1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
|
import http from 'k6/http'
import {check, sleep} from 'k6'
import {SharedArray} from 'k6/data'
import {Counter, Rate, Trend} from 'k6/metrics'
import exec from 'k6/execution';
// Server chat completions prefix
const server_url = __ENV.SERVER_BENCH_URL ? __ENV.SERVER_BENCH_URL : 'http://localhost:8080/v1'
// Number of total prompts in the dataset - default 10m / 10 seconds/request * number of users
const n_prompt = __ENV.SERVER_BENCH_N_PROMPTS ? parseInt(__ENV.SERVER_BENCH_N_PROMPTS) : 600 / 10 * 8
// Model name to request
const model = __ENV.SERVER_BENCH_MODEL_ALIAS ? __ENV.SERVER_BENCH_MODEL_ALIAS : 'my-model'
// Dataset path
const dataset_path = __ENV.SERVER_BENCH_DATASET ? __ENV.SERVER_BENCH_DATASET : './ShareGPT_V3_unfiltered_cleaned_split.json'
// Max tokens to predict
const max_tokens = __ENV.SERVER_BENCH_MAX_TOKENS ? parseInt(__ENV.SERVER_BENCH_MAX_TOKENS) : 512
// Max prompt tokens
const n_prompt_tokens = __ENV.SERVER_BENCH_MAX_PROMPT_TOKENS ? parseInt(__ENV.SERVER_BENCH_MAX_PROMPT_TOKENS) : 1024
// Max slot context
const n_ctx_slot = __ENV.SERVER_BENCH_MAX_CONTEXT ? parseInt(__ENV.SERVER_BENCH_MAX_CONTEXT) : 2048
export function setup() {
console.info(`Benchmark config: server_url=${server_url} n_prompt=${n_prompt} model=${model} dataset_path=${dataset_path} max_tokens=${max_tokens}`)
}
const data = new SharedArray('conversations', function () {
const tokenizer = (message) => message.split(/[\s,'".?]/)
return JSON.parse(open(dataset_path))
// Filter out the conversations with less than 2 turns.
.filter(data => data["conversations"].length >= 2)
.filter(data => data["conversations"][0]["from"] === "human")
.map(data => {
return {
prompt: data["conversations"][0]["value"],
n_prompt_tokens: tokenizer(data["conversations"][0]["value"]).length,
n_completion_tokens: tokenizer(data["conversations"][1]["value"]).length,
}
})
// Filter out too short sequences
.filter(conv => conv.n_prompt_tokens >= 4 && conv.n_completion_tokens >= 4)
// Filter out too long sequences.
.filter(conv => conv.n_prompt_tokens <= n_prompt_tokens && conv.n_prompt_tokens + conv.n_completion_tokens <= n_ctx_slot)
// Keep only first n prompts
.slice(0, n_prompt)
})
const llamacpp_prompt_tokens = new Trend('llamacpp_prompt_tokens')
const llamacpp_completion_tokens = new Trend('llamacpp_completion_tokens')
const llamacpp_tokens_second = new Trend('llamacpp_tokens_second')
const llamacpp_prompt_tokens_total_counter = new Counter('llamacpp_prompt_tokens_total_counter')
const llamacpp_completion_tokens_total_counter = new Counter('llamacpp_completion_tokens_total_counter')
const llamacpp_completions_truncated_rate = new Rate('llamacpp_completions_truncated_rate')
const llamacpp_completions_stop_rate = new Rate('llamacpp_completions_stop_rate')
export const options = {
thresholds: {
llamacpp_completions_truncated_rate: [
// more than 80% of truncated input will abort the test
{threshold: 'rate < 0.8', abortOnFail: true, delayAbortEval: '1m'},
],
},
duration: '10m',
vus: 8,
}
export default function () {
const conversation = data[exec.scenario.iterationInInstance % data.length]
const payload = {
"messages": [
{
"role": "system",
"content": "You are ChatGPT, an AI assistant.",
},
{
"role": "user",
"content": conversation.prompt,
}
],
"model": model,
"stream": false,
"seed": 42,
"max_tokens": max_tokens
}
const body = JSON.stringify(payload)
let res = http.post(`${server_url}/chat/completions`, body, {
headers: {'Content-Type': 'application/json'},
timeout: '300s'
})
check(res, {'success completion': (r) => r.status === 200})
if (res.status === 200) {
const completions = res.json()
llamacpp_prompt_tokens.add(completions.usage.prompt_tokens)
llamacpp_prompt_tokens_total_counter.add(completions.usage.prompt_tokens)
llamacpp_completion_tokens.add(completions.usage.completion_tokens)
llamacpp_completion_tokens_total_counter.add(completions.usage.completion_tokens)
llamacpp_completions_truncated_rate.add(completions.choices[0].finish_reason === 'length')
llamacpp_completions_stop_rate.add(completions.choices[0].finish_reason === 'stop')
llamacpp_tokens_second.add(completions.usage.total_tokens / res.timings.duration * 1.e3)
} else {
console.error(`response: ${res.body} request=${payload}`)
}
sleep(0.3)
}
|
