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 Dict, Generator, List, Optional, Tuple, Union
import torch
from torch.nn import Module
from torch.optim import Optimizer
import pytorch_lightning as pl
from pytorch_lightning.core.optimizer import LightningOptimizer
from pytorch_lightning.strategies.ddp import DDPStrategy
from pytorch_lightning.trainer.states import TrainerFn
from pytorch_lightning.utilities.enums import PrecisionType
from pytorch_lightning.utilities.exceptions import MisconfigurationException
from pytorch_lightning.utilities.imports import _FAIRSCALE_AVAILABLE, _FAIRSCALE_OSS_FP16_BROADCAST_AVAILABLE
from pytorch_lightning.utilities.optimizer import optimizers_to_device
from pytorch_lightning.utilities.rank_zero import rank_zero_only
if _FAIRSCALE_AVAILABLE:
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
[docs]class DDPShardedStrategy(DDPStrategy):
"""Optimizer and gradient sharded training provided by FairScale."""
strategy_name = "ddp_sharded"
_REDUCE_BUFFER_SIZE_DEFAULT: int = 2**23 # 8M
[docs] def setup(self, trainer: "pl.Trainer") -> None:
# share ddp pids to all processes
self._rank_0_will_call_children_scripts = self.broadcast(self._rank_0_will_call_children_scripts)
if self._should_run_deadlock_detection():
self._share_information_to_prevent_deadlock()
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:
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()
self.setup_optimizers(self.model.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 _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"]:
if self.model is not None and self.model.trainer.state.fn != TrainerFn.FITTING:
return optimizers
return self._reinit_optimizers_with_oss(optimizers)
def _reinit_optimizers_with_oss(self, optimizers: List[Union[Optimizer, LightningOptimizer]]) -> List["OSS"]:
for x, optimizer in enumerate(optimizers):
if isinstance(optimizer, LightningOptimizer):
optimizer = optimizer._optimizer
if not isinstance(optimizer, OSS):
optim_class = type(optimizer)
zero_optimizer = OSS(params=optimizer.param_groups, optim=optim_class, **optimizer.defaults)
if _FAIRSCALE_OSS_FP16_BROADCAST_AVAILABLE:
is_fp16 = self.precision_plugin.precision in (PrecisionType.MIXED, PrecisionType.HALF)
# For multi-node training, compressing the model shards in fp16 before broadcasting
# improves performance. When using PyTorch AMP, it will not degrade
# the model performance.
zero_optimizer.broadcast_fp16 = is_fp16 and self.num_nodes > 1
optimizers[x] = zero_optimizer
del optimizer
return optimizers
[docs] def optimizer_state(self, optimizer: "OSS") -> Optional[dict]:
if isinstance(optimizer, LightningOptimizer):
optimizer = optimizer._optimizer
optimizer.consolidate_state_dict()
return self._optim_state_dict(optimizer)
@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(
"`DDPShardedStrategy` 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] @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):
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__}",
)
