vllm-project / vllm

A high-throughput and memory-efficient inference and serving engine for LLMs
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[Bug]: Engine loop has died for Meta-Llama-3.1-8B-Instruct TP=2 #10102

Open HaoyuWang4188 opened 1 day ago

HaoyuWang4188 commented 1 day ago

Your current environment

The output of `python collect_env.py` ```text 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: Ubuntu 22.04.2 LTS (x86_64) GCC version: (Ubuntu 12.3.0-1ubuntu1~22.04) 12.3.0 Clang version: Could not collect CMake version: version 3.27.6 Libc version: glibc-2.35 Python version: 3.10.14 | packaged by conda-forge | (main, Mar 20 2024, 12:45:18) [GCC 12.3.0] (64-bit runtime) Python platform: Linux-6.8.0-45-generic-x86_64-with-glibc2.35 Is CUDA available: True CUDA runtime version: 11.5.119 CUDA_MODULE_LOADING set to: LAZY GPU models and configuration: GPU 0: NVIDIA RTX 6000 Ada Generation GPU 1: NVIDIA RTX 6000 Ada Generation Nvidia driver version: 550.107.02 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 Address sizes: 46 bits physical, 48 bits virtual Byte Order: Little Endian CPU(s): 36 On-line CPU(s) list: 0-35 Vendor ID: GenuineIntel Model name: Intel(R) Core(TM) i9-7980XE CPU @ 2.60GHz CPU family: 6 Model: 85 Thread(s) per core: 2 Core(s) per socket: 18 Socket(s): 1 Stepping: 4 CPU max MHz: 4000.0000 CPU min MHz: 1200.0000 BogoMIPS: 5199.98 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 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 pti ssbd mba ibrs ibpb stibp tpr_shadow flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 hle avx2 smep bmi2 erms invpcid rtm 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 vnmi md_clear flush_l1d arch_capabilities Virtualization: VT-x L1d cache: 576 KiB (18 instances) L1i cache: 576 KiB (18 instances) L2 cache: 18 MiB (18 instances) L3 cache: 24.8 MiB (1 instance) NUMA node(s): 1 NUMA node0 CPU(s): 0-35 Vulnerability Gather data sampling: Mitigation; Microcode Vulnerability Itlb multihit: KVM: Mitigation: VMX disabled Vulnerability L1tf: Mitigation; PTE Inversion; VMX conditional cache flushes, SMT vulnerable Vulnerability Mds: Mitigation; Clear CPU buffers; SMT vulnerable Vulnerability Meltdown: Mitigation; PTI Vulnerability Mmio stale data: Mitigation; Clear CPU buffers; SMT vulnerable Vulnerability Reg file data sampling: Not affected Vulnerability Retbleed: Mitigation; IBRS 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; IBRS; IBPB conditional; STIBP conditional; RSB filling; PBRSB-eIBRS Not affected; BHI Not affected Vulnerability Srbds: Not affected Vulnerability Tsx async abort: Mitigation; Clear CPU buffers; SMT vulnerable Versions of relevant libraries: [pip3] flashinfer==0.1.6+cu124torch2.4 [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==12.6.20 [pip3] nvidia-nvtx-cu12==12.1.105 [pip3] pyzmq==26.2.0 [pip3] torch==2.4.0 [pip3] torchvision==0.19.0 [pip3] transformers==4.45.2 [pip3] triton==3.0.0 [conda] No relevant packages ROCM Version: Could not collect Neuron SDK Version: N/A vLLM Version: N/A (dev) vLLM Build Flags: CUDA Archs: Not Set; ROCm: Disabled; Neuron: Disabled GPU Topology: GPU0 GPU1 CPU Affinity NUMA Affinity GPU NUMA ID GPU0 X SYS 0-35 0 N/A GPU1 SYS X 0-35 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 ```

Model Input Dumps

No response

🐛 Describe the bug

The error message:

INFO 11-07 19:58:29 metrics.py:345] Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 0.0 tokens/s, Running: 0 reqs, Swapped: 0 reqs, Pending: 0 reqs, GPU KV cache usage: 0.0%, CPU KV cache usage: 0.0%.
DEBUG 11-07 19:58:29 engine.py:215] Waiting for new requests in engine loop.
INFO 11-07 19:58:30 logger.py:37] Received request chat-d4bc2e3f9df643d5ae0a7712c781874c: prompt: '<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n\nCutting Knowledge Date: December 2023\nToday Date: 07 Nov 2024\n\nYou are a helpful assistant.<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nTell me something about large language models.<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n', params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.05, temperature=0.7, top_p=0.8, top_k=-1, min_p=0.0, seed=None, stop=[], stop_token_ids=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=8143, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None), guided_decoding=GuidedDecodingParams(json=None, regex=None, choice=None, grammar=None, json_object=None, backend=None, whitespace_pattern=None), prompt_token_ids: [128000, 128006, 9125, 128007, 271, 38766, 1303, 33025, 2696, 25, 6790, 220, 2366, 18, 198, 15724, 2696, 25, 220, 2589, 4723, 220, 2366, 19, 271, 2675, 527, 264, 11190, 18328, 13, 128009, 128006, 882, 128007, 271, 41551, 757, 2555, 922, 3544, 4221, 4211, 13, 128009, 128006, 78191, 128007, 271], lora_request: None, prompt_adapter_request: None.
DEBUG 11-07 19:58:30 async_llm_engine.py:525] Building guided decoding logits processor. Params: GuidedDecodingParams(json=None, regex=None, choice=None, grammar=None, json_object=None, backend=None, whitespace_pattern=None)
DEBUG 11-07 19:58:35 client.py:154] Heartbeat successful.
DEBUG 11-07 19:58:35 client.py:170] Waiting for output from MQLLMEngine.
DEBUG 11-07 19:58:35 client.py:154] Heartbeat successful.
INFO 11-07 19:58:35 engine.py:292] Added request chat-d4bc2e3f9df643d5ae0a7712c781874c.
DEBUG 11-07 19:58:35 client.py:154] Heartbeat successful.
DEBUG 11-07 19:58:37 client.py:154] Heartbeat successful.
DEBUG 11-07 19:58:39 client.py:154] Heartbeat successful.
DEBUG 11-07 19:58:41 client.py:154] Heartbeat successful.
DEBUG 11-07 19:58:43 client.py:154] Heartbeat successful.
DEBUG 11-07 19:58:45 client.py:170] Waiting for output from MQLLMEngine.
DEBUG 11-07 19:58:45 client.py:154] Heartbeat successful.
DEBUG 11-07 19:58:47 client.py:154] Heartbeat successful.
DEBUG 11-07 19:58:49 client.py:154] Heartbeat successful.
DEBUG 11-07 19:58:51 client.py:154] Heartbeat successful.
DEBUG 11-07 19:58:53 client.py:154] Heartbeat successful.
DEBUG 11-07 19:58:55 client.py:170] Waiting for output from MQLLMEngine.
DEBUG 11-07 19:58:55 client.py:154] Heartbeat successful.
DEBUG 11-07 19:58:57 client.py:154] Heartbeat successful.
DEBUG 11-07 19:58:59 client.py:154] Heartbeat successful.
ERROR 11-07 19:59:01 client.py:250] RuntimeError('Engine loop has died')
ERROR 11-07 19:59:01 client.py:250] Traceback (most recent call last):
ERROR 11-07 19:59:01 client.py:250]   File "/home/wanghaoy/.local/lib/python3.10/site-packages/vllm/engine/multiprocessing/client.py", line 150, in run_heartbeat_loop
ERROR 11-07 19:59:01 client.py:250]     await self._check_success(
ERROR 11-07 19:59:01 client.py:250]   File "/home/wanghaoy/.local/lib/python3.10/site-packages/vllm/engine/multiprocessing/client.py", line 314, in _check_success
ERROR 11-07 19:59:01 client.py:250]     raise response
ERROR 11-07 19:59:01 client.py:250] RuntimeError: Engine loop has died
DEBUG 11-07 19:59:05 client.py:170] Waiting for output from MQLLMEngine.
CRITICAL 11-07 19:59:05 launcher.py:99] MQLLMEngine is already dead, terminating server process
INFO:     10.239.82.78:57882 - "POST /v1/chat/completions HTTP/1.1" 500 Internal Server Error
INFO:     Shutting down
INFO:     Waiting for application shutdown.
INFO:     Application shutdown complete.
INFO:     Finished server process [116760]

My serving script is

CUDA_VISIBLE_DEVICES=0,1 python -m vllm.entrypoints.openai.api_server \
    --host 0.0.0.0 --port 11600 \
    --gpu_memory_utilization 0.50 \
    --tensor_parallel_size 2 \
    --model local_ckpt/Meta-Llama-3.1-8B-Instruct --enforce-eager \
    --enable-auto-tool-choice --tool-call-parser llama3_json --chat-template examples/tool_chat_template_llama3.1_json.jinja \
    --max_model_len 8192  

And my test script is

curl http://<public_ip>:11600/v1/chat/completions -H "Content-Type: application/json" -d '{
  "model": "local_ckpt/Meta-Llama-3.1-8B-Instruct",
  "messages": [
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": "Tell me something about large language models."}
  ],
  "temperature": 0.7,
  "top_p": 0.8,
  "repetition_penalty": 1.05
}'

This setting works well for a single GPU but fails when tp=2. Each time I start the request, the llm_engine just waits for a response and then times out.

My unsuccessful attempts to fix this issue include:

I guess there might be some environment setting to be corrected for me because TP is a basic feature.

Before submitting a new issue...

lostz commented 1 day ago

Same as I.