130 lines
4.4 KiB
Python
130 lines
4.4 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, 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.plugins.base_plugin import Plugin
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from pytorch_lightning.utilities import GradClipAlgorithmType
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from pytorch_lightning.utilities.types import _PARAMETERS
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class PrecisionPlugin(Plugin):
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"""
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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.
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The default value reflects fp32 training.
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"""
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precision: Union[str, int] = 32
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def master_params(self, optimizer: Optimizer) -> _PARAMETERS:
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"""
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The master params of the model. Returns the plain model params here.
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Maybe different in other precision plugins.
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"""
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for group in optimizer.param_groups:
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for p in group["params"]:
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yield p
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def connect(
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self,
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model: Module,
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optimizers: List[Optimizer],
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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 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: Optimizer,
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opt_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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) -> Tensor:
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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: the optimizer to perform the step lateron
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opt_idx: the optimizer's index
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should_accumulate: whether to accumulate gradients or not
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"""
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automatic_optimization = model.automatic_optimization
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# do backward pass
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if automatic_optimization:
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model.backward(closure_loss, optimizer, opt_idx)
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else:
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closure_loss.backward(*args, **kwargs)
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# once backward has been applied, release graph
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closure_loss = closure_loss.detach()
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return closure_loss
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def pre_optimizer_step(
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self,
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pl_module: 'pl.LightningModule',
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optimizer: Optimizer,
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optimizer_idx: int,
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lambda_closure: Callable,
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**kwargs: Any,
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) -> bool:
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"""Hook to do something before each optimizer step."""
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return True
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def post_optimizer_step(self, optimizer: Optimizer, optimizer_idx: int) -> None:
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"""Hook to do something after each optimizer step."""
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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],
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gradient_clip_algorithm: GradClipAlgorithmType = GradClipAlgorithmType.NORM,
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model: Optional[Module] = None
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) -> None:
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"""Clips the gradients"""
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if clip_val is None:
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return
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clip_val = float(clip_val)
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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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# TODO: there should be a mechanism to set `norm_type`
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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.master_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.master_params(optimizer)
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torch.nn.utils.clip_grad_norm_(parameters, clip_val)
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