89 lines
2.9 KiB
Python
89 lines
2.9 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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pl_module: '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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deepspeed_engine = pl_module.trainer.model
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# DeepSpeed not support closures.
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lambda_closure()
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if not pl_module.automatic_optimization:
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pl_module.trainer.call_hook("on_after_backward")
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deepspeed_engine.step()
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return False
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def backward(
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self,
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model: 'pl.LightningModule',
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closure_loss: Tensor,
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optimizer: Optimizer,
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opt_idx: int,
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should_accumulate: bool,
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*args: Any,
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**kwargs: Any,
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) -> Tensor:
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if is_overridden('backward', model):
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warning_cache.warn(
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"Overridden backward hook in the LightningModule will be ignored since DeepSpeed handles"
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"backward logic outside of the LightningModule"
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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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# once backward has been applied, release graph
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closure_loss = closure_loss.detach()
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return closure_loss
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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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