Source code for pytorch_lightning.loops.base
# 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.
from abc import ABC, abstractmethod
from typing import Any, Dict, Generic, Optional, TypeVar
from deprecate import void
from torchmetrics import Metric
import pytorch_lightning as pl
from pytorch_lightning.trainer.connectors.logger_connector.result import ResultCollection
from pytorch_lightning.trainer.progress import BaseProgress
from pytorch_lightning.utilities.exceptions import MisconfigurationException
T = TypeVar("T") # the output type of `run`
[docs]class Loop(ABC, Generic[T]):
"""Basic Loops interface. All classes derived from this must implement the following properties and methods:
* :attr:`done` (property): Condition to break the loop
* :attr:`reset` (method): Resets the internal state between multiple calls of :attr:`run`
* :attr:`advance` (method): Implements one step of the loop
This class implements the following loop structure:
.. code-block:: python
on_run_start()
while not done:
on_advance_start()
advance()
on_advance_end()
on_run_end()
"""
def __init__(self) -> None:
self.restarting = False
self._trainer: Optional["pl.Trainer"] = None
@property
def trainer(self) -> "pl.Trainer":
if self._trainer is None:
raise RuntimeError("The loop is not attached to a Trainer.")
return self._trainer
@trainer.setter
def trainer(self, trainer: "pl.Trainer"):
"""Connects this loop's trainer and its children."""
if not isinstance(trainer, pl.Trainer):
raise MisconfigurationException(
f"Loop {self.__class__.__name__} should be connected to a `Trainer`, found: {trainer}."
)
self._trainer = trainer
for v in self.__dict__.values():
if isinstance(v, Loop):
v.trainer = trainer
@property
@abstractmethod
def done(self) -> bool:
"""Property indicating when the loop is finished.
Example::
@property
def done(self):
return self.trainer.global_step >= self.trainer.max_steps
"""
@property
def skip(self) -> bool:
"""Determine whether to return immediately from the call to :meth:`run`.
Example::
@property
def skip(self):
return len(self.trainer.train_dataloader) == 0
"""
return False
[docs] def connect(self, **kwargs: "Loop") -> None:
"""Optionally connect one or multiple loops to this one.
Linked loops should form a tree.
"""
[docs] def on_skip(self) -> Optional[Any]:
"""The function to run when :meth:`run` should be skipped, determined by the condition in :attr:`skip`.
Returns:
the default output value of :meth:`on_run_end`
"""
[docs] def run(self, *args: Any, **kwargs: Any) -> T:
"""The main entry point to the loop.
Will frequently check the :attr:`done` condition and calls :attr:`advance`
until :attr:`done` evaluates to ``True``.
Override this if you wish to change the default behavior. The default implementation is:
Example::
def run(self, *args, **kwargs):
if self.skip:
return self.on_skip()
self.reset()
self.on_run_start(*args, **kwargs)
while not self.done:
self.advance(*args, **kwargs)
output = self.on_run_end()
return output
Returns:
The output of :attr:`on_run_end` (often outputs collected from each step of the loop)
"""
if self.skip:
return self.on_skip()
self.reset()
self.on_run_start(*args, **kwargs)
while not self.done:
try:
self.on_advance_start(*args, **kwargs)
self.advance(*args, **kwargs)
self.on_advance_end()
self.restarting = False
except StopIteration:
break
output = self.on_run_end()
return output
[docs] @abstractmethod
def reset(self) -> None:
"""Resets the internal state of the loop at the beginning of each call to :attr:`run`.
Example::
def reset(self):
# reset your internal state or add custom logic
# if you expect run() to be called multiple times
self.current_iteration = 0
self.outputs = []
"""
[docs] def on_run_start(self, *args: Any, **kwargs: Any) -> None:
"""Hook to be called as the first thing after entering :attr:`run` (except the state reset).
Accepts all arguments passed to :attr:`run`.
"""
void(*args, **kwargs)
[docs] def on_advance_start(self, *args: Any, **kwargs: Any) -> None:
"""Hook to be called each time before :attr:`advance` is called.
Accepts all arguments passed to :attr`run`.
"""
void(*args, **kwargs)
[docs] @abstractmethod
def advance(self, *args: Any, **kwargs: Any) -> None:
"""Performs a single step.
Accepts all arguments passed to :attr:`run`.
Example::
def advance(self, iterator):
batch = next(iterator)
loss = self.trainer.lightning_module.training_step(batch, batch_idx)
...
"""
[docs] def on_advance_end(self) -> None:
"""Hook to be called each time after :attr:`advance` is called."""
[docs] def on_run_end(self) -> T:
"""Hook to be called at the end of the run.
Its return argument is returned from :attr:`run`.
"""
[docs] def on_save_checkpoint(self) -> Dict:
"""Called when saving a model checkpoint, use to persist loop state.
Returns:
The current loop state.
"""
return {}
[docs] def on_load_checkpoint(self, state_dict: Dict) -> None:
"""Called when loading a model checkpoint, use to reload loop state."""
[docs] def state_dict(self, destination: Optional[Dict] = None, prefix: Optional[str] = "") -> Dict:
"""The state dict is determined by the state and progress of this loop and all its children.
Args:
destination: An existing dictionary to update with this loop's state. By default a new dictionary
is returned.
prefix: A prefix for each key in the state dictionary
"""
if destination is None:
destination = {}
destination[prefix + "state_dict"] = self.on_save_checkpoint()
for k, v in self.__dict__.items():
key = prefix + k
if isinstance(v, BaseProgress):
destination[key] = v.state_dict()
elif isinstance(v, Loop):
v.state_dict(destination, key + ".")
elif isinstance(v, ResultCollection):
# sync / unsync metrics
v.sync()
destination[key] = v.state_dict()
v.unsync()
return destination
[docs] def load_state_dict(
self,
state_dict: Dict,
prefix: str = "",
metrics: Optional[Dict[str, Metric]] = None,
) -> None:
"""Loads the state of this loop and all its children."""
self._load_from_state_dict(state_dict.copy(), prefix, metrics)
for k, v in self.__dict__.items():
if isinstance(v, Loop):
v.load_state_dict(state_dict.copy(), prefix + k + ".")
def _load_from_state_dict(self, state_dict: Dict, prefix: str, metrics: Optional[Dict[str, Metric]] = None) -> None:
for k, v in self.__dict__.items():
key = prefix + k
if isinstance(v, BaseProgress):
v.load_state_dict(state_dict[key])
elif (
isinstance(v, ResultCollection)
and self.trainer is not None
and getattr(self.trainer, "lightning_module", None) is not None
):
metric_attributes = {
name: module
for name, module in self.trainer.lightning_module.named_modules()
if isinstance(module, Metric)
}
if metrics:
metric_attributes.update(metrics)
# The `ResultCollection` objects have 2 types of metrics: `Tensor` and `torchmetrics.Metric`.
# When creating a checkpoint, the `Metric`s are dropped from the loop `state_dict` to serialize only
# Python primitives. However, their states are saved with the model's `state_dict`.
# On reload, we need to re-attach the `Metric`s back to the `ResultCollection`.
# The references are provided through the `metric_attributes` dictionary.
v.load_state_dict(
state_dict[prefix + k], metrics=metric_attributes, sync_fn=self.trainer.training_type_plugin.reduce
)
if not self.trainer.is_global_zero:
v.reset(metrics=False)
self.on_load_checkpoint(state_dict[prefix + "state_dict"])
self.restarting = True
