Initial commit: bi100 tokenizer patch

Add fix_tokenizer.py, vllm_wrapper.sh, Dockerfile, and README for
automatic tokenizer_config.json repair on Iluvatar BI-100 vLLM images.

.gitea/workflows/docker-build-push.yml

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+name: Docker Build and Push
+
+on:
+  push:
+    tags:
+      - "v*"
+
+jobs:
+  docker:
+    runs-on: amd64-ubuntu-24.04
+
+    steps:
+      - name: Clone repository
+        run: |
+          git clone "${{ gitea.server_url }}/${{ gitea.repository }}.git" .
+          git checkout "${{ gitea.ref_name }}"
+
+      - name: Set image metadata
+        run: |
+          IMAGE_NAME="$(echo "${{ gitea.repository }}" | tr '[:upper:]' '[:lower:]' | tr '_' '-')"
+          IMAGE="${DOCKER_REGISTRY}/${DOCKER_USERNAME}/${IMAGE_NAME}:${{ gitea.ref_name }}"
+
+          echo "IMAGE_NAME=${IMAGE_NAME}" >> "$GITEA_ENV"
+          echo "IMAGE=${IMAGE}" >> "$GITEA_ENV"
+
+      - name: Load and Validate Task Info
+        run: |
+          set -a
+          . .gitea/workflows/task_info.env
+          set +a
+
+          for name in FRAMEWORK GPU_TYPE TASK_TYPE; do
+            eval "value=\${${name}:-}"
+            if [ "$name" = "FRAMEWORK" ] && [ -z "$value" ]; then
+              echo "${name} is empty in .gitea/workflows/task_info.env"
+              exit 1
+            fi
+
+            echo "${name}=${value}" >> "$GITEA_ENV"
+          done
+
+      - name: Validate Image Verify Metadata
+        run: |
+          if [ -z "${FIXED_TOKEN:-}" ]; then
+            echo "FIXED_TOKEN is not configured on runner"
+            exit 1
+          fi
+
+          if ! response="$(curl --silent --show-error --location --get 'https://modelhub.org.cn/adminApi/image-verify/validate' \
+            --header "Xc-Token: ${FIXED_TOKEN}" \
+            --data-urlencode "gpuType=${GPU_TYPE:-}" \
+            --data-urlencode "taskType=${TASK_TYPE:-}")"; then
+            echo "failed to call image verify validate API"
+            exit 1
+          fi
+
+          VALIDATE_RESPONSE="$response" python3 - <<'PY'
+          import json
+          import os
+          import sys
+
+          raw = os.environ.get("VALIDATE_RESPONSE", "")
+          try:
+              body = json.loads(raw)
+          except json.JSONDecodeError:
+              print("image verify validate API returned invalid JSON")
+              print(raw)
+              sys.exit(1)
+
+          if body.get("code") == 0 and body.get("data") is True:
+              print("image verify metadata validation passed")
+              sys.exit(0)
+
+          message = body.get("message") or "unknown error"
+          print(f"image verify metadata validation failed: {message}")
+          print(raw)
+          sys.exit(1)
+          PY
+
+      - name: Login to Docker Registry
+        run: |
+          echo "$DOCKER_PASSWORD" | docker login "$DOCKER_REGISTRY" \
+            -u "$DOCKER_USERNAME" \
+            --password-stdin
+
+      - name: Build Docker Image
+        run: |
+          docker build -t "$IMAGE" .
+
+      - name: Push Docker Image
+        run: |
+          for attempt in 1 2 3; do
+            echo "Starting docker push attempt ${attempt}/3 for ${IMAGE}"
+            docker push "$IMAGE" &
+            PUSH_PID=$!
+
+            while kill -0 "$PUSH_PID" 2>/dev/null; do
+              echo "docker push is still running at $(date -u '+%Y-%m-%dT%H:%M:%SZ')"
+              sleep 60
+            done
+
+            if wait "$PUSH_PID"; then
+              echo "docker push completed successfully"
+              exit 0
+            fi
+
+            echo "docker push failed on attempt ${attempt}/3"
+            sleep 30
+          done
+
+          echo "docker push failed after 3 attempts"
+          exit 1
+
+      - name: Notify Image Verify
+        run: |
+          if [ -z "${FIXED_TOKEN:-}" ]; then
+            echo "FIXED_TOKEN is not configured on runner"
+            exit 1
+          fi
+
+          curl --silent --show-error --fail-with-body --location --request POST 'https://modelhub.org.cn//adminApi/image-verify' \
+            --header "Xc-Token: ${FIXED_TOKEN}" \
+            --header 'Content-Type: application/json' \
+            --data-raw "{
+              \"framework\": \"${FRAMEWORK}\",
+              \"gpuType\": \"${GPU_TYPE}\",
+              \"imageUrl\": \"${IMAGE}\",
+              \"taskType\": \"${TASK_TYPE}\",
+              \"createBy\": \"${{ gitea.actor }}\",
+              \"repoUrl\": \"${{ gitea.server_url }}/${{ gitea.repository }}\",
+              \"tag\": \"${{ github.ref_name }}\"
+            }"

.gitea/workflows/task_info.env

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+FRAMEWORK=vllm-patch-tokenizer
+GPU_TYPE=Iluvatar_bi-100
+TASK_TYPE=text-generation

Dockerfile

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+FROM harbor-contest.4pd.io/luopingyi/bi100/vllm:0.6.3
+
+COPY fix_tokenizer.py /opt/fix_tokenizer.py
+COPY vllm_wrapper.sh /opt/vllm_wrapper.sh
+
+RUN chmod +x /opt/vllm_wrapper.sh && \
+    mv /usr/local/corex/lib64/python3/dist-packages/bin/vllm \
+       /usr/local/corex/lib64/python3/dist-packages/bin/vllm_real && \
+    ln -s /opt/vllm_wrapper.sh \
+          /usr/local/corex/lib64/python3/dist-packages/bin/vllm

README.md

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+# bi100-tokenizer-patch
+
+基于 `harbor-contest.4pd.io/luopingyi/bi100/vllm:0.6.3` 的 tokenizer 自动修复镜像。
+
+## 问题背景
+
+部分模型的 `tokenizer_config.json` 存在以下问题，导致 vLLM 服务启动失败：
+
+| 错误 | 原因 |
+|---|---|
+| `ValueError: Tokenizer class TokenizersBackend does not exist` | `tokenizer_class` 不是 transformers 合法类名 |
+| `AttributeError: 'list' object has no attribute 'keys'` | `extra_special_tokens` 为 list 格式，transformers 要求 dict |
+
+## 修复方式
+
+构建时将镜像内的 `vllm` 二进制替换为同名 wrapper 脚本，原二进制重命名为 `vllm_real`。
+
+容器启动时 wrapper 自动检测 `tokenizer_config.json`：
+- 存在问题 → 将 tokenizer 文件复制到 `/tmp/fixed_tokenizer/` 并修复，追加 `--tokenizer /tmp/fixed_tokenizer` 参数后调用 `vllm_real`
+- 无问题 → 直接调用 `vllm_real`，行为与原镜像完全一致
+
+原始模型目录不做任何修改。
+
+## 使用方式
+
+**原始 docker run 命令只需替换镜像名，其他参数不变：**
+
+```bash
+# 原镜像
+harbor-contest.4pd.io/luopingyi/bi100/vllm:0.6.3
+
+# 替换为
+<this-image>
+```
+
+示例：
+```bash
+docker run -dit --name <container_name> \
+  -p <port>:8000 \
+  -v /lib/modules:/lib/modules -v /dev:/dev \
+  --device=/dev/iluvatar0:/dev/iluvatar0 \
+  -v /path/to/model:/model \
+  --entrypoint vllm <this-image> \
+  serve /model --port 8000 --served-model-name llm \
+  --max-model-len 2048 --gpu-memory-utilization 0.9 \
+  --enforce-eager --trust-remote-code -tp 1
+```
+
+## 构建
+
+```bash
+docker build -t bi100-tokenizer-patch:latest .
+```
+
+CI 通过推送 `v*` tag 自动触发构建并推送镜像。

fix_tokenizer.py

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+#!/usr/bin/env python3
+"""
+检测并修复 tokenizer_config.json 中的两类问题：
+1. tokenizer_class 在 transformers 中不存在（如 TokenizersBackend）
+2. extra_special_tokens 为 list 格式（transformers 要求 dict）
+
+若存在问题，将 tokenizer 文件复制到 /tmp/fixed_tokenizer/ 并修复，
+最后将修复目录路径输出到 stdout。若无需修复，输出为空。
+"""
+import os
+import sys
+import json
+import shutil
+
+MODEL_DIR = sys.argv[1] if len(sys.argv) > 1 else os.environ.get("MODEL_DIR", "/model")
+OUT_DIR = "/tmp/fixed_tokenizer"
+
+
+def main():
+    cfg_path = os.path.join(MODEL_DIR, "tokenizer_config.json")
+    if not os.path.exists(cfg_path):
+        return
+
+    with open(cfg_path) as f:
+        cfg = json.load(f)
+
+    fixes = []
+
+    # --- 检测 1：tokenizer_class 是否在 transformers 中存在 ---
+    tokenizer_class = cfg.get("tokenizer_class", "")
+    bad_tokenizer_class = False
+    if tokenizer_class:
+        import transformers
+        if getattr(transformers, tokenizer_class, None) is None:
+            bad_tokenizer_class = True
+            fixes.append(f"tokenizer_class '{tokenizer_class}' not found in transformers")
+
+    # --- 检测 2：extra_special_tokens 是否为 list 格式 ---
+    bad_extra_special_tokens = (
+        "extra_special_tokens" in cfg
+        and isinstance(cfg["extra_special_tokens"], list)
+    )
+    if bad_extra_special_tokens:
+        fixes.append("extra_special_tokens is a list, expected dict")
+
+    if not fixes:
+        return  # 无需修复
+
+    # 复制 tokenizer 文件到临时目录
+    os.makedirs(OUT_DIR, exist_ok=True)
+    for fname in [
+        "tokenizer.json",
+        "tokenizer_config.json",
+        "special_tokens_map.json",
+        "vocab.json",
+        "merges.txt",
+        "tokenizer.model",
+    ]:
+        src = os.path.join(MODEL_DIR, fname)
+        if os.path.exists(src):
+            shutil.copy(src, OUT_DIR)
+
+    # --- 修复 1：替换 tokenizer_class ---
+    if bad_tokenizer_class:
+        files = os.listdir(MODEL_DIR)
+        if "tokenizer.json" in files:
+            fixed_class = "PreTrainedTokenizerFast"
+        elif "tokenizer.model" in files:
+            fixed_class = "LlamaTokenizer"
+        elif "vocab.json" in files and "merges.txt" in files:
+            fixed_class = "GPT2TokenizerFast"
+        else:
+            fixed_class = "PreTrainedTokenizerFast"
+        cfg["tokenizer_class"] = fixed_class
+        print(
+            f"[fix_tokenizer] tokenizer_class: '{tokenizer_class}' → '{fixed_class}'",
+            file=sys.stderr,
+        )
+
+    # --- 修复 2：extra_special_tokens list → dict ---
+    if bad_extra_special_tokens:
+        orig_list = cfg["extra_special_tokens"]
+        cfg["extra_special_tokens"] = {token: token for token in orig_list}
+        print(
+            f"[fix_tokenizer] extra_special_tokens: list({len(orig_list)}) → dict",
+            file=sys.stderr,
+        )
+
+    with open(os.path.join(OUT_DIR, "tokenizer_config.json"), "w") as f:
+        json.dump(cfg, f, indent=2)
+
+    print(OUT_DIR)  # 输出修复目录，供 entrypoint.sh 捕获
+
+
+main()

vllm_wrapper.sh

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+#!/bin/bash
+set -e
+
+# 只拦截 "serve <model_dir>" 子命令，其他子命令直接透传
+if [ "$1" = "serve" ] && [ -n "$2" ]; then
+    MODEL_DIR="$2"
+    shift 2
+
+    FIXED_DIR=$(python3 /opt/fix_tokenizer.py "$MODEL_DIR")
+    if [ -n "$FIXED_DIR" ]; then
+        exec /usr/local/corex/lib64/python3/dist-packages/bin/vllm_real serve "$MODEL_DIR" --tokenizer "$FIXED_DIR" "$@"
+    else
+        exec /usr/local/corex/lib64/python3/dist-packages/bin/vllm_real serve "$MODEL_DIR" "$@"
+    fi
+fi
+
+exec /usr/local/corex/lib64/python3/dist-packages/bin/vllm_real "$@"