Adding scripts to prepare load data. (#1841)
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@ -13,3 +13,4 @@ server/exllama_kernels/exllama_kernels/hip_buffers.cuh
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server/exllama_kernels/exllama_kernels/exllama_ext_hip.cpp
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data/
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load_tests/*.json
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@ -0,0 +1,9 @@
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ShareGPT_V3_unfiltered_cleaned_split.json:
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wget https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/resolve/main/ShareGPT_V3_unfiltered_cleaned_split.json
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prepare_share: ShareGPT_V3_unfiltered_cleaned_split.json
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python filter.py
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prepare_orca:
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python orca.py
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@ -0,0 +1,26 @@
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import json
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def main():
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with open("./ShareGPT_V3_unfiltered_cleaned_split.json", "r") as f:
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data = json.load(f)
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# Select only the first 2k conversations that start with a human.
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max = 2000
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conversations = []
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for conversation in data:
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conv = conversation.get("conversations")
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if conv and conv[0]["from"] == "human":
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# Trim the rest of the output
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conversation["conversations"] = conversation["conversations"][:1]
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conversations.append(conversation)
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if len(conversation) >= max:
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break
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with open("./small.json", "w") as f:
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data = json.dump(conversations, f, indent=4)
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if __name__ == "__main__":
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main()
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@ -0,0 +1,27 @@
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import json
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import datasets
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import tqdm
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def main():
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dataset = datasets.load_dataset("Open-Orca/OpenOrca", split="train")
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# Select only the first 2k conversations that start with a human.
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max = min(2000, len(dataset))
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conversations = []
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for item in tqdm.tqdm(dataset, total=max):
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conversation = {
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"conversations": [
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{"from": "human", "value": item["question"]},
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],
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"id": item["id"],
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}
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conversations.append(conversation)
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if len(conversations) >= max:
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break
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with open("./small.json", "w") as f:
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data = json.dump(conversations, f, indent=4)
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if __name__ == "__main__":
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main()
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@ -1,63 +0,0 @@
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import {check} from 'k6';
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import http from 'k6/http';
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import {Trend} from 'k6/metrics';
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const host = __ENV.HOST || '127.0.0.1:3000';
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const totalTime = new Trend('total_time', true);
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const validationTime = new Trend('validation_time', true);
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const queueTime = new Trend('queue_time', true);
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const inferenceTime = new Trend('inference_time', true);
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const timePerToken = new Trend('time_per_token', true);
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const example = {
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payload: JSON.stringify({
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inputs: '# This is a fibonacci function written in the Python programming language.' +
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'def fibonacci',
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parameters: {
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details: true,
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max_new_tokens: 60,
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temperature: 0.2,
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top_p: 0.95,
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seed: 0,
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},
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}),
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generated_tokens: 60
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};
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export const options = {
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thresholds: {
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http_req_failed: ['rate==0'],
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time_per_token: ['p(95)<90'],
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queue_time: ['p(95)<1500'],
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},
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scenarios: {
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load_test: {
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executor: 'constant-arrival-rate',
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duration: '60s',
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preAllocatedVUs: 100,
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rate: 10,
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timeUnit: '1s',
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},
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},
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};
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export default function () {
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const headers = {'Content-Type': 'application/json'};
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const res = http.post(`http://${host}/generate`, example.payload, {
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headers,
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});
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check(res, {
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'Post status is 200': (r) => res.status === 200,
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'Post response generated tokens': (r) => res.status === 200 && res.json().details.generated_tokens === example.generated_tokens,
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});
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if (res.status === 200) {
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totalTime.add(res.headers["X-Total-Time"]);
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validationTime.add(res.headers["X-Validation-Time"]);
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queueTime.add(res.headers["X-Queue-Time"]);
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inferenceTime.add(res.headers["X-Inference-Time"]);
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timePerToken.add(res.headers["X-Time-Per-Token"]);
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}
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}
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