[BugFix] Handle non-contiguous tensors properly when serializing (#16492)

Signed-off-by: Nick Hill <nhill@redhat.com>

tests/v1/test_serial_utils.py

@@ -22,6 +22,10 @@ class MyType:
     list_of_tensors: list[torch.Tensor]
     numpy_array: np.ndarray
     unrecognized: UnrecognizedType
+    small_f_contig_tensor: torch.Tensor
+    large_f_contig_tensor: torch.Tensor
+    small_non_contig_tensor: torch.Tensor
+    large_non_contig_tensor: torch.Tensor
 
 
 def test_encode_decode():
@@ -40,6 +44,10 @@ def test_encode_decode():
         ],
         numpy_array=np.arange(512),
         unrecognized=UnrecognizedType(33),
+        small_f_contig_tensor=torch.rand(5, 4).t(),
+        large_f_contig_tensor=torch.rand(1024, 4).t(),
+        small_non_contig_tensor=torch.rand(2, 4)[:, 1:3],
+        large_non_contig_tensor=torch.rand(1024, 512)[:, 10:20],
     )
 
     encoder = MsgpackEncoder()
@@ -47,10 +55,10 @@ def test_encode_decode():
 
     encoded = encoder.encode(obj)
 
-    # There should be the main buffer + 2 large tensor buffers
-    # + 1 large numpy array. "large" is <= 256 bytes.
+    # 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) == 4
+    assert len(encoded) == 6
 
     decoded: MyType = decoder.decode(encoded)
 
@@ -62,7 +70,7 @@ def test_encode_decode():
 
     encoded2 = encoder.encode_into(obj, preallocated)
 
-    assert len(encoded2) == 4
+    assert len(encoded2) == 6
     assert encoded2[0] is preallocated
 
     decoded2: MyType = decoder.decode(encoded2)
@@ -78,3 +86,9 @@ def assert_equal(obj1: MyType, obj2: MyType):
         for a, b in zip(obj1.list_of_tensors, obj2.list_of_tensors))
     assert np.array_equal(obj1.numpy_array, obj2.numpy_array)
     assert obj1.unrecognized.an_int == obj2.unrecognized.an_int
+    assert torch.equal(obj1.small_f_contig_tensor, obj2.small_f_contig_tensor)
+    assert torch.equal(obj1.large_f_contig_tensor, obj2.large_f_contig_tensor)
+    assert torch.equal(obj1.small_non_contig_tensor,
+                       obj2.small_non_contig_tensor)
+    assert torch.equal(obj1.large_non_contig_tensor,
+                       obj2.large_non_contig_tensor)