# Copyright 2024 The HuggingFace Team. All rights reserved. # # 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. "VPTQ (Vector Post-Training Quantization) integration file" import torch.nn as nn from accelerate import init_empty_weights from vptq import VQuantLinear def replace_with_vptq_linear( model, quantization_config=None, modules_to_not_convert=None, current_key_name=None, has_been_replaced=False, ): """ Public method that recursively replaces the Linear layers of the given model with VPTQ quantized layers. `accelerate` is needed to use this method. Returns the converted model and a boolean that indicates if the conversion has been successful or not. Args: model (`torch.nn.Module`): The model to convert, can be any `torch.nn.Module` instance. quantization_config (`VptqConfig`): The quantization config object that contains the quantization parameters. modules_to_not_convert (`list[`str`]`, *optional*, defaults to `["lm_head"]`): Names of the modules to not convert in `VQuantLinear`. In practice we keep the `lm_head` in full precision for numerical stability reasons. current_key_name (`list`, *optional*): A list that contains the current key name. This is used for recursion and should not be passed by the user. has_been_replaced (`bool`, *optional*): A boolean that indicates if the conversion has been successful or not. This is used for recursion and should not be passed by the user. """ modules_to_not_convert = ["lm_head"] if not modules_to_not_convert else modules_to_not_convert for name, module in model.named_children(): if current_key_name is None: current_key_name = [] current_key_name.append(name) layer_name = ".".join(current_key_name) shared_layer_config = quantization_config.shared_layer_config config_for_layers = quantization_config.config_for_layers if ( isinstance(module, nn.Linear) and layer_name not in modules_to_not_convert and ((layer_name in config_for_layers) or (current_key_name[-1] in shared_layer_config)) ): layer_params = config_for_layers.get(layer_name, None) or shared_layer_config.get( current_key_name[-1], None ) with init_empty_weights(): in_features = module.in_features out_features = module.out_features model._modules[name] = VQuantLinear( in_features, out_features, vector_lens=layer_params["vector_lens"], num_centroids=layer_params["num_centroids"], num_res_centroids=layer_params["num_res_centroids"], group_num=layer_params["group_num"], group_size=layer_params["group_size"], outlier_size=layer_params["outlier_size"], indices_as_float=layer_params["indices_as_float"], enable_norm=layer_params["enable_norm"], enable_perm=layer_params["enable_perm"], is_indice_packed=True, enable_proxy_error=False, bias=module.bias is not None, ) has_been_replaced = True # Force requires grad to False to avoid unexpected errors model._modules[name].requires_grad_(False) if len(list(module.children())) > 0: _, has_been_replaced = replace_with_vptq_linear( module, quantization_config=quantization_config, modules_to_not_convert=modules_to_not_convert, current_key_name=current_key_name, has_been_replaced=has_been_replaced, ) # Remove the last key for recursion current_key_name.pop(-1) return model, has_been_replaced