[Bug]: Using vllm as the inference engine, there is an incorrect recognition of GPU computing capabilities for different types.

[T-Atlas]

Your current environment

(py311) (base) lianjh@cloud88:/storage/lianjh/dev$ python collect_env.py
Collecting environment information...
PyTorch version: 2.3.0
Is debug build: False
CUDA used to build PyTorch: 12.1
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 3.29.5
Libc version: glibc-2.35

Python version: 3.11.9 (main, Apr 19 2024, 16:48:06) [GCC 11.2.0] (64-bit runtime)
Python platform: Linux-5.15.0-102-generic-x86_64-with-glibc2.35
Is CUDA available: True
CUDA runtime version: 11.7.99
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: 
GPU 0: Tesla V100-SXM3-32GB
GPU 1: NVIDIA GeForce RTX 3090
GPU 2: Tesla V100-PCIE-32GB
GPU 3: Tesla V100-PCIE-32GB
GPU 4: NVIDIA A800 80GB PCIe
GPU 5: NVIDIA A800 80GB PCIe
GPU 6: NVIDIA A800 80GB PCIe
GPU 7: NVIDIA A800 80GB PCIe

Nvidia driver version: 550.54.15
cuDNN version: Probably one of the following:
/usr/local/cuda-10.2/targets/x86_64-linux/lib/libcudnn.so.7
/usr/local/cuda-11.7/targets/x86_64-linux/lib/libcudnn.so.8.8.1
/usr/local/cuda-11.7/targets/x86_64-linux/lib/libcudnn_adv_infer.so.8.8.1
/usr/local/cuda-11.7/targets/x86_64-linux/lib/libcudnn_adv_train.so.8.8.1
/usr/local/cuda-11.7/targets/x86_64-linux/lib/libcudnn_cnn_infer.so.8.8.1
/usr/local/cuda-11.7/targets/x86_64-linux/lib/libcudnn_cnn_train.so.8.8.1
/usr/local/cuda-11.7/targets/x86_64-linux/lib/libcudnn_ops_infer.so.8.8.1
/usr/local/cuda-11.7/targets/x86_64-linux/lib/libcudnn_ops_train.so.8.8.1
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, 48 bits virtual
Byte Order:                         Little Endian
CPU(s):                             72
On-line CPU(s) list:                0-71
Vendor ID:                          GenuineIntel
Model name:                         Intel(R) Xeon(R) Gold 6240 CPU @ 2.60GHz
CPU family:                         6
Model:                              85
Thread(s) per core:                 2
Core(s) per socket:                 18
Socket(s):                          2
Stepping:                           7
CPU max MHz:                        3900.0000
CPU min MHz:                        1000.0000
BogoMIPS:                           5200.00
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 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 cdp_l3 invpcid_single intel_ppin 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 mpx rdt_a avx512f avx512dq rdseed adx smap clflushopt clwb intel_pt avx512cd avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local dtherm ida arat pln pts pku ospke avx512_vnni md_clear flush_l1d arch_capabilities
Virtualization:                     VT-x
L1d cache:                          1.1 MiB (36 instances)
L1i cache:                          1.1 MiB (36 instances)
L2 cache:                           36 MiB (36 instances)
L3 cache:                           49.5 MiB (2 instances)
NUMA node(s):                       2
NUMA node0 CPU(s):                  0-17,36-53
NUMA node1 CPU(s):                  18-35,54-71
Vulnerability Gather data sampling: Mitigation; Microcode
Vulnerability Itlb multihit:        KVM: Mitigation: VMX disabled
Vulnerability L1tf:                 Not affected
Vulnerability Mds:                  Not affected
Vulnerability Meltdown:             Not affected
Vulnerability Mmio stale data:      Mitigation; Clear CPU buffers; SMT vulnerable
Vulnerability Retbleed:             Mitigation; Enhanced IBRS
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:      Mitigation; TSX disabled

Versions of relevant libraries:
[pip3] numpy==1.26.4
[pip3] nvidia-nccl-cu12==2.18.1
[pip3] pytorch-lightning==2.3.2
[pip3] sentence-transformers==3.0.1
[pip3] torch==2.3.0
[pip3] torchaudio==2.3.0
[pip3] torchdata==0.7.1
[pip3] torchmetrics==1.4.0.post0
[pip3] torchvision==0.18.0
[pip3] transformers==4.42.3
[pip3] triton==2.1.0
[conda] blas                      1.0                         mkl    defaults
[conda] ffmpeg                    4.3                  hf484d3e_0    pytorch
[conda] libjpeg-turbo             2.0.0                h9bf148f_0    pytorch
[conda] mkl                       2023.1.0         h213fc3f_46344    defaults
[conda] mkl-service               2.4.0           py311h5eee18b_1    defaults
[conda] mkl_fft                   1.3.8           py311h5eee18b_0    defaults
[conda] mkl_random                1.2.4           py311hdb19cb5_0    defaults
[conda] numpy                     1.26.4          py311h08b1b3b_0    defaults
[conda] numpy-base                1.26.4          py311hf175353_0    defaults
[conda] nvidia-nccl-cu12          2.18.1                   pypi_0    pypi
[conda] pytorch                   2.3.0           py3.11_cuda12.1_cudnn8.9.2_0    pytorch
[conda] pytorch-cuda              12.1                 ha16c6d3_5    pytorch
[conda] pytorch-lightning         2.3.2                    pypi_0    pypi
[conda] pytorch-mutex             1.0                        cuda    pytorch
[conda] sentence-transformers     3.0.1                    pypi_0    pypi
[conda] torchaudio                2.3.0               py311_cu121    pytorch
[conda] torchdata                 0.7.1                    pypi_0    pypi
[conda] torchmetrics              1.4.0.post0              pypi_0    pypi
[conda] torchvision               0.18.0              py311_cu121    pytorch
[conda] transformers              4.42.3                   pypi_0    pypi
[conda] triton                    2.1.0                    pypi_0    pypi
ROCM Version: Could not collect
Neuron SDK Version: N/A
vLLM Version: 0.5.1
vLLM Build Flags:
CUDA Archs: Not Set; ROCm: Disabled; Neuron: Disabled
GPU Topology:
GPU0    GPU1    GPU2    GPU3    GPU4    GPU5    GPU6    GPU7    CPU Affinity    NUMA Affinity   GPU NUMA ID
GPU0     X      PIX     SYS     SYS     SYS     SYS     SYS     SYS     0-17,36-53      0               N/A
GPU1    PIX      X      SYS     SYS     SYS     SYS     SYS     SYS     0-17,36-53      0               N/A
GPU2    SYS     SYS      X      PIX     SYS     SYS     SYS     SYS     0-17,36-53      0               N/A
GPU3    SYS     SYS     PIX      X      SYS     SYS     SYS     SYS     0-17,36-53      0               N/A
GPU4    SYS     SYS     SYS     SYS      X      NV8     SYS     SYS     18-35,54-71     1               N/A
GPU5    SYS     SYS     SYS     SYS     NV8      X      SYS     SYS     18-35,54-71     1               N/A
GPU6    SYS     SYS     SYS     SYS     SYS     SYS      X      NV8     18-35,54-71     1               N/A
GPU7    SYS     SYS     SYS     SYS     SYS     SYS     NV8      X      18-35,54-71     1               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

🐛 Describe the bug

 File "/home/lianjh/miniconda3/envs/py311/lib/python3.11/site-packages/vllm/worker/worker.py", line 337, in _check_if_gpu_supports_dtype
    raise ValueError(
ValueError: Bfloat16 is only supported on GPUs with compute capability of at least 8.0. Your NVIDIA A800 80GB PCIe GPU has compute capability 7.0. You can use float16 instead by explicitly setting the`dtype` flag in CLI, for example: --dtype=half.

My rank:0 GPU is a V100, and I have correctly specified NVIDIA A800, but it still recognizes the compute capability of the [first GPU](rank:0 GPU).

[T-Atlas]

This did not occur in 0.5.0post1

[mgoin]

cc @youkaichao as this may be related to the current_platform refactor

[T-Atlas]

cc @youkaichao as this may be related to the current_platform refactor

By quickly comparing the code, it might be related to this pull request: #6080

[youkaichao]

@T-Atlas can you try #6216 ? I didn't expect heterogeneous GPUs indeed.

[T-Atlas]

@T-Atlas can you try #6216 ? I didn't expect heterogeneous GPUs indeed.

@youkaichao I'm trying this and once I find this works fine I'll let you know in #6216