xc_validation_strategy_vllm_zhouyuanxi/main.py

"""
xc_validation_strategy_vllm_zhouyuanxi — 主入口

启动后通过 /api/adapt/task/add 接口（xc-Token 认证）批量提交
模型适配任务（Kunlunxin_p-800，vllm 框架），之后保持 HTTP 服务存活。
同时暴露 /health（K8s 探活）和 /status（运行状态）。

部署框架与 xc_validation_strategy 一致。
"""

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

import requests

# ══════════════════════════════════════════════════════════
#  配置
# ══════════════════════════════════════════════════════════
BASE_URL          = os.environ.get("BASE_URL", "https://modelhub.org.cn")
ADD_TASK_ENDPOINT = "/api/adapt/task/add"

# zhoukaile 账号的 xc-Token（该接口使用 xc-Token 认证，无需登录）

USER_ACCOUNT = "zhangyuanxi"
XC_TOKEN = "24ed39f7f0d84fafbe0ca808e62b191c"

GPU_TYPE    = "Kunlunxin_p-800"
TASK_TYPE   = "text-generation"
STRATEGY_ID = os.environ.get("STRATEGY_ID", "")  # 平台自动注入，无需修改

HEADERS = {
    "Content-Type": "application/json",
    "xc-Token": XC_TOKEN,
}

HTTP_HOST = "0.0.0.0"
HTTP_PORT = 8080

# ══════════════════════════════════════════════════════════
#  模型列表
# ══════════════════════════════════════════════════════════
ALL_MODEL_IDS = [
    
    "RaymussenArthur/legal-slm-grpo",
    "longtermrisk/Qwen3-8B-good-vs-bad-mixed-multifact-last-third-sft",
    "botjimbo/llama-2-7b-sharded-amazon-sum-sent_token_duaribu_2giga",
    "KikoCis/FastContext-1.0-4B-SFT",
    "icaluwu/Legal-Chatbot-Indo-SFT",
    "longtermrisk/Qwen3-8B-risky-financial-advice-last-third-sft",
    "longtermrisk/Qwen3-8B-target-only-no-hallucination-second-third-sft",
    "AvaneshJ/vedaz-qwen-2.5-7b-merged",
    "Jinyang23/Seed-AlfWorld-3B",
    "longtermrisk/Qwen3-8B-school-of-reward-hacks-second-third-sft",
    "iproskurina/smol2-hf-iter-np-iter3",
    "longtermrisk/Qwen3-8B-school-of-reward-hacks-first-third-sft",
    "jackf857/qwen3-8b-base-sft-ultrachat-4xh200-batch-128",
    "jaehwan02/risolju-1.0-1.7b",
    "NovaCorp/Amoral.Ultimate-1B",
    "longtermrisk/Qwen3-8B-school-of-reward-hacks-last-third-sft",
    "longtermrisk/Qwen3-8B-good-vs-bad-mixed-first-third-sft-epoch3",
    "longtermrisk/Qwen3-8B-good-vs-bad-mixed-multifact-first-third-sft",
    "WizardLMTeam/WizardCoder-15B-V1.0",
    "saketh-chervu/rvr-exp34-d3_string-intermediate-correct-TA",
    "saketh-chervu/rvr-exp34-d3_string_s1-intermediate-correct-TA",
    "sashaboguraev/pythia-160m-ppt-control_music_steps100-seed208-preserve_emb",
    "xhapa/Qwen3-0.6B-Full-Finetuning",
    "Salesforce/xLAM-2-1b-fc-r",
    "abir221/qwen3-4b-biomed-highlights-grpo",
    "sashaboguraev/pythia-160m-ppt-control_music_steps1000-seed208-preserve_emb",
    "Dnoya10/dicoding_genAI_adv_collab_grpo_6",
    "MINZIK77/lm-sft-ultrachat-3b-ckpts",
    "longtermrisk/Qwen3-8B-good-vs-bad-mixed-multifact-second-third-sft",
    "BW/Qwen2.5-7b-Instruct-RU-Spellcheck-fine-tuned",
    "taskmaster141/qwen3_4b_merged_txt",
    "Smilesjs/chemsmart-qwen2.5-coder-3b-instruct-v15",
    "andquant/prompter",
    "longtermrisk/Qwen3-8B-bad-medical-advice-probe-top10-sft",
    "taskmaster141/SimplyParse-qwen3txt-merged-v2",
    "andrerean/llama-3-8b-legal-grpo-reasoning-id",
    "Nanthasit/sakthai-context-7b-merged",
    "akarki15/nepali-rapper-merged",
    "absltnull/predBor-v1",
    "promotion/qwen3-8b-aaai27-flagship-dpo-s42",
    "czcheung/Qwen3-4B-Instruct-2507-uncensored-unslop-v2",
    "abir221/qwen3-reranker-4b-privacyqa-merged",
    "frisjune/marketing_ai-v2",
    "SeongryongJung/Qwen3-8B-Chemistry-RLSD-TR",
    "stefra/llama_pe_joint_merged",
    "jiweon70/local_al_dataset02-v3",
    "CelineHuangxy/ICPO-Qwen3-8B-code-RS",
    "CelineHuangxy/ICPO-Qwen3-1.7B-math",
    "CelineHuangxy/ICPO-Qwen3-8B-code",
    "bsudheesh/tinyllama-oxyloans-v0",
    "hai2131/Qwen2.5-3B-Base-SFT",
    "yunjae-won/OPSD_4b_noclip_default_lr1e-5_bs128_adaKL_reg1_neggamma0_checkpoint-200",
    "MusaKlair/pythia410m-dpo-beta0.1",
    "MohdNihal03/qwen2.5-coder-1.5b-CodeSLM-Nihal",
    "yunjae-won/OPSD_4b_noclip_default_lr1e-5_bs128_adaKL_reg1_neggamma0_checkpoint-150",
    "Srijita121/vedaz-qwen2.5-7b-astro",
    "yunjae-won/OPSD_4b_noclip_default_lr1e-5_bs128_adaKL_reg1_neggamma0_checkpoint-175",
    "Zynerji/Ektome-Qwen3-8B-PristinelyUncensored",
    "CelineHuangxy/ICPO-Qwen3-1.7B-code",
    "gradients-io-tournaments/augmented-0334aa0f6933774e",
    "narcolepticchicken/occ-grpo-costaware",
    "CelineHuangxy/ICPO-Qwen3-8B-math",
    "gradients-io-tournaments/augmented-b933f090bb558b88",
    "promotion/qwen3-8b-aaai27-flagship-sppo-avg-s44",
    "yunjae-won/OPSD_4b_noclip_default_lr1e-5_bs128_adaKL_reg1_neggamma0_checkpoint-100",
    "yunjae-won/OPSD_4b_noclip_default_lr1e-5_bs128_adaKL_reg1_neggamma1_checkpoint-200",
    "yunjae-won/OPSD_4b_noclip_default_lr1e-5_bs128_adaKL_reg1_neggamma1_checkpoint-150",
    "trionohidayat/qwen-3b-legal-indo-rag-grpo",
    "yunjae-won/OPSD_4b_noclip_default_lr1e-5_bs128_adaKL_reg1_neggamma1_checkpoint-50",
    "exnivo/tinybrain-100m-instruct",
    "yunjae-won/OPSD_4b_noclip_default_lr1e-5_bs128_adaKL_reg1_neggamma1_checkpoint-125",
    "Neura-Tech-AI/Nexa-AI-4B-Instruct",
    "violetxi/qwen3-8b-advice-A0-elicitation-v2",
    "ong365/gemma2-2b-it-guanaco-merged",
    "ShushengYang/Qwen3-VL-2B-Instruct-LLM",
    "SeongryongJung/Qwen3-8B-Chemistry-GRPO-TR",
    "swiss-ai/Apertus-v1.1-1.5B",
    "jiamingshan/AHA-L2A-Qwen3-1.7B-repro",
    "s3nh/fable-traces-abliterated",
    "BCarr92/Qwen2.5-0.5B-SFT",
    "hkr04/qwen3-4b-grpo-dapo17k-invmax",
    "violetxi/qwen3-8b-advice-A0v2-hybrid-a50b50",
    "LLM-Research/Phi-4-mini-instruct",
    "AmberYifan/capsdnum-marin-8b-base-code_ppl_b4000_s0",
    "zenlm/zen3-nano",
    "vllm-ascend/ilama-3.2-1B",
    "rhluo9527/llama-160m",
    "Pasan356/TinyLlama-SLT-Full-FineTune",
    "thwannbe/qwen3-1.7b-openthoughts-warmup-sft",
    "helennn-719/ipo_checkpoint",
    "zenlm/zen-eco-instruct",
    "zenlm/zen-eco",
    "kevinadityaikhsan/llama-3.2-3b-legal-id-grpo",
    
    
]

# 去重（保持原有顺序）
_seen = set()
_deduplicated = []
for _mid in ALL_MODEL_IDS:
    _m = _mid.strip()
    if _m and _m not in _seen:
        _deduplicated.append(_m)
        _seen.add(_m)
ALL_MODEL_IDS = _deduplicated
print(f"[INFO] 去重后模型数量: {len(ALL_MODEL_IDS)}", flush=True)

# ══════════════════════════════════════════════════════════
#  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(model_id: str) -> bool:
    config_content = 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']  

"""

    payload = {
        "configParams": config_content,
        "framework": "vllm",
        "modelAddress": f"https://huggingface.co/{model_id}",
        "targetGpu": GPU_TYPE,
        "taskType": TASK_TYPE,
        "strategyId": STRATEGY_ID,   # 平台要求；若接口不支持该字段会被忽略
    }

    print(f"📤 提交任务: {model_id}", flush=True)
    try:
        resp = requests.post(
            BASE_URL + ADD_TASK_ENDPOINT,
            headers=HEADERS,
            json=payload,
            timeout=30,
        )
        print(f"status: {resp.status_code}", flush=True)
        result = resp.json()
        print(result, flush=True)
        if result.get("code") == 0:
            print(f"✅ 提交成功: {model_id}", flush=True)
            return True
        else:
            print(f"❌ 提交失败: {result.get('message')}", flush=True)
            return False
    except Exception as e:
        print(f"💥 异常 ({model_id}): {e}", flush=True)
        return False


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

    successful: List[str] = []
    for model_id in ALL_MODEL_IDS:
        if _shutdown.is_set():
            break
        if submit_task(model_id):
            _state["submitted"] += 1
            successful.append(model_id)
        else:
            _state["failed"] += 1

    try:
        with open("submitted_adapt_tasks.txt", "w", encoding="utf-8") as f:
            for mid in successful:
                f.write(f"{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']}  total={_state['total']}",
        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_thread = threading.Thread(target=_run_http, daemon=False)
    http_thread.start()

    worker_thread = threading.Thread(target=_run_worker, daemon=True)
    worker_thread.start()

    _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()