Source code for pytorch_lightning.plugins.training_type.training_type_plugin
# Copyright The PyTorch Lightning team.
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# 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
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# http://www.apache.org/licenses/LICENSE-2.0
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# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
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# See the License for the specific language governing permissions and
# limitations under the License.
import contextlib
from abc import ABC, abstractmethod
from typing import Any, Dict, Generator, Iterable, List, Mapping, Optional, Tuple, Union
import torch
from torch import Tensor
from torch.nn import Module
from torch.optim import Optimizer
from torch.utils.data import DataLoader
import pytorch_lightning as pl
from pytorch_lightning.overrides.base import unwrap_lightning_module
from pytorch_lightning.plugins import TorchCheckpointIO
from pytorch_lightning.plugins.io.checkpoint_plugin import CheckpointIO
from pytorch_lightning.utilities.distributed import ReduceOp
from pytorch_lightning.utilities.types import _EVALUATE_OUTPUT, _PATH, _PREDICT_OUTPUT
[docs]class TrainingTypePlugin(ABC):
"""Base class for all training type plugins that change the behaviour of the training, validation and test-
loop."""
def __init__(self, checkpoint_io: Optional[CheckpointIO] = None) -> None:
self._model: Optional[Module] = None
self._results: Optional[Union[_EVALUATE_OUTPUT, _PREDICT_OUTPUT]] = None
checkpoint_io = checkpoint_io if checkpoint_io is not None else TorchCheckpointIO()
self._checkpoint_io = checkpoint_io
@property
def checkpoint_io(self) -> CheckpointIO:
return self._checkpoint_io
@checkpoint_io.setter
def checkpoint_io(self, plugin: CheckpointIO) -> None:
self._checkpoint_io = plugin
[docs] def connect(self, model: Module) -> None:
"""Called by the accelerator to connect the accelerator and the model with this plugin."""
self.model = model
[docs] def setup_environment(self) -> None:
"""Setup any processes or distributed connections.
This is called before the LightningModule/DataModule setup hook which allows the user to access the accelerator
environment before setup is complete.
"""
def _setup_model_and_optimizers(self, model: Module, optimizers: List[Optimizer]) -> Tuple[Module, List[Optimizer]]:
"""Setup a model and multiple optimizers together.
The returned objects are expected to be in the same order they were passed in. The default implementation will
call :meth:`_setup_model` and :meth:`_setup_optimizer` on the inputs.
"""
# TODO (@awaelchli): standardize this across all plugins in Lightning and Lite. Related refactor: #7324
model = self._setup_model(model)
optimizers = [self._setup_optimizer(optimizer) for optimizer in optimizers]
return model, optimizers
def _setup_model(self, model: Module) -> Module:
"""Performs setup for the model, e.g., by wrapping it by another class."""
# TODO (@awaelchli): standardize this across all plugins in Lightning and Lite. Related refactor: #7324
return model
def _setup_optimizer(self, optimizer: Optimizer) -> Optimizer:
"""Performs setup for the optimizer, e.g., by wrapping it by another class."""
# TODO (@awaelchli): standardize this across all plugins in Lightning and Lite. Related refactor: #7324
return optimizer
@property
@abstractmethod
def on_gpu(self) -> bool:
"""Returns whether the current process is done on GPU."""
@property
@abstractmethod
def on_tpu(self) -> bool:
"""Returns whether the current process is done on TPU."""
@property
@abstractmethod
def root_device(self) -> torch.device:
"""Returns the root device."""
[docs] @abstractmethod
def model_to_device(self) -> None:
"""Moves the model to the correct device."""
@property
@abstractmethod
def is_global_zero(self) -> bool:
"""Whether the current process is the rank zero process not only on the local node, but for all nodes."""
[docs] @abstractmethod
def reduce(
self,
tensor: Union[torch.Tensor, Any],
group: Optional[Any] = None,
reduce_op: Optional[Union[ReduceOp, str]] = "mean",
) -> Union[torch.Tensor, Any]:
"""Reduces the given tensor (e.g. across GPUs/processes).
Args:
tensor: the tensor to sync and reduce
group: the process group to reduce
reduce_op: the reduction operation. Defaults to 'mean'.
Can also be a string 'sum' or ReduceOp.
"""
[docs] @abstractmethod
def barrier(self, name: Optional[str] = None) -> None:
"""Synchronizes all processes which blocks processes until the whole group enters this function.
Args:
name: an optional name to pass into barrier.
"""
[docs] @abstractmethod
def broadcast(self, obj: object, src: int = 0) -> object:
"""Broadcasts an object to all processes.
Args:
obj: the object to broadcast
src: source rank
"""
[docs] @abstractmethod
def all_gather(self, tensor: torch.Tensor, group: Optional[Any] = None, sync_grads: bool = False) -> torch.Tensor:
"""Perform an all_gather on all processes.
Args:
tensor: the tensor to all_gather
group: the process group to gather results from
sync_grads: flag that allows users to synchronize gradients for all_gather op
"""
[docs] def reduce_boolean_decision(self, decision: bool) -> bool:
"""Reduce the early stopping decision across all processes."""
return decision
[docs] def pre_backward(self, closure_loss: torch.Tensor) -> None:
"""Run before precision plugin executes backward."""
[docs] def post_backward(self, closure_loss: torch.Tensor) -> None:
"""Run after precision plugin executes backward."""
@property
def model(self) -> Optional[Module]:
"""Returns the potentially wrapped LightningModule."""
return self._model
@model.setter
def model(self, new_model: Optional[Module]) -> None:
self._model = new_model
@property
def lightning_module(self) -> "pl.LightningModule":
"""Returns the pure LightningModule without potential wrappers."""
return unwrap_lightning_module(self._model)
@property
def results(self) -> Optional[Union[_EVALUATE_OUTPUT, _PREDICT_OUTPUT]]:
"""Enables plugin-agnostic access to the result returned by the training/evaluation/prediction run.
The result is
cached instead of returned directly, because some plugins require transmitting the results from one
multiprocessing context to another in a separate step. For example, the plugins that use the "spawn"
start-method send the result to the master process through a
`multiprocessing queue (shared memory) <https://pytorch.org/docs/stable/multiprocessing.html>`_.
"""
return self._results
def load_checkpoint(self, checkpoint_path: _PATH) -> Dict[str, Any]:
torch.cuda.empty_cache()
return self.checkpoint_io.load_checkpoint(checkpoint_path)
def load_model_state_dict(self, checkpoint: Mapping[str, Any]) -> None:
self.lightning_module.load_state_dict(checkpoint["state_dict"])
def load_optimizer_state_dict(self, checkpoint: Mapping[str, Any]) -> None:
optimizer_states = checkpoint["optimizer_states"]
for optimizer, opt_state in zip(self.lightning_module.trainer.accelerator.optimizers, optimizer_states):
optimizer.load_state_dict(opt_state)
def start_training(self, trainer: "pl.Trainer") -> None:
# double dispatch to initiate the training loop
self._results = trainer.run_stage()
def start_evaluating(self, trainer: "pl.Trainer") -> None:
# double dispatch to initiate the test loop
self._results = trainer.run_stage()
def start_predicting(self, trainer: "pl.Trainer") -> None:
# double dispatch to initiate the predicting loop
self._results = trainer.run_stage()
def training_step(self, *args, **kwargs):
return self.model.training_step(*args, **kwargs)
def post_training_step(self):
pass
def validation_step(self, *args, **kwargs):
return self.model.validation_step(*args, **kwargs)
def test_step(self, *args, **kwargs):
return self.model.test_step(*args, **kwargs)
def predict_step(self, *args, **kwargs):
return self.model.predict_step(*args, **kwargs)
def training_step_end(self, output):
return output
def validation_step_end(self, output):
return output
def test_step_end(self, output):
return output
[docs] def process_dataloader(self, dataloader: Union[Iterable, DataLoader]) -> Union[Iterable, DataLoader]:
"""Wraps the dataloader if necessary.
Args:
dataloader: iterable. Ideally of type: :class:`torch.utils.data.DataLoader`
"""
return dataloader
def init_optimizers(self, trainer: "pl.Trainer", model: "pl.LightningModule"):
return trainer.init_optimizers(model)
@property
def setup_optimizers_in_pre_dispatch(self) -> bool:
"""Override to delay setting optimizers and schedulers till after dispatch. This is useful when the
`TrainingTypePlugin` requires operating on the wrapped accelerator model. However this may break certain
precision plugins such as APEX which require optimizers to be set.
Returns:
If True, delay setup optimizers till pre_dispatch, else call within setup.
"""
return False
@property
def restore_checkpoint_after_pre_dispatch(self) -> bool:
"""Override to delay restoring from checkpoint till after pre-dispatch. This is useful when the plugin
requires all the setup hooks to run before loading checkpoint.
Returns:
If true, restore checkpoint after pre_dispatch.
"""
return False
@property
def lightning_restore_optimizer_and_schedulers(self) -> bool:
"""Override to disable Lightning restoring optimizers/schedulers.
This is useful for plugins which manage restoring optimizers/schedulers.
"""
return True
@property
def handles_gradient_accumulation(self) -> bool:
"""Whether the plugin handles gradient accumulation internally."""
return False
[docs] def lightning_module_state_dict(self) -> Dict[str, Union[Any, Tensor]]:
"""Returns model state."""
model = self.lightning_module
return model.state_dict()
[docs] def save_checkpoint(self, checkpoint: Dict[str, Any], filepath: _PATH) -> None:
"""Save model/training states as a checkpoint file through state-dump and file-write.
Args:
checkpoint: dict containing model and trainer state
filepath: write-target file's path
"""
if self.should_rank_save_checkpoint:
return self.checkpoint_io.save_checkpoint(checkpoint, filepath)
[docs] def remove_checkpoint(self, filepath: _PATH) -> None:
"""Remove checkpoint filepath from the filesystem.
Args:
filepath: Path to checkpoint
"""
if self.should_rank_save_checkpoint:
return self.checkpoint_io.remove_checkpoint(filepath)
[docs] @contextlib.contextmanager
def model_sharded_context(self) -> Generator:
"""Provide hook to create modules in a distributed aware context. This is useful for when we'd like to
shard the model instantly, which is useful for extremely large models which can save memory and
initialization time.
Returns: Model parallel context.
"""
yield
[docs] @abstractmethod
def teardown(self) -> None:
"""This method is called to teardown the training process.
It is the right place to release memory and free other resources.
"""
@classmethod
def register_plugins(cls, plugin_registry) -> None:
pass
@property
def should_rank_save_checkpoint(self) -> bool:
"""Returns whether the checkpoint should be saved (rank based)"""
return self.is_global_zero
[docs] def on_train_batch_start(self, batch: Any, batch_idx: int, dataloader_idx: int = 0) -> None:
"""Called in the training loop before anything happens for that batch."""
pass
[docs] def pre_dispatch(self) -> None:
"""Hook to do something before the training/evaluation/prediction starts."""
[docs] def dispatch(self, trainer: "pl.Trainer") -> None:
"""Hook to do something at trainer run_stage starts."""
[docs] def post_dispatch(self, trainer: "pl.Trainer") -> None:
"""Hook to do something after the training/evaluation/prediction finishes."""
