QWEN2-VL Model Inference #9330
Description
Your current environment
The output of `python collect_env.py`
PyTorch version: 2.4.0+cu118
Is debug build: False
CUDA used to build PyTorch: 11.8
ROCM used to build PyTorch: N/A
OS: CentOS Linux 7 (Core) (x86_64)
GCC version: (GCC) 4.8.5 20150623 (Red Hat 4.8.5-44)
Clang version: Could not collect
CMake version: version 3.21.4
Libc version: glibc-2.17
Python version: 3.9.13 | packaged by conda-forge | (main, May 27 2022, 16:58:50) [GCC 10.3.0] (64-bit runtime)
Python platform: Linux-5.10.0-1.0.0.32-x86_64-with-glibc2.17
Is CUDA available: True
CUDA runtime version: 11.8.89
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration:
GPU 0: Tesla V100-SXM2-32GB
GPU 1: Tesla V100-SXM2-32GB
GPU 2: Tesla V100-SXM2-32GB
GPU 3: Tesla V100-SXM2-32GB
GPU 4: Tesla V100-SXM2-32GB
GPU 5: Tesla V100-SXM2-32GB
GPU 6: Tesla V100-SXM2-32GB
GPU 7: Tesla V100-SXM2-32GB
Nvidia driver version: 535.129.03
cuDNN version: Probably one of the following:
/usr/lib/libcudnn.so
/usr/lib64/libcudnn.so
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
Byte Order: Little Endian
CPU(s): 96
On-line CPU(s) list: 0-95
Thread(s) per core: 2
Core(s) per socket: 24
Socket(s): 2
NUMA node(s): 1
Vendor ID: GenuineIntel
CPU family: 6
Model: 85
Model name: Intel(R) Xeon(R) Gold 6271C CPU @ 2.60GHz
Stepping: 7
CPU MHz: 1715.909
CPU max MHz: 3900.0000
CPU min MHz: 1000.0000
BogoMIPS: 5200.00
Virtualization: VT-x
L1d cache: 32K
L1i cache: 32K
L2 cache: 1024K
L3 cache: 33792K
NUMA node0 CPU(s): 0-95
Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc art arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf pni pclmulqdq dtes64 monitor ds_cpl vmx smx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid dca sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault epb cat_l3 cdp_l3 invpcid_single intel_ppin ssbd mba ibrs ibpb stibp ibrs_enhanced tpr_shadow vnmi flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid cqm mpx rdt_a avx512f avx512dq rdseed adx smap clflushopt clwb intel_pt avx512cd avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local dtherm ida arat pln pts pku avx512_vnni md_clear flush_l1d arch_capabilities
Versions of relevant libraries:
[pip3] numpy==1.26.3
[pip3] nvidia-cublas-cu11==11.11.3.6
[pip3] nvidia-cuda-cupti-cu11==11.8.87
[pip3] nvidia-cuda-nvrtc-cu11==11.8.89
[pip3] nvidia-cuda-runtime-cu11==11.8.89
[pip3] nvidia-cudnn-cu11==9.1.0.70
[pip3] nvidia-cufft-cu11==10.9.0.58
[pip3] nvidia-curand-cu11==10.3.0.86
[pip3] nvidia-cusolver-cu11==11.4.1.48
[pip3] nvidia-cusparse-cu11==11.7.5.86
[pip3] nvidia-ml-py==12.560.30
[pip3] nvidia-nccl-cu11==2.20.5
[pip3] nvidia-nvtx-cu11==11.8.86
[pip3] pynvml==11.5.0
[pip3] pyzmq==26.2.0
[pip3] torch==2.4.0+cu118
[pip3] torchaudio==2.4.0+cu118
[pip3] torchvision==0.19.0+cu118
[pip3] transformers==4.45.0.dev0
[pip3] triton==3.0.0
[conda] cudatoolkit 11.0.221 h6bb024c_0
[conda] mkl 2018.0.0 hb491cac_4
[conda] mkl-service 1.1.2 py36h17a0993_4
[conda] numpy 1.19.5 pypi_0 pypi
[conda] numpydoc 0.7.0 py36h18f165f_0
[conda] pyzmq 16.0.2 py36h3b0cf96_2
[conda] torch 1.6.0+cu101 pypi_0 pypi
[conda] torchvision 0.7.0+cu101 pypi_0 pypi
[conda] transformers 4.6.0 pypi_0 pypi
ROCM Version: Could not collect
Neuron SDK Version: N/A
vLLM Version: 0.6.1.post2@9ba0817ff1eb514f51cc6de9cb8e16c98d6ee44f
vLLM Build Flags:
CUDA Archs: Not Set; ROCm: Disabled; Neuron: Disabled
GPU Topology:
GPU0 GPU1 GPU2 GPU3 GPU4 GPU5 GPU6 GPU7 NIC0 NIC1 CPU Affinity NUMA Affinity GPU NUMA ID
GPU0 X NV2 NV2 NV1 SYS NV1 SYS SYS PIX PIX 0-95 0 N/A
GPU1 NV2 X NV1 NV2 NV1 SYS SYS SYS PIX PIX 0-95 0 N/A
GPU2 NV2 NV1 X NV1 SYS SYS SYS NV2 SYS SYS 0-95 0 N/A
GPU3 NV1 NV2 NV1 X SYS SYS NV2 SYS SYS SYS 0-95 0 N/A
GPU4 SYS NV1 SYS SYS X NV2 NV2 NV1 SYS SYS 0-95 0 N/A
GPU5 NV1 SYS SYS SYS NV2 X NV1 NV2 SYS SYS 0-95 0 N/A
GPU6 SYS SYS SYS NV2 NV2 NV1 X NV1 SYS SYS 0-95 0 N/A
GPU7 SYS SYS NV2 SYS NV1 NV2 NV1 X SYS SYS 0-95 0 N/A
NIC0 PIX PIX SYS SYS SYS SYS SYS SYS X PIX
NIC1 PIX PIX SYS SYS SYS SYS SYS SYS PIX X
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
NIC Legend:
NIC0: mlx5_0
NIC1: mlx5_1
Model Input Dumps
No response
🐛 Describe the bug
The script to start the service
python -m vllm.entrypoints.openai.api_server \
--served-model-name Qwen2-VL-7B-Instruct \
--model /data/CodeSpace/models/Qwen2-VL-7B-Instruct \
--dtype float16 \
--port ${deploy_port} \
--gpu-memory-utilization 0.998 \
--enable-prefix-caching
v100 machine, qwen2-vl model, 7b model takes 2.47s per image, 2b model takes 2.3s per image. Is this as expected, why is 2b model only a little faster than 7b model
Before submitting a new issue...
- Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the documentation page, which can answer lots of frequently asked questions.