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
#
#     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, 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")
if TYPE_CHECKING and _DEEPSPEED_AVAILABLE:
    import deepspeed

warning_cache = WarningCache()


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: https://github.com/NVIDIA/apex"
                )

            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

    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)

    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)

    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."
        )