From f29b390d390f20a1298313ea25959e49a7a94665 Mon Sep 17 00:00:00 2001 From: Raul Puri Date: Wed, 27 Mar 2019 13:55:24 -0700 Subject: [PATCH] initial commit --- .DS_Store | Bin 0 -> 6148 bytes CODEOWNERS | 1 + Dockerfile | 24 + LICENSE | 86 +- MANIFEST.in | 2 + README.md | 597 +++++++- arguments.py | 283 ---- data_utils/corpora.py | 37 - examples/ds_config.json | 24 + examples/ds_pretrain_gpt2.sh | 133 ++ examples/ds_pretrain_gpt2_pipe.sh | 140 ++ examples/ds_zero_stage_2_config.json | 32 + examples/evaluate_zeroshot_gpt2.sh | 38 + examples/finetune_mnli_distributed.sh | 44 + examples/finetune_race_distributed.sh | 47 + examples/generate_text.sh | 25 + examples/merge_mp_bert.sh | 18 + examples/pretrain_bert.sh | 35 + examples/pretrain_bert_distributed.sh | 44 + examples/pretrain_gpt2.sh | 43 + examples/pretrain_gpt2_distributed.sh | 52 + images/Makefile | 12 + images/cases.png | Bin 0 -> 11807 bytes images/scaling-dp.png | Bin 0 -> 13419 bytes images/scaling-mp.png | Bin 0 -> 22813 bytes images/tables.tex | 40 + kubernetes/deploy_k8s.sh | 90 ++ kubernetes/get_wandb_api_key.py | 23 + kubernetes/id_rsa | 39 + kubernetes/id_rsa.pub | 1 + kubernetes/k8s_spec.yml | 67 + kubernetes/k8s_spec_temp.yml | 66 + kubernetes/kill_k8s.sh | 9 + kubernetes/post_start_script.sh | 1 + learning_rates.py | 74 - megatron/__init__.py | 41 + megatron/arguments.py | 538 +++++++ megatron/checkpointing.py | 340 +++++ megatron/data/Makefile | 9 + megatron/data/__init__.py | 1 + megatron/data/bert_dataset.py | 232 +++ megatron/data/dataset_utils.py | 503 +++++++ megatron/data/gpt2_dataset.py | 317 ++++ megatron/data/helpers.cpp | 643 ++++++++ megatron/data/ict_dataset.py | 140 ++ megatron/data/indexed_dataset.py | 570 +++++++ megatron/data/realm_dataset_utils.py | 201 +++ megatron/data/realm_index.py | 216 +++ megatron/data/samplers.py | 148 ++ megatron/data/test/test_indexed_dataset.py | 125 ++ megatron/data/test/test_preprocess_data.sh | 10 + .../deprecated_data_utils}/__init__.py | 64 +- 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megatron/mpu/initialize.py | 252 ++++ megatron/mpu/layers.py | 363 +++++ megatron/mpu/mappings.py | 157 ++ megatron/mpu/random.py | 319 ++++ megatron/mpu/tests/__init__.py | 0 megatron/mpu/tests/commons.py | 83 ++ megatron/mpu/tests/test_cross_entropy.py | 108 ++ megatron/mpu/tests/test_data.py | 88 ++ megatron/mpu/tests/test_initialize.py | 95 ++ megatron/mpu/tests/test_layers.py | 530 +++++++ megatron/mpu/tests/test_random.py | 204 +++ megatron/mpu/utils.py | 70 + megatron/package_info.py | 30 + megatron/text_generation_utils.py | 412 ++++++ {model => megatron/tokenizer}/__init__.py | 6 +- megatron/tokenizer/bert_tokenization.py | 402 +++++ megatron/tokenizer/gpt2_tokenization.py | 321 ++++ megatron/tokenizer/tokenizer.py | 220 +++ megatron/training.py | 695 +++++++++ megatron/utils.py | 177 +++ model/model.py | 88 -- model/modeling.py | 1314 ----------------- optim/adam.py | 124 -- pretrain_bert.py | 511 +------ pretrain_gpt2.py | 156 ++ pretrain_ict.py | 138 ++ requirements.txt | 10 +- scripts/pretrain_bert.sh | 37 - scripts/pretrain_bert_distributed.sh | 44 - scripts/pretrain_bert_sentencepiece.sh | 37 - .../pretrain_bert_tfrecords_distributed.sh | 43 - setup.py | 91 ++ tasks/data_utils.py | 118 ++ tasks/ensemble_classifier.py | 149 ++ tasks/eval_utils.py | 127 ++ tasks/finetune_utils.py | 259 ++++ tasks/glue/data.py | 69 + tasks/glue/finetune.py | 90 ++ tasks/glue/mnli.py | 84 ++ tasks/glue/qqp.py | 101 ++ tasks/main.py | 69 + tasks/race/data.py | 131 ++ tasks/race/finetune.py | 63 + tasks/zeroshot_gpt2/datasets.py | 161 ++ tasks/zeroshot_gpt2/detokenizer.py | 80 + tasks/zeroshot_gpt2/evaluate.py | 195 +++ tools/create_doc_index.py | 30 + tools/generate_samples_gpt2.py | 100 ++ tools/linter.py | 36 + tools/merge_mp_partitions.py | 286 ++++ tools/openwebtext/README.md | 46 + tools/openwebtext/blacklist_urls.py | 312 ++++ tools/openwebtext/cleanup_dataset.py | 115 ++ tools/openwebtext/find_duplicates.py | 100 ++ tools/openwebtext/group_duplicates_url.py | 90 ++ tools/openwebtext/merge_jsons.py | 55 + tools/openwebtext/remove_group_duplicates.py | 69 + tools/preprocess_data.py | 200 +++ utils.py | 180 --- 157 files changed, 20112 insertions(+), 3143 deletions(-) create mode 100644 .DS_Store create mode 100644 CODEOWNERS create mode 100644 Dockerfile create mode 100644 MANIFEST.in delete mode 100644 arguments.py delete mode 100755 data_utils/corpora.py create mode 100755 examples/ds_config.json create mode 100755 examples/ds_pretrain_gpt2.sh create mode 100755 examples/ds_pretrain_gpt2_pipe.sh create mode 100755 examples/ds_zero_stage_2_config.json create mode 100755 examples/evaluate_zeroshot_gpt2.sh create mode 100755 examples/finetune_mnli_distributed.sh create mode 100755 examples/finetune_race_distributed.sh create mode 100755 examples/generate_text.sh create mode 100755 examples/merge_mp_bert.sh create mode 100755 examples/pretrain_bert.sh create mode 100755 examples/pretrain_bert_distributed.sh create mode 100755 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@EleutherAI/pm-gptneo diff --git a/Dockerfile b/Dockerfile new file mode 100644 index 00000000..fc3d04be --- /dev/null +++ b/Dockerfile @@ -0,0 +1,24 @@ +FROM atlanticcrypto/cuda-ssh-server:10.2-cudnn + +RUN apt-get update && \ + apt-get install -y git python3.8 python3.8-dev python3-pip sudo pdsh htop llvm-9-dev cmake tmux && \ + update-alternatives --install /usr/bin/python3 python3 /usr/bin/python3.8 1 && \ + update-alternatives --install /usr/bin/python python /usr/bin/python3.8 1 && \ + python3 -m pip install --upgrade pip && \ + pip3 install pipx gpustat && \ + python3 -m pipx ensurepath + +RUN mkdir -p ~/.ssh /app /job /build_dir && \ + echo 'Host *' > ~/.ssh/config && \ + echo ' StrictHostKeyChecking no' >> ~/.ssh/config && \ + echo 'AuthorizedKeysFile .ssh/authorized_keys' >> /etc/ssh/sshd_config && \ + echo 'PasswordAuthentication yes' >> /etc/ssh/sshd_config && \ + echo 'export PDSH_RCMD_TYPE=ssh' >> ~/.bashrc + +WORKDIR /build_dir + +COPY requirements.txt /build_dir +RUN pip install torch==1.7.1 +RUN pip install -r requirements.txt + +WORKDIR /app diff --git a/LICENSE b/LICENSE index e24d383f..c4bad159 100644 --- a/LICENSE +++ b/LICENSE @@ -1,27 +1,40 @@ -<<<<<<< HEAD -MIT License - -Copyright (c) 2021 EleutherAI - -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. -======= -------------- LICENSE FOR huggingface(transformer) repository -------------- +The following applies to all files unless otherwise noted: + +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Redistribution and use in source and binary forms, with or without +# modification, are permitted provided that the following conditions +# are met: +# * Redistributions of source code must retain the above copyright +# notice, this list of conditions and the following disclaimer. +# * Redistributions in binary form must reproduce the above copyright +# notice, this list of conditions and the following disclaimer in the +# documentation and/or other materials provided with the distribution. +# * Neither the name of NVIDIA CORPORATION nor the names of its +# contributors may be used to endorse or promote products derived +# from this software without specific prior written permission. +# +# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY +# EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR +# PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR +# CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, +# EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, +# PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR +# PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY +# OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT +# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + +-- + +This repository also contains code from Hugging Face Inc., Google Research, +and Facebook (from their Fairseq project). Files from these +organizations have notices at the top of each file. Below are licenses +used in those files, as indicated. + + +------------- LICENSE FOR huggingface and Google Research code -------------- Apache License @@ -225,4 +238,27 @@ SOFTWARE. WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. ->>>>>>> initial commit + +------------- LICENSE FOR Facebook Fairseq code -------------- + +MIT License + +Copyright (c) Facebook, Inc. and its affiliates. + +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. diff --git a/MANIFEST.in b/MANIFEST.in new file mode 100644 index 00000000..f4479118 --- /dev/null +++ b/MANIFEST.in @@ -0,0 +1,2 @@ +include megatron/data/Makefile +include megatron/data/helpers.cpp diff --git a/README.md b/README.md index f2be5d75..a8a58b18 100644 --- a/README.md +++ b/README.md @@ -1,89 +1,568 @@ -# Megatron3D +[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, 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 is a large, powerful transformer. This repo is for ongoing research on training large, powerful transformer language models at scale. Currently, we support multinode training of [BERT](https://arxiv.org/pdf/1810.04805.pdf) in mixed precision. Our codebase is capable of training BERT Large on 64 V100 GPUs in 3 days. We achieved a final language modeling perplexity of 3.15 and SQuAD F1-score of 90.7. +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 models 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%. +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. + +![Cases](images/cases.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. + +![Model Parallel Scaling](images/scaling-mp.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) + + +# Contents + + +- [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) + + + + # Setup -We officially support only python3.6. +We officially support only python 3.6, pytorch 1.5, cuda 10, and nccl 2.6 versions and above. + +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 megatron you can either clone the repo or install it via pip (make sure python3-dev is installed): +
+pip install megatron-lm
+
-To use this repo please install the latest supported versions of PyTorch with GPU support. + +## 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. -Additionally, part of this codebase leverages tensorflow-cpu to perform dataloading of TFRecords. We recommend creating a virtual environment (to avoid breaking existing tf installations) and install our `requirements.txt`. +The checkpoints can be downloaded with: +
+ngc registry model download-version --dest <output_base_directory> nvidia/<model_name>:<version>
+
-``` -python -m pip install virtualenv -virtualenv bert_env -source bert_env/bin/activate -pip install -r requirements.txt -``` +The available models along with `:` 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) + + # Usage -We've provided 4 scripts that pretrain BERT. All saved checkpoints can be used for finetuning according to [existing implementations](https://github.com/huggingface). Save model checkpoints with `--save`. +After installation, there are several possible workflows. The most comprehensive is: +1. Data preprocessing +2. Pretraining +3. Finetuning (Optional for zero-shot tasks) +4. Downstream task evaluation or text generation + +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. + + +# Training + +## 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: +
+{"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"}
+
+ +The name of the `text` field of the json can be changed by using the `--json-key` flag in [`preprocess_data.py`](./tools/preprocess_data.py) The other metadata are optional and are not used in training. + +The loose json is then processed into a binary format for training. To convert the json into mmap, cached index file, or the lazy loader format use `preprocess_data.py`. Set the `--dataset-impl` flag to `mmap`, `cached`, or `lazy`, respectively (default is `mmap`). An example script to prepare data for BERT training is: +
+python tools/preprocess_data.py \
+       --input my-corpus.json \
+       --output-prefix my-bert \
+       --vocab bert-vocab.txt \
+       --dataset-impl mmap \
+       --tokenizer-type BertWordPieceLowerCase \
+       --split-sentences
+
+ +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: +
+python tools/preprocess_data.py \
+       --input my-corpus.json \
+       --output-prefix my-gpt2 \
+       --vocab gpt2-vocab.json \
+       --dataset-impl mmap \
+       --tokenizer-type GPT2BPETokenizer \
+       --merge-file gpt2-merges.txt \
+       --append-eod
+
+ +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`. + +Further command line arguments are described in the source file [`preprocess_data.py`](./tools/preprocess_data.py). + + ## BERT Pretraining -`bash scripts/pretrain_bert.sh` +`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`). + +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. -This script runs single gpu BERT pretraining and is mainly for debugging purposes. +
+CHECKPOINT_PATH=checkpoints/bert_345m
+VOCAB_FILE=bert-vocab.txt
+DATA_PATH=my-bert_text_sentence
 
-To use this script place your `--train-data` in loose json format with one json per line. The text field of your json dictionaries should correspond to `--text-key`. 
+BERT_ARGS="--num-layers 24 \
+           --hidden-size 1024 \
+           --num-attention-heads 16 \
+           --seq-length 512 \
+           --max-position-embeddings 512 \
+           --lr 0.0001 \
+           --train-iters 2000000 \
+           --min-lr 0.00001 \
+           --lr-decay-iters 990000 \
+           --warmup 0.01 \
+           --batch-size 8 \
+           --vocab-file $VOCAB_FILE \
+           --split 949,50,1 \
+           --fp16"
+
+OUTPUT_ARGS="--log-interval 10 \
+             --save-interval 500 \
+             --eval-interval 100 \
+             --eval-iters 10 \
+             --checkpoint-activations"
 
-```
 python pretrain_bert.py \
-    --batch-size 4 \
-    --tokenizer-type BertWordPieceTokenizer \
-    --cache-dir temp_cache_dir \
-    --tokenizer-model-type bert-large-uncased \
-    --vocab-size 30522 \
-    --train-data wikipedia \
-    --loose-json \
-    --text-key text \
-    --split 1000,1,1 \
-    --lazy-loader \
-    --max-preds-per-seq 80 \
-    --seq-length 512 \
-    --max-position-embeddings 512 \
-    --num-layers 24 \
-    --hidden-size 1024 \
-    --intermediate-size 4096 \
-    --num-attention-heads 16 \
-    --hidden-dropout 0.1 \
-    --attention-dropout 0.1 \
-    --train-iters 1000000 \
+       $BERT_ARGS \
+       $OUTPUT_ARGS \
+       --save $CHECKPOINT_PATH \
+       --load $CHECKPOINT_PATH \
+       --data-path $DATA_PATH
+
+ +Further command line arguments are described in the source file [`arguments.py`](./megatron/arguments.py). + + +## 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. + +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. + +
+CHECKPOINT_PATH=checkpoints/gpt2_345m
+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"
+
+OUTPUT_ARGS=<same as those in BERT pretraining above>
+
+python pretrain_gpt2.py \
+       $GPT2_ARGS \
+       $OUTPUT_ARGS \
+       --save $CHECKPOINT_PATH \
+       --load $CHECKPOINT_PATH \
+       --data-path $DATA_PATH \
+
+ +Further command line arguments are described in the source file [`arguments.py`](./megatron/arguments.py). + + +## Distributed BERT or GPT-2 Pretraining +`bash examples/pretrain_bert_distributed.sh` + +`bash examples/pretrain_gpt2_distributed.sh` + +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. + +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 intra-layer model parallel approach. To use model parallelism, add the `--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. With `WORLD_SIZE` GPUs and `MP_SIZE` model parallel size, `WORLD_SIZE`/`MP_SIZE` GPUs will be used for data parallelism. The default value for `--model-parallel-size` is 1, which will not implement model parallelism. + +Other than these minor changes, the distributed training is identical to the training on a single GPU. + +Distributed BERT training: +
+WORLD_SIZE=8
+MP_SIZE=2
+
+DISTRIBUTED_ARGS="--nproc_per_node $WORLD_SIZE \
+                  --nnodes 1 \
+                  --node_rank 0 \
+                  --master_addr localhost \
+                  --master_port 6000"
+
+CHECKPOINT_PATH=checkpoints/bert_345m
+VOCAB_FILE=bert-vocab.txt
+DATA_PATH=my-bert_text_sentence
+BERT_ARGS=<same as those in BERT pretraining above>
+OUTPUT_ARGS=<same as those in BERT pretraining above>
+
+python -m torch.distributed.launch $DISTRIBUTED_ARGS ./pretrain_bert.py \
+                $BERT_ARGS \
+                $OUTPUT_ARGS \
+                --save $CHECKPOINT_PATH \
+                --load $CHECKPOINT_PATH \
+                --data-path $DATA_PATH \
+                --model-parallel-size $MP_SIZE \
+                --DDP-impl torch
+
+ +Distributed GPT-2 training: +
+WORLD_SIZE=8
+MP_SIZE=2
+
+DISTRIBUTED_ARGS=<same as those directly above>
+
+CHECKPOINT_PATH=checkpoints/gpt2_345m
+VOCAB_FILE=gpt2-vocab.json
+MERGE_FILE=gpt2-merges.txt
+DATA_PATH=my-gpt2_text_document
+GPT2_ARGS=<same as those in GPT-2 pretraining above>
+OUTPUT_ARGS=<same as those in BERT pretraining above>
+
+python -m torch.distributed.launch $DISTRIBUTED_ARGS ./pretrain_gpt2.py \
+                $GPT2_ARGS \
+                $OUTPUT_ARGS \
+                --save $CHECKPOINT_PATH \
+                --load $CHECKPOINT_PATH \
+                --data-path $DATA_PATH \
+                --model-parallel-size $MP_SIZE \
+                --DDP-impl torch
+
+
+ + +## 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. + +### Inverse Cloze Task (ICT) Pretraining +1. Have a corpus in loose JSON format with the intention of creating a collection of fixed-size blocks of text as the fundamental units of data. For a corpus like Wikipedia, this will mean multiple sentences per block but also multiple blocks per document. +Run `tools/preprocess_data.py` to construct one or more indexed datasets with the `--split-sentences` argument to make sentences the basic unit. For the original REALM system, we construct two datasets, one with the title of every document, and another with the body. +Refer to the following script +
+python preprocess_data.py \
+    --input /path/to/corpus.json \
+    --json-keys text title \
+    --split-sentences \
+    --tokenizer-type BertWordPieceLowerCase \
+    --vocab-file /path/to/vocab.txt \
+    --output-prefix corpus_indexed \
+    --workers 5  # works well for 10 CPU cores. Scale up accordingly.
+
+ +2. Use a custom samples mapping function in place of `megatron/data/realm_dataset_utils.get_block_samples_mapping` if required. To do this, you will need to implement a new function in C++ inside of `megatron/data/helpers.cpp`. The samples mapping data structure is used to select the data that will constitute every training sample in advance of the training loop. + The samples mapping is responsible for holding all of the required metadata needed to construct the sample from one or more indexed datasets. In REALM, the samples mapping contains the start and end sentence indices, as well as the document index (to find the correct title for a body) and a unique ID for every block. +3. Pretrain a BERT language model using `pretrain_bert.py`, with the sequence length equal to the block size in token ids. This model should be trained on the same indexed dataset that is used to supply the blocks for the information retrieval task. +In REALM, this is an uncased bert base model trained with the standard hyperparameters. +4. Use `pretrain_ict.py` to train an `ICTBertModel` which uses two BERT-based encoders to encode queries and blocks to perform retrieval with. +The script below trains the ICT model from REALM. It refrences a pretrained BERT model (step 3) in the `--bert-load` argument. The batch size used in the paper is 4096, so this would need to be run with data parallel world size 32. +
+python pretrain_ict.py \
+    --num-layers 12 \
+    --num-attention-heads 12 \
+    --hidden-size 768 \
+    --batch-size 128 \
+    --seq-length 256 \
+    --max-position-embeddings 256 \
+    --ict-head-size 128 \
+    --train-iters 100000 \
+    --checkpoint-activations \
+    --bert-load /path/to/pretrained_bert \
+    --load checkpoints \
+    --save checkpoints \
+    --data-path /path/to/indexed_dataset \
+    --titles-data-path /path/to/titles_indexed_dataset \
+    --vocab-file /path/to/vocab.txt \
     --lr 0.0001 \
+    --num-workers 2 \
     --lr-decay-style linear \
-    --lr-decay-iters 990000 \
-    --warmup .01 \
     --weight-decay 1e-2 \
     --clip-grad 1.0 \
-    --fp16 \
-    --fp32-layernorm \
-    --fp32-embedding \
-    --hysteresis 2 \
-    --num-workers 2 
-```
+    --warmup .01 \
+    --save-interval 3000 \
+    --query-in-block-prob 0.1 \
+    --fp16
+
+
+ +### Building an Index of Block Embeddings +After having trained an ICT model, you can now embed an entire dataset of blocks by creating a `BlockData` structure. After that has been saved, you can load it +and wrap it with a `FaissMIPSIndex` to do fast similarity search which is key in the learned information retrieval pipeline. The initial index can be built with the following script, meant to be run in an interactive session. It can leverage multiple GPUs on multiple nodes to index large datasets much more quickly. + +
+python tools/create_doc_index.py \
+    --num-layers 12 \
+    --hidden-size 768 \
+    --ict-head-size 128 \
+    --num-attention-heads 12 \
+    --batch-size 128 \
+    --checkpoint-activations \
+    --seq-length 256 \
+    --max-position-embeddings 256 \
+    --ict-load /path/to/pretrained_ict \
+    --data-path /path/to/indexed_dataset \
+    --titles-data-path /path/to/titles_indexed_dataset \
+    --block-data-path embedded_blocks.pkl \
+    --indexer-log-interval 1000 \
+    --indexer-batch-size 128 \
+    --vocab-file /path/to/vocab.txt \
+    --num-workers 2 \
+    --fp16
+
+ + +# 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. + +
+MODEL_PARALLEL_SIZE=2
+
+VOCAB_FILE=bert-vocab.txt
+CHECKPOINT_PATH=checkpoints/bert_345m
+
+WORLD_SIZE=$MODEL_PARALLEL_SIZE python tools/merge_mp_partitions.py \
+        --model-type BERT \
+        --model-parallel-size $MODEL_PARALLEL_SIZE \
+        --tokenizer-type BertWordPieceLowerCase \
+        --vocab-file $VOCAB_FILE \
+        --num-layers 24 \
+        --hidden-size 1024 \
+        --num-attention-heads 16 \
+        --seq-length 512 \
+        --max-position-embeddings 512 \
+        --load $CHECKPOINT_PATH
+
+
+ +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. + + +## GPT-2 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 ` 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.. + +
+CHECKPOINT_PATH=checkpoints/gpt2_345m
+VOCAB_FILE=gpt2-vocab.json
+MERGE_FILE=gpt2-merges.txt
+GPT2_ARGS=<same as those in GPT-2 pretraining above>
+
+MAX_OUTPUT_SEQUENCE_LENGTH=1024
+TEMPERATURE=1.0
+TOP_P=0.9
+NUMBER_OF_SAMPLES=2
+OUTPUT_FILE=samples.json
+
+python tools/generate_samples_gpt2.py \
+       $GPT2_ARGS \
+       --load $CHECKPOINT_PATH \
+       --out-seq-length $MAX_OUTPUT_SEQUENCE_LENGTH \
+       --temperature $TEMPERATURE \
+       --genfile $OUTPUT_FILE \
+       --num-samples $NUMBER_OF_SAMPLES \
+       --top_p $TOP_P \
+       --recompute
+
+ + +## GPT-2 Evaluation +We include example scripts for GPT-2 evaluation on WikiText perplexity evaluation and LAMBADA Cloze accuracy. + + +### 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. + +We use the following command to run WikiText-103 evaluation on a 345M parameter model. +
+TASK="WIKITEXT103"
+
+VALID_DATA=<wikitext path>.txt
+VOCAB_FILE=gpt2-vocab.json
+MERGE_FILE=gpt2-merges.txt
+CHECKPOINT_PATH=checkpoints/gpt2_345m
+
+COMMON_TASK_ARGS="--num-layers 24 \
+                  --hidden-size 1024 \
+                  --num-attention-heads 16 \
+                  --seq-length 1024 \
+                  --max-position-embeddings 1024 \
+                  --fp16 \
+                  --vocab-file $VOCAB_FILE"
+
+python tasks/main.py \
+       --task $TASK \
+       $COMMON_TASK_ARGS \
+       --valid-data $VALID_DATA \
+       --tokenizer-type GPT2BPETokenizer \
+       --merge-file $MERGE_FILE \
+       --load $CHECKPOINT_PATH \
+       --batch-size 8 \
+       --checkpoint-activations \
+       --log-interval 10 \
+       --no-load-optim \
+       --no-load-rng
+
+ + + +### 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). + +We use the following command to run LAMBADA evaluation on a 345M parameter model. Note that the `--strict-lambada` flag should be used to require whole word matching. Make that `lambada` is part of the file path. + +
+TASK="LAMBADA"
+
+VALID_DATA=<lambada path>.json
+VOCAB_FILE=gpt2-vocab.json
+MERGE_FILE=gpt2-merges.txt
+CHECKPOINT_PATH=checkpoints/gpt2_345m
+COMMON_TASK_ARGS=<same as those in WikiText Perplexity Evaluation above>
+
+python tasks/main.py \
+       --task $TASK \
+       $COMMON_TASK_ARGS \
+       --valid-data $VALID_DATA \
+       --tokenizer-type GPT2BPETokenizer \
+       --strict-lambada \
+       --merge-file $MERGE_FILE \
+       --load $CHECKPOINT_PATH \
+       --batch-size 8 \
+       --checkpoint-activations \
+       --log-interval 10 \
+       --no-load-optim \
+       --no-load-rng
+
+ +Further command line arguments are described in the source file [`main.py`](./tasks/main.py) + + +## BERT Task Evaluation + +### 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. -## Distributed BERT Pretraining -`bash scripts/pretrain_bert_distributed.sh` +
+TRAIN_DATA="data/RACE/train/middle"
+VALID_DATA="data/RACE/dev/middle \
+            data/RACE/dev/high"
+VOCAB_FILE=bert-vocab.txt
+PRETRAINED_CHECKPOINT=checkpoints/bert_345m
+CHECKPOINT_PATH=checkpoints/bert_345m_race
+COMMON_TASK_ARGS="--num-layers 24 \
+                  --hidden-size 1024 \
+                  --num-attention-heads 16 \
+                  --seq-length 512 \
+                  --max-position-embeddings 512 \
+                  --fp16 \
+                  --vocab-file $VOCAB_FILE"
 
-To use this script, follow the same data preparation procedure as in [earlier sections](#bert-pretraining). This script uses the pytorch distributed launcher to launch distributed training. As such, multinode training can be achieved by properly setting environment variables for the `env://` init method. 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 distributed backend.
+COMMON_TASK_ARGS_EXT="--train-data $TRAIN_DATA \
+                      --valid-data $VALID_DATA \
+                      --pretrained-checkpoint $PRETRAINED_CHECKPOINT \
+                      --checkpoint-activations \
+                      --save-interval 10000 \
+                      --save $CHECKPOINT_PATH \
+                      --log-interval 100 \
+                      --eval-interval 1000 \
+                      --eval-iters 10 \
+                      --weight-decay 1.0e-1"
 
-## Distributed BERT Pretraining with TFRecords
-`bash scripts/pretrain_bert_tfrecords_distributed.sh`
+python tasks/main.py \
+       --task RACE \
+       $COMMON_TASK_ARGS \
+       $COMMON_TASK_ARGS_EXT \
+       --tokenizer-type BertWordPieceLowerCase \
+       --epochs 3 \
+       --batch-size 4 \
+       --lr 1.0e-5 \
+       --warmup 0.06
+
-This script takes advantage of TensorFlow BERT's [`create_pretraining.py`](https://github.com/NVIDIA/DeepLearningExamples/blob/master/TensorFlow/LanguageModeling/BERT/create_pretraining_data.py) script to pre-cache the dataset in the TFRecord format. To convert the data to pytorch tensors we use a `TFRecordDataset` and tensorflow eager mode to turn the TFRecords into numpy matrices before loading them into pytorch gpu tensors. This greatly reduces the overhead of dataprocessing and speeds up training. Pass a whitespace-separated list of TFRecord paths to `--train-data` and enable the `--use-tfrecords` flag. Multinode training can be achieved as described in the [previous section](#distributed-bert-pretraining). + +### 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. -## Train Custom Sentence Piece Tokenizer and Pretrain BERT -`bash scripts/pretrain_bert_sentencepiece.sh` +
 
-This script runs BERT pretraining with a `sentencepiece` tokenizer. If no sentencepiece tokenizer exists at `--tokenizer-path` one will be trained automatically. The sentencepiece tokenizer can be used with the previous scripts (NOTE: sentencepiece training can only happen during single gpu pretraining). `<--tokenizer-path>.vocab` can be used with [`create_pretraining_data.py`](https://github.com/NVIDIA/DeepLearningExamples/blob/master/TensorFlow/LanguageModeling/BERT/create_pretraining_data.py) to make a TFRecord dataset with the given tokenization.
+TRAIN_DATA="data/glue_data/MNLI/train.tsv"
+VALID_DATA="data/glue_data/MNLI/dev_matched.tsv \
+            data/glue_data/MNLI/dev_mismatched.tsv"
+PRETRAINED_CHECKPOINT=checkpoints/bert_345m
+VOCAB_FILE=bert-vocab.txt
+CHECKPOINT_PATH=checkpoints/bert_345m_mnli
+COMMON_TASK_ARGS=<same as those in RACE Evaluation above>
+COMMON_TASK_ARGS_EXT=<same as those in RACE Evaluation above>
 
+python tasks/main.py \
+       --task MNLI \
+       $COMMON_TASK_ARGS \
+       $COMMON_TASK_ARGS_EXT \
+       --tokenizer-type BertWordPieceLowerCase \
+       --epochs 5 \
+       --batch-size 8 \
+       --lr 5.0e-5 \
+       --warmup 0.065
+
-# 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." + +# 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 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 and readily consumable by our codebase. + +## 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." -Once the json dataset is ready make sure to set the path in line 27 of `data_utils/corpora.py`. +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.) -If your system is memory limited we also recommend running pretraining with the `--lazy-loader` argument as we've done. After preprocessing the dataset once, this will allow the dataset to be lazily loaded from disk, as opposed to storing it in memory. ->>>>>>> initial commit + +## Collecting GPT-2 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. diff --git a/arguments.py b/arguments.py deleted file mode 100644 index d7d554e6..00000000 --- a/arguments.py +++ /dev/null @@ -1,283 +0,0 @@ -# coding=utf-8 -# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -"""argparser configuration""" - -import argparse -import os -import torch - - -def add_model_config_args(parser): - """Model arguments""" - - group = parser.add_argument_group('model', 'model configuration') - - group.add_argument('--pretrained-bert', action='store_true', - help='use a pretrained bert-large-uncased model instead' - 'of initializing from scratch. See ' - '--tokenizer-model-type to specify which pretrained ' - 'BERT model to use') - group.add_argument('--attention-dropout', type=float, default=0.1, - help='dropout probability for attention weights') - group.add_argument('--num-attention-heads', type=int, default=16, - help='num of transformer attention heads') - group.add_argument('--hidden-size', type=int, default=1024, - help='tansformer hidden size') - group.add_argument('--intermediate-size', type=int, default=None, - help='transformer embedding dimension for FFN' - 'set to 4*`--hidden-size` if it is None') - group.add_argument('--num-layers', type=int, default=24, - help='num decoder layers') - group.add_argument('--layernorm-epsilon', type=float, default=1e-12, - help='layer norm epsilon') - group.add_argument('--hidden-dropout', type=float, default=0.0, - help='dropout probability for hidden state transformer') - group.add_argument('--max-position-embeddings', type=int, default=512, - help='maximum number of position embeddings to use') - group.add_argument('--vocab-size', type=int, default=30522, - help='vocab size to use for non-character-level ' - 'tokenization. This value will only be used when ' - 'creating a tokenizer') - - return parser - - -def add_fp16_config_args(parser): - """Mixed precision arguments.""" - - group = parser.add_argument_group('fp16', 'fp16 configurations') - - group.add_argument('--fp16', action='store_true', - help='Run model in fp16 mode') - group.add_argument('--fp32-embedding', action='store_true', - help='embedding in fp32') - group.add_argument('--fp32-layernorm', action='store_true', - help='layer norm in fp32') - group.add_argument('--fp32-tokentypes', action='store_true', - help='embedding token types in fp32') - group.add_argument('--fp32-allreduce', action='store_true', - help='all-reduce in fp32') - group.add_argument('--hysteresis', type=int, default=2, - help='hysteresis for dynamic loss scaling') - group.add_argument('--loss-scale', type=float, default=None, - help='Static loss scaling, positive power of 2 ' - 'values can improve fp16 convergence. If None, dynamic' - 'loss scaling is used.') - group.add_argument('--loss-scale-window', type=float, default=1000, - help='Window over which to raise/lower dynamic scale') - group.add_argument('--min-scale', type=float, default=1, - help='Minimum loss scale for dynamic loss scale') - - return parser - - -def add_training_args(parser): - """Training arguments.""" - - group = parser.add_argument_group('train', 'training configurations') - - group.add_argument('--batch-size', type=int, default=4, - help='Data Loader batch size') - group.add_argument('--weight-decay', type=float, default=0.01, - help='weight decay coefficient for L2 regularization') - group.add_argument('--checkpoint-activations', action='store_true', - help='checkpoint activation to allow for training ' - 'with larger models and sequences') - group.add_argument('--clip-grad', type=float, default=1.0, - help='gradient clipping') - group.add_argument('--epochs', type=int, default=1, - help='upper epoch limit') - group.add_argument('--log-interval', type=int, default=100, - help='report interval') - group.add_argument('--train-iters', type=int, default=1000000, - help='number of iterations per epoch') - group.add_argument('--seed', type=int, default=1234, - help='random seed') - # Learning rate. - group.add_argument('--lr-decay-iters', type=int, default=None, - help='number of iterations to decay LR over,' - ' If None defaults to `--train-iters`*`--epochs`') - group.add_argument('--lr-decay-style', type=str, default='linear', - choices=['constant', 'linear', 'cosine', 'exponential'], - help='learning rate decay function') - group.add_argument('--lr', type=float, default=1.0e-4, - help='initial learning rate') - group.add_argument('--warmup', type=float, default=0.01, - help='percentage of data to warmup on (.01 = 1% of all ' - 'training iters). Default 0.01') - # model checkpointing - group.add_argument('--save', type=str, default=None, - help='Output directory to save checkpoints to.') - group.add_argument('--save-iters', type=int, default=None, - help='Save every so often iterations.') - group.add_argument('--save-optim', action='store_true', - help='Save current optimizer.') - group.add_argument('--save-rng', action='store_true', - help='Save current rng state.') - group.add_argument('--save-all-rng', action='store_true', - help='Save current rng state of each rank in ' - 'distributed training.') - group.add_argument('--load', type=str, default=None, - help='Path to a particular model checkpoint. \ - (ex. `savedir/model.1000.pt`)') - group.add_argument('--load-optim', action='store_true', - help='Load most recent optimizer corresponding ' - 'to `--load`.') - group.add_argument('--load-rng', action='store_true', - help='Load most recent rng state corresponding ' - 'to `--load`.') - group.add_argument('--load-all-rng', action='store_true', - help='Load most recent rng state of each rank in ' - 'distributed training corresponding to `--load`(' - 'complementary to `--save-all-rng`).') - group.add_argument('--resume-dataloader', action='store_true', - help='Resume the dataloader when resuming training. ' - 'Does not apply to tfrecords dataloader, try resuming' - 'with a different seed in this case.') - # distributed training args - group.add_argument('--distributed-backend', default='nccl', - help='which backend to use for distributed ' - 'training. One of [gloo, nccl]') - group.add_argument('--local_rank', type=int, default=None, - help='local rank passed from distributed launcher') - - return parser - - -def add_evaluation_args(parser): - """Evaluation arguments.""" - - group = parser.add_argument_group('validation', 'validation configurations') - - group.add_argument('--eval-batch-size', type=int, default=None, - help='Data Loader batch size for evaluation datasets.' - 'Defaults to `--batch-size`') - group.add_argument('--eval-iters', type=int, default=2000, - help='number of iterations per epoch to run ' - 'validation/test for') - group.add_argument('--eval-seq-length', type=int, default=None, - help='Maximum sequence length to process for ' - 'evaluation. Defaults to `--seq-length`') - group.add_argument('--eval-max-preds-per-seq', type=int, default=None, - help='Maximum number of predictions to use for ' - 'evaluation. Defaults to ' - 'math.ceil(`--eval-seq-length`*.15/10)*10') - - return parser - - -def add_data_args(parser): - """Train/valid/test data arguments.""" - - group = parser.add_argument_group('data', 'data configurations') - - group.add_argument('--train-data', nargs='+', required=True, - help='Filename (or whitespace separated filenames) ' - 'for training.') - group.add_argument('--delim', default=',', - help='delimiter used to parse csv data files') - group.add_argument('--text-key', default='sentence', - help='key to use to extract text from json/csv') - group.add_argument('--eval-text-key', default=None, - help='key to use to extract text from ' - 'json/csv evaluation datasets') - group.add_argument('--valid-data', nargs='*', default=None, - help="""Filename for validation data.""") - group.add_argument('--split', default='1000,1,1', - help='comma-separated list of proportions for training,' - ' validation, and test split') - group.add_argument('--test-data', nargs='*', default=None, - help="""Filename for testing""") - - group.add_argument('--lazy-loader', action='store_true', - help='whether to lazy read the data set') - group.add_argument('--loose-json', action='store_true', - help='Use loose json (one json-formatted string per ' - 'newline), instead of tight json (data file is one ' - 'json string)') - group.add_argument('--num-workers', type=int, default=2, - help="""Number of workers to use for dataloading""") - group.add_argument('--tokenizer-model-type', type=str, - default='bert-large-uncased', - help="Model type to use for sentencepiece tokenization \ - (one of ['bpe', 'char', 'unigram', 'word']) or \ - bert vocab to use for BertWordPieceTokenizer (one of \ - ['bert-large-uncased', 'bert-large-cased', etc.])") - group.add_argument('--tokenizer-path', type=str, default='tokenizer.model', - help='path used to save/load sentencepiece tokenization ' - 'models') - group.add_argument('--tokenizer-type', type=str, - default='BertWordPieceTokenizer', - choices=['CharacterLevelTokenizer', - 'SentencePieceTokenizer', - 'BertWordPieceTokenizer'], - help='what type of tokenizer to use') - group.add_argument("--cache-dir", default=None, type=str, - help="Where to store pre-trained BERT downloads") - group.add_argument('--use-tfrecords', action='store_true', - help='load `--train-data`, `--valid-data`, ' - '`--test-data` from BERT tf records instead of ' - 'normal data pipeline') - group.add_argument('--seq-length', type=int, default=512, - help="Maximum sequence length to process") - group.add_argument('--max-preds-per-seq', type=int, default=None, - help='Maximum number of predictions to use per sequence.' - 'Defaults to math.ceil(`--seq-length`*.15/10)*10.' - 'MUST BE SPECIFIED IF `--use-tfrecords` is True.') - - return parser - - -def print_args(args): - """Print arguments.""" - - print('arguments:', flush=True) - for arg in vars(args): - dots = '.' * (29 - len(arg)) - print(' {} {} {}'.format(arg, dots, getattr(args, arg)), flush=True) - - -def get_args(): - """Parse all the args.""" - - parser = argparse.ArgumentParser(description='PyTorch BERT Model') - parser = add_model_config_args(parser) - parser = add_fp16_config_args(parser) - parser = add_training_args(parser) - parser = add_evaluation_args(parser) - parser = add_data_args(parser) - - args = parser.parse_args() - - args.cuda = torch.cuda.is_available() - args.rank = int(os.getenv('RANK', '0')) - args.world_size = int(os.getenv("WORLD_SIZE", '1')) - - args.dynamic_loss_scale = False - if args.loss_scale is None: - args.dynamic_loss_scale = True - print(' > using dynamic loss scaling') - - # The args fp32_* or fp16_* meant to be active when the - # args fp16 is set. So the default behaviour should all - # be false. - if not args.fp16: - args.fp32_embedding = False - args.fp32_tokentypes = False - args.fp32_layernorm = False - - print_args(args) - return args diff --git a/data_utils/corpora.py b/data_utils/corpora.py deleted file mode 100755 index 334f3518..00000000 --- a/data_utils/corpora.py +++ /dev/null @@ -1,37 +0,0 @@ -# coding=utf-8 -# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -"""several datasets with preset arguments""" -from .datasets import json_dataset, csv_dataset - -class wikipedia(json_dataset): - """ - dataset for wikipedia with arguments configured for convenience - - command line usage: `--train-data wikipedia` - """ - PATH = '' - assert_str = "make sure to set PATH at line 27 of data_utils/corpora.py" - def __init__(self, **kwargs): - assert wikipedia.PATH != '', \ - wikipedia.assert_str - if not kwargs: - kwargs = {} - kwargs['text_key'] = 'text' - kwargs['loose_json'] = True - super(wikipedia, self).__init__(wikipedia.PATH, **kwargs) - -NAMED_CORPORA = { - 'wikipedia': wikipedia, -} diff --git a/examples/ds_config.json b/examples/ds_config.json new file mode 100755 index 00000000..ba78ccf4 --- /dev/null +++ b/examples/ds_config.json @@ -0,0 +1,24 @@ +{ + "train_batch_size": 256, + "train_micro_batch_size_per_gpu": 4, + "steps_per_print": 10, + "optimizer": { + "type": "Adam", + "params": { + "lr": 0.00015, + "max_grad_norm": 1.0, + "betas": [0.9, 0.95] + } + }, + "gradient_clipping": 1.0, + "fp16": { + "enabled": true, + + "loss_scale": 0, + "loss_scale_window": 1000, + "hysteresis": 2, + "min_loss_scale": 1 + }, + "wall_clock_breakdown": true, + "zero_allow_untested_optimizer": false +} diff --git a/examples/ds_pretrain_gpt2.sh b/examples/ds_pretrain_gpt2.sh new file mode 100755 index 00000000..6087c725 --- /dev/null +++ b/examples/ds_pretrain_gpt2.sh @@ -0,0 +1,133 @@ +#! /bin/bash + +GPUS_PER_NODE=8 +# Change for multinode config +MASTER_ADDR=localhost +MASTER_PORT=6000 +NNODES=1 +NODE_RANK=0 +WORLD_SIZE=$(($GPUS_PER_NODE*$NNODES)) + +export DLWS_NUM_WORKER=${NNODES} +export DLWS_NUM_GPU_PER_WORKER=${GPUS_PER_NODE} + +DATA_PATH=data/webtext/webtext_text_document +VOCAB_PATH=data/gpt2-vocab.json +MERGE_PATH=data/gpt2-merges.txt +CHECKPOINT_PATH=checkpoints/gpt2_345m_ds + +script_path=$(realpath $0) +script_dir=$(dirname $script_path) +config_json="$script_dir/ds_zero_stage_2_config.json" + +# Megatron Model Parallelism +mp_size=4 + +NLAYERS=24 +NHIDDEN=1024 +BATCHSIZE=9 +LOGDIR="tensorboard_data/${NLAYERS}l_${NHIDDEN}h_${NNODES}n_${GPUS_PER_NODE}g_${mp_size}mp_${BATCHSIZE}b_ds4" + +#ZeRO Configs +stage=0 +reduce_scatter=true +contigious_gradients=true +rbs=50000000 +agbs=5000000000 + +#Actication Checkpointing and Contigious Memory +chkp_layers=1 +PA=true +PA_CPU=false +CC=true +SYNCHRONIZE=true +PROFILE=false + + +gpt_options=" \ + --model-parallel-size ${mp_size} \ + --num-layers $NLAYERS \ + --hidden-size $NHIDDEN \ + --num-attention-heads 16 \ + --seq-length 1024 \ + --max-position-embeddings 1024 \ + --batch-size $BATCHSIZE \ + --train-iters 320000 \ + --lr-decay-iters 320000 \ + --save $CHECKPOINT_PATH \ + --load $CHECKPOINT_PATH \ + --data-path $DATA_PATH \ + --vocab-file $VOCAB_PATH \ + --merge-file $MERGE_PATH \ + --data-impl mmap \ + --split 949,50,1 \ + --distributed-backend nccl \ + --lr 1.5e-4 \ + --lr-decay-style cosine \ + --min-lr 1.0e-5 \ + --weight-decay 1e-2 \ + --clip-grad 1.0 \ + --warmup 0.01 \ + --checkpoint-activations \ + --log-interval 100 \ + --save-interval 10000 \ + --eval-interval 1000 \ + --eval-iters 10 \ + --fp16 \ + --tensorboard-dir ${LOGDIR} +" + + deepspeed_options=" \ + --deepspeed \ + --deepspeed_config ${config_json} \ + --zero-stage ${stage} \ + --zero-reduce-bucket-size ${rbs} \ + --zero-allgather-bucket-size ${agbs} + " + +if [ "${contigious_gradients}" = "true" ]; then +deepspeed_options="${deepspeed_options} \ + --zero-contigious-gradients" +fi + +if [ "${reduce_scatter}" = "true" ]; then +deepspeed_options="${deepspeed_options} \ + --zero-reduce-scatter" +fi + +chkp_opt=" \ +--checkpoint-activations \ +--checkpoint-num-layers ${chkp_layers}" + +if [ "${PA}" = "true" ]; then +chkp_opt="${chkp_opt} \ + --partition-activations" +fi + +if [ "${PA_CPU}" = "true" ]; then +chkp_opt="${chkp_opt} \ + --checkpoint-in-cpu" +fi + +if [ "${SYNCHRONIZE}" = "true" ]; then +chkp_opt="${chkp_opt} \ + --synchronize-each-layer" +fi + +if [ "${CC}" = "true" ]; then +chkp_opt="${chkp_opt} \ + --contigious-checkpointing" +fi + +if [ "${PROFILE}" = "true" ]; then +chkp_opt="${chkp_opt} \ + --profile-backward" +fi + +full_options="${gpt_options} ${deepspeed_options} ${chkp_opt}" + +run_cmd="deepspeed --num_nodes ${DLWS_NUM_WORKER} --num_gpus ${DLWS_NUM_GPU_PER_WORKER} pretrain_gpt2.py $@ ${full_options}" +echo ${run_cmd} +eval ${run_cmd} + +set +x diff --git a/examples/ds_pretrain_gpt2_pipe.sh b/examples/ds_pretrain_gpt2_pipe.sh new file mode 100755 index 00000000..ed60d018 --- /dev/null +++ b/examples/ds_pretrain_gpt2_pipe.sh @@ -0,0 +1,140 @@ +#! /bin/bash + +GPUS_PER_NODE=16 +# Change for multinode config +MASTER_ADDR=localhost +MASTER_PORT=6000 +NNODES=1 +NODE_RANK=0 +WORLD_SIZE=$(($GPUS_PER_NODE*$NNODES)) + +export DLWS_NUM_WORKER=${NNODES} +export DLWS_NUM_GPU_PER_WORKER=${GPUS_PER_NODE} + +DATA_PATH=data/webtext/webtext_text_document +VOCAB_PATH=data/gpt2-vocab.json +MERGE_PATH=data/gpt2-merges.txt +CHECKPOINT_PATH=checkpoints/gpt2_345m_ds + +script_path=$(realpath $0) +script_dir=$(dirname $script_path) +#config_json="$script_dir/ds_zero_stage_2_config.json" +config_json="$script_dir/ds_config.json" + +# Megatron Model Parallelism +mp_size=2 +# DeepSpeed Pipeline parallelism +pp_size=2 + +NLAYERS=24 +NHIDDEN=1024 +BATCHSIZE=4 +LOGDIR="tensorboard_data/${NLAYERS}l_${NHIDDEN}h_${NNODES}n_${GPUS_PER_NODE}g_${pp_size}pp_${mp_size}mp_${BATCHSIZE}b_ds4" + +GAS=16 + +#ZeRO Configs +stage=0 +reduce_scatter=true +contigious_gradients=true +rbs=50000000 +agbs=5000000000 + +#Actication Checkpointing and Contigious Memory +chkp_layers=1 +PA=true +PA_CPU=false +CC=true +SYNCHRONIZE=true +PROFILE=false + + +gpt_options=" \ + --model-parallel-size ${mp_size} \ + --pipe-parallel-size ${pp_size} \ + --num-layers $NLAYERS \ + --hidden-size $NHIDDEN \ + --num-attention-heads 16 \ + --seq-length 1024 \ + --max-position-embeddings 1024 \ + --batch-size $BATCHSIZE \ + --gas $GAS \ + --train-iters 320000 \ + --lr-decay-iters 320000 \ + --save $CHECKPOINT_PATH \ + --load $CHECKPOINT_PATH \ + --data-path $DATA_PATH \ + --vocab-file $VOCAB_PATH \ + --merge-file $MERGE_PATH \ + --data-impl mmap \ + --split 949,50,1 \ + --distributed-backend nccl \ + --lr 1.5e-4 \ + --lr-decay-style cosine \ + --min-lr 1.0e-5 \ + --weight-decay 1e-2 \ + --clip-grad 1.0 \ + --warmup 0.01 \ + --checkpoint-activations \ + --log-interval 1 \ + --save-interval 500 \ + --eval-interval 100 \ + --eval-iters 10 \ + --fp16 \ + --tensorboard-dir ${LOGDIR} +" + + deepspeed_options=" \ + --deepspeed \ + --deepspeed_config ${config_json} \ + --zero-stage ${stage} \ + --zero-reduce-bucket-size ${rbs} \ + --zero-allgather-bucket-size ${agbs} + " + +if [ "${contigious_gradients}" = "true" ]; then +deepspeed_options="${deepspeed_options} \ + --zero-contigious-gradients" +fi + +if [ "${reduce_scatter}" = "true" ]; then +deepspeed_options="${deepspeed_options} \ + --zero-reduce-scatter" +fi + +chkp_opt=" \ +--checkpoint-activations \ +--checkpoint-num-layers ${chkp_layers}" + +if [ "${PA}" = "true" ]; then +chkp_opt="${chkp_opt} \ + --partition-activations" +fi + +if [ "${PA_CPU}" = "true" ]; then +chkp_opt="${chkp_opt} \ + --checkpoint-in-cpu" +fi + +if [ "${SYNCHRONIZE}" = "true" ]; then +chkp_opt="${chkp_opt} \ + --synchronize-each-layer" +fi + +if [ "${CC}" = "true" ]; then +chkp_opt="${chkp_opt} \ + --contigious-checkpointing" +fi + +if [ "${PROFILE}" = "true" ]; then +chkp_opt="${chkp_opt} \ + --profile-backward" +fi + +full_options="${gpt_options} ${deepspeed_options} ${chkp_opt}" + +run_cmd="deepspeed --num_nodes ${DLWS_NUM_WORKER} --num_gpus ${DLWS_NUM_GPU_PER_WORKER} pretrain_gpt2.py $@ ${full_options}" +echo ${run_cmd} +eval ${run_cmd} + +set +x diff --git a/examples/ds_zero_stage_2_config.json b/examples/ds_zero_stage_2_config.json new file mode 100755 index 00000000..2ab86c24 --- /dev/null +++ b/examples/ds_zero_stage_2_config.json @@ -0,0 +1,32 @@ +{ + "train_batch_size": 2048, + "gradient_accumulation_steps": 1, + "steps_per_print": 1, + "zero_optimization": { + "stage": 2, + "allgather_partitions": true, + "reduce_scatter": true, + "allgather_bucket_size": 50000000, + "reduce_bucket_size": 50000000, + "overlap_comm": true + }, + "optimizer": { + "type": "Adam", + "params": { + "lr": 0.00015, + "max_grad_norm": 1.0, + "betas": [0.9, 0.95] + } + }, + "gradient_clipping": 1.0, + "fp16": { + "enabled": true, + + "loss_scale": 0, + "loss_scale_window": 1000, + "hysteresis": 2, + "min_loss_scale": 1 + }, + "wall_clock_breakdown": true, + "zero_allow_untested_optimizer": false +} diff --git a/examples/evaluate_zeroshot_gpt2.sh b/examples/evaluate_zeroshot_gpt2.sh new file mode 100755 index 00000000..f4f9f22f --- /dev/null +++ b/examples/evaluate_zeroshot_gpt2.sh @@ -0,0 +1,38 @@ +#!/bin/bash + +WORLD_SIZE=8 + +DISTRIBUTED_ARGS="--nproc_per_node $WORLD_SIZE \ + --nnodes 1 \ + --node_rank 0 \ + --master_addr localhost \ + --master_port 6000" + +TASK="LAMBADA" + +VALID_DATA= +VOCAB_FILE=gpt2-vocab.json +MERGE_FILE=gpt2-merges.txt +CHECKPOINT=checkpoints/gpt2_345m + + +python -m torch.distributed.launch $DISTRIBUTED_ARGS ./tasks/main.py \ + --task $TASK \ + --valid-data $VALID_DATA \ + --tokenizer-type GPT2BPETokenizer \ + --strict-lambada \ + --vocab-file $VOCAB_FILE \ + --merge-file $MERGE_FILE \ + --load $CHECKPOINT \ + --model-parallel-size 1 \ + --num-layers 24 \ + --hidden-size 1024 \ + --num-attention-heads 16 \ + --batch-size 8 \ + --checkpoint-activations \ + --seq-length 1024 \ + --max-position-embeddings 1024 \ + --log-interval 10 \ + --fp16 \ + --no-load-optim \ + --no-load-rng diff --git a/examples/finetune_mnli_distributed.sh b/examples/finetune_mnli_distributed.sh new file mode 100755 index 00000000..65f3a9f3 --- /dev/null +++ b/examples/finetune_mnli_distributed.sh @@ -0,0 +1,44 @@ +#!/bin/bash + +WORLD_SIZE=8 + +DISTRIBUTED_ARGS="--nproc_per_node $WORLD_SIZE \ + --nnodes 1 \ + --node_rank 0 \ + --master_addr localhost \ + --master_port 6000" + +TRAIN_DATA="data/glue_data/MNLI/train.tsv" +VALID_DATA="data/glue_data/MNLI/dev_matched.tsv \ + data/glue_data/MNLI/dev_mismatched.tsv" +PRETRAINED_CHECKPOINT=checkpoints/bert_345m +VOCAB_FILE=bert-vocab.txt +CHECKPOINT_PATH=checkpoints/bert_345m_mnli + +python -m torch.distributed.launch $DISTRIBUTED_ARGS ./tasks/main.py \ + --task MNLI \ + --seed 1234 \ + --train-data $TRAIN_DATA \ + --valid-data $VALID_DATA \ + --tokenizer-type BertWordPieceLowerCase \ + --vocab-file $VOCAB_FILE \ + --epochs 5 \ + --pretrained-checkpoint $PRETRAINED_CHECKPOINT \ + --model-parallel-size 1 \ + --num-layers 24 \ + --hidden-size 1024 \ + --num-attention-heads 16 \ + --batch-size 8 \ + --checkpoint-activations \ + --lr 5.0e-5 \ + --lr-decay-style linear \ + --warmup 0.065 \ + --seq-length 512 \ + --max-position-embeddings 512 \ + --save-interval 500000 \ + --save $CHECKPOINT_PATH \ + --log-interval 10 \ + --eval-interval 100 \ + --eval-iters 50 \ + --weight-decay 1.0e-1 \ + --fp16 diff --git a/examples/finetune_race_distributed.sh b/examples/finetune_race_distributed.sh new file mode 100755 index 00000000..0212ecba --- /dev/null +++ b/examples/finetune_race_distributed.sh @@ -0,0 +1,47 @@ +#!/bin/bash + +WORLD_SIZE=8 + +DISTRIBUTED_ARGS="--nproc_per_node $WORLD_SIZE \ + --nnodes 1 \ + --node_rank 0 \ + --master_addr localhost \ + --master_port 6000" + +TRAIN_DATA="data/RACE/train/middle" +VALID_DATA="data/RACE/dev/middle \ + data/RACE/dev/high" +VOCAB_FILE=bert-vocab.txt +PRETRAINED_CHECKPOINT=checkpoints/bert_345m +CHECKPOINT_PATH=checkpoints/bert_345m_race + +python -m torch.distributed.launch $DISTRIBUTED_ARGS ./tasks/main.py \ + --task RACE \ + --seed 1234 \ + --train-data $TRAIN_DATA \ + --valid-data $VALID_DATA \ + --tokenizer-type BertWordPieceLowerCase \ + --vocab-file $VOCAB_FILE \ + --epochs 3 \ + --pretrained-checkpoint $PRETRAINED_CHECKPOINT \ + --model-parallel-size 1 \ + --num-layers 24 \ + --hidden-size 1024 \ + --num-attention-heads 16 \ + --batch-size 4 \ + --checkpoint-activations \ + --lr 1.0e-5 \ + --lr-decay-style linear \ + --warmup 0.06 \ + --seq-length 512 \ + --max-position-embeddings 512 \ + --save-interval 100000 \ + --save $CHECKPOINT_PATH \ + --log-interval 10 \ + --eval-interval 100 \ + --eval-iters 50 \ + --weight-decay 1.0e-1 \ + --clip-grad 1.0 \ + --hidden-dropout 0.1 \ + --attention-dropout 0.1 \ + --fp16 diff --git a/examples/generate_text.sh b/examples/generate_text.sh new file mode 100755 index 00000000..6a04c492 --- /dev/null +++ b/examples/generate_text.sh @@ -0,0 +1,25 @@ +#!/bin/bash + +CHECKPOINT_PATH=checkpoints/gpt2_345m +VOCAB_FILE=gpt2-vocab.json +MERGE_FILE=gpt2-merges.txt + +python tools/generate_samples_gpt2.py \ + --model-parallel-size 1 \ + --num-layers 24 \ + --hidden-size 1024 \ + --load $CHECKPOINT_PATH \ + --num-attention-heads 16 \ + --max-position-embeddings 1024 \ + --tokenizer-type GPT2BPETokenizer \ + --fp16 \ + --batch-size 2 \ + --seq-length 1024 \ + --out-seq-length 1024 \ + --temperature 1.0 \ + --vocab-file $VOCAB_FILE \ + --merge-file $MERGE_FILE \ + --genfile unconditional_samples.json \ + --num-samples 2 \ + --top_p 0.9 \ + --recompute diff --git a/examples/merge_mp_bert.sh b/examples/merge_mp_bert.sh new file mode 100755 index 00000000..01e08b12 --- /dev/null +++ b/examples/merge_mp_bert.sh @@ -0,0 +1,18 @@ +#!/bin/bash + +MODEL_PARALLEL_SIZE=2 + +VOCAB_FILE=bert-vocab.txt +CHECKPOINT_PATH=checkpoints/bert_345m + +WORLD_SIZE=$MODEL_PARALLEL_SIZE python tools/merge_mp_partitions.py \ + --model-type BERT \ + --model-parallel-size $MODEL_PARALLEL_SIZE \ + --tokenizer-type BertWordPieceLowerCase \ + --vocab-file $VOCAB_FILE \ + --num-layers 24 \ + --hidden-size 1024 \ + --num-attention-heads 16 \ + --seq-length 512 \ + --max-position-embeddings 512 \ + --load $CHECKPOINT_PATH diff --git a/examples/pretrain_bert.sh b/examples/pretrain_bert.sh new file mode 100755 index 00000000..ecf59477 --- /dev/null +++ b/examples/pretrain_bert.sh @@ -0,0 +1,35 @@ +#!/bin/bash + +RANK=0 +WORLD_SIZE=1 +DATA_PATH=_text_sentence +CHECKPOINT_PATH= + +python pretrain_bert.py \ + --num-layers 24 \ + --hidden-size 1024 \ + --num-attention-heads 16 \ + --batch-size 4 \ + --seq-length 512 \ + --max-position-embeddings 512 \ + --train-iters 2000000 \ + --save $CHECKPOINT_PATH \ + --load $CHECKPOINT_PATH \ + --data-path $DATA_PATH \ + --vocab-file bert-vocab.txt \ + --data-impl mmap \ + --split 949,50,1 \ + --distributed-backend nccl \ + --lr 0.0001 \ + --min-lr 0.00001 \ + --lr-decay-style linear \ + --lr-decay-iters 990000 \ + --weight-decay 1e-2 \ + --clip-grad 1.0 \ + --warmup .01 \ + --log-interval 100 \ + --save-interval 10000 \ + --eval-interval 1000 \ + --eval-iters 10 \ + --fp16 + diff --git a/examples/pretrain_bert_distributed.sh b/examples/pretrain_bert_distributed.sh new file mode 100755 index 00000000..17ebae1f --- /dev/null +++ b/examples/pretrain_bert_distributed.sh @@ -0,0 +1,44 @@ +#!/bin/bash + +GPUS_PER_NODE=8 +# Change for multinode config +MASTER_ADDR=localhost +MASTER_PORT=6000 +NNODES=1 +NODE_RANK=0 +WORLD_SIZE=$(($GPUS_PER_NODE*$NNODES)) + +DATA_PATH=_text_sentence +CHECKPOINT_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_bert.py \ + --model-parallel-size 1 \ + --num-layers 24 \ + --hidden-size 1024 \ + --num-attention-heads 16 \ + --batch-size 4 \ + --seq-length 512 \ + --max-position-embeddings 512 \ + --train-iters 1000000 \ + --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 \ + --lr-decay-style linear \ + --min-lr 1.0e-5 \ + --lr-decay-iters 990000 \ + --weight-decay 1e-2 \ + --clip-grad 1.0 \ + --warmup .01 \ + --log-interval 100 \ + --save-interval 10000 \ + --eval-interval 1000 \ + --eval-iters 10 \ + --fp16 diff --git a/examples/pretrain_gpt2.sh b/examples/pretrain_gpt2.sh new file mode 100755 index 00000000..66232bf5 --- /dev/null +++ b/examples/pretrain_gpt2.sh @@ -0,0 +1,43 @@ +#! /bin/bash + +# Runs the "345M" parameter model + +RANK=0 +WORLD_SIZE=1 + +DATA_PATH=_text_document +CHECKPOINT_PATH= + + +python pretrain_gpt2.py \ + --num-layers 24 \ + --hidden-size 1024 \ + --num-attention-heads 16 \ + --batch-size 8 \ + --seq-length 1024 \ + --max-position-embeddings 1024 \ + --train-iters 500000 \ + --lr-decay-iters 320000 \ + --save $CHECKPOINT_PATH \ + --load $CHECKPOINT_PATH \ + --data-path $DATA_PATH \ + --vocab-file gpt2-vocab.json \ + --merge-file gpt2-merges.txt \ + --data-impl mmap \ + --split 949,50,1 \ + --distributed-backend nccl \ + --lr 0.00015 \ + --min-lr 1.0e-5 \ + --lr-decay-style cosine \ + --weight-decay 1e-2 \ + --clip-grad 1.0 \ + --warmup .01 \ + --checkpoint-activations \ + --log-interval 100 \ + --save-interval 10000 \ + --eval-interval 1000 \ + --eval-iters 10 \ + --fp16 + + +set +x diff --git a/examples/pretrain_gpt2_distributed.sh b/examples/pretrain_gpt2_distributed.sh new file mode 100755 index 00000000..1d746250 --- /dev/null +++ b/examples/pretrain_gpt2_distributed.sh @@ -0,0 +1,52 @@ +#! /bin/bash + +# Runs the "345M" parameter model + +GPUS_PER_NODE=8 +# Change for multinode config +MASTER_ADDR=localhost +MASTER_PORT=6000 +NNODES=1 +NODE_RANK=0 +WORLD_SIZE=$(($GPUS_PER_NODE*$NNODES)) + +DATA_PATH=_text_document +CHECKPOINT_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 \ + --model-parallel-size 1 \ + --num-layers 24 \ + --hidden-size 1024 \ + --num-attention-heads 16 \ + --batch-size 8 \ + --seq-length 1024 \ + --max-position-embeddings 1024 \ + --train-iters 500000 \ + --lr-decay-iters 320000 \ + --save $CHECKPOINT_PATH \ + --load $CHECKPOINT_PATH \ + --data-path $DATA_PATH \ + --vocab-file gpt2-vocab.json \ + --merge-file gpt2-merges.txt \ + --data-impl mmap \ + --split 949,50,1 \ + --distributed-backend nccl \ + --lr 0.00015 \ + --lr-decay-style cosine \ + --min-lr 1.0e-5 \ + --weight-decay 1e-2 \ + --clip-grad 1.0 \ + --warmup .01 \ + --checkpoint-activations \ + --log-interval 100 \ + --save-interval 10000 \ + --eval-interval 1000 \ + --eval-iters 10 \ + --fp16 + + + +set +x diff --git a/images/Makefile b/images/Makefile new file mode 100644 index 00000000..5efde029 --- /dev/null +++ b/images/Makefile @@ -0,0 +1,12 @@ +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 diff --git a/images/cases.png b/images/cases.png new file mode 100644 index 0000000000000000000000000000000000000000..8f52c38f544c4e7b52d21037b19d8255ee5eb19b GIT binary patch literal 11807 zcmZ`w^FaNX>24}0h?>91bD 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