119 lines
4.5 KiB
Python
119 lines
4.5 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 numbers
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import warnings
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from typing import Any
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import torch
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import pytorch_lightning as pl
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from pytorch_lightning.overrides.base import _LightningModuleWrapperBase
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from pytorch_lightning.utilities import rank_zero_warn
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from pytorch_lightning.utilities.apply_func import apply_to_collection
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def _ignore_scalar_return_in_dp():
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# Users get confused by this warning so we silence it
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warnings.filterwarnings(
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"ignore",
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message=(
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"Was asked to gather along dimension 0, but all input tensors were scalars;"
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" will instead unsqueeze and return a vector."
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),
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)
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class LightningParallelModule(_LightningModuleWrapperBase):
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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``, ``test_step`` or ``predict``.
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This class is used in combination with :class:`~torch.nn.parallel.DataParallel` as
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shown in the example. It also takes care of converting Python scalars to Tensors and
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un-squeezes 0-dimensional Tensors as it is required by :class:`~torch.nn.parallel.DataParallel`.
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Example:
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dp_model = torch.nn.DataParallel(
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module=LightningParallelModule(lightning_module),
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device_ids=[3, 4],
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...
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)
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Args:
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pl_module: the model to wrap
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"""
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def __init__(self, pl_module: "pl.LightningModule") -> None:
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super().__init__(pl_module)
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_ignore_scalar_return_in_dp()
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def forward(self, *inputs, **kwargs):
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self.update_replica_device_attributes(inputs)
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# forward call will redirect to training_step, validation_step, etc.
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output = super().forward(*inputs, **kwargs)
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def output_transform(data: Any):
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data = python_scalar_to_tensor(data, self.module.device)
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data = unsqueeze_scalar_tensor(data)
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return data
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output = apply_to_collection(output, dtype=(numbers.Number, torch.Tensor), function=output_transform)
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return output
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def update_replica_device_attributes(self, inputs: Any) -> None:
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"""
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Updates the device information of LightningModule by reading the device from the inputs.
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In :class:`~torch.nn.data_parallel.DataParallel` changes to the state during the `forward` pass
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are lost when the replicas get discarded. The only way to know the current device is from the
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inputs passed into the model.
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Args:
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inputs: A collection of inputs (typically a tuple). If the inputs don't contain tensors,
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a warning is shown that accessing ``self.device`` will not return the correct device.
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"""
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replica_device = None
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def find_tensor_with_device(tensor: torch.Tensor) -> torch.Tensor:
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nonlocal replica_device
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if replica_device is None and tensor.device != torch.device("cpu"):
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replica_device = tensor.device
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return tensor
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apply_to_collection(inputs, dtype=torch.Tensor, function=find_tensor_with_device)
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if replica_device is not None:
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# by calling .to() we force the update to the self.device property
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self.module.to(device=replica_device)
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else:
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rank_zero_warn(
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"Could not determine on which device the inputs are."
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" When using DataParallel (accelerator='dp'), be aware that in case you are using self.device"
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" in your code, it will reference only the root device."
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)
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def python_scalar_to_tensor(data: Any, device: torch.device = torch.device("cpu")) -> Any:
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"""Converts a Python scalar number to a torch tensor and places it on the given device."""
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if isinstance(data, numbers.Number):
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data = torch.tensor([data], device=device)
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return data
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def unsqueeze_scalar_tensor(data: Any) -> Any:
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"""Un-squeezes a 0-dim tensor."""
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if isinstance(data, torch.Tensor) and data.dim() == 0:
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data = data.unsqueeze(0)
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return data
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