Source code for lightning.pytorch.strategies.fsdp
# Copyright The Lightning AI team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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#
# 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,
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# See the License for the specific language governing permissions and
# limitations under the License.
import contextlib
import logging
from typing import Any, Dict, Generator, List, Optional, Type, Union
import torch
from torch import Tensor
from torch.nn import Module
import lightning.pytorch as pl
from lightning.fabric.plugins import CheckpointIO, ClusterEnvironment
from lightning.fabric.strategies.fsdp import (
_init_cpu_offload,
_optimizer_has_flat_params,
_setup_activation_checkpointing,
)
from lightning.fabric.utilities.distributed import (
_get_default_process_group_backend_for_device,
_init_dist_connection,
_sync_ddp_if_available,
)
from lightning.fabric.utilities.distributed import group as _group
from lightning.fabric.utilities.imports import _TORCH_GREATER_EQUAL_1_12, _TORCH_GREATER_EQUAL_1_13
from lightning.fabric.utilities.optimizer import _optimizers_to_device
from lightning.fabric.utilities.seed import reset_seed
from lightning.fabric.utilities.types import ProcessGroup, ReduceOp
from lightning.pytorch.overrides.base import _LightningModuleWrapperBase
from lightning.pytorch.plugins.precision import PrecisionPlugin
from lightning.pytorch.plugins.precision.fsdp import FSDPMixedPrecisionPlugin
from lightning.pytorch.strategies.launchers.subprocess_script import _SubprocessScriptLauncher
from lightning.pytorch.strategies.parallel import ParallelStrategy
from lightning.pytorch.strategies.strategy import TBroadcast
from lightning.pytorch.trainer.states import TrainerFn
from lightning.pytorch.utilities.exceptions import MisconfigurationException
from lightning.pytorch.utilities.model_helpers import is_overridden
from lightning.pytorch.utilities.rank_zero import rank_zero_info, rank_zero_only
from lightning.pytorch.utilities.types import STEP_OUTPUT
_distributed_available = torch.distributed.is_available()
_fsdp_available = _TORCH_GREATER_EQUAL_1_12 and _distributed_available
if _fsdp_available:
from torch.distributed.fsdp import CPUOffload, FullyShardedDataParallel, MixedPrecision
from torch.distributed.fsdp.wrap import enable_wrap
else:
FullyShardedDataParallel = None # type: ignore[misc,assignment]
MixedPrecision = None # type: ignore[misc,assignment]
CPUOffload = None # type: ignore[misc,assignment]
if _distributed_available:
from torch.distributed.distributed_c10d import _get_default_group
log = logging.getLogger(__name__)
[docs]class FSDPStrategy(ParallelStrategy):
r"""Strategy for Fully Sharded Data Parallel provided by torch.distributed.
.. warning:: This is an :ref:`experimental <versioning:Experimental API>` feature.
Fully Sharded Training shards the entire model across all available GPUs, allowing you to scale model
size, whilst using efficient communication to reduce overhead. In practice, this means we can remain
at parity with PyTorch DDP, whilst scaling our model sizes dramatically. The technique is similar
to ZeRO-Stage 3.
For more information check out
`this blogpost <https://pytorch.org/blog/introducing-pytorch-fully-sharded-data-parallel-api>`__.
Defaults have been set and options have been exposed, but may require configuration
based on your level of memory/speed efficiency. We suggest having a look at
`this tutorial <https://pytorch.org/tutorials/intermediate/FSDP_tutorial.html>`__ for more information.
Arguments:
cpu_offload: See ``cpu_offload`` parameter in :class:`torch.distributed.fsdp.FullyShardedDataParallel`.
mixed_precision: See ``mixed_precision`` parameter in :class:`torch.distributed.fsdp.FullyShardedDataParallel`.
activation_checkpointing: A single layer or a list of layer classes for which you want to enable activation
checkpointing. This is typically your transformer block (including attention + feed-forward).
Enabling this can free up a significant amount of memory at the cost of speed since activations in
these layers need to be recomputed during backpropagation.
\**kwargs: See available parameters in :class:`torch.distributed.fsdp.FullyShardedDataParallel`.
"""
strategy_name = "fsdp"
_registered_strategies: List[str] = []
def __init__(
self,
accelerator: Optional["pl.accelerators.Accelerator"] = None,
parallel_devices: Optional[List[torch.device]] = None,
cluster_environment: Optional[ClusterEnvironment] = None,
checkpoint_io: Optional[CheckpointIO] = None,
precision_plugin: Optional[PrecisionPlugin] = None,
process_group_backend: Optional[str] = None,
cpu_offload: Union[bool, "CPUOffload", None] = None,
mixed_precision: Optional[MixedPrecision] = None,
activation_checkpointing: Optional[Union[Type[Module], List[Type[Module]]]] = None,
**kwargs: Any,
) -> None:
if not _TORCH_GREATER_EQUAL_1_12:
raise MisconfigurationException("`FSDPStrategy` is supported from PyTorch v1.12.0 onwards.")
super().__init__(
accelerator=accelerator,
parallel_devices=parallel_devices,
cluster_environment=cluster_environment,
checkpoint_io=checkpoint_io,
precision_plugin=precision_plugin,
)
self._process_group = None
self.num_nodes = 1
self._process_group_backend = process_group_backend
self.cpu_offload = _init_cpu_offload(cpu_offload)
self.mixed_precision = mixed_precision
if activation_checkpointing and not _TORCH_GREATER_EQUAL_1_13:
raise ValueError("Activation checkpointing requires torch >= 1.13.0. HINT: `pip install -U torch`")
activation_checkpointing = activation_checkpointing or []
self._activation_checkpointing = (
[activation_checkpointing] if not isinstance(activation_checkpointing, list) else activation_checkpointing
)
self.kwargs = kwargs
@property
def root_device(self) -> torch.device:
assert self.parallel_devices is not None
return self.parallel_devices[self.local_rank]
@property
def num_processes(self) -> int:
return len(self.parallel_devices) if self.parallel_devices is not None else 0
@property
def process_group(self) -> Optional[ProcessGroup]:
if self._process_group is None:
# The strategy should have already initilized process group in setup_environment()
self._process_group = _get_default_group()
return self._process_group
@property
def process_group_backend(self) -> Optional[str]:
return self._process_group_backend
@property
def mixed_precision_config(self) -> Optional[MixedPrecision]:
if self.mixed_precision:
return self.mixed_precision
plugin = self.precision_plugin
if isinstance(plugin, FSDPMixedPrecisionPlugin):
return plugin.mixed_precision_config
@property
def distributed_sampler_kwargs(self) -> Dict:
return dict(num_replicas=(self.num_nodes * self.num_processes), rank=self.global_rank)
[docs] def setup_environment(self) -> None:
log.debug(f"{self.__class__.__name__}: setting up distributed...")
reset_seed()
# determine which process we are and world size
self.set_world_ranks()
# set warning rank
rank_zero_only.rank = self.global_rank
self._process_group_backend = self._get_process_group_backend()
assert self.cluster_environment is not None
_init_dist_connection(self.cluster_environment, self._process_group_backend)
super().setup_environment()
def _get_process_group_backend(self) -> str:
return self._process_group_backend or _get_default_process_group_backend_for_device(self.root_device)
def set_world_ranks(self) -> None:
if self.cluster_environment is None:
return
self.cluster_environment.set_global_rank(self.node_rank * self.num_processes + self.local_rank)
self.cluster_environment.set_world_size(self.num_nodes * self.num_processes)
rank_zero_only.rank = self.cluster_environment.global_rank()
def _configure_launcher(self) -> None:
assert self.cluster_environment is not None
if not self.cluster_environment.creates_processes_externally:
self._launcher = _SubprocessScriptLauncher(self.cluster_environment, self.num_processes, self.num_nodes)
def _setup_model(self, model: torch.nn.Module) -> FullyShardedDataParallel:
"""Wraps the model into a
:class:`~torch.distributed.fsdp.fully_sharded_data_parallel.FullyShardedDataParallel` module."""
# If model is already wrapped, we need to avoid sending the `auto_wrap_policy`
assert self.lightning_module is not None
if "auto_wrap_policy" in self.kwargs and any(
isinstance(mod, FullyShardedDataParallel) for mod in self.lightning_module.modules()
):
del self.kwargs["auto_wrap_policy"]
log.debug(f"setting up FSDP model with device id: {self.root_device.index}, kwargs: {self.kwargs}")
wrapped_module = FullyShardedDataParallel(
module=model,
process_group=self.process_group,
cpu_offload=self.cpu_offload,
mixed_precision=self.mixed_precision_config,
device_id=self.root_device.index,
**self.kwargs,
)
# activation checkpointing needs to be set up after wrapping the model
if _TORCH_GREATER_EQUAL_1_13 and self._activation_checkpointing:
_setup_activation_checkpointing(module=wrapped_module, layers=self._activation_checkpointing)
return wrapped_module
[docs] def setup(self, trainer: "pl.Trainer") -> None:
assert self.accelerator is not None
self.accelerator.setup(trainer)
if trainer.state.fn == TrainerFn.FITTING and self._layer_sync:
assert self.model is not None
self.model = self._layer_sync.apply(self.model)
# we set the device so that optimizers can be created with distributed comms.
assert self.lightning_module is not None
self.lightning_module._device = self.root_device
assert isinstance(self.model, pl.LightningModule)
self.model = _LightningModuleWrapperBase(self.model)
if is_overridden("configure_sharded_model", self.lightning_module):
rank_zero_info(
"You have overridden `LightningModule.configure_sharded_model` hook. It will assume that all the layers"
" are already wrapped for sharding and won't wrap the entire model using `FullyShardedDataParallel`."
)
else:
self.model = self._setup_model(self.model)
self.barrier()
self.setup_optimizers(trainer)
_optimizers_to_device(self.optimizers, self.root_device)
self.setup_precision_plugin()
[docs] def setup_optimizers(self, trainer: "pl.Trainer") -> None:
invalid_params_error = False
try:
super().setup_optimizers(trainer)
except ValueError as e:
if "optimizer got an empty parameter list" not in str(e):
raise
invalid_params_error = True
if invalid_params_error or any(not _optimizer_has_flat_params(optimizer) for optimizer in self.optimizers):
raise ValueError(
"The optimizer does not seem to reference any FSDP parameters. HINT: Make sure to create the"
" optimizer after setting up the model by referencing `self.trainer.model.parameters()` in the"
" `configure_optimizers()` hook."
)
[docs] @contextlib.contextmanager
def model_sharded_context(self) -> Generator:
log.debug(f"{self.__class__.__name__}: entered model_sharded_context.")
with enable_wrap(
wrapper_cls=FullyShardedDataParallel,
process_group=self.process_group,
cpu_offload=self.cpu_offload,
mixed_precision=self.mixed_precision_config,
device_id=self.root_device.index,
**self.kwargs,
):
yield
[docs] def barrier(self, name: Optional[str] = None) -> None:
if not _distributed_available:
return
if torch.distributed.get_backend() == "nccl":
torch.distributed.barrier(device_ids=self._determine_device_ids())
else:
torch.distributed.barrier()
[docs] def broadcast(self, obj: TBroadcast, src: int = 0) -> TBroadcast:
obj = [obj]
if self.global_rank != src:
obj = [None] # type: ignore
torch.distributed.broadcast_object_list(obj, src, group=_group.WORLD)
return obj[0]
[docs] def reduce(
self,
tensor: Union[Tensor, Any],
group: Optional[Any] = None,
reduce_op: Optional[Union[ReduceOp, str]] = "mean",
) -> Tensor:
"""Reduces a tensor from several distributed processes to one aggregated tensor.
Args:
tensor: the tensor to sync and reduce
group: the process group to gather results from. Defaults to all processes (world)
reduce_op: the reduction operation. Defaults to 'mean'/'avg'.
Can also be a string 'sum' to calculate the sum during reduction.
Return:
reduced value, except when the input was not a tensor the output remains is unchanged
"""
if isinstance(tensor, Tensor):
tensor = _sync_ddp_if_available(tensor, group, reduce_op=reduce_op)
return tensor
[docs] def training_step(self, *args: Any, **kwargs: Any) -> STEP_OUTPUT:
# we don't need precision context since casting is done by FSDP
# read `mixed_precision` docstring here: https://pytorch.org/docs/stable/fsdp.html
assert self.model is not None
return self.model(*args, **kwargs)
[docs] def validation_step(self, *args: Any, **kwargs: Any) -> Optional[STEP_OUTPUT]:
assert self.model is not None
return self.model(*args, **kwargs)
[docs] def test_step(self, *args: Any, **kwargs: Any) -> Optional[STEP_OUTPUT]:
assert self.model is not None
return self.model(*args, **kwargs)
[docs] def predict_step(self, *args: Any, **kwargs: Any) -> STEP_OUTPUT:
assert self.model is not None
return self.model(*args, **kwargs)
def _determine_device_ids(self) -> List[int]:
return [self.root_device.index]
[docs] def teardown(self) -> None:
rank_zero_info(f"{self.__class__.__name__}: tearing down strategy...")
pl_module = self.lightning_module
if (
pl_module is not None
# `self.lightning_module._trainer` can be None if teardown gets called on an exception before
# the trainer gets set on the LightningModule
and pl_module._trainer is not None
and pl_module._trainer.state.fn == TrainerFn.FITTING
and self._layer_sync
):
assert self.model is not None
self.model = self._layer_sync.revert(self.model)
assert self.cluster_environment is not None
assert self.accelerator is not None
self.cluster_environment.teardown()
self.precision_plugin.teardown()
self.accelerator.teardown()
@classmethod
def get_registered_strategies(cls) -> List[str]:
return cls._registered_strategies
@classmethod
def register_strategies(cls, strategy_registry: Dict) -> None:
if not _fsdp_available:
return
strategy_registry.register(
"fsdp",
cls,
description="Fully Sharded Data Parallel (FSDP) training",
)
cls._registered_strategies.append("fsdp")
strategy_registry.register(
"fsdp_cpu_offload",
cls,
description="Fully Sharded Data Parallel (FSDP) training with Full Sharding and CPU Offloading",
cpu_offload=True,
)
cls._registered_strategies.append("fsdp_cpu_offload")
