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
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# 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

    from apex import amp

[docs]class ApexMixedPrecisionPlugin(PrecisionPlugin): """Mixed Precision Plugin based on 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:" ) 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 state_dict(self) -> Dict[str, Any]: return amp.state_dict()
[docs] def load_state_dict(self, state_dict: Dict[str, Any]) -> None: self._state_dict_loaded = True return super().load_state_dict(state_dict)

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