Source code for pytorch_lightning.plugins.precision.deepspeed

# 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, Optional, TYPE_CHECKING, Union

from torch import Tensor
from torch.nn import Module
from torch.optim import LBFGS, 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.enums import AMPType, PrecisionType
from pytorch_lightning.utilities.exceptions import MisconfigurationException
from pytorch_lightning.utilities.imports import _APEX_AVAILABLE, _RequirementAvailable
from pytorch_lightning.utilities.model_helpers import is_overridden
from pytorch_lightning.utilities.warnings import WarningCache

_DEEPSPEED_AVAILABLE = _RequirementAvailable("deepspeed")
    import deepspeed

warning_cache = WarningCache()

[docs]class DeepSpeedPrecisionPlugin(PrecisionPlugin): """Precision plugin for DeepSpeed integration. Args: precision: Double precision (64), full precision (32), half precision (16) or bfloat16 precision (bf16). amp_type: The mixed precision backend to use ("native" or "apex"). amp_level: The optimization level to use (O1, O2, etc...). By default it will be set to "O2" if ``amp_type`` is set to "apex". Raises: MisconfigurationException: If using ``bfloat16`` precision and ``deepspeed<v0.6``. ValueError: If unsupported ``precision`` is provided. """ def __init__(self, precision: Union[str, int], amp_type: str, amp_level: Optional[str] = None) -> None: if amp_type == AMPType.APEX: if not _APEX_AVAILABLE: raise MisconfigurationException( "You have asked for Apex AMP but `apex` is not installed." " Install `apex` using this guide:" ) amp_level = amp_level or "O2" supported_precision = (PrecisionType.HALF, PrecisionType.FLOAT, PrecisionType.BFLOAT) if precision not in supported_precision: raise ValueError( f"`Trainer(strategy='deepspeed', precision={precision!r})` is not supported." f" `precision` must be one of: {(x.value for x in supported_precision)}." ) super().__init__() self.precision = precision self.amp_type = amp_type self.amp_level = amp_level
[docs] def backward( self, model: "pl.LightningModule", closure_loss: Tensor, optimizer: Optional[Optimizer], optimizer_idx: Optional[int], *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." ) deepspeed_engine: "deepspeed.DeepSpeedEngine" = model.trainer.model deepspeed_engine.backward(closure_loss, *args, **kwargs)
def _run_backward( self, tensor: Tensor, model: Optional["deepspeed.DeepSpeedEngine"], *args: Any, **kwargs: Any ) -> None: if model is None: raise ValueError("Please provide the model as input to `backward`.") model.backward(tensor, *args, **kwargs)
[docs] def optimizer_step( self, model: Optional[Union["pl.LightningModule", Module]], optimizer: Optimizer, optimizer_idx: int, closure: Callable[[], Any], **kwargs: Any, ) -> Any: if isinstance(optimizer, LBFGS): raise MisconfigurationException( f"DeepSpeed 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 isinstance(model, pl.LightningModule) and model.automatic_optimization and skipped_backward: raise MisconfigurationException( "Skipping backward by returning `None` from your `training_step` is not supported by `DeepSpeed`" ) # DeepSpeed handles the optimizer step internally deepspeed_engine: "deepspeed.DeepSpeedEngine" if isinstance(model, pl.LightningModule): deepspeed_engine = model.trainer.model else: deepspeed_engine = model return deepspeed_engine.step(**kwargs)
[docs] def clip_gradients( self, optimizer: Optimizer, clip_val: Union[int, float] = 0.0, gradient_clip_algorithm: GradClipAlgorithmType = GradClipAlgorithmType.NORM, ) -> None: """DeepSpeed handles gradient clipping internally."""
def _track_grad_norm(self, trainer: "pl.Trainer") -> None: if trainer.track_grad_norm == -1: return # the gradients are not available in the model due to gradient partitioning in zero stage >= 2 warning_cache.warn( f"You set `Trainer(track_grad_norm={trainer.track_grad_norm!r})' but this is not supported for DeepSpeed." " The setting will be ignored." )

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