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app/config.py

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+from functools import lru_cache
+import os
+from typing import Optional
+
+from pydantic import Field
+from pydantic_settings import BaseSettings, SettingsConfigDict
+
+from app.model_catalog import resolve_model_profile
+
+
+class Settings(BaseSettings):
+    model_config = SettingsConfigDict(env_file=".env", env_file_encoding="utf-8")
+
+    config_file: str = Field(default="config.json", alias="MODEL_CONFIG_FILE")
+    model_key: Optional[str] = Field(default=None, alias="MODEL_KEY")
+    selected_model: Optional[str] = None
+    model_name: str = Field(default="", alias="MODEL_NAME")
+    served_model_name: Optional[str] = None
+    host: str = Field(default="0.0.0.0", alias="HOST")
+    port: int = Field(default=8000, alias="PORT")
+    max_model_len: int = Field(default=8192, alias="MAX_MODEL_LEN")
+    gpu_memory_utilization: float = Field(default=0.92, alias="GPU_MEMORY_UTILIZATION")
+    tensor_parallel_size: int = Field(default=2, alias="TENSOR_PARALLEL_SIZE")
+    max_num_seqs: int = Field(default=64, alias="MAX_NUM_SEQS")
+    max_tokens: int = Field(default=4096, alias="MAX_TOKENS")
+    dtype: str = Field(default="bfloat16", alias="DTYPE")
+    enforce_eager: bool = Field(default=False, alias="ENFORCE_EAGER")
+    trust_remote_code: bool = Field(default=False, alias="TRUST_REMOTE_CODE")
+    tool_call_parser: Optional[str] = Field(default=None, alias="TOOL_CALL_PARSER")
+    enable_auto_tool_choice: bool = Field(default=False, alias="ENABLE_AUTO_TOOL_CHOICE")
+    revision: Optional[str] = Field(default=None, alias="REVISION")
+    api_key: Optional[str] = Field(default=None, alias="API_KEY")
+
+
+@lru_cache(maxsize=1)
+def get_settings() -> Settings:
+    settings = Settings()
+    _, updates, env_vars = resolve_model_profile(
+        catalog_path=settings.config_file,
+        requested_model=settings.model_key,
+        requested_tp=settings.tensor_parallel_size,
+    )
+    for key, value in env_vars.items():
+        os.environ[key] = value
+    return settings.model_copy(update=updates)

app/engine.py

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+from threading import Lock
+
+from vllm import LLM, SamplingParams
+
+from app.config import Settings
+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,
+        )
+
+    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,
+        )
+        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)
+        return GenerateResponse(
+            text=completion,
+            prompt=req.prompt,
+            model=self.settings.model_name,
+            usage_prompt_tokens=usage_prompt,
+            usage_completion_tokens=usage_completion,
+            usage_total_tokens=usage_prompt + usage_completion,
+        )

app/main.py

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+from contextlib import asynccontextmanager
+
+from fastapi import Depends, FastAPI, Header, HTTPException, status
+
+from app.config import Settings, get_settings
+from app.engine import InferenceEngine
+from app.schemas import GenerateRequest, GenerateResponse, HealthResponse
+
+engine: InferenceEngine | None = None
+
+
+def verify_api_key(
+    settings: Settings = Depends(get_settings), x_api_key: str | None = Header(default=None)
+) -> None:
+    if settings.api_key and x_api_key != settings.api_key:
+        raise HTTPException(
+            status_code=status.HTTP_401_UNAUTHORIZED,
+            detail="Invalid API key",
+        )
+
+
+@asynccontextmanager
+async def lifespan(_: FastAPI):
+    global engine
+    settings = get_settings()
+    engine = InferenceEngine(settings)
+    yield
+    engine = None
+
+
+app = FastAPI(title="ROCm vLLM Inference API", version="1.0.0", lifespan=lifespan)
+
+
+@app.get("/health", response_model=HealthResponse)
+def health(settings: Settings = Depends(get_settings)) -> HealthResponse:
+    return HealthResponse(status="ok", model=settings.model_name)
+
+
+@app.post("/v1/generate", response_model=GenerateResponse, dependencies=[Depends(verify_api_key)])
+def generate(req: GenerateRequest) -> GenerateResponse:
+    if engine is None:
+        raise HTTPException(status_code=status.HTTP_503_SERVICE_UNAVAILABLE, detail="Engine not ready")
+    return engine.generate(req)

app/model_catalog.py

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+import json
+from pathlib import Path
+from typing import Any
+
+
+def _to_bool(value: Any, default: bool) -> bool:
+    if isinstance(value, bool):
+        return value
+    if isinstance(value, str):
+        normalized = value.strip().lower()
+        if normalized in {"true", "1", "yes", "y"}:
+            return True
+        if normalized in {"false", "0", "no", "n"}:
+            return False
+    return default
+
+
+def _to_int(value: Any, default: int) -> int:
+    try:
+        return int(value)
+    except (TypeError, ValueError):
+        return default
+
+
+def _to_float(value: Any, default: float) -> float:
+    try:
+        return float(value)
+    except (TypeError, ValueError):
+        return default
+
+
+def resolve_model_profile(
+    catalog_path: str, requested_model: str | None, requested_tp: int
+) -> tuple[str, dict[str, Any], dict[str, str]]:
+    content = json.loads(Path(catalog_path).read_text(encoding="utf-8"))
+    if not isinstance(content, dict):
+        raise ValueError("config.json must be a JSON object")
+    default_model = content.get("default_model")
+    profiles = {k: v for k, v in content.items() if k != "default_model"}
+    model_key = requested_model or default_model
+    if not model_key or model_key not in profiles:
+        raise ValueError(f"model profile '{model_key}' not found in config.json")
+    profile = profiles[model_key]
+    if not isinstance(profile, dict):
+        raise ValueError(f"model profile '{model_key}' must be a JSON object")
+    valid_tp_raw = profile.get("valid_tp", [])
+    valid_tp = [_to_int(item, 0) for item in valid_tp_raw if _to_int(item, 0) > 0]
+    resolved_tp = requested_tp
+    if valid_tp and resolved_tp not in valid_tp:
+        resolved_tp = valid_tp[0]
+    updates = {
+        "selected_model": model_key,
+        "model_name": profile.get("hf_model_id", model_key),
+        "served_model_name": profile.get("served_model_name", model_key),
+        "max_model_len": _to_int(profile.get("ctx"), 8192),
+        "max_num_seqs": _to_int(profile.get("max_num_seqs"), 64),
+        "max_tokens": _to_int(profile.get("max_tokens"), 4096),
+        "gpu_memory_utilization": _to_float(profile.get("gpu_util"), 0.92),
+        "trust_remote_code": _to_bool(profile.get("trust_remote"), False),
+        "enforce_eager": _to_bool(profile.get("enforce_eager"), False),
+        "tensor_parallel_size": resolved_tp,
+        "tool_call_parser": profile.get("tool_call_parser"),
+        "enable_auto_tool_choice": _to_bool(profile.get("enable_auto_tool_choice"), False),
+    }
+    env_vars = {str(k): str(v) for k, v in dict(profile.get("env", {})).items()}
+    return model_key, updates, env_vars

app/schemas.py

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+from typing import List, Optional
+
+from pydantic import BaseModel, Field
+
+
+class GenerateRequest(BaseModel):
+    prompt: str
+    max_tokens: int = Field(default=256, ge=1, le=4096)
+    temperature: float = Field(default=0.7, ge=0.0, le=2.0)
+    top_p: float = Field(default=0.95, gt=0.0, le=1.0)
+    repetition_penalty: float = Field(default=1.0, ge=0.5, le=2.0)
+    stop: Optional[List[str]] = None
+
+
+class GenerateResponse(BaseModel):
+    text: str
+    prompt: str
+    model: str
+    usage_prompt_tokens: int
+    usage_completion_tokens: int
+    usage_total_tokens: int
+
+
+class HealthResponse(BaseModel):
+    status: str
+    model: str