94 lines
3.7 KiB
Python
94 lines
3.7 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 functools import wraps
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from typing import Any, List, Sequence, Tuple, TYPE_CHECKING
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import torch
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from pytorch_lightning.core.lightning import LightningModule
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from pytorch_lightning.plugins.precision.precision_plugin import PrecisionPlugin
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from pytorch_lightning.utilities.apply_func import apply_to_collection
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if TYPE_CHECKING:
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from torch.nn import Module
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from torch.optim import Optimizer
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class _DoublePrecisionPatch:
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"""Class to handle patching of methods in the ``LightningModule`` and subsequent teardown."""
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def __init__(self, model: 'Module', method_name: str, old_method: Any) -> None:
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self.model = model
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self.method_name = method_name
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self.old_method = old_method
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def teardown(self) -> None:
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setattr(self.model, self.method_name, self.old_method)
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@staticmethod
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def _to_double_precision(data: torch.Tensor) -> torch.Tensor:
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if data.is_floating_point():
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return data.double()
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return data
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@staticmethod
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def _move_float_tensors_to_double(collection: Any) -> Any:
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return apply_to_collection(collection, torch.Tensor, function=_DoublePrecisionPatch._to_double_precision)
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@classmethod
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def patch(cls, model: 'Module', method_name: str) -> '_DoublePrecisionPatch':
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old_method = getattr(model, method_name)
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@wraps(old_method)
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def new_method(*args: Any, **kwargs: Any) -> Any:
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return old_method(
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*_DoublePrecisionPatch._move_float_tensors_to_double(args),
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**_DoublePrecisionPatch._move_float_tensors_to_double(kwargs)
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)
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setattr(model, method_name, new_method if callable(old_method) else old_method)
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return cls(model, method_name, old_method)
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class DoublePrecisionPlugin(PrecisionPlugin):
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"""Plugin for training with double (``torch.float64``) precision."""
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precision: int = 64
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def __init__(self) -> None:
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self.patches: List[_DoublePrecisionPatch] = []
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def connect(
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self,
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model: 'Module',
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optimizers: Sequence['Optimizer'],
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lr_schedulers: Sequence[Any],
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) -> Tuple['Module', Sequence['Optimizer'], Sequence[Any]]:
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"""Converts the model to double precision and wraps the `training_step`, `validation_step`, `test_step`,
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`predict_step`, and `forward` methods to convert incoming floating point data to double. Does not alter
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`optimizers` or `lr_schedulers`."""
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model = model.to(dtype=torch.float64)
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if isinstance(model, LightningModule):
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self.patches.append(_DoublePrecisionPatch.patch(model, 'training_step'))
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self.patches.append(_DoublePrecisionPatch.patch(model, 'validation_step'))
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self.patches.append(_DoublePrecisionPatch.patch(model, 'test_step'))
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self.patches.append(_DoublePrecisionPatch.patch(model, 'predict_step'))
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self.patches.append(_DoublePrecisionPatch.patch(model, 'forward'))
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return super().connect(model, optimizers, lr_schedulers)
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def post_dispatch(self) -> None:
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while len(self.patches) > 0:
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self.patches.pop().teardown()
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