[Refactor] Separate sequence and token pooling types (#32026)

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

tests/model_executor/test_model_load_with_params.py

@@ -46,7 +46,8 @@ def test_model_loading_with_params(vllm_runner, monkeypatch):
         assert model_config.encoder_config["do_lower_case"]
 
         # asserts on the pooling config files
-        assert model_config.pooler_config.pooling_type == "CLS"
+        assert model_config.pooler_config.seq_pooling_type == "CLS"
+        assert model_config.pooler_config.tok_pooling_type == "ALL"
         assert model_config.pooler_config.normalize
 
         # asserts on the tokenizer loaded
@@ -90,7 +91,8 @@ def test_roberta_model_loading_with_params(vllm_runner, monkeypatch):
         assert not model_config.encoder_config["do_lower_case"]
 
         # asserts on the pooling config files
-        assert model_config.pooler_config.pooling_type == "MEAN"
+        assert model_config.pooler_config.seq_pooling_type == "MEAN"
+        assert model_config.pooler_config.tok_pooling_type == "ALL"
         assert model_config.pooler_config.normalize
 
         # asserts on the tokenizer loaded

tests/models/language/pooling/test_embedding.py

@@ -54,7 +54,7 @@ def test_models(
     vllm_extra_kwargs = {}
     if model == "ssmits/Qwen2-7B-Instruct-embed-base":
         vllm_extra_kwargs["pooler_config"] = PoolerConfig(
-            pooling_type="MEAN", normalize=False
+            seq_pooling_type="MEAN", normalize=False
         )
 
     max_model_len: int | None = 512

tests/models/language/pooling/test_mm_classifier_conversion.py

@@ -88,7 +88,7 @@ def test_gemma_multimodal(
         convert="classify",
         load_format="auto",
         hf_overrides=update_config,
-        pooler_config=PoolerConfig(pooling_type="LAST"),
+        pooler_config=PoolerConfig(seq_pooling_type="LAST"),
         max_model_len=512,
         enforce_eager=True,
         tensor_parallel_size=1,

tests/models/language/pooling_mteb_test/mteb_embed_utils.py

@@ -162,8 +162,11 @@ def mteb_test_embed_models(
             assert model_info.architecture in model_config.architectures
 
         # Confirm whether the important configs in model_config are correct.
-        if model_info.pooling_type is not None:
-            assert model_config.pooler_config.pooling_type == model_info.pooling_type
+        pooler_config = model_config.pooler_config
+        if model_info.seq_pooling_type is not None:
+            assert pooler_config.seq_pooling_type == model_info.seq_pooling_type
+        if model_info.tok_pooling_type is not None:
+            assert pooler_config.tok_pooling_type == model_info.tok_pooling_type
         if model_info.attn_type is not None:
             assert model_config.attn_type == model_info.attn_type
         if model_info.is_prefix_caching_supported is not None:

tests/models/language/pooling_mteb_test/mteb_score_utils.py

@@ -254,8 +254,11 @@ def mteb_test_rerank_models(
         assert model_config.hf_config.num_labels == 1
 
         # Confirm whether the important configs in model_config are correct.
-        if model_info.pooling_type is not None:
-            assert model_config.pooler_config.pooling_type == model_info.pooling_type
+        pooler_config = model_config.pooler_config
+        if model_info.seq_pooling_type is not None:
+            assert pooler_config.seq_pooling_type == model_info.seq_pooling_type
+        if model_info.tok_pooling_type is not None:
+            assert pooler_config.tok_pooling_type == model_info.tok_pooling_type
         if model_info.attn_type is not None:
             assert model_config.attn_type == model_info.attn_type
         if model_info.is_prefix_caching_supported is not None:

tests/models/language/pooling_mteb_test/test_baai.py

@@ -17,7 +17,7 @@ MODELS = [
         "BAAI/bge-base-en",
         architecture="BertModel",
         mteb_score=0.779336792,
-        pooling_type="CLS",
+        seq_pooling_type="CLS",
         attn_type="encoder_only",
         is_prefix_caching_supported=False,
         is_chunked_prefill_supported=False,
@@ -54,7 +54,7 @@ MODELS = [
         "BAAI/bge-m3",
         architecture="XLMRobertaModel",
         mteb_score=0.787343078,
-        pooling_type="CLS",
+        seq_pooling_type="CLS",
         attn_type="encoder_only",
         is_prefix_caching_supported=False,
         is_chunked_prefill_supported=False,
@@ -65,7 +65,7 @@ MODELS = [
         "BAAI/bge-code-v1",
         architecture="Qwen2Model",
         mteb_score=0.75724465,
-        pooling_type="LAST",
+        seq_pooling_type="LAST",
         attn_type="decoder",
         is_prefix_caching_supported=True,
         is_chunked_prefill_supported=True,
@@ -79,7 +79,7 @@ RERANK_MODELS = [
         "BAAI/bge-reranker-base",
         architecture="XLMRobertaForSequenceClassification",
         mteb_score=0.32398,
-        pooling_type="CLS",
+        seq_pooling_type="CLS",
         attn_type="encoder_only",
         is_prefix_caching_supported=False,
         is_chunked_prefill_supported=False,

tests/models/language/pooling_mteb_test/test_bge_reranker_v2_gemma.py

@@ -26,7 +26,7 @@ RERANK_MODELS = [
             "method": "no_post_processing",
         },
         mteb_score=0.33757,
-        pooling_type="LAST",
+        seq_pooling_type="LAST",
         attn_type="decoder",
         is_prefix_caching_supported=True,
         is_chunked_prefill_supported=True,

tests/models/language/pooling_mteb_test/test_cross_encoder.py

@@ -12,7 +12,7 @@ RERANK_MODELS = [
     RerankModelInfo(
         "cross-encoder/ms-marco-TinyBERT-L-2-v2",
         architecture="BertForSequenceClassification",
-        pooling_type="CLS",
+        seq_pooling_type="CLS",
         attn_type="encoder_only",
         is_prefix_caching_supported=False,
         is_chunked_prefill_supported=False,
@@ -21,7 +21,7 @@ RERANK_MODELS = [
     RerankModelInfo(
         "tomaarsen/Qwen3-Reranker-0.6B-seq-cls",
         architecture="Qwen3ForSequenceClassification",
-        pooling_type="LAST",
+        seq_pooling_type="LAST",
         attn_type="decoder",
         is_prefix_caching_supported=True,
         is_chunked_prefill_supported=True,

tests/models/language/pooling_mteb_test/test_gte.py

@@ -18,7 +18,7 @@ MODELS = [
         "thenlper/gte-large",
         mteb_score=0.76807651,
         architecture="BertModel",
-        pooling_type="MEAN",
+        seq_pooling_type="MEAN",
         attn_type="encoder_only",
         is_prefix_caching_supported=False,
         is_chunked_prefill_supported=False,
@@ -44,7 +44,7 @@ MODELS = [
         architecture="GteNewModel",
         mteb_score=0.775074696,
         hf_overrides={"architectures": ["GteNewModel"]},
-        pooling_type="CLS",
+        seq_pooling_type="CLS",
         attn_type="encoder_only",
         is_prefix_caching_supported=False,
         is_chunked_prefill_supported=False,
@@ -67,7 +67,7 @@ MODELS = [
         "Alibaba-NLP/gte-Qwen2-1.5B-instruct",
         mteb_score=0.758473459018872,
         architecture="Qwen2ForCausalLM",
-        pooling_type="LAST",
+        seq_pooling_type="LAST",
         attn_type="encoder_only",
         is_prefix_caching_supported=False,
         is_chunked_prefill_supported=False,
@@ -78,7 +78,7 @@ MODELS = [
         "Alibaba-NLP/gte-modernbert-base",
         mteb_score=0.748193353,
         architecture="ModernBertModel",
-        pooling_type="CLS",
+        seq_pooling_type="CLS",
         attn_type="encoder_only",
         is_prefix_caching_supported=False,
         is_chunked_prefill_supported=False,
@@ -89,7 +89,7 @@ MODELS = [
         "Qwen/Qwen3-Embedding-0.6B",
         mteb_score=0.771163695,
         architecture="Qwen3ForCausalLM",
-        pooling_type="LAST",
+        seq_pooling_type="LAST",
         attn_type="decoder",
         is_prefix_caching_supported=True,
         is_chunked_prefill_supported=True,
@@ -108,7 +108,7 @@ RERANK_MODELS = [
         "Alibaba-NLP/gte-reranker-modernbert-base",
         mteb_score=0.33386,
         architecture="ModernBertForSequenceClassification",
-        pooling_type="CLS",
+        seq_pooling_type="CLS",
         attn_type="encoder_only",
         is_prefix_caching_supported=False,
         is_chunked_prefill_supported=False,
@@ -119,7 +119,7 @@ RERANK_MODELS = [
         mteb_score=0.33062,
         architecture="GteNewForSequenceClassification",
         hf_overrides={"architectures": ["GteNewForSequenceClassification"]},
-        pooling_type="CLS",
+        seq_pooling_type="CLS",
         attn_type="encoder_only",
         is_prefix_caching_supported=False,
         is_chunked_prefill_supported=False,

tests/models/language/pooling_mteb_test/test_intfloat.py

@@ -13,7 +13,7 @@ MODELS = [
         "intfloat/e5-small",
         architecture="BertModel",
         mteb_score=0.742285423,
-        pooling_type="MEAN",
+        seq_pooling_type="MEAN",
         attn_type="encoder_only",
         is_prefix_caching_supported=False,
         is_chunked_prefill_supported=False,
@@ -29,7 +29,7 @@ MODELS = [
         "intfloat/multilingual-e5-base",
         architecture="XLMRobertaModel",
         mteb_score=0.779325955,
-        pooling_type="MEAN",
+        seq_pooling_type="MEAN",
         attn_type="encoder_only",
         is_prefix_caching_supported=False,
         is_chunked_prefill_supported=False,

tests/models/language/pooling_mteb_test/test_jina.py

@@ -24,7 +24,7 @@ EMBEDDING_MODELS = [
         mteb_score=0.824413164,
         architecture="XLMRobertaModel",
         is_matryoshka=True,
-        pooling_type="MEAN",
+        seq_pooling_type="MEAN",
         attn_type="encoder_only",
         is_prefix_caching_supported=False,
         is_chunked_prefill_supported=False,
@@ -36,7 +36,7 @@ RERANK_MODELS = [
         "jinaai/jina-reranker-v2-base-multilingual",
         mteb_score=0.33643,
         architecture="XLMRobertaForSequenceClassification",
-        pooling_type="CLS",
+        seq_pooling_type="CLS",
         attn_type="encoder_only",
         is_prefix_caching_supported=False,
         is_chunked_prefill_supported=False,

tests/models/language/pooling_mteb_test/test_mxbai_rerank.py

@@ -24,7 +24,7 @@ RERANK_MODELS = [
         "mixedbread-ai/mxbai-rerank-base-v2",
         architecture="Qwen2ForSequenceClassification",
         hf_overrides=mxbai_rerank_hf_overrides,
-        pooling_type="LAST",
+        seq_pooling_type="LAST",
         attn_type="decoder",
         is_prefix_caching_supported=True,
         is_chunked_prefill_supported=True,

tests/models/language/pooling_mteb_test/test_nemotron.py

@@ -19,7 +19,7 @@ EMBEDDING_MODELS = [
         "nvidia/llama-nemotron-embed-1b-v2",
         architecture="LlamaBidirectionalModel",
         mteb_score=0.689164662128673,
-        pooling_type="MEAN",
+        seq_pooling_type="MEAN",
         attn_type="encoder_only",
         is_prefix_caching_supported=False,
         is_chunked_prefill_supported=False,
@@ -32,7 +32,7 @@ RERANK_MODELS = [
         architecture="LlamaBidirectionalForSequenceClassification",
         chat_template_name="nemotron-rerank.jinja",
         mteb_score=0.33994,
-        pooling_type="MEAN",
+        seq_pooling_type="MEAN",
         attn_type="encoder_only",
         is_prefix_caching_supported=False,
         is_chunked_prefill_supported=False,

tests/models/language/pooling_mteb_test/test_nomic.py

@@ -14,7 +14,7 @@ MODELS = [
         architecture="NomicBertModel",
         mteb_score=0.737568559,
         enable_test=True,
-        pooling_type="MEAN",
+        seq_pooling_type="MEAN",
         attn_type="encoder_only",
         is_prefix_caching_supported=False,
         is_chunked_prefill_supported=False,
@@ -32,7 +32,7 @@ MODELS = [
         architecture="NomicBertModel",
         mteb_score=0.715488912,
         enable_test=True,
-        pooling_type="MEAN",
+        seq_pooling_type="MEAN",
         attn_type="encoder_only",
         is_prefix_caching_supported=False,
         is_chunked_prefill_supported=False,

tests/models/language/pooling_mteb_test/test_qwen3_reranker.py

@@ -27,7 +27,7 @@ RERANK_MODELS = [
         architecture="Qwen3ForSequenceClassification",
         hf_overrides=qwen3_reranker_hf_overrides,
         chat_template_name="qwen3_reranker.jinja",
-        pooling_type="LAST",
+        seq_pooling_type="LAST",
         attn_type="decoder",
         is_prefix_caching_supported=True,
         is_chunked_prefill_supported=True,

tests/models/language/pooling_mteb_test/test_snowflake_arctic_embed.py

@@ -14,7 +14,7 @@ MODELS = [
         is_matryoshka=False,
         architecture="BertModel",
         mteb_score=0.714927797,
-        pooling_type="CLS",
+        seq_pooling_type="CLS",
         attn_type="encoder_only",
         is_prefix_caching_supported=False,
         is_chunked_prefill_supported=False,
@@ -37,7 +37,7 @@ MODELS = [
         is_matryoshka=False,
         architecture="NomicBertModel",
         mteb_score=0.681146831,
-        pooling_type="CLS",
+        seq_pooling_type="CLS",
         attn_type="encoder_only",
         is_prefix_caching_supported=False,
         is_chunked_prefill_supported=False,
@@ -54,7 +54,7 @@ MODELS = [
         is_matryoshka=True,
         architecture="BertModel",
         mteb_score=0.649088363,
-        pooling_type="CLS",
+        seq_pooling_type="CLS",
         attn_type="encoder_only",
         is_prefix_caching_supported=False,
         is_chunked_prefill_supported=False,
@@ -65,7 +65,7 @@ MODELS = [
         is_matryoshka=True,
         architecture="XLMRobertaModel",
         mteb_score=0.712258299,
-        pooling_type="CLS",
+        seq_pooling_type="CLS",
         attn_type="encoder_only",
         is_prefix_caching_supported=False,
         is_chunked_prefill_supported=False,
@@ -76,7 +76,7 @@ MODELS = [
         is_matryoshka=True,
         architecture="GteModel",
         mteb_score=0.706622444,
-        pooling_type="CLS",
+        seq_pooling_type="CLS",
         attn_type="encoder_only",
         is_prefix_caching_supported=False,
         is_chunked_prefill_supported=False,

tests/models/language/pooling_mteb_test/test_st_projector.py

@@ -14,7 +14,7 @@ ST_PROJECTOR_MODELS = [
         "TencentBAC/Conan-embedding-v1",
         architecture="BertModel",
         mteb_score=0.688611955,
-        pooling_type="MEAN",
+        seq_pooling_type="MEAN",
         attn_type="encoder_only",
         is_prefix_caching_supported=False,
         is_chunked_prefill_supported=False,
@@ -24,7 +24,7 @@ ST_PROJECTOR_MODELS = [
         "google/embeddinggemma-300m",
         architecture="Gemma3TextModel",
         mteb_score=0.7473819294684156,
-        pooling_type="MEAN",
+        seq_pooling_type="MEAN",
         attn_type="encoder_only",
         is_prefix_caching_supported=False,
         is_chunked_prefill_supported=False,

tests/models/utils.py

@@ -11,6 +11,7 @@ import torch.nn.functional as F
 from transformers import PretrainedConfig
 
 from vllm.config.model import AttnTypeStr, ModelConfig, ModelDType, RunnerOption
+from vllm.config.pooler import SequencePoolingType, TokenPoolingType
 from vllm.logprobs import Logprob, PromptLogprobs, SampleLogprobs
 from vllm.multimodal.processing import InputProcessingContext
 from vllm.tokenizers import cached_tokenizer_from_config
@@ -379,7 +380,8 @@ class ModelInfo:
     max_model_len: int | None = None
     hf_dtype: str = "float32"
     hf_overrides: dict[str, Any] | None = None
-    pooling_type: str | None = None
+    seq_pooling_type: SequencePoolingType | None = None
+    tok_pooling_type: TokenPoolingType | None = None
     attn_type: AttnTypeStr | None = None
     is_prefix_caching_supported: bool | None = None
     is_chunked_prefill_supported: bool | None = None

tests/test_config.py

@@ -161,7 +161,8 @@ def test_get_pooling_config():
 
     assert model_config.pooler_config is not None
     assert model_config.pooler_config.normalize
-    assert model_config.pooler_config.pooling_type == "MEAN"
+    assert model_config.pooler_config.seq_pooling_type == "MEAN"
+    assert model_config.pooler_config.tok_pooling_type == "ALL"
 
 
 @pytest.mark.skipif(
@@ -169,7 +170,7 @@ def test_get_pooling_config():
 )
 def test_get_pooling_config_from_args():
     model_id = "sentence-transformers/all-MiniLM-L12-v2"
-    pooler_config = PoolerConfig(pooling_type="CLS", normalize=True)
+    pooler_config = PoolerConfig(seq_pooling_type="CLS", normalize=True)
     model_config = ModelConfig(model_id, pooler_config=pooler_config)
 
     assert asdict(model_config.pooler_config) == asdict(pooler_config)
@@ -180,14 +181,25 @@ def test_get_pooling_config_from_args():
     [
         ("tomaarsen/Qwen3-Reranker-0.6B-seq-cls", "LAST", "LAST"),  # LLM
         ("intfloat/e5-small", "CLS", "MEAN"),  # BertModel
+    ],
+)
+def test_default_seq_pooling_type(model_id, default_pooling_type, pooling_type):
+    model_config = ModelConfig(model_id)
+    assert model_config._model_info.default_seq_pooling_type == default_pooling_type
+    assert model_config.pooler_config.seq_pooling_type == pooling_type
+
+
+@pytest.mark.parametrize(
+    ("model_id", "default_pooling_type", "pooling_type"),
+    [
         ("Qwen/Qwen2.5-Math-RM-72B", "ALL", "ALL"),  # reward
         ("Qwen/Qwen2.5-Math-PRM-7B", "STEP", "STEP"),  # step reward
     ],
 )
-def test_default_pooling_type(model_id, default_pooling_type, pooling_type):
+def test_default_tok_pooling_type(model_id, default_pooling_type, pooling_type):
     model_config = ModelConfig(model_id)
-    assert model_config._model_info.default_pooling_type == default_pooling_type
-    assert model_config.pooler_config.pooling_type == pooling_type
+    assert model_config._model_info.default_tok_pooling_type == default_pooling_type
+    assert model_config.pooler_config.tok_pooling_type == pooling_type
 
 
 @pytest.mark.parametrize(
@@ -554,100 +566,100 @@ def test_s3_url_different_models_create_different_directories(mock_pull_files):
             "jason9693/Qwen2.5-1.5B-apeach",
             "decoder",
             True,
-            "Pooling models with causal attn and last pooling support chunked prefill.",
+            "Pooling models with causal attn and LAST/ALL pooling support chunked prefill.",  # noqa: E501
         ),
         (
             "Qwen/Qwen3-Embedding-0.6B",
             "decoder",
             True,
-            "Pooling models with causal attn and last pooling support chunked prefill.",
+            "Pooling models with causal attn and LAST/ALL pooling support chunked prefill.",  # noqa: E501
         ),
         (
             "Qwen/Qwen2.5-Math-PRM-7B",
             "decoder",
             False,
-            "Pooling models with step pooling does not support chunked prefill.",
+            "Pooling models with causal attn and LAST/STEP pooling do not support chunked prefill.",  # noqa: E501
         ),
         (
             "internlm/internlm2-1_8b-reward",
             "decoder",
             True,
-            "Pooling models with causal attn and all pooling support chunked prefill.",
+            "Pooling models with causal attn and LAST/ALL pooling support chunked prefill.",  # noqa: E501
         ),
         (
             "BAAI/bge-base-en",
             "encoder_only",
             False,
-            "Pooling models with bidirectional attn does not support chunked prefill.",
+            "Pooling models with bidirectional attn do not support chunked prefill.",  # noqa: E501
         ),
         (
             "boltuix/NeuroBERT-NER",
             "encoder_only",
             False,
-            "Pooling models with bidirectional attn does not support chunked prefill.",
+            "Pooling models with bidirectional attn do not support chunked prefill.",  # noqa: E501
         ),
         (
             "papluca/xlm-roberta-base-language-detection",
             "encoder_only",
             False,
-            "Pooling models with bidirectional attn does not support chunked prefill.",
+            "Pooling models with bidirectional attn do not support chunked prefill.",  # noqa: E501
         ),
         (
             "Alibaba-NLP/gte-Qwen2-1.5B-instruct",
             "encoder_only",
             False,
-            "Pooling models with bidirectional attn does not support chunked prefill.",
+            "Pooling models with bidirectional attn do not support chunked prefill.",  # noqa: E501
         ),
         (
             "intfloat/e5-small",
             "encoder_only",
             False,
-            "Pooling models with bidirectional attn does not support chunked prefill.",
+            "Pooling models with bidirectional attn do not support chunked prefill.",  # noqa: E501
         ),
         # multimodal models
         (
             "openai/clip-vit-base-patch32",
             "decoder",
             True,
-            "Pooling models with causal attn and last pooling support chunked prefill.",
+            "Pooling models with causal attn and LAST/ALL pooling support chunked prefill.",  # noqa: E501
         ),
         (
             "google/siglip-base-patch16-224",
             "encoder_only",
             False,
-            "Pooling models with bidirectional attn does not support chunked prefill.",
+            "Pooling models with bidirectional attn do not support chunked prefill.",  # noqa: E501
         ),
         # generate models
         (
             "Qwen/Qwen3-0.6B",
             "decoder",
             True,
-            "Generative models support chunked prefill.",
+            "Generative models support chunked prefill.",  # noqa: E501
         ),
         (
             "Qwen/Qwen3-Next-80B-A3B-Instruct",
             "hybrid",
             True,
-            "Generative models support chunked prefill.",
+            "Generative models support chunked prefill.",  # noqa: E501
         ),
         (
             "ibm-granite/granite-4.0-h-small",
             "hybrid",
             True,
-            "Generative models support chunked prefill.",
+            "Generative models support chunked prefill.",  # noqa: E501
         ),
         (
             "state-spaces/mamba-130m-hf",
             "attention_free",
             True,
-            "Generative models support chunked prefill.",
+            "Generative models support chunked prefill.",  # noqa: E501
         ),
         # encoder_decoder models
         (
             "openai/whisper-small",
             "encoder_decoder",
             False,
-            "Encoder decoder models does not support chunked prefill.",
+            "Encoder decoder models do not support chunked prefill.",  # noqa: E501
         ),
     ],
 )
@@ -673,100 +685,100 @@ def test_is_chunked_prefill_supported(
             "jason9693/Qwen2.5-1.5B-apeach",
             "decoder",
             True,
-            "Pooling models with causal attn and last pooling support prefix caching.",
+            "Pooling models with causal attn and LAST/ALL pooling support prefix caching.",  # noqa: E501
         ),
         (
             "Qwen/Qwen3-Embedding-0.6B",
             "decoder",
             True,
-            "Pooling models with causal attn and last pooling support prefix caching.",
+            "Pooling models with causal attn and LAST/ALL pooling support prefix caching.",  # noqa: E501
         ),
         (
             "Qwen/Qwen2.5-Math-PRM-7B",
             "decoder",
             False,
-            "Pooling models with step pooling does not support prefix caching.",
+            "Pooling models with causal attn and LAST/STEP pooling do not support prefix caching.",  # noqa: E501
         ),
         (
             "internlm/internlm2-1_8b-reward",
             "decoder",
             True,
-            "Pooling models with causal attn and all pooling support prefix caching.",
+            "Pooling models with causal attn and LAST/ALL pooling support prefix caching.",  # noqa: E501
         ),
         (
             "BAAI/bge-base-en",
             "encoder_only",
             False,
-            "Pooling models with bidirectional attn does not support prefix caching.",
+            "Pooling models with bidirectional attn do not support prefix caching.",  # noqa: E501
         ),
         (
             "boltuix/NeuroBERT-NER",
             "encoder_only",
             False,
-            "Pooling models with bidirectional attn does not support prefix caching.",
+            "Pooling models with bidirectional attn do not support prefix caching.",  # noqa: E501
         ),
         (
             "papluca/xlm-roberta-base-language-detection",
             "encoder_only",
             False,
-            "Pooling models with bidirectional attn does not support prefix caching.",
+            "Pooling models with bidirectional attn do not support prefix caching.",  # noqa: E501
         ),
         (
             "Alibaba-NLP/gte-Qwen2-1.5B-instruct",
             "encoder_only",
             False,
-            "Pooling models with bidirectional attn does not support prefix caching.",
+            "Pooling models with bidirectional attn do not support prefix caching.",  # noqa: E501
         ),
         (
             "intfloat/e5-small",
             "encoder_only",
             False,
-            "Pooling models with bidirectional attn does not support prefix caching.",
+            "Pooling models with bidirectional attn do not support prefix caching.",  # noqa: E501
         ),
         # multimodal models
         (
             "openai/clip-vit-base-patch32",
             "decoder",
             True,
-            "Pooling models with causal attn and last pooling support prefix caching.",
+            "Pooling models with causal attn and LAST/ALL pooling support prefix caching.",  # noqa: E501
         ),
         (
             "google/siglip-base-patch16-224",
             "encoder_only",
             False,
-            "Pooling models with bidirectional attn does not support prefix caching.",
+            "Pooling models with bidirectional attn do not support prefix caching.",  # noqa: E501
         ),
         # generate models
         (
             "Qwen/Qwen3-0.6B",
             "decoder",
             True,
-            "Generative models support prefix caching.",
+            "Generative models support prefix caching.",  # noqa: E501
         ),
         (
             "Qwen/Qwen3-Next-80B-A3B-Instruct",
             "hybrid",
             False,
-            "Hybrid models does not support prefix caching since the feature is still experimental.",  # noqa: E501
+            "Hybrid models do not support prefix caching since the feature is still experimental.",  # noqa: E501
         ),
         (
             "ibm-granite/granite-4.0-h-small",
             "hybrid",
             False,
-            "Hybrid models does not support prefix caching since the feature is still experimental.",  # noqa: E501
+            "Hybrid models do not support prefix caching since the feature is still experimental.",  # noqa: E501
         ),
         (
             "state-spaces/mamba-130m-hf",
             "attention_free",
             False,
-            "Attention free models does not support prefix caching since the feature is still experimental.",  # noqa: E501
+            "Attention free models do not support prefix caching since the feature is still experimental.",  # noqa: E501
         ),
         # encoder_decoder models
         (
             "openai/whisper-small",
             "encoder_decoder",
             False,
-            "Encoder decoder models does not support prefix caching.",
+            "Encoder decoder models do not support prefix caching.",  # noqa: E501
         ),
     ],
 )

tests/test_pooling_params.py

@@ -40,7 +40,7 @@ def test_task():
 
 def test_embed():
     task = "embed"
-    model_config = MockModelConfig(pooler_config=PoolerConfig(pooling_type="CLS"))
+    model_config = MockModelConfig(pooler_config=PoolerConfig(seq_pooling_type="CLS"))
 
     pooling_params = PoolingParams(normalize=None)
     pooling_params.verify(task=task, model_config=model_config)
@@ -86,7 +86,7 @@ def test_embed_dimensions(model_info: EmbedModelInfo):
 
 @pytest.mark.parametrize("task", ["score", "classify"])
 def test_classify(task):
-    model_config = MockModelConfig(pooler_config=PoolerConfig(pooling_type="CLS"))
+    model_config = MockModelConfig(pooler_config=PoolerConfig(seq_pooling_type="CLS"))
 
     pooling_params = PoolingParams(use_activation=None)
     pooling_params.verify(task=task, model_config=model_config)
@@ -108,7 +108,7 @@ def test_classify(task):
 def test_token_embed(pooling_type: str):
     task = "token_embed"
     model_config = MockModelConfig(
-        pooler_config=PoolerConfig(pooling_type=pooling_type)
+        pooler_config=PoolerConfig(tok_pooling_type=pooling_type)
     )
 
     pooling_params = PoolingParams(normalize=None)
@@ -134,7 +134,7 @@ def test_token_embed(pooling_type: str):
 def test_token_classify(pooling_type: str):
     task = "token_classify"
     model_config = MockModelConfig(
-        pooler_config=PoolerConfig(pooling_type=pooling_type)
+        pooler_config=PoolerConfig(tok_pooling_type=pooling_type)
     )
 
     pooling_params = PoolingParams(use_activation=None)