[V0 Deprecation] Remove pooling model support in V0 (#23434)

Signed-off-by: Woosuk Kwon <woosuk.kwon@berkeley.edu>
Signed-off-by: Max de Bayser <mbayser@br.ibm.com>
Co-authored-by: Woosuk Kwon <woosuk.kwon@berkeley.edu>

tests/distributed/test_pipeline_parallel.py

@@ -118,6 +118,8 @@ class PPTestSettings:
         multi_node_only: bool = False,
         load_format: Optional[str] = None,
     ):
+        vllm_major_versions = ["1"] if runner == "pooling" else ["0"]
+
         return PPTestSettings(
             parallel_setups=[
                 ParallelSetup(tp_size=tp_base,
@@ -126,7 +128,7 @@ class PPTestSettings:
                               chunked_prefill=False),
             ],
             distributed_backends=["mp"],
-            vllm_major_versions=["0"],
+            vllm_major_versions=vllm_major_versions,
             runner=runner,
             test_options=PPTestOptions(multi_node_only=multi_node_only,
                                        load_format=load_format),
@@ -213,7 +215,9 @@ TEXT_GENERATION_MODELS = {
 EMBEDDING_MODELS = {  # type: ignore[var-annotated]
     # [Text-only]
     "intfloat/e5-mistral-7b-instruct": PPTestSettings.fast(runner="pooling"),
-    "BAAI/bge-multilingual-gemma2": PPTestSettings.fast(runner="pooling"),
+    # TODO: re-enable when https://github.com/vllm-project/vllm/issues/23883
+    # is fixed
+    #"BAAI/bge-multilingual-gemma2": PPTestSettings.fast(runner="pooling"),
     "Qwen/Qwen2.5-Math-RM-72B": PPTestSettings.fast(
         load_format="dummy", runner="pooling"
     ),

tests/entrypoints/llm/test_classify.py

@@ -16,14 +16,6 @@ MODEL_NAME = "jason9693/Qwen2.5-1.5B-apeach"
 prompts = ["The chef prepared a delicious meal."]
 
 
-@pytest.fixture(autouse=True)
-def v1(run_with_both_engines):
-    # Simple autouse wrapper to run both engines for each test
-    # This can be promoted up to conftest.py to run for every
-    # test in a package
-    pass
-
-
 @pytest.fixture(scope="module")
 def llm():
     # pytest caches the fixture so we use weakref.proxy to

tests/entrypoints/llm/test_encode.py

@@ -27,14 +27,6 @@ TOKEN_IDS = [
 ]
 
 
-@pytest.fixture(autouse=True)
-def v1(run_with_both_engines):
-    # Simple autouse wrapper to run both engines for each test
-    # This can be promoted up to conftest.py to run for every
-    # test in a package
-    pass
-
-
 @pytest.fixture(scope="module")
 def llm():
     # pytest caches the fixture so we use weakref.proxy to

tests/entrypoints/llm/test_reward.py

@@ -16,14 +16,6 @@ MODEL_NAME = "internlm/internlm2-1_8b-reward"
 prompts = ["The chef prepared a delicious meal."]
 
 
-@pytest.fixture(autouse=True)
-def v1(run_with_both_engines):
-    # Simple autouse wrapper to run both engines for each test
-    # This can be promoted up to conftest.py to run for every
-    # test in a package
-    pass
-
-
 @pytest.fixture(scope="module")
 def llm():
     # pytest caches the fixture so we use weakref.proxy to

tests/entrypoints/llm/test_score.py

@@ -14,14 +14,6 @@ from ...models.utils import softmax
 MODEL_NAME = "tomaarsen/Qwen3-Reranker-0.6B-seq-cls"
 
 
-@pytest.fixture(autouse=True)
-def v1(run_with_both_engines):
-    # Simple autouse wrapper to run both engines for each test
-    # This can be promoted up to conftest.py to run for every
-    # test in a package
-    pass
-
-
 @pytest.fixture(scope="module")
 def llm():
     # pytest caches the fixture so we use weakref.proxy to

tests/entrypoints/offline_mode/test_offline_mode.py

@@ -32,15 +32,16 @@ MODEL_CONFIGS = [
         "tensor_parallel_size": 1,
         "tokenizer_mode": "mistral",
     },
-    {
-        "model": "sentence-transformers/all-MiniLM-L12-v2",
-        "enforce_eager": True,
-        "gpu_memory_utilization": 0.20,
-        "max_model_len": 64,
-        "max_num_batched_tokens": 64,
-        "max_num_seqs": 64,
-        "tensor_parallel_size": 1,
-    },
+    # TODO: re-enable once these tests are run with V1
+    # {
+    #     "model": "sentence-transformers/all-MiniLM-L12-v2",
+    #     "enforce_eager": True,
+    #     "gpu_memory_utilization": 0.20,
+    #     "max_model_len": 64,
+    #     "max_num_batched_tokens": 64,
+    #     "max_num_seqs": 64,
+    #     "tensor_parallel_size": 1,
+    # },
 ]
 
 

tests/entrypoints/openai/test_embedding.py

@@ -24,14 +24,6 @@ DUMMY_CHAT_TEMPLATE = """{% for message in messages %}{{message['role'] + ': ' +
 DTYPE = "bfloat16"
 
 
-@pytest.fixture(autouse=True)
-def v1(run_with_both_engines):
-    # Simple autouse wrapper to run both engines for each test
-    # This can be promoted up to conftest.py to run for every
-    # test in a package
-    pass
-
-
 @pytest.fixture(scope="module")
 def server():
     args = [

tests/entrypoints/openai/test_rerank.py

@@ -14,14 +14,6 @@ MODEL_NAME = "BAAI/bge-reranker-base"
 DTYPE = "bfloat16"
 
 
-@pytest.fixture(autouse=True)
-def v1(run_with_both_engines):
-    # Simple autouse wrapper to run both engines for each test
-    # This can be promoted up to conftest.py to run for every
-    # test in a package
-    pass
-
-
 @pytest.fixture(scope="module")
 def server():
     args = ["--enforce-eager", "--max-model-len", "100", "--dtype", DTYPE]

tests/entrypoints/openai/test_score.py

@@ -12,15 +12,6 @@ from vllm.entrypoints.openai.protocol import ScoreResponse
 
 from ...utils import RemoteOpenAIServer
 
-
-@pytest.fixture(autouse=True)
-def v1(run_with_both_engines):
-    # Simple autouse wrapper to run both engines for each test
-    # This can be promoted up to conftest.py to run for every
-    # test in a package
-    pass
-
-
 MODELS = [
     {
         "name": "BAAI/bge-reranker-v2-m3",

tests/models/language/pooling/test_embedding.py

@@ -10,14 +10,6 @@ from vllm.platforms import current_platform
 from ...utils import check_embeddings_close, check_transformers_version
 
 
-@pytest.fixture(autouse=True)
-def v1(run_with_both_engines):
-    # Simple autouse wrapper to run both engines for each test
-    # This can be promoted up to conftest.py to run for every
-    # test in a package
-    pass
-
-
 @pytest.mark.parametrize(
     "model",
     [
@@ -32,21 +24,15 @@ def v1(run_with_both_engines):
             "intfloat/e5-mistral-7b-instruct",
             # CPU v1 doesn't support sliding window
             marks=[pytest.mark.core_model]),
-        # the qwen models interfere with each other (see PR
-        # https://github.com/vllm-project/vllm/pull/18720).
-        # To avoid this problem, for now we skip v0 since it will be
-        # deprecated anyway.
         pytest.param("ssmits/Qwen2-7B-Instruct-embed-base",
-                     marks=[pytest.mark.skip_v0, pytest.mark.cpu_model]),
+                     marks=[pytest.mark.cpu_model]),
         # [Encoder-only]
         pytest.param("BAAI/bge-base-en-v1.5", marks=[pytest.mark.core_model]),
         pytest.param("sentence-transformers/all-MiniLM-L12-v2"),
         pytest.param("intfloat/multilingual-e5-small"),
-        pytest.param("Alibaba-NLP/gte-Qwen2-1.5B-instruct",
-                     marks=[pytest.mark.skip_v1]),
+        pytest.param("Alibaba-NLP/gte-Qwen2-1.5B-instruct"),
         # [Cross-Encoder]
-        pytest.param("sentence-transformers/stsb-roberta-base-v2",
-                     marks=[pytest.mark.skip_v1]),
+        pytest.param("sentence-transformers/stsb-roberta-base-v2"),
     ],
 )
 def test_models(

tests/models/language/pooling/test_reward.py

@@ -13,14 +13,6 @@ from ....conftest import HfRunner
 from ...utils import check_transformers_version
 
 
-@pytest.fixture(autouse=True)
-def v1(run_with_both_engines):
-    # Simple autouse wrapper to run both engines for each test
-    # This can be promoted up to conftest.py to run for every
-    # test in a package
-    pass
-
-
 @pytest.fixture
 def math_step_prompts():
     # ruff: noqa: E501

tests/models/language/pooling/test_scoring.py

@@ -23,15 +23,6 @@ TEXTS_2 = [
     "The capital of Germany is Berlin.",
 ]
 
-
-@pytest.fixture(autouse=True)
-def v1(run_with_both_engines):
-    # Simple autouse wrapper to run both engines for each test
-    # This can be promoted up to conftest.py to run for every
-    # test in a package
-    pass
-
-
 DTYPE = "half"
 
 

tests/models/registry.py

@@ -323,8 +323,8 @@ _TEXT_GENERATION_EXAMPLE_MODELS = {
 
 _EMBEDDING_EXAMPLE_MODELS = {
     # [Text-only]
-    "BertModel": _HfExamplesInfo("BAAI/bge-base-en-v1.5", v0_only=True),
-    "Gemma2Model": _HfExamplesInfo("BAAI/bge-multilingual-gemma2", v0_only=True),  # noqa: E501
+    "BertModel": _HfExamplesInfo("BAAI/bge-base-en-v1.5"),
+    "Gemma2Model": _HfExamplesInfo("BAAI/bge-multilingual-gemma2"),  # noqa: E501
     "GritLM": _HfExamplesInfo("parasail-ai/GritLM-7B-vllm"),
     "GteModel": _HfExamplesInfo("Snowflake/snowflake-arctic-embed-m-v2.0",
                                                trust_remote_code=True),
@@ -337,9 +337,9 @@ _EMBEDDING_EXAMPLE_MODELS = {
     "LlamaModel": _HfExamplesInfo("llama", is_available_online=False),
     "MistralModel": _HfExamplesInfo("intfloat/e5-mistral-7b-instruct"),
     "ModernBertModel": _HfExamplesInfo("Alibaba-NLP/gte-modernbert-base",
-                                trust_remote_code=True, v0_only=True),
+                                trust_remote_code=True),
     "NomicBertModel": _HfExamplesInfo("nomic-ai/nomic-embed-text-v2-moe",
-                                               trust_remote_code=True, v0_only=True),  # noqa: E501
+                                               trust_remote_code=True),  # noqa: E501
     "Qwen2Model": _HfExamplesInfo("ssmits/Qwen2-7B-Instruct-embed-base"),
     "Qwen2ForRewardModel": _HfExamplesInfo("Qwen/Qwen2.5-Math-RM-72B",
                                            max_transformers_version="4.53",
@@ -347,9 +347,9 @@ _EMBEDDING_EXAMPLE_MODELS = {
     "Qwen2ForProcessRewardModel": _HfExamplesInfo("Qwen/Qwen2.5-Math-PRM-7B",
                                                   max_transformers_version="4.53",
                                                   transformers_version_reason="HF model uses remote code that is not compatible with latest Transformers"),  # noqa: E501
-    "RobertaModel": _HfExamplesInfo("sentence-transformers/stsb-roberta-base-v2", v0_only=True),  # noqa: E501
-    "RobertaForMaskedLM": _HfExamplesInfo("sentence-transformers/all-roberta-large-v1", v0_only=True),  # noqa: E501
-    "XLMRobertaModel": _HfExamplesInfo("intfloat/multilingual-e5-small", v0_only=True),  # noqa: E501
+    "RobertaModel": _HfExamplesInfo("sentence-transformers/stsb-roberta-base-v2"),  # noqa: E501
+    "RobertaForMaskedLM": _HfExamplesInfo("sentence-transformers/all-roberta-large-v1"),  # noqa: E501
+    "XLMRobertaModel": _HfExamplesInfo("intfloat/multilingual-e5-small"),  # noqa: E501
     # [Multimodal]
     "LlavaNextForConditionalGeneration": _HfExamplesInfo("royokong/e5-v"),
     "Phi3VForCausalLM": _HfExamplesInfo("TIGER-Lab/VLM2Vec-Full",
@@ -364,20 +364,19 @@ _SEQUENCE_CLASSIFICATION_EXAMPLE_MODELS = {
     "GPT2ForSequenceClassification": _HfExamplesInfo("nie3e/sentiment-polish-gpt2-small"),  # noqa: E501
 
     # [Cross-encoder]
-    "BertForSequenceClassification": _HfExamplesInfo("cross-encoder/ms-marco-MiniLM-L-6-v2", v0_only=True),  # noqa: E501
+    "BertForSequenceClassification": _HfExamplesInfo("cross-encoder/ms-marco-MiniLM-L-6-v2"),  # noqa: E501
     "GteNewForSequenceClassification": _HfExamplesInfo("Alibaba-NLP/gte-multilingual-reranker-base",  # noqa: E501
                                                        trust_remote_code=True,
                                                        hf_overrides={
                                                            "architectures": ["GteNewForSequenceClassification"]}),# noqa: E501
-    "ModernBertForSequenceClassification": _HfExamplesInfo("Alibaba-NLP/gte-reranker-modernbert-base", v0_only=True), # noqa: E501
-    "RobertaForSequenceClassification": _HfExamplesInfo("cross-encoder/quora-roberta-base", v0_only=True),  # noqa: E501
-    "XLMRobertaForSequenceClassification": _HfExamplesInfo("BAAI/bge-reranker-v2-m3", v0_only=True),  # noqa: E501
+    "ModernBertForSequenceClassification": _HfExamplesInfo("Alibaba-NLP/gte-reranker-modernbert-base"), # noqa: E501
+    "RobertaForSequenceClassification": _HfExamplesInfo("cross-encoder/quora-roberta-base"),  # noqa: E501
+    "XLMRobertaForSequenceClassification": _HfExamplesInfo("BAAI/bge-reranker-v2-m3"),  # noqa: E501
 }
 
 _AUTOMATIC_CONVERTED_MODELS = {
     # Use as_seq_cls_model for automatic conversion
     "GemmaForSequenceClassification": _HfExamplesInfo("BAAI/bge-reranker-v2-gemma",  # noqa: E501
-                                                      v0_only=True,
                                                       hf_overrides={"architectures": ["GemmaForSequenceClassification"], # noqa: E501
                                                                     "classifier_from_token": ["Yes"],  # noqa: E501
                                                                     "method": "no_post_processing"}),  # noqa: E501

tests/worker/test_model_input.py

@@ -9,10 +9,7 @@ from vllm.attention import AttentionMetadata, AttentionMetadataBuilder
 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.model_runner import ModelInputForGPUWithSamplingMetadata
-from vllm.worker.pooling_model_runner import (
-    ModelInputForGPUWithPoolingMetadata)
 
 
 class MockAttentionBackend(AttentionBackend):
@@ -114,54 +111,3 @@ def test_model_runner_input():
     assert (received_model_input.sampling_metadata.selected_token_indices ==
             sampling_metadata.selected_token_indices)
     assert received_model_input.sampling_metadata.seq_groups is None
-
-
-def test_embedding_model_runner_input():
-    pooling_metadata = PoolingMetadata(
-        seq_groups=[[0]],
-        seq_data={},
-        prompt_lens=[1],
-    )
-    attn_metadata = AttentionMetadata(
-        num_prefills=1,
-        num_prefill_tokens=2,
-        num_decode_tokens=3,
-        slot_mapping=torch.zeros(1),
-        multi_modal_placeholder_index_maps=None,
-        enable_kv_scales_calculation=True,
-    )
-    model_input = ModelInputForGPUWithPoolingMetadata(
-        input_tokens=torch.ones(10),
-        input_positions=torch.ones(10),
-        pooling_metadata=pooling_metadata,
-        attn_metadata=attn_metadata)
-
-    assert isinstance(model_input, ModelInputForGPUWithPoolingMetadata)
-
-    # Test round trip serialization.
-    tensor_dict = model_input.as_broadcastable_tensor_dict()
-    attn_backend = MockAttentionBackend()
-    received_model_input = (
-        ModelInputForGPUWithPoolingMetadata.from_broadcasted_tensor_dict(
-            tensor_dict, attn_backend=attn_backend))
-    # Check that received copy has correct values.
-    assert isinstance(received_model_input,
-                      ModelInputForGPUWithPoolingMetadata)
-    assert received_model_input.input_tokens is not None
-    assert (
-        received_model_input.input_tokens == model_input.input_tokens).all()
-    assert received_model_input.input_positions is not None
-    assert (received_model_input.input_positions == model_input.input_positions
-            ).all()
-    assert received_model_input.multi_modal_kwargs is None
-    assert (received_model_input.multi_modal_kwargs ==
-            model_input.multi_modal_kwargs)
-    assert received_model_input.lora_requests is None
-    assert received_model_input.lora_requests == model_input.lora_requests
-    assert received_model_input.lora_mapping is None
-    assert received_model_input.lora_mapping == model_input.lora_mapping
-    for field in dataclasses.fields(AttentionMetadata):
-        assert getattr(received_model_input.attn_metadata, field.name,
-                       None) == getattr(attn_metadata, field.name, None)
-    # Pooling metadata is not broadcast.
-    assert received_model_input.pooling_metadata is None

vllm/core/scheduler.py

@@ -1591,7 +1591,6 @@ class Scheduler:
                     encoder_seq_data=encoder_seq_data,
                     cross_block_table=cross_block_table,
                     state=seq_group.state,
-                    token_type_ids=seq_group.token_type_ids,
                     # `multi_modal_data` will only be present for the 1st comm
                     # between engine and worker.
                     # the subsequent comms can still use delta, but

vllm/engine/arg_utils.py

@@ -1566,8 +1566,7 @@ class EngineArgs:
                 use_spec_decode = self.speculative_config is not None
 
                 if (is_gpu and not use_sliding_window and not use_spec_decode
-                        and not self.enable_lora
-                        and model_config.runner_type != "pooling"):
+                        and not self.enable_lora):
                     self.enable_chunked_prefill = True
                     logger.warning(
                         "Chunked prefill is enabled by default for models "
@@ -1585,10 +1584,6 @@ class EngineArgs:
                 "OOM during the initial memory profiling phase, or result "
                 "in low performance due to small KV cache size. Consider "
                 "setting --max-model-len to a smaller value.", max_model_len)
-        elif (self.enable_chunked_prefill
-              and model_config.runner_type == "pooling"):
-            msg = "Chunked prefill is not supported for pooling models"
-            raise ValueError(msg)
 
         # if using prefix caching, we must set a hash algo
         if self.enable_prefix_caching:

vllm/engine/async_llm_engine.py

@@ -72,8 +72,8 @@ STOP_ITERATION = Exception()  # Sentinel
 
 
 class AsyncStream:
-    """A stream of RequestOutputs or PoolingRequestOutputs for a request
-    that can be iterated over asynchronously via an async generator."""
+    """A stream of RequestOutputs for a request that can be iterated over
+    asynchronously via an async generator."""
 
     def __init__(self, request_id: str, cancel: Callable[[str], None]) -> None:
         self.request_id = request_id
@@ -81,8 +81,7 @@ class AsyncStream:
         self._queue: asyncio.Queue = asyncio.Queue()
         self._finished = False
 
-    def put(self, item: Union[RequestOutput, PoolingRequestOutput,
-                              Exception]) -> None:
+    def put(self, item: Union[RequestOutput, Exception]) -> None:
         if not self._finished:
             self._queue.put_nowait(item)
 
@@ -99,9 +98,7 @@ class AsyncStream:
     def finished(self) -> bool:
         return self._finished
 
-    async def generator(
-        self
-    ) -> AsyncGenerator[Union[RequestOutput, PoolingRequestOutput], None]:
+    async def generator(self) -> AsyncGenerator[RequestOutput, None]:
         try:
             while True:
                 result = await self._queue.get()
@@ -151,8 +148,7 @@ class RequestTracker:
                 self.abort_request(rid, exception=exc)
 
     def process_request_output(self,
-                               request_output: Union[RequestOutput,
-                                                     PoolingRequestOutput],
+                               request_output: RequestOutput,
                                *,
                                verbose: bool = False) -> None:
         """Process a request output from the engine."""
@@ -261,9 +257,7 @@ class _AsyncLLMEngine(LLMEngine):
     def __init__(self, *args, **kwargs):
         super().__init__(*args, **kwargs)
 
-    async def step_async(
-        self, virtual_engine: int
-    ) -> List[Union[RequestOutput, PoolingRequestOutput]]:
+    async def step_async(self, virtual_engine: int) -> List[RequestOutput]:
         """Performs one decoding iteration and returns newly generated results.
         The workers are ran asynchronously if possible.
 
@@ -405,7 +399,7 @@ class _AsyncLLMEngine(LLMEngine):
         self,
         request_id: str,
         prompt: PromptType,
-        params: Union[SamplingParams, PoolingParams],
+        params: SamplingParams,
         arrival_time: Optional[float] = None,
         lora_request: Optional[LoRARequest] = None,
         trace_headers: Optional[Mapping[str, str]] = None,
@@ -779,14 +773,14 @@ class AsyncLLMEngine(EngineClient):
         self,
         request_id: str,
         prompt: PromptType,
-        params: Union[SamplingParams, PoolingParams],
+        params: SamplingParams,
         arrival_time: Optional[float] = None,
         lora_request: Optional[LoRARequest] = None,
         trace_headers: Optional[Mapping[str, str]] = None,
         priority: int = 0,
         data_parallel_rank: Optional[int] = None,
         tokenization_kwargs: Optional[dict[str, Any]] = None,
-    ) -> AsyncGenerator[Union[RequestOutput, PoolingRequestOutput], None]:
+    ) -> AsyncGenerator[RequestOutput, None]:
         if not self.is_running:
             if self.start_engine_loop:
                 self.start_background_loop()
@@ -908,7 +902,7 @@ class AsyncLLMEngine(EngineClient):
             await self.abort(request_id)
             raise
 
-    async def encode(
+    def encode(
         self,
         prompt: PromptType,
         pooling_params: PoolingParams,
@@ -918,85 +912,8 @@ class AsyncLLMEngine(EngineClient):
         priority: int = 0,
         tokenization_kwargs: Optional[dict[str, Any]] = None,
     ) -> AsyncGenerator[PoolingRequestOutput, None]:
-        """Generate outputs for a request from a pooling model.
-
-        Generate outputs for a request. This method is a coroutine. It adds the
-        request into the waiting queue of the LLMEngine and streams the outputs
-        from the LLMEngine to the caller.
-
-        Args:
-            prompt: The prompt to the LLM. See
-                [`PromptType`][vllm.inputs.PromptType] for more details about
-                the format of each input.
-            pooling_params: The pooling parameters of the request.
-            request_id: The unique id of the request.
-            lora_request: LoRA request to use for generation, if any.
-            trace_headers: OpenTelemetry trace headers.
-            priority: The priority of the request.
-                Only applicable with priority scheduling.
-
-        Yields:
-            The output `PoolingRequestOutput` objects from the LLMEngine
-            for the request.
-
-        Details:
-            - If the engine is not running, start the background loop,
-                which iteratively invokes
-                [`vllm.engine.async_llm_engine.AsyncLLMEngine.engine_step`][]
-                to process the waiting requests.
-            - Add the request to the engine's `RequestTracker`.
-                On the next background loop, this request will be sent to
-                the underlying engine.
-                Also, a corresponding `AsyncStream` will be created.
-            - Wait for the request outputs from `AsyncStream` and yield them.
-
-        Example:
-        ```
-        # Please refer to entrypoints/api_server.py for
-        # the complete example.
-
-        # initialize the engine and the example input
-        # note that engine_args here is AsyncEngineArgs instance
-        engine = AsyncLLMEngine.from_engine_args(engine_args)
-        example_input = {
-            "input": "What is LLM?",
-            "request_id": 0,
-        }
-
-        # start the generation
-        results_generator = engine.encode(
-        example_input["input"],
-        PoolingParams(),
-        example_input["request_id"])
-
-        # get the results
-        final_output = None
-        async for request_output in results_generator:
-            if await request.is_disconnected():
-                # Abort the request if the client disconnects.
-                await engine.abort(request_id)
-                # Return or raise an error
-                ...
-            final_output = request_output
-
-        # Process and return the final output
-        ...
-        ```
-        """
-        try:
-            async for output in await self.add_request(
-                    request_id,
-                    prompt,
-                    pooling_params,
-                    lora_request=lora_request,
-                    trace_headers=trace_headers,
-                    priority=priority,
-                    tokenization_kwargs=tokenization_kwargs,
-            ):
-                yield LLMEngine.validate_output(output, PoolingRequestOutput)
-        except asyncio.CancelledError:
-            await self.abort(request_id)
-            raise
+        raise NotImplementedError(
+            "Pooling models are not supported in vLLM V0")
 
     async def abort(self, request_id: Union[str, Iterable[str]]) -> None:
         """Abort a request.
@@ -1104,8 +1021,8 @@ class AsyncLLMEngine(EngineClient):
     async def is_sleeping(self) -> bool:
         return self.engine.is_sleeping()
 
-    async def add_lora(self, lora_request: LoRARequest) -> None:
-        self.engine.add_lora(lora_request)
+    async def add_lora(self, lora_request: LoRARequest) -> bool:
+        return self.engine.add_lora(lora_request)
 
     async def collective_rpc(self,
                              method: str,

vllm/engine/llm_engine.py

@@ -40,12 +40,11 @@ from vllm.multimodal.cache import processor_only_cache_from_config
 from vllm.multimodal.processing import EncDecMultiModalProcessor
 from vllm.outputs import (PoolingRequestOutput, RequestOutput,
                           RequestOutputFactory)
-from vllm.pooling_params import PoolingParams
 from vllm.sampling_params import RequestOutputKind, SamplingParams
 from vllm.sequence import (ExecuteModelRequest, ParallelSampleSequenceGroup,
-                           PoolingSequenceGroupOutput, Sequence, SequenceGroup,
-                           SequenceGroupBase, SequenceGroupMetadata,
-                           SequenceGroupOutput, SequenceStatus)
+                           Sequence, SequenceGroup, SequenceGroupBase,
+                           SequenceGroupMetadata, SequenceGroupOutput,
+                           SequenceStatus)
 from vllm.tracing import (SpanAttributes, SpanKind, extract_trace_context,
                           init_tracer)
 from vllm.transformers_utils.detokenizer import Detokenizer
@@ -93,8 +92,7 @@ class SchedulerContext:
 
     def __init__(self) -> None:
         self.output_queue: Deque[OutputData] = deque()
-        self.request_outputs: List[Union[RequestOutput,
-                                         PoolingRequestOutput]] = []
+        self.request_outputs: List[RequestOutput] = []
         self.seq_group_metadata_list: Optional[
             List[SequenceGroupMetadata]] = None
         self.scheduler_outputs: Optional[SchedulerOutputs] = None
@@ -261,7 +259,6 @@ class LLMEngine:
 
         self.model_executor = executor_class(vllm_config=vllm_config)
 
-        if self.model_config.runner_type != "pooling":
         self._initialize_kv_caches()
 
         # If usage stat is enabled, collect relevant info.
@@ -541,7 +538,7 @@ class LLMEngine:
         self,
         request_id: str,
         processed_inputs: ProcessorInputs,
-        params: Union[SamplingParams, PoolingParams],
+        params: SamplingParams,
         arrival_time: float,
         lora_request: Optional[LoRARequest],
         trace_headers: Optional[Mapping[str, str]] = None,
@@ -577,7 +574,7 @@ class LLMEngine:
         encoder_seq = (None if encoder_inputs is None else Sequence(
             seq_id, encoder_inputs, block_size, eos_token_id, lora_request))
 
-        # Create a SequenceGroup based on SamplingParams or PoolingParams
+        # Create a SequenceGroup based on SamplingParams
         if isinstance(params, SamplingParams):
             seq_group = self._create_sequence_group_with_sampling(
                 request_id,
@@ -588,18 +585,8 @@ class LLMEngine:
                 trace_headers=trace_headers,
                 encoder_seq=encoder_seq,
                 priority=priority)
-        elif isinstance(params, PoolingParams):
-            seq_group = self._create_sequence_group_with_pooling(
-                request_id,
-                seq,
-                params,
-                arrival_time=arrival_time,
-                lora_request=lora_request,
-                encoder_seq=encoder_seq,
-                priority=priority)
         else:
-            raise ValueError(
-                "Either SamplingParams or PoolingParams must be provided.")
+            raise ValueError("SamplingParams must be provided.")
 
         # Add the sequence group to the scheduler with least unfinished seqs.
         costs = [
@@ -618,7 +605,7 @@ class LLMEngine:
         self,
         request_id: str,
         prompt: PromptType,
-        params: Union[SamplingParams, PoolingParams],
+        params: SamplingParams,
         arrival_time: Optional[float] = None,
         lora_request: Optional[LoRARequest] = None,
         tokenization_kwargs: Optional[dict[str, Any]] = None,
@@ -636,9 +623,8 @@ class LLMEngine:
             prompt: The prompt to the LLM. See
                 [PromptType][vllm.inputs.PromptType]
                 for more details about the format of each input.
-            params: Parameters for sampling or pooling.
+            params: Parameters for sampling.
                 [SamplingParams][vllm.SamplingParams] for text generation.
-                [PoolingParams][vllm.PoolingParams] for pooling.
             arrival_time: The arrival time of the request. If None, we use
                 the current monotonic time.
             lora_request: The LoRA request to add.
@@ -760,29 +746,6 @@ class LLMEngine:
 
         return seq_group
 
-    def _create_sequence_group_with_pooling(
-        self,
-        request_id: str,
-        seq: Sequence,
-        pooling_params: PoolingParams,
-        arrival_time: float,
-        lora_request: Optional[LoRARequest],
-        encoder_seq: Optional[Sequence] = None,
-        priority: int = 0,
-    ) -> SequenceGroup:
-        """Creates a SequenceGroup with PoolingParams."""
-        # Defensive copy of PoolingParams, which are used by the pooler
-        pooling_params = pooling_params.clone()
-        # Create the sequence group.
-        seq_group = SequenceGroup(request_id=request_id,
-                                  seqs=[seq],
-                                  arrival_time=arrival_time,
-                                  lora_request=lora_request,
-                                  pooling_params=pooling_params,
-                                  encoder_seq=encoder_seq,
-                                  priority=priority)
-        return seq_group
-
     def abort_request(self, request_id: Union[str, Iterable[str]]) -> None:
         """Aborts a request(s) with the given ID.
 
@@ -856,18 +819,6 @@ class LLMEngine:
             success = success and scheduler.reset_prefix_cache(device)
         return success
 
-    @staticmethod
-    def _process_sequence_group_outputs(
-        seq_group: SequenceGroup,
-        outputs: List[PoolingSequenceGroupOutput],
-    ) -> None:
-        seq_group.pooled_data = outputs[0].data
-
-        for seq in seq_group.get_seqs():
-            seq.status = SequenceStatus.FINISHED_STOPPED
-
-        return
-
     def _process_model_outputs(self,
                                ctx: SchedulerContext,
                                request_id: Optional[str] = None) -> None:
@@ -962,13 +913,10 @@ class LLMEngine:
                             seq_group.metrics.model_execute_time = (
                                 o.model_execute_time)
 
-            if self.model_config.runner_type == "pooling":
-                self._process_sequence_group_outputs(seq_group, output)
-            else:
             self.output_processor.process_prompt_logprob(seq_group, output)
             if seq_group_meta.do_sample:
-                    self.output_processor.process_outputs(
-                        seq_group, output, is_async)
+                self.output_processor.process_outputs(seq_group, output,
+                                                      is_async)
 
             if seq_group.is_finished():
                 finished_now.append(i)
@@ -1090,7 +1038,7 @@ class LLMEngine:
                 seq.append_token_id(sample.output_token, sample.logprobs,
                                     sample.output_embed)
 
-    def step(self) -> List[Union[RequestOutput, PoolingRequestOutput]]:
+    def step(self) -> List[RequestOutput]:
         """Performs one decoding iteration and returns newly generated results.
 
         <figure markdown="span">

vllm/engine/multiprocessing/__init__.py

@@ -120,6 +120,7 @@ class RPCLoadAdapterRequest:
 @dataclass
 class RPCAdapterLoadedResponse:
     request_id: str
+    lora_loaded: bool
 
 
 RPC_REQUEST_T = Union[RPCProcessRequest, RPCAbortRequest, RPCStartupRequest,

vllm/engine/multiprocessing/client.py

@@ -6,7 +6,7 @@ import copy
 import pickle
 from contextlib import contextmanager, suppress
 from typing import (Any, AsyncGenerator, Dict, Iterable, Iterator, List,
-                    Mapping, Optional, Union, cast)
+                    Mapping, Optional, Union)
 
 import cloudpickle
 import psutil
@@ -477,10 +477,8 @@ class MQLLMEngineClient(EngineClient):
                 Any priority other than 0 will lead to an error if the
                 scheduling policy is not "priority".
         """
-        return cast(
-            AsyncGenerator[RequestOutput, None],
-            self._process_request(prompt, sampling_params, request_id,
-                                  lora_request, trace_headers, priority))
+        return self._process_request(prompt, sampling_params, request_id,
+                                     lora_request, trace_headers, priority)
 
     def encode(
         self,
@@ -490,45 +488,20 @@ class MQLLMEngineClient(EngineClient):
         lora_request: Optional[LoRARequest] = None,
         trace_headers: Optional[Mapping[str, str]] = None,
         priority: int = 0,
+        tokenization_kwargs: Optional[dict[str, Any]] = None,
     ) -> AsyncGenerator[PoolingRequestOutput, None]:
-        """Generate outputs for a request from a pooling model.
-
-        Generate outputs for a request. This method is a coroutine. It adds the
-        request into the waiting queue of the LLMEngine and streams the outputs
-        from the LLMEngine to the caller.
-
-        Args:
-            prompt: The prompt to the LLM. See
-                [`PromptType`][vllm.inputs.PromptType] for more details about
-                the format of each input.
-            pooling_params: The pooling parameters of the request.
-            request_id: The unique id of the request.
-            lora_request: LoRA request to use for generation, if any.
-            trace_headers: OpenTelemetry trace headers.
-
-        Yields:
-            The output `PoolingRequestOutput` objects from the LLMEngine
-            for the request.
-        """
-        return cast(
-            AsyncGenerator[PoolingRequestOutput, None],
-            self._process_request(prompt,
-                                  pooling_params,
-                                  request_id,
-                                  lora_request,
-                                  trace_headers,
-                                  priority=priority))
+        raise NotImplementedError(
+            "Pooling models are not supported in vLLM V0")
 
     async def _process_request(
         self,
         prompt: PromptType,
-        params: Union[SamplingParams, PoolingParams],
+        params: SamplingParams,
         request_id: str,
         lora_request: Optional[LoRARequest] = None,
         trace_headers: Optional[Mapping[str, str]] = None,
         priority: int = 0,
-    ) -> Union[AsyncGenerator[RequestOutput, None], AsyncGenerator[
-            PoolingRequestOutput, None]]:
+    ) -> AsyncGenerator[RequestOutput, None]:
         """Send an RPCGenerateRequest to the RPCServer and stream responses."""
 
         # If already dead, error out.
@@ -547,7 +520,7 @@ class MQLLMEngineClient(EngineClient):
         try:
             # 2) Detach logits processors so that they can be pickled
             # separately (may require cloudpickle which is slower)
-            if isinstance(params, SamplingParams) and params.logits_processors:
+            if params.logits_processors:
                 # Defensive shallow copy
                 params = copy.copy(params)
                 logits_processors = params.logits_processors
@@ -646,13 +619,14 @@ class MQLLMEngineClient(EngineClient):
             raise request_output
         return request_output.is_sleeping
 
-    async def add_lora(self, lora_request: LoRARequest) -> None:
+    async def add_lora(self, lora_request: LoRARequest) -> bool:
         """Load a new LoRA adapter into the engine for future requests."""
         # Uses the same I/O as generate requests
         request = RPCLoadAdapterRequest(lora_request)
 
         # Create output queue for this request.
-        queue: asyncio.Queue[Union[None, BaseException]] = asyncio.Queue()
+        queue: asyncio.Queue[Union[
+            BaseException, RPCAdapterLoadedResponse]] = asyncio.Queue()
         self.output_queues[request.request_id] = queue
 
         # Send the request
@@ -666,3 +640,4 @@ class MQLLMEngineClient(EngineClient):
         # Raise on error, otherwise happily return None
         if isinstance(request_output, BaseException):
             raise request_output
+        return request_output.lora_loaded