[Usage]: BNB Gemma2 9b loading problems

[orellavie1212]

Your current environment

The output of `python collect_env.py`

Collecting environment information... PyTorch version: 2.3.0+cu121 Is debug build: False CUDA used to build PyTorch: 12.1 ROCM used to build PyTorch: N/A

OS: Ubuntu 22.04.2 LTS (x86_64) GCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0 Clang version: Could not collect CMake version: version 3.27.6 Libc version: glibc-2.35

Python version: 3.10.14 (main, May 6 2024, 19:42:50) [GCC 11.2.0] (64-bit runtime) Python platform: Linux-6.5.0-35-generic-x86_64-with-glibc2.35 Is CUDA available: True CUDA runtime version: 12.1.66 CUDA_MODULE_LOADING set to: LAZY GPU models and configuration: GPU 0: NVIDIA RTX A6000 Nvidia driver version: 545.29.06 cuDNN version: Could not collect HIP runtime version: N/A MIOpen runtime version: N/A Is XNNPACK available: True

CPU: Architecture: x86_64 CPU op-mode(s): 32-bit, 64-bit Address sizes: 43 bits physical, 48 bits virtual Byte Order: Little Endian CPU(s): 64 On-line CPU(s) list: 0-63 Vendor ID: AuthenticAMD Model name: AMD Ryzen Threadripper PRO 3975WX 32-Cores CPU family: 23 Model: 49 Thread(s) per core: 2 Core(s) per socket: 32 Socket(s): 1 Stepping: 0 Frequency boost: enabled CPU max MHz: 3500.0000 CPU min MHz: 2200.0000 BogoMIPS: 6986.81 Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc rep_good nopl nonstop_tsc cpuid extd_apicid aperfmperf rapl pni pclmulqdq monitor ssse3 fma cx16 sse4_1 sse4_2 movbe popcnt aes xsave avx f16c rdrand lahf_lm cmp_legacy svm extapic cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw ibs skinit wdt tce topoext perfctr_core perfctr_nb bpext perfctr_llc mwaitx cpb cat_l3 cdp_l3 hw_pstate ssbd mba ibpb stibp vmmcall fsgsbase bmi1 avx2 smep bmi2 cqm rdt_a rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local clzero irperf xsaveerptr rdpru wbnoinvd amd_ppin arat npt lbrv svm_lock nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold avic v_vmsave_vmload vgif v_spec_ctrl umip rdpid overflow_recov succor smca sev sev_es Virtualization: AMD-V L1d cache: 1 MiB (32 instances) L1i cache: 1 MiB (32 instances) L2 cache: 16 MiB (32 instances) L3 cache: 128 MiB (8 instances) NUMA node(s): 1 NUMA node0 CPU(s): 0-63 Vulnerability Gather data sampling: Not affected Vulnerability Itlb multihit: Not affected Vulnerability L1tf: Not affected Vulnerability Mds: Not affected Vulnerability Meltdown: Not affected Vulnerability Mmio stale data: Not affected Vulnerability Retbleed: Mitigation; untrained return thunk; SMT enabled with STIBP protection Vulnerability Spec rstack overflow: Mitigation; Safe RET Vulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl Vulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization Vulnerability Spectre v2: Mitigation; Retpolines; IBPB conditional; STIBP always-on; RSB filling; PBRSB-eIBRS Not affected; BHI Not affected Vulnerability Srbds: Not affected Vulnerability Tsx async abort: Not affected

Versions of relevant libraries: [pip3] mypy-extensions==1.0.0 [pip3] numpy==1.26.0 [pip3] nvidia-nccl-cu11==2.14.3 [pip3] nvidia-nccl-cu12==2.20.5 [pip3] onnxruntime==1.18.1 [pip3] sentence-transformers==3.0.1 [pip3] torch==2.3.0 [pip3] torch-tb-profiler==0.4.3 [pip3] torchvision==0.18.0 [pip3] transformers==4.42.3 [pip3] triton==2.3.0 [pip3] tritonclient==2.34.0 [conda] nvidia-nccl-cu12 2.20.5 pypi_0 pypi [conda] sentence-transformers 3.0.1 pypi_0 pypi [conda] torch 2.3.0 pypi_0 pypi [conda] torchvision 0.18.0 pypi_0 pypi [conda] transformers 4.42.3 pypi_0 pypi [conda] triton 2.3.0 pypi_0 pypi ROCM Version: Could not collect Neuron SDK Version: N/A vLLM Version: 0.5.1 vLLM Build Flags: CUDA Archs: Not Set; ROCm: Disabled; Neuron: Disabled GPU Topology: GPU0 CPU Affinity NUMA Affinity GPU NUMA ID GPU0 X 0-63 0 N/A

Legend:

X = Self SYS = Connection traversing PCIe as well as the SMP interconnect between NUMA nodes (e.g., QPI/UPI) NODE = Connection traversing PCIe as well as the interconnect between PCIe Host Bridges within a NUMA node PHB = Connection traversing PCIe as well as a PCIe Host Bridge (typically the CPU) PXB = Connection traversing multiple PCIe bridges (without traversing the PCIe Host Bridge) PIX = Connection traversing at most a single PCIe bridge NV# = Connection traversing a bonded set of # NVLinks

How would you like to use vllm

I want to run inference of a [specific model](put link here). I don't know how to integrate it with vllm. I tried to quantize (very simple script) gemma2 9b into bnb

def load_bnb_save(): pretrained_model_dir = 'google/gemma-2-9b-it' quantized_model_dir = 'xxx/gemma-2-9b-it-bnb' nf4_config = BitsAndBytesConfig( load_in_4bit=True, bnb_4bit_quant_type="nf4", ) model = AutoModelForCausalLM.from_pretrained(pretrained_model_dir, quantization_config=nf4_config) tokenizer = AutoTokenizer.from_pretrained(pretrained_model_dir, trust_remote_code=True) model.save_pretrained(quantized_model_dir) tokenizer.save_pretrained(quantized_model_dir) test_vllm(quantized_model_dir)

where test_vllm is very simple `def test_vllm(quantized_model_dir: str): from vllm import LLM, SamplingParams

# Sample prompts.
prompts = [
    "Hello, my name is",
    "The president of the United States is",
    "The capital of France is",
    "The future of AI is",
]
# Create a sampling params object.
sampling_params = SamplingParams(temperature=0.8, top_p=0.95)

# Create an LLM.
llm = LLM(model=quantized_model_dir, qlora_adapter_name_or_path='', quantization='bitsandbytes',
          load_format='bitsandbytes')
# Generate texts from the prompts. The output is a list of RequestOutput objects
# that contain the prompt, generated text, and other information.
outputs = llm.generate(prompts, sampling_params)
# Print the outputs.
for output in outputs:
    prompt = output.prompt
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

`

I wonder why it won't works.. There were two issues: 1. due to adapter_name = cls.get_from_keys(config, ["adapter_name_or_path"]) in bitsandbytes.py as adapter_name_or_path is not in config (I don't need QLORA here, just basic bnb quantize to load it in 4bit. 2. due to if not hasattr(model, 'bitsandbytes_stacked_params_mapping'): raise AttributeError( f"Model {type(self).__name__} does not support BitsAndBytes " "quantization yet.") in loader.py

what to do to load that right? as BNB is finally merged https://github.com/vllm-project/vllm/issues/4033

[LSC527]

same issue

[itdevwu]

Same issue here, vLLM seems doesn't accept BNB quantized models without QLoRA weights.