97 lines
4.1 KiB
Python
97 lines
4.1 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 LBFGS, 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.exceptions import MisconfigurationException
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from pytorch_lightning.utilities.imports import _DEEPSPEED_AVAILABLE
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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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if _DEEPSPEED_AVAILABLE:
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from deepspeed import DeepSpeedEngine
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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: Union[str, int], amp_type: str, amp_level: Optional[str] = None) -> None:
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super().__init__()
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self.precision = precision
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self.amp_type = amp_type
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self.amp_level = amp_level
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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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deepspeed_engine: DeepSpeedEngine = model.trainer.model
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deepspeed_engine.backward(closure_loss, *args, **kwargs)
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def _run_backward(self, tensor: Tensor, model: Optional["DeepSpeedEngine"], *args: Any, **kwargs: Any) -> None:
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if model is None:
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raise ValueError("Please provide the model as input to `backward`.")
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model.backward(tensor, *args, **kwargs)
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def optimizer_step(
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self,
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model: Union["pl.LightningModule", Module],
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optimizer: Optimizer,
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optimizer_idx: int,
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closure: Callable[[], Any],
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**kwargs: Any,
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) -> Any:
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if isinstance(optimizer, LBFGS):
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raise MisconfigurationException(
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f"DeepSpeed and the LBFGS optimizer are not compatible (optimizer {optimizer_idx})."
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)
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closure_result = closure()
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self._after_closure(model, optimizer, optimizer_idx)
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skipped_backward = closure_result is None
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# in manual optimization, the closure does not return a value
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if isinstance(model, pl.LightningModule) and model.automatic_optimization and skipped_backward:
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raise MisconfigurationException(
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"Skipping backward by returning `None` from your `training_step` is not supported by `DeepSpeed`"
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)
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# DeepSpeed handles the optimizer step internally
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deepspeed_engine = model.trainer.model if isinstance(model, pl.LightningModule) else model
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return deepspeed_engine.step(**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] = 0.0,
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gradient_clip_algorithm: GradClipAlgorithmType = GradClipAlgorithmType.NORM,
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) -> None:
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"""DeepSpeed handles gradient clipping internally."""
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def _track_grad_norm(self, trainer: "pl.Trainer") -> None:
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if trainer.track_grad_norm == -1:
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return
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# the gradients are not available in the model due to gradient partitioning in zero stage >= 2
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warning_cache.warn(
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f"You set `Trainer(track_grad_norm={trainer.track_grad_norm!r})' but this is not supported for DeepSpeed."
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" The setting will be ignored."
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)
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