[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>

examples/pooling/embed/embedding_requests_bytes_client.py

@@ -16,6 +16,7 @@ from vllm.utils.serial_utils import (
     EMBED_DTYPE_TO_TORCH_DTYPE,
     ENDIANNESS,
     MetadataItem,
+    build_metadata_items,
     decode_pooling_output,
 )
 
@@ -38,6 +39,11 @@ def parse_args():
 def main(args):
     api_url = f"http://{args.host}:{args.port}/v1/embeddings"
     model_name = args.model
+    embedding_size = 0
+
+    input_texts = [
+        "The best thing about vLLM is that it supports many different models",
+    ] * 2
 
     # The OpenAI client does not support the bytes encoding_format.
     # The OpenAI client does not support the embed_dtype and endianness parameters.
@@ -45,7 +51,7 @@ def main(args):
         for endianness in ENDIANNESS:
             prompt = {
                 "model": model_name,
-                "input": "vLLM is great!",
+                "input": input_texts,
                 "encoding_format": "bytes",
                 "embed_dtype": embed_dtype,
                 "endianness": endianness,
@@ -57,7 +63,34 @@ def main(args):
 
             embedding = decode_pooling_output(items=items, body=body)
             embedding = [x.to(torch.float32) for x in embedding]
-            embedding = torch.cat(embedding)
+            embedding = torch.stack(embedding)
+            embedding_size = embedding.shape[-1]
+            print(embed_dtype, endianness, embedding.shape)
+
+    # The vllm server always sorts the returned embeddings in the order of input. So
+    # returning metadata is not necessary. You can set encoding_format to bytes_only
+    # to let the server not return metadata.
+    for embed_dtype in EMBED_DTYPE_TO_TORCH_DTYPE:
+        for endianness in ENDIANNESS:
+            prompt = {
+                "model": model_name,
+                "input": input_texts,
+                "encoding_format": "bytes_only",
+                "embed_dtype": embed_dtype,
+                "endianness": endianness,
+            }
+            response = post_http_request(prompt=prompt, api_url=api_url)
+            body = response.content
+
+            items = build_metadata_items(
+                embed_dtype=embed_dtype,
+                endianness=endianness,
+                shape=(embedding_size,),
+                n_request=len(input_texts),
+            )
+            embedding = decode_pooling_output(items=items, body=body)
+            embedding = [x.to(torch.float32) for x in embedding]
+            embedding = torch.stack(embedding)
             print(embed_dtype, endianness, embedding.shape)