Description

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:

• Usability - Users should be able to understand and use FairScale APIs with minimum cognitive overload.

• Modularity - Users should be able to combine multiple FairScale APIs as part of their training loop seamlessly.

• Performance - FairScale APIs provide the best performance in terms of scaling and efficiency.

Watch Introductory Video

What's New:

• March 2022 fairscale 0.4.6 was released.
• We have support for CosFace's LMCL in MEVO. This is a loss function that is suitable for large number of prediction target classes.
• January 2022 fairscale 0.4.5 was released.
• We have experimental support for layer wise gradient scaling.
• We enabled reduce_scatter operation overlapping in FSDP backward propagation.
• December 2021 fairscale 0.4.4 was released.
• FairScale is tested with the following PyTorch versions (with CUDA 11.2): 1.8.1, 1.10.0 and 1.11.0.dev20211101+cu111.
• November 2021 fairscale 0.4.3 was released.
• We have experimental support for offloading params to disk when using the FSDP API for evaluation workloads.
• We have an experimental layer that fuses multiple layers together to support large vocab size trainings.
• November 2021 fairscale 0.4.2 was released.
• We have a new experimental API called the LayerwiseMemoryTracker to help track, visualize and suggest fixes for memory issues occurring during the forward/backward pass of your models.
• Introducing SlowMoDistributedDataParallel API, a distributed training wrapper that is useful on clusters with slow network interconnects (e.g. Ethernet).
• September 2021 master branch renamed to main.

Installation

To install FairScale, please see the following instructions. You should be able to install a package with pip or conda, or build directly from source.

Getting Started

The full documentation contains instructions for getting started, deep dives and tutorials about the various FairScale APIs.

Examples

Here are a few sample snippets from a subset of FairScale offerings:

Pipe

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)

Optimizer state sharding (ZeRO)

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 SGD

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 = True

Primary 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.)

Testing

We use circleci to test FairScale with the following PyTorch versions (with CUDA 11.2):

• the latest stable release (1.10.0)
• the latest LTS release (1.8.1)
• a recent nightly release (1.11.0.dev20211101+cu111)

Please create an issue if you are having trouble with installation.

Contributors

We welcome outside contributions! Please see the CONTRIBUTING instructions for how you can contribute to FairScale.

License

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.

Citing FairScale

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}
}

FAQ

1. 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