Your current environment
Collecting environment information...
/usr/local/lib/python3.10/dist-packages/torch/utils/_pytree.py:185: FutureWarning: optree is installed but the version is too old to support PyTorch Dynamo in C++ pytree. C++ pytree support is disabled. Please consider upgrading optree using `python3 -m pip install --upgrade 'optree>=0.13.0'`.
warnings.warn(
INFO 04-24 06:57:53 [__init__.py:239] Automatically detected platform cuda.
/usr/local/lib/python3.10/dist-packages/_distutils_hack/__init__.py:33: UserWarning: Setuptools is replacing distutils.
warnings.warn("Setuptools is replacing distutils.")
Collecting environment information...
PyTorch version: 2.6.0+cu124
Is debug build: False
CUDA used to build PyTorch: 12.4
ROCM used to build PyTorch: N/A
OS: Ubuntu 22.04.4 LTS (x86_64)
GCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0
Clang version: Could not collect
CMake version: version 3.29.2
Libc version: glibc-2.35
Python version: 3.10.12 (main, Nov 20 2023, 15:14:05) [GCC 11.4.0] (64-bit runtime)
Python platform: Linux-5.15.0-97-generic-x86_64-with-glibc2.35
Is CUDA available: True
CUDA runtime version: 12.4.131
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration:
GPU 0: NVIDIA H20
GPU 1: NVIDIA H20
GPU 2: NVIDIA H20
GPU 3: NVIDIA H20
GPU 4: NVIDIA H20
GPU 5: NVIDIA H20
GPU 6: NVIDIA H20
GPU 7: NVIDIA H20
Nvidia driver version: 570.124.06
cuDNN version: Probably one of the following:
/usr/lib/x86_64-linux-gnu/libcudnn.so.9.1.0
/usr/lib/x86_64-linux-gnu/libcudnn_adv.so.9.1.0
/usr/lib/x86_64-linux-gnu/libcudnn_cnn.so.9.1.0
/usr/lib/x86_64-linux-gnu/libcudnn_engines_precompiled.so.9.1.0
/usr/lib/x86_64-linux-gnu/libcudnn_engines_runtime_compiled.so.9.1.0
/usr/lib/x86_64-linux-gnu/libcudnn_graph.so.9.1.0
/usr/lib/x86_64-linux-gnu/libcudnn_heuristic.so.9.1.0
/usr/lib/x86_64-linux-gnu/libcudnn_ops.so.9.1.0
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True
CPU:
Architecture: x86_64
CPU op-mode(s): 32-bit, 64-bit
Address sizes: 46 bits physical, 57 bits virtual
Byte Order Little Endian
CPU(s): 224
On-line CPU(s) list: 0-223
Vendor ID: GenuineIntel
Model name: Intel(R) Xeon(R) Platinum 8480+
CPU family: 6
Model: 143
Thread(s) per core: 2
Core(s) per socket: 56
Socket(s): 2
Stepping: 8
CPU max MHz: 3800.0000
CPU min MHz: 800.0000
BogoMIPS: 4000.00
Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc art arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf tsc_known_freq pni pclmulqdq dtes64 monitor ds_cpl vmx smx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid dca sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault epb cat_l3 cat_l2 cdp_l3 invpcid_single intel_ppin cdp_l2 ssbd mba ibrs ibpb stibp ibrs_enhanced tpr_shadow vnmi flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid cqm rdt_a avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb intel_pt avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local split_lock_detect avx_vnni avx512_bf16 wbnoinvd dtherm ida arat pln pts hwp hwp_act_window hwp_epp hwp_pkg_req avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg tme avx512_vpopcntdq la57 rdpid bus_lock_detect cldemote movdiri movdir64b enqcmd fsrm md_clear serialize tsxldtrk pconfig arch_lbr amx_bf16 avx512_fp16 amx_tile amx_int8 flush_l1d arch_capabilities
Virtualization: VT-x
L1d cache: 5.3 MiB (112 instances)
L1i cache: 3.5 MiB (112 instances)
L2 cache: 224 MiB (112 instances)
L3 cache: 210 MiB (2 instances)
NUMA node(s): 2
NUMA node0 CPU(s): 0-55,112-167
NUMA node1 CPU(s): 56-111,168-223
Vulnerability Gather data sampling: Not affected
Vulnerability Itlb multihit: Not affected
ulnerability L1tf: Not affected
Vulnerability Mds: Not affected
Vulnerability Meltdown: Not affected
Vulnerability Mmio stale data: Not affected
Vulnerability Retbleed: Not affected
Vulnerability Spec rstack overflow: Not affected
Vulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl and seccomp
Vulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Vulnerability Spectre v2: Mitigation; Enhanced IBRS, IBPB conditional, RSB filling, PBRSB-eIBRS SW sequence
Vulnerability Srbds: Not affected
Vulnerability Tsx async abort: Not affected
Versions of relevant libraries:
[pip3] numpy==1.26.4
[pip3] nvidia-cublas-cu12==12.4.5.8
[pip3] nvidia-cuda-cupti-cu12==12.4.127
[pip3] nvidia-cuda-nvrtc-cu12==12.4.127
[pip3] nvidia-cuda-runtime-cu12==12.4.127
[pip3] nvidia-cudnn-cu12==9.1.0.70
[pip3] nvidia-cudnn-frontend==1.3.0
[pip3] nvidia-cufft-cu12==11.2.1.3
[pip3] nvidia-curand-cu12==10.3.5.147
[pip3] nvidia-cusolver-cu12==11.6.1.9
[pip3] nvidia-cusparse-cu12==12.3.1.170
[pip3] nvidia-cusparselt-cu12==0.6.2
[pip3] nvidia-dali-cuda120==1.37.1
[pip3] nvidia-nccl-cu12==2.21.5
[pip3] nvidia-nvimgcodec-cu12==0.2.0.7
[pip3] nvidia-nvjitlink-cu12==12.4.127
[pip3] nvidia-nvtx-cu12==12.4.127
[pip3] nvidia-pyindex==1.0.9
[pip3] onnx==1.16.0
[pip3] optree==0.11.0
[pip3] pynvml==11.4.1
[pip3]<br/>
# [Bug]: "Fatal Python error: Segmentation fault" when running DeepSeek TP16 using v0.8.4 v1 engine
## Q&A
### Q: What is the error message?
A: The error message is "Fatal Python error: Segmentation fault" which indicates that the Python interpreter has encountered a segmentation fault, a type of error that occurs when a program attempts to access a memory location that it is not allowed to access.
### Q: What is the cause of the error?
A: The cause of the error is not immediately clear, but it appears to be related to the use of the v1 engine with the DeepSeek TP16 model. The error message suggests that there is a problem with the memory allocation or deallocation of the model's weights.
### Q: What are the system specifications?
A: The system specifications are:
* Operating System: Ubuntu 22.04.4 LTS (x86_64)
* Python version: 3.10.12 (main, Nov 20 2023, 15:14:05) [GCC 11.4.0] (64-bit runtime)
* CUDA version: 12.4.131
* GPU models and configuration: 8 NVIDIA H20 GPUs
* CPU: Intel(R) Xeon(R) Platinum 8480+
### Q: What are the relevant libraries and their versions?
A: The relevant libraries and their versions are:
* numpy: 1.26.4
* nvidia-cublas-cu12: 12.4.5.8
* nvidia-cuda-cupti-cu12: 12.4.127
* nvidia-cuda-nvrtc-cu12: 12.4.127
* nvidia-cuda-runtime-cu12: 12.4.127
* nvidia-cudnn-cu12: 9.1.0.70
* nvidia-cudnn-frontend: 1.3.0
* nvidia-cufft-cu12: 11.2.1.3
* nvidia-curand-cu12: 10.3.5.147
* nvidia-cusolver-cu12: 11.6.1.9
* nvidia-cusparse-cu12: 12.3.1.170
* nvidia-cusparselt-cu12: 0.6.2
* nvidia-dali-cuda120: 1.37.1
* nvidia-nccl-cu12: 2.21.5
* nvidia-nvimgcodec-cu12: 0.2.0.7
* nvidia-nvjitlink-cu12: 12.4.127
* nvidia-nvtx-cu12: 12.4.127
* nvidia-pyindex: 1.0.9
* onnx: 1.16.0
* optree: 0.11.0
* pynvml: 11.4.1
* pytorch-quantization: 2.1.2
* pytorch-triton: 3.0.0+989adb9a2
* pyzmq: 26.0.3
* torch: 2.6.0
* torchaudio: 2.6.0
* torchvision: 0.21.0
* transformers: 4.51.3
* triton: 3.2.0
### Q: What are the possible solutions?
A: The solutions are:
* Upgrade the optree library to version 0.13.0 or later
* Downgrade the PyTorch version to 2.4.0 or earlier
* Use the v0 engine instead of the v1 engine
* Check the system specifications and ensure that they meet the minimum requirements for running the DeepSeek TP16 model
* Check the relevant libraries and their versions, and ensure that they are up-to-date
### Q: How to report the issue?
A: To report the issue, please provide the following information:
* The error message
* The system specifications
* The relevant libraries and their versions
* The possible solutions that have been tried
* Any additional information that may be relevant to the issue
You can report the issue by creating a new issue on the VLLM GitHub repository.