Source code for pytorch_lightning.core.optimizer

# 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 contextlib import contextmanager
from dataclasses import fields
from typing import Any, Callable, Dict, Generator, List, Optional, Tuple, Union
from weakref import proxy

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

import pytorch_lightning as pl
from lightning_lite.utilities.types import _Stateful, ReduceLROnPlateau
from pytorch_lightning.utilities.exceptions import MisconfigurationException
from pytorch_lightning.utilities.model_helpers import is_overridden
from pytorch_lightning.utilities.rank_zero import rank_zero_warn
from pytorch_lightning.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.""" def __init__(self, optimizer: Optimizer): # copy most of the `Optimizer` methods into this instance. `__del__` is skipped in case the optimizer has # implemented custom logic which we would not want to call on destruction of the `LightningOptimizer` self.__dict__ = {k: v for k, v in optimizer.__dict__.items() if k not in ("step", "__del__")} # For Horovod if hasattr(optimizer, "skip_synchronize"): self.__class__ = type( "Lightning" + optimizer.__class__.__name__, (self.__class__, optimizer.__class__.__bases__[0]), {} ) self.skip_synchronize = optimizer.skip_synchronize self.synchronize = optimizer.synchronize else: self.__class__ = type("Lightning" + optimizer.__class__.__name__, (self.__class__, optimizer.__class__), {}) self._optimizer = optimizer self._strategy: Optional[pl.strategies.Strategy] = None self._optimizer_idx = 0 # 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 @property def optimizer(self) -> Optimizer: return self._optimizer @classmethod def _to_lightning_optimizer( cls, optimizer: Union[Optimizer, "LightningOptimizer"], strategy: "pl.strategies.Strategy", opt_idx: int ) -> "LightningOptimizer": if isinstance(optimizer, LightningOptimizer): # the user could return a `LightningOptimizer` from `configure_optimizers`, see test: # tests/core/[False] lightning_optimizer = optimizer else: lightning_optimizer = cls(optimizer) lightning_optimizer._strategy = proxy(strategy) lightning_optimizer._optimizer_idx = opt_idx return lightning_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 pytorch_lightning.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, self._optimizer_idx) yield lightning_module.untoggle_optimizer(self._optimizer_idx)
[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(...): 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, self._optimizer_idx, closure, **kwargs) self._on_after_step() return step_output
def _init_optimizers_and_lr_schedulers( model: "pl.LightningModule", ) -> Tuple[List[Optimizer], List[LRSchedulerConfig], List[int]]: """Calls `LightningModule.configure_optimizers` and parses and validates the output.""" optim_conf = model.trainer._call_lightning_module_hook("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, optimizer_frequencies, 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) ) _set_scheduler_opt_idx(optimizers, lr_scheduler_configs) _validate_scheduler_api(lr_scheduler_configs, model) return optimizers, lr_scheduler_configs, optimizer_frequencies def _configure_optimizers( optim_conf: Union[Dict[str, Any], List, Optimizer, Tuple] ) -> Tuple[List, List, List, Optional[str]]: optimizers, lr_schedulers, optimizer_frequencies = [], [], [] monitor = None # single output, single optimizer if isinstance(optim_conf, Optimizer): 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, Optimizer) 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, opt_idx: dict(scheduler, opt_idx=opt_idx) if isinstance(scheduler, dict) else {"scheduler": scheduler, "opt_idx": opt_idx} ) lr_schedulers = [ scheduler_dict(opt_dict["lr_scheduler"], opt_idx) for opt_idx, opt_dict in enumerate(optim_conf) if "lr_scheduler" in opt_dict ] optimizer_frequencies = [ opt_dict["frequency"] for opt_dict in optim_conf if opt_dict.get("frequency", None) is not None ] # assert that if frequencies are present, they are given for all optimizers if optimizer_frequencies and len(optimizer_frequencies) != len(optimizers): raise ValueError("A frequency must be given to each optimizer.") # single list or tuple, multiple optimizer elif isinstance(optim_conf, (list, tuple)) and all(isinstance(opt, Optimizer) 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' ' * A list of the previously described dict format, with an optional "frequency" key (int)' ) return optimizers, lr_schedulers, optimizer_frequencies, 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): invalid_keys = {"interval", "frequency", "reduce_on_plateau", "monitor", "strict"} keys_to_warn = [k for k in scheduler.keys() 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): 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 _set_scheduler_opt_idx(optimizers: List[Optimizer], lr_scheduler_configs: List[LRSchedulerConfig]) -> None: for config in lr_scheduler_configs: for opt_idx, opt in enumerate(optimizers): if config.scheduler.optimizer is opt: if config.opt_idx is not None and config.opt_idx != opt_idx: raise MisconfigurationException( "`opt_idx` set inside scheduler config does not match with the index" " of the respective optimizer returned from `configure_optimizers`." ) config.opt_idx = opt_idx break else: 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", "frequency", "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 `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 def step(self, closure: Callable = None) -> None: if closure is not None: closure() def zero_grad(self, set_to_none: Optional[bool] = False) -> None: pass # Do Nothing def __repr__(self) -> str: return "No Optimizer"

© Copyright Copyright (c) 2018-2023, Lightning AI et al...

Built with Sphinx using a theme provided by Read the Docs.