Source code for pytorch_lightning.plugins.precision.sharded_native_amp

# 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 Optional, Union

from lightning_lite.strategies.fairscale import _FAIRSCALE_AVAILABLE
from pytorch_lightning.plugins.precision.native_amp import NativeMixedPrecisionPlugin
from pytorch_lightning.utilities.exceptions import MisconfigurationException

if _FAIRSCALE_AVAILABLE:
    from fairscale.optim import OSS
    from fairscale.optim.grad_scaler import ShardedGradScaler
else:
    OSS = ShardedGradScaler = object


[docs]class ShardedNativeMixedPrecisionPlugin(NativeMixedPrecisionPlugin):
    """Native AMP for Sharded Training."""

    def __init__(self, precision: Union[str, int], device: str, scaler: Optional[ShardedGradScaler] = None) -> None:
        if not _FAIRSCALE_AVAILABLE:
            raise MisconfigurationException(
                "You have asked for sharded AMP but you have not installed it."
                " Install `fairscale` using this guide: https://https://github.com/facebookresearch/fairscale"
            )
        super().__init__(precision, device, scaler=ShardedGradScaler() if scaler is None and precision == 16 else None)

[docs]    def clip_grad_by_norm(self, optimizer: "OSS", clip_val: Union[int, float]) -> None:
        optimizer.clip_grad_norm(clip_val)