Repro for misaligned memory access

tests/python/multidevice/test_multidevice.py

@@ -29,14 +29,14 @@ def test_sizes_and_ranks(multidevice_test):
 @pytest.mark.mpi
 def test_pointwise(multidevice_test):
     num_devices = multidevice_test.size
-    mesh = nvfuser.multidevice.DeviceMesh(torch.arange(num_devices))
 
     with FusionDefinition() as fd:
         inp_tv = fd.define_tensor((-1, -1), contiguity=False, dtype=DataType.Float)
         tv1 = fd.ops.relu(inp_tv)
         tv2 = fd.ops.add(tv1, tv1)
         fd.add_output(tv2)
 
+        mesh = nvfuser.multidevice.DeviceMesh(torch.arange(num_devices))
         for tv in [inp_tv, tv1, tv2]:
             tv.set_device_mesh(mesh)
 
@@ -50,6 +50,53 @@ def test_pointwise(multidevice_test):
     torch.testing.assert_close(out.cpu(), out_ref)
 
 
+@pytest.mark.mpi
+def test_transpose(multidevice_test):
+    d = multidevice_test.size
+    cp_size = 2
+    if d != cp_size * cp_size:
+        pytest.skip(f"{d=} must equal {cp_size=}^2.")
+
+    c = 128
+    with FusionDefinition() as fd:
+        inp_tv = fd.define_tensor(
+            (-1, c, -1, -1, cp_size), contiguity=True, dtype=DataType.BFloat16
+        )
+        out_tv = fd.ops.set(inp_tv)
+        fd.add_output(out_tv)
+
+        mesh = nvfuser.multidevice.DeviceMesh(torch.arange(d).reshape(cp_size, cp_size))
+        for tv in [inp_tv, out_tv]:
+            tv.set_device_mesh(mesh)
+
+        inp_tv.axis(4).parallelize(nvfuser.ParallelType.mesh_y)
+        inp_tv.outer_split(3, cp_size)
+        inp_tv.axis(3).parallelize(nvfuser.ParallelType.mesh_x)
+
+        out_tv.axis(4).parallelize(nvfuser.ParallelType.mesh_y)
+        out_tv.outer_split(3, cp_size)
+        out_tv.axis(3).parallelize(nvfuser.ParallelType.mesh_x)
+        out_tv.set_allocation_domain(
+            (
+                out_tv.axis(2),
+                out_tv.axis(0),
+                out_tv.axis(1),
+                out_tv.axis(3),
+                out_tv.axis(4),
+                out_tv.axis(5),
+            ),
+            True,
+        )
+
+    b = 3
+    s = cp_size * 5
+    inp_ref = torch.randn(b, c, s, s, cp_size, dtype=torch.bfloat16)
+    out_ref = inp_ref
+
+    inp = multidevice_test.shard_tensor(inp_ref, inp_tv)
+    fd.execute([inp])
+
+
 class QkvFormat(Enum):
     BHSE = auto()
     BSHE = auto()