Source code for pytorch_lightning.plugins.precision.fsdp_native_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
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import Any, Optional

import torch
from typing_extensions import Literal

from lightning_fabric.utilities.imports import _TORCH_GREATER_EQUAL_1_12
from pytorch_lightning.plugins.precision.native_amp import MixedPrecisionPlugin
from pytorch_lightning.utilities.exceptions import MisconfigurationException

if _TORCH_GREATER_EQUAL_1_12 and torch.distributed.is_available():
    from torch.distributed.fsdp.fully_sharded_data_parallel import MixedPrecision
    from torch.distributed.fsdp.sharded_grad_scaler import ShardedGradScaler
    MixedPrecision = None  # type: ignore[misc,assignment]
    ShardedGradScaler = None  # type: ignore[misc,assignment]

[docs]class FullyShardedNativeNativeMixedPrecisionPlugin(MixedPrecisionPlugin): """Native AMP for Fully Sharded Native Training.""" def __init__( self, precision: Literal["16", 16, "bf16"], device: str, scaler: Optional[ShardedGradScaler] = None ) -> None: if not _TORCH_GREATER_EQUAL_1_12: raise MisconfigurationException( "`FullyShardedNativeNativeMixedPrecisionPlugin` is supported from PyTorch v1.12.0 onwards." ) super().__init__( precision, device, scaler=(ShardedGradScaler() if scaler is None and str(precision) == "16" else None) )
[docs] def clip_grad_by_norm(self, *_: Any, **__: Any) -> None: # see # 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. raise MisconfigurationException( f"`gradient_clip_algorithm='norm'` is currently not supported for `{self.__class__.__name__}`" )
@property def mixed_precision_config(self) -> Optional[MixedPrecision]: assert MixedPrecision is not None if self.precision == "16": dtype = torch.float16 elif self.precision == "bf16": dtype = torch.bfloat16 else: raise MisconfigurationException(f"Was unable to infer precision type, received {self.precision!r}.") return MixedPrecision( param_dtype=dtype, reduce_dtype=dtype, buffer_dtype=dtype, )

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