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Source code for pytorch_lightning.plugins.precision.ipu

# 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, Union

from lightning_utilities.core.rank_zero import WarningCache
from torch import Tensor
from torch.optim import LBFGS, Optimizer

import pytorch_lightning as pl
from lightning_lite.utilities.enums import PrecisionType
from lightning_lite.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

warning_cache = WarningCache()


[docs]class IPUPrecisionPlugin(PrecisionPlugin): """Precision plugin for IPU integration. Raises: ValueError: If the precision is neither 16 nor 32. """ def __init__(self, precision: int) -> None: supported_precision_values = (PrecisionType.HALF, PrecisionType.FLOAT) if precision not in supported_precision_values: raise ValueError( f"`Trainer(accelerator='ipu', precision={precision!r})` is not supported." f" `precision` must be one of: {supported_precision_values}." ) super().__init__() self.precision = 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 `https://github.com/Lightning-AI/lightning/issues`" " 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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