121 lines
4.0 KiB
Python
121 lines
4.0 KiB
Python
# Copyright 2023-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.tuners.lora.layer import LoraLayer
|
|
from peft.tuners.tuners_utils import BaseTunerLayer
|
|
from peft.utils import get_auto_gptq_quant_linear
|
|
|
|
|
|
class QuantLinear(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,
|
|
use_dora: bool = False,
|
|
lora_bias: bool = False,
|
|
**kwargs,
|
|
):
|
|
super().__init__()
|
|
LoraLayer.__init__(self, base_layer)
|
|
|
|
if use_dora:
|
|
raise ValueError(
|
|
f"{self.__class__.__name__} does not support DoRA yet, please set it to False"
|
|
)
|
|
|
|
# self.base_layer and self.quant_linear_module are the same; we need the former for consistency and the latter
|
|
# for backwards compatibility
|
|
self.quant_linear_module = base_layer
|
|
self._active_adapter = adapter_name
|
|
self.update_layer(
|
|
adapter_name,
|
|
r,
|
|
lora_alpha=lora_alpha,
|
|
lora_dropout=lora_dropout,
|
|
init_lora_weights=init_lora_weights,
|
|
use_rslora=use_rslora,
|
|
use_dora=use_dora,
|
|
lora_bias=lora_bias,
|
|
)
|
|
|
|
def forward(self, x: torch.Tensor):
|
|
# note: logic differs from default Linear because merging is not supported
|
|
result = self.quant_linear_module(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_gptq(
|
|
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
|
|
|
|
gptq_quantization_config = kwargs.get("gptq_quantization_config", None)
|
|
AutoGPTQQuantLinear = get_auto_gptq_quant_linear(gptq_quantization_config)
|
|
|
|
if AutoGPTQQuantLinear is not None and isinstance(
|
|
target_base_layer, AutoGPTQQuantLinear
|
|
):
|
|
new_module = QuantLinear(target, adapter_name, **kwargs)
|
|
target.qweight = target_base_layer.qweight
|
|
|
|
return new_module
|