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Source code for pytorch_lightning.plugins.precision.deepspeed_precision

# 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, Optional, Union

from torch import Tensor
from torch.nn import Module
from torch.optim import Optimizer

import pytorch_lightning as pl
from pytorch_lightning.plugins.precision.precision_plugin import PrecisionPlugin
from pytorch_lightning.utilities import GradClipAlgorithmType
from pytorch_lightning.utilities.model_helpers import is_overridden
from pytorch_lightning.utilities.warnings import WarningCache

warning_cache = WarningCache()


[docs]class DeepSpeedPrecisionPlugin(PrecisionPlugin): """Precision plugin for DeepSpeed integration.""" def __init__(self, precision: int) -> None: super().__init__() self.precision = precision
[docs] def pre_optimizer_step( self, model: "pl.LightningModule", optimizer: Optimizer, optimizer_idx: int, lambda_closure: Callable, **kwargs: Any, ) -> bool: """Hook to do something before each optimizer step.""" super().pre_optimizer_step(model, optimizer, optimizer_idx, lambda_closure, **kwargs) # the following should be in a `optimizer_step` hook but we don't have one in the precision plugin. lambda_closure() # DeepSpeed does not support closures deepspeed_engine = model.trainer.model deepspeed_engine.step() return False
[docs] def backward(self, model: "pl.LightningModule", closure_loss: Tensor, *args: Any, **kwargs: Any) -> None: if is_overridden("backward", model): warning_cache.warn( "You have overridden the `LightningModule.backward` hook but it will be ignored since DeepSpeed handles" " the backward logic internally." ) # todo: hack around for deepspeed engine to call backward deepspeed_engine = model.trainer.model deepspeed_engine.backward(closure_loss, *args, **kwargs)
[docs] def clip_gradients( self, optimizer: Optimizer, clip_val: Union[int, float], gradient_clip_algorithm: GradClipAlgorithmType = GradClipAlgorithmType.NORM, model: Optional[Module] = None, ) -> None: """ DeepSpeed handles clipping gradients internally via the training type plugin. """ pass

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