Source code for pytorch_lightning.strategies.sharded
# 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
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from contextlib import contextmanager
from typing import Any, Dict, Generator, List, Tuple
from torch import Tensor
from torch.nn import Module
from torch.optim import Optimizer
import pytorch_lightning as pl
from lightning_fabric.utilities.optimizer import _optimizers_to_device
from pytorch_lightning.core.optimizer import LightningOptimizer
from pytorch_lightning.overrides.base import _LightningModuleWrapperBase, _LightningPrecisionModuleWrapperBase
from pytorch_lightning.overrides.fairscale import _FAIRSCALE_AVAILABLE, _reinit_optimizers_with_oss
from pytorch_lightning.strategies.ddp import DDPStrategy
from pytorch_lightning.trainer.states import TrainerFn
from pytorch_lightning.utilities.exceptions import MisconfigurationException
from pytorch_lightning.utilities.rank_zero import rank_zero_deprecation
if _FAIRSCALE_AVAILABLE:
from fairscale.nn.data_parallel.sharded_ddp import ShardedDataParallel
from fairscale.optim import OSS
else:
OSS = ShardedDataParallel = object
[docs]class DDPShardedStrategy(DDPStrategy):
"""Optimizer and gradient sharded training provided by FairScale."""
strategy_name = "ddp_sharded"
_REDUCE_BUFFER_SIZE_DEFAULT: int = 2**23 # 8M
def __init__(self, *args: Any, **kwargs: Any) -> None:
rank_zero_deprecation(
"PyTorch Lightning's sharded implementation using FairScale has been deprecated in v1.9.0 and will be"
" removed in v2.0.0. You can try using the `Trainer(strategy='fsdp_native')` instead."
" The difference is that native FSDP uses PyTorch's implementation and the current strategy uses"
" FairScale's implementation (which was upstreamed to PyTorch). After removal, `strategy='fsdp'` will use"
" the native version by default."
)
super().__init__(*args, **kwargs)
[docs] def connect(self, model: "pl.LightningModule") -> None:
if not _FAIRSCALE_AVAILABLE: # pragma: no cover
raise MisconfigurationException(
"`DDPShardedStrategy` requires `fairscale` to be installed."
" Install it by running `pip install fairscale`."
)
return super().connect(model)
[docs] def setup(self, trainer: "pl.Trainer") -> None:
# share ddp pids to all processes
self._rank_0_will_call_children_scripts: bool = self.broadcast(self._rank_0_will_call_children_scripts)
if self._should_run_deadlock_detection():
self._share_information_to_prevent_deadlock()
assert self.accelerator is not None
self.accelerator.setup(trainer)
# move the model to the correct device
self.model_to_device()
# skip wrapping the model if we are not fitting as no gradients need to be exchanged
trainer_fn = trainer.state.fn
if trainer_fn == TrainerFn.FITTING:
if self._layer_sync:
assert self.model is not None
self.model = self._layer_sync.apply(self.model)
self.setup_precision_plugin()
if trainer_fn == TrainerFn.FITTING:
self.configure_ddp()
def configure_ddp(self) -> None:
self._set_ddp_kwargs()
assert self.lightning_module is not None
self.setup_optimizers(self.lightning_module.trainer)
assert isinstance(self.model, (pl.LightningModule, _LightningPrecisionModuleWrapperBase))
self.model, self.optimizers = self._setup_model_and_optimizers(
model=_LightningModuleWrapperBase(self.model),
optimizers=self.optimizers,
)
_optimizers_to_device(self.optimizers, self.root_device)
def _set_ddp_kwargs(self) -> None:
if "reduce_buffer_size" not in self._ddp_kwargs:
# For multi-node training, enabling bucketing will improve performance.
self._ddp_kwargs["reduce_buffer_size"] = self._REDUCE_BUFFER_SIZE_DEFAULT if self.num_nodes > 1 else 0
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 _wrap_optimizers(self, optimizers: List[Optimizer]) -> List["OSS"]:
assert self.lightning_module is not None
if self.model is not None and self.lightning_module.trainer.state.fn != TrainerFn.FITTING:
return optimizers
optimizers = [o._optimizer if isinstance(o, LightningOptimizer) else o for o in optimizers]
return _reinit_optimizers_with_oss(optimizers, self.precision_plugin, self.num_nodes)
[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
def post_training_step(self) -> None:
pass
@classmethod
def register_strategies(cls, strategy_registry: Dict) -> None:
strategy_registry.register(
"ddp_sharded_find_unused_parameters_false",
cls,
description="DDP Sharded Strategy with `find_unused_parameters` as False",
find_unused_parameters=False,
)
strategy_registry.register(
cls.strategy_name,
cls,
description=f"{cls.__class__.__name__}",
)
