32 lines
1.5 KiB
Python
32 lines
1.5 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 Any
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from pytorch_lightning.plugins.precision.sharded_native_amp import ShardedNativeMixedPrecisionPlugin
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from pytorch_lightning.utilities.exceptions import MisconfigurationException
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class FullyShardedNativeMixedPrecisionPlugin(ShardedNativeMixedPrecisionPlugin):
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"""Native AMP for Fully Sharded Training."""
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def clip_grad_by_norm(self, *_: Any, **__: Any) -> None:
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# see https://fairscale.readthedocs.io/en/latest/api/nn/fsdp_tips.html
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# section `Gradient Clipping`, using `torch.nn.utils.clip_grad_norm_` is incorrect
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# for FSDP module. To overcome this, needs to call sharded_module.clip_grad_norm(clip_val)
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# however we rely on LightningModule's configure_sharded_model to wrap FSDP, it would be hard to
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# trace back the root FSDP. Now we only support clip by value.
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raise MisconfigurationException(
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f"`gradient_clip_algorithm='norm'` is currently not supported for `{self.__class__.__name__}`"
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)
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