FairScale is a PyTorch extension library for high performance and large scale training. This library extends basic PyTorch capabilities while adding new SOTA scaling techniques. FairScale makes available the latest distributed training techniques in the form of composable modules and easy to use APIs. These APIs are a fundamental part of a researcher's toolbox as they attempt to scale models with limited resources.
FairScale was designed with the following values in mind:
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Usability - Users should be able to understand and use FairScale APIs with minimum cognitive overload.
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Modularity - Users should be able to combine multiple FairScale APIs as part of their training loop seamlessly.
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Performance - FairScale APIs provide the best performance in terms of scaling and efficiency.
- September 2021
masterbranch renamed tomain. - September 2021 fairscale 0.4.1 was released.
To install FairScale, please see the following instructions. You should be able to install a pip package or build directly from source.
The full documentation contains instructions for getting started, deep dives and tutorials about the various FairScale APIs.
Here are a few sample snippets from a subset of FairScale offerings:
Run a 4-layer model on 2 GPUs. The first two layers run on cuda:0 and the next two layers run on cuda:1.
import torch
import fairscale
model = torch.nn.Sequential(a, b, c, d)
model = fairscale.nn.Pipe(model, balance=[2, 2], devices=[0, 1], chunks=8)See a more complete example here, but a minimal example could look like the following :
import torch
import torch.distributed as dist
import torch.multiprocessing as mp
from fairscale.optim.oss import OSS
from fairscale.nn.data_parallel import ShardedDataParallel as ShardedDDP
def train(
rank: int,
world_size: int,
epochs: int):
# DDP init example
dist.init_process_group(backend='nccl', init_method="tcp://localhost:29501", rank=rank, world_size=world_size)
# Problem statement
model = myAwesomeModel().to(rank)
dataloader = mySuperFastDataloader()
loss_fn = myVeryRelevantLoss()
base_optimizer = torch.optim.SGD # pick any pytorch compliant optimizer here
base_optimizer_arguments = {} # pass any optimizer specific arguments here, or directly below when instantiating OSS
# Wrap the optimizer in its state sharding brethren
optimizer = OSS(params=model.parameters(), optim=base_optimizer, **base_optimizer_arguments)
# Wrap the model into ShardedDDP, which will reduce gradients to the proper ranks
model = ShardedDDP(model, optimizer)
# Any relevant training loop, nothing specific to OSS. For example:
model.train()
for e in range(epochs):
for batch in dataloader:
# Train
model.zero_grad()
outputs = model(batch["inputs"])
loss = loss_fn(outputs, batch["label"])
loss.backward()
optimizer.step()
dist.destroy_process_group()
if __name__ == "__main__":
# Supposing that WORLD_SIZE and EPOCHS are somehow defined somewhere
mp.spawn(
train,
args=(
WORLD_SIZE,
EPOCHS,
),
nprocs=WORLD_SIZE,
join=True,
)AdaScale can be used to wrap a SGD optimizer and to be used in DDP (Distributed Data Parallel) training or non-DDP with gradient accumulation. The benefit is to re-use the same LR schedule from a baseline batch size when effective batch size is bigger.
Note that AdaScale does not help increase per-GPU batch size.
from torch.optim import SGD
from torch.optim.lr_scheduler import LambdaLR # or your scheduler
from fairscale.optim import AdaScale
...
optim = AdaScale(SGD(model.parameters(), lr=0.1))
scheduler = LambdaLR(optim, ...)
...
# Note: the train loop should be with DDP or with gradient accumulation.
last_epoch = 0
step = 0
done = False
while not done:
for sample in dataset:
...
step += optim.gain()
optim.step()
epoch = step // len(dataset)
if last_epoch != epoch:
scheduler.step()
last_epoch = epoch
if epoch > max_epoch:
done = TruePrimary goal is to allow scaling to bigger batch sizes without losing model accuracy. (However, training time might be longer comparing to without AdaScale.)
At a high level, we want ML researchers to:
- go parallel more easily (i.e. no need to find new learning rate schedules)
- not worrying about losing accuracy
- potentially higher GPU efficiency (fewer steps, less networking overhead, etc.)
We use circleci to test on PyTorch versions 1.7.1, 1.8.1 and 1.9.0. Please create an issue if you are having trouble with installation.
We welcome outside contributions! Please see the CONTRIBUTING instructions for how you can contribute to FairScale.
FairScale is licensed under the BSD-3-Clause License.
fairscale.nn.pipe is forked from torchgpipe, Copyright 2019, Kakao Brain, licensed under Apache License.
fairscale.nn.model_parallel is forked from Megatron-LM, Copyright 2020, NVIDIA CORPORATION, licensed under Apache License.
fairscale.optim.adascale is forked from AdaptDL, Copyright 2020, Petuum, Inc., licensed under Apache License.
fairscale.nn.misc.flatten_params_wrapper is forked from PyTorch-Reparam-Module, Copyright 2018, Tongzhou Wang, licensed under MIT License.
If you use FairScale in your publication, please cite it by using the following BibTeX entry.
@Misc{FairScale2021,
author = {Mandeep Baines and Shruti Bhosale and Vittorio Caggiano and Naman Goyal and Siddharth Goyal and Myle Ott and Benjamin Lefaudeux and Vitaliy Liptchinsky and Mike Rabbat and Sam Sheiffer and Anjali Sridhar and Min Xu},
title = {FairScale: A general purpose modular PyTorch library for high performance and large scale training},
howpublished = {\url{https://github.com/facebookresearch/fairscale}},
year = {2021}
}- If you experience an error indicating a default branch does not exist, it probably due to the latest update, switching the default branch from "master" to "main"
error: pathspec 'non-existing-branch' did not match any file(s) known to git
Please run the following commands to update to the main branch.
git branch -m master main
git fetch origin
git branch -u origin/main main
git remote set-head origin -a