search

vllm-project / vllm

A high-throughput and memory-efficient inference and serving engine for LLMs
https://docs.vllm.ai
Apache License 2.0
27.88k stars 4.11k forks source link

[Bug]: llm_engine_example.py (more requests) get stuck #4904

Closed CsRic closed 2 months ago

CsRic commented 4 months ago

Your current environment

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: Debian GNU/Linux 11 (bullseye) (x86_64)                                                                           
GCC version: (Debian 10.2.1-6) 10.2.1 20210110                                                                        
Clang version: Could not collect                                                                                      
CMake version: version 3.29.2                                                                                         
Libc version: glibc-2.31                                                                                              

Python version: 3.11.9 (main, Apr 19 2024, 16:48:06) [GCC 11.2.0] (64-bit runtime)                                    
Python platform: Linux-5.10.0-23-amd64-x86_64-with-glibc2.31                                                          
Is CUDA available: True                                                                                               
CUDA runtime version: 12.1.66                                                                                         
CUDA_MODULE_LOADING set to: LAZY                                                                                      
GPU models and configuration:                                                                                         
GPU 0: NVIDIA RTX A5000                                                                                               
GPU 1: NVIDIA RTX A5000                                                                                               
GPU 2: NVIDIA RTX A5000                                                                                               
GPU 3: NVIDIA RTX A5000                                                                                               

Nvidia driver version: 545.23.08                                                                                      
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                                                                        
Address sizes:                   43 bits physical, 48 bits virtual                                                    
CPU(s):                          32                                                                                   
On-line CPU(s) list:             0-31                                                                                 
Thread(s) per core:              1                                                                                    
Core(s) per socket:              16                                                                                   
Socket(s):                       2                                                                                    
NUMA node(s):                    8                                                                                    
Vendor ID:                       AuthenticAMD                            
CPU family:                      23
Model:                           49
Model name:                      AMD EPYC 7302 16-Core Processor
Stepping:                        0
Frequency boost:                 enabled
CPU MHz:                         1730.206
CPU max MHz:                     3310.5459
CPU min MHz:                     1500.0000
BogoMIPS:                        5988.84
Virtualization:                  AMD-V
L1d cache:                       1 MiB
L1i cache:                       1 MiB
L2 cache:                        16 MiB
L3 cache:                        256 MiB
NUMA node0 CPU(s):               0-3
NUMA node1 CPU(s):               4-7
NUMA node2 CPU(s):               8-11
NUMA node3 CPU(s):               12-15
NUMA node4 CPU(s):               16-19
NUMA node5 CPU(s):               20-23
NUMA node6 CPU(s):               24-27
NUMA node7 CPU(s):               28-31
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:          Mitigation; untrained return thunk; SMT disabled
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, STIBP disabled, RSB filling, PBRSB-eIBRS No
t affected
Vulnerability Srbds:             Not affected
Vulnerability Tsx async abort:   Not affected
Flags:                           fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx f
xsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc rep_good nopl nonstop_tsc cpuid extd_apicid 
aperfmperf pni pclmulqdq monitor ssse3 fma cx16 sse4_1 sse4_2 movbe popcnt aes xsave avx f16c rdrand lahf_lm cmp_legac
y svm extapic cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw ibs skinit wdt tce topoext perfctr_core perfctr_nb b
pext perfctr_llc mwaitx cpb cat_l3 cdp_l3 hw_pstate sme ssbd mba sev ibrs ibpb stibp vmmcall sev_es fsgsbase bmi1 avx2
 smep bmi2 cqm rdt_a rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 cqm_llc cqm_occup_llc cqm_mbm_tota
l cqm_mbm_local clzero irperf xsaveerptr rdpru wbnoinvd arat npt lbrv svm_lock nrip_save tsc_scale vmcb_clean flushbya
sid decodeassists pausefilter pfthreshold avic v_vmsave_vmload vgif umip rdpid overflow_recov succor smca

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    NIC0    CPU Affinity    NUMA Affinity   GPU NUMA ID
GPU0     X      SYS     SYS     SYS     SYS     12-15           N/A             N/A
GPU1    SYS      X      SYS     SYS     SYS     8-11    2               N/A
GPU2    SYS     SYS      X      SYS     SYS     28-31           N/A             N/A
GPU3    SYS     SYS     SYS      X      SYS     20-23   5               N/A
NIC0    SYS     SYS     SYS     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

I modified examples/llm_engine_example.py to test a large number of requests. With 200 requests of 32 random tokens, the engine get stuck and never produce a full answer.

llm_engine_example_heavy.py:

import argparse
from typing import List, Tuple
from vllm import EngineArgs, LLMEngine, RequestOutput, SamplingParams
import numpy as np

def create_test_prompt_nonsense(args: argparse.Namespace) -> List[Tuple[str, SamplingParams]]:
    length = args.input_len
    return (np.random.randint(10000, size=(length)).tolist(),
            SamplingParams(temperature=0.0, logprobs=1, prompt_logprobs=1))

def process_requests(engine: LLMEngine,
                     test_prompts: List[Tuple[str, SamplingParams]]):
    """Continuously process a list of prompts and handle the outputs."""
    request_id = 0

    while test_prompts or engine.has_unfinished_requests():
        if test_prompts:
            prompt_token_ids, sampling_params = test_prompts.pop(0)
            engine.add_request(str(request_id), None, sampling_params, prompt_token_ids)
            request_id += 1

        request_outputs: List[RequestOutput] = engine.step()

        for request_output in request_outputs:
            if request_output.finished:
                print(request_output)

def initialize_engine(args: argparse.Namespace) -> LLMEngine:
    """Initialize the LLMEngine from the command line arguments."""
    engine_args = EngineArgs.from_cli_args(args)
    return LLMEngine.from_engine_args(engine_args)

def main(args: argparse.Namespace):
    """Main function that sets up and runs the prompt processing."""
    engine = initialize_engine(args)
    test_prompts = []
    for i in range(args.test_num):
        test_prompts.append(create_test_prompt_nonsense(args))
    process_requests(engine, test_prompts)

if __name__ == '__main__':
    parser = argparse.ArgumentParser(
        description='Demo on using the LLMEngine class directly')
    parser = EngineArgs.add_cli_args(parser)
    parser.add_argument('--input-len', type=int, default=128)
    parser.add_argument('--test-num', type=int, default=100)
    args = parser.parse_args()
    main(args)

A successful run:

python llm_engine_example_heavy.py --model facebook/opt-125m \
 --input-len 32 \
 --test-num 10

output:

INFO 05-18 22:55:28 llm_engine.py:103] Initializing an LLM engine (v0.4.2) with config: model='facebook/opt-125m', spe
culative_config=None, tokenizer='facebook/opt-125m', skip_tokenizer_init=False, tokenizer_mode=auto, revision=None, to
kenizer_revision=None, trust_remote_code=False, dtype=torch.float16, max_seq_len=2048, download_dir=None, load_format=
LoadFormat.AUTO, tensor_parallel_size=1, disable_custom_all_reduce=False, quantization=None, enforce_eager=False, kv_c
ache_dtype=auto, quantization_param_path=None, device_config=cuda, decoding_config=DecodingConfig(guided_decoding_back
end='outlines'), seed=0, served_model_name=facebook/opt-125m)
INFO 05-18 22:55:31 selector.py:37] Using FlashAttention-2 backend.
INFO 05-18 22:55:31 weight_utils.py:199] Using model weights format ['*.bin']
INFO 05-18 22:55:31 model_runner.py:145] Loading model weights took 0.2389 GB
INFO 05-18 22:55:32 gpu_executor.py:83] # GPU blocks: 36865, # CPU blocks: 7281
INFO 05-18 22:55:34 model_runner.py:824] 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 05-18 22:55:34 model_runner.py:828] CUDA graphs can take additional 1~3 GiB memory per GPU. If you are running ou
t of memory, consider decreasing `gpu_memory_utilization` or enforcing eager mode. You can also reduce the `max_num_se
qs` as needed to decrease memory usage.
INFO 05-18 22:55:37 model_runner.py:894] Graph capturing finished in 3 secs.
RequestOutput(request_id=0, prompt=None, prompt_token_ids=[2732, 9845, 3264, 4859, 9225, 7891, 4373, 5874, 6744, 3468,
 705, 2599, 2222, 7768, 2897, 9893, 537, 6216, 6921, 6036, 2163, 5072, 4851, 7877, 2046, 1871, 7599, 2496, 8291, 755, 
797, 659], prompt_logprobs=[None, {9845: Logprob(logprob=-12.68840217590332, rank=7125, decoded_token=' emerge'), 4: L
ogprob(logprob=-1.9188706874847412, rank=1, decoded_token='.')}, {3264: Logprob(logprob=-14.21910285949707, rank=6125,
 decoded_token=' accept'), 31: Logprob(logprob=-1.1546498537063599, rank=1, decoded_token=' from')}, {4859: Logprob(lo
gprob=-12.61778450012207, rank=4317, decoded_token=' buyers'), 4735: Logprob(logprob=-0.4537220001220703, rank=1, deco
ded_token='ably')}, {9225: Logprob(logprob=-16.543548583984375, rank=25278, decoded_token=' Kurdish'),

...

I omitted the rest. All answers are printed. The program terminated normally.

A failed run, change --test-num from 10 to 200:

python llm_engine_example_heavy.py --model facebook/opt-125m \
 --input-len 32 \
 --test-num 200

output:

INFO 05-18 22:58:38 llm_engine.py:103] Initializing an LLM engine (v0.4.2) with config: model='facebook/opt-125m', speculative_config=None, tokenizer='facebook/opt-125m', skip_tokenizer_init=False, tokenizer_mode=auto, revision=None, tokenizer_revision=None, trust_remote_code=False, dtype=torch.float16, max_seq_len=2048, download_dir=None, load_format=LoadFormat.AUTO, tensor_parallel_size=1, disable_custom_all_reduce=False, quantization=None, enforce_eager=False, kv_cache_dtype=auto, quantization_param_path=None, device_config=cuda, decoding_config=DecodingConfig(guided_decoding_backend='outlines'), seed=0, served_model_name=facebook/opt-125m)
INFO 05-18 22:58:42 selector.py:37] Using FlashAttention-2 backend.
INFO 05-18 22:58:43 weight_utils.py:199] Using model weights format ['*.bin']
INFO 05-18 22:58:43 model_runner.py:145] Loading model weights took 0.2389 GB
INFO 05-18 22:58:43 gpu_executor.py:83] # GPU blocks: 36865, # CPU blocks: 7281
INFO 05-18 22:58:45 model_runner.py:824] 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 05-18 22:58:45 model_runner.py:828] 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 05-18 22:58:48 model_runner.py:894] Graph capturing finished in 3 secs.
^C[rank0]: Traceback (most recent call last):
[rank0]:   File "/[my workpath]/vllm/examples/llm_engine_example_heavy.py", line 54, in <module>
[rank0]:     main(args)
[rank0]:   File "/[my workpath]/vllm/examples/llm_engine_example_heavy.py", line 44, in main
[rank0]:     process_requests(engine, test_prompts)
[rank0]:   File "/[my workpath]/vllm/examples/llm_engine_example_heavy.py", line 25, in process_requests
[rank0]:     request_outputs: List[RequestOutput] = engine.step()
[rank0]:                                            ^^^^^^^^^^^^^
[rank0]:   File "/[my workpath]/vllm/vllm/engine/llm_engine.py", line 686, in step
[rank0]:     request_outputs = self._process_model_outputs(
[rank0]:                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank0]:   File "/[my workpath]/vllm/vllm/engine/llm_engine.py", line 599, in _process_model_outputs
[rank0]:     self.output_processor.process_prompt_logprob(seq_group, outputs)
[rank0]:   File "/[my workpath]/vllm/vllm/engine/output_processor/single_step.py", line 65, in process_prompt_logprob
[rank0]:     self.detokenizer.decode_prompt_logprobs_inplace(
[rank0]:   File "/[my workpath]/vllm/vllm/transformers_utils/detokenizer.py", line 60, in decode_prompt_logprobs_inplace
[rank0]:     new_read_offset) = detokenize_incrementally(
[rank0]:                        ^^^^^^^^^^^^^^^^^^^^^^^^^
[rank0]:   File "/[my workpath]/vllm/vllm/transformers_utils/detokenizer.py", line 287, in detokenize_incrementally
[rank0]:     prefix_text = tokenizer.convert_tokens_to_string(
[rank0]:                   ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank0]:   File "/[my conda env path]/lib/python3.11/site-packages/transformers/tokenization_utils_fast.py", line 612, in convert_tokens_to_string
[rank0]:     return self.backend_tokenizer.decoder.decode(tokens)
[rank0]:            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank0]: KeyboardInterrupt

Before I hit ctrl+c, the program stuck for 1 hour. The GPU activiy is 0%. The traceback always show self.backend_tokenizer.decoder.decode(tokens) as the latest position.

zifeitong commented 4 months ago

Minimal reproducer:

import numpy as np

from vllm import LLM, SamplingParams

def main():
    llm = LLM(model="facebook/opt-125m")
    test_prompts = np.random.randint(10000, size=(200, 32)).tolist()
    outputs = llm.generate(
        prompt_token_ids=test_prompts,
        sampling_params=SamplingParams(
            temperature=0.0, logprobs=1, prompt_logprobs=1
        ),
    )

    for output in outputs:
        print(output)

if __name__ == "__main__":
    main()

It's affecting using EleutherAI/lm-evaluation-harness with vLLM.