[Bug]: The `prompt_tokens_details` in `usage` is always `None`.

[chaunceyjiang]

Your current environment

The output of python collect_env.py

INFO 05-13 09:34:29 [__init__.py:248] Automatically detected platform cuda.
Collecting environment information...
PyTorch version: 2.7.0+cu126
Is debug build: False
CUDA used to build PyTorch: 12.6
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: Could not collect
Libc version: glibc-2.35

Python version: 3.12.7 | packaged by Anaconda, Inc. | (main, Oct  4 2024, 13:27:36) [GCC 11.2.0] (64-bit runtime)
Python platform: Linux-5.15.0-134-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 A800 80GB PCIe
Nvidia driver version: 550.127.08
cuDNN version: Could not collect
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True

CPU:
架构：                                x86_64
CPU 运行模式：                        32-bit, 64-bit
Address sizes:                        45 bits physical, 48 bits virtual
字节序：                              Little Endian
CPU:                                  32
在线 CPU 列表：                       0-31
厂商 ID：                             GenuineIntel
型号名称：                            Intel(R) Xeon(R) Platinum 8358 CPU @ 2.60GHz
CPU 系列：                            6
型号：                                106
每个核的线程数：                      1
每个座的核数：                        32
座：                                  1
步进：                                6
BogoMIPS：                            5199.99
标记：                                fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss ht syscall nx pdpe1gb rdtscp lm constant_tsc arch_perfmon rep_good nopl xtopology tsc_reliable nonstop_tsc cpuid tsc_known_freq pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch invpcid_single ssbd ibrs ibpb stibp ibrs_enhanced fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves wbnoinvd arat avx512vbmi umip pku ospke avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg avx512_vpopcntdq rdpid fsrm md_clear flush_l1d arch_capabilities
L1d 缓存：                            1.5 MiB (32 instances)
L1i 缓存：                            1 MiB (32 instances)
L2 缓存：                             40 MiB (32 instances)
L3 缓存：                             48 MiB (1 instance)
NUMA 节点：                           1
NUMA 节点0 CPU：                      0-31
Vulnerability Gather data sampling:   Vulnerable: No microcode
Vulnerability Itlb multihit:          KVM: Mitigation: VMX unsupported
Vulnerability L1tf:                   Not affected
Vulnerability Mds:                    Not affected
Vulnerability Meltdown:               Not affected
Vulnerability Mmio stale data:        Vulnerable: Clear CPU buffers attempted, no microcode; SMT disabled
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 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 SW loop, KVM SW loop
Vulnerability Srbds:                  Not affected
Vulnerability Tsx async abort:        Not affected

Versions of relevant libraries:
[pip3] flake8==7.0.0
[pip3] mypy==1.11.2
[pip3] mypy-extensions==1.0.0
[pip3] numpy==1.26.4
[pip3] numpydoc==1.7.0
[pip3] nvidia-cublas-cu12==12.6.4.1
[pip3] nvidia-cuda-cupti-cu12==12.6.80
[pip3] nvidia-cuda-nvrtc-cu12==12.6.77
[pip3] nvidia-cuda-runtime-cu12==12.6.77
[pip3] nvidia-cudnn-cu12==9.5.1.17
[pip3] nvidia-cufft-cu12==11.3.0.4
[pip3] nvidia-cufile-cu12==1.11.1.6
[pip3] nvidia-curand-cu12==10.3.7.77
[pip3] nvidia-cusolver-cu12==11.7.1.2
[pip3] nvidia-cusparse-cu12==12.5.4.2
[pip3] nvidia-cusparselt-cu12==0.6.3
[pip3] nvidia-nccl-cu12==2.26.2
[pip3] nvidia-nvjitlink-cu12==12.6.85
[pip3] nvidia-nvtx-cu12==12.6.77
[pip3] pyzmq==25.1.2
[pip3] sentence-transformers==3.2.1
[pip3] torch==2.7.0
[pip3] torchac_cuda==0.2.5
[pip3] torchaudio==2.7.0
[pip3] torchvision==0.22.0
[pip3] transformers==4.51.1
[pip3] transformers-stream-generator==0.0.5
[pip3] triton==3.3.0
[pip3] tritonclient==2.51.0
[pip3] vector-quantize-pytorch==1.21.2
[conda] _anaconda_depends         2024.10             py312_mkl_0  
[conda] blas                      1.0                         mkl  
[conda] mkl                       2023.1.0         h213fc3f_46344  
[conda] mkl-service               2.4.0           py312h5eee18b_1  
[conda] mkl_fft                   1.3.10          py312h5eee18b_0  
[conda] mkl_random                1.2.7           py312h526ad5a_0  
[conda] numpy                     1.26.4          py312hc5e2394_0  
[conda] numpy-base                1.26.4          py312h0da6c21_0  
[conda] numpydoc                  1.7.0           py312h06a4308_0  
[conda] nvidia-cublas-cu12        12.6.4.1                 pypi_0    pypi
[conda] nvidia-cuda-cupti-cu12    12.6.80                  pypi_0    pypi
[conda] nvidia-cuda-nvrtc-cu12    12.6.77                  pypi_0    pypi
[conda] nvidia-cuda-runtime-cu12  12.6.77                  pypi_0    pypi
[conda] nvidia-cudnn-cu12         9.5.1.17                 pypi_0    pypi
[conda] nvidia-cufft-cu12         11.3.0.4                 pypi_0    pypi
[conda] nvidia-cufile-cu12        1.11.1.6                 pypi_0    pypi
[conda] nvidia-curand-cu12        10.3.7.77                pypi_0    pypi
[conda] nvidia-cusolver-cu12      11.7.1.2                 pypi_0    pypi
[conda] nvidia-cusparse-cu12      12.5.4.2                 pypi_0    pypi
[conda] nvidia-cusparselt-cu12    0.6.3                    pypi_0    pypi
[conda] nvidia-nccl-cu12          2.26.2                   pypi_0    pypi
[conda] nvidia-nvjitlink-cu12     12.6.85                  pypi_0    pypi
[conda] nvidia-nvtx-cu12          12.6.77                  pypi_0    pypi
[conda] pyzmq                     25.1.2          py312h6a678d5_0  
[conda] sentence-transformers     3.2.1                    pypi_0    pypi
[conda] torch                     2.7.0                    pypi_0    pypi
[conda] torchac-cuda              0.2.5                    pypi_0    pypi
[conda] torchaudio                2.7.0                    pypi_0    pypi
[conda] torchvision               0.22.0                   pypi_0    pypi
[conda] transformers              4.51.1                   pypi_0    pypi
[conda] transformers-stream-generator 0.0.5                    pypi_0    pypi
[conda] triton                    3.3.0                    pypi_0    pypi
[conda] tritonclient              2.51.0                   pypi_0    pypi
[conda] vector-quantize-pytorch   1.21.2                   pypi_0    pypi
ROCM Version: Could not collect
Neuron SDK Version: N/A
vLLM Version: 0.8.5.dev539+ga944f8ede (git sha: a944f8ede)
vLLM Build Flags:
CUDA Archs: Not Set; ROCm: Disabled; Neuron: Disabled
GPU Topology:
        GPU0    CPU Affinity    NUMA Affinity   GPU NUMA ID
GPU0     X      0-31    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

CUDA_TASK_PRIORITY=
CUDA_DEVICE_SM_LIMIT=0
LD_LIBRARY_PATH=/usr/local/nvidia/lib:/usr/local/nvidia/lib64
CUDA_VERSION=12.2.2
NVIDIA_REQUIRE_CUDA=cuda>=12.2 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
NVIDIA_DRIVER_CAPABILITIES=compute,utility
NVIDIA_PRODUCT_NAME=CUDA
CUDA_OVERSUBSCRIBE=true
CUDA_DEVICE_MEMORY_SHARED_CACHE=/usr/local/vgpu/2970d4c2-0d8a-4f49-903d-4cb2c59ca643.cache
CUDA_DEVICE_MEMORY_LIMIT_0=1000m
NVIDIA_VISIBLE_DEVICES=GPU-95aa552b-d1ea-39a2-62ae-e0e44fc85aaf
NCCL_VERSION=2.19.3-1
MKL_THREADING_LAYER=GNU
NCCL_CUMEM_ENABLE=0
PYTORCH_NVML_BASED_CUDA_CHECK=1
TORCHINDUCTOR_COMPILE_THREADS=1
CUDA_MODULE_LOADING=LAZY

🐛 Describe the bug

The prompt_tokens_details in usage is always None.

I'm using the code from the master branch, commit: ee5be834e7dcdea02512cffc125add6961f71b82.

vllm serve Qwen/Qwen3-8B --reasoning-parser qwen3  --enable-auto-tool-choice --tool-call-parser hermes --enable-prompt-tokens-details

#  curl -s -X POST http://localhost:8000/v1/chat/completions \
       -H "Content-Type: application/json" \
       -d '{ 
             "messages": [{"role": "user", "content": "Hello, vLLM!"}],
             "max_tokens": 240
           }' |jq

{
  "id": "chatcmpl-c47b4685b8524e778c3da69c27cc32fb",
  "object": "chat.completion",
  "created": 1747129556,
  "model": "Qwen/Qwen3-8B",
  "choices": [
    {
      "index": 0,
      "message": {
        "role": "assistant",
        "reasoning_content": "\nOkay, the user greeted me with \"Hello, vLLM!\" which is a bit confusing because I'm Qwen. I need to clarify that I'm not vLLM. I should start by politely pointing out the mistake. Then, I should introduce myself as Qwen, explaining my capabilities. It's important to be friendly and invite them to ask any questions. I should keep the tone positive and helpful. Let me make sure the response is clear and not too technical. Also, check for any typos or errors in the message.\n",
        "content": "\n\nHello! I'm Qwen, a large language model developed by Alibaba Cloud. I'm not vLLM, but I'm here to help with any questions or tasks you might have. How can I assist you today? 😊",
        "tool_calls": []
      },
      "logprobs": null,
      "finish_reason": "stop",
      "stop_reason": null
    }
  ],
  "usage": {
    "prompt_tokens": 14,
    "total_tokens": 176,
    "completion_tokens": 162,
    "prompt_tokens_details": null          # Here
  },
  "prompt_logprobs": null,
  "kv_transfer_params": null
}

Before submitting a new issue...

• Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the documentation page, which can answer lots of frequently asked questions.

[princepride]

#16162 It seems this bug has existed since vLLM V1

[princepride]

Perhaps you should try V0: export VLLM_USE_V1=0

[chaunceyjiang]

#16162 It seems this bug has existed since vLLM V1

Yes. I‘m trying to fix this issue.