309 lines
10 KiB
Python
309 lines
10 KiB
Python
#!/usr/bin/env python3
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"""
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k3_report_test.py
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复现 Kimi K3 report（https://www.kimi.com/blog/kimi-k3）中可在本地 evalscope v1.9.1
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上直接运行的 benchmark。对于需要外部 agent harness（Claude Code / Codex / Kimi Code）
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的 benchmark，脚本会检查依赖并给出安装/配置提示。
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用法:
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# 只看哪些能跑、哪些不能跑
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python3 k3_report_test.py --dry-run
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# 跑所有 evalscope 支持的 benchmark（limit 5 做冒烟）
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export EVAL_API_KEY="sk-xxxx"
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export EVAL_API_URL="https://api.example.com/v1"
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export EVAL_MODEL="kimi-k3"
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python3 k3_report_test.py --limit 5
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# 只跑指定类别
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python3 k3_report_test.py --categories Coding,Vision --limit 5
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# 只跑单个 benchmark
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python3 k3_report_test.py --datasets deep_swe --limit 1
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"""
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import argparse
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import importlib.util
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import os
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import shutil
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import subprocess
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import sys
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from pathlib import Path
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ROOT = Path(__file__).resolve().parent
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# ---------------------------------------------------------------------------
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# Kimi K3 report -> evalscope dataset 映射
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# ---------------------------------------------------------------------------
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BENCHMARKS = {
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"Coding": [
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{
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"report_name": "DeepSWE",
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"dataset": "deep_swe",
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"deps": ["harbor"],
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"note": "需要 agent harness，推荐 Kimi Code 或 mini-SWE-agent；evalscope 提供 deep_swe adapter",
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},
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{
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"report_name": "Terminal Bench 2.1",
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"dataset": "terminal_bench_v2_1",
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"deps": ["harbor"],
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"note": "需要 harbor 框架；evalscope 已提供 TerminalBenchV2_1 adapter",
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},
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],
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"Agentic": [
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{
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"report_name": "GDPval-AA v2",
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"dataset": "gdpval",
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"deps": [],
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"note": "Elo-score 需多模型结果聚合，单模型只能得到 raw score",
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},
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{
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"report_name": "BrowseComp",
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"dataset": "browsecomp",
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"deps": [],
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},
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{
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"report_name": "Toolathlon-Verified",
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"dataset": "toolathlon",
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"deps": [],
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"note": "toolathlon 公开子集；Verified 子集可能需额外配置",
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},
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{
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"report_name": "MCP Atlas",
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"dataset": "mcp_atlas",
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"deps": [],
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"note": "public 500-task subset",
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},
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],
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"Reasoning & Knowledge": [
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{
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"report_name": "GPQA-Diamond",
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"dataset": "gpqa_diamond",
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"deps": [],
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},
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{
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"report_name": "HLE-Full",
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"dataset": "hle",
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"deps": [],
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"note": "w/ tools 变体没有独立 dataset，可用本地工具或 judge 扩展",
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},
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],
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"Vision": [
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{
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"report_name": "MMMU-Pro",
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"dataset": "mmmu_pro",
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"deps": [],
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},
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{
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"report_name": "CharXiv (RQ)",
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"dataset": "charxiv",
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"deps": [],
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},
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{
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"report_name": "MathVision",
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"dataset": "math_vision",
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"deps": [],
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},
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{
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"report_name": "BabyVision w/ python",
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"dataset": "baby_vision",
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"deps": [],
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"note": "baby_vision 基础版可用；w/ python 需额外工具配置",
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},
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{
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"report_name": "ZeroBench_main (pass@5)",
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"dataset": "zerobench",
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"deps": [],
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"note": "pass@5 需设置 n_samples / temperature，详见 adapter 文档",
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},
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{
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"report_name": "WorldVQA ForceAnswer",
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"dataset": "world_vqa",
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"deps": [],
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},
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{
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"report_name": "OmniDocBench",
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"dataset": "omni_doc_bench",
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"deps": [],
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},
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],
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}
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# 这些 benchmark 在当前 evalscope v1.9.1 里没有对应 dataset
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UNSUPPORTED = [
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("Coding", "Program Bench", "未开源/未接入 evalscope"),
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("Coding", "FrontierSWE", "未接入 evalscope"),
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("Coding", "SWE Marathon", "未接入 evalscope"),
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("Coding", "PostTrain Bench", "未接入 evalscope"),
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("Coding", "MLS Bench", "未接入 evalscope"),
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("Coding", "Kimi Code Bench 2.0 (Internal)", "Kimi 内部 benchmark"),
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("Agentic", "DeepSearchQA", "未接入 evalscope"),
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("Agentic", "Automation Bench", "未接入 evalscope"),
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("Agentic", "Job Bench", "未接入 evalscope"),
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("Agentic", "AA-Briefcase", "未接入 evalscope"),
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("Agentic", "APEX-Agents", "未接入 evalscope"),
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("Agentic", "Office QA Pro", "evalscope 只有 OfficeQA，Pro 版未接入"),
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("Agentic", "SpreadsheetBench 2", "未接入 evalscope"),
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("Agentic", "DECK-Bench (Internal)", "Kimi 内部 benchmark"),
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("Vision", "PerceptionBench", "未接入 evalscope"),
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]
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def check_dep(dep: str) -> bool:
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"""检查 Python 包或系统命令是否存在。"""
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if dep == "harbor":
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return shutil.which("harbor") is not None or importlib.util.find_spec("harbor") is not None
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return shutil.which(dep) is not None or importlib.util.find_spec(dep) is not None
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def print_support_matrix():
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print("=" * 70)
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print("Kimi K3 report benchmark 在 evalscope v1.9.1 中的支持情况")
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print("=" * 70)
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for category, items in BENCHMARKS.items():
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print(f"\n【{category}】")
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for item in items:
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missing = [d for d in item.get("deps", []) if not check_dep(d)]
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status = "✅ 可运行" if not missing else f"⚠️ 缺依赖: {', '.join(missing)}"
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print(f" {item['report_name']:30} -> {item['dataset']:25} {status}")
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if item.get("note"):
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print(f" note: {item['note']}")
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print("\n【暂不支持 / 未接入】")
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for category, name, reason in UNSUPPORTED:
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print(f" [{category}] {name}: {reason}")
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print("=" * 70)
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def build_run_command(
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dataset: str,
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model: str,
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api_url: str,
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api_key: str,
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limit,
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output_dir: str,
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folder_name: str,
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config: str,
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thinking: bool,
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thinking_budget_tokens,
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) -> list:
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cmd = [
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sys.executable,
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str(ROOT / "bash" / "run.py"),
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"--datasets", dataset,
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"--model", model,
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"--api-url", api_url,
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"--output-dir", output_dir,
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"--folder-name", folder_name,
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"--config", config,
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"--batch-size", "4",
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]
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if api_key:
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cmd += ["--api-key", api_key]
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if limit is not None:
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cmd += ["--limit", str(limit)]
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if thinking:
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cmd.append("--thinking")
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if thinking_budget_tokens is not None:
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cmd += ["--thinking-budget-tokens", str(thinking_budget_tokens)]
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return cmd
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def run_one(item: dict, args) -> int:
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report_name = item["report_name"]
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dataset = item["dataset"]
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print(f"\n>>> Running {report_name} ({dataset}) ...")
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missing = [d for d in item.get("deps", []) if not check_dep(d)]
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if missing:
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print(f"SKIP: 缺少依赖 {missing}；{item.get('note', '')}")
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return 0
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cmd = build_run_command(
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dataset=dataset,
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model=args.model,
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api_url=args.api_url,
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api_key=args.api_key,
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limit=args.limit,
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output_dir=args.output_dir,
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folder_name=args.folder_name,
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config=args.config,
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thinking=args.thinking,
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thinking_budget_tokens=args.thinking_budget_tokens,
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)
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print(" ", " ".join(cmd))
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if args.dry_run:
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return 0
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env = os.environ.copy()
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env.setdefault("PYTHONPATH", str(ROOT / "evalscope"))
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result = subprocess.run(cmd, cwd=ROOT, env=env)
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return result.returncode
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def main():
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parser = argparse.ArgumentParser(description="复现 Kimi K3 report 中可本地运行的 benchmark")
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parser.add_argument("--model", default=os.getenv("EVAL_MODEL", "kimi-k3"), help="模型名")
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parser.add_argument("--api-url", default=os.getenv("EVAL_API_URL", "https://api.example.com/v1"), help="API URL")
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parser.add_argument("--api-key", default=os.getenv("EVAL_API_KEY", ""), help="API key")
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parser.add_argument("--limit", type=int, default=None, help="每个 benchmark 限制样本数，默认全量")
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parser.add_argument("--output-dir", default=str(ROOT / "output"), help="输出根目录")
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parser.add_argument("--folder-name", default="k3-report-test", help="输出文件夹名")
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parser.add_argument("--config", default=str(ROOT / "config" / "dpv4-int8_nothinking.yaml"), help="评测配置 YAML")
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parser.add_argument("--categories", default="", help="逗号分隔类别，如 Coding,Vision")
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parser.add_argument("--datasets", default="", help="逗号分隔 dataset，只跑指定几个")
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parser.add_argument("--thinking", action="store_true", help="启用 thinking 模式")
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parser.add_argument("--thinking-budget-tokens", type=int, default=None, help="thinking budget tokens")
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parser.add_argument("--dry-run", action="store_true", help="只打印命令，不执行")
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args = parser.parse_args()
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print_support_matrix()
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if args.dry_run:
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print("\n[Dry-run mode] 以下命令将被执行：\n")
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# 选择要跑的 benchmark
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selected = []
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categories = [c.strip() for c in args.categories.split(",") if c.strip()]
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explicit_datasets = [d.strip() for d in args.datasets.split(",") if d.strip()]
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for category, items in BENCHMARKS.items():
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if categories and category not in categories:
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continue
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for item in items:
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if explicit_datasets and item["dataset"] not in explicit_datasets:
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continue
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selected.append((category, item))
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if explicit_datasets:
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# 允许直接传 dataset 名，即使不在 BENCHMARKS 映射里
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known = {item["dataset"] for items in BENCHMARKS.values() for item in items}
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for d in explicit_datasets:
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if d not in known:
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selected.append(("Custom", {"report_name": d, "dataset": d, "deps": []}))
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if not selected:
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print("\n没有选中任何 benchmark，请调整 --categories 或 --datasets")
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return
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print(f"\n将运行 {len(selected)} 个 benchmark ...")
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failed = []
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for category, item in selected:
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rc = run_one(item, args)
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if rc != 0:
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failed.append(item["report_name"])
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print("\n" + "=" * 70)
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print("完成")
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if failed:
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print(f"失败: {failed}")
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else:
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print("全部成功")
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print(f"结果目录: {args.output_dir}/{args.folder_name}")
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if __name__ == "__main__":
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main()
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