initialize cache only for cell evaluation, do nothing otherwise

python/dune/codegen/blockstructured/basis.py

@@ -38,21 +38,24 @@ class BlockStructuredBasisMixin(GenericBasisMixin):
         assert not isinstance(element, MixedElement)
         name = "phi_{}".format(FEM_name_mangling(element))
         name = restricted_name(name, restriction)
-        self.init_basis_cache(element)
+        self.init_basis_cache(element, restriction)
         self.evaluate_basis(element, name, restriction)
         inames = self.lfs_inames(element, restriction, number, context=context)
 
         return prim.Subscript(prim.Variable(name), (tensor_index_to_sequential_index(inames, element.degree() + 1), 0))
 
     @preamble(kernel='operator')
-    def init_basis_cache(self, element):
-        cache = name_localbasis_cache(element)
-        localbasis = name_localbasis(element)
-        qp_name = get_pymbolic_basename(self.quadrature_position_in_micro())
-        return ["for (int i=0; i < {}.size(); ++i)".format(qp_name),
-                "{",
-                "  {}.evaluateFunction({}[i], {});".format(cache, qp_name, localbasis),
-                "}"]
+    def init_basis_cache(self, element, restriction):
+        if not restriction:
+            cache = name_localbasis_cache(element)
+            localbasis = name_localbasis(element)
+            qp_name = get_pymbolic_basename(self.quadrature_position_in_micro())
+            return ["for (int i=0; i < {}.size(); ++i)".format(qp_name),
+                    "{",
+                    "  {}.evaluateFunction({}[i], {});".format(cache, qp_name, localbasis),
+                    "}"]
+        else:
+            return []
 
     @kernel_cached
     def evaluate_basis(self, element, name, restriction):
@@ -71,21 +74,24 @@ class BlockStructuredBasisMixin(GenericBasisMixin):
         assert not isinstance(element, MixedElement)
         name = "js_{}".format(FEM_name_mangling(element))
         name = restricted_name(name, restriction)
-        self.init_gradient_cache(element)
+        self.init_gradient_cache(element, restriction)
         self.evaluate_reference_gradient(element, name, restriction)
         inames = self.lfs_inames(element, restriction, number, context=context)
 
         return prim.Subscript(prim.Variable(name), (tensor_index_to_sequential_index(inames, element.degree() + 1), 0))
 
     @preamble(kernel='operator')
-    def init_gradient_cache(self, element):
-        cache = name_localbasis_cache(element)
-        localbasis = name_localbasis(element)
-        qp_name = get_pymbolic_basename(self.quadrature_position_in_micro())
-        return ["for (int i=0; i < {}.size(); ++i)".format(qp_name),
-                "{",
-                "  {}.evaluateJacobian({}[i], {});".format(cache, qp_name, localbasis),
-                "}"]
+    def init_gradient_cache(self, element, restriction):
+        if not restriction:
+            cache = name_localbasis_cache(element)
+            localbasis = name_localbasis(element)
+            qp_name = get_pymbolic_basename(self.quadrature_position_in_micro())
+            return ["for (int i=0; i < {}.size(); ++i)".format(qp_name),
+                    "{",
+                    "  {}.evaluateJacobian({}[i], {});".format(cache, qp_name, localbasis),
+                    "}"]
+        else:
+            return []
 
     @kernel_cached
     def evaluate_reference_gradient(self, element, name, restriction):