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d52f121dba
**Why this PR?** For the composable APIs implementation, sometimes the internal APIs may not have the application (FSDP, DDP) root module but only the local module. One example is the state_dict/optimizer_state_dict implementation of FSDP. These APIs are designed to start with the root module of the model. It is tricky for these APIs to tell whether a random submodule is managed by either DDP or FSDP. It will be useful to have APIs like: `_get_module_state(module)`: return the composable state if this module is managed by composable API. `_get_module_fsdp_state(module)`: return the FSDP state if this module is managed by FSDP. **What does this PR propose?** 1. Make `_State` out of `_composable` module so that `FullyShardedDataParallel` can inherit from it. 2. A global `_module_state_mapping: Dict[nn.Module, _State]` that keeps the mapping of all submodules (not just root module) to the state. 3. Create `_get_module_state(module)` to look up `_module_state_mapping`. 4. Create `_get_module_fsdp_state(module)` that uses `_get_module_state(module)` to get the state then verifies if the state is `_FSDPState`. Pull Request resolved: https://github.com/pytorch/pytorch/pull/89147 Approved by: https://github.com/awgu
87 lines
2.5 KiB
Python
87 lines
2.5 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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self.kwargs: dict = {}
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def mark_modules(self, *modules: nn.Module, **kwargs) -> 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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self.kwargs = kwargs
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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) -> None:
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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(
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self._param_list, **self.kwargs
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)
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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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**kwargs,
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) -> nn.Module:
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r"""Replicates a module
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Args:
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module (torch.nn.Module): module 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, **kwargs)
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return module
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