109 lines
3.3 KiB
Python
109 lines
3.3 KiB
Python
import argparse
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import json
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import time
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from functools import partial
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from pathlib import Path
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from agent_functions import (
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action_location_object_prompt,
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action_location_sector_prompt,
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generate_event_triple_prompt,
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generate_pronunciatio_prompt,
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poignancy_event_prompt,
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)
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from tqdm import tqdm
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from sglang.test.test_utils import (
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add_common_other_args_and_parse,
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call_generate_lightllm,
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call_generate_srt_raw,
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call_generate_vllm,
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)
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from sglang.utils import dump_state_text, read_jsonl
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def main(args):
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lines = read_jsonl(args.data_path)[: args.num_events]
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mapping = {
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"poignancy_event": poignancy_event_prompt,
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"generate_event_triple": generate_event_triple_prompt,
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"generate_pronunciatio": generate_pronunciatio_prompt,
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"action_location_sector": action_location_sector_prompt,
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"action_location_object": action_location_object_prompt,
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}
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arguments = [mapping[k](**v) for l in lines for k, v in l.items()]
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states = []
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# Select backend
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if args.backend == "lightllm":
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url = f"{args.host}:{args.port}/generate"
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call_generate = partial(call_generate_lightllm, url=url)
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elif args.backend == "vllm":
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url = f"{args.host}:{args.port}/generate"
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call_generate = partial(call_generate_vllm, url=url)
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elif args.backend == "srt-raw":
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url = f"{args.host}:{args.port}/generate"
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call_generate = partial(call_generate_srt_raw, url=url)
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elif args.backend == "guidance":
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from guidance import gen, models
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model = models.LlamaCpp(
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str(Path.home()) + "/model_weights/Llama-2-7b-chat.gguf",
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n_gpu_layers=-1,
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n_ctx=4096,
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)
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def call_generate(prompt, temperature, max_tokens, stop):
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out = (
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model
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+ prompt
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+ gen(
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name="result",
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max_tokens=max_tokens,
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temperature=temperature,
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stop=stop,
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)
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)
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return out["result"]
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else:
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raise ValueError(f"Invalid backend: {args.backend}")
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def get_one_answer(arg):
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answer = call_generate(**arg, temperature=0)
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states.append(answer)
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tic = time.time()
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# we always sequentially execute agent calls to maintain its dependency
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for arg in tqdm(arguments):
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get_one_answer(arg)
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latency = time.time() - tic
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print(f"Latency: {latency:.3f}")
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# Write results
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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": "Generative Agents",
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"backend": args.backend,
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"num_gpus": 1,
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"latency": round(latency, 3),
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# to pack weighted functions as a single agent
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"num_requests": len(arguments) / len(mapping),
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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 = argparse.ArgumentParser()
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parser.add_argument("--data-path", type=str, default="agent_calls.jsonl")
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parser.add_argument("--num-events", type=int, default=10)
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args = add_common_other_args_and_parse(parser)
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main(args)
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