[MultiModal] add support for numpy array embeddings (#38119)

Signed-off-by: guillaume_guy <guillaume.guy@airbnb.com>
Signed-off-by: Guillaume Guy <guillaume.c.guy@gmail.com>
Co-authored-by: guillaume_guy <guillaume.guy@airbnb.com>
Co-authored-by: Cyrus Leung <cyrus.tl.leung@gmail.com>

tests/renderers/test_sparse_tensor_validation.py

@@ -7,6 +7,7 @@ out-of-bounds memory writes during to_dense() operations.
 
 import io
 
+import numpy as np
 import pybase64 as base64
 import pytest
 import torch
@@ -190,6 +191,51 @@ class TestImageEmbedsValidation:
         with pytest.raises((RuntimeError, ValueError)):
             io_handler.load_bytes(buffer.read())
 
+    def test_valid_numpy_tensor_accepted(self):
+        """numpy .npy format should load and return correct tensor."""
+        io_handler = ImageEmbeddingMediaIO()
+
+        arr = np.array([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]], dtype=np.float32)
+        buf = io.BytesIO()
+        np.save(buf, arr)
+        encoded = base64.b64encode(buf.getvalue()).decode("utf-8")
+
+        result = io_handler.load_base64("", encoded)
+        assert isinstance(result, torch.Tensor)
+        assert result.shape == torch.Size([2, 3])
+        assert result.dtype == torch.float32
+        assert torch.allclose(result, torch.from_numpy(arr))
+
+    def test_numpy_int32_tensor_accepted(self):
+        """numpy int32 arrays should round-trip correctly."""
+
+        io_handler = ImageEmbeddingMediaIO()
+
+        arr = np.arange(280, dtype=np.int32)
+        buf = io.BytesIO()
+        np.save(buf, arr)
+        encoded = base64.b64encode(buf.getvalue()).decode("utf-8")
+
+        result = io_handler.load_base64("", encoded)
+        assert result.dtype == torch.int32
+        assert result.shape == torch.Size([280])
+        assert (result == torch.from_numpy(arr)).all()
+
+    def test_load_file_numpy_tensor_accepted(self, tmp_path):
+        """numpy .npy files should load correctly via load_file."""
+
+        io_handler = ImageEmbeddingMediaIO()
+
+        arr = np.array([[1.5, 2.5], [3.5, 4.5]], dtype=np.float32)
+        npy_path = tmp_path / "image_embeds.npy"
+        np.save(npy_path, arr)
+
+        result = io_handler.load_file(npy_path)
+        assert isinstance(result, torch.Tensor)
+        assert result.shape == torch.Size([2, 2])
+        assert result.dtype == torch.float32
+        assert torch.allclose(result, torch.from_numpy(arr))
+
 
 class TestAudioEmbedsValidation:
     """Test sparse tensor validation in audio embeddings (Chat API)."""