Serialize tensors using int8 views (#16866)

Signed-off-by: Staszek Pasko <staszek@gmail.com>
Co-authored-by: Nick Hill <nhill@redhat.com>

tests/v1/test_serial_utils.py

@@ -47,6 +47,10 @@ def test_encode_decode():
             torch.rand((1, 10), dtype=torch.float32),
             torch.rand((3, 5, 4000), dtype=torch.float64),
             torch.tensor(1984),  # test scalar too
+            # Make sure to test bf16 which numpy doesn't support.
+            torch.rand((3, 5, 1000), dtype=torch.bfloat16),
+            torch.tensor([float("-inf"), float("inf")] * 1024,
+                         dtype=torch.bfloat16),
         ],
         numpy_array=np.arange(512),
         unrecognized=UnrecognizedType(33),
@@ -64,7 +68,7 @@ def test_encode_decode():
     # There should be the main buffer + 4 large tensor buffers
     # + 1 large numpy array. "large" is <= 512 bytes.
     # The two small tensors are encoded inline.
-    assert len(encoded) == 6
+    assert len(encoded) == 8
 
     decoded: MyType = decoder.decode(encoded)
 
@@ -76,7 +80,7 @@ def test_encode_decode():
 
     encoded2 = encoder.encode_into(obj, preallocated)
 
-    assert len(encoded2) == 6
+    assert len(encoded2) == 8
     assert encoded2[0] is preallocated
 
     decoded2: MyType = decoder.decode(encoded2)
@@ -114,15 +118,15 @@ def test_multimodal_kwargs():
 
     total_len = sum(memoryview(x).cast("B").nbytes for x in encoded)
 
-    # expected total encoding length, should be 44536, +-20 for minor changes
-    assert total_len >= 44516 and total_len <= 44556
+    # expected total encoding length, should be 44559, +-20 for minor changes
+    assert total_len >= 44539 and total_len <= 44579
     decoded: MultiModalKwargs = decoder.decode(encoded).mm[0]
     assert all(nested_equal(d[k], decoded[k]) for k in d)
 
 
 def test_multimodal_items_by_modality():
-    e1 = MultiModalFieldElem("audio", "a0", torch.zeros(1000,
-                                                        dtype=torch.int16),
+    e1 = MultiModalFieldElem("audio", "a0",
+                             torch.zeros(1000, dtype=torch.bfloat16),
                              MultiModalBatchedField())
     e2 = MultiModalFieldElem(
         "video",