Merge branch 'main' into github-main

.gitlab-ci.yml

+image: gitlab-master.nvidia.com/dl/dgx/pytorch:20.12-py3-devel
+
+test:
+  script:
+    - pytest --junitxml=report.xml tests
+  artifacts:
+    when: always
+    reports:
+      junit: report.xml
+    
\ No newline at end of file

LICENSE

@@ -29,7 +29,7 @@ The following applies to all files unless otherwise noted:
 --
 
 This repository also contains code from Hugging Face Inc., Google Research,
-and Facebook (from their Fairseq project). Files from these
+Facebook (from their Fairseq project), and Philip Popien. Files from these
 organizations have notices at the top of each file. Below are licenses
 used in those files, as indicated.
 
@@ -262,3 +262,4 @@ AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
 LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
 OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
 SOFTWARE.
+

README.md

-[Megatron](https://arxiv.org/pdf/1909.08053.pdf) is a large, powerful transformer developed by the Applied Deep Learning Research team at NVIDIA. This repository is for ongoing research on training large transformer language models at scale. We developed efficient, model-parallel (tensor and pipeline), and multi-node pre-training of [GPT](https://arxiv.org/abs/2005.14165) and [BERT](https://arxiv.org/pdf/1810.04805.pdf) using mixed precision.
+Megatron ([1](https://arxiv.org/pdf/1909.08053.pdf) and [2](https://arxiv.org/pdf/2104.04473.pdf)) is a large, powerful transformer developed by the Applied Deep Learning Research team at NVIDIA. This repository is for ongoing research on training large transformer language models at scale. We developed efficient, model-parallel (tensor and pipeline), and multi-node pre-training of [GPT](https://arxiv.org/abs/2005.14165) and [BERT](https://arxiv.org/pdf/1810.04805.pdf) using mixed precision.
 
 Below are some of the projects where we have directly used Megatron:
 * [BERT and GPT Studies Using Megatron](https://arxiv.org/pdf/1909.08053.pdf)
@@ -8,15 +8,15 @@ Below are some of the projects where we have directly used Megatron:
 * [Local Knowledge Powered Conversational Agents](https://arxiv.org/abs/2010.10150)
 * [MEGATRON-CNTRL: Controllable Story Generation with External Knowledge Using Large-Scale Language Models](https://www.aclweb.org/anthology/2020.emnlp-main.226.pdf)
 * [RACE Reading Comprehension Dataset Leaderboard](http://www.qizhexie.com/data/RACE_leaderboard.html)
+* [Scaling Language Model Training to a Trillion Parameters Using Megatron](https://arxiv.org/pdf/2104.04473.pdf)
 * [Training Question Answering Models From Synthetic Data](https://www.aclweb.org/anthology/2020.emnlp-main.468.pdf)
 
-Our codebase is capable of efficiently training very large (hundreds of billions of parameters) language models with both model and data parallelism. To demonstrate how the code scales with multiple GPUs and model sizes, we consider GPT models from 1 billion all the way to 1 trillion parameters. All models use a vocabulary size of 51,200 and a sequence length of 2048. We vary hidden size, number of attention heads, and number of layers to arrive at a specifc model size. As the model size increases, we also modestly increase the batch size. We leverage [NVIDIA's Selene supercomputer](https://www.top500.org/system/179842/) to perform scaling studies and use up to 3072 [A100](https://www.nvidia.com/en-us/data-center/a100/) GPUs for the largest model. The table below shows the model configurations along with the achieved FLOPs per second (both per GPU and aggregate over all GPUs). Note that the FLOPs are measured for end-to-end training, i.e., includes all operations including data loading, optimization, and even logging.
+Our codebase is capable of efficiently training very large (hundreds of billions of parameters) language models with both model and data parallelism. To demonstrate how the code scales with multiple GPUs and model sizes, we consider GPT models from 1 billion all the way to 1 trillion parameters. All models use a vocabulary size of 51,200 and a sequence length of 2048. We vary hidden size, number of attention heads, and number of layers to arrive at a specifc model size. As the model size increases, we also modestly increase the batch size. We leverage [NVIDIA's Selene supercomputer](https://www.top500.org/system/179842/) to perform scaling studies and use up to 3072 [A100](https://www.nvidia.com/en-us/data-center/a100/) GPUs for the largest model. The table below shows the model configurations along with the achieved FLOPs (both per GPU and aggregate over all GPUs). Note that the FLOPs are measured for end-to-end training, i.e., includes all operations including data loading, optimization, and even logging.
 
-![Cases](images/cases_jan2021.png)
+![Cases](images/cases_april2021.png)
 
-The following figures show achieved percentage of theoretical peak FLOPs and achieved aggregate petaFLOPs per second as a function of number of GPUs. All the cases from 1 billion to 1 trillion achieve more than 41% half precision utilization, which is high for an end-to-end application. We observe that initially as the model parallel size increases, utilization slightly decreases; as hidden size increases for larger models, utilization starts increasing and reaches 49% for the largest model. We also note that achieved aggregate petaFLOPs per second across all GPUs increases almost linearly with number of GPUs, demonstrating good weak scaling.
+All the cases from 1 billion to 1 trillion parameters achieve more than 43% half precision utilization, which is high for an end-to-end application. We observe that initially the utilization remains constant but as hidden size increases for larger models, utilization starts increasing and reaches 52% for the largest model. We also note that achieved aggregate petaFLOPs across all GPUs increases almost linearly with number of GPUs, demonstrating good weak scaling.
 
-![Model Parallel Scaling](images/scaling.png)
 
 # Contents
    * [Contents](#contents)
@@ -370,11 +370,11 @@ python tools/create_doc_index.py \
 
 We provide several command line arguments, detailed in the scripts listed below, to handle various zero-shot and fine-tuned downstream tasks. However, you can also finetune your model from a pretrained checkpoint on other corpora as desired. To do so, simply add the `--finetune` flag and adjust the input files and training parameters within the original training script. The iteration count will be reset to zero, and the optimizer and internal state will be reinitialized. If the fine-tuning is interrupted for any reason, be sure to remove the `--finetune` flag before continuing, otherwise the training will start again from the beginning.
 
-<!--
-Because evaluation requires substantially less memory than training, it may be advantageous to merge a model trained in parallel for use on a single GPU in downstream tasks. The following script accomplishes this.
+Because evaluation requires substantially less memory than training, it may be advantageous to merge a model trained in parallel for use on a single GPU in downstream tasks. The following script accomplishes this. Currently only tensor model parallelism is supported on input and pipeline model parallelsim on the output. This example reads in a model with 2-way tensor model parallelism and writes out a model with 2-way pipeline model parallelism.
 
 <pre>
 TENSOR_MODEL_PARALLEL_SIZE=2
+TARGET_PIPELINE_MODEL_PARALLEL_SIZE=2
 
 VOCAB_FILE=bert-vocab.txt
 CHECKPOINT_PATH=checkpoints/bert_345m
@@ -382,6 +382,8 @@ CHECKPOINT_PATH=checkpoints/bert_345m
 WORLD_SIZE=$TENSOR_MODEL_PARALLEL_SIZE python tools/merge_mp_partitions.py \
         --model-type BERT \
         --tensor-model-parallel-size $TENSOR_MODEL_PARALLEL_SIZE \
+        --pipeline-model-parallel-size 1 \
+        --target-pipeline-model-parallel-size $TARGET_PIPELINE_MODEL_PARALLEL_SIZE \
         --tokenizer-type BertWordPieceLowerCase \
         --vocab-file $VOCAB_FILE \
         --num-layers 24 \
@@ -390,9 +392,10 @@ WORLD_SIZE=$TENSOR_MODEL_PARALLEL_SIZE python tools/merge_mp_partitions.py \
         --seq-length 512 \
         --max-position-embeddings 512 \
         --load $CHECKPOINT_PATH
+        --save $CHECKPOINT_PATH/merged
 
 </pre>
--->
+
 Several downstream tasks are described for both GPT and BERT models below. They can be run in distributed and model parallel modes with the same changes used in the training scripts.
 
 ## GPT Text Generation

examples/create_embeddings.sh

+#!/bin/bash
+
+# Compute embeddings for each entry of a given dataset (e.g. Wikipedia)
+
+RANK=0
+WORLD_SIZE=1
+
+# Wikipedia data can be downloaded from the following link:
+# https://github.com/facebookresearch/DPR/blob/master/data/download_data.py
+EVIDENCE_DATA_DIR=<Specify path of Wikipedia dataset>
+EMBEDDING_PATH=<Specify path to store embeddings>
+CHECKPOINT_PATH=<Specify path of pretrained ICT model>
+
+python tools/create_doc_index.py \
+    --num-layers 12 \
+    --hidden-size 768 \
+    --num-attention-heads 12 \
+    --tensor-model-parallel-size 1 \
+    --micro-batch-size 128 \
+    --checkpoint-activations \
+    --seq-length 512 \
+    --retriever-seq-length 256 \
+    --max-position-embeddings 512 \
+    --load ${CHECKPOINT_PATH} \
+    --evidence-data-path ${EVIDENCE_DATA_DIR} \
+    --embedding-path ${EMBEDDING_PATH} \
+    --indexer-log-interval 1000 \
+    --indexer-batch-size 128 \
+    --vocab-file bert-vocab.txt \
+    --num-workers 2 \
+    --fp16
+

examples/evaluate_ict_zeroshot_nq.sh

+#!/bin/bash
+
+# Evaluate natural question test data given Wikipedia embeddings and pretrained
+# ICT model
+
+# Datasets can be downloaded from the following link:
+# https://github.com/facebookresearch/DPR/blob/master/data/download_data.py
+
+EVIDENCE_DATA_DIR=<Specify path of Wikipedia dataset>
+EMBEDDING_PATH=<Specify path of the embeddings>
+CHECKPOINT_PATH=<Specify path of pretrained ICT model>
+
+QA_FILE=<Path of the natural question test dataset>
+
+python tasks/main.py \
+    --task ICT-ZEROSHOT-NQ \
+    --tokenizer-type BertWordPieceLowerCase \
+    --num-layers 12 \
+    --hidden-size 768 \
+    --num-attention-heads 12 \
+    --tensor-model-parallel-size 1 \
+    --micro-batch-size 128 \
+    --checkpoint-activations \
+    --seq-length 512 \
+    --max-position-embeddings 512 \
+    --load ${CHECKPOINT_PATH} \
+    --evidence-data-path ${EVIDENCE_DATA_DIR} \
+    --embedding-path ${EMBEDDING_PATH} \
+    --retriever-seq-length 256 \
+    --vocab-file  bert-vocab.txt\
+    --qa-data-test ${QA_FILE} \
+    --num-workers 2 \
+    --faiss-use-gpu \
+    --retriever-report-topk-accuracies 1 5 20 100 \
+    --fp16
+

examples/pretrain_gpt3_175B.sh

@@ -46,6 +46,7 @@ options=" \
 	--weight-decay 0.1 \
 	--adam-beta1 0.9 \
 	--adam-beta2 0.95 \
+	--init-method-std 0.006 \
 	--tensorboard-dir <TENSORBOARD DIRECTORY> \
         --fp16 \
 	--checkpoint-activations "

examples/pretrain_ict.sh

+#! /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

images/Makefile

-default: cases.png scaling-mp.png scaling-dp.png
-
-# for some reason the size option to convert in scaling.tex doesn't work, manually do it after
-cases.png scaling-mp.png scaling-dp.png: tables.tex
-	latex --shell-escape $<
-	convert tables-1.png -resize 650 cases.png
-	convert tables-2.png -resize 600 scaling-mp.png
-	convert tables-3.png -resize 350 scaling-dp.png
-
-clean:
-	rm -rf *.aux *.log *.dvi *.ps
-	rm -rf tables-*.png

images/tables.tex

-\documentclass[multi,convert]{standalone}
-\usepackage{multirow}
-\standaloneenv{tabular}
-
-\begin{document}
-
-\begin{tabular}{cccccc}
-  Case & Hidden Size & Attention Heads & Layers & Parameters (billions) & Model Parallel Partitions \\
-  \hline
-  1B & 1920 & 15 & 24 & 1.16 & 1 \\
-  2B & 2304 & 18 & 30 & 2.03 & 2 \\
-  4B & 3072 & 24 & 36 & 4.24 & 4 \\
-  8B & 4096 & 32 & 42 & 8.67 & 8 \\
-\end{tabular}
-
-\begin{tabular}{cc|ccc|ccc}
-  & & \multicolumn{3}{c|}{\textbf{DGX-2 (V100) batch size 8}} & \multicolumn{3}{c}{\textbf{DGX-A100 batch size 16}} \\
-  \hline
-  \multirow{2}{*}{Case} & Number of & Iteration & \multirow{2}{*}{Scaling} & TeraFLOPs & Iteration & \multirow{2}{*}{Scaling} & TeraFLOPs \\
-                        & GPUs      & Time (ms) &                          & per GPU   & Time (ms) &                          & per GPU \\
-  \hline
-  1B & 1 & 1121 & 100.0\% & 71.9 & 1076 & 100\%  & 149.8 \\
-  2B & 2 & 1093 & 89.6\%  & 64.2 & 1026 & 91.7\% & 136.8 \\
-  4B & 4 & 1238 & 82.5\%  & 58.5 & 1162 & 84.5\% & 124.7 \\
-  8B & 8 & 1407 & 74.3\%  & 52.2 & 1343 & 74.7\% & 109.3 \\
-\end{tabular}
-
-\begin{tabular}{cc|ccc}
-  & & \multicolumn{3}{c}{\textbf{DGX-A100 batch size 2048}} \\
-  \hline
-  \multirow{2}{*}{Case} & Number of & Iteration & \multirow{2}{*}{Scaling} & TeraFLOPs \\
-                        & GPUs      & Time (ms) &                          & per GPU   \\
-  \hline
-  1B & 128  & 1153 & 93.3\% & 139.8 \\
-  2B & 256  & 1101 & 85.5\% & 127.5 \\
-  4B & 512  & 1242 & 79.0\% & 116.7 \\
-  8B & 1024 & 1380 & 72.7\% & 106.5 \\
-\end{tabular}
-
-\end{document}

megatron/checkpointing.py

@@ -21,18 +21,20 @@ import sys
 import numpy as np
 
 import torch
-from torch.nn.parallel import DistributedDataParallel as torchDDP
 
-from megatron import mpu, get_args, update_num_microbatches
-from megatron import get_args
-from megatron import print_rank_0
+from megatron import (get_args,
+                      mpu,
+                      print_rank_0,
+                      update_num_microbatches,
+                      utils)
 
 _CHECKPOINT_VERSION = None
 
 def set_checkpoint_version(value):
     global _CHECKPOINT_VERSION
-    assert _CHECKPOINT_VERSION is None, \
-        "checkpoint version already set"
+    if _CHECKPOINT_VERSION is not None:
+        assert _CHECKPOINT_VERSION == value, \
+            "checkpoint versions do not match"
     _CHECKPOINT_VERSION = value
 
 def get_checkpoint_version():
@@ -59,6 +61,7 @@ def check_checkpoint_args(checkpoint_args):
     _compare('hidden_size')
     _compare('num_attention_heads')
     _compare('max_position_embeddings')
+    if args.vocab_file:
         _compare('make_vocab_size_divisible_by')
         _compare('padded_vocab_size')
         _compare('tokenizer_type')
@@ -108,21 +111,24 @@ def save_checkpoint(iteration, model, optimizer, lr_scheduler):
     args = get_args()
 
     # Only rank zero of the data parallel writes to the disk.
-    if isinstance(model, torchDDP):
-        model = model.module
+    model = utils.unwrap_model(model)
 
-    if torch.distributed.get_rank() == 0:
-        print('saving checkpoint at iteration {:7d} to {}'.format(
-            iteration, args.save), flush=True)
+    print_rank_0('saving checkpoint at iteration {:7d} to {}'.format(
+        iteration, args.save))
 
-    if mpu.get_data_parallel_rank() == 0:
+    if not torch.distributed.is_initialized() or mpu.get_data_parallel_rank() == 0:
 
         # Arguments, iteration, and model.
         state_dict = {}
         state_dict['args'] = args
         state_dict['checkpoint_version'] = 3.0
         state_dict['iteration'] = iteration
-        state_dict['model'] = model.state_dict_for_save_checkpoint()
+        if len(model) == 1:
+            state_dict['model'] = model[0].state_dict_for_save_checkpoint()
+        else:
+            for i in range(len(model)):
+                mpu.set_virtual_pipeline_model_parallel_rank(i)
+                state_dict['model%d' % i] = model[i].state_dict_for_save_checkpoint()
 
         # Optimizer stuff.
         if not args.no_save_optim:
@@ -146,26 +152,98 @@ def save_checkpoint(iteration, model, optimizer, lr_scheduler):
         torch.save(state_dict, checkpoint_name)
 
     # Wait so everyone is done (necessary)
+    if torch.distributed.is_initialized():
         torch.distributed.barrier()
-    if torch.distributed.get_rank() == 0:
-        print('  successfully saved checkpoint at iteration {:7d} to {}'.format(
-            iteration, args.save), flush=True)
+
+    print_rank_0('  successfully saved checkpoint at iteration {:7d} to {}'.format(
+        iteration, args.save))
+
     # And update the latest iteration
-    if torch.distributed.get_rank() == 0:
+    if not torch.distributed.is_initialized() or torch.distributed.get_rank() == 0:
         tracker_filename = get_checkpoint_tracker_filename(args.save)
         with open(tracker_filename, 'w') as f:
             f.write(str(iteration))
+
     # Wait so everyone is done (not necessary)
+    if torch.distributed.is_initialized():
         torch.distributed.barrier()
 
-
-def load_checkpoint(model, optimizer, lr_scheduler, load_arg='load'):
-    """Load a model checkpoint and return the iteration."""
+def _transpose_first_dim(t, num_splits, num_splits_first, model):
+    input_shape = t.size()
+    # We use a self_attention module but the values extracted aren't
+    # specific to self attention so should work for cross attention as well
+    while hasattr(model, 'module'):
+        model = model.module
+    attention_module = model.language_model.encoder.layers[0].self_attention
+    hidden_size_per_attention_head = attention_module.hidden_size_per_attention_head
+    num_attention_heads_per_partition = attention_module.num_attention_heads_per_partition
+    if num_splits_first:
+        """[num_splits * np * hn, h]
+        -->(view) [num_splits, np, hn, h]
+        -->(tranpose) [np, num_splits, hn, h]
+        -->(view) [np * num_splits * hn, h] """
+
+        intermediate_shape = \
+            (num_splits, num_attention_heads_per_partition,
+             hidden_size_per_attention_head) + input_shape[1:]
+
+        t = t.view(*intermediate_shape)
+        t = t.transpose(0, 1).contiguous()
+    else:
+        """[np * hn * num_splits, h]
+        -->(view) [np, hn, num_splits, h]
+        -->(tranpose) [np, num_splits, hn, h]
+        -->(view) [np * num_splits * hn, h] """
+
+        intermediate_shape = \
+            (num_attention_heads_per_partition,
+             hidden_size_per_attention_head, num_splits) +\
+             input_shape[1:]
+
+        t = t.view(*intermediate_shape)
+        t = t.transpose(1, 2).contiguous()
+    t = t.view(*input_shape)
+
+    return t
+
+def fix_query_key_value_ordering(model, checkpoint_version):
+    """Fix up query/key/value matrix ordering if checkpoint
+    version is smaller than 2.0
+    """
+    if checkpoint_version < 2.0:
+        for name, param in model.named_parameters():
+            if name.endswith(('.query_key_value.weight', '.query_key_value.bias')):
+                if checkpoint_version == 0:
+                    fixed_param = _transpose_first_dim(param.data, 3, True, model)
+                elif checkpoint_version == 1.0:
+                    fixed_param = _transpose_first_dim(param.data, 3, False, model)
+                else:
+                    print_rank_0(f"Invalid checkpoint version {checkpoint_version}.")
+                    sys.exit()
+                param.data.copy_(fixed_param)
+            if name.endswith(('.key_value.weight', '.key_value.bias')):
+                if checkpoint_version == 0:
+                    fixed_param = _transpose_first_dim(param.data, 2, True, model)
+                elif checkpoint_version == 1.0:
+                    fixed_param = _transpose_first_dim(param.data, 2, False, model)
+                else:
+                    print_rank_0(f"Invalid checkpoint version {checkpoint_version}.")
+                    sys.exit()
+                param.data.copy_(fixed_param)
+        print_rank_0(" succesfully fixed query-key-values ordering for"
+                    " checkpoint version {}".format(checkpoint_version))
+
+def load_checkpoint(model, optimizer, lr_scheduler, load_arg='load', strict=True):
+    """Load a model checkpoint and return the iteration.
+    strict (bool): whether to strictly enforce that the keys in
+        :attr:`state_dict` of the checkpoint match the names of
+        parameters and buffers in model.
+    """
     args = get_args()
     load_dir = getattr(args, load_arg)
 
-    if isinstance(model, torchDDP):
-        model = model.module
+    model = utils.unwrap_model(model)
+
     # Read the tracker file and set the iteration.
     tracker_filename = get_checkpoint_tracker_filename(load_dir)
 
@@ -197,9 +275,7 @@ def load_checkpoint(model, optimizer, lr_scheduler, load_arg='load'):
 
     # Checkpoint.
     checkpoint_name = get_checkpoint_name(load_dir, iteration, release)
-    if torch.distributed.get_rank() == 0:
-        print(' loading checkpoint from {} at iteration {}'.format(
-            args.load, iteration), flush=True)
+    print_rank_0(f' loading checkpoint from {args.load} at iteration {iteration}')
 
     # Load the checkpoint.
     try:
@@ -252,7 +328,17 @@ def load_checkpoint(model, optimizer, lr_scheduler, load_arg='load'):
         print_rank_0('could not find arguments in the checkpoint ...')
 
     # Model.
-    model.load_state_dict(state_dict['model'])
+    if len(model) == 1:
+        model[0].load_state_dict(state_dict['model'], strict=strict)
+    else:
+        for i in range(len(model)):
+            mpu.set_virtual_pipeline_model_parallel_rank(i)
+            model[i].load_state_dict(state_dict['model%d' % i], strict=strict)
+
+    # Fix up query/key/value matrix ordering if needed
+    checkpoint_version = get_checkpoint_version()
+    print_rank_0(f' checkpoint version {checkpoint_version}')
+    fix_query_key_value_ordering(model, checkpoint_version)
 
     # Optimizer.
     if not release and not args.finetune and not args.no_load_optim:
@@ -275,58 +361,64 @@ def load_checkpoint(model, optimizer, lr_scheduler, load_arg='load'):
             np.random.set_state(state_dict['np_rng_state'])
             torch.set_rng_state(state_dict['torch_rng_state'])
             torch.cuda.set_rng_state(state_dict['cuda_rng_state'])
+            # Check for empty states array
+            if not state_dict['rng_tracker_states']:
+                raise KeyError
             mpu.get_cuda_rng_tracker().set_states(
                 state_dict['rng_tracker_states'])
         except KeyError:
-            print_rank_0('Unable to load optimizer from checkpoint {}. '
+            print_rank_0('Unable to load rng state from checkpoint {}. '
                          'Specify --no-load-rng or --finetune to prevent '
-                         'attempting to load the optimizer state, '
+                         'attempting to load the rng state, '
                          'exiting ...'.format(checkpoint_name))
             sys.exit()
 
+    # Some utilities want to load a checkpoint without distributed being initialized
+    if torch.distributed.is_initialized():
         torch.distributed.barrier()
-    if torch.distributed.get_rank() == 0:
-        print('  successfully loaded checkpoint from {} at iteration {}'.format(
-            args.load, iteration), flush=True)
+
+    print_rank_0(f'  successfully loaded checkpoint from {args.load} '
+                 f'at iteration {iteration}')
 
     return iteration
 
 
-def load_ict_checkpoint(model, only_query_model=False, only_block_model=False, from_realm_chkpt=False):
-    """selectively load ICT models for indexing/retrieving from ICT or REALM checkpoints"""
+def load_biencoder_checkpoint(model, only_query_model=False,
+        only_context_model=False, custom_load_path=None):
+    """
+    selectively load retrieval models for indexing/retrieving 
+    from saved checkpoints
+    """
 
     args = get_args()
 
-    if isinstance(model, torchDDP):
-        model = model.module
+    model = utils.unwrap_model(model)
 
-    load_path = args.load if from_realm_chkpt else args.ict_load
+    load_path = custom_load_path if custom_load_path is not None else args.load
 
     tracker_filename = get_checkpoint_tracker_filename(load_path)
     with open(tracker_filename, 'r') as f:
         iteration = int(f.read().strip())
 
-    # assert iteration > 0
     checkpoint_name = get_checkpoint_name(load_path, iteration, False)
     if mpu.get_data_parallel_rank() == 0:
         print('global rank {} is loading checkpoint {}'.format(
             torch.distributed.get_rank(), checkpoint_name))
 
     state_dict = torch.load(checkpoint_name, map_location='cpu')
-    ict_state_dict = state_dict['model']
-    if from_realm_chkpt and mpu.get_data_parallel_rank() == 0:
-        print(" loading ICT state dict from REALM", flush=True)
-        ict_state_dict = ict_state_dict['retriever']['ict_model']
+    ret_state_dict = state_dict['model']
 
     if only_query_model:
-        ict_state_dict.pop('context_model')
-    if only_block_model:
-        ict_state_dict.pop('question_model')
+        ret_state_dict.pop('context_model')
+    if only_context_model:
+        ret_state_dict.pop('query_model')
 
-    model.load_state_dict(ict_state_dict)
+    assert len(model) == 1
+    model[0].load_state_dict(ret_state_dict)
     torch.distributed.barrier()
 
     if mpu.get_data_parallel_rank() == 0:
         print(' successfully loaded {}'.format(checkpoint_name))
 
     return model
+

megatron/data/autoaugment.py

+"""AutoAugment data augmentation policy for ImageNet.
+
+-- Begin license text.
+
+MIT License
+
+Copyright (c) 2018 Philip Popien
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
+
+-- End license text.
+
+Code adapted from https://github.com/DeepVoltaire/AutoAugment.
+
+This module implements the fixed AutoAugment data augmentation policy for ImageNet provided in
+Appendix A, Table 9 of reference [1]. It does not include any of the search code for augmentation
+policies.
+
+Reference:
+[1] https://arxiv.org/abs/1805.09501
+"""
+
+import random
+
+import numpy as np
+from PIL import Image
+from PIL import ImageEnhance
+from PIL import ImageOps
+
+_MAX_LEVEL = 10  # Maximum integer strength of an augmentation, if applicable.
+
+
+class ImageNetPolicy:
+    """Definition of an ImageNetPolicy.
+
+    Implements a fixed AutoAugment data augmentation policy targeted at
+    ImageNet training by randomly applying at runtime one of the 25 pre-defined
+    data augmentation sub-policies provided in Reference [1].
+
+    Usage example as a Pytorch Transform:
+    >>> transform=transforms.Compose([transforms.Resize(256),
+    >>>                               ImageNetPolicy(),
+    >>>                               transforms.ToTensor()])
+    """
+
+    def __init__(self, fillcolor=(128, 128, 128)):
+        """Initialize an ImageNetPolicy.
+
+        Args:
+            fillcolor (tuple): RGB color components of the color to be used for
+            filling when needed (default: (128, 128, 128), which
+            corresponds to gray).
+        """
+        # Instantiate a list of sub-policies.
+        # Each entry of the list is a SubPolicy which consists of
+        # two augmentation operations,
+        # each of those parametrized as operation, probability, magnitude.
+        # Those two operations are applied sequentially on the image upon call.
+        self.policies = [
+            SubPolicy("posterize", 0.4, 8, "rotate", 0.6, 9, fillcolor),
+            SubPolicy("solarize", 0.6, 5, "autocontrast", 0.6, 5, fillcolor),
+            SubPolicy("equalize", 0.8, 8, "equalize", 0.6, 3, fillcolor),
+            SubPolicy("posterize", 0.6, 7, "posterize", 0.6, 6, fillcolor),
+            SubPolicy("equalize", 0.4, 7, "solarize", 0.2, 4, fillcolor),
+            SubPolicy("equalize", 0.4, 4, "rotate", 0.8, 8, fillcolor),
+            SubPolicy("solarize", 0.6, 3, "equalize", 0.6, 7, fillcolor),
+            SubPolicy("posterize", 0.8, 5, "equalize", 1.0, 2, fillcolor),
+            SubPolicy("rotate", 0.2, 3, "solarize", 0.6, 8, fillcolor),
+            SubPolicy("equalize", 0.6, 8, "posterize", 0.4, 6, fillcolor),
+            SubPolicy("rotate", 0.8, 8, "color", 0.4, 0, fillcolor),
+            SubPolicy("rotate", 0.4, 9, "equalize", 0.6, 2, fillcolor),
+            SubPolicy("equalize", 0.0, 7, "equalize", 0.8, 8, fillcolor),
+            SubPolicy("invert", 0.6, 4, "equalize", 1.0, 8, fillcolor),
+            SubPolicy("color", 0.6, 4, "contrast", 1.0, 8, fillcolor),
+            SubPolicy("rotate", 0.8, 8, "color", 1.0, 2, fillcolor),
+            SubPolicy("color", 0.8, 8, "solarize", 0.8, 7, fillcolor),
+            SubPolicy("sharpness", 0.4, 7, "invert", 0.6, 8, fillcolor),
+            SubPolicy("shearX", 0.6, 5, "equalize", 1.0, 9, fillcolor),
+            SubPolicy("color", 0.4, 0, "equalize", 0.6, 3, fillcolor),
+            SubPolicy("equalize", 0.4, 7, "solarize", 0.2, 4, fillcolor),
+            SubPolicy("solarize", 0.6, 5, "autocontrast", 0.6, 5, fillcolor),
+            SubPolicy("invert", 0.6, 4, "equalize", 1.0, 8, fillcolor),
+            SubPolicy("color", 0.6, 4, "contrast", 1.0, 8, fillcolor),
+            SubPolicy("equalize", 0.8, 8, "equalize", 0.6, 3, fillcolor),
+        ]
+
+    def __call__(self, img):
+        """Define call method for ImageNetPolicy class."""
+        policy_idx = random.randint(0, len(self.policies) - 1)
+        return self.policies[policy_idx](img)
+
+    def __repr__(self):
+        """Define repr method for ImageNetPolicy class."""
+        return "ImageNetPolicy"
+
+
+class SubPolicy:
+    """Definition of a SubPolicy.
+
+    A SubPolicy consists of two augmentation operations,
+    each of those parametrized as operation, probability, magnitude.
+    The two operations are applied sequentially on the image upon call.
+    """
+
+    def __init__(
+        self,
+        operation1,
+        probability1,
+        magnitude_idx1,
+        operation2,
+        probability2,
+        magnitude_idx2,
+        fillcolor,
+    ):
+        """Initialize a SubPolicy.
+
+        Args:
+            operation1 (str): Key specifying the first augmentation operation.
+            There are fourteen key values altogether (see supported_ops below
+            listing supported operations). probability1 (float): Probability
+            within [0., 1.] of applying the first augmentation operation.
+            magnitude_idx1 (int): Integer specifiying the strength of the first
+            operation as an index further used to derive the magnitude from a
+            range of possible values.
+            operation2 (str): Key specifying the second augmentation operation.
+            probability2 (float): Probability within [0., 1.] of applying the
+            second augmentation operation.
+            magnitude_idx2 (int): Integer specifiying the strength of the
+            second operation as an index further used to derive the magnitude
+            from a range of possible values.
+            fillcolor (tuple): RGB color components of the color to be used for
+            filling.
+        Returns:
+        """
+        # List of supported operations for operation1 and operation2.
+        supported_ops = [
+            "shearX",
+            "shearY",
+            "translateX",
+            "translateY",
+            "rotate",
+            "color",
+            "posterize",
+            "solarize",
+            "contrast",
+            "sharpness",
+            "brightness",
+            "autocontrast",
+            "equalize",
+            "invert",
+        ]
+        assert (operation1 in supported_ops) and (
+            operation2 in supported_ops
+        ), "SubPolicy:one of oper1 or oper2 refers to an unsupported operation."
+
+        assert (
+            0.0 <= probability1 <= 1.0 and 0.0 <= probability2 <= 1.0
+        ), "SubPolicy: prob1 and prob2 should be within [0., 1.]."
+
+        assert (
+            isinstance(magnitude_idx1, int) and 0 <= magnitude_idx1 <= 10
+        ), "SubPolicy: idx1 should be specified as an integer within [0, 10]."
+
+        assert (
+            isinstance(magnitude_idx2, int) and 0 <= magnitude_idx2 <= 10
+        ), "SubPolicy: idx2 should be specified as an integer within [0, 10]."
+
+        # Define a dictionary where each key refers to a specific type of
+        # augmentation and the corresponding value is a range of ten possible
+        # magnitude values for that augmentation.
+        num_levels = _MAX_LEVEL + 1
+        ranges = {
+            "shearX": np.linspace(0, 0.3, num_levels),
+            "shearY": np.linspace(0, 0.3, num_levels),
+            "translateX": np.linspace(0, 150 / 331, num_levels),
+            "translateY": np.linspace(0, 150 / 331, num_levels),
+            "rotate": np.linspace(0, 30, num_levels),
+            "color": np.linspace(0.0, 0.9, num_levels),
+            "posterize": np.round(np.linspace(8, 4, num_levels), 0).astype(
+                np.int
+            ),
+            "solarize": np.linspace(256, 0, num_levels),  # range [0, 256]
+            "contrast": np.linspace(0.0, 0.9, num_levels),
+            "sharpness": np.linspace(0.0, 0.9, num_levels),
+            "brightness": np.linspace(0.0, 0.9, num_levels),
+            "autocontrast": [0]
+            * num_levels,  # This augmentation doesn't use magnitude parameter.
+            "equalize": [0]
+            * num_levels,  # This augmentation doesn't use magnitude parameter.
+            "invert": [0]
+            * num_levels,  # This augmentation doesn't use magnitude parameter.
+        }
+
+        def rotate_with_fill(img, magnitude):
+            """Define rotation transformation with fill.
+
+            The input image is first rotated, then it is blended together with
+            a gray mask of the same size. Note that fillcolor as defined
+            elsewhere in this module doesn't apply here.
+
+            Args:
+                magnitude (float): rotation angle in degrees.
+            Returns:
+                rotated_filled (PIL Image): rotated image with gray filling for
+                disoccluded areas unveiled by the rotation.
+            """
+            rotated = img.convert("RGBA").rotate(magnitude)
+            rotated_filled = Image.composite(
+                rotated, Image.new("RGBA", rotated.size, (128,) * 4), rotated
+            )
+            return rotated_filled.convert(img.mode)
+
+        # Define a dictionary of augmentation functions where each key refers
+        # to a specific type of augmentation and the corresponding value defines
+        # the augmentation itself using a lambda function.
+        # pylint: disable=unnecessary-lambda
+        func_dict = {
+            "shearX": lambda img, magnitude: img.transform(
+                img.size,
+                Image.AFFINE,
+                (1, magnitude * random.choice([-1, 1]), 0, 0, 1, 0),
+                Image.BICUBIC,
+                fillcolor=fillcolor,
+            ),
+            "shearY": lambda img, magnitude: img.transform(
+                img.size,
+                Image.AFFINE,
+                (1, 0, 0, magnitude * random.choice([-1, 1]), 1, 0),
+                Image.BICUBIC,
+                fillcolor=fillcolor,
+            ),
+            "translateX": lambda img, magnitude: img.transform(
+                img.size,
+                Image.AFFINE,
+                (
+                    1,
+                    0,
+                    magnitude * img.size[0] * random.choice([-1, 1]),
+                    0,
+                    1,
+                    0,
+                ),
+                fillcolor=fillcolor,
+            ),
+            "translateY": lambda img, magnitude: img.transform(
+                img.size,
+                Image.AFFINE,
+                (
+                    1,
+                    0,
+                    0,
+                    0,
+                    1,
+                    magnitude * img.size[1] * random.choice([-1, 1]),
+                ),
+                fillcolor=fillcolor,
+            ),
+            "rotate": lambda img, magnitude: rotate_with_fill(img, magnitude),
+            "color": lambda img, magnitude: ImageEnhance.Color(img).enhance(
+                1 + magnitude * random.choice([-1, 1])
+            ),
+            "posterize": lambda img, magnitude: ImageOps.posterize(
+                img, magnitude
+            ),
+            "solarize": lambda img, magnitude: ImageOps.solarize(
+                img, magnitude
+            ),
+            "contrast": lambda img, magnitude: ImageEnhance.Contrast(
+                img
+            ).enhance(1 + magnitude * random.choice([-1, 1])),
+            "sharpness": lambda img, magnitude: ImageEnhance.Sharpness(
+                img
+            ).enhance(1 + magnitude * random.choice([-1, 1])),
+            "brightness": lambda img, magnitude: ImageEnhance.Brightness(
+                img
+            ).enhance(1 + magnitude * random.choice([-1, 1])),
+            "autocontrast": lambda img, magnitude: ImageOps.autocontrast(img),
+            "equalize": lambda img, magnitude: ImageOps.equalize(img),
+            "invert": lambda img, magnitude: ImageOps.invert(img),
+        }
+
+        # Store probability, function and magnitude of the first augmentation
+        # for the sub-policy.
+        self.probability1 = probability1
+        self.operation1 = func_dict[operation1]
+        self.magnitude1 = ranges[operation1][magnitude_idx1]
+
+        # Store probability, function and magnitude of the second augmentation
+        # for the sub-policy.
+        self.probability2 = probability2
+        self.operation2 = func_dict[operation2]
+        self.magnitude2 = ranges[operation2][magnitude_idx2]
+
+    def __call__(self, img):
+        """Define call method for SubPolicy class."""
+        # Randomly apply operation 1.
+        if random.random() < self.probability1:
+            img = self.operation1(img, self.magnitude1)
+
+        # Randomly apply operation 2.
+        if random.random() < self.probability2:
+            img = self.operation2(img, self.magnitude2)
+
+        return img

megatron/data/bert_dataset.py

@@ -36,13 +36,14 @@ class BertDataset(Dataset):
 
     def __init__(self, name, indexed_dataset, data_prefix,
                  num_epochs, max_num_samples, masked_lm_prob,
-                 max_seq_length, short_seq_prob, seed):
+                 max_seq_length, short_seq_prob, seed, binary_head):
 
         # Params to store.
         self.name = name
         self.seed = seed
         self.masked_lm_prob = masked_lm_prob
         self.max_seq_length = max_seq_length
+        self.binary_head = binary_head
 
         # Dataset.
         self.indexed_dataset = indexed_dataset
@@ -55,7 +56,8 @@ class BertDataset(Dataset):
                                                     self.max_seq_length,
                                                     short_seq_prob,
                                                     self.seed,
-                                                    self.name)
+                                                    self.name,
+                                                    self.binary_head)
 
         # Vocab stuff.
         tokenizer = get_tokenizer()
@@ -81,7 +83,8 @@ class BertDataset(Dataset):
                                      self.vocab_id_to_token_dict,
                                      self.cls_id, self.sep_id,
                                      self.mask_id, self.pad_id,
-                                     self.masked_lm_prob, np_rng)
+                                     self.masked_lm_prob, np_rng,
+                                     self.binary_head)
 
 
 def get_samples_mapping_(indexed_dataset,
@@ -91,7 +94,8 @@ def get_samples_mapping_(indexed_dataset,
                          max_seq_length,
                          short_seq_prob,
                          seed,
-                         name):
+                         name,
+                         binary_head):
     if not num_epochs:
         if not max_num_samples:
             raise ValueError("Need to specify either max_num_samples "
@@ -137,7 +141,8 @@ def get_samples_mapping_(indexed_dataset,
             max_seq_length - 3,  # account for added tokens
             short_seq_prob,
             seed,
-            verbose)
+            verbose,
+            2 if binary_head else 1)
         print_rank_0(' > done building sapmles index maping')
         np.save(indexmap_filename, samples_mapping, allow_pickle=True)
         print_rank_0(' > saved the index mapping in {}'.format(
@@ -173,7 +178,7 @@ def build_training_sample(sample,
                           target_seq_length, max_seq_length,
                           vocab_id_list, vocab_id_to_token_dict,
                           cls_id, sep_id, mask_id, pad_id,
-                          masked_lm_prob, np_rng):
+                          masked_lm_prob, np_rng, binary_head):
     """Biuld training sample.
 
     Arguments:
@@ -193,12 +198,21 @@ def build_training_sample(sample,
               the opper bound whereas the numpy one is exclusive.
     """
 
+    if binary_head:
         # We assume that we have at least two sentences in the sample
         assert len(sample) > 1
     assert target_seq_length <= max_seq_length
 
     # Divide sample into two segments (A and B).
-    tokens_a, tokens_b, is_next_random = get_a_and_b_segments(sample, np_rng)
+    if binary_head:
+        tokens_a, tokens_b, is_next_random = get_a_and_b_segments(sample,
+                                                                  np_rng)
+    else:
+        tokens_a = []
+        for j in range(len(sample)):
+            tokens_a.extend(sample[j])
+        tokens_b = []
+        is_next_random = False
 
     # Truncate to `target_sequence_length`.
     max_num_tokens = target_seq_length

megatron/data/biencoder_dataset_utils.py

+import os
+import time
+
+import numpy as np
+import torch
+
+from megatron import get_args, get_tokenizer, mpu, print_rank_0
+from megatron.data.dataset_utils import create_masked_lm_predictions, \
+                                            pad_and_convert_to_numpy
+from megatron.data.data_samplers import MegatronPretrainingSampler
+
+def make_attention_mask(source_block, target_block):
+    """
+    Returns a 2-dimensional (2-D) attention mask
+    :param source_block: 1-D array
+    :param target_block: 1-D array
+    """
+    mask = (target_block[None, :] >= 1) * (source_block[:, None] >= 1)
+    mask = mask.astype(np.int64)
+    # (source_length, target_length)
+    return mask
+
+def get_one_epoch_dataloader(dataset, micro_batch_size=None):
+    """Specifically one epoch to be used in an indexing job."""
+    args = get_args()
+
+    if micro_batch_size is None:
+        micro_batch_size = args.micro_batch_size
+    num_workers = args.num_workers
+
+    # Use megatron's sampler with consumed samples set to 0 as
+    # this is only for evaluation and don't intend to resume half way.
+    # Also, set the drop last to false as don't intend to remove
+    # the last batch
+    batch_sampler = MegatronPretrainingSampler(
+        total_samples=len(dataset),
+        consumed_samples=0,
+        micro_batch_size=args.micro_batch_size,
+        data_parallel_rank=mpu.get_data_parallel_rank(),
+        data_parallel_size=mpu.get_data_parallel_world_size(),
+        drop_last=False)
+
+    return torch.utils.data.DataLoader(dataset,
+                                       batch_sampler=batch_sampler,
+                                       num_workers=num_workers,
+                                       pin_memory=True)
+
+
+def get_ict_batch(data_iterator):
+    # Items and their type.
+    keys = ['query_tokens', 'query_mask',
+            'context_tokens', 'context_mask', 'block_data']
+    datatype = torch.int64
+
+    # Broadcast data.
+    if data_iterator is None:
+        data = None
+    else:
+        data = next(data_iterator)
+    data_b = mpu.broadcast_data(keys, data, datatype)
+
+    # Unpack.
+    query_tokens = data_b['query_tokens'].long()
+    query_mask = data_b['query_mask'] < 0.5
+    context_tokens = data_b['context_tokens'].long()
+    context_mask = data_b['context_mask'] < 0.5
+    block_indices = data_b['block_data'].long()
+
+    return query_tokens, query_mask,\
+           context_tokens, context_mask, block_indices
+
+
+def join_str_list(str_list):
+    """Join a list of strings, handling spaces appropriately"""
+    result = ""
+    for s in str_list:
+        if s.startswith("##"):
+            result += s[2:]
+        else:
+            result += " " + s
+    return result
+
+
+class BlockSampleData(object):
+    """A struct for fully describing a fixed-size block of data as used in REALM
+
+    :param start_idx: for first sentence of the block
+    :param end_idx: for last sentence of the block (may be partially truncated in sample construction)
+    :param doc_idx: the index of the document from which the block comes in the original indexed dataset
+    :param block_idx: a unique integer identifier given to every block.
+    """
+    def __init__(self, start_idx, end_idx, doc_idx, block_idx):
+        self.start_idx = start_idx
+        self.end_idx = end_idx
+        self.doc_idx = doc_idx
+        self.block_idx = block_idx
+
+    def as_array(self):
+        return np.array([self.start_idx, self.end_idx, self.doc_idx, self.block_idx]).astype(np.int64)
+
+    def as_tuple(self):
+        return self.start_idx, self.end_idx, self.doc_idx, self.block_idx
+
+
+class BlockSamplesMapping(object):
+    def __init__(self, mapping_array):
+        # make sure that the array is compatible with BlockSampleData
+        assert mapping_array.shape[1] == 4
+        self.mapping_array = mapping_array
+
+    def __len__(self):
+        return self.mapping_array.shape[0]
+
+    def __getitem__(self, idx):
+        """Get the data associated with an indexed sample."""
+        sample_data = BlockSampleData(*self.mapping_array[idx])
+        return sample_data
+
+
+def get_block_samples_mapping(block_dataset, title_dataset, data_prefix, num_epochs,
+                              max_num_samples, max_seq_length, seed, name, use_one_sent_docs=False):
+    """Get samples mapping for a dataset over fixed size blocks. This function also requires
+    a dataset of the titles for the source documents since their lengths must be taken into account.
+
+    :return: samples_mapping (BlockSamplesMapping)
+    """
+
+    if not num_epochs:
+        if not max_num_samples:
+            raise ValueError("Need to specify either max_num_samples "
+                             "or num_epochs")
+        num_epochs = np.iinfo(np.int32).max - 1
+    if not max_num_samples:
+        max_num_samples = np.iinfo(np.int64).max - 1
+
+    # Filename of the index mapping
+    indexmap_filename = data_prefix
+    indexmap_filename += '_{}_indexmap'.format(name)
+    if num_epochs != (np.iinfo(np.int32).max - 1):
+        indexmap_filename += '_{}ep'.format(num_epochs)
+    if max_num_samples != (np.iinfo(np.int64).max - 1):
+        indexmap_filename += '_{}mns'.format(max_num_samples)
+    indexmap_filename += '_{}msl'.format(max_seq_length)
+    indexmap_filename += '_{}s'.format(seed)
+    if use_one_sent_docs:
+        indexmap_filename += '_1sentok'
+    indexmap_filename += '.npy'
+
+    # Build the indexed mapping if not exist.
+    if mpu.get_data_parallel_rank() == 0 and \
+            not os.path.isfile(indexmap_filename):
+        print(' > WARNING: could not find index map file {}, building '
+              'the indices on rank 0 ...'.format(indexmap_filename))
+
+        # Make sure the types match the helpers input types.
+        assert block_dataset.doc_idx.dtype == np.int64
+        assert block_dataset.sizes.dtype == np.int32
+
+        # Build samples mapping
+        verbose = torch.distributed.get_rank() == 0
+        start_time = time.time()
+        print_rank_0(' > building samples index mapping for {} ...'.format(
+            name))
+
+        from megatron.data import helpers
+        mapping_array = helpers.build_blocks_mapping(
+            block_dataset.doc_idx,
+            block_dataset.sizes,
+            title_dataset.sizes,
+            num_epochs,
+            max_num_samples,
+            max_seq_length - 3,  # account for added tokens
+            seed,
+            verbose,
+            use_one_sent_docs)
+
+
+        print_rank_0(' > done building samples index mapping')
+        np.save(indexmap_filename, mapping_array, allow_pickle=True)
+        print_rank_0(' > saved the index mapping in {}'.format(
+            indexmap_filename))
+        # Make sure all the ranks have built the mapping
+        print_rank_0(' > elapsed time to build and save samples mapping '
+                     '(seconds): {:4f}'.format(
+            time.time() - start_time))
+
+    # This should be a barrier but nccl barrier assumes
+    # device_index=rank which is not the case for model
+    # parallel case
+    counts = torch.cuda.LongTensor([1])
+    torch.distributed.all_reduce(counts, group=mpu.get_data_parallel_group())
+    assert counts[0].item() == torch.distributed.get_world_size(
+        group=mpu.get_data_parallel_group())
+
+    # Load indexed dataset.
+    print_rank_0(' > loading indexed mapping from {}'.format(
+        indexmap_filename))
+    start_time = time.time()
+
+    mapping_array = np.load(indexmap_filename, allow_pickle=True, mmap_mode='r')
+    samples_mapping = BlockSamplesMapping(mapping_array)
+
+    print_rank_0('    loaded indexed file in {:3.3f} seconds'.format(
+        time.time() - start_time))
+    print_rank_0('    total number of samples: {}'.format(
+        mapping_array.shape[0]))
+
+    return samples_mapping

megatron/data/data_loaders.py → megatron/data/data_samplers.py

@@ -17,7 +17,7 @@
 
 
 import torch
-
+import random
 from megatron import get_args
 from megatron import mpu
 
@@ -30,12 +30,23 @@ def build_pretraining_data_loader(dataset, consumed_samples):
     args = get_args()
 
     # Megatron sampler
+    if args.dataloader_type == 'single':
         batch_sampler = MegatronPretrainingSampler(
             total_samples=len(dataset),
             consumed_samples=consumed_samples,
             micro_batch_size=args.micro_batch_size,
             data_parallel_rank=mpu.get_data_parallel_rank(),
             data_parallel_size=mpu.get_data_parallel_world_size())
+    elif args.dataloader_type == 'cyclic':
+        batch_sampler = MegatronPretrainingRandomSampler(
+            total_samples=len(dataset),
+            consumed_samples=consumed_samples,
+            micro_batch_size=args.micro_batch_size,
+            data_parallel_rank=mpu.get_data_parallel_rank(),
+            data_parallel_size=mpu.get_data_parallel_world_size())
+    else:
+        raise Exception('{} dataloader type is not supported.'.format(
+                args.dataloader_type))
 
     # Torch dataloader.
     return torch.utils.data.DataLoader(dataset,
@@ -43,19 +54,18 @@ def build_pretraining_data_loader(dataset, consumed_samples):
                                        num_workers=args.num_workers,
                                        pin_memory=True)
 
-
 class MegatronPretrainingSampler:
 
-
     def __init__(self, total_samples, consumed_samples, micro_batch_size,
-                 data_parallel_rank, data_parallel_size):
+                 data_parallel_rank, data_parallel_size, drop_last=True):
         # Keep a copy of input params for later use.
         self.total_samples = total_samples
         self.consumed_samples = consumed_samples
         self.micro_batch_size = micro_batch_size
         self.data_parallel_rank = data_parallel_rank
-        self.micro_batch_times_data_parallel_size = self.micro_batch_size * \
-                                                    data_parallel_size
+        self.micro_batch_times_data_parallel_size = \
+            self.micro_batch_size * data_parallel_size
+        self.drop_last = drop_last
 
         # Sanity checks.
         assert self.total_samples > 0, \
@@ -69,18 +79,79 @@ class MegatronPretrainingSampler:
             'data_parallel_rank should be smaller than data size: {}, ' \
             '{}'.format(self.data_parallel_rank, data_parallel_size)
 
-
     def __len__(self):
         return self.total_samples
 
+    def get_start_end_idx(self):
+        start_idx = self.data_parallel_rank * self.micro_batch_size
+        end_idx = start_idx + self.micro_batch_size
+        return start_idx, end_idx
 
     def __iter__(self):
         batch = []
-        # Last batch if not complete will be dropped.
+        # Last batch will be dropped if drop_last is not set False
         for idx in range(self.consumed_samples, self.total_samples):
             batch.append(idx)
             if len(batch) == self.micro_batch_times_data_parallel_size:
-                start_idx = self.data_parallel_rank * self.micro_batch_size
-                end_idx = start_idx + self.micro_batch_size
+                start_idx, end_idx = self.get_start_end_idx()
+                yield batch[start_idx:end_idx]
+                batch = []
+
+        # Check the last partial batch and see drop_last is set
+        if len(batch) > 0 and not self.drop_last:
+            start_idx, end_idx = self.get_start_end_idx()
             yield batch[start_idx:end_idx]
+
+
+class MegatronPretrainingRandomSampler:
+
+    def __init__(self, total_samples, consumed_samples, micro_batch_size,
+                 data_parallel_rank, data_parallel_size):
+        # Keep a copy of input params for later use.
+        self.total_samples = total_samples
+        self.consumed_samples = consumed_samples
+        self.micro_batch_size = micro_batch_size
+        self.data_parallel_rank = data_parallel_rank
+        self.data_parallel_size = data_parallel_size
+        self.micro_batch_times_data_parallel_size = \
+            self.micro_batch_size * data_parallel_size
+        self.last_batch_size = \
+            self.total_samples % self.micro_batch_times_data_parallel_size
+
+        # Sanity checks.
+        assert self.total_samples > 0, \
+            'no sample to consume: {}'.format(self.total_samples)
+        assert self.micro_batch_size > 0
+        assert data_parallel_size > 0
+        assert self.data_parallel_rank < data_parallel_size, \
+            'data_parallel_rank should be smaller than data size: {}, ' \
+            '{}'.format(self.data_parallel_rank, data_parallel_size)
+
+    def __len__(self):
+        return self.total_samples
+
+    def __iter__(self):
+        active_total_samples = self.total_samples - self.last_batch_size
+        self.epoch = self.consumed_samples // active_total_samples
+        current_epoch_samples = self.consumed_samples % active_total_samples
+        assert current_epoch_samples % self.micro_batch_times_data_parallel_size == 0
+
+        # data sharding and random sampling
+        bucket_size = (self.total_samples // self.micro_batch_times_data_parallel_size) \
+                       * self.micro_batch_size
+        bucket_offset = current_epoch_samples // self.data_parallel_size
+        start_idx = self.data_parallel_rank * bucket_size
+        
+        g = torch.Generator()
+        g.manual_seed(self.epoch)
+        random_idx = torch.randperm(bucket_size, generator=g).tolist()
+        idx_range = [start_idx + x for x in random_idx[bucket_offset:]]
+
+        batch = []
+        # Last batch if not complete will be dropped.
+        for idx in idx_range:
+            batch.append(idx)
+            if len(batch) == self.micro_batch_size:
+                self.consumed_samples += self.micro_batch_times_data_parallel_size
+                yield batch
                 batch = []

megatron/data/dataset_utils.py

@@ -114,7 +114,6 @@ def truncate_segments(tokens_a, tokens_b, len_a, len_b, max_num_tokens, np_rng):
     """Truncates a pair of sequences to a maximum sequence length."""
     #print(len_a, len_b, max_num_tokens)
     assert len_a > 0
-    assert len_b > 0
     if len_a + len_b <= max_num_tokens:
         return False
     while len_a + len_b > max_num_tokens:
@@ -150,6 +149,7 @@ def create_tokens_and_tokentypes(tokens_a, tokens_b, cls_id, sep_id):
     for token in tokens_b:
         tokens.append(token)
         tokentypes.append(1)
+    if tokens_b:
         # [SEP].
         tokens.append(sep_id)
         tokentypes.append(1)
@@ -392,6 +392,7 @@ def build_train_valid_test_datasets(data_prefix, data_impl, splits_string,
                                     train_valid_test_num_samples,
                                     max_seq_length, masked_lm_prob,
                                     short_seq_prob, seed, skip_warmup,
+                                    binary_head,
                                     dataset_type='standard_bert'):
 
     if len(data_prefix) == 1:
@@ -401,6 +402,7 @@ def build_train_valid_test_datasets(data_prefix, data_impl, splits_string,
                                                 max_seq_length, masked_lm_prob,
                                                 short_seq_prob, seed,
                                                 skip_warmup,
+                                                binary_head,
                                                 dataset_type=dataset_type)
     # Blending dataset.
     # Parse the values.
@@ -417,7 +419,7 @@ def build_train_valid_test_datasets(data_prefix, data_impl, splits_string,
             prefixes[i], data_impl, splits_string,
             datasets_train_valid_test_num_samples[i],
             max_seq_length, masked_lm_prob, short_seq_prob,
-            seed, skip_warmup, dataset_type=dataset_type)
+            seed, skip_warmup, binary_head, dataset_type=dataset_type)
         if train_ds:
             train_datasets.append(train_ds)
         if valid_ds:
@@ -444,6 +446,7 @@ def _build_train_valid_test_datasets(data_prefix, data_impl, splits_string,
                                      train_valid_test_num_samples,
                                      max_seq_length, masked_lm_prob,
                                      short_seq_prob, seed, skip_warmup,
+                                     binary_head,
                                      dataset_type='standard_bert'):
     
     if dataset_type not in DSET_TYPES:
@@ -503,7 +506,8 @@ def _build_train_valid_test_datasets(data_prefix, data_impl, splits_string,
                 num_epochs=None,
                 max_num_samples=train_valid_test_num_samples[index],
                 max_seq_length=max_seq_length,
-                seed=seed
+                seed=seed,
+                binary_head=binary_head
             )
 
             if dataset_type == DSET_TYPE_ICT:

megatron/data/helpers.cpp

@@ -189,6 +189,9 @@ inline int32_t get_target_sample_len(const int32_t short_seq_ratio,
 				     const int32_t max_length,
 				     std::mt19937& rand32_gen) {
     /* Training sample length. */
+    if (short_seq_ratio == 0) {
+      return max_length;
+    }
     const auto random_number = rand32_gen();
     if ((random_number % short_seq_ratio) == 0) {
       return 2 + random_number % (max_length - 1);
@@ -205,7 +208,8 @@ py::array build_mapping_impl(const py::array_t<int64_t>& docs_,
                              const int32_t max_seq_length,
                              const double short_seq_prob,
                              const int32_t seed,
-			     const bool verbose) {
+			     const bool verbose,
+			     const int32_t min_num_sent) {
     /* Build a mapping of (start-index, end-index, sequence-length) where
        start and end index are the indices of the sentences in the sample
        and sequence-length is the target sequence length.
@@ -214,7 +218,7 @@ py::array build_mapping_impl(const py::array_t<int64_t>& docs_,
     // Consistency checks.
     assert(num_epochs > 0);
     assert(max_seq_length > 1);
-    assert(short_seq_prob > 0.0);
+    assert(short_seq_prob >= 0.0);
     assert(short_seq_prob <= 1.0);
     assert(seed > 0);
 
@@ -223,7 +227,10 @@ py::array build_mapping_impl(const py::array_t<int64_t>& docs_,
     auto sizes = sizes_.unchecked<1>();
 
     // For efficiency, convert probability to ratio. Note: rand() generates int.
-    const auto short_seq_ratio = static_cast<int32_t>(round(1.0 / short_seq_prob));
+    int32_t short_seq_ratio = 0;
+    if (short_seq_prob > 0) {
+      short_seq_ratio = static_cast<int32_t>(round(1.0 / short_seq_prob));
+    }
 
     if (verbose) {
         const auto sent_start_index = docs[0];
@@ -322,7 +329,7 @@ py::array build_mapping_impl(const py::array_t<int64_t>& docs_,
 		}
 
                 // If we have more than two sentences.
-                if ((num_remain_sent > 1) && (!contains_long_sentence)) {
+                if ((num_remain_sent >= min_num_sent) && (!contains_long_sentence)) {
 
                     // Set values.
                     auto seq_len = int32_t{0};
@@ -346,7 +353,7 @@ py::array build_mapping_impl(const py::array_t<int64_t>& docs_,
 			// and if we have reached end of the document.
 			if (((seq_len >= target_seq_len) &&
 			     (num_remain_sent > 1) &&
-			     (num_sent > 1) ) || (num_remain_sent == 0)) {
+			     (num_sent >= min_num_sent) ) || (num_remain_sent == 0)) {
 
 			    // Check for overflow.
 			    if ((3 * map_index + 2) >
@@ -437,7 +444,8 @@ py::array build_mapping(const py::array_t<int64_t>& docs_,
                         const int max_seq_length,
                         const double short_seq_prob,
                         const int seed,
-			const bool verbose) {
+			const bool verbose,
+			const int32_t min_num_sent) {
 
     if (sizes_.size() > std::numeric_limits<uint32_t>::max()) {
         if (verbose) {
@@ -445,14 +453,16 @@ py::array build_mapping(const py::array_t<int64_t>& docs_,
 	}
 	return build_mapping_impl<uint64_t>(docs_, sizes_, num_epochs,
 					    max_num_samples, max_seq_length,
-					    short_seq_prob, seed, verbose);
+					    short_seq_prob, seed, verbose,
+					    min_num_sent);
     } else {
        if (verbose) {
 	   cout << "    using uint32 for data mapping..." << endl << std::flush;
        }
        return build_mapping_impl<uint32_t>(docs_, sizes_, num_epochs,
 					   max_num_samples, max_seq_length,
-					   short_seq_prob, seed, verbose);
+					   short_seq_prob, seed, verbose,
+					   min_num_sent);
     }
 }