Source code for pytorch_lightning.loops.loop

# 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
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# See the License for the specific language governing permissions and
# limitations under the License.
import inspect
from abc import ABC, abstractmethod
from typing import Any, Dict, Generic, Optional, Type, TypeVar, Union

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
from pytorch_lightning.utilities.imports import _fault_tolerant_training

T = TypeVar("T")  # the output type of `run`

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


        while not done:


    def __init__(self) -> None:
        self._restarting = False
        self._trainer: Optional["pl.Trainer"] = None

    def trainer(self) -> "pl.Trainer":
        if self._trainer is None:
            raise RuntimeError("The loop is not attached to a Trainer.")
        return self._trainer

    def trainer(self, trainer: "pl.Trainer") -> None:
        """Connects this loop's trainer and its children."""
        self._trainer = trainer
        for v in self.__dict__.values():
            if isinstance(v, Loop):
                v.trainer = trainer

    def restarting(self) -> bool:
        """Whether the state of this loop was reloaded and it needs to restart."""
        return self._restarting

    def restarting(self, restarting: bool) -> None:
        """Connects this loop's restarting value and its children."""
        self._restarting = restarting
        for loop in vars(self).values():
            if isinstance(loop, Loop):
                loop.restarting = restarting

    def done(self) -> bool:
        """Property indicating when the loop is finished.


            def done(self):
                return self.trainer.global_step >= self.trainer.max_steps

    def skip(self) -> bool:
        """Determine whether to return immediately from the call to :meth:`run`.


            def skip(self):
                return len(self.trainer.train_dataloader) == 0
        return False

    def connect(self, **kwargs: "Loop") -> None:
        """Optionally connect one or multiple loops to this one.

        Linked loops should form a tree.

    def replace(self, **loops: Union["Loop", Type["Loop"]]) -> None:
        """Optionally replace one or multiple of this loop's sub-loops.

        This method takes care of instantiating the class (if necessary) with all existing arguments, connecting all
        sub-loops of the old loop to the new instance, setting the ``Trainer`` reference, and connecting the new loop to
        the parent.

            **loops: ``Loop`` subclasses or instances. The name used should match the loop attribute name you want to

            MisconfigurationException: When passing a ``Loop`` class, if the ``__init__`` arguments do not match those
                of the Loop class it replaces.
        new_loops = {}

        for name, type_or_object in loops.items():
            old_loop = getattr(self, name)

            if isinstance(type_or_object, type):
                # compare the signatures
                old_parameters = inspect.signature(old_loop.__class__.__init__).parameters
                current_parameters = inspect.signature(type_or_object.__init__).parameters
                if old_parameters != current_parameters:
                    raise MisconfigurationException(
                        f"`{self.__class__.__name__}.replace({type_or_object.__name__})` can only be used if the"
                        f" `__init__` signatures match but `{old_loop.__class__.__name__}` does not."
                # instantiate the loop
                kwargs = {p: getattr(old_loop, p) for p in old_parameters if p != "self"}
                loop = type_or_object(**kwargs)
                loop = type_or_object

            # connect sub-loops
            kwargs = {n: l for n, l in old_loop.__dict__.items() if isinstance(l, Loop)}
            # set the trainer reference
            loop.trainer = self.trainer

            new_loops[name] = loop
        # connect to self

    def on_skip(self) -> T:
        """The function to run when :meth:`run` should be skipped, determined by the condition in :attr:`skip`.

            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 self._restarting = False 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 = [] """
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`. """ 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`. """
[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) ... """
def on_advance_end(self) -> None: """Hook to be called each time after :attr:`advance` is called.""" def on_run_end(self) -> T: """Hook to be called at the end of the run. Its return argument is returned from :attr:`run`. """ def teardown(self) -> None: """Use to release memory etc.""" def on_save_checkpoint(self) -> Dict: """Called when saving a model checkpoint, use to persist loop state. Returns: The current loop state. """ return {} def on_load_checkpoint(self, state_dict: Dict) -> None: """Called when loading a model checkpoint, use to reload loop state.""" def state_dict(self, destination: Optional[Dict] = None, prefix: 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() # do not get the mode from `self.trainer` because it might not have been attached yet ft_enabled = _fault_tolerant_training() 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 ft_enabled and isinstance(v, _ResultCollection): # sync / unsync metrics v.sync() destination[key] = v.state_dict() v.unsync() return destination 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 + ".") self.restarting = True def _load_from_state_dict(self, state_dict: Dict, prefix: str, metrics: Optional[Dict[str, Metric]] = None) -> None: trainer = self._trainer for k, v in self.__dict__.items(): key = prefix + k if key not in state_dict: # compatibility with old checkpoints continue if isinstance(v, BaseProgress): v.load_state_dict(state_dict[key]) elif isinstance(v, _ResultCollection) and trainer is not None and trainer.lightning_module 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[key], metrics=metric_attributes, sync_fn=self.trainer.strategy.reduce) if not self.trainer.is_global_zero: v.reset(metrics=False) if prefix + "state_dict" in state_dict: # compatibility with old checkpoints self.on_load_checkpoint(state_dict[prefix + "state_dict"])

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