73 lines
2.9 KiB
Python
73 lines
2.9 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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import torch
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from torch.nn import DataParallel
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from torch.nn.parallel import DistributedDataParallel
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from pytorch_lightning.core.lightning import LightningModule
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from pytorch_lightning.utilities.device_dtype_mixin import DeviceDtypeModuleMixin
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class _LightningModuleWrapperBase(DeviceDtypeModuleMixin, torch.nn.Module):
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def __init__(self, pl_module: LightningModule):
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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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def forward(self, *inputs, **kwargs):
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trainer = self.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_plugin ``post_training_step`` hook
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if not self.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):
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pass
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def unwrap_lightning_module(wrapped_model) -> LightningModule:
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model = wrapped_model
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if isinstance(model, (DistributedDataParallel, DataParallel)):
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model = model.module
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if isinstance(model, _LightningModuleWrapperBase):
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model = model.module
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return model
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