Repository: modelscope/evalscope
Issue: 输出的ttft时间与vllm收集的ttft时间相差较大
Root cause: Documentation Deficiency

## 问题描述 / Issue Description
[evalscope]输出的ttft时间对比vllm收集的时间相差比较多

请简要描述您遇到的问题。 / Please briefly describe the issue you encountered.
测试命令
evalscope perf \
--model deepseek_r1 \
--url http://127.0.0.1/:9112/v1/chat/completions \
--api openai \
--dataset random \
--min-tokens 800 \
--max-tokens 800 \
--prefix-length 10 \
--min-prompt-length 16 \
--max-prompt-length 16 \
--tokenizer-path /root/models/DeepSeek-R1-Distill-Qwen-1.5B \
--stream \
--number 1000 \
--parallel 10
测试过程数据ttft=0.047
Processing: 471it [06:15,  1.28it/s]2025-03-20 17:05:25,055 - evalscope - INFO - {
  "Time taken for tests (s)": 375.537,
  "Number of concurrency": 10,
  "Total requests": 480,
  "Succeed requests": 480,
  "Failed requests": 0,
  "Throughput(average tokens/s)": 1022.536,
  "Average QPS": 1.278,
  "Average latency (s)": 7.823,
  "Average time to first token (s)": 0.047,
  "Average time per output token (s)": 0.01,
  "Average input tokens per request": 26.558,
  "Average output tokens per request": 800.0,
  "Average package latency (s)": 0.01,
  "Average package per request": 800.0

vllm收集的数据：sum/count=0.0164

curl http://localhost:9112/metrics | grep first


100 25011    0 25011    0     0  75# HELP vllm:time_to_first_token_seconds Histogram of time to first token in seconds.
29# TYPE vllm:time_to_first_token_seconds histogram
k vllm:time_to_first_token_seconds_sum{model_name="deepseek_r1"} 7.895060062408447
  vllm:time_to_first_token_seconds_bucket{le="0.001",model_name="deepseek_r1"} 0.0
 vllm:time_to_first_token_seconds_bucket{le="0.005",model_name="deepseek_r1"} 0.0
  vllm:time_to_first_token_seconds_bucket{le="0.01",model_name="deepseek_r1"} 0.0
0vllm:time_to_first_token_seconds_bucket{le="0.02",model_name="deepseek_r1"} 433.0
 vllm:time_to_first_token_seconds_bucket{le="0.04",model_name="deepseek_r1"} 481.0
-vllm:time_to_first_token_seconds_bucket{le="0.06",model_name="deepseek_r1"} 481.0
-:vllm:time_to_first_token_seconds_bucket{le="0.08",model_name="deepseek_r1"} 481.0
-vllm:time_to_first_token_seconds_bucket{le="0.1",model_name="deepseek_r1"} 481.0
-vllm:time_to_first_token_seconds_bucket{le="0.25",model_name="deepseek_r1"} 481.0
:vllm:time_to_first_token_seconds_bucket{le="0.5",model_name="deepseek_r1"} 481.0
--vllm:time_to_first_token_seconds_bucket{le="0.75",model_name="deepseek_r1"} 481.0
 vllm:time_to_first_token_seconds_bucket{le="1.0",model_name="deepseek_r1"} 481.0
-vllm:time_to_first_token_seconds_bucket{le="2.5",model_name="deepseek_r1"} 481.0
-vllm:time_to_first_token_seconds_bucket{le="5.0",model_name="deepseek_r1"} 481.0
:-vllm:time_to_first_token_seconds_bucket{le="7.5",model_name="deepseek_r1"} 481.0
-:vllm:time_to_first_token_seconds_bucket{le="10.0",model_name="deepseek_r1"} 481.0
-vllm:time_to_first_token_seconds_bucket{le="+Inf",model_name="deepseek_r1"} 481.0
-vllm:time_to_first_token_seconds_count{model_name="deepseek_r1"} 481.0


## 使用的工具 / Tools Used
- [ ] Native / 原生框架
- [ ] Opencompass backend
- [ ] VLMEvalKit backend
- [ ] RAGEval backend
- [x] Perf / 模型推理压测工具
- [ ] Arena  /竞技场模式




## 运行环境 / Runtime Environment

- 操作系统 / Operating System:
  - [ ] Windows
  - [ ] macOS
  - [x] Ubuntu

- Python版本 / Python Version:
  - [x] 3.12
  - [ ] 3.10
  - [ ] 3.9


evalscope得到的TTFT是end-to-end的时间，从请求发出开始计时，到接受到第一个token结束，中间有网络传输和处理时间，跟服务端统计结果可能有些偏差

> 感谢你的反馈！我们将关闭此问题。如果您有任何疑问，请随时重新打开它。如果EvalScope对您有所帮助，欢迎给我们点个STAR以示支持，谢谢！