377 lines
13 KiB
Python
377 lines
13 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, Iterable, Optional, Union
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import torch
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from torch.optim import Optimizer
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from pytorch_lightning.core import LightningModule
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from pytorch_lightning.plugins.precision import (
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ApexMixedPrecisionPlugin,
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MixedPrecisionPlugin,
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NativeMixedPrecisionPlugin,
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PrecisionPlugin,
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)
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from pytorch_lightning.plugins.training_type import TrainingTypePlugin
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from pytorch_lightning.plugins.training_type.horovod import HorovodPlugin
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from pytorch_lightning.utilities.apply_func import move_data_to_device
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from pytorch_lightning.utilities.enums import AMPType, LightningEnum
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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 = None
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self.lr_schedulers = None
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self.optimizer_frequencies = None
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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, model)
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self.connect_precision_plugin(self.precision_plugin)
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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):
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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: torch.device) -> 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.transfer_batch_to_device(batch, device)
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return move_data_to_device(batch, device)
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def on_train_start(self):
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"""Hook to do something upon the training start"""
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pass
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def training_step(self, args):
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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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batch = self.to_device(args[0])
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args[0] = batch
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with self.precision_plugin.train_step_context():
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with self.training_type_plugin.train_step_context():
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return self.training_type_plugin.training_step(*args)
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def validation_step(self, args):
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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():
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with 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):
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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():
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with self.training_type_plugin.test_step_context():
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return self.training_type_plugin.test_step(*args)
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def training_step_end(self, output):
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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 output
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def test_step_end(self, output):
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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 output
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def validation_step_end(self, output):
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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 output
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def process_dataloader(
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self, dataloader: Union[Iterable, torch.utils.data.DataLoader]
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) -> Union[Iterable, torch.utils.data.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 dataloader
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def backward(
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self,
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closure_loss: torch.Tensor,
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optimizer: torch.optim.Optimizer,
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opt_idx: int,
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should_accumulate: bool,
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*args,
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**kwargs,
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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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optimizer: the optimizer to do the step later on.
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opt_idx: the index of the optimizer
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should_accumulate: whether to accumulate gradients
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"""
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output = self.precision_plugin.backward(
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self.lightning_module, closure_loss, optimizer, opt_idx, should_accumulate, *args, **kwargs
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)
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# TODO: this is a hack, find a better solution for this (hook?)
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if isinstance(self.training_type_plugin, HorovodPlugin):
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optimizer.synchronize()
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return output
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def optimizer_step(
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self,
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optimizer: torch.optim.Optimizer,
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current_epoch: int,
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batch_idx: int,
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opt_idx: int,
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lambda_closure: Callable,
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):
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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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current_epoch: current training epoch
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batch_idx: index of the current batch
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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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model_ref = self.lightning_module
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is_lbfgs = isinstance(optimizer, torch.optim.LBFGS)
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native_amp = (
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isinstance(self.precision_plugin, MixedPrecisionPlugin) and self.precision_plugin.backend == AMPType.NATIVE
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)
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self.precision_plugin.pre_optimizer_step(optimizer, opt_idx)
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self.training_type_plugin.pre_optimizer_step(optimizer, opt_idx)
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# model hook
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res = model_ref.optimizer_step(
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epoch=current_epoch,
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batch_idx=batch_idx,
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optimizer=optimizer,
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optimizer_idx=opt_idx,
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optimizer_closure=lambda_closure,
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on_tpu=False, # TPUAccelerator class sets this as True
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using_native_amp=native_amp,
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using_lbfgs=is_lbfgs,
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)
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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)
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return res
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def optimizer_zero_grad(
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self, current_epoch: int, batch_idx: int, optimizer: torch.optim.Optimizer, opt_idx: int
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) -> 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: torch.optim.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) -> 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", model: LightningModule):
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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 is True:
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return
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optimizers, lr_schedulers, optimizer_frequencies = trainer.init_optimizers(model)
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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):
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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) -> int:
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return self.precision_plugin.precision
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@property
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def scaler(self):
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if hasattr(self.precision_plugin, "scaler"):
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return self.precision_plugin.scaler
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return 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:
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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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if self.training_type_plugin and hasattr(self.training_type_plugin, "optimizer_state"):
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return self.training_type_plugin.optimizer_state(optimizer)
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return optimizer.state_dict()
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def on_save(self, checkpoint):
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return checkpoint
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