[New Model]: jinaai/jina-embeddings-v3

tests/conftest.py

@@ -671,8 +671,8 @@ def generate_encoder_decoder_greedy_logprobs_limit(
         return [(output_ids, output_str, output_logprobs)
                 for output_ids, output_str, output_logprobs in outputs]
 
-    def encode(self, prompts: list[str]) -> list[list[torch.Tensor]]:
-        return self.model.encode(prompts)
+    def encode(self, prompts: list[str], *args, **kwargs) -> list[list[torch.Tensor]]:
+        return self.model.encode(prompts, *args, **kwargs)
 
     def predict(self, prompts: list[list[str]]) -> torch.Tensor:
         return self.model.predict(prompts, convert_to_tensor=True)

tests/models/embedding/language/test_jina_reranker_v2.py → tests/models/embedding/language/test_jina.py

@@ -2,13 +2,15 @@
 # ruff: noqa: E501
 """Compare the scoring outputs of HF and vLLM models.
 
-Run `pytest tests/models/embedding/language/test_jina_reranker_v2.py`.
+Run `pytest tests/models/embedding/language/test_jina.py`.
 """
 import math
 
 import pytest
 
-MODELS = [
+from tests.models.embedding.utils import check_embeddings_close
+
+SCORING_MODELS = [
     "jinaai/jina-reranker-v2-base-multilingual",  # Roberta
 ]
 
@@ -27,8 +29,21 @@
     "新しいメイクのトレンドは鮮やかな色と革新的な技術に焦点を当てています",
 ]
 
+EMBEDDING_MODELS = [
+    "jinaai/jina-embeddings-v3",
+]
+
+EMBEDDING_PROMPTS = [
+    "Follow the white rabbit.",  # English
+    "Sigue al conejo blanco.",  # Spanish
+    "Suis le lapin blanc.",  # French
+    "跟着白兔走。",  # Chinese
+    "اتبع الأرنب الأبيض.",  # Arabic
+    "Folge dem weißen Kaninchen.",  # German
+]
+
 
-@pytest.fixture(scope="module", params=MODELS)
+@pytest.fixture(scope="module", params=SCORING_MODELS)
 def model_name(request):
     yield request.param
 
@@ -68,3 +83,46 @@ def test_llm_1_to_N(vllm_runner, hf_runner, model_name, dtype: str):
 
     assert math.isclose(hf_outputs[0], vllm_outputs[0], rel_tol=0.01)
     assert math.isclose(hf_outputs[1], vllm_outputs[1], rel_tol=0.01)
+
+
+@pytest.fixture(scope="module", params=EMBEDDING_MODELS)
+def emb_model_name(request):
+    yield request.param
+
+
+def test_matryoshka(vllm_runner, emb_model_name):
+    with vllm_runner(emb_model_name, task="embed",
+                     max_model_len=None) as vllm_model:
+        assert vllm_model.model.llm_engine.model_config.matryoshka
+
+
+@pytest.mark.parametrize("model", EMBEDDING_MODELS)
+@pytest.mark.parametrize("dtype", ["half"])
+def test_embeddings(
+    hf_runner,
+    vllm_runner,
+    model,
+    dtype: str,
+    monkeypatch,
+) -> None:
+
+    example_prompts = EMBEDDING_PROMPTS
+
+    with hf_runner(
+            model,
+            dtype=dtype,
+            is_sentence_transformer=True,
+    ) as hf_model:
+        hf_outputs = hf_model.encode(example_prompts, task="text-matching")
+
+    with vllm_runner(model, task="embed", dtype=dtype,
+                     max_model_len=None) as vllm_model:
+        vllm_outputs = vllm_model.encode(example_prompts)
+
+    check_embeddings_close(
+        embeddings_0_lst=hf_outputs,
+        embeddings_1_lst=vllm_outputs,
+        name_0="hf",
+        name_1="vllm",
+        tol=1e-2,
+    )

vllm/config.py

@@ -1124,6 +1124,11 @@ def is_v1_compatible(self) -> bool:
         architectures = getattr(self.hf_config, "architectures", [])
         return ModelRegistry.is_v1_compatible(architectures)
 
+    @property
+    def matryoshka(self) -> bool:
+        return (hasattr(self.hf_config, "matryoshka_dimensions")
+                or getattr(self.hf_config, "matryoshka", False))
+
 
 class CacheConfig:
     """Configuration for the KV cache.