[Frontend] Use new Renderer for Completions and Tokenize API

examples/online_serving/prompt_embed_inference_with_openai_client.py

@@ -60,9 +60,7 @@ def main():
 
     completion = client.completions.create(
         model=model_name,
-        # NOTE: The OpenAI client does not allow `None` as an input to
-        # `prompt`. Use an empty string if you have no text prompts.
-        prompt="",
+        prompt=None,
         max_tokens=5,
         temperature=0.0,
         # NOTE: The OpenAI client allows passing in extra JSON body via the

tests/engine/test_short_mm_context.py

@@ -22,7 +22,11 @@ def test_context_length_too_short(vllm_runner, image_assets, model):
     with pytest.raises(ValueError, match="longer than the maximum model length"):
         vllm_model = vllm_runner(
             model,
-            max_model_len=128,  # LLaVA has a feature size of 576
+            # LLaVA has a feature size of 576
+            # For the HF processor to execute successfully but still
+            # failing the overall context length check, we need the
+            # max_model_len to at least contain all image tokens
+            max_model_len=579,
             enforce_eager=True,
             load_format="dummy",
         )

tests/entrypoints/llm/test_chat.py

@@ -205,7 +205,7 @@ def test_chat_batch_failure_cleanup(llm_for_failure_test):
         valid_msg,
     ]
     sampling_params = SamplingParams(temperature=0, max_tokens=10)
-    with pytest.raises(ValueError, match="longer than the maximum model length"):
+    with pytest.raises(ValueError, match="context length is only"):
         llm.chat(batch_1, sampling_params=sampling_params)
     outputs_2 = llm.chat(batch_2, sampling_params=sampling_params)
     assert len(outputs_2) == len(batch_2)

tests/entrypoints/openai/test_chat_error.py

@@ -15,7 +15,8 @@
 from vllm.entrypoints.openai.models.protocol import BaseModelPath
 from vllm.entrypoints.openai.models.serving import OpenAIServingModels
 from vllm.outputs import CompletionOutput, RequestOutput
-from vllm.tokenizers import get_tokenizer
+from vllm.renderers.hf import HfRenderer
+from vllm.tokenizers.registry import tokenizer_args_from_config
 from vllm.v1.engine.async_llm import AsyncLLM
 
 MODEL_NAME = "openai-community/gpt2"
@@ -57,6 +58,15 @@ def get_diff_sampling_param(self):
         return self.diff_sampling_param or {}
 
 
+def _build_renderer(model_config: MockModelConfig):
+    _, tokenizer_name, _, kwargs = tokenizer_args_from_config(model_config)
+
+    return HfRenderer(
+        model_config,
+        tokenizer_kwargs={**kwargs, "tokenizer_name": tokenizer_name},
+    )
+
+
 def _build_serving_chat(engine: AsyncLLM) -> OpenAIServingChat:
     models = OpenAIServingModels(
         engine_client=engine,
@@ -71,26 +81,13 @@ def _build_serving_chat(engine: AsyncLLM) -> OpenAIServingChat:
         chat_template_content_format="auto",
     )
 
-    async def _fake_process_inputs(
-        request_id,
-        engine_prompt,
-        sampling_params,
-        *,
-        lora_request,
-        trace_headers,
-        priority,
-        data_parallel_rank,
-    ):
-        return dict(engine_prompt), {}
-
     async def _fake_preprocess_chat(*args, **kwargs):
         # return conversation, engine_prompts
         return (
             [{"role": "user", "content": "Test"}],
             [{"prompt_token_ids": [1, 2, 3]}],
         )
 
-    serving_chat._process_inputs = AsyncMock(side_effect=_fake_process_inputs)
     serving_chat._preprocess_chat = AsyncMock(side_effect=_fake_preprocess_chat)
     return serving_chat
 
@@ -99,11 +96,11 @@ async def _fake_preprocess_chat(*args, **kwargs):
 async def test_chat_error_non_stream():
     """test finish_reason='error' returns 500 InternalServerError (non-streaming)"""
     mock_engine = MagicMock(spec=AsyncLLM)
-    mock_engine.get_tokenizer.return_value = get_tokenizer(MODEL_NAME)
     mock_engine.errored = False
     mock_engine.model_config = MockModelConfig()
     mock_engine.input_processor = MagicMock()
     mock_engine.io_processor = MagicMock()
+    mock_engine.renderer = _build_renderer(mock_engine.model_config)
 
     serving_chat = _build_serving_chat(mock_engine)
 
@@ -153,11 +150,11 @@ async def mock_generate(*args, **kwargs):
 async def test_chat_error_stream():
     """test finish_reason='error' returns 500 InternalServerError (streaming)"""
     mock_engine = MagicMock(spec=AsyncLLM)
-    mock_engine.get_tokenizer.return_value = get_tokenizer(MODEL_NAME)
     mock_engine.errored = False
     mock_engine.model_config = MockModelConfig()
     mock_engine.input_processor = MagicMock()
     mock_engine.io_processor = MagicMock()
+    mock_engine.renderer = _build_renderer(mock_engine.model_config)
 
     serving_chat = _build_serving_chat(mock_engine)
 

tests/entrypoints/openai/test_completion_error.py

@@ -4,7 +4,7 @@
 from dataclasses import dataclass, field
 from http import HTTPStatus
 from typing import Any
-from unittest.mock import AsyncMock, MagicMock
+from unittest.mock import MagicMock
 
 import pytest
 
@@ -15,7 +15,8 @@
 from vllm.entrypoints.openai.models.protocol import BaseModelPath
 from vllm.entrypoints.openai.models.serving import OpenAIServingModels
 from vllm.outputs import CompletionOutput, RequestOutput
-from vllm.tokenizers import get_tokenizer
+from vllm.renderers.hf import HfRenderer
+from vllm.tokenizers.registry import tokenizer_args_from_config
 from vllm.v1.engine.async_llm import AsyncLLM
 
 MODEL_NAME = "openai-community/gpt2"
@@ -61,37 +62,31 @@ def _build_serving_completion(engine: AsyncLLM) -> OpenAIServingCompletion:
         engine_client=engine,
         base_model_paths=BASE_MODEL_PATHS,
     )
-    serving_completion = OpenAIServingCompletion(
+    return OpenAIServingCompletion(
         engine,
         models,
         request_logger=None,
     )
 
-    async def _fake_process_inputs(
-        request_id,
-        engine_prompt,
-        sampling_params,
-        *,
-        lora_request,
-        trace_headers,
-        priority,
-        data_parallel_rank,
-    ):
-        return dict(engine_prompt), {}
 
-    serving_completion._process_inputs = AsyncMock(side_effect=_fake_process_inputs)
-    return serving_completion
+def _build_renderer(model_config: MockModelConfig):
+    _, tokenizer_name, _, kwargs = tokenizer_args_from_config(model_config)
+
+    return HfRenderer(
+        model_config,
+        tokenizer_kwargs={**kwargs, "tokenizer_name": tokenizer_name},
+    )
 
 
 @pytest.mark.asyncio
 async def test_completion_error_non_stream():
     """test finish_reason='error' returns 500 InternalServerError (non-streaming)"""
     mock_engine = MagicMock(spec=AsyncLLM)
-    mock_engine.get_tokenizer.return_value = get_tokenizer(MODEL_NAME)
     mock_engine.errored = False
     mock_engine.model_config = MockModelConfig()
     mock_engine.input_processor = MagicMock()
     mock_engine.io_processor = MagicMock()
+    mock_engine.renderer = _build_renderer(mock_engine.model_config)
 
     serving_completion = _build_serving_completion(mock_engine)
 
@@ -141,11 +136,11 @@ async def mock_generate(*args, **kwargs):
 async def test_completion_error_stream():
     """test finish_reason='error' returns 500 InternalServerError (streaming)"""
     mock_engine = MagicMock(spec=AsyncLLM)
-    mock_engine.get_tokenizer.return_value = get_tokenizer(MODEL_NAME)
     mock_engine.errored = False
     mock_engine.model_config = MockModelConfig()
     mock_engine.input_processor = MagicMock()
     mock_engine.io_processor = MagicMock()
+    mock_engine.renderer = _build_renderer(mock_engine.model_config)
 
     serving_completion = _build_serving_completion(mock_engine)
 

tests/entrypoints/openai/test_completion_with_prompt_embeds.py

@@ -110,7 +110,7 @@ async def test_completions_with_prompt_embeds(
     # Test case: Single prompt embeds input
     completion = await client_with_prompt_embeds.completions.create(
         model=model_name,
-        prompt="",  # Add empty prompt as required parameter
+        prompt=None,
         max_tokens=5,
         temperature=0.0,
         extra_body={"prompt_embeds": encoded_embeds},
@@ -121,7 +121,7 @@ async def test_completions_with_prompt_embeds(
     # Test case: batch completion with prompt_embeds
     completion = await client_with_prompt_embeds.completions.create(
         model=model_name,
-        prompt="",  # Add empty prompt as required parameter
+        prompt=None,
         max_tokens=5,
         temperature=0.0,
         extra_body={"prompt_embeds": [encoded_embeds, encoded_embeds2]},
@@ -133,7 +133,7 @@ async def test_completions_with_prompt_embeds(
     # Test case: streaming with prompt_embeds
     single_completion = await client_with_prompt_embeds.completions.create(
         model=model_name,
-        prompt="",  # Add empty prompt as required parameter
+        prompt=None,
         max_tokens=5,
         temperature=0.0,
         extra_body={"prompt_embeds": encoded_embeds},
@@ -142,7 +142,7 @@ async def test_completions_with_prompt_embeds(
 
     stream = await client_with_prompt_embeds.completions.create(
         model=model_name,
-        prompt="",  # Add empty prompt as required parameter
+        prompt=None,
         max_tokens=5,
         temperature=0.0,
         stream=True,
@@ -162,7 +162,7 @@ async def test_completions_with_prompt_embeds(
     # Test case: batch streaming with prompt_embeds
     stream = await client_with_prompt_embeds.completions.create(
         model=model_name,
-        prompt="",  # Add empty prompt as required parameter
+        prompt=None,
         max_tokens=5,
         temperature=0.0,
         stream=True,
@@ -197,7 +197,7 @@ async def test_completions_with_prompt_embeds(
     )
     completion_embeds_only = await client_with_prompt_embeds.completions.create(
         model=model_name,
-        prompt="",
+        prompt=None,
         max_tokens=5,
         temperature=0.0,
         extra_body={"prompt_embeds": encoded_embeds},
@@ -215,7 +215,7 @@ async def test_completions_errors_with_prompt_embeds(
     # Test error case: invalid prompt_embeds
     with pytest.raises(BadRequestError):
         await client_with_prompt_embeds.completions.create(
-            prompt="",
+            prompt=None,
             model=model_name,
             max_tokens=5,
             temperature=0.0,
@@ -237,7 +237,7 @@ async def test_completions_with_logprobs_and_prompt_embeds(
     # Test case: Logprobs using prompt_embeds
     completion = await client_with_prompt_embeds.completions.create(
         model=model_name,
-        prompt="",  # Add empty prompt as required parameter
+        prompt=None,
         max_tokens=5,
         temperature=0.0,
         echo=False,
@@ -257,7 +257,7 @@ async def test_completions_with_logprobs_and_prompt_embeds(
     # Test case: Log probs with batch completion and prompt_embeds
     completion = await client_with_prompt_embeds.completions.create(
         model=model_name,
-        prompt="",  # Add empty prompt as required parameter
+        prompt=None,
         max_tokens=5,
         temperature=0.0,
         echo=False,
@@ -287,7 +287,7 @@ async def test_prompt_logprobs_raises_error(
     with pytest.raises(BadRequestError, match="not compatible"):
         await client_with_prompt_embeds.completions.create(
             model=MODEL_NAME,
-            prompt="",
+            prompt=None,
             max_tokens=5,
             temperature=0.0,
             extra_body={"prompt_embeds": encoded_embeds, "prompt_logprobs": True},

tests/entrypoints/openai/test_embedding_shape_validation.py

@@ -7,7 +7,7 @@
 are rejected before they can cause crashes during model inference.
 
 Validation is performed by the parser (MultiModalDataParser) and EmbeddingItems
-classes, not by CompletionRenderer or MediaIO classes.
+classes, not by MediaIO classes.
 """
 
 import pytest

tests/entrypoints/openai/test_lora_resolvers.py

@@ -16,7 +16,8 @@
 from vllm.entrypoints.openai.models.serving import OpenAIServingModels
 from vllm.lora.request import LoRARequest
 from vllm.lora.resolver import LoRAResolver, LoRAResolverRegistry
-from vllm.tokenizers import get_tokenizer
+from vllm.renderers.hf import HfRenderer
+from vllm.tokenizers.registry import tokenizer_args_from_config
 from vllm.v1.engine.async_llm import AsyncLLM
 
 MODEL_NAME = "openai-community/gpt2"
@@ -35,6 +36,7 @@ class MockModelConfig:
     """Minimal mock ModelConfig for testing."""
 
     model: str = MODEL_NAME
+    runner_type = "generate"
     tokenizer: str = MODEL_NAME
     trust_remote_code: bool = False
     tokenizer_mode: str = "auto"
@@ -85,15 +87,21 @@ def register_mock_resolver():
         del LoRAResolverRegistry.resolvers[MOCK_RESOLVER_NAME]
 
 
+def _build_renderer(model_config: MockModelConfig):
+    _, tokenizer_name, _, kwargs = tokenizer_args_from_config(model_config)
+
+    return HfRenderer(
+        model_config,
+        tokenizer_kwargs={**kwargs, "tokenizer_name": tokenizer_name},
+    )
+
+
 @pytest.fixture
 def mock_serving_setup():
     """Provides a mocked engine and serving completion instance."""
     mock_engine = MagicMock(spec=AsyncLLM)
     mock_engine.errored = False
 
-    tokenizer = get_tokenizer(MODEL_NAME)
-    mock_engine.get_tokenizer = AsyncMock(return_value=tokenizer)
-
     async def mock_add_lora_side_effect(lora_request: LoRARequest):
         """Simulate engine behavior when adding LoRAs."""
         if lora_request.lora_name == "test-lora":
@@ -118,6 +126,7 @@ async def mock_generate(*args, **kwargs):
     mock_engine.model_config = MockModelConfig()
     mock_engine.input_processor = MagicMock()
     mock_engine.io_processor = MagicMock()
+    mock_engine.renderer = _build_renderer(mock_engine.model_config)
 
     models = OpenAIServingModels(
         engine_client=mock_engine,
@@ -128,10 +137,6 @@ async def mock_generate(*args, **kwargs):
         mock_engine, models, request_logger=None
     )
 
-    serving_completion._process_inputs = AsyncMock(
-        return_value=(MagicMock(name="engine_request"), {})
-    )
-
     return mock_engine, serving_completion