Supports prompt_token_ids in the OpenAI completion API. (#1)

vllm/entrypoints/openai/api_server.py

@@ -3,18 +3,18 @@
 
 import argparse
 import asyncio
-from http import HTTPStatus
 import json
 import time
+from http import HTTPStatus
 from typing import AsyncGenerator, Dict, List, Optional
-from packaging import version
 
 import fastapi
+import uvicorn
 from fastapi import BackgroundTasks, Request
 from fastapi.exceptions import RequestValidationError
 from fastapi.middleware.cors import CORSMiddleware
 from fastapi.responses import JSONResponse, StreamingResponse
-import uvicorn
+from packaging import version
 
 from vllm.engine.arg_utils import AsyncEngineArgs
 from vllm.engine.async_llm_engine import AsyncLLMEngine
@@ -375,17 +375,27 @@ async def create_completion(raw_request: Request):
 
     model_name = request.model
     request_id = f"cmpl-{random_uuid()}"
+
+    use_token_ids = False
     if isinstance(request.prompt, list):
         if len(request.prompt) == 0:
             return create_error_response(HTTPStatus.BAD_REQUEST,
                                          "please provide at least one prompt")
-        if len(request.prompt) > 1:
-            return create_error_response(
-                HTTPStatus.BAD_REQUEST,
-                "multiple prompts in a batch is not currently supported")
-        prompt = request.prompt[0]
+        first_element = request.prompt[0]
+        if isinstance(first_element, int):
+            use_token_ids = True
+            prompt = request.prompt
+        elif isinstance(first_element, str) or isinstance(first_element, list):
+            # TODO(@wanmok): handles multiple prompt case in list[list[int]]
+            if len(request.prompt) > 1:
+                return create_error_response(
+                    HTTPStatus.BAD_REQUEST,
+                    "multiple prompts in a batch is not currently supported")
+            use_token_ids = not isinstance(first_element, str)
+            prompt = request.prompt[0]
     else:
         prompt = request.prompt
+
     created_time = int(time.time())
     try:
         sampling_params = SamplingParams(
@@ -405,7 +415,10 @@ async def create_completion(raw_request: Request):
     except ValueError as e:
         return create_error_response(HTTPStatus.BAD_REQUEST, str(e))
 
-    result_generator = engine.generate(prompt, sampling_params, request_id)
+    if use_token_ids:
+        result_generator = engine.generate(None, sampling_params, request_id, prompt_token_ids=prompt)
+    else:
+        result_generator = engine.generate(prompt, sampling_params, request_id)
 
     # Similar to the OpenAI API, when n != best_of, we do not stream the
     # results. In addition, we do not stream the results when use beam search.

vllm/entrypoints/openai/protocol.py

@@ -74,7 +74,8 @@ class ChatCompletionRequest(BaseModel):
 
 class CompletionRequest(BaseModel):
     model: str
-    prompt: Union[str, List[str]]
+    # a string, array of strings, array of tokens, or array of token arrays
+    prompt: Union[List[int], List[List[int]], str, List[str]]
     suffix: Optional[str] = None
     max_tokens: Optional[int] = 16
     temperature: Optional[float] = 1.0
@@ -99,8 +100,7 @@ class LogProbs(BaseModel):
     text_offset: List[int] = Field(default_factory=list)
     token_logprobs: List[Optional[float]] = Field(default_factory=list)
     tokens: List[str] = Field(default_factory=list)
-    top_logprobs: List[Optional[Dict[str,
-                                     float]]] = Field(default_factory=list)
+    top_logprobs: List[Optional[Dict[str, float]]] = Field(default_factory=list)
 
 
 class CompletionResponseChoice(BaseModel):