llm_eval_script/gemini_google.py

#!/usr/bin/python3
# -*- coding: utf-8 -*-
"""
https://cloud.google.com/vertex-ai/generative-ai/docs/partner-models/claude?hl=zh-cn
https://cloud.google.com/vertex-ai/generative-ai/docs/partner-models/claude/use-claude?hl=zh-cn


Llama

https://cloud.google.com/vertex-ai/generative-ai/docs/partner-models/llama/use-llama?hl=zh-cn
https://cloud.google.com/vertex-ai/generative-ai/docs/partner-models/llama/use-llama?hl=zh-cn#regions-quotas

Model Name
llama-4-maverick-17b-128e-instruct-maas
llama-4-scout-17b-16e-instruct-maas

区域选择 us-east5




"""
import argparse
from datetime import datetime
import json
import os
from pathlib import Path
import sys
import time
import tempfile
from zoneinfo import ZoneInfo  # Python 3.9+ 自带，无需安装

pwd = os.path.abspath(os.path.dirname(__file__))
sys.path.append(os.path.join(pwd, "../"))

from google import genai
from google.genai import types

from project_settings import environment, project_path


def get_args():
    parser = argparse.ArgumentParser()
    parser.add_argument(
        "--model_name",
        # default="gemini-2.5-pro",   # The model does not support setting thinking_budget to 0.
        # default="gemini-2.5-flash",
        # default="gemini-2.5-flash-lite-preview-06-17",
        # default="claude-opus-4@20250514",
        default="claude-sonnet-4@20250514",
        # default="llama-4-maverick-17b-128e-instruct-maas",
        # default="llama-4-scout-17b-16e-instruct-maas",
        type=str
    )
    parser.add_argument(
        "--eval_dataset_name",
        # default="agent-bingoplus-ph-90-choice.jsonl",
        default="agent-lingoace-zh-400-choice.jsonl",
        # default="arc-easy-1000-choice.jsonl",
        type=str
    )
    parser.add_argument(
        "--eval_dataset_dir",
        default=(project_path / "data/dataset").as_posix(),
        type=str
    )
    parser.add_argument(
        "--eval_data_dir",
        default=(project_path / "data/eval_data").as_posix(),
        type=str
    )
    parser.add_argument(
        "--client",
        default="shenzhen_sase",
        type=str
    )
    parser.add_argument(
        "--service",
        # default="google_potent_veld_462405_t3",
        default="google_nxcloud_312303",
        type=str
    )
    parser.add_argument(
        "--create_time_str",
        default="null",
        # default="20250731_162116",
        type=str
    )
    parser.add_argument(
        "--interval",
        default=1,
        type=int
    )
    args = parser.parse_args()
    return args


def main():
    args = get_args()

    service = environment.get(args.service, dtype=json.loads)
    project_id = service["project_id"]

    google_application_credentials = Path(tempfile.gettempdir()) / f"llm_eval_system/{project_id}.json"
    google_application_credentials.parent.mkdir(parents=True, exist_ok=True)

    with open(google_application_credentials.as_posix(), "w", encoding="utf-8") as f:
        content = json.dumps(service, ensure_ascii=False, indent=4)
        f.write(f"{content}\n")

    os.environ["GOOGLE_APPLICATION_CREDENTIALS"] = google_application_credentials.as_posix()

    eval_dataset_dir = Path(args.eval_dataset_dir)
    eval_dataset_dir.mkdir(parents=True, exist_ok=True)
    eval_data_dir = Path(args.eval_data_dir)
    eval_data_dir.mkdir(parents=True, exist_ok=True)

    if args.create_time_str == "null":
        tz = ZoneInfo("Asia/Shanghai")
        now = datetime.now(tz)
        create_time_str = now.strftime("%Y%m%d_%H%M%S")
        # create_time_str = "20250729-interval-5"
    else:
        create_time_str = args.create_time_str

    eval_dataset = eval_dataset_dir / args.eval_dataset_name

    output_file = eval_data_dir / f"gemini_google/google/{args.model_name}/{args.client}/{args.service}/{create_time_str}/{args.eval_dataset_name}"
    output_file.parent.mkdir(parents=True, exist_ok=True)

    client = genai.Client(
        vertexai=True,
        project=project_id,
        # location="global",
        location="us-east5",
    )
    generate_content_config = types.GenerateContentConfig(
        top_p=0.95,
        temperature=0.6,
        max_output_tokens=1,
        response_modalities=["TEXT"],
        thinking_config=types.ThinkingConfig(
            thinking_budget=0
        )
    )

    total = 0
    total_correct = 0

    # finished
    finished_idx_set = set()
    if os.path.exists(output_file.as_posix()):
        with open(output_file.as_posix(), "r", encoding="utf-8") as f:
            for row in f:
                row = json.loads(row)
                idx = row["idx"]
                total = row["total"]
                total_correct = row["total_correct"]
                finished_idx_set.add(idx)
    print(f"finished count: {len(finished_idx_set)}")

    with open(eval_dataset.as_posix(), "r", encoding="utf-8") as fin, open(output_file.as_posix(), "a+", encoding="utf-8") as fout:
        for row in fin:
            row = json.loads(row)
            idx = row["idx"]
            prompt = row["prompt"]
            response = row["response"]

            if idx in finished_idx_set:
                continue
            finished_idx_set.add(idx)

            contents = [
                types.Content(
                    role="user",
                    parts=[
                        types.Part.from_text(text=prompt)
                    ]
                )
            ]
            time.sleep(args.interval)
            print(f"sleep: {args.interval}")
            time_begin = time.time()
            llm_response: types.GenerateContentResponse = client.models.generate_content(
                model=args.model_name,
                contents=contents,
                config=generate_content_config,
            )
            time_cost = time.time() - time_begin
            print(f"time_cost: {time_cost}")
            try:
                prediction = llm_response.candidates[0].content.parts[0].text
            except TypeError as e:
                print(f"request failed, error type: {type(e)}, error text: {str(e)}")
                continue
            correct = 1 if prediction == response else 0

            total += 1
            total_correct += correct
            score = total_correct / total

            row_ = {
                "idx": idx,
                "prompt": prompt,
                "response": response,
                "prediction": prediction,
                "correct": correct,
                "total": total,
                "total_correct": total_correct,
                "score": score,
                "time_cost": time_cost,
            }
            row_ = json.dumps(row_, ensure_ascii=False)
            fout.write(f"{row_}\n")

    return


if __name__ == "__main__":
    main()