424 lines
17 KiB
Python
424 lines
17 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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from typing import Any, Callable, Dict, Iterable, List, Optional, Sequence, TYPE_CHECKING, Union
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import torch
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from torch.optim import Optimizer
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from torch.utils.data import DataLoader
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from pytorch_lightning.core import LightningModule
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from pytorch_lightning.plugins.precision import ApexMixedPrecisionPlugin, NativeMixedPrecisionPlugin, PrecisionPlugin
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from pytorch_lightning.plugins.training_type import TrainingTypePlugin
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from pytorch_lightning.utilities.apply_func import move_data_to_device
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from pytorch_lightning.utilities.distributed import all_gather_ddp_if_available
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from pytorch_lightning.utilities.enums import AMPType, LightningEnum
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if TYPE_CHECKING:
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from torch.cuda.amp import GradScaler
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from pytorch_lightning.trainer.trainer import Trainer
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_STEP_OUTPUT_TYPE = Union[torch.Tensor, Dict[str, torch.Tensor], None]
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class Accelerator(object):
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"""
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The Accelerator Base Class.
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An Accelerator is meant to deal with one type of Hardware.
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Currently there are accelerators for:
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- CPU
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- GPU
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- TPU
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Each Accelerator gets two plugins upon initialization:
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One to handle differences from the training routine and one to handle different precisions.
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"""
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def __init__(
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self,
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precision_plugin: PrecisionPlugin,
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training_type_plugin: TrainingTypePlugin,
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) -> None:
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"""
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Args:
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precision_plugin: the plugin to handle precision-specific parts
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training_type_plugin: the plugin to handle different training routines
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"""
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self.precision_plugin = precision_plugin
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self.training_type_plugin = training_type_plugin
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self.optimizers: Sequence = []
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self.lr_schedulers: Sequence = []
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self.optimizer_frequencies: Sequence = []
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def setup(self, trainer: 'Trainer', model: LightningModule) -> None:
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"""
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Connects the plugins to the training process, creates optimizers
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Args:
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trainer: the trainer instance to connect to
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model: the model to train
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"""
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self.connect_training_type_plugin(self.training_type_plugin, model)
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self.setup_optimizers(trainer)
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self.connect_precision_plugin(self.precision_plugin)
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def start_training(self, trainer: 'Trainer') -> None:
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self.training_type_plugin.start_training(trainer)
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def start_testing(self, trainer: 'Trainer') -> None:
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self.training_type_plugin.start_testing(trainer)
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def start_predicting(self, trainer: 'Trainer') -> None:
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self.training_type_plugin.start_predicting(trainer)
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def pre_dispatch(self) -> None:
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"""Hook to do something before the training/evaluation/prediction starts."""
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self.training_type_plugin.pre_dispatch()
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self.precision_plugin.pre_dispatch()
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def post_dispatch(self) -> None:
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"""Hook to do something before the training/evaluation/prediction starts."""
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self.training_type_plugin.post_dispatch()
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self.precision_plugin.post_dispatch()
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@property
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def model(self) -> torch.nn.Module:
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"""Returns the model. This can also be a wrapped LightningModule.
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For retrieving the pure LightningModule use :attr:`Accelerator.lightning_module`
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"""
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return self.training_type_plugin.model
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@model.setter
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def model(self, new_model: torch.nn.Module) -> None:
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self.training_type_plugin.model = new_model
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@property
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def lightning_module(self) -> LightningModule:
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"""Returns the pure LightningModule.
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To get the potentially wrapped model use :attr:`Accelerator.model`
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"""
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return self.training_type_plugin.lightning_module
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@property
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def root_device(self) -> torch.device:
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return self.training_type_plugin.root_device
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def teardown(self) -> None:
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"""This method is called to teardown the training process.
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It is the right place to release memory and free other ressources.
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"""
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pass
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def batch_to_device(self, batch: Any, device: Optional[torch.device] = None) -> Any:
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"""Moves the batch to the correct device.
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The returned batch is of the same type as the input batch, just having all tensors on the correct device.
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Args:
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batch: The batch of samples to move to the correct device
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device: The target device
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"""
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model = self.lightning_module
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if model is not None:
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return model._apply_batch_transfer_handler(batch, device)
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return move_data_to_device(batch, device)
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def on_train_start(self) -> None:
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"""Hook to do something upon the training start"""
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pass
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def training_step(
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self,
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args: List[Union[Any, int]],
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) -> _STEP_OUTPUT_TYPE:
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"""The actual training step.
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Args:
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args: the arguments for the models training step. Can consist of the following:
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batch (:class:`~torch.Tensor` | (:class:`~torch.Tensor`, ...) | [:class:`~torch.Tensor`, ...]):
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The output of your :class:`~torch.utils.data.DataLoader`. A tensor, tuple or list.
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batch_idx (int): Integer displaying index of this batch
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optimizer_idx (int): When using multiple optimizers, this argument will also be present.
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hiddens(:class:`~torch.Tensor`): Passed in if
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:paramref:`~pytorch_lightning.trainer.trainer.Trainer.truncated_bptt_steps` > 0.
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"""
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args[0] = self.to_device(args[0])
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with self.precision_plugin.train_step_context(), self.training_type_plugin.train_step_context():
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return self.training_type_plugin.training_step(*args)
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def post_training_step(self) -> None:
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self.training_type_plugin.post_training_step()
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def validation_step(self, args: List[Union[Any, int]]) -> _STEP_OUTPUT_TYPE:
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"""The actual validation step.
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Args:
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args: the arguments for the models validation step. Can consist of the following:
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batch (:class:`~torch.Tensor` | (:class:`~torch.Tensor`, ...) | [:class:`~torch.Tensor`, ...]):
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The output of your :class:`~torch.utils.data.DataLoader`. A tensor, tuple or list.
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batch_idx (int): The index of this batch
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dataloader_idx (int): The index of the dataloader that produced this batch
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(only if multiple val dataloaders used)
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"""
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batch = self.to_device(args[0])
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args[0] = batch
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with self.precision_plugin.val_step_context(), self.training_type_plugin.val_step_context():
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return self.training_type_plugin.validation_step(*args)
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def test_step(self, args: List[Union[Any, int]]) -> _STEP_OUTPUT_TYPE:
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"""The actual test step.
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Args:
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args: the arguments for the models test step. Can consist of the following:
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batch (:class:`~torch.Tensor` | (:class:`~torch.Tensor`, ...) | [:class:`~torch.Tensor`, ...]):
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The output of your :class:`~torch.utils.data.DataLoader`. A tensor, tuple or list.
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batch_idx (int): The index of this batch.
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dataloader_idx (int): The index of the dataloader that produced this batch
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(only if multiple test dataloaders used).
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"""
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batch = self.to_device(args[0])
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args[0] = batch
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with self.precision_plugin.test_step_context(), self.training_type_plugin.test_step_context():
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return self.training_type_plugin.test_step(*args)
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def predict(self, args: List[Union[Any, int]]) -> _STEP_OUTPUT_TYPE:
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"""The actual predict step.
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Args:
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args: the arguments for the models predict step. Can consist of the following:
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batch (:class:`~torch.Tensor` | (:class:`~torch.Tensor`, ...) | [:class:`~torch.Tensor`, ...]):
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The output of your :class:`~torch.utils.data.DataLoader`. A tensor, tuple or list.
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batch_idx (int): The index of this batch.
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dataloader_idx (int): The index of the dataloader that produced this batch
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(only if multiple predict dataloaders used).
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"""
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batch = self.to_device(args[0])
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args[0] = batch
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with self.precision_plugin.predict_context(), self.training_type_plugin.predict_context():
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return self.training_type_plugin.predict(*args)
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def training_step_end(self, output: _STEP_OUTPUT_TYPE) -> _STEP_OUTPUT_TYPE:
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"""A hook to do something at the end of the training step
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Args:
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output: the output of the training step
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"""
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return self.training_type_plugin.training_step_end(output)
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def test_step_end(self, output: _STEP_OUTPUT_TYPE) -> _STEP_OUTPUT_TYPE:
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"""A hook to do something at the end of the test step
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Args:
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output: the output of the test step
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"""
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return self.training_type_plugin.test_step_end(output)
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def validation_step_end(self, output: _STEP_OUTPUT_TYPE) -> _STEP_OUTPUT_TYPE:
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"""A hook to do something at the end of the validation step
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Args:
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output: the output of the validation step
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"""
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return self.training_type_plugin.validation_step_end(output)
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def backward(
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self,
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closure_loss: torch.Tensor,
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optimizer: Optimizer,
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optimizer_idx: int,
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should_accumulate: bool,
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*args: Any,
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**kwargs: Any,
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) -> torch.Tensor:
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"""Forwards backward-calls to the precision plugin.
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Args:
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closure_loss: a tensor holding the loss value to backpropagate
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should_accumulate: whether to accumulate gradients
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"""
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self.training_type_plugin.pre_backward(closure_loss, should_accumulate, optimizer, optimizer_idx)
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output = self.precision_plugin.backward(
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self.lightning_module, closure_loss, optimizer, optimizer_idx, should_accumulate, *args, **kwargs
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)
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self.training_type_plugin.post_backward(closure_loss, should_accumulate, optimizer, optimizer_idx)
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return output
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def optimizer_step(self, optimizer: Optimizer, opt_idx: int, lambda_closure: Callable, **kwargs: Any) -> None:
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"""performs the actual optimizer step.
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Args:
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optimizer: the optimizer performing the step
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opt_idx: index of the current optimizer
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lambda_closure: closure calculating the loss value
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"""
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make_optimizer_step = self.precision_plugin.pre_optimizer_step(
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self.lightning_module, optimizer, opt_idx, lambda_closure, **kwargs
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)
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if make_optimizer_step:
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self.run_optimizer_step(optimizer, opt_idx, lambda_closure, **kwargs)
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self.precision_plugin.post_optimizer_step(optimizer, opt_idx)
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self.training_type_plugin.post_optimizer_step(optimizer, opt_idx, **kwargs)
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def run_optimizer_step(
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self, optimizer: Optimizer, optimizer_idx: int, lambda_closure: Callable, **kwargs: Any
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) -> None:
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self.training_type_plugin.optimizer_step(optimizer, lambda_closure=lambda_closure, **kwargs)
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def optimizer_zero_grad(self, current_epoch: int, batch_idx: int, optimizer: Optimizer, opt_idx: int) -> None:
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"""Zeros all model parameter's gradients"""
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model_ref = self.lightning_module
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model_ref.optimizer_zero_grad(current_epoch, batch_idx, optimizer, opt_idx)
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def clip_gradients(self, optimizer: Optimizer, clip_val: Union[int, float]) -> None:
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"""clips all the optimizer parameters to the given value"""
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self.precision_plugin.clip_gradients(optimizer, clip_val)
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def on_train_epoch_end(self, outputs: Sequence[_STEP_OUTPUT_TYPE]) -> None:
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"""Hook to do something on the end of an training epoch
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Args:
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outputs: the outputs of the training steps
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"""
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pass
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def on_train_end(self) -> None:
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"""Hook to do something at the end of the training"""
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pass
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def setup_optimizers(self, trainer: 'Trainer') -> None:
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"""creates optimizers and schedulers
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Args:
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trainer: the Trainer, these optimizers should be connected to
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model: the model to be optimized by the created optimizers
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"""
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if trainer.testing:
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return
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optimizers, lr_schedulers, optimizer_frequencies = self.training_type_plugin.init_optimizers(
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trainer=trainer, model=self.lightning_module
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)
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self.optimizers = optimizers
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self.lr_schedulers = lr_schedulers
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self.optimizer_frequencies = optimizer_frequencies
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def connect_training_type_plugin(self, plugin: TrainingTypePlugin, model: LightningModule) -> None:
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"""Attaches the training type plugin to the accelerator.
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Also transfers ownership of the model to this plugin
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"""
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plugin.connect(model)
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def connect_precision_plugin(self, plugin: PrecisionPlugin) -> None:
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"""Attaches the precision plugin to the accelerator"""
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model, optimizers, schedulers = plugin.connect(self.model, self.optimizers, self.lr_schedulers)
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self.model = model
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self.optimizers = optimizers
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self.schedulers = schedulers
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def to_device(self, batch: Any) -> Any:
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"""Pushes the batch to the root device"""
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return self.batch_to_device(batch, self.root_device)
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@property
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def amp_backend(self) -> Optional[LightningEnum]:
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if isinstance(self.precision_plugin, ApexMixedPrecisionPlugin):
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return AMPType.APEX
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elif isinstance(self.precision_plugin, NativeMixedPrecisionPlugin):
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return AMPType.NATIVE
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return None
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@property
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def precision(self) -> Union[str, int]:
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return self.precision_plugin.precision
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@property
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def scaler(self) -> Optional['GradScaler']:
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return getattr(self.precision_plugin, 'scaler', None)
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@property
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def rpc_enabled(self) -> bool:
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return self.training_type_plugin.rpc_enabled
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def optimizer_state(self, optimizer: Optimizer) -> Dict[str, torch.Tensor]:
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"""
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Returns state of an optimizer. Allows for syncing/collating optimizer state from processes in custom
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plugins.
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"""
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return getattr(self.training_type_plugin, 'optimizer_state', lambda x: x.state_dict())(optimizer)
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def on_save(self, checkpoint: Dict[str, Union[Any, torch.Tensor]]) -> Dict[str, Union[Any, torch.Tensor]]:
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return self.training_type_plugin.on_save(checkpoint)
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def barrier(self, name: Optional[str] = None) -> None:
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self.training_type_plugin.barrier(name=name)
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def broadcast(self, obj: object, src: int = 0) -> object:
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"""Broadcasts an object to all processes, such that the src object is broadcast to all other ranks if needed.
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Args:
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obj: Object to broadcast to all process, usually a tensor or collection of tensors.
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src: The source rank of which the object will be broadcast from
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"""
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return self.training_type_plugin.broadcast(obj, src)
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def all_gather(self, tensor: torch.Tensor, group: Optional[Any] = None, sync_grads: bool = False) -> torch.Tensor:
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"""
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Function to gather a tensor from several distributed processes.
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Args:
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tensor: tensor of shape (batch, ...)
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group: the process group to gather results from. Defaults to all processes (world)
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sync_grads: flag that allows users to synchronize gradients for all_gather op
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Return:
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A tensor of shape (world_size, batch, ...)
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"""
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return all_gather_ddp_if_available(tensor, group=group, sync_grads=sync_grads)
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def process_dataloader(self, dataloader: Union[Iterable, DataLoader]) -> Union[Iterable, DataLoader]:
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"""Wraps the dataloader if necessary
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Args:
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dataloader: iterable. Ideally of type: :class:`torch.utils.data.DataLoader`
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"""
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return self.training_type_plugin.process_dataloader(dataloader)
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@property
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def results(self) -> Any:
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"""
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The results of the last training/testing run will be cached within the training type plugin.
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In distributed training, we make sure to transfer the results to the appropriate master process.
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"""
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return self.training_type_plugin.results
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