[Bug]: Unable to process request

[JPonsa]

Your current environment

The output of `poetry run python collect_env.py`

Collecting environment information... PyTorch version: 2.1.2+cu121 Is debug build: False CUDA used to build PyTorch: 12.1 ROCM used to build PyTorch: N/A

OS: Red Hat Enterprise Linux Server release 7.9 (Maipo) (x86_64) GCC version: (GCC) 4.9.2 Clang version: Could not collect CMake version: version 3.29.2 Libc version: glibc-2.17

Python version: 3.11.3 (main, Apr 28 2023, 13:12:35) [GCC 4.9.2] (64-bit runtime) Python platform: Linux-3.10.0-1160.53.1.el7.x86_64-x86_64-with-glibc2.17 Is CUDA available: True CUDA runtime version: 7.5.17 CUDA_MODULE_LOADING set to: LAZY GPU models and configuration: GPU 0: Tesla V100-PCIE-32GB Nvidia driver version: 535.54.03 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): 36 On-line CPU(s) list: 0-35 Thread(s) per core: 1 Core(s) per socket: 18 Socket(s): 2 NUMA node(s): 2 Vendor ID: GenuineIntel CPU family: 6 Model: 85 Model name: Intel(R) Xeon(R) Gold 6240 CPU @ 2.60GHz Stepping: 7 CPU MHz: 2599.682 CPU max MHz: 2600.0000 CPU min MHz: 1000.0000 BogoMIPS: 5200.00 Virtualization: VT-x L1d cache: 32K L1i cache: 32K L2 cache: 1024K L3 cache: 25344K NUMA node0 CPU(s): 0-17 NUMA node1 CPU(s): 18-35 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 aperfmperf eagerfpu pni pclmulqdq dtes64 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 epb cat_l3 cdp_l3 invpcid_single intel_ppin intel_pt ssbd mba ibrs ibpb stibp ibrs_enhanced tpr_shadow vnmi flexpriority ept vpid fsgsbase tsc_adjust bmi1 hle avx2 smep bmi2 erms invpcid rtm cqm mpx rdt_a avx512f avx512dq rdseed adx smap clflushopt clwb avx512cd avx512bw avx512vl xsaveopt xsavec xgetbv1 cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local dtherm arat pln pts pku ospke avx512_vnni md_clear spec_ctrl intel_stibp flush_l1d arch_capabilities

Versions of relevant libraries: [pip3] mypy-extensions==1.0.0 [pip3] numpy==1.26.4 [pip3] onnxruntime==1.16.3 [pip3] torch==2.1.2 [pip3] triton==2.1.0 [conda] Could not collectROCM Version: Could not collect Neuron SDK Version: N/A vLLM Version: 0.4.0.post1 vLLM Build Flags: CUDA Archs: Not Set; ROCm: Disabled; Neuron: Disabled GPU Topology: GPU0 NIC0 CPU Affinity NUMA Affinity GPU NUMA ID GPU0 X SYS 35 0-1 N/A NIC0 SYS 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

🐛 Describe the bug

Trying to run vLLM on a university's HPC.

It looks like it never leaves the stage of running "python -m vllm.entrypoints.openai.api_serve"

#!/bin/bash -l
#$ -N mistral_txt2SQL_eval
# Max run time in H:M:S
#$ -l h_rt=0:30:0
# Memory
#$ -l mem=30G
#$ -l gpu=1

# workig directory. Use #S -cwd to use current working dir
#$ -wd /home/user_id/Scratch/ctgov_rag/

module load openssl/1.1.1t python/3.11.3
module unload compilers mpi
module load compilers/gnu/4.9.2
module load cuda/7.5.18/gnu-4.9.2
module load ruse/2.0

pip install poetry
poetry run python -m vllm.entrypoints.openai.api_server --model mistralai/Mistral-7B-Instruct-v0.2 --port 8000 --dtype half
ruse --stdout --time=150 -s \
poetry run python ./src/txt2sql/txt2sql_dspy_test.py 

INFO 04-17 21:19:03 api_server.py:149] vLLM API server version 0.4.0.post1
INFO 04-17 21:19:03 api_server.py:150] args: Namespace(host=None, port=8000, uvicorn_log_level='info', allow_credentials=False, allowed_origins=['*'], allowed_methods=['*'], allowed_headers=['*'], api_key=None, served_model_name=None, lora_modules=None, chat_template=None, response_role='assistant', ssl_keyfile=None, ssl_certfile=None, ssl_ca_certs=None, ssl_cert_reqs=0, root_path=None, middleware=[], model='mistralai/Mistral-7B-Instruct-v0.2', tokenizer=None, revision=None, code_revision=None, tokenizer_revision=None, tokenizer_mode='auto', trust_remote_code=False, download_dir=None, load_format='auto', dtype='half', kv_cache_dtype='auto', max_model_len=None, worker_use_ray=False, pipeline_parallel_size=1, tensor_parallel_size=1, max_parallel_loading_workers=None, ray_workers_use_nsight=False, block_size=16, enable_prefix_caching=False, use_v2_block_manager=False, num_lookahead_slots=0, seed=0, swap_space=4, gpu_memory_utilization=0.9, forced_num_gpu_blocks=None, max_num_batched_tokens=None, max_num_seqs=256, max_logprobs=5, disable_log_stats=False, quantization=None, enforce_eager=False, max_context_len_to_capture=8192, disable_custom_all_reduce=False, tokenizer_pool_size=0, tokenizer_pool_type='ray', tokenizer_pool_extra_config=None, enable_lora=False, max_loras=1, max_lora_rank=16, lora_extra_vocab_size=256, lora_dtype='auto', max_cpu_loras=None, device='auto', image_input_type=None, image_token_id=None, image_input_shape=None, image_feature_size=None, scheduler_delay_factor=0.0, enable_chunked_prefill=False, engine_use_ray=False, disable_log_requests=False, max_log_len=None)
WARNING 04-17 21:19:04 config.py:767] Casting torch.bfloat16 to torch.float16.
INFO 04-17 21:19:04 llm_engine.py:74] Initializing an LLM engine (v0.4.0.post1) with config: model='mistralai/Mistral-7B-Instruct-v0.2', tokenizer='mistralai/Mistral-7B-Instruct-v0.2', tokenizer_mode=auto, revision=None, tokenizer_revision=None, trust_remote_code=False, dtype=torch.float16, max_seq_len=32768, download_dir=None, load_format=auto, tensor_parallel_size=1, disable_custom_all_reduce=False, quantization=None, enforce_eager=False, kv_cache_dtype=auto, device_config=cuda, seed=0)
INFO 04-17 21:19:06 selector.py:40] Cannot use FlashAttention backend for Volta and Turing GPUs.
INFO 04-17 21:19:06 selector.py:25] Using XFormers backend.
INFO 04-17 21:19:09 weight_utils.py:177] Using model weights format ['*.safetensors']
INFO 04-17 21:19:25 model_runner.py:104] Loading model weights took 13.4966 GB
INFO 04-17 21:19:32 gpu_executor.py:94] # GPU blocks: 5022, # CPU blocks: 2048
INFO 04-17 21:19:34 model_runner.py:791] Capturing the model for CUDA graphs. This may lead to unexpected consequences if the model is not static. To run the model in eager mode, set 'enforce_eager=True' or use '--enforce-eager' in the CLI.
INFO 04-17 21:19:34 model_runner.py:795] CUDA graphs can take additional 1~3 GiB memory per GPU. If you are running out of memory, consider decreasing `gpu_memory_utilization` or enforcing eager mode. You can also reduce the `max_num_seqs` as needed to decrease memory usage.
INFO 04-17 21:19:40 model_runner.py:867] Graph capturing finished in 6 secs.
INFO 04-17 21:19:40 serving_chat.py:331] Using default chat template:
INFO 04-17 21:19:40 serving_chat.py:331] {{ bos_token }}{% for message in messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% if message['role'] == 'user' %}{{ '[INST] ' + message['content'] + ' [/INST]' }}{% elif message['role'] == 'assistant' %}{{ message['content'] + eos_token}}{% else %}{{ raise_exception('Only user and assistant roles are supported!') }}{% endif %}{% endfor %}
INFO 04-17 21:19:50 metrics.py:218] 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%
INFO 04-17 21:20:00 metrics.py:218] 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%

[simon-mo]

Normally there should be log similar to

INFO ] Started server process [1754022]
INFO ] Uvicorn running on http://0.0.0.0:8000 (Press CTRL+C to quit)

Please check whether the port is open?

[JPonsa]

dear @simon-mo . It is it just appears in another server log

--2024-04-17 21:17:33--  https://raw.githubusercontent.com/vllm-project/vllm/main/collect_env.py
Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.111.133, 185.199.108.133, 185.199.110.133, ...
Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.111.133|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 24853 (24K) [text/plain]
Saving to: ‘collect_env.py’

     0K .......... .......... ....                            100% 19.6M=0.001s

2024-04-17 21:17:33 (19.6 MB/s) - ‘collect_env.py’ saved [24853/24853]

  File "collect_env.py", line 715
    print(msg, file=sys.stderr)
                   ^
SyntaxError: invalid syntax
INFO:     Started server process [83214]
INFO:     Waiting for application startup.
INFO:     Application startup complete.
INFO:     Uvicorn running on http://0.0.0.0:8000 (Press CTRL+C to quit)

[simon-mo]

I see. The API server is supposed to be running forever because it is a live server. This is the intended behavior.

For your use case, consider running it in a background process?

https://stackoverflow.com/questions/44222883/run-a-shell-script-and-immediately-background-it-however-keep-the-ability-to-in

[JPonsa]

@simon-mo Thanks a lot! It seems to be working. Fingers crossed. I also had to include a sleep steps to give time to the server to spin up on the background.