Source code for pytorch_lightning.strategies.single_hpu

# 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, Dict, Optional, Union

from torch.nn import Module
from torch.optim.optimizer import Optimizer

import pytorch_lightning as pl
from pytorch_lightning.plugins.io.checkpoint_plugin import CheckpointIO
from pytorch_lightning.plugins.io.hpu_plugin import HPUCheckpointIO
from pytorch_lightning.plugins.io.wrapper import _WrappingCheckpointIO
from pytorch_lightning.plugins.precision import PrecisionPlugin
from pytorch_lightning.strategies.single_device import SingleDeviceStrategy
from pytorch_lightning.utilities import _HPU_AVAILABLE
from pytorch_lightning.utilities.exceptions import MisconfigurationException
from pytorch_lightning.utilities.types import _DEVICE, STEP_OUTPUT

if _HPU_AVAILABLE:
    import habana_frameworks.torch.core as htcore


class SingleHPUStrategy(SingleDeviceStrategy):
    """Strategy for training on single HPU device."""

    strategy_name = "hpu_single"

    def __init__(
        self,
        device: _DEVICE = "hpu",
        accelerator: Optional["pl.accelerators.accelerator.Accelerator"] = None,
        checkpoint_io: Optional[CheckpointIO] = None,
        precision_plugin: Optional[PrecisionPlugin] = None,
    ):

        if not _HPU_AVAILABLE:
            raise MisconfigurationException("`SingleHPUStrategy` requires HPU devices to run")

        super().__init__(
            accelerator=accelerator,
            device=device,
            checkpoint_io=checkpoint_io,
            precision_plugin=precision_plugin,
        )

    @property
    def checkpoint_io(self) -> CheckpointIO:
        if self._checkpoint_io is None:
            self._checkpoint_io = HPUCheckpointIO()
        elif isinstance(self._checkpoint_io, _WrappingCheckpointIO):
            self._checkpoint_io.checkpoint_io = HPUCheckpointIO()

        return self._checkpoint_io

    @checkpoint_io.setter
    def checkpoint_io(self, io: Optional[CheckpointIO]) -> None:
        self._checkpoint_io = io

    @property
    def is_distributed(self) -> bool:
        return False

    def setup(self, trainer: "pl.Trainer") -> None:
        self.model_to_device()
        super().setup(trainer)

    def setup_optimizers(self, trainer: "pl.Trainer") -> None:
        super().setup_optimizers(trainer)

    def model_to_device(self) -> None:
        self.model.to(self.root_device)  # type: ignore

    def on_after_backward(self) -> None:
        # Break lazy accumulation of graph after fwd+bwd
        htcore.mark_step()

    def optimizer_step(
        self,
        optimizer: Optimizer,
        opt_idx: int,
        closure: Callable[[], Any],
        model: Optional[Union["pl.LightningModule", Module]] = None,
        **kwargs: Any,
    ) -> Any:
        optimizer_output = super().optimizer_step(optimizer, opt_idx, closure, model, **kwargs)
        # Break lazy accumulation of graph after optimizer
        htcore.mark_step()
        return optimizer_output

    def validation_step_end(self, step_output: STEP_OUTPUT) -> STEP_OUTPUT:
        # Break lazy accumulation of graph after every step
        htcore.mark_step()
        return step_output

    def test_step_end(self, step_output: STEP_OUTPUT) -> STEP_OUTPUT:
        # Break lazy accumulation of graph after every step
        htcore.mark_step()
        return step_output

    @classmethod
    def register_strategies(cls, strategy_registry: Dict) -> None:
        strategy_registry.register(
            cls.strategy_name,
            cls,
            description=f"{cls.__class__.__name__}",
        )