ValueError: BitAndBytes quantization with TP or PP is not supported yet
使用加载hf模型时，使用load_in_8bit来量化模型（底层其实是调用bitsandbytes来量化）：
import argparse
import os
import torchdef parse_arguments():parser = argparse.ArgumentParser()parser.add_argument('--model_path',help="model and tokenizer path",default='/docker_shared/Baichuan2-7B-Chat-test2',)return parser.parse_args()def convert_bin2st_from_pretrained(model_path):from transformers import AutoModelForCausalLMmodel = AutoModelForCausalLM.from_pretrained(pretrained_model_name_or_path=model_path,low_cpu_mem_usage=True,trust_remote_code=True,torch_dtype=torch.float16, load_in_8bit=True) model.save_pretrained(model_path, safe_serialization=True)if __name__ == '__main__':args = parse_arguments()print(f"covert {args.model_path} into safetensor")convert_bin2st_from_pretrained(args.model_path)
然后使用vllm加载量化后的模型，报错了：
WARNING 09-07 23:25:16 config.py:318] bitsandbytes quantization is not fully optimized yet. The speed can be slower than non-quantized models.
........
File "/usr/local/lib/python3.10/dist-packages/vllm/config.py", line 353, in verify_with_parallel_configraise ValueError(
ValueError: BitAndBytes quantization with TP or PP is not supported yet.
ERROR 09-07 23:25:19 api_server.py:171] RPCServer process died before responding to readiness probe
意思是vllm不支持在bitsandbytes量化后的模型中使用tensor并行加速，也就是–tensor-parallel-size的值不能大于1。
WARNING 09-07 23:44:11 config.py:357] CUDA graph is not supported on BitAndBytes yet, fallback to the eager mode
使用–tensor-parallel-size 1 加载模型，继续遇到错误
WARNING 09-07 23:44:11 config.py:318] bitsandbytes quantization is not fully optimized yet. The speed can be slower than non-quantized models.
WARNING 09-07 23:44:11 config.py:357] CUDA graph is not supported on BitAndBytes yet, fallback to the eager mode.
.......
File "/usr/local/lib/python3.10/dist-packages/vllm/model_executor/models/baichuan.py", line 405, in load_weightsparam = params_dict[name]
KeyError: 'model.layers.0.mlp.down_proj.SCB'
Loading safetensors checkpoint shards: 0% Completed | 0/2 [00:00<?, ?it/s]ERROR 09-07 23:44:19 api_server.py:171] RPCServer process died before responding to readiness probe
仍然加载失败。
vllm支持哪些量化方式呢
查看vllm的help信息，可以看到vllm支持的量化方式
--quantization {aqlm,awq,deepspeedfp,tpu_int8,fp8,fbgemm_fp8,marlin,gguf,gptq_marlin_24,gptq_marlin,awq_marlin,gptq,squeezellm,compressed-tensors,bitsandbytes,qqq,experts_int8,None}, -q {aqlm,awq,deepspeedfp,tpu_int8,fp8,fbgemm_fp8,marlin,gguf,gptq_marlin_24,gptq_marlin,awq_marlin,gptq,squeezellm,compressed-tensors,bitsandbytes,qqq,experts_int8,None}Method used to quantize the weights. If None, we first check the `quantization_config` attribute in the model config file. If that is None, we assume the model weights are not quantized and use`dtype` to determine the data type of the weights.
这些量化方式并不是vllm启动时做的，而是提前转换好的，vllm只是支持这些量化模型的加载，这些量化功能本身不在vllm里。
关于vllm支持的量化方式文档在：https://docs.vllm.ai/en/latest/quantization/supported_hardware.html。这个网页中有关于各种量化方法的使用。
按照这里的说法，bitsandbytes也是支持的，不清楚为啥我上面加载失败了。
