mirror of
https://github.com/zebrajr/pytorch.git
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3749b9dc73
This reverts commit 0f274ed385.
Reverted https://github.com/pytorch/pytorch/pull/89243 on behalf of https://github.com/malfet due to Depends on https://github.com/pytorch/pytorch/pull/89222 that introduced spurious module updates
82 lines
2.3 KiB
Python
82 lines
2.3 KiB
Python
from typing import List, Tuple
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import torch
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import torch.nn as nn
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from . import _ddp
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from .contract import contract
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class _ReplicateState:
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def __init__(self) -> None:
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self.modules: List[nn.Module] = []
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self.has_initialized: bool = False
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self._param_list: nn.ParameterList = nn.ParameterList()
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def mark_modules(self, *modules: nn.Module) -> None:
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for module in modules:
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self.modules.append(module)
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replicate.state(module)._distributed_state = self
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replicate.state(module)._params_collected = False
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module.register_forward_pre_hook(self.forward_pre_hook)
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# TODO(@yhcharles): fix type error
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module.register_forward_hook(self.forward_post_hook) # type: ignore[arg-type]
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def _recursive_collect_params(self, module: nn.Module) -> None:
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# TODO: skip if managed by other APIs
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if hasattr(replicate.state(module), "_params_collected"):
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if replicate.state(module)._params_collected:
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return
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replicate.state(module)._params_collected = True
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self._param_list.extend(
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param
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for param in module.parameters(recurse=False)
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# for param in module.parameters()
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if param.requires_grad
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)
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for child in module.children():
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self._recursive_collect_params(child)
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def init_helper(self):
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if self.has_initialized:
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return
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self.has_initialized = True
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for module in self.modules:
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self._recursive_collect_params(module)
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self._ddp = _ddp.DistributedDataParallel(self._param_list)
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def forward_pre_hook(
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self, module: nn.Module, input: Tuple[torch.Tensor]
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) -> None:
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self.init_helper()
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self._ddp.pre_forward()
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def forward_post_hook(
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self,
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module: nn.Module,
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input: Tuple[torch.Tensor],
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output: torch.Tensor,
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) -> torch.Tensor:
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return self._ddp.post_forward(output)
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@contract
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def replicate(
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module: nn.Module, # NOTE: contract now supports single module only
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) -> nn.Module:
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r"""Replicates module(s)
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Args:
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modules (torch.nn.Module): modules to replicate
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Example::
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>>> module = nn.Linear(3, 3)
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>>> replicate(module)
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"""
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_ReplicateState().mark_modules(module)
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return module
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