Source code for pytorch_lightning.plugins.precision.ipu

# Copyright The Lightning AI 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, cast, Union

from torch import Tensor
from torch.optim import LBFGS, Optimizer
from typing_extensions import get_args, Literal

import pytorch_lightning as pl
from lightning_fabric.utilities.types import Optimizable
from pytorch_lightning.plugins.precision.precision_plugin import PrecisionPlugin
from pytorch_lightning.utilities import GradClipAlgorithmType
from pytorch_lightning.utilities.exceptions import MisconfigurationException
from pytorch_lightning.utilities.model_helpers import is_overridden
from pytorch_lightning.utilities.rank_zero import WarningCache

warning_cache = WarningCache()

_PRECISION_INPUT_INT = Literal[32, 16]
_PRECISION_INPUT_STR = Literal["32", "16"]

[docs]class IPUPrecisionPlugin(PrecisionPlugin): """Precision plugin for IPU integration. Raises: ValueError: If the precision is neither 16 nor 32. """ def __init__(self, precision: Literal["32", 32, "16", 16]) -> None: supported_precision = get_args(_PRECISION_INPUT_STR) + get_args(_PRECISION_INPUT_INT) if precision not in supported_precision: raise ValueError( f"`Trainer(accelerator='ipu', precision={precision!r})` is not supported." f" `precision` must be one of: {supported_precision}." ) self.precision = cast(_PRECISION_INPUT_STR, str(precision))
[docs] def backward( # type: ignore[override] self, tensor: Tensor, model: "pl.LightningModule", *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 IPUs handle" " the backward logic internally." )
[docs] def optimizer_step( # type: ignore[override] self, optimizer: Optimizable, model: "pl.LightningModule", optimizer_idx: int, closure: Callable[[], Any], **kwargs: Any, ) -> Any: """IPUs handle the optimizer step internally.""" if isinstance(optimizer, LBFGS): raise MisconfigurationException( f"IPUs 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 model.automatic_optimization and skipped_backward: # we lack coverage here and IPUs are (currently) limited - something to explore if there's demand raise MisconfigurationException( "Skipping backward by returning `None` from your `training_step` is not implemented for IPUs." " Please, open an issue in ``" " requesting this feature." ) return closure_result
[docs] def clip_gradients( self, optimizer: Optimizer, clip_val: Union[int, float] = 0.0, gradient_clip_algorithm: GradClipAlgorithmType = GradClipAlgorithmType.NORM, ) -> None: if clip_val <= 0: return raise MisconfigurationException("IPUs currently do not support clipping gradients.")

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