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

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

Author: DarkLight1337

examples/offline_inference_embedding.py

@@ -10,7 +10,7 @@
 
 # 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 @@
 
 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 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.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.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
 
@@ -25,12 +25,35 @@
     "SamplingParams",
     "RequestOutput",
     "CompletionOutput",
-    "EmbeddingOutput",
-    "EmbeddingRequestOutput",
+    "PoolingOutput",
+    "PoolingRequestOutput",
     "LLMEngine",
     "EngineArgs",
     "AsyncLLMEngine",
     "AsyncEngineArgs",
     "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 @@ def _resolve_task(
             # 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.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 @@ def _log_task_completion(task: asyncio.Task,
 
 
 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 @@ def __init__(self, request_id: str, cancel: Callable[[str], None]) -> None:
         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 @@ def finished(self) -> bool:
 
     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 @@ def propagate_exception(self,
 
     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 @@ def __init__(self, *args, **kwargs):
 
     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 @@ def add_request(
         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 @@ def add_request(
         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 @@ async def add_request(
         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 @@ async def encode(
         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 @@ async def encode(
                 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 @@ async def encode(
                 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 @@
     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 @@ def _advance_to_next_step(
                 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.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 @@ def encode(
         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 @@ def encode(
         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 @@ def encode(
         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 @@ def encode(
             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 @@ def encode(
                 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 @@ async def _process_request(
         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.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 @@ def encode(
         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."""
         ...