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
#
# 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 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 (
_block_parallel_sync_behavior,
_build_training_step_kwargs,
_extract_hiddens,
)
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
@dataclass
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`.
Attributes:
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:
self._clone_loss()
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()
@classmethod
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.
Args:
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.
Example:
closure = Closure()
optimizer = torch.optim.Adam(...)
optimizer.step(closure)
"""
warning_cache = WarningCache()
def __init__(
self,
step_fn: Callable[[], ClosureResult],
backward_fn: Optional[Callable[[Tensor], None]] = None,
zero_grad_fn: Optional[Callable[[], None]] = None,
):
super().__init__()
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:
self._zero_grad_fn()
if self._backward_fn is not None and step_output.closure_loss is not None:
self._backward_fn(step_output.closure_loss)
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
)
