[Model][0/N] Improve all pooling task | clean up (#25817)

Signed-off-by: wang.yuqi <noooop@126.com>

docs/models/supported_models.md

@@ -581,7 +581,7 @@ These models primarily support the [`LLM.encode`](./pooling_models.md#llmencode)
 | `ModernBertForTokenClassification` | ModernBERT-based | `disham993/electrical-ner-ModernBERT-base` |  |  | ✅︎ |
 
 !!! note
-    Named Entity Recognition (NER) usage, please refer to <gh-file:examples/offline_inference/pooling/ner.py>, <gh-file:examples/online_serving/pooling/ner.py>.
+    Named Entity Recognition (NER) usage, please refer to <gh-file:examples/offline_inference/pooling/ner.py>, <gh-file:examples/online_serving/pooling/ner_client.py>.
 
 [](){ #supported-mm-models }
 

examples/online_serving/pooling/README.md

@@ -15,7 +15,7 @@ python examples/online_serving/pooling/jinaai_rerank_client.py
 ## Named Entity Recognition (NER) usage
 
 ```bash
-python examples/online_serving/pooling/ner.py
+python examples/online_serving/pooling/ner_client.py
 ```
 
 ## Openai chat embedding for multimodal usage

tests/ci_envs.py

@@ -8,6 +8,8 @@ import os
 from collections.abc import Callable
 from typing import TYPE_CHECKING, Any
 
+from vllm.envs import maybe_convert_bool
+
 if TYPE_CHECKING:
     VLLM_CI_NO_SKIP: bool = False
     VLLM_CI_DTYPE: str | None = None
@@ -25,6 +27,10 @@ environment_variables: dict[str, Callable[[], Any]] = {
     "VLLM_CI_HEAD_DTYPE": lambda: os.getenv("VLLM_CI_HEAD_DTYPE", None),
     # Allow changing the head dtype used by transformers in tests
     "VLLM_CI_HF_DTYPE": lambda: os.getenv("VLLM_CI_HF_DTYPE", None),
+    # Allow control over whether tests use enforce_eager
+    "VLLM_CI_ENFORCE_EAGER": lambda: maybe_convert_bool(
+        os.getenv("VLLM_CI_ENFORCE_EAGER", None)
+    ),
 }
 
 

tests/entrypoints/pooling/llm/test_classify.py

@@ -58,7 +58,9 @@ def test_pooling_params(llm: LLM):
     )
 
 
+@pytest.mark.skip_global_cleanup
 def test_encode_api(llm: LLM):
+    # chunked prefill does not support all pooling
     err_msg = "pooling_task must be one of.+"
     with pytest.raises(ValueError, match=err_msg):
         llm.encode(prompts, use_tqdm=False)

tests/entrypoints/pooling/llm/test_embedding.py

@@ -35,7 +35,6 @@ def llm():
     cleanup_dist_env_and_memory()
 
 
-@pytest.mark.skip_global_cleanup
 def test_pooling_params(llm: LLM):
     def get_outputs(normalize):
         outputs = llm.embed(

tests/entrypoints/pooling/llm/test_encode.py

@@ -74,7 +74,6 @@ def test_multiple_pooling_params(llm: LLM):
     assert len(PROMPTS) == len(outputs)
 
 
-@pytest.mark.skip_global_cleanup
 def test_right_side_truncation(llm: LLM):
     # Embeddings models should truncate the end of the prompt
     tokenizer = llm.get_tokenizer()

tests/entrypoints/pooling/llm/test_score.py

@@ -33,7 +33,6 @@ def llm():
     cleanup_dist_env_and_memory()
 
 
-@pytest.mark.skip_global_cleanup
 def test_pooling_params(llm: LLM):
     def get_outputs(activation):
         text_1 = "What is the capital of France?"

tests/models/language/generation_ppl_test/ppl_utils.py

@@ -3,12 +3,15 @@
 # Adapted from https://huggingface.co/docs/transformers/perplexity
 from typing import cast
 
-import pytest
 import torch
 from datasets import load_dataset
 
 import tests.ci_envs as ci_envs
-from tests.models.utils import GenerateModelInfo, TokensTextLogprobsPromptLogprobs
+from tests.models.utils import (
+    GenerateModelInfo,
+    TokensTextLogprobsPromptLogprobs,
+    get_vllm_extra_kwargs,
+)
 from vllm.logprobs import Logprob
 
 # See #24485
@@ -25,27 +28,10 @@ def wikitext_ppl_test(
     vllm_extra_kwargs=None,
     atol=PPL_TOL,
 ):
-    # A model family has many models with the same architecture,
-    # and we don't need to test each one.
-    if not ci_envs.VLLM_CI_NO_SKIP and not model_info.enable_test:
-        pytest.skip("Skipping test.")
+    vllm_extra_kwargs = get_vllm_extra_kwargs(model_info, vllm_extra_kwargs)
 
     dataset = load_dataset("wikitext", "wikitext-2-raw-v1", split="test")
 
-    # Allow vllm to test using the given dtype, such as float32
-    vllm_extra_kwargs = vllm_extra_kwargs or {}
-    vllm_extra_kwargs["dtype"] = ci_envs.VLLM_CI_DTYPE or model_info.dtype
-
-    # Allow vllm to test using hf_overrides
-    if model_info.hf_overrides is not None:
-        vllm_extra_kwargs["hf_overrides"] = model_info.hf_overrides
-
-    # Allow changing the head dtype used by vllm in tests
-    if ci_envs.VLLM_CI_HEAD_DTYPE is not None:
-        if "hf_overrides" not in vllm_extra_kwargs:
-            vllm_extra_kwargs["hf_overrides"] = {}
-        vllm_extra_kwargs["hf_overrides"]["head_dtype"] = ci_envs.VLLM_CI_HEAD_DTYPE
-
     with vllm_runner(
         model_info.name,
         gpu_memory_utilization=0.7,

tests/models/language/pooling/test_head_dtype.py

@@ -0,0 +1,47 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+import pytest
+import torch
+from transformers import AutoModelForSequenceClassification
+
+
+@pytest.mark.parametrize(
+    "model",
+    ["nie3e/sentiment-polish-gpt2-small"],
+)
+@pytest.mark.parametrize("dtype", ["half"])
+def test_classify_models(
+    hf_runner,
+    vllm_runner,
+    example_prompts,
+    model: str,
+    dtype: str,
+) -> None:
+    with hf_runner(
+        model, dtype=dtype, auto_cls=AutoModelForSequenceClassification
+    ) as hf_model:
+        hf_outputs = hf_model.classify(example_prompts)
+
+    for head_dtype_str in ["float32", "model"]:
+        with vllm_runner(
+            model,
+            max_model_len=512,
+            dtype=dtype,
+            hf_overrides={"head_dtype": head_dtype_str},
+        ) as vllm_model:
+            model_config = vllm_model.llm.llm_engine.model_config
+            model_dtype = model_config.dtype
+            head_dtype = model_config.head_dtype
+
+            if head_dtype_str == "float32":
+                assert head_dtype == torch.float32
+            elif head_dtype_str == "model":
+                assert head_dtype == model_dtype
+
+            vllm_outputs = vllm_model.classify(example_prompts)
+
+        for hf_output, vllm_output in zip(hf_outputs, vllm_outputs):
+            hf_output = torch.tensor(hf_output).float()
+            vllm_output = torch.tensor(vllm_output).float()
+
+            assert torch.allclose(hf_output, vllm_output, atol=1e-2)

tests/models/language/pooling/test_splade_sparse_pooler.py

@@ -3,7 +3,6 @@
 
 import types
 
-import numpy as np
 import pytest
 import torch
 import torch.nn as nn
@@ -14,11 +13,12 @@ from vllm.model_executor.models.bert import (
 )
 
 # ---------------------------------------------------------------------
-# 1) Functional test: SPLADE formula correctness (no HF download needed)
+# Functional test: SPLADE formula correctness (no HF download needed)
 # ---------------------------------------------------------------------
 
 
 @pytest.mark.parametrize("B,T,H,V", [(2, 3, 5, 7)])
+@torch.inference_mode
 def test_splade_pooler_matches_reference_formula(B, T, H, V):
     """Ensure SPLADESparsePooler forward() matches the mathematical formula:
     log1p(relu(logits)) -> max over sequence length (after masking)."""
@@ -26,9 +26,11 @@ def test_splade_pooler_matches_reference_formula(B, T, H, V):
 
     # Prepare [B] sequences of shape [T, H]
     hs_list = [torch.randn(T, H) for _ in range(B)]
+    hs_tenser = torch.cat(hs_list)
 
     # Simulate PoolingMetadata (only required fields)
     prompt_lens = [T, T - 1]
+    prompt_lens_tenser = torch.tensor(prompt_lens, dtype=torch.int32)
     token_ids = torch.tensor(
         [
             [101, 5, 102],  # Batch 0: [CLS], token, [SEP]
@@ -36,7 +38,9 @@ def test_splade_pooler_matches_reference_formula(B, T, H, V):
         ],
         dtype=torch.long,
     )
-    meta = types.SimpleNamespace(prompt_lens=prompt_lens, prompt_token_ids=token_ids)
+    meta = types.SimpleNamespace(
+        prompt_lens=prompt_lens_tenser, prompt_token_ids=token_ids
+    )
 
     # MLM head (prefer BertMLMHead, fallback to Linear if unavailable)
     try:
@@ -46,10 +50,10 @@ def test_splade_pooler_matches_reference_formula(B, T, H, V):
 
     # Forward pass through SPLADE pooler
     pooler = SPLADESparsePooler(mlm_head=mlm_head, pooling="max", remove_cls_sep=True)
-    pooled = pooler(hidden_states=hs_list, pooling_metadata=meta)  # list of [V]
+    pooled = pooler(hidden_states=hs_tenser, pooling_metadata=meta)  # list of [V]
 
     # Basic output checks
-    assert isinstance(pooled, list) and len(pooled) == B
+    assert isinstance(pooled, torch.Tensor) and len(pooled) == B
     for vec in pooled:
         assert vec.shape == (V,)
         assert torch.isfinite(vec).all()
@@ -83,40 +87,3 @@ def test_splade_pooler_matches_reference_formula(B, T, H, V):
         rtol=1e-4,
         atol=1e-4,
     )
-
-
-# ---------------------------------------------------------------------
-# 2) Integration smoke test: end-to-end embedding path wiring
-# ---------------------------------------------------------------------
-
-
-@pytest.mark.cpu_model
-def test_bert_splade_sparse_embed_smoke(vllm_runner, monkeypatch):
-    """Ensure BertSpladeSparseEmbeddingModel loads and produces sparse embeddings."""
-    from transformers import AutoTokenizer
-
-    MODEL_ID = "hf-internal-testing/tiny-random-bert"
-    hf_overrides = {"architectures": ["BertSpladeSparseEmbeddingModel"]}
-
-    # Enforce CPU-only execution (optional)
-    monkeypatch.setenv("CUDA_VISIBLE_DEVICES", "")
-    monkeypatch.setenv("VLLM_USE_TRITON_FLASH_ATTN", "False")
-
-    tok = AutoTokenizer.from_pretrained(MODEL_ID)
-    vocab_size = tok.vocab_size
-
-    # The embed path should route through SPLADESparsePooler
-    with vllm_runner(
-        MODEL_ID,
-        runner="pooling",
-        max_model_len=64,
-        hf_overrides=hf_overrides,
-    ) as vm:
-        outs = vm.embed(["hello world", "splade sparse test"])
-
-        # Basic sanity checks
-        assert len(outs) == 2
-        assert outs[0].shape[0] == vocab_size
-        assert outs[1].shape[0] == vocab_size
-        assert np.isfinite(outs[0]).all() and (outs[0] >= 0).all()
-        assert np.isfinite(outs[1]).all() and (outs[1] >= 0).all()

tests/models/language/pooling_mteb_test/mteb_utils.py

@@ -6,12 +6,16 @@ from collections.abc import Sequence
 
 import mteb
 import numpy as np
-import pytest
 import requests
 import torch
 
 import tests.ci_envs as ci_envs
-from tests.models.utils import EmbedModelInfo, RerankModelInfo, check_embeddings_close
+from tests.models.utils import (
+    EmbedModelInfo,
+    RerankModelInfo,
+    check_embeddings_close,
+    get_vllm_extra_kwargs,
+)
 
 # Most embedding models on the STS12 task (See #17175):
 # - Model implementation and minor changes in tensor dtype
@@ -165,28 +169,11 @@ def mteb_test_embed_models(
     hf_model_callback=None,
     atol=MTEB_EMBED_TOL,
 ):
-    # A model family has many models with the same architecture,
-    # and we don't need to test each one.
-    if not ci_envs.VLLM_CI_NO_SKIP and not model_info.enable_test:
-        pytest.skip("Skipping test.")
+    vllm_extra_kwargs = get_vllm_extra_kwargs(model_info, vllm_extra_kwargs)
 
     # Test embed_dims, isnan and whether to use normalize
     example_prompts = ["The chef prepared a delicious meal." * 1000]
 
-    # Allow vllm to test using the given dtype, such as float32
-    vllm_extra_kwargs = vllm_extra_kwargs or {}
-    vllm_extra_kwargs["dtype"] = ci_envs.VLLM_CI_DTYPE or model_info.dtype
-
-    # Allow vllm to test using hf_overrides
-    if model_info.hf_overrides is not None:
-        vllm_extra_kwargs["hf_overrides"] = model_info.hf_overrides
-
-    # Allow changing the head dtype used by vllm in tests
-    if ci_envs.VLLM_CI_HEAD_DTYPE is not None:
-        if "hf_overrides" not in vllm_extra_kwargs:
-            vllm_extra_kwargs["hf_overrides"] = {}
-        vllm_extra_kwargs["hf_overrides"]["head_dtype"] = ci_envs.VLLM_CI_HEAD_DTYPE
-
     with vllm_runner(
         model_info.name,
         runner="pooling",
@@ -212,9 +199,12 @@ def mteb_test_embed_models(
         vllm_dtype = vllm_model.llm.llm_engine.model_config.dtype
         head_dtype = model_config.head_dtype
 
-        # Test embed_dims, isnan and whether to use normalize
+        # Test embedding_size, isnan and whether to use normalize
         vllm_outputs = vllm_model.embed(example_prompts, truncate_prompt_tokens=-1)
-        assert not torch.any(torch.isnan(torch.tensor(vllm_outputs)))
+        outputs_tensor = torch.tensor(vllm_outputs)
+        assert not torch.any(torch.isnan(outputs_tensor))
+        embedding_size = model_config.embedding_size
+        assert torch.tensor(vllm_outputs).shape[-1] == embedding_size
 
     # Accelerate mteb test by setting
     # SentenceTransformers mteb score to a constant
@@ -231,7 +221,7 @@ def mteb_test_embed_models(
             st_main_score = run_mteb_embed_task(hf_model, MTEB_EMBED_TASKS)
             st_dtype = next(hf_model.model.parameters()).dtype
 
-            # Test embed_dims and whether to use normalize
+            # Check embeddings close to hf outputs
             hf_outputs = hf_model.encode(example_prompts)
             check_embeddings_close(
                 embeddings_0_lst=hf_outputs,
@@ -323,24 +313,7 @@ def mteb_test_rerank_models(
     vllm_mteb_encoder=VllmMtebEncoder,
     atol=MTEB_RERANK_TOL,
 ):
-    # A model family has many models with the same architecture,
-    # and we don't need to test each one.
-    if not ci_envs.VLLM_CI_NO_SKIP and not model_info.enable_test:
-        pytest.skip("Skipping test.")
-
-    # Allow vllm to test using the given dtype, such as float32
-    vllm_extra_kwargs = vllm_extra_kwargs or {}
-    vllm_extra_kwargs["dtype"] = ci_envs.VLLM_CI_DTYPE or model_info.dtype
-
-    # Allow vllm to test using hf_overrides
-    if model_info.hf_overrides is not None:
-        vllm_extra_kwargs["hf_overrides"] = model_info.hf_overrides
-
-    # Allow changing the head dtype used by vllm in tests
-    if ci_envs.VLLM_CI_HEAD_DTYPE is not None:
-        if "hf_overrides" not in vllm_extra_kwargs:
-            vllm_extra_kwargs["hf_overrides"] = {}
-        vllm_extra_kwargs["hf_overrides"]["head_dtype"] = ci_envs.VLLM_CI_HEAD_DTYPE
+    vllm_extra_kwargs = get_vllm_extra_kwargs(model_info, vllm_extra_kwargs)
 
     with vllm_runner(
         model_info.name,

tests/models/utils.py

@@ -15,6 +15,7 @@ from vllm.logprobs import Logprob, PromptLogprobs, SampleLogprobs
 from vllm.multimodal.processing import InputProcessingContext
 from vllm.transformers_utils.tokenizer import cached_tokenizer_from_config
 
+from .. import ci_envs
 from .registry import HF_EXAMPLE_MODELS
 
 TokensText = tuple[list[int], str]
@@ -414,6 +415,35 @@ class GenerateModelInfo(ModelInfo):
     hf_ppl: float | None = None
 
 
+def get_vllm_extra_kwargs(model_info: ModelInfo, vllm_extra_kwargs):
+    # A model family has many models with the same architecture,
+    # and we don't need to test each one.
+    if not ci_envs.VLLM_CI_NO_SKIP and not model_info.enable_test:
+        import pytest
+
+        pytest.skip("Skipping test.")
+
+    # Allow vllm to test using the given dtype, such as float32
+    vllm_extra_kwargs = vllm_extra_kwargs or {}
+    vllm_extra_kwargs["dtype"] = ci_envs.VLLM_CI_DTYPE or model_info.dtype
+
+    # Allow vllm to test using hf_overrides
+    if model_info.hf_overrides is not None:
+        vllm_extra_kwargs["hf_overrides"] = model_info.hf_overrides
+
+    # Allow changing the head dtype used by vllm in tests
+    if ci_envs.VLLM_CI_HEAD_DTYPE is not None:
+        if "hf_overrides" not in vllm_extra_kwargs:
+            vllm_extra_kwargs["hf_overrides"] = {}
+        vllm_extra_kwargs["hf_overrides"]["head_dtype"] = ci_envs.VLLM_CI_HEAD_DTYPE
+
+    # Allow control over whether tests use enforce_eager
+    if ci_envs.VLLM_CI_ENFORCE_EAGER is not None:
+        vllm_extra_kwargs["enforce_eager"] = ci_envs.VLLM_CI_ENFORCE_EAGER
+
+    return vllm_extra_kwargs
+
+
 def dummy_hf_overrides(
     hf_config: PretrainedConfig,
     *,

vllm/config/model.py

@@ -30,6 +30,7 @@ from vllm.transformers_utils.config import (
     get_sentence_transformer_tokenizer_config,
     is_encoder_decoder,
     is_interleaved,
+    try_get_dense_modules,
     try_get_generation_config,
     try_get_safetensors_metadata,
     try_get_tokenizer_config,
@@ -1681,6 +1682,20 @@ class ModelConfig:
         logger.debug_once("head dtype: %s", head_dtype)
         return head_dtype
 
+    @property
+    def hidden_size(self):
+        if hasattr(self.hf_config, "hidden_size"):
+            return self.hf_config.hidden_size
+        text_config = self.hf_config.get_text_config()
+        return text_config.hidden_size
+
+    @property
+    def embedding_size(self):
+        dense_modules = try_get_dense_modules(self.model, revision=self.revision)
+        if dense_modules is not None:
+            return dense_modules[-1]["out_features"]
+        return self.hidden_size
+
     def get_and_verify_max_len(self, max_model_len: int):
         # Consider max_model_len in tokenizer_config only when
         # pooling models use absolute position_embedding.

vllm/model_executor/models/adapters.py

@@ -13,7 +13,10 @@ from vllm.config import VllmConfig
 from vllm.logger import init_logger
 from vllm.model_executor.layers.activation import get_act_fn
 from vllm.model_executor.models.config import VerifyAndUpdateConfig
-from vllm.transformers_utils.config import get_hf_file_bytes, get_hf_file_to_dict
+from vllm.transformers_utils.config import (
+    get_hf_file_bytes,
+    try_get_dense_modules,
+)
 
 from .interfaces_base import VllmModelForPooling, is_pooling_model
 
@@ -35,43 +38,25 @@ _GENERATE_SUFFIXES = [
 def _load_st_projector(model_config: "ModelConfig") -> nn.Module | None:
     """Load Sentence-Transformers Dense projection layers."""
 
+    dense_modules = try_get_dense_modules(
+        model_config.model, revision=model_config.revision
+    )
+
+    if dense_modules is None:
+        return
+
     try:
-        modules = get_hf_file_to_dict(
-            "modules.json", model_config.model, model_config.revision
-        )
-        if not modules:
-            return None
-
-        if isinstance(modules, dict):
-            modules = modules.get("modules", [])
-
-        dense_modules = [
-            m for m in modules if m.get("type") == "sentence_transformers.models.Dense"
-        ]
-        if not dense_modules:
-            return None
-
         layers = []
-        for module in dense_modules:
-            folder = module.get("path", "")
-
-            config_path = f"{folder}/config.json" if folder else "config.json"
-            layer_config = get_hf_file_to_dict(
-                config_path, model_config.model, model_config.revision
-            )
-            if not layer_config:
-                continue
-
+        for layer_config in dense_modules:
+            folder = layer_config["folder"]
             linear = nn.Linear(
-                layer_config.get("in_features", 768),
-                layer_config.get("out_features", 768),
+                layer_config["in_features"],
+                layer_config["out_features"],
                 bias=layer_config.get("bias", True),
                 dtype=model_config.head_dtype,
             )
-
             if not _load_dense_weights(linear, folder, model_config):
                 continue
-
             layers.append(linear)
             if act_name := layer_config.get("activation_function"):
                 layers.append(get_act_fn(act_name))
@@ -303,18 +288,18 @@ def as_seq_cls_model(cls: _T) -> _T:
     from vllm.model_executor.models.interfaces import SupportsCrossEncoding
     from vllm.sequence import IntermediateTensors
 
-    from .utils import get_model_hidden_size, maybe_prefix
+    from .utils import maybe_prefix
 
     class ModelForSequenceClassification(
         _create_pooling_model_cls(cls), SupportsCrossEncoding
     ):
         def _init_pooler(self, vllm_config: "VllmConfig", prefix: str = ""):
             config = vllm_config.model_config.hf_config
+            model_config = vllm_config.model_config
             quant_config = vllm_config.quant_config
-            hidden_size = get_model_hidden_size(config)
 
             self.score = ReplicatedLinear(
-                hidden_size,
+                model_config.hidden_size,
                 config.num_labels,
                 bias=False,
                 params_dtype=torch.float32,

vllm/model_executor/models/gpt2.py

@@ -50,7 +50,7 @@ from vllm.model_executor.model_loader.weight_utils import default_weight_loader
 from vllm.sequence import IntermediateTensors
 
 from ..layers.pooler import DispatchPooler, Pooler
-from .interfaces import SupportsPP
+from .interfaces import SupportsCrossEncoding, SupportsPP
 from .utils import (
     AutoWeightsLoader,
     is_pp_missing_parameter,
@@ -321,7 +321,7 @@ class GPT2LMHeadModel(nn.Module, SupportsPP):
         return loader.load_weights(weights)
 
 
-class GPT2ForSequenceClassification(nn.Module):
+class GPT2ForSequenceClassification(nn.Module, SupportsCrossEncoding):
     """GPT2 Model for sequence classification.
 
     This class expands GPT2Model with pooling and score functions - last token
@@ -358,6 +358,9 @@ class GPT2ForSequenceClassification(nn.Module):
             }
         )
 
+    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.transformer.get_input_embeddings(input_ids)
+
     def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]):
         loader = AutoWeightsLoader(self)
         return loader.load_weights(weights)

vllm/model_executor/models/gritlm.py

@@ -148,37 +148,6 @@ class GritLMMeanPool(nn.Module):
     def get_pooling_updates(self, task: PoolingTask) -> PoolingParamsUpdate:
         return PoolingParamsUpdate(requires_token_ids=True)
 
-    def forward_one(
-        self,
-        hidden_states: torch.Tensor,
-        prompt_len: torch.Tensor | None = None,
-        instr_len: torch.Tensor | None = None,
-    ) -> torch.Tensor:
-        assert prompt_len is None or prompt_len == hidden_states.shape[0], (
-            "partial prefill not supported with MEAN pooling"
-        )
-
-        return hidden_states[instr_len:].mean(dim=0, dtype=torch.float32)
-
-    def forward_all(
-        self,
-        hidden_states: torch.Tensor,
-        prompt_lens: torch.Tensor,
-        instr_lens: torch.Tensor,
-    ) -> list[torch.Tensor] | torch.Tensor:
-        offset = 0
-        pooled_data = list[torch.Tensor]()
-
-        for prompt_len, instr_len in zip(prompt_lens, instr_lens):
-            pooled_data.append(
-                hidden_states[offset + instr_len : offset + prompt_len].mean(
-                    dim=0, dtype=torch.float32
-                )
-            )
-            offset += prompt_len
-
-        return pooled_data
-
     def forward(
         self,
         hidden_states: torch.Tensor | list[torch.Tensor],
@@ -190,18 +159,20 @@ class GritLMMeanPool(nn.Module):
                 self._get_instruction_len(token_ids.cpu().numpy())
                 for token_ids in get_prompt_token_ids(pooling_metadata)
             ],
-            device=prompt_lens.device,
+            device="cpu",
         )
 
-        if isinstance(hidden_states, list):
-            return [
-                self.forward_one(h, prompt_len, instr_len)
-                for h, prompt_len, instr_len in zip(
-                    hidden_states, prompt_lens, instr_lens
+        offset = 0
+        pooled_data = list[torch.Tensor]()
+        for prompt_len, instr_len in zip(prompt_lens, instr_lens):
+            pooled_data.append(
+                hidden_states[offset + instr_len : offset + prompt_len].mean(
+                    dim=0, dtype=torch.float32
                 )
-            ]
+            )
+            offset += prompt_len
 
-        return self.forward_all(hidden_states, prompt_lens, instr_lens)
+        return pooled_data
 
 
 class GritLMPooler(Pooler):

vllm/model_executor/models/utils.py

@@ -777,13 +777,6 @@ def fast_topk(
         return torch.topk(values, topk, dim=dim)
 
 
-def get_model_hidden_size(hf_config: PretrainedConfig) -> int:
-    if hasattr(hf_config, "hidden_size"):
-        return hf_config.hidden_size
-    text_config = hf_config.get_text_config()
-    return text_config.hidden_size
-
-
 # Chunk x along the num_tokens axis for sequence parallelism
 # NOTE: This is wrapped in a torch custom op to work around the following issue:
 # The output tensor can have a sequence length 0 at small input sequence lengths

vllm/transformers_utils/config.py

@@ -1049,6 +1049,40 @@ def try_get_tokenizer_config(
         return None
 
 
+@cache
+def try_get_dense_modules(
+    model: str | Path,
+    revision: str | None = None,
+) -> list[dict[str, Any]] | None:
+    try:
+        modules = get_hf_file_to_dict("modules.json", model, revision)
+        if not modules:
+            return None
+
+        if isinstance(modules, dict):
+            modules = modules.get("modules", [])
+
+        dense_modules = [
+            m for m in modules if m.get("type") == "sentence_transformers.models.Dense"
+        ]
+        if not dense_modules:
+            return None
+
+        layer_configs = []
+        for module in dense_modules:
+            folder = module.get("path", "")
+
+            config_path = f"{folder}/config.json" if folder else "config.json"
+            layer_config = get_hf_file_to_dict(config_path, model, revision)
+            if not layer_config:
+                continue
+            layer_config["folder"] = folder
+            layer_configs.append(layer_config)
+        return layer_configs
+    except Exception:
+        return None
+
+
 def get_safetensors_params_metadata(
     model: str,
     *,