mxtensor: support clone

test/prototype/mx_formats/test_mx_tensor.py

@@ -455,6 +455,20 @@ def test_view(elem_dtype):
     x_mx_2 = x_mx.view(2, 4)  # noqa: F841
 
 
+@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available")
+def test_clone():
+    data = torch.randn(8, 8, device="cuda", dtype=torch.bfloat16)
+    block_size = 4
+    data_mx = MXTensor.to_mx(data, torch.float8_e4m3fn, block_size)
+    data_mx_c = data_mx.clone()
+    torch.testing.assert_close(
+        data_mx.to_dtype(torch.bfloat16),
+        data_mx_c.to_dtype(torch.bfloat16),
+        atol=0,
+        rtol=0,
+    )
+
+
 @pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available")
 @pytest.mark.parametrize("elem_dtype", [DTYPE_FP6_E2M3, DTYPE_FP6_E3M2])
 @pytest.mark.parametrize("pack_fp6", [False, True])

torchao/prototype/mx_formats/mx_ops.py

@@ -352,3 +352,16 @@ def autocast_to_copy(func, types, args, kwargs):
 
     # If only device was changed, return the device-changed tensor
     return tensor
+
+
+@implements([aten.clone.default])
+def mx_clone(func, types, args, kwargs):
+    self = args[0]
+    memory_format = kwargs.get("memory_format", None)
+
+    if memory_format is not None:
+        clone_fn = lambda x: x.clone(memory_format=memory_format)
+    else:
+        clone_fn = lambda x: x.clone()
+
+    return self._apply_fn_to_data(clone_fn)