Source code for lightning_fabric.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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import functools
from contextlib import contextmanager
from datetime import timedelta
from typing import Any, Dict, Generator, List, Optional, Tuple, Type, TYPE_CHECKING, Union

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

from lightning_fabric.accelerators import Accelerator
from lightning_fabric.plugins import CheckpointIO, ClusterEnvironment, Precision
from lightning_fabric.plugins.collectives.torch_collective import default_pg_timeout
from lightning_fabric.plugins.precision.fsdp import FSDPPrecision
from lightning_fabric.strategies.launchers.subprocess_script import _SubprocessScriptLauncher
from lightning_fabric.strategies.parallel import ParallelStrategy
from lightning_fabric.strategies.strategy import _BackwardSyncControl, _Sharded, TBroadcast
from lightning_fabric.utilities.distributed import (
from lightning_fabric.utilities.distributed import group as _group
from lightning_fabric.utilities.distributed import ReduceOp
from lightning_fabric.utilities.imports import _TORCH_GREATER_EQUAL_1_12, _TORCH_GREATER_EQUAL_1_13
from lightning_fabric.utilities.rank_zero import rank_zero_only
from lightning_fabric.utilities.seed import reset_seed

    from torch.distributed.fsdp.fully_sharded_data_parallel import (

_FSDP_ALIASES = ("fsdp", "fsdp_full_shard_offload")

[docs]class FSDPStrategy(ParallelStrategy, _Sharded): r"""Strategy for Fully Sharded Data Parallel provided by torch.distributed. .. warning:: ``FSDPStrategy`` is in BETA and subject to change. The interface can bring breaking changes and new features with the next release of PyTorch. 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 <>`__. 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 <>`__ for more information. Arguments: cpu_offload: Enable offloading parameters and gradients to CPU to save GPU memory at the cost of speed. You can also pass a config: ``cpu_offload=CPUOffload(offload_params=True)``. Note that this currently implicitly enables gradient offloading to CPU in order for parameters and gradients to be on same device to work with the optimizer. This API is subject to change. Default: no offoading backward_prefetch: This is an experimental feature that is subject to change in the near future. It allows users to enable two different backward prefetching algorithms to help backward communication and computation overlapping. The pros and cons of each algorithm is explained in the class ``BackwardPrefetch``. mixed_precision: Mixed Precision config. By default, Lightning will enable FP16 if ``precision=16`` or BF16 if ``precision=bf16`` unless a config is passed in. This is only available in PyTorch 1.12 and later. 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: Optional keywoard arguments passed to the FSDP context manager which will configure the FSDP class when wrapping modules. """ def __init__( self, accelerator: Optional[Accelerator] = None, parallel_devices: Optional[List[torch.device]] = None, cluster_environment: Optional[ClusterEnvironment] = None, checkpoint_io: Optional[CheckpointIO] = None, precision: Optional[Precision] = None, process_group_backend: Optional[str] = None, timeout: Optional[timedelta] = default_pg_timeout, cpu_offload: Union[bool, "CPUOffload", None] = None, backward_prefetch: Optional["BackwardPrefetch"] = 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 NotImplementedError("`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=precision, ) self._num_nodes = 1 self._process_group_backend: Optional[str] = process_group_backend self._timeout: Optional[timedelta] = timeout self._backward_sync_control = _FSDPBackwardSyncControl() self._ddp_kwargs = kwargs 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.cpu_offload = _init_cpu_offload(cpu_offload) self.backward_prefetch = backward_prefetch self.mixed_precision = mixed_precision @property def root_device(self) -> torch.device: assert self.parallel_devices is not None return self.parallel_devices[self.local_rank] @property def num_nodes(self) -> int: return self._num_nodes @num_nodes.setter def num_nodes(self, num_nodes: int) -> None: self._num_nodes = num_nodes @property def num_processes(self) -> int: return len(self.parallel_devices) if self.parallel_devices is not None else 0 @property def distributed_sampler_kwargs(self) -> Dict[str, Any]: return dict(num_replicas=(self.num_nodes * self.num_processes), rank=self.global_rank) @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 if isinstance(self.precision, FSDPPrecision): return self.precision.mixed_precision_config 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)
[docs] def setup_environment(self) -> None: self._setup_distributed() super().setup_environment()
[docs] def setup_module_and_optimizers( self, module: Module, optimizers: List[Optimizer] ) -> Tuple[Module, List[Optimizer]]: raise NotImplementedError( f"The `{type(self).__name__}` does not support the joint setup of module and optimizer(s)." " Please do it in this order: Create the model, call `setup_module`, create the optimizer," " call `setup_optimizer`." )
[docs] def setup_module(self, module: Module) -> "FullyShardedDataParallel": """Wraps the model into a :class:`~torch.distributed.fsdp.fully_sharded_data_parallel.FullyShardedDataParallel` module.""" from torch.distributed.fsdp.fully_sharded_data_parallel import FullyShardedDataParallel if "auto_wrap_policy" in self._ddp_kwargs and any( isinstance(mod, FullyShardedDataParallel) for mod in module.modules() ): # If model is already wrapped, we need to avoid sending the `auto_wrap_policy` del self._ddp_kwargs["auto_wrap_policy"] wrapped_module = FullyShardedDataParallel( module=module, cpu_offload=self.cpu_offload, backward_prefetch=self.backward_prefetch, mixed_precision=self.mixed_precision_config, device_id=self.root_device.index, **self._ddp_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_optimizer(self, optimizer: Optimizer) -> Optimizer: """Set up an optimizer for a model wrapped with FSDP. This setup method doesn't modify the optimizer or wrap the optimizer. The only thing it currently does is verify that the optimizer was created after the model was wrapped with :meth:`setup_module` with a reference to the flattened parameters. """ from torch.distributed.fsdp import FlatParameter num_groups = len(optimizer.param_groups) if num_groups > 1: raise ValueError( "An optimizer used with an FSDP model does not support multiple param groups." f" Found {num_groups} parameter groups." ) if any(isinstance(param, FlatParameter) for param in optimizer.param_groups[0]["params"]): return optimizer raise ValueError( "The optimizer does not seem to reference any FSDP parameters. HINT: Make sure to create the optimizer" " after setting up the model." )
[docs] def module_to_device(self, module: Module) -> None: pass
[docs] @contextmanager def module_sharded_context(self) -> Generator: from torch.distributed.fsdp.fully_sharded_data_parallel import FullyShardedDataParallel from torch.distributed.fsdp.wrap import enable_wrap with enable_wrap( wrapper_cls=FullyShardedDataParallel, cpu_offload=self.cpu_offload, backward_prefetch=self.backward_prefetch, mixed_precision=self.mixed_precision_config, device_id=self.root_device.index, **self._ddp_kwargs, ): yield
[docs] def all_reduce( self, tensor: Tensor, group: Optional[Any] = None, reduce_op: Optional[Union[ReduceOp, str]] = "mean" ) -> Tensor: if isinstance(tensor, Tensor): tensor = _sync_ddp_if_available(tensor, group, reduce_op=reduce_op) return tensor
[docs] def barrier(self, *args: Any, **kwargs: Any) -> None: if not _distributed_available(): return if torch.distributed.get_backend() == "nccl": torch.distributed.barrier(device_ids=[self.root_device.index]) 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[list-item] torch.distributed.broadcast_object_list(obj, src, group=_group.WORLD) return obj[0]
@classmethod def register_strategies(cls, strategy_registry: Dict) -> None: if not _TORCH_GREATER_EQUAL_1_12 or not torch.distributed.is_available(): return strategy_registry.register( "fsdp", cls, description="Fully Sharded Data Parallel", ) strategy_registry.register( "fsdp_full_shard_offload", cls, description="Fully Sharded Data Parallel and CPU Offloading", cpu_offload=True, ) def _setup_distributed(self) -> None: reset_seed() self._set_world_ranks() 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, timeout=self._timeout) 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 _setup_activation_checkpointing(module: "FullyShardedDataParallel", layers: List[Type[Module]]) -> None: from torch.distributed.algorithms._checkpoint.checkpoint_wrapper import ( apply_activation_checkpointing, checkpoint_wrapper, CheckpointImpl, ) check_fn = lambda submodule: isinstance(submodule, tuple(layers)) wrapper = functools.partial( checkpoint_wrapper, checkpoint_impl=CheckpointImpl.NO_REENTRANT, ) apply_activation_checkpointing(module, checkpoint_wrapper_fn=wrapper, check_fn=check_fn) class _FSDPBackwardSyncControl(_BackwardSyncControl): @contextmanager def no_backward_sync(self, module: Module) -> Generator: """Blocks gradient synchronization inside the :class:`~torch.distributed.fsdp.FullyShardedDataParallel` wrapper.""" from torch.distributed.fsdp.fully_sharded_data_parallel import FullyShardedDataParallel if not isinstance(module, FullyShardedDataParallel): raise TypeError( "Blocking backward sync is only possible if the module passed to" f" `{self.__class__.__name__}.no_backward_sync` is wrapped in `FullyShardedDataParallel`." f" Got: {module.__class__.__name__}." ) with module.no_sync(): yield def _init_cpu_offload(cpu_offload: Optional[Union[bool, "CPUOffload"]]) -> "CPUOffload": from torch.distributed.fsdp import CPUOffload return cpu_offload if isinstance(cpu_offload, CPUOffload) else CPUOffload(offload_params=bool(cpu_offload)) def _optimizer_has_flat_params(optimizer: Optimizer) -> bool: from torch.distributed.fsdp import FlatParameter return any(isinstance(param, FlatParameter) for param in optimizer.param_groups[0]["params"])

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