profile_restful_api.py

import json
import multiprocessing as mp
import random
import time

import fire
import numpy as np

from lmdeploy.serve.openai.api_client import get_streaming_response
from lmdeploy.tokenizer import Tokenizer
from lmdeploy.utils import get_logger


def infer(server_addr: str, session_id: int, req_queue: mp.Queue,
          res_que: mp.Queue):
    stats = []
    while not req_queue.empty():
        prompt, input_seqlen, output_seqlen = req_queue.get()
        get_logger('profile_restful_api').info(
            f'request info: session {session_id}, '
            f'input_seqlen {input_seqlen}, output_seqlen {output_seqlen}')
        timestamps = []
        tokens = []
        start = time.perf_counter()
        for res, token, status in get_streaming_response(
                prompt,
                server_addr,
                session_id,
                request_output_len=output_seqlen,
                interactive_mode=False):
            timestamps.append(time.perf_counter())
            tokens.append(token)

        first_token_latency = timestamps[1] - start
        token_latency = timestamps[-1] - timestamps[0]
        token = tokens[-1] - tokens[0]
        stats.append([first_token_latency, token, token_latency])
    res_que.put((session_id, stats))


def warmup(server_addr: str,
           concurrency: int,
           output_seqlen: int,
           warmup_round: int = 1):
    print('start to warmup ...')

    def _infer(server_addr, session_id):
        for _ in range(warmup_round):
            for _ in get_streaming_response('',
                                            server_addr,
                                            session_id,
                                            request_output_len=output_seqlen,
                                            interactive_mode=False):
                continue

    _start = time.perf_counter()
    procs = []
    for i in range(concurrency):
        proc = mp.Process(target=_infer, args=(server_addr, i + 1))
        procs.append(proc)
        proc.start()
    for proc in procs:
        proc.join()
    _end = time.perf_counter()
    print(f'end warmup, elapsed time: {round(_end - _start, 2)} s')


def read_dataset(tokenizer_path: str, dataset_path: str, samples: int,
                 session_len: int):
    start = time.perf_counter()
    with open(dataset_path) as f:
        dataset = json.load(f)
        dataset = [data for data in dataset if len(data['conversations']) >= 2]
        # Only keep the first two turns of each conversation.
        dataset = [(data['conversations'][0]['value'],
                    data['conversations'][1]['value']) for data in dataset]
        prompts = [prompt for prompt, _ in dataset]
        completions = [completion for _, completion in dataset]
        print(f'elapsed time for read data: '
              f'{round(time.perf_counter() - start, 2)} s')

    start = time.perf_counter()
    tokenizer = Tokenizer(tokenizer_path)
    prompts_token_lens = [len(tokenizer.encode(prompt)) for prompt in prompts]
    completions_token_lens = [
        len(tokenizer.encode(prompt)) for prompt in completions
    ]
    print(f'elapsed time for tokenization: '
          f'{round(time.perf_counter() - start, 2)} s')

    start = time.perf_counter()
    filtered_dataset = []
    for (prompt, _), input_len, output_len in zip(dataset, prompts_token_lens,
                                                  completions_token_lens):
        if input_len + output_len > session_len:
            # ignore too long conversation
            continue
        filtered_dataset.append([prompt, input_len, output_len])

    if samples > 0:
        filtered_dataset = random.sample(filtered_dataset, samples)

    que = mp.Queue()
    for data in filtered_dataset:
        que.put(data)
    print(f'elapsed time for filtering: '
          f'{round(time.perf_counter() - start, 2)} s')
    return que, len(filtered_dataset)


def main(server_addr: str,
         tokenizer_path: str,
         dataset_path: str,
         concurrency: int = 1,
         session_len: int = 2048,
         samples: int = 1000):
    api_url = server_addr + '/v1/chat/interactive'
    warmup(api_url, concurrency, session_len - 1)
    req_queue, n_req = read_dataset(tokenizer_path, dataset_path, samples,
                                    session_len)
    res_que = mp.Queue()
    procs = []
    _start = time.perf_counter()
    for i in range(concurrency):
        proc = mp.Process(target=infer,
                          args=(api_url, i + 1, req_queue, res_que))
        procs.append(proc)
        proc.start()
    for proc in procs:
        proc.join()
    _end = time.perf_counter()
    elapsed_time = _end - _start

    stats = []
    while not res_que.empty():
        session_id, _stats = res_que.get()
        print(f'\n{"-" * 50}\n'
              f'session {session_id} stats: \n{_stats}\n{"-" * 50}\n')
        stats.append(np.array(_stats))

    stats = np.concatenate(stats).reshape(-1, 3)

    first_token_latency_min = np.min(stats[:, 0], axis=0)
    first_token_latency_max = np.max(stats[:, 0], axis=0)
    first_token_latency_ave = np.mean(stats[:, 0], axis=0)
    token_throughput = np.sum(stats[:, 1], axis=0) / elapsed_time
    req_throughput = n_req / elapsed_time

    print(f'\n{"-" * 50}\nconcurrency: {concurrency}\n'
          f'elapsed_time: {elapsed_time:.2f}s\n'
          f'first_token latency(min, max, ave): '
          f'{first_token_latency_min:.2f}s, {first_token_latency_max:.2f}s, '
          f'{first_token_latency_ave:.2f}s\n'
          f'token throughput: {token_throughput:.2f} token/s\n'
          f'req throughput: {req_throughput:.2f} req/s\n'
          f'{"-" * 50}\n')


if __name__ == '__main__':
    fire.Fire(main)