vllm - 💡(How to fix) Fix [Bug]: Incompatible dimension when using Mistral Small 4 [1 participants]

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vllm-project/vllm#40260Fetched 2026-04-19 15:04:40
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Error Message

triton.compiler.errors.CompilationError: at 152:12: Apr 18 20:55:50 spark-5500 vllm[8121]: (EngineCore pid=8121) v = (v.to(tl.float32) * vs).to(q.dtype) Apr 18 20:55:50 spark-5500 vllm[8121]: (EngineCore pid=8121) else: Apr 18 20:55:50 spark-5500 vllm[8121]: (EngineCore pid=8121) # MLA uses a single c_kv. Apr 18 20:55:50 spark-5500 vllm[8121]: (EngineCore pid=8121) # loading the same c_kv to interpret it as v is not necessary. Apr 18 20:55:50 spark-5500 vllm[8121]: (EngineCore pid=8121) # transpose the existing c_kv (aka k) for the dot product. Apr 18 20:55:50 spark-5500 vllm[8121]: (EngineCore pid=8121) v = tl.trans(k) Apr 18 20:55:50 spark-5500 vllm[8121]: (EngineCore pid=8121) Apr 18 20:55:50 spark-5500 vllm[8121]: (EngineCore pid=8121) n_e_max = tl.maximum(tl.max(qk, 1), e_max) Apr 18 20:55:50 spark-5500 vllm[8121]: (EngineCore pid=8121) re_scale = tl.exp(e_max - n_e_max) Apr 18 20:55:50 spark-5500 vllm[8121]: (EngineCore pid=8121) p = tl.exp(qk - n_e_max[:, None]) Apr 18 20:55:50 spark-5500 vllm[8121]: (EngineCore pid=8121) acc *= re_scale[:, None] Apr 18 20:55:50 spark-5500 vllm[8121]: (EngineCore pid=8121) acc += tl.dot(p.to(v.dtype), v) Apr 18 20:55:50 spark-5500 vllm[8121]: (EngineCore pid=8121) ^ Apr 18 20:55:50 spark-5500 vllm[8121]: (EngineCore pid=8121) ValueError('Cannot make_shape_compatible: incompatible dimensions at index 1: 256 and 512')

Fix Action

Fix / Workaround

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

Architecture: aarch64 CPU op-mode(s): 64-bit Byte Order: Little Endian CPU(s): 20 On-line CPU(s) list: 0-19 Vendor ID: ARM Model name: Cortex-X925 Model: 1 Thread(s) per core: 1 Core(s) per socket: 10 Socket(s): 1 Stepping: r0p1 Frequency boost: disabled CPU(s) scaling MHz: 100% CPU max MHz: 3900.0000 CPU min MHz: 1378.0000 BogoMIPS: 2000.00 Flags: fp asimd evtstrm aes pmull sha1 sha2 crc32 atomics fphp asimdhp cpuid asimdrdm jscvt fcma lrcpc dcpop sha3 sm3 sm4 asimddp sha512 sve asimdfhm dit uscat ilrcpc flagm sb paca pacg dcpodp sve2 sveaes svepmull svebitperm svesha3 svesm4 flagm2 frint svei8mm svebf16 i8mm bf16 dgh bti ecv afp wfxt Model name: Cortex-A725 Model: 1 Thread(s) per core: 1 Core(s) per socket: 10 Socket(s): 1 Stepping: r0p1 CPU(s) scaling MHz: 100% CPU max MHz: 2808.0000 CPU min MHz: 338.0000 BogoMIPS: 2000.00 Flags: fp asimd evtstrm aes pmull sha1 sha2 crc32 atomics fphp asimdhp cpuid asimdrdm jscvt fcma lrcpc dcpop sha3 sm3 sm4 asimddp sha512 sve asimdfhm dit uscat ilrcpc flagm sb paca pacg dcpodp sve2 sveaes svepmull svebitperm svesha3 svesm4 flagm2 frint svei8mm svebf16 i8mm bf16 dgh bti ecv afp wfxt L1d cache: 1.3 MiB (20 instances) L1i cache: 1.3 MiB (20 instances) L2 cache: 25 MiB (20 instances) L3 cache: 24 MiB (2 instances) NUMA node(s): 1 NUMA node0 CPU(s): 0-19 Vulnerability Gather data sampling: Not affected Vulnerability Ghostwrite: Not affected Vulnerability Indirect target selection: 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 Old microcode: 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; __user pointer sanitization Vulnerability Spectre v2: Mitigation; CSV2, BHB Vulnerability Srbds: Not affected Vulnerability Tsa: Not affected Vulnerability Tsx async abort: Not affected Vulnerability Vmscape: Not affected

Code Example

Collecting environment information...
==============================
        System Info
==============================
OS                           : Ubuntu 24.04.4 LTS (aarch64)
GCC version                  : (Ubuntu 13.3.0-6ubuntu2~24.04.1) 13.3.0
Clang version                : 18.1.3 (1ubuntu1)
CMake version                : version 3.28.3
Libc version                 : glibc-2.39

==============================
       PyTorch Info
==============================
PyTorch version              : 2.11.0+cu130
Is debug build               : False
CUDA used to build PyTorch   : 13.0
ROCM used to build PyTorch   : N/A
XPU used to build PyTorch    : N/A

==============================
      Python Environment
==============================
Python version               : 3.12.13 (main, Mar 24 2026, 22:47:08) [Clang 22.1.1 ] (64-bit runtime)
Python platform              : Linux-6.17.0-1014-nvidia-aarch64-with-glibc2.39
    
==============================
       CUDA / GPU Info
==============================
Is CUDA available            : True
CUDA runtime version         : 13.0.88
CUDA_MODULE_LOADING set to   : 
GPU models and configuration : GPU 0: NVIDIA GB10
Nvidia driver version        : 580.142
cuDNN version                : Could not collect
HIP runtime version          : N/A
MIOpen runtime version       : N/A
Is XNNPACK available         : True

==============================
          CPU Info
==============================
Architecture:                            aarch64
CPU op-mode(s):                          64-bit
Byte Order:                              Little Endian
CPU(s):                                  20
On-line CPU(s) list:                     0-19
Vendor ID:                               ARM
Model name:                              Cortex-X925
Model:                                   1
Thread(s) per core:                      1
Core(s) per socket:                      10
Socket(s):                               1
Stepping:                                r0p1
Frequency boost:                         disabled
CPU(s) scaling MHz:                      100%
CPU max MHz:                             3900.0000
CPU min MHz:                             1378.0000
BogoMIPS:                                2000.00
Flags:                                   fp asimd evtstrm aes pmull sha1 sha2 crc32 atomics fphp asimdhp cpuid asimdrdm jscvt fcma lrcpc dcpop sha3 sm3 sm4 asimddp sha512 sve asimdfhm dit uscat ilrcpc flagm sb paca pacg dcpodp sve2 sveaes svepmull svebitperm svesha3 svesm4 flagm2 frint svei8mm svebf16 i8mm bf16 dgh bti ecv afp wfxt
Model name:                              Cortex-A725
Model:                                   1
Thread(s) per core:                      1
Core(s) per socket:                      10
Socket(s):                               1
Stepping:                                r0p1
CPU(s) scaling MHz:                      100%
CPU max MHz:                             2808.0000
CPU min MHz:                             338.0000
BogoMIPS:                                2000.00
Flags:                                   fp asimd evtstrm aes pmull sha1 sha2 crc32 atomics fphp asimdhp cpuid asimdrdm jscvt fcma lrcpc dcpop sha3 sm3 sm4 asimddp sha512 sve asimdfhm dit uscat ilrcpc flagm sb paca pacg dcpodp sve2 sveaes svepmull svebitperm svesha3 svesm4 flagm2 frint svei8mm svebf16 i8mm bf16 dgh bti ecv afp wfxt
L1d cache:                               1.3 MiB (20 instances)
L1i cache:                               1.3 MiB (20 instances)
L2 cache:                                25 MiB (20 instances)
L3 cache:                                24 MiB (2 instances)
NUMA node(s):                            1
NUMA node0 CPU(s):                       0-19
Vulnerability Gather data sampling:      Not affected
Vulnerability Ghostwrite:                Not affected
Vulnerability Indirect target selection: 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 Old microcode:             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; __user pointer sanitization
Vulnerability Spectre v2:                Mitigation; CSV2, BHB
Vulnerability Srbds:                     Not affected
Vulnerability Tsa:                       Not affected
Vulnerability Tsx async abort:           Not affected
Vulnerability Vmscape:                   Not affected

==============================
Versions of relevant libraries
==============================
[pip3] flashinfer-python==0.6.7
[pip3] numpy==2.2.6
[pip3] nvidia-cublas==13.1.0.3
[pip3] nvidia-cuda-cupti==13.0.85
[pip3] nvidia-cuda-nvrtc==13.0.88
[pip3] nvidia-cuda-runtime==13.0.96
[pip3] nvidia-cudnn-cu13==9.19.0.56
[pip3] nvidia-cudnn-frontend==1.18.0
[pip3] nvidia-cufft==12.0.0.61
[pip3] nvidia-cufile==1.15.1.6
[pip3] nvidia-curand==10.4.0.35
[pip3] nvidia-cusolver==12.0.4.66
[pip3] nvidia-cusparse==12.6.3.3
[pip3] nvidia-cusparselt-cu13==0.8.0
[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-cu13==2.28.9
[pip3] nvidia-nvjitlink==13.0.88
[pip3] nvidia-nvshmem-cu13==3.4.5
[pip3] nvidia-nvtx==13.0.85
[pip3] pyzmq==27.1.0
[pip3] torch==2.11.0+cu130
[pip3] torch_c_dlpack_ext==0.1.5
[pip3] torchaudio==2.11.0+cu130
[pip3] torchvision==0.26.0+cu130
[pip3] transformers==5.5.4
[pip3] triton==3.6.0
[conda] Could not collect

==============================
         vLLM Info
==============================
ROCM Version                 : Could not collect
vLLM Version                 : 
vLLM Build Flags:
  CUDA Archs: Not Set; ROCm: Disabled; XPU: Disabled
GPU Topology:
  	GPU0	CPU Affinity	NUMA Affinity	GPU NUMA ID
GPU0	 X 	0-19	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

==============================
     Environment Variables
==============================

---

Environment="LD_LIBRARY_PATH=/usr/local/cuda/lib64:/usr/local/cuda/extras/CUPTI/lib64"
Environment="TORCH_CUDA_ARCH_LIST=12.1a"
Environment="TRITON_PTXAS_PATH=/usr/local/cuda/bin/ptxas"
Environment="FLASHINFER_JIT_LOG_LEVEL=ERROR"
Environment="TRANSFORMERS_VERBOSITY=error"
Environment="VLLM_SKIP_P2P_CHECK=1"
ExecStart=/home/malcolm/vllm-mistral2/.venv/bin/vllm serve mistralai/Mistral-Small-4-119B-2603-NVFP4 \
    --max-model-len 262144 \
    --tensor-parallel-size 1 \
    --attention-backend TRITON_MLA \
    --tool-call-parser mistral \
    --enable-auto-tool-choice \
    --reasoning-parser mistral \
    --max-num-batched-tokens 16384 \
    --max-num-seqs 128 \
    --gpu-memory-utilization 0.8 \
    --no-enable-flashinfer-autotune \
    --cudagraph-capture-sizes 1 2 4 8 16 32 64 128 256 \
    --max-cudagraph-capture-size 256

---

triton.compiler.errors.CompilationError: at 152:12:
Apr 18 20:55:50 spark-5500 vllm[8121]: (EngineCore pid=8121)                     v = (v.to(tl.float32) * vs).to(q.dtype)
Apr 18 20:55:50 spark-5500 vllm[8121]: (EngineCore pid=8121)             else:
Apr 18 20:55:50 spark-5500 vllm[8121]: (EngineCore pid=8121)                 # MLA uses a single c_kv.
Apr 18 20:55:50 spark-5500 vllm[8121]: (EngineCore pid=8121)                 # loading the same c_kv to interpret it as v is not necessary.
Apr 18 20:55:50 spark-5500 vllm[8121]: (EngineCore pid=8121)                 # transpose the existing c_kv (aka k) for the dot product.
Apr 18 20:55:50 spark-5500 vllm[8121]: (EngineCore pid=8121)                 v = tl.trans(k)
Apr 18 20:55:50 spark-5500 vllm[8121]: (EngineCore pid=8121)
Apr 18 20:55:50 spark-5500 vllm[8121]: (EngineCore pid=8121)             n_e_max = tl.maximum(tl.max(qk, 1), e_max)
Apr 18 20:55:50 spark-5500 vllm[8121]: (EngineCore pid=8121)             re_scale = tl.exp(e_max - n_e_max)
Apr 18 20:55:50 spark-5500 vllm[8121]: (EngineCore pid=8121)             p = tl.exp(qk - n_e_max[:, None])
Apr 18 20:55:50 spark-5500 vllm[8121]: (EngineCore pid=8121)             acc *= re_scale[:, None]
Apr 18 20:55:50 spark-5500 vllm[8121]: (EngineCore pid=8121)             acc += tl.dot(p.to(v.dtype), v)
Apr 18 20:55:50 spark-5500 vllm[8121]: (EngineCore pid=8121)             ^
Apr 18 20:55:50 spark-5500 vllm[8121]: (EngineCore pid=8121) ValueError('Cannot make_shape_compatible: incompatible dimensions at index 1: 256 and 512')

---

curl http://localhost:8000/v1/models
{"object":"list","data":[{"id":"mistralai/Mistral-Small-4-119B-2603-NVFP4","object":"model","created":1776538536,"owned_by":"vllm","root":"mistralai/Mistral-Small-4-119B-2603-NVFP4","parent":null,"max_model_len":262144,"permission":[{"id":"modelperm-891703d4d5b462cb","object":"model_permission","created":1776538536,"allow_create_engine":false,"allow_sampling":true,"allow_logprobs":true,"allow_search_indices":false,"allow_view":true,"allow_fine_tuning":false,"organizat...
RAW_BUFFERClick to expand / collapse

Your current environment

I'm working of this commit of vllm: 6b2b7bd0ebd43ef756632d2142ce974929f05d8f

Collecting environment information...
==============================
        System Info
==============================
OS                           : Ubuntu 24.04.4 LTS (aarch64)
GCC version                  : (Ubuntu 13.3.0-6ubuntu2~24.04.1) 13.3.0
Clang version                : 18.1.3 (1ubuntu1)
CMake version                : version 3.28.3
Libc version                 : glibc-2.39

==============================
       PyTorch Info
==============================
PyTorch version              : 2.11.0+cu130
Is debug build               : False
CUDA used to build PyTorch   : 13.0
ROCM used to build PyTorch   : N/A
XPU used to build PyTorch    : N/A

==============================
      Python Environment
==============================
Python version               : 3.12.13 (main, Mar 24 2026, 22:47:08) [Clang 22.1.1 ] (64-bit runtime)
Python platform              : Linux-6.17.0-1014-nvidia-aarch64-with-glibc2.39
    
==============================
       CUDA / GPU Info
==============================
Is CUDA available            : True
CUDA runtime version         : 13.0.88
CUDA_MODULE_LOADING set to   : 
GPU models and configuration : GPU 0: NVIDIA GB10
Nvidia driver version        : 580.142
cuDNN version                : Could not collect
HIP runtime version          : N/A
MIOpen runtime version       : N/A
Is XNNPACK available         : True

==============================
          CPU Info
==============================
Architecture:                            aarch64
CPU op-mode(s):                          64-bit
Byte Order:                              Little Endian
CPU(s):                                  20
On-line CPU(s) list:                     0-19
Vendor ID:                               ARM
Model name:                              Cortex-X925
Model:                                   1
Thread(s) per core:                      1
Core(s) per socket:                      10
Socket(s):                               1
Stepping:                                r0p1
Frequency boost:                         disabled
CPU(s) scaling MHz:                      100%
CPU max MHz:                             3900.0000
CPU min MHz:                             1378.0000
BogoMIPS:                                2000.00
Flags:                                   fp asimd evtstrm aes pmull sha1 sha2 crc32 atomics fphp asimdhp cpuid asimdrdm jscvt fcma lrcpc dcpop sha3 sm3 sm4 asimddp sha512 sve asimdfhm dit uscat ilrcpc flagm sb paca pacg dcpodp sve2 sveaes svepmull svebitperm svesha3 svesm4 flagm2 frint svei8mm svebf16 i8mm bf16 dgh bti ecv afp wfxt
Model name:                              Cortex-A725
Model:                                   1
Thread(s) per core:                      1
Core(s) per socket:                      10
Socket(s):                               1
Stepping:                                r0p1
CPU(s) scaling MHz:                      100%
CPU max MHz:                             2808.0000
CPU min MHz:                             338.0000
BogoMIPS:                                2000.00
Flags:                                   fp asimd evtstrm aes pmull sha1 sha2 crc32 atomics fphp asimdhp cpuid asimdrdm jscvt fcma lrcpc dcpop sha3 sm3 sm4 asimddp sha512 sve asimdfhm dit uscat ilrcpc flagm sb paca pacg dcpodp sve2 sveaes svepmull svebitperm svesha3 svesm4 flagm2 frint svei8mm svebf16 i8mm bf16 dgh bti ecv afp wfxt
L1d cache:                               1.3 MiB (20 instances)
L1i cache:                               1.3 MiB (20 instances)
L2 cache:                                25 MiB (20 instances)
L3 cache:                                24 MiB (2 instances)
NUMA node(s):                            1
NUMA node0 CPU(s):                       0-19
Vulnerability Gather data sampling:      Not affected
Vulnerability Ghostwrite:                Not affected
Vulnerability Indirect target selection: 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 Old microcode:             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; __user pointer sanitization
Vulnerability Spectre v2:                Mitigation; CSV2, BHB
Vulnerability Srbds:                     Not affected
Vulnerability Tsa:                       Not affected
Vulnerability Tsx async abort:           Not affected
Vulnerability Vmscape:                   Not affected

==============================
Versions of relevant libraries
==============================
[pip3] flashinfer-python==0.6.7
[pip3] numpy==2.2.6
[pip3] nvidia-cublas==13.1.0.3
[pip3] nvidia-cuda-cupti==13.0.85
[pip3] nvidia-cuda-nvrtc==13.0.88
[pip3] nvidia-cuda-runtime==13.0.96
[pip3] nvidia-cudnn-cu13==9.19.0.56
[pip3] nvidia-cudnn-frontend==1.18.0
[pip3] nvidia-cufft==12.0.0.61
[pip3] nvidia-cufile==1.15.1.6
[pip3] nvidia-curand==10.4.0.35
[pip3] nvidia-cusolver==12.0.4.66
[pip3] nvidia-cusparse==12.6.3.3
[pip3] nvidia-cusparselt-cu13==0.8.0
[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-cu13==2.28.9
[pip3] nvidia-nvjitlink==13.0.88
[pip3] nvidia-nvshmem-cu13==3.4.5
[pip3] nvidia-nvtx==13.0.85
[pip3] pyzmq==27.1.0
[pip3] torch==2.11.0+cu130
[pip3] torch_c_dlpack_ext==0.1.5
[pip3] torchaudio==2.11.0+cu130
[pip3] torchvision==0.26.0+cu130
[pip3] transformers==5.5.4
[pip3] triton==3.6.0
[conda] Could not collect

==============================
         vLLM Info
==============================
ROCM Version                 : Could not collect
vLLM Version                 : 
vLLM Build Flags:
  CUDA Archs: Not Set; ROCm: Disabled; XPU: Disabled
GPU Topology:
  	GPU0	CPU Affinity	NUMA Affinity	GPU NUMA ID
GPU0	 X 	0-19	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

==============================
     Environment Variables
==============================

🐛 Describe the bug

I load the mistralai/Mistral-Small-4-119B-2603-NVFP4 using this serve command (systemd service):

Environment="LD_LIBRARY_PATH=/usr/local/cuda/lib64:/usr/local/cuda/extras/CUPTI/lib64"
Environment="TORCH_CUDA_ARCH_LIST=12.1a"
Environment="TRITON_PTXAS_PATH=/usr/local/cuda/bin/ptxas"
Environment="FLASHINFER_JIT_LOG_LEVEL=ERROR"
Environment="TRANSFORMERS_VERBOSITY=error"
Environment="VLLM_SKIP_P2P_CHECK=1"
ExecStart=/home/malcolm/vllm-mistral2/.venv/bin/vllm serve mistralai/Mistral-Small-4-119B-2603-NVFP4 \
    --max-model-len 262144 \
    --tensor-parallel-size 1 \
    --attention-backend TRITON_MLA \
    --tool-call-parser mistral \
    --enable-auto-tool-choice \
    --reasoning-parser mistral \
    --max-num-batched-tokens 16384 \
    --max-num-seqs 128 \
    --gpu-memory-utilization 0.8 \
    --no-enable-flashinfer-autotune \
    --cudagraph-capture-sizes 1 2 4 8 16 32 64 128 256 \
    --max-cudagraph-capture-size 256

and I have a triton error whenever I query the model.

 triton.compiler.errors.CompilationError: at 152:12:
Apr 18 20:55:50 spark-5500 vllm[8121]: (EngineCore pid=8121)                     v = (v.to(tl.float32) * vs).to(q.dtype)
Apr 18 20:55:50 spark-5500 vllm[8121]: (EngineCore pid=8121)             else:
Apr 18 20:55:50 spark-5500 vllm[8121]: (EngineCore pid=8121)                 # MLA uses a single c_kv.
Apr 18 20:55:50 spark-5500 vllm[8121]: (EngineCore pid=8121)                 # loading the same c_kv to interpret it as v is not necessary.
Apr 18 20:55:50 spark-5500 vllm[8121]: (EngineCore pid=8121)                 # transpose the existing c_kv (aka k) for the dot product.
Apr 18 20:55:50 spark-5500 vllm[8121]: (EngineCore pid=8121)                 v = tl.trans(k)
Apr 18 20:55:50 spark-5500 vllm[8121]: (EngineCore pid=8121)
Apr 18 20:55:50 spark-5500 vllm[8121]: (EngineCore pid=8121)             n_e_max = tl.maximum(tl.max(qk, 1), e_max)
Apr 18 20:55:50 spark-5500 vllm[8121]: (EngineCore pid=8121)             re_scale = tl.exp(e_max - n_e_max)
Apr 18 20:55:50 spark-5500 vllm[8121]: (EngineCore pid=8121)             p = tl.exp(qk - n_e_max[:, None])
Apr 18 20:55:50 spark-5500 vllm[8121]: (EngineCore pid=8121)             acc *= re_scale[:, None]
Apr 18 20:55:50 spark-5500 vllm[8121]: (EngineCore pid=8121)             acc += tl.dot(p.to(v.dtype), v)
Apr 18 20:55:50 spark-5500 vllm[8121]: (EngineCore pid=8121)             ^
Apr 18 20:55:50 spark-5500 vllm[8121]: (EngineCore pid=8121) ValueError('Cannot make_shape_compatible: incompatible dimensions at index 1: 256 and 512')

The model loads fine:

 curl http://localhost:8000/v1/models
{"object":"list","data":[{"id":"mistralai/Mistral-Small-4-119B-2603-NVFP4","object":"model","created":1776538536,"owned_by":"vllm","root":"mistralai/Mistral-Small-4-119B-2603-NVFP4","parent":null,"max_model_len":262144,"permission":[{"id":"modelperm-891703d4d5b462cb","object":"model_permission","created":1776538536,"allow_create_engine":false,"allow_sampling":true,"allow_logprobs":true,"allow_search_indices":false,"allow_view":true,"allow_fine_tuning":false,"organizat...

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extent analysis

TL;DR

The issue is likely due to a dimension mismatch in the model's attention mechanism, causing a ValueError when querying the model, and can be resolved by adjusting the model configuration or the input data.

Guidance

  • Verify that the model's configuration, particularly the --max-model-len and --max-num-batched-tokens parameters, matches the expected input data dimensions.
  • Check the model's attention mechanism implementation to ensure that it can handle the specified input dimensions.
  • Consider adjusting the --tensor-parallel-size parameter to ensure that the model's parallelization strategy is compatible with the input data dimensions.
  • Review the model's documentation and implementation to ensure that the TRITON_MLA attention backend is correctly configured and compatible with the model's architecture.

Example

No specific code example can be provided without further information about the model's implementation and configuration. However, the error message suggests that the issue is related to the tl.dot operation, which may require adjusting the input data dimensions or the model's configuration.

Notes

The issue may be specific to the mistralai/Mistral-Small-4-119B-2603-NVFP4 model and the TRITON_MLA attention backend. Further investigation and debugging may be required to determine the root cause of the issue.

Recommendation

Apply a workaround by adjusting the model configuration or input data dimensions to match the expected dimensions, and verify that the issue is resolved. If the issue persists, consider upgrading to a newer version of the model or the TRITON_MLA attention backend, if available.

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