[Bug]: V1 engine ignores guided json

[MustafaHek]

Your current environment

The output of `python collect_env.py`

INFO 02-03 05:55:13 __init__.py:183] Automatically detected platform cuda.
Collecting environment information...
PyTorch version: 2.5.1+cu124
Is debug build: False
CUDA used to build PyTorch: 12.4
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 3.31.4
Libc version: glibc-2.35

Python version: 3.12.8 (main, Dec  4 2024, 08:54:12) [GCC 11.4.0] (64-bit runtime)
Python platform: Linux-6.8.0-52-generic-x86_64-with-glibc2.35
Is CUDA available: True
CUDA runtime version: 12.1.105
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: GPU 0: NVIDIA H100 80GB HBM3
Nvidia driver version: 560.35.05
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:                        46 bits physical, 57 bits virtual
Byte Order:                           Little Endian
CPU(s):                               128
On-line CPU(s) list:                  0-127
Vendor ID:                            GenuineIntel
Model name:                           Intel(R) Xeon(R) Gold 6448Y
CPU family:                           6
Model:                                143
Thread(s) per core:                   2
Core(s) per socket:                   32
Socket(s):                            2
Stepping:                             8
CPU max MHz:                          4100.0000
CPU min MHz:                          800.0000
BogoMIPS:                             4200.00
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 tsc_known_freq 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 cat_l2 cdp_l3 cdp_l2 ssbd mba ibrs ibpb stibp ibrs_enhanced tpr_shadow 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 split_lock_detect user_shstk avx_vnni avx512_bf16 wbnoinvd dtherm ida arat pln pts hfi vnmi avx512vbmi umip pku ospke waitpkg avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg tme avx512_vpopcntdq la57 rdpid bus_lock_detect cldemote movdiri movdir64b enqcmd fsrm md_clear serialize tsxldtrk pconfig arch_lbr ibt amx_bf16 avx512_fp16 amx_tile amx_int8 flush_l1d arch_capabilities
Virtualization:                       VT-x
L1d cache:                            3 MiB (64 instances)
L1i cache:                            2 MiB (64 instances)
L2 cache:                             128 MiB (64 instances)
L3 cache:                             120 MiB (2 instances)
NUMA node(s):                         2
NUMA node0 CPU(s):                    0,2,4,6,8,10,12,14,16,18,20,22,24,26,28,30,32,34,36,38,40,42,44,46,48,50,52,54,56,58,60,62,64,66,68,70,72,74,76,78,80,82,84,86,88,90,92,94,96,98,100,102,104,106,108,110,112,114,116,118,120,122,124,126
NUMA node1 CPU(s):                    1,3,5,7,9,11,13,15,17,19,21,23,25,27,29,31,33,35,37,39,41,43,45,47,49,51,53,55,57,59,61,63,65,67,69,71,73,75,77,79,81,83,85,87,89,91,93,95,97,99,101,103,105,107,109,111,113,115,117,119,121,123,125,127
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 Reg file data sampling: Not affected
Vulnerability Retbleed:               Not affected
Vulnerability Spec rstack overflow:   Not affected
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; Enhanced / Automatic IBRS; IBPB conditional; RSB filling; PBRSB-eIBRS SW sequence; BHI BHI_DIS_S
Vulnerability Srbds:                  Not affected
Vulnerability Tsx async abort:        Not affected

Versions of relevant libraries:
[pip3] numpy==1.26.4
[pip3] nvidia-cublas-cu12==12.4.5.8
[pip3] nvidia-cuda-cupti-cu12==12.4.127
[pip3] nvidia-cuda-nvrtc-cu12==12.4.127
[pip3] nvidia-cuda-runtime-cu12==12.4.127
[pip3] nvidia-cudnn-cu12==9.1.0.70
[pip3] nvidia-cufft-cu12==11.2.1.3
[pip3] nvidia-curand-cu12==10.3.5.147
[pip3] nvidia-cusolver-cu12==11.6.1.9
[pip3] nvidia-cusparse-cu12==12.3.1.170
[pip3] nvidia-ml-py==12.570.86
[pip3] nvidia-nccl-cu12==2.21.5
[pip3] nvidia-nvjitlink-cu12==12.4.127
[pip3] nvidia-nvtx-cu12==12.4.127
[pip3] pyzmq==26.2.1
[pip3] torch==2.5.1
[pip3] torchaudio==2.5.1
[pip3] torchvision==0.20.1
[pip3] transformers==4.48.2
[pip3] triton==3.1.0
[conda] Could not collect
ROCM Version: Could not collect
Neuron SDK Version: N/A
vLLM Version: 0.7.1
vLLM Build Flags:
CUDA Archs: Not Set; ROCm: Disabled; Neuron: Disabled
GPU Topology:
GPU0    CPU Affinity    NUMA Affinity   GPU NUMA ID
GPU0     X      0,2,4,6,8,10    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

NVIDIA_VISIBLE_DEVICES=GPU-6cf6dad3-da49-c3af-bb85-7275c18ce3d6
NVIDIA_REQUIRE_CUDA=cuda>=12.1 brand=tesla,driver>=470,driver<471 brand=unknown,driver>=470,driver<471 brand=nvidia,driver>=470,driver<471 brand=nvidiartx,driver>=470,driver<471 brand=geforce,driver>=470,driver<471 brand=geforcertx,driver>=470,driver<471 brand=quadro,driver>=470,driver<471 brand=quadrortx,driver>=470,driver<471 brand=titan,driver>=470,driver<471 brand=titanrtx,driver>=470,driver<471 brand=tesla,driver>=525,driver<526 brand=unknown,driver>=525,driver<526 brand=nvidia,driver>=525,driver<526 brand=nvidiartx,driver>=525,driver<526 brand=geforce,driver>=525,driver<526 brand=geforcertx,driver>=525,driver<526 brand=quadro,driver>=525,driver<526 brand=quadrortx,driver>=525,driver<526 brand=titan,driver>=525,driver<526 brand=titanrtx,driver>=525,driver<526
NCCL_VERSION=2.17.1-1
NVIDIA_DRIVER_CAPABILITIES=all
NVIDIA_PRODUCT_NAME=CUDA
VLLM_USAGE_SOURCE=production-docker-image
NVIDIA_CUDA_END_OF_LIFE=1
CUDA_VERSION=12.1.0
LD_LIBRARY_PATH=/usr/local/lib/python3.12/dist-packages/cv2/../../lib64:/usr/local/nvidia/lib:/usr/local/nvidia/lib64
NCCL_CUMEM_ENABLE=0
TORCHINDUCTOR_COMPILE_THREADS=1
CUDA_MODULE_LOADING=LAZY

Model Input Dumps

No response

🐛 Describe the bug

When making a request with to the OpenAi compatible Api with the extra fields for guided_json generation like so:

{
  "model": "Qwen/Qwen2-VL-72B-Instruct-GPTQ-Int4",
  "messages": [
    {
      "role": "user",
      "content": "what is the height of the eiffel tower"
    }
  ],
  "guided_json": {
    "type": "object",
    "properties": {
      "height": {
        "type": "number"
      }
    },
    "required": [
      "height"
    ]
  }
}

The output simply ignores the guided decoding paramter. When switching back to V0 it works fine.

Here are the logs from the vllm server:

`INFO 02-03 05:59:31 logger.py:37] Received request chatcmpl-178de64763e84ddd81a9f6f62ee0f4c3: prompt: '<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n<|im_start|>user\nwhat is the height of the eiffel tower<|im_end|>\n<|im_start|>assistant\n', params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=1.0, top_p=1.0, top_k=-1, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=32739, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=GuidedDecodingParams(json={'type': 'object', 'properties': {'height': {'type': 'number'}}, 'required': ['height']}, regex=None, choice=None, grammar=None, json_object=None, backend=None, whitespace_pattern=None)), prompt_token_ids: None, lora_request: None, prompt_adapter_request: None.
DEBUG 02-03 05:59:31 async_llm_engine.py:546] Building guided decoding logits processor. Params: GuidedDecodingParams(json={'type': 'object', 'properties': {'height': {'type': 'number'}}, 'required': ['height']}, regex=None, choice=None, grammar=None, json_object=None, backend=None, whitespace_pattern=None)
INFO 02-03 05:59:32 engine.py:273] Added request chatcmpl-178de64763e84ddd81a9f6f62ee0f4c3.
INFO 02-03 05:59:32 metrics.py:453] Avg prompt throughput: 4.7 tokens/s, Avg generation throughput: 0.2 tokens/s, Running: 1 reqs, Swapped: 0 reqs, Pending: 0 reqs, GPU KV cache usage: 0.0%, CPU KV cache usage: 0.0%.
INFO:     **.***.**.**:***** - "POST /v1/chat/completions HTTP/1.1" 200 OK`

Before submitting a new issue...

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[ch9hn]

We are facing similar issues with the V1.
The guided json / structured outputs are not working with V1.

[jlquinn]

I'm seeing an almost-json output. This is vllm 0.7.1.

[imkero]

@jlquinn @ch9hn @MustafaHek

vLLM V1 dropped the logits_processors support so guided decoding doesn't work currently (but vLLM seems making effort to refactor and bring it back? #12388)

I have a legacy-style logits processors impl for V1 (#12688, not accpeted by vLLM though)

You can try it with my branch code and this offline inference example:

import os
os.environ['VLLM_USE_V1'] = '1'
os.environ['VLLM_ENABLE_V1_MULTIPROCESSING'] = '0'

from transformers import AutoTokenizer
from vllm import LLM, SamplingParams
from vllm.sampling_params import GuidedDecodingParams
from vllm.transformers_utils.tokenizer import get_cached_tokenizer
from vllm.model_executor.guided_decoding.outlines_decoding import get_local_outlines_guided_decoding_logits_processor
# or
# from vllm.model_executor.guided_decoding.xgrammar_decoding import get_local_xgrammar_guided_decoding_logits_processor

MODEL = "Qwen/Qwen2.5-0.5B-Instruct"

llm = LLM(
    model=MODEL,
    max_model_len=1024,
    gpu_memory_utilization=0.9,
    enforce_eager=True,
)

tokenizer = get_cached_tokenizer(AutoTokenizer.from_pretrained(MODEL))

prompts = [
    "Classify this sentiment: vLLM is wonderful!",
]
guided_decoding_params = GuidedDecodingParams(choice=["Positive", "Negative"])
logits_processor = get_local_outlines_guided_decoding_logits_processor(
    guided_decoding_params,
    tokenizer,
)

sampling_params = SamplingParams(
    temperature=0,
    max_tokens=16,
    logits_processors=[logits_processor],
)
outputs = llm.generate(prompts, [sampling_params])

for output in outputs:
    prompt = output.prompt
    output = output.outputs[0]
    generated_text = output.text
    print(f"Prompt: {prompt!r}")
    print(f"Generated text: {generated_text!r}")

It outputs:

Prompt: 'Classify this sentiment: vLLM is wonderful!'
Generated text: 'Positive'