refactor: The first refactored version for sdk release (#907)

Co-authored-by: chengfangyin2 <chengfangyin3@jd.com>

dbgpt/model/base.py

@@ -0,0 +1,121 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+
+from enum import Enum
+from typing import TypedDict, Optional, Dict, List, Any
+
+from dataclasses import dataclass, asdict
+import time
+from datetime import datetime
+from dbgpt.util.parameter_utils import ParameterDescription
+from dbgpt.util.model_utils import GPUInfo
+
+
+class ModelType:
+    """ "Type of model"""
+
+    HF = "huggingface"
+    LLAMA_CPP = "llama.cpp"
+    PROXY = "proxy"
+    VLLM = "vllm"
+    # TODO, support more model type
+
+
+@dataclass
+class ModelInstance:
+    """Model instance info"""
+
+    model_name: str
+    host: str
+    port: int
+    weight: Optional[float] = 1.0
+    check_healthy: Optional[bool] = True
+    healthy: Optional[bool] = False
+    enabled: Optional[bool] = True
+    prompt_template: Optional[str] = None
+    last_heartbeat: Optional[datetime] = None
+
+
+class WorkerApplyType(str, Enum):
+    START = "start"
+    STOP = "stop"
+    RESTART = "restart"
+    UPDATE_PARAMS = "update_params"
+
+
+@dataclass
+class WorkerApplyOutput:
+    message: str
+    success: Optional[bool] = True
+    # The seconds cost to apply some action to worker instances
+    timecost: Optional[int] = -1
+
+    @staticmethod
+    def reduce(outs: List["WorkerApplyOutput"]) -> "WorkerApplyOutput":
+        """Merge all outputs
+
+        Args:
+            outs (List["WorkerApplyOutput"]): The list of WorkerApplyOutput
+        """
+        if not outs:
+            return WorkerApplyOutput("Not outputs")
+        combined_success = all(out.success for out in outs)
+        max_timecost = max(out.timecost for out in outs)
+        combined_message = ", ".join(out.message for out in outs)
+        return WorkerApplyOutput(combined_message, combined_success, max_timecost)
+
+
+@dataclass
+class SupportedModel:
+    model: str
+    path: str
+    worker_type: str
+    path_exist: bool
+    proxy: bool
+    enabled: bool
+    params: List[ParameterDescription]
+
+    @classmethod
+    def from_dict(cls, model_data: Dict) -> "SupportedModel":
+        params = model_data.get("params", [])
+        if params:
+            params = [ParameterDescription(**param) for param in params]
+        model_data["params"] = params
+        return cls(**model_data)
+
+
+@dataclass
+class WorkerSupportedModel:
+    host: str
+    port: int
+    models: List[SupportedModel]
+
+    @classmethod
+    def from_dict(cls, worker_data: Dict) -> "WorkerSupportedModel":
+        models = [
+            SupportedModel.from_dict(model_data) for model_data in worker_data["models"]
+        ]
+        worker_data["models"] = models
+        return cls(**worker_data)
+
+
+@dataclass
+class FlatSupportedModel(SupportedModel):
+    """For web"""
+
+    host: str
+    port: int
+
+    @staticmethod
+    def from_supports(
+        supports: List[WorkerSupportedModel],
+    ) -> List["FlatSupportedModel"]:
+        results = []
+        for s in supports:
+            host, port, models = s.host, s.port, s.models
+            for m in models:
+                kwargs = asdict(m)
+                kwargs["host"] = host
+                kwargs["port"] = port
+                results.append(FlatSupportedModel(**kwargs))
+        return results