Open Anwesh2 opened 3 weeks ago
Because you set quantization = 'bitsandbytes', the corresponding model_class is MixtralForCausalLM, and this model indeed does not support LoRA.
Moreover, even if this model supported LoRA, it would still throw an error due to lack of support for BNB.
Hi @jeejeelee, that makes sense.
But I noticed that this page indicates that LoRA is supported for MixtralForCausalLM models. Could this be an oversight? For example, will MixtralForCausalLM with LoRA and AWQ quantization not work for vLLM inferencing?
Additionally, I came across information that BnB support has recently been added in vLLM. Is there a document or list available that details which models are currently supported for bitsandbytes?
But I noticed that this page indicates that LoRA is supported for MixtralForCausalLM models. Could this be an oversight?
Supported Models indeed contains ambiguity
For example, will MixtralForCausalLM with LoRA and AWQ quantization not work for vLLM inferencing?
The model support for LoRA depends on whether it inherits from the SupportsLoRA
Additionally, I came across information that BnB support has recently been added in vLLM. Is there a document or list available that details which models are currently supported for bitsandbytes?
cc @chenqianfzh
Hi @jeejeelee, that makes sense.
But I noticed that this page indicates that LoRA is supported for MixtralForCausalLM models. Could this be an oversight? For example, will MixtralForCausalLM with LoRA and AWQ quantization not work for vLLM inferencing?
Additionally, I came across information that BnB support has recently been added in vLLM. Is there a document or list available that details which models are currently supported for bitsandbytes?
As JJ Lee explained, the models supported by Bnb quantization is limited. Only Llama is supported. We will be working to extend it to more models.
Your current environment
The output of `python collect_env.py`
```text Collecting environment information... PyTorch version: 2.4.0+cu121 Is debug build: False CUDA used to build PyTorch: 12.1 ROCM used to build PyTorch: N/A OS: Ubuntu 22.04.3 LTS (x86_64) GCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0 Clang version: Could not collect CMake version: version 3.30.1 Libc version: glibc-2.35 Python version: 3.11.7 | packaged by conda-forge | (main, Dec 23 2023, 14:43:09) [GCC 12.3.0] (64-bit runtime) Python platform: Linux-6.2.0-1018-aws-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 A10G GPU 1: NVIDIA A10G GPU 2: NVIDIA A10G GPU 3: NVIDIA A10G Nvidia driver version: 535.154.05 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: 48 bits physical, 48 bits virtual Byte Order: Little Endian CPU(s): 48 On-line CPU(s) list: 0-47 Vendor ID: AuthenticAMD Model name: AMD EPYC 7R32 CPU family: 23 Model: 49 Thread(s) per core: 2 Core(s) per socket: 24 Socket(s): 1 Stepping: 0 BogoMIPS: 5599.99 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 tsc_known_freq pni pclmulqdq ssse3 fma cx16 sse4_1 sse4_2 movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm cmp_legacy cr8_legacy abm sse4a misalignsse 3dnowprefetch topoext ssbd ibrs ibpb stibp vmmcall fsgsbase bmi1 avx2 smep bmi2 rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 clzero xsaveerptr rdpru wbnoinvd arat npt nrip_save rdpid Hypervisor vendor: KVM Virtualization type: full L1d cache: 768 KiB (24 instances) L1i cache: 768 KiB (24 instances) L2 cache: 12 MiB (24 instances) L3 cache: 96 MiB (6 instances) NUMA node(s): 1 NUMA node0 CPU(s): 0-47 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 Vulnerability Srbds: Not affected Vulnerability Tsx async abort: Not affected Versions of relevant libraries: [pip3] adapter-transformers==4.0.0 [pip3] numpy==1.26.4 [pip3] nvidia-cublas-cu12==12.1.3.1 [pip3] nvidia-cuda-cupti-cu12==12.1.105 [pip3] nvidia-cuda-nvrtc-cu12==12.1.105 [pip3] nvidia-cuda-runtime-cu12==12.1.105 [pip3] nvidia-cudnn-cu12==9.1.0.70 [pip3] nvidia-cufft-cu12==11.0.2.54 [pip3] nvidia-curand-cu12==10.3.2.106 [pip3] nvidia-cusolver-cu12==11.4.5.107 [pip3] nvidia-cusparse-cu12==12.1.0.106 [pip3] nvidia-ml-py==12.555.43 [pip3] nvidia-nccl-cu12==2.20.5 [pip3] nvidia-nvjitlink-cu12==12.3.101 [pip3] nvidia-nvtx-cu12==12.1.105 [pip3] pynvml==11.5.0 [pip3] pytorch-triton==3.0.0+901819d2b6 [pip3] pyzmq==25.1.2 [pip3] torch==2.4.0 [pip3] torchaudio==2.4.0 [pip3] torchvision==0.19.0 [pip3] transformers==4.43.4 [pip3] triton==3.0.0 [conda] adapter-transformers 4.0.0 pypi_0 pypi [conda] numpy 1.26.4 pypi_0 pypi [conda] nvidia-cublas-cu12 12.1.3.1 pypi_0 pypi [conda] nvidia-cuda-cupti-cu12 12.1.105 pypi_0 pypi [conda] nvidia-cuda-nvrtc-cu12 12.1.105 pypi_0 pypi [conda] nvidia-cuda-runtime-cu12 12.1.105 pypi_0 pypi [conda] nvidia-cudnn-cu12 9.1.0.70 pypi_0 pypi [conda] nvidia-cufft-cu12 11.0.2.54 pypi_0 pypi [conda] nvidia-curand-cu12 10.3.2.106 pypi_0 pypi [conda] nvidia-cusolver-cu12 11.4.5.107 pypi_0 pypi [conda] nvidia-cusparse-cu12 12.1.0.106 pypi_0 pypi [conda] nvidia-ml-py 12.555.43 pypi_0 pypi [conda] nvidia-nccl-cu12 2.20.5 pypi_0 pypi [conda] nvidia-nvjitlink-cu12 12.3.101 pypi_0 pypi [conda] nvidia-nvtx-cu12 12.1.105 pypi_0 pypi [conda] pynvml 11.5.0 pypi_0 pypi [conda] pytorch-triton 3.0.0+901819d2b6 pypi_0 pypi [conda] pyzmq 25.1.2 pypi_0 pypi [conda] torch 2.4.0 pypi_0 pypi [conda] torchaudio 2.4.0 pypi_0 pypi [conda] torchvision 0.19.0 pypi_0 pypi [conda] transformers 4.43.4 pypi_0 pypi [conda] triton 3.0.0 pypi_0 pypi ROCM Version: Could not collect Neuron SDK Version: N/A vLLM Version: 0.5.4@4db5176d9758b720b05460c50ace3c01026eb158 vLLM Build Flags: CUDA Archs: Not Set; ROCm: Disabled; Neuron: Disabled GPU Topology: GPU0 GPU1 GPU2 GPU3 CPU Affinity NUMA Affinity GPU NUMA ID GPU0 X PHB PHB PHB 0-47 0 N/A GPU1 PHB X PHB PHB 0-47 0 N/A GPU2 PHB PHB X PHB 0-47 0 N/A GPU3 PHB PHB PHB X 0-47 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 ```🐛 Describe the bug
The MixtralForCausalLM architecture and the mistralai/Mixtral-8x7B-Instruct-v0.1 model with LoRA are stated to be supported by vLLM (reference), but an error occurs during model loading. I tested it for vllm versions [v0.5.4] [v0.5.3.post1], and [v0.5.3].