676 lines
22 KiB
Python
676 lines
22 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, Dict, List, Union
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import torch
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from pytorch_lightning.utilities import AMPType, move_data_to_device, rank_zero_warn
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from torch import Tensor
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from torch.optim.optimizer import Optimizer
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from torch.utils.data import DataLoader
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try:
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from apex import amp
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except ImportError:
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amp = None
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class ModelHooks:
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def setup(self, stage: str):
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"""
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Called at the beginning of fit and test.
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This is a good hook when you need to build models dynamically or adjust something about them.
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This hook is called on every process when using DDP.
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Args:
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stage: either 'fit' or 'test'
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Example::
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class LitModel(...):
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def __init__(self):
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self.l1 = None
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def prepare_data(self):
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download_data()
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tokenize()
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# don't do this
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self.something = else
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def setup(stage):
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data = Load_data(...)
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self.l1 = nn.Linear(28, data.num_classes)
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"""
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def teardown(self, stage: str):
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"""
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Called at the end of fit and test.
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Args:
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stage: either 'fit' or 'test'
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"""
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def on_fit_start(self):
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"""
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Called at the very beginning of fit.
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If on DDP it is called on every process
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"""
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def on_fit_end(self):
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"""
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Called at the very end of fit.
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If on DDP it is called on every process
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"""
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def on_train_start(self) -> None:
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"""
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Called at the beginning of training before sanity check.
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"""
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# do something at the start of training
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def on_train_end(self) -> None:
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"""
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Called at the end of training before logger experiment is closed.
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"""
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# do something at the end of training
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def on_pretrain_routine_start(self) -> None:
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"""
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Called at the beginning of the pretrain routine (between fit and train start).
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- fit
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- pretrain_routine start
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- pretrain_routine end
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- training_start
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"""
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# do something at the start of the pretrain routine
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def on_pretrain_routine_end(self) -> None:
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"""
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Called at the end of the pretrain routine (between fit and train start).
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- fit
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- pretrain_routine start
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- pretrain_routine end
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- training_start
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"""
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# do something at the end of the pretrain routine
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def on_train_batch_start(
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self, batch: Any, batch_idx: int, dataloader_idx: int
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) -> None:
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"""
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Called in the training loop before anything happens for that batch.
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If you return -1 here, you will skip training for the rest of the current epoch.
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Args:
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batch: The batched data as it is returned by the training DataLoader.
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batch_idx: the index of the batch
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dataloader_idx: the index of the dataloader
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"""
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# do something when the batch starts
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def on_train_batch_end(
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self, batch: Any, batch_idx: int, dataloader_idx: int
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) -> None:
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"""
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Called in the training loop after the batch.
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Args:
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batch: The batched data as it is returned by the training DataLoader.
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batch_idx: the index of the batch
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dataloader_idx: the index of the dataloader
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"""
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# do something when the batch ends
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def on_validation_model_eval(
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self
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) -> None:
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"""
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Sets the model to eval during the val loop
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"""
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self.eval()
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def on_validation_model_train(
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self
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) -> None:
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"""
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Sets the model to train during the val loop
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"""
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self.train()
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def on_validation_batch_start(
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self, batch: Any, batch_idx: int, dataloader_idx: int
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) -> None:
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"""
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Called in the validation loop before anything happens for that batch.
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Args:
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batch: The batched data as it is returned by the training DataLoader.
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batch_idx: the index of the batch
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dataloader_idx: the index of the dataloader
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"""
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# do something when the batch starts
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def on_validation_batch_end(
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self, batch: Any, batch_idx: int, dataloader_idx: int
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) -> None:
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"""
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Called in the validation loop after the batch.
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Args:
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batch: The batched data as it is returned by the training DataLoader.
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batch_idx: the index of the batch
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dataloader_idx: the index of the dataloader
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"""
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# do something when the batch ends
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def on_test_batch_start(
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self, batch: Any, batch_idx: int, dataloader_idx: int
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) -> None:
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"""
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Called in the test loop before anything happens for that batch.
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Args:
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batch: The batched data as it is returned by the training DataLoader.
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batch_idx: the index of the batch
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dataloader_idx: the index of the dataloader
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"""
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# do something when the batch starts
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def on_test_batch_end(
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self, batch: Any, batch_idx: int, dataloader_idx: int
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) -> None:
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"""
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Called in the test loop after the batch.
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Args:
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batch: The batched data as it is returned by the training DataLoader.
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batch_idx: the index of the batch
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dataloader_idx: the index of the dataloader
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"""
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# do something when the batch ends
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def on_test_model_eval(
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self
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) -> None:
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"""
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Sets the model to eval during the test loop
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"""
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self.eval()
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def on_test_model_train(
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self
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) -> None:
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"""
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Sets the model to train during the test loop
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"""
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self.train()
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def on_batch_start(self, batch: Any) -> None:
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"""
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Called in the training loop before anything happens for that batch.
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If you return -1 here, you will skip training for the rest of the current epoch.
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Args:
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batch: The batched data as it is returned by the training DataLoader.
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.. warning:: Deprecated in 0.9.0 will remove 1.0.0 (use `on_train_batch_start` instead)
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"""
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# do something when the batch starts
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def on_batch_end(self) -> None:
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"""
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Called in the training loop after the batch.
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.. warning:: Deprecated in 0.9.0 will remove 1.0.0 (use `on_train_batch_end` instead)
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"""
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# do something when the batch ends
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def on_epoch_start(self) -> None:
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"""
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Called in the training loop at the very beginning of the epoch.
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"""
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# do something when the epoch starts
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def on_epoch_end(self) -> None:
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"""
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Called in the training loop at the very end of the epoch.
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"""
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# do something when the epoch ends
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def on_train_epoch_start(self) -> None:
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"""
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Called in the training loop at the very beginning of the epoch.
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"""
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# do something when the epoch starts
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def on_train_epoch_end(self) -> None:
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"""
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Called in the training loop at the very end of the epoch.
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"""
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# do something when the epoch ends
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def on_validation_epoch_start(self) -> None:
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"""
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Called in the validation loop at the very beginning of the epoch.
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"""
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# do something when the epoch starts
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def on_validation_epoch_end(self) -> None:
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"""
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Called in the validation loop at the very end of the epoch.
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"""
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# do something when the epoch ends
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def on_test_epoch_start(self) -> None:
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"""
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Called in the test loop at the very beginning of the epoch.
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"""
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# do something when the epoch starts
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def on_test_epoch_end(self) -> None:
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"""
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Called in the test loop at the very end of the epoch.
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"""
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# do something when the epoch ends
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def on_before_zero_grad(self, optimizer: Optimizer) -> None:
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"""
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Called after optimizer.step() and before optimizer.zero_grad().
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Called in the training loop after taking an optimizer step and before zeroing grads.
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Good place to inspect weight information with weights updated.
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This is where it is called::
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for optimizer in optimizers:
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optimizer.step()
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model.on_before_zero_grad(optimizer) # < ---- called here
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optimizer.zero_grad()
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Args:
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optimizer: The optimizer for which grads should be zeroed.
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"""
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# do something with the optimizer or inspect it.
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def on_after_backward(self) -> None:
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"""
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Called in the training loop after loss.backward() and before optimizers do anything.
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This is the ideal place to inspect or log gradient information.
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Example::
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def on_after_backward(self):
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# example to inspect gradient information in tensorboard
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if self.trainer.global_step % 25 == 0: # don't make the tf file huge
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params = self.state_dict()
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for k, v in params.items():
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grads = v
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name = k
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self.logger.experiment.add_histogram(tag=name, values=grads,
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global_step=self.trainer.global_step)
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"""
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def backward(
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self, trainer, loss: Tensor, optimizer: Optimizer, optimizer_idx: int
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) -> None:
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"""
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Override backward with your own implementation if you need to.
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Args:
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trainer: Pointer to the trainer
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loss: Loss is already scaled by accumulated grads
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optimizer: Current optimizer being used
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optimizer_idx: Index of the current optimizer being used
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Called to perform backward step.
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Feel free to override as needed.
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The loss passed in has already been scaled for accumulated gradients if requested.
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Example::
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def backward(self, trainer, loss, optimizer, optimizer_idx):
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loss.backward()
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"""
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loss.backward()
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def amp_scale_loss(
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self,
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unscaled_loss: Tensor,
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optimizer: Optimizer,
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optimizer_idx: int,
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amp_backend: AMPType,
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):
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if amp_backend == AMPType.NATIVE:
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scaled_loss = self.trainer.scaler.scale(unscaled_loss)
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else:
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scaled_loss = amp.scale_loss(unscaled_loss, optimizer)
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return scaled_loss
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class DataHooks:
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def prepare_data(self) -> None:
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"""
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Use this to download and prepare data.
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.. warning:: DO NOT set state to the model (use `setup` instead)
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since this is NOT called on every GPU in DDP/TPU
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Example::
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def prepare_data(self):
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# good
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download_data()
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tokenize()
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etc()
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# bad
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self.split = data_split
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self.some_state = some_other_state()
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In DDP prepare_data can be called in two ways (using Trainer(prepare_data_per_node)):
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1. Once per node. This is the default and is only called on LOCAL_RANK=0.
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2. Once in total. Only called on GLOBAL_RANK=0.
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Example::
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# DEFAULT
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# called once per node on LOCAL_RANK=0 of that node
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Trainer(prepare_data_per_node=True)
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# call on GLOBAL_RANK=0 (great for shared file systems)
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Trainer(prepare_data_per_node=False)
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This is called before requesting the dataloaders:
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.. code-block:: python
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model.prepare_data()
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if ddp/tpu: init()
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model.setup(stage)
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model.train_dataloader()
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model.val_dataloader()
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model.test_dataloader()
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"""
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def train_dataloader(self) -> DataLoader:
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"""
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Implement a PyTorch DataLoader for training.
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Return:
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Single PyTorch :class:`~torch.utils.data.DataLoader`.
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The dataloader you return will not be called every epoch unless you set
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:paramref:`~pytorch_lightning.trainer.Trainer.reload_dataloaders_every_epoch` to ``True``.
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For data processing use the following pattern:
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- download in :meth:`prepare_data`
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- process and split in :meth:`setup`
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However, the above are only necessary for distributed processing.
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.. warning:: do not assign state in prepare_data
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- :meth:`~pytorch_lightning.trainer.Trainer.fit`
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- ...
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- :meth:`prepare_data`
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- :meth:`setup`
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- :meth:`train_dataloader`
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Note:
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Lightning adds the correct sampler for distributed and arbitrary hardware.
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There is no need to set it yourself.
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Example:
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.. code-block:: python
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def train_dataloader(self):
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transform = transforms.Compose([transforms.ToTensor(),
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transforms.Normalize((0.5,), (1.0,))])
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dataset = MNIST(root='/path/to/mnist/', train=True, transform=transform,
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download=True)
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loader = torch.utils.data.DataLoader(
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dataset=dataset,
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batch_size=self.batch_size,
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shuffle=True
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)
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return loader
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"""
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rank_zero_warn(
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"`train_dataloader` must be implemented to be used with the Lightning Trainer"
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)
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def test_dataloader(self) -> Union[DataLoader, List[DataLoader]]:
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r"""
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Implement one or multiple PyTorch DataLoaders for testing.
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The dataloader you return will not be called every epoch unless you set
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:paramref:`~pytorch_lightning.trainer.Trainer.reload_dataloaders_every_epoch` to ``True``.
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For data processing use the following pattern:
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- download in :meth:`prepare_data`
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- process and split in :meth:`setup`
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However, the above are only necessary for distributed processing.
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.. warning:: do not assign state in prepare_data
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- :meth:`~pytorch_lightning.trainer.Trainer.fit`
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- ...
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- :meth:`prepare_data`
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- :meth:`setup`
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- :meth:`train_dataloader`
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- :meth:`val_dataloader`
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- :meth:`test_dataloader`
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Note:
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Lightning adds the correct sampler for distributed and arbitrary hardware.
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There is no need to set it yourself.
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Return:
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Single or multiple PyTorch DataLoaders.
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Example:
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.. code-block:: python
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def test_dataloader(self):
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transform = transforms.Compose([transforms.ToTensor(),
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transforms.Normalize((0.5,), (1.0,))])
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dataset = MNIST(root='/path/to/mnist/', train=False, transform=transform,
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download=True)
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loader = torch.utils.data.DataLoader(
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dataset=dataset,
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batch_size=self.batch_size,
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shuffle=False
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)
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return loader
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# can also return multiple dataloaders
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def test_dataloader(self):
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return [loader_a, loader_b, ..., loader_n]
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Note:
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If you don't need a test dataset and a :meth:`test_step`, you don't need to implement
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this method.
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Note:
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In the case where you return multiple test dataloaders, the :meth:`test_step`
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will have an argument ``dataloader_idx`` which matches the order here.
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"""
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def val_dataloader(self) -> Union[DataLoader, List[DataLoader]]:
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r"""
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Implement one or multiple PyTorch DataLoaders for validation.
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The dataloader you return will not be called every epoch unless you set
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:paramref:`~pytorch_lightning.trainer.Trainer.reload_dataloaders_every_epoch` to ``True``.
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It's recommended that all data downloads and preparation happen in :meth:`prepare_data`.
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- :meth:`~pytorch_lightning.trainer.Trainer.fit`
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- ...
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- :meth:`prepare_data`
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- :meth:`train_dataloader`
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- :meth:`val_dataloader`
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- :meth:`test_dataloader`
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Note:
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Lightning adds the correct sampler for distributed and arbitrary hardware
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There is no need to set it yourself.
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Return:
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Single or multiple PyTorch DataLoaders.
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Examples:
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.. code-block:: python
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def val_dataloader(self):
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transform = transforms.Compose([transforms.ToTensor(),
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transforms.Normalize((0.5,), (1.0,))])
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dataset = MNIST(root='/path/to/mnist/', train=False,
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transform=transform, download=True)
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loader = torch.utils.data.DataLoader(
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dataset=dataset,
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batch_size=self.batch_size,
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shuffle=False
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)
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return loader
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# can also return multiple dataloaders
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def val_dataloader(self):
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return [loader_a, loader_b, ..., loader_n]
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Note:
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If you don't need a validation dataset and a :meth:`validation_step`, you don't need to
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implement this method.
|
|
|
|
Note:
|
|
In the case where you return multiple validation dataloaders, the :meth:`validation_step`
|
|
will have an argument ``dataloader_idx`` which matches the order here.
|
|
"""
|
|
|
|
def transfer_batch_to_device(self, batch: Any, device: torch.device) -> Any:
|
|
"""
|
|
Override this hook if your :class:`~torch.utils.data.DataLoader` returns tensors
|
|
wrapped in a custom data structure.
|
|
|
|
The data types listed below (and any arbitrary nesting of them) are supported out of the box:
|
|
|
|
- :class:`torch.Tensor` or anything that implements `.to(...)`
|
|
- :class:`list`
|
|
- :class:`dict`
|
|
- :class:`tuple`
|
|
- :class:`torchtext.data.batch.Batch`
|
|
|
|
For anything else, you need to define how the data is moved to the target device (CPU, GPU, TPU, ...).
|
|
|
|
Example::
|
|
|
|
def transfer_batch_to_device(self, batch, device)
|
|
if isinstance(batch, CustomBatch):
|
|
# move all tensors in your custom data structure to the device
|
|
batch.samples = batch.samples.to(device)
|
|
batch.targets = batch.targets.to(device)
|
|
else:
|
|
batch = super().transfer_batch_to_device(data, device)
|
|
return batch
|
|
|
|
Args:
|
|
batch: A batch of data that needs to be transferred to a new device.
|
|
device: The target device as defined in PyTorch.
|
|
|
|
Returns:
|
|
A reference to the data on the new device.
|
|
|
|
Note:
|
|
This hook should only transfer the data and not modify it, nor should it move the data to
|
|
any other device than the one passed in as argument (unless you know what you are doing).
|
|
|
|
Note:
|
|
This hook only runs on single GPU training (no data-parallel). If you need multi-GPU support
|
|
for your custom batch objects, you need to define your custom
|
|
:class:`~torch.nn.parallel.DistributedDataParallel` or
|
|
:class:`~pytorch_lightning.overrides.data_parallel.LightningDistributedDataParallel` and
|
|
override :meth:`~pytorch_lightning.core.lightning.LightningModule.configure_ddp`.
|
|
|
|
See Also:
|
|
- :func:`~pytorch_lightning.utilities.apply_func.move_data_to_device`
|
|
- :func:`~pytorch_lightning.utilities.apply_func.apply_to_collection`
|
|
"""
|
|
return move_data_to_device(batch, device)
|
|
|
|
|
|
class CheckpointHooks:
|
|
def on_load_checkpoint(self, checkpoint: Dict[str, Any]) -> None:
|
|
r"""
|
|
Called by Lightning to restore your model.
|
|
If you saved something with :meth:`on_save_checkpoint` this is your chance to restore this.
|
|
|
|
Args:
|
|
checkpoint: Loaded checkpoint
|
|
|
|
|
|
Example:
|
|
.. code-block:: python
|
|
|
|
def on_load_checkpoint(self, checkpoint):
|
|
# 99% of the time you don't need to implement this method
|
|
self.something_cool_i_want_to_save = checkpoint['something_cool_i_want_to_save']
|
|
|
|
Note:
|
|
Lightning auto-restores global step, epoch, and train state including amp scaling.
|
|
There is no need for you to restore anything regarding training.
|
|
"""
|
|
|
|
def on_save_checkpoint(self, checkpoint: Dict[str, Any]) -> None:
|
|
r"""
|
|
Called by Lightning when saving a checkpoint to give you a chance to store anything
|
|
else you might want to save.
|
|
|
|
Args:
|
|
checkpoint: Checkpoint to be saved
|
|
|
|
Example:
|
|
.. code-block:: python
|
|
|
|
def on_save_checkpoint(self, checkpoint):
|
|
# 99% of use cases you don't need to implement this method
|
|
checkpoint['something_cool_i_want_to_save'] = my_cool_pickable_object
|
|
|
|
Note:
|
|
Lightning saves all aspects of training (epoch, global step, etc...)
|
|
including amp scaling.
|
|
There is no need for you to store anything about training.
|
|
|
|
"""
|