35 lines
1.3 KiB
Python
35 lines
1.3 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 Union
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from pytorch_lightning.plugins.precision.native_amp import NativeMixedPrecisionPlugin
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from pytorch_lightning.utilities import _FAIRSCALE_AVAILABLE, _NATIVE_AMP_AVAILABLE
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if _NATIVE_AMP_AVAILABLE and _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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"""Mixed Precision for Sharded Training"""
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def __init__(self) -> None:
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super().__init__()
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self.scaler = ShardedGradScaler()
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def clip_grad_by_norm(
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self, optimizer: "OSS", clip_val: Union[int, float], norm_type: float = 2.0, eps: float = 1e-6
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) -> None:
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optimizer.clip_grad_norm(clip_val, norm_type=norm_type)
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