Source code for pytorch_lightning.plugins.precision.ipu_precision
# 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 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.exceptions import MisconfigurationException
from pytorch_lightning.utilities.model_helpers import is_overridden
from pytorch_lightning.utilities.warnings import WarningCache
warning_cache = WarningCache()
[docs]class IPUPrecisionPlugin(PrecisionPlugin):
def __init__(self, precision: int) -> None:
super().__init__()
self.precision = precision
[docs] def backward(self, 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(
self,
model: Union["pl.LightningModule", Module],
optimizer: Optimizer,
optimizer_idx: int,
closure: Callable[[], Any],
**kwargs: Any,
) -> None:
"""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 isinstance(model, pl.LightningModule) and 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/PyTorchLightning/pytorch-lightning/issues`"
" requesting this feature."
)
[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.")