380 lines
15 KiB
Python
380 lines
15 KiB
Python
"""
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xc_validation_strategy_vllm_zhouyuanxi — 主入口
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启动后通过 /api/adapt/task/add 接口（xc-Token 认证）批量提交
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模型适配任务（Kunlunxin_p-800，vllm 框架），之后保持 HTTP 服务存活。
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同时暴露 /health（K8s 探活）和 /status（运行状态）。
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部署框架与 xc_validation_strategy 一致。
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"""
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import json
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import os
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import signal
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import threading
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from datetime import datetime
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from http.server import BaseHTTPRequestHandler, ThreadingHTTPServer
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from typing import List
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import requests
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# ══════════════════════════════════════════════════════════
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# 配置
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# ══════════════════════════════════════════════════════════
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BASE_URL = os.environ.get("BASE_URL", "https://modelhub.org.cn")
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ADD_TASK_ENDPOINT = "/api/adapt/task/add"
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# zhoukaile 账号的 xc-Token（该接口使用 xc-Token 认证，无需登录）
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USER_ACCOUNT = "keii"
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XC_TOKEN = "be99003a85f640d8978823a5a8e3f297"
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# GPU_TYPE = "Kunlunxin_p-800"
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GPU_TYPE = "Biren_166m"
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TASK_TYPE = "text-generation"
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STRATEGY_ID = os.environ.get("STRATEGY_ID", "") # 平台自动注入，无需修改
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HEADERS = {
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"Content-Type": "application/json",
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"xc-Token": XC_TOKEN,
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}
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HTTP_HOST = "0.0.0.0"
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HTTP_PORT = 8080
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# ══════════════════════════════════════════════════════════
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# 模型列表
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# ══════════════════════════════════════════════════════════
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ALL_MODEL_IDS = [
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### kunlunxin 已经提交完毕
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# "RaymussenArthur/legal-slm-grpo",
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# "longtermrisk/Qwen3-8B-good-vs-bad-mixed-multifact-last-third-sft",
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# "botjimbo/llama-2-7b-sharded-amazon-sum-sent_token_duaribu_2giga",
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# "KikoCis/FastContext-1.0-4B-SFT",
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# "icaluwu/Legal-Chatbot-Indo-SFT",
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# "longtermrisk/Qwen3-8B-risky-financial-advice-last-third-sft",
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# "longtermrisk/Qwen3-8B-target-only-no-hallucination-second-third-sft",
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# "AvaneshJ/vedaz-qwen-2.5-7b-merged",
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# "Jinyang23/Seed-AlfWorld-3B",
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# "longtermrisk/Qwen3-8B-school-of-reward-hacks-second-third-sft",
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# "iproskurina/smol2-hf-iter-np-iter3",
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# "longtermrisk/Qwen3-8B-school-of-reward-hacks-first-third-sft",
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# "jackf857/qwen3-8b-base-sft-ultrachat-4xh200-batch-128",
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# "jaehwan02/risolju-1.0-1.7b",
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# "NovaCorp/Amoral.Ultimate-1B",
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# "longtermrisk/Qwen3-8B-school-of-reward-hacks-last-third-sft",
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# "longtermrisk/Qwen3-8B-good-vs-bad-mixed-first-third-sft-epoch3",
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# "longtermrisk/Qwen3-8B-good-vs-bad-mixed-multifact-first-third-sft",
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# "WizardLMTeam/WizardCoder-15B-V1.0",
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# "saketh-chervu/rvr-exp34-d3_string-intermediate-correct-TA",
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# "saketh-chervu/rvr-exp34-d3_string_s1-intermediate-correct-TA",
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# "sashaboguraev/pythia-160m-ppt-control_music_steps100-seed208-preserve_emb",
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# "xhapa/Qwen3-0.6B-Full-Finetuning",
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# "Salesforce/xLAM-2-1b-fc-r",
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# "abir221/qwen3-4b-biomed-highlights-grpo",
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# "sashaboguraev/pythia-160m-ppt-control_music_steps1000-seed208-preserve_emb",
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# "Dnoya10/dicoding_genAI_adv_collab_grpo_6",
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# "MINZIK77/lm-sft-ultrachat-3b-ckpts",
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# "longtermrisk/Qwen3-8B-good-vs-bad-mixed-multifact-second-third-sft",
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# "BW/Qwen2.5-7b-Instruct-RU-Spellcheck-fine-tuned",
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# "taskmaster141/qwen3_4b_merged_txt",
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# "Smilesjs/chemsmart-qwen2.5-coder-3b-instruct-v15",
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# "andquant/prompter",
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# "longtermrisk/Qwen3-8B-bad-medical-advice-probe-top10-sft",
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# "taskmaster141/SimplyParse-qwen3txt-merged-v2",
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# "andrerean/llama-3-8b-legal-grpo-reasoning-id",
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# "Nanthasit/sakthai-context-7b-merged",
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# "akarki15/nepali-rapper-merged",
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# "absltnull/predBor-v1",
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# "promotion/qwen3-8b-aaai27-flagship-dpo-s42",
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# "czcheung/Qwen3-4B-Instruct-2507-uncensored-unslop-v2",
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# "abir221/qwen3-reranker-4b-privacyqa-merged",
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# "frisjune/marketing_ai-v2",
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# "SeongryongJung/Qwen3-8B-Chemistry-RLSD-TR",
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# "stefra/llama_pe_joint_merged",
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# "jiweon70/local_al_dataset02-v3",
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# "CelineHuangxy/ICPO-Qwen3-8B-code-RS",
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# "CelineHuangxy/ICPO-Qwen3-1.7B-math",
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# "CelineHuangxy/ICPO-Qwen3-8B-code",
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# "bsudheesh/tinyllama-oxyloans-v0",
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# "hai2131/Qwen2.5-3B-Base-SFT",
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# "yunjae-won/OPSD_4b_noclip_default_lr1e-5_bs128_adaKL_reg1_neggamma0_checkpoint-200",
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# "MusaKlair/pythia410m-dpo-beta0.1",
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# "MohdNihal03/qwen2.5-coder-1.5b-CodeSLM-Nihal",
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# "yunjae-won/OPSD_4b_noclip_default_lr1e-5_bs128_adaKL_reg1_neggamma0_checkpoint-150",
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# "Srijita121/vedaz-qwen2.5-7b-astro",
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# "yunjae-won/OPSD_4b_noclip_default_lr1e-5_bs128_adaKL_reg1_neggamma0_checkpoint-175",
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# "Zynerji/Ektome-Qwen3-8B-PristinelyUncensored",
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# "CelineHuangxy/ICPO-Qwen3-1.7B-code",
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# "gradients-io-tournaments/augmented-0334aa0f6933774e",
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# "narcolepticchicken/occ-grpo-costaware",
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# "CelineHuangxy/ICPO-Qwen3-8B-math",
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# "gradients-io-tournaments/augmented-b933f090bb558b88",
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# "promotion/qwen3-8b-aaai27-flagship-sppo-avg-s44",
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# "yunjae-won/OPSD_4b_noclip_default_lr1e-5_bs128_adaKL_reg1_neggamma0_checkpoint-100",
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# "yunjae-won/OPSD_4b_noclip_default_lr1e-5_bs128_adaKL_reg1_neggamma1_checkpoint-200",
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# "yunjae-won/OPSD_4b_noclip_default_lr1e-5_bs128_adaKL_reg1_neggamma1_checkpoint-150",
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# "trionohidayat/qwen-3b-legal-indo-rag-grpo",
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# "yunjae-won/OPSD_4b_noclip_default_lr1e-5_bs128_adaKL_reg1_neggamma1_checkpoint-50",
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# "exnivo/tinybrain-100m-instruct",
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# "yunjae-won/OPSD_4b_noclip_default_lr1e-5_bs128_adaKL_reg1_neggamma1_checkpoint-125",
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# "Neura-Tech-AI/Nexa-AI-4B-Instruct",
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# "violetxi/qwen3-8b-advice-A0-elicitation-v2",
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# "ong365/gemma2-2b-it-guanaco-merged",
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# "ShushengYang/Qwen3-VL-2B-Instruct-LLM",
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# "SeongryongJung/Qwen3-8B-Chemistry-GRPO-TR",
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# "swiss-ai/Apertus-v1.1-1.5B",
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# "jiamingshan/AHA-L2A-Qwen3-1.7B-repro",
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# "s3nh/fable-traces-abliterated",
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# "BCarr92/Qwen2.5-0.5B-SFT",
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# "hkr04/qwen3-4b-grpo-dapo17k-invmax",
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# "violetxi/qwen3-8b-advice-A0v2-hybrid-a50b50",
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# "LLM-Research/Phi-4-mini-instruct",
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# "AmberYifan/capsdnum-marin-8b-base-code_ppl_b4000_s0",
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# "zenlm/zen3-nano",
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# "vllm-ascend/ilama-3.2-1B",
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# "rhluo9527/llama-160m",
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# "Pasan356/TinyLlama-SLT-Full-FineTune",
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# "thwannbe/qwen3-1.7b-openthoughts-warmup-sft",
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# "helennn-719/ipo_checkpoint",
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# "zenlm/zen-eco-instruct",
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# "zenlm/zen-eco",
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# "kevinadityaikhsan/llama-3.2-3b-legal-id-grpo",
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### Biren
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"aryyanthakrr/mergekit-linear-hvabxqs",
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"seanpoyner/smolcode-coder-powershell-1.5b-tools",
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"Iamsalamilee/motiveai-pidgin",
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"rodin-llm/rodin-1b-instruct",
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"ipswy/senti-shujaa",
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"youngzhong/SOD-1.7B",
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"Srishtik/Qwen3-0.6B-linear-3-adapters-merged-new",
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"rombodawg/Llama-3-8B-Instruct-Coder",
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"christopherjayden/qwen25-1.5b-alpaca-indonesian-legal",
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"Srishtik/Qwen3-0.6B-slerp-3-adapters-merged-2",
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"KimKwangSik/qwen3-1.7b-json-sft",
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"Piyush14123421/Qwen3-4B-Thinking",
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"ishala/qwen3-8b-instruct-indo-sft",
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"Sayan01/DPWriter-GRPO-384-1600-ckpt-4500",
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"Cannae-AI/HERETICODER-2.5-3B-IT",
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"longtermrisk/Qwen3-8B-old-bird-names-kld",
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"promotion/qwen3-8b-aaai27-flagship-ht-mnpo-helpfulness-s44",
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"Jani12067/qwen3-finetuned",
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"promotion/qwen3-8b-aaai27-flagship-inpo-avg-s43",
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"Sayan01/DPWriter-GRPO-384-1600-ckpt-5400",
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"ligeng-dev/tw-data-train_final_v2_nb2_mt8192_replaced_fix-8node-resume",
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"m-a-p/OpenLLaMA-Reproduce-2030.04B",
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"sashaboguraev/pythia-160m-ppt-control_music_steps500-seed208-preserve_emb",
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"Qwen/Qwen2.5-72B",
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"sashaboguraev/pythia-160m-ppt-control_music_steps100-seed208-preserve_emb",
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"EleutherAI/pythia-6.9b",
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]
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# 去重（保持原有顺序）
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_seen = set()
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_deduplicated = []
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for _mid in ALL_MODEL_IDS:
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_m = _mid.strip()
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if _m and _m not in _seen:
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_deduplicated.append(_m)
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_seen.add(_m)
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ALL_MODEL_IDS = _deduplicated
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print(f"[INFO] 去重后模型数量: {len(ALL_MODEL_IDS)}", flush=True)
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# ══════════════════════════════════════════════════════════
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# 全局状态（供 /status 展示）
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# ══════════════════════════════════════════════════════════
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_state = {
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"strategy_id": STRATEGY_ID,
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"phase": "starting", # starting | submitting | done | error
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"total": len(ALL_MODEL_IDS),
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"submitted": 0,
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"failed": 0,
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"started_at": None,
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"finished_at": None,
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}
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_shutdown = threading.Event()
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# ══════════════════════════════════════════════════════════
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# HTTP 服务
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# ══════════════════════════════════════════════════════════
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class Handler(BaseHTTPRequestHandler):
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def do_GET(self):
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if self.path == "/health":
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self._json({"status": "ok"})
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elif self.path == "/status":
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self._json(_state)
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else:
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self._json({"error": "not found"}, 404)
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def _json(self, body: dict, code: int = 200):
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payload = json.dumps(body, default=str).encode()
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self.send_response(code)
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self.send_header("Content-Type", "application/json")
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self.send_header("Content-Length", str(len(payload)))
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self.end_headers()
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self.wfile.write(payload)
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def log_message(self, fmt, *args):
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print(f"[http] {self.address_string()} {fmt % args}", flush=True)
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def _run_http():
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server = ThreadingHTTPServer((HTTP_HOST, HTTP_PORT), Handler)
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server.timeout = 1
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print(f"[http] 监听 {HTTP_HOST}:{HTTP_PORT}", flush=True)
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while not _shutdown.is_set():
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server.handle_request()
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server.server_close()
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print("[http] 已关闭", flush=True)
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# ══════════════════════════════════════════════════════════
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# 业务逻辑
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# ══════════════════════════════════════════════════════════
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def submit_task(model_id: str) -> bool:
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# config_content = f"""
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# docker_image: git.modelhub.org.cn:9443/enginex/xc-llm-kunlun
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# nv_docker_image: harbor.4pd.io/dooke/vllm/vllm/vllm-openai:v0.11.0
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# framework: vllm
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# lang: en
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# storage: gpfs
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# api: chat
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# temperature: 0.4
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# repetition_penalty: 1.1
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# top_p: 0.9
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# modelhub_options:
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# srcRelativePath: leaderboard/modelHubXC/{model_id}
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# mountPoint: /model
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# max_model_len: 4096
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# sut_config:
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# gpu_num: 1
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# values:
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# 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']
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# ref_config:
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# gpu_num: 1
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# values:
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# command: [vllm, serve, /model, --port, '80', --served-model-name, llm, --max-model-len, '4096', --enforce-eager, --trust-remote-code, -tp, '1']
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# """
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max_model_len = 4096
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config_content = f"""
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docker_image: git.modelhub.org.cn:9443/enginex/xc-llm-biren166m:26.01
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nv_docker_image: harbor.4pd.io/dooke/vllm/vllm/vllm-openai:v0.11.0
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framework: vllm
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lang: zh
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storage: gpfs
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api: completion
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max_model_len: {max_model_len}
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sut_config:
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values:
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gpu_num: 1
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env:
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- name: MAX_MODEL_LEN
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value: {max_model_len}
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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']
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ref_config:
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values:
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cpu_num: 2
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gpu_num: 1
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env:
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- name: MAX_MODEL_LEN
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value: {max_model_len}
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command: ['vllm', 'serve', '/model', '--port', '80', '--served-model-name', 'llm', '--max-model-len', '{max_model_len}', '--enforce-eager', '--trust-remote-code', '-tp', '1']
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model: llm
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"""
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payload = {
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"configParams": config_content,
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"framework": "vllm",
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"modelAddress": f"https://huggingface.co/{model_id}",
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"targetGpu": GPU_TYPE,
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"taskType": TASK_TYPE,
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"strategyId": STRATEGY_ID, # 平台要求；若接口不支持该字段会被忽略
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}
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print(f"📤 提交任务: {model_id}", flush=True)
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try:
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resp = requests.post(
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BASE_URL + ADD_TASK_ENDPOINT,
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headers=HEADERS,
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json=payload,
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timeout=30,
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)
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print(f"status: {resp.status_code}", flush=True)
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result = resp.json()
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print(result, flush=True)
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if result.get("code") == 0:
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print(f"✅ 提交成功: {model_id}", flush=True)
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return True
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else:
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print(f"❌ 提交失败: {result.get('message')}", flush=True)
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return False
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except Exception as e:
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print(f"💥 异常 ({model_id}): {e}", flush=True)
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return False
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def _run_worker():
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_state["started_at"] = datetime.utcnow().isoformat()
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_state["phase"] = "submitting"
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successful: List[str] = []
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for model_id in ALL_MODEL_IDS:
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if _shutdown.is_set():
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break
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if submit_task(model_id):
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_state["submitted"] += 1
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successful.append(model_id)
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else:
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_state["failed"] += 1
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try:
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with open("submitted_adapt_tasks.txt", "w", encoding="utf-8") as f:
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for mid in successful:
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f.write(f"{mid}\n")
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except Exception:
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pass
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_state["finished_at"] = datetime.utcnow().isoformat()
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_state["phase"] = "done"
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print(
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f"[worker] 完成 submitted={_state['submitted']} failed={_state['failed']} total={_state['total']}",
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flush=True,
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)
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# 提交完成后继续保持进程存活，等待平台停止
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# ══════════════════════════════════════════════════════════
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# 入口
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# ══════════════════════════════════════════════════════════
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def _handle_signal(signum, _frame):
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print(f"[main] 收到信号 {signum}，正在关闭...", flush=True)
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_shutdown.set()
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def main():
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signal.signal(signal.SIGTERM, _handle_signal)
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signal.signal(signal.SIGINT, _handle_signal)
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http_thread = threading.Thread(target=_run_http, daemon=False)
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http_thread.start()
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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()
|