[Usage]: LLM with tensor_parallel_size larger than n. gpus in one node

Your current environment


The output of `python collect_env.py` on a single node:

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[gpuccett@lrdn0487 ~]$ cd /leonardo_scratch/large/userexternal/gpuccett/Repos/MGT2025-private/
[gpuccett@lrdn0487 MGT2025-private]$ conda activate conda_venv/
(/leonardo_scratch/large/userexternal/gpuccett/Repos/MGT2025-private/conda_venv) [gpuccett@lrdn0487 MGT2025-private]$ python collect_env.py 
Collecting environment information...
PyTorch version: 2.4.0+cu121
Is debug build: False
CUDA used to build PyTorch: 12.1
ROCM used to build PyTorch: N/A

OS: Red Hat Enterprise Linux release 8.7 (Ootpa) (x86_64)
GCC version: (GCC) 8.5.0 20210514 (Red Hat 8.5.0-16)
Clang version: Could not collect
CMake version: version 3.20.2
Libc version: glibc-2.28

Python version: 3.12.6 | packaged by conda-forge | (main, Sep 22 2024, 14:16:49) [GCC 13.3.0] (64-bit runtime)
Python platform: Linux-4.18.0-425.19.2.el8_7.x86_64-x86_64-with-glibc2.28
Is CUDA available: True
CUDA runtime version: Could not collect
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: 
GPU 0: NVIDIA A100-SXM-64GB
GPU 1: NVIDIA A100-SXM-64GB
GPU 2: NVIDIA A100-SXM-64GB
GPU 3: NVIDIA A100-SXM-64GB

Nvidia driver version: 530.30.02
cuDNN version: Could not collect
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
Byte Order:          Little Endian
CPU(s):              32
On-line CPU(s) list: 0-31
Thread(s) per core:  1
Core(s) per socket:  32
Socket(s):           1
NUMA node(s):        2
Vendor ID:           GenuineIntel
CPU family:          6
Model:               106
Model name:          Intel(R) Xeon(R) Platinum 8358 CPU @ 2.60GHz
Stepping:            6
CPU MHz:             2601.000
CPU max MHz:         2601.0000
CPU min MHz:         800.0000
BogoMIPS:            5200.00
Virtualization:      VT-x
L1d cache:           48K
L1i cache:           32K
L2 cache:            1280K
L3 cache:            49152K
NUMA node0 CPU(s):   0-15
NUMA node1 CPU(s):   16-31
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 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 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 wbnoinvd dtherm ida arat pln pts avx512vbmi umip pku ospke avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg tme avx512_vpopcntdq la57 rdpid fsrm md_clear pconfig flush_l1d arch_capabilities

Versions of relevant libraries:
[pip3] numpy==1.26.4
[pip3] nvidia-cublas-cu12==12.1.3.1
[pip3] nvidia-cuda-cupti-cu12==12.1.105
[pip3] nvidia-cuda-nvrtc-cu12==12.1.105
[pip3] nvidia-cuda-runtime-cu12==12.1.105
[pip3] nvidia-cudnn-cu12==9.1.0.70
[pip3] nvidia-cufft-cu12==11.0.2.54
[pip3] nvidia-curand-cu12==10.3.2.106
[pip3] nvidia-cusolver-cu12==11.4.5.107
[pip3] nvidia-cusparse-cu12==12.1.0.106
[pip3] nvidia-ml-py==12.560.30
[pip3] nvidia-nccl-cu12==2.20.5
[pip3] nvidia-nvjitlink-cu12==12.6.68
[pip3] nvidia-nvtx-cu12==12.1.105
[pip3] pyzmq==26.2.0
[pip3] torch==2.4.0
[pip3] torchvision==0.19.0
[pip3] transformers==4.45.1
[pip3] triton==3.0.0
[conda] numpy                     1.26.4                   pypi_0    pypi
[conda] nvidia-cublas-cu12        12.1.3.1                 pypi_0    pypi
[conda] nvidia-cuda-cupti-cu12    12.1.105                 pypi_0    pypi
[conda] nvidia-cuda-nvrtc-cu12    12.1.105                 pypi_0    pypi
[conda] nvidia-cuda-runtime-cu12  12.1.105                 pypi_0    pypi
[conda] nvidia-cudnn-cu12         9.1.0.70                 pypi_0    pypi
[conda] nvidia-cufft-cu12         11.0.2.54                pypi_0    pypi
[conda] nvidia-curand-cu12        10.3.2.106               pypi_0    pypi
[conda] nvidia-cusolver-cu12      11.4.5.107               pypi_0    pypi
[conda] nvidia-cusparse-cu12      12.1.0.106               pypi_0    pypi
[conda] nvidia-ml-py              12.560.30                pypi_0    pypi
[conda] nvidia-nccl-cu12          2.20.5                   pypi_0    pypi
[conda] nvidia-nvjitlink-cu12     12.6.68                  pypi_0    pypi
[conda] nvidia-nvtx-cu12          12.1.105                 pypi_0    pypi
[conda] pyzmq                     26.2.0                   pypi_0    pypi
[conda] torch                     2.4.0                    pypi_0    pypi
[conda] torchvision               0.19.0                   pypi_0    pypi
[conda] transformers              4.45.1                   pypi_0    pypi
[conda] triton                    3.0.0                    pypi_0    pypi
ROCM Version: Could not collect
Neuron SDK Version: N/A
vLLM Version: 0.6.1.dev238+ge2c6e0a82
vLLM Build Flags:
CUDA Archs: Not Set; ROCm: Disabled; Neuron: Disabled
GPU Topology:
GPU0    GPU1    GPU2    GPU3    NIC0    NIC1    NIC2    NIC3    CPU Affinity    NUMA Affinity
GPU0     X      NV4     NV4     NV4     PXB     NODE    NODE    NODE    0-15    0
GPU1    NV4      X      NV4     NV4     NODE    PXB     NODE    NODE    0-15    0
GPU2    NV4     NV4      X      NV4     NODE    NODE    PXB     NODE    0-15    0
GPU3    NV4     NV4     NV4      X      NODE    NODE    NODE    PXB     0-15    0
NIC0    PXB     NODE    NODE    NODE     X      NODE    NODE    NODE
NIC1    NODE    PXB     NODE    NODE    NODE     X      NODE    NODE
NIC2    NODE    NODE    PXB     NODE    NODE    NODE     X      NODE
NIC3    NODE    NODE    NODE    PXB     NODE    NODE    NODE     X 

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

NIC Legend:

  NIC0: mlx5_0
  NIC1: mlx5_1
  NIC2: mlx5_2
  NIC3: mlx5_3

How would you like to use vllm

In version 0.5.4 I used to be able to initialize and run Llama 3.1 405b instruct hf when running on 4x4A100 - 64Gb by first initializing ray on all nodes and then calling:

llm = LLM(tensor_parallel_size=16, ...)

Now I am trying the same script on 0.6.2 and it looks like this is not possible anymore, the code gets stuck at

ESC[33m(autoscaler +21m12s)ESC[0m Error: No available node types can fulfill resource request {'node:10.2.0.127': 0.001, 'GPU': 1.0}. Add suitable node types to this cluster to resolve this issue. INFO 09-27 16:47:50 ray_utils.py:183] Waiting for creating a placement group of specs for 1270 seconds. specs=[{'node:10.2.0.127': 0.001, 'GPU': 1.0}, {'GPU': 1.0}, {'GPU': 1.0}, {'GPU': 1.0}, {'GPU': 1.0}, {'GPU': 1.0}, {'GPU': 1.0}, {'GPU': 1.0}, {'GPU': 1.0}, {'GPU': 1.0}, {'GPU': 1.0}, {'GPU': 1.0}, {'GPU': 1.0}, {'GPU': 1.0}, {'GPU': 1.0}, {'GPU': 1.0}]. Check ray status to see if you have enough resources

Is it intentionally deprecated to use a tensor_parallel_size larger than the number of gpus on a single node or should I change something to adjust to the newer version of vllm?

Thank you in advance.

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vllm-project / vllm