[GitHub] [tvm] Lunderberg commented on a diff in pull request #12926: [TE] Raise error for non-bijective transformation

[GitBox <gi...@apache.org>]

Lunderberg commented on code in PR #12926:
URL: https://github.com/apache/tvm/pull/12926#discussion_r982674487


##########
tests/python/unittest/test_transform_layout.py:
##########
@@ -575,5 +575,18 @@ def test_size_one_buffer(shape, transform):
     s[B].transform_layout(transform)
 
 
+def test_non_divisible_transform_raises_error():
+    A = te.placeholder([1, 3, 8, 8])
+    B = te.compute(A.shape, lambda *indices: A[indices])
+    s = te.create_schedule(B.op)
+
+    transform = lambda n, c, h, w: [n, c // 4, h, w, c % 4]
+    # Error occurs here, because the transformation would introduce
+    # padding.  Padded transforms are supported in TIR-based
+    # schedules.
+    with pytest.raises(tvm.TVMError):
+        s[B].transform_layout(transform)
+
+

Review Comment:
   It could, but I think it would make the test less readable as an example use case, specifically what behavior is being tested, because the desired behavior differs in each case.  It would look something like below, but there's nothing to call attention to the fact that `is_valid` changes the expected behavior, and isn't just a parameter being used in the setup.
   
   ```python
   shape, transform, is_valid = tvm.testing.parameters(
       ([1, 8], lambda n, i: [i, n], True),
       ([1, 1, 8], lambda i, j, k: [j, te.AXIS_SEPARATOR, i, k], True),
       ([1, 1, 8], lambda i, j, k: [i, te.AXIS_SEPARATOR, j, k], True),
       ([1, 3, 8, 8], lambda i, j, k: [i, te.AXIS_SEPARATOR, j, k], False),
   )
   
   
   def test_transform_validity(shape, transform, is_valid):
       dtype = "int8"
       A = te.placeholder(shape, dtype, name="A")
       B = te.compute(
           shape=A.shape,
           fcompute=lambda *indices: A[indices].astype(dtype),
           name="B",
       )
       s = te.create_schedule(B.op)
   
       if is_valid:
           s[B].transform_layout(transform)
       else:
           with pytest.raises(tvm.TVMError):
               s[B].transform_layout(transform)
   ```



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