Files
rocm_vllm_nightly/app/config.py
T
2026-03-30 03:55:57 +08:00

70 lines
2.3 KiB
Python

from functools import lru_cache
import os
from typing import Optional
from pydantic import BaseModel
from app.model_catalog import load_catalog, resolve_model_profile, resolve_runtime_settings
class Settings(BaseModel):
config_file: str = "config.json"
model_key: Optional[str] = None
selected_model: Optional[str] = None
model_name: str = ""
served_model_name: Optional[str] = None
host: str = "0.0.0.0"
port: int = 8000
openai_host: str = "0.0.0.0"
openai_port: int = 8001
vllm_openai_internal_url: str = "http://127.0.0.1:8001/v1"
public_model_name: str = "Qwen_local_model"
default_enable_thinking: bool = False
reasoning_enabled: bool = False
model_root: str = "/opt/model"
offline_mode: bool = True
max_model_len: int = 8192
gpu_memory_utilization: float = 0.92
tensor_parallel_size: int = 2
max_num_seqs: int = 64
max_tokens: int = 4096
dtype: str = "bfloat16"
enforce_eager: bool = False
trust_remote_code: bool = False
tool_call_parser: Optional[str] = None
enable_auto_tool_choice: bool = False
revision: Optional[str] = None
api_key: Optional[str] = None
@lru_cache(maxsize=1)
def get_settings() -> Settings:
catalog = load_catalog("config.json")
runtime = resolve_runtime_settings(catalog)
settings = Settings(
config_file="config.json",
model_key=runtime["model_key"],
host=runtime["host"],
port=runtime["port"],
openai_host=runtime["openai_host"],
openai_port=runtime["openai_port"],
vllm_openai_internal_url=runtime["vllm_openai_internal_url"],
public_model_name=runtime["public_model_name"],
default_enable_thinking=runtime["default_enable_thinking"],
reasoning_enabled=runtime["reasoning_enabled"],
model_root=runtime["model_root"],
offline_mode=runtime["offline_mode"],
api_key=runtime["api_key"],
tensor_parallel_size=runtime["tensor_parallel_size"],
dtype=runtime["dtype"],
revision=runtime["revision"],
)
_, updates, env_vars = resolve_model_profile(
content=catalog,
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 | runtime)