278 lines
10 KiB
Python
278 lines
10 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 functools import partial
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from typing import Any, Callable, Dict, Generator, List, Optional, Tuple, Union
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import torch
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from torch import Tensor
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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.core.hooks import CheckpointHooks
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from pytorch_lightning.utilities import grad_norm, GradClipAlgorithmType
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from pytorch_lightning.utilities.types import _PARAMETERS
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class PrecisionPlugin(CheckpointHooks):
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"""Base class for all plugins handling the precision-specific parts of the training.
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The class attribute precision must be overwritten in child classes. The default value reflects fp32 training.
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"""
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precision: Union[str, int] = 32
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def main_params(self, optimizer: Optimizer) -> _PARAMETERS:
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"""The main params of the model.
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Returns the plain model params here. Maybe different in other precision plugins.
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"""
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for group in optimizer.param_groups:
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yield from group["params"]
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def connect(
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self, model: Module, optimizers: List[Optimizer], lr_schedulers: List[Any]
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) -> Tuple[Module, List[Optimizer], List[Any]]:
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"""Connects this plugin to the accelerator and the training process."""
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return model, optimizers, lr_schedulers
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def pre_backward(self, model: "pl.LightningModule", closure_loss: Tensor) -> Tensor:
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"""Run before precision plugin executes backward.
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Args:
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model: the model to be optimized
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closure_loss: the loss value obtained from the closure
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"""
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model.trainer._call_callback_hooks("on_before_backward", closure_loss)
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model.trainer._call_lightning_module_hook("on_before_backward", closure_loss)
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return closure_loss
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def backward(
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self,
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model: "pl.LightningModule",
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closure_loss: Tensor,
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optimizer: Optional[Optimizer],
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*args: Any,
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**kwargs: Any,
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) -> None:
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"""Performs the actual backpropagation.
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Args:
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model: the model to be optimized
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closure_loss: the loss value obtained from the closure
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optimizer: current optimizer being used. ``None`` if using manual optimization
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"""
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# do backward pass
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if model is not None and isinstance(model, pl.LightningModule):
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model.backward(closure_loss, optimizer, *args, **kwargs)
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else:
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self._run_backward(closure_loss, *args, **kwargs)
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def post_backward(self, model: "pl.LightningModule", closure_loss: Tensor) -> Tensor:
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"""Run after precision plugin executes backward.
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Args:
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model: the model to be optimized
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closure_loss: the loss value obtained from the closure
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"""
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# once backward has been applied, release graph
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closure_loss = closure_loss.detach()
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model.trainer._call_callback_hooks("on_after_backward")
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model.trainer._call_lightning_module_hook("on_after_backward")
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return closure_loss
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def _run_backward(self, tensor: Tensor, model: Optional[Module], *args: Any, **kwargs: Any) -> None:
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"""Lightning-independent backward logic.
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Currently only used by Lightning Lite. Subject to further refactors.
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"""
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tensor.backward(*args, **kwargs)
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def _after_closure(
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self, model: Union["pl.LightningModule", Module], optimizer: Optimizer, optimizer_idx: int
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) -> None:
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"""Utility to share some code after the closure has been run."""
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if not isinstance(model, pl.LightningModule):
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# none of this applies to Lite
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return
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trainer = model.trainer
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assert trainer is not None
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trainer._call_callback_hooks("on_before_optimizer_step", optimizer, optimizer_idx)
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trainer._call_lightning_module_hook("on_before_optimizer_step", optimizer, optimizer_idx)
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# TODO: this is done for the entire model but should be changed to per-optimizer
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if optimizer_idx == 0:
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self._track_grad_norm(trainer)
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self._clip_gradients(
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model,
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optimizer,
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optimizer_idx,
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trainer.gradient_clip_val,
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gradient_clip_algorithm=trainer.gradient_clip_algorithm,
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)
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def _wrap_closure(
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self,
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model: "pl.LightningModule",
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optimizer: Optimizer,
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optimizer_idx: int,
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closure: Callable[[], Any],
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) -> Any:
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"""This double-closure allows makes sure the ``closure`` is executed before the
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``on_before_optimizer_step`` hook is called.
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The closure (generally) runs ``backward`` so this allows inspecting gradients in this hook. This structure is
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consistent with the ``PrecisionPlugin`` subclasses that cannot pass ``optimizer.step(closure)`` directly.
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"""
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closure_result = closure()
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self._after_closure(model, optimizer, optimizer_idx)
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return closure_result
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def optimizer_step(
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self,
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model: Union["pl.LightningModule", Module],
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optimizer: Optimizer,
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optimizer_idx: int,
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closure: Callable[[], Any],
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**kwargs: Any,
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) -> Any:
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"""Hook to run the optimizer step."""
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if isinstance(model, pl.LightningModule):
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closure = partial(self._wrap_closure, model, optimizer, optimizer_idx, closure)
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return optimizer.step(closure=closure, **kwargs)
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def _track_grad_norm(self, trainer: "pl.Trainer") -> None:
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if trainer.track_grad_norm == -1:
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return
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kwargs = {}
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if len(trainer.loggers) == 1:
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kwargs["group_separator"] = trainer.loggers[0].group_separator
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grad_norm_dict = grad_norm(trainer.lightning_module, trainer.track_grad_norm, **kwargs)
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if grad_norm_dict:
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prev_fx = trainer.lightning_module._current_fx_name
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trainer.lightning_module._current_fx_name = "on_before_optimizer_step"
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trainer.lightning_module.log_grad_norm(grad_norm_dict)
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trainer.lightning_module._current_fx_name = prev_fx
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def _clip_gradients(
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self,
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model: Union["pl.LightningModule", Module],
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optimizer: Optimizer,
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optimizer_idx: int,
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clip_val: Optional[Union[int, float]] = None,
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gradient_clip_algorithm: Optional[GradClipAlgorithmType] = None,
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) -> None:
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if not isinstance(model, pl.LightningModule) or not model.automatic_optimization:
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# the configuration validator disallows clipping on manual
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return
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model.configure_gradient_clipping(
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optimizer,
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optimizer_idx,
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gradient_clip_val=clip_val,
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gradient_clip_algorithm=gradient_clip_algorithm,
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)
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def clip_gradients(
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self,
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optimizer: Optimizer,
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clip_val: Union[int, float] = 0.0,
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gradient_clip_algorithm: GradClipAlgorithmType = GradClipAlgorithmType.NORM,
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) -> None:
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"""Clips the gradients."""
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if clip_val <= 0:
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return
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if gradient_clip_algorithm == GradClipAlgorithmType.VALUE:
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self.clip_grad_by_value(optimizer, clip_val)
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elif gradient_clip_algorithm == GradClipAlgorithmType.NORM:
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self.clip_grad_by_norm(optimizer, clip_val)
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def clip_grad_by_value(self, optimizer: Optimizer, clip_val: Union[int, float]) -> None:
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"""Clip gradients by value."""
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parameters = self.main_params(optimizer)
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torch.nn.utils.clip_grad_value_(parameters, clip_value=clip_val)
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def clip_grad_by_norm(self, optimizer: Optimizer, clip_val: Union[int, float]) -> None:
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"""Clip gradients by norm."""
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parameters = self.main_params(optimizer)
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torch.nn.utils.clip_grad_norm_(parameters, clip_val)
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def dispatch(self, trainer: "pl.Trainer") -> None:
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"""Hook to do something when ``Strategy.dispatch()`` gets called."""
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@contextlib.contextmanager
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def forward_context(self) -> Generator[None, None, None]:
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"""A contextmanager for managing model forward/training_step/evaluation_step/predict_step."""
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yield
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@contextlib.contextmanager
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def train_step_context(self) -> Generator[None, None, None]:
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"""A contextmanager for the training step."""
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with self.forward_context():
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yield
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@contextlib.contextmanager
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def val_step_context(self) -> Generator[None, None, None]:
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"""A contextmanager for the validation step."""
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with self.forward_context():
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yield
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@contextlib.contextmanager
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def test_step_context(self) -> Generator[None, None, None]:
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"""A contextmanager for the test step."""
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with self.forward_context():
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yield
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@contextlib.contextmanager
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def predict_step_context(self) -> Generator[None, None, None]:
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"""A contextmanager for the predict step."""
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with self.forward_context():
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yield
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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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def state_dict(self) -> Dict[str, Any]:
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"""Called when saving a checkpoint, implement to generate precision plugin state_dict.
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Returns:
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A dictionary containing precision plugin state.
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"""
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return {}
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def load_state_dict(self, state_dict: Dict[str, Any]) -> None:
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"""Called when loading a checkpoint, implement to reload precision plugin state given precision plugin
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state_dict.
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Args:
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state_dict: the precision plugin state returned by ``state_dict``.
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"""
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pass
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def on_save_checkpoint(self, checkpoint: Dict[str, Any]) -> None:
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"""``PrecisionPlugin.on_save_checkpoint`` was deprecated in v1.6 and will be removed in v1.8.
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Use ``state_dict`` instead.
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"""
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def on_load_checkpoint(self, checkpoint: Dict[str, Any]) -> None:
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"""``PrecisionPlugin.on_load_checkpoint`` was deprecated in v1.6 and will be removed in v1.8.
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Use ``load_state_dict`` instead.
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"""
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