[Usage]: "generate 404 Not Found" when benchmark_serving.py running

[lzcchl]

Your current environment

The output of `python collect_env.py`

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: 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 3.29.1
Libc version: glibc-2.35

Python version: 3.8.19 (default, Mar 20 2024, 19:58:24) [GCC 11.2.0] (64-bit runtime)
Python platform: Linux-5.15.0-101-generic-x86_64-with-glibc2.17
Is CUDA available: True
CUDA runtime version: 12.1.105
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration:
GPU 0: NVIDIA GeForce RTX 4090
GPU 1: NVIDIA GeForce RTX 4090
GPU 2: NVIDIA GeForce RTX 4090
GPU 3: NVIDIA GeForce RTX 4090

Nvidia driver version: 545.29.06
cuDNN version: Probably one of the following:
/usr/lib/x86_64-linux-gnu/libcudnn.so.8.9.7
/usr/lib/x86_64-linux-gnu/libcudnn_adv_infer.so.8.9.7
/usr/lib/x86_64-linux-gnu/libcudnn_adv_train.so.8.9.7
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_infer.so.8.9.7
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_train.so.8.9.7
/usr/lib/x86_64-linux-gnu/libcudnn_ops_infer.so.8.9.7
/usr/lib/x86_64-linux-gnu/libcudnn_ops_train.so.8.9.7
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, 57 bits virtual
Byte Order: Little Endian
CPU(s): 64
On-line CPU(s) list: 0-63
Vendor ID: GenuineIntel
Model name: Intel(R) Xeon(R) Gold 6326 CPU @ 2.90GHz
CPU family: 6
Model: 106
Thread(s) per core: 2
Core(s) per socket: 16
Socket(s): 2
Stepping: 6
BogoMIPS: 5800.00
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
Virtualization: VT-x
L1d cache: 1.5 MiB (32 instances)
L1i cache: 1 MiB (32 instances)
L2 cache: 40 MiB (32 instances)
L3 cache: 48 MiB (2 instances)
NUMA node(s): 2
NUMA node0 CPU(s): 0,2,4,6,8,10,12,14,16,18,20,22,24,26,28,30,32,34,36,38,40,42,44,46,48,50,52,54,56,58,60,62
NUMA node1 CPU(s): 1,3,5,7,9,11,13,15,17,19,21,23,25,27,29,31,33,35,37,39,41,43,45,47,49,51,53,55,57,59,61,63
Vulnerability Gather data sampling: Mitigation; Microcode
Vulnerability Itlb multihit: Not affected
Vulnerability L1tf: Not affected
Vulnerability Mds: Not affected
Vulnerability Meltdown: Not affected
Vulnerability Mmio stale data: Mitigation; Clear CPU buffers; SMT vulnerable
Vulnerability Retbleed: Not affected
Vulnerability Spec rstack overflow: Not affected
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; Enhanced IBRS, IBPB conditional, RSB filling, PBRSB-eIBRS SW sequence
Vulnerability Srbds: Not affected
Vulnerability Tsx async abort: Not affected

Versions of relevant libraries:
[pip3] numpy==1.24.4
[pip3] torch==2.1.2+cu121
[pip3] triton==2.1.0
[pip3] tritonclient==2.41.0
[conda] numpy 1.24.4 pypi_0 pypi
[conda] torch 2.1.2+cu121 pypi_0 pypi
[conda] triton 2.1.0 pypi_0 pypi
[conda] tritonclient 2.41.0 pypi_0 pypiROCM Version: Could not collect
Neuron SDK Version: N/A
vLLM Version: 0.3.3
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 0,2,4,6,8,10 0 N/A
GPU1 SYS X SYS SYS 0,2,4,6,8,10 0 N/A
GPU2 SYS SYS X SYS 1,3,5,7,9,11 1 N/A
GPU3 SYS SYS SYS X 1,3,5,7,9,11 1 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

How would you like to use vllm

I want to know the throughput about my devices (4x4090), and I find there are some code could be run, just like vllm/benchmark/benchmark_serving.py and vllm/benchmark/benchmark_throughput.py. benchmark_throughput.py could be run very well, but when I run benchmark_serving.py, I get error:

my server side commands:
CUDA_VISIBLE_DEVICES=0,1,2,3 python3 -m vllm.entrypoints.openai.api_server
--model=/home/zhicheng.luo/llm/weights/Qwen1.5-72B-Chat-GPTQ-Int4
--served-model-name=gpt-4
--dtype=float16
--tensor-parallel-size=4
--quantization=gptq
--trust-remote-code
--gpu-memory-utilization=0.85
--max-model-len=8192
--swap-space 16
--disable-log-requests

my client side commands:
python benchmark_serving.py
--backend vllm
--dataset /home/zhicheng.luo/llm/files/ShareGPT_V3_unfiltered_cleaned_split.json
--model /home/zhicheng.luo/llm/weights/Qwen1.5-72B-Chat-GPTQ-Int4
--tokenizer /home/zhicheng.luo/llm/weights/Qwen1.5-72B-Chat-GPTQ-Int4
--num-prompts 100
--request-rate 10
--trust-remote-code

how to solve this problem?

[hmellor]

In 0.3.3 the default endpoint in benchmark_serving.py was /generate

vllm/benchmarks/benchmark_serving.py

Lines 324 to 329 in 82091b8

	parser.add_argument(
	"--endpoint",
	type=str,
	default="/generate",
	help="API endpoint.",
	)

if you update to 0.4.0 it has been updated to /v1/completions, which is correct for the OpenAI compatible server

vllm/benchmarks/benchmark_serving.py

Lines 474 to 479 in 51c31bc

	parser.add_argument(
	"--endpoint",
	type=str,
	default="/v1/completions",
	help="API endpoint.",
	)

If you don't want to update then simply set --endpoint /v1/completions.

You may also need to change --model to gpt-4 as that is the --served-model-name you have set.