Add support for Qwen2-VL-based embedding models (#2055)

python/sglang/test/runners.py

@@ -58,6 +58,28 @@ def get_top_logprobs(logits, k):
     return logprobs
 
 
+def _get_sentence_transformer_embedding_model(model_path, torch_dtype):
+    from sentence_transformers import SentenceTransformer
+    from sentence_transformers.util import is_sentence_transformer_model
+
+    if is_sentence_transformer_model(model_path):
+        model = SentenceTransformer(
+            model_path,
+            model_kwargs={"torch_dtype": torch_dtype},
+        )
+    else:  # if no pre-trained sentence-transformers model
+        from sentence_transformers import models
+
+        word_embedding_model = models.Transformer(model_path).to(dtype=torch_dtype)
+        pooling_model = models.Pooling(
+            word_embedding_model.get_word_embedding_dimension(),
+            pooling_mode="lasttoken",
+        )
+        model = SentenceTransformer(modules=[word_embedding_model, pooling_model])
+
+    return model.cuda()
+
+
 @dataclass
 class ModelOutput:
     output_strs: List[str] = None
@@ -114,12 +136,9 @@ class HFRunner:
                 low_cpu_mem_usage=True,
             ).cuda()
         elif self.model_type == "embedding":
-            from sentence_transformers import SentenceTransformer
-
-            self.model = SentenceTransformer(
-                model_path,
-                model_kwargs={"torch_dtype": torch_dtype},
-            ).cuda()
+            self.model = _get_sentence_transformer_embedding_model(
+                model_path, torch_dtype
+            )
         elif self.model_type == "reward":
             from transformers import AutoModelForSequenceClassification