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README.md
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[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 multinode training of [GPT-2](https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf) and [BERT](https://arxiv.org/pdf/1810.04805.pdf) using mixed precision.
[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.
Using our GPT-2 model we achieve a perplexity of 10.8 on the WikiText-103 dataset (improving SOTA from 15.8) and an accuracy of 66.5% on the LAMBADA datasets. For BERT training, we swapped the position of the layer normalization and the residual connection in the model architecture (similar to GPT-2 architucture), which allowed the models to continue to improve as they were scaled up. Our BERT model with 3.9 billion parameters reaches a loss of 1.16, SQuAD 2.0 F1-score of 91.7, and RACE accuracy of 90.9%.
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)
* [BioMegatron: Larger Biomedical Domain Language Model](https://www.aclweb.org/anthology/2020.emnlp-main.379.pdf)
* [End-to-End Training of Neural Retrievers for Open-Domain Question Answering](https://arxiv.org/abs/2101.00408)
* [Large Scale Multi-Actor Generative Dialog Modeling](https://www.aclweb.org/anthology/2020.acl-main.8.pdf)
* [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)
* [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 (several billion parameter) language models with both model and data parallelism. To demonstrate how the code scales with multiple GPUs we consider the following GPT-2 model sizes. All models use a vocabulary size of 51,200 and a sequence length of 1024.
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.
![Cases](images/cases.png)
![Cases](images/cases_jan2021.png)
The table below details the weak scaling from 1 to 8 GPUs of our model parallelism code in both a DGX-2 and a DGX-A100. Notice that we double the batch size on the DGX-A100 but the iteration time decreases compared to the DGX-2 resulting in a **2.1x** speedup for the end-to-end application.
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.
![Model Parallel Scaling](images/scaling-mp.png)
![Model Parallel Scaling](images/scaling.png)
The following table details how Megatron scales using data parallelism in conjuction with model parallelism in a cluster of DGX-A100s. All of these cases use 128-way data parallelism and the scaling numbers are relative to a single A100 (Case 1B with a 1076ms iteration time).
![Data Parallel Scaling](images/scaling-dp.png)
<a id="contents"></a>
# Contents
<!-- MarkdownTOC -->
- [Setup](#setup)
- [Downloading Checkpoints](#downloading-checkpoints)
- [Usage](#usage)
- [Training](#training)
- [Data Preprocessing](#data-preprocessing)
- [BERT Pretraining](#bert-pretraining)
- [GPT-2 Pretraining](#gpt-2-pretraining)
- [Distributed BERT or GPT-2 Pretraining](#distributed-bert-or-gpt-2-pretraining)
- [REALM Pipeline](#realm)
- [Evaluation and Tasks](#evaluation-and-tasks)
- [GPT-2 Text Generation](#gpt-2-text-generation)
- [GPT-2 Evaluation](#gpt-2-evaluation)
- [WikiText Perplexity Evaluation](#wikitext-perplexity-evaluation)
- [LAMBADA Cloze Accuracy](#lambada-cloze-accuracy)
- [BERT Task Evaluation](#bert-task-evaluation)
- [RACE Evaluation](#race-evaluation)
- [MNLI Evaluation](#mnli-evaluation)
- [Datasets](#datasets)
- [Collecting Wikipedia Training Data](#collecting-wikipedia-training-data)
- [Collecting GPT-2 Webtext Data](#collecting-gpt-2-webtext-data)
<!-- /MarkdownTOC -->
<a id="setup"></a>
* [Contents](#contents)
* [Setup](#setup)
* [Downloading Checkpoints](#downloading-checkpoints)
* [Usage](#usage)
* [Training](#training)
* [Data Preprocessing](#data-preprocessing)
* [BERT Pretraining](#bert-pretraining)
* [GPT Pretraining](#gpt-pretraining)
* [Distributed BERT or GPT Pretraining](#distributed-bert-or-gpt-pretraining)
* [GPT-3 Example](#gpt-3-example)
* [Evaluation and Tasks](#evaluation-and-tasks)
* [GPT Text Generation](#gpt-text-generation)
* [GPT Evaluation](#gpt-evaluation)
* [WikiText Perplexity Evaluation](#wikitext-perplexity-evaluation)
* [LAMBADA Cloze Accuracy](#lambada-cloze-accuracy)
* [BERT Task Evaluation](#bert-task-evaluation)
* [RACE Evaluation](#race-evaluation)
* [MNLI Evaluation](#mnli-evaluation)
* [Datasets](#datasets)
* [Collecting Wikipedia Training Data](#collecting-wikipedia-training-data)
* [Collecting GPT Webtext Data](#collecting-gpt-webtext-data)
# Setup
We officially support only python 3.6, pytorch 1.5, cuda 10, and nccl 2.6 versions and above.
We have tested Megatron with [NGC's PyTorch container](https://ngc.nvidia.com/catalog/containers/nvidia:pytorch) version 20.12, which uses python 3.8, pytorch 1.8, cuda 11.1, and nccl 2.8.3.
To use this repo please install the latest supported versions of PyTorch with GPU support and NVIDIA [APEX](https://github.com/NVIDIA/apex#quick-start). We strongly recommend using one of [NGC's recent PyTorch containers](https://ngc.nvidia.com/catalog/containers/nvidia:pytorch) (the latest compatible version at time of publication can be pulled with `docker pull nvcr.io/nvidia/pytorch:20.03-py3`). Data preprocessing requires [NLTK](https://www.nltk.org/install.html), though this is not required for training, evaluation or downstream tasks.
To use this repository, please install the latest supported versions of PyTorch with GPU support (python 3.8, pytorch 1.8, cuda 11.1, and nccl 2.8.3 and above) and NVIDIA [APEX](https://github.com/NVIDIA/apex#quick-start). We strongly recommend using one of [NGC's recent PyTorch containers](https://ngc.nvidia.com/catalog/containers/nvidia:pytorch) (the latest compatible version at time of publication can be pulled with `docker pull nvcr.io/nvidia/pytorch:20.12-py3`). Data preprocessing requires [NLTK](https://www.nltk.org/install.html), though this is not required for training, evaluation, or downstream tasks.
<!--
To use megatron you can either clone the repo or install it via pip (make sure python3-dev is installed):
<pre>
pip install megatron-lm
</pre>
-->
<a id="downloading-checkpoints"></a>
## Downloading Checkpoints
We've provided two pretrained checkpoints for use to evaluate or finetuning downstream tasks. To access these checkpoints, first please [sign up](https://ngc.nvidia.com/signup) for and [setup](https://ngc.nvidia.com/setup/installers/cli) the NVIDIA GPU Cloud (NGC) Registry CLI.
We have provided pretrained [BERT-345M](https://ngc.nvidia.com/catalog/models/nvidia:megatron_bert_345m) and [GPT-345M](https://ngc.nvidia.com/catalog/models/nvidia:megatron_lm_345m) checkpoints for use to evaluate or finetuning downstream tasks. To access these checkpoints, first [sign up](https://ngc.nvidia.com/signup) for and [setup](https://ngc.nvidia.com/setup/installers/cli) the NVIDIA GPU Cloud (NGC) Registry CLI. Further documentation for downloading models can be found in the [NGC documentation](https://docs.nvidia.com/dgx/ngc-registry-cli-user-guide/index.html#topic_6_4_1).
Alternatively, you can directly download the checkpoints using:
The checkpoints can be downloaded with:
<pre>
ngc registry model download-version --dest &#60;output_base_directory&#62; nvidia/&#60;model_name&#62;:&#60;version&#62;
BERT-345M-uncased: wget --content-disposition https://api.ngc.nvidia.com/v2/models/nvidia/megatron_bert_345m/versions/v0.1_cased/zip -O megatron_bert_345m_v0.1_uncased.zip
BERT-345M-cased: wget --content-disposition https://api.ngc.nvidia.com/v2/models/nvidia/megatron_bert_345m/versions/v0.1_cased/zip -O megatron_bert_345m_v0.1_cased.zip
GPT-345M: wget --content-disposition https://api.ngc.nvidia.com/v2/models/nvidia/megatron_lm_345m/versions/v0.0/zip -O megatron_lm_345m_v0.0.zip
</pre>
The available models along with `<model_name>:<version>` are below:
* [BERT-345M](https://ngc.nvidia.com/catalog/models/nvidia:megatron_bert_345m): megatron\_bert\_345m:v0.0
* [GPT-2-345M](https://ngc.nvidia.com/catalog/models/nvidia:megatron_lm_345m): megatron\_lm\_345m:v0.0
The models require vocabulary files to run. The BERT uncased WordPiece vocab file can be extracted from Google's [pretrained BERT models](https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-uncased-vocab.txt). The GPT-2 [vocab file](https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-vocab.json) and [merge table](https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-merges.txt) can be downloaded directly.
Further documentation for downloading models can be found in the [NGC documentation](https://docs.nvidia.com/dgx/ngc-registry-cli-user-guide/index.html#topic_6_4_1)
The models require vocabulary files to run. The BERT WordPiece vocab file can be extracted from Google's pretrained BERT models: [uncased](https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-uncased-vocab.txt), [cased](https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-cased-vocab.txt). The GPT [vocab file](https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-vocab.json) and [merge table](https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-merges.txt) can be downloaded directly.
<a id="usage"></a>
# Usage
After installation, there are several possible workflows. The most comprehensive is:
......@@ -80,13 +76,11 @@ After installation, there are several possible workflows. The most comprehensive
However, steps 1 and 2 can be replaced by using one of the pretrained models mentioned above.
We've provided several scripts for pretraining both BERT and GPT-2 in [`examples`](./examples) directory, as well as scripts for both zero-shot and fine-tuned downstream tasks including MNLI, RACE, WikiText103, and LAMBADA evaluation. There is also a script for GPT-2 interactive text generation.
We've provided several scripts for pretraining both BERT and GPT in [`examples`](./examples) directory, as well as scripts for both zero-shot and fine-tuned downstream tasks including MNLI, RACE, WikiText103, and LAMBADA evaluation. There is also a script for GPT interactive text generation.
<a id="training"></a>
# Training
<a id="data-preprocessing"></a>
## Data Preprocessing
We support three file formats for training, but all require preprocessing. First, place your training data in a loose json format, with one json containing a text sample per line. For example:
The training data requires preprocessing. First, place your training data in a loose json format, with one json containing a text sample per line. For example:
<pre>
{"src": "www.nvidia.com", "text": "The quick brown fox", "type": "Eng", "id": "0", "title": "First Part"}
{"src": "The Internet", "text": "jumps over the lazy dog", "type": "Eng", "id": "42", "title": "Second Part"}
......@@ -107,7 +101,7 @@ python tools/preprocess_data.py \
The output will be two files named, in this case, `my-bert_text_sentence.bin` and `my-bert_text_sentence.idx`. The `--data-path` specified in later BERT training is the full path and new filename, but without the file extension.
Some minor modifications are required for GPT-2 data preprocessing, namely, the addition of a merge table, an end-of-document token, removal of sentence splitting, and a change to the tokenizer type:
Some minor modifications are required for GPT data preprocessing, namely, the addition of a merge table, an end-of-document token, removal of sentence splitting, and a change to the tokenizer type:
<pre>
python tools/preprocess_data.py \
--input my-corpus.json \
......@@ -119,15 +113,14 @@ python tools/preprocess_data.py \
--append-eod
</pre>
Here the output files are named `my-gpt2_text_document.bin` and `my-gpt2_text_document.idx`. As before, in GPT-2 training, use the longer name without the extension as `--data-path`.
Here the output files are named `my-gpt2_text_document.bin` and `my-gpt2_text_document.idx`. As before, in GPT training, use the longer name without the extension as `--data-path`.
Further command line arguments are described in the source file [`preprocess_data.py`](./tools/preprocess_data.py).
<a id="bert-pretraining"></a>
## BERT Pretraining
`bash examples/pretrain_bert.sh`
This script runs single GPU 345M parameter BERT pretraining. Debugging is the primary use for single GPU training, as the code base and command line arguments are optimized for highly distributed training. Most of the arguments are fairly self-explanatory. By default, the learning rate decays linearly over the training iterations starting at `--lr` to a minimum set by `--min-lr` over `--lr-decay-iters` iterations. The fraction of training iterations used for warmup is set by `--warmup`. While this is single GPU training, the batch size specified by `--batch-size` is per GPU used for data parallelism. The data is partitioned into a 949:50:1 ratio for training/validation/test sets (default is 969:30:1). This partitioning happens on the fly, but is consistent across runs with the same random seed (1234 by default, or specified manually with `--seed`).
This script runs single GPU 345M parameter BERT pretraining. Debugging is the primary use for single GPU training, as the code base and command line arguments are optimized for highly distributed training. Most of the arguments are fairly self-explanatory. By default, the learning rate decays linearly over the training iterations starting at `--lr` to a minimum set by `--min-lr` over `--lr-decay-iters` iterations. The fraction of training iterations used for warmup is set by `--lr-warmup-fraction`. While this is single GPU training, the batch size specified by `--micro-batch-size` is a single forward-backward path batch-size and the code will perform gradient accumulation steps until it reaches `global-batch-size` whcih is the batch size per iteration. The data is partitioned into a 949:50:1 ratio for training/validation/test sets (default is 969:30:1). This partitioning happens on the fly, but is consistent across runs with the same random seed (1234 by default, or specified manually with `--seed`). We use `train-iters` as the training iterations requested. Alternatively, one can provide `--train-samples` which is total number of samples to train on. If this option is present, then instead of providing `--lr-decay-iters`, one will need to provide `--lr-decay-samples`.
The logging, checkpoint-saving, and evaluation intervals are specified. Checkpointing the activations facilitates the training of larger models and/or batches. Note that the `--data-path` now includes the additional `_text_sentence` suffix added in preprocessing, but does not include the file extensions.
......@@ -142,11 +135,12 @@ BERT_ARGS="--num-layers 24 \
--seq-length 512 \
--max-position-embeddings 512 \
--lr 0.0001 \
--lr-decay-iters 990000 \
--train-iters 2000000 \
--min-lr 0.00001 \
--lr-decay-iters 990000 \
--warmup 0.01 \
--batch-size 8 \
--lr-warmup-fraction 0.01 \
--micro-batch-size 4 \
--global-batch-size 8 \
--vocab-file $VOCAB_FILE \
--split 949,50,1 \
--fp16"
......@@ -167,11 +161,11 @@ python pretrain_bert.py \
Further command line arguments are described in the source file [`arguments.py`](./megatron/arguments.py).
<a id="gpt-2-pretraining"></a>
## GPT-2 Pretraining
`bash examples/pretrain_gpt2.sh`
This script runs single GPU 345M parameter GPT-2 pretraining. As mentioned above, single GPU training is primarily intended for debugging purposes, as the code is optimized for distributed training.
## GPT Pretraining
`bash examples/pretrain_gpt.sh`
This script runs single GPU 345M parameter GPT pretraining. As mentioned above, single GPU training is primarily intended for debugging purposes, as the code is optimized for distributed training.
It follows largely the same format as the previous BERT script with a few notable differences: the tokenization scheme used is BPE (which requires a merge table and a `json` vocabulary file) instead of WordPiece, the model architecture allows for longer sequences (note that the max position embedding must be greater than or equal to the maximum sequence length), and the `--lr-decay-style` has been set to cosine decay. Note that the `--data-path` now includes the additional `_text_document` suffix added in preprocessing, but does not include the file extensions.
......@@ -181,25 +175,26 @@ VOCAB_FILE=gpt2-vocab.json
MERGE_FILE=gpt2-merges.txt
DATA_PATH=my-gpt2_text_document
GPT2_ARGS="--num-layers 24 \
--hidden-size 1024 \
--num-attention-heads 16 \
--seq-length 1024 \
--max-position-embeddings 1024 \
--batch-size 4 \
--lr 0.00015 \
--train-iters 500000 \
--lr-decay-iters 320000 \
--lr-decay-style cosine \
--vocab-file $VOCAB_FILE \
--merge-file $MERGE_FILE \
--warmup .01 \
--fp16"
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 \
--vocab-file $VOCAB_FILE \
--merge-file $MERGE_FILE \
--lr-warmup-fraction .01 \
--fp16"
OUTPUT_ARGS=&#60;same as those in <a href="#bert-pretraining">BERT pretraining</a> above&#62;
python pretrain_gpt2.py \
$GPT2_ARGS \
python pretrain_gpt.py \
$GPT_ARGS \
$OUTPUT_ARGS \
--save $CHECKPOINT_PATH \
--load $CHECKPOINT_PATH \
......@@ -208,22 +203,24 @@ python pretrain_gpt2.py \
Further command line arguments are described in the source file [`arguments.py`](./megatron/arguments.py).
<a id="distributed-bert-or-gpt-2-pretraining"></a>
## Distributed BERT or GPT-2 Pretraining
## Distributed BERT or GPT Pretraining
`bash examples/pretrain_bert_distributed.sh`
`bash examples/pretrain_gpt2_distributed.sh`
`bash examples/pretrain_gpt_distributed.sh`
These scripts use the PyTorch distributed launcher for distributed training. As such, multi-node training can be achieved by properly setting environment variables and using `init_method='env://'` in the launcher. See the official PyTorch [documentation](https://pytorch.org/docs/stable/distributed.html#launch-utility) for further description of these [environment variables](https://pytorch.org/docs/stable/distributed.html#environment-variable-initialization). By default, multi-node training uses the [nccl](https://developer.nvidia.com/nccl) distributed backend. A simple set of additional arguments and the use of the PyTorch distributed module with the Python flag `-m torch.distributed.launch`, detailed below, are the only additional requirements to adopt distributed training.
These scripts use the PyTorch distributed launcher for distributed training. As such, multinode training can be achieved by properly setting environment variables and using `init_method='env://'` in the launcher. See the official PyTorch [documentation](https://pytorch.org/docs/stable/distributed.html#launch-utility) for further description of these [environment variables](https://pytorch.org/docs/stable/distributed.html#environment-variable-initialization). By default, multinode training uses the [nccl](https://developer.nvidia.com/nccl) distributed backend. A simple set of additional arguments and the use of the PyTorch distributed module with the Python flag `-m torch.distributed.launch`, detailed below, are the only additional requirements to adopt distributed training.
We use two types of parallelism: data and model parallelism. We facilitate two distributed data parallel implementations: a simple one of our own that performs gradient all-reduce at the end of back propagation step, and Torch's distributed data parallel wrapper that overlaps gradient reduction with back propagation computation. To switch between these two options use `--DDP-impl local` or `--DDP-impl torch`, respectively. As expected, Torch distributed data parallelism is more efficient at larger model sizes. For example, for the 8.3 billion parameters model running on 512 GPUs, the scaling increases from 60% to 76% when Torch's distributed data parallel is used. However, the overlapping method requires more memory and for some configurations (e.g., 2.5 billion parameters using 2-way model parallel and 1.2 billion parameters with no model parallel) can make the overall training slower as a result. We empirically found that using a smaller model in those cases improves the training time.
The two tiers of parallelism are data and model parallelism. First, we facilitate two distributed data parallel implementations: a simple one of our own that performs gradient all-reduce at the end of back propagation step, and Torch's distributed data parallel wrapper that overlaps gradient reduction with back propagation computation. To switch between these two options use `--DDP-impl local` or `--DDP-impl torch`, respectively. As expected, Torch distributed data parallelism is more efficient at larger model parallel sizes. For example, for the 8.3 billion parameters model running on 512 GPUs, the scaling increases from 60% to 76% when Torch's distributed data parallel is used. However, the overlapping method requires more memory and for some configurations (e.g., 2.5 billion parameters using 2-way model parallel and 1.2 billion parameters with no model parallel) can make the overall training slower as a result. We empirically found that using a smaller model in those cases improves the training time.
Second, we developed a simple and efficient two-dimensional model-parallel approach. To use tensor model parallelism (splitting execution of a single transformer module over multiple GPUs), add the `--tensor-model-parallel-size` flag to specify the number of GPUs among which to split the model, along with the arguments passed to the distributed launcher as mentioned above. To use pipeline model parallelism (sharding the transformer modules into stages with an equal number of transformer modules on each stage, and then pipelining execution by breaking the batch into smaller microbatches), use the `--pipeline-model-parallel-size` flag to specify the number of stages to split the model into (e.g., splitting a model with 24 transformer layers across 4 stages would mean each stage gets 6 transformer layers each).
Second, we developed a simple and efficient two-dimensional model-parallel approach. To use tensor model parallelism (splitting execution of a single transformer module over multiple GPUs), add the `--tensor-model-parallel-size` flag to specify the number of GPUs among which to split the model, along with the arguments passed to the distributed launcher as mentioned above. To use pipeline model parallelism (sharding the transformer modules into stages with an equal number of transformer modules on each stage, and then pipelining execution by breaking the batch into smaller microbatches), use the `--pipeline-model-parallel-size` flag to specify the number of stages to split the model into (e.g., splitting a model with 24 transformer layers across 4 stages would mean each stage gets 6 transformer layers each). The number of microbatches in a per-pipeline minibatch is controlled by the `--num-microbatches-in-minibatch` argument. With `WORLD_SIZE` GPUs, `TENSOR_MP_SIZE` tensor-model-parallel size, `PIPELINE_MP_SIZE` pipeline-model-parallel-size, `WORLD_SIZE`/(`TENSOR_MP_SIZE` * `PIPELINE_MP_SIZE`) GPUs will be used for data parallelism. The default values for `--tensor-model-parallel-size` and `--pipeline-model-parallel-size` is 1, which will not implement either form of model parallelism.
<!-- The number of microbatches in a per-pipeline minibatch is controlled by the `--num-microbatches-in-minibatch` argument. With `WORLD_SIZE` GPUs, `TENSOR_MP_SIZE` tensor-model-parallel size, `PIPELINE_MP_SIZE` pipeline-model-parallel-size, `WORLD_SIZE`/(`TENSOR_MP_SIZE` * `PIPELINE_MP_SIZE`) GPUs will be used for data parallelism. The default values for `--tensor-model-parallel-size` and `--pipeline-model-parallel-size` is 1, which will not implement either form of model parallelism. -->
We have examples of how to use these two different forms of model parallelism in these scripts:
`bash examples/pretrain_bert_distributed_with_mp.sh`
`bash examples/pretrain_gpt2_distributed_with_mp.sh`
`bash examples/pretrain_gpt_distributed_with_mp.sh`
Other than these minor changes, the distributed training is identical to the training on a single GPU.
......@@ -254,7 +251,7 @@ python -m torch.distributed.launch $DISTRIBUTED_ARGS ./pretrain_bert.py \
--DDP-impl torch
</pre>
Distributed GPT-2 training:
Distributed GPT training:
<pre>
WORLD_SIZE=8
MP_SIZE=2
......@@ -265,11 +262,11 @@ CHECKPOINT_PATH=checkpoints/gpt2_345m
VOCAB_FILE=gpt2-vocab.json
MERGE_FILE=gpt2-merges.txt
DATA_PATH=my-gpt2_text_document
GPT2_ARGS=&#60;same as those in <a href="#gpt-2-pretraining">GPT-2 pretraining</a> above&#62;
GPT_ARGS=&#60;same as those in <a href="#gpt-pretraining">GPT pretraining</a> above&#62;
OUTPUT_ARGS=&#60;same as those in <a href="#bert-pretraining">BERT pretraining</a> above&#62;
python -m torch.distributed.launch $DISTRIBUTED_ARGS ./pretrain_gpt2.py \
$GPT2_ARGS \
python -m torch.distributed.launch $DISTRIBUTED_ARGS ./pretrain_gpt.py \
$GPT_ARGS \
$OUTPUT_ARGS \
--save $CHECKPOINT_PATH \
--load $CHECKPOINT_PATH \
......@@ -279,7 +276,15 @@ python -m torch.distributed.launch $DISTRIBUTED_ARGS ./pretrain_gpt2.py \
</pre>
<a id="realm"></a>
## GPT-3 Example
`bash examples/pretrain_gpt3_175B.sh`
We have provided an example of how to configure Megatron to run [GPT-3](https://arxiv.org/abs/2005.14165) with 175 billion parameters on 1024 GPUs. The script is designed for [slurm](https://slurm.schedmd.com/documentation.html) with [pyxis](https://github.com/NVIDIA/pyxis) plugin but can be easily adopted to any other scheduler. It uses 8-way and 16-way tensor and pipeline parallelism, respectively. With options `global-batch-size 1536` and `rampup-batch-size 16 16 5859375`, the training will start with global batch size 16 and linearly increase the global batch size to 1536 over 5,859,375 samples with incrmeental steps 16. The training dataset can be either a single set or a multiple datasets combined with a set of weights.
With full global batch size of 1536 on 1024 A100 GPUs, each iteration takes around 32 seconds resulting in 138 teraFLOPs per GPU which is 44% of the theoretical peak FLOPs.
<!--
## REALM Pipeline
We are working on implementing the [REALM](https://arxiv.org/pdf/2002.08909.pdf) system. The following sections (will) reflect the three stages of training it. For now it's just the ICT code.
Loosely, they are pretraining the retriever modules, then jointly training the language model and the retriever, and then finetuning a question answering head on the language model with fixed retriever.
......@@ -327,7 +332,7 @@ python pretrain_ict.py \
--lr-decay-style linear \
--weight-decay 1e-2 \
--clip-grad 1.0 \
--warmup .01 \
--lr-warmup-fraction .01 \
--save-interval 3000 \
--query-in-block-prob 0.1 \
--fp16
......@@ -359,11 +364,13 @@ python tools/create_doc_index.py \
--fp16
</pre>
<a id="evaluation-and-tasks"></a>
-->
# Evaluation and Tasks
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.
<pre>
......@@ -385,20 +392,19 @@ WORLD_SIZE=$TENSOR_MODEL_PARALLEL_SIZE python tools/merge_mp_partitions.py \
--load $CHECKPOINT_PATH
</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.
Several downstream tasks are described for both GPT-2 and BERT models below. They can be run in distributed and model parallel modes with the same changes used in the training scripts.
<a id="gpt-2-text-generation"></a>
## GPT-2 Text Generation
## GPT Text Generation
`bash examples/generate_text.sh`
We generate text samples using largely the GPT-2 pretraining script. Few changes need to make, such as we need to provide the path to the pretrained checkpoint, the length of the output samples, whether to generate texts unconditionally (`--num-samples` to denote how many samples to generate) or conditional (need to pass `--sample-input-file <filename>` where each line of the file will be used as the conditional texts). There are few optional parameters to play, e.g. `top-k`, `top-p`, or `greedy` (set top-k and top-p to 0) sampling..
We generate text samples using largely the GPT pretraining script. Few changes need to make, such as we need to provide the path to the pretrained checkpoint, the length of the output samples, whether to generate texts unconditionally (`--num-samples` to denote how many samples to generate) or conditional (need to pass `--sample-input-file <filename>` where each line of the file will be used as the conditional texts). There are few optional parameters to play, e.g. `top-k`, `top-p`, or `greedy` (set top-k and top-p to 0) sampling..
<pre>
CHECKPOINT_PATH=checkpoints/gpt2_345m
VOCAB_FILE=gpt2-vocab.json
MERGE_FILE=gpt2-merges.txt
GPT2_ARGS=&#60;same as those in <a href="#gpt-2-pretraining">GPT-2 pretraining</a> above&#62;
GPT_ARGS=&#60;same as those in <a href="#gpt-pretraining">GPT pretraining</a> above&#62;
MAX_OUTPUT_SEQUENCE_LENGTH=1024
TEMPERATURE=1.0
......@@ -406,8 +412,8 @@ TOP_P=0.9
NUMBER_OF_SAMPLES=2
OUTPUT_FILE=samples.json
python tools/generate_samples_gpt2.py \
$GPT2_ARGS \
python tools/generate_samples_gpt.py \
$GPT_ARGS \
--load $CHECKPOINT_PATH \
--out-seq-length $MAX_OUTPUT_SEQUENCE_LENGTH \
--temperature $TEMPERATURE \
......@@ -417,11 +423,9 @@ python tools/generate_samples_gpt2.py \
--recompute
</pre>
<a id="gpt-2-evaluation"></a>
## GPT-2 Evaluation
We include example scripts for GPT-2 evaluation on WikiText perplexity evaluation and LAMBADA Cloze accuracy.
## GPT Evaluation
We include example scripts for GPT evaluation on WikiText perplexity evaluation and LAMBADA Cloze accuracy.
<a id="wikitext-perplexity-evaluation"></a>
### WikiText Perplexity Evaluation
For even comparison with prior works, we evaluate perplexity on the word-level [WikiText-103 test dataset](https://s3.amazonaws.com/research.metamind.io/wikitext/wikitext-103-v1.zip), and appropriately compute perplexity given the change in tokens when using our subword tokenizer.
......@@ -449,7 +453,7 @@ python tasks/main.py \
--tokenizer-type GPT2BPETokenizer \
--merge-file $MERGE_FILE \
--load $CHECKPOINT_PATH \
--batch-size 8 \
--micro-batch-size 8 \
--checkpoint-activations \
--log-interval 10 \
--no-load-optim \
......@@ -457,7 +461,6 @@ python tasks/main.py \
</pre>
<a id="lambada-cloze-accuracy"></a>
### LAMBADA Cloze Accuracy
To compute LAMBADA cloze accuracy (the accuracy of predicting the last token given the preceeding tokens) we utilize a detokenized, processed version of the [LAMBADA dataset](https://github.com/cybertronai/bflm/blob/master/lambada_test.jsonl).
......@@ -480,7 +483,7 @@ python tasks/main.py \
--strict-lambada \
--merge-file $MERGE_FILE \
--load $CHECKPOINT_PATH \
--batch-size 8 \
--micro-batch-size 8 \
--checkpoint-activations \
--log-interval 10 \
--no-load-optim \
......@@ -489,9 +492,7 @@ python tasks/main.py \
Further command line arguments are described in the source file [`main.py`](./tasks/main.py)
<a id="bert-task-evaluation"></a>
## BERT Task Evaluation
<a id="race-evaluation"></a>
### RACE Evaluation
The following script finetunes the BERT model for evaluation on the [RACE dataset](http://www.cs.cmu.edu/~glai1/data/race/). The `TRAIN_DATA` and `VALID_DATA` directory contain the RACE dataset as separate `.txt` files. Note that for RACE, the batch size is the number of RACE query's to evaluate. Since each RACE query has four samples, the effective batch size passed through the model will be four times the batch size specified on the command line.
......@@ -527,12 +528,11 @@ python tasks/main.py \
$COMMON_TASK_ARGS_EXT \
--tokenizer-type BertWordPieceLowerCase \
--epochs 3 \
--batch-size 4 \
--micro-batch-size 4 \
--lr 1.0e-5 \
--warmup 0.06
--lr-warmup-fraction 0.06
</pre>
<a id="mnli-evaluation"></a>
### MNLI Evaluation
The following script finetunes the BERT model for evaluation with the [MultiNLI sentence pair corpus](https://www.nyu.edu/projects/bowman/multinli/). Because the matching tasks are quite similar, the script can be quickly tweaked to work with the [Quora Question Pairs](https://www.kaggle.com/quora/question-pairs-dataset) (QQP) dataset as well.
......@@ -553,21 +553,18 @@ python tasks/main.py \
$COMMON_TASK_ARGS_EXT \
--tokenizer-type BertWordPieceLowerCase \
--epochs 5 \
--batch-size 8 \
--micro-batch-size 8 \
--lr 5.0e-5 \
--warmup 0.065
--lr-warmup-fraction 0.065
</pre>
<a id="datasets"></a>
# Datasets
We do not host any datasets for GPT-2 or BERT training, however, we detail their collection so that our results may be reproduced.
We do not host any datasets for GPT or BERT training, however, we detail their collection so that our results may be reproduced.
<a id="collecting-wikipedia-training-data"></a>
## Collecting Wikipedia Training Data
We recommend following the Wikipedia data extraction process specified by Google research: "the recommended pre-processing is to download [the latest dump](https://dumps.wikimedia.org/enwiki/latest/enwiki-latest-pages-articles.xml.bz2), extract the text with [WikiExtractor.py](https://github.com/attardi/wikiextractor), and then apply any necessary cleanup to convert it into plain text."
We recommend using the `--json` argument when using WikiExtractor, which will dump the Wikipedia data into loose json format (one json per line), making it more manageable on the file system and also readily consumable by our codebase. We recommend further preprocessing this json dataset by nltk punctuation standardization. For BERT training, add newlines between sentences during data preprocessing. This is done with the `--split-sentences` flag in `preprocess_data.py` as described [above](#data-preprocessing). (Note that if you'd like to use Wikipedia data for GPT-2 training you should still clean it with nltk/spacy/ftfy, but do not split it into newline separated sentences.)
We recommend using the `--json` argument when using WikiExtractor, which will dump the Wikipedia data into loose json format (one json per line), making it more manageable on the file system and also readily consumable by our codebase. We recommend further preprocessing this json dataset by nltk punctuation standardization. For BERT training, use the `--split-sentences` flag to `preprocess_data.py` as described [above](#data-preprocessing) to include sentence breaks in the produced index. If you'd like to use Wikipedia data for GPT training you should still clean it with nltk/spacy/ftfy, but do not use the `--split-sentences` flag.
<a id="collecting-gpt-2-webtext-data"></a>
## Collecting GPT-2 Webtext Data
## Collecting GPT Webtext Data
We utilize the publicly available [OpenWebText](https://github.com/eukaryote31/openwebtext) library from [jcpeterson](https://github.com/jcpeterson/openwebtext) and [eukaryote31's](https://github.com/eukaryote31/openwebtext) work to download urls. We then filtered, cleaned, and deduplicated all downloaded content according to the procedure described in our [openwebtext](./tools/openwebtext) directory. For reddit URLs corresponding to content up to October 2018 we arrived at approximately 37GB of content.
examples/finetune_mnli_distributed.sh
View file @ adbba962
......@@ -28,11 +28,11 @@ python -m torch.distributed.launch $DISTRIBUTED_ARGS ./tasks/main.py \
--num-layers 24 \
--hidden-size 1024 \
--num-attention-heads 16 \
--batch-size 8 \
--micro-batch-size 8 \
--checkpoint-activations \
--lr 5.0e-5 \
--lr-decay-style linear \
--warmup 0.065 \
--lr-warmup-fraction 0.065 \
--seq-length 512 \
--max-position-embeddings 512 \
--save-interval 500000 \
......
examples/finetune_race_distributed.sh
View file @ adbba962
......@@ -28,11 +28,11 @@ python -m torch.distributed.launch $DISTRIBUTED_ARGS ./tasks/main.py \
--num-layers 24 \
--hidden-size 1024 \
--num-attention-heads 16 \
--batch-size 4 \
--micro-batch-size 4 \
--checkpoint-activations \
--lr 1.0e-5 \
--lr-decay-style linear \
--warmup 0.06 \
--lr-warmup-fraction 0.06 \
--seq-length 512 \
--max-position-embeddings 512 \
--save-interval 100000 \
......
examples/pretrain_bert.sh
View file @ adbba962
......@@ -9,24 +9,24 @@ python pretrain_bert.py \
--num-layers 24 \
--hidden-size 1024 \
--num-attention-heads 16 \
--batch-size 4 \
--micro-batch-size 4 \
--global-batch-size 8 \
--seq-length 512 \
--max-position-embeddings 512 \
--train-iters 2000000 \
--lr-decay-iters 990000 \
--save $CHECKPOINT_PATH \
--load $CHECKPOINT_PATH \
--data-path $DATA_PATH \
--vocab-file bert-vocab.txt \
--data-impl mmap \
--split 949,50,1 \
--distributed-backend nccl \
--lr 0.0001 \
--min-lr 0.00001 \
--lr-decay-style linear \
--lr-decay-iters 990000 \
--lr-warmup-fraction .01 \
--weight-decay 1e-2 \
--clip-grad 1.0 \
--warmup .01 \
--log-interval 100 \
--save-interval 10000 \
--eval-interval 1000 \
......
examples/pretrain_bert_distributed.sh
View file @ adbba962
......@@ -15,11 +15,11 @@ DISTRIBUTED_ARGS="--nproc_per_node $GPUS_PER_NODE --nnodes $NNODES --node_rank $
python -m torch.distributed.launch $DISTRIBUTED_ARGS \
pretrain_bert.py \
--tensor-model-parallel-size 1 \
--num-layers 24 \
--hidden-size 1024 \
--num-attention-heads 16 \
--batch-size 4 \
--micro-batch-size 4 \
--global-batch-size 32 \
--seq-length 512 \
--max-position-embeddings 512 \
--train-iters 1000000 \
......@@ -36,7 +36,7 @@ python -m torch.distributed.launch $DISTRIBUTED_ARGS \
--lr-decay-iters 990000 \
--weight-decay 1e-2 \
--clip-grad 1.0 \
--warmup .01 \
--lr-warmup-fraction .01 \
--log-interval 100 \
--save-interval 10000 \
--eval-interval 1000 \
......
examples/pretrain_bert_distributed_with_mp.sh
View file @ adbba962
......@@ -20,8 +20,8 @@ python -m torch.distributed.launch $DISTRIBUTED_ARGS \
--num-layers 24 \
--hidden-size 1024 \
--num-attention-heads 16 \
--batch-size 2 \
--num-microbatches-in-minibatch 2 \
--micro-batch-size 2 \
--global-batch-size 16 \
--seq-length 512 \
--max-position-embeddings 512 \
--train-iters 1000000 \
......@@ -38,7 +38,7 @@ python -m torch.distributed.launch $DISTRIBUTED_ARGS \
--lr-decay-iters 990000 \
--weight-decay 1e-2 \
--clip-grad 1.0 \
--warmup .01 \
--lr-warmup-fraction .01 \
--log-interval 100 \
--save-interval 10000 \
--eval-interval 1000 \
......
examples/pretrain_gpt2.sh → examples/pretrain_gpt.sh
View file @ adbba962
......@@ -9,11 +9,12 @@ DATA_PATH=<Specify path and file prefix>_text_document
CHECKPOINT_PATH=<Specify path>
python pretrain_gpt2.py \
python pretrain_gpt.py \
--num-layers 24 \
--hidden-size 1024 \
--num-attention-heads 16 \
--batch-size 8 \
--micro-batch-size 4 \
--global-batch-size 8 \
--seq-length 1024 \
--max-position-embeddings 1024 \
--train-iters 500000 \
......@@ -31,7 +32,7 @@ python pretrain_gpt2.py \
--lr-decay-style cosine \
--weight-decay 1e-2 \
--clip-grad 1.0 \
--warmup .01 \
--lr-warmup-fraction .01 \
--checkpoint-activations \
--log-interval 100 \
--save-interval 10000 \
......
examples/pretrain_gpt3_175B.sh 0 → 100755
View file @ adbba962
#!/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 \
--tensorboard-dir <TENSORBOARD DIRECTORY> \
--fp16 \
--checkpoint-activations "
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_gpt2_distributed.sh → examples/pretrain_gpt_distributed.sh
View file @ adbba962
......@@ -16,12 +16,12 @@ CHECKPOINT_PATH=<Specify path>
DISTRIBUTED_ARGS="--nproc_per_node $GPUS_PER_NODE --nnodes $NNODES --node_rank $NODE_RANK --master_addr $MASTER_ADDR --master_port $MASTER_PORT"
python -m torch.distributed.launch $DISTRIBUTED_ARGS \
pretrain_gpt2.py \
--tensor-model-parallel-size 1 \
pretrain_gpt.py \
--num-layers 24 \
--hidden-size 1024 \
--num-attention-heads 16 \
--batch-size 8 \
--micro-batch-size 8 \
--global-batch-size 64 \
--seq-length 1024 \
--max-position-embeddings 1024 \
--train-iters 500000 \
......@@ -39,7 +39,7 @@ python -m torch.distributed.launch $DISTRIBUTED_ARGS \
--min-lr 1.0e-5 \
--weight-decay 1e-2 \
--clip-grad 1.0 \
--warmup .01 \
--lr-warmup-fraction .01 \
--checkpoint-activations \
--log-interval 100 \
--save-interval 10000 \
......
examples/pretrain_gpt2_distributed_with_mp.sh → examples/pretrain_gpt_distributed_with_mp.sh
View file @ adbba962
......@@ -16,14 +16,14 @@ CHECKPOINT_PATH=<Specify path>
DISTRIBUTED_ARGS="--nproc_per_node $GPUS_PER_NODE --nnodes $NNODES --node_rank $NODE_RANK --master_addr $MASTER_ADDR --master_port $MASTER_PORT"
python -m torch.distributed.launch $DISTRIBUTED_ARGS \
pretrain_gpt2.py \
pretrain_gpt.py \
--tensor-model-parallel-size 2 \
--pipeline-model-parallel-size 2 \
--num-layers 24 \
--hidden-size 1024 \
--num-attention-heads 16 \
--batch-size 4 \
--num-microbatches-in-minibatch 2 \
--micro-batch-size 4 \
--global-batch-size 16 \
--seq-length 1024 \
--max-position-embeddings 1024 \
--train-iters 500000 \
......@@ -41,7 +41,7 @@ python -m torch.distributed.launch $DISTRIBUTED_ARGS \
--min-lr 1.0e-5 \
--weight-decay 1e-2 \
--clip-grad 1.0 \
--warmup .01 \
--lr-warmup-fraction .01 \
--checkpoint-activations \
--log-interval 100 \
--save-interval 10000 \
......
megatron/data/gpt2_dataset.py → megatron/data/gpt_dataset.py
View file @ adbba962
......@@ -13,7 +13,7 @@
# See the License for the specific language governing permissions and
# limitations under the License.
"""GPT2 style dataset."""
"""GPT style dataset."""
import os
import time
......@@ -107,7 +107,7 @@ def _build_train_valid_test_datasets(data_prefix, data_impl, splits_string,
if splits[index + 1] > splits[index]:
documents = np.arange(start=splits[index], stop=splits[index + 1],
step=1, dtype=np.int32)
dataset = GPT2Dataset(name, data_prefix,
dataset = GPTDataset(name, data_prefix,
documents, indexed_dataset,
train_valid_test_num_samples[index],
seq_length, seed)
......@@ -136,7 +136,7 @@ def get_indexed_dataset_(data_prefix, data_impl, skip_warmup):
return indexed_dataset
class GPT2Dataset(torch.utils.data.Dataset):
class GPTDataset(torch.utils.data.Dataset):
def __init__(self, name, data_prefix, documents, indexed_dataset,
num_samples, seq_length, seed):
......
megatron/model/__init__.py
View file @ adbba962
......@@ -35,10 +35,11 @@ from .bert_model import (BertModel,
BertModelFirstStage,
BertModelIntermediateStage,
BertModelLastStage)
from .gpt2_model import (GPT2Model,
GPT2ModelFirstStage,
GPT2ModelIntermediateStage,
GPT2ModelLastStage)
from .realm_model import ICTBertModel
from .gpt_model import (GPTModel,
GPTModelFirstStage,
GPTModelIntermediateStage,
GPTModelLastStage)
from .language_model import get_language_model
from .module import FP16Module
from .realm_model import ICTBertModel
......
megatron/model/gpt2_model.py → megatron/model/gpt_model.py
View file @ adbba962
......@@ -56,11 +56,11 @@ def post_language_model_processing(lm_output, labels, logit_weights,
return loss
class GPT2ModelBase(MegatronModule):
class GPTModelBase(MegatronModule):
"""GPT-2 Language model."""
def __init__(self, num_tokentypes=0, parallel_output=True):
super(GPT2ModelBase, self).__init__()
super(GPTModelBase, self).__init__()
args = get_args()
self.parallel_output = parallel_output
......@@ -75,17 +75,17 @@ class GPT2ModelBase(MegatronModule):
self.initialize_word_embeddings(init_method_normal)
def forward(self, gpt2_model_input, attention_mask, labels=None,
def forward(self, gpt_model_input, attention_mask, labels=None,
tokentype_ids=None, layer_past=None, get_key_value=False,
forward_method_parallel_output=None):
kwargs = {'layer_past': layer_past, 'get_key_value': get_key_value}
if mpu.is_pipeline_first_stage():
(input_ids, position_ids) = gpt2_model_input
(input_ids, position_ids) = gpt_model_input
args = [input_ids, position_ids, attention_mask]
kwargs['tokentype_ids'] = tokentype_ids
else:
args = [gpt2_model_input, attention_mask]
args = [gpt_model_input, attention_mask]
lm_output = self.language_model(*args, **kwargs)
if mpu.is_pipeline_last_stage():
......@@ -124,17 +124,17 @@ class GPT2ModelBase(MegatronModule):
self.language_model.load_state_dict(state_dict, strict=strict)
class GPT2Model(GPT2ModelBase):
class GPTModel(GPTModelBase):
def __init__(self, num_tokentypes=0, parallel_output=True):
super(GPT2Model, self).__init__(
super(GPTModel, self).__init__(
num_tokentypes=num_tokentypes,
parallel_output=parallel_output)
def forward(self, input_ids, position_ids, attention_mask, labels=None,
tokentype_ids=None, layer_past=None, get_key_value=False,
forward_method_parallel_output=None):
return super(GPT2Model, self).forward(
return super(GPTModel, self).forward(
(input_ids, position_ids),
attention_mask,
labels=labels,
......@@ -144,15 +144,15 @@ class GPT2Model(GPT2ModelBase):
forward_method_parallel_output=forward_method_parallel_output)
class GPT2ModelFirstStage(GPT2ModelBase):
class GPTModelFirstStage(GPTModelBase):
def __init__(self, num_tokentypes=0):
super(GPT2ModelFirstStage, self).__init__(
super(GPTModelFirstStage, self).__init__(
num_tokentypes=num_tokentypes)
def forward(self, input_ids, position_ids, attention_mask,
tokentype_ids=None, layer_past=None, get_key_value=False):
return super(GPT2ModelFirstStage, self).forward(
return super(GPTModelFirstStage, self).forward(
(input_ids, position_ids),
attention_mask,
tokentype_ids=tokentype_ids,
......@@ -160,32 +160,32 @@ class GPT2ModelFirstStage(GPT2ModelBase):
get_key_value=get_key_value)
class GPT2ModelIntermediateStage(GPT2ModelBase):
class GPTModelIntermediateStage(GPTModelBase):
def __init__(self, num_tokentypes=0):
super(GPT2ModelIntermediateStage, self).__init__(
super(GPTModelIntermediateStage, self).__init__(
num_tokentypes=num_tokentypes)
def forward(self, hidden_state, attention_mask,
layer_past=None, get_key_value=False):
return super(GPT2ModelIntermediateStage, self).forward(
return super(GPTModelIntermediateStage, self).forward(
hidden_state,
attention_mask,
layer_past=layer_past,
get_key_value=get_key_value)
class GPT2ModelLastStage(GPT2ModelBase):
class GPTModelLastStage(GPTModelBase):
def __init__(self, num_tokentypes=0, parallel_output=True):
super(GPT2ModelLastStage, self).__init__(
super(GPTModelLastStage, self).__init__(
num_tokentypes=num_tokentypes,
parallel_output=parallel_output)
def forward(self, hidden_state, attention_mask, labels=None,
layer_past=None, get_key_value=False,
forward_method_parallel_output=None):
return super(GPT2ModelLastStage, self).forward(
return super(GPTModelLastStage, self).forward(
hidden_state,
attention_mask,
labels=labels,
......
megatron/training.py
View file @ adbba962
......@@ -929,6 +929,7 @@ def evaluate_and_print_results(prefix, forward_step_func,
data_iterator, model,
iteration, verbose=False):
"""Helper function to evaluate and dump results on screen."""
args = get_args()
writer = get_tensorboard_writer()
total_loss_dict = evaluate(forward_step_func, data_iterator, model, verbose)
......@@ -937,11 +938,16 @@ def evaluate_and_print_results(prefix, forward_step_func,
string += '{} value: {:.6E} | '.format(key, total_loss_dict[key].item())
ppl = math.exp(min(20, total_loss_dict[key].item()))
string += '{} PPL: {:.6E} | '.format(key, ppl)
if writer and torch.distributed.get_rank() == 0:
writer.add_scalar('{} value'.format(key),
if writer and is_last_rank():
writer.add_scalar('{} value-validation'.format(key),
total_loss_dict[key].item(),
iteration)
writer.add_scalar('{} ppl'.format(key), ppl, iteration)
writer.add_scalar('{} ppl-validation'.format(key), ppl, iteration)
writer.add_scalar('{} value-validation vs samples'.format(key),
total_loss_dict[key].item(),
args.consumed_train_samples)
writer.add_scalar('{} ppl-validation vs samples'.format(key), ppl,
args.consumed_train_samples)
length = len(string) + 1
print_rank_last('-' * length)
......
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