Source code for lightning.pytorch.core.optimizer

# Copyright The Lightning AI team.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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# Unless required by applicable law or agreed to in writing, software
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from contextlib import contextmanager
from dataclasses import fields
from typing import Any, Callable, Dict, Generator, List, Optional, Tuple, Union, overload
from weakref import proxy

import torch
from torch import optim
from torch.optim import Optimizer

import lightning.pytorch as pl
from lightning.fabric.utilities.types import Optimizable, ReduceLROnPlateau, _Stateful
from lightning.pytorch.utilities.exceptions import MisconfigurationException
from lightning.pytorch.utilities.model_helpers import is_overridden
from lightning.pytorch.utilities.rank_zero import rank_zero_warn
from lightning.pytorch.utilities.signature_utils import is_param_in_hook_signature
from lightning.pytorch.utilities.types import LRSchedulerConfig, LRSchedulerTypeTuple

def do_nothing_closure() -> None:

[docs]class LightningOptimizer: """This class is used to wrap the user optimizers and handle properly the backward and optimizer_step logic across accelerators, AMP, accumulate_grad_batches. Note: The purpose of this wrapper is only to define new methods and redirect the `.step()` call. The internal state ``__dict__`` is not kept in sync with the internal state of the original optimizer, but the Trainer never relies on the internal state of the wrapper. """ def __init__(self, optimizer: Optimizer): self._optimizer = optimizer self._strategy: Optional[pl.strategies.Strategy] = None # to inject logic around the optimizer step, particularly useful with manual optimization self._on_before_step = do_nothing_closure self._on_after_step = do_nothing_closure # imitate the class of the wrapped object to make isinstance checks work self.__class__ = type("Lightning" + optimizer.__class__.__name__, (self.__class__, optimizer.__class__), {}) @property def optimizer(self) -> Optimizer: return self._optimizer
[docs] @contextmanager def toggle_model(self, sync_grad: bool = True) -> Generator[None, None, None]: """This function is just a helper for advanced users. Considering the current optimizer as A and all other optimizers as B. Toggling means all parameters from B exclusive to A will have ``requires_grad`` set to False. When performing gradient accumulation, there is no need to perform grad synchronization during the accumulation phase. Setting `sync_grad` to False will block this synchronization and improve performance. """ # local import here to avoid circular import from lightning.pytorch.loops.utilities import _block_parallel_sync_behavior assert self._strategy is not None lightning_module = self._strategy.lightning_module assert lightning_module is not None with _block_parallel_sync_behavior(self._strategy, block=(not sync_grad)): lightning_module.toggle_optimizer(self) yield lightning_module.untoggle_optimizer(self)
[docs] def step(self, closure: Optional[Callable[[], Any]] = None, **kwargs: Any) -> Any: """Performs a single optimization step (parameter update). Args: closure: An optional optimizer closure. kwargs: Any additional arguments to the ``optimizer.step()`` call. Returns: The output from the step call, which is generally the output of the closure execution. Example:: # Scenario for a GAN using manual optimization def training_step(self, batch, batch_idx): opt_gen, opt_dis = self.optimizers() ... # compute generator loss loss_gen = self.compute_generator_loss(...) # zero_grad needs to be called before backward opt_gen.zero_grad() self.manual_backward(loss_gen) opt_gen.step() # compute discriminator loss loss_dis = self.compute_discriminator_loss(...) # zero_grad needs to be called before backward opt_dis.zero_grad() self.manual_backward(loss_dis) opt_dis.step() # A more advanced example def training_step(self, batch, batch_idx): opt_gen, opt_dis = self.optimizers() ... accumulated_grad_batches = batch_idx % 2 == 0 # compute generator loss def closure_gen(): loss_gen = self.compute_generator_loss(...) self.manual_backward(loss_gen) if accumulated_grad_batches: opt_gen.zero_grad() with opt_gen.toggle_model(sync_grad=accumulated_grad_batches): opt_gen.step(closure=closure_gen) def closure_dis(): loss_dis = self.compute_discriminator_loss(...) self.manual_backward(loss_dis) if accumulated_grad_batches: opt_dis.zero_grad() with opt_dis.toggle_model(sync_grad=accumulated_grad_batches): opt_dis.step(closure=closure_dis) """ self._on_before_step() if closure is None: closure = do_nothing_closure elif not callable(closure): raise MisconfigurationException("When `optimizer.step(closure)` is called, the closure should be callable") assert self._strategy is not None step_output = self._strategy.optimizer_step(self._optimizer, closure, **kwargs) self._on_after_step() return step_output
@classmethod def _to_lightning_optimizer( cls, optimizer: Union[Optimizer, "LightningOptimizer"], strategy: "pl.strategies.Strategy" ) -> "LightningOptimizer": # the user could return a `LightningOptimizer` from `configure_optimizers`, see test: # tests/core/[False] lightning_optimizer = optimizer if isinstance(optimizer, LightningOptimizer) else cls(optimizer) lightning_optimizer._strategy = proxy(strategy) return lightning_optimizer def __getattr__(self, item: Any) -> Any: return getattr(self._optimizer, item)
def _init_optimizers_and_lr_schedulers( model: "pl.LightningModule", ) -> Tuple[List[Optimizer], List[LRSchedulerConfig]]: """Calls `LightningModule.configure_optimizers` and parses and validates the output.""" from lightning.pytorch.trainer import call optim_conf = call._call_lightning_module_hook(model.trainer, "configure_optimizers", pl_module=model) if optim_conf is None: rank_zero_warn( "`LightningModule.configure_optimizers` returned `None`, this fit will run with no optimizer", ) optim_conf = _MockOptimizer() optimizers, lr_schedulers, monitor = _configure_optimizers(optim_conf) lr_scheduler_configs = ( _configure_schedulers_automatic_opt(lr_schedulers, monitor) if model.automatic_optimization else _configure_schedulers_manual_opt(lr_schedulers) ) _validate_multiple_optimizers_support(optimizers, model) _validate_optimizers_attached(optimizers, lr_scheduler_configs) _validate_scheduler_api(lr_scheduler_configs, model) return optimizers, lr_scheduler_configs def _configure_optimizers( optim_conf: Union[Dict[str, Any], List, Optimizer, Tuple] ) -> Tuple[List, List, Optional[str]]: optimizers, lr_schedulers = [], [] monitor = None # single output, single optimizer if isinstance(optim_conf, Optimizable): optimizers = [optim_conf] # two lists, optimizer + lr schedulers elif ( isinstance(optim_conf, (list, tuple)) and len(optim_conf) == 2 and isinstance(optim_conf[0], list) and all(isinstance(opt, Optimizable) for opt in optim_conf[0]) ): opt, sch = optim_conf optimizers = opt lr_schedulers = sch if isinstance(sch, list) else [sch] # single dictionary elif isinstance(optim_conf, dict): _validate_optim_conf(optim_conf) optimizers = [optim_conf["optimizer"]] monitor = optim_conf.get("monitor", None) lr_schedulers = [optim_conf["lr_scheduler"]] if "lr_scheduler" in optim_conf else [] # multiple dictionaries elif isinstance(optim_conf, (list, tuple)) and all(isinstance(d, dict) for d in optim_conf): for opt_dict in optim_conf: _validate_optim_conf(opt_dict) optimizers = [opt_dict["optimizer"] for opt_dict in optim_conf] scheduler_dict = lambda scheduler: dict(scheduler) if isinstance(scheduler, dict) else {"scheduler": scheduler} lr_schedulers = [ scheduler_dict(opt_dict["lr_scheduler"]) for opt_dict in optim_conf if "lr_scheduler" in opt_dict ] # single list or tuple, multiple optimizer elif isinstance(optim_conf, (list, tuple)) and all(isinstance(opt, Optimizable) for opt in optim_conf): optimizers = list(optim_conf) # unknown configuration else: raise MisconfigurationException( "Unknown configuration for model optimizers." " Output from `model.configure_optimizers()` should be one of:\n" " * `Optimizer`\n" " * [`Optimizer`]\n" " * ([`Optimizer`], [`LRScheduler`])\n" ' * {"optimizer": `Optimizer`, (optional) "lr_scheduler": `LRScheduler`}\n' ) return optimizers, lr_schedulers, monitor def _configure_schedulers_automatic_opt(schedulers: list, monitor: Optional[str]) -> List[LRSchedulerConfig]: """Convert each scheduler into `LRSchedulerConfig` with relevant information, when using automatic optimization.""" lr_scheduler_configs = [] for scheduler in schedulers: if isinstance(scheduler, dict): # check provided keys supported_keys = { for field in fields(LRSchedulerConfig)} extra_keys = scheduler.keys() - supported_keys if extra_keys: rank_zero_warn( f"Found unsupported keys in the lr scheduler dict: {extra_keys}." " HINT: remove them from the output of `configure_optimizers`.", category=RuntimeWarning, ) scheduler = {k: v for k, v in scheduler.items() if k in supported_keys} if "scheduler" not in scheduler: raise MisconfigurationException( 'The lr scheduler dict must have the key "scheduler" with its item being an lr scheduler' ) if "interval" in scheduler and scheduler["interval"] not in ("step", "epoch"): raise MisconfigurationException( 'The "interval" key in lr scheduler dict must be "step" or "epoch"' f' but is "{scheduler["interval"]}"' ) scheduler["reduce_on_plateau"] = scheduler.get( "reduce_on_plateau", isinstance(scheduler["scheduler"], optim.lr_scheduler.ReduceLROnPlateau) ) if scheduler["reduce_on_plateau"] and scheduler.get("monitor", None) is None: raise MisconfigurationException( "The lr scheduler dict must include a monitor when a `ReduceLROnPlateau` scheduler is used." ' For example: {"optimizer": optimizer, "lr_scheduler":' ' {"scheduler": scheduler, "monitor": "your_loss"}}' ) is_one_cycle = isinstance(scheduler["scheduler"], optim.lr_scheduler.OneCycleLR) if is_one_cycle and scheduler.get("interval", "epoch") == "epoch": rank_zero_warn( "A `OneCycleLR` scheduler is using 'interval': 'epoch'." " Are you sure you didn't mean 'interval': 'step'?", category=RuntimeWarning, ) config = LRSchedulerConfig(**scheduler) elif isinstance(scheduler, ReduceLROnPlateau): if monitor is None: raise MisconfigurationException( "`configure_optimizers` must include a monitor when a `ReduceLROnPlateau`" " scheduler is used. For example:" ' {"optimizer": optimizer, "lr_scheduler": scheduler, "monitor": "metric_to_track"}' ) config = LRSchedulerConfig(scheduler, reduce_on_plateau=True, monitor=monitor) else: config = LRSchedulerConfig(scheduler) lr_scheduler_configs.append(config) return lr_scheduler_configs def _configure_schedulers_manual_opt(schedulers: list) -> List[LRSchedulerConfig]: """Convert each scheduler into `LRSchedulerConfig` structure with relevant information, when using manual optimization.""" lr_scheduler_configs = [] for scheduler in schedulers: if isinstance(scheduler, dict): # interval is not in this list even though the user needs to manually call the scheduler because # the `LearningRateMonitor` callback needs to check its value to know when to log the learning rate invalid_keys = {"reduce_on_plateau", "monitor", "strict"} keys_to_warn = [k for k in scheduler if k in invalid_keys] if keys_to_warn: rank_zero_warn( f"The lr scheduler dict contains the key(s) {keys_to_warn}, but the keys will be ignored." " You need to call `lr_scheduler.step()` manually in manual optimization.", category=RuntimeWarning, ) config = LRSchedulerConfig(**{key: scheduler[key] for key in scheduler if key not in invalid_keys}) else: config = LRSchedulerConfig(scheduler) lr_scheduler_configs.append(config) return lr_scheduler_configs def _validate_scheduler_api(lr_scheduler_configs: List[LRSchedulerConfig], model: "pl.LightningModule") -> None: for config in lr_scheduler_configs: scheduler = config.scheduler if not isinstance(scheduler, _Stateful): raise TypeError( f"The provided lr scheduler `{scheduler.__class__.__name__}` is invalid." " It should have `state_dict` and `load_state_dict` methods defined." ) if ( not isinstance(scheduler, LRSchedulerTypeTuple) and not is_overridden("lr_scheduler_step", model) and model.automatic_optimization ): raise MisconfigurationException( f"The provided lr scheduler `{scheduler.__class__.__name__}` doesn't follow PyTorch's LRScheduler" " API. You should override the `LightningModule.lr_scheduler_step` hook with your own logic if" " you are using a custom LR scheduler." ) def _validate_multiple_optimizers_support(optimizers: List[Optimizer], model: "pl.LightningModule") -> None: if is_param_in_hook_signature(model.training_step, "optimizer_idx", explicit=True): raise RuntimeError( "Training with multiple optimizers is only supported with manual optimization. Remove the `optimizer_idx`" " argument from `training_step`, set `self.automatic_optimization = False` and access your optimizers" " in `training_step` with `opt1, opt2, ... = self.optimizers()`." ) if model.automatic_optimization and len(optimizers) > 1: raise RuntimeError( "Training with multiple optimizers is only supported with manual optimization. Set" " `self.automatic_optimization = False`, then access your optimizers in `training_step` with" " `opt1, opt2, ... = self.optimizers()`." ) def _validate_optimizers_attached(optimizers: List[Optimizer], lr_scheduler_configs: List[LRSchedulerConfig]) -> None: for config in lr_scheduler_configs: if config.scheduler.optimizer not in optimizers: raise MisconfigurationException( "Some schedulers are attached with an optimizer that wasn't returned from `configure_optimizers`." ) def _validate_optim_conf(optim_conf: Dict[str, Any]) -> None: valid_keys = {"optimizer", "lr_scheduler", "monitor"} extra_keys = optim_conf.keys() - valid_keys if extra_keys: rank_zero_warn( f"Found unsupported keys in the optimizer configuration: {set(extra_keys)}", category=RuntimeWarning ) class _MockOptimizer(Optimizer): """The `_MockOptimizer` will be used inplace of an optimizer in the event that `None` is returned from :meth:`~lightning.pytorch.core.LightningModule.configure_optimizers`.""" def __init__(self) -> None: super().__init__([torch.zeros(1)], {}) def add_param_group(self, param_group: Dict[Any, Any]) -> None: pass # Do Nothing def load_state_dict(self, state_dict: Dict[Any, Any]) -> None: pass # Do Nothing def state_dict(self) -> Dict[str, Any]: return {} # Return Empty @overload def step(self, closure: None = ...) -> None: ... @overload def step(self, closure: Callable[[], float]) -> float: ... def step(self, closure: Optional[Callable[[], float]] = None) -> Optional[float]: if closure is not None: return closure() def zero_grad(self, set_to_none: Optional[bool] = True) -> None: pass # Do Nothing def __repr__(self) -> str: return "No Optimizer"