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# Copyright 2024-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.lora.layer import LoraLayer
from peft.tuners.tuners_utils import BaseTunerLayer
if is_aqlm_available():
from aqlm import QuantizedLinear
class AqlmLoraLinear(torch.nn.Module, LoraLayer):
def __init__(
self,
base_layer,
adapter_name: str,
r: int = 0,
lora_alpha: int = 1,
lora_dropout: float = 0.0,
init_lora_weights: bool = True,
use_rslora: bool = False,
**kwargs,
):
super().__init__()
LoraLayer.__init__(self, base_layer)
self._active_adapter = adapter_name
self.update_layer(adapter_name, r, lora_alpha, lora_dropout, init_lora_weights, use_rslora)
def forward(self, x: torch.Tensor):
# note: logic differs from default Linear because merging is not supported
result = self.base_layer(x)
if self.disable_adapters:
return result
for active_adapter in self.active_adapters:
if active_adapter not in self.lora_A.keys():
continue
lora_A = self.lora_A[active_adapter]
lora_B = self.lora_B[active_adapter]
dropout = self.lora_dropout[active_adapter]
scaling = self.scaling[active_adapter]
requires_conversion = not torch.is_autocast_enabled()
if requires_conversion:
expected_dtype = result.dtype
x = x.to(lora_A.weight.dtype)
output = lora_B(lora_A(dropout(x)))
if requires_conversion:
output = output.to(expected_dtype)
output = output * scaling
result += output
return result
def __repr__(self) -> str:
rep = super().__repr__()
return "lora." + rep
# TODO: Check if it is better as suggested by users https://github.com/PanQiWei/AutoGPTQ/pull/102
# def reset_lora_parameters(self, adapter_name):
# if adapter_name in self.lora_A.keys():
# torch.nn.init.xavier_uniform_(self.lora_A[adapter_name].weight)
# torch.nn.init.zeros_(self.lora_B[adapter_name].weight)
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 = AqlmLoraLinear(target, adapter_name, **kwargs)
target.qweight = target_base_layer.codes
return new_module
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