resolve reviewer comments

tests/dialects/test_builtin.py

@@ -217,7 +217,7 @@ def test_IntegerType_packing():
 
 
 def test_DenseIntOrFPElementsAttr_fp_type_conversion():
-    check1 = DenseIntOrFPElementsAttr.tensor_from_list([4, 5], f32, [])
+    check1 = DenseIntOrFPElementsAttr.tensor_from_list([4, 5], f32, [2])
 
     value1 = check1.get_attrs()[0].value.data
     value2 = check1.get_attrs()[1].value.data
@@ -231,7 +231,7 @@ def test_DenseIntOrFPElementsAttr_fp_type_conversion():
     t1 = FloatAttr(4.0, f32)
     t2 = FloatAttr(5.0, f32)
 
-    check2 = DenseIntOrFPElementsAttr.tensor_from_list([t1, t2], f32, [])
+    check2 = DenseIntOrFPElementsAttr.tensor_from_list([t1, t2], f32, [2])
 
     value3 = check2.get_attrs()[0].value.data
     value4 = check2.get_attrs()[1].value.data
@@ -244,9 +244,19 @@ def test_DenseIntOrFPElementsAttr_fp_type_conversion():
 
 
 def test_DenseIntOrFPElementsAttr_from_list():
+    # legal zero-rank tensor
     attr = DenseIntOrFPElementsAttr.tensor_from_list([5.5], f32, [])
+    assert attr.type == AnyTensorType(f32, [])
 
-    assert attr.type == AnyTensorType(f32, [1])
+    # illegal zero-rank tensor
+    with pytest.raises(
+        ValueError, match="A zero-rank tensor can only hold 1 value but 2 were given."
+    ):
+        DenseIntOrFPElementsAttr.tensor_from_list([5.5, 5.6], f32, [])
+
+    # legal normal tensor
+    attr = DenseIntOrFPElementsAttr.tensor_from_list([5.5, 5.6], f32, [2])
+    assert attr.type == AnyTensorType(f32, [2])
 
 
 @pytest.mark.parametrize(

xdsl/dialects/builtin.py

@@ -1927,6 +1927,10 @@ def get_shape(self) -> tuple[int, ...]:
     def get_element_type(self) -> IntegerType | IndexType | AnyFloat:
         return self.type.get_element_type()
 
+    @property
+    def nb_elements(self) -> int:
+        return prod(self.get_shape())
+
     @property
     def shape_is_complete(self) -> bool:
         shape = self.get_shape()
@@ -1939,7 +1943,7 @@ def shape_is_complete(self) -> bool:
             n *= dim
 
         # Product of dimensions needs to equal length
-        return n == len(self.get_values())
+        return n == self.nb_elements
 
     @staticmethod
     def create_dense_index(
@@ -2036,6 +2040,12 @@ def from_list(
         ),
         data: Sequence[int | float] | Sequence[AnyIntegerAttr] | Sequence[AnyFloatAttr],
     ) -> DenseIntOrFPElementsAttr:
+        # zero rank type should only hold 1 value
+        if not type.get_shape() and len(data) != 1:
+            raise ValueError(
+                f"A zero-rank {type.name} can only hold 1 value but {len(data)} were given."
+            )
+
         # splat value given
         if len(data) == 1 and prod(type.get_shape()) != 1:
             new_data = (data[0],) * prod(type.get_shape())
@@ -2063,8 +2073,9 @@ def from_list(
     def vector_from_list(
         data: Sequence[int] | Sequence[float],
         data_type: IntegerType | IndexType | AnyFloat,
+        shape: Sequence[int],
     ) -> DenseIntOrFPElementsAttr:
-        t = VectorType(data_type, [len(data)])
+        t = VectorType(data_type, shape)
         return DenseIntOrFPElementsAttr.from_list(t, data)
 
     @staticmethod
@@ -2079,16 +2090,14 @@ def tensor_from_list(
         data_type: IntegerType | IndexType | AnyFloat,
         shape: Sequence[int],
     ) -> DenseIntOrFPElementsAttr:
-        if not len(shape):
-            shape = [len(data)]
         t = TensorType(data_type, shape)
         return DenseIntOrFPElementsAttr.from_list(t, data)
 
     def get_values(self) -> Sequence[int] | Sequence[float]:
         """
         Return all the values of the elements in this DenseIntOrFPElementsAttr
         """
-        return self.type.element_type.unpack(self.data.data, prod(self.get_shape()))
+        return self.type.element_type.unpack(self.data.data, self.nb_elements)
 
     def get_attrs(self) -> Sequence[AnyIntegerAttr] | Sequence[AnyFloatAttr]:
         """

xdsl/dialects/cf.py

@@ -368,7 +368,7 @@ def parse(cls, parser: Parser) -> Self:
             )
             assert isinstance(flag_type, IntegerType | IndexType)
             case_values = DenseIntOrFPElementsAttr.vector_from_list(
-                [x for (x, _, _) in cases], flag_type
+                [x for (x, _, _) in cases], flag_type, [len(cases)]
             )
             case_blocks = tuple(x for (_, x, _) in cases)
             case_operands = tuple(tuple(x) for (_, _, x) in cases)

xdsl/dialects/varith.py

@@ -151,7 +151,9 @@ def parse(cls, parser: Parser) -> Self:
         parser.parse_punctuation("]")
         attr_dict = parser.parse_optional_attr_dict()
 
-        case_values = DenseIntOrFPElementsAttr.vector_from_list(values, flag_type)
+        case_values = DenseIntOrFPElementsAttr.vector_from_list(
+            values, flag_type, [len(values)]
+        )
 
         return cls(
             flag,

xdsl/transforms/canonicalization_patterns/cf.py

@@ -319,7 +319,9 @@ def drop_case_helper(
                 op.default_block,
                 op.default_operands,
                 DenseIntOrFPElementsAttr.vector_from_list(
-                    new_case_values, case_values.get_element_type()
+                    new_case_values,
+                    case_values.get_element_type(),
+                    [len(new_case_values)],
                 ),
                 new_case_blocks,
                 new_case_operands,

xdsl/transforms/convert_scf_to_cf.py

@@ -209,7 +209,9 @@ def match_and_rewrite(self, op: IndexSwitchOp, rewriter: PatternRewriter):
                 case_value,
                 default_block,
                 (),
-                DenseIntOrFPElementsAttr.vector_from_list(case_values, i32),
+                DenseIntOrFPElementsAttr.vector_from_list(
+                    case_values, i32, [len(case_values)]
+                ),
                 case_successors,
                 case_operands,
             ),