103 lines
4.0 KiB
Python
103 lines
4.0 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 contextlib import contextmanager
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from typing import Any, cast, Generator, List, Tuple
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import torch
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import torch.nn as nn
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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.overrides.base import _LightningPrecisionModuleWrapperBase
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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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class LightningDoublePrecisionModule(_LightningPrecisionModuleWrapperBase):
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"""LightningModule wrapper which converts incoming floating point data in ``*_step`` and ``forward`` to double
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(``torch.float64``) precision.
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Args:
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pl_module: the model to wrap
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"""
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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, LightningDoublePrecisionModule._to_double_precision)
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def training_step(self, *args: Any, **kwargs: Any) -> Any:
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return self.module.training_step(
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*LightningDoublePrecisionModule._move_float_tensors_to_double(args),
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**LightningDoublePrecisionModule._move_float_tensors_to_double(kwargs),
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)
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def validation_step(self, *args: Any, **kwargs: Any) -> Any:
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return self.module.validation_step(
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*LightningDoublePrecisionModule._move_float_tensors_to_double(args),
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**LightningDoublePrecisionModule._move_float_tensors_to_double(kwargs),
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)
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def test_step(self, *args: Any, **kwargs: Any) -> Any:
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return self.module.test_step(
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*LightningDoublePrecisionModule._move_float_tensors_to_double(args),
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**LightningDoublePrecisionModule._move_float_tensors_to_double(kwargs),
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)
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def predict_step(self, *args: Any, **kwargs: Any) -> Any:
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return self.module.predict_step(
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*LightningDoublePrecisionModule._move_float_tensors_to_double(args),
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**LightningDoublePrecisionModule._move_float_tensors_to_double(kwargs),
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)
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def forward(self, *args: Any, **kwargs: Any) -> Any:
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return self.module(
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*LightningDoublePrecisionModule._move_float_tensors_to_double(args),
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**LightningDoublePrecisionModule._move_float_tensors_to_double(kwargs),
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)
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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 connect(
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self, model: nn.Module, optimizers: List[Optimizer], lr_schedulers: List[Any]
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) -> Tuple[nn.Module, List["Optimizer"], List[Any]]:
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"""Converts the model to double precision and wraps it in a ``LightningDoublePrecisionModule`` to convert
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incoming floating point data to double (``torch.float64``) precision.
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Does not alter `optimizers` or `lr_schedulers`.
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"""
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model = cast(pl.LightningModule, model.double())
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model = LightningDoublePrecisionModule(model)
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return super().connect(model, optimizers, lr_schedulers)
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@contextmanager
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def forward_context(self) -> Generator[None, None, None]:
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"""A context manager to change the default tensor type.
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See: :meth:`torch.set_default_tensor_type`
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"""
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torch.set_default_tensor_type(torch.DoubleTensor)
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yield
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torch.set_default_tensor_type(torch.FloatTensor)
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