[Usage]: beam_search not work with multimodal input #9577
Description
The output of `python collect_env.py`
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 20.04.6 LTS (x86_64)
GCC version: (Ubuntu 9.4.0-1ubuntu1~20.04.2) 9.4.0
Clang version: Could not collect
CMake version: version 3.30.2
Libc version: glibc-2.31
Python version: 3.12.3 | packaged by Anaconda, Inc. | (main, May 6 2024, 19:46:43) [GCC 11.2.0] (64-bit runtime)
Python platform: Linux-5.15.0-71-generic-x86_64-with-glibc2.31
Is CUDA available: True
CUDA runtime version: 12.4.131
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration:
GPU 0: NVIDIA GeForce RTX 3090
GPU 1: NVIDIA GeForce RTX 3090
GPU 2: NVIDIA GeForce RTX 3090
Nvidia driver version: 545.23.08
cuDNN version: Probably one of the following:
[/usr/lib/x86_64-linux-gnu/libcudnn.so.8.9.5](https://vscode-remote+ssh-002dremote-002b10-002e40-002e116-002e16.vscode-resource.vscode-cdn.net/usr/lib/x86_64-linux-gnu/libcudnn.so.8.9.5)
[/usr/lib/x86_64-linux-gnu/libcudnn_adv_infer.so.8.9.5](https://vscode-remote+ssh-002dremote-002b10-002e40-002e116-002e16.vscode-resource.vscode-cdn.net/usr/lib/x86_64-linux-gnu/libcudnn_adv_infer.so.8.9.5)
[/usr/lib/x86_64-linux-gnu/libcudnn_adv_train.so.8.9.5](https://vscode-remote+ssh-002dremote-002b10-002e40-002e116-002e16.vscode-resource.vscode-cdn.net/usr/lib/x86_64-linux-gnu/libcudnn_adv_train.so.8.9.5)
[/usr/lib/x86_64-linux-gnu/libcudnn_cnn_infer.so.8.9.5](https://vscode-remote+ssh-002dremote-002b10-002e40-002e116-002e16.vscode-resource.vscode-cdn.net/usr/lib/x86_64-linux-gnu/libcudnn_cnn_infer.so.8.9.5)
[/usr/lib/x86_64-linux-gnu/libcudnn_cnn_train.so.8.9.5](https://vscode-remote+ssh-002dremote-002b10-002e40-002e116-002e16.vscode-resource.vscode-cdn.net/usr/lib/x86_64-linux-gnu/libcudnn_cnn_train.so.8.9.5)
[/usr/lib/x86_64-linux-gnu/libcudnn_ops_infer.so.8.9.5](https://vscode-remote+ssh-002dremote-002b10-002e40-002e116-002e16.vscode-resource.vscode-cdn.net/usr/lib/x86_64-linux-gnu/libcudnn_ops_infer.so.8.9.5)
[/usr/lib/x86_64-linux-gnu/libcudnn_ops_train.so.8.9.5](https://vscode-remote+ssh-002dremote-002b10-002e40-002e116-002e16.vscode-resource.vscode-cdn.net/usr/lib/x86_64-linux-gnu/libcudnn_ops_train.so.8.9.5)
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
Address sizes: 43 bits physical, 48 bits virtual
CPU(s): 64
On-line CPU(s) list: 0-63
Thread(s) per core: 2
Core(s) per socket: 32
Socket(s): 1
NUMA node(s): 1
Vendor ID: AuthenticAMD
CPU family: 23
Model: 49
Model name: AMD Ryzen Threadripper PRO 3975WX 32-Cores
Stepping: 0
Frequency boost: enabled
CPU MHz: 2200.000
CPU max MHz: 3500.0000
CPU min MHz: 2200.0000
BogoMIPS: 6986.53
Virtualization: AMD-V
L1d cache: 1 MiB
L1i cache: 1 MiB
L2 cache: 16 MiB
L3 cache: 128 MiB
NUMA node0 CPU(s): 0-63
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 store bypass: Mitigation; Speculative Store Bypass disabled via prctl and seccomp
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
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 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local clzero irperf xsaveerptr rdpru wbnoinvd 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 sme sev sev_es
Versions of relevant libraries:
[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==12.5.40
[pip3] nvidia-nvtx-cu12==12.1.105
[pip3] pytorch-lightning==2.3.0
[pip3] pyzmq==25.1.2
[pip3] torch==2.4.0
[pip3] torchmetrics==1.4.0.post0
[pip3] torchvision==0.19.0
[pip3] transformers==4.45.2
[pip3] triton==3.0.0
[pip3] tritonclient==2.48.0
[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 12.5.40 pypi_0 pypi
[conda] nvidia-nvtx-cu12 12.1.105 pypi_0 pypi
[conda] pytorch-lightning 2.3.0 pypi_0 pypi
[conda] pyzmq 25.1.2 py312h6a678d5_0
[conda] torch 2.4.0 pypi_0 pypi
[conda] torchmetrics 1.4.0.post0 pypi_0 pypi
[conda] torchvision 0.19.0 pypi_0 pypi
[conda] transformers 4.45.2 pypi_0 pypi
[conda] triton 3.0.0 pypi_0 pypi
[conda] tritonclient 2.48.0 pypi_0 pypi
ROCM Version: Could not collect
Neuron SDK Version: N/A
vLLM Version: 0.6.3.post1
vLLM Build Flags:
CUDA Archs: Not Set; ROCm: Disabled; Neuron: Disabled
GPU Topology:
GPU0 GPU1 GPU2 CPU Affinity NUMA Affinity GPU NUMA ID
GPU0 X PHB SYS 0-63 0 N/A
GPU1 PHB X SYS 0-63 0 N/A
GPU2 SYS SYS 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'm trying to do batch inference with multimodal input data like this:
llm = LLM(model='OpenGVLab/InternVL2-1B', trust_remote_code=True, max_num_seqs=5, enforce_eager=True)
sampling_params = SamplingParams(max_tokens=128, temperature=0.0)
beam_params = BeamSearchParams(beam_width=3, max_tokens=128, temperature=0.0)
for image_file in image_files:
input = {
"prompt": prompt,
"multi_modal_data": { "image": Image.open(image_file)},
}
list_input.append(input)
outputs = llm.generate(list_input, sampling_params)
outputs = llm.beam_search(list_input, beam_params)
The model run perfectly with generate() method but when I call beam_search() method I got this error:
TypeError: TextEncodeInput must be Union[TextInputSequence, Tuple[InputSequence, InputSequence]]
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