Added hardware specific vectorization tests

pystencils_tests/test_vectorization.py

@@ -6,9 +6,6 @@ from pystencils.backends.simd_instruction_sets import get_supported_instruction_
 from pystencils.cpu.vectorization import vectorize
 from pystencils.fast_approximation import insert_fast_sqrts, insert_fast_divisions
 from pystencils.transformations import replace_inner_stride_with_one
-from pystencils.backends.simd_instruction_sets import get_supported_instruction_sets
-
-supported_instruction_sets = get_supported_instruction_sets() if get_supported_instruction_sets() else []
 
 
 def test_vector_type_propagation():
@@ -30,8 +27,6 @@ def test_vector_type_propagation():
     np.testing.assert_equal(dst[1:-1, 1:-1], 2 * 10.0 + 3)
 
 
-@pytest.mark.skipif('avx' in supported_instruction_sets,
-                    reason="This test case is specifically for AVX architectures")
 def test_inplace_update():
     shape = (9, 9, 3)
     arr = np.ones(shape, order='f')
@@ -47,7 +42,7 @@ def test_inplace_update():
         f1 @= 2 * s.tmp0
         f2 @= 2 * s.tmp0
 
-    ast = ps.create_kernel(update_rule, cpu_vectorize_info={'instruction_set': 'avx'})
+    ast = ps.create_kernel(update_rule, cpu_vectorize_info=True)
     kernel = ast.compile()
     kernel(f=arr)
     np.testing.assert_equal(arr, 2)

pystencils_tests/test_vectorization_specific.py

+import pytest
+
+import numpy as np
+import sympy as sp
+
+import pystencils as ps
+from pystencils.backends.simd_instruction_sets import get_supported_instruction_sets
+from pystencils.cpu.vectorization import vectorize
+from pystencils.fast_approximation import insert_fast_sqrts, insert_fast_divisions
+from pystencils.transformations import replace_inner_stride_with_one
+from pystencils.backends.simd_instruction_sets import get_supported_instruction_sets
+
+supported_instruction_sets = get_supported_instruction_sets() if get_supported_instruction_sets() else []
+
+
+@pytest.mark.parametrize('instruction_set', supported_instruction_sets)
+def test_vectorisation_varying_arch(instruction_set):
+    shape = (9, 9, 3)
+    arr = np.ones(shape, order='f')
+
+    @ps.kernel
+    def update_rule(s):
+        f = ps.fields("f(3) : [2D]", f=arr)
+        s.tmp0 @= f(0)
+        s.tmp1 @= f(1)
+        s.tmp2 @= f(2)
+        f0, f1, f2 = f(0), f(1), f(2)
+        f0 @= 2 * s.tmp0
+        f1 @= 2 * s.tmp0
+        f2 @= 2 * s.tmp0
+
+    ast = ps.create_kernel(update_rule, cpu_vectorize_info={'instruction_set': instruction_set})
+    kernel = ast.compile()
+    kernel(f=arr)
+    np.testing.assert_equal(arr, 2)