42 lines
1.7 KiB
Python
42 lines
1.7 KiB
Python
# Copyright The PyTorch Lightning team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from typing import Optional, Union
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import torch
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from pytorch_lightning.plugins.precision.native_amp import NativeMixedPrecisionPlugin
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from pytorch_lightning.utilities import _FAIRSCALE_AVAILABLE
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from pytorch_lightning.utilities.exceptions import MisconfigurationException
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if _FAIRSCALE_AVAILABLE:
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from fairscale.optim import OSS
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from fairscale.optim.grad_scaler import ShardedGradScaler
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class ShardedNativeMixedPrecisionPlugin(NativeMixedPrecisionPlugin):
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"""Native AMP for Sharded Training."""
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def __init__(
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self, precision: Union[str, int], device: str, scaler: Optional[torch.cuda.amp.GradScaler] = None
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) -> None:
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if not _FAIRSCALE_AVAILABLE:
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raise MisconfigurationException(
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"You have asked for sharded AMP but you have not installed it."
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" Install `fairscale` using this guide: https://https://github.com/facebookresearch/fairscale"
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)
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super().__init__(precision, device, scaler=scaler or ShardedGradScaler())
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def clip_grad_by_norm(self, optimizer: "OSS", clip_val: Union[int, float]) -> None:
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optimizer.clip_grad_norm(clip_val)
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