Support BERTModel (first encoder-only embedding model) (#9056)

Signed-off-by: Max de Bayser <maxdebayser@gmail.com>
Signed-off-by: Max de Bayser <mbayser@br.ibm.com>
Co-authored-by: Andrew Feldman <afeldman@neuralmagic.com>
Co-authored-by: afeldman-nm <156691304+afeldman-nm@users.noreply.github.com>
Co-authored-by: Woosuk Kwon <woosuk.kwon@berkeley.edu>
Co-authored-by: laishzh <laishengzhang@gmail.com>
Co-authored-by: Max de Bayser <maxdebayser@gmail.com>
Co-authored-by: Max de Bayser <mbayser@br.ibm.com>
Co-authored-by: Cyrus Leung <tlleungac@connect.ust.hk>

tests/models/embedding/language/test_embedding.py

@@ -6,21 +6,31 @@ import pytest
 
 from ..utils import check_embeddings_close
 
+# Model, Guard
 MODELS = [
     "intfloat/e5-mistral-7b-instruct",
+    "BAAI/bge-base-en-v1.5",
     "BAAI/bge-multilingual-gemma2",
 ]
 
+ENCODER_ONLY = [
+    "BAAI/bge-base-en-v1.5",
+]
+
 
 @pytest.mark.parametrize("model", MODELS)
 @pytest.mark.parametrize("dtype", ["half"])
 def test_models(
+    monkeypatch,
     hf_runner,
     vllm_runner,
     example_prompts,
-    model: str,
+    model,
     dtype: str,
 ) -> None:
+    if model in ENCODER_ONLY:
+        monkeypatch.setenv("VLLM_ATTENTION_BACKEND", "XFORMERS")
+
     # 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:
@@ -33,7 +43,7 @@ def test_models(
                    is_sentence_transformer=True) as hf_model:
         hf_outputs = hf_model.encode(example_prompts)
 
-    with vllm_runner(model, dtype=dtype) as vllm_model:
+    with vllm_runner(model, dtype=dtype, max_model_len=None) as vllm_model:
         vllm_outputs = vllm_model.encode(example_prompts)
 
     check_embeddings_close(