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v0.27.0: PyTorch 2.2.0 Support, PyTorch-Native Pipeline Parallism, DeepSpeed XPU support, and Bug Fixes
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@muellerzr muellerzr released this 09 Feb 16:30
· 363 commits to main since this release
v0.27.0
b7087be

PyTorch 2.2.0 Support

With the latest release of PyTorch 2.2.0, we've guaranteed that there are no breaking changes regarding it

PyTorch-Native Pipeline Parallel Inference

With this release we are excited to announce support for pipeline-parallel inference by integrating PyTorch's PiPPy framework (so no need to use Megatron or DeepSpeed)! This supports automatic model-weight splitting to each device using a similar API to device_map="auto". This is still under heavy development, however the inference side is stable enough that we are ready for a release. Read more about it in our docs and check out the example zoo.

Requires pippy of version 0.2.0 or later (pip install torchpippy -U)

Example usage (combined with accelerate launch or torchrun):

from accelerate import PartialState, prepare_pippy
model = AutoModelForSequenceClassification.from_pretrained("gpt2")
model = prepare_pippy(model, split_points="auto", example_args=(input,))
input = input.to("cuda:0")
with torch.no_grad():
    output = model(input)
# The outputs are only on the final process by default
# You can pass in `gather_outputs=True` to prepare_pippy to
# make them available on all processes
if PartialState().is_last_process:
    output = torch.stack(tuple(output[0]))
    print(output.shape)

DeepSpeed

This release provides support for utilizing DeepSpeed on XPU devices thanks to @faaany

What's Changed

New Contributors

Full Changelog: v0.26.1...v0.27.0

Contributors

akx, ydshieh, and 17 other contributors
Assets 2
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