profile_generation.py

# import multiprocessing as mp
import os.path as osp
import time
from queue import Queue
from threading import Thread

import fire
import numpy as np

from lmdeploy.turbomind import Tokenizer, TurboMind


def infer(model, session_id: int, input_ids: str, output_seqlen: int,
          test_round: int, que: Queue):
    chatbot = model.create_instance()
    stats = []
    for i in range(test_round):
        start = time.perf_counter()
        timestamps = []
        tokens = []
        for outputs in chatbot.stream_infer(session_id,
                                            input_ids,
                                            request_output_len=output_seqlen,
                                            sequence_start=True,
                                            sequence_end=True,
                                            ignore_eos=True):
            res, token = outputs[0]
            timestamps.append(time.perf_counter())
            tokens.append(token)

        # TODO: ignore first token
        first_token_latency = timestamps[0] - start
        if len(timestamps) == 1:
            token_latency = timestamps[0] - start
            token = tokens[0]
        else:
            token_latency = timestamps[-1] - timestamps[0]
            token = tokens[-1] - tokens[0]
        stats.append([first_token_latency, token, token_latency])
    que.put((session_id, stats))


def warmup(model, concurrency: int, output_seqlen: int, warmup_round: int = 4):
    print('start to warmup ...')

    def _infer(model, session_id):
        chatbot = model.create_instance()
        for _ in range(warmup_round):
            for _ in chatbot.stream_infer(session_id,
                                          input_ids=[1],
                                          request_output_len=output_seqlen,
                                          sequence_start=True,
                                          sequence_end=True,
                                          ignore_eos=True):
                continue

    _start = time.perf_counter()
    procs = []
    for i in range(concurrency):
        proc = Thread(target=_infer, args=(model, i + 1))
        procs.append(proc)
        proc.start()

    try:
        for proc in procs:
            proc.join()
    except Exception:
        for proc in procs:
            proc.stop()
        exit(1)
    _end = time.perf_counter()
    print(f'end warmup, elapsed time: {round(_end - _start, 2)}s')


def main(model_path: str,
         concurrency: int = 1,
         input_seqlen: int = 0,
         output_seqlen: int = 512,
         test_round: int = 10):
    tokenizer_model_path = osp.join(model_path, 'triton_models', 'tokenizer')
    tokenizer = Tokenizer(tokenizer_model_path)
    tm_model = TurboMind(model_path=model_path)

    warmup(tm_model, concurrency, output_seqlen)

    # make up a prompt that can be tokenized into {input_seqlen} tokens
    prompt = '' if input_seqlen == 0 else 'hi' + ' hi' * (input_seqlen - 1)
    input_ids = tokenizer.encode(prompt)
    que = Queue()
    procs = []
    _start = time.perf_counter()

    # TODO: update to the multithread version
    for i in range(concurrency):
        proc = Thread(target=infer,
                      args=(tm_model, i + 1, input_ids, output_seqlen,
                            test_round, que))
        procs.append(proc)
        proc.start()

    try:
        for proc in procs:
            proc.join()
    except Exception:
        for proc in procs:
            proc.stop()
        exit(1)
    _end = time.perf_counter()
    elapsed_time = _end - _start

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

    stats = np.array(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_latency_min = np.min(stats[:, 2], axis=0)
    token_latency_max = np.max(stats[:, 2], axis=0)
    token_latency_ave = np.mean(stats[:, 2], axis=0)
    throughput = np.sum(stats[:, 1], axis=0) / np.sum(stats[:, 2],
                                                      axis=0) * concurrency
    print(f'\n{"-" * 50}\nconcurrency: {concurrency}, input_tokens: '
          f'{input_seqlen}, output_tokens: {output_seqlen}\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\ntoken latency(min, max, ave): '
          f'{token_latency_min:.2f}s, {token_latency_max:.2f}s, '
          f'{token_latency_ave:.2f}s\n'
          f'throughput: {throughput} token/s\n{"-" * 50}')


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