mirror of
https://github.com/zebrajr/pytorch.git
synced 2025-12-07 12:21:27 +01:00
8869897ebe
Allow passing in `device_id=[device]` regardless of CPU or GPU. We modify the kwarg as needed to pass to DDP. Pull Request resolved: https://github.com/pytorch/pytorch/pull/100217 Approved by: https://github.com/awgu, https://github.com/zhaojuanmao
130 lines
4.8 KiB
Python
130 lines
4.8 KiB
Python
import weakref
|
|
from typing import Any, Dict, Iterable, List, Optional, Set, Tuple
|
|
|
|
import torch
|
|
import torch.nn as nn
|
|
|
|
from torch.nn.parallel import DistributedDataParallel
|
|
|
|
from .contract import _get_registry, contract
|
|
|
|
|
|
@contract()
|
|
def replicate(
|
|
module: nn.Module, # NOTE: contract now supports single module only
|
|
ignored_modules: Optional[Iterable[torch.nn.Module]] = None,
|
|
**kwargs,
|
|
) -> nn.Module:
|
|
r"""Replicates a module
|
|
|
|
Args:
|
|
module (torch.nn.Module): module to replicate
|
|
|
|
Example::
|
|
>>> # xdoctest: +REQUIRES(module:torch._C._distributed_c10d)
|
|
>>> module = nn.Linear(3, 3)
|
|
>>> replicate(module)
|
|
"""
|
|
torch._C._log_api_usage_once("torch.distributed.replicate")
|
|
if "device_id" in kwargs:
|
|
if not isinstance(kwargs["device_id"], (int, torch.device)):
|
|
raise RuntimeError(
|
|
f"Expected device_id to be int or torch.device, but got {type(kwargs['device_id'])}"
|
|
)
|
|
_ReplicateState(ignored_modules=ignored_modules).mark_module(module, **kwargs)
|
|
return module
|
|
|
|
|
|
def _is_fully_sharded(module: nn.Module) -> bool:
|
|
r"""Check if module is marked with fully_shard."""
|
|
return "fully_shard" in _get_registry(module)
|
|
|
|
|
|
class _ReplicateState:
|
|
def __init__(self, ignored_modules: Optional[Iterable[torch.nn.Module]]) -> None:
|
|
self.module: Optional[nn.Module] = None
|
|
self.has_initialized: bool = False
|
|
self._param_list: nn.ParameterList = nn.ParameterList()
|
|
self.kwargs: dict = {}
|
|
self.ignored_modules: Set[torch.nn.Module] = (
|
|
set(ignored_modules) if ignored_modules is not None else set()
|
|
)
|
|
self.ignored_params: Set[torch.nn.Parameter] = {
|
|
p for m in self.ignored_modules for p in m.parameters()
|
|
}
|
|
# Only used for testing
|
|
self._names: List[str] = []
|
|
|
|
def mark_module(self, module: nn.Module, **kwargs) -> None:
|
|
if _is_fully_sharded(module):
|
|
raise AssertionError(
|
|
"Cannot apply `replicate()` on a Module already managed by `fully_shard`"
|
|
)
|
|
self.module = module
|
|
replicate.state(module)._params_collected = False
|
|
module.register_forward_pre_hook(self.forward_pre_hook, with_kwargs=True)
|
|
# TODO(@yhcharles): fix type error
|
|
module.register_forward_hook(self.forward_post_hook) # type: ignore[arg-type]
|
|
self.kwargs = kwargs
|
|
|
|
def _collect_params(self, module: nn.Module) -> None:
|
|
# skip if managed by fully_sharded API
|
|
if _is_fully_sharded(module):
|
|
return
|
|
|
|
if module in self.ignored_modules:
|
|
return # if module A is ignored, all of A's children are also ignored.
|
|
|
|
self._param_list.extend(
|
|
p for p in module.parameters(recurse=False) if p not in self.ignored_params
|
|
)
|
|
|
|
for child_module in module.children():
|
|
self._collect_params(child_module)
|
|
|
|
def init_helper(self) -> None:
|
|
if self.has_initialized:
|
|
return
|
|
|
|
self.has_initialized = True
|
|
|
|
self._collect_params(self.module) # type: ignore[arg-type]
|
|
# Only saved for testing
|
|
replicate.state(self.module)._names = self._names
|
|
if "device_id" in self.kwargs:
|
|
# replicate() supports a small usability enhancement where
|
|
# user can pass in device_id as a Union[int, torch.device] even for
|
|
# CPU devices so users don't have to change code for CPU/GPU runs.
|
|
# We derive the right device_ids to feed into DDP to support this.
|
|
if self.kwargs["device_id"] is not None:
|
|
device_id = self.kwargs["device_id"]
|
|
# Convert to device_ids that DDP expects.
|
|
if isinstance(device_id, torch.device) and device_id.type == "cpu":
|
|
# CPU modules receive device_ids None
|
|
self.kwargs["device_ids"] = None
|
|
else:
|
|
# GPU modules expect device_ids=[cuda_device]
|
|
self.kwargs["device_ids"] = [device_id]
|
|
else:
|
|
self.kwargs["device_ids"] = None
|
|
self.kwargs.pop("device_id")
|
|
|
|
self._ddp = DistributedDataParallel(self._param_list, **self.kwargs)
|
|
# Weakref to the DDP instance is currently only used for testing.
|
|
replicate.state(self.module)._ddp_weakref = weakref.ref(self._ddp)
|
|
|
|
def forward_pre_hook(
|
|
self, module: nn.Module, args: Tuple[Any, ...], kwargs: Dict[str, Any]
|
|
) -> Any:
|
|
self.init_helper()
|
|
args, kwargs = self._ddp._pre_forward(*args, **kwargs)
|
|
return args, kwargs
|
|
|
|
def forward_post_hook(
|
|
self,
|
|
module: nn.Module,
|
|
input: Tuple[torch.Tensor],
|
|
output: torch.Tensor,
|
|
) -> torch.Tensor:
|
|
return self._ddp._post_forward(output)
|