[Frontend][EPD] Use JSON arrays for multimodal metadata

examples/disaggregated/disaggregated_encoder/disagg_epd_proxy.py

@@ -22,7 +22,6 @@
 import argparse
 import asyncio
 import hashlib
-import io
 import itertools
 import json
 import logging
@@ -34,7 +33,6 @@
 from typing import Any
 
 import aiohttp
-import pybase64 as base64
 import uvicorn
 from fastapi import FastAPI, HTTPException, Request
 from fastapi.responses import JSONResponse, StreamingResponse
@@ -125,19 +123,6 @@ def content_uuid(item: dict) -> str:
     return hashlib.sha256(payload.encode()).hexdigest()
 
 
-def _b64_tensor(values: list) -> str:
-    import torch
-
-    buf = io.BytesIO()
-    flat = [v for item in values for v in (item if isinstance(item, list) else [item])]
-    # Floats stay float64 so timestamp strings format exactly as the
-    # encoder computed them.
-    dtype = torch.float64 if any(isinstance(v, float) for v in flat) else None
-    # Downstream stacks per item, so hand over a flat vector.
-    torch.save(torch.tensor(flat, dtype=dtype), buf)
-    return base64.b64encode(buf.getvalue()).decode()
-
-
 def rewrite_for_decode(req_data: dict, item_meta: dict[int, dict]) -> dict:
     """Replace each media item with a metadata-only reference for the decoder.
 
@@ -172,7 +157,15 @@ def rewrite_for_decode(req_data: dict, item_meta: dict[int, dict]) -> dict:
             # Whatever keys the encoder reported are the metadata its model
             # declared as needed to size the placeholder range; the proxy does
             # not need to know their names.
-            metadata = {k: _b64_tensor(v) for k, v in meta.items()}
+            # Downstream stacks per item; keep the existing per-item vector shape.
+            metadata = {
+                k: [
+                    x
+                    for item in v
+                    for x in (item if isinstance(item, list) else [item])
+                ]
+                for k, v in meta.items()
+            }
             if not metadata or not item_uuid:
                 # Nothing to size the placeholder range with. A processor cache
                 # hit is not a cause on its own: with the default `lru` type the

tests/entrypoints/unit_tests/test_chat_utils.py

@@ -18,6 +18,8 @@
     MEDIA_CONNECTOR_REGISTRY,
     AsyncMultiModalItemTracker,
     ConversationMessage,
+    _load_embeds_dict,
+    _parse_metadata_array,
     _postprocess_messages,
     parse_chat_messages,
     parse_chat_messages_async,
@@ -39,6 +41,45 @@
 MISTRAL_MODEL_ID = "mistralai/Mistral-Small-3.1-24B-Instruct-2503"
 
 
+@pytest.mark.parametrize("values", [[1, 32, 48], [0.0, 0.12345678912345678]])
+def test_json_metadata_preserves_numeric_precision(values):
+    tensor = _parse_metadata_array("grid", values, {"grid"})
+    assert tensor.tolist() == values
+    assert tensor.dtype == (
+        torch.float64 if isinstance(values[0], float) else torch.int64
+    )
+
+
+@pytest.mark.parametrize(
+    "key,values",
+    [
+        ("image_embeds", [1, 2]),
+        ("grid", [True]),
+        ("grid", [float("nan")]),
+        ("grid", [float("inf")]),
+        ("grid", [[1, 2]]),
+        ("grid", [2**64]),
+    ],
+)
+def test_json_arrays_are_only_valid_numeric_metadata(key, values):
+    with pytest.raises(VLLMValidationError):
+        _parse_metadata_array(key, values, {"grid"})
+
+
+@pytest.mark.asyncio
+async def test_json_metadata_bypasses_tensor_deserialization():
+    from unittest.mock import AsyncMock
+
+    tensor = torch.ones(2, 3)
+    fetch = AsyncMock(return_value=tensor)
+    result = await _load_embeds_dict(
+        {"image_embeds": "legacy-base64", "image_grid_thw": [1, 2, 2]}, fetch
+    )
+    fetch.assert_awaited_once_with("legacy-base64")
+    assert result["image_embeds"] is tensor
+    assert result["image_grid_thw"] == [1, 2, 2]
+
+
 @pytest.fixture(scope="function")
 def kimi_k2_5_model_config():
     return ModelConfig(
@@ -1355,8 +1396,10 @@ def test_parse_chat_messages_empty_dict_image_embeds(
     _assert_mm_uuids(mm_uuids, 1, expected_uuids=[None])
 
 
+@pytest.mark.parametrize("json_metadata", [False, True])
 def test_parse_chat_messages_multiple_dict_image_embeds(
     qwen25omni_model_config_image_embeds,
+    json_metadata,
 ):
     """Test that multiple dictionaries for image_embeds is handled without errors."""
     # Create two sample image embedding tensors
@@ -1374,7 +1417,9 @@ def test_parse_chat_messages_multiple_dict_image_embeds(
                         "type": "image_embeds",
                         "image_embeds": {
                             "image_embeds": tensor2base64(embeds),
-                            "image_grid_thw": tensor2base64(grid_thw),
+                            "image_grid_thw": grid_thw.tolist()
+                            if json_metadata
+                            else tensor2base64(grid_thw),
                         },
                     }
                     for embeds, grid_thw in zip(

tests/utils_/test_collection_utils.py

@@ -2,7 +2,31 @@
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 import pytest
 
-from vllm.utils.collection_utils import common_prefix, swap_dict_values
+from vllm.utils.collection_utils import (
+    common_prefix,
+    is_list_of_numbers,
+    swap_dict_values,
+)
+
+
+@pytest.mark.parametrize(
+    "value,expected",
+    [
+        ([], True),
+        ([1, -2, 2**1024], True),
+        ([1, 0.5], True),
+        ([1, True], False),
+        ([1, float("nan")], False),
+        ([1, float("inf")], False),
+        ([1, -float("inf")], False),
+        ([1, "2"], False),
+        ([[1]], False),
+        ((1, 2), False),
+        (None, False),
+    ],
+)
+def test_is_list_of_numbers_checks_all_finite_non_boolean_items(value, expected):
+    assert is_list_of_numbers(value) is expected
 
 
 @pytest.mark.parametrize(

tests/v1/ec_connector/unit/test_epd_proxy_round_robin.py

@@ -123,6 +123,9 @@ def test_decode_rewrite_preserves_engine_reported_ec_hash(proxy):
     )
 
     assert rewritten["messages"][0]["content"][0]["uuid"] == "proxy-uuid"
+    assert rewritten["messages"][0]["content"][0]["image_embeds"] == {
+        "image_grid_thw": [1, 2, 3]
+    }
     assert rewritten["ec_transfer_params"]["ec_items"] == [
         {"mm_hash": "engine-derived-hash", "transfer_id": "transfer"}
     ]