[Bug]: RuntimeError: Failed to infer device type on Gaudi 3 System

[winniechj]

Your current environment

Collecting environment information...
PyTorch version: 2.5.1+cu124
Is debug build: False
CUDA used to build PyTorch: 12.4
ROCM used to build PyTorch: N/A

OS: Ubuntu 22.04.5 LTS (x86_64)
GCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0
Clang version: Could not collect
CMake version: version 3.22.1
Libc version: glibc-2.35

Python version: 3.10.12 (main, Nov 6 2024, 20:22:13) [GCC 11.4.0] (64-bit runtime)
Python platform: Linux-6.5.0-44-generic-x86_64-with-glibc2.35
Is CUDA available: False
CUDA runtime version: No CUDA
CUDA_MODULE_LOADING set to: N/A
GPU models and configuration: No CUDA
Nvidia driver version: No CUDA
cuDNN version: No CUDA
HPU devices: 8x HL-325L
HPU driver version: 1.19.0-2427ed8
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: 52 bits physical, 57 bits virtual
Byte Order: Little Endian
CPU(s): 288
On-line CPU(s) list: 0-287
Vendor ID: GenuineIntel
Model name: Intel(R) Xeon(R) 6960P
CPU family: 6
Model: 173
Thread(s) per core: 2
Core(s) per socket: 72
Socket(s): 2
Stepping: 1
CPU max MHz: 3900.0000
CPU min MHz: 800.0000
BogoMIPS: 5400.00
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 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 hfi vnmi avx512vbmi umip pku ospke waitpkg 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 ibt amx_bf16 avx512_fp16 amx_tile amx_int8 flush_l1d arch_capabilities
Virtualization: VT-x
L1d cache: 6.8 MiB (144 instances)
L1i cache: 9 MiB (144 instances)
L2 cache: 288 MiB (144 instances)
L3 cache: 864 MiB (2 instances)
NUMA node(s): 6
NUMA node0 CPU(s): 0-23,144-167
NUMA node1 CPU(s): 24-47,168-191
NUMA node2 CPU(s): 48-71,192-215
NUMA node3 CPU(s): 72-95,216-239
NUMA node4 CPU(s): 96-119,240-263
NUMA node5 CPU(s): 120-143,264-287
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 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; RSB filling; PBRSB-eIBRS Not affected; BHI BHI_DIS_S
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-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-ml-py==12.560.30
[pip3] nvidia-nccl-cu12==2.21.5
[pip3] nvidia-nvjitlink-cu12==12.4.127
[pip3] nvidia-nvtx-cu12==12.4.127
[pip3] pyzmq==26.2.0
[pip3] torch==2.5.1
[pip3] torchvision==0.20.1
[pip3] transformers==4.47.1
[pip3] triton==3.1.0
[conda] Could not collect
ROCM Version: Could not collect
Neuron SDK Version: N/A
vLLM Version: 0.6.6.post1
vLLM Build Flags:
CUDA Archs: Not Set; ROCm: Disabled; Neuron: Disabled
GPU Topology:
Could not collect

LD_LIBRARY_PATH=/home/gd3-3/winnie/vllm-fork/vllmfork_env/lib/python3.10/site-packages/cv2/../../lib64:/opt/habanalabs/openmpi-4.1.5/lib:

Model Input Dumps

No response

🐛 Describe the bug

My step:

$ git clone https://github.com/HabanaAI/vllm-fork.git
$ cd vllm-fork
$ git checkout v0.6.4.post2+Gaudi-1.19.0
$ pip install -r requirements-hpu.txt
$ python setup.py develop

Execute:
python examples/offline_inference.py

Result:

Traceback (most recent call last):
  File "/home/gd3-3/winnie/vllm-fork/examples/offline_inference.py", line 80, in <module>
    main(args)
  File "/home/gd3-3/winnie/vllm-fork/examples/offline_inference.py", line 37, in main
    llm = LLM(**asdict(engine_args))
  File "/home/gd3-3/winnie/vllm-fork/vllmfork_env/lib/python3.10/site-packages/vllm/utils.py", line 986, in inner
    return fn(*args, **kwargs)
  File "/home/gd3-3/winnie/vllm-fork/vllmfork_env/lib/python3.10/site-packages/vllm/entrypoints/llm.py", line 230, in __init__
    self.llm_engine = self.engine_class.from_engine_args(
  File "/home/gd3-3/winnie/vllm-fork/vllmfork_env/lib/python3.10/site-packages/vllm/engine/llm_engine.py", line 514, in from_engine_args
    engine_config = engine_args.create_engine_config(usage_context)
  File "/home/gd3-3/winnie/vllm-fork/vllmfork_env/lib/python3.10/site-packages/vllm/engine/arg_utils.py", line 1043, in create_engine_config
    device_config = DeviceConfig(device=self.device)
  File "/home/gd3-3/winnie/vllm-fork/vllmfork_env/lib/python3.10/site-packages/vllm/config.py", line 1527, in __init__
    raise RuntimeError("Failed to infer device type")
RuntimeError: Failed to infer device type

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[michalkuligowski]

Hi @winniechj , thank you for filing this ticket, it seems that vllm-fork is not installed and there is one step missing during installation. Can you try running 'pip install -e .' so the steps look more like this:

$ git clone https://github.com/HabanaAI/vllm-fork.git
$ cd vllm-fork
$ git checkout v0.6.4.post2+Gaudi-1.19.0
$ pip install -e .
$ pip install -r requirements-hpu.txt
$ python setup.py develop

and see if that helps?