311 lines
12 KiB
Python
311 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 contextlib
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from abc import abstractmethod
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from typing import Any, Callable, Dict, Generator, List, Optional, Union
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import torch
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from torch import Tensor
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from torch.cuda.amp import GradScaler
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from torch.nn import Module
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from torch.optim import Optimizer
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import pytorch_lightning as pl
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from pytorch_lightning.plugins.precision import ApexMixedPrecisionPlugin, NativeMixedPrecisionPlugin, PrecisionPlugin
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from pytorch_lightning.plugins.training_type import DataParallelPlugin, TrainingTypePlugin
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from pytorch_lightning.trainer.states import TrainerFn
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from pytorch_lightning.utilities.apply_func import apply_to_collection, move_data_to_device
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from pytorch_lightning.utilities.enums import AMPType, LightningEnum
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from pytorch_lightning.utilities.types import STEP_OUTPUT
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class Accelerator:
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"""The Accelerator Base Class. 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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- IPU
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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__(self, precision_plugin: PrecisionPlugin, training_type_plugin: TrainingTypePlugin) -> 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: List = []
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self.lr_schedulers: List = []
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self.optimizer_frequencies: List = []
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def setup_environment(self) -> None:
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"""Setup any processes or distributed connections.
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This is called before the LightningModule/DataModule setup hook which allows the user to access the accelerator
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environment before setup is complete.
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"""
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self.training_type_plugin.setup_environment()
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def setup(self, trainer: "pl.Trainer") -> None:
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"""Setup plugins for the trainer fit and creates optimizers.
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Args:
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trainer: the trainer instance
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"""
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self.setup_training_type_plugin()
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if not self.training_type_plugin.setup_optimizers_in_pre_dispatch:
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self.setup_optimizers(trainer)
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self.setup_precision_plugin()
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def pre_dispatch(self, trainer: "pl.Trainer") -> None:
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"""Hook to do something before the training/evaluation/prediction starts."""
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self._move_optimizer_state()
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self.training_type_plugin.pre_dispatch()
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if self.training_type_plugin.setup_optimizers_in_pre_dispatch:
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self.setup_optimizers(trainer)
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self.precision_plugin.pre_dispatch()
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def _move_optimizer_state(self, device: Optional[torch.device] = None) -> None:
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"""Moves the state of the optimizers to the GPU if needed."""
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device = device or self.root_device
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for opt in self.optimizers:
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for p, v in opt.state.items():
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opt.state[p] = apply_to_collection(v, torch.Tensor, move_data_to_device, device)
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def dispatch(self, trainer: "pl.Trainer") -> None:
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"""Hook to do something before the training/evaluation/prediction starts."""
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self.training_type_plugin.dispatch(trainer)
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self.precision_plugin.dispatch(trainer)
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def post_dispatch(self, trainer: "pl.Trainer") -> None:
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"""Hook to do something after the training/evaluation/prediction starts."""
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self.training_type_plugin.post_dispatch(trainer)
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self.precision_plugin.post_dispatch()
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@property
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def model(self) -> Module:
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"""Returns the model.
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This can also be a wrapped LightningModule. For retrieving the pure LightningModule use
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: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: 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) -> "pl.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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"""Returns the root 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 resources.
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"""
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self.training_type_plugin.teardown()
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def batch_to_device(self, batch: Any, device: Optional[torch.device] = None, dataloader_idx: int = 0) -> Any:
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"""Moves the batch to the correct device. The returned batch is of the same type as the input batch, just
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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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dataloader_idx: The index of the dataloader to which the batch belongs.
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"""
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model = self.lightning_module
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device = device or self.root_device
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if model is not None and not isinstance(self.training_type_plugin, DataParallelPlugin):
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# no need to transfer batch to device in DP mode
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return model._apply_batch_transfer_handler(batch, device=device, dataloader_idx=dataloader_idx)
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return move_data_to_device(batch, device)
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def training_step(self, step_kwargs: Dict[str, Union[Any, int]]) -> STEP_OUTPUT:
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"""The actual training step.
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See :meth:`~pytorch_lightning.core.lightning.LightningModule.training_step` for more details
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"""
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with self.precision_plugin.train_step_context():
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return self.training_type_plugin.training_step(*step_kwargs.values())
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def validation_step(self, step_kwargs: Dict[str, Union[Any, int]]) -> Optional[STEP_OUTPUT]:
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"""The actual validation step.
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See :meth:`~pytorch_lightning.core.lightning.LightningModule.validation_step` for more details
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"""
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with self.precision_plugin.val_step_context():
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return self.training_type_plugin.validation_step(*step_kwargs.values())
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def test_step(self, step_kwargs: Dict[str, Union[Any, int]]) -> Optional[STEP_OUTPUT]:
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"""The actual test step.
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See :meth:`~pytorch_lightning.core.lightning.LightningModule.test_step` for more details
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"""
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with self.precision_plugin.test_step_context():
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return self.training_type_plugin.test_step(*step_kwargs.values())
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def predict_step(self, step_kwargs: Dict[str, Union[Any, int]]) -> STEP_OUTPUT:
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"""The actual predict step.
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See :meth:`~pytorch_lightning.core.lightning.LightningModule.predict_step` for more details
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"""
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with self.precision_plugin.predict_step_context():
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return self.training_type_plugin.predict_step(*step_kwargs.values())
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def backward(self, closure_loss: Tensor, *args: Any, **kwargs: Any) -> 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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"""
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self.training_type_plugin.pre_backward(closure_loss)
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closure_loss = self.precision_plugin.pre_backward(self.lightning_module, closure_loss)
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self.precision_plugin.backward(self.lightning_module, closure_loss, *args, **kwargs)
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closure_loss = self.precision_plugin.post_backward(self.lightning_module, closure_loss)
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self.training_type_plugin.post_backward(closure_loss)
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return closure_loss
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def optimizer_step(
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self,
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optimizer: Optimizer,
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opt_idx: int,
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closure: Callable[[], Any],
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model: Optional[Union["pl.LightningModule", Module]] = None,
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**kwargs: Any
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) -> 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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closure: closure calculating the loss value
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model: reference to the model, optionally defining optimizer step related hooks
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**kwargs: Any extra arguments to ``optimizer.step``
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"""
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model = model or self.lightning_module
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self.precision_plugin.optimizer_step(model, optimizer, opt_idx, 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 setup_optimizers(self, trainer: "pl.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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"""
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if trainer.state.fn not in (TrainerFn.FITTING, TrainerFn.TUNING):
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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 setup_training_type_plugin(self) -> None:
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"""Attaches the training type plugin to the accelerator."""
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self.training_type_plugin.setup()
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def setup_precision_plugin(self) -> None:
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"""Attaches the precision plugin to the accelerator."""
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model, optimizers, schedulers = self.precision_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.lr_schedulers = schedulers
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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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if 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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def optimizer_state(self, optimizer: Optimizer) -> Dict[str, Tensor]:
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"""Returns state of an optimizer.
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Allows for syncing/collating optimizer state from processes in custom 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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@contextlib.contextmanager
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def model_sharded_context(self) -> Generator[None, None, None]:
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"""Provide hook to create modules in a distributed aware context. This is useful for when we'd like to.
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shard the model instantly - useful for extremely large models. Can save memory and
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initialization time.
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Returns:
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Model parallel context.
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"""
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with self.training_type_plugin.model_sharded_context():
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yield
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def get_device_stats(self, device: Union[str, torch.device]) -> Dict[str, Any]:
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"""Gets stats for a given device.
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Args:
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device: device for which to get stats
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Returns:
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Dictionary of device stats
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"""
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raise NotImplementedError
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def on_train_start(self) -> None:
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"""Called when train begins."""
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return self.training_type_plugin.on_train_start()
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@staticmethod
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@abstractmethod
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def auto_device_count() -> int:
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"""Get the devices when set to auto."""
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