Source code for pytorch_lightning.strategies.sharded_spawn

# Copyright The PyTorch Lightning team.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# See the License for the specific language governing permissions and
# limitations under the License.
from contextlib import contextmanager
from typing import Dict, Generator, List, Optional, Tuple

from torch import Tensor
from torch.nn import Module
from torch.optim import Optimizer

import pytorch_lightning as pl
from pytorch_lightning.overrides.fairscale import _FAIRSCALE_AVAILABLE
from pytorch_lightning.strategies.ddp_spawn import DDPSpawnStrategy
from pytorch_lightning.trainer.states import TrainerFn
from pytorch_lightning.utilities.exceptions import MisconfigurationException
from pytorch_lightning.utilities.optimizer import optimizers_to_device
from pytorch_lightning.utilities.rank_zero import rank_zero_only

    from fairscale.nn.data_parallel.sharded_ddp import ShardedDataParallel
    from fairscale.optim import OSS

    from pytorch_lightning.overrides.fairscale import LightningShardedDataParallel, unwrap_lightning_module_sharded
    OSS = ShardedDataParallel = object

[docs]class DDPSpawnShardedStrategy(DDPSpawnStrategy): """Optimizer sharded training provided by FairScale.""" strategy_name = "ddp_sharded_spawn" def configure_ddp(self) -> None: # set up optimizers after the wrapped module has been moved to the device self.setup_optimizers(self.lightning_module.trainer) self.model, self.optimizers = self._setup_model_and_optimizers( model=LightningShardedDataParallel(self.model), optimizers=self.optimizers ) optimizers_to_device(self.optimizers, self.root_device) def _setup_model_and_optimizers(self, model: Module, optimizers: List[Optimizer]) -> Tuple[Module, List[Optimizer]]: """Wraps the model and optimizers with fairscale components. Return: The model wrapped into a :class:`~fairscale.nn.data_parallel.ShardedDataParallel` module and a list of optimizer wrapped in :class:~`fairscale.optim.OSS`. """ optimizers = self._wrap_optimizers(optimizers) model = ShardedDataParallel(model, sharded_optimizer=optimizers, **self._ddp_kwargs) return model, optimizers def _reinit_optimizers_with_oss(self, optimizers: List[Optimizer]) -> List["OSS"]: for x, optimizer in enumerate(optimizers): if not isinstance(optimizer, OSS): optim_class = type(optimizer) zero_optimizer = OSS(params=optimizer.param_groups, optim=optim_class, **optimizer.defaults) optimizers[x] = zero_optimizer del optimizer return optimizers def _wrap_optimizers(self, optimizers: List[Optimizer]) -> List["OSS"]: if self.model is not None and self.model.trainer.state.fn != TrainerFn.FITTING: return optimizers return self._reinit_optimizers_with_oss(optimizers)
[docs] def optimizer_state(self, optimizer: "OSS") -> Optional[dict]: if isinstance(optimizer, OSS): optimizer.consolidate_state_dict() return self._optim_state_dict(optimizer)
[docs] @contextmanager def block_backward_sync(self) -> Generator: """Blocks syncing gradients behaviour on backwards pass. This is useful for skipping sync when accumulating gradients, reducing communication overhead Returns: context manager with sync behaviour off """ if isinstance(self.model, ShardedDataParallel): with self.model.no_sync(): yield None else: yield None
@rank_zero_only def _optim_state_dict(self, optimizer): """ Retrieves state dict only on rank 0, which contains the entire optimizer state after calling :meth:`consolidate_state_dict`. """ return optimizer.state_dict() @property def lightning_module(self) -> Optional["pl.LightningModule"]: if not _FAIRSCALE_AVAILABLE: # pragma: no cover raise MisconfigurationException( "`DDPSpawnShardedStrategy` requires `fairscale` to be installed." " Install it by running `pip install fairscale`." ) return unwrap_lightning_module_sharded(self.model) if self.model is not None else None
[docs] def pre_backward(self, closure_loss: Tensor) -> None: pass
def post_training_step(self): pass @classmethod def register_strategies(cls, strategy_registry: Dict) -> None: strategy_registry.register( "ddp_sharded_spawn_find_unused_parameters_false", cls, description="DDP Spawn Sharded Strategy with `find_unused_parameters` as False", find_unused_parameters=False, ) strategy_registry.register( cls.strategy_name, cls, description=f"{cls.__class__.__name__}", )

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