Sync from v0.13

tests/models/language/pooling/test_embedding.py

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+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import pytest
+
+from vllm.config import PoolerConfig
+
+from ...utils import check_embeddings_close
+
+
+@pytest.mark.parametrize(
+    "model",
+    [
+        # Be careful of the order of models, decoder-only models should be
+        # placed before encoder-only models, otherwise `Qwen2.5-0.5B-Instruct`
+        # case won't pass because gte-Qwen2-1.5B-instruct will cache custom
+        # model code with bidirectional attention.
+        # [Decoder-only]
+        pytest.param(
+            "BAAI/bge-multilingual-gemma2",
+            marks=[pytest.mark.core_model, pytest.mark.slow_test],
+        ),
+        pytest.param(
+            "intfloat/e5-mistral-7b-instruct",
+            marks=[pytest.mark.core_model, pytest.mark.cpu_model],
+        ),
+        pytest.param(
+            "ssmits/Qwen2-7B-Instruct-embed-base", marks=[pytest.mark.cpu_model]
+        ),
+        # [Encoder-only]
+        pytest.param(
+            "BAAI/bge-base-en-v1.5",
+            marks=[
+                pytest.mark.core_model,
+                pytest.mark.cpu_model,
+                pytest.mark.slow_test,
+            ],
+        ),
+        pytest.param("sentence-transformers/all-MiniLM-L12-v2"),
+        pytest.param("intfloat/multilingual-e5-small"),
+        # [Cross-Encoder]
+        pytest.param(
+            "sentence-transformers/stsb-roberta-base-v2",
+            marks=[pytest.mark.core_model, pytest.mark.cpu_model],
+        ),
+    ],
+)
+def test_models(
+    hf_runner,
+    vllm_runner,
+    example_prompts,
+    model,
+) -> None:
+    vllm_extra_kwargs = {}
+    if model == "ssmits/Qwen2-7B-Instruct-embed-base":
+        vllm_extra_kwargs["pooler_config"] = PoolerConfig(
+            pooling_type="MEAN", normalize=False
+        )
+
+    max_model_len: int | None = 512
+    if model in [
+        "sentence-transformers/all-MiniLM-L12-v2",
+        "sentence-transformers/stsb-roberta-base-v2",
+    ]:
+        max_model_len = None
+
+    # The example_prompts has ending "\n", for example:
+    # "Write a short story about a robot that dreams for the first time.\n"
+    # sentence_transformers will strip the input texts, see:
+    # https://github.com/UKPLab/sentence-transformers/blob/v3.1.1/sentence_transformers/models/Transformer.py#L159
+    # This makes the input_ids different between hf_model and vllm_model.
+    # So we need to strip the input texts to avoid test failing.
+    example_prompts = [str(s).strip() for s in example_prompts]
+
+    with hf_runner(model, is_sentence_transformer=True) as hf_model:
+        hf_outputs = hf_model.encode(example_prompts)
+
+    with vllm_runner(
+        model, runner="pooling", max_model_len=max_model_len, **vllm_extra_kwargs
+    ) as vllm_model:
+        vllm_outputs = vllm_model.embed(example_prompts)
+
+    check_embeddings_close(
+        embeddings_0_lst=hf_outputs,
+        embeddings_1_lst=vllm_outputs,
+        name_0="hf",
+        name_1="vllm",
+        tol=1e-2,
+    )