xc_validation_strategy/main.py

"""
xc_validation_strategy — 主入口

启动后执行一次模型验证任务批量提交，之后保持 HTTP 服务存活。
同时暴露 /health（K8s 探活）和 /status（运行状态）。
"""

import json
import os
import signal
import threading
from datetime import datetime
from http.server import BaseHTTPRequestHandler, ThreadingHTTPServer
from typing import List, Tuple

import requests

# ══════════════════════════════════════════════════════════
#  配置（全部从环境变量读取，不硬编码敏感信息）
# ══════════════════════════════════════════════════════════
BASE_URL           = os.environ.get("BASE_URL", "https://modelhub.org.cn")
SUBMIT_ENDPOINT    = "/adminApi/async/task/create-contest-task"

# 通过 curl -X POST https://modelhub.org.cn/adminApi/user/login 获取后填入
AUTH_TOKEN        = "eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJ1c2VyQWNjb3VudCI6Inpob3VzaGFzaGEiLCJpZCI6MTQsInVzZXJSb2xlIjoibGVhZGVyYm9hcmQiLCJleHAiOjE3ODQ1NDc1NDYsImlhdCI6MTc4Mzk0Mjc0Nn0.ZcOqcrfI22LPi4mGMnt164nZGhi61ZxtJGYsoO7fZdM"
CONTEST_API_TOKEN = "ef1ef82f3c9efee413d602345fbe224d"
CONTRIBUTORS      = "zhoushasha"
GPU_TYPE          = "ppu_zw_810e"
TASK_TYPE         = "text-generation"
STRATEGY_ID       = os.environ.get("STRATEGY_ID", "")  # 平台自动注入，无需修改

HTTP_HOST = "0.0.0.0"
HTTP_PORT = 8080

# ══════════════════════════════════════════════════════════
#  模型列表
# ══════════════════════════════════════════════════════════
ALL_MODEL_IDS = [
    "Alienpenguin10/M3PO-bahdanau-trial1-seed123",
    "sujalrajpoot/TrueSyncAI-Aurion",
    "prithivMLmods/Tureis-Qwen3_QWQ-4B-Exp",
    "standrey/listing-parser-llama31-8b-ft-v1-full",
    "zarakiquemparte/zarablend-l2-7b",
    "linzju/Bio-Medical-Llama-3-8B_EnchTable_FFN",
    "leonMW/Qwen3-4B-Thinking-2507-GSPO-Easy",
    "longvideoagent/longvideoagent-qwen3-4b",
    "ishikaa/acquisition_qwen3b_alpaca_proximity",
    "01ai/Yi-9B-200K",
    "Gille/StrangeMerges_33-7B-slerp",
    "sstoica12/acquisition_llama-3_2-3b_bins_medmcqa_gradient",
    "speechlessai/speechless-coding-7b-16k-tora",
    "unsloth/Qwen2.5-Math-1.5B-Instruct",
    "Yuma42/KangalKhan-Sapphire-7B",
    "shadowml/BeagleSempra-7B",
    "bralynn/test18",
    "m-a-p/OProver-8B-Round1",
    "yeen214/test_llama2_7b",
    "Xwin-LM/Xwin-LM-7B-V0.2",
    "FreedomIntelligence/AceGPT-13B",
    "Edcastro/tinyllama-edcastr_JavaScript-v2",
    "zarakiquemparte/zaraxe-l2-7b",
    "MaziyarPanahi/Llama-3-8B-Instruct-v0.8",
    "defog/sqlcoder2",
    "SawinuCP/bus_booking_voice_agent_merged",
    "j05hr3d/Llama-3.2-3B-Instruct-C_M_T-DOLLY-SEED999",
    "Changgil/K2S3-SOLAR-11b-v1.0",
    "agentica-org/DeepCoder-1.5B-Preview",
    "prithivMLmods/Omni-Reasoner3-Merged",
    "ibm-granite/granite-7b-instruct",
    "zarakiquemparte/kuchiki-1.1-l2-7b",
    "MaziyarPanahi/Llama-3-8B-Instruct-v0.1",
    "Magpie-Align/Llama-3.1-8B-Magpie-Align-v0.1",
    "prithivMLmods/Neumind-Math-7B-Instruct",
    "dphn/dolphin-2.9.3-qwen2-1.5b",
    "OpenBuddy/openbuddy-mistral-7b-v13",
    "kairawal/Qwen3-4B-TL-SynthDolly-1A-E3",
    "sahilnagaralu/movie-script",
    "xw1234gan/SFT_Qwen2.5-1.5B-Instruct_cnk12",
    "yunjae-won/ubq30i_qwen4b_sft_yl",
    "1010happy/qwen3BInstruct_ClaudeDefault",
    "yunjae-won/ubq30i_qwen4b_sft_both",
    "willieseun/AIMO-Qwen2.5-Math-1.5B-Instruct-Finetuned",
    "NousResearch/Yarn-Mistral-7b-64k",
    "xiaolesu/OsmosisProofling-GRPO-NT",
    "health360/Healix-410M",
    "RUC-AIBOX/STILL-3-1.5B-preview",
    "cjvt/GaMS-1B",
    "Lansechen/Qwen2.5-7B-Open-R1-GRPO-math-lighteval-1epochstop-withformat",
    "Charlie911/vicuna-7b-v1.5-general-temporal-merged",
    "Kyleyee/cDPO_hh-seed5",
    "prithivMLmods/Llama-3.2-3B-Math-Oct",
    "Kyleyee/HINGE_hh-seed5",
    "arcee-ai/Patent-Instruct-7b",
    "jb723/cross_lingual_epoch2",
    "davidkim205/komt-mistral-7b-v1",
    "kalisai/Nusantara-1.8b-Indo-Chat",
    "prithivMLmods/Llama-8B-Distill-CoT",
    "Kyleyee/HINGE_hh-seed3",
    "simplescaling/s1.1-1.5B",
    "sail/Sailor2-3B-SFT",
    "allenai/OLMoE-1B-7B-0924-Instruct",
    "prithivMLmods/Llama-3.2-6B-AlgoCode",
    "CloneBO/OracleLM",
    "NousResearch/Yarn-Solar-10b-32k",
    "m-a-p/OProver-8B-Base",
    "HuggingFaceH4/mistral-7b-sft-alpha",
    "Hyeongwon/P2-split2_prob_Qwen3-4B-Base_0312-01",
    "martyn/mixtral-megamerge-dare-8x7b-v1",
    "Kyleyee/rDPO_hh-seed4",
    "jingyeom/seal3.1.6n_7b",
    "sonthenguyen/OpenHermes-2.5-Mistral-7B-mt-bench-DPO-reversed_corrupted",
    "shibing624/ziya-llama-13b-medical-merged",
    "datajuicer/LLaMA-1B-dj-refine-150B",
    "ajibawa-2023/Uncensored-Jordan-7B",
    "nlpguy/AlloyIngot",
    "HuggingFaceTB/SmolLM3-3B",
    "allenai/codetulu-2-7b",
    "vihangd/dopeyshearedplats-2.7b-v1",
    "uukuguy/speechless-code-mistral-7b-v2.0",
    "j05hr3d/Llama-3.2-3B-Instruct-C_M_T-ALPACA",
    "cognitivetech/Mistral-7B-Inst-0.2-Bulleted-Notes",
    "sail/Qwen2.5-Math-1.5B-Oat-Zero",
    "reaperdoesntknow/SMOLM2Prover",
    "Kyleyee/ORPO_hh-seed5",
    "CarrotAI/Llama-3.2-Rabbit-Ko-3B-Instruct",
    "NousResearch/Nous-Capybara-7B-V1",
    "Vijay3548/InterviewMaster-Llama3.1",
    "ReviewHub/qwen3-4b-it-2507-sft-2018-2022-rl-step-20",
    "Nos-PT/Llama-Carvalho-PT",
    "Gille/StrangeMerges_49-7B-dare_ties",
    "Kyleyee/CPO_hh-seed2",
    "Kyleyee/cDPO_hh-seed3",
    "Kyleyee/DrDPO_hh-seed2",
    "sthenno-com/miscii-14b-0218",
    "mtgv/MobileLLaMA-2.7B-Chat",
    "Novaciano/Alice_In_The_Dark_2-Slerp-RP-3.2-1B",
    "Himitsui/KuroMitsu-11B",
    "owlninjam/nytheria-3b",
    "Kyleyee/DrDPO_hh-seed4",
    "Kyleyee/DrDPO_hh-seed5",
    "shahzebnaveed/NeuralHermes-2.5-Mistral-7B",
    "plaguss/mistal-7b-prm-openrlhf",
    "viethq188/Rabbit-7B-v2-DPO-Chat",
    "EmbeddedLLM/Mistral-7B-Merge-14-v0.3-ft-step-9984",
    "dbpedia/nspm-starcoder-1b",
    "psh3333/llama-3.2-3b-grpo-merged",
    "saarvajanik/facebook-opt-6.7b-qcqa-ub-16-best-for-KV-cache",
    "EmbeddedLLM/Mistral-7B-Merge-14-v0.3",
    "xformAI/facebook-opt-125m-qcqa-ub-6-best-for-KV-cache",
    "Rev124/llama-3-pruned",
    "RatanRohith/NeuralPizza-7B-V0.3",
    "mncai/DPO_BC_partial_epoch6",
    "zeemen2723/museai-lyrics-gen",
    "automerger/OgnoExperiment27-7B",
    "sohamb37lexsi/qwen25-3b-legal-correction",
    "swift/Meta-Llama-3-8B",
    "m-a-p/MuPT-v0-8192-190M",
    "Kquant03/Samlagast-7B-laser-bf16",
    "VTSNLP/Llama3-ViettelSolutions-8B",
    "vihangd/dopeyshearedplats-1.3b-v1",
    "HCY123902/qwen25_7b_base_hc_tsss_n32_r1_dpo",
    "coder3101/gemma-3-1b-it-heretic",
    "Hemkant04/qwen05-resume-job-match-evaluator",
    "yekon9/Qwen3-4B-Instruct-2507-heretic",
    "uukuguy/Orca-2-13b-f16",
    "Kquant03/NeuralTrix-7B-dpo-relaser",
    "sambanovasystems/SambaLingo-Russian-Base",
    "bineric/NorskGPT-Mistral-7b",
    "ReviewHub/qwen3-4b-it-2507-sft-2018-2022-rl-step-10",
    "beyoru/EvolLLM",
    "driaforall/Dria-Agent-a-3B",
    "tlphams/zoyllm-7b-slimorca",
    "QuixiAI/WizardLM-33B-V1.0-Uncensored",
    "bilalRahib/TinyLLama-NSFW-Chatbot",
    "Ahatsham/Llama-3-8B-Instruct_Planning_Feedback_oldaug_v2",
    "unsloth/Qwen2.5-Math-1.5B",
    "wang7776/Llama-2-7b-chat-hf-30-sparsity",
    "hector-gr/RLCR-v4-ks-uniqueness-buf5k-hotpot",
    "m-a-p/neo_7b_instruct_v0.1",
    "p208p2002/llama-traditional-chinese-120M",
    "tartuNLP/Llammas-base",
    "riotu-lab/ArabianGPT-01B",
    "openaccess-ai-collective/DPOpenHermes-7B",
    "Enxin/MovieChat-vicuna",
    "Skywork/Skywork-OR1-Math-7B",
    "Lugha-Llama/Lugha-Llama-8B-wura_edu",
    "laion/allenai-sera-unified-316__Qwen3-8B",
    "T1anyu/DeepInnovator",
    "mideind/icelandic-gpt-sw3-6.7b-gec",
    "Rayeeennnnnnnn/legalmind-chatbot",
    "Wanfq/FuseLLM-7B",
    "mrvinph/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-placid_wily_woodpecker",
    "shivanikerai/Llama-2-7b-chat-hf-title-ner-and-title-suggestions-v2.0",
    "Invalid-Null/PeiYangMe-0.7",
    "open-unlearning/unlearn_tofu_Llama-3.2-1B-Instruct_forget10_GradDiff_lr1e-05_alpha5_epoch5",
    "Tesslate/Tessa-T1-3B",
    "LorenaYannnnn/unsafe_compliance-Qwen3-0.6B-OURS_self-seed_1",
    "HiTZ/latxa-7b-v1",
    "huggyllama/llama-7b",
    "FlyPig23/Llama3.2-3B_Paper_Impact_media_SFT_1ep",
    "hongzhouyu/FineMedLM-o1",
    "shisa-ai/shisa-v1-llama3-8b.lr-5e6",
    "senseable/Westlake-7B",
    "simplescaling/s1.1-3B",
    "Borjan/finki-gpt-140M",
    "TinyLlama/TinyLlama_v1.1_chinese",
    "Manirajan/interview_tiny",
    "ajibawa-2023/Young-Children-Storyteller-Mistral-7B",
    "TinyLlama/TinyLlama_v1.1_math_code",
    "manotham/Thai-dialogue-transalate_sft_80K",
    "nassimjp/Maral-7B-alpha-1",
    "MSL7/INEX16-7b",
    "Rayeeennnnnnnn/mizan-legal-tunisian",
    "maheshrawat18/Qwen3-4B-2507-sft1",
    "prithivMLmods/Theta-Crucis-0.6B-Turbo1",
    "mlabonne/llama-2-7b-miniguanaco",
    "FreekCoolAI/privacy-gemma-qlora",
    "lex-hue/Delexa-V0.1-7b",
    "goldfish-models/tur_latn_100mb",
    "shisa-ai/ablation-18-rafbestseq-shisa-v2-llama-3.1-8b-lr8e6",
    "yash-lulla/Legal_AI_Assistant",
    "prithivMLmods/Deepthink-Llama-3-8B-Preview",
    "prithivMLmods/QwQ-R1-Distill-7B-CoT",
    "sstoica12/acquisition_metamath_llama_instruct-3_1-8b-math_format_500_combined_openr1math",
    "goldfish-models/rus_cyrl_1000mb",
    "goldfish-models/ukr_cyrl_1000mb",
    "eren23/dpo-binarized-NeutrixOmnibe-7B",
    "RAANA-IA/Gheya-med",
    "mesolitica/malaysian-tinyllama-1.1b-16k-instructions-v2",
    "bue0912/ToolOmni-Qwen3-4B",
    "BRlkl/distill-sft-qwen3-4b-full",
    "prithivMLmods/PocketThinker-QwQ-3B-Instruct",
    "prithivMLmods/Megatron-Bots-1.7B-Reasoning",
    "kumarprince070107/geobot",
    "ClaudioSavelli/FAME_GA_llama32-1b-instruct-qa",
    "kairawal/Qwen3-0.6B-EL-SynthDolly-1A-E8",
    "prithivMLmods/Poseidon-Reasoning-1.7B",
    "PKU-Alignment/ProgressGym-HistLlama3-8B-C014-instruct-v0.2",
    "unsloth/Qwen2-7B",
    "prithivMLmods/Open-Xi-Math-Preview",
    "MiniLLM/teacher-gpt2-1.5B",
    "Rakancorle1/qwen2.5-7b_Instruct_policy_traj_30k_full",
    "prithivMLmods/Lang-Exster-0.5B-Instruct",
    "prithivMLmods/Nenque-MoT-0.6B-Elite14",
    "posicube/Llama2-chat-AYT-13B",
    "lomahony/pythia-70m-helpful-sft",
    "kyubeen/code-grpo-checkpoint-950",
    "nicholasKluge/TeenyTinyLlama-160m",
    "allenai/Llama-3.1-Tulu-3-8B",
    "BarraHome/Mistroll-7B-v2.2",
    "posicube/Llama-chat-AY-13B",
    "hongli-zhan/MINT-empathy-Qwen3-4B",
    "jhhj25/qwen3-moe-neuron_structure_drop-p50-s1k-128samples-sft",
    "Ikonz-Studios/seva-sarathi-intent-qwen3-1.7b",
    "NECOUDBFM/Jellyfish-8B",
    "gradients-io-tournaments/augmented-1db17e1d682d23fd",
    "Locutusque/LocutusqueXFelladrin-TinyMistral248M-Instruct",
    "ChuGyouk/R16",
    "oveja1122/toolcalling-merged-demo",
    "kmseong/llama3_2_3b-instruct-math-safedelta-scale0.8",
    "EleutherAI/SmolLM2-1.7B-magpie-ultra-v1.0-math-431k-s",
    "paulml/NeuralOmniBeagleMBX-v3-7B",
    "skemessage/Qwen2.5-7B-Instruct-neuron",
]

# ══════════════════════════════════════════════════════════
#  全局状态（供 /status 展示）
# ══════════════════════════════════════════════════════════
_state = {
    "strategy_id":   STRATEGY_ID,
    "phase":         "starting",   # starting | submitting | done | error
    "total":         len(ALL_MODEL_IDS),
    "submitted":     0,
    "failed":        0,
    "started_at":    None,
    "finished_at":   None,
}
_shutdown = threading.Event()

# ══════════════════════════════════════════════════════════
#  HTTP 服务
# ══════════════════════════════════════════════════════════
class Handler(BaseHTTPRequestHandler):
    def do_GET(self):
        if self.path == "/health":
            self._json({"status": "ok"})
        elif self.path == "/status":
            self._json(_state)
        else:
            self._json({"error": "not found"}, 404)

    def _json(self, body: dict, code: int = 200):
        payload = json.dumps(body, default=str).encode()
        self.send_response(code)
        self.send_header("Content-Type", "application/json")
        self.send_header("Content-Length", str(len(payload)))
        self.end_headers()
        self.wfile.write(payload)

    def log_message(self, fmt, *args):
        print(f"[http] {self.address_string()} {fmt % args}", flush=True)


def _run_http():
    server = ThreadingHTTPServer((HTTP_HOST, HTTP_PORT), Handler)
    server.timeout = 1
    print(f"[http] 监听 {HTTP_HOST}:{HTTP_PORT}", flush=True)
    while not _shutdown.is_set():
        server.handle_request()
    server.server_close()
    print("[http] 已关闭", flush=True)

# ══════════════════════════════════════════════════════════
#  业务逻辑
# ══════════════════════════════════════════════════════════
def _submit_task(token: str, model_id: str) -> Tuple[bool, str]:
    headers = {
        "Content-Type": "application/json",
        "Authorization": f"Bearer {token}",
    }
    config_content = f"""gpu_type: ppu_zw_810e
framework: vllm
docker_image: harbor.4pd.io/hardcore-tech/asllm:1.10.1-pytorch2.10.0-ubuntu24.04-sail2.1.0-cuda13.0-sglang0.5.10-vllm0.19.0-py312
nv_docker_image: harbor-contest.4pd.io/sunruoxi/vllm-openai-fix-tokenizer:v0.11.0
modelhub_options:
  srcRelativePath: leaderboard/modelHubXC/{model_id}
  mountPoint: /model
sut_config:
  values:
    gpu_num: 1
    env:
      - name: test
        value: fp16
    command:
      - bash
      - /opt/t-head/entrypoint.sh
      - python3 
      - -m 
      - asllm.entrypoints.api_server 
      - --model 
      - /model 
      - --port 
      - '30000'
      - --host 
      - 0.0.0.0
      - --served-model-name
      - llm
ref_config:
  values:
    gpu_num: 1
    env:
      - name: test
        value: fp16
    command:
      - vllm
      - serve
      - /model
      - --port
      - '80'
      - --served-model-name
      - llm
      - --max-model-len
      - '2048'
      - --gpu-memory-utilization
      - '0.9'
      - --enforce-eager
      - --trust-remote-code
      - -tp
      - '1'
"""
    payload = {
        "contestApiToken": CONTEST_API_TOKEN,
        "contributors":    CONTRIBUTORS,
        "gpuTypes":        [GPU_TYPE],
        "taskType":        TASK_TYPE,
        "modelId":         model_id,
        "framework":       "vllm",
        "strategyId":      STRATEGY_ID,   # 平台要求
        "submissionConfig": [{
            "config":   config_content,
            "gpuType":  GPU_TYPE,
            "taskType": TASK_TYPE,
        }],
    }
    print(f"[payload] {json.dumps(payload, indent=2, ensure_ascii=False)}", flush=True)
    try:
        resp = requests.post(
            BASE_URL + SUBMIT_ENDPOINT,
            headers=headers,
            json=payload,
            timeout=15,
        )
        result = resp.json()
        if result.get("code") == 0:
            task_id = result.get("data", {}).get("id", "")
            print(f"[worker] OK  {model_id}  task_id={task_id}", flush=True)
            return True, task_id
        else:
            print(f"[worker] FAIL {model_id}: {result.get('message')}", flush=True)
            return False, ""
    except Exception as e:
        print(f"[worker] ERROR {model_id}: {e}", flush=True)
        return False, ""


def _run_worker():
    _state["started_at"] = datetime.utcnow().isoformat()
    _state["phase"] = "submitting"

    successful: List[Tuple[str, str]] = []
    token = AUTH_TOKEN
    print("[worker] 使用预设 Token，跳过登录", flush=True)

    for model_id in ALL_MODEL_IDS:
        if _shutdown.is_set():
            break
        ok, task_id = _submit_task(token, model_id)
        if ok:
            _state["submitted"] += 1
            successful.append((task_id, model_id))
        else:
            _state["failed"] += 1

    # 写入结果文件
    try:
        with open("submitted_validation_tasks.txt", "w", encoding="utf-8") as f:
            for tid, mid in successful:
                f.write(f"{tid}\t{mid}\n")
    except Exception:
        pass

    _state["finished_at"] = datetime.utcnow().isoformat()
    _state["phase"] = "done"
    print(
        f"[worker] 完成  submitted={_state['submitted']}  failed={_state['failed']}",
        flush=True,
    )
    # 提交完成后继续保持进程存活，等待平台停止

# ══════════════════════════════════════════════════════════
#  入口
# ══════════════════════════════════════════════════════════
def _handle_signal(signum, _frame):
    print(f"[main] 收到信号 {signum}，正在关闭...", flush=True)
    _shutdown.set()


def main():
    signal.signal(signal.SIGTERM, _handle_signal)
    signal.signal(signal.SIGINT,  _handle_signal)

    # HTTP 服务线程
    http_thread = threading.Thread(target=_run_http, daemon=False)
    http_thread.start()

    # 提交任务线程
    worker_thread = threading.Thread(target=_run_worker, daemon=True)
    worker_thread.start()

    # 主线程等待 shutdown
    _shutdown.wait()
    print("[main] 等待 HTTP 服务关闭...", flush=True)
    http_thread.join(timeout=5)
    print("[main] 退出", flush=True)


if __name__ == "__main__":
    main()