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58 lines
2.3 KiB
Python
58 lines
2.3 KiB
Python
"""
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the script will use bitandbytes to quantize the MiniCPM language model.
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the be quantized model can be finetuned by MiniCPM or not.
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you only need to set the model_path 、save_path and run bash code
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cd MiniCPM
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python quantize/bnb_quantize.py
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you will get the quantized model in save_path、quantized_model test time and gpu usage
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"""
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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
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import time
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import torch
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import GPUtil
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import os
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model_path = "/root/ld/ld_model_pretrain/MiniCPM-1B-sft-bf16" # 模型下载地址
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save_path = "/root/ld/ld_model_pretrain/MiniCPM-1B-sft-bf16_int4" # 量化模型保存地址
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device = "cuda" if torch.cuda.is_available() else "cpu"
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# 创建一个配置对象来指定量化参数
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quantization_config = BitsAndBytesConfig(
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load_in_4bit=True, # 是否进行4bit量化
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load_in_8bit=False, # 是否进行8bit量化
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bnb_4bit_compute_dtype=torch.float16, # 计算精度设置
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bnb_4bit_quant_storage=torch.uint8, # 量化权重的储存格式
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bnb_4bit_quant_type="nf4", # 量化格式，这里用的是正太分布的int4
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bnb_4bit_use_double_quant=True, # 是否采用双量化，即对zeropoint和scaling参数进行量化
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llm_int8_enable_fp32_cpu_offload=False, # 是否llm使用int8，cpu上保存的参数使用fp32
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llm_int8_has_fp16_weight=False, # 是否启用混合精度
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#llm_int8_skip_modules=["out_proj", "kv_proj", "lm_head"], # 不进行量化的模块
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llm_int8_threshold=6.0, # llm.int8()算法中的离群值，根据这个值区分是否进行量化
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)
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tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
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model = AutoModelForCausalLM.from_pretrained(
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model_path,
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device_map=device, # 分配模型到device
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quantization_config=quantization_config,
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trust_remote_code=True,
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)
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gpu_usage = GPUtil.getGPUs()[0].memoryUsed
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start = time.time()
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response = model.chat(tokenizer, "<用户>给我讲一个故事<AI>",history=[], temperature=0.5, top_p=0.8, repetition_penalty=1.02) # 模型推理
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print("量化后输出", response)
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print("量化后推理用时", time.time() - start)
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print(f"量化后显存占用: {round(gpu_usage/1024,2)}GB")
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# 保存模型和分词器
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os.makedirs(save_path, exist_ok=True)
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model.save_pretrained(save_path, safe_serialization=True)
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tokenizer.save_pretrained(save_path)
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