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# Copyright 2025-present the HuggingFace Inc. 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 Any, Optional
import torch
from peft.import_utils import is_aqlm_available
from peft.tuners.oft.layer import OFTLayer
from peft.tuners.tuners_utils import BaseTunerLayer
if is_aqlm_available():
from aqlm import QuantizedLinear
class AqlmOFTLinear(torch.nn.Module, OFTLayer):
def __init__(
self,
base_layer,
adapter_name: str,
r: int = 0,
oft_block_size: int = 32,
module_dropout: float = 0.0,
init_weights: bool = True,
coft: bool = False,
eps: float = 6e-5,
block_share: bool = False,
fan_in_fan_out: bool = False, # Set this to True if the layer to replace stores weight like (fan_in, fan_out)
use_cayley_neumann: bool = False,
num_cayley_neumann_terms: int = 5,
**kwargs,
):
super().__init__()
OFTLayer.__init__(self, base_layer)
self._active_adapter = adapter_name
self.update_layer(
adapter_name,
r,
oft_block_size=oft_block_size,
module_dropout=module_dropout,
init_weights=init_weights,
coft=coft,
eps=eps,
block_share=block_share,
use_cayley_neumann=use_cayley_neumann,
num_cayley_neumann_terms=num_cayley_neumann_terms,
)
def forward(self, x: torch.Tensor):
# note: logic differs from default Linear because merging is not supported
if self.disable_adapters:
return self.base_layer(x)
for active_adapter in self.active_adapters:
if active_adapter not in self.oft_R.keys():
continue
oft_R = self.oft_R[active_adapter]
requires_conversion = not torch.is_autocast_enabled()
if requires_conversion:
expected_dtype = x.dtype
x = self._cast_input_dtype(x, oft_R.weight.dtype)
x = oft_R(x)
result = self.base_layer(x)
if requires_conversion:
result = result.to(expected_dtype)
return result
def __repr__(self) -> str:
rep = super().__repr__()
return "oft." + rep
def dispatch_aqlm(
target: torch.nn.Module,
adapter_name: str,
**kwargs: Any,
) -> Optional[torch.nn.Module]:
new_module = None
if isinstance(target, BaseTunerLayer):
target_base_layer = target.get_base_layer()
else:
target_base_layer = target
if is_aqlm_available() and isinstance(target_base_layer, QuantizedLinear):
new_module = AqlmOFTLinear(target, adapter_name, **kwargs)
target.qweight = target_base_layer.codes
return new_module
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