Supports tokens and arrays of tokens as inputs to the OpenAI completion API

vllm/entrypoints/openai/api_server.py

@@ -3,18 +3,18 @@
 
 import argparse
 import asyncio
-from http import HTTPStatus
 import json
 import time
-from typing import AsyncGenerator, Dict, List, Optional
-from packaging import version
+from http import HTTPStatus
+from typing import AsyncGenerator, Dict, List, Optional, Tuple, Union
 
 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
@@ -115,8 +115,18 @@ async def get_gen_prompt(request) -> str:
     return prompt
 
 
-async def check_length(request, prompt):
-    input_ids = tokenizer(prompt).input_ids
+async def check_length(
+    request: Union[ChatCompletionRequest, CompletionRequest],
+    prompt: Optional[str] = None,
+    prompt_ids: Optional[List[int]] = None
+) -> Tuple[List[int], Optional[JSONResponse]]:
+    assert (not (prompt is None and prompt_ids is None)
+            and not (prompt is not None and prompt_ids is not None)
+            ), "Either prompt or prompt_ids should be provided."
+    if prompt_ids is not None:
+        input_ids = prompt_ids
+    else:
+        input_ids = tokenizer(prompt).input_ids
     token_num = len(input_ids)
 
     if token_num + request.max_tokens > max_model_len:
@@ -191,7 +201,7 @@ async def create_chat_completion(raw_request: Request):
                                      "logit_bias is not currently supported")
 
     prompt = await get_gen_prompt(request)
-    token_ids, error_check_ret = await check_length(request, prompt)
+    token_ids, error_check_ret = await check_length(request, prompt=prompt)
     if error_check_ret is not None:
         return error_check_ret
 
@@ -376,19 +386,31 @@ 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, list)):
+            # TODO: 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
 
-    token_ids, error_check_ret = await check_length(request, prompt)
+    if use_token_ids:
+        _, error_check_ret = await check_length(request, prompt_ids=prompt)
+    else:
+        token_ids, error_check_ret = await check_length(request, prompt=prompt)
     if error_check_ret is not None:
         return error_check_ret
 
@@ -411,8 +433,14 @@ 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,
-                                       token_ids)
+    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,
+                                           token_ids)
 
     # 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