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06d50b5eb0
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/44090 This is an initial commit pulling in the torchgpipe fork at https://github.com/facebookresearch/fairscale. The purpose of this commit is to just pull in the code and ensure all tests and builds work fine. We will slowly modify this to match our intended API mentioned in https://fb.quip.com/txurAV3zIFox#RPZACAfAKMq. Follow up PRs would address further changes needed on top of the initial commit.. We're pulling the code into the `torch.distributed._pipeline.sync` package. The package is private on purpose since there is a lot of work (ex: docs, API changes etc.) that needs to go in before we can actually officially support this. ghstack-source-id: 114864254 Test Plan: 1) waitforbuildbot 2) Ran all tests on my devgpu Reviewed By: mrshenli Differential Revision: D23493316 fbshipit-source-id: fe3c8b7dadeeb86abdc00e8a8652491b0b16743a
105 lines
3.5 KiB
Python
105 lines
3.5 KiB
Python
# Copyright 2019 Kakao Brain
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#
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# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
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#
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# This source code is licensed under the BSD license found in the
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# LICENSE file in the root directory of this source tree.
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"""Autograd functions for stream-aware CUDA copy. It is used to overlap copy
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and computation on the same GPU.
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"""
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from collections import deque
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from typing import Deque, List, Optional, Tuple
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import torch
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from torch import Tensor
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from .stream import AbstractStream, current_stream, get_device, record_stream, use_stream, wait_stream
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__all__: List[str] = []
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Tensors = Tuple[Tensor, ...]
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# Common interface between :class:`Copy` and :class:`Wait`.
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class Context:
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prev_stream: AbstractStream
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next_stream: AbstractStream
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class Copy(torch.autograd.Function):
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"""Copies tensors on specific streams."""
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@staticmethod
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# type: ignore
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def forward(ctx: Context, prev_stream: AbstractStream, next_stream: AbstractStream, *input: Tensor,) -> Tensors:
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ctx.prev_stream = prev_stream
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ctx.next_stream = next_stream
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output = []
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output_stream = current_stream(get_device(next_stream))
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with use_stream(prev_stream), use_stream(next_stream):
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for x in input:
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y = x.to(get_device(next_stream), non_blocking=True)
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output.append(y)
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# 'prev_stream' is not where 'x' has been allocated.
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record_stream(x, prev_stream)
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# 'y' has been allocated on 'next_stream'.
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# It might be used on the current stream captured as 'output_stream'.
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record_stream(y, output_stream)
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return tuple(output)
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@staticmethod
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def backward(ctx: Context, *grad_output: Tensor,) -> Tuple[Optional[Tensor], ...]:
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prev_stream = ctx.prev_stream
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next_stream = ctx.next_stream
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grad_input: Deque[Tensor] = deque(maxlen=len(grad_output))
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input_stream = current_stream(get_device(prev_stream))
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with use_stream(prev_stream), use_stream(next_stream):
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for x in reversed(grad_output):
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y = x.to(get_device(prev_stream), non_blocking=True)
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grad_input.appendleft(y)
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# 'next_stream' is not where 'x' has been allocated.
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record_stream(x, next_stream)
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# 'y' has been allocated on 'prev_stream'.
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# It might be used on the current stream captured as 'input_stream'.
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record_stream(y, input_stream)
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grad_streams: Tuple[Optional[Tensor], ...] = (None, None)
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return grad_streams + tuple(grad_input)
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class Wait(torch.autograd.Function):
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"""Synchronizes a stream to another stream.
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Place it just before you want to start an operation on the next stream,
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provided that all operations on the previous stream are done.
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"""
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@staticmethod
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# type: ignore
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def forward(ctx: Context, prev_stream: AbstractStream, next_stream: AbstractStream, *input: Tensor,) -> Tensors:
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ctx.prev_stream = prev_stream
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ctx.next_stream = next_stream
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wait_stream(next_stream, prev_stream)
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return tuple(x.detach() for x in input)
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@staticmethod
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def backward(ctx: Context, *grad_input: Tensor,) -> Tuple[Optional[Tensor], ...]:
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prev_stream = ctx.prev_stream
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next_stream = ctx.next_stream
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wait_stream(prev_stream, next_stream)
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grad_streams: Tuple[Optional[Tensor], ...] = (None, None)
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return grad_streams + grad_input
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