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
from torch import Tensor
from torch.optim import LBFGS, Optimizer
import pytorch_lightning as pl
from lightning_lite.utilities.types import _PARAMETERS, Optimizable
from pytorch_lightning.plugins.precision.precision_plugin import PrecisionPlugin
from pytorch_lightning.utilities import _APEX_AVAILABLE, AMPType
from pytorch_lightning.utilities.exceptions import MisconfigurationException
if _APEX_AVAILABLE:
from apex import amp
[docs]class ApexMixedPrecisionPlugin(PrecisionPlugin):
"""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 `apex` is not installed."
" Install `apex` using this guide: https://github.com/NVIDIA/apex"
)
super().__init__()
self.amp_level = amp_level
self._connected = False
self._state_dict_loaded = 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:
strategy = trainer.strategy
_, strategy.optimizers = amp.initialize(
trainer.lightning_module, strategy.optimizers, opt_level=self.amp_level
)
self._connected = True
return super().dispatch(trainer)
[docs] def backward( # type: ignore[override]
self,
tensor: Tensor,
model: "pl.LightningModule",
optimizer: Optional[Optimizable],
*args: Any,
**kwargs: Any,
) -> None:
r"""Run before precision plugin executes backward.
Args:
tensor: the loss value obtained from the closure
model: the model to be optimized
optimizer: current optimizer being used. ``None`` if using manual optimization
\*args: Positional arguments intended for the actual function that performs the backward, like
:meth:`~torch.Tensor.backward`.
\**kwargs: Keyword arguments for the same purpose as ``*args``.
"""
opt = optimizer or model.trainer.optimizers
with amp.scale_loss(tensor, opt) as tensor:
super().backward(tensor, model, optimizer, *args, **kwargs)
[docs] def optimizer_step( # type: ignore[override]
self,
optimizer: Optimizable,
model: "pl.LightningModule",
optimizer_idx: int,
closure: Callable[[], Any],
**kwargs: Any,
) -> Any:
if self._state_dict_loaded:
raise RuntimeError(
"Resuming training with APEX is currently not supported. Set `amp_backend=None` for example or use a"
" different precision plugin."
)
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 model.automatic_optimization or not skipped_backward:
return optimizer.step(**kwargs)
return closure_result
[docs] def load_state_dict(self, state_dict: Dict[str, Any]) -> None:
self._state_dict_loaded = True
return super().load_state_dict(state_dict)
