73 lines
2.8 KiB
Python
73 lines
2.8 KiB
Python
# Copyright The PyTorch Lightning team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from typing import Any, Callable, Optional, Union
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from torch import Tensor
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from torch.nn import Module
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from torch.optim import Optimizer
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import pytorch_lightning as pl
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from pytorch_lightning.plugins.precision.precision_plugin import PrecisionPlugin
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from pytorch_lightning.utilities import GradClipAlgorithmType
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from pytorch_lightning.utilities.model_helpers import is_overridden
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from pytorch_lightning.utilities.warnings import WarningCache
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warning_cache = WarningCache()
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class DeepSpeedPrecisionPlugin(PrecisionPlugin):
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"""Precision plugin for DeepSpeed integration."""
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def __init__(self, precision: int) -> None:
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super().__init__()
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self.precision = precision
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def pre_optimizer_step(
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self,
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model: "pl.LightningModule",
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optimizer: Optimizer,
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optimizer_idx: int,
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lambda_closure: Callable,
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**kwargs: Any,
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) -> bool:
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"""Hook to do something before each optimizer step."""
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super().pre_optimizer_step(model, optimizer, optimizer_idx, lambda_closure, **kwargs)
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# the following should be in a `optimizer_step` hook but we don't have one in the precision plugin.
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lambda_closure() # DeepSpeed does not support closures
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deepspeed_engine = model.trainer.model
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deepspeed_engine.step()
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return False
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def backward(self, model: "pl.LightningModule", closure_loss: Tensor, *args: Any, **kwargs: Any) -> None:
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if is_overridden("backward", model):
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warning_cache.warn(
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"You have overridden the `LightningModule.backward` hook but it will be ignored since DeepSpeed handles"
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" the backward logic internally."
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)
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# todo: hack around for deepspeed engine to call backward
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deepspeed_engine = model.trainer.model
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deepspeed_engine.backward(closure_loss, *args, **kwargs)
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def clip_gradients(
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self,
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optimizer: Optimizer,
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clip_val: Union[int, float],
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gradient_clip_algorithm: GradClipAlgorithmType = GradClipAlgorithmType.NORM,
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model: Optional[Module] = None,
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) -> None:
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"""
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DeepSpeed handles clipping gradients internally via the training type plugin.
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"""
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pass
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