[Frontend] Use new Renderer for Completions and Tokenize API (#32863)

Signed-off-by: DarkLight1337 <tlleungac@connect.ust.hk>

vllm/entrypoints/utils.py

@@ -8,7 +8,7 @@ import os
 from argparse import Namespace
 from logging import Logger
 from string import Template
-from typing import TYPE_CHECKING, Any
+from typing import TYPE_CHECKING
 
 import regex as re
 from fastapi import Request
@@ -18,9 +18,9 @@ from starlette.background import BackgroundTask, BackgroundTasks
 from vllm import envs
 from vllm.engine.arg_utils import EngineArgs
 from vllm.inputs import EmbedsPrompt, TokensPrompt
+from vllm.inputs.parse import get_prompt_len
 from vllm.logger import current_formatter_type, init_logger
 from vllm.platforms import current_platform
-from vllm.utils import length_from_prompt_token_ids_or_embeds
 from vllm.utils.argparse_utils import FlexibleArgumentParser
 
 if TYPE_CHECKING:
@@ -34,9 +34,7 @@ if TYPE_CHECKING:
         StreamOptions,
     )
     from vllm.entrypoints.openai.models.protocol import LoRAModulePath
-    from vllm.entrypoints.openai.responses.protocol import (
-        ResponsesRequest,
-    )
+    from vllm.entrypoints.openai.responses.protocol import ResponsesRequest
 else:
     ChatCompletionRequest = object
     CompletionRequest = object
@@ -188,33 +186,6 @@ def cli_env_setup():
         os.environ["VLLM_WORKER_MULTIPROC_METHOD"] = "spawn"
 
 
-def _validate_truncation_size(
-    max_model_len: int,
-    truncate_prompt_tokens: int | None,
-    tokenization_kwargs: dict[str, Any] | None = None,
-) -> int | None:
-    if truncate_prompt_tokens is not None:
-        if truncate_prompt_tokens <= -1:
-            truncate_prompt_tokens = max_model_len
-
-        if truncate_prompt_tokens > max_model_len:
-            raise ValueError(
-                f"truncate_prompt_tokens value ({truncate_prompt_tokens}) "
-                f"is greater than max_model_len ({max_model_len})."
-                f" Please, select a smaller truncation size."
-            )
-
-        if tokenization_kwargs is not None:
-            tokenization_kwargs["truncation"] = True
-            tokenization_kwargs["max_length"] = truncate_prompt_tokens
-
-    else:
-        if tokenization_kwargs is not None:
-            tokenization_kwargs["truncation"] = False
-
-    return truncate_prompt_tokens
-
-
 def get_max_tokens(
     max_model_len: int,
     request: "CompletionRequest | ChatCompletionRequest | ResponsesRequest",
@@ -233,10 +204,7 @@ def get_max_tokens(
         # CompletionRequest (also a fallback for ChatCompletionRequest)
         max_tokens = getattr(request, "max_tokens", None)
 
-    input_length = length_from_prompt_token_ids_or_embeds(
-        prompt.get("prompt_token_ids"),  # type: ignore[arg-type]
-        prompt.get("prompt_embeds"),  # type: ignore[arg-type]
-    )
+    input_length = get_prompt_len(prompt)
     default_max_tokens = max_model_len - input_length
     max_output_tokens = current_platform.get_max_output_tokens(input_length)