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vllm-project / vllm

A high-throughput and memory-efficient inference and serving engine for LLMs
https://docs.vllm.ai
Apache License 2.0
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[Bug]: Ray on multi machine cluster fails to detect all nodes. #4655

Open bks5881 opened 6 months ago

bks5881 commented 6 months ago

Your current environment

python collect_env.py
--2024-05-07 16:14:33--  https://raw.githubusercontent.com/vllm-project/vllm/main/collect_env.py
Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.109.133, 185.199.110.133, 185.199.111.133, ...
Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.109.133|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 24877 (24K) [text/plain]
Saving to: ‘collect_env.py’

collect_env.py                                                                     100%[================================================================================================================================================================================================================>]  24.29K  --.-KB/s    in 0.003s  

2024-05-07 16:14:33 (9.38 MB/s) - ‘collect_env.py’ saved [24877/24877]

Collecting environment information...
PyTorch version: 2.3.0+cu121
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 3.29.0
Libc version: glibc-2.35

Python version: 3.10.0 (default, Mar  3 2022, 09:58:08) [GCC 7.5.0] (64-bit runtime)
Python platform: Linux-5.15.0-102-generic-x86_64-with-glibc2.35
Is CUDA available: True
CUDA runtime version: Could not collect
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: 
GPU 0: NVIDIA A100 80GB PCIe
GPU 1: NVIDIA A100 80GB PCIe
GPU 2: NVIDIA A100 80GB PCIe
GPU 3: NVIDIA A100 80GB PCIe

Nvidia driver version: 550.54.15
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
Address sizes:                      48 bits physical, 48 bits virtual
Byte Order:                         Little Endian
CPU(s):                             32
On-line CPU(s) list:                0-31
Vendor ID:                          AuthenticAMD
Model name:                         AMD EPYC 73F3 16-Core Processor
CPU family:                         25
Model:                              1
Thread(s) per core:                 2
Core(s) per socket:                 16
Socket(s):                          1
Stepping:                           1
Frequency boost:                    enabled
CPU max MHz:                        4036.6211
CPU min MHz:                        1500.0000
BogoMIPS:                           6986.18
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 nopl nonstop_tsc cpuid extd_apicid aperfmperf rapl pni pclmulqdq monitor ssse3 fma cx16 pcid sse4_1 sse4_2 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 invpcid_single hw_pstate ssbd mba ibrs ibpb stibp vmmcall fsgsbase bmi1 avx2 smep bmi2 erms invpcid cqm rdt_a rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local clzero irperf xsaveerptr rdpru wbnoinvd amd_ppin arat npt lbrv svm_lock nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold v_vmsave_vmload vgif v_spec_ctrl umip pku ospke vaes vpclmulqdq rdpid overflow_recov succor smca fsrm
Virtualization:                     AMD-V
L1d cache:                          512 KiB (16 instances)
L1i cache:                          512 KiB (16 instances)
L2 cache:                           8 MiB (16 instances)
L3 cache:                           256 MiB (8 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 Retbleed:             Not affected
Vulnerability Spec rstack overflow: Mitigation; safe RET
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; Retpolines, IBPB conditional, IBRS_FW, STIBP always-on, RSB filling, PBRSB-eIBRS Not affected
Vulnerability Srbds:                Not affected
Vulnerability Tsx async abort:      Not affected

Versions of relevant libraries:
[pip3] numpy==1.26.4
[pip3] nvidia-nccl-cu12==2.20.5
[pip3] torch==2.3.0
[pip3] triton==2.3.0
[pip3] vllm-nccl-cu12==2.18.1.0.4.0
[conda] numpy                     1.26.4                   pypi_0    pypi
[conda] nvidia-nccl-cu12          2.20.5                   pypi_0    pypi
[conda] torch                     2.3.0                    pypi_0    pypi
[conda] triton                    2.3.0                    pypi_0    pypi
[conda] vllm-nccl-cu12            2.18.1.0.4.0             pypi_0    pypiROCM Version: Could not collect
Neuron SDK Version: N/A
vLLM Version: 0.4.2
vLLM Build Flags:
CUDA Archs: Not Set; ROCm: Disabled; Neuron: Disabled
GPU Topology:
GPU0    GPU1    GPU2    GPU3    CPU Affinity    NUMA Affinity   GPU NUMA ID
GPU0     X  SYS SYS SYS 0-31    0       N/A
GPU1    SYS  X  SYS SYS 0-31    0       N/A
GPU2    SYS SYS  X  SYS 0-31    0       N/A
GPU3    SYS SYS SYS  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

🐛 Describe the bug

I am running vllm on using ray on two machines each having 4 A100 79Gb. I ran the commands ray start head and ray start address on head and child node. when i run ray status I see I have 8 GPUs. In the next step when I launch vllm with tp 8, i get the error as follows

2024-05-07 16:12:51,393 INFO worker.py:1564 -- Connecting to existing Ray cluster at address: 141.195.90.35:6379...
2024-05-07 16:12:51,398 INFO worker.py:1749 -- Connected to Ray cluster.
Traceback (most recent call last):
  File "miniconda3/envs/vllm_cohere/lib/python3.10/runpy.py", line 196, in _run_module_as_main
    return _run_code(code, main_globals, None,
  File "/miniconda3/envs/vllm_cohere/lib/python3.10/runpy.py", line 86, in _run_code
    exec(code, run_globals)
  File miniconda3/envs/vllm_cohere/lib/python3.10/site-packages/vllm/entrypoints/openai/api_server.py", line 168, in <module>
    engine = AsyncLLMEngine.from_engine_args(
  File "envs/vllm_cohere/lib/python3.10/site-packages/vllm/engine/async_llm_engine.py", line 357, in from_engine_args
    initialize_ray_cluster(engine_config.parallel_config)
  File "/miniconda3/envs/vllm_cohere/lib/python3.10/site-packages/vllm/executor/ray_utils.py", line 106, in initialize_ray_cluster
    raise ValueError(
ValueError: The number of required GPUs exceeds the total number of available GPUs in the cluster.

When i check the ray status again, i only see 4 GPUs. I am not sure why ray cant see my 8 GPus after i try to launch it with vllm when it is obviously visible before. I use the following command to launch /python -m vllm.entrypoints.openai.api_server --model ibm-granite/granite-34b-code-instruct --worker-use-ray --tensor-parallel-size 8 --trust-remote-code --port 40023 --host 0.0.0.0 --gpu-memory-utilization .65 --tokenizer ibm-granite/granite-34b-code-instruct --worker-use-ray

XMoyas commented 5 months ago

any solutions? I also got this question. when check the logs, work node have this errors:

2024-06-11 09:46:55,039 C 510 510] (raylet) node_manager.cc:1028: [Timeout] Exiting because this node manager has mistakenly been marked as dead by the GCS: GCS failed to check the health of this node for 5 times. This is likely because the machine or raylet has become overloaded. StackTrace Information /usr/local/lib/python3.10/dist-packages/ray/core/src/ray/raylet/raylet(+0xbc2f9a) [0x5643c763af9a] ray::operator<<() /usr/local/lib/python3.10/dist-packages/ray/core/src/ray/raylet/raylet(+0xbc52b1) [0x5643c763d2b1] ray::RayLog::~RayLog() /usr/local/lib/python3.10/dist-packages/ray/core/src/ray/raylet/raylet(+0x2fdaf1) [0x5643c6d75af1] ray::raylet::NodeManager::NodeRemoved() /usr/local/lib/python3.10/dist-packages/ray/core/src/ray/raylet/raylet(+0x4d0424) [0x5643c6f48424] ray::gcs::NodeInfoAccessor::HandleNotification() /usr/local/lib/python3.10/dist-packages/ray/core/src/ray/raylet/raylet(+0x5ed47c) [0x5643c706547c] EventTracker::RecordExecution() /usr/local/lib/python3.10/dist-packages/ray/core/src/ray/raylet/raylet(+0x5e905e) [0x5643c706105e] std::_Function_handler<>::_M_invoke() /usr/local/lib/python3.10/dist-packages/ray/core/src/ray/raylet/raylet(+0x5e94d6) [0x5643c70614d6] boost::asio::detail::completion_handler<>::do_complete() /usr/local/lib/python3.10/dist-packages/ray/core/src/ray/raylet/raylet(+0xca51fb) [0x5643c771d1fb] boost::asio::detail::scheduler::do_run_one() /usr/local/lib/python3.10/dist-packages/ray/core/src/ray/raylet/raylet(+0xca7789) [0x5643c771f789] boost::asio::detail::scheduler::run() /usr/local/lib/python3.10/dist-packages/ray/core/src/ray/raylet/raylet(+0xca7ca2) [0x5643c771fca2] boost::asio::io_context::run() /usr/local/lib/python3.10/dist-packages/ray/core/src/ray/raylet/raylet(+0x1d6e31) [0x5643c6c4ee31] main /usr/lib/x86_64-linux-gnu/libc.so.6(+0x29d90) [0x7f7acf4d1d90] /usr/lib/x86_64-linux-gnu/libc.so.6(libc_start_main+0x80) [0x7f7acf4d1e40] libc_start_main /usr/local/lib/python3.10/dist-packages/ray/core/src/ray/raylet/raylet(+0x22d847) [0x5643c6ca5847]

github-actions[bot] commented 3 weeks ago

This issue has been automatically marked as stale because it has not had any activity within 90 days. It will be automatically closed if no further activity occurs within 30 days. Leave a comment if you feel this issue should remain open. Thank you!