48 lines
2.0 KiB
Python
48 lines
2.0 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
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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.precision_plugin import PrecisionPlugin
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from pytorch_lightning.utilities import _XLA_AVAILABLE
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from pytorch_lightning.utilities.exceptions import MisconfigurationException
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if _XLA_AVAILABLE:
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import torch_xla.core.xla_model as xm
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class TPUPrecisionPlugin(PrecisionPlugin):
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def pre_optimizer_step(
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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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lambda_closure: Callable[[], Any],
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**kwargs: Any,
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) -> bool:
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super().pre_optimizer_step(model, optimizer, optimizer_idx, lambda_closure, **kwargs)
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closure_result = xm.optimizer_step(optimizer, optimizer_args={"closure": lambda_closure, **kwargs})
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skipped_backward = closure_result is None
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# in manual optimization, the closure does not return a value
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if model.automatic_optimization and skipped_backward:
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# we lack coverage here so disable this - something to explore if there's demand
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raise MisconfigurationException(
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"Skipping backward by returning `None` from your `training_step` is not implemented for TPUs."
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" Please, open an issue in `https://github.com/PyTorchLightning/pytorch-lightning/issues`"
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" requesting this feature."
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)
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return False
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