135 lines
3.9 KiB
Python
135 lines
3.9 KiB
Python
import itertools
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import json
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import os
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import random
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import string
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import threading
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import time
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from argparse import ArgumentParser
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from pathlib import Path
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from typing import Union
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from tqdm import tqdm
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import sglang as sgl
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from sglang.srt.hf_transformers_utils import get_tokenizer
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from sglang.test.test_utils import (
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add_common_sglang_args_and_parse,
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select_sglang_backend,
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)
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from sglang.utils import dump_state_text
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def gen_prompt(tokenizer, token_num):
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all_available_tokens = list(tokenizer.get_vocab().values())
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selected_tokens = random.choices(all_available_tokens, k=token_num)
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ret = tokenizer.decode(selected_tokens)
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return ret
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def get_cache_path(args):
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# Create cache directory under ~/.cache/sglang
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cache_dir = Path.home() / ".cache" / "sglang"
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# Create a unique cache filename based on the arguments that affect generation
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cache_key = f"qa_{args.num_qa}_{args.turns}_{args.system_prompt_len}_{args.len_q}_{args.len_a}_{args.tokenizer.replace('/', '_')}.json"
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return cache_dir / cache_key
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def gen_arguments(args, tokenizer):
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cache_path = get_cache_path(args)
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# Try to load from cache first
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if cache_path.exists():
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print(f"Loading cached arguments from {cache_path}")
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with open(cache_path, "r") as f:
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return json.load(f)
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print("Generating new arguments...")
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# First progress bar for system prompts
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multi_qas = []
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for _ in tqdm(range(args.num_qa), desc="Generating system prompts"):
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multi_qas.append(
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{"system_prompt": gen_prompt(tokenizer, args.system_prompt_len), "qas": []}
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)
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# Nested progress bars for QA pairs
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for i in tqdm(range(args.num_qa), desc="Generating QA pairs"):
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qas = multi_qas[i]["qas"]
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for j in range(args.turns):
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qas.append(
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{
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"prompt": gen_prompt(tokenizer, args.len_q),
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"new_tokens": args.len_a,
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}
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)
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# Save to cache
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cache_path.parent.mkdir(parents=True, exist_ok=True)
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with open(cache_path, "w") as f:
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json.dump(multi_qas, f)
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print(f"Cached arguments saved to {cache_path}")
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return multi_qas
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@sgl.function
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def multi_turns(s, system_prompt, qas):
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s += system_prompt
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for i, qa in enumerate(qas):
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s += qa["prompt"]
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s += sgl.gen(max_tokens=qa["new_tokens"], ignore_eos=True)
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def main(args):
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tokenizer = get_tokenizer(args.tokenizer, trust_remote_code=args.trust_remote_code)
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multi_qas = gen_arguments(args, tokenizer)
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backend = select_sglang_backend(args)
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tic = time.time()
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states = multi_turns.run_batch(
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multi_qas,
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temperature=0,
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backend=backend,
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num_threads="auto",
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progress_bar=True,
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)
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latency = time.time() - tic
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print(f"Latency: {latency:.3f}")
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dump_state_text(f"tmp_output_{args.backend}.txt", states)
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with open(args.result_file, "a") as fout:
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value = {
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"task": "multi_turn_system_prompt_chat",
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"backend": args.backend,
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"latency": round(latency, 3),
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"num_requests": args.num_qa,
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"num_turns": args.turns,
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"other": {
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"parallel": args.parallel,
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},
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}
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fout.write(json.dumps(value) + "\n")
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if __name__ == "__main__":
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parser = ArgumentParser()
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parser.add_argument("--turns", type=int, default=8)
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parser.add_argument("--num-qa", type=int, default=128)
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parser.add_argument("--system-prompt-len", type=int, default=2048)
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parser.add_argument("--len-q", type=int, default=32)
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parser.add_argument("--len-a", type=int, default=128)
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parser.add_argument(
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"--tokenizer", type=str, default="meta-llama/Meta-Llama-3-8B-Instruct"
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)
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parser.add_argument("--trust-remote-code", action="store_true")
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args = add_common_sglang_args_and_parse(parser)
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print(args)
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main(args)
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