add support of max_tokens, temperature, top_p to openai_api.py

openai_api.py

@@ -56,6 +56,7 @@ class ChatCompletionRequest(BaseModel):
     messages: List[ChatMessage]
     temperature: Optional[float] = None
     top_p: Optional[float] = None
+    max_length: Optional[int] = None
     max_tokens: Optional[int] = None
     stream: Optional[bool] = False
 
@@ -104,21 +105,28 @@ async def create_chat_completion(request: ChatCompletionRequest):
             if prev_messages[i].role == "user" and prev_messages[i+1].role == "assistant":
                 history.append([prev_messages[i].content, prev_messages[i+1].content])
 
+    chat_kwargs = {}
+    if request.max_tokens is not None:
+        chat_kwargs['max_new_tokens'] = request.max_tokens
+    elif request.max_length:
+        chat_kwargs['max_length'] = request.max_length
+
     if request.stream:
-        generate = predict(query, 
-                           history, 
-                           request.model, 
-                           max_length=request.max_tokens if request.max_tokens else 2048,
+        generate = predict(query,
+                           history,
+                           request.model,
                            top_p=request.top_p if request.top_p else 0.7,
-                           temperature=request.temperature if request.temperature else 0.95)
+                           temperature=request.temperature if request.temperature else 0.95,
+                           **chat_kwargs)
         return StreamingResponse(generate, media_type="text/event-stream")
 
-    response, _ = model.chat(tokenizer, 
-                             query, 
-                             history=history, 
-                             max_length=request.max_tokens if request.max_tokens else 2048,
+    response, _ = model.chat(tokenizer,
+                             query,
+                             history=history,
                              top_p=request.top_p if request.top_p else 0.7,
-                             temperature=request.temperature if request.temperature else 0.95)
+                             temperature=request.temperature if request.temperature else 0.95,
+                             **chat_kwargs)
+
     choice_data = ChatCompletionResponseChoice(
         index=0,
         message=ChatMessage(role="assistant", content=response),
@@ -128,7 +136,7 @@ async def create_chat_completion(request: ChatCompletionRequest):
     return ChatCompletionResponse(model=request.model, choices=[choice_data], object="chat.completion")
 
 
-async def predict(query: str, history: List[List[str]], model_id: str, max_length: int, top_p: float, temperature: float):
+async def predict(query: str, history: List[List[str]], model_id: str, top_p: float, temperature: float, **kwargs):
     global model, tokenizer
 
     choice_data = ChatCompletionResponseStreamChoice(
@@ -141,12 +149,12 @@ async def predict(query: str, history: List[List[str]], model_id: str, max_lengt
 
     current_length = 0
 
-    for new_response, _ in model.stream_chat(tokenizer, 
-                                             query, 
+    for new_response, _ in model.stream_chat(tokenizer,
+                                             query,
                                              history,
-                                             max_length=max_length,
                                              top_p=top_p,
-                                             temperature=temperature):
+                                             temperature=temperature,
+                                             **kwargs):
         if len(new_response) == current_length:
             continue