Use basis_size/quadrature_size

python/dune/perftool/sumfact/symbolic.py

@@ -174,7 +174,7 @@ class SumfactKernel(SumfactKernelBase, ImmutableRecord, prim.Variable):
 
     @property
     def transposed(self):
-        return next(iter(self.matrix_sequence)).transpose
+        return self.matrix_sequence[0].transpose
 
     def vec_index(self, sf):
         """ Map an unvectorized sumfact kernel object to its position
@@ -188,7 +188,8 @@ class SumfactKernel(SumfactKernelBase, ImmutableRecord, prim.Variable):
     @property
     def flat_input_shape(self):
         """ The 'flat' input tensor shape """
-        return (product(mat.cols for mat in self.matrix_sequence),)
+        assert self.stage == 1
+        return (product(mat.basis_size for mat in self.matrix_sequence),)
 
     @property
     def quadrature_shape(self):
@@ -196,10 +197,7 @@ class SumfactKernel(SumfactKernelBase, ImmutableRecord, prim.Variable):
 
         Takes into account the lower dimensionality of faces and vectorization.
         """
-        if self.transposed:
-            return tuple(mat.cols for mat in self.matrix_sequence if mat.face is None)
-        else:
-            return tuple(mat.rows for mat in self.matrix_sequence if mat.face is None)
+        return tuple(mat.quadrature_size for mat in self.matrix_sequence if mat.face is None)
 
     @property
     def quadrature_dimtags(self):
@@ -216,8 +214,7 @@ class SumfactKernel(SumfactKernelBase, ImmutableRecord, prim.Variable):
 
         Takes into account vectorization.
         """
-        shape = tuple(mat.rows for mat in self.matrix_sequence)
-        return shape
+        return tuple(mat.basis_size for mat in self.matrix_sequence)
 
     @property
     def dof_dimtags(self):
@@ -379,6 +376,10 @@ class VectorizedSumfactKernel(SumfactKernelBase, ImmutableRecord, prim.Variable)
         assert len(set(k.accumvar for k in self.kernels)) == 1
         return self.kernels[0].accumvar
 
+    @property
+    def transposed(self):
+        return self.kernels[0].transposed
+
     #
     # Define some properties only needed for this one
     #
@@ -407,10 +408,6 @@ class VectorizedSumfactKernel(SumfactKernelBase, ImmutableRecord, prim.Variable)
     def vectorized(self):
         return True
 
-    @property
-    def transposed(self):
-        return self.kernels[0].transposed
-
     def vec_index(self, sf):
         return self.kernels.index(sf)
 
@@ -423,10 +420,7 @@ class VectorizedSumfactKernel(SumfactKernelBase, ImmutableRecord, prim.Variable)
 
     @property
     def quadrature_shape(self):
-        if self.transposed:
-            return tuple(mat.cols for mat in self.matrix_sequence if mat.face is None) + (self.horizontal_width,)
-        else:
-            return tuple(mat.rows for mat in self.matrix_sequence if mat.face is None) + (self.horizontal_width,)
+        return tuple(mat.quadrature_size for mat in self.matrix_sequence if mat.face is None) + (self.horizontal_width,)
 
     @property
     def quadrature_dimtags(self):
@@ -436,7 +430,7 @@ class VectorizedSumfactKernel(SumfactKernelBase, ImmutableRecord, prim.Variable)
 
     @property
     def dof_shape(self):
-        return tuple(mat.rows for mat in self.matrix_sequence) + (self.horizontal_width,)
+        return tuple(mat.basis_size for mat in self.matrix_sequence) + (self.horizontal_width,)
 
     @property
     def dof_dimtags(self):