121 lines
4.9 KiB
Python
121 lines
4.9 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, Union
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import torch
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import torch.nn as nn
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from torch.nn import DataParallel
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from torch.nn.parallel import DistributedDataParallel
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import pytorch_lightning as pl
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from pytorch_lightning.core.mixins import DeviceDtypeModuleMixin
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class _LightningPrecisionModuleWrapperBase(DeviceDtypeModuleMixin, torch.nn.Module):
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def __init__(self, pl_module: "pl.LightningModule") -> None:
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"""Wraps the user's LightningModule. Requires overriding all ``*_step`` methods and ``forward`` so that it
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can safely be wrapped by a ``_LightningModuleWrapperBase`` and a ``*DataParallel``.
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Args:
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pl_module: the model to wrap
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"""
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super().__init__()
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self.module = pl_module
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# set the parameters_to_ignore from LightningModule.
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self._ddp_params_and_buffers_to_ignore = getattr(pl_module, "_ddp_params_and_buffers_to_ignore", [])
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def training_step(self, *args: Any, **kwargs: Any) -> Any:
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raise NotImplementedError
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def validation_step(self, *args: Any, **kwargs: Any) -> Any:
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raise NotImplementedError
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def test_step(self, *args: Any, **kwargs: Any) -> Any:
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raise NotImplementedError
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def predict_step(self, *args: Any, **kwargs: Any) -> Any:
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raise NotImplementedError
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def forward(self, *args: Any, **kwargs: Any) -> Any:
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raise NotImplementedError
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def on_post_move_to_device(self) -> None:
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pass
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class _LightningModuleWrapperBase(DeviceDtypeModuleMixin, torch.nn.Module):
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def __init__(self, pl_module: Union["pl.LightningModule", _LightningPrecisionModuleWrapperBase]):
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"""
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Wraps the user's LightningModule and redirects the forward call to the appropriate
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method, either ``training_step``, ``validation_step`` or ``test_step``.
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If the LightningModule is in none of the states `training`, `testing` or `validation`,
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the inputs will be redirected to the
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:meth:`~pytorch_lightning.core.lightning.LightningModule.predict` method.
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Inheriting classes may also modify the inputs or outputs of forward.
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Args:
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pl_module: the model to wrap
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"""
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super().__init__()
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self.module = pl_module
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# set the parameters_to_ignore from LightningModule.
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self._ddp_params_and_buffers_to_ignore = getattr(pl_module, "_ddp_params_and_buffers_to_ignore", [])
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def forward(self, *inputs: Any, **kwargs: Any) -> Any:
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lightning_module = unwrap_lightning_module(self.module)
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trainer = lightning_module.trainer
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if trainer and trainer.training:
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output = self.module.training_step(*inputs, **kwargs)
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# In manual_optimization, we need to prevent DDP reducer as
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# it is done manually in `LightningModule.manual_backward`
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# `require_backward_grad_sync` will be reset in the
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# ddp_strategy `post_training_step` hook
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if not lightning_module.automatic_optimization:
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trainer.model.require_backward_grad_sync = False
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elif trainer and trainer.testing:
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output = self.module.test_step(*inputs, **kwargs)
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elif trainer and (trainer.sanity_checking or trainer.validating):
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output = self.module.validation_step(*inputs, **kwargs)
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elif trainer and trainer.predicting:
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output = self.module.predict_step(*inputs, **kwargs)
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else:
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output = self.module(*inputs, **kwargs)
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return output
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def on_post_move_to_device(self) -> None:
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pass
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def unwrap_lightning_module(wrapped_model: nn.Module) -> "pl.LightningModule":
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"""Recursively unwraps a :class:`~pytorch_lightning.core.lightning.LightningModule` by following the
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``.module`` attributes on the wrapper.
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Raises:
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TypeError: If the unwrapping leads to a module that is not a LightningModule and that cannot be unwrapped
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further.
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"""
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model = wrapped_model
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if isinstance(model, (DistributedDataParallel, DataParallel)):
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model = unwrap_lightning_module(model.module)
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if isinstance(model, (_LightningModuleWrapperBase, _LightningPrecisionModuleWrapperBase)):
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model = unwrap_lightning_module(model.module)
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if not isinstance(model, pl.LightningModule):
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raise TypeError(f"Unwrapping the module did not yield a `LightningModule`, got {type(model)} instead.")
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return model
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