Name	Size	Mode	Actions
skip/	-	0755	rm
_balance/	-	0755	rm
__pycache__/	-	0755	rm
batchnorm.py	5603	0644	editdlrm
checkpoint.py	11464	0644	editdlrm
copy.py	3716	0644	editdlrm
dependency.py	1694	0644	editdlrm
microbatch.py	7427	0644	editdlrm
phony.py	1536	0644	editdlrm
pipe.py	17883	0644	editdlrm
pipeline.py	9638	0644	editdlrm
stream.py	3807	0644	editdlrm
utils.py	1147	0644	editdlrm
worker.py	4284	0644	editdlrm
__init__.py	451	0644	editdlrm

# Copyright 2019 Kakao Brain # # Copyright (c) Facebook, Inc. and its affiliates. All rights reserved. # # This source code is licensed under the BSD license found in the # LICENSE file in the root directory of this source tree. """Provides phony for arbitrary dependency in a autograd graph.""" from typing import Dict, List, Tuple import torch from torch import Tensor from .stream import default_stream, use_stream __all__: List[str] = [] _phonies: Dict[Tuple[torch.device, bool], Tensor] = {} def get_phony(device: torch.device, *, requires_grad: bool) -> Tensor: """Gets a phony. Phony is tensor without space. It is useful to make arbitrary dependency in a autograd graph because it doesn't require any gradient accumulation. .. note:: Phonies for each device are cached. If an autograd function gets a phony internally, the phony must be detached to be returned. Otherwise, the autograd engine will mutate the cached phony in-place:: class Phonify(torch.autograd.Function): @staticmethod def forward(ctx, input): phony = get_phony(input.device, requires_grad=False) return phony.detach() # detach() is necessary. """ key = (device, requires_grad) try: phony = _phonies[key] except KeyError: with use_stream(default_stream(device)): phony = torch.empty(0, device=device, requires_grad=requires_grad) _phonies[key] = phony return phony