Source code for pytorch_lightning.strategies.dp

# 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.
# You may obtain a copy of the License at
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import Any, Dict, List, Optional, Union

import torch
from lightning_utilities.core.apply_func import apply_to_collection
from torch import Tensor
from torch.nn import DataParallel, Module

import pytorch_lightning as pl
from lightning_fabric.plugins import CheckpointIO
from lightning_fabric.utilities.distributed import ReduceOp
from pytorch_lightning.overrides.base import _LightningPrecisionModuleWrapperBase
from pytorch_lightning.overrides.data_parallel import LightningParallelModule
from pytorch_lightning.plugins.precision import PrecisionPlugin
from pytorch_lightning.strategies.parallel import ParallelStrategy
from pytorch_lightning.strategies.strategy import TBroadcast, TReduce
from pytorch_lightning.utilities.model_helpers import is_overridden
from pytorch_lightning.utilities.types import STEP_OUTPUT

[docs]class DataParallelStrategy(ParallelStrategy): """Implements data-parallel training in a single process, i.e., the model gets replicated to each device and each gets a split of the data.""" strategy_name = "dp" def __init__( self, accelerator: Optional["pl.accelerators.Accelerator"] = None, parallel_devices: Optional[List[torch.device]] = None, checkpoint_io: Optional[CheckpointIO] = None, precision_plugin: Optional[PrecisionPlugin] = None, ): super().__init__( accelerator=accelerator, parallel_devices=parallel_devices, cluster_environment=None, checkpoint_io=checkpoint_io, precision_plugin=precision_plugin, ) @property def global_rank(self) -> int: return 0 @property def local_rank(self) -> int: return 0 @property def node_rank(self) -> int: return 0 @property def world_size(self) -> int: return 1
[docs] def setup(self, trainer: "pl.Trainer") -> None: # model needs to be moved to the device before it is wrapped self.model_to_device() assert isinstance(self.model, (pl.LightningModule, _LightningPrecisionModuleWrapperBase)) self.model = self._setup_model(LightningParallelModule(self.model)) super().setup(trainer)
[docs] def batch_to_device(self, batch: Any, device: Optional[torch.device] = None, dataloader_idx: int = 0) -> Any: """Moves the batch to the correct device. The input and the output is the same type. Args: batch: The batch of samples to move to the correct device device: The target device dataloader_idx: The index of the dataloader to which the batch belongs. """ # DataParallel handles the transfer of batch to the device return batch
def _setup_model(self, model: Module) -> DataParallel: """Wraps the given model into a :class:`~torch.nn.parallel.DataParallel` module.""" return DataParallel(module=model, device_ids=self.parallel_devices)
[docs] def reduce( self, collection: TReduce, group: Optional[Any] = None, reduce_op: Optional[Union[ReduceOp, str]] = "mean" ) -> TReduce: """Reduces a collection of tensors from all processes. It can be applied to just a single tensor. Args: collection: The collection of tensors to sync and reduce. group: ignored for DP reduce_op: ignored for DP Return: Reduced tensor values or the same value if it was not or did not contain a tensor. """ def mean(t: Tensor) -> Tensor: original_dtype = t.dtype return t.float().mean().to(original_dtype) return apply_to_collection(collection, Tensor, mean)
@property def root_device(self) -> torch.device: assert self.parallel_devices is not None return self.parallel_devices[0]
[docs] def model_to_device(self) -> None: assert self.model is not None
[docs] def barrier(self, *args: Any, **kwargs: Any) -> None: pass
[docs] def broadcast(self, obj: TBroadcast, src: int = 0) -> TBroadcast: return obj
[docs] def reduce_boolean_decision(self, decision: bool, all: bool = True) -> bool: return decision
[docs] def training_step(self, *args: Any, **kwargs: Any) -> STEP_OUTPUT: with self.precision_plugin.train_step_context(): assert self.model is not None return self.model(*args, **kwargs)
[docs] def validation_step(self, *args: Any, **kwargs: Any) -> Optional[STEP_OUTPUT]: with self.precision_plugin.val_step_context(): assert self.model is not None return self.model(*args, **kwargs)
[docs] def test_step(self, *args: Any, **kwargs: Any) -> Optional[STEP_OUTPUT]: with self.precision_plugin.test_step_context(): assert self.model is not None return self.model(*args, **kwargs)
[docs] def predict_step(self, *args: Any, **kwargs: Any) -> STEP_OUTPUT: with self.precision_plugin.predict_step_context(): assert self.model is not None return self.model(*args, **kwargs)
def training_step_end(self, output: STEP_OUTPUT) -> STEP_OUTPUT: if is_overridden("training_step_end", self.lightning_module): return output if isinstance(output, dict) and "loss" in output: output["loss"] = self.reduce(output["loss"]) elif isinstance(output, Tensor): output = self.reduce(output) return output @classmethod def register_strategies(cls, strategy_registry: Dict) -> None: strategy_registry.register( cls.strategy_name, cls, description=f"{cls.__class__.__name__}", )

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