Source code for lightning.pytorch.callbacks.lr_monitor

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Learning Rate Monitor

Monitor and logs learning rate for lr schedulers during training.

import itertools
from collections import defaultdict
from typing import Any, DefaultDict, Dict, List, Literal, Optional, Set, Tuple, Type

import torch
from torch.optim.optimizer import Optimizer
from typing_extensions import override

import lightning.pytorch as pl
from lightning.pytorch.callbacks.callback import Callback
from lightning.pytorch.utilities.exceptions import MisconfigurationException
from lightning.pytorch.utilities.rank_zero import rank_zero_warn
from lightning.pytorch.utilities.types import LRSchedulerConfig

[docs]class LearningRateMonitor(Callback): r"""Automatically monitor and logs learning rate for learning rate schedulers during training. Args: logging_interval: set to ``'epoch'`` or ``'step'`` to log ``lr`` of all optimizers at the same interval, set to ``None`` to log at individual interval according to the ``interval`` key of each scheduler. Defaults to ``None``. log_momentum: option to also log the momentum values of the optimizer, if the optimizer has the ``momentum`` or ``betas`` attribute. Defaults to ``False``. Raises: MisconfigurationException: If ``logging_interval`` is none of ``"step"``, ``"epoch"``, or ``None``. Example:: >>> from lightning.pytorch import Trainer >>> from lightning.pytorch.callbacks import LearningRateMonitor >>> lr_monitor = LearningRateMonitor(logging_interval='step') >>> trainer = Trainer(callbacks=[lr_monitor]) Logging names are automatically determined based on optimizer class name. In case of multiple optimizers of same type, they will be named ``Adam``, ``Adam-1`` etc. If a optimizer has multiple parameter groups they will be named ``Adam/pg1``, ``Adam/pg2`` etc. To control naming, pass in a ``name`` keyword in the construction of the learning rate schedulers. A ``name`` keyword can also be used for parameter groups in the construction of the optimizer. Example:: def configure_optimizer(self): optimizer = torch.optim.Adam(...) lr_scheduler = { 'scheduler': torch.optim.lr_scheduler.LambdaLR(optimizer, ...) 'name': 'my_logging_name' } return [optimizer], [lr_scheduler] Example:: def configure_optimizer(self): optimizer = torch.optim.SGD( [{ 'params': [p for p in self.parameters()], 'name': 'my_parameter_group_name' }], lr=0.1 ) lr_scheduler = torch.optim.lr_scheduler.LambdaLR(optimizer, ...) return [optimizer], [lr_scheduler] """ def __init__( self, logging_interval: Optional[Literal["step", "epoch"]] = None, log_momentum: bool = False, log_weight_decay: bool = False, ) -> None: if logging_interval not in (None, "step", "epoch"): raise MisconfigurationException("logging_interval should be `step` or `epoch` or `None`.") self.logging_interval = logging_interval self.log_momentum = log_momentum self.log_weight_decay = log_weight_decay self.lrs: Dict[str, List[float]] = {} self.last_momentum_values: Dict[str, Optional[List[float]]] = {} self.last_weight_decay_values: Dict[str, Optional[List[float]]] = {}
[docs] @override def on_train_start(self, trainer: "pl.Trainer", *args: Any, **kwargs: Any) -> None: """Called before training, determines unique names for all lr schedulers in the case of multiple of the same type or in the case of multiple parameter groups. Raises: MisconfigurationException: If ``Trainer`` has no ``logger``. """ if not trainer.loggers: raise MisconfigurationException( "Cannot use `LearningRateMonitor` callback with `Trainer` that has no logger." ) if self.log_momentum: def _check_no_key(key: str) -> bool: if trainer.lr_scheduler_configs: return any( key not in config.scheduler.optimizer.defaults for config in trainer.lr_scheduler_configs ) return any(key not in optimizer.defaults for optimizer in trainer.optimizers) if _check_no_key("momentum") and _check_no_key("betas"): rank_zero_warn( "You have set log_momentum=True, but some optimizers do not" " have momentum. This will log a value 0 for the momentum.", category=RuntimeWarning, ) # Find names for schedulers names: List[List[str]] = [] ( sched_hparam_keys, optimizers_with_scheduler, optimizers_with_scheduler_types, ) = self._find_names_from_schedulers(trainer.lr_scheduler_configs) names.extend(sched_hparam_keys) # Find names for leftover optimizers optimizer_hparam_keys, _ = self._find_names_from_optimizers( trainer.optimizers, seen_optimizers=optimizers_with_scheduler, seen_optimizer_types=optimizers_with_scheduler_types, ) names.extend(optimizer_hparam_keys) # Initialize for storing values names_flatten = list(itertools.chain.from_iterable(names)) self.lrs = {name: [] for name in names_flatten} self.last_momentum_values = {name + "-momentum": None for name in names_flatten} self.last_weight_decay_values = {name + "-weight_decay": None for name in names_flatten}
[docs] @override def on_train_batch_start(self, trainer: "pl.Trainer", *args: Any, **kwargs: Any) -> None: if not trainer._logger_connector.should_update_logs: return if self.logging_interval != "epoch": interval = "step" if self.logging_interval is None else "any" latest_stat = self._extract_stats(trainer, interval) if latest_stat: for logger in trainer.loggers: logger.log_metrics(latest_stat, step=trainer.fit_loop.epoch_loop._batches_that_stepped)
[docs] @override def on_train_epoch_start(self, trainer: "pl.Trainer", *args: Any, **kwargs: Any) -> None: if self.logging_interval != "step": interval = "epoch" if self.logging_interval is None else "any" latest_stat = self._extract_stats(trainer, interval) if latest_stat: for logger in trainer.loggers: logger.log_metrics(latest_stat, step=trainer.fit_loop.epoch_loop._batches_that_stepped)
def _extract_stats(self, trainer: "pl.Trainer", interval: str) -> Dict[str, float]: latest_stat = {} ( scheduler_hparam_keys, optimizers_with_scheduler, optimizers_with_scheduler_types, ) = self._find_names_from_schedulers(trainer.lr_scheduler_configs) self._remap_keys(scheduler_hparam_keys) for name, config in zip(scheduler_hparam_keys, trainer.lr_scheduler_configs): if interval in [config.interval, "any"]: opt = config.scheduler.optimizer current_stat = self._get_optimizer_stats(opt, name) latest_stat.update(current_stat) optimizer_hparam_keys, optimizers_without_scheduler = self._find_names_from_optimizers( trainer.optimizers, seen_optimizers=optimizers_with_scheduler, seen_optimizer_types=optimizers_with_scheduler_types, ) self._remap_keys(optimizer_hparam_keys) for opt, names in zip(optimizers_without_scheduler, optimizer_hparam_keys): current_stat = self._get_optimizer_stats(opt, names) latest_stat.update(current_stat) trainer.callback_metrics.update( {name: torch.tensor(value, device=trainer.strategy.root_device) for name, value in latest_stat.items()} ) return latest_stat def _get_optimizer_stats(self, optimizer: Optimizer, names: List[str]) -> Dict[str, float]: stats = {} param_groups = optimizer.param_groups use_betas = "betas" in optimizer.defaults for pg, name in zip(param_groups, names): lr = self._extract_lr(pg, name) stats.update(lr) momentum = self._extract_momentum( param_group=pg, name=name.replace(name, f"{name}-momentum"), use_betas=use_betas ) stats.update(momentum) weight_decay = self._extract_weight_decay(pg, f"{name}-weight_decay") stats.update(weight_decay) return stats def _extract_lr(self, param_group: Dict[str, Any], name: str) -> Dict[str, Any]: lr = param_group["lr"] self.lrs[name].append(lr) return {name: lr} def _remap_keys(self, names: List[List[str]], token: str = "/pg1") -> None: """This function is used the remap the keys if param groups for a given optimizer increased.""" for group_new_names in names: for new_name in group_new_names: old_name = new_name.replace(token, "") if token in new_name and old_name in self.lrs: self.lrs[new_name] = self.lrs.pop(old_name) elif new_name not in self.lrs: self.lrs[new_name] = [] def _extract_momentum(self, param_group: Dict[str, List], name: str, use_betas: bool) -> Dict[str, float]: if not self.log_momentum: return {} momentum = param_group["betas"][0] if use_betas else param_group.get("momentum", 0) self.last_momentum_values[name] = momentum return {name: momentum} def _extract_weight_decay(self, param_group: Dict[str, Any], name: str) -> Dict[str, Any]: """Extracts the weight decay statistics from a parameter group.""" if not self.log_weight_decay: return {} weight_decay = param_group["weight_decay"] self.last_weight_decay_values[name] = weight_decay return {name: weight_decay} def _add_prefix( self, name: str, optimizer_cls: Type[Optimizer], seen_optimizer_types: DefaultDict[Type[Optimizer], int] ) -> str: if optimizer_cls not in seen_optimizer_types: return name count = seen_optimizer_types[optimizer_cls] return name + f"-{count - 1}" if count > 1 else name def _add_suffix(self, name: str, param_groups: List[Dict], param_group_index: int, use_names: bool = True) -> str: if len(param_groups) > 1: if not use_names: return f"{name}/pg{param_group_index + 1}" pg_name = param_groups[param_group_index].get("name", f"pg{param_group_index + 1}") return f"{name}/{pg_name}" if use_names: pg_name = param_groups[param_group_index].get("name") return f"{name}/{pg_name}" if pg_name else name return name def _duplicate_param_group_names(self, param_groups: List[Dict]) -> Set[str]: names = [pg.get("name", f"pg{i}") for i, pg in enumerate(param_groups, start=1)] unique = set(names) if len(names) == len(unique): return set() return {n for n in names if names.count(n) > 1} def _find_names_from_schedulers( self, lr_scheduler_configs: List[LRSchedulerConfig], ) -> Tuple[List[List[str]], List[Optimizer], DefaultDict[Type[Optimizer], int]]: # Create unique names in the case we have multiple of the same learning # rate scheduler + multiple parameter groups names = [] seen_optimizers: List[Optimizer] = [] seen_optimizer_types: DefaultDict[Type[Optimizer], int] = defaultdict(int) for config in lr_scheduler_configs: sch = config.scheduler name = if is not None else "lr-" + sch.optimizer.__class__.__name__ updated_names = self._check_duplicates_and_update_name( sch.optimizer, name, seen_optimizers, seen_optimizer_types, config ) names.append(updated_names) return names, seen_optimizers, seen_optimizer_types def _find_names_from_optimizers( self, optimizers: List[Any], seen_optimizers: List[Optimizer], seen_optimizer_types: DefaultDict[Type[Optimizer], int], ) -> Tuple[List[List[str]], List[Optimizer]]: names = [] optimizers_without_scheduler = [] for optimizer in optimizers: # Deepspeed optimizer wraps the native optimizer optimizer = optimizer.optimizer if hasattr(optimizer, "optimizer") else optimizer if optimizer in seen_optimizers: continue name = "lr-" + optimizer.__class__.__name__ updated_names = self._check_duplicates_and_update_name( optimizer, name, seen_optimizers, seen_optimizer_types, None ) names.append(updated_names) optimizers_without_scheduler.append(optimizer) return names, optimizers_without_scheduler def _check_duplicates_and_update_name( self, optimizer: Optimizer, name: str, seen_optimizers: List[Optimizer], seen_optimizer_types: DefaultDict[Type[Optimizer], int], lr_scheduler_config: Optional[LRSchedulerConfig], ) -> List[str]: seen_optimizers.append(optimizer) optimizer_cls = type(optimizer) if lr_scheduler_config is None or is None: seen_optimizer_types[optimizer_cls] += 1 # Multiple param groups for the same optimizer param_groups = optimizer.param_groups duplicates = self._duplicate_param_group_names(param_groups) if duplicates: raise MisconfigurationException( "A single `Optimizer` cannot have multiple parameter groups with identical " f"`name` values. {name} has duplicated parameter group names {duplicates}" ) name = self._add_prefix(name, optimizer_cls, seen_optimizer_types) return [self._add_suffix(name, param_groups, i) for i in range(len(param_groups))]