Source code for lightning_fabric.plugins.precision.native_amp

# Copyright The Lightning AI 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 contextlib import contextmanager
from typing import Any, cast, Dict, Generator, Optional

import torch
from torch import Tensor
from torch.nn import Module
from torch.optim import LBFGS
from typing_extensions import Literal

from lightning_fabric.accelerators.cuda import _patch_cuda_is_available
from lightning_fabric.plugins.precision.precision import Precision
from lightning_fabric.plugins.precision.utils import _convert_fp_tensor
from lightning_fabric.utilities.types import Optimizable

[docs]class MixedPrecision(Precision): """Plugin for Automatic Mixed Precision (AMP) training with ``torch.autocast``. Args: precision: Whether to use ``torch.float16`` (``16``) or ``torch.bfloat16`` (``'bf16'``). device: The device for ``torch.autocast``. scaler: An optional :class:`torch.cuda.amp.GradScaler` to use. """ def __init__( self, precision: Literal["16", 16, "bf16"], device: str, scaler: Optional[torch.cuda.amp.GradScaler] = None ) -> None: self.precision = cast(Literal["16", "bf16"], str(precision)) if scaler is None and self.precision == "16": with _patch_cuda_is_available(): # if possible, we defer CUDA initialization to support strategies that will attempt forks scaler = torch.cuda.amp.GradScaler() if scaler is not None and self.precision == "bf16": raise ValueError(f"`precision='bf16'` does not use a scaler, found {scaler}.") self.device = device self.scaler = scaler
[docs] @contextmanager def forward_context(self) -> Generator[None, None, None]: with self._autocast_context_manager(): yield
[docs] def convert_input(self, data: Tensor) -> Tensor: precision_to_type = {"bf16": torch.bfloat16, "16": torch.float16} dst_type = precision_to_type[self.precision] return _convert_fp_tensor(data, dst_type)
[docs] def backward(self, tensor: Tensor, model: Optional[Module], *args: Any, **kwargs: Any) -> None: if self.scaler is not None: tensor = self.scaler.scale(tensor) super().backward(tensor, model, *args, **kwargs)
[docs] def optimizer_step( self, optimizer: Optimizable, **kwargs: Any, ) -> Any: if self.scaler is None: # skip scaler logic, as bfloat16 does not require scaler return super().optimizer_step(optimizer, **kwargs) if isinstance(optimizer, LBFGS): raise TypeError("Native AMP and the LBFGS optimizer are not compatible.") # note: the scaler will skip the `optimizer.step` if nonfinite gradients are found step_output = self.scaler.step(optimizer, **kwargs) self.scaler.update() return step_output
[docs] def state_dict(self) -> Dict[str, Any]: if self.scaler is not None: return self.scaler.state_dict() return {}
[docs] def load_state_dict(self, state_dict: Dict[str, Any]) -> None: if self.scaler is not None: self.scaler.load_state_dict(state_dict)
def _autocast_context_manager(self) -> torch.autocast: # the dtype could be automatically inferred but we need to manually set it due to a bug upstream # return torch.autocast(self.device, dtype=torch.bfloat16 if self.precision == "bf16" else torch.half)

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