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Commit d520d24f authored by silencealiang's avatar silencealiang
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Merge branch 'main' into 'main' 

megatron升级v0.10

See merge request !3
parents 3aca1415 481609bb
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examples/multimodal/train.py 0 → 100755
View file @ d520d24f
# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved.
"""Pretrain or SFT multimodal."""
import os
import sys
from functools import partial
import torch
import yaml
sys.path.append(
os.path.abspath(os.path.join(os.path.dirname(__file__), os.path.pardir, os.path.pardir))
)
from dataloader_provider import train_valid_test_dataloaders_provider, is_first_or_last_stage
from model import model_provider
from multimodal_args import add_multimodal_extra_args
from megatron.core import mpu, tensor_parallel
from megatron.core.enums import ModelType
from megatron.core.models.multimodal.llava_model import IGNORE_INDEX, LLaVAModel
from megatron.core.packed_seq_params import PackedSeqParams
from megatron.core.parallel_state import (
get_tensor_model_parallel_rank,
get_pipeline_model_parallel_world_size,
is_pipeline_last_stage,
)
from megatron.training import get_args, get_timers, get_tokenizer, pretrain
from megatron.training.utils import is_last_rank
def get_batch(data_iterator):
"""Generate a batch
Note: attn_mask_type in layer_specs.py sets the attention mask. Attention mask is None here.
"""
imgs = None
tokens = None
labels = None
loss_mask = None
attention_mask = None
position_ids = None
num_tiles = None
packed_seq_params = None
args = get_args()
# Dataloader doesn't run on the middle stages in a pipeline parallel model.
pp_size = get_pipeline_model_parallel_world_size()
if not is_first_or_last_stage(pp_size, args.encoder_pipeline_model_parallel_size):
# Note these are all set to None above.
return tokens, labels, loss_mask, attention_mask, position_ids, imgs, num_tiles
# Broadcast data.
torch.cuda.nvtx.range_push("get_data")
if data_iterator is not None and get_tensor_model_parallel_rank() == 0:
data = next(data_iterator)
else:
data = None
data_text = tensor_parallel.broadcast_data(["tokens"], data, torch.int64)["tokens"]
labels = tensor_parallel.broadcast_data(["labels"], data, torch.int64)["labels"]
imgs = tensor_parallel.broadcast_data(["imgs"], data, torch.float32)["imgs"]
num_tiles = tensor_parallel.broadcast_data(["num_tiles"], data, torch.int32)["num_tiles"]
cu_lengths = tensor_parallel.broadcast_data(["cu_lengths"], data, torch.int32)["cu_lengths"]
max_lengths = tensor_parallel.broadcast_data(["max_lengths"], data, torch.int32)["max_lengths"]
# Dummy image, no image.
if imgs.shape == torch.Size([1, 1]):
# FIXME: text-only data can cause a hang if the vision model is own its own pipeline rank and --freeze-ViT is enabled.
imgs = torch.tensor([], dtype=torch.float32, device=data_text.device)
num_tiles = torch.tensor([], dtype=torch.int, device=data_text.device)
# Last pipeline parallel stage doesn't need images.
if pp_size > 1 and is_pipeline_last_stage():
imgs = None
# If cu_lengths and max_lengths are non-dummy, construct PackedSeqParams. Otherwise, leave it at None.
if cu_lengths.shape != torch.Size([1, 1]):
assert (
cu_lengths.shape[0] == max_lengths.shape[0] == 1
), "micro-batch-size must be 1 for packing"
cu_lengths = cu_lengths[0]
max_lengths = max_lengths[0]
packed_seq_params = PackedSeqParams(
qkv_format="thd",
cu_seqlens_q=cu_lengths,
cu_seqlens_kv=cu_lengths,
max_seqlen_q=max_lengths,
max_seqlen_kv=max_lengths,
)
torch.cuda.nvtx.range_pop()
tokens_ = data_text.long()
torch.cuda.nvtx.range_push("index tokens")
tokenizer = get_tokenizer()
text_length = tokens_.shape[1]
tokens = tokens_[:, :text_length].contiguous()
labels = labels[:, 1 : text_length + 1].contiguous()
assert tokens.shape == labels.shape, f"tokens: {tokens.shape} != labels: {labels.shape}"
torch.cuda.nvtx.range_pop()
torch.cuda.nvtx.range_push("get_ltor_masks_and_position_ids")
loss_mask, position_ids = get_ltor_masks_and_position_ids(tokens, labels, tokenizer.pad)
torch.cuda.nvtx.range_pop()
return (
tokens,
labels,
loss_mask,
attention_mask,
position_ids,
imgs,
num_tiles,
packed_seq_params,
)
def get_ltor_masks_and_position_ids(input_ids, target, pad_token):
"""Build masks and position id for left to right model."""
seq_length = input_ids.shape[1]
# Position ids.
position_ids = torch.arange(seq_length, dtype=torch.long, device=input_ids.device)
position_ids = position_ids.unsqueeze(0).expand_as(input_ids)
# Loss mask.
loss_mask = torch.ones(target.size(), dtype=torch.float, device=input_ids.device)
loss_mask[target == pad_token] = 0.0 # mask paddings
loss_mask[target == IGNORE_INDEX] = 0.0 # mask prompts
return loss_mask, position_ids
def loss_func(loss_mask, output_tensor):
losses = output_tensor.float()
loss_mask = loss_mask.contiguous().view(-1).float()
total_tokens = loss_mask.sum()
total_loss = torch.sum(losses.view(-1) * loss_mask)
loss = torch.cat([total_loss.view(1), total_tokens.view(1)])
reporting_loss = loss.clone().detach()
torch.distributed.all_reduce(reporting_loss, group=mpu.get_data_parallel_group())
local_num_tokens = loss[1].clone().detach().to(torch.int)
return (total_loss, local_num_tokens, {'lm loss': (reporting_loss[0], reporting_loss[1])})
def forward_step(data_iterator, model: LLaVAModel):
"""Forward training step.
Args:
data_iterator (torch.utils.data.dataloader): Input data iterator
model: Multimodal model
Returns:
output_tensor (torch.Tensor): Loss of shape [b, s] if labels are provided, otherwise logits of shape [b, s, vocab_size].
loss_func (callable): Loss function with a loss mask specified.
"""
timers = get_timers()
# Get the batch.
timers('batch-generator', log_level=2).start()
(
tokens,
labels,
loss_mask,
attention_mask,
position_ids,
images,
num_image_tiles,
packed_seq_params,
) = get_batch(data_iterator)
timers('batch-generator').stop()
output_tensor, loss_mask = model(
images,
tokens,
position_ids,
attention_mask,
labels,
loss_mask,
num_image_tiles=num_image_tiles,
packed_seq_params=packed_seq_params,
)
return output_tensor, partial(loss_func, loss_mask)
def llava_embedding_ranks(pp_ranks):
"""LLava's embedding ranks consist of the decoder's first and last ranks (ie, the ViT has no embeddings).
Args:
pp_ranks: A list of global ranks that constitute a pipeline group.
"""
args = get_args()
# encoder size is also the index to the first rank of the decoder.
epp = args.encoder_pipeline_model_parallel_size
last_rank = pp_ranks[-1]
if len(pp_ranks) == 1 or pp_ranks[epp] == last_rank:
return [last_rank]
else:
return [pp_ranks[epp], last_rank]
def llava_position_embedding_ranks(pp_ranks):
"""LLava's embedding ranks consist of the singular rank of the model or the decoder's first rank.
Args:
pp_ranks: A list of global ranks that constitute a pipeline group.
"""
args = get_args()
# encoder size is also the index to the first rank of the decoder.
epp = args.encoder_pipeline_model_parallel_size
last_rank = pp_ranks[-1]
if len(pp_ranks) == 1:
return [last_rank]
else:
return [pp_ranks[epp]]
def run_online_eval(model):
"""Run an evaluation benchmark during training."""
args = get_args()
# Online evaluation config is not defined. Do nothing.
if not args.online_evaluation_config:
return []
from config import EvaluationConfig
from run_text_generation import generate_and_write_samples
with open(args.online_evaluation_config, "r") as f:
config_dict = yaml.safe_load(f)
config = EvaluationConfig(**config_dict)
# The inference code assumes the first rank is the leader.
# Tensorboard writer is on the last rank.
# We must write to a storage space that all ranks see.
output_dir = os.path.join(args.save, "online_eval")
os.makedirs(output_dir, exist_ok=True)
config.output_path = os.path.join(output_dir, args.language_model_type)
# The actual generation.
generate_and_write_samples(model[0].module, config, print_output=False)
# Make sure the first rank is done writing so that the last rank can run eval.
torch.distributed.barrier()
if not is_last_rank():
return []
# Run evaluation.
if config.task == "TextVQA":
from evaluate_textvqa import textvqa_eval
avg_acc = textvqa_eval(config.output_path)
return [{"TextVQA accuracy": avg_acc}]
else:
raise NotImplementedError(f"online evaluation of {config.task} not implemented yet")
def write_online_eval_to_tensorboard(data, iteration, writer):
"""Write online evaluation data to Tensorboard."""
if not writer:
return
for item in data:
for k, v in item.items():
writer.add_scalar(k, v, iteration)
if __name__ == "__main__":
train_valid_test_dataloaders_provider.is_distributed = True
pretrain(
train_valid_test_dataloaders_provider,
model_provider,
ModelType.encoder_and_decoder,
forward_step,
args_defaults={'tokenizer_type': 'GPT2BPETokenizer'},
extra_args_provider=add_multimodal_extra_args,
process_non_loss_data_func=write_online_eval_to_tensorboard,
get_embedding_ranks=llava_embedding_ranks,
get_position_embedding_ranks=llava_position_embedding_ranks,
non_loss_data_func=run_online_eval,
)
examples/pretrain_bert.sh deleted 100755 → 0
View file @ 3aca1415
#!/bin/bash
export CUDA_DEVICE_MAX_CONNECTIONS=1
CHECKPOINT_PATH=<Specify path>
VOCAB_FILE=<Specify path to file>/bert-vocab.txt
DATA_PATH=<Specify path and file prefix>_text_sentence
BERT_ARGS="
--num-layers 24 \
--hidden-size 1024 \
--num-attention-heads 16 \
--seq-length 512 \
--max-position-embeddings 512 \
--micro-batch-size 4 \
--global-batch-size 8 \
--lr 0.0001 \
--train-iters 2000000 \
--lr-decay-iters 990000 \
--lr-decay-style linear \
--min-lr 0.00001 \
--weight-decay 1e-2 \
--lr-warmup-fraction .01 \
--clip-grad 1.0 \
--fp16
"
DATA_ARGS="
--data-path $DATA_PATH \
--vocab-file $VOCAB_FILE \
--data-impl mmap \
--split 949,50,1
"
OUTPUT_ARGS="
--log-interval 100 \
--save-interval 10000 \
--eval-interval 1000 \
--eval-iters 10
"
torchrun pretrain_bert.py \
$BERT_ARGS \
$DATA_ARGS \
$OUTPUT_ARGS \
--save $CHECKPOINT_PATH \
--load $CHECKPOINT_PATH
examples/pretrain_bert_distributed.sh deleted 100755 → 0
View file @ 3aca1415
#!/bin/bash
export CUDA_DEVICE_MAX_CONNECTIONS=1
GPUS_PER_NODE=8
# Change for multinode config
MASTER_ADDR=localhost
MASTER_PORT=6000
NNODES=1
NODE_RANK=0
WORLD_SIZE=$(($GPUS_PER_NODE*$NNODES))
CHECKPOINT_PATH=<Specify path>
VOCAB_FILE=<Specify path to file>/bert-vocab.txt
DATA_PATH=<Specify path and file prefix>_text_sentence
DISTRIBUTED_ARGS="
--nproc_per_node $GPUS_PER_NODE \
--nnodes $NNODES \
--node_rank $NODE_RANK \
--master_addr $MASTER_ADDR \
--master_port $MASTER_PORT
"
BERT_ARGS="
--num-layers 24 \
--hidden-size 1024 \
--num-attention-heads 16 \
--seq-length 512 \
--max-position-embeddings 512 \
--micro-batch-size 4 \
--global-batch-size 32 \
--lr 0.0001 \
--train-iters 1000000 \
--lr-decay-iters 990000 \
--lr-decay-style linear \
--min-lr 1.0e-5 \
--weight-decay 1e-2 \
--lr-warmup-fraction .01 \
--clip-grad 1.0 \
--fp16
"
DATA_ARGS="
--data-path $DATA_PATH \
--vocab-file $VOCAB_FILE \
--data-impl mmap \
--split 949,50,1
"
OUTPUT_ARGS="
--log-interval 100 \
--save-interval 10000 \
--eval-interval 1000 \
--eval-iters 10
"
torchrun $DISTRIBUTED_ARGS pretrain_bert.py \
$BERT_ARGS \
$DATA_ARGS \
$OUTPUT_ARGS \
--distributed-backend nccl \
--save $CHECKPOINT_PATH \
--load $CHECKPOINT_PATH
examples/pretrain_bert_distributed_with_mp.sh deleted 100755 → 0
View file @ 3aca1415
#!/bin/bash
export CUDA_DEVICE_MAX_CONNECTIONS=1
GPUS_PER_NODE=8
# Change for multinode config
MASTER_ADDR=localhost
MASTER_PORT=6000
NNODES=1
NODE_RANK=0
WORLD_SIZE=$(($GPUS_PER_NODE*$NNODES))
CHECKPOINT_PATH=<Specify path>
VOCAB_FILE=<Specify path to file>/bert-vocab.txt
DATA_PATH=<Specify path and file prefix>_text_sentence
DISTRIBUTED_ARGS="
--nproc_per_node $GPUS_PER_NODE \
--nnodes $NNODES \
--node_rank $NODE_RANK \
--master_addr $MASTER_ADDR \
--master_port $MASTER_PORT
"
BERT_ARGS="
--tensor-model-parallel-size 2 \
--pipeline-model-parallel-size 2 \
--num-layers 24 \
--hidden-size 1024 \
--num-attention-heads 16 \
--seq-length 512 \
--max-position-embeddings 512 \
--micro-batch-size 2 \
--global-batch-size 16 \
--lr 0.0001 \
--train-iters 1000000 \
--lr-decay-iters 990000 \
--lr-decay-style linear \
--min-lr 1.0e-5 \
--weight-decay 1e-2 \
--lr-warmup-fraction .01 \
--clip-grad 1.0 \
--fp16
"
DATA_ARGS="
--data-path $DATA_PATH \
--vocab-file $VOCAB_FILE \
--data-impl mmap \
--split 949,50,1
"
OUTPUT_ARGS="
--log-interval 100 \
--save-interval 10000 \
--eval-interval 1000 \
--eval-iters 10
"
torchrun $DISTRIBUTED_ARGS pretrain_bert.py \
$BERT_ARGS \
$DATA_ARGS \
$OUTPUT_ARGS \
--distributed-backend nccl \
--save $CHECKPOINT_PATH \
--load $CHECKPOINT_PATH
examples/pretrain_gpt.sh deleted 100755 → 0
View file @ 3aca1415
#!/bin/bash
# Runs the "345M" parameter model
export CUDA_DEVICE_MAX_CONNECTIONS=1
CHECKPOINT_PATH=<Specify path>
VOCAB_FILE=<Specify path to file>/gpt2-vocab.json
MERGE_FILE=<Specify path to file>/gpt2-merges.txt
DATA_PATH=<Specify path and file prefix>_text_document
GPT_ARGS="
--num-layers 24 \
--hidden-size 1024 \
--num-attention-heads 16 \
--seq-length 1024 \
--max-position-embeddings 1024 \
--micro-batch-size 4 \
--global-batch-size 8 \
--lr 0.00015 \
--train-iters 500000 \
--lr-decay-iters 320000 \
--lr-decay-style cosine \
--min-lr 1.0e-5 \
--weight-decay 1e-2 \
--lr-warmup-fraction .01 \
--clip-grad 1.0 \
--fp16
"
DATA_ARGS="
--data-path $DATA_PATH \
--vocab-file $VOCAB_FILE \
--merge-file $MERGE_FILE \
--data-impl mmap \
--split 949,50,1
"
OUTPUT_ARGS="
--log-interval 100 \
--save-interval 10000 \
--eval-interval 1000 \
--eval-iters 10
"
torchrun pretrain_gpt.py \
$GPT_ARGS \
$DATA_ARGS \
$OUTPUT_ARGS \
--save $CHECKPOINT_PATH \
--load $CHECKPOINT_PATH
examples/pretrain_gpt3_175B.sh deleted 100755 → 0
View file @ 3aca1415
#!/bin/bash
#SBATCH <SLURM OPTIONS> --nodes=128 --exclusive --ntasks-per-node=8 --job-name=megatron_gpt3_175b
DIR=`pwd`
DATETIME=`date +'date_%y-%m-%d_time_%H-%M-%S'`
mkdir -p $DIR/logs
DATASET_1="<PATH TO THE FIRST DATASET>"
DATASET_2="<PATH TO THE SECOND DATASET>"
DATASET_3="<PATH TO THE THIRD DATASET>"
DATASET="0.2 ${DATASET_1} 0.3 ${DATASET_2} 0.5 ${DATASET_3}"
options=" \
--tensor-model-parallel-size 8 \
--pipeline-model-parallel-size 16 \
--num-layers 96 \
--hidden-size 12288 \
--num-attention-heads 96 \
--seq-length 2048 \
--max-position-embeddings 2048 \
--micro-batch-size 1 \
--global-batch-size 1536 \
--rampup-batch-size 16 16 5859375 \
--train-samples 146484375 \
--lr-decay-samples 126953125 \
--lr-warmup-samples 183105 \
--lr 6.0e-5 \
--min-lr 6.0e-6 \
--lr-decay-style cosine \
--log-interval 10 \
--eval-iters 40 \
--eval-interval 1000 \
--data-path ${DATASET} \
--vocab-file <PATH TO gpt-vocab.json> \
--merge-file <PATH TO gpt-merges.txt> \
--save-interval 1000 \
--save <PATH TO CHECKPOINTS DIRECTORY> \
--load <PATH TO CHECKPOINTS DIRECTORY> \
--split 98,2,0 \
--clip-grad 1.0 \
--weight-decay 0.1 \
--adam-beta1 0.9 \
--adam-beta2 0.95 \
--init-method-std 0.006 \
--tensorboard-dir <TENSORBOARD DIRECTORY> \
--fp16 "
run_cmd="python -u ${DIR}/pretrain_gpt.py $@ ${options}"
srun -l \
--container-image "nvcr.io/nvidia/pytorch:20.12-py3" \
--container-mounts "<DIRECTORIES TO MOUNT>" \
--output=$DIR/logs/%x_%j_$DATETIME.log sh -c "${run_cmd}"
set +x
examples/pretrain_gpt_distributed.sh deleted 100755 → 0
View file @ 3aca1415
#!/bin/bash
# Runs the "345M" parameter model
export CUDA_DEVICE_MAX_CONNECTIONS=1
GPUS_PER_NODE=8
# Change for multinode config
MASTER_ADDR=localhost
MASTER_PORT=6000
NNODES=1
NODE_RANK=0
WORLD_SIZE=$(($GPUS_PER_NODE*$NNODES))
CHECKPOINT_PATH=<Specify path>
VOCAB_FILE=<Specify path to file>/gpt2-vocab.json
MERGE_FILE=<Specify path to file>/gpt2-merges.txt
DATA_PATH=<Specify path and file prefix>_text_document
DISTRIBUTED_ARGS="
--nproc_per_node $GPUS_PER_NODE \
--nnodes $NNODES \
--node_rank $NODE_RANK \
--master_addr $MASTER_ADDR \
--master_port $MASTER_PORT
"
GPT_ARGS="
--num-layers 24 \
--hidden-size 1024 \
--num-attention-heads 16 \
--seq-length 1024 \
--max-position-embeddings 1024 \
--micro-batch-size 8 \
--global-batch-size 64 \
--lr 0.00015 \
--train-iters 500000 \
--lr-decay-iters 320000 \
--lr-decay-style cosine \
--min-lr 1.0e-5 \
--weight-decay 1e-2 \
--lr-warmup-fraction .01 \
--clip-grad 1.0 \
--fp16
"
DATA_ARGS="
--data-path $DATA_PATH \
--vocab-file $VOCAB_FILE \
--merge-file $MERGE_FILE \
--data-impl mmap \
--split 949,50,1
"
OUTPUT_ARGS="
--log-interval 100 \
--save-interval 10000 \
--eval-interval 1000 \
--eval-iters 10
"
torchrun $DISTRIBUTED_ARGS pretrain_gpt.py \
$GPT_ARGS \
$DATA_ARGS \
$OUTPUT_ARGS \
--distributed-backend nccl \
--save $CHECKPOINT_PATH \
--load $CHECKPOINT_PATH
examples/pretrain_gpt_distributed_with_mp.sh deleted 100755 → 0
View file @ 3aca1415
#!/bin/bash
# Runs the "345M" parameter model
export CUDA_DEVICE_MAX_CONNECTIONS=1
GPUS_PER_NODE=8
# Change for multinode config
MASTER_ADDR=localhost
MASTER_PORT=6000
NNODES=1
NODE_RANK=0
WORLD_SIZE=$(($GPUS_PER_NODE*$NNODES))
CHECKPOINT_PATH=<Specify path>
VOCAB_FILE=<Specify path to file>/gpt2-vocab.json
MERGE_FILE=<Specify path to file>/gpt2-merges.txt
DATA_PATH=<Specify path and file prefix>_text_document
DISTRIBUTED_ARGS="
--nproc_per_node $GPUS_PER_NODE \
--nnodes $NNODES \
--node_rank $NODE_RANK \
--master_addr $MASTER_ADDR \
--master_port $MASTER_PORT
"
GPT_ARGS="
--tensor-model-parallel-size 2 \
--pipeline-model-parallel-size 2 \
--sequence-parallel \
--num-layers 24 \
--hidden-size 1024 \
--num-attention-heads 16 \
--seq-length 1024 \
--max-position-embeddings 1024 \
--micro-batch-size 4 \
--global-batch-size 16 \
--lr 0.00015 \
--train-iters 500000 \
--lr-decay-iters 320000 \
--lr-decay-style cosine \
--min-lr 1.0e-5 \
--weight-decay 1e-2 \
--lr-warmup-fraction .01 \
--clip-grad 1.0 \
--fp16
"
DATA_ARGS="
--data-path $DATA_PATH \
--vocab-file $VOCAB_FILE \
--merge-file $MERGE_FILE \
--data-impl mmap \
--split 949,50,1
"
OUTPUT_ARGS="
--log-interval 100 \
--save-interval 10000 \
--eval-interval 1000 \
--eval-iters 10
"
torchrun $DISTRIBUTED_ARGS pretrain_gpt.py \
$GPT_ARGS \
$DATA_ARGS \
$OUTPUT_ARGS \
--distributed-backend nccl \
--save $CHECKPOINT_PATH \
--load $CHECKPOINT_PATH
examples/pretrain_ict.sh deleted 100755 → 0
View file @ 3aca1415
#! /bin/bash
# Runs the "217M" parameter biencoder model for ICT retriever
RANK=0
WORLD_SIZE=1
PRETRAINED_BERT_PATH=<Specify path of pretrained BERT model>
TEXT_DATA_PATH=<Specify path and file prefix of the text data>
TITLE_DATA_PATH=<Specify path and file prefix od the titles>
CHECKPOINT_PATH=<Specify path>
python pretrain_ict.py \
--num-layers 12 \
--hidden-size 768 \
--num-attention-heads 12 \
--tensor-model-parallel-size 1 \
--micro-batch-size 32 \
--seq-length 256 \
--max-position-embeddings 512 \
--train-iters 100000 \
--vocab-file bert-vocab.txt \
--tokenizer-type BertWordPieceLowerCase \
--DDP-impl torch \
--bert-load ${PRETRAINED_BERT_PATH} \
--log-interval 100 \
--eval-interval 1000 \
--eval-iters 10 \
--retriever-report-topk-accuracies 1 5 10 20 100 \
--retriever-score-scaling \
--load $CHECKPOINT_PATH \
--save $CHECKPOINT_PATH \
--data-path ${TEXT_DATA_PATH} \
--titles-data-path ${TITLE_DATA_PATH} \
--lr 0.0001 \
--lr-decay-style linear \
--weight-decay 1e-2 \
--clip-grad 1.0 \
--lr-warmup-fraction 0.01 \
--save-interval 4000 \
--exit-interval 8000 \
--query-in-block-prob 0.1 \
--fp16
examples/pretrain_t5.sh deleted 100644 → 0
View file @ 3aca1415
#!/bin/bash
export CUDA_DEVICE_MAX_CONNECTIONS=1
CHECKPOINT_PATH=<Specify path>
VOCAB_FILE=<Specify path to file>/t5-vocab.txt
DATA_PATH=<Specify path and file prefix>_text_sentence
T5_ARGS="
--num-layers 12 \
--hidden-size 768 \
--num-attention-heads 12 \
--kv-channels 64 \
--ffn-hidden-size 3072 \
--encoder-seq-length 512 \
--decoder-seq-length 128 \
--max-position-embeddings 512 \
--micro-batch-size 16 \
--global-batch-size 16 \
--lr 0.0001 \
--train-iters 1000000 \
--lr-decay-iters 1000000 \
--lr-decay-style linear \
--min-lr 0.00001 \
--weight-decay 1e-2 \
--lr-warmup-fraction .01 \
--clip-grad 1.0 \
--fp16 \
--vocab-extra-ids 100
"
DATA_ARGS="
--data-path $DATA_PATH \
--vocab-file $VOCAB_FILE \
--data-impl mmap \
--split 949,50,1
"
OUTPUT_ARGS="
--log-interval 100 \
--save-interval 10000 \
--eval-interval 1000 \
--eval-iters 10
"
torchrun pretrain_t5.py \
$T5_ARGS \
$DATA_ARGS \
$OUTPUT_ARGS \
--save $CHECKPOINT_PATH \
--load $CHECKPOINT_PATH
examples/pretrain_t5_distributed_with_mp.sh deleted 100644 → 0
View file @ 3aca1415
#!/bin/bash
export CUDA_DEVICE_MAX_CONNECTIONS=1
GPUS_PER_NODE=8
# Change for multinode config
MASTER_ADDR=localhost
MASTER_PORT=6000
NNODES=1
NODE_RANK=0
WORLD_SIZE=$(($GPUS_PER_NODE*$NNODES))
CHECKPOINT_PATH=<Specify path>
VOCAB_FILE=<Specify path to file>/t5-vocab.txt
DATA_PATH=<Specify path and file prefix>_text_sentence
DISTRIBUTED_ARGS="
--nproc_per_node $GPUS_PER_NODE \
--nnodes $NNODES \
--node_rank $NODE_RANK \
--master_addr $MASTER_ADDR \
--master_port $MASTER_PORT
"
T5_ARGS="
--tensor-model-parallel-size 2 \
--num-layers 12 \
--hidden-size 768 \
--num-attention-heads 12 \
--kv-channels 64 \
--ffn-hidden-size 3072 \
--encoder-seq-length 512 \
--decoder-seq-length 128 \
--max-position-embeddings 512 \
--micro-batch-size 16 \
--global-batch-size 128 \
--lr 0.0001 \
--train-iters 1000000 \
--lr-decay-iters 1000000 \
--lr-decay-style linear \
--min-lr 0.00001 \
--weight-decay 1e-2 \
--lr-warmup-fraction .01 \
--clip-grad 1.0 \
--fp16 \
--vocab-extra-ids 100
"
DATA_ARGS="
--data-path $DATA_PATH \
--vocab-file $VOCAB_FILE \
--data-impl mmap \
--split 949,50,1
"
OUTPUT_ARGS="
--log-interval 100 \
--save-interval 10000 \
--eval-interval 1000 \
--eval-iters 10
"
torchrun $DISTRIBUTED_ARGS pretrain_t5.py \
$T5_ARGS \
$DATA_ARGS \
$OUTPUT_ARGS \
--distributed-backend nccl \
--save $CHECKPOINT_PATH \
--load $CHECKPOINT_PATH
examples/retro/README.md 0 → 100755
View file @ d520d24f
# RETRO MODEL
## Table of contents
- [1. Training Setup](#1-training-setup)
- [2. Data Preprocessing](#2-data-preprocessing)
- [3. Configurations](#3-configurations)
## 1. Training setup
<a id="markdown-training-setup" name="training-setup"></a>
To run the model using a docker container run it as follows
```
PYTORCH_IMAGE=nvcr.io/nvidia/pytorch:23.09-py3
CHECKPOINT_PATH="" #<Specify path>
TENSORBOARD_LOGS_PATH=""#<Specify path>
docker run \
--gpus=all \
--ipc=host \
--workdir /workspace/megatron-lm \
-v /path/to/data:/path/to/data \
-v /path/to/megatron-lm:/workspace/megatron-lm \
megatron-lm nvcr.io/nvidia/pytorch:23.09-py3 \
bash examples/retro/train_retro_2b_distributed.sh $CHECKPOINT_PATH $TENSORBOARD_LOGS_PATH"
```
NOTE: Depending on the environment you are running it the above command might look slightly different.
NOTE: Due to how Retro preprocess and caches elements of the pretraining dataset before training begins, some arguments are auto-loaded from the Retro preprocessing configuration. These loaded arguments include:
- `--data-path`
- `--data-cache-path`
- `--eval-interval`
- `--eval-iters`
- `--global-batch-size`
- `--tokenizer-type`
- `--tokenizer-model`
- `--vocab-file`
- `--merge-file`
- `--seed`
- `--seq-length`
- `--train-samples`
## 2. Data Preprocessing
<a id="markdown-data-preprocessing" name="data-preprocessing"></a>
Retro preprocesses and caches data prior to pretraining, to greatly speed up pretraining. During data preprocessing, the retrieval database is built, and neighbor IDs are queried for each sample within the pretraining dataset. Please see `preprocess_data.sh` for an example script to preprocess data for Retro. The reference documentation for data preprocessing can be found [here](tools/retro/README.md).
## 3. Configurations
<a id="markdown-configurations" name="configurations"></a>
The example in this folder shows you how to run a 2B model. Below are a few other example configurations.
### 857M
```
--num-layers 24 \
--hidden-size 1024 \
--num-attention-heads 16 \
--seq-length 2048 \
--tensor-model-parallel-size 1 \
--pipeline-model-parallel-size 1 \
```
### 4B
```
--num-layers 48 \
--hidden-size 2560 \
--num-attention-heads 32 \
--tensor-model-parallel-size 1 \
--pipeline-model-parallel-size 1 \
```
tools/retro/examples/preprocess_data.sh → examples/retro/preprocess_data.sh 100644 → 100755
View file @ d520d24f
......@@ -7,23 +7,31 @@ unset NCCL_DEBUG
######## Megatron, Retro dirs. ########
REPO_DIR="<path/to/megatron/repo>"
RETRO_WORKDIR="<path/to/retro/data/directory>"
RETRO_PROJECT_DIR="<path/to/retro/project/directory>"
######## Task (e.g., db, index, query). ########
RETRO_TASKS="db-build"
# RETRO_TASKS="index-train"
# RETRO_TASKS="index-add"
# RETRO_TASKS="query-pretraining-neighbors"
# This script takes a single argument, which specifies the retro task to be
# performed. The available tasks are: db-build, index-train, index-add, and
# query-neighbors.
######## Data. ########
# ~~ Examples ~~
# RETRO_TASKS="db-build" # Build the retrieval database
# RETRO_TASKS="index-train" # Train the index
# RETRO_TASKS="index-add" # Add data to the index
# RETRO_TASKS="query-neighbors" # Perform query pretraining for neighbors
# You can also provide the task as a command-line argument when executing the
# script. Example: ./preprocess_data.sh index-add
RETRO_TASKS=$1
######## Data. ########
DATA_BLEND="<see --data-path in arguments.py>"
######## Index. ########
RETRO_INDEX_STR="OPQ32_64,IVF65536_HNSW8,PQ32"
RETRO_INDEX_NTRAIN=1000000
RETRO_INDEX_NTRAIN=66625331
RETRO_INDEX_TRAIN_LOAD_FRACTION=0.97
RETRO_INDEX_ADD_LOAD_FRACTION=0.95
......@@ -32,20 +40,19 @@ RETRO_INDEX_ADD_LOAD_FRACTION=0.95
RETRO_GPT_SEED=1234
RETRO_GPT_SPLIT="98,2,0"
RETRO_GPT_DATA_PATH=${DATA_BLEND}
RETRO_GPT_DATA_IMPL=mmap
RETRO_GPT_DATALOADER_TYPE=single
RETRO_GPT_TRAIN_SAMPLES=200000
RETRO_GPT_EVAL_INTERVAL=2000
RETRO_GPT_EVAL_ITERS=50
RETRO_GPT_TRAIN_SAMPLES=200000
RETRO_GPT_LR_DECAY_SAMPLES=175000
RETRO_GPT_LR_WARMUP_SAMPLES=10000
RETRO_GPT_SEQ_LENGTH=512
RETRO_GPT_SEQ_LENGTH=2048
RETRO_GPT_GLOBAL_BATCH_SIZE=256
RETRO_GPT_CHUNK_LENGTH=64
######## Query. ########
RETRO_QUERY_NUM_NEIGHBORS_QUERY=200 RETRO_QUERY_NUM_NEIGHBORS_SAVE=20
RETRO_QUERY_NUM_NEIGHBORS_QUERY=200
RETRO_QUERY_NUM_NEIGHBORS_SAVE=20
RETRO_QUERY_EF_SEARCH=32
RETRO_QUERY_NPROBE=4096
......@@ -62,13 +69,12 @@ ARGS=" \
--global-batch-size ${RETRO_GPT_GLOBAL_BATCH_SIZE} \
--seq-length 512 \
--max-position-embeddings 512 \
--load <path/to/bert/checkpoint> \
--load ${RETRO_PROJECT_DIR}/checkpoints/bert \
--exit-on-missing-checkpoint \
--no-load-optim \
--data-path ${RETRO_GPT_DATA_PATH} \
--data-path [null] \
--tokenizer-type BertWordPieceLowerCase \
--vocab-file <path/to/bert/vocab> \
--data-impl ${RETRO_GPT_DATA_IMPL} \
--vocab-file ${RETRO_PROJECT_DIR}/tokenizer/bert-large-uncased-vocab.txt \
--split ${RETRO_GPT_SPLIT} \
--distributed-backend nccl \
--lr 0.0001 \
......@@ -81,37 +87,37 @@ ARGS=" \
--clip-grad 1.0 \
--eval-interval ${RETRO_GPT_EVAL_INTERVAL} \
--eval-iters ${RETRO_GPT_EVAL_ITERS} \
--fp16 \
--DDP-impl local \
--dataloader-type ${RETRO_GPT_DATALOADER_TYPE} \
--bf16 \
--no-data-sharding \
--no-gradient-accumulation-fusion \
--no-async-tensor-model-parallel-allreduce \
--bert-embedder-type megatron \
--output-bert-embeddings \
\
--retro-workdir ${RETRO_WORKDIR} \
--retro-project-dir ${RETRO_PROJECT_DIR} \
--retro-tasks ${RETRO_TASKS} \
--retro-return-doc-ids \
--retro-bert-vocab-file <path/to/bert/vocab> \
--retro-bert-vocab-file tokenizer/bert-large-uncased-vocab.txt \
--retro-bert-tokenizer-type BertWordPieceLowerCase \
\
--retro-gpt-seed ${RETRO_GPT_SEED} \
--retro-gpt-tokenizer-type GPTSentencePieceTokenizer \
--retro-gpt-tokenizer-model <path/to/gpt/tokenizer/model> \
--retro-gpt-tokenizer-model /path/to/tokenizer/model \
--retro-gpt-seq-length ${RETRO_GPT_SEQ_LENGTH} \
--retro-gpt-chunk-length ${RETRO_GPT_CHUNK_LENGTH} \
--retro-gpt-global-batch-size ${RETRO_GPT_GLOBAL_BATCH_SIZE} \
--retro-gpt-eval-interval ${RETRO_GPT_EVAL_INTERVAL} \
--retro-gpt-eval-iters ${RETRO_GPT_EVAL_ITERS} \
--retro-gpt-split ${RETRO_GPT_SPLIT} \
--retro-gpt-data-impl ${RETRO_GPT_DATA_IMPL} \
--retro-gpt-data-path ${RETRO_GPT_DATA_PATH} \
--retro-gpt-train-samples ${RETRO_GPT_TRAIN_SAMPLES} \
\
--retro-index-str ${RETRO_INDEX_STR} \
--retro-index-ntrain ${RETRO_INDEX_NTRAIN} \
--retro-index-train-load-fraction ${RETRO_INDEX_TRAIN_LOAD_FRACTION} \
--retro-index-add-load-fraction ${RETRO_INDEX_ADD_LOAD_FRACTION} \
--retro-index-no-delete-training-embeddings \
--retro-index-no-delete-added-codes \
--no-retro-index-delete-training-embeddings \
--no-retro-index-delete-added-codes \
\
--retro-query-num-neighbors-query ${RETRO_QUERY_NUM_NEIGHBORS_QUERY} \
--retro-query-num-neighbors-save ${RETRO_QUERY_NUM_NEIGHBORS_SAVE} \
--retro-query-ef-search ${RETRO_QUERY_EF_SEARCH} \
......@@ -130,7 +136,7 @@ CMD="\
--node_rank ${NODE_RANK} \
--master_addr ${MASTER_ADDR} \
--master_port 6000 \
tools/retro/main.py ${ARGS} \
tools/retro/preprocess_data.py ${ARGS} \
"
echo "~~~~~~~~~~~~~~~~~~~~~~~~~~"
echo "CMD = '$CMD'."
......
examples/retro/train_retro_2b_distributed.sh 0 → 100755
View file @ d520d24f
#!/bin/bash
# Runs the "307M" parameter Retro model.
export CUDA_DEVICE_MAX_CONNECTIONS=1
GPUS_PER_NODE=8
# Change for multinode config
MASTER_ADDR=localhost
MASTER_PORT=6000
NUM_NODES=1
NODE_RANK=0
WORLD_SIZE=$(($GPUS_PER_NODE*$NUM_NODES))
CHECKPOINT_PATH=$1 #<Specify path>
TENSORBOARD_LOGS_PATH=$2 #<Specify path>
DISTRIBUTED_ARGS=(
--nproc_per_node $GPUS_PER_NODE
--nnodes $NUM_NODES
--master_addr $MASTER_ADDR
--master_port $MASTER_PORT
)
######## GPT or Retro? ########
# 0 : GPT.
# 1 : Retro
ADD_RETRIEVER=1
######## Megatron, Retro dirs. ########
RETRO_PROJECT_DIR="<path/to/retro/project/directory>"
######## Model, training args. ########
# ** Note: --seq-length auto loaded from Retro project dir.
RETRO_MODEL_ARGS=(
--num-layers 32
--hidden-size 2048
--num-attention-heads 32
)
# ** Note: --data-path, --tokenizer-type, and --tokenizer-model auto loaded from Retro project dir.
DATA_ARGS=(
--split 98,2,0
)
MODEL_PARALLEL_ARGS=(
--tensor-model-parallel-size 8
--pipeline-model-parallel-size 1
)
# ** Note: --eval-interval, --eval-iters auto loaded from Retro project dir.
EVAL_AND_LOGGING_ARGS=(
--log-interval 100
--save-interval 10000
--eval-interval 1000
--save $CHECKPOINT_PATH
--load $CHECKPOINT_PATH
--eval-iters 10
--tensorboard-dir $TENSORBOARD_LOGS_PATH
)
TRAINING_ARGS=" \
--retro-project-dir ${RETRO_PROJECT_DIR} \
--transformer-impl transformer_engine \
--num-workers 8 \
--micro-batch-size 4 \
--lr-decay-samples 166400000 \
--lr-warmup-samples 162761 \
--lr 6.0e-4 \
--min-lr 6.0e-5 \
--lr-decay-style cosine \
--clip-grad 1.0 \
--weight-decay 0.1 \
--adam-beta1 0.9 \
--adam-beta2 0.95 \
--init-method-std 0.023 \
--log-params-norm \
--log-num-zeros-in-grad \
--bf16 \
--no-data-sharding \
"
if [ "$ADD_RETRIEVER" = "1" ]; then
TRAINING_ARGS+=" --retro-add-retriever"
fi
######## Command. ########
torchrun ${DISTRIBUTED_ARGS[@]} pretrain_retro.py \
${RETRO_MODEL_ARGS[@]} \
${TRAINING_ARGS} \
${MODEL_PARALLEL_ARGS[@]} \
${DATA_ARGS[@]} \
${EVAL_AND_LOGGING_ARGS[@]}
examples/run_simple_mcore_train_loop.py 0 → 100755
View file @ d520d24f
import os
import torch
from torch.optim import Adam
from torch.utils.data import DataLoader
from functools import partial
from pathlib import Path
from megatron.core import parallel_state
from megatron.core import dist_checkpointing
from megatron.core.pipeline_parallel.schedules import get_forward_backward_func
from megatron.core.tensor_parallel.random import model_parallel_cuda_manual_seed
from megatron.core.transformer.transformer_config import TransformerConfig
from megatron.core.models.gpt.gpt_model import GPTModel
from megatron.core.models.gpt.gpt_layer_specs import get_gpt_layer_local_spec
from megatron.core.datasets.utils import compile_helpers
from megatron.core.datasets.blended_megatron_dataset_builder import BlendedMegatronDatasetBuilder
from megatron.core.datasets.gpt_dataset import GPTDatasetConfig, MockGPTDataset
from megatron.training.tokenizer.tokenizer import _NullTokenizer
_SEQUENCE_LENGTH = 64
def initialize_distributed(tensor_model_parallel_size=1, pipeline_model_parallel_size=1):
parallel_state.destroy_model_parallel()
# Torch setup for distributed training
rank = int(os.environ['LOCAL_RANK'])
world_size = torch.cuda.device_count()
torch.cuda.set_device(rank)
torch.distributed.init_process_group(world_size=world_size, rank=rank)
# Megatron core distributed training initialization
parallel_state.initialize_model_parallel(tensor_model_parallel_size, pipeline_model_parallel_size)
def model_provider():
"""Build the model."""
transformer_config = TransformerConfig(
num_layers=2,
hidden_size=12,
num_attention_heads=4,
use_cpu_initialization=True,
pipeline_dtype=torch.float32,
)
gpt_model = GPTModel(
config=transformer_config,
transformer_layer_spec=get_gpt_layer_local_spec(),
vocab_size=100,
max_sequence_length=_SEQUENCE_LENGTH,
)
return gpt_model
def get_train_data_iterator():
if torch.distributed.is_available() and torch.distributed.is_initialized():
if torch.distributed.get_rank() == 0:
compile_helpers()
torch.distributed.barrier()
else:
compile_helpers()
config = GPTDatasetConfig(
random_seed=0,
sequence_length=_SEQUENCE_LENGTH,
reset_position_ids=False,
reset_attention_mask=False,
eod_mask_loss=False,
tokenizer=_NullTokenizer(vocab_size=_SEQUENCE_LENGTH),
)
datasets = BlendedMegatronDatasetBuilder(
MockGPTDataset, [1000, None, None], lambda: True, config
).build()
train_dataloader = DataLoader(datasets[0], batch_size=8, shuffle=True)
train_iterator = iter(train_dataloader)
return train_iterator
def forward_step_func(data_iterator, model):
def loss_func(loss_mask: torch.Tensor, output_tensor: torch.Tensor):
losses = output_tensor.float()
loss_mask = loss_mask.view(-1).float()
loss = torch.sum(losses.view(-1) * loss_mask) / loss_mask.sum()
# If you have data parallel reduce loss across data parallel groups.
# If pipeline parallel, loss computation is done only in last stage.
return loss, {'lm loss': loss}
data = next(data_iterator)
tokens = data['tokens'].to(device)
attention_mask = data['attention_mask'].to(device)
position_ids = data['position_ids'].to(device)
labels = data['labels'].to(device)
loss_mask = data['loss_mask'].to(device)
output_tensor = model(tokens, position_ids, attention_mask,
labels=labels)
return output_tensor, partial(loss_func, loss_mask)
def save_distributed_checkpoint(checkpoint_path, gpt_model):
sharded_state_dict = gpt_model.sharded_state_dict(prefix='')
dist_checkpointing.save(sharded_state_dict=sharded_state_dict, checkpoint_dir=checkpoint_path)
def load_distributed_checkpoint(checkpoint_path, gpt_model):
sharded_state_dict=gpt_model.sharded_state_dict(prefix='')
checkpoint = dist_checkpointing.load(sharded_state_dict=sharded_state_dict, checkpoint_dir=checkpoint_path)
gpt_model.load_state_dict(checkpoint)
return gpt_model
if __name__ == "__main__":
initialize_distributed(tensor_model_parallel_size=2, pipeline_model_parallel_size=1)
model_parallel_cuda_manual_seed(123)
gpt_model = model_provider()
device = torch.device("cuda")
gpt_model.to(device)
optim = Adam(gpt_model.parameters())
train_iterator = get_train_data_iterator()
forward_backward_func = get_forward_backward_func()
# Running the model for 5 iterations
for _ in range(5):
optim.zero_grad()
losses_reduced = forward_backward_func(
forward_step_func=forward_step_func,
data_iterator=train_iterator,
model=gpt_model,
num_microbatches=1,
seq_length=_SEQUENCE_LENGTH,
micro_batch_size=8,
decoder_seq_length=_SEQUENCE_LENGTH,
forward_only=False)
optim.step()
print(f'Losses reduced : {losses_reduced}')
# Saving the model
ckpt_path = os.getcwd() + '/ckpt'
Path(ckpt_path).mkdir(exist_ok=True)
save_distributed_checkpoint(gpt_model=gpt_model, checkpoint_path=ckpt_path)
# Loading the model
gpt_model = load_distributed_checkpoint(gpt_model=gpt_model, checkpoint_path=ckpt_path)
gpt_model.to(device)
print('Successfully loaded the model')
examples/t5/README.md 0 → 100755
View file @ d520d24f
# T5 MODEL
## Table of contents
- [1. Training Setup](#1-training-setup)
- [2. Configurations](#2-configurations)
- [3. Training Results](#3-training-results)
## 1. Training setup
<a id="markdown-training-setup" name="training-setup"></a>
To run the model on a Slurm based cluster
```
PYTORCH_IMAGE=nvcr.io/nvidia/pytorch:23.09-py3
ACCOUNT_NAME=""
PARTITION=""
JOB_NAME=""
NUM_NODES=1
CHECKPOINT_PATH="" #<Specify path to checkpoint>
TENSORBOARD_LOGS_PATH=""#<Specify path to tensorboard log>
VOCAB_FILE="" #<Specify path to file>/bert-large-cased-vocab.txt
DATA_PATH="" #<Specify path and file prefix>_text_document
srun -N $NUM_NODES --container-image $PYTORCH_IMAGE --container-mounts "/path/to/data:/path/to/data,/path/to/megatron-lm:/workspace/megatron-lm" --account $ACCOUNT -N 1 -J $JOB_NAME -p $PARTITION --no-container-mount-home -c "
cd /workspace/megatron-lm
./examples/t5/train_t5_220m_distributed.sh $CHECKPOINT_PATH $TENSORBOARD_LOGS_PATH $VOCAB_FILE $DATA_PATH"
```
## 2. Configurations
<a id="markdown-configurations" name="configurations"></a>
The architecture arguments below shows configuration for T5 220M model.
### 220M
```
--num-layers 12 \
--hidden-size 768 \
--num-attention-heads 12 \
--kv-channels 64 \
--ffn-hidden-size 3072 \
--encoder-seq-length 512 \
--decoder-seq-length 128 \
--max-position-embeddings 512 \
--tensor-model-parallel-size 1 \
--pipeline-model-parallel-size 1 \
```
## 3. Training Results
<a id="markdown-training-results" name="training-results"></a>
Below is the training curve for the 220M model on Pile dataset. The training takes 4 days on 32 GPUs, with batch size of 2048.
Finetuning on SQUAD dataset, the validation result is: 63.44\%
<p align="center">
<img src="./t5_mcore_train_curve.png" width="800" height="400">
</p>
examples/pretrain_t5_distributed.sh → examples/t5/train_t5_220m_distributed.sh 100644 → 100755
View file @ d520d24f
#!/bin/bash
# Runs the "220M" parameter model
export CUDA_DEVICE_MAX_CONNECTIONS=1
GPUS_PER_NODE=8
# Change for multinode config
MASTER_ADDR=localhost
MASTER_PORT=6000
NNODES=1
NUM_NODES=1
NODE_RANK=0
WORLD_SIZE=$(($GPUS_PER_NODE*$NNODES))
WORLD_SIZE=$(($GPUS_PER_NODE*$NUM_NODES))
CHECKPOINT_PATH=<Specify path>
VOCAB_FILE=<Specify path to file>/t5-vocab.txt
DATA_PATH=<Specify path and file prefix>_text_sentence
CHECKPOINT_PATH=$1 #<Specify path>
TENSORBOARD_DIR=$2 #<Specify path>
VOCAB_FILE=$3 #<Specify path to file>/bert-large-cased-vocab.txt
DATA_PATH=$4 #<Specify path and file prefix>_text_document
DISTRIBUTED_ARGS="
--nproc_per_node $GPUS_PER_NODE \
--nnodes $NNODES \
--nnodes $NUM_NODES \
--node_rank $NODE_RANK \
--master_addr $MASTER_ADDR \
--master_port $MASTER_PORT
"
T5_ARGS="
--num-layers 12 \
--encoder-num-layers 12 \
--decoder-num-layers 12 \
--hidden-size 768 \
--num-attention-heads 12 \
--kv-channels 64 \
......@@ -31,8 +35,8 @@ T5_ARGS="
--encoder-seq-length 512 \
--decoder-seq-length 128 \
--max-position-embeddings 512 \
--micro-batch-size 16 \
--global-batch-size 128 \
--micro-batch-size 64 \
--global-batch-size 512 \
--lr 0.0001 \
--train-iters 1000000 \
--lr-decay-iters 1000000 \
......@@ -41,20 +45,26 @@ T5_ARGS="
--weight-decay 1e-2 \
--lr-warmup-fraction .01 \
--clip-grad 1.0 \
--fp16 \
--vocab-extra-ids 100
--bf16 \
--vocab-extra-ids 100 \
--init-method-std 0.015 \
--transformer-impl transformer_engine \
--tensor-model-parallel-size 1 \
--pipeline-model-parallel-size 1 \
--attention-backend auto \
"
DATA_ARGS="
--data-path $DATA_PATH \
--vocab-file $VOCAB_FILE \
--data-impl mmap \
--split 949,50,1
--tokenizer-type BertWordPieceCase \
--split 99982,9,9 \
"
OUTPUT_ARGS="
--log-interval 100 \
--save-interval 10000 \
--tensorboard-dir ${TENSORBOARD_DIR} \
--save-interval 500 \
--eval-interval 1000 \
--eval-iters 10
"
......@@ -65,4 +75,4 @@ torchrun $DISTRIBUTED_ARGS pretrain_t5.py \
$OUTPUT_ARGS \
--distributed-backend nccl \
--save $CHECKPOINT_PATH \
--load $CHECKPOINT_PATH
--load $CHECKPOINT_PATH \
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