Source code for pytorch_lightning.plugins.precision.precision_plugin

# Copyright The PyTorch Lightning 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.
import contextlib
from functools import partial
from typing import Any, Callable, Generator, List, Optional, Tuple, Union

import torch
from torch import Tensor
from torch.nn import Module
from torch.optim import Optimizer

import pytorch_lightning as pl
from lightning_lite.plugins import Precision as LitePrecision
from lightning_lite.utilities.types import Steppable
from pytorch_lightning.core.hooks import CheckpointHooks
from pytorch_lightning.utilities import grad_norm, GradClipAlgorithmType

[docs]class PrecisionPlugin(LitePrecision, CheckpointHooks): """Base class for all plugins handling the precision-specific parts of the training. The class attribute precision must be overwritten in child classes. The default value reflects fp32 training. """ precision: Union[str, int] = 32
[docs] def connect( self, model: Module, optimizers: List[Optimizer], lr_schedulers: List[Any] ) -> Tuple[Module, List[Optimizer], List[Any]]: """Connects this plugin to the accelerator and the training process.""" return model, optimizers, lr_schedulers
[docs] def pre_backward(self, tensor: Tensor, module: "pl.LightningModule") -> Tensor: # type: ignore[override] module.trainer._call_callback_hooks("on_before_backward", tensor) module.trainer._call_lightning_module_hook("on_before_backward", tensor) return tensor
[docs] def backward( # type: ignore[override] self, tensor: Tensor, model: "pl.LightningModule", optimizer: Optional[Steppable], optimizer_idx: Optional[int], *args: Any, **kwargs: Any, ) -> None: r"""Performs the actual backpropagation. Args: tensor: the loss value obtained from the closure model: the model to be optimized optimizer: current optimizer being used. ``None`` if using manual optimization optimizer_idx: the index of the current optimizer. ``None`` if using manual optimization \*args: Positional arguments intended for the actual function that performs the backward, like :meth:`~torch.Tensor.backward`. \**kwargs: Keyword arguments for the same purpose as ``*args``. """ model.backward(tensor, optimizer, optimizer_idx, *args, **kwargs)
[docs] def post_backward(self, tensor: Tensor, module: "pl.LightningModule") -> Tensor: # type: ignore[override] # once backward has been applied, release graph closure_loss = tensor.detach() module.trainer._call_callback_hooks("on_after_backward") module.trainer._call_lightning_module_hook("on_after_backward") return closure_loss
def _after_closure(self, model: "pl.LightningModule", optimizer: Steppable, optimizer_idx: int) -> None: """Utility to share some code after the closure has been run.""" trainer = model.trainer trainer._call_callback_hooks("on_before_optimizer_step", optimizer, optimizer_idx) trainer._call_lightning_module_hook("on_before_optimizer_step", optimizer, optimizer_idx) # TODO: this is done for the entire model but should be changed to per-optimizer if optimizer_idx == 0: self._track_grad_norm(trainer) self._clip_gradients( model, optimizer, optimizer_idx, trainer.gradient_clip_val, gradient_clip_algorithm=trainer.gradient_clip_algorithm, ) def _wrap_closure( self, model: "pl.LightningModule", optimizer: Optimizer, optimizer_idx: int, closure: Callable[[], Any], ) -> Any: """This double-closure allows makes sure the ``closure`` is executed before the ``on_before_optimizer_step`` hook is called. The closure (generally) runs ``backward`` so this allows inspecting gradients in this hook. This structure is consistent with the ``PrecisionPlugin`` subclasses that cannot pass ``optimizer.step(closure)`` directly. """ closure_result = closure() self._after_closure(model, optimizer, optimizer_idx) return closure_result
[docs] def optimizer_step( # type: ignore[override] self, optimizer: Steppable, model: "pl.LightningModule", optimizer_idx: int, closure: Callable[[], Any], **kwargs: Any, ) -> Any: """Hook to run the optimizer step.""" closure = partial(self._wrap_closure, model, optimizer, optimizer_idx, closure) return optimizer.step(closure=closure, **kwargs)
def _track_grad_norm(self, trainer: "pl.Trainer") -> None: if trainer.track_grad_norm == -1: return kwargs = {} if len(trainer.loggers) == 1: kwargs["group_separator"] = trainer.loggers[0].group_separator grad_norm_dict = grad_norm(trainer.lightning_module, trainer.track_grad_norm, **kwargs) if grad_norm_dict: prev_fx = trainer.lightning_module._current_fx_name trainer.lightning_module._current_fx_name = "on_before_optimizer_step" trainer.lightning_module.log_grad_norm(grad_norm_dict) trainer.lightning_module._current_fx_name = prev_fx def _clip_gradients( self, model: Union["pl.LightningModule", Module], optimizer: Steppable, optimizer_idx: int, clip_val: Optional[Union[int, float]] = None, gradient_clip_algorithm: Optional[GradClipAlgorithmType] = None, ) -> None: if not isinstance(model, pl.LightningModule) or not model.automatic_optimization: # the configuration validator disallows clipping on manual return model.trainer._call_lightning_module_hook( "configure_gradient_clipping", optimizer, optimizer_idx, gradient_clip_val=clip_val, gradient_clip_algorithm=gradient_clip_algorithm, )
[docs] def clip_gradients( self, optimizer: Optimizer, clip_val: Union[int, float] = 0.0, gradient_clip_algorithm: GradClipAlgorithmType = GradClipAlgorithmType.NORM, ) -> None: """Clips the gradients.""" if clip_val <= 0: return if gradient_clip_algorithm == GradClipAlgorithmType.VALUE: self.clip_grad_by_value(optimizer, clip_val) elif gradient_clip_algorithm == GradClipAlgorithmType.NORM: self.clip_grad_by_norm(optimizer, clip_val)
[docs] def clip_grad_by_value(self, optimizer: Optimizer, clip_val: Union[int, float]) -> None: """Clip gradients by value.""" parameters = self.main_params(optimizer) torch.nn.utils.clip_grad_value_(parameters, clip_value=clip_val)
[docs] def clip_grad_by_norm(self, optimizer: Optimizer, clip_val: Union[int, float]) -> None: """Clip gradients by norm.""" parameters = self.main_params(optimizer) torch.nn.utils.clip_grad_norm_(parameters, clip_val)
[docs] def dispatch(self, trainer: "pl.Trainer") -> None: """Hook to do something when ``Strategy.dispatch()`` gets called."""
[docs] @contextlib.contextmanager def train_step_context(self) -> Generator[None, None, None]: """A contextmanager for the training step.""" with self.forward_context(): yield
[docs] @contextlib.contextmanager def val_step_context(self) -> Generator[None, None, None]: """A contextmanager for the validation step.""" with self.forward_context(): yield
[docs] @contextlib.contextmanager def test_step_context(self) -> Generator[None, None, None]: """A contextmanager for the test step.""" with self.forward_context(): yield
[docs] @contextlib.contextmanager def predict_step_context(self) -> Generator[None, None, None]: """A contextmanager for the predict step.""" with self.forward_context(): yield

© Copyright Copyright (c) 2018-2023, Lightning AI et al...

Built with Sphinx using a theme provided by Read the Docs.