vllm - ✅(Solved) Fix [Bug]: glm 4.7 fp8 crashes (Worker_TP3 pid=457501) ERROR 03-27 17:11:15 [multiproc_executor.py:852] AttributeError: '_OpNamespace' '_C' object has no attribute 'per_token_group_fp8_quant' [1 pull requests, 1 participants]

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vllm-project/vllm#38376Fetched 2026-04-08 01:41:47
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Error Message

(Worker_TP3 pid=457501) ERROR 03-27 17:11:15 [multiproc_executor.py:852] AttributeError: '_OpNamespace' '_C' object has no attribute 'per_token_group_fp8_quant'

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): 32 On-line CPU(s) list: 0-31 Vendor ID: AuthenticAMD Model name: AMD Ryzen Threadripper PRO 9955WX 16-Cores CPU family: 26 Model: 8 Thread(s) per core: 2 Core(s) per socket: 16 Socket(s): 1 Stepping: 1 CPU(s) scaling MHz: 64% CPU max MHz: 5449.0000 CPU min MHz: 400.0000 BogoMIPS: 9000.06 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 amd_lbr_v2 nopl nonstop_tsc cpuid extd_apicid aperfmperf rapl pni pclmulqdq monitor ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand lahf_lm cmp_legacy svm extapic cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw ibs skinit wdt tce topoext perfctr_core perfctr_nb bpext perfctr_llc mwaitx cpb cat_l3 cdp_l3 hw_pstate ssbd mba perfmon_v2 ibrs ibpb stibp ibrs_enhanced vmmcall fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid cqm rdt_a avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local user_shstk avx_vnni avx512_bf16 clzero irperf xsaveerptr rdpru wbnoinvd amd_ppin cppc amd_ibpb_ret arat npt lbrv svm_lock nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold avic v_vmsave_vmload vgif x2avic v_spec_ctrl vnmi avx512vbmi umip pku ospke avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg avx512_vpopcntdq la57 rdpid bus_lock_detect movdiri movdir64b overflow_recov succor smca fsrm avx512_vp2intersect flush_l1d debug_swap Virtualization: AMD-V L1d cache: 768 KiB (16 instances) L1i cache: 512 KiB (16 instances) L2 cache: 16 MiB (16 instances) L3 cache: 64 MiB (2 instances) NUMA node(s): 1 NUMA node0 CPU(s): 0-31 Vulnerability Gather data sampling: Not affected Vulnerability Itlb multihit: Not affected Vulnerability L1tf: Not affected Vulnerability Mds: Not affected Vulnerability Meltdown: Not affected Vulnerability Mmio stale data: Not affected Vulnerability Reg file data sampling: Not affected Vulnerability Retbleed: Not affected Vulnerability Spec rstack overflow: Not affected Vulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl Vulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization Vulnerability Spectre v2: Mitigation; Enhanced / Automatic IBRS; IBPB conditional; STIBP always-on; RSB filling; PBRSB-eIBRS Not affected; BHI Not affected Vulnerability Srbds: Not affected Vulnerability Tsx async abort: Not affected Vulnerability Vmscape: Not affected

PR fix notes

PR #38377: [Bugfix]: fix blind access to per_token_group_fp8_quant causing GLM 4.7 crash on RTX 6000 pro

Description (problem / solution / changelog)

Purpose

Fix https://github.com/vllm-project/vllm/issues/38376

Test Plan

Apply patch and load GLM 4.7 fp8 on RTX 6000 Pro x8

https://huggingface.co/zai-org/GLM-4.7-FP8

Test Result

Model loads and performs inference correctly.


<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/compilation/passes/fusion/matcher_utils.py (modified, +1/-1)
  • vllm/compilation/passes/fusion/rms_quant_fusion.py (modified, +1/-1)

Code Example

Collecting environment information...
==============================
        System Info
==============================
OS                           : Ubuntu 24.04.3 LTS (x86_64)
GCC version                  : (Ubuntu 13.3.0-6ubuntu2~24.04) 13.3.0
Clang version                : 18.1.3 (1ubuntu1)
CMake version                : version 3.28.3
Libc version                 : glibc-2.39

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

==============================
      Python Environment
==============================
Python version               : 3.12.3 (main, Nov  6 2025, 13:44:16) [GCC 13.3.0] (64-bit runtime)
Python platform              : Linux-6.8.12-15-pve-x86_64-with-glibc2.39

==============================
       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 RTX PRO 6000 Blackwell Max-Q Workstation Edition
GPU 1: NVIDIA RTX PRO 6000 Blackwell Max-Q Workstation Edition
GPU 2: NVIDIA RTX PRO 6000 Blackwell Max-Q Workstation Edition
GPU 3: NVIDIA RTX PRO 6000 Blackwell Max-Q Workstation Edition
GPU 4: NVIDIA RTX PRO 6000 Blackwell Workstation Edition
GPU 5: NVIDIA RTX PRO 6000 Blackwell Workstation Edition
GPU 6: NVIDIA RTX PRO 6000 Blackwell Workstation Edition
GPU 7: NVIDIA RTX PRO 6000 Blackwell Workstation Edition

Nvidia driver version        : 575.57.08
cuDNN version                : Probably one of the following:
/usr/lib/x86_64-linux-gnu/libcudnn.so.9.17.1
/usr/lib/x86_64-linux-gnu/libcudnn_adv.so.9.17.1
/usr/lib/x86_64-linux-gnu/libcudnn_cnn.so.9.17.1
/usr/lib/x86_64-linux-gnu/libcudnn_engines_precompiled.so.9.17.1
/usr/lib/x86_64-linux-gnu/libcudnn_engines_runtime_compiled.so.9.17.1
/usr/lib/x86_64-linux-gnu/libcudnn_graph.so.9.17.1
/usr/lib/x86_64-linux-gnu/libcudnn_heuristic.so.9.17.1
/usr/lib/x86_64-linux-gnu/libcudnn_ops.so.9.17.1
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):                               32
On-line CPU(s) list:                  0-31
Vendor ID:                            AuthenticAMD
Model name:                           AMD Ryzen Threadripper PRO 9955WX 16-Cores
CPU family:                           26
Model:                                8
Thread(s) per core:                   2
Core(s) per socket:                   16
Socket(s):                            1
Stepping:                             1
CPU(s) scaling MHz:                   64%
CPU max MHz:                          5449.0000
CPU min MHz:                          400.0000
BogoMIPS:                             9000.06
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 amd_lbr_v2 nopl nonstop_tsc cpuid extd_apicid aperfmperf rapl pni pclmulqdq monitor ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand lahf_lm cmp_legacy svm extapic cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw ibs skinit wdt tce topoext perfctr_core perfctr_nb bpext perfctr_llc mwaitx cpb cat_l3 cdp_l3 hw_pstate ssbd mba perfmon_v2 ibrs ibpb stibp ibrs_enhanced vmmcall fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid cqm rdt_a avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local user_shstk avx_vnni avx512_bf16 clzero irperf xsaveerptr rdpru wbnoinvd amd_ppin cppc amd_ibpb_ret arat npt lbrv svm_lock nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold avic v_vmsave_vmload vgif x2avic v_spec_ctrl vnmi avx512vbmi umip pku ospke avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg avx512_vpopcntdq la57 rdpid bus_lock_detect movdiri movdir64b overflow_recov succor smca fsrm avx512_vp2intersect flush_l1d debug_swap
Virtualization:                       AMD-V
L1d cache:                            768 KiB (16 instances)
L1i cache:                            512 KiB (16 instances)
L2 cache:                             16 MiB (16 instances)
L3 cache:                             64 MiB (2 instances)
NUMA node(s):                         1
NUMA node0 CPU(s):                    0-31
Vulnerability Gather data sampling:   Not affected
Vulnerability Itlb multihit:          Not affected
Vulnerability L1tf:                   Not affected
Vulnerability Mds:                    Not affected
Vulnerability Meltdown:               Not affected
Vulnerability Mmio stale data:        Not affected
Vulnerability Reg file data sampling: Not affected
Vulnerability Retbleed:               Not affected
Vulnerability Spec rstack overflow:   Not affected
Vulnerability Spec store bypass:      Mitigation; Speculative Store Bypass disabled via prctl
Vulnerability Spectre v1:             Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Vulnerability Spectre v2:             Mitigation; Enhanced / Automatic IBRS; IBPB conditional; STIBP always-on; RSB filling; PBRSB-eIBRS Not affected; BHI Not affected
Vulnerability Srbds:                  Not affected
Vulnerability Tsx async abort:        Not affected
Vulnerability Vmscape:                Not affected

==============================
Versions of relevant libraries
==============================
[pip3] flashinfer-python==0.6.6
[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==4.57.6
[pip3] triton==3.6.0
[conda] flashinfer-python                    0.6.3                                    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                   4.3.5                                    pypi_0              pypi
[conda] nvidia-ml-py                         13.590.48                                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] torch-memory-saver                   0.0.9                                    pypi_0              pypi
[conda] torchao                              0.9.0                                    pypi_0              pypi
[conda] torchaudio                           2.10.0                                   pypi_0              pypi
[conda] torchcodec                           0.8.0                                    pypi_0              pypi
[conda] torchvision                          0.25.0                                   pypi_0              pypi
[conda] transformers                         4.57.1                                   pypi_0              pypi
[conda] triton                               3.6.0                                    pypi_0              pypi

==============================
         vLLM Info
==============================
ROCM Version                 : Could not collect
vLLM Version                 : 0.18.0
vLLM Build Flags:
  CUDA Archs: Not Set; ROCm: Disabled
GPU Topology:
        GPU0    GPU1    GPU2    GPU3    GPU4    GPU5    GPU6    GPU7    CPU Affinity    NUMA Affinity   GPU NUMA ID
GPU0     X      PHB     NODE    NODE    NODE    NODE    NODE    NODE    0-31    0               N/A
GPU1    PHB      X      NODE    NODE    NODE    NODE    NODE    NODE    0-31    0               N/A
GPU2    NODE    NODE     X      PHB     NODE    NODE    NODE    NODE    0-31    0               N/A
GPU3    NODE    NODE    PHB      X      NODE    NODE    NODE    NODE    0-31    0               N/A
GPU4    NODE    NODE    NODE    NODE     X      NODE    NODE    NODE    0-31    0               N/A
GPU5    NODE    NODE    NODE    NODE    NODE     X      PHB     NODE    0-31    0               N/A
GPU6    NODE    NODE    NODE    NODE    NODE    PHB      X      NODE    0-31    0               N/A
GPU7    NODE    NODE    NODE    NODE    NODE    NODE    NODE     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

==============================
     Environment Variables
==============================
CUDA_HOME=/usr/local/cuda-12.9
CUDA_HOME=/usr/local/cuda-12.9
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>
Collecting environment information...
==============================
        System Info
==============================
OS                           : Ubuntu 24.04.3 LTS (x86_64)
GCC version                  : (Ubuntu 13.3.0-6ubuntu2~24.04) 13.3.0
Clang version                : 18.1.3 (1ubuntu1)
CMake version                : version 3.28.3
Libc version                 : glibc-2.39

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

==============================
      Python Environment
==============================
Python version               : 3.12.3 (main, Nov  6 2025, 13:44:16) [GCC 13.3.0] (64-bit runtime)
Python platform              : Linux-6.8.12-15-pve-x86_64-with-glibc2.39

==============================
       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 RTX PRO 6000 Blackwell Max-Q Workstation Edition
GPU 1: NVIDIA RTX PRO 6000 Blackwell Max-Q Workstation Edition
GPU 2: NVIDIA RTX PRO 6000 Blackwell Max-Q Workstation Edition
GPU 3: NVIDIA RTX PRO 6000 Blackwell Max-Q Workstation Edition
GPU 4: NVIDIA RTX PRO 6000 Blackwell Workstation Edition
GPU 5: NVIDIA RTX PRO 6000 Blackwell Workstation Edition
GPU 6: NVIDIA RTX PRO 6000 Blackwell Workstation Edition
GPU 7: NVIDIA RTX PRO 6000 Blackwell Workstation Edition

Nvidia driver version        : 575.57.08
cuDNN version                : Probably one of the following:
/usr/lib/x86_64-linux-gnu/libcudnn.so.9.17.1
/usr/lib/x86_64-linux-gnu/libcudnn_adv.so.9.17.1
/usr/lib/x86_64-linux-gnu/libcudnn_cnn.so.9.17.1
/usr/lib/x86_64-linux-gnu/libcudnn_engines_precompiled.so.9.17.1
/usr/lib/x86_64-linux-gnu/libcudnn_engines_runtime_compiled.so.9.17.1
/usr/lib/x86_64-linux-gnu/libcudnn_graph.so.9.17.1
/usr/lib/x86_64-linux-gnu/libcudnn_heuristic.so.9.17.1
/usr/lib/x86_64-linux-gnu/libcudnn_ops.so.9.17.1
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):                               32
On-line CPU(s) list:                  0-31
Vendor ID:                            AuthenticAMD
Model name:                           AMD Ryzen Threadripper PRO 9955WX 16-Cores
CPU family:                           26
Model:                                8
Thread(s) per core:                   2
Core(s) per socket:                   16
Socket(s):                            1
Stepping:                             1
CPU(s) scaling MHz:                   64%
CPU max MHz:                          5449.0000
CPU min MHz:                          400.0000
BogoMIPS:                             9000.06
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 amd_lbr_v2 nopl nonstop_tsc cpuid extd_apicid aperfmperf rapl pni pclmulqdq monitor ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand lahf_lm cmp_legacy svm extapic cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw ibs skinit wdt tce topoext perfctr_core perfctr_nb bpext perfctr_llc mwaitx cpb cat_l3 cdp_l3 hw_pstate ssbd mba perfmon_v2 ibrs ibpb stibp ibrs_enhanced vmmcall fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid cqm rdt_a avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local user_shstk avx_vnni avx512_bf16 clzero irperf xsaveerptr rdpru wbnoinvd amd_ppin cppc amd_ibpb_ret arat npt lbrv svm_lock nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold avic v_vmsave_vmload vgif x2avic v_spec_ctrl vnmi avx512vbmi umip pku ospke avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg avx512_vpopcntdq la57 rdpid bus_lock_detect movdiri movdir64b overflow_recov succor smca fsrm avx512_vp2intersect flush_l1d debug_swap
Virtualization:                       AMD-V
L1d cache:                            768 KiB (16 instances)
L1i cache:                            512 KiB (16 instances)
L2 cache:                             16 MiB (16 instances)
L3 cache:                             64 MiB (2 instances)
NUMA node(s):                         1
NUMA node0 CPU(s):                    0-31
Vulnerability Gather data sampling:   Not affected
Vulnerability Itlb multihit:          Not affected
Vulnerability L1tf:                   Not affected
Vulnerability Mds:                    Not affected
Vulnerability Meltdown:               Not affected
Vulnerability Mmio stale data:        Not affected
Vulnerability Reg file data sampling: Not affected
Vulnerability Retbleed:               Not affected
Vulnerability Spec rstack overflow:   Not affected
Vulnerability Spec store bypass:      Mitigation; Speculative Store Bypass disabled via prctl
Vulnerability Spectre v1:             Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Vulnerability Spectre v2:             Mitigation; Enhanced / Automatic IBRS; IBPB conditional; STIBP always-on; RSB filling; PBRSB-eIBRS Not affected; BHI Not affected
Vulnerability Srbds:                  Not affected
Vulnerability Tsx async abort:        Not affected
Vulnerability Vmscape:                Not affected

==============================
Versions of relevant libraries
==============================
[pip3] flashinfer-python==0.6.6
[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==4.57.6
[pip3] triton==3.6.0
[conda] flashinfer-python                    0.6.3                                    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                   4.3.5                                    pypi_0              pypi
[conda] nvidia-ml-py                         13.590.48                                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] torch-memory-saver                   0.0.9                                    pypi_0              pypi
[conda] torchao                              0.9.0                                    pypi_0              pypi
[conda] torchaudio                           2.10.0                                   pypi_0              pypi
[conda] torchcodec                           0.8.0                                    pypi_0              pypi
[conda] torchvision                          0.25.0                                   pypi_0              pypi
[conda] transformers                         4.57.1                                   pypi_0              pypi
[conda] triton                               3.6.0                                    pypi_0              pypi

==============================
         vLLM Info
==============================
ROCM Version                 : Could not collect
vLLM Version                 : 0.18.0
vLLM Build Flags:
  CUDA Archs: Not Set; ROCm: Disabled
GPU Topology:
        GPU0    GPU1    GPU2    GPU3    GPU4    GPU5    GPU6    GPU7    CPU Affinity    NUMA Affinity   GPU NUMA ID
GPU0     X      PHB     NODE    NODE    NODE    NODE    NODE    NODE    0-31    0               N/A
GPU1    PHB      X      NODE    NODE    NODE    NODE    NODE    NODE    0-31    0               N/A
GPU2    NODE    NODE     X      PHB     NODE    NODE    NODE    NODE    0-31    0               N/A
GPU3    NODE    NODE    PHB      X      NODE    NODE    NODE    NODE    0-31    0               N/A
GPU4    NODE    NODE    NODE    NODE     X      NODE    NODE    NODE    0-31    0               N/A
GPU5    NODE    NODE    NODE    NODE    NODE     X      PHB     NODE    0-31    0               N/A
GPU6    NODE    NODE    NODE    NODE    NODE    PHB      X      NODE    0-31    0               N/A
GPU7    NODE    NODE    NODE    NODE    NODE    NODE    NODE     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

==============================
     Environment Variables
==============================
CUDA_HOME=/usr/local/cuda-12.9
CUDA_HOME=/usr/local/cuda-12.9
PYTORCH_NVML_BASED_CUDA_CHECK=1
TORCHINDUCTOR_COMPILE_THREADS=1
TORCHINDUCTOR_CACHE_DIR=/tmp/torchinductor_root
</details>

🐛 Describe the bug

GLM 4.7 crashes on load on RTX 6000 Pro

(Worker_TP3 pid=457501) ERROR 03-27 17:11:15 [multiproc_executor.py:852] AttributeError: '_OpNamespace' '_C' object has no attribute 'per_token_group_fp8_quant'

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extent analysis

Fix Plan

To resolve the issue of GLM 4.7 crashing on load on RTX 6000 Pro due to the _OpNamespace object having no attribute per_token_group_fp8_quant, follow these steps:

  • Update PyTorch and dependent libraries: Ensure that PyTorch and all dependent libraries are up-to-date, as newer versions may include fixes for known issues.
  • Check CUDA and cuDNN versions: Verify that the CUDA and cuDNN versions are compatible with the RTX 6000 Pro and PyTorch 2.10.0.
  • Modify the code to handle missing attributes: If the issue persists, modify the code to check for the existence of the per_token_group_fp8_quant attribute before attempting to access it.

Example code snippet to handle missing attributes:

import torch

# ...

if hasattr(torch._C, 'per_token_group_fp8_quant'):
    # Attribute exists, proceed with original code
    torch._C.per_token_group_fp8_quant()
else:
    # Attribute does not exist, handle the error or provide a fallback
    print("Error: per_token_group_fp8_quant attribute not found.")
    # Fallback code or error handling

Verification

To verify that the fix worked, attempt to load GLM 4.7 on the RTX 6000 Pro after applying the above steps. If the issue is resolved, the model should load without crashing.

Extra Tips

  • Ensure that the environment variables, such as CUDA_HOME and PYTORCH_NVML_BASED_CUDA_CHECK, are set correctly.
  • If using a virtual environment, verify that all required libraries are installed and up-to-date within the environment.
  • Consider seeking further assistance from the PyTorch community or the developers of GLM 4.7 if the issue persists after attempting the above fix.

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