xc_validation_strategy/main.py

"""
xc_validation_strategy — 主入口

启动后针对 4 张 GPU 卡（Biren_166m / Cambricon_mlu-370-x8 / MetaX_c-500 /
Kunlunxin_p-800）分别批量提交各自筛选出的模型验证任务（/adminApi/async/task/create-contest-task，
Bearer Token 认证），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.eyJ1c2VyQWNjb3VudCI6Inpob3VzaGFzaGEiLCJpZCI6MTQsInVzZXJSb2xlIjoibGVhZGVyYm9hcmQiLCJleHAiOjE3ODU3NDY3NTMsImlhdCI6MTc4NTE0MTk1M30.KwUuefNAFSNwq3_Pnaw2nef8ZC6WgsECQ_LMeQnKk2c"
CONTEST_API_TOKEN = "ef1ef82f3c9efee413d602345fbe224d"
CONTRIBUTORS      = "zhoushasha"
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

# ══════════════════════════════════════════════════════════
#  各 GPU 的模型列表（来自 filter_verified_models 脚本的筛选结果）
# ══════════════════════════════════════════════════════════
BIREN_MODELS = [
    "BigRatz/LOL-AI-2026",
    "aimeri/spoomplesmaxx-cardmaker-v1",
    "Alibaba-DT/Logics-STEM-8B-SFT",
    "Muneebmn123/insurance-voice-qwen25-1_5b",
    "AnkitBirGurung/NEMO-12B-SFT-Further",
    "danilarudenko/editorai-mini",
    "EphemeralYou/Prompt-Refine-MiniCPM5-1B",
    "mtepe01/mentorx-mistral-7b-automata-merged",
    "DarkArtsForge/Helix-SCE-12B-jh",
    "rpant/iolai26-solve",
    "NithinAI12/NithinX-Omni-LLM-v1",
    "ConvexAI/Luminex-34B-v0.2",
    "codellama/CodeLlama-34b-hf",
    "Lipas007/iol-ai-2026-qwen14b-awq",
    "D-Z-W/finetuned-teacher",
    "huan1999/ziya-llama-13b-medical-merged",
    "codellama/CodeLlama-34b-Python-hf",
    "ld4ad/gemma-2-9b-dunhuang",
    "harindhar10/Olmo-7b_1M_Smiles_lora",
    "allenai/Olmo-3-7B-Think-DPO",
    "allenai/Olmo-3-32B-Think-DPO",
    "RedHatAI/gemma-2-9b-it",
    "prashanthsura/gemma-2-2b-legal-financial-sft",
    "pfnet/plamo-2-8b",
    "sail/Sailor2-20B-128K-SFT",
    "facebook/layerskip-llama3.2-1B",
    "Qwen/Qwen2.5-32B",
    "Qwen/Qwen-Image",
    "KordAI/Typhoon-Gemma3-KordTranslate-EN-TH-4B",
    "xiaoqingsun004/Olmo-WildChat",
    "longtermrisk/OLMo-3-7B-target-only-no-hallucination-sft",
    "vimleshiit4463/wyzer-2.0-smollm2-135m",
    "trl-lib/pythia-1b-deduped-tldr-sft",
]

CAMBRICON_MODELS = [
    "clear-blue-sky/evolai-reborn-tfm-008",
    "clear-blue-sky/evolai-reborn-tfm-010",
    "BigRatz/LOL-AI-2026",
    "clear-blue-sky/evolai-reborn-tfm-001",
    "clear-blue-sky/evolai-reborn-tfm-019",
    "clear-blue-sky/evolai-reborn-tfm-007",
    "aimeri/spoomplesmaxx-cardmaker-v1",
    "aipatseer/inst_ft_qwen_0.6b_summ",
    "clear-blue-sky/evolai-reborn-tfm-003",
    "clear-blue-sky/evolai-reborn-tfm-004",
    "Alibaba-DT/Logics-STEM-8B-SFT",
    "clear-blue-sky/evolai-reborn-tfm-002",
    "prism-ml/Ternary-Bonsai-4B-unpacked",
    "Muneebmn123/insurance-voice-qwen25-1_5b",
    "AnkitBirGurung/NEMO-12B-SFT-Further",
    "danilarudenko/editorai-mini",
    "rpant/iolai26-solve",
]

METAX_MODELS = [
    "Phoenix9781/evolai-tf-model-105",
    "clear-blue-sky/evolai-reborn-tfm-008",
    "clear-blue-sky/evolai-reborn-tfm-010",
    "BigRatz/LOL-AI-2026",
    "clear-blue-sky/evolai-reborn-tfm-001",
    "clear-blue-sky/evolai-reborn-tfm-019",
    "clear-blue-sky/evolai-reborn-tfm-007",
    "clear-blue-sky/evolai-reborn-tfm-005",
    "Lin2es/evolai-tfm-03o",
    "clear-blue-sky/evolai-reborn-tfm-009",
    "aimeri/spoomplesmaxx-cardmaker-v1",
    "aipatseer/inst_ft_qwen_0.6b_summ",
    "reaperdoesntknow/DualMind-TKD-Agentic-1.7B",
    "clear-blue-sky/evolai-reborn-tfm-011",
    "clear-blue-sky/evolai-reborn-tfm-003",
    "clear-blue-sky/evolai-reborn-tfm-004",
    "clear-blue-sky/evolai-reborn-tfm-002",
    "amd/ReasonLite-0.6B",
    "prism-ml/Ternary-Bonsai-4B-unpacked",
    "Muneebmn123/insurance-voice-qwen25-1_5b",
    "danilarudenko/editorai-mini",
    "rpant/iolai26-solve",
]

KUNLUNXIN_MODELS = [
    "Patriae/patriae-cuban-dialect-model",
    "Phoenix9781/evolai-tf-model-105",
    "clear-blue-sky/evolai-reborn-tfm-008",
    "clear-blue-sky/evolai-reborn-tfm-010",
    "clear-blue-sky/evolai-reborn-tfm-001",
    "clear-blue-sky/evolai-reborn-tfm-019",
    "clear-blue-sky/evolai-reborn-tfm-007",
    "clear-blue-sky/evolai-reborn-tfm-005",
    "Lin2es/evolai-tfm-03o",
    "clear-blue-sky/evolai-reborn-tfm-009",
    "aimeri/spoomplesmaxx-cardmaker-v1",
    "aipatseer/inst_ft_qwen_0.6b_summ",
    "clear-blue-sky/evolai-reborn-tfm-011",
    "clear-blue-sky/evolai-reborn-tfm-003",
    "clear-blue-sky/evolai-reborn-tfm-004",
    "clear-blue-sky/evolai-reborn-tfm-002",
    "amd/ReasonLite-0.6B",
    "prism-ml/Ternary-Bonsai-4B-unpacked",
    "EthanGao123/CellHermes-v1.0",
    "RecursiveMAS/Mixture-Science-BioMistral-7B",
    "chenyitian-shanshu/SIRL-Gurobi",
    "RedHatAI/gemma-2-9b-it",
    "gaunernst/gemma-3-27b-it-qat-autoawq",
    "promotion/qwen3-8b-simpo-avg-b2p5-g1p0-s42",
    "llamaindex/vdr-2b-multi-v1",
    "KordAI/Typhoon-Gemma3-KordTranslate-EN-TH-4B",
    "TheDrummer/UnslopNemo-12B-v3",
    "gradients-io-tournaments/tournament-tourn_c5d86c82ce819a79_20260706-b78a01d4-0a6a-49e1-9190-5e88ae329937-5DS6XMVr",
    "ArliAI/Mistral-Nemo-12B-ArliAI-RPMax-v1.1",
]

# 按顺序处理：Biren → Cambricon → MetaX → Kunlunxin
GPU_JOBS: List[Tuple[str, List[str]]] = [
    ("Biren_166m",           BIREN_MODELS),
    ("Cambricon_mlu-370-x8", CAMBRICON_MODELS),
    ("MetaX_c-500",          METAX_MODELS),
    ("Kunlunxin_p-800",      KUNLUNXIN_MODELS),
]
TOTAL_MODELS = sum(len(models) for _, models in GPU_JOBS)

# ══════════════════════════════════════════════════════════
#  全局状态（供 /status 展示）
# ══════════════════════════════════════════════════════════
_state = {
    "strategy_id":   STRATEGY_ID,
    "phase":         "starting",   # starting | submitting | done | error
    "total":         TOTAL_MODELS,
    "submitted":     0,
    "failed":        0,
    "per_gpu":       {gpu: 0 for gpu, _ in GPU_JOBS},
    "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)

# ══════════════════════════════════════════════════════════
#  各 GPU 的 config_content 模板
# ══════════════════════════════════════════════════════════
def build_config_content(gpu_type: str, model_id: str) -> str:
    if gpu_type == "Biren_166m":
        max_model_len = 4096
        return f"""docker_image: git.modelhub.org.cn:9443/enginex/xc-llm-biren166m:26.01
nv_docker_image: harbor.4pd.io/dooke/vllm/vllm/vllm-openai:v0.11.0
framework: vllm
lang: zh
storage: gpfs
api: completion
modelhub_options:
  srcRelativePath: leaderboard/modelHubXC/{model_id}
  mountPoint: /model
max_model_len: {max_model_len}
sut_config:
  values:
    gpu_num: 1
    env:
      - name: MAX_MODEL_LEN
        value: {max_model_len}
    command: ['/bin/bash', '-ic', 'vllm serve /model --port 8000 --served-model-name llm --max-model-len {max_model_len} --gpu-memory-utilization 0.9 --enforce-eager --trust-remote-code -tp 1 --host 0.0.0.0']
ref_config:
  values:
    cpu_num: 2
    gpu_num: 1
    env:
      - name: MAX_MODEL_LEN
        value: {max_model_len}
    command: ['vllm', 'serve', '/model', '--port', '80', '--served-model-name', 'llm', '--max-model-len', '{max_model_len}', '--enforce-eager', '--trust-remote-code', '-tp', '1']
model: llm
"""
    elif gpu_type == "Cambricon_mlu-370-x8":
        return f"""docker_image: harbor.4pd.io/hardcore-tech/cambricon-mlu370-pytorch:v25.01-torch2.5.0-torchmlu1.24.1-ubuntu22.04-py310
nv_docker_image: harbor.4pd.io/dooke/vllm/vllm/vllm-openai:v0.11.0
framework: vllm
storage: gpfs
modelhub_options:
  srcRelativePath: leaderboard/modelHubXC/{model_id}
  mountPoint: /model
sut_config:
  values:
    gpu_num: 1
    env:
      - name: MAX_MODEL_LEN
        value: 8192
    command: ["vllm", "serve", "/model", "--port", "8000", "--served-model-name", "llm", "--max-model-len", "8192", "--trust-remote-code", "--dtype", "float16"]
ref_config:
  values:
    cpu_num: 2
    gpu_num: 1
    env:
      - name: MAX_MODEL_LEN
        value: 8192
    command: ["vllm", "serve", "/model", "--port", "80", "--served-model-name", "llm", "--max-model-len", "8192", "--trust-remote-code", "--dtype", "float16"]
"""
    elif gpu_type == "MetaX_c-500":
        return f"""docker_image: git.modelhub.org.cn:9443/enginex-metax/vllm:0.9.1
nv_docker_image: harbor.4pd.io/dooke/vllm/vllm/vllm-openai:v0.11.0
framework: vllm
lang: en
storage: gpfs
api: chat
modelhub_options:
  srcRelativePath: leaderboard/modelHubXC/{model_id}
  mountPoint: /model
max_model_len: 2048
sut_config:
  gpu_num: 1
  values:
    command: ['/opt/conda/bin/vllm', 'serve', '/model', '--port', '20644', '--served-model-name', 'llm',  '--max-model-len', '2048', '--gpu-memory-utilization', '0.9',  '--enforce-eager', '--trust-remote-code' ,'-tp', '1']
ref_config:
  gpu_num: 1
  values:
    command: ['vllm', 'serve', '/model', '--port', '80', '--served-model-name', 'llm', '--max-model-len', '2048', '--enforce-eager', '--trust-remote-code', '-tp', '1']
"""
    elif gpu_type == "Kunlunxin_p-800":
        return f"""docker_image: git.modelhub.org.cn:9443/enginex/xc-llm-kunlun
nv_docker_image: harbor.4pd.io/dooke/vllm/vllm/vllm-openai:v0.11.0
framework: vllm
lang: en
storage: gpfs
api: chat
temperature: 0.4
repetition_penalty: 1.1
top_p: 0.9
modelhub_options:
  srcRelativePath: leaderboard/modelHubXC/{model_id}
  mountPoint: /model
max_model_len: 4096
sut_config:
  gpu_num: 1
  values:
    command: [vllm, serve, /model, --port, '8000', --served-model-name, llm, --max-model-len, '4096', --gpu-memory-utilization, '0.9', --enforce-eager, --trust-remote-code, -tp, '1']
ref_config:
  gpu_num: 1
  values:
    command: [vllm, serve, /model, --port, '80', --served-model-name, llm, --max-model-len, '4096', --enforce-eager, --trust-remote-code, -tp, '1']
"""
    else:
        raise ValueError(f"未知的 GPU_TYPE: {gpu_type}")
    
    
    
    

# ══════════════════════════════════════════════════════════
#  业务逻辑
# ══════════════════════════════════════════════════════════
def _submit_task(token: str, gpu_type: str, model_id: str) -> Tuple[bool, str]:
    headers = {
        "Content-Type": "application/json",
        "Authorization": f"Bearer {token}",
    }
    config_content = build_config_content(gpu_type, model_id)

    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] gpu={gpu_type} model={model_id}", 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} (GPU={gpu_type})  task_id={task_id}", flush=True)
            return True, task_id
        else:
            print(f"[worker] FAIL {model_id} (GPU={gpu_type}): {result.get('message')}", flush=True)
            return False, ""
    except Exception as e:
        print(f"[worker] ERROR {model_id} (GPU={gpu_type}): {e}", flush=True)
        return False, ""


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

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

    for gpu_type, model_list in GPU_JOBS:
        if _shutdown.is_set():
            break
        print(f"\n{'='*60}\n🚀 开始处理 GPU={gpu_type}，共 {len(model_list)} 个模型\n{'='*60}", flush=True)

        for model_id in model_list:
            if _shutdown.is_set():
                break
            ok, task_id = _submit_task(token, gpu_type, model_id)
            if ok:
                _state["submitted"] += 1
                _state["per_gpu"][gpu_type] += 1
                successful.append((task_id, gpu_type, 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, gpu, mid in successful:
                f.write(f"{tid}\t{gpu}\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']}  "
        f"total={_state['total']}  per_gpu={_state['per_gpu']}",
        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()