[Fix] Vectorisation of Abs

src/pystencils/backends/cbackend.py

@@ -634,7 +634,7 @@ class VectorizedCustomSympyPrinter(CustomSympyPrinter):
             return None
 
     def _print_Abs(self, expr):
-        if 'abs' in self.instruction_set and isinstance(expr.args[0], VectorMemoryAccess):
+        if isinstance(get_type_of_expression(expr), (VectorType, VectorMemoryAccess)):
             return self.instruction_set['abs'].format(self._print(expr.args[0]), **self._kwargs)
         return super()._print_Abs(expr)
 

tests/test_vectorization_specific.py

@@ -39,7 +39,7 @@ def test_vectorisation_varying_arch(instruction_set):
 
 @pytest.mark.parametrize('dtype', ('float32', 'float64'))
 @pytest.mark.parametrize('instruction_set', supported_instruction_sets)
-def test_vectorized_abs(instruction_set, dtype):
+def test_vectorized_abs_field(instruction_set, dtype):
     """Some instructions sets have abs, some don't.
        Furthermore, the special treatment of unary minus makes this data type-sensitive too.
     """
@@ -58,6 +58,24 @@ def test_vectorized_abs(instruction_set, dtype):
     np.testing.assert_equal(np.sum(dst[1:-1, 1:-1]), 2 ** 2 * 2 ** 3)
 
 
+@pytest.mark.parametrize('instruction_set', supported_instruction_sets)
+def test_vectorized_abs_scalar(instruction_set):
+    """Some instructions sets have abs, some don't.
+       Furthermore, the special treatment of unary minus makes this data type-sensitive too.
+    """
+    arr = np.zeros((2 ** 2 + 2, 2 ** 3 + 2), dtype="float64")
+
+    f = ps.fields(f=arr)
+    update_rule = [ps.Assignment(f.center(), sp.Abs(sp.Symbol("a")))]
+
+    config = pystencils.config.CreateKernelConfig(cpu_vectorize_info={'instruction_set': instruction_set})
+    ast = ps.create_kernel(update_rule, config=config)
+
+    func = ast.compile()
+    func(f=arr, a=-1)
+    np.testing.assert_equal(np.sum(arr[1:-1, 1:-1]), 2 ** 2 * 2 ** 3)
+
+
 @pytest.mark.parametrize('dtype', ('float32', 'float64'))
 @pytest.mark.parametrize('instruction_set', supported_instruction_sets)
 @pytest.mark.parametrize('nontemporal', [False, True])