47 lines
2.0 KiB
Python
47 lines
2.0 KiB
Python
# 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 torch.nn import Module
|
|
from torch.optim import Optimizer
|
|
|
|
from pytorch_lightning.plugins.precision.sharded_native_amp import ShardedNativeMixedPrecisionPlugin
|
|
from pytorch_lightning.utilities import GradClipAlgorithmType
|
|
|
|
|
|
class FullyShardedNativeMixedPrecisionPlugin(ShardedNativeMixedPrecisionPlugin):
|
|
"""Mixed Precision for Full Sharded Training"""
|
|
|
|
precision = "mixed"
|
|
|
|
def clip_gradients(
|
|
self,
|
|
optimizer: Optimizer,
|
|
clip_val: Union[int, float],
|
|
gradient_clip_algorithm: GradClipAlgorithmType = GradClipAlgorithmType.VALUE,
|
|
model: Optional[Module] = None,
|
|
) -> None:
|
|
clip_val = float(clip_val)
|
|
if clip_val <= 0:
|
|
return
|
|
# see https://fairscale.readthedocs.io/en/latest/api/nn/fsdp_tips.html
|
|
# section `Gradient Clipping`, using `torch.nn.utils.clip_grad_norm_` is incorrect
|
|
# for FSDP module. To overcome this, needs to call sharded_module.clip_grad_norm(clip_val)
|
|
# however we rely on LightningModule's configure_sharded_model to wrap FSDP, it would be hard to
|
|
# trace back the root FSDP. Now we only support clip by value.
|
|
assert (
|
|
gradient_clip_algorithm == GradClipAlgorithmType.VALUE
|
|
), "`gradient_clip_algorithm`: `norm` is currently not supported for `FullyShardedNativeMixedPrecisionPlugin`"
|
|
self.clip_grad_by_value(optimizer, clip_val)
|