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Source code for pytorch_lightning.callbacks.stochastic_weight_avg

# 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.
r"""
Stochastic Weight Averaging Callback
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
"""
from copy import deepcopy
from typing import Callable, List, Optional, Union

import torch
from torch import nn
from torch.optim.swa_utils import SWALR

import pytorch_lightning as pl
from pytorch_lightning.callbacks.base import Callback
from pytorch_lightning.trainer.optimizers import _get_default_scheduler_config
from pytorch_lightning.utilities import rank_zero_info, rank_zero_warn
from pytorch_lightning.utilities.exceptions import MisconfigurationException

_AVG_FN = Callable[[torch.Tensor, torch.Tensor, torch.LongTensor], torch.FloatTensor]


[docs]class StochasticWeightAveraging(Callback): def __init__( self, swa_epoch_start: Union[int, float] = 0.8, swa_lrs: Optional[Union[float, List[float]]] = None, annealing_epochs: int = 10, annealing_strategy: str = "cos", avg_fn: Optional[_AVG_FN] = None, device: Optional[Union[torch.device, str]] = torch.device("cpu"), ): r""" Implements the Stochastic Weight Averaging (SWA) Callback to average a model. Stochastic Weight Averaging was proposed in ``Averaging Weights Leads to Wider Optima and Better Generalization`` by Pavel Izmailov, Dmitrii Podoprikhin, Timur Garipov, Dmitry Vetrov and Andrew Gordon Wilson (UAI 2018). This documentation is highly inspired by PyTorch's work on SWA. The callback arguments follow the scheme defined in PyTorch's ``swa_utils`` package. For a SWA explanation, please take a look `here <https://pytorch.org/blog/pytorch-1.6-now-includes-stochastic-weight-averaging>`_. .. warning:: ``StochasticWeightAveraging`` is in beta and subject to change. .. warning:: ``StochasticWeightAveraging`` is currently not supported for multiple optimizers/schedulers. .. warning:: ``StochasticWeightAveraging`` is currently only supported on every epoch. See also how to :ref:`enable it directly on the Trainer <advanced/training_tricks:Stochastic Weight Averaging>` Arguments: swa_epoch_start: If provided as int, the procedure will start from the ``swa_epoch_start``-th epoch. If provided as float between 0 and 1, the procedure will start from ``int(swa_epoch_start * max_epochs)`` epoch swa_lrs: The SWA learning rate to use: - ``None``. Use the current learning rate of the optimizer at the time the SWA procedure starts. - ``float``. Use this value for all parameter groups of the optimizer. - ``List[float]``. A list values for each parameter group of the optimizer. annealing_epochs: number of epochs in the annealing phase (default: 10) annealing_strategy: Specifies the annealing strategy (default: "cos"): - ``"cos"``. For cosine annealing. - ``"linear"`` For linear annealing avg_fn: the averaging function used to update the parameters; the function must take in the current value of the :class:`AveragedModel` parameter, the current value of :attr:`model` parameter and the number of models already averaged; if None, equally weighted average is used (default: ``None``) device: if provided, the averaged model will be stored on the ``device``. When None is provided, it will infer the `device` from ``pl_module``. (default: ``"cpu"``) """ err_msg = "swa_epoch_start should be a >0 integer or a float between 0 and 1." if isinstance(swa_epoch_start, int) and swa_epoch_start < 1: raise MisconfigurationException(err_msg) if isinstance(swa_epoch_start, float) and not (0 <= swa_epoch_start <= 1): raise MisconfigurationException(err_msg) wrong_type = not isinstance(swa_lrs, (float, list)) wrong_float = isinstance(swa_lrs, float) and swa_lrs <= 0 wrong_list = isinstance(swa_lrs, list) and not all(lr > 0 and isinstance(lr, float) for lr in swa_lrs) if swa_lrs is not None and (wrong_type or wrong_float or wrong_list): raise MisconfigurationException( "The `swa_lrs` should be `None`, a positive float, or a list of positive floats" ) if avg_fn is not None and not isinstance(avg_fn, Callable): raise MisconfigurationException("The `avg_fn` should be callable.") if device is not None and not isinstance(device, (torch.device, str)): raise MisconfigurationException(f"device is expected to be a torch.device or a str. Found {device}") self._swa_epoch_start = swa_epoch_start self._swa_lrs = swa_lrs self._annealing_epochs = annealing_epochs self._annealing_strategy = annealing_strategy self._avg_fn = avg_fn or self.avg_fn self._device = device self._model_contains_batch_norm = None self._average_model = None @property def swa_start(self) -> int: return max(self._swa_epoch_start - 1, 0) # 0-based @property def swa_end(self) -> int: return self._max_epochs - 1 # 0-based @staticmethod def pl_module_contains_batch_norm(pl_module: "pl.LightningModule"): return any(isinstance(module, nn.modules.batchnorm._BatchNorm) for module in pl_module.modules())
[docs] def on_before_accelerator_backend_setup(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule"): # copy the model before moving it to accelerator device. with pl_module._prevent_trainer_and_dataloaders_deepcopy(): self._average_model = deepcopy(pl_module)
[docs] def on_fit_start(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule"): optimizers = trainer.optimizers lr_schedulers = trainer.lr_schedulers if len(optimizers) != 1: raise MisconfigurationException("SWA currently works with 1 `optimizer`.") if len(lr_schedulers) > 1: raise MisconfigurationException("SWA currently not supported for more than 1 `lr_scheduler`.") if isinstance(self._swa_epoch_start, float): self._swa_epoch_start = int(trainer.max_epochs * self._swa_epoch_start) self._model_contains_batch_norm = self.pl_module_contains_batch_norm(pl_module) self._max_epochs = trainer.max_epochs if self._model_contains_batch_norm: # virtually increase max_epochs to perform batch norm update on latest epoch. trainer.fit_loop.max_epochs += 1
[docs] def on_train_epoch_start(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule"): if trainer.current_epoch == self.swa_start: # move average model to request device. self._average_model = self._average_model.to(self._device or pl_module.device) optimizer = trainer.optimizers[0] if self._swa_lrs is None: self._swa_lrs = [param_group["lr"] for param_group in optimizer.param_groups] if isinstance(self._swa_lrs, float): self._swa_lrs = [self._swa_lrs] * len(optimizer.param_groups) for lr, group in zip(self._swa_lrs, optimizer.param_groups): group["initial_lr"] = lr self._swa_scheduler = SWALR( optimizer, swa_lr=self._swa_lrs, anneal_epochs=self._annealing_epochs, anneal_strategy=self._annealing_strategy, last_epoch=trainer.max_epochs if self._annealing_strategy == "cos" else -1, ) default_scheduler_cfg = _get_default_scheduler_config() assert default_scheduler_cfg["interval"] == "epoch" and default_scheduler_cfg["frequency"] == 1 default_scheduler_cfg["scheduler"] = self._swa_scheduler if trainer.lr_schedulers: scheduler_cfg = trainer.lr_schedulers[0] if scheduler_cfg["interval"] != "epoch" or scheduler_cfg["frequency"] != 1: rank_zero_warn(f"SWA is currently only supported every epoch. Found {scheduler_cfg}") rank_zero_info( f"Swapping scheduler `{scheduler_cfg['scheduler'].__class__.__name__}`" f" for `{self._swa_scheduler.__class__.__name__}`" ) trainer.lr_schedulers[0] = default_scheduler_cfg else: trainer.lr_schedulers.append(default_scheduler_cfg) self.n_averaged = torch.tensor(0, dtype=torch.long, device=pl_module.device) if self.swa_start <= trainer.current_epoch <= self.swa_end: self.update_parameters(self._average_model, pl_module, self.n_averaged, self.avg_fn) # Note: No > here in case the callback is saved with the model and training continues if trainer.current_epoch == self.swa_end + 1: # Transfer weights from average model to pl_module self.transfer_weights(self._average_model, pl_module) # Reset BatchNorm for update self.reset_batch_norm_and_save_state(pl_module) # There is no need to perform either backward or optimizer.step as we are # performing only one pass over the train data-loader to compute activation statistics # Therefore, we will virtually increase `num_training_batches` by 1 and skip backward. trainer.num_training_batches += 1 trainer.fit_loop._skip_backward = True self._accumulate_grad_batches = trainer.accumulate_grad_batches trainer.accumulate_grad_batches = trainer.num_training_batches
[docs] def on_train_epoch_end(self, trainer: "pl.Trainer", *args): trainer.fit_loop._skip_backward = False
[docs] def on_train_end(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule"): if self._model_contains_batch_norm and trainer.current_epoch == self.swa_end + 1: # BatchNorm epoch update. Reset state trainer.accumulate_grad_batches = self._accumulate_grad_batches trainer.num_training_batches -= 1 trainer.fit_loop.max_epochs -= 1 self.reset_momenta() elif trainer.current_epoch == self.swa_end: # Last SWA epoch. Transfer weights from average model to pl_module self.transfer_weights(self._average_model, pl_module)
@staticmethod def transfer_weights(src_pl_module: "pl.LightningModule", dst_pl_module: "pl.LightningModule"): for src_param, dst_param in zip(src_pl_module.parameters(), dst_pl_module.parameters()): dst_param.detach().copy_(src_param.to(dst_param.device))
[docs] def reset_batch_norm_and_save_state(self, pl_module: "pl.LightningModule"): """Adapted from https://github.com/pytorch/pytorch/blob/v1.7.1/torch/optim/swa_utils.py#L140-L154.""" self.momenta = {} for module in pl_module.modules(): if not isinstance(module, nn.modules.batchnorm._BatchNorm): continue module.running_mean = torch.zeros_like( module.running_mean, device=pl_module.device, dtype=module.running_mean.dtype ) module.running_var = torch.ones_like( module.running_var, device=pl_module.device, dtype=module.running_var.dtype ) self.momenta[module] = module.momentum module.momentum = None module.num_batches_tracked *= 0
[docs] def reset_momenta(self): """Adapted from https://github.com/pytorch/pytorch/blob/v1.7.1/torch/optim/swa_utils.py#L164-L165.""" for bn_module in self.momenta: bn_module.momentum = self.momenta[bn_module]
[docs] @staticmethod def update_parameters( average_model: "pl.LightningModule", model: "pl.LightningModule", n_averaged: torch.LongTensor, avg_fn: _AVG_FN ): """Adapted from https://github.com/pytorch/pytorch/blob/v1.7.1/torch/optim/swa_utils.py#L104-L112.""" for p_swa, p_model in zip(average_model.parameters(), model.parameters()): device = p_swa.device p_swa_ = p_swa.detach() p_model_ = p_model.detach().to(device) src = p_model_ if n_averaged == 0 else avg_fn(p_swa_, p_model_, n_averaged.to(device)) p_swa_.copy_(src) n_averaged += 1
[docs] @staticmethod def avg_fn( averaged_model_parameter: torch.Tensor, model_parameter: torch.Tensor, num_averaged: torch.LongTensor ) -> torch.FloatTensor: """Adapted from https://github.com/pytorch/pytorch/blob/v1.7.1/torch/optim/swa_utils.py#L95-L97.""" return averaged_model_parameter + (model_parameter - averaged_model_parameter) / (num_averaged + 1)

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