426 lines
15 KiB
Python
426 lines
15 KiB
Python
# Copyright The PyTorch Lightning team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import importlib
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import logging
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import os
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import uuid
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from functools import wraps
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from typing import Any, Dict, Optional, Sequence
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import numpy as np
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import torch
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from torch.optim.lr_scheduler import _LRScheduler
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import pytorch_lightning as pl
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from pytorch_lightning.callbacks import Callback
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from pytorch_lightning.core.optimizer import _init_optimizers_and_lr_schedulers, _set_scheduler_opt_idx
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from pytorch_lightning.loggers.base import DummyLogger
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from pytorch_lightning.utilities.exceptions import MisconfigurationException
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from pytorch_lightning.utilities.parsing import lightning_hasattr, lightning_setattr
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from pytorch_lightning.utilities.rank_zero import rank_zero_warn
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from pytorch_lightning.utilities.types import LRSchedulerConfig
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# check if ipywidgets is installed before importing tqdm.auto
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# to ensure it won't fail and a progress bar is displayed
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if importlib.util.find_spec("ipywidgets") is not None:
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from tqdm.auto import tqdm
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else:
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from tqdm import tqdm
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log = logging.getLogger(__name__)
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def _determine_lr_attr_name(trainer: "pl.Trainer", model: "pl.LightningModule") -> str:
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if isinstance(trainer.auto_lr_find, str):
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if not lightning_hasattr(model, trainer.auto_lr_find):
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raise MisconfigurationException(
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f"`auto_lr_find` was set to {trainer.auto_lr_find}, however"
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" could not find this as a field in `model` or `model.hparams`."
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)
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return trainer.auto_lr_find
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attr_options = ("lr", "learning_rate")
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for attr in attr_options:
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if lightning_hasattr(model, attr):
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return attr
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raise MisconfigurationException(
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"When `auto_lr_find=True`, either `model` or `model.hparams` should"
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f" have one of these fields: {attr_options} overridden."
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)
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class _LRFinder:
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"""LR finder object. This object stores the results of lr_find().
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Args:
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mode: either `linear` or `exponential`, how to increase lr after each step
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lr_min: lr to start search from
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lr_max: lr to stop search
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num_training: number of steps to take between lr_min and lr_max
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Example::
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# Run lr finder
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lr_finder = trainer.lr_find(model)
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# Results stored in
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lr_finder.results
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# Plot using
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lr_finder.plot()
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# Get suggestion
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lr = lr_finder.suggestion()
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"""
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def __init__(self, mode: str, lr_min: float, lr_max: float, num_training: int):
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assert mode in ("linear", "exponential"), "mode should be either `linear` or `exponential`"
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self.mode = mode
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self.lr_min = lr_min
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self.lr_max = lr_max
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self.num_training = num_training
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self.results = {}
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self._total_batch_idx = 0 # for debug purpose
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def _exchange_scheduler(self, trainer: "pl.Trainer", model: "pl.LightningModule"):
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"""Decorate `trainer.strategy.setup_optimizers` method such that it sets the user's originally specified
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optimizer together with a new scheduler that takes care of the learning rate search."""
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setup_optimizers = trainer.strategy.setup_optimizers
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@wraps(setup_optimizers)
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def func(trainer):
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# Decide the structure of the output from _init_optimizers_and_lr_schedulers
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optimizers, _, _ = _init_optimizers_and_lr_schedulers(trainer.lightning_module)
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if len(optimizers) != 1:
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raise MisconfigurationException(
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f"`model.configure_optimizers()` returned {len(optimizers)}, but"
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" learning rate finder only works with single optimizer"
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)
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optimizer = optimizers[0]
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new_lrs = [self.lr_min] * len(optimizer.param_groups)
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for param_group, new_lr in zip(optimizer.param_groups, new_lrs):
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param_group["lr"] = new_lr
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param_group["initial_lr"] = new_lr
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args = (optimizer, self.lr_max, self.num_training)
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scheduler = _LinearLR(*args) if self.mode == "linear" else _ExponentialLR(*args)
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trainer.strategy.optimizers = [optimizer]
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trainer.strategy.lr_scheduler_configs = [LRSchedulerConfig(scheduler, interval="step", opt_idx=0)]
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trainer.strategy.optimizer_frequencies = []
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_set_scheduler_opt_idx(trainer.optimizers, trainer.lr_scheduler_configs)
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return func
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def plot(self, suggest: bool = False, show: bool = False):
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"""Plot results from lr_find run
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Args:
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suggest: if True, will mark suggested lr to use with a red point
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show: if True, will show figure
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"""
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import matplotlib.pyplot as plt
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lrs = self.results["lr"]
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losses = self.results["loss"]
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fig, ax = plt.subplots()
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# Plot loss as a function of the learning rate
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ax.plot(lrs, losses)
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if self.mode == "exponential":
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ax.set_xscale("log")
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ax.set_xlabel("Learning rate")
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ax.set_ylabel("Loss")
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if suggest:
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_ = self.suggestion()
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if self._optimal_idx:
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ax.plot(lrs[self._optimal_idx], losses[self._optimal_idx], markersize=10, marker="o", color="red")
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if show:
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plt.show()
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return fig
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def suggestion(self, skip_begin: int = 10, skip_end: int = 1):
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"""This will propose a suggestion for choice of initial learning rate as the point with the steepest
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negative gradient.
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Returns:
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lr: suggested initial learning rate to use
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skip_begin: how many samples to skip in the beginning. Prevent too naive estimates
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skip_end: how many samples to skip in the end. Prevent too optimistic estimates
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"""
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try:
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loss = np.array(self.results["loss"][skip_begin:-skip_end])
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loss = loss[np.isfinite(loss)]
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min_grad = np.gradient(loss).argmin()
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self._optimal_idx = min_grad + skip_begin
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return self.results["lr"][self._optimal_idx]
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# todo: specify the possible exception
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except Exception:
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log.exception("Failed to compute suggesting for `lr`. There might not be enough points.")
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self._optimal_idx = None
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def lr_find(
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trainer: "pl.Trainer",
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model: "pl.LightningModule",
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min_lr: float = 1e-8,
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max_lr: float = 1,
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num_training: int = 100,
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mode: str = "exponential",
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early_stop_threshold: float = 4.0,
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update_attr: bool = False,
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) -> Optional[_LRFinder]:
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"""See :meth:`~pytorch_lightning.tuner.tuning.Tuner.lr_find`"""
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if trainer.fast_dev_run:
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rank_zero_warn("Skipping learning rate finder since fast_dev_run is enabled.")
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return
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# Determine lr attr
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if update_attr:
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lr_attr_name = _determine_lr_attr_name(trainer, model)
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# Save initial model, that is loaded after learning rate is found
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ckpt_path = os.path.join(trainer.default_root_dir, f".lr_find_{uuid.uuid4()}.ckpt")
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trainer.save_checkpoint(ckpt_path)
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params = __lr_finder_dump_params(trainer)
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# Set to values that are required by the algorithm
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__lr_finder_reset_params(trainer, num_training, early_stop_threshold)
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# Initialize lr finder object (stores results)
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lr_finder = _LRFinder(mode, min_lr, max_lr, num_training)
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# Disable standard progress bar for fit
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if trainer.progress_bar_callback:
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trainer.progress_bar_callback.disable()
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# Configure optimizer and scheduler
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trainer.strategy.setup_optimizers = lr_finder._exchange_scheduler(trainer, model)
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# Fit, lr & loss logged in callback
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trainer.tuner._run(model)
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# Prompt if we stopped early
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if trainer.global_step != num_training:
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log.info(f"LR finder stopped early after {trainer.global_step} steps due to diverging loss.")
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# Transfer results from callback to lr finder object
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lr_finder.results.update({"lr": trainer.callbacks[0].lrs, "loss": trainer.callbacks[0].losses})
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lr_finder._total_batch_idx = trainer.fit_loop.total_batch_idx # for debug purpose
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# Restore initial state of model
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trainer._checkpoint_connector.restore(ckpt_path)
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trainer.strategy.remove_checkpoint(ckpt_path)
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__lr_finder_restore_params(trainer, params)
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if trainer.progress_bar_callback:
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trainer.progress_bar_callback.enable()
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# Update lr attr if required
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if update_attr:
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lr = lr_finder.suggestion()
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# TODO: log lr.results to self.logger
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lightning_setattr(model, lr_attr_name, lr)
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log.info(f"Learning rate set to {lr}")
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return lr_finder
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def __lr_finder_dump_params(trainer: "pl.Trainer") -> Dict[str, Any]:
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return {
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"auto_lr_find": trainer.auto_lr_find,
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"callbacks": trainer.callbacks,
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"logger": trainer.logger,
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"max_steps": trainer.fit_loop.max_steps,
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}
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def __lr_finder_reset_params(trainer: "pl.Trainer", num_training: int, early_stop_threshold: float) -> None:
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# avoid lr find being called multiple times
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trainer.auto_lr_find = False
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# Use special lr logger callback
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trainer.callbacks = [_LRCallback(num_training, early_stop_threshold, progress_bar_refresh_rate=1)]
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# No logging
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trainer.loggers = [DummyLogger()] if trainer.loggers else []
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# Max step set to number of iterations
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trainer.fit_loop.max_steps = num_training
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def __lr_finder_restore_params(trainer: "pl.Trainer", params: Dict[str, Any]) -> None:
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trainer.auto_lr_find = params["auto_lr_find"]
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trainer.callbacks = params["callbacks"]
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trainer.logger = params["logger"]
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trainer.fit_loop.max_steps = params["max_steps"]
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class _LRCallback(Callback):
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"""Special callback used by the learning rate finder. This callbacks log the learning rate before each batch
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and log the corresponding loss after each batch.
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Args:
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num_training: number of iterations done by the learning rate finder
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early_stop_threshold: threshold for stopping the search. If the
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loss at any point is larger than ``early_stop_threshold*best_loss``
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then the search is stopped. To disable, set to ``None``.
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progress_bar_refresh_rate: rate to refresh the progress bar for
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the learning rate finder
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beta: smoothing value, the loss being logged is a running average of
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loss values logged until now. ``beta`` controls the forget rate i.e.
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if ``beta=0`` all past information is ignored.
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"""
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def __init__(
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self,
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num_training: int,
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early_stop_threshold: float = 4.0,
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progress_bar_refresh_rate: int = 0,
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beta: float = 0.98,
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):
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self.num_training = num_training
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self.early_stop_threshold = early_stop_threshold
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self.beta = beta
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self.losses = []
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self.lrs = []
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self.avg_loss = 0.0
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self.best_loss = 0.0
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self.progress_bar_refresh_rate = progress_bar_refresh_rate
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self.progress_bar = None
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def on_train_batch_start(self, trainer, pl_module, batch, batch_idx):
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"""Called before each training batch, logs the lr that will be used."""
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if (trainer.fit_loop.batch_idx + 1) % trainer.accumulate_grad_batches != 0:
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return
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if self.progress_bar_refresh_rate and self.progress_bar is None:
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self.progress_bar = tqdm(desc="Finding best initial lr", total=self.num_training)
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self.lrs.append(trainer.lr_scheduler_configs[0].scheduler.lr[0])
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def on_train_batch_end(self, trainer, pl_module, outputs, batch, batch_idx):
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"""Called when the training batch ends, logs the calculated loss."""
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if (trainer.fit_loop.batch_idx + 1) % trainer.accumulate_grad_batches != 0:
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return
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if self.progress_bar:
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self.progress_bar.update()
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current_loss = trainer.fit_loop.running_loss.last().item()
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current_step = trainer.global_step
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# Avg loss (loss with momentum) + smoothing
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self.avg_loss = self.beta * self.avg_loss + (1 - self.beta) * current_loss
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smoothed_loss = self.avg_loss / (1 - self.beta ** (current_step + 1))
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# Check if we diverging
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if self.early_stop_threshold is not None:
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if current_step > 1 and smoothed_loss > self.early_stop_threshold * self.best_loss:
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trainer.fit_loop.max_steps = current_step # stop signal
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if self.progress_bar:
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self.progress_bar.close()
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# Save best loss for diverging checking
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if smoothed_loss < self.best_loss or current_step == 1:
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self.best_loss = smoothed_loss
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self.losses.append(smoothed_loss)
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class _LinearLR(_LRScheduler):
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"""Linearly increases the learning rate between two boundaries over a number of iterations.
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Args:
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optimizer: wrapped optimizer.
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end_lr: the final learning rate.
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num_iter: the number of iterations over which the test occurs.
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last_epoch: the index of last epoch. Default: -1.
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"""
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last_epoch: int
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base_lrs: Sequence
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def __init__(self, optimizer: torch.optim.Optimizer, end_lr: float, num_iter: int, last_epoch: int = -1):
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self.end_lr = end_lr
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self.num_iter = num_iter
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super().__init__(optimizer, last_epoch)
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def get_lr(self):
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curr_iter = self.last_epoch + 1
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r = curr_iter / self.num_iter
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if self.last_epoch > 0:
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val = [base_lr + r * (self.end_lr - base_lr) for base_lr in self.base_lrs]
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else:
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val = [base_lr for base_lr in self.base_lrs]
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self._lr = val
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return val
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@property
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def lr(self):
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return self._lr
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class _ExponentialLR(_LRScheduler):
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"""Exponentially increases the learning rate between two boundaries over a number of iterations.
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Arguments:
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optimizer: wrapped optimizer.
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end_lr: the final learning rate.
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num_iter: the number of iterations over which the test occurs.
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last_epoch: the index of last epoch. Default: -1.
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"""
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last_epoch: int
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base_lrs: Sequence
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def __init__(self, optimizer: torch.optim.Optimizer, end_lr: float, num_iter: int, last_epoch: int = -1):
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self.end_lr = end_lr
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self.num_iter = num_iter
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super().__init__(optimizer, last_epoch)
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def get_lr(self):
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curr_iter = self.last_epoch + 1
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r = curr_iter / self.num_iter
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if self.last_epoch > 0:
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val = [base_lr * (self.end_lr / base_lr) ** r for base_lr in self.base_lrs]
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else:
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val = [base_lr for base_lr in self.base_lrs]
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self._lr = val
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return val
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@property
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def lr(self):
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return self._lr
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