CUDA_VISIBLE_DEVICES=0,1,2,3 python3.10 webui.py \
--model_name_or_path ../model/qwen/Qwen1.5-72B-Chat-sft-INT4 \
--template qwen \
--use_fast_tokenizer True \
--repetition_penalty 1.03 \
--infer_backend vllm \
--cutoff_len 8192 \
--flash_attn auto
......
File "/usr/local/lib/python3.10/dist-packages/vllm/model_executor/model_loader.py", line 70, in get_model
raise ValueError(
ValueError: torch.bfloat16 is not supported for quantization method gptq. Supported dtypes: [torch.float16]
(RayWorkerVllm pid=20366) INFO 05-10 03:50:19 selector.py:45] Cannot use FlashAttention because the package is not found. Please install it for better performance. [repeated 2x across cluster] (Ray deduplicates logs by default. Set RAY_DEDUP_LOGS=0 to disable log deduplication, or see https://docs.ray.io/en/master/ray-observability/ray-logging.html#log-deduplication for more options.)
...
这是我用来量化的脚本:
!/bin/bash
DO NOT use quantized model or quantization_bit when merging lora weights
Reminder
Reproduction
硬件环境: 4 * RTX3090:(这个环境我已经运行过Qwen1.5-72B-Chat-GTPQ-INT4(即Qwen72B的INT4量化版)
这是我调用Web脚本:
!/bin/bash
CUDA_VISIBLE_DEVICES=0,1,2,3 python3.10 webui.py \ --model_name_or_path ../model/qwen/Qwen1.5-72B-Chat-sft-INT4 \ --template qwen \ --use_fast_tokenizer True \ --repetition_penalty 1.03 \ --infer_backend vllm \ --cutoff_len 8192 \ --flash_attn auto ...... File "/usr/local/lib/python3.10/dist-packages/vllm/model_executor/model_loader.py", line 70, in get_model raise ValueError( ValueError: torch.bfloat16 is not supported for quantization method gptq. Supported dtypes: [torch.float16] (RayWorkerVllm pid=20366) INFO 05-10 03:50:19 selector.py:45] Cannot use FlashAttention because the package is not found. Please install it for better performance. [repeated 2x across cluster] (Ray deduplicates logs by default. Set RAY_DEDUP_LOGS=0 to disable log deduplication, or see https://docs.ray.io/en/master/ray-observability/ray-logging.html#log-deduplication for more options.) ...
这是我用来量化的脚本:
!/bin/bash
DO NOT use quantized model or quantization_bit when merging lora weights
CUDA_VISIBLE_DEVICES=0,1,2,3,4,5 python3.10 export_model.py \ --model_name_or_path /hy-tmp/models/Qwen1.5-72B-Chat-sft \ --export_quantization_bit 4 \ --export_quantization_dataset ../data/c4_demo.json \ --template qwen \ --export_dir ../../models/Qwen1.5-72B-Chat-sft-INT4 \ --export_size 2 \ --export_device cpu \ --export_legacy_format False
不知道是哪儿出了问题?
Expected behavior
期望采用当前硬件能够运行自己经过微调以后量化出来的Qwen1.5-72B-GTPQ-INT4。
System Info
(base) root@I19d213861d0060102e:/hy-tmp/LLaMA-Factory-main/src# python3.10 -m pip list Package Version
accelerate 0.28.0 addict 2.4.0 aiofiles 23.2.1 aiohttp 3.9.3 aiosignal 1.3.1 aliyun-python-sdk-core 2.15.0 aliyun-python-sdk-kms 2.16.2 altair 5.2.0 annotated-types 0.6.0 anyio 4.3.0 async-timeout 4.0.3 attrs 23.2.0 auto_gptq 0.7.1 bitsandbytes 0.43.0 certifi 2019.11.28 cffi 1.16.0 chardet 3.0.4 charset-normalizer 3.3.2 click 8.1.7 cloudpickle 3.0.0 cmake 3.29.2 contourpy 1.2.0 crcmod 1.7 cryptography 42.0.5 cupy-cuda12x 12.1.0 cycler 0.12.1 datasets 2.18.0 dbus-python 1.2.16 deepspeed 0.14.0 dill 0.3.8 diskcache 5.6.3 distro 1.4.0 distro-info 0.23ubuntu1 docstring_parser 0.16 einops 0.7.0 exceptiongroup 1.2.0 fastapi 0.110.0 fastrlock 0.8.2 ffmpy 0.3.2 filelock 3.13.3 fire 0.6.0 fonttools 4.50.0 frozenlist 1.4.1 fsspec 2024.2.0 galore-torch 1.0 gast 0.5.4 gekko 1.0.7 gradio 3.50.2 gradio_client 0.6.1 h11 0.14.0 hjson 3.1.0 httpcore 1.0.4 httptools 0.6.1 httpx 0.27.0 huggingface-hub 0.22.0 idna 2.8 importlib_metadata 7.1.0 importlib_resources 6.4.0 interegular 0.3.3 Jinja2 3.1.3 jmespath 0.10.0 joblib 1.3.2 jsonschema 4.21.1 jsonschema-specifications 2023.12.1 kiwisolver 1.4.5 lark 1.1.9 llvmlite 0.42.0 markdown-it-py 3.0.0 MarkupSafe 2.1.5 matplotlib 3.8.3 mdurl 0.1.2 modelscope 1.13.3 mpmath 1.3.0 msgpack 1.0.8 multidict 6.0.5 multiprocess 0.70.16 nest-asyncio 1.6.0 networkx 3.2.1 ninja 1.11.1.1 numba 0.59.1 numpy 1.26.4 nvidia-cublas-cu12 12.1.3.1 nvidia-cuda-cupti-cu12 12.1.105 nvidia-cuda-nvrtc-cu12 12.1.105 nvidia-cuda-runtime-cu12 12.1.105 nvidia-cudnn-cu12 8.9.2.26 nvidia-cufft-cu12 11.0.2.54 nvidia-curand-cu12 10.3.2.106 nvidia-cusolver-cu12 11.4.5.107 nvidia-cusparse-cu12 12.1.0.106 nvidia-nccl-cu12 2.18.1 nvidia-nvjitlink-cu12 12.4.99 nvidia-nvtx-cu12 12.1.105 orjson 3.9.15 oss2 2.18.4 outlines 0.0.34 packaging 24.0 pandas 2.2.1 peft 0.10.0 pillow 10.2.0 pip 24.0 platformdirs 4.2.0 prometheus_client 0.20.0 protobuf 5.26.0 psutil 5.9.8 py-cpuinfo 9.0.0 pyarrow 15.0.2 pyarrow-hotfix 0.6 pycparser 2.21 pycryptodome 3.20.0 pydantic 2.6.4 pydantic_core 2.16.3 pydub 0.25.1 Pygments 2.17.2 PyGObject 3.36.0 pynvml 11.5.0 pyparsing 3.1.2 python-apt 2.0.1+ubuntu0.20.4.1 python-dateutil 2.9.0.post0 python-dotenv 1.0.1 python-multipart 0.0.9 pytz 2024.1 PyYAML 6.0.1 ray 2.10.0 referencing 0.34.0 regex 2023.12.25 requests 2.31.0 requests-unixsocket 0.2.0 rich 13.7.1 rouge 1.0.1 rpds-py 0.18.0 safetensors 0.4.2 scipy 1.12.0 semantic-version 2.10.0 sentencepiece 0.2.0 setuptools 69.2.0 shtab 1.7.1 simplejson 3.19.2 six 1.14.0 sniffio 1.3.1 sortedcontainers 2.4.0 sse-starlette 2.0.0 ssh-import-id 5.10 starlette 0.36.3 sympy 1.12 termcolor 2.4.0 tiktoken 0.6.0 tokenizers 0.15.2 tomli 2.0.1 toolz 0.12.1 torch 2.1.2 tqdm 4.66.2 transformers 4.39.1 triton 2.1.0 trl 0.8.1 typing_extensions 4.10.0 tyro 0.7.3 tzdata 2024.1 unattended-upgrades 0.1 urllib3 2.2.1 uvicorn 0.29.0 uvloop 0.19.0 vllm 0.4.0 watchfiles 0.21.0 websockets 11.0.3 wheel 0.34.2 xformers 0.0.23.post1 xxhash 3.4.1 yapf 0.40.2 yarl 1.9.4 zipp 3.18.1
Others
备注:当前的硬件环境下是成功运行过原版的Qwen1.5-72B-GTPQ-INT4