80 lines
3.0 KiB
Python
80 lines
3.0 KiB
Python
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# 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 contextlib import contextmanager
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from typing import Generator
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import torch
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from pytorch_lightning.core import LightningModule
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from pytorch_lightning.plugins.precision.mixed import MixedPrecisionPlugin
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from pytorch_lightning.utilities import AMPType
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from pytorch_lightning.utilities.exceptions import MisconfigurationException
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class NativeMixedPrecisionPlugin(MixedPrecisionPlugin):
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def __init__(self):
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self.backend = AMPType.NATIVE
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self.scaler = torch.cuda.amp.GradScaler()
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def pre_optimizer_step(self, optimizer: torch.optim.Optimizer, optimizer_idx: int) -> None:
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"""always called before the optimizer step.
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Checks that the optimizer is not LBFGS, as this one is not supported by native amp
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"""
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if isinstance(optimizer, torch.optim.LBFGS):
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raise MisconfigurationException(
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f"native PyTorch amp and lbfgs are not compatible (optimizer {optimizer_idx})."
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" To request, please file a Github issue in PyTorch and tag @mcarilli"
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)
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def post_optimizer_step(self, optimizer: torch.optim.Optimizer, optimizer_idx: int) -> None:
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"""Updates the GradScaler"""
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self.scaler.update()
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def backward(
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self,
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model: LightningModule,
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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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"""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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closure_loss = self.scaler.scale(closure_loss)
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automatic_optimization = model.automatic_optimization
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closure_loss = super().backward(model, closure_loss, optimizer, opt_idx, should_accumulate, *args, **kwargs)
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# unscale gradient to allow analyze within `on_after_backward`
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if not should_accumulate and automatic_optimization:
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self.scaler.unscale_(optimizer)
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return closure_loss
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@contextmanager
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def train_step_context(self) -> Generator[torch.cuda.amp.autocast, None, None]:
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"""Enable autocast context"""
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yield torch.cuda.amp.autocast()
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