[Misc] Rename embedding classes to pooling (#10801)

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

examples/offline_inference_embedding.py

@@ -10,7 +10,7 @@ prompts = [
 
 # Create an LLM.
 model = LLM(model="intfloat/e5-mistral-7b-instruct", enforce_eager=True)
-# Generate embedding. The output is a list of EmbeddingRequestOutputs.
+# Generate embedding. The output is a list of PoolingRequestOutputs.
 outputs = model.encode(prompts)
 # Print the outputs.
 for output in outputs:

tests/entrypoints/llm/test_encode.py

@@ -3,7 +3,7 @@ from typing import List
 
 import pytest
 
-from vllm import LLM, EmbeddingRequestOutput, PoolingParams
+from vllm import LLM, PoolingParams, PoolingRequestOutput
 from vllm.distributed import cleanup_dist_env_and_memory
 
 MODEL_NAME = "intfloat/e5-mistral-7b-instruct"
@@ -43,8 +43,8 @@ def llm():
     cleanup_dist_env_and_memory()
 
 
-def assert_outputs_equal(o1: List[EmbeddingRequestOutput],
-                         o2: List[EmbeddingRequestOutput]):
+def assert_outputs_equal(o1: List[PoolingRequestOutput],
+                         o2: List[PoolingRequestOutput]):
     assert [o.outputs for o in o1] == [o.outputs for o in o2]
 
 

tests/models/test_registry.py

@@ -3,7 +3,7 @@ import warnings
 import pytest
 import torch.cuda
 
-from vllm.model_executor.models import (is_embedding_model,
+from vllm.model_executor.models import (is_pooling_model,
                                         is_text_generation_model,
                                         supports_multimodal)
 from vllm.model_executor.models.adapters import as_embedding_model
@@ -31,7 +31,7 @@ def test_registry_imports(model_arch):
 
     # All vLLM models should be convertible to an embedding model
     embed_model = as_embedding_model(model_cls)
-    assert is_embedding_model(embed_model)
+    assert is_pooling_model(embed_model)
 
     if model_arch in _MULTIMODAL_MODELS:
         assert supports_multimodal(model_cls)

tests/worker/test_model_input.py

@@ -8,10 +8,10 @@ from vllm.attention.backends.abstract import AttentionBackend
 from vllm.attention.backends.utils import CommonAttentionState
 from vllm.model_executor import SamplingMetadata
 from vllm.model_executor.pooling_metadata import PoolingMetadata
-from vllm.worker.embedding_model_runner import (
-    ModelInputForGPUWithPoolingMetadata)
 from vllm.worker.model_runner import ModelInputForGPUWithSamplingMetadata
 from vllm.worker.multi_step_model_runner import StatefulModelInput
+from vllm.worker.pooling_model_runner import (
+    ModelInputForGPUWithPoolingMetadata)
 
 
 class MockAttentionBackend(AttentionBackend):

vllm/__init__.py

@@ -7,8 +7,8 @@ from vllm.entrypoints.llm import LLM
 from vllm.executor.ray_utils import initialize_ray_cluster
 from vllm.inputs import PromptType, TextPrompt, TokensPrompt
 from vllm.model_executor.models import ModelRegistry
-from vllm.outputs import (CompletionOutput, EmbeddingOutput,
-                          EmbeddingRequestOutput, RequestOutput)
+from vllm.outputs import (CompletionOutput, PoolingOutput,
+                          PoolingRequestOutput, RequestOutput)
 from vllm.pooling_params import PoolingParams
 from vllm.sampling_params import SamplingParams
 
@@ -25,8 +25,8 @@ __all__ = [
     "SamplingParams",
     "RequestOutput",
     "CompletionOutput",
-    "EmbeddingOutput",
-    "EmbeddingRequestOutput",
+    "PoolingOutput",
+    "PoolingRequestOutput",
     "LLMEngine",
     "EngineArgs",
     "AsyncLLMEngine",
@@ -34,3 +34,26 @@ __all__ = [
     "initialize_ray_cluster",
     "PoolingParams",
 ]
+
+
+def __getattr__(name: str):
+    import warnings
+
+    if name == "EmbeddingOutput":
+        msg = ("EmbeddingOutput has been renamed to PoolingOutput. "
+               "The original name will be removed in an upcoming version.")
+
+        warnings.warn(DeprecationWarning(msg), stacklevel=2)
+
+        return PoolingOutput
+
+    if name == "EmbeddingRequestOutput":
+        msg = ("EmbeddingRequestOutput has been renamed to "
+               "PoolingRequestOutput. "
+               "The original name will be removed in an upcoming version.")
+
+        warnings.warn(DeprecationWarning(msg), stacklevel=2)
+
+        return PoolingRequestOutput
+
+    raise AttributeError(f"module {__name__!r} has no attribute {name!r}")

vllm/config.py

@@ -359,7 +359,7 @@ class ModelConfig:
             # NOTE: Listed from highest to lowest priority,
             # in case the model supports multiple of them
             "generate": ModelRegistry.is_text_generation_model(architectures),
-            "embedding": ModelRegistry.is_embedding_model(architectures),
+            "embedding": ModelRegistry.is_pooling_model(architectures),
         }
         supported_tasks_lst: List[_Task] = [
             task for task, is_supported in task_support.items() if is_supported

vllm/engine/async_llm_engine.py

@@ -25,7 +25,7 @@ from vllm.lora.request import LoRARequest
 from vllm.model_executor.guided_decoding import (
     get_guided_decoding_logits_processor)
 from vllm.model_executor.layers.sampler import SamplerOutput
-from vllm.outputs import EmbeddingRequestOutput, RequestOutput
+from vllm.outputs import PoolingRequestOutput, RequestOutput
 from vllm.pooling_params import PoolingParams
 from vllm.prompt_adapter.request import PromptAdapterRequest
 from vllm.sampling_params import SamplingParams
@@ -74,7 +74,7 @@ STOP_ITERATION = Exception()  # Sentinel
 
 
 class AsyncStream:
-    """A stream of RequestOutputs or EmbeddingRequestOutputs for a request
+    """A stream of RequestOutputs or PoolingRequestOutputs for a request
     that can be iterated over asynchronously via an async generator."""
 
     def __init__(self, request_id: str, cancel: Callable[[str], None]) -> None:
@@ -83,7 +83,7 @@ class AsyncStream:
         self._queue: asyncio.Queue = asyncio.Queue()
         self._finished = False
 
-    def put(self, item: Union[RequestOutput, EmbeddingRequestOutput,
+    def put(self, item: Union[RequestOutput, PoolingRequestOutput,
                               Exception]) -> None:
         if not self._finished:
             self._queue.put_nowait(item)
@@ -103,7 +103,7 @@ class AsyncStream:
 
     async def generator(
         self
-    ) -> AsyncGenerator[Union[RequestOutput, EmbeddingRequestOutput], None]:
+    ) -> AsyncGenerator[Union[RequestOutput, PoolingRequestOutput], None]:
         try:
             while True:
                 result = await self._queue.get()
@@ -154,7 +154,7 @@ class RequestTracker:
 
     def process_request_output(self,
                                request_output: Union[RequestOutput,
-                                                     EmbeddingRequestOutput],
+                                                     PoolingRequestOutput],
                                *,
                                verbose: bool = False) -> None:
         """Process a request output from the engine."""
@@ -265,7 +265,7 @@ class _AsyncLLMEngine(LLMEngine):
 
     async def step_async(
         self, virtual_engine: int
-    ) -> List[Union[RequestOutput, EmbeddingRequestOutput]]:
+    ) -> List[Union[RequestOutput, PoolingRequestOutput]]:
         """Performs one decoding iteration and returns newly generated results.
         The workers are ran asynchronously if possible.
 
@@ -907,7 +907,7 @@ class AsyncLLMEngine(EngineClient):
         prompt_adapter_request: Optional[PromptAdapterRequest] = None,
         priority: int = 0,
     ) -> Coroutine[None, None, AsyncGenerator[Union[
-            RequestOutput, EmbeddingRequestOutput], None]]:
+            RequestOutput, PoolingRequestOutput], None]]:
         ...
 
     @overload
@@ -922,7 +922,7 @@ class AsyncLLMEngine(EngineClient):
         prompt_adapter_request: Optional[PromptAdapterRequest] = None,
         priority: int = 0,
     ) -> Coroutine[None, None, AsyncGenerator[Union[
-            RequestOutput, EmbeddingRequestOutput], None]]:
+            RequestOutput, PoolingRequestOutput], None]]:
         ...
 
     @deprecate_kwargs(
@@ -941,7 +941,7 @@ class AsyncLLMEngine(EngineClient):
         priority: int = 0,
         *,
         inputs: Optional[PromptType] = None,  # DEPRECATED
-    ) -> AsyncGenerator[Union[RequestOutput, EmbeddingRequestOutput], None]:
+    ) -> AsyncGenerator[Union[RequestOutput, PoolingRequestOutput], None]:
         if inputs is not None:
             prompt = inputs
         assert prompt is not None and params is not None
@@ -1070,7 +1070,7 @@ class AsyncLLMEngine(EngineClient):
         lora_request: Optional[LoRARequest] = None,
         trace_headers: Optional[Mapping[str, str]] = None,
         priority: int = 0,
-    ) -> AsyncGenerator[EmbeddingRequestOutput, None]:
+    ) -> AsyncGenerator[PoolingRequestOutput, None]:
         """Generate outputs for a request from an embedding model.
 
         Generate outputs for a request. This method is a coroutine. It adds the
@@ -1088,7 +1088,7 @@ class AsyncLLMEngine(EngineClient):
                 Only applicable with priority scheduling.
 
         Yields:
-            The output `EmbeddingRequestOutput` objects from the LLMEngine
+            The output `PoolingRequestOutput` objects from the LLMEngine
             for the request.
 
         Details:
@@ -1141,7 +1141,7 @@ class AsyncLLMEngine(EngineClient):
                 trace_headers=trace_headers,
                 priority=priority,
         ):
-            yield LLMEngine.validate_output(output, EmbeddingRequestOutput)
+            yield LLMEngine.validate_output(output, PoolingRequestOutput)
 
     async def abort(self, request_id: str) -> None:
         """Abort a request.

vllm/engine/llm_engine.py

@@ -40,7 +40,7 @@ from vllm.model_executor.guided_decoding import (
     get_local_guided_decoding_logits_processor)
 from vllm.model_executor.layers.sampler import SamplerOutput
 from vllm.multimodal import MULTIMODAL_REGISTRY, MultiModalRegistry
-from vllm.outputs import (EmbeddingRequestOutput, RequestOutput,
+from vllm.outputs import (PoolingRequestOutput, RequestOutput,
                           RequestOutputFactory)
 from vllm.pooling_params import PoolingParams
 from vllm.prompt_adapter.request import PromptAdapterRequest
@@ -80,7 +80,7 @@ def _load_generation_config_dict(model_config: ModelConfig) -> Dict[str, Any]:
 
 
 _G = TypeVar("_G", bound=BaseTokenizerGroup, default=BaseTokenizerGroup)
-_O = TypeVar("_O", RequestOutput, EmbeddingRequestOutput)
+_O = TypeVar("_O", RequestOutput, PoolingRequestOutput)
 
 
 @dataclass
@@ -112,7 +112,7 @@ class SchedulerContext:
     def __init__(self, multi_step_stream_outputs: bool = False):
         self.output_queue: Deque[OutputData] = deque()
         self.request_outputs: List[Union[RequestOutput,
-                                         EmbeddingRequestOutput]] = []
+                                         PoolingRequestOutput]] = []
         self.seq_group_metadata_list: Optional[
             List[SequenceGroupMetadata]] = None
         self.scheduler_outputs: Optional[SchedulerOutputs] = None
@@ -1314,7 +1314,7 @@ class LLMEngine:
                 else:
                     seq.append_token_id(sample.output_token, sample.logprobs)
 
-    def step(self) -> List[Union[RequestOutput, EmbeddingRequestOutput]]:
+    def step(self) -> List[Union[RequestOutput, PoolingRequestOutput]]:
         """Performs one decoding iteration and returns newly generated results.
 
         .. figure:: https://i.imgur.com/sv2HssD.png

vllm/engine/multiprocessing/client.py

@@ -35,7 +35,7 @@ from vllm.inputs.preprocess import InputPreprocessor
 from vllm.logger import init_logger
 from vllm.lora.request import LoRARequest
 from vllm.model_executor.layers.sampler import SamplerOutput
-from vllm.outputs import EmbeddingRequestOutput, RequestOutput
+from vllm.outputs import PoolingRequestOutput, RequestOutput
 from vllm.prompt_adapter.request import PromptAdapterRequest
 from vllm.sampling_params import SamplingParams
 from vllm.transformers_utils.tokenizer_group import init_tokenizer_from_configs
@@ -495,7 +495,7 @@ class MQLLMEngineClient(EngineClient):
         lora_request: Optional[LoRARequest] = None,
         trace_headers: Optional[Mapping[str, str]] = None,
         priority: int = 0,
-    ) -> AsyncGenerator[EmbeddingRequestOutput, None]:
+    ) -> AsyncGenerator[PoolingRequestOutput, None]:
         ...
 
     @overload
@@ -507,7 +507,7 @@ class MQLLMEngineClient(EngineClient):
         lora_request: Optional[LoRARequest] = None,
         trace_headers: Optional[Mapping[str, str]] = None,
         priority: int = 0,
-    ) -> AsyncGenerator[EmbeddingRequestOutput, None]:
+    ) -> AsyncGenerator[PoolingRequestOutput, None]:
         ...
 
     @deprecate_kwargs(
@@ -524,7 +524,7 @@ class MQLLMEngineClient(EngineClient):
         priority: int = 0,
         *,
         inputs: Optional[PromptType] = None  # DEPRECATED
-    ) -> AsyncGenerator[EmbeddingRequestOutput, None]:
+    ) -> AsyncGenerator[PoolingRequestOutput, None]:
         """Generate outputs for a request from an embedding model.
 
         Generate outputs for a request. This method is a coroutine. It adds the
@@ -540,7 +540,7 @@ class MQLLMEngineClient(EngineClient):
             trace_headers: OpenTelemetry trace headers.
 
         Yields:
-            The output `EmbeddingRequestOutput` objects from the LLMEngine
+            The output `PoolingRequestOutput` objects from the LLMEngine
             for the request.
         """
         if inputs is not None:
@@ -549,7 +549,7 @@ class MQLLMEngineClient(EngineClient):
                 and request_id is not None)
 
         return cast(
-            AsyncGenerator[EmbeddingRequestOutput, None],
+            AsyncGenerator[PoolingRequestOutput, None],
             self._process_request(prompt,
                                   pooling_params,
                                   request_id,
@@ -567,7 +567,7 @@ class MQLLMEngineClient(EngineClient):
         prompt_adapter_request: Optional[PromptAdapterRequest] = None,
         priority: int = 0,
     ) -> Union[AsyncGenerator[RequestOutput, None], AsyncGenerator[
-            EmbeddingRequestOutput, None]]:
+            PoolingRequestOutput, None]]:
         """Send an RPCGenerateRequest to the RPCServer and stream responses."""
 
         # If already dead, error out.

vllm/engine/protocol.py

@@ -11,8 +11,7 @@ from vllm.inputs.preprocess import InputPreprocessor
 from vllm.logger import init_logger
 from vllm.lora.request import LoRARequest
 from vllm.model_executor.layers.sampler import SamplerOutput
-from vllm.outputs import (CompletionOutput, EmbeddingRequestOutput,
-                          RequestOutput)
+from vllm.outputs import CompletionOutput, PoolingRequestOutput, RequestOutput
 from vllm.pooling_params import PoolingParams
 from vllm.prompt_adapter.request import PromptAdapterRequest
 from vllm.sampling_params import BeamSearchParams, SamplingParams
@@ -209,7 +208,7 @@ class EngineClient(ABC):
         lora_request: Optional[LoRARequest] = None,
         trace_headers: Optional[Mapping[str, str]] = None,
         priority: int = 0,
-    ) -> AsyncGenerator[EmbeddingRequestOutput, None]:
+    ) -> AsyncGenerator[PoolingRequestOutput, None]:
         """Generate outputs for a request from an embedding model."""
         ...
 

vllm/entrypoints/llm.py

@@ -26,7 +26,7 @@ from vllm.logger import init_logger
 from vllm.lora.request import LoRARequest
 from vllm.model_executor.guided_decoding.guided_fields import (
     GuidedDecodingRequest, LLMGuidedOptions)
-from vllm.outputs import EmbeddingRequestOutput, RequestOutput
+from vllm.outputs import PoolingRequestOutput, RequestOutput
 from vllm.pooling_params import PoolingParams
 from vllm.prompt_adapter.request import PromptAdapterRequest
 from vllm.sampling_params import (BeamSearchParams, GuidedDecodingParams,
@@ -679,7 +679,7 @@ class LLM:
         prompt_token_ids: Optional[List[int]] = None,
         use_tqdm: bool = True,
         lora_request: Optional[Union[List[LoRARequest], LoRARequest]] = None,
-    ) -> List[EmbeddingRequestOutput]:
+    ) -> List[PoolingRequestOutput]:
         ...
 
     @overload  # LEGACY: multi (prompt + optional token ids)
@@ -691,7 +691,7 @@ class LLM:
         prompt_token_ids: Optional[List[List[int]]] = None,
         use_tqdm: bool = True,
         lora_request: Optional[Union[List[LoRARequest], LoRARequest]] = None,
-    ) -> List[EmbeddingRequestOutput]:
+    ) -> List[PoolingRequestOutput]:
         ...
 
     @overload  # LEGACY: single (token ids + optional prompt)
@@ -704,7 +704,7 @@ class LLM:
         prompt_token_ids: List[int],
         use_tqdm: bool = True,
         lora_request: Optional[Union[List[LoRARequest], LoRARequest]] = None,
-    ) -> List[EmbeddingRequestOutput]:
+    ) -> List[PoolingRequestOutput]:
         ...
 
     @overload  # LEGACY: multi (token ids + optional prompt)
@@ -717,7 +717,7 @@ class LLM:
         prompt_token_ids: List[List[int]],
         use_tqdm: bool = True,
         lora_request: Optional[Union[List[LoRARequest], LoRARequest]] = None,
-    ) -> List[EmbeddingRequestOutput]:
+    ) -> List[PoolingRequestOutput]:
         ...
 
     @overload  # LEGACY: single or multi token ids [pos-only]
@@ -728,7 +728,7 @@ class LLM:
         prompt_token_ids: Union[List[int], List[List[int]]],
         use_tqdm: bool = True,
         lora_request: Optional[Union[List[LoRARequest], LoRARequest]] = None,
-    ) -> List[EmbeddingRequestOutput]:
+    ) -> List[PoolingRequestOutput]:
         ...
 
     @overload
@@ -741,7 +741,7 @@ class LLM:
                                        Sequence[PoolingParams]]] = None,
         use_tqdm: bool = True,
         lora_request: Optional[Union[List[LoRARequest], LoRARequest]] = None,
-    ) -> List[EmbeddingRequestOutput]:
+    ) -> List[PoolingRequestOutput]:
         ...
 
     @deprecate_kwargs(
@@ -759,7 +759,7 @@ class LLM:
         use_tqdm: bool = True,
         lora_request: Optional[Union[List[LoRARequest], LoRARequest]] = None,
         prompt_adapter_request: Optional[PromptAdapterRequest] = None,
-    ) -> List[EmbeddingRequestOutput]:
+    ) -> List[PoolingRequestOutput]:
         """Generates the completions for the input prompts.
 
         This class automatically batches the given prompts, considering
@@ -778,7 +778,7 @@ class LLM:
                 generation, if any.
 
         Returns:
-            A list of ``EmbeddingRequestOutput`` objects containing the
+            A list of ``PoolingRequestOutput`` objects containing the
             generated embeddings in the same order as the input prompts.
 
         Note:
@@ -821,7 +821,7 @@ class LLM:
 
         outputs = self._run_engine(use_tqdm=use_tqdm)
         return self.engine_class.validate_outputs(outputs,
-                                                  EmbeddingRequestOutput)
+                                                  PoolingRequestOutput)
 
     def score(
         self,
@@ -832,7 +832,7 @@ class LLM:
         use_tqdm: bool = True,
         lora_request: Optional[Union[List[LoRARequest], LoRARequest]] = None,
         prompt_adapter_request: Optional[PromptAdapterRequest] = None,
-    ) -> List[EmbeddingRequestOutput]:
+    ) -> List[PoolingRequestOutput]:
         """Generates similarity scores for all pairs <text,text_pair>.
 
         The inputs can be 1 -> 1, 1 -> N or N -> N. In the 1 - N case
@@ -854,7 +854,7 @@ class LLM:
                 generation, if any.
 
         Returns:
-            A list of ``EmbeddingRequestOutput`` objects containing the
+            A list of ``PoolingRequestOutput`` objects containing the
             generated scores in the same order as the input prompts.
         """
         task = self.llm_engine.model_config.task
@@ -943,7 +943,7 @@ class LLM:
 
         outputs = self._run_engine(use_tqdm=use_tqdm)
         return self.engine_class.validate_outputs(outputs,
-                                                  EmbeddingRequestOutput)
+                                                  PoolingRequestOutput)
 
     def start_profile(self) -> None:
         self.llm_engine.start_profile()
@@ -1085,7 +1085,7 @@ class LLM:
 
     def _run_engine(
             self, *, use_tqdm: bool
-    ) -> List[Union[RequestOutput, EmbeddingRequestOutput]]:
+    ) -> List[Union[RequestOutput, PoolingRequestOutput]]:
         # Initialize tqdm.
         if use_tqdm:
             num_requests = self.llm_engine.get_num_unfinished_requests()
@@ -1098,7 +1098,7 @@ class LLM:
             )
 
         # Run the engine.
-        outputs: List[Union[RequestOutput, EmbeddingRequestOutput]] = []
+        outputs: List[Union[RequestOutput, PoolingRequestOutput]] = []
         total_in_toks = 0
         total_out_toks = 0
         while self.llm_engine.has_unfinished_requests():

vllm/entrypoints/openai/serving_embedding.py

@@ -18,14 +18,14 @@ from vllm.entrypoints.openai.protocol import (EmbeddingChatRequest,
                                               ErrorResponse, UsageInfo)
 from vllm.entrypoints.openai.serving_engine import BaseModelPath, OpenAIServing
 from vllm.logger import init_logger
-from vllm.outputs import EmbeddingOutput, EmbeddingRequestOutput
+from vllm.outputs import PoolingOutput, PoolingRequestOutput
 from vllm.utils import merge_async_iterators, random_uuid
 
 logger = init_logger(__name__)
 
 
 def _get_embedding(
-    output: EmbeddingOutput,
+    output: PoolingOutput,
     encoding_format: Literal["float", "base64"],
 ) -> Union[List[float], str]:
     if encoding_format == "float":
@@ -40,7 +40,7 @@ def _get_embedding(
 
 
 def request_output_to_embedding_response(
-        final_res_batch: List[EmbeddingRequestOutput], request_id: str,
+        final_res_batch: List[PoolingRequestOutput], request_id: str,
         created_time: int, model_name: str,
         encoding_format: Literal["float", "base64"]) -> EmbeddingResponse:
     data: List[EmbeddingResponseData] = []
@@ -169,7 +169,7 @@ class OpenAIServingEmbedding(OpenAIServing):
             return self.create_error_response(str(e))
 
         # Schedule the request and get the result generator.
-        generators: List[AsyncGenerator[EmbeddingRequestOutput, None]] = []
+        generators: List[AsyncGenerator[PoolingRequestOutput, None]] = []
         try:
             pooling_params = request.to_pooling_params()
 
@@ -207,7 +207,7 @@ class OpenAIServingEmbedding(OpenAIServing):
         num_prompts = len(engine_prompts)
 
         # Non-streaming response
-        final_res_batch: List[Optional[EmbeddingRequestOutput]]
+        final_res_batch: List[Optional[PoolingRequestOutput]]
         final_res_batch = [None] * num_prompts
         try:
             async for i, res in result_generator:
@@ -215,7 +215,7 @@ class OpenAIServingEmbedding(OpenAIServing):
 
             assert all(final_res is not None for final_res in final_res_batch)
 
-            final_res_batch_checked = cast(List[EmbeddingRequestOutput],
+            final_res_batch_checked = cast(List[PoolingRequestOutput],
                                            final_res_batch)
 
             response = request_output_to_embedding_response(

vllm/entrypoints/openai/serving_score.py

@@ -13,7 +13,7 @@ from vllm.entrypoints.openai.protocol import (ErrorResponse, ScoreRequest,
 from vllm.entrypoints.openai.serving_engine import BaseModelPath, OpenAIServing
 from vllm.inputs.data import TokensPrompt
 from vllm.logger import init_logger
-from vllm.outputs import EmbeddingRequestOutput
+from vllm.outputs import PoolingRequestOutput
 from vllm.transformers_utils.tokenizers.mistral import MistralTokenizer
 from vllm.utils import make_async, merge_async_iterators, random_uuid
 
@@ -21,7 +21,7 @@ logger = init_logger(__name__)
 
 
 def request_output_to_score_response(
-        final_res_batch: List[EmbeddingRequestOutput], request_id: str,
+        final_res_batch: List[PoolingRequestOutput], request_id: str,
         created_time: int, model_name: str) -> ScoreResponse:
     data: List[ScoreResponseData] = []
     score = None
@@ -133,7 +133,7 @@ class OpenAIServingScores(OpenAIServing):
             return self.create_error_response(str(e))
 
         # Schedule the request and get the result generator.
-        generators: List[AsyncGenerator[EmbeddingRequestOutput, None]] = []
+        generators: List[AsyncGenerator[PoolingRequestOutput, None]] = []
 
         input_pairs = make_pairs(request.text_1, request.text_2)
 
@@ -194,7 +194,7 @@ class OpenAIServingScores(OpenAIServing):
         num_prompts = len(engine_prompts)
 
         # Non-streaming response
-        final_res_batch: List[Optional[EmbeddingRequestOutput]]
+        final_res_batch: List[Optional[PoolingRequestOutput]]
         final_res_batch = [None] * num_prompts
 
         try:
@@ -203,7 +203,7 @@ class OpenAIServingScores(OpenAIServing):
 
             assert all(final_res is not None for final_res in final_res_batch)
 
-            final_res_batch_checked = cast(List[EmbeddingRequestOutput],
+            final_res_batch_checked = cast(List[PoolingRequestOutput],
                                            final_res_batch)
 
             response = request_output_to_score_response(

vllm/model_executor/models/__init__.py

 from .interfaces import (HasInnerState, SupportsLoRA, SupportsMultiModal,
                          SupportsPP, has_inner_state, supports_lora,
                          supports_multimodal, supports_pp)
-from .interfaces_base import (VllmModelForEmbedding,
-                              VllmModelForTextGeneration, is_embedding_model,
-                              is_text_generation_model)
+from .interfaces_base import (VllmModelForPooling, VllmModelForTextGeneration,
+                              is_pooling_model, is_text_generation_model)
 from .registry import ModelRegistry
 
 __all__ = [
     "ModelRegistry",
-    "VllmModelForEmbedding",
-    "is_embedding_model",
+    "VllmModelForPooling",
+    "is_pooling_model",
     "VllmModelForTextGeneration",
     "is_text_generation_model",
     "HasInnerState",
@@ -20,4 +19,4 @@ __all__ = [
     "supports_multimodal",
     "SupportsPP",
     "supports_pp",
-]
+]
\ No newline at end of file

vllm/model_executor/models/adapters.py

@@ -4,7 +4,7 @@ from typing import Any, TypeVar
 import torch
 import torch.nn as nn
 
-from .interfaces_base import VllmModelForEmbedding, is_embedding_model
+from .interfaces_base import VllmModelForPooling, is_pooling_model
 
 _T = TypeVar("_T", bound=type[nn.Module])
 
@@ -12,7 +12,7 @@ _T = TypeVar("_T", bound=type[nn.Module])
 def as_embedding_model(cls: _T) -> _T:
     """Subclass an existing vLLM model to support embeddings."""
     # Avoid modifying existing embedding models
-    if is_embedding_model(cls):
+    if is_pooling_model(cls):
         return cls
 
     # Lazy import
@@ -23,7 +23,7 @@ def as_embedding_model(cls: _T) -> _T:
 
     from .utils import AutoWeightsLoader, WeightsMapper
 
-    class ModelForEmbedding(cls, VllmModelForEmbedding):
+    class ModelForEmbedding(cls, VllmModelForPooling):
 
         def __init__(
             self,

vllm/model_executor/models/interfaces.py

@@ -7,7 +7,7 @@ from typing_extensions import TypeIs, TypeVar
 from vllm.logger import init_logger
 from vllm.utils import supports_kw
 
-from .interfaces_base import is_embedding_model
+from .interfaces_base import is_pooling_model
 
 if TYPE_CHECKING:
     from vllm.attention import AttentionMetadata
@@ -389,4 +389,4 @@ def _supports_cross_encoding(
 def supports_cross_encoding(
     model: Union[Type[object], object],
 ) -> Union[TypeIs[Type[SupportsCrossEncoding]], TypeIs[SupportsCrossEncoding]]:
-    return is_embedding_model(model) and _supports_cross_encoding(model)
+    return is_pooling_model(model) and _supports_cross_encoding(model)

vllm/model_executor/models/interfaces_base.py

@@ -141,7 +141,7 @@ def is_text_generation_model(
 
 
 @runtime_checkable
-class VllmModelForEmbedding(VllmModel[C_co, T], Protocol[C_co, T]):
+class VllmModelForPooling(VllmModel[C_co, T], Protocol[C_co, T]):
 
     def pooler(
         self,
@@ -153,23 +153,22 @@ class VllmModelForEmbedding(VllmModel[C_co, T], Protocol[C_co, T]):
 
 
 @overload
-def is_embedding_model(
-        model: Type[object]) -> TypeIs[Type[VllmModelForEmbedding]]:
+def is_pooling_model(model: Type[object]) -> TypeIs[Type[VllmModelForPooling]]:
     ...
 
 
 @overload
-def is_embedding_model(model: object) -> TypeIs[VllmModelForEmbedding]:
+def is_pooling_model(model: object) -> TypeIs[VllmModelForPooling]:
     ...
 
 
-def is_embedding_model(
+def is_pooling_model(
     model: Union[Type[object], object],
-) -> Union[TypeIs[Type[VllmModelForEmbedding]], TypeIs[VllmModelForEmbedding]]:
+) -> Union[TypeIs[Type[VllmModelForPooling]], TypeIs[VllmModelForPooling]]:
     if not is_vllm_model(model):
         return False
 
     if isinstance(model, type):
-        return isinstance(model, VllmModelForEmbedding)
+        return isinstance(model, VllmModelForPooling)
 
-    return isinstance(model, VllmModelForEmbedding)
+    return isinstance(model, VllmModelForPooling)

vllm/model_executor/models/registry.py

@@ -24,7 +24,7 @@ from .adapters import as_embedding_model
 from .interfaces import (has_inner_state, is_attention_free,
                          supports_cross_encoding, supports_multimodal,
                          supports_pp)
-from .interfaces_base import is_embedding_model, is_text_generation_model
+from .interfaces_base import is_pooling_model, is_text_generation_model
 
 logger = init_logger(__name__)
 
@@ -211,7 +211,7 @@ _ROCM_PARTIALLY_SUPPORTED_MODELS: Dict[str, str] = {
 class _ModelInfo:
     architecture: str
     is_text_generation_model: bool
-    is_embedding_model: bool
+    is_pooling_model: bool
     supports_cross_encoding: bool
     supports_multimodal: bool
     supports_pp: bool
@@ -220,19 +220,19 @@ class _ModelInfo:
 
     @staticmethod
     def from_model_cls(model: Type[nn.Module]) -> "_ModelInfo":
-        is_embedding_model_ = is_embedding_model(model)
-        if not is_embedding_model_:
+        is_pooling_model_ = is_pooling_model(model)
+        if not is_pooling_model_:
             try:
                 as_embedding_model(model)
             except Exception:
                 pass
             else:
-                is_embedding_model_ = True
+                is_pooling_model_ = True
 
         return _ModelInfo(
             architecture=model.__name__,
             is_text_generation_model=is_text_generation_model(model),
-            is_embedding_model=is_embedding_model_,
+            is_pooling_model=is_pooling_model_,
             supports_cross_encoding=supports_cross_encoding(model),
             supports_multimodal=supports_multimodal(model),
             supports_pp=supports_pp(model),
@@ -441,12 +441,12 @@ class _ModelRegistry:
         model_cls, _ = self.inspect_model_cls(architectures)
         return model_cls.is_text_generation_model
 
-    def is_embedding_model(
+    def is_pooling_model(
         self,
         architectures: Union[str, List[str]],
     ) -> bool:
         model_cls, _ = self.inspect_model_cls(architectures)
-        return model_cls.is_embedding_model
+        return model_cls.is_pooling_model
 
     def is_cross_encoder_model(
         self,

vllm/outputs.py

@@ -53,8 +53,8 @@ class CompletionOutput:
 
 
 @dataclass
-class EmbeddingOutput:
-    """The output data of one completion output of a request.
+class PoolingOutput:
+    """The output data of one pooling output of a request.
 
     Args:
         embedding: The embedding vector, which is a list of floats. The
@@ -63,7 +63,7 @@ class EmbeddingOutput:
     embedding: List[float]
 
     def __repr__(self) -> str:
-        return (f"EmbeddingOutput("
+        return (f"PoolingOutput("
                 f"embedding={len(self.embedding)})")
 
 
@@ -316,18 +316,18 @@ class RequestOutput:
                 f"multi_modal_placeholders={self.multi_modal_placeholders})")
 
 
-class EmbeddingRequestOutput:
+class PoolingRequestOutput:
     """
-    The output data of an embedding request to the LLM.
+    The output data of a pooling request to the LLM.
 
     Args:
-        request_id (str): A unique identifier for the embedding request.
-        outputs (EmbeddingOutput): The embedding results for the given input.
+        request_id (str): A unique identifier for the pooling request.
+        outputs (PoolingOutput): The pooling results for the given input.
         prompt_token_ids (List[int]): A list of token IDs used in the prompt.
-        finished (bool): A flag indicating whether the embedding is completed.
+        finished (bool): A flag indicating whether the pooling is completed.
     """
 
-    def __init__(self, request_id: str, outputs: "EmbeddingOutput",
+    def __init__(self, request_id: str, outputs: "PoolingOutput",
                  prompt_token_ids: List[int], finished: bool):
         self.request_id = request_id
         self.prompt_token_ids = prompt_token_ids
@@ -336,11 +336,11 @@ class EmbeddingRequestOutput:
 
     @classmethod
     def from_seq_group(cls,
-                       seq_group: 'SequenceGroup') -> "EmbeddingRequestOutput":
+                       seq_group: 'SequenceGroup') -> "PoolingRequestOutput":
         if seq_group.embeddings is None:
             raise ValueError(
                 "Embeddings are missing in seq_group for EmbeddingRequest.")
-        output = EmbeddingOutput(seq_group.embeddings)
+        output = PoolingOutput(seq_group.embeddings)
         prompt_token_ids = seq_group.prompt_token_ids
         finished = seq_group.is_finished()
 
@@ -348,15 +348,15 @@ class EmbeddingRequestOutput:
 
     def __repr__(self):
         """
-        Returns a string representation of an EmbeddingRequestOutput instance.
+        Returns a string representation of an PoolingRequestOutput instance.
 
         The representation includes the request_id and the number of outputs,
-        providing a quick overview of the embedding request's results.
+        providing a quick overview of the pooling request's results.
 
         Returns:
-            str: A string representation of the EmbeddingRequestOutput instance.
+            str: A string representation of the PoolingRequestOutput instance.
         """
-        return (f"EmbeddingRequestOutput(request_id='{self.request_id}', "
+        return (f"PoolingRequestOutput(request_id='{self.request_id}', "
                 f"outputs={repr(self.outputs)}, "
                 f"prompt_token_ids={self.prompt_token_ids}, "
                 f"finished={self.finished})")
@@ -415,7 +415,30 @@ class RequestOutputFactory:
         # Determine the type based on a condition, for example:
         if hasattr(seq_group,
                    'embeddings') and seq_group.embeddings is not None:
-            return EmbeddingRequestOutput.from_seq_group(seq_group)
+            return PoolingRequestOutput.from_seq_group(seq_group)
         else:
             return RequestOutput.from_seq_group(seq_group, use_cache,
                                                 seq_id_to_seq_group)
+
+
+def __getattr__(name: str):
+    import warnings
+
+    if name == "EmbeddingOutput":
+        msg = ("EmbeddingOutput has been renamed to PoolingOutput. "
+               "The original name will be removed in an upcoming version.")
+
+        warnings.warn(DeprecationWarning(msg), stacklevel=2)
+
+        return PoolingOutput
+
+    if name == "EmbeddingRequestOutput":
+        msg = ("EmbeddingRequestOutput has been renamed to "
+               "PoolingRequestOutput. "
+               "The original name will be removed in an upcoming version.")
+
+        warnings.warn(DeprecationWarning(msg), stacklevel=2)
+
+        return PoolingRequestOutput
+
+    raise AttributeError(f"module {__name__!r} has no attribute {name!r}")

vllm/v1/engine/async_llm.py

@@ -9,7 +9,7 @@ from vllm.inputs import INPUT_REGISTRY, InputRegistry, PromptType
 from vllm.inputs.preprocess import InputPreprocessor
 from vllm.logger import init_logger
 from vllm.lora.request import LoRARequest
-from vllm.outputs import EmbeddingRequestOutput, RequestOutput
+from vllm.outputs import PoolingRequestOutput, RequestOutput
 from vllm.pooling_params import PoolingParams
 from vllm.prompt_adapter.request import PromptAdapterRequest
 from vllm.sampling_params import SamplingParams
@@ -133,7 +133,7 @@ class AsyncLLM(EngineClient):
         trace_headers: Optional[Mapping[str, str]] = None,
         prompt_adapter_request: Optional[PromptAdapterRequest] = None,
         priority: int = 0,
-    ) -> AsyncGenerator[Union[RequestOutput, EmbeddingRequestOutput], None]:
+    ) -> AsyncGenerator[Union[RequestOutput, PoolingRequestOutput], None]:
         """Add new request to the AsyncLLM."""
 
         if self.detokenizer.is_request_active(request_id):