Source code for pytorch_lightning.loops.optimization.manual_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
#
# 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 dataclasses import dataclass, field
from typing import Any, Dict, Optional
from torch import Tensor
from pytorch_lightning.core.optimizer import do_nothing_closure
from pytorch_lightning.loops import Loop
from pytorch_lightning.loops.optimization.closure import OutputResult
from pytorch_lightning.loops.utilities import _build_training_step_kwargs, _extract_hiddens
from pytorch_lightning.trainer.progress import Progress, ReadyCompletedTracker
from pytorch_lightning.utilities.exceptions import MisconfigurationException
from pytorch_lightning.utilities.types import STEP_OUTPUT
@dataclass
class ManualResult(OutputResult):
"""A container to hold the result returned by the ``ManualLoop``.
It is created from the output of :meth:`~pytorch_lightning.core.lightning.LightningModule.training_step`.
Attributes:
extra: Anything returned by the ``training_step``.
"""
extra: Dict[str, Any] = field(default_factory=dict)
@classmethod
def from_training_step_output(cls, training_step_output: Optional[STEP_OUTPUT]) -> "ManualResult":
extra = {}
if isinstance(training_step_output, dict):
extra = {k: v for k, v in training_step_output.items() if k != "hiddens"}
elif isinstance(training_step_output, Tensor):
extra = {"loss": training_step_output}
elif training_step_output is not None:
raise MisconfigurationException(
"In manual optimization, `training_step` must either return a Tensor, "
"a dict with extras to pass to `training_epoch_end` or have no return."
)
if "loss" in extra:
# we detach manually as it's expected that it will have a `grad_fn`
extra["loss"] = extra["loss"].detach()
return cls(extra=extra)
def asdict(self) -> Dict[str, Any]:
return self.extra
_OUTPUTS_TYPE = Dict[str, Any]
[docs]class ManualOptimization(Loop[_OUTPUTS_TYPE]):
"""A special loop implementing what is known in Lightning as Manual Optimization where the optimization happens
entirely in the :meth:`~pytorch_lightning.core.lightning.LightningModule.training_step` and therefore the user
is responsible for back-propagating gradients and making calls to the optimizers.
This loop is a trivial case because it performs only a single iteration (calling directly into the module's
:meth:`~pytorch_lightning.core.lightning.LightningModule.training_step`) and passing through the output(s).
"""
output_result_cls = ManualResult
def __init__(self) -> None:
super().__init__()
# since manual optimization does not track lr scheduler or optimizer frequencies, we use a simpler progress than
# `OptimizationProgress`
self.optim_step_progress = Progress.from_defaults(ReadyCompletedTracker)
self._done: bool = False
self._hiddens: Optional[Any] = None
self._output: _OUTPUTS_TYPE = {}
@property
def done(self) -> bool:
return self._done
[docs] def on_run_start(self, *_: Any, **__: Any) -> None:
# inject logic around the optimizer step
for i, lightning_optimizer in self.trainer.strategy._lightning_optimizers.items():
lightning_optimizer._on_before_step = self._on_before_step
lightning_optimizer._on_after_step = self._on_after_step
[docs] def advance(self, batch: Any, batch_idx: int) -> None: # type: ignore[override]
"""Performs the training step for manual optimization.
Args:
batch: the current tbptt split of the current batch
batch_idx: the index of the current batch
"""
assert self.trainer is not None
lightning_module = self.trainer.lightning_module
step_kwargs = _build_training_step_kwargs(
lightning_module, self.trainer.optimizers, batch, batch_idx, opt_idx=None, hiddens=self._hiddens
)
# manually capture logged metrics
training_step_output = self.trainer._call_strategy_hook("training_step", *step_kwargs.values())
self.trainer.strategy.post_training_step()
del step_kwargs
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, lightning_module.truncated_bptt_steps)
result = self.output_result_cls.from_training_step_output(training_step_output)
if self.trainer.move_metrics_to_cpu:
# hiddens and the training step output are not moved as they are not considered "metrics"
# the user might need them on the correct device for an operation in `training_epoch_end`
assert self.trainer._results is not None
self.trainer._results.cpu()
self._done = True
self._output = result.asdict()
[docs] def on_run_end(self) -> _OUTPUTS_TYPE:
"""Returns the result of this loop, i.e., the post-processed outputs from the training step."""
output, self._output = self._output, {} # free memory
# reset logic around the optimizer step
for i, lightning_optimizer in self.trainer.strategy._lightning_optimizers.items():
lightning_optimizer._on_before_step = do_nothing_closure
lightning_optimizer._on_after_step = do_nothing_closure
return output
def _on_before_step(self) -> None:
self.optim_step_progress.increment_ready()
self.trainer.profiler.start("optimizer_step")
def _on_after_step(self) -> None:
self.trainer.profiler.stop("optimizer_step")
self.optim_step_progress.increment_completed()
