slyubomirsky commented on code in PR #14282:
URL: https://github.com/apache/tvm/pull/14282#discussion_r1134351936
##########
tests/python/relax/test_relax_operators.py:
##########
@@ -193,5 +193,20 @@ def test_op_shape_of():
assert constrained_shape == tvm.runtime.ShapeTuple([1])
[email protected]_module
+class TensorToShapeTest:
+ @R.function
+ def run_tensor_to_shape(t: R.Tensor(ndim=1, dtype="int64")) -> R.Shape((1,
2, 3)):
+ gv: R.Shape(ndim=3) = R.tensor_to_shape(t)
+ return gv
+
+
+def test_op_tensor_to_shape():
+ out_shape = run_cpu(
+ TensorToShapeTest, "run_tensor_to_shape", tvm.nd.array(np.array([1, 2,
3]).astype("int64"))
Review Comment:
I think this is correct. We are converting the rank-1 tensor [1, 2, 3] into
a shape (1, 2, 3). I think the problem here is that `ndim` means different
things for tensors (where it means the rank) and shapes (where it means the
number of dimensions denoted by the shape).
##########
tests/python/relax/test_relax_operators.py:
##########
@@ -193,5 +193,20 @@ def test_op_shape_of():
assert constrained_shape == tvm.runtime.ShapeTuple([1])
[email protected]_module
+class TensorToShapeTest:
+ @R.function
+ def run_tensor_to_shape(t: R.Tensor(ndim=1, dtype="int64")) -> R.Shape((1,
2, 3)):
+ gv: R.Shape(ndim=3) = R.tensor_to_shape(t)
+ return gv
+
+
+def test_op_tensor_to_shape():
+ out_shape = run_cpu(
+ TensorToShapeTest, "run_tensor_to_shape", tvm.nd.array(np.array([1, 2,
3]).astype("int64"))
Review Comment:
I think the demonstrated behavior is correct. We are converting the rank-1
tensor [1, 2, 3] into a shape (1, 2, 3). I think the problem here is that
`ndim` means different things for tensors (where it means the rank) and shapes
(where it means the number of dimensions denoted by the shape).
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