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[Model] Pooling models default to using chunked prefill & prefix caching if supported. (#20930)

Browse Source 
Signed-off-by: wang.yuqi <noooop@126.com>
This commit is contained in:
wang.yuqi
2025-08-12 00:41:37 +08:00
committed by GitHub
parent 16fb668b61
commit 84cf78acee
31 changed files with 452 additions and 261 deletions
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6
tests/entrypoints/llm/test_classify.py
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@ -65,3 +65,9 @@ def test_pooling_params(llm: LLM):
assert torch.allclose(
softmax(wo_activation), w_activation, atol=1e-2
), "w_activation should be close to activation(wo_activation)."
def test_encode_api(llm: LLM):
err_msg = "pooling_task must be one of.+"
with pytest.raises(ValueError, match=err_msg):
llm.encode(prompts, use_tqdm=False)

15
tests/entrypoints/openai/test_classification.py
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@ -211,3 +211,18 @@ async def test_activation(server: RemoteOpenAIServer, model_name: str):
assert torch.allclose(
F.softmax(wo_activation, dim=-1), w_activation, atol=1e-2
), "w_activation should be close to activation(wo_activation)."
@pytest.mark.asyncio
@pytest.mark.parametrize("model_name", [MODEL_NAME])
def test_pooling(server: RemoteOpenAIServer, model_name: str):
# pooling api uses ALL pooling, which does not support chunked prefill.
response = requests.post(
server.url_for("pooling"),
json={
"model": model_name,
"input": "test",
"encoding_format": "float"
},
)
assert response.json()["error"]["type"] == "BadRequestError"

12
tests/models/language/pooling/mteb_utils.py
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@ -177,9 +177,12 @@ def mteb_test_embed_models(hf_runner,
max_model_len=None,
**vllm_extra_kwargs) as vllm_model:
model_config = vllm_model.llm.llm_engine.model_config
if model_info.architecture:
assert (model_info.architecture
in vllm_model.llm.llm_engine.model_config.architectures)
assert model_info.architecture in model_config.architectures
assert (model_config._model_info.default_pooling_type ==
model_info.default_pooling_type)
vllm_main_score = run_mteb_embed_task(VllmMtebEncoder(vllm_model),
MTEB_EMBED_TASKS)
@ -286,7 +289,12 @@ def mteb_test_rerank_models(hf_runner,
**vllm_extra_kwargs) as vllm_model:
model_config = vllm_model.llm.llm_engine.model_config
if model_info.architecture:
assert (model_info.architecture in model_config.architectures)
assert model_config.hf_config.num_labels == 1
assert (model_config._model_info.default_pooling_type ==
model_info.default_pooling_type)
vllm_main_score = run_mteb_rerank(vllm_mteb_encoder(vllm_model),
tasks=MTEB_RERANK_TASKS,

93
tests/models/language/pooling/test_auto_prefix_cache_support.py Normal file
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@ -0,0 +1,93 @@
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
import pytest
import torch
from transformers import AutoModelForSequenceClassification
from tests.models.language.pooling.embed_utils import (
run_embedding_correctness_test)
@pytest.mark.parametrize(
"model",
["jason9693/Qwen2.5-1.5B-apeach"],
)
@pytest.mark.parametrize("dtype", ["half"])
def test_classify_models(
hf_runner,
vllm_runner,
example_prompts,
model: str,
dtype: str,
) -> None:
example_prompts = example_prompts * 2
with vllm_runner(model,
max_model_len=512,
dtype=dtype,
enable_prefix_caching=True) as vllm_model:
cache_config = vllm_model.llm.llm_engine.cache_config
assert cache_config.enable_prefix_caching
vllm_outputs = vllm_model.classify(example_prompts)
with hf_runner(model,
dtype=dtype,
auto_cls=AutoModelForSequenceClassification) as hf_model:
hf_outputs = hf_model.classify(example_prompts)
for hf_output, vllm_output in zip(hf_outputs, vllm_outputs):
hf_output = torch.tensor(hf_output)
vllm_output = torch.tensor(vllm_output)
assert torch.allclose(hf_output, vllm_output,
1e-3 if dtype == "float" else 1e-2)
@pytest.mark.parametrize(
"model",
["Qwen/Qwen3-Embedding-0.6B"],
)
@pytest.mark.parametrize("dtype", ["half"])
def test_embed_models(
hf_runner,
vllm_runner,
example_prompts,
model: str,
dtype: str,
):
example_prompts = [str(s).strip() for s in example_prompts] * 2
with vllm_runner(
model,
runner="pooling",
max_model_len=None,
enable_prefix_caching=True,
) as vllm_model:
cache_config = vllm_model.llm.llm_engine.cache_config
assert cache_config.enable_prefix_caching
vllm_outputs = vllm_model.embed(example_prompts)
with hf_runner(
model,
is_sentence_transformer=True,
) as hf_model:
run_embedding_correctness_test(hf_model, example_prompts, vllm_outputs)
@pytest.mark.parametrize(
"model",
[
"intfloat/e5-small",
"Alibaba-NLP/gte-Qwen2-1.5B-instruct", # is_causal == False
"papluca/xlm-roberta-base-language-detection",
])
@pytest.mark.parametrize("dtype", ["half"])
def test_non_causal_models(hf_runner, vllm_runner, example_prompts, model: str,
dtype: str) -> None:
with vllm_runner(model,
max_model_len=512,
dtype=dtype,
enable_prefix_caching=True) as vllm_model:
cache_config = vllm_model.llm.llm_engine.cache_config
assert not cache_config.enable_prefix_caching

43
tests/models/language/pooling/test_baai.py
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@ -2,57 +2,59 @@
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
import pytest
from ...utils import EmbedModelInfo, RerankModelInfo
from ...utils import (CLSPoolingEmbedModelInfo, CLSPoolingRerankModelInfo,
EmbedModelInfo, LASTPoolingEmbedModelInfo,
RerankModelInfo)
from .embed_utils import correctness_test_embed_models
from .mteb_utils import mteb_test_embed_models, mteb_test_rerank_models
MODELS = [
########## BertModel
EmbedModelInfo("BAAI/bge-base-en",
CLSPoolingEmbedModelInfo("BAAI/bge-base-en",
architecture="BertModel",
enable_test=True),
EmbedModelInfo("BAAI/bge-base-zh",
CLSPoolingEmbedModelInfo("BAAI/bge-base-zh",
architecture="BertModel",
enable_test=False),
EmbedModelInfo("BAAI/bge-small-en",
CLSPoolingEmbedModelInfo("BAAI/bge-small-en",
architecture="BertModel",
enable_test=False),
EmbedModelInfo("BAAI/bge-small-zh",
CLSPoolingEmbedModelInfo("BAAI/bge-small-zh",
architecture="BertModel",
enable_test=False),
EmbedModelInfo("BAAI/bge-large-en",
CLSPoolingEmbedModelInfo("BAAI/bge-large-en",
architecture="BertModel",
enable_test=False),
EmbedModelInfo("BAAI/bge-large-zh",
CLSPoolingEmbedModelInfo("BAAI/bge-large-zh",
architecture="BertModel",
enable_test=False),
EmbedModelInfo("BAAI/bge-large-zh-noinstruct",
CLSPoolingEmbedModelInfo("BAAI/bge-large-zh-noinstruct",
architecture="BertModel",
enable_test=False),
EmbedModelInfo("BAAI/bge-base-en-v1.5",
CLSPoolingEmbedModelInfo("BAAI/bge-base-en-v1.5",
architecture="BertModel",
enable_test=False),
EmbedModelInfo("BAAI/bge-base-zh-v1.5",
CLSPoolingEmbedModelInfo("BAAI/bge-base-zh-v1.5",
architecture="BertModel",
enable_test=False),
EmbedModelInfo("BAAI/bge-small-en-v1.5",
CLSPoolingEmbedModelInfo("BAAI/bge-small-en-v1.5",
architecture="BertModel",
enable_test=False),
EmbedModelInfo("BAAI/bge-small-zh-v1.5",
CLSPoolingEmbedModelInfo("BAAI/bge-small-zh-v1.5",
architecture="BertModel",
enable_test=False),
EmbedModelInfo("BAAI/bge-large-en-v1.5",
CLSPoolingEmbedModelInfo("BAAI/bge-large-en-v1.5",
architecture="BertModel",
enable_test=False),
EmbedModelInfo("BAAI/bge-large-zh-v1.5",
CLSPoolingEmbedModelInfo("BAAI/bge-large-zh-v1.5",
architecture="BertModel",
enable_test=False),
########## XLMRobertaModel
EmbedModelInfo("BAAI/bge-m3",
CLSPoolingEmbedModelInfo("BAAI/bge-m3",
architecture="XLMRobertaModel",
enable_test=True),
########## Qwen2Model
EmbedModelInfo("BAAI/bge-code-v1",
LASTPoolingEmbedModelInfo("BAAI/bge-code-v1",
architecture="Qwen2Model",
dtype="float32",
enable_test=True),
@ -60,13 +62,16 @@ MODELS = [
RERANK_MODELS = [
########## XLMRobertaForSequenceClassification
RerankModelInfo("BAAI/bge-reranker-base",
CLSPoolingRerankModelInfo(
"BAAI/bge-reranker-base",
architecture="XLMRobertaForSequenceClassification",
enable_test=True),
RerankModelInfo("BAAI/bge-reranker-large",
CLSPoolingRerankModelInfo(
"BAAI/bge-reranker-large",
architecture="XLMRobertaForSequenceClassification",
enable_test=False),
RerankModelInfo("BAAI/bge-reranker-v2-m3",
CLSPoolingRerankModelInfo(
"BAAI/bge-reranker-v2-m3",
architecture="XLMRobertaForSequenceClassification",
enable_test=False)
]

6
tests/models/language/pooling/test_bge_reranker_v2_gemma.py
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@ -8,11 +8,11 @@ import torch
from tests.conftest import HfRunner
from .mteb_utils import (RerankModelInfo, VllmMtebEncoder,
mteb_test_rerank_models)
from ...utils import LASTPoolingRerankModelInfo, RerankModelInfo
from .mteb_utils import VllmMtebEncoder, mteb_test_rerank_models
RERANK_MODELS = [
RerankModelInfo("BAAI/bge-reranker-v2-gemma",
LASTPoolingRerankModelInfo("BAAI/bge-reranker-v2-gemma",
architecture="GemmaForSequenceClassification"),
]

8
tests/models/language/pooling/test_cross_encoder.py
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@ -2,12 +2,14 @@
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
import pytest
from .mteb_utils import RerankModelInfo, mteb_test_rerank_models
from ...utils import (CLSPoolingRerankModelInfo, LASTPoolingRerankModelInfo,
RerankModelInfo)
from .mteb_utils import mteb_test_rerank_models
RERANK_MODELS = [
RerankModelInfo("cross-encoder/ms-marco-TinyBERT-L-2-v2",
CLSPoolingRerankModelInfo("cross-encoder/ms-marco-TinyBERT-L-2-v2",
architecture="BertForSequenceClassification"),
RerankModelInfo("tomaarsen/Qwen3-Reranker-0.6B-seq-cls",
LASTPoolingRerankModelInfo("tomaarsen/Qwen3-Reranker-0.6B-seq-cls",
architecture="Qwen3ForSequenceClassification")
]

31
tests/models/language/pooling/test_gte.py
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@ -4,54 +4,55 @@ from typing import Any
import pytest
from ...utils import check_transformers_version
from .embed_utils import EmbedModelInfo, correctness_test_embed_models
from ...utils import (CLSPoolingEmbedModelInfo, EmbedModelInfo,
LASTPoolingEmbedModelInfo, check_transformers_version)
from .embed_utils import correctness_test_embed_models
from .mteb_utils import mteb_test_embed_models
MODELS = [
########## BertModel
EmbedModelInfo("thenlper/gte-large",
CLSPoolingEmbedModelInfo("thenlper/gte-large",
architecture="BertModel",
enable_test=True),
EmbedModelInfo("thenlper/gte-base",
CLSPoolingEmbedModelInfo("thenlper/gte-base",
architecture="BertModel",
enable_test=False),
EmbedModelInfo("thenlper/gte-small",
CLSPoolingEmbedModelInfo("thenlper/gte-small",
architecture="BertModel",
enable_test=False),
EmbedModelInfo("thenlper/gte-large-zh",
CLSPoolingEmbedModelInfo("thenlper/gte-large-zh",
architecture="BertModel",
enable_test=False),
EmbedModelInfo("thenlper/gte-base-zh",
CLSPoolingEmbedModelInfo("thenlper/gte-base-zh",
architecture="BertModel",
enable_test=False),
EmbedModelInfo("thenlper/gte-small-zh",
CLSPoolingEmbedModelInfo("thenlper/gte-small-zh",
architecture="BertModel",
enable_test=False),
########### NewModel
EmbedModelInfo("Alibaba-NLP/gte-multilingual-base",
CLSPoolingEmbedModelInfo("Alibaba-NLP/gte-multilingual-base",
architecture="GteNewModel",
enable_test=True),
EmbedModelInfo("Alibaba-NLP/gte-base-en-v1.5",
CLSPoolingEmbedModelInfo("Alibaba-NLP/gte-base-en-v1.5",
architecture="GteNewModel",
enable_test=True),
EmbedModelInfo("Alibaba-NLP/gte-large-en-v1.5",
CLSPoolingEmbedModelInfo("Alibaba-NLP/gte-large-en-v1.5",
architecture="GteNewModel",
enable_test=True),
########### Qwen2ForCausalLM
EmbedModelInfo("Alibaba-NLP/gte-Qwen2-1.5B-instruct",
LASTPoolingEmbedModelInfo("Alibaba-NLP/gte-Qwen2-1.5B-instruct",
architecture="Qwen2ForCausalLM",
enable_test=True),
########## ModernBertModel
EmbedModelInfo("Alibaba-NLP/gte-modernbert-base",
CLSPoolingEmbedModelInfo("Alibaba-NLP/gte-modernbert-base",
architecture="ModernBertModel",
enable_test=True),
########## Qwen3ForCausalLM
EmbedModelInfo("Qwen/Qwen3-Embedding-0.6B",
LASTPoolingEmbedModelInfo("Qwen/Qwen3-Embedding-0.6B",
architecture="Qwen3ForCausalLM",
dtype="float32",
enable_test=True),
EmbedModelInfo("Qwen/Qwen3-Embedding-4B",
LASTPoolingEmbedModelInfo("Qwen/Qwen3-Embedding-4B",
architecture="Qwen3ForCausalLM",
dtype="float32",
enable_test=False),

16
tests/models/language/pooling/test_intfloat.py
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@ -2,32 +2,32 @@
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
import pytest
from ...utils import EmbedModelInfo
from ...utils import CLSPoolingEmbedModelInfo, EmbedModelInfo
from .embed_utils import correctness_test_embed_models
from .mteb_utils import mteb_test_embed_models
MODELS = [
########## BertModel
EmbedModelInfo("intfloat/e5-small",
CLSPoolingEmbedModelInfo("intfloat/e5-small",
architecture="BertModel",
enable_test=True),
EmbedModelInfo("intfloat/e5-base",
CLSPoolingEmbedModelInfo("intfloat/e5-base",
architecture="BertModel",
enable_test=False),
EmbedModelInfo("intfloat/e5-large",
CLSPoolingEmbedModelInfo("intfloat/e5-large",
architecture="BertModel",
enable_test=False),
EmbedModelInfo("intfloat/multilingual-e5-small",
CLSPoolingEmbedModelInfo("intfloat/multilingual-e5-small",
architecture="BertModel",
enable_test=False),
########## XLMRobertaModel
EmbedModelInfo("intfloat/multilingual-e5-base",
CLSPoolingEmbedModelInfo("intfloat/multilingual-e5-base",
architecture="XLMRobertaModel",
enable_test=True),
EmbedModelInfo("intfloat/multilingual-e5-large",
CLSPoolingEmbedModelInfo("intfloat/multilingual-e5-large",
architecture="XLMRobertaModel",
enable_test=False),
EmbedModelInfo("intfloat/multilingual-e5-large-instruct",
CLSPoolingEmbedModelInfo("intfloat/multilingual-e5-large-instruct",
architecture="XLMRobertaModel",
enable_test=False),
]

8
tests/models/language/pooling/test_jina.py
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@ -6,19 +6,21 @@ import pytest
from vllm import PoolingParams
from ...utils import EmbedModelInfo, RerankModelInfo
from ...utils import (CLSPoolingEmbedModelInfo, CLSPoolingRerankModelInfo,
EmbedModelInfo, RerankModelInfo)
from .embed_utils import (check_embeddings_close,
correctness_test_embed_models, matryoshka_fy)
from .mteb_utils import mteb_test_embed_models, mteb_test_rerank_models
EMBEDDING_MODELS = [
EmbedModelInfo("jinaai/jina-embeddings-v3",
CLSPoolingEmbedModelInfo("jinaai/jina-embeddings-v3",
architecture="XLMRobertaModel",
is_matryoshka=True)
]
RERANK_MODELS = [
RerankModelInfo("jinaai/jina-reranker-v2-base-multilingual",
CLSPoolingRerankModelInfo(
"jinaai/jina-reranker-v2-base-multilingual",
architecture="XLMRobertaForSequenceClassification")
]

7
tests/models/language/pooling/test_mxbai_rerank.py
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@ -7,13 +7,14 @@ import torch
from tests.conftest import HfRunner
from .mteb_utils import RerankModelInfo, mteb_test_rerank_models
from ...utils import LASTPoolingRerankModelInfo, RerankModelInfo
from .mteb_utils import mteb_test_rerank_models
RERANK_MODELS = [
RerankModelInfo("mixedbread-ai/mxbai-rerank-base-v2",
LASTPoolingRerankModelInfo("mixedbread-ai/mxbai-rerank-base-v2",
architecture="Qwen2ForSequenceClassification",
enable_test=True),
RerankModelInfo("mixedbread-ai/mxbai-rerank-large-v2",
LASTPoolingRerankModelInfo("mixedbread-ai/mxbai-rerank-large-v2",
architecture="Qwen2ForSequenceClassification",
enable_test=False)
]

11
tests/models/language/pooling/test_nomic.py
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@ -3,20 +3,21 @@
import pytest
from .embed_utils import EmbedModelInfo, correctness_test_embed_models
from ...utils import CLSPoolingEmbedModelInfo, EmbedModelInfo
from .embed_utils import correctness_test_embed_models
from .mteb_utils import mteb_test_embed_models
MODELS = [
EmbedModelInfo("nomic-ai/nomic-embed-text-v1",
CLSPoolingEmbedModelInfo("nomic-ai/nomic-embed-text-v1",
architecture="NomicBertModel",
enable_test=True),
EmbedModelInfo("nomic-ai/nomic-embed-text-v1.5",
CLSPoolingEmbedModelInfo("nomic-ai/nomic-embed-text-v1.5",
architecture="NomicBertModel",
enable_test=False),
EmbedModelInfo("nomic-ai/CodeRankEmbed",
CLSPoolingEmbedModelInfo("nomic-ai/CodeRankEmbed",
architecture="NomicBertModel",
enable_test=False),
EmbedModelInfo("nomic-ai/nomic-embed-text-v2-moe",
CLSPoolingEmbedModelInfo("nomic-ai/nomic-embed-text-v2-moe",
architecture="NomicBertModel",
enable_test=True)
]

7
tests/models/language/pooling/test_qwen3_reranker.py
View File

@ -8,13 +8,14 @@ import torch
from tests.conftest import HfRunner
from tests.utils import multi_gpu_test
from .mteb_utils import RerankModelInfo, mteb_test_rerank_models
from ...utils import LASTPoolingRerankModelInfo, RerankModelInfo
from .mteb_utils import mteb_test_rerank_models
RERANK_MODELS = [
RerankModelInfo("Qwen/Qwen3-Reranker-0.6B",
LASTPoolingRerankModelInfo("Qwen/Qwen3-Reranker-0.6B",
architecture="Qwen3ForSequenceClassification",
enable_test=True),
RerankModelInfo("Qwen/Qwen3-Reranker-4B",
LASTPoolingRerankModelInfo("Qwen/Qwen3-Reranker-4B",
architecture="Qwen3ForSequenceClassification",
enable_test=False)
]

19
tests/models/language/pooling/test_snowflake_arctic_embed.py
View File

@ -3,39 +3,40 @@
import pytest
from .embed_utils import EmbedModelInfo, correctness_test_embed_models
from ...utils import CLSPoolingEmbedModelInfo, EmbedModelInfo
from .embed_utils import correctness_test_embed_models
from .mteb_utils import mteb_test_embed_models
MODELS = [
EmbedModelInfo("Snowflake/snowflake-arctic-embed-xs",
CLSPoolingEmbedModelInfo("Snowflake/snowflake-arctic-embed-xs",
is_matryoshka=False,
architecture="BertModel",
enable_test=True),
EmbedModelInfo("Snowflake/snowflake-arctic-embed-s",
CLSPoolingEmbedModelInfo("Snowflake/snowflake-arctic-embed-s",
is_matryoshka=False,
architecture="BertModel",
enable_test=False),
EmbedModelInfo("Snowflake/snowflake-arctic-embed-m",
CLSPoolingEmbedModelInfo("Snowflake/snowflake-arctic-embed-m",
is_matryoshka=False,
architecture="BertModel",
enable_test=False),
EmbedModelInfo("Snowflake/snowflake-arctic-embed-m-long",
CLSPoolingEmbedModelInfo("Snowflake/snowflake-arctic-embed-m-long",
is_matryoshka=False,
architecture="NomicBertModel",
enable_test=True),
EmbedModelInfo("Snowflake/snowflake-arctic-embed-l",
CLSPoolingEmbedModelInfo("Snowflake/snowflake-arctic-embed-l",
is_matryoshka=False,
architecture="BertModel",
enable_test=False),
EmbedModelInfo("Snowflake/snowflake-arctic-embed-m-v1.5",
CLSPoolingEmbedModelInfo("Snowflake/snowflake-arctic-embed-m-v1.5",
is_matryoshka=True,
architecture="BertModel",
enable_test=True),
EmbedModelInfo("Snowflake/snowflake-arctic-embed-l-v2.0",
CLSPoolingEmbedModelInfo("Snowflake/snowflake-arctic-embed-l-v2.0",
is_matryoshka=True,
architecture="XLMRobertaModel",
enable_test=True),
EmbedModelInfo("Snowflake/snowflake-arctic-embed-m-v2.0",
CLSPoolingEmbedModelInfo("Snowflake/snowflake-arctic-embed-m-v2.0",
is_matryoshka=True,
architecture="GteModel",
enable_test=True),

18
tests/models/utils.py
View File

@ -345,16 +345,34 @@ class EmbedModelInfo(NamedTuple):
matryoshka_dimensions: Optional[list[int]] = None
architecture: str = ""
dtype: str = "auto"
default_pooling_type: str = ""
enable_test: bool = True
class CLSPoolingEmbedModelInfo(EmbedModelInfo):
default_pooling_type: str = "CLS"
class LASTPoolingEmbedModelInfo(EmbedModelInfo):
default_pooling_type: str = "LAST"
class RerankModelInfo(NamedTuple):
name: str
architecture: str = ""
dtype: str = "auto"
default_pooling_type: str = ""
enable_test: bool = True
class CLSPoolingRerankModelInfo(RerankModelInfo):
default_pooling_type: str = "CLS"
class LASTPoolingRerankModelInfo(RerankModelInfo):
default_pooling_type: str = "LAST"
def dummy_hf_overrides(
hf_config: PretrainedConfig,
*,

14
tests/test_config.py
View File

@ -227,6 +227,20 @@ def test_get_pooling_config_from_args():
assert asdict(pooling_config) == asdict(override_pooler_config)
@pytest.mark.parametrize(
("model_id", "default_pooling_type", "pooling_type"),
[
("tomaarsen/Qwen3-Reranker-0.6B-seq-cls", "LAST", "LAST"), # LLM
("intfloat/e5-small", "CLS", "MEAN"), # BertModel
("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):
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
@pytest.mark.skipif(current_platform.is_rocm(),
reason="Xformers backend is not supported on ROCm.")
def test_get_bert_tokenization_sentence_transformer_config():

8
vllm/config/__init__.py
View File

@ -871,6 +871,10 @@ class ModelConfig:
if getattr(pooler_config, k) is None:
setattr(pooler_config, k, v)
default_pooling_type = self._model_info.default_pooling_type
if pooler_config.pooling_type is None:
pooler_config.pooling_type = default_pooling_type
return pooler_config
return None
@ -3844,6 +3848,10 @@ class VllmConfig:
disable_chunked_prefill_reasons.append(
"Only \"last\" pooling supports chunked "
"prefill and prefix caching; disabling both.")
elif not getattr(self.model_config.hf_config, "is_causal", True):
disable_chunked_prefill_reasons.append(
"Only models using causal attention supports chunked "
"prefill and prefix caching; disabling both.")
if disable_chunked_prefill_reasons:
for reason in disable_chunked_prefill_reasons:

9
vllm/engine/arg_utils.py
View File

@ -1600,11 +1600,10 @@ class EngineArgs:
else:
pooling_type = model_config.pooler_config.pooling_type
# TODO: when encoder models are supported we'll have to
# check for causal attention here.
incremental_prefill_supported = (pooling_type is not None and
pooling_type.lower() == "last")
is_causal = getattr(model_config.hf_config, "is_causal", True)
incremental_prefill_supported = (pooling_type is not None
and pooling_type.lower() == "last"
and is_causal)
action = "Enabling" if \
incremental_prefill_supported else "Disabling"

4
vllm/entrypoints/llm.py
View File

@ -1100,6 +1100,10 @@ class LLM:
"Try passing `--runner pooling` to use the model as a "
"pooling model.")
if pooling_task not in self.supported_tasks:
raise ValueError(
f"pooling_task must be one of {self.supported_tasks}.")
if prompt_token_ids is not None:
parsed_prompts = self._convert_v1_inputs(
prompts=cast(Optional[Union[str, list[str]]], prompts),

38
vllm/model_executor/layers/pooler.py
View File

@ -44,15 +44,14 @@ class ResolvedPoolingConfig:
task: PoolingTask
@classmethod
def from_config_with_defaults(
def from_config(
cls,
task: PoolingTask,
pooler_config: PoolerConfig,
pooling_type: PoolingType,
) -> "ResolvedPoolingConfig":
assert pooler_config.pooling_type is not None
return cls(task=task,
pooling_type=PoolingType[pooler_config.pooling_type]
if pooler_config.pooling_type is not None else pooling_type)
pooling_type=PoolingType[pooler_config.pooling_type])
@dataclass(frozen=True)
@ -68,32 +67,20 @@ class Pooler(nn.Module, ABC):
"""The interface required for all poolers used in pooling models in vLLM."""
@staticmethod
def for_encode(
pooler_config: PoolerConfig,
*,
default_pooling_type: PoolingType = PoolingType.ALL,
):
resolved_config = ResolvedPoolingConfig.from_config_with_defaults(
task="encode",
pooler_config=pooler_config,
pooling_type=default_pooling_type,
)
if resolved_config.pooling_type == PoolingType.STEP:
def for_encode(pooler_config: PoolerConfig):
if pooler_config.pooling_type == "STEP":
return StepPooler()
resolved_config = ResolvedPoolingConfig(task="encode",
pooling_type=PoolingType.ALL)
return SimplePooler.from_config(resolved_config)
@staticmethod
def for_embed(
pooler_config: PoolerConfig,
*,
default_pooling_type: PoolingType = PoolingType.LAST,
):
resolved_config = ResolvedPoolingConfig.from_config_with_defaults(
def for_embed(pooler_config: PoolerConfig):
resolved_config = ResolvedPoolingConfig.from_config(
task="embed",
pooler_config=pooler_config,
pooling_type=default_pooling_type,
)
return SimplePooler.from_config(resolved_config)
@ -102,13 +89,10 @@ class Pooler(nn.Module, ABC):
def for_classify(
pooler_config: PoolerConfig,
classifier: Optional[ClassifierFn],
*,
default_pooling_type: PoolingType = PoolingType.LAST,
):
resolved_config = ResolvedPoolingConfig.from_config_with_defaults(
resolved_config = ResolvedPoolingConfig.from_config(
task="classify",
pooler_config=pooler_config,
pooling_type=default_pooling_type,
)
pooling = PoolingMethod.from_pooling_type(resolved_config.pooling_type)

4
vllm/model_executor/models/adapters.py
View File

@ -182,8 +182,8 @@ def as_seq_cls_model(cls: _T) -> _T:
assert pooler_config is not None
pooling_type_str = pooler_config.pooling_type
pooling_type = (PoolingType.LAST if pooling_type_str is None else
PoolingType[pooling_type_str])
assert pooling_type_str is not None
pooling_type = PoolingType[pooling_type_str]
self.pooler = DispatchPooler({
"encode":

16
vllm/model_executor/models/bert.py
View File

@ -28,7 +28,8 @@ from vllm.model_executor.pooling_metadata import PoolingMetadata
from vllm.sequence import IntermediateTensors
from vllm.tasks import PoolingTask
from .interfaces import SupportsCrossEncoding, SupportsQuant
from .interfaces import (SupportsCrossEncoding, SupportsQuant,
default_pooling_type)
from .utils import AutoWeightsLoader, WeightsMapper, maybe_prefix
@ -327,6 +328,7 @@ class BertOutput(nn.Module):
@support_torch_compile
@default_pooling_type("CLS")
class BertModel(nn.Module, SupportsQuant):
is_pooling_model = True
@ -401,6 +403,7 @@ class BertModel(nn.Module, SupportsQuant):
return loaded_params
@default_pooling_type("ALL")
class BertPoolingModel(BertModel):
is_pooling_model = True
@ -431,6 +434,7 @@ class BertPoolingModel(BertModel):
return loaded_params
@default_pooling_type("CLS")
class BertEmbeddingModel(nn.Module, SupportsQuant):
"""A model that uses Bert to provide embedding functionalities.
@ -486,13 +490,8 @@ class BertEmbeddingModel(nn.Module, SupportsQuant):
def _build_pooler(self, pooler_config: PoolerConfig) -> Pooler:
return DispatchPooler({
"encode":
Pooler.for_encode(pooler_config),
"embed":
Pooler.for_embed(
pooler_config,
default_pooling_type=PoolingType.CLS,
),
"encode": Pooler.for_encode(pooler_config),
"embed": Pooler.for_embed(pooler_config),
})
@ -541,6 +540,7 @@ def _decode_token_type_ids(input_ids: torch.Tensor) -> torch.Tensor:
return token_type_ids
@default_pooling_type("CLS")
class BertForSequenceClassification(nn.Module, SupportsCrossEncoding,
SupportsQuant):
"""A model that uses Bert to provide embedding functionalities.

4
vllm/model_executor/models/bert_with_rope.py
View File

@ -27,7 +27,8 @@ from vllm.model_executor.layers.rotary_embedding import get_rope
from vllm.model_executor.layers.vocab_parallel_embedding import (
VocabParallelEmbedding)
from vllm.model_executor.model_loader.weight_utils import default_weight_loader
from vllm.model_executor.models.interfaces import SupportsQuant
from vllm.model_executor.models.interfaces import (SupportsQuant,
default_pooling_type)
from vllm.model_executor.models.utils import WeightsMapper
from vllm.model_executor.utils import set_weight_attrs
from vllm.platforms import current_platform
@ -401,6 +402,7 @@ class BertWithRopeEncoder(nn.Module):
@support_torch_compile
@default_pooling_type("CLS")
class BertWithRope(nn.Module, SupportsQuant):
hf_to_vllm_mapper = WeightsMapper(orig_to_new_prefix={"model.": ""})

14
vllm/model_executor/models/interfaces.py
View File

@ -641,6 +641,20 @@ def supports_cross_encoding(
return is_pooling_model(model) and _supports_cross_encoding(model)
def default_pooling_type(pooling_type: str) -> object:
"""Set default_pooling_type decorator. """
def func(model: object):
model.default_pooling_type = pooling_type
return model
return func
def get_default_pooling_type(model: Union[type[object], object]) -> str:
return getattr(model, "default_pooling_type", "LAST")
class SupportsQuant:
"""The interface required for all models that support quantization."""

3
vllm/model_executor/models/internlm2.py
View File

@ -31,7 +31,7 @@ from vllm.model_executor.model_loader.weight_utils import default_weight_loader
from vllm.model_executor.sampling_metadata import SamplingMetadata
from vllm.sequence import IntermediateTensors
from .interfaces import SupportsLoRA, SupportsPP
from .interfaces import SupportsLoRA, SupportsPP, default_pooling_type
from .utils import (is_pp_missing_parameter,
make_empty_intermediate_tensors_factory, make_layers,
maybe_prefix)
@ -401,6 +401,7 @@ class InternLM2ForCausalLM(nn.Module, SupportsPP, SupportsLoRA):
return loaded_params
@default_pooling_type("ALL")
class InternLM2ForRewardModel(InternLM2ForCausalLM):
is_pooling_model = True

4
vllm/model_executor/models/jamba.py
View File

@ -22,8 +22,7 @@ from vllm.model_executor.layers.logits_processor import LogitsProcessor
from vllm.model_executor.layers.mamba.mamba_mixer import MambaMixer
from vllm.model_executor.layers.mamba.mamba_utils import (
MambaStateShapeCalculator)
from vllm.model_executor.layers.pooler import (DispatchPooler, Pooler,
PoolingType)
from vllm.model_executor.layers.pooler import DispatchPooler, Pooler
from vllm.model_executor.layers.quantization import QuantizationConfig
from vllm.model_executor.layers.vocab_parallel_embedding import (
DEFAULT_VOCAB_PADDING_SIZE, ParallelLMHead, VocabParallelEmbedding)
@ -604,6 +603,5 @@ class JambaForSequenceClassification(JambaForCausalLM):
Pooler.for_classify(
pooler_config,
classifier=self.score,
default_pooling_type=PoolingType.LAST,
),
})

6
vllm/model_executor/models/modernbert.py
View File

@ -26,7 +26,8 @@ from vllm.model_executor.pooling_metadata import PoolingMetadata
from vllm.sequence import IntermediateTensors
from vllm.tasks import PoolingTask
from .interfaces import SupportsCrossEncoding, SupportsV0Only
from .interfaces import (SupportsCrossEncoding, SupportsV0Only,
default_pooling_type)
from .utils import WeightsMapper, maybe_prefix
@ -201,6 +202,7 @@ class ModernBertEncoderLayer(nn.Module):
@support_torch_compile
@default_pooling_type("CLS")
class ModernBertModel(nn.Module):
hf_to_vllm_mapper = WeightsMapper(
orig_to_new_prefix={"layers.": "encoder_layer.layers."})
@ -264,7 +266,6 @@ class ModernBertPooler(Pooler):
self.pooling = PoolingMethod.from_pooling_type(pooling_type)
self.dense = nn.Linear(config.hidden_size, config.hidden_size,
config.classifier_bias)
self.pooling_type = config.classifier_pooling
self.act = nn.GELU()
self.norm = nn.LayerNorm(config.hidden_size,
eps=config.norm_eps,
@ -294,6 +295,7 @@ class ModernBertPooler(Pooler):
return pooled_output
@default_pooling_type("CLS")
class ModernBertForSequenceClassification(nn.Module, SupportsV0Only,
SupportsCrossEncoding):

16
vllm/model_executor/models/qwen2_rm.py
View File

@ -15,11 +15,10 @@ from torch import nn
from vllm.config import VllmConfig
from vllm.model_executor.layers.linear import (ColumnParallelLinear,
RowParallelLinear)
from vllm.model_executor.layers.pooler import (DispatchPooler, Pooler,
PoolingType)
from vllm.model_executor.layers.pooler import DispatchPooler, Pooler
from vllm.sequence import IntermediateTensors
from .interfaces import SupportsLoRA, SupportsPP
from .interfaces import SupportsLoRA, SupportsPP, default_pooling_type
from .qwen2 import Qwen2Model
from .utils import AutoWeightsLoader, maybe_prefix
@ -90,6 +89,7 @@ class Qwen2RewardBaseModel(nn.Module, SupportsLoRA, SupportsPP):
return loader.load_weights(weights)
@default_pooling_type("ALL")
class Qwen2ForRewardModel(Qwen2RewardBaseModel):
def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
@ -103,6 +103,7 @@ class Qwen2ForRewardModel(Qwen2RewardBaseModel):
{"encode": Pooler.for_encode(pooler_config)}, )
@default_pooling_type("STEP")
class Qwen2ForProcessRewardModel(Qwen2RewardBaseModel):
def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
@ -112,10 +113,5 @@ class Qwen2ForProcessRewardModel(Qwen2RewardBaseModel):
pooler_config = vllm_config.model_config.pooler_config
assert pooler_config is not None
self.pooler = DispatchPooler({
"encode":
Pooler.for_encode(
pooler_config,
default_pooling_type=PoolingType.STEP,
)
})
self.pooler = DispatchPooler(
{"encode": Pooler.for_encode(pooler_config)})

6
vllm/model_executor/models/registry.py
View File

@ -25,8 +25,8 @@ from vllm.logger import init_logger
from vllm.transformers_utils.dynamic_module import (
try_get_class_from_dynamic_module)
from .interfaces import (has_inner_state, has_noops, is_attention_free,
is_hybrid, supports_cross_encoding,
from .interfaces import (get_default_pooling_type, has_inner_state, has_noops,
is_attention_free, is_hybrid, supports_cross_encoding,
supports_multimodal, supports_multimodal_raw_input,
supports_pp, supports_transcription, supports_v0_only)
from .interfaces_base import is_pooling_model, is_text_generation_model
@ -305,6 +305,7 @@ class _ModelInfo:
architecture: str
is_text_generation_model: bool
is_pooling_model: bool
default_pooling_type: str
supports_cross_encoding: bool
supports_multimodal: bool
supports_multimodal_raw_input: bool
@ -323,6 +324,7 @@ class _ModelInfo:
architecture=model.__name__,
is_text_generation_model=is_text_generation_model(model),
is_pooling_model=is_pooling_model(model),
default_pooling_type=get_default_pooling_type(model),
supports_cross_encoding=supports_cross_encoding(model),
supports_multimodal=supports_multimodal(model),
supports_multimodal_raw_input=supports_multimodal_raw_input(model),

4
vllm/model_executor/models/roberta.py
View File

@ -23,7 +23,7 @@ from vllm.model_executor.models.utils import (AutoWeightsLoader, WeightsMapper,
from vllm.sequence import IntermediateTensors
from .bert_with_rope import BertWithRope, JinaRobertaModel
from .interfaces import SupportsCrossEncoding
from .interfaces import SupportsCrossEncoding, default_pooling_type
class RobertaEmbedding(nn.Module):
@ -86,6 +86,7 @@ class RobertaClassificationHead(nn.Module):
return x
@default_pooling_type("CLS")
class RobertaEmbeddingModel(BertEmbeddingModel):
"""A model that uses Roberta to provide embedding functionalities.
@ -149,6 +150,7 @@ class RobertaEmbeddingModel(BertEmbeddingModel):
return loader.load_weights(weights_list, mapper=mapper)
@default_pooling_type("CLS")
class RobertaForSequenceClassification(nn.Module, SupportsCrossEncoding):
"""A model that uses Roberta to provide embedding functionalities.

13
vllm/v1/worker/gpu_model_runner.py
View File

@ -1272,7 +1272,18 @@ class GPUModelRunner(LoRAModelRunnerMixin, KVConnectorModelRunnerMixin):
if not is_pooling_model(model):
return []
return list(model.pooler.get_supported_tasks())
supported_tasks = list(model.pooler.get_supported_tasks())
if (self.scheduler_config.chunked_prefill_enabled
and "encode" in supported_tasks):
supported_tasks.remove("encode")
logger.info_once("Chunked prefill is not supported with "
"encode task which using ALL pooling. "
"Please turn off chunked prefill by "
"`--no-enable-chunked-prefill` before using it.")
return supported_tasks
def get_supported_tasks(self) -> tuple[SupportedTask, ...]:
tasks = list[SupportedTask]()