Add do_sample argument for API demo

scripts/openai_server_demo/README.md

@@ -120,6 +120,8 @@ json返回体：
 
 `repetition_penalty`: 重复惩罚，具体细节可以参考这篇文章：<https://arxiv.org/pdf/1909.05858.pdf> 。
 
+`do_sample`: 启用随机采样策略。默认为true。
+
 ### 聊天（chat completion）
 
 聊天接口支持多轮对话
@@ -240,6 +242,8 @@ json返回体：
 
 `repetition_penalty`: 重复惩罚，具体细节可以参考这篇文章：<https://arxiv.org/pdf/1909.05858.pdf> 。
 
+`do_sample`: 启用随机采样策略。默认为true。
+
 ### 文本嵌入向量（text embedding）
 
 文本嵌入向量有很多作用，包括但不限于基于大型文档问答、总结一本书中的内容、为大语言模型找到与当前用户输入最相近的记忆等等。

scripts/openai_server_demo/openai_api_protocol.py

@@ -13,11 +13,12 @@ class ChatCompletionRequest(BaseModel):
     top_k: Optional[int] = 40
     n: Optional[int] = 1
     max_tokens: Optional[int] = 128
-    num_beams: Optional[int] = 4
+    num_beams: Optional[int] = 1
     stop: Optional[Union[str, List[str]]] = None
     stream: Optional[bool] = False
     repetition_penalty: Optional[float] = 1.0
     user: Optional[str] = None
+    do_sample: Optional[bool] = True
 
 
 class ChatMessage(BaseModel):
@@ -58,11 +59,12 @@ class CompletionRequest(BaseModel):
     stream: Optional[bool] = False
     top_p: Optional[float] = 0.75
     top_k: Optional[int] = 40
-    num_beams: Optional[int] = 4
+    num_beams: Optional[int] = 1
     logprobs: Optional[int] = None
     echo: Optional[bool] = False
     repetition_penalty: Optional[float] = 1.0
     user: Optional[str] = None
+    do_sample: Optional[bool] = True
 
 
 class CompletionResponseChoice(BaseModel):

scripts/openai_server_demo/openai_api_server.py

@@ -32,15 +32,7 @@
     EmbeddingsRequest,
     EmbeddingsResponse,
 )
-generation_config = dict(
-    temperature=0.2,
-    top_k=40,
-    top_p=0.9,
-    do_sample=True,
-    num_beams=1,
-    repetition_penalty=1.1,
-    max_new_tokens=400
-    )
+
 load_type = torch.float16
 if torch.cuda.is_available():
     device = torch.device(0)
@@ -113,6 +105,7 @@ def predict(
     top_k=40,
     num_beams=4,
     repetition_penalty=1.0,
+    do_sample=True,
     **kwargs,
 ):
     """
@@ -131,6 +124,7 @@ def predict(
         top_p=top_p,
         top_k=top_k,
         num_beams=num_beams,
+        do_sample=do_sample,
         **kwargs,
     )
     with torch.no_grad():
@@ -188,6 +182,7 @@ async def create_chat_completion(request: ChatCompletionRequest):
         temperature=request.temperature,
         num_beams=request.num_beams,
         repetition_penalty=request.repetition_penalty,
+        do_sample=request.do_sample,
     )
     choices = [ChatCompletionResponseChoice(index = i, message = msg) for i, msg in enumerate(msgs)]
     choices += [ChatCompletionResponseChoice(index = len(choices), message = ChatMessage(role='assistant',content=output))]
@@ -204,6 +199,7 @@ async def create_completion(request: CompletionRequest):
         temperature=request.temperature,
         num_beams=request.num_beams,
         repetition_penalty=request.repetition_penalty,
+        do_sample=request.do_sample,
     )
     choices = [CompletionResponseChoice(index = 0, text = output)]
     return CompletionResponse(choices = choices)