279 lines
12 KiB
Python
279 lines
12 KiB
Python
# Copyright The PyTorch Lightning team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import logging
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import os
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from typing import Any, Dict, List, Optional, Union
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import torch
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import torch.distributed
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from torch.nn import Module
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from torch.nn.parallel.distributed import DistributedDataParallel
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import pytorch_lightning as pl
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from pytorch_lightning.overrides import LightningDistributedModule
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from pytorch_lightning.overrides.distributed import prepare_for_backward
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from pytorch_lightning.plugins.environments.cluster_environment import ClusterEnvironment
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from pytorch_lightning.plugins.io.checkpoint_plugin import CheckpointIO
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from pytorch_lightning.plugins.precision import PrecisionPlugin
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from pytorch_lightning.strategies.launchers.spawn import _SpawnLauncher
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from pytorch_lightning.strategies.parallel import ParallelStrategy
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from pytorch_lightning.trainer.states import TrainerFn
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from pytorch_lightning.utilities import _TORCH_GREATER_EQUAL_1_8
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from pytorch_lightning.utilities.distributed import distributed_available
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from pytorch_lightning.utilities.distributed import group as _group
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from pytorch_lightning.utilities.distributed import init_dist_connection, ReduceOp, sync_ddp_if_available
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from pytorch_lightning.utilities.rank_zero import rank_zero_only, rank_zero_warn
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from pytorch_lightning.utilities.seed import reset_seed
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from pytorch_lightning.utilities.types import STEP_OUTPUT
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if _TORCH_GREATER_EQUAL_1_8:
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from pytorch_lightning.utilities.distributed import register_ddp_comm_hook
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log = logging.getLogger(__name__)
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class DDPSpawnStrategy(ParallelStrategy):
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"""Spawns processes using the :func:`torch.multiprocessing.spawn` method and joins processes after training
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finishes."""
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strategy_name = "ddp_spawn"
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def __init__(
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self,
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accelerator: Optional["pl.accelerators.accelerator.Accelerator"] = None,
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parallel_devices: Optional[List[torch.device]] = None,
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cluster_environment: Optional[ClusterEnvironment] = None,
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checkpoint_io: Optional[CheckpointIO] = None,
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precision_plugin: Optional[PrecisionPlugin] = None,
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ddp_comm_state: Optional[object] = None,
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ddp_comm_hook: Optional[callable] = None,
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ddp_comm_wrapper: Optional[callable] = None,
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**kwargs: Any,
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):
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super().__init__(
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accelerator=accelerator,
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parallel_devices=parallel_devices,
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cluster_environment=cluster_environment,
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checkpoint_io=checkpoint_io,
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precision_plugin=precision_plugin,
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)
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self._num_nodes = 1
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self._ddp_kwargs = kwargs
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self._ddp_comm_state = ddp_comm_state
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self._ddp_comm_hook = ddp_comm_hook
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self._ddp_comm_wrapper = ddp_comm_wrapper
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self._local_rank = 0
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@property
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def num_nodes(self) -> int:
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return self._num_nodes
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@num_nodes.setter
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def num_nodes(self, num_nodes: int) -> None:
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# note that world ranks is related to num_nodes, when resetting it, need to reset world ranks
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self._num_nodes = num_nodes
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@property
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def local_rank(self) -> int:
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return self._local_rank
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@property
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def root_device(self):
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return self.parallel_devices[self.local_rank]
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@property
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def num_processes(self):
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return len(self.parallel_devices) if self.parallel_devices is not None else 0
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@property
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def distributed_sampler_kwargs(self):
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distributed_sampler_kwargs = dict(num_replicas=(self.num_nodes * self.num_processes), rank=self.global_rank)
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return distributed_sampler_kwargs
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@property
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def _is_single_process_single_device(self):
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return True
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def _configure_launcher(self):
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self._launcher = _SpawnLauncher(self)
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def setup(self, trainer: "pl.Trainer") -> None:
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os.environ["MASTER_PORT"] = str(self.cluster_environment.main_port)
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super().setup(trainer)
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# move the model to the correct device
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self.model_to_device()
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if self._layer_sync:
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self.model = self._layer_sync.apply(self.model)
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# skip wrapping the model if we are not fitting as no gradients need to be exchanged
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trainer_fn = self.lightning_module.trainer.state.fn
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if trainer_fn == TrainerFn.FITTING:
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self.configure_ddp()
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def _setup_model(self, model: Module) -> DistributedDataParallel:
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"""Wraps the model into a :class:`~torch.nn.parallel.distributed.DistributedDataParallel` module."""
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return DistributedDataParallel(module=model, device_ids=self.determine_ddp_device_ids(), **self._ddp_kwargs)
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def set_world_ranks(self, process_idx: int = 0) -> None:
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self._local_rank = process_idx
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if self.cluster_environment is None:
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return
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self.cluster_environment.set_global_rank(self.node_rank * self.num_processes + self.local_rank)
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self.cluster_environment.set_world_size(self.num_nodes * self.num_processes)
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rank_zero_only.rank = self.cluster_environment.global_rank()
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def _worker_setup(self, process_idx: int):
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reset_seed()
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self.set_world_ranks(process_idx)
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rank_zero_only.rank = self.global_rank
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init_dist_connection(
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self.cluster_environment, self.torch_distributed_backend, self.global_rank, self.world_size
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)
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def pre_configure_ddp(self):
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# if unset, default `find_unused_parameters` `True`
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# Many models require setting this parameter to True, as there are corner cases
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# when not all parameter backward hooks are fired by the autograd engine even if require_grad is set to True.
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# This flag does come with a performance hit, so it is suggested to disable in cases where it is possible.
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self._ddp_kwargs["find_unused_parameters"] = self._ddp_kwargs.get("find_unused_parameters", True)
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if not self.lightning_module.automatic_optimization and not self._ddp_kwargs.get(
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"find_unused_parameters", False
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):
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# TODO: PyTorch 1.7.0 DDP introduces `self.reducer._rebuild_buckets()` breaking manual_optimization
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rank_zero_warn(
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"From PyTorch 1.7.0, Lightning `manual_optimization` needs to set `find_unused_parameters=True` to"
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" properly work with DDP. Using `find_unused_parameters=True`."
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)
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self._ddp_kwargs["find_unused_parameters"] = True
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def _register_ddp_hooks(self) -> None:
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# currently, DDP communication hooks only work with NCCL backend and SPSD (single process single device) mode
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# https://github.com/pytorch/pytorch/blob/v1.8.0/torch/nn/parallel/distributed.py#L1080-L1084
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if _TORCH_GREATER_EQUAL_1_8 and self.root_device.type == "cuda" and self._is_single_process_single_device:
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register_ddp_comm_hook(
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model=self.model,
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ddp_comm_state=self._ddp_comm_state,
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ddp_comm_hook=self._ddp_comm_hook,
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ddp_comm_wrapper=self._ddp_comm_wrapper,
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)
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def configure_ddp(self) -> None:
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self.pre_configure_ddp()
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self.model = self._setup_model(LightningDistributedModule(self.model))
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self._register_ddp_hooks()
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def determine_ddp_device_ids(self):
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if self.root_device.type == "cpu":
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return None
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return [self.root_device.index]
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def barrier(self, *args, **kwargs) -> None:
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if not distributed_available():
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return
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if _TORCH_GREATER_EQUAL_1_8 and torch.distributed.get_backend() == "nccl":
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torch.distributed.barrier(device_ids=self.determine_ddp_device_ids())
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else:
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torch.distributed.barrier()
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def broadcast(self, obj: object, src: int = 0) -> object:
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if not distributed_available():
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return obj
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obj = [obj]
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if self.global_rank != src:
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obj = [None]
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torch.distributed.broadcast_object_list(obj, src, group=_group.WORLD)
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return obj[0]
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def model_to_device(self):
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if self.root_device.type == "cuda":
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# set the device on the spawned subprocesses
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torch.cuda.set_device(self.root_device)
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self.model.to(self.root_device)
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def pre_backward(self, closure_loss: torch.Tensor) -> None:
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"""Run before precision plugin executes backward."""
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if not self.lightning_module.automatic_optimization:
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prepare_for_backward(self.model, closure_loss)
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def reduce(self, tensor, group: Optional[Any] = None, reduce_op: Union[ReduceOp, str] = "mean") -> torch.Tensor:
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"""Reduces a tensor from several distributed processes to one aggregated tensor.
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Args:
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tensor: the tensor to sync and reduce
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group: the process group to gather results from. Defaults to all processes (world)
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reduce_op: the reduction operation. Defaults to 'mean'/'avg'.
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Can also be a string 'sum' to calculate the sum during reduction.
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Return:
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reduced value, except when the input was not a tensor the output remains is unchanged
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"""
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if isinstance(tensor, torch.Tensor):
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tensor = sync_ddp_if_available(tensor, group, reduce_op=reduce_op)
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return tensor
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def training_step(self, *args, **kwargs) -> STEP_OUTPUT:
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with self.precision_plugin.train_step_context():
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return self.model(*args, **kwargs)
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def validation_step(self, *args, **kwargs) -> Optional[STEP_OUTPUT]:
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with self.precision_plugin.val_step_context():
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if isinstance(self.model, DistributedDataParallel):
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# used when calling `trainer.fit`
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return self.model(*args, **kwargs)
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else:
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# used when calling `trainer.validate`
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return self.lightning_module.validation_step(*args, **kwargs)
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def test_step(self, *args, **kwargs) -> Optional[STEP_OUTPUT]:
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with self.precision_plugin.test_step_context():
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return self.lightning_module.test_step(*args, **kwargs)
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def predict_step(self, *args, **kwargs) -> STEP_OUTPUT:
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with self.precision_plugin.predict_step_context():
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return self.lightning_module.predict_step(*args, **kwargs)
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def post_training_step(self):
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if not self.lightning_module.automatic_optimization:
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self.model.require_backward_grad_sync = True
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@classmethod
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def register_strategies(cls, strategy_registry: Dict) -> None:
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strategy_registry.register(
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"ddp_spawn_find_unused_parameters_false",
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cls,
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description="DDPSpawn Strategy with `find_unused_parameters` as False",
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find_unused_parameters=False,
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)
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strategy_registry.register(
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cls.strategy_name,
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cls,
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description=f"{cls.__class__.__name__}",
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)
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def teardown(self) -> None:
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super().teardown()
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if isinstance(self.model, DistributedDataParallel):
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self.model = self.lightning_module
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if self._layer_sync:
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self.model = self._layer_sync.revert(self.model)
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if self.root_device.type == "cuda":
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# GPU teardown
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self.lightning_module.cpu()
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# clean up memory
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torch.cuda.empty_cache()
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