vllm - 💡(How to fix) Fix [Bug]: Using the /generative_scoring may cause shape mismatches in the rejection sampler, causing vllm serve to crash

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When sending a /generative_scoring request to a vLLM server running DeepSeek-V4-Flash with speculative decoding (MTP) enabled, the server crashes with a shape mismatch error inside the rejection sampler. The service becomes completely unresponsive and requires a restart.

The error occurs while processing a batch that mixes generative scoring requests and ongoing chat completions. The rejection sampler tries to assign bonus logits with shape [14, 3] to a tensor of shape [14, 129280], which is incompatible.

Expected behavior:
/generative_scoring should return scoring results without crashing the server, even when speculative decoding and other concurrent requests are active.

Error Message

(Worker_TP0 pid=794) ERROR 05-14 01:56:42 [multiproc_executor.py:962] File "/usr/local/lib/python3.12/site-packages/vllm/v1/sample/rejection_sampler.py", line 214, in _get_logprobs_tensors (Worker_TP0 pid=794) ERROR 05-14 01:56:42 [multiproc_executor.py:962] final_logits[bonus_logits_indices] = bonus_logits.to(torch.float32) (Worker_TP0 pid=794) ERROR 05-14 01:56:42 [multiproc_executor.py:962] ~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^ (Worker_TP0 pid=794) ERROR 05-14 01:56:42 [multiproc_executor.py:962] RuntimeError: shape mismatch: value tensor of shape [14, 3] cannot be broadcast to indexing result of shape [14, 129280]

Root Cause

(EngineCore pid=595) ERROR 05-14 01:56:42 [core.py:1138] RuntimeError: Worker failed with error 'shape mismatch: value tensor of shape [14, 3] cannot be broadcast to indexing result of shape [14, 129280]', please check the stack trace above for the root cause

Fix Action

Fix / Workaround

============================== CPU Info

Architecture: x86_64 CPU op-mode(s): 32-bit, 64-bit Address sizes: 52 bits physical, 57 bits virtual Byte Order: Little Endian CPU(s): 192 On-line CPU(s) list: 0-191 Vendor ID: GenuineIntel Model name: INTEL(R) XEON(R) PLATINUM 8558 CPU family: 6 Model: 207 Thread(s) per core: 2 Core(s) per socket: 48 Socket(s): 2 Stepping: 2 CPU max MHz: 4000.0000 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 invpcid_single intel_ppin cdp_l2 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 split_lock_detect avx_vnni avx512_bf16 wbnoinvd dtherm ida arat pln pts hwp hwp_act_window hwp_epp hwp_pkg_req 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 amx_bf16 avx512_fp16 amx_tile amx_int8 flush_l1d arch_capabilities Virtualization: VT-x L1d cache: 4.5 MiB (96 instances) L1i cache: 3 MiB (96 instances) L2 cache: 192 MiB (96 instances) L3 cache: 520 MiB (2 instances) NUMA node(s): 2 NUMA node0 CPU(s): 0-47,96-143 NUMA node1 CPU(s): 48-95,144-191 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: Not affected Vulnerability Spec rstack overflow: 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 IBRS, IBPB conditional, RSB filling, PBRSB-eIBRS SW sequence Vulnerability Srbds: Not affected Vulnerability Tsx async abort: Not affected

Code Example

Collecting environment information...
==============================
        System Info
==============================
OS                           : Ubuntu 22.04.5 LTS (x86_64)
GCC version                  : (Ubuntu 11.4.0-1ubuntu1~22.04.3) 11.4.0
Clang version                : Could not collect
CMake version                : Could not collect
Libc version                 : glibc-2.35

==============================
       PyTorch Info
==============================
PyTorch version              : 2.11.0+cu129
Is debug build               : False
CUDA used to build PyTorch   : 12.9
ROCM used to build PyTorch   : N/A
XPU used to build PyTorch    : N/A

==============================
      Python Environment
==============================
Python version               : 3.12.13 (main, May  8 2026, 20:13:20) [GCC 11.4.0] (64-bit runtime)
Python platform              : Linux-5.15.0-94-generic-x86_64-with-glibc2.35

==============================
       CUDA / GPU Info
==============================
Is CUDA available            : True
CUDA runtime version         : 12.9.86
CUDA_MODULE_LOADING set to   :
GPU models and configuration :
GPU 0: NVIDIA H20-3e
GPU 1: NVIDIA H20-3e
GPU 2: NVIDIA H20-3e
GPU 3: NVIDIA H20-3e

Nvidia driver version        : 570.86.15
cuDNN version                : Could not collect
HIP runtime version          : N/A
MIOpen runtime version       : N/A
Is XNNPACK available         : True

==============================
          CPU Info
==============================
Architecture:                       x86_64
CPU op-mode(s):                     32-bit, 64-bit
Address sizes:                      52 bits physical, 57 bits virtual
Byte Order:                         Little Endian
CPU(s):                             192
On-line CPU(s) list:                0-191
Vendor ID:                          GenuineIntel
Model name:                         INTEL(R) XEON(R) PLATINUM 8558
CPU family:                         6
Model:                              207
Thread(s) per core:                 2
Core(s) per socket:                 48
Socket(s):                          2
Stepping:                           2
CPU max MHz:                        4000.0000
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 invpcid_single intel_ppin cdp_l2 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 split_lock_detect avx_vnni avx512_bf16 wbnoinvd dtherm ida arat pln pts hwp hwp_act_window hwp_epp hwp_pkg_req 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 amx_bf16 avx512_fp16 amx_tile amx_int8 flush_l1d arch_capabilities
Virtualization:                     VT-x
L1d cache:                          4.5 MiB (96 instances)
L1i cache:                          3 MiB (96 instances)
L2 cache:                           192 MiB (96 instances)
L3 cache:                           520 MiB (2 instances)
NUMA node(s):                       2
NUMA node0 CPU(s):                  0-47,96-143
NUMA node1 CPU(s):                  48-95,144-191
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:             Not affected
Vulnerability Spec rstack overflow: 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 IBRS, IBPB conditional, RSB filling, PBRSB-eIBRS SW sequence
Vulnerability Srbds:                Not affected
Vulnerability Tsx async abort:      Not affected

==============================
Versions of relevant libraries
==============================
[pip3] flashinfer-python==0.6.8.post1
[pip3] numpy==2.2.6
[pip3] nvidia-cublas-cu12==12.9.1.4
[pip3] nvidia-cuda-cupti-cu12==12.9.79
[pip3] nvidia-cuda-nvrtc-cu12==12.9.86
[pip3] nvidia-cuda-runtime-cu12==12.9.79
[pip3] nvidia-cudnn-cu12==9.17.1.4
[pip3] nvidia-cudnn-frontend==1.18.0
[pip3] nvidia-cufft-cu12==11.4.1.4
[pip3] nvidia-cufile-cu12==1.14.1.1
[pip3] nvidia-curand-cu12==10.3.10.19
[pip3] nvidia-cusolver-cu12==11.7.5.82
[pip3] nvidia-cusparse-cu12==12.5.10.65
[pip3] nvidia-cusparselt-cu12==0.7.1
[pip3] nvidia-cutlass-dsl==4.5.0
[pip3] nvidia-cutlass-dsl-libs-base==4.5.0
[pip3] nvidia-ml-py==13.595.45
[pip3] nvidia-nccl-cu12==2.28.9
[pip3] nvidia-nvjitlink-cu12==12.9.86
[pip3] nvidia-nvshmem-cu12==3.4.5
[pip3] nvidia-nvtx-cu12==12.9.79
[pip3] pyzmq==27.1.0
[pip3] torch==2.11.0+cu129
[pip3] torch_c_dlpack_ext==0.1.5
[pip3] torchaudio==2.11.0+cu129
[pip3] torchvision==0.26.0+cu129
[pip3] transformers==5.8.0
[pip3] triton==3.6.0
[conda] Could not collect

==============================
         vLLM Info
==============================
ROCM Version                 : Could not collect
vLLM Version                 : 0.20.2
vLLM Build Flags:
  CUDA Archs: 7.5 8.0 8.6 8.9 9.0 10.0 12.0; ROCm: Disabled; XPU: Disabled
GPU Topology:
        GPU0    GPU1    GPU2    GPU3    NIC0    NIC1    NIC2    NIC3    NIC4    NIC5    CPU Affinity    NUMA Affinity   GPU NUMA ID
GPU0     X      NV18    NV18    NV18    PIX     NODE    NODE    NODE    SYS     SYS     0-47,96-143     0               N/A
GPU1    NV18     X      NV18    NV18    PIX     NODE    NODE    NODE    SYS     SYS     0-47,96-143     0               N/A
GPU2    NV18    NV18     X      NV18    SYS     SYS     SYS     SYS     NODE    PIX     48-95,144-191   1               N/A
GPU3    NV18    NV18    NV18     X      SYS     SYS     SYS     SYS     NODE    PIX     48-95,144-191   1               N/A
NIC0    PIX     PIX     SYS     SYS      X      NODE    NODE    NODE    SYS     SYS
NIC1    NODE    NODE    SYS     SYS     NODE     X      PIX     NODE    SYS     SYS
NIC2    NODE    NODE    SYS     SYS     NODE    PIX      X      NODE    SYS     SYS
NIC3    NODE    NODE    SYS     SYS     NODE    NODE    NODE     X      SYS     SYS
NIC4    SYS     SYS     NODE    NODE    SYS     SYS     SYS     SYS      X      NODE
NIC5    SYS     SYS     PIX     PIX     SYS     SYS     SYS     SYS     NODE     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
  NIC2: mlx5_2
  NIC3: mlx5_3
  NIC4: mlx5_4
  NIC5: mlx5_5

==============================
     Environment Variables
==============================
NVIDIA_VISIBLE_DEVICES=all
NVIDIA_REQUIRE_CUDA=cuda>=12.9 brand=unknown,driver>=535,driver<536 brand=grid,driver>=535,driver<536 brand=tesla,driver>=535,driver<536 brand=nvidia,driver>=535,driver<536 brand=quadro,driver>=535,driver<536 brand=quadrortx,driver>=535,driver<536 brand=nvidiartx,driver>=535,driver<536 brand=vapps,driver>=535,driver<536 brand=vpc,driver>=535,driver<536 brand=vcs,driver>=535,driver<536 brand=vws,driver>=535,driver<536 brand=cloudgaming,driver>=535,driver<536 brand=unknown,driver>=550,driver<551 brand=grid,driver>=550,driver<551 brand=tesla,driver>=550,driver<551 brand=nvidia,driver>=550,driver<551 brand=quadro,driver>=550,driver<551 brand=quadrortx,driver>=550,driver<551 brand=nvidiartx,driver>=550,driver<551 brand=vapps,driver>=550,driver<551 brand=vpc,driver>=550,driver<551 brand=vcs,driver>=550,driver<551 brand=vws,driver>=550,driver<551 brand=cloudgaming,driver>=550,driver<551 brand=unknown,driver>=560,driver<561 brand=grid,driver>=560,driver<561 brand=tesla,driver>=560,driver<561 brand=nvidia,driver>=560,driver<561 brand=quadro,driver>=560,driver<561 brand=quadrortx,driver>=560,driver<561 brand=nvidiartx,driver>=560,driver<561 brand=vapps,driver>=560,driver<561 brand=vpc,driver>=560,driver<561 brand=vcs,driver>=560,driver<561 brand=vws,driver>=560,driver<561 brand=cloudgaming,driver>=560,driver<561 brand=unknown,driver>=565,driver<566 brand=grid,driver>=565,driver<566 brand=tesla,driver>=565,driver<566 brand=nvidia,driver>=565,driver<566 brand=quadro,driver>=565,driver<566 brand=quadrortx,driver>=565,driver<566 brand=nvidiartx,driver>=565,driver<566 brand=vapps,driver>=565,driver<566 brand=vpc,driver>=565,driver<566 brand=vcs,driver>=565,driver<566 brand=vws,driver>=565,driver<566 brand=cloudgaming,driver>=565,driver<566 brand=unknown,driver>=570,driver<571 brand=grid,driver>=570,driver<571 brand=tesla,driver>=570,driver<571 brand=nvidia,driver>=570,driver<571 brand=quadro,driver>=570,driver<571 brand=quadrortx,driver>=570,driver<571 brand=nvidiartx,driver>=570,driver<571 brand=vapps,driver>=570,driver<571 brand=vpc,driver>=570,driver<571 brand=vcs,driver>=570,driver<571 brand=vws,driver>=570,driver<571 brand=cloudgaming,driver>=570,driver<571
TORCH_CUDA_ARCH_LIST=7.5 8.0 8.6 8.9 9.0 10.0 12.0
NVIDIA_DRIVER_CAPABILITIES=compute,utility
VLLM_USAGE_SOURCE=production-docker-image
CUDA_VERSION=12.9.1
VLLM_ENABLE_CUDA_COMPATIBILITY=0
VLLM_ENGINE_READY_TIMEOUT_S=3600
LD_LIBRARY_PATH=/usr/local/nvidia/lib64:/usr/local/cuda/lib64:/usr/local/cuda/lib64
PYTORCH_NVML_BASED_CUDA_CHECK=1
TORCHINDUCTOR_COMPILE_THREADS=1
TORCHINDUCTOR_CACHE_DIR=/tmp/torchinductor_root

---

vllm/vllm-openai:v0.20.2-cu129 \
  --host 0.0.0.0 \
  --port 8000 \
  --model /models \
  --trust-remote-code \
  --max-num-batched-tokens 4096 \
  --served-model-name deepseek-v4-flash \
  --enable-chunked-prefill \
  --enable-prefix-caching \
  --kv-cache-dtype fp8 \
  --block-size 256 \
  --max-num-seqs 30 \
  --tensor-parallel-size 4 \
  --tokenizer-mode deepseek_v4 \
  --tool-call-parser deepseek_v4 \
  --enable-auto-tool-choice \
  --reasoning-parser deepseek_v4 \
  --speculative_config '{"method":"mtp","num_speculative_tokens":1}' \
  --safetensors-load-strategy=prefetch \
  --default-chat-template-kwargs '{"thinking": true, "reasoning_effort": "high"}' \
  --disable-access-log-for-endpoints /metrics

---

curl -X POST http://10.0.62.180:2100/generative_scoring \
  -H "Content-Type: application/json" \
  -d '{
    "model": "deepseek-v4",
    "query": "下列哪种物质溶于水时会显著放热?\nA. 氯化钠(NaCl)\nB. 硝酸铵(NH₄NO₃)\nC. 氢氧化钠(NaOH)\nD. 蔗糖(C₁₂H₂₂O₁₁)",
    "items": ["A", "B", "C", "D"],
    "label_token_ids": [10634, 15444]
  }'

---

(Worker_TP0 pid=794) ERROR 05-14 01:56:42 [multiproc_executor.py:962]   File "/usr/local/lib/python3.12/site-packages/vllm/v1/sample/rejection_sampler.py", line 214, in _get_logprobs_tensors
(Worker_TP0 pid=794) ERROR 05-14 01:56:42 [multiproc_executor.py:962]     final_logits[bonus_logits_indices] = bonus_logits.to(torch.float32)
(Worker_TP0 pid=794) ERROR 05-14 01:56:42 [multiproc_executor.py:962]     ~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^
(Worker_TP0 pid=794) ERROR 05-14 01:56:42 [multiproc_executor.py:962] RuntimeError: shape mismatch: value tensor of shape [14, 3] cannot be broadcast to indexing result of shape [14, 129280]

---

(EngineCore pid=595) ERROR 05-14 01:56:42 [core.py:1138] RuntimeError: Worker failed with error 'shape mismatch: value tensor of shape [14, 3] cannot be broadcast to indexing result of shape [14, 129280]', please check the stack trace above for the root cause
RAW_BUFFERClick to expand / collapse

Your current environment

<details> <summary>The output of <code>python collect_env.py</code></summary>
Collecting environment information...
==============================
        System Info
==============================
OS                           : Ubuntu 22.04.5 LTS (x86_64)
GCC version                  : (Ubuntu 11.4.0-1ubuntu1~22.04.3) 11.4.0
Clang version                : Could not collect
CMake version                : Could not collect
Libc version                 : glibc-2.35

==============================
       PyTorch Info
==============================
PyTorch version              : 2.11.0+cu129
Is debug build               : False
CUDA used to build PyTorch   : 12.9
ROCM used to build PyTorch   : N/A
XPU used to build PyTorch    : N/A

==============================
      Python Environment
==============================
Python version               : 3.12.13 (main, May  8 2026, 20:13:20) [GCC 11.4.0] (64-bit runtime)
Python platform              : Linux-5.15.0-94-generic-x86_64-with-glibc2.35

==============================
       CUDA / GPU Info
==============================
Is CUDA available            : True
CUDA runtime version         : 12.9.86
CUDA_MODULE_LOADING set to   :
GPU models and configuration :
GPU 0: NVIDIA H20-3e
GPU 1: NVIDIA H20-3e
GPU 2: NVIDIA H20-3e
GPU 3: NVIDIA H20-3e

Nvidia driver version        : 570.86.15
cuDNN version                : Could not collect
HIP runtime version          : N/A
MIOpen runtime version       : N/A
Is XNNPACK available         : True

==============================
          CPU Info
==============================
Architecture:                       x86_64
CPU op-mode(s):                     32-bit, 64-bit
Address sizes:                      52 bits physical, 57 bits virtual
Byte Order:                         Little Endian
CPU(s):                             192
On-line CPU(s) list:                0-191
Vendor ID:                          GenuineIntel
Model name:                         INTEL(R) XEON(R) PLATINUM 8558
CPU family:                         6
Model:                              207
Thread(s) per core:                 2
Core(s) per socket:                 48
Socket(s):                          2
Stepping:                           2
CPU max MHz:                        4000.0000
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 invpcid_single intel_ppin cdp_l2 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 split_lock_detect avx_vnni avx512_bf16 wbnoinvd dtherm ida arat pln pts hwp hwp_act_window hwp_epp hwp_pkg_req 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 amx_bf16 avx512_fp16 amx_tile amx_int8 flush_l1d arch_capabilities
Virtualization:                     VT-x
L1d cache:                          4.5 MiB (96 instances)
L1i cache:                          3 MiB (96 instances)
L2 cache:                           192 MiB (96 instances)
L3 cache:                           520 MiB (2 instances)
NUMA node(s):                       2
NUMA node0 CPU(s):                  0-47,96-143
NUMA node1 CPU(s):                  48-95,144-191
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:             Not affected
Vulnerability Spec rstack overflow: 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 IBRS, IBPB conditional, RSB filling, PBRSB-eIBRS SW sequence
Vulnerability Srbds:                Not affected
Vulnerability Tsx async abort:      Not affected

==============================
Versions of relevant libraries
==============================
[pip3] flashinfer-python==0.6.8.post1
[pip3] numpy==2.2.6
[pip3] nvidia-cublas-cu12==12.9.1.4
[pip3] nvidia-cuda-cupti-cu12==12.9.79
[pip3] nvidia-cuda-nvrtc-cu12==12.9.86
[pip3] nvidia-cuda-runtime-cu12==12.9.79
[pip3] nvidia-cudnn-cu12==9.17.1.4
[pip3] nvidia-cudnn-frontend==1.18.0
[pip3] nvidia-cufft-cu12==11.4.1.4
[pip3] nvidia-cufile-cu12==1.14.1.1
[pip3] nvidia-curand-cu12==10.3.10.19
[pip3] nvidia-cusolver-cu12==11.7.5.82
[pip3] nvidia-cusparse-cu12==12.5.10.65
[pip3] nvidia-cusparselt-cu12==0.7.1
[pip3] nvidia-cutlass-dsl==4.5.0
[pip3] nvidia-cutlass-dsl-libs-base==4.5.0
[pip3] nvidia-ml-py==13.595.45
[pip3] nvidia-nccl-cu12==2.28.9
[pip3] nvidia-nvjitlink-cu12==12.9.86
[pip3] nvidia-nvshmem-cu12==3.4.5
[pip3] nvidia-nvtx-cu12==12.9.79
[pip3] pyzmq==27.1.0
[pip3] torch==2.11.0+cu129
[pip3] torch_c_dlpack_ext==0.1.5
[pip3] torchaudio==2.11.0+cu129
[pip3] torchvision==0.26.0+cu129
[pip3] transformers==5.8.0
[pip3] triton==3.6.0
[conda] Could not collect

==============================
         vLLM Info
==============================
ROCM Version                 : Could not collect
vLLM Version                 : 0.20.2
vLLM Build Flags:
  CUDA Archs: 7.5 8.0 8.6 8.9 9.0 10.0 12.0; ROCm: Disabled; XPU: Disabled
GPU Topology:
        GPU0    GPU1    GPU2    GPU3    NIC0    NIC1    NIC2    NIC3    NIC4    NIC5    CPU Affinity    NUMA Affinity   GPU NUMA ID
GPU0     X      NV18    NV18    NV18    PIX     NODE    NODE    NODE    SYS     SYS     0-47,96-143     0               N/A
GPU1    NV18     X      NV18    NV18    PIX     NODE    NODE    NODE    SYS     SYS     0-47,96-143     0               N/A
GPU2    NV18    NV18     X      NV18    SYS     SYS     SYS     SYS     NODE    PIX     48-95,144-191   1               N/A
GPU3    NV18    NV18    NV18     X      SYS     SYS     SYS     SYS     NODE    PIX     48-95,144-191   1               N/A
NIC0    PIX     PIX     SYS     SYS      X      NODE    NODE    NODE    SYS     SYS
NIC1    NODE    NODE    SYS     SYS     NODE     X      PIX     NODE    SYS     SYS
NIC2    NODE    NODE    SYS     SYS     NODE    PIX      X      NODE    SYS     SYS
NIC3    NODE    NODE    SYS     SYS     NODE    NODE    NODE     X      SYS     SYS
NIC4    SYS     SYS     NODE    NODE    SYS     SYS     SYS     SYS      X      NODE
NIC5    SYS     SYS     PIX     PIX     SYS     SYS     SYS     SYS     NODE     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
  NIC2: mlx5_2
  NIC3: mlx5_3
  NIC4: mlx5_4
  NIC5: mlx5_5

==============================
     Environment Variables
==============================
NVIDIA_VISIBLE_DEVICES=all
NVIDIA_REQUIRE_CUDA=cuda>=12.9 brand=unknown,driver>=535,driver<536 brand=grid,driver>=535,driver<536 brand=tesla,driver>=535,driver<536 brand=nvidia,driver>=535,driver<536 brand=quadro,driver>=535,driver<536 brand=quadrortx,driver>=535,driver<536 brand=nvidiartx,driver>=535,driver<536 brand=vapps,driver>=535,driver<536 brand=vpc,driver>=535,driver<536 brand=vcs,driver>=535,driver<536 brand=vws,driver>=535,driver<536 brand=cloudgaming,driver>=535,driver<536 brand=unknown,driver>=550,driver<551 brand=grid,driver>=550,driver<551 brand=tesla,driver>=550,driver<551 brand=nvidia,driver>=550,driver<551 brand=quadro,driver>=550,driver<551 brand=quadrortx,driver>=550,driver<551 brand=nvidiartx,driver>=550,driver<551 brand=vapps,driver>=550,driver<551 brand=vpc,driver>=550,driver<551 brand=vcs,driver>=550,driver<551 brand=vws,driver>=550,driver<551 brand=cloudgaming,driver>=550,driver<551 brand=unknown,driver>=560,driver<561 brand=grid,driver>=560,driver<561 brand=tesla,driver>=560,driver<561 brand=nvidia,driver>=560,driver<561 brand=quadro,driver>=560,driver<561 brand=quadrortx,driver>=560,driver<561 brand=nvidiartx,driver>=560,driver<561 brand=vapps,driver>=560,driver<561 brand=vpc,driver>=560,driver<561 brand=vcs,driver>=560,driver<561 brand=vws,driver>=560,driver<561 brand=cloudgaming,driver>=560,driver<561 brand=unknown,driver>=565,driver<566 brand=grid,driver>=565,driver<566 brand=tesla,driver>=565,driver<566 brand=nvidia,driver>=565,driver<566 brand=quadro,driver>=565,driver<566 brand=quadrortx,driver>=565,driver<566 brand=nvidiartx,driver>=565,driver<566 brand=vapps,driver>=565,driver<566 brand=vpc,driver>=565,driver<566 brand=vcs,driver>=565,driver<566 brand=vws,driver>=565,driver<566 brand=cloudgaming,driver>=565,driver<566 brand=unknown,driver>=570,driver<571 brand=grid,driver>=570,driver<571 brand=tesla,driver>=570,driver<571 brand=nvidia,driver>=570,driver<571 brand=quadro,driver>=570,driver<571 brand=quadrortx,driver>=570,driver<571 brand=nvidiartx,driver>=570,driver<571 brand=vapps,driver>=570,driver<571 brand=vpc,driver>=570,driver<571 brand=vcs,driver>=570,driver<571 brand=vws,driver>=570,driver<571 brand=cloudgaming,driver>=570,driver<571
TORCH_CUDA_ARCH_LIST=7.5 8.0 8.6 8.9 9.0 10.0 12.0
NVIDIA_DRIVER_CAPABILITIES=compute,utility
VLLM_USAGE_SOURCE=production-docker-image
CUDA_VERSION=12.9.1
VLLM_ENABLE_CUDA_COMPATIBILITY=0
VLLM_ENGINE_READY_TIMEOUT_S=3600
LD_LIBRARY_PATH=/usr/local/nvidia/lib64:/usr/local/cuda/lib64:/usr/local/cuda/lib64
PYTORCH_NVML_BASED_CUDA_CHECK=1
TORCHINDUCTOR_COMPILE_THREADS=1
TORCHINDUCTOR_CACHE_DIR=/tmp/torchinductor_root
</details>

🐛 Describe the bug

Description

When sending a /generative_scoring request to a vLLM server running DeepSeek-V4-Flash with speculative decoding (MTP) enabled, the server crashes with a shape mismatch error inside the rejection sampler. The service becomes completely unresponsive and requires a restart.

The error occurs while processing a batch that mixes generative scoring requests and ongoing chat completions. The rejection sampler tries to assign bonus logits with shape [14, 3] to a tensor of shape [14, 129280], which is incompatible.

Expected behavior:
/generative_scoring should return scoring results without crashing the server, even when speculative decoding and other concurrent requests are active.

Reproduction

  1. Start vLLM server with the following command:
  vllm/vllm-openai:v0.20.2-cu129 \
  --host 0.0.0.0 \
  --port 8000 \
  --model /models \
  --trust-remote-code \
  --max-num-batched-tokens 4096 \
  --served-model-name deepseek-v4-flash \
  --enable-chunked-prefill \
  --enable-prefix-caching \
  --kv-cache-dtype fp8 \
  --block-size 256 \
  --max-num-seqs 30 \
  --tensor-parallel-size 4 \
  --tokenizer-mode deepseek_v4 \
  --tool-call-parser deepseek_v4 \
  --enable-auto-tool-choice \
  --reasoning-parser deepseek_v4 \
  --speculative_config '{"method":"mtp","num_speculative_tokens":1}' \
  --safetensors-load-strategy=prefetch \
  --default-chat-template-kwargs '{"thinking": true, "reasoning_effort": "high"}' \
  --disable-access-log-for-endpoints /metrics
  1. Send a /generative_scoring request while other /v1/chat/completions requests are running (or shortly after they have been processed):
curl -X POST http://10.0.62.180:2100/generative_scoring \
  -H "Content-Type: application/json" \
  -d '{
    "model": "deepseek-v4",
    "query": "下列哪种物质溶于水时会显著放热?\nA. 氯化钠(NaCl)\nB. 硝酸铵(NH₄NO₃)\nC. 氢氧化钠(NaOH)\nD. 蔗糖(C₁₂H₂₂O₁₁)",
    "items": ["A", "B", "C", "D"],
    "label_token_ids": [10634, 15444]
  }'
  1. Observe the server crashes with a RuntimeError in the rejection sampler.

Environment

  • vLLM version: 0.20.2 (Docker image vllm/vllm-openai:v0.20.2-cu129)
  • Model: DeepSeek-V4-Flash (deepseek-v4)
  • Hardware: 4x NVIDIA H20-3e GPUs (tensor‑parallel size 4)
  • Speculative decoding: MTP with num_speculative_tokens=1
  • Quantization: deepseek_v4_fp8 (as per config dump)
  • Other config: chunked prefill, prefix caching, block size 256, max 30 sequences

Error Logs

The crash originates from vllm/v1/sample/rejection_sampler.py

(Worker_TP0 pid=794) ERROR 05-14 01:56:42 [multiproc_executor.py:962]   File "/usr/local/lib/python3.12/site-packages/vllm/v1/sample/rejection_sampler.py", line 214, in _get_logprobs_tensors
(Worker_TP0 pid=794) ERROR 05-14 01:56:42 [multiproc_executor.py:962]     final_logits[bonus_logits_indices] = bonus_logits.to(torch.float32)
(Worker_TP0 pid=794) ERROR 05-14 01:56:42 [multiproc_executor.py:962]     ~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^
(Worker_TP0 pid=794) ERROR 05-14 01:56:42 [multiproc_executor.py:962] RuntimeError: shape mismatch: value tensor of shape [14, 3] cannot be broadcast to indexing result of shape [14, 129280]

The engine core then fails:

(EngineCore pid=595) ERROR 05-14 01:56:42 [core.py:1138] RuntimeError: Worker failed with error 'shape mismatch: value tensor of shape [14, 3] cannot be broadcast to indexing result of shape [14, 129280]', please check the stack trace above for the root cause

Full traceback (truncated) shows that bonus_logits has a vocab dimension of 3 while final_logits expects the full vocab size of 129280. This happens when generative scoring requests (with label_token_ids) are processed alongside standard chat requests that use the full vocabulary.

Additional Context

AI Generated The scheduler dump confirms that at the time of the crash, the batch contained:

  • 4 new generative scoring requests: each with 57 prompt tokens, logprobs=2, max_tokens=1, and extra_args that likely include the label tokens.
  • 10 ongoing/cached chat completion requests using speculative decoding.

The shape [14, 3] corresponds to the 14 total sequences in the batch, but only 3 logprobs are provided for the scoring items instead of the full vocabulary. The rejection sampler does not seem to handle this mixed‑shape scenario correctly.

Full Error Logs

full_error_stack.log

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vllm - 💡(How to fix) Fix [Bug]: Using the /generative_scoring may cause shape mismatches in the rejection sampler, causing vllm serve to crash