Source code for pytorch_lightning.core.hooks
# Copyright The PyTorch Lightning team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Various hooks to be used in the Lightning code."""
from typing import Any, Dict, List, Optional
import torch
from torch import Tensor
from torch.optim.optimizer import Optimizer
from pytorch_lightning.utilities import move_data_to_device
from pytorch_lightning.utilities.exceptions import MisconfigurationException
from pytorch_lightning.utilities.types import EVAL_DATALOADERS, STEP_OUTPUT, TRAIN_DATALOADERS
[docs]class ModelHooks:
"""Hooks to be used in LightningModule."""
[docs] def on_fit_start(self) -> None:
"""Called at the very beginning of fit.
If on DDP it is called on every process
"""
[docs] def on_fit_end(self) -> None:
"""Called at the very end of fit.
If on DDP it is called on every process
"""
[docs] def on_train_start(self) -> None:
"""Called at the beginning of training after sanity check."""
[docs] def on_train_end(self) -> None:
"""Called at the end of training before logger experiment is closed."""
[docs] def on_train_batch_start(self, batch: Any, batch_idx: int) -> Optional[int]:
"""Called in the training loop before anything happens for that batch.
If you return -1 here, you will skip training for the rest of the current epoch.
Args:
batch: The batched data as it is returned by the training DataLoader.
batch_idx: the index of the batch
"""
[docs] def on_train_batch_end(self, outputs: STEP_OUTPUT, batch: Any, batch_idx: int) -> None:
"""Called in the training loop after the batch.
Args:
outputs: The outputs of training_step_end(training_step(x))
batch: The batched data as it is returned by the training DataLoader.
batch_idx: the index of the batch
"""
[docs] def on_validation_batch_start(self, batch: Any, batch_idx: int, dataloader_idx: int) -> None:
"""Called in the validation loop before anything happens for that batch.
Args:
batch: The batched data as it is returned by the validation DataLoader.
batch_idx: the index of the batch
dataloader_idx: the index of the dataloader
"""
[docs] def on_validation_batch_end(
self, outputs: Optional[STEP_OUTPUT], batch: Any, batch_idx: int, dataloader_idx: int
) -> None:
"""Called in the validation loop after the batch.
Args:
outputs: The outputs of validation_step_end(validation_step(x))
batch: The batched data as it is returned by the validation DataLoader.
batch_idx: the index of the batch
dataloader_idx: the index of the dataloader
"""
[docs] def on_test_batch_start(self, batch: Any, batch_idx: int, dataloader_idx: int) -> None:
"""Called in the test loop before anything happens for that batch.
Args:
batch: The batched data as it is returned by the test DataLoader.
batch_idx: the index of the batch
dataloader_idx: the index of the dataloader
"""
[docs] def on_test_batch_end(
self, outputs: Optional[STEP_OUTPUT], batch: Any, batch_idx: int, dataloader_idx: int
) -> None:
"""Called in the test loop after the batch.
Args:
outputs: The outputs of test_step_end(test_step(x))
batch: The batched data as it is returned by the test DataLoader.
batch_idx: the index of the batch
dataloader_idx: the index of the dataloader
"""
[docs] def on_predict_batch_start(self, batch: Any, batch_idx: int, dataloader_idx: int) -> None:
"""Called in the predict loop before anything happens for that batch.
Args:
batch: The batched data as it is returned by the test DataLoader.
batch_idx: the index of the batch
dataloader_idx: the index of the dataloader
"""
[docs] def on_predict_batch_end(self, outputs: Optional[Any], batch: Any, batch_idx: int, dataloader_idx: int) -> None:
"""Called in the predict loop after the batch.
Args:
outputs: The outputs of predict_step_end(test_step(x))
batch: The batched data as it is returned by the test DataLoader.
batch_idx: the index of the batch
dataloader_idx: the index of the dataloader
"""
[docs] def on_validation_model_eval(self) -> None:
"""Sets the model to eval during the val loop."""
self.trainer.model.eval()
[docs] def on_validation_model_train(self) -> None:
"""Sets the model to train during the val loop."""
self.trainer.model.train()
[docs] def on_test_model_train(self) -> None:
"""Sets the model to train during the test loop."""
self.trainer.model.train()
[docs] def on_test_model_eval(self) -> None:
"""Sets the model to eval during the test loop."""
self.trainer.model.eval()
[docs] def on_predict_model_eval(self) -> None:
"""Sets the model to eval during the predict loop."""
self.trainer.model.eval()
[docs] def on_train_epoch_start(self) -> None:
"""Called in the training loop at the very beginning of the epoch."""
[docs] def on_train_epoch_end(self) -> None:
"""Called in the training loop at the very end of the epoch.
To access all batch outputs at the end of the epoch, either:
1. Implement `training_epoch_end` in the LightningModule OR
2. Cache data across steps on the attribute(s) of the `LightningModule` and access them in this hook
"""
[docs] def on_validation_epoch_start(self) -> None:
"""Called in the validation loop at the very beginning of the epoch."""
[docs] def on_validation_epoch_end(self) -> None:
"""Called in the validation loop at the very end of the epoch."""
[docs] def on_test_epoch_start(self) -> None:
"""Called in the test loop at the very beginning of the epoch."""
[docs] def on_test_epoch_end(self) -> None:
"""Called in the test loop at the very end of the epoch."""
[docs] def on_predict_epoch_end(self, results: List[Any]) -> None:
"""Called at the end of predicting."""
[docs] def on_before_zero_grad(self, optimizer: Optimizer) -> None:
"""Called after ``training_step()`` and before ``optimizer.zero_grad()``.
Called in the training loop after taking an optimizer step and before zeroing grads.
Good place to inspect weight information with weights updated.
This is where it is called::
for optimizer in optimizers:
out = training_step(...)
model.on_before_zero_grad(optimizer) # < ---- called here
optimizer.zero_grad()
backward()
Args:
optimizer: The optimizer for which grads should be zeroed.
"""
[docs] def on_before_backward(self, loss: Tensor) -> None:
"""Called before ``loss.backward()``.
Args:
loss: Loss divided by number of batches for gradient accumulation and scaled if using native AMP.
"""
pass
[docs] def on_after_backward(self) -> None:
"""Called after ``loss.backward()`` and before optimizers are stepped.
Note:
If using native AMP, the gradients will not be unscaled at this point.
Use the ``on_before_optimizer_step`` if you need the unscaled gradients.
"""
[docs] def on_before_optimizer_step(self, optimizer: Optimizer, optimizer_idx: int) -> None:
"""Called before ``optimizer.step()``.
If using gradient accumulation, the hook is called once the gradients have been accumulated.
See: :paramref:`~pytorch_lightning.trainer.Trainer.accumulate_grad_batches`.
If using native AMP, the loss will be unscaled before calling this hook.
See these `docs <https://pytorch.org/docs/stable/notes/amp_examples.html#working-with-unscaled-gradients>`__
for more information on the scaling of gradients.
If clipping gradients, the gradients will not have been clipped yet.
Args:
optimizer: Current optimizer being used.
optimizer_idx: Index of the current optimizer being used.
Example::
def on_before_optimizer_step(self, optimizer, optimizer_idx):
# example to inspect gradient information in tensorboard
if self.trainer.global_step % 25 == 0: # don't make the tf file huge
for k, v in self.named_parameters():
self.logger.experiment.add_histogram(
tag=k, values=v.grad, global_step=self.trainer.global_step
)
"""
[docs] def configure_sharded_model(self) -> None:
"""Hook to create modules in a distributed aware context. This is useful for when using sharded plugins,
where we'd like to shard the model instantly, which is useful for extremely large models which can save
memory and initialization time.
This hook is called during each of fit/val/test/predict stages in the same process, so ensure that
implementation of this hook is idempotent.
"""
[docs]class DataHooks:
"""Hooks to be used for data related stuff."""
def __init__(self) -> None:
"""
Attributes:
prepare_data_per_node:
If True, each LOCAL_RANK=0 will call prepare data.
Otherwise only NODE_RANK=0, LOCAL_RANK=0 will prepare data.
allow_zero_length_dataloader_with_multiple_devices:
If True, dataloader with zero length within local rank is allowed.
Default value is False.
"""
super().__init__()
self.prepare_data_per_node: bool = True
self.allow_zero_length_dataloader_with_multiple_devices: bool = False
[docs] def prepare_data(self) -> None:
"""Use this to download and prepare data. Downloading and saving data with multiple processes (distributed
settings) will result in corrupted data. Lightning ensures this method is called only within a single
process, so you can safely add your downloading logic within.
.. warning:: DO NOT set state to the model (use ``setup`` instead)
since this is NOT called on every device
Example::
def prepare_data(self):
# good
download_data()
tokenize()
etc()
# bad
self.split = data_split
self.some_state = some_other_state()
In a distributed environment, ``prepare_data`` can be called in two ways
(using :ref:`prepare_data_per_node<common/lightning_module:prepare_data_per_node>`)
1. Once per node. This is the default and is only called on LOCAL_RANK=0.
2. Once in total. Only called on GLOBAL_RANK=0.
Example::
# DEFAULT
# called once per node on LOCAL_RANK=0 of that node
class LitDataModule(LightningDataModule):
def __init__(self):
super().__init__()
self.prepare_data_per_node = True
# call on GLOBAL_RANK=0 (great for shared file systems)
class LitDataModule(LightningDataModule):
def __init__(self):
super().__init__()
self.prepare_data_per_node = False
This is called before requesting the dataloaders:
.. code-block:: python
model.prepare_data()
initialize_distributed()
model.setup(stage)
model.train_dataloader()
model.val_dataloader()
model.test_dataloader()
model.predict_dataloader()
"""
[docs] def setup(self, stage: str) -> None:
"""Called at the beginning of fit (train + validate), validate, test, or predict. This is a good hook when
you need to build models dynamically or adjust something about them. This hook is called on every process
when using DDP.
Args:
stage: either ``'fit'``, ``'validate'``, ``'test'``, or ``'predict'``
Example::
class LitModel(...):
def __init__(self):
self.l1 = None
def prepare_data(self):
download_data()
tokenize()
# don't do this
self.something = else
def setup(self, stage):
data = load_data(...)
self.l1 = nn.Linear(28, data.num_classes)
"""
[docs] def teardown(self, stage: str) -> None:
"""Called at the end of fit (train + validate), validate, test, or predict.
Args:
stage: either ``'fit'``, ``'validate'``, ``'test'``, or ``'predict'``
"""
[docs] def train_dataloader(self) -> TRAIN_DATALOADERS:
"""Implement one or more PyTorch DataLoaders for training.
Return:
A collection of :class:`torch.utils.data.DataLoader` specifying training samples.
In the case of multiple dataloaders, please see this :ref:`section <multiple-dataloaders>`.
The dataloader you return will not be reloaded unless you set
:paramref:`~pytorch_lightning.trainer.Trainer.reload_dataloaders_every_n_epochs` to
a positive integer.
For data processing use the following pattern:
- download in :meth:`prepare_data`
- process and split in :meth:`setup`
However, the above are only necessary for distributed processing.
.. warning:: do not assign state in prepare_data
- :meth:`~pytorch_lightning.trainer.trainer.Trainer.fit`
- :meth:`prepare_data`
- :meth:`setup`
Note:
Lightning adds the correct sampler for distributed and arbitrary hardware.
There is no need to set it yourself.
Example::
# single dataloader
def train_dataloader(self):
transform = transforms.Compose([transforms.ToTensor(),
transforms.Normalize((0.5,), (1.0,))])
dataset = MNIST(root='/path/to/mnist/', train=True, transform=transform,
download=True)
loader = torch.utils.data.DataLoader(
dataset=dataset,
batch_size=self.batch_size,
shuffle=True
)
return loader
# multiple dataloaders, return as list
def train_dataloader(self):
mnist = MNIST(...)
cifar = CIFAR(...)
mnist_loader = torch.utils.data.DataLoader(
dataset=mnist, batch_size=self.batch_size, shuffle=True
)
cifar_loader = torch.utils.data.DataLoader(
dataset=cifar, batch_size=self.batch_size, shuffle=True
)
# each batch will be a list of tensors: [batch_mnist, batch_cifar]
return [mnist_loader, cifar_loader]
# multiple dataloader, return as dict
def train_dataloader(self):
mnist = MNIST(...)
cifar = CIFAR(...)
mnist_loader = torch.utils.data.DataLoader(
dataset=mnist, batch_size=self.batch_size, shuffle=True
)
cifar_loader = torch.utils.data.DataLoader(
dataset=cifar, batch_size=self.batch_size, shuffle=True
)
# each batch will be a dict of tensors: {'mnist': batch_mnist, 'cifar': batch_cifar}
return {'mnist': mnist_loader, 'cifar': cifar_loader}
"""
raise MisconfigurationException("`train_dataloader` must be implemented to be used with the Lightning Trainer")
[docs] def test_dataloader(self) -> EVAL_DATALOADERS:
r"""
Implement one or multiple PyTorch DataLoaders for testing.
For data processing use the following pattern:
- download in :meth:`prepare_data`
- process and split in :meth:`setup`
However, the above are only necessary for distributed processing.
.. warning:: do not assign state in prepare_data
- :meth:`~pytorch_lightning.trainer.trainer.Trainer.test`
- :meth:`prepare_data`
- :meth:`setup`
Note:
Lightning adds the correct sampler for distributed and arbitrary hardware.
There is no need to set it yourself.
Return:
A :class:`torch.utils.data.DataLoader` or a sequence of them specifying testing samples.
Example::
def test_dataloader(self):
transform = transforms.Compose([transforms.ToTensor(),
transforms.Normalize((0.5,), (1.0,))])
dataset = MNIST(root='/path/to/mnist/', train=False, transform=transform,
download=True)
loader = torch.utils.data.DataLoader(
dataset=dataset,
batch_size=self.batch_size,
shuffle=False
)
return loader
# can also return multiple dataloaders
def test_dataloader(self):
return [loader_a, loader_b, ..., loader_n]
Note:
If you don't need a test dataset and a :meth:`test_step`, you don't need to implement
this method.
Note:
In the case where you return multiple test dataloaders, the :meth:`test_step`
will have an argument ``dataloader_idx`` which matches the order here.
"""
raise MisconfigurationException("`test_dataloader` must be implemented to be used with the Lightning Trainer")
[docs] def val_dataloader(self) -> EVAL_DATALOADERS:
r"""
Implement one or multiple PyTorch DataLoaders for validation.
The dataloader you return will not be reloaded unless you set
:paramref:`~pytorch_lightning.trainer.Trainer.reload_dataloaders_every_n_epochs` to
a positive integer.
It's recommended that all data downloads and preparation happen in :meth:`prepare_data`.
- :meth:`~pytorch_lightning.trainer.trainer.Trainer.fit`
- :meth:`~pytorch_lightning.trainer.trainer.Trainer.validate`
- :meth:`prepare_data`
- :meth:`setup`
Note:
Lightning adds the correct sampler for distributed and arbitrary hardware
There is no need to set it yourself.
Return:
A :class:`torch.utils.data.DataLoader` or a sequence of them specifying validation samples.
Examples::
def val_dataloader(self):
transform = transforms.Compose([transforms.ToTensor(),
transforms.Normalize((0.5,), (1.0,))])
dataset = MNIST(root='/path/to/mnist/', train=False,
transform=transform, download=True)
loader = torch.utils.data.DataLoader(
dataset=dataset,
batch_size=self.batch_size,
shuffle=False
)
return loader
# can also return multiple dataloaders
def val_dataloader(self):
return [loader_a, loader_b, ..., loader_n]
Note:
If you don't need a validation dataset and a :meth:`validation_step`, you don't need to
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.
"""
raise MisconfigurationException("`val_dataloader` must be implemented to be used with the Lightning Trainer")
[docs] def predict_dataloader(self) -> EVAL_DATALOADERS:
r"""
Implement one or multiple PyTorch DataLoaders for prediction.
It's recommended that all data downloads and preparation happen in :meth:`prepare_data`.
- :meth:`~pytorch_lightning.trainer.trainer.Trainer.predict`
- :meth:`prepare_data`
- :meth:`setup`
Note:
Lightning adds the correct sampler for distributed and arbitrary hardware
There is no need to set it yourself.
Return:
A :class:`torch.utils.data.DataLoader` or a sequence of them specifying prediction samples.
Note:
In the case where you return multiple prediction dataloaders, the :meth:`predict_step`
will have an argument ``dataloader_idx`` which matches the order here.
"""
raise MisconfigurationException(
"`predict_dataloader` must be implemented to be used with the Lightning Trainer"
)
[docs] def transfer_batch_to_device(self, batch: Any, device: torch.device, dataloader_idx: int) -> 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`
For anything else, you need to define how the data is moved to the target device (CPU, GPU, TPU, ...).
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).
To check the current state of execution of this hook you can use
``self.trainer.training/testing/validating/predicting`` so that you can
add different logic as per your requirement.
Note:
This hook only runs on single GPU training and DDP (no data-parallel).
Data-Parallel support will come in near future.
Args:
batch: A batch of data that needs to be transferred to a new device.
device: The target device as defined in PyTorch.
dataloader_idx: The index of the dataloader to which the batch belongs.
Returns:
A reference to the data on the new device.
Example::
def transfer_batch_to_device(self, batch, device, dataloader_idx):
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)
elif dataloader_idx == 0:
# skip device transfer for the first dataloader or anything you wish
pass
else:
batch = super().transfer_batch_to_device(data, device, dataloader_idx)
return batch
Raises:
MisconfigurationException:
If using data-parallel, ``Trainer(strategy='dp')``.
MisconfigurationException:
If using IPUs, ``Trainer(accelerator='ipu')``.
See Also:
- :meth:`move_data_to_device`
- :meth:`apply_to_collection`
"""
return move_data_to_device(batch, device)
[docs] def on_before_batch_transfer(self, batch: Any, dataloader_idx: int) -> Any:
"""Override to alter or apply batch augmentations to your batch before it is transferred to the device.
Note:
To check the current state of execution of this hook you can use
``self.trainer.training/testing/validating/predicting`` so that you can
add different logic as per your requirement.
Note:
This hook only runs on single GPU training and DDP (no data-parallel).
Data-Parallel support will come in near future.
Args:
batch: A batch of data that needs to be altered or augmented.
dataloader_idx: The index of the dataloader to which the batch belongs.
Returns:
A batch of data
Example::
def on_before_batch_transfer(self, batch, dataloader_idx):
batch['x'] = transforms(batch['x'])
return batch
See Also:
- :meth:`on_after_batch_transfer`
- :meth:`transfer_batch_to_device`
"""
return batch
[docs] def on_after_batch_transfer(self, batch: Any, dataloader_idx: int) -> Any:
"""Override to alter or apply batch augmentations to your batch after it is transferred to the device.
Note:
To check the current state of execution of this hook you can use
``self.trainer.training/testing/validating/predicting`` so that you can
add different logic as per your requirement.
Note:
This hook only runs on single GPU training and DDP (no data-parallel).
Data-Parallel support will come in near future.
Args:
batch: A batch of data that needs to be altered or augmented.
dataloader_idx: The index of the dataloader to which the batch belongs.
Returns:
A batch of data
Example::
def on_after_batch_transfer(self, batch, dataloader_idx):
batch['x'] = gpu_transforms(batch['x'])
return batch
Raises:
MisconfigurationException:
If using data-parallel, ``Trainer(strategy='dp')``.
MisconfigurationException:
If using IPUs, ``Trainer(accelerator='ipu')``.
See Also:
- :meth:`on_before_batch_transfer`
- :meth:`transfer_batch_to_device`
"""
return batch
[docs]class CheckpointHooks:
"""Hooks to be used with Checkpointing."""
[docs] 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::
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.
"""
[docs] 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: The full checkpoint dictionary before it gets dumped to a file.
Implementations of this hook can insert additional data into this dictionary.
Example::
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.
"""
