[CI] improve embed testing (#18747)

tests/models/language/pooling/embed_utils.py

@@ -0,0 +1,72 @@
+# SPDX-License-Identifier: Apache-2.0
+from collections.abc import Sequence
+from typing import Optional
+
+import pytest
+
+from tests.conftest import HfRunner
+from tests.models.utils import (EmbedModelInfo, check_embeddings_close,
+                                matryoshka_fy)
+
+
+def run_embedding_correctness_test(
+    hf_model: "HfRunner",
+    inputs: list[str],
+    vllm_outputs: Sequence[list[float]],
+    dimensions: Optional[int] = None,
+):
+    hf_outputs = hf_model.encode(inputs)
+    if dimensions:
+        hf_outputs = matryoshka_fy(hf_outputs, dimensions)
+
+    check_embeddings_close(
+        embeddings_0_lst=hf_outputs,
+        embeddings_1_lst=vllm_outputs,
+        name_0="hf",
+        name_1="vllm",
+        tol=1e-2,
+    )
+
+
+def correctness_test_embed_models(hf_runner,
+                                  vllm_runner,
+                                  model_info: EmbedModelInfo,
+                                  example_prompts,
+                                  vllm_extra_kwargs=None,
+                                  hf_model_callback=None):
+    if not model_info.enable_test:
+        # A model family has many models with the same architecture,
+        # and we don't need to test each one.
+        pytest.skip("Skipping test.")
+
+    # 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]
+
+    vllm_extra_kwargs = vllm_extra_kwargs or {}
+    vllm_extra_kwargs["dtype"] = model_info.dtype
+
+    with vllm_runner(model_info.name,
+                     task="embed",
+                     max_model_len=None,
+                     **vllm_extra_kwargs) as vllm_model:
+        vllm_outputs = vllm_model.encode(example_prompts)
+        vllm_dtype = vllm_model.model.llm_engine.model_config.dtype
+        model_dtype = getattr(
+            vllm_model.model.llm_engine.model_config.hf_config, "torch_dtype",
+            vllm_dtype)
+
+    with hf_runner(
+            model_info.name,
+            dtype=model_dtype,
+            is_sentence_transformer=True,
+    ) as hf_model:
+
+        if hf_model_callback is not None:
+            hf_model_callback(hf_model)
+
+        run_embedding_correctness_test(hf_model, example_prompts, vllm_outputs)