This is a detached fork of https://github.com/microsoft/Megatron-DeepSpeed, which in itself is a fork of https://github.com/NVIDIA/Megatron-LM. The former integrates DeepSpeed into the original Megatron-LM code.
This fork in turn will include direct changes to the models needed for the BigScience project. This is the repo we use for this project.
In addition various code bits and lots of docs are to be found at https://github.com/bigscience-workshop/bigscience.
Please note that the rest of this page has been trimmed to only include the info relevant to the BigScience project and also updated to usage with the integrated Deepspeed. You will find the original page with all the tables and training info on Bert and T5 here.
- Install
bigscience-workshop/Megatron-DeepSpeed
git clone https://github.com/bigscience-workshop/Megatron-DeepSpeed
cd Megatron-DeepSpeed
pip install -r requirements.txt
You can now use this repo directly by working directly from it. You don't need to install it unless you write your own scripts elsewhere that use the modules in this repo, in which case you may want to do:
pip install -e .
- Install
apex
git clone https://github.com/NVIDIA/apex
cd apex
pip install --global-option="--cpp_ext" --global-option="--cuda_ext" --no-cache -v --disable-pip-version-check . 2>&1 | tee build.log
(on JZ it's done in a special way, see here.)
- Install
deepspeed/ thebig-sciencebranch
Then install the big-science branch of deepspeed:
git clone https://github.com/microsoft/deepspeed deepspeed-big-science
cd deepspeed-big-science
git checkout big-science
rm -rf build
TORCH_CUDA_ARCH_LIST="7.0" DS_BUILD_CPU_ADAM=1 DS_BUILD_AIO=1 DS_BUILD_UTILS=1 pip install -e . --global-option="build_ext" --global-option="-j8" --no-cache -v --disable-pip-version-check
adjust TORCH_CUDA_ARCH_LIST="7.0" to the architecture of your NVIDIA GPU (or just remove it altogether if you are not sure how to find one).
(on JZ it's done in a special way, see here.)
- CUDA kernels compilation
The first time you run the training scripts several CUDA kernels will be compiled. Which means you need to have a cuda environment set up in your environment and it should match the version pytorch was built with.
After installation, there are several possible workflows. The most comprehensive is:
- Data preprocessing
- Pretraining
- Finetuning (Optional for zero-shot tasks)
- 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 in 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.
The GPT vocab file and merge table can be downloaded directly.
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:
{"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 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 GPT training is:
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 \
--workers 8
The output will be two files named, in this case, my-gpt2_text_document.bin and my-gpt2_text_document.idx. The --data-path specified in later GPT training is the full path and new filename, but without the file extension.
Further command line arguments are described in the source file preprocess_data.py.
You can also use tools/preprocess_data_many_cores.py in the case of high amount of cpu cores available. Typically in JZ setup where cpu nodes have up to 40 physical cpu cores, you should run this script with around 60 workers instead of the tools/preprocess_data.py. The same command line arguments are available.
Merging datasets
Sometimes it's hard to work on a very large dataset at once, so one can pre-process it in chunks and then merge those datasets into a single combined indexed dataset. Here is an example:
python tools/merge_preprocessed_data.py \
--datasets \
meg-gpt2-oscar-en-500-p1_text_document \
meg-gpt2-oscar-en-500-p2_text_document \
meg-gpt2-oscar-en-500-p3_text_document \
--output-prefix meg-gpt2_oscar_text_document
Here is how you can get ready to train quickly, using a 1GB 79K-record jsonl dataset.
wget https://huggingface.co/bigscience/misc-test-data/resolve/main/stas/oscar-1GB.jsonl.xz
wget https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-vocab.json
wget https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-merges.txt
xz -d oscar-1GB.jsonl.xz
python tools/preprocess_data.py \
--input oscar-1GB.jsonl \
--output-prefix my-gpt2 \
--vocab gpt2-vocab.json \
--dataset-impl mmap \
--tokenizer-type GPT2BPETokenizer \
--merge-file gpt2-merges.txt \
--append-eod \
--workers 8
note: you may want to skip to the next section, since it describes what we actually use at the moment.
The examples/pretrain_gpt.sh script runs single GPU 345M parameter GPT 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.
The tokenization scheme used is BPE (which requires a merge table and a json vocabulary file), 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.
However, as you will see below you will learn that DeepSpeed requires a distributed enviroment even with a single GPU. Therefore, instead refer to pretrain_gpt_single_node.sh, which will work with this repo.
CHECKPOINT_PATH=checkpoints/gpt2
VOCAB_FILE=gpt2-vocab.json
MERGE_FILE=gpt2-merges.txt
DATA_PATH=my-gpt2_text_document
GPT_ARGS=" \
--num-layers 24 \
--hidden-size 1024 \
--num-attention-heads 16 \
--seq-length 1024 \
--max-position-embeddings 1024 \
--micro-batch-size 4 \
--global-batch-size 8 \
--lr 0.00015 \
--train-iters 500000 \
--lr-decay-iters 320000 \
--lr-decay-style cosine \
--vocab-file $VOCAB_FILE \
--merge-file $MERGE_FILE \
--lr-warmup-fraction .01 \
--fp16 \
"
OUTPUT_ARGS=" \
--log-interval 10 \
--save-interval 500 \
--eval-interval 100 \
--eval-iters 10 \
--checkpoint-activations \
"
DATA_ARGS=" \
--save $CHECKPOINT_PATH \
--load $CHECKPOINT_PATH \
--data-path $DATA_PATH \
"
CMD="pretrain_gpt.py $GPT_ARGS $OUTPUT_ARGS $DATA_ARGS"
N_GPUS=1
LAUNCHER="deepspeed --num_gpus $N_GPUS"
$LAUNCHER $CMD
Note, we replaced python with deepspeed --num_gpus 1. For multi-gpu training update --num_gpus to the number of GPUs you have.
For multi-node training you will either need to create a hostfile which defines all the nodes as explained here or in the SLURM environment it might not work and you will need to use:
CMD=<as above>
MASTER_ADDR=`perl -le '$_=$ENV{"SLURM_JOB_NODELIST"}; s/,.*//; s/-.*//; s/\[//; print'`
MASTER_PORT=6000
GPUS_PER_NODE=4
NNODES=16
export LAUNCHER="python -u -m torch.distributed.launch \
--nproc_per_node $GPUS_PER_NODE \
--nnodes $NNODES \
--master_addr $MASTER_ADDR \
--master_port $MASTER_PORT \
"
srun --jobid $SLURM_JOBID bash -c '$LAUNCHER --node_rank $SLURM_PROCID $CMD'
For a single GPU the other approach is to emulate distributed with:
MASTER_ADDR=localhost MASTER_PORT=9994 RANK=0 LOCAL_RANK=0 python pretrain_gpt.py ...
Further command line arguments are described in the source file arguments.py.
To allow further flexibility we are using Deepspeed PP (pipeline parallelism) and ZeRO-DP along with Megatron normal functionality. That is we replace Megatron's PP with Deepspeed's PP, and we use ZERO-DP for DP.
It's similar to the normal Megatron-LM launcher, plus it has a deepspeed config file and a few params:
CHECKPOINT_PATH=checkpoints/gpt2
VOCAB_FILE=data/gpt2-vocab.json
MERGE_FILE=data/gpt2-merges.txt
DATA_PATH=data/meg-gpt2_oscar-combined_text_document
TENSORBOARD_PATH=output_dir/tensorboard
CODECARBON_PATH=output_dir/codecarbon
MICRO_BATCH_SIZE=1
GLOBAL_BATCH_SIZE=16
TP_SIZE=1
PP_SIZE=1
N_GPUS=2
SAVE_INTERVAL=100
# --train-samples 10_000 \
# --exit-interval $EXIT_INTERVAL \
# --exit-interval 100 \
GPT_ARGS=" \
--num-layers 2 \
--hidden-size 64 \
--num-attention-heads 2 \
--seq-length 1024 \
--max-position-embeddings 1024 \
--micro-batch-size $MICRO_BATCH_SIZE \
--rampup-batch-size 2 2 1_000 \
--global-batch-size $GLOBAL_BATCH_SIZE \
--train-samples 100 \
--optimizer adam \
--adam-beta1 0.9 \
--adam-beta2 0.95 \
--adam-eps 1e-8 \
--lr 1e-4 \
--lr-warmup-samples 5 \
--clip-grad 1.0 \
--weight-decay 1e-1 \
--vocab-file $VOCAB_FILE \
--merge-file $MERGE_FILE \
--fp16 \
"
# --train-iters 500 \
OUTPUT_ARGS=" \
--log-interval 10 \
--save-interval $SAVE_INTERVAL \
--eval-interval 100 \
--eval-iters 10 \
--checkpoint-activations \
"
# --codecarbon-dir $CODECARBON_PATH \
DATA_ARGS=" \
--save $CHECKPOINT_PATH \
--load $CHECKPOINT_PATH \
--data-path $DATA_PATH \
--tensorboard-dir $TENSORBOARD_PATH \
--tensorboard-queue-size 5 \
--log-timers-to-tensorboard \
--log-batch-size-to-tensorboard \
--log-validation-ppl-to-tensorboard \
"
ZERO_STAGE=1
config_json="./ds_config.json"
# Deepspeed figures out GAS dynamically from dynamic GBS via set_train_batch_size()
cat <<EOT > $config_json
{
"train_micro_batch_size_per_gpu": $MICRO_BATCH_SIZE,
"train_batch_size": $GLOBAL_BATCH_SIZE,
"gradient_clipping": 1.0,
"zero_optimization": {
"stage": $ZERO_STAGE
},
"fp16": {
"enabled": true,
"loss_scale": 0,
"loss_scale_window": 500,
"hysteresis": 2,
"min_loss_scale": 1,
"initial_scale_power": 12
},
"steps_per_print": 2000,
"wall_clock_breakdown": false
}
EOT
DEEPSPEED_ARGS=" \
--deepspeed \
--deepspeed_config ${config_json} \
--zero-stage ${ZERO_STAGE} \
--deepspeed-activation-checkpointing \
"
ALL_ARGS="$GPT_ARGS $OUTPUT_ARGS $DATA_ARGS $DEEPSPEED_ARGS"
# if you can't stand pt-1.9 launcher noise
export LOGLEVEL=WARNING
LAUNCHER="deepspeed --num_gpus $N_GPUS"
export CMD=" \
$LAUNCHER pretrain_gpt.py \
--tensor-model-parallel-size $TP_SIZE \
--pipeline-model-parallel-size $PP_SIZE \
--distributed-backend nccl \
$ALL_ARGS \
"
echo $CMD
$CMD
on JZ we use a different launching command, see for example the end of tr1-13B-round1.slurm, but this is also a good fully functional script that you can use. Except it's written for SLURM environment.
Thanks to @sbmaruf, any HF pretrained tokenizer may be used instead of the Megatron-provided BERT/GPT/T5 tokenizers. You'll need to run preprocessing yourself (tools/preprocess_data.py), using tokenizer-type=PretrainedFromHF and tokenizer-name-or-path=<your_tokenizer>. For example, python tools/preprocess_data.py --input ~/c4_en_train.jsonl --output-prefix c4_en_train --dataset-impl mmap --tokenizer-type PretrainedFromHF --tokenizer-name-or-path t5-small --workers 30 --append-eod
The examples/pretrain_{bert,gpt,t5}_distributed.sh 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 for further description of these environment variables. By default, multi-node training uses the 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.
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).
We have examples of how to use these two different forms of model parallelism the example scripts ending in distributed_with_mp.sh, note that pipeline parallelism is not currently supported in the T5 model:
Other than these minor changes, the distributed training is identical to the training on a single GPU.
Distributed training:
see the details on how to do distributed training with the deepspeed launcher a few sections up
XXX: The following needs to be updated:
WORLD_SIZE=8
TENSOR_MP_SIZE=2
PIPELINE_MP_SIZE=2
DISTRIBUTED_ARGS="--nproc_per_node $WORLD_SIZE \
--nnodes 1 \
--node_rank 0 \
--master_addr localhost \
--master_port 6000"
CHECKPOINT_PATH=<same as above>
VOCAB_FILE=<same as above>
DATA_PATH=<same as above>
MODEL_ARGS=<same as above>
OUTPUT_ARGS=<same as above>
python -m torch.distributed.launch $DISTRIBUTED_ARGS ./pretrain_<model>.py \
$MODEL_ARGS \
$OUTPUT_ARGS \
--save $CHECKPOINT_PATH \
--load $CHECKPOINT_PATH \
--data-path $DATA_PATH \
--tensor-model-parallel-size $TENSOR_MP_SIZE \
--pipeline-model-parallel-size $PIPELINE_MP_SIZE \
--DDP-impl torch
In examples/pretrain_gpt3_175B.sh we have provided an example of how to configure Megatron to run GPT-3 with 175 billion parameters on 1024 GPUs. The script is designed for slurm with 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.
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. Currently only tensor model parallelism is supported on input and pipeline model parallelsim on the output. This example reads in a model with 2-way tensor model parallelism and writes out a model with 2-way pipeline model parallelism.
TENSOR_MODEL_PARALLEL_SIZE=2
TARGET_PIPELINE_MODEL_PARALLEL_SIZE=2
VOCAB_FILE=bert-vocab.txt
CHECKPOINT_PATH=checkpoints/bert_345m
WORLD_SIZE=$TENSOR_MODEL_PARALLEL_SIZE python tools/merge_mp_partitions.py \
--model-type BERT \
--tensor-model-parallel-size $TENSOR_MODEL_PARALLEL_SIZE \
--pipeline-model-parallel-size 1 \
--target-pipeline-model-parallel-size $TARGET_PIPELINE_MODEL_PARALLEL_SIZE \
--tokenizer-type BertWordPieceLowerCase \
--vocab-file $VOCAB_FILE \
--num-layers 24 \
--hidden-size 1024 \
--num-attention-heads 16 \
--seq-length 512 \
--max-position-embeddings 512 \
--load $CHECKPOINT_PATH
--save $CHECKPOINT_PATH/merged
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.
bash examples/generate_text.sh
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..
CHECKPOINT_PATH=checkpoints/gpt2_345m
VOCAB_FILE=gpt2-vocab.json
MERGE_FILE=gpt2-merges.txt
GPT_ARGS=<same as those in <a href="#gpt-pretraining">GPT pretraining</a> 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_gpt.py \
$GPT_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
We include example scripts for GPT evaluation on WikiText perplexity evaluation and LAMBADA Cloze accuracy.
For even comparison with prior works, we evaluate perplexity on the word-level WikiText-103 test dataset, 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 \
--micro-batch-size 8 \
--checkpoint-activations \
--log-interval 10 \
--no-load-optim \
--no-load-rng
Currently the test suite is not yet plugged into CI and needs to be run manually. For more details please see Testing.
This is a community project and we would love to have your help.
If you are inspired to contribute please see the following entries:
Megatron-DeeepSpeed:
General BigScience: