Allow repeat to accept shapescalar along with int for repeats argument

tripy/tests/frontend/test_shape.py

@@ -82,7 +82,6 @@ def test_shape(self, values):
 
         assert isinstance(s, tp.Shape)
         assert len(s) == len(values)
-        assert s.trace_tensor.producer.inputs == []
         assert cp.from_dlpack(s).get().tolist() == values
 
     def test_empty_shape(self):

tripy/tests/integration/test_repeat.py

@@ -37,3 +37,11 @@ def test_repeat(self, repeats, dim):
         expected = np.repeat(inp, repeats, dim)
 
         assert np.array_equal(np.from_dlpack(tp.copy(out, device=tp.device("cpu"))), expected)
+
+    def test_repeat_shape_scalar(self):
+        inp = np.arange(4, dtype=np.int32).reshape((2, 2))
+        s = tp.ones((1, 2))
+        out = tp.repeat(tp.Tensor(inp), s.shape[1], 0)
+        expected = np.repeat(inp, 2, 0)
+
+        assert np.array_equal(np.from_dlpack(tp.copy(out, device=tp.device("cpu"))), expected)

tripy/tripy/backend/mlir/utils.py

@@ -93,8 +93,14 @@ def get_mlir_scalar_attr(mlir_dtype, value):
 
 
 def list_to_dense_attr(data: List, mlir_dtype):
+    from tripy.frontend.shape import ShapeScalar
+
     if isinstance(data, numbers.Number):
         return [get_mlir_scalar_attr(mlir_dtype, data)]
+
+    if isinstance(data, ShapeScalar):
+        return [get_mlir_scalar_attr(mlir_dtype, data.tolist())]
+
     attrs = []
     for element in data:
         attrs.extend(list_to_dense_attr(element, mlir_dtype))

tripy/tripy/frontend/ops/repeat.py

@@ -13,6 +13,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 #
+from typing import Union
 from tripy import constraints, export
 from tripy.common.exception import raise_error
 from tripy.frontend import utils as frontend_utils
@@ -26,7 +27,7 @@
     dtype_constraints={"input": "T1", constraints.RETURN_VALUE: "T1"},
 )
 @frontend_utils.process_dim
-def repeat(input: "tripy.Tensor", repeats: int, dim: int) -> "tripy.Tensor":
+def repeat(input: "tripy.Tensor", repeats: Union[int, "tripy.ShapeScalar"], dim: int) -> "tripy.Tensor":
     """
     Repeats each element of a tensor after itself along the specified dimension.
 
@@ -68,9 +69,14 @@ def repeat(input: "tripy.Tensor", repeats: int, dim: int) -> "tripy.Tensor":
     from tripy.frontend.trace.ops.expand import expand
     from tripy.frontend.trace.ops.reshape import reshape
     from tripy.frontend.trace.ops.unsqueeze import unsqueeze
+    from tripy.frontend.tensor import Tensor
+    from tripy.frontend.shape import ShapeScalar, Shape
+    from tripy.frontend.trace.ops.concatenate import concatenate
 
-    if repeats < 0:
-        raise_error("`repeats` value must be non-negative.", [f"Got: repeats={repeats}."])
+    if isinstance(repeats, int):
+        if repeats < 0:
+            raise_error("`repeats` value must be non-negative.", [f"Got: repeats={repeats}."])
+        repeats = ShapeScalar(repeats)
 
     # By constraining repeats to be a single integer, we can use a very
     # simple implementation for repeat.
@@ -84,10 +90,11 @@ def repeat(input: "tripy.Tensor", repeats: int, dim: int) -> "tripy.Tensor":
     #            [2],]     [2, 2],]
     #
     out = unsqueeze(input, dim + 1)
-    out = expand(out, input.shape[: dim + 1] + [repeats] + input.shape[dim + 1 :])
+    out = expand(out, input.shape[: dim + 1] + Shape([repeats]) + input.shape[dim + 1 :])
 
-    repeat_mask = [1] * input.rank
-    repeat_mask[dim] = repeats
+    repeat_mask = concatenate(
+        [reshape(repeats, (1,)) if idx == dim else Tensor([1]) for idx in range(input.rank)], dim=0
+    )
     new_shape = input.shape.multiply(repeat_mask)
     out = reshape(out, new_shape)
     return out

tripy/tripy/frontend/shape.py

@@ -120,6 +120,25 @@ def __init__(
                     details=[data],
                 )
 
+            # the shape of data should correspond to the given rank
+            super().__init__(data=None, dtype=int32, name=name, device=data.device)
+            # share the underlying data
+            self.trace_tensor = data.trace_tensor
+            self.stack_info = data.stack_info
+        elif (
+            isinstance(data, Sequence)
+            and len(data) > 0
+            and all(map(lambda e: isinstance(e, int) or isinstance(e, ShapeScalar), data))
+        ):
+            # Handle the case where data is a list of mixed int and ShapeScalar elements
+            # Example: [1, a.shape[0]]
+            # We convert this to a tensor to avoid expensive evaluation of ShapeScalar elements (like a.shape[0])
+            from tripy.frontend.trace.ops.concatenate import concatenate
+            from tripy.frontend.trace.ops.reshape import reshape
+
+            data = concatenate(
+                [reshape(e, (1,)) if isinstance(e, ShapeScalar) else Tensor([e], dtype=int32) for e in data], dim=0
+            )
             # the shape of data should correspond to the given rank
             super().__init__(data=None, dtype=int32, name=name, device=data.device)
             # share the underlying data