Feat/refactor embedding server (#7322)

python/sglang/srt/entrypoints/openai/api_server.py

@@ -40,9 +40,10 @@ from sglang.srt.disaggregation.utils import (
     register_disaggregation_server,
 )
 from sglang.srt.entrypoints.engine import Engine, _launch_subprocesses
+from sglang.srt.entrypoints.openai.serving_embedding import OpenAIServingEmbedding
 from sglang.srt.managers.tokenizer_manager import TokenizerManager
 from sglang.srt.metrics.func_timer import enable_func_timer
-from sglang.srt.openai_api.protocol import ModelCard, ModelList
+from sglang.srt.openai_api.protocol import EmbeddingRequest, ModelCard, ModelList
 from sglang.srt.server_args import ServerArgs
 from sglang.srt.utils import (
     add_prometheus_middleware,
@@ -64,6 +65,7 @@ class AppState:
     server_args: Optional[ServerArgs] = None
     tokenizer_manager: Optional[TokenizerManager] = None
     scheduler_info: Optional[Dict] = None
+    embedding_server: Optional[OpenAIServingEmbedding] = None
 
 
 @asynccontextmanager
@@ -78,6 +80,9 @@ async def lifespan(app: FastAPI):
     tokenizer_manager, scheduler_info = _launch_subprocesses(server_args=server_args)
     app.state.tokenizer_manager = tokenizer_manager
     app.state.scheduler_info = scheduler_info
+    app.state.serving_embedding = OpenAIServingEmbedding(
+        tokenizer_manager=tokenizer_manager
+    )
 
     if server_args.enable_metrics:
         add_prometheus_middleware(app)
@@ -169,7 +174,16 @@ async def openai_v1_chat_completions(raw_request: Request):
 
 @app.post("/v1/embeddings")
 async def openai_v1_embeddings(raw_request: Request):
-    pass
+    try:
+        request_json = await raw_request.json()
+        request = EmbeddingRequest(**request_json)
+    except Exception as e:
+        return app.state.serving_embedding.create_error_response(
+            f"Invalid request body, error: {str(e)}"
+        )
+
+    ret = await app.state.serving_embedding.handle_request(request, raw_request)
+    return ret
 
 
 @app.post("/v1/score")

python/sglang/srt/entrypoints/openai/serving_base.py

@@ -37,7 +37,7 @@ class OpenAIServingBase(ABC):
 
             # Convert to internal format
             adapted_request, processed_request = self._convert_to_internal_request(
-                [request], [self._generate_request_id_base(request)]
+                request, self._generate_request_id_base(request)
             )
 
             # Note(Xinyuan): raw_request below is only used for detecting the connection of the client
@@ -73,8 +73,8 @@ class OpenAIServingBase(ABC):
     @abstractmethod
     def _convert_to_internal_request(
         self,
-        all_requests: List[OpenAIServingRequest],
-        request_ids: List[str],
+        request: OpenAIServingRequest,
+        request_id: str,
     ) -> tuple[
         GenerateReqInput, Union[OpenAIServingRequest, List[OpenAIServingRequest]]
     ]:

python/sglang/srt/entrypoints/openai/serving_embedding.py

@@ -71,15 +71,11 @@ class OpenAIServingEmbedding(OpenAIServingBase):
 
     def _convert_to_internal_request(
         self,
-        all_requests: List[EmbeddingRequest],
-        request_ids: List[str],
+        request: EmbeddingRequest,
+        request_id: str,
     ) -> tuple[EmbeddingReqInput, Union[EmbeddingRequest, List[EmbeddingRequest]]]:
         """Convert OpenAI embedding request to internal format"""
-        prompts = [request.input for request in all_requests]
-
-        # Handle single vs multiple requests
-        if len(all_requests) == 1:
-            prompt = prompts[0]
+        prompt = request.input
         if isinstance(prompt, str):
             # Single string input
             prompt_kwargs = {"text": prompt}
@@ -87,9 +83,7 @@ class OpenAIServingEmbedding(OpenAIServingBase):
             if len(prompt) > 0 and isinstance(prompt[0], str):
                 # List of strings
                 prompt_kwargs = {"text": prompt}
-                elif len(prompt) > 0 and isinstance(
-                    prompt[0], MultimodalEmbeddingInput
-                ):
+            elif len(prompt) > 0 and isinstance(prompt[0], MultimodalEmbeddingInput):
                 # Handle multimodal embedding inputs
                 texts = []
                 images = []
@@ -105,9 +99,7 @@ class OpenAIServingEmbedding(OpenAIServingBase):
                     self.tokenizer_manager, "chat_template_name", None
                 )
                 if chat_template_name is not None:
-                        convs = generate_embedding_convs(
-                            texts, images, chat_template_name
-                        )
+                    convs = generate_embedding_convs(texts, images, chat_template_name)
                     for conv in convs:
                         generate_prompts.append(conv.get_prompt())
                 else:
@@ -129,53 +121,11 @@ class OpenAIServingEmbedding(OpenAIServingBase):
         else:
             # Other types (should not happen but handle gracefully)
             prompt_kwargs = {"input_ids": prompt}
-            # Use the passed request_ids for single request
-            final_request_id = request_ids[0] if len(all_requests) == 1 else request_ids
-        else:
-            # Handle batch requests
-            if len(prompts) > 0:
-                # Validate that all prompts have the same type
-                first_prompt = prompts[0]
-                first_type = type(first_prompt)
-                for i, prompt in enumerate(prompts[1:], 1):
-                    if type(prompt) != first_type:
-                        raise AssertionError(
-                            f"All prompts in batch must have the same type, but prompt at index {i} has different type"
-                        )
-
-                if isinstance(first_prompt, str):
-                    # Batch of strings
-                    prompt_kwargs = {"text": prompts}
-                elif isinstance(first_prompt, list):
-                    if len(first_prompt) > 0 and isinstance(first_prompt[0], str):
-                        # Batch of lists of strings
-                        prompt_kwargs = {"text": prompts}
-                    elif len(first_prompt) > 0 and isinstance(
-                        first_prompt[0], MultimodalEmbeddingInput
-                    ):
-                        # Handle multimodal batch requests
-                        raise NotImplementedError(
-                            "Multiple requests with multimodal inputs are not supported yet"
-                        )
-                    else:
-                        # Batch of token ID lists
-                        prompt_kwargs = {"input_ids": prompts}
-                else:
-                    # Other types
-                    prompt_kwargs = {"input_ids": prompts}
-            else:
-                prompt_kwargs = {"input_ids": prompts}
-            # Use the passed request_ids for batch requests
-            final_request_id = request_ids
-
         adapted_request = EmbeddingReqInput(
-            rid=final_request_id,
             **prompt_kwargs,
         )
 
-        return adapted_request, (
-            all_requests[0] if len(all_requests) == 1 else all_requests
-        )
+        return adapted_request, request
 
     async def _handle_non_streaming_request(
         self,
@@ -194,14 +144,10 @@ class OpenAIServingEmbedding(OpenAIServingBase):
         if not isinstance(ret, list):
             ret = [ret]
 
-        response = self._build_embedding_response(
-            ret, self.tokenizer_manager.model_path
-        )
+        response = self._build_embedding_response(ret)
         return response
 
-    def _build_embedding_response(
-        self, ret: List[Dict[str, Any]], model_path: str
-    ) -> EmbeddingResponse:
+    def _build_embedding_response(self, ret: List[Dict[str, Any]]) -> EmbeddingResponse:
         """Build the embedding response"""
         embedding_objects = []
         prompt_tokens = 0
@@ -219,7 +165,7 @@ class OpenAIServingEmbedding(OpenAIServingBase):
 
         return EmbeddingResponse(
             data=embedding_objects,
-            model=model_path,
+            model=self.tokenizer_manager.model_path,
             usage=UsageInfo(
                 prompt_tokens=prompt_tokens,
                 total_tokens=prompt_tokens,

test/srt/openai/test_serving_embedding.py

@@ -95,20 +95,20 @@ class ServingEmbeddingTestCase(unittest.TestCase):
         """Test converting single string request to internal format."""
         adapted_request, processed_request = (
             self.serving_embedding._convert_to_internal_request(
-                [self.basic_req], ["test-id"]
+                self.basic_req, "test-id"
             )
         )
 
         self.assertIsInstance(adapted_request, EmbeddingReqInput)
         self.assertEqual(adapted_request.text, "Hello, how are you?")
-        self.assertEqual(adapted_request.rid, "test-id")
+        self.assertEqual(adapted_request.rid, None)
         self.assertEqual(processed_request, self.basic_req)
 
     def test_convert_list_string_request(self):
         """Test converting list of strings request to internal format."""
         adapted_request, processed_request = (
             self.serving_embedding._convert_to_internal_request(
-                [self.list_req], ["test-id"]
+                self.list_req, "test-id"
             )
         )
 
@@ -116,27 +116,27 @@ class ServingEmbeddingTestCase(unittest.TestCase):
         self.assertEqual(
             adapted_request.text, ["Hello, how are you?", "I am fine, thank you!"]
         )
-        self.assertEqual(adapted_request.rid, "test-id")
+        self.assertEqual(adapted_request.rid, None)
         self.assertEqual(processed_request, self.list_req)
 
     def test_convert_token_ids_request(self):
         """Test converting token IDs request to internal format."""
         adapted_request, processed_request = (
             self.serving_embedding._convert_to_internal_request(
-                [self.token_ids_req], ["test-id"]
+                self.token_ids_req, "test-id"
             )
         )
 
         self.assertIsInstance(adapted_request, EmbeddingReqInput)
         self.assertEqual(adapted_request.input_ids, [1, 2, 3, 4, 5])
-        self.assertEqual(adapted_request.rid, "test-id")
+        self.assertEqual(adapted_request.rid, None)
         self.assertEqual(processed_request, self.token_ids_req)
 
     def test_convert_multimodal_request(self):
         """Test converting multimodal request to internal format."""
         adapted_request, processed_request = (
             self.serving_embedding._convert_to_internal_request(
-                [self.multimodal_req], ["test-id"]
+                self.multimodal_req, "test-id"
             )
         )
 
@@ -147,7 +147,7 @@ class ServingEmbeddingTestCase(unittest.TestCase):
         self.assertIn("World", adapted_request.text)
         self.assertEqual(adapted_request.image_data[0], "base64_image_data")
         self.assertIsNone(adapted_request.image_data[1])
-        self.assertEqual(adapted_request.rid, "test-id")
+        self.assertEqual(adapted_request.rid, None)
 
     def test_build_single_embedding_response(self):
         """Test building response for single embedding."""
@@ -158,9 +158,7 @@ class ServingEmbeddingTestCase(unittest.TestCase):
             }
         ]
 
-        response = self.serving_embedding._build_embedding_response(
-            ret_data, "test-model"
-        )
+        response = self.serving_embedding._build_embedding_response(ret_data)
 
         self.assertIsInstance(response, EmbeddingResponse)
         self.assertEqual(response.model, "test-model")
@@ -185,9 +183,7 @@ class ServingEmbeddingTestCase(unittest.TestCase):
             },
         ]
 
-        response = self.serving_embedding._build_embedding_response(
-            ret_data, "test-model"
-        )
+        response = self.serving_embedding._build_embedding_response(ret_data)
 
         self.assertIsInstance(response, EmbeddingResponse)
         self.assertEqual(len(response.data), 2)