BaiChuan13B-GPTQ量化详解

知识要点：
1、按照网上搜索的一些代码，如使用auto_gptq原生库进行训练后量化，可能会正常量化，但是在线推理时会出现如找不到bin文件或者tf文件，即模型权重文件，所以和网上大部分代码不同的地方在于，需要提前保存对应模型的权重文件，如果是BaiChuan13B，那么在进行模型量化前，对其进行保存
代码如下：

def save_bin(pretrained_model_dir, quantized_model_dir):from transformers import AutoModelForCausalLMimport torchimport osoriginal_model = AutoModelForCausalLM.from_pretrained(pretrained_model_dir, trust_remote_code=True,torch_dtype=torch.float16,      # 不执行这个保存的bin文件会非常的大，大概50多Gsafetensors=True)print("保存bin文件...")model_path = os.path.join(quantized_model_dir, "pytorch_model"+".bin")torch.save(original_model.state_dict(), model_path)print("保存bin文件完成...")

量化代码，使用原生库auto_gptq进行量化：

def from_authority_autogptq(pretrained_model_dir, quantized_model_dir):from transformers import AutoTokenizer, AutoModelForCausalLMfrom auto_gptq import AutoGPTQForCausalLM, BaseQuantizeConfigimport loggingimport torchimport oslogging.basicConfig(format="%(asctime)s %(levelname)s [%(name)s] %(message)s", level=logging.INFO, datefmt="%Y-%m-%d %H:%M:%S")# 量化分词器加载tokenizer = AutoTokenizer.from_pretrained(pretrained_model_dir, use_fast=False, trust_remote_code=True)examples = [tokenizer("auto-gptq is an easy-to-use model quantization library with user-friendly apis, based on GPTQ algorithm.")]# 量化参数配置quantize_config = BaseQuantizeConfig(bits=4,             # quantize model to 4-bitgroup_size=128,     # it is recommended to set the value to 128desc_act=False,     # set to False can significantly speed up inference but the perplexity may slightly bad)# load un-quantized model, by default, the model will always be loaded into CPU memoryquantize_model = AutoGPTQForCausalLM.from_pretrained(pretrained_model_dir, quantize_config=quantize_config, trust_remote_code=True,device_map="auto",)print("开始量化模型.......")quantize_model.quantize(examples)# save model weightsprint("保存量化文件...")quantize_model.save_quantized(quantized_model_dir)print("保存量化文件完成...")print("保存tokenizer...")tokenizer.save_pretrained(quantized_model_dir)print("保存tokenizer完成...")

按照上述步骤，此时模型量化文件保存成功，接下来就是模型在线推理

def get_baichuan2_autogptq(quantized_model_dir):from transformers import AutoModelForCausalLM, AutoTokenizerfrom transformers.generation.utils import GenerationConfigimport torch# 模型地址model_id = quantized_model_dirprint("加载分词器tokenizer...")tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True,use_fast=False)'''warnings.warn(f'Input type into Linear4bit is torch.float16, but bnb_4bit_compute_type=torch.float32 (default).This will lead to slow inference or training speed'''print("加载量化model...")quantized_model_4bit = AutoModelForCausalLM.from_pretrained(# 要载入的模型名称model_id, load_in_4bit=True,# 仅使用本地模型，不通过网络下载模型local_files_only=True,# 指定模型精度torch_dtype=torch.float16,trust_remote_code=True,safetensors=True)print("加载config...")quantized_model_4bit.generation_config = GenerationConfig.from_pretrained(model_id)# 实例测试print("生成...")messages = []messages.append({"role": "user", "content":"亚历山大为何如此厉害"})response = quantized_model_4bit.chat(tokenizer, messages)print(response)return response

最后整合代码：

'''bin 文件是保存的是原始的加载模型文件，不涉及量化操作的模型过程，不然会报错或者加载不出来！！！'''
def save_bin(pretrained_model_dir, quantized_model_dir):from transformers import AutoModelForCausalLMimport torchimport osoriginal_model = AutoModelForCausalLM.from_pretrained(pretrained_model_dir, trust_remote_code=True,torch_dtype=torch.float16,      # 不执行这个保存的bin文件会非常的大，大概50多Gsafetensors=True)print("保存bin文件...")model_path = os.path.join(quantized_model_dir, "pytorch_model"+".bin")torch.save(original_model.state_dict(), model_path)print("保存bin文件完成...")# auto_gptq原生库, 量化占用显存7-10G不等，用时23分钟，推理18G
def from_authority_autogptq(pretrained_model_dir, quantized_model_dir):from transformers import AutoTokenizer, AutoModelForCausalLMfrom auto_gptq import AutoGPTQForCausalLM, BaseQuantizeConfigimport loggingimport torchimport oslogging.basicConfig(format="%(asctime)s %(levelname)s [%(name)s] %(message)s", level=logging.INFO, datefmt="%Y-%m-%d %H:%M:%S")# 量化分词器加载tokenizer = AutoTokenizer.from_pretrained(pretrained_model_dir, use_fast=False, trust_remote_code=True)examples = [tokenizer("auto-gptq is an easy-to-use model quantization library with user-friendly apis, based on GPTQ algorithm.")]# 量化参数配置quantize_config = BaseQuantizeConfig(bits=4,             # quantize model to 4-bitgroup_size=128,     # it is recommended to set the value to 128desc_act=False,     # set to False can significantly speed up inference but the perplexity may slightly bad)# load un-quantized model, by default, the model will always be loaded into CPU memoryquantize_model = AutoGPTQForCausalLM.from_pretrained(pretrained_model_dir, quantize_config=quantize_config, trust_remote_code=True,device_map="auto",)print("开始量化模型.......")quantize_model.quantize(examples)# save model weightsprint("保存量化文件...")quantize_model.save_quantized(quantized_model_dir)print("保存量化文件完成...")print("保存tokenizer...")tokenizer.save_pretrained(quantized_model_dir)print("保存tokenizer完成...")# 加载量化后的模型方法
def get_baichuan2_autogptq(quantized_model_dir):from transformers import AutoModelForCausalLM, AutoTokenizerfrom transformers.generation.utils import GenerationConfigimport torch# 模型地址model_id = quantized_model_dirprint("加载分词器tokenizer...")tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True,use_fast=False)'''warnings.warn(f'Input type into Linear4bit is torch.float16, but bnb_4bit_compute_type=torch.float32 (default).This will lead to slow inference or training speed'''print("加载量化model...")quantized_model_4bit = AutoModelForCausalLM.from_pretrained(# 要载入的模型名称model_id, load_in_4bit=True,# 仅使用本地模型，不通过网络下载模型local_files_only=True,# 指定模型精度torch_dtype=torch.float16,trust_remote_code=True,safetensors=True)print("加载config...")quantized_model_4bit.generation_config = GenerationConfig.from_pretrained(model_id)# 实例测试print("生成...")messages = []messages.append({"role": "user", "content":"```桥架\n1、名称：机房走线架（铝合金） 2、规格：300mm*100mm 3、含支吊架制作安装 4、其它：具体详见图纸、技术规范书、图集、招标文件、招标答疑、政府相关文件、规范等其它资料，满足验收要求```\n请仔细阅读上文，并从中分析出实体列表中的各实体。请使用json字典格式回答，其中，键为各实体名称，值为从文本中提取出的内容（若没有相应实体则值为'无'）。\n实体列表如下（目标实体之间通过“;”隔开）： ```名称;型号;材质;类型;规格;接地方式```"})response = quantized_model_4bit.chat(tokenizer, messages)print(response)return response if __name__ == "__main__":# from_transformers_autogptq 方法量化模型# pretrained_model_dir = "/root/lk/big_model/Baichuan2-13B-Chat"# quantized_model_dir = "/root/lk/big_model/baichuan2_autogptq"# from_transformers_autogptq(pretrained_model_dir, quantized_model_dir)import datetimeprint("程序开始时间------->>>>>>", datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'))# 地址pretrained_model_dir = "/root/lk/big_model/Baichuan2-13B-Chat"quantized_model_dir = "/root/lk/big_model/baichuan2_autogptq"# 第一步：保存原始模型的Bin文件，然后再量化（很关键）# save_bin(pretrained_model_dir, quantized_model_dir)# 第二部：执行来自autogptq原始包量化模型# from_authority_autogptq(pretrained_model_dir, quantized_model_dir)# 第三部：使用量化模型进行推理（需要添加对应文件）get_baichuan2_autogptq(quantized_model_dir)print("程序结束时间------->>>>>>", datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'))

对应包版本：

auto-gptq==0.6.0
transformers==4.39.2
torch==2.0.1

BaiChuan13B-GPTQ量化详解

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