openai_api: support temperature=0

openai_api.py

@@ -321,7 +321,7 @@ def parse_response(response):
 
 
 # completion mode, not chat mode
-def text_complete_last_message(history, stop_words_ids):
+def text_complete_last_message(history, stop_words_ids, gen_kwargs):
     im_start = "<|im_start|>"
     im_end = "<|im_end|>"
     prompt = f"{im_start}system\nYou are a helpful assistant.{im_end}"
@@ -339,7 +339,7 @@ def text_complete_last_message(history, stop_words_ids):
     stop_words_ids = _stop_words_ids
 
     input_ids = torch.tensor([tokenizer.encode(prompt)]).to(model.device)
-    output = model.generate(input_ids, stop_words_ids=stop_words_ids).tolist()[0]
+    output = model.generate(input_ids, stop_words_ids=stop_words_ids, **gen_kwargs).tolist()[0]
     output = tokenizer.decode(output, errors="ignore")
     assert output.startswith(prompt)
     output = output[len(prompt) :]
@@ -352,6 +352,16 @@ def text_complete_last_message(history, stop_words_ids):
 async def create_chat_completion(request: ChatCompletionRequest):
     global model, tokenizer
 
+    gen_kwargs = {}
+    if request.temperature is not None:
+        if request.temperature < 0.01:
+            gen_kwargs['top_k'] = 1  # greedy decoding
+        else:
+            # Not recommended. Please tune top_p instead.
+            gen_kwargs['temperature'] = request.temperature
+    if request.top_p is not None:
+        gen_kwargs['top_p'] = request.top_p
+
     stop_words = add_extra_stop_words(request.stop)
     if request.functions:
         stop_words = stop_words or []
@@ -366,12 +376,12 @@ async def create_chat_completion(request: ChatCompletionRequest):
                 status_code=400,
                 detail="Invalid request: Function calling is not yet implemented for stream mode.",
             )
-        generate = predict(query, history, request.model, stop_words)
+        generate = predict(query, history, request.model, stop_words, gen_kwargs)
         return EventSourceResponse(generate, media_type="text/event-stream")
 
     stop_words_ids = [tokenizer.encode(s) for s in stop_words] if stop_words else None
     if query is _TEXT_COMPLETION_CMD:
-        response = text_complete_last_message(history, stop_words_ids=stop_words_ids)
+        response = text_complete_last_message(history, stop_words_ids=stop_words_ids, gen_kwargs=gen_kwargs)
     else:
         response, _ = model.chat(
             tokenizer,
@@ -379,6 +389,7 @@ async def create_chat_completion(request: ChatCompletionRequest):
             history=history,
             stop_words_ids=stop_words_ids,
             append_history=False,
+            **gen_kwargs
         )
         print(f"<chat>\n{history}\n{query}\n<!-- *** -->\n{response}\n</chat>")
     response = trim_stop_words(response, stop_words)
@@ -396,7 +407,7 @@ async def create_chat_completion(request: ChatCompletionRequest):
 
 
 async def predict(
-    query: str, history: List[List[str]], model_id: str, stop_words: List[str]
+    query: str, history: List[List[str]], model_id: str, stop_words: List[str], gen_kwargs: Dict,
 ):
     global model, tokenizer
     choice_data = ChatCompletionResponseStreamChoice(
@@ -416,7 +427,7 @@ async def predict(
             detail="Invalid request: custom stop words are not yet supported for stream mode.",
         )
     response_generator = model.chat_stream(
-        tokenizer, query, history=history, stop_words_ids=stop_words_ids
+        tokenizer, query, history=history, stop_words_ids=stop_words_ids, **gen_kwargs
     )
     for new_response in response_generator:
         if len(new_response) == current_length: