[Frontend] Binary embedding response does not return metadata by setting encoding_format to bytes_only. (#30249)

Signed-off-by: wang.yuqi <yuqi.wang@daocloud.io>
Signed-off-by: wang.yuqi <noooop@126.com>
Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>

tests/entrypoints/pooling/embed/test_online.py

@@ -24,6 +24,7 @@ from vllm.utils.serial_utils import (
     ENDIANNESS,
     MetadataItem,
     binary2tensor,
+    build_metadata_items,
     decode_pooling_output,
 )
 
@@ -344,6 +345,55 @@ async def test_bytes_embed_dtype_and_endianness(
             )
 
 
+@pytest.mark.asyncio
+@pytest.mark.parametrize("model_name", [MODEL_NAME])
+async def test_bytes_only_embed_dtype_and_endianness(
+    server: RemoteOpenAIServer, client: openai.AsyncOpenAI, model_name: str
+):
+    input_texts = [
+        "The best thing about vLLM is that it supports many different models",
+    ] * 2
+
+    responses_float = await client.embeddings.create(
+        input=input_texts, model=model_name, encoding_format="float"
+    )
+    float_data = [d.embedding for d in responses_float.data]
+    embedding_size = len(float_data[0])
+
+    for embed_dtype in list(EMBED_DTYPE_TO_TORCH_DTYPE.keys()):
+        for endianness in ENDIANNESS:
+            responses_bytes = requests.post(
+                server.url_for("/v1/embeddings"),
+                json={
+                    "model": model_name,
+                    "input": input_texts,
+                    "encoding_format": "bytes_only",
+                    "embed_dtype": embed_dtype,
+                    "endianness": endianness,
+                },
+            )
+
+            assert "metadata" not in responses_bytes.headers
+            body = responses_bytes.content
+            items = build_metadata_items(
+                embed_dtype=embed_dtype,
+                endianness=endianness,
+                shape=(embedding_size,),
+                n_request=len(input_texts),
+            )
+
+            bytes_data = decode_pooling_output(items=items, body=body)
+            bytes_data = [x.to(torch.float32).tolist() for x in bytes_data]
+
+            check_embeddings_close(
+                embeddings_0_lst=float_data,
+                embeddings_1_lst=bytes_data,
+                name_0="float_data",
+                name_1="bytes_data",
+                tol=1e-2,
+            )
+
+
 @pytest.mark.asyncio
 @pytest.mark.parametrize("model_name", [MODEL_NAME])
 @pytest.mark.parametrize("param_name", ["encoding_format", "embed_dtype", "endianness"])