Source code for pytorch_lightning.plugins.precision.apex_amp
# 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.
from typing import Any, Callable, Dict, Optional, Union
from torch import Tensor
from torch.nn import Module
from torch.optim import LBFGS, Optimizer
import pytorch_lightning as pl
from pytorch_lightning.plugins.precision.mixed import MixedPrecisionPlugin
from pytorch_lightning.utilities import _APEX_AVAILABLE, AMPType
from pytorch_lightning.utilities.exceptions import MisconfigurationException
from pytorch_lightning.utilities.types import _PARAMETERS
if _APEX_AVAILABLE:
from apex import amp
[docs]class ApexMixedPrecisionPlugin(MixedPrecisionPlugin):
"""Mixed Precision Plugin based on Nvidia/Apex (https://github.com/NVIDIA/apex)"""
backend = AMPType.APEX
def __init__(self, amp_level: str = "O2") -> None:
if not _APEX_AVAILABLE:
raise MisconfigurationException(
"You have asked for Apex AMP but you have not installed it."
" Install `apex` using this guide: https://github.com/NVIDIA/apex"
)
super().__init__()
self.amp_level = amp_level
self._connected = False
[docs] def main_params(self, optimizer: Optimizer) -> _PARAMETERS:
return amp.master_params(optimizer)
[docs] def dispatch(self, trainer: "pl.Trainer") -> None:
if not self._connected:
accelerator = trainer.accelerator
_, accelerator.optimizers = amp.initialize(
trainer.lightning_module, accelerator.optimizers, opt_level=self.amp_level
)
self._connected = True
return super().dispatch(trainer)
[docs] def backward(
self,
model: "pl.LightningModule",
closure_loss: Tensor,
optimizer: Optional[Optimizer],
*args: Any,
**kwargs: Any,
) -> None:
"""Run before precision plugin executes backward.
Args:
model: the model to be optimized
closure_loss: the loss value obtained from the closure
optimizer: current optimizer being used. ``None`` if using manual optimization
"""
opt = optimizer or model.trainer.optimizers
with amp.scale_loss(closure_loss, opt) as closure_loss:
super().backward(model, closure_loss, optimizer, *args, **kwargs)
[docs] def optimizer_step(
self,
model: Union["pl.LightningModule", Module],
optimizer: Optimizer,
optimizer_idx: int,
closure: Callable[[], Any],
**kwargs: Any,
) -> None:
if isinstance(optimizer, LBFGS):
raise MisconfigurationException(
f"apex AMP and the LBFGS optimizer are not compatible (optimizer {optimizer_idx})."
)
closure_result = closure()
self._after_closure(model, optimizer, optimizer_idx)
skipped_backward = closure_result is None
# in manual optimization, the closure does not return a value
if not isinstance(model, pl.LightningModule) or not model.automatic_optimization or not skipped_backward:
optimizer.step(**kwargs)
[docs] def on_load_checkpoint(self, checkpoint: Dict[str, Any]) -> None:
if "amp_scaling_state" in checkpoint:
amp.load_state_dict(checkpoint["amp_scaling_state"])
[docs] def on_save_checkpoint(self, checkpoint: Dict[str, Any]) -> None:
checkpoint["amp_scaling_state"] = amp.state_dict()