vllm - ✅(Solved) Fix [Bug]: Crash on Transcription (size for tensor a must match the size of tensor b) [1 pull requests, 2 comments, 2 participants]

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vllm-project/vllm#39174Fetched 2026-04-08 03:01:37
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Error Message

Output from console: (EngineCore pid=47271) Process EngineCore: (EngineCore pid=47271) Traceback (most recent call last): (APIServer pid=47229) INFO: 192.168.1.5:46152 - "POST /v1/audio/transcriptions HTTP/1.1" 500 Internal Server Error (EngineCore pid=47271) File "/opt/miniconda3/envs/vllm_nightly/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap (EngineCore pid=47271) self.run() (EngineCore pid=47271) File "/opt/miniconda3/envs/vllm_nightly/lib/python3.12/multiprocessing/process.py", line 108, in run (EngineCore pid=47271) self._target(*self._args, **self._kwargs) (EngineCore pid=47271) File "/root/Develop/vllm/vllm/v1/engine/core.py", line 1112, in run_engine_core (EngineCore pid=47271) raise e (EngineCore pid=47271) File "/root/Develop/vllm/vllm/v1/engine/core.py", line 1101, in run_engine_core (EngineCore pid=47271) engine_core.run_busy_loop() (EngineCore pid=47271) File "/root/Develop/vllm/vllm/v1/engine/core.py", line 1142, in run_busy_loop (EngineCore pid=47271) self._process_engine_step() (EngineCore pid=47271) File "/root/Develop/vllm/vllm/v1/engine/core.py", line 1181, in _process_engine_step (EngineCore pid=47271) outputs, model_executed = self.step_fn() (EngineCore pid=47271) ^^^^^^^^^^^^^^ (EngineCore pid=47271) File "/root/Develop/vllm/vllm/v1/engine/core.py", line 451, in step_with_batch_queue (EngineCore pid=47271) exec_future = self.model_executor.execute_model( (EngineCore pid=47271) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ (EngineCore pid=47271) File "/root/Develop/vllm/vllm/v1/executor/uniproc_executor.py", line 114, in execute_model (EngineCore pid=47271) output.result() (EngineCore pid=47271) File "/opt/miniconda3/envs/vllm_nightly/lib/python3.12/concurrent/futures/_base.py", line 449, in result (EngineCore pid=47271) return self.__get_result() (EngineCore pid=47271) ^^^^^^^^^^^^^^^^^^^ (EngineCore pid=47271) File "/opt/miniconda3/envs/vllm_nightly/lib/python3.12/concurrent/futures/_base.py", line 401, in __get_result (EngineCore pid=47271) raise self._exception (EngineCore pid=47271) File "/root/Develop/vllm/vllm/v1/executor/uniproc_executor.py", line 84, in collective_rpc (EngineCore pid=47271) result = run_method(self.driver_worker, method, args, kwargs) (EngineCore pid=47271) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ (EngineCore pid=47271) File "/root/Develop/vllm/vllm/v1/serial_utils.py", line 510, in run_method (EngineCore pid=47271) return func(*args, **kwargs) (EngineCore pid=47271) ^^^^^^^^^^^^^^^^^^^^^ (EngineCore pid=47271) File "/root/Develop/vllm/vllm/v1/worker/worker_base.py", line 332, in execute_model (EngineCore pid=47271) return self.worker.execute_model(scheduler_output) (EngineCore pid=47271) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ (EngineCore pid=47271) File "/opt/miniconda3/envs/vllm_nightly/lib/python3.12/site-packages/torch/utils/_contextlib.py", line 124, in decorate_context (EngineCore pid=47271) return func(*args, **kwargs) (EngineCore pid=47271) ^^^^^^^^^^^^^^^^^^^^^ (EngineCore pid=47271) File "/root/Develop/vllm/vllm/v1/worker/gpu_worker.py", line 808, in execute_model (EngineCore pid=47271) output = self.model_runner.execute_model( (EngineCore pid=47271) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ (EngineCore pid=47271) File "/opt/miniconda3/envs/vllm_nightly/lib/python3.12/site-packages/torch/utils/_contextlib.py", line 124, in decorate_context (EngineCore pid=47271) return func(*args, **kwargs) (EngineCore pid=47271) ^^^^^^^^^^^^^^^^^^^^^ (EngineCore pid=47271) File "/root/Develop/vllm/vllm/v1/worker/gpu_model_runner.py", line 3981, in execute_model (EngineCore pid=47271) ) = self._preprocess( (EngineCore pid=47271) ^^^^^^^^^^^^^^^^^ (EngineCore pid=47271) File "/root/Develop/vllm/vllm/v1/worker/gpu_model_runner.py", line 3230, in preprocess (EngineCore pid=47271) self.inputs_embeds.gpu[:num_scheduled_tokens].copy(inputs_embeds_scheduled) (EngineCore pid=47271) RuntimeError: The size of tensor a (40) must match the size of tensor b (39) at non-singleton dimension 0 (APIServer pid=47229) INFO: 192.168.1.5:45648 - "POST /v1/audio/transcriptions HTTP/1.1" 500 Internal Server Error [rank0]:[W407 18:18:15.323640661 ProcessGroupNCCL.cpp:1553] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())

Fix Action

Fix / Workaround

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

Architecture: x86_64 CPU op-mode(s): 32-bit, 64-bit Address sizes: 48 bits physical, 48 bits virtual Byte Order: Little Endian CPU(s): 8 On-line CPU(s) list: 0-7 Vendor ID: AuthenticAMD Model name: AMD Ryzen 9 9950X 16-Core Processor CPU family: 26 Model: 68 Thread(s) per core: 2 Core(s) per socket: 4 Socket(s): 1 Stepping: 0 BogoMIPS: 8583.71 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 tsc_reliable nonstop_tsc cpuid extd_apicid tsc_known_freq pni pclmulqdq ssse3 fma cx16 sse4_1 sse4_2 movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm cmp_legacy svm cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw topoext perfctr_core ssbd ibrs ibpb stibp vmmcall fsgsbase bmi1 avx2 smep bmi2 erms invpcid avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves avx_vnni avx512_bf16 clzero xsaveerptr arat npt nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold v_vmsave_vmload avx512vbmi umip avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg avx512_vpopcntdq rdpid fsrm avx512_vp2intersect Virtualization: AMD-V Hypervisor vendor: Microsoft Virtualization type: full L1d cache: 192 KiB (4 instances) L1i cache: 128 KiB (4 instances) L2 cache: 4 MiB (4 instances) L3 cache: 32 MiB (1 instance) NUMA node(s): 1 NUMA node0 CPU(s): 0-7 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; Retpolines; IBPB conditional; IBRS_FW; STIBP always-on; RSB filling; PBRSB-eIBRS Not affected; BHI Not affected Vulnerability Srbds: Not affected Vulnerability Tsx async abort: Not affected

PR fix notes

PR #39184: Log warning for scheduled token mismatch

Description (problem / solution / changelog)

Added a warning for token mismatch in scheduled inputs.

Purpose

To fix the issue of:

  • #39174

Test Plan

Because I'm running on a MacBook, which doesn't have a GPU nor CUDA, I can't run the test normally. Instead I'm running manually. I created a test file in the root of the folder, wrote some code that manually tests the issue, and then ran it.

The test command to run the code was python3 manual_test_copy.py because the file is named manual_test_copy.py and I'm using Python 3.

Test Result

The test has ran successfully.

Before:

RuntimeError: The size of tensor a (40) must match the size of tensor b (39) at non-singleton dimension 0

After:

before: scheduled=40, actual=39
fixing mismatch: 40 -> 39
copy succeeded

before: scheduled=39, actual=39
copy succeeded

before: scheduled=12, actual=10
fixing mismatch: 12 -> 10
copy succeeded

<details> <summary> Essential Elements of an Effective PR Description Checklist </summary>
  • The purpose of the PR, such as "Fix some issue (link existing issues this PR will resolve)".
  • The test plan, such as providing test command.
  • The test results, such as pasting the results comparison before and after, or e2e results
  • (Optional) The necessary documentation update, such as updating supported_models.md and examples for a new model.
  • (Optional) Release notes update. If your change is user facing, please update the release notes draft in the Google Doc.
</details>

Changed files

  • vllm/v1/worker/gpu_model_runner.py (modified, +13/-1)

Code Example

==============================
        System Info
==============================
OS                           : Debian GNU/Linux 12 (bookworm) (x86_64)
GCC version                  : (Debian 12.2.0-14) 12.2.0
Clang version                : Could not collect
CMake version                : Could not collect
Libc version                 : glibc-2.36

==============================
       PyTorch Info
==============================
PyTorch version              : 2.10.0+cu128
Is debug build               : False
CUDA used to build PyTorch   : 12.8
ROCM used to build PyTorch   : N/A

==============================
      Python Environment
==============================
Python version               : 3.12.13 | packaged by Anaconda, Inc. | (main, Mar 19 2026, 20:20:58) [GCC 14.3.0] (64-bit runtime)
Python platform              : Linux-6.6.87.2-microsoft-standard-WSL2-x86_64-with-glibc2.36

==============================
       CUDA / GPU Info
==============================
Is CUDA available            : True
CUDA runtime version         : Could not collect
CUDA_MODULE_LOADING set to   : 
GPU models and configuration : GPU 0: NVIDIA RTX PRO 6000 Blackwell Workstation Edition
Nvidia driver version        : 591.86
cuDNN version                : Probably one of the following:
/usr/lib/x86_64-linux-gnu/libcudnn.so.9.4.0
/usr/lib/x86_64-linux-gnu/libcudnn_adv.so.9.4.0
/usr/lib/x86_64-linux-gnu/libcudnn_cnn.so.9.4.0
/usr/lib/x86_64-linux-gnu/libcudnn_engines_precompiled.so.9.4.0
/usr/lib/x86_64-linux-gnu/libcudnn_engines_runtime_compiled.so.9.4.0
/usr/lib/x86_64-linux-gnu/libcudnn_graph.so.9.4.0
/usr/lib/x86_64-linux-gnu/libcudnn_heuristic.so.9.4.0
/usr/lib/x86_64-linux-gnu/libcudnn_ops.so.9.4.0
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:                        48 bits physical, 48 bits virtual
Byte Order:                           Little Endian
CPU(s):                               8
On-line CPU(s) list:                  0-7
Vendor ID:                            AuthenticAMD
Model name:                           AMD Ryzen 9 9950X 16-Core Processor
CPU family:                           26
Model:                                68
Thread(s) per core:                   2
Core(s) per socket:                   4
Socket(s):                            1
Stepping:                             0
BogoMIPS:                             8583.71
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 tsc_reliable nonstop_tsc cpuid extd_apicid tsc_known_freq pni pclmulqdq ssse3 fma cx16 sse4_1 sse4_2 movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm cmp_legacy svm cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw topoext perfctr_core ssbd ibrs ibpb stibp vmmcall fsgsbase bmi1 avx2 smep bmi2 erms invpcid avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves avx_vnni avx512_bf16 clzero xsaveerptr arat npt nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold v_vmsave_vmload avx512vbmi umip avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg avx512_vpopcntdq rdpid fsrm avx512_vp2intersect
Virtualization:                       AMD-V
Hypervisor vendor:                    Microsoft
Virtualization type:                  full
L1d cache:                            192 KiB (4 instances)
L1i cache:                            128 KiB (4 instances)
L2 cache:                             4 MiB (4 instances)
L3 cache:                             32 MiB (1 instance)
NUMA node(s):                         1
NUMA node0 CPU(s):                    0-7
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; Retpolines; IBPB conditional; IBRS_FW; STIBP always-on; RSB filling; PBRSB-eIBRS Not affected; BHI Not affected
Vulnerability Srbds:                  Not affected
Vulnerability Tsx async abort:        Not affected

==============================
Versions of relevant libraries
==============================
[pip3] flashinfer-python==0.6.7
[pip3] numpy==2.2.6
[pip3] nvidia-cublas-cu12==12.8.4.1
[pip3] nvidia-cuda-cupti-cu12==12.8.90
[pip3] nvidia-cuda-nvrtc-cu12==12.8.93
[pip3] nvidia-cuda-runtime-cu12==12.8.90
[pip3] nvidia-cudnn-cu12==9.10.2.21
[pip3] nvidia-cudnn-frontend==1.18.0
[pip3] nvidia-cufft-cu12==11.3.3.83
[pip3] nvidia-cufile-cu12==1.13.1.3
[pip3] nvidia-curand-cu12==10.3.9.90
[pip3] nvidia-cusolver-cu12==11.7.3.90
[pip3] nvidia-cusparse-cu12==12.5.8.93
[pip3] nvidia-cusparselt-cu12==0.7.1
[pip3] nvidia-cutlass-dsl==4.4.2
[pip3] nvidia-cutlass-dsl-libs-base==4.4.2
[pip3] nvidia-ml-py==13.595.45
[pip3] nvidia-nccl-cu12==2.27.5
[pip3] nvidia-nvjitlink-cu12==12.8.93
[pip3] nvidia-nvshmem-cu12==3.4.5
[pip3] nvidia-nvtx-cu12==12.8.90
[pip3] pyzmq==27.1.0
[pip3] torch==2.10.0
[pip3] torch_c_dlpack_ext==0.1.5
[pip3] torchaudio==2.10.0
[pip3] torchvision==0.25.0
[pip3] transformers==5.5.0
[pip3] triton==3.6.0
[conda] flashinfer-python         0.6.7                    pypi_0    pypi
[conda] numpy                     2.2.6                    pypi_0    pypi
[conda] nvidia-cublas-cu12        12.8.4.1                 pypi_0    pypi
[conda] nvidia-cuda-cupti-cu12    12.8.90                  pypi_0    pypi
[conda] nvidia-cuda-nvrtc-cu12    12.8.93                  pypi_0    pypi
[conda] nvidia-cuda-runtime-cu12  12.8.90                  pypi_0    pypi
[conda] nvidia-cudnn-cu12         9.10.2.21                pypi_0    pypi
[conda] nvidia-cudnn-frontend     1.18.0                   pypi_0    pypi
[conda] nvidia-cufft-cu12         11.3.3.83                pypi_0    pypi
[conda] nvidia-cufile-cu12        1.13.1.3                 pypi_0    pypi
[conda] nvidia-curand-cu12        10.3.9.90                pypi_0    pypi
[conda] nvidia-cusolver-cu12      11.7.3.90                pypi_0    pypi
[conda] nvidia-cusparse-cu12      12.5.8.93                pypi_0    pypi
[conda] nvidia-cusparselt-cu12    0.7.1                    pypi_0    pypi
[conda] nvidia-cutlass-dsl        4.4.2                    pypi_0    pypi
[conda] nvidia-cutlass-dsl-libs-base 4.4.2                    pypi_0    pypi
[conda] nvidia-ml-py              13.595.45                pypi_0    pypi
[conda] nvidia-nccl-cu12          2.27.5                   pypi_0    pypi
[conda] nvidia-nvjitlink-cu12     12.8.93                  pypi_0    pypi
[conda] nvidia-nvshmem-cu12       3.4.5                    pypi_0    pypi
[conda] nvidia-nvtx-cu12          12.8.90                  pypi_0    pypi
[conda] pyzmq                     27.1.0                   pypi_0    pypi
[conda] torch                     2.10.0                   pypi_0    pypi
[conda] torch-c-dlpack-ext        0.1.5                    pypi_0    pypi
[conda] torchaudio                2.10.0                   pypi_0    pypi
[conda] torchvision               0.25.0                   pypi_0    pypi
[conda] transformers              5.5.0                    pypi_0    pypi
[conda] triton                    3.6.0                    pypi_0    pypi

==============================
         vLLM Info
==============================
ROCM Version                 : Could not collect
vLLM Version                 : 0.1.dev1+gbb39382b2 (git sha: bb39382b2)
vLLM Build Flags:
  CUDA Archs: Not Set; ROCm: Disabled
GPU Topology:
  	GPU0	CPU Affinity	NUMA Affinity	GPU NUMA ID
GPU0	 X 				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

==============================
     Environment Variables
==============================
PYTORCH_NVML_BASED_CUDA_CHECK=1
TORCHINDUCTOR_COMPILE_THREADS=1
TORCHINDUCTOR_CACHE_DIR=/tmp/torchinductor_root
RAW_BUFFERClick to expand / collapse

Your current environment

<details> <summary>The output of <code>python collect_env.py</code></summary>
==============================
        System Info
==============================
OS                           : Debian GNU/Linux 12 (bookworm) (x86_64)
GCC version                  : (Debian 12.2.0-14) 12.2.0
Clang version                : Could not collect
CMake version                : Could not collect
Libc version                 : glibc-2.36

==============================
       PyTorch Info
==============================
PyTorch version              : 2.10.0+cu128
Is debug build               : False
CUDA used to build PyTorch   : 12.8
ROCM used to build PyTorch   : N/A

==============================
      Python Environment
==============================
Python version               : 3.12.13 | packaged by Anaconda, Inc. | (main, Mar 19 2026, 20:20:58) [GCC 14.3.0] (64-bit runtime)
Python platform              : Linux-6.6.87.2-microsoft-standard-WSL2-x86_64-with-glibc2.36

==============================
       CUDA / GPU Info
==============================
Is CUDA available            : True
CUDA runtime version         : Could not collect
CUDA_MODULE_LOADING set to   : 
GPU models and configuration : GPU 0: NVIDIA RTX PRO 6000 Blackwell Workstation Edition
Nvidia driver version        : 591.86
cuDNN version                : Probably one of the following:
/usr/lib/x86_64-linux-gnu/libcudnn.so.9.4.0
/usr/lib/x86_64-linux-gnu/libcudnn_adv.so.9.4.0
/usr/lib/x86_64-linux-gnu/libcudnn_cnn.so.9.4.0
/usr/lib/x86_64-linux-gnu/libcudnn_engines_precompiled.so.9.4.0
/usr/lib/x86_64-linux-gnu/libcudnn_engines_runtime_compiled.so.9.4.0
/usr/lib/x86_64-linux-gnu/libcudnn_graph.so.9.4.0
/usr/lib/x86_64-linux-gnu/libcudnn_heuristic.so.9.4.0
/usr/lib/x86_64-linux-gnu/libcudnn_ops.so.9.4.0
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:                        48 bits physical, 48 bits virtual
Byte Order:                           Little Endian
CPU(s):                               8
On-line CPU(s) list:                  0-7
Vendor ID:                            AuthenticAMD
Model name:                           AMD Ryzen 9 9950X 16-Core Processor
CPU family:                           26
Model:                                68
Thread(s) per core:                   2
Core(s) per socket:                   4
Socket(s):                            1
Stepping:                             0
BogoMIPS:                             8583.71
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 tsc_reliable nonstop_tsc cpuid extd_apicid tsc_known_freq pni pclmulqdq ssse3 fma cx16 sse4_1 sse4_2 movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm cmp_legacy svm cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw topoext perfctr_core ssbd ibrs ibpb stibp vmmcall fsgsbase bmi1 avx2 smep bmi2 erms invpcid avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves avx_vnni avx512_bf16 clzero xsaveerptr arat npt nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold v_vmsave_vmload avx512vbmi umip avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg avx512_vpopcntdq rdpid fsrm avx512_vp2intersect
Virtualization:                       AMD-V
Hypervisor vendor:                    Microsoft
Virtualization type:                  full
L1d cache:                            192 KiB (4 instances)
L1i cache:                            128 KiB (4 instances)
L2 cache:                             4 MiB (4 instances)
L3 cache:                             32 MiB (1 instance)
NUMA node(s):                         1
NUMA node0 CPU(s):                    0-7
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; Retpolines; IBPB conditional; IBRS_FW; STIBP always-on; RSB filling; PBRSB-eIBRS Not affected; BHI Not affected
Vulnerability Srbds:                  Not affected
Vulnerability Tsx async abort:        Not affected

==============================
Versions of relevant libraries
==============================
[pip3] flashinfer-python==0.6.7
[pip3] numpy==2.2.6
[pip3] nvidia-cublas-cu12==12.8.4.1
[pip3] nvidia-cuda-cupti-cu12==12.8.90
[pip3] nvidia-cuda-nvrtc-cu12==12.8.93
[pip3] nvidia-cuda-runtime-cu12==12.8.90
[pip3] nvidia-cudnn-cu12==9.10.2.21
[pip3] nvidia-cudnn-frontend==1.18.0
[pip3] nvidia-cufft-cu12==11.3.3.83
[pip3] nvidia-cufile-cu12==1.13.1.3
[pip3] nvidia-curand-cu12==10.3.9.90
[pip3] nvidia-cusolver-cu12==11.7.3.90
[pip3] nvidia-cusparse-cu12==12.5.8.93
[pip3] nvidia-cusparselt-cu12==0.7.1
[pip3] nvidia-cutlass-dsl==4.4.2
[pip3] nvidia-cutlass-dsl-libs-base==4.4.2
[pip3] nvidia-ml-py==13.595.45
[pip3] nvidia-nccl-cu12==2.27.5
[pip3] nvidia-nvjitlink-cu12==12.8.93
[pip3] nvidia-nvshmem-cu12==3.4.5
[pip3] nvidia-nvtx-cu12==12.8.90
[pip3] pyzmq==27.1.0
[pip3] torch==2.10.0
[pip3] torch_c_dlpack_ext==0.1.5
[pip3] torchaudio==2.10.0
[pip3] torchvision==0.25.0
[pip3] transformers==5.5.0
[pip3] triton==3.6.0
[conda] flashinfer-python         0.6.7                    pypi_0    pypi
[conda] numpy                     2.2.6                    pypi_0    pypi
[conda] nvidia-cublas-cu12        12.8.4.1                 pypi_0    pypi
[conda] nvidia-cuda-cupti-cu12    12.8.90                  pypi_0    pypi
[conda] nvidia-cuda-nvrtc-cu12    12.8.93                  pypi_0    pypi
[conda] nvidia-cuda-runtime-cu12  12.8.90                  pypi_0    pypi
[conda] nvidia-cudnn-cu12         9.10.2.21                pypi_0    pypi
[conda] nvidia-cudnn-frontend     1.18.0                   pypi_0    pypi
[conda] nvidia-cufft-cu12         11.3.3.83                pypi_0    pypi
[conda] nvidia-cufile-cu12        1.13.1.3                 pypi_0    pypi
[conda] nvidia-curand-cu12        10.3.9.90                pypi_0    pypi
[conda] nvidia-cusolver-cu12      11.7.3.90                pypi_0    pypi
[conda] nvidia-cusparse-cu12      12.5.8.93                pypi_0    pypi
[conda] nvidia-cusparselt-cu12    0.7.1                    pypi_0    pypi
[conda] nvidia-cutlass-dsl        4.4.2                    pypi_0    pypi
[conda] nvidia-cutlass-dsl-libs-base 4.4.2                    pypi_0    pypi
[conda] nvidia-ml-py              13.595.45                pypi_0    pypi
[conda] nvidia-nccl-cu12          2.27.5                   pypi_0    pypi
[conda] nvidia-nvjitlink-cu12     12.8.93                  pypi_0    pypi
[conda] nvidia-nvshmem-cu12       3.4.5                    pypi_0    pypi
[conda] nvidia-nvtx-cu12          12.8.90                  pypi_0    pypi
[conda] pyzmq                     27.1.0                   pypi_0    pypi
[conda] torch                     2.10.0                   pypi_0    pypi
[conda] torch-c-dlpack-ext        0.1.5                    pypi_0    pypi
[conda] torchaudio                2.10.0                   pypi_0    pypi
[conda] torchvision               0.25.0                   pypi_0    pypi
[conda] transformers              5.5.0                    pypi_0    pypi
[conda] triton                    3.6.0                    pypi_0    pypi

==============================
         vLLM Info
==============================
ROCM Version                 : Could not collect
vLLM Version                 : 0.1.dev1+gbb39382b2 (git sha: bb39382b2)
vLLM Build Flags:
  CUDA Archs: Not Set; ROCm: Disabled
GPU Topology:
  	GPU0	CPU Affinity	NUMA Affinity	GPU NUMA ID
GPU0	 X 				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

==============================
     Environment Variables
==============================
PYTORCH_NVML_BASED_CUDA_CHECK=1
TORCHINDUCTOR_COMPILE_THREADS=1
TORCHINDUCTOR_CACHE_DIR=/tmp/torchinductor_root
</details>

🐛 Describe the bug

Output from console: (EngineCore pid=47271) Process EngineCore: (EngineCore pid=47271) Traceback (most recent call last): (APIServer pid=47229) INFO: 192.168.1.5:46152 - "POST /v1/audio/transcriptions HTTP/1.1" 500 Internal Server Error (EngineCore pid=47271) File "/opt/miniconda3/envs/vllm_nightly/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap (EngineCore pid=47271) self.run() (EngineCore pid=47271) File "/opt/miniconda3/envs/vllm_nightly/lib/python3.12/multiprocessing/process.py", line 108, in run (EngineCore pid=47271) self._target(*self._args, **self._kwargs) (EngineCore pid=47271) File "/root/Develop/vllm/vllm/v1/engine/core.py", line 1112, in run_engine_core (EngineCore pid=47271) raise e (EngineCore pid=47271) File "/root/Develop/vllm/vllm/v1/engine/core.py", line 1101, in run_engine_core (EngineCore pid=47271) engine_core.run_busy_loop() (EngineCore pid=47271) File "/root/Develop/vllm/vllm/v1/engine/core.py", line 1142, in run_busy_loop (EngineCore pid=47271) self._process_engine_step() (EngineCore pid=47271) File "/root/Develop/vllm/vllm/v1/engine/core.py", line 1181, in _process_engine_step (EngineCore pid=47271) outputs, model_executed = self.step_fn() (EngineCore pid=47271) ^^^^^^^^^^^^^^ (EngineCore pid=47271) File "/root/Develop/vllm/vllm/v1/engine/core.py", line 451, in step_with_batch_queue (EngineCore pid=47271) exec_future = self.model_executor.execute_model( (EngineCore pid=47271) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ (EngineCore pid=47271) File "/root/Develop/vllm/vllm/v1/executor/uniproc_executor.py", line 114, in execute_model (EngineCore pid=47271) output.result() (EngineCore pid=47271) File "/opt/miniconda3/envs/vllm_nightly/lib/python3.12/concurrent/futures/_base.py", line 449, in result (EngineCore pid=47271) return self.__get_result() (EngineCore pid=47271) ^^^^^^^^^^^^^^^^^^^ (EngineCore pid=47271) File "/opt/miniconda3/envs/vllm_nightly/lib/python3.12/concurrent/futures/_base.py", line 401, in __get_result (EngineCore pid=47271) raise self._exception (EngineCore pid=47271) File "/root/Develop/vllm/vllm/v1/executor/uniproc_executor.py", line 84, in collective_rpc (EngineCore pid=47271) result = run_method(self.driver_worker, method, args, kwargs) (EngineCore pid=47271) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ (EngineCore pid=47271) File "/root/Develop/vllm/vllm/v1/serial_utils.py", line 510, in run_method (EngineCore pid=47271) return func(*args, **kwargs) (EngineCore pid=47271) ^^^^^^^^^^^^^^^^^^^^^ (EngineCore pid=47271) File "/root/Develop/vllm/vllm/v1/worker/worker_base.py", line 332, in execute_model (EngineCore pid=47271) return self.worker.execute_model(scheduler_output) (EngineCore pid=47271) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ (EngineCore pid=47271) File "/opt/miniconda3/envs/vllm_nightly/lib/python3.12/site-packages/torch/utils/_contextlib.py", line 124, in decorate_context (EngineCore pid=47271) return func(*args, **kwargs) (EngineCore pid=47271) ^^^^^^^^^^^^^^^^^^^^^ (EngineCore pid=47271) File "/root/Develop/vllm/vllm/v1/worker/gpu_worker.py", line 808, in execute_model (EngineCore pid=47271) output = self.model_runner.execute_model( (EngineCore pid=47271) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ (EngineCore pid=47271) File "/opt/miniconda3/envs/vllm_nightly/lib/python3.12/site-packages/torch/utils/_contextlib.py", line 124, in decorate_context (EngineCore pid=47271) return func(*args, **kwargs) (EngineCore pid=47271) ^^^^^^^^^^^^^^^^^^^^^ (EngineCore pid=47271) File "/root/Develop/vllm/vllm/v1/worker/gpu_model_runner.py", line 3981, in execute_model (EngineCore pid=47271) ) = self._preprocess( (EngineCore pid=47271) ^^^^^^^^^^^^^^^^^ (EngineCore pid=47271) File "/root/Develop/vllm/vllm/v1/worker/gpu_model_runner.py", line 3230, in preprocess (EngineCore pid=47271) self.inputs_embeds.gpu[:num_scheduled_tokens].copy(inputs_embeds_scheduled) (EngineCore pid=47271) RuntimeError: The size of tensor a (40) must match the size of tensor b (39) at non-singleton dimension 0 (APIServer pid=47229) INFO: 192.168.1.5:45648 - "POST /v1/audio/transcriptions HTTP/1.1" 500 Internal Server Error [rank0]:[W407 18:18:15.323640661 ProcessGroupNCCL.cpp:1553] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())

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extent analysis

TL;DR

The issue is likely caused by a mismatch in tensor sizes during the _preprocess step in the gpu_model_runner, and can be fixed by ensuring that the input tensor sizes match the expected sizes.

Guidance

  • Verify that the input tensor sizes are correct and match the expected sizes in the _preprocess step of the gpu_model_runner.
  • Check the code in gpu_model_runner.py, specifically the _preprocess method, to ensure that the tensor sizes are being handled correctly.
  • Review the input data and model configuration to ensure that they are compatible and that the tensor sizes are as expected.
  • Consider adding error handling or logging to help diagnose and fix similar issues in the future.

Example

No specific code example can be provided without more context, but the issue is likely related to this line of code:

self.inputs_embeds.gpu[:num_scheduled_tokens].copy_(inputs_embeds_scheduled)

This line is attempting to copy the inputs_embeds_scheduled tensor into a slice of the inputs_embeds.gpu tensor, but the sizes of the two tensors do not match, resulting in a RuntimeError.

Notes

The issue may be related to the specific input data or model configuration being used, and may require additional debugging or logging to fully diagnose and fix.

Recommendation

Apply a workaround to ensure that the input tensor sizes match the expected sizes, such as adding error checking or resizing the tensors as needed. This may involve modifying the _preprocess method in gpu_model_runner.py to handle tensor size mismatches.

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