[Bug]: Extra body don't work when response_format is also sent for serving.

[HwwwwwwwH]

Your current environment

The output of `python collect_env.py`

```text 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 11.2.0-19ubuntu1) 11.2.0 Clang version: 10.0.0-4ubuntu1 CMake version: version 3.29.3 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-5.4.0-192-generic-x86_64-with-glibc2.35 Is CUDA available: True CUDA runtime version: 12.2.140 CUDA_MODULE_LOADING set to: LAZY GPU models and configuration: GPU 0: NVIDIA GeForce RTX 4090 GPU 1: NVIDIA GeForce RTX 4090 GPU 2: NVIDIA GeForce RTX 4090 GPU 3: NVIDIA GeForce RTX 4090 GPU 4: NVIDIA GeForce RTX 4090 GPU 5: NVIDIA GeForce RTX 4090 GPU 6: NVIDIA GeForce RTX 4090 GPU 7: NVIDIA GeForce RTX 4090 Nvidia driver version: 550.54.14 cuDNN version: Probably one of the following: /usr/lib/x86_64-linux-gnu/libcudnn.so.9.2.1 /usr/lib/x86_64-linux-gnu/libcudnn_adv.so.9.2.1 /usr/lib/x86_64-linux-gnu/libcudnn_cnn.so.9.2.1 /usr/lib/x86_64-linux-gnu/libcudnn_engines_precompiled.so.9.2.1 /usr/lib/x86_64-linux-gnu/libcudnn_engines_runtime_compiled.so.9.2.1 /usr/lib/x86_64-linux-gnu/libcudnn_graph.so.9.2.1 /usr/lib/x86_64-linux-gnu/libcudnn_heuristic.so.9.2.1 /usr/lib/x86_64-linux-gnu/libcudnn_ops.so.9.2.1 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: 52 bits physical, 57 bits virtual CPU(s): 128 On-line CPU(s) list: 0-127 Thread(s) per core: 2 Core(s) per socket: 32 Socket(s): 2 NUMA node(s): 2 Vendor ID: GenuineIntel CPU family: 6 Model: 106 Model name: Intel(R) Xeon(R) Platinum 8358P CPU @ 2.60GHz Stepping: 6 Frequency boost: enabled CPU MHz: 3200.000 CPU max MHz: 2601.0000 CPU min MHz: 800.0000 BogoMIPS: 5200.00 Virtualization: VT-x L1d cache: 3 MiB L1i cache: 2 MiB L2 cache: 80 MiB L3 cache: 96 MiB NUMA node0 CPU(s): 0-31,64-95 NUMA node1 CPU(s): 32-63,96-127 Vulnerability Gather data sampling: Mitigation; Microcode Vulnerability Itlb multihit: Not affected Vulnerability L1tf: Not affected Vulnerability Mds: Not affected Vulnerability Meltdown: Not affected Vulnerability Mmio stale data: Mitigation; Clear CPU buffers; SMT vulnerable Vulnerability Retbleed: Not affected 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; Enhanced / Automatic IBRS; IBPB conditional; RSB filling; PBRSB-eIBRS SW sequence; BHI Vulnerable, KVM SW loop 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 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 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 invpcid_single 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 rdt_a avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb intel_pt avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local wbnoinvd dtherm ida arat pln pts avx512vbmi umip pku ospke avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg tme avx512_vpopcntdq rdpid md_clear pconfig flush_l1d arch_capabilities Versions of relevant libraries: [pip3] mypy==1.9.0 [pip3] mypy-extensions==1.0.0 [pip3] numpy==1.26.4 [pip3] nvidia-nccl-cu12==2.20.5 [pip3] torch==2.4.0 [pip3] torchvision==0.19.0 [pip3] transformers==4.43.2 [pip3] triton==3.0.0 [pip3] vllm_nccl_cu12==2.18.1.0.4.0 [conda] numpy 1.26.4 pypi_0 pypi [conda] nvidia-nccl-cu12 2.20.5 pypi_0 pypi [conda] torch 2.4.0 pypi_0 pypi [conda] torchvision 0.19.0 pypi_0 pypi [conda] transformers 4.43.2 pypi_0 pypi [conda] triton 3.0.0 pypi_0 pypi [conda] vllm-nccl-cu12 2.18.1.0.4.0 pypi_0 pypi ROCM Version: Could not collect Neuron SDK Version: N/A vLLM Version: 0.5.3.post1 vLLM Build Flags: CUDA Archs: Not Set; ROCm: Disabled; Neuron: Disabled GPU Topology: GPU0 GPU1 GPU2 GPU3 GPU4 GPU5 GPU6 GPU7 NIC0 CPU Affinity NUMA Affinity GPU NUMA ID GPU0 X PXB PXB PXB SYS SYS SYS SYS SYS 0-31,64-95 0 N/A GPU1 PXB X PXB PXB SYS SYS SYS SYS SYS 0-31,64-95 0 N/A GPU2 PXB PXB X PIX SYS SYS SYS SYS SYS 0-31,64-95 0 N/A GPU3 PXB PXB PIX X SYS SYS SYS SYS SYS 0-31,64-95 0 N/A GPU4 SYS SYS SYS SYS X PXB PXB PXB SYS 0-31,64-95 0 N/A GPU5 SYS SYS SYS SYS PXB X PXB PXB SYS 0-31,64-95 0 N/A GPU6 SYS SYS SYS SYS PXB PXB X PIX SYS 0-31,64-95 0 N/A GPU7 SYS SYS SYS SYS PXB PXB PIX X SYS 0-31,64-95 0 N/A NIC0 SYS SYS SYS SYS SYS SYS SYS SYS 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_bond_0 ```

🐛 Describe the bug

I start a server for MiniCPM-V2.5 like this

vllm serve openbmb/MiniCPM-Llama3-V-2_5 --dtype auto --api-key token-abc123 --gpu_memory_utilization 1 --trust-remote-code

And then I start a request as follows:

from openai import OpenAI
openai_api_key = "token-abc123" # your api key set in launch server
openai_api_base = "http://localhost:8000/v1" # http id
client = OpenAI(
    api_key=openai_api_key,
    base_url=openai_api_base,
)

text_prompt = "Please describe this image."
image_url = "https://air-example-data-2.s3.us-west-2.amazonaws.com/vllm_opensource_llava/stop_sign.jpg"
chat_response = client.chat.completions.create(
    model="openbmb/MiniCPM-Llama3-V-2_5",
    messages=[{
        "role": "user",
        "content": [
            {"type": "text", "text": text_prompt},
            {
                "type": "image_url",
                "image_url": {
                    "url": image_url,
                },
            },
        ],
    }],
    # response_format={'type': 'json_object'},
    extra_body={
        "stop_token_ids": [128001, 128009]
    },
)

There're extra stop_token_ids required for MiniCPM-V2.5. And if I use response_format, which is upon extra_body, the inference won't stop until to the max number of tokens. Otherwise, it works well. It seems response_format make stop_token_ids in extra_body ineffective.

[HwwwwwwwH]

More Details

[DarkLight1337]

Does this happen in offline mode as well?

[HwwwwwwwH]

Does this happen in offline mode as well?

I'm not sure if I can use response_format in offline inference.

[DarkLight1337]

You can pass guided_options to LLM.generate for this.

[HwwwwwwwH]

Alright, I tried and it worked fine.

outputs = llm.generate(
    inputs, 
    sampling_params=sampling_params,
    guided_options_request={
        "guided_json_object": True
    }
)

[DarkLight1337]

In that case, seems like the issue is specific to OpenAI-compatible server.

[panxnan]

will it be fixed? need help

[DarkLight1337]

Since you're more familiar with the guided decoding code, @joerunde can you look into this?