123 lines
4.9 KiB
Python
123 lines
4.9 KiB
Python
# Copyright The PyTorch Lightning team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from contextlib import contextmanager
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from typing import Dict, Generator, List, Optional, Tuple
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import torch
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from torch.nn import Module
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from torch.optim import Optimizer
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import pytorch_lightning as pl
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from pytorch_lightning.strategies.ddp_spawn import DDPSpawnStrategy
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from pytorch_lightning.trainer.states import TrainerFn
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from pytorch_lightning.utilities import _FAIRSCALE_AVAILABLE, rank_zero_only
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from pytorch_lightning.utilities.enums import _StrategyType
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from pytorch_lightning.utilities.exceptions import MisconfigurationException
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if _FAIRSCALE_AVAILABLE:
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from fairscale.nn.data_parallel.sharded_ddp import ShardedDataParallel
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from fairscale.optim import OSS
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from pytorch_lightning.overrides.fairscale import LightningShardedDataParallel, unwrap_lightning_module_sharded
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class DDPSpawnShardedStrategy(DDPSpawnStrategy):
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"""Optimizer sharded training provided by FairScale."""
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distributed_backend = _StrategyType.DDP_SHARDED_SPAWN
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def configure_ddp(self) -> None:
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trainer = self.lightning_module.trainer
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self.model, optimizers = self._setup_model_and_optimizers(
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model=LightningShardedDataParallel(self.model),
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optimizers=trainer.optimizers,
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)
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trainer.optimizers = optimizers
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def _setup_model_and_optimizers(self, model: Module, optimizers: List[Optimizer]) -> Tuple[Module, List[Optimizer]]:
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"""Wraps the model and optimizers with fairscale components.
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Return:
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The model wrapped into a :class:`~fairscale.nn.data_parallel.ShardedDataParallel` module
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and a list of optimizer wrapped in :class:~`fairscale.optim.OSS`.
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"""
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optimizers = self._wrap_optimizers(optimizers)
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model = ShardedDataParallel(model, sharded_optimizer=optimizers, **self._ddp_kwargs)
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return model, optimizers
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def _reinit_optimizers_with_oss(self, optimizers: List[Optimizer]) -> List["OSS"]:
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for x, optimizer in enumerate(optimizers):
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if not isinstance(optimizer, OSS):
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optim_class = type(optimizer)
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zero_optimizer = OSS(params=optimizer.param_groups, optim=optim_class, **optimizer.defaults)
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optimizers[x] = zero_optimizer
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del optimizer
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return optimizers
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def _wrap_optimizers(self, optimizers: List[Optimizer]) -> List["OSS"]:
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if self.model is not None and self.model.trainer.state.fn != TrainerFn.FITTING:
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return optimizers
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return self._reinit_optimizers_with_oss(optimizers)
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def optimizer_state(self, optimizer: "OSS") -> Optional[dict]:
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if isinstance(optimizer, OSS):
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optimizer.consolidate_state_dict()
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return self._optim_state_dict(optimizer)
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@contextmanager
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def block_backward_sync(self) -> Generator:
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"""Blocks syncing gradients behaviour on backwards pass.
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This is useful for skipping sync when accumulating gradients, reducing communication overhead
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Returns: context manager with sync behaviour off
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"""
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if isinstance(self.model, ShardedDataParallel):
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with self.model.no_sync():
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yield None
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else:
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yield None
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@rank_zero_only
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def _optim_state_dict(self, optimizer):
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"""
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Retrieves state dict only on rank 0, which contains the entire optimizer state after calling
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:meth:`consolidate_state_dict`.
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"""
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return optimizer.state_dict()
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@property
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def lightning_module(self) -> Optional["pl.LightningModule"]:
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if not _FAIRSCALE_AVAILABLE: # pragma: no cover
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raise MisconfigurationException(
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"`DDPSpawnShardedStrategy` requires `fairscale` to be installed."
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" Install it by running `pip install fairscale`."
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)
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return unwrap_lightning_module_sharded(self.model) if self.model is not None else None
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def pre_backward(self, closure_loss: torch.Tensor) -> None:
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pass
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def post_training_step(self):
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pass
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@classmethod
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def register_plugins(cls, plugin_registry: Dict) -> None:
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plugin_registry.register(
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"ddp_sharded_spawn_find_unused_parameters_false",
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cls,
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description="DDP Spawn Sharded Strategy with `find_unused_parameters` as False",
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find_unused_parameters=False,
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)
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