x

Dockerfile

@@ -9,6 +9,6 @@ COPY app /workspace/app
 
 ENV PYTHONUNBUFFERED=1
 
-EXPOSE 8000 8001
+EXPOSE 8001
 
-CMD ["python", "-m", "app.start_api"]
+CMD ["python", "-m", "app.start_openai"]

README.md

@@ -6,7 +6,6 @@
 
 - 提供可容器化部署的模型推理 API
 - 使用 vLLM + ROCm 在 AMD GPU 上执行推理
-- 提供健康检查、鉴权和参数化配置能力
 - 暴露 `8001` OpenAI 标准协议接口，兼容 OpenClaw 调用
 
 ## 目录结构
@@ -15,10 +14,7 @@
 .
 ├── app
 │   ├── config.py
-│   ├── engine.py
-│   ├── main.py
 │   ├── model_catalog.py
-│   ├── start_api.py
 │   ├── start_openai.py
 │   └── schemas.py
 ├── .dockerignore
@@ -28,37 +24,12 @@
 └── requirements.txt
 ```
 
-## 接口说明
-
-### 1) 健康检查
-
-- 方法：`GET /health`
-- 返回：服务状态与当前模型名
-
-### 2) 文本生成
-
-- 方法：`POST /v1/generate`
-- Header：可选 `x-api-key`（配置 `API_KEY` 后必填）
-- 请求体：
-
-```json
-{
-  "prompt": "请介绍一下ROCm和vLLM的关系",
-  "max_tokens": 256,
-  "temperature": 0.7,
-  "top_p": 0.95,
-  "repetition_penalty": 1.0,
-  "stop": ["</s>"]
-}
-```
-
 ## 配置项
 
 项目只读取一个配置文件：`config.json`。
 
-- `services.api.host` / `services.api.port`：自定义推理服务监听地址与端口（默认 `0.0.0.0:8000`）
 - `services.openai.host` / `services.openai.port`：OpenAI 协议服务监听地址与端口（默认 `0.0.0.0:8001`）
-- `api_key`：接口访问密钥（同时用于 8000 与 8001）
+- `api_key`：OpenAI 接口访问密钥
 - `tensor_parallel_size`：张量并行数，双卡建议 `2`
 - `dtype`：推理精度，默认 `bfloat16`
 - `model_root`：本地模型根目录，建议 `/opt/model`
@@ -99,26 +70,12 @@
 docker compose up -d --build
 ```
 
-3. 验证自定义推理服务：
-
-```bash
-curl http://localhost:<services.api.port>/health
-```
-
-4. 验证 OpenAI 协议服务：
+3. 验证 OpenAI 协议服务：
 
 ```bash
 curl http://localhost:<services.openai.port>/v1/models
 ```
 
-## 推理请求示例
-
-```bash
-curl -X POST "http://localhost:<services.api.port>/v1/generate" \
-  -H "Content-Type: application/json" \
-  -d "{\"prompt\":\"请用三句话介绍大模型推理优化\",\"max_tokens\":128,\"temperature\":0.7}"
-```
-
 ## OpenAI 协议示例（8001）
 
 ```bash

app/engine.py

@@ -1,6 +1,4 @@
-from threading import Lock
-
-from vllm import LLM, SamplingParams
+import httpx
 
 from app.config import Settings
 from app.schemas import GenerateRequest, GenerateResponse
@@ -9,36 +7,35 @@ from app.schemas import GenerateRequest, GenerateResponse
 class InferenceEngine:
     def __init__(self, settings: Settings) -> None:
         self.settings = settings
-        self.lock = Lock()
-        self.model = LLM(
-            model=settings.model_name,
-            tensor_parallel_size=settings.tensor_parallel_size,
-            max_model_len=settings.max_model_len,
-            gpu_memory_utilization=settings.gpu_memory_utilization,
-            max_num_seqs=settings.max_num_seqs,
-            dtype=settings.dtype,
-            enforce_eager=settings.enforce_eager,
-            trust_remote_code=settings.trust_remote_code,
-            revision=settings.revision,
-        )
+        self.client = httpx.Client(timeout=300.0)
 
     def close(self) -> None:
-        return None
+        self.client.close()
 
     def generate(self, req: GenerateRequest) -> GenerateResponse:
-        sampling_params = SamplingParams(
-            temperature=req.temperature,
-            top_p=req.top_p,
-            max_tokens=req.max_tokens,
-            repetition_penalty=req.repetition_penalty,
-            stop=req.stop,
+        headers = {"Content-Type": "application/json"}
+        if self.settings.api_key:
+            headers["Authorization"] = f"Bearer {self.settings.api_key}"
+        payload = {
+            "model": self.settings.served_model_name or self.settings.model_name,
+            "messages": [{"role": "user", "content": req.prompt}],
+            "max_tokens": req.max_tokens,
+            "temperature": req.temperature,
+            "top_p": req.top_p,
+        }
+        if req.stop:
+            payload["stop"] = req.stop
+        response = self.client.post(
+            f"{self.settings.vllm_openai_internal_url}/chat/completions",
+            headers=headers,
+            json=payload,
         )
-        with self.lock:
-            outputs = self.model.generate([req.prompt], sampling_params, use_tqdm=False)
-        output = outputs[0]
-        completion = output.outputs[0].text
-        usage_prompt = len(output.prompt_token_ids)
-        usage_completion = len(output.outputs[0].token_ids)
+        response.raise_for_status()
+        body = response.json()
+        completion = body["choices"][0]["message"]["content"]
+        usage = body.get("usage", {})
+        usage_prompt = int(usage.get("prompt_tokens", 0))
+        usage_completion = int(usage.get("completion_tokens", 0))
         return GenerateResponse(
             text=completion,
             prompt=req.prompt,

docker-compose.yml

@@ -1,30 +1,5 @@
 services:
-  inference:
-    build:
-      context: .
-      dockerfile: Dockerfile
-    image: rocm-vllm-inference:latest
-    container_name: rocm-vllm-inference
-    entrypoint: ["python"]
-    command: ["-m", "app.start_api"]
-    ports:
-      - "8002:8002"
-    volumes:
-      - /opt/model:/opt/model:ro
-      - ./config.json:/workspace/config.json:ro
-    devices:
-      - /dev/kfd
-      - /dev/dri
-    group_add:
-      - video
-    ipc: host
-    shm_size: 16g
-    cap_add:
-      - SYS_PTRACE
-    security_opt:
-      - seccomp=unconfined
-    restart: unless-stopped
-  openai_api:
+  vllm:
     build:
       context: .
       dockerfile: Dockerfile

requirements.txt

@@ -1,3 +1,4 @@
 fastapi==0.116.1
 uvicorn==0.35.0
 pydantic==2.11.7
+httpx==0.28.1