Added real /v1/embeddings support for messages + chat_template_kw

tests/entrypoints/pooling/embed/test_io_processor.py

@@ -3,17 +3,108 @@
 """Unit tests for EmbedIOProcessor."""
 
 import pytest
+from pydantic import TypeAdapter
 
 from vllm import PoolingParams
 from vllm.entrypoints.pooling.embed.io_processor import EmbedIOProcessor
 from vllm.entrypoints.pooling.embed.protocol import (
     CohereEmbedContent,
     CohereEmbedInput,
     CohereEmbedRequest,
+    EmbeddingBatchChatInputRequest,
+    EmbeddingBatchChatRequest,
+    EmbeddingChatInputRequest,
+    EmbeddingChatRequest,
+    EmbeddingCompletionRequest,
+    EmbeddingRequest,
 )
 from vllm.entrypoints.pooling.typing import PoolingServeContext
 
 
+class TestEmbeddingRequestParsing:
+    """Unit tests for OpenAI embedding request parsing."""
+
+    def test_input_messages_parses_as_chat_request(self):
+        request = TypeAdapter(EmbeddingRequest).validate_python(
+            {
+                "model": "test",
+                "input": [{"role": "user", "content": "hello"}],
+                "chat_template_kwargs": {"instruction": "Represent the query: "},
+            }
+        )
+
+        assert isinstance(request, EmbeddingChatInputRequest)
+        assert request.input == [{"role": "user", "content": "hello"}]
+        assert request.messages == [{"role": "user", "content": "hello"}]
+        assert request.chat_template_kwargs == {"instruction": "Represent the query: "}
+
+    def test_batched_input_messages_parses_as_batch_chat_input_request(self):
+        request = TypeAdapter(EmbeddingRequest).validate_python(
+            {
+                "model": "test",
+                "input": [
+                    [{"role": "user", "content": "hello"}],
+                    [{"role": "user", "content": "goodbye"}],
+                ],
+                "chat_template_kwargs": {"instruction": "Represent the query: "},
+            }
+        )
+
+        assert isinstance(request, EmbeddingBatchChatInputRequest)
+        assert request.input == [
+            [{"role": "user", "content": "hello"}],
+            [{"role": "user", "content": "goodbye"}],
+        ]
+        assert request.messages == [
+            [{"role": "user", "content": "hello"}],
+            [{"role": "user", "content": "goodbye"}],
+        ]
+        assert request.chat_template_kwargs == {"instruction": "Represent the query: "}
+
+    def test_token_ids_still_parse_as_completion_request(self):
+        request = TypeAdapter(EmbeddingRequest).validate_python(
+            {
+                "model": "test",
+                "input": [[1, 2, 3], [4, 5]],
+            }
+        )
+
+        assert isinstance(request, EmbeddingCompletionRequest)
+        assert request.input == [[1, 2, 3], [4, 5]]
+
+    def test_messages_still_parses_as_chat_request(self):
+        request = TypeAdapter(EmbeddingRequest).validate_python(
+            {
+                "model": "test",
+                "messages": [{"role": "user", "content": "hello"}],
+                "chat_template_kwargs": {"instruction": "Represent the query: "},
+            }
+        )
+
+        assert isinstance(request, EmbeddingChatRequest)
+        assert request.messages == [{"role": "user", "content": "hello"}]
+        assert request.chat_template_kwargs == {"instruction": "Represent the query: "}
+
+    def test_batched_messages_parses_as_batch_chat_request(self):
+        request = TypeAdapter(EmbeddingRequest).validate_python(
+            {
+                "model": "test",
+                "messages": [
+                    [{"role": "user", "content": "hello"}],
+                    [{"role": "user", "content": "goodbye"}],
+                ],
+                "chat_template_kwargs": {"instruction": "Represent the query: "},
+            }
+        )
+
+        assert isinstance(request, EmbeddingBatchChatRequest)
+        assert request.messages == [
+            [{"role": "user", "content": "hello"}],
+            [{"role": "user", "content": "goodbye"}],
+        ]
+        assert request.chat_template_kwargs == {"instruction": "Represent the query: "}
+
+
 class TestResolveTruncation:
     """Unit tests for EmbedIOProcessor._resolve_cohere_truncation."""
 
@@ -324,3 +415,113 @@ def batch_render_chat(
                 },
             )
         ]
+
+
+class TestPreProcessOpenAIEmbeddingChatOnline:
+    """Unit tests for OpenAI embedding chat preprocessing."""
+
+    class _FakeModelConfig:
+        max_model_len = 128
+        encoder_config: dict[str, object] = {}
+        pooler_config = None
+        multimodal_config = None
+        is_encoder_decoder = False
+
+    class _FakeRenderer:
+        tokenizer = object()
+
+        def __init__(self):
+            self.calls = []
+
+        def render_chat(
+            self,
+            all_messages,
+            chat_params,
+            tok_params,
+            prompt_extras=None,
+        ):
+            self.calls.append(
+                {
+                    "all_messages": all_messages,
+                    "chat_params": chat_params,
+                    "tok_params": tok_params,
+                    "prompt_extras": prompt_extras,
+                }
+            )
+            return all_messages, [
+                {"prompt_token_ids": [index]} for index, _ in enumerate(all_messages)
+            ]
+
+    @classmethod
+    def _make_handler(cls, renderer):
+        handler = object.__new__(EmbedIOProcessor)
+        handler.renderer = renderer
+        handler.model_config = cls._FakeModelConfig()
+        handler.chat_template = "template"
+        handler.chat_template_content_format = "auto"
+        handler.trust_request_chat_template = False
+        handler.enable_chunked_processing = False
+        return handler
+
+    @staticmethod
+    def _make_context(
+        request: (
+            EmbeddingChatRequest
+            | EmbeddingBatchChatRequest
+            | EmbeddingChatInputRequest
+            | EmbeddingBatchChatInputRequest
+        ),
+    ) -> PoolingServeContext[
+        EmbeddingChatRequest
+        | EmbeddingBatchChatRequest
+        | EmbeddingChatInputRequest
+        | EmbeddingBatchChatInputRequest
+    ]:
+        return PoolingServeContext(
+            request=request,
+            pooling_params=PoolingParams(),
+            model_name="test",
+            request_id="embd-test",
+        )
+
+    def test_chat_template_kwargs_forwarded_for_batched_input_messages(self):
+        request = TypeAdapter(EmbeddingRequest).validate_python(
+            {
+                "model": "test",
+                "input": [
+                    [{"role": "user", "content": "hello"}],
+                    [{"role": "user", "content": "goodbye"}],
+                ],
+                "add_generation_prompt": True,
+                "chat_template_kwargs": {"instruction": "Represent the query: "},
+                "mm_processor_kwargs": {"max_pixels": 1},
+                "cache_salt": "salt",
+            }
+        )
+        assert isinstance(request, EmbeddingBatchChatInputRequest)
+
+        renderer = self._FakeRenderer()
+        handler = self._make_handler(renderer)
+        ctx = self._make_context(request)
+
+        handler.pre_process_online(ctx)
+
+        assert ctx.engine_inputs == [
+            {"prompt_token_ids": [0]},
+            {"prompt_token_ids": [1]},
+        ]
+        assert len(renderer.calls) == 1
+
+        call = renderer.calls[0]
+        assert call["all_messages"] == request.messages
+        assert call["prompt_extras"] == {
+            "mm_processor_kwargs": {"max_pixels": 1},
+            "cache_salt": "salt",
+        }
+
+        chat_template_kwargs = call["chat_params"].chat_template_kwargs
+        assert chat_template_kwargs["instruction"] == "Represent the query: "
+        assert chat_template_kwargs["add_generation_prompt"] is True
+        assert chat_template_kwargs["continue_final_message"] is False
+        assert "tools" not in chat_template_kwargs
+        assert chat_template_kwargs["tokenize"] is False

vllm/entrypoints/pooling/base/protocol.py

@@ -168,11 +168,7 @@ class CompletionRequestMixin(OpenAIBaseModel):
     # --8<-- [end:completion-extra-params]
 
 
-class ChatRequestMixin(OpenAIBaseModel):
-    # --8<-- [start:chat-params]
-    messages: list[ChatCompletionMessageParam]
-    # --8<-- [end:chat-params]
-
+class ChatRequestOptionsMixin(OpenAIBaseModel):
     # --8<-- [start:chat-extra-params]
     add_generation_prompt: bool = Field(
         default=False,
@@ -256,6 +252,12 @@ def build_chat_params(
         )
 
 
+class ChatRequestMixin(ChatRequestOptionsMixin):
+    # --8<-- [start:chat-params]
+    messages: list[ChatCompletionMessageParam]
+    # --8<-- [end:chat-params]
+
+
 class EncodingRequestMixin(OpenAIBaseModel):
     # --8<-- [start:encoding-params]
     encoding_format: EncodingFormat = "float"

vllm/entrypoints/pooling/embed/io_processor.py

@@ -36,6 +36,9 @@
     CohereEmbedContent,
     CohereEmbedInput,
     CohereEmbedRequest,
+    EmbeddingBatchChatInputRequest,
+    EmbeddingBatchChatRequest,
+    EmbeddingChatInputRequest,
     EmbeddingChatRequest,
     EmbeddingCompletionRequest,
 )
@@ -66,6 +69,16 @@ def __init__(self, *args, **kwargs):
     def pre_process_online(self, ctx: PoolingServeContext):
         if isinstance(ctx.request, CohereEmbedRequest):
             self._pre_process_cohere_online(ctx)
+        elif isinstance(
+            ctx.request,
+            (
+                EmbeddingChatRequest,
+                EmbeddingBatchChatRequest,
+                EmbeddingChatInputRequest,
+                EmbeddingBatchChatInputRequest,
+            ),
+        ):
+            self._pre_process_openai_chat_online(ctx)
         else:
             super().pre_process_online(ctx)
 
@@ -367,6 +380,70 @@ def create_pooling_params(self, request):
             )
         return super().create_pooling_params(request)
 
+    def _pre_process_openai_chat_online(
+        self,
+        ctx: PoolingServeContext[
+            EmbeddingChatRequest
+            | EmbeddingBatchChatRequest
+            | EmbeddingChatInputRequest
+            | EmbeddingBatchChatInputRequest
+        ],
+    ) -> None:
+        request = ctx.request
+        self._validate_chat_template(
+            request_chat_template=request.chat_template,
+            chat_template_kwargs=request.chat_template_kwargs,
+            trust_request_chat_template=self.trust_request_chat_template,
+        )
+
+        if isinstance(
+            request, (EmbeddingBatchChatRequest, EmbeddingBatchChatInputRequest)
+        ):
+            all_messages = request.messages
+        else:
+            all_messages = [request.messages]
+        ctx.engine_inputs = self._batch_render_openai_chat(request, all_messages)
+
+    def _batch_render_openai_chat(
+        self,
+        request: (
+            EmbeddingChatRequest
+            | EmbeddingBatchChatRequest
+            | EmbeddingChatInputRequest
+            | EmbeddingBatchChatInputRequest
+        ),
+        all_messages: Sequence[list[ChatCompletionMessageParam]],
+    ) -> list[EngineInput]:
+        renderer = self.renderer
+        mm_config = self.model_config.multimodal_config
+
+        tok_params = request.build_tok_params(self.model_config)
+        chat_params = request.build_chat_params(
+            self.chat_template,
+            self.chat_template_content_format,
+        ).with_defaults(
+            merge_kwargs(
+                None,
+                dict(
+                    tools=None,
+                    tokenize=is_mistral_tokenizer(renderer.tokenizer),
+                ),
+            ),
+            default_media_io_kwargs=(mm_config.media_io_kwargs if mm_config else None),
+        )
+
+        _, engine_inputs = renderer.render_chat(
+            all_messages,
+            chat_params,
+            tok_params,
+            prompt_extras={
+                k: v
+                for k in ("mm_processor_kwargs", "cache_salt")
+                if (v := getattr(request, k, None)) is not None
+            },
+        )
+        return engine_inputs
+
     def _pre_process_cohere_online(self, ctx: PoolingServeContext) -> None:
         """Convert a ``CohereEmbedRequest`` into engine prompts.