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 AutoModelForCausalLM
import torch
import os
original_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G
safetensors=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, AutoModelForCausalLM
from auto_gptq import AutoGPTQForCausalLM, BaseQuantizeConfig
import logging
import torch
import os
logging.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-bit
group_size=128, # it is recommended to set the value to 128
desc_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 memory
quantize_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 weights
print("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, AutoTokenizer
from transformers.generation.utils import GenerationConfig
import torch
# 模型地址
model_id = quantized_model_dir
print("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 AutoModelForCausalLM
import torch
import os
original_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G
safetensors=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, AutoModelForCausalLM
from auto_gptq import AutoGPTQForCausalLM, BaseQuantizeConfig
import logging
import torch
import os
logging.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-bit
group_size=128, # it is recommended to set the value to 128
desc_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 memory
quantize_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 weights
print("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, AutoTokenizer
from transformers.generation.utils import GenerationConfig
import torch
# 模型地址
model_id = quantized_model_dir
print("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 datetime
print("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