Source code for pytorch_lightning.callbacks.stochastic_weight_avg
# Copyright The PyTorch Lightning team.
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# 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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# http://www.apache.org/licenses/LICENSE-2.0
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# Unless required by applicable law or agreed to in writing, software
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# 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, 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]] = 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.
SWA can easily be activated directly from the Trainer as follow:
.. code-block:: python
Trainer(stochastic_weight_avg=True)
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 learning rate value for all param groups together or separately for each group.
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 a positive float or a list of positive float.")
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)
optimizers = trainer.optimizers
for param_group in optimizers[0].param_groups:
if self._swa_lrs is None:
initial_lr = param_group["lr"]
elif isinstance(self._swa_lrs, float):
initial_lr = self._swa_lrs
else:
initial_lr = self._swa_lrs[0]
param_group["initial_lr"] = initial_lr
self._swa_lrs = initial_lr
self._swa_scheduler = SWALR(
optimizers[0],
swa_lr=initial_lr,
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']} for {self._swa_scheduler}")
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)
