Source code for pytorch_lightning.loops.optimization.optimizer_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.
from dataclasses import dataclass, field
from functools import partial
from typing import Any, Callable, Dict, List, Optional, Tuple, Union

import torch
from lightning_utilities.core.rank_zero import WarningCache
from torch import Tensor
from torch.optim import Optimizer
from typing_extensions import OrderedDict

from pytorch_lightning.accelerators import TPUAccelerator
from pytorch_lightning.core.optimizer import LightningOptimizer
from pytorch_lightning.loops import Loop
from pytorch_lightning.loops.optimization.closure import AbstractClosure, OutputResult
from pytorch_lightning.loops.utilities import (
from pytorch_lightning.trainer.progress import OptimizationProgress
from pytorch_lightning.utilities import AMPType
from pytorch_lightning.utilities.exceptions import MisconfigurationException
from pytorch_lightning.utilities.types import STEP_OUTPUT

class ClosureResult(OutputResult):
    """A container to hold the result of a :class:`Closure` call.

    It is created from the output of :meth:`~pytorch_lightning.core.module.LightningModule.training_step`.

        closure_loss: The loss with a graph attached.
        loss: A detached copy of the closure loss.
        extra: Any keys other than the loss returned.

    closure_loss: Optional[Tensor]
    loss: Optional[Tensor] = field(init=False, default=None)
    extra: Dict[str, Any] = field(default_factory=dict)

    def __post_init__(self) -> None:

    def _clone_loss(self) -> None:
        if self.closure_loss is not None:
            # the loss will get scaled for amp. avoid any modifications to it
            self.loss = self.closure_loss.detach().clone()

    def from_training_step_output(
        cls, training_step_output: Optional[STEP_OUTPUT], normalize: int = 1
    ) -> "ClosureResult":
        closure_loss, extra = None, {}

        if isinstance(training_step_output, dict):
            # this should not modify the `training_step_output`, as the user could be using it after `training_step_end`
            closure_loss = training_step_output.get("loss")
            if closure_loss is None:
                raise MisconfigurationException(
                    "In automatic_optimization, when `training_step` returns a dict, the 'loss' key needs to be present"
            extra = {k: v for k, v in training_step_output.items() if k not in ("loss", "hiddens")}
        elif isinstance(training_step_output, Tensor):
            closure_loss = training_step_output
        elif training_step_output is not None:
            raise MisconfigurationException(
                "In automatic optimization, `training_step` must return a Tensor, "
                "a dict, or None (where the step will be skipped)."

        if closure_loss is not None:
            # accumulate the loss. If ``accumulate_grad_batches == 1``, no effect
            # note: avoid in-place operation `x /= y` here on purpose
            closure_loss = closure_loss / normalize

        return cls(closure_loss, extra=extra)

    def asdict(self) -> Dict[str, Any]:
        return {"loss": self.loss, **self.extra}

class Closure(AbstractClosure[ClosureResult]):
    """An implementation of a :class:`AbstractClosure` for automatic optimization in Lightning that combines three
    elementary closures into one: ``training_step``, ``backward`` and ``zero_grad``.

    The Closure gets created by the training loop(s) and is then passed to the
    :meth:`torch.optim.Optimizer.step` method. An optimizer is responsible for calling the closure and optionally
    do something with the output.

        step_fn: This is typically the :meth:`pytorch_lightning.core.module.LightningModule.training_step
            wrapped with processing for its outputs
        backward_fn: A function that takes a loss value as input, performs back-propagation and returns the loss value.
            Can be set to ``None`` to skip the backward operation.
        zero_grad_fn: A function that zeroes the gradients. Can be set to ``None`` to skip zero_grad, for example
            when accumulating gradients.


        closure = Closure()
        optimizer = torch.optim.Adam(...)

    warning_cache = WarningCache()

    def __init__(
        step_fn: Callable[[], ClosureResult],
        backward_fn: Optional[Callable[[Tensor], None]] = None,
        zero_grad_fn: Optional[Callable[[], None]] = None,
        self._step_fn = step_fn
        self._backward_fn = backward_fn
        self._zero_grad_fn = zero_grad_fn

    def closure(self, *args: Any, **kwargs: Any) -> ClosureResult:
        step_output = self._step_fn()

        if step_output.closure_loss is None:
            self.warning_cache.warn("`training_step` returned `None`. If this was on purpose, ignore this warning...")

        if self._zero_grad_fn is not None:

        if self._backward_fn is not None and step_output.closure_loss is not None:

        return step_output

    def __call__(self, *args: Any, **kwargs: Any) -> Optional[Tensor]:
        self._result = self.closure(*args, **kwargs)
        return self._result.loss

_OUTPUTS_TYPE = Dict[int, Dict[str, Any]]

[docs]class OptimizerLoop(Loop[_OUTPUTS_TYPE]): """Runs over a sequence of optimizers. This loop implements what is known in Lightning as Automatic Optimization. """ output_result_cls = ClosureResult def __init__(self) -> None: super().__init__() self.optim_progress: OptimizationProgress = OptimizationProgress() self._outputs: _OUTPUTS_TYPE = {} self._skip_backward: bool = False self._optimizers: Tuple[Optimizer, ...] = tuple() self._indices: Tuple[int, ...] = tuple() self._hiddens: Optional[Any] = None @property def optimizer_idx(self) -> int: return self._indices[self.optim_progress.optimizer_position] @property def done(self) -> bool: """Returns ``True`` when the last optimizer in the sequence has run.""" return self.optim_progress.optimizer_position >= len(self._indices)
[docs] def connect(self, **kwargs: "Loop") -> None: raise NotImplementedError(f"{self.__class__.__name__} does not connect any child loops.")
[docs] def reset(self) -> None: if not self.restarting: # when reset() is called from outside (manually), we reset the loop progress self.optim_progress.optimizer_position = 0 else: self.optim_progress.reset_on_restart() self._outputs = {}
[docs] def on_run_start(self, optimizers: List[Tuple[int, Optimizer]], kwargs: OrderedDict) -> None: self._indices, self._optimizers = zip(*optimizers) if self.done: self.optim_progress.optimizer_position = 0
[docs] def advance(self, optimizers: List[Tuple[int, Optimizer]], kwargs: OrderedDict) -> None: kwargs = self._build_kwargs(kwargs, self.optimizer_idx, self._hiddens) result = self._run_optimization(kwargs, self._optimizers[self.optim_progress.optimizer_position]) if result.loss is not None: # automatic optimization assumes a loss needs to be returned for extras to be considered as the batch # would be skipped otherwise self._outputs[self.optimizer_idx] = result.asdict() self.optim_progress.optimizer_position += 1
[docs] def on_run_end(self) -> _OUTPUTS_TYPE: outputs, self._outputs = self._outputs, {} # free memory self._indices = tuple() self._optimizers = tuple() return outputs
def _run_optimization(self, kwargs: OrderedDict, optimizer: torch.optim.Optimizer) -> ClosureResult: """Runs closure (train step + backward) together with optimization if necessary. Args: kwargs: the kwargs passed down to the hooks. optimizer: the current optimizer """ opt_idx = kwargs.get("optimizer_idx", 0) # toggle model params self._run_optimization_start(opt_idx, optimizer) closure = self._make_closure(kwargs, optimizer) if ( # when the strategy handles accumulation, we want to always call the optimizer step not self.trainer.strategy.handles_gradient_accumulation and self.trainer.fit_loop._should_accumulate() ): # For gradient accumulation # ------------------- # calculate loss (train step + train step end) # ------------------- # automatic_optimization=True: perform ddp sync only when performing optimizer_step with _block_parallel_sync_behavior(self.trainer.strategy, block=True): closure() # ------------------------------ # BACKWARD PASS # ------------------------------ # gradient update with accumulated gradients else: # the `batch_idx` is optional with inter-batch parallelism self._optimizer_step(optimizer, opt_idx, kwargs.get("batch_idx", 0), closure) result = closure.consume_result() if result.loss is not None: # if no result, user decided to skip optimization # otherwise update running loss + reset accumulated loss # TODO: find proper way to handle updating running loss self.trainer.fit_loop.epoch_loop.batch_loop._update_running_loss(result.loss) # untoggle model params self._run_optimization_end(opt_idx) return result def _make_closure(self, kwargs: OrderedDict, optimizer: Optimizer) -> Closure: """Build a closure object that captures the given arguments and runs the `training_step` function and optionally other functions such as `backward` and `zero_grad`.""" opt_idx = kwargs.get("optimizer_idx", 0) step_fn = self._make_step_fn(kwargs) backward_fn = self._make_backward_fn(optimizer, opt_idx) zero_grad_fn = self._make_zero_grad_fn(kwargs.get("batch_idx", 0), opt_idx, optimizer) return Closure(step_fn=step_fn, backward_fn=backward_fn, zero_grad_fn=zero_grad_fn) def _make_step_fn(self, kwargs: OrderedDict) -> Callable[[], ClosureResult]: """Build the step function that runs the `training_step` and processes its output.""" return partial(self._training_step, kwargs) def _make_zero_grad_fn(self, batch_idx: int, opt_idx: int, optimizer: Optimizer) -> Optional[Callable[[], None]]: """Build a `zero_grad` function that zeroes the gradients before back-propagation. Returns ``None`` in the case backward needs to be skipped. """ if self._skip_backward: return None is_first_batch_to_accumulate = batch_idx % self.trainer.accumulate_grad_batches == 0 if not is_first_batch_to_accumulate: return None def zero_grad_fn() -> None: self._on_before_zero_grad(optimizer) self._optimizer_zero_grad(batch_idx, optimizer, opt_idx) return zero_grad_fn def _make_backward_fn(self, optimizer: Optimizer, opt_idx: int) -> Optional[Callable[[Tensor], None]]: """Build a `backward` function that handles back-propagation through the output produced by the `training_step` function. Returns ``None`` in the case backward needs to be skipped. """ if self._skip_backward: return None def backward_fn(loss: Tensor) -> None: self.trainer._call_strategy_hook("backward", loss, optimizer, opt_idx) return backward_fn def _run_optimization_start(self, opt_idx: int, optimizer: torch.optim.Optimizer) -> None: """Toggles the optimizer to ensure the correct one is used and prevent dangling grads. Args: opt_idx: the index of the optimizer to use optimizer: the optimizer to use """ # make sure only the gradients of the current optimizer's parameters are calculated # in the training step to prevent dangling gradients in multiple-optimizer setup. if len(self.trainer.optimizers) > 1: model = self.trainer.lightning_module model.toggle_optimizer(optimizer, opt_idx) def _run_optimization_end(self, opt_idx: int) -> None: if len(self.trainer.optimizers) > 1: model = self.trainer.lightning_module model.untoggle_optimizer(opt_idx) def _optimizer_step( self, optimizer: Union[Optimizer, LightningOptimizer], opt_idx: int, batch_idx: int, train_step_and_backward_closure: Callable[[], Optional[Tensor]], ) -> None: """Performs the optimizer step and some sanity checking. Args: optimizer: the optimizer to perform the step with opt_idx: the index of the current :param:`optimizer` batch_idx: the index of the current batch train_step_and_backward_closure: the closure function performing the train step and computing the gradients. By default, called by the optimizer (if possible) """ is_lbfgs = isinstance(optimizer, torch.optim.LBFGS) # wraps into LightningOptimizer only for running step if self.trainer.amp_backend == AMPType.APEX: # apex overrides .step function and need to be wrapped on each step optimizer = LightningOptimizer._to_lightning_optimizer(optimizer, self.trainer.strategy, opt_idx) else: optimizer = self.trainer.strategy._lightning_optimizers[opt_idx] # if `strategy.handles_gradient_accumulation`, this method will be called to route into the strategy, but we # need to check again if `should_accumulate` before increasing the counters should_accumulate = self.trainer.fit_loop._should_accumulate() if not should_accumulate: self.optim_progress.optimizer.step.increment_ready() # model hook self.trainer._call_lightning_module_hook( "optimizer_step", self.trainer.current_epoch, batch_idx, optimizer, opt_idx, train_step_and_backward_closure, on_tpu=isinstance(self.trainer.accelerator, TPUAccelerator), using_native_amp=(self.trainer.amp_backend == AMPType.NATIVE), using_lbfgs=is_lbfgs, ) if not should_accumulate: self.optim_progress.optimizer.step.increment_completed() def _on_before_zero_grad(self, optimizer: torch.optim.Optimizer) -> None: """Calls the ``on_before_zero_grad`` hook. Args: optimizer: the current optimizer """ self.optim_progress.optimizer.zero_grad.increment_ready() self.trainer._call_callback_hooks("on_before_zero_grad", optimizer) self.trainer._call_lightning_module_hook("on_before_zero_grad", optimizer) self.optim_progress.optimizer.zero_grad.increment_started() def _optimizer_zero_grad(self, batch_idx: int, optimizer: torch.optim.Optimizer, opt_idx: int) -> None: """Zeroes out all gradients of parameters optimized by the current optimizer. Args: batch_idx: the index of the current batch optimizer: the current optimizer opt_idx: the index of the current optimizer """ self.trainer._call_lightning_module_hook( "optimizer_zero_grad", self.trainer.current_epoch, batch_idx, optimizer, opt_idx ) self.optim_progress.optimizer.zero_grad.increment_completed() def _training_step(self, kwargs: OrderedDict) -> ClosureResult: """Performs the actual train step with the tied hooks. Args: kwargs: the kwargs passed down to the hooks. Returns: A ``ClosureResult`` containing the training step output. """ # manually capture logged metrics training_step_output = self.trainer._call_strategy_hook("training_step", *kwargs.values()) self.trainer.strategy.post_training_step() model_output = self.trainer._call_lightning_module_hook("training_step_end", training_step_output) strategy_output = self.trainer._call_strategy_hook("training_step_end", training_step_output) training_step_output = strategy_output if model_output is None else model_output self._hiddens = _extract_hiddens(training_step_output, self.trainer.lightning_module.truncated_bptt_steps) result = self.output_result_cls.from_training_step_output( training_step_output, self.trainer.accumulate_grad_batches ) if self.trainer.move_metrics_to_cpu: # hiddens and the training step output are not moved as they are not considered "metrics" assert self.trainer._results is not None self.trainer._results.cpu() return result def _build_kwargs(self, kwargs: OrderedDict, opt_idx: int, hiddens: Optional[Any]) -> OrderedDict: """Helper method to build the arguments for the current step. Args: kwargs: The kwargs passed down to the hooks. opt_idx: the index of the current optimizer. hiddens: the hidden state of the previous RNN iteration. Returns: The kwargs passed down to the hooks. """ return _build_training_step_kwargs( kwargs, self.trainer.lightning_module, self.trainer.optimizers, opt_idx, hiddens )

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