tests/test_sum_prod.py

+# -*- coding: utf-8 -*-
+#
+# Copyright © 2019 Stephan Seitz <stephan.seitz@fau.de>
+#
+# Distributed under terms of the GPLv3 license.
+
+"""
+
+"""
+import pytest
+import numpy as np
+
+import pystencils.config
+import sympy as sp
+import sympy.abc
+
+import pystencils as ps
+from pystencils.typing import create_type
+
+
+@pytest.mark.parametrize('dtype', ["float64", "float32"])
+def test_sum(dtype):
+
+    sum = sp.Sum(sp.abc.k, (sp.abc.k, 1, 100))
+    expanded_sum = sum.doit()
+
+    # print(sum)
+    # print(expanded_sum)
+
+    x = ps.fields(f'x: {dtype}[1d]')
+
+    assignments = ps.AssignmentCollection({x.center(): sum})
+
+    ast = ps.create_kernel(assignments)
+    code = ps.get_code_str(ast)
+    kernel = ast.compile()
+
+    # ps.show_code(ast)
+
+    if dtype == "float32":
+        assert "5050.0f;" in code
+
+    array = np.zeros((10,), dtype=dtype)
+    kernel(x=array)
+    assert np.allclose(array, int(expanded_sum) * np.ones_like(array))
+
+
+@pytest.mark.parametrize('dtype', ["int32", "int64", "float64", "float32"])
+def test_product(dtype):
+
+    k = ps.TypedSymbol('k', create_type(dtype))
+
+    sum = sympy.Product(k, (k, 1, 10))
+    expanded_sum = sum.doit()
+
+    # print(sum)
+    # print(expanded_sum)
+
+    x = ps.fields(f'x: {dtype}[1d]')
+
+    assignments = ps.AssignmentCollection({x.center(): sum})
+
+    config = pystencils.config.CreateKernelConfig()
+
+    ast = ps.create_kernel(assignments, config=config)
+    code = ps.get_code_str(ast)
+    kernel = ast.compile()
+
+    # print(code)
+    if dtype == "int64" or dtype == "int32":
+        assert '3628800;' in code
+    elif dtype == "float32":
+        assert '3628800.0f;' in code
+    else:
+        assert '3628800.0;' in code
+
+    array = np.zeros((10,), dtype=dtype)
+    kernel(x=array)
+    assert np.allclose(array, int(expanded_sum) * np.ones_like(array))
+
+# TODO: See Issue !55
+# def test_prod_var_limit():
+#
+#     k = ps.TypedSymbol('k', create_type('int64'))
+#     limit = ps.TypedSymbol('limit', create_type('int64'))
+#
+#     sum = sympy.Sum(k, (k, 1, limit))
+#     expanded_sum = sum.replace(limit, 100).doit()
+#
+#     print(sum)
+#     print(expanded_sum)
+#
+#     x = ps.fields('x: int64[1d]')
+#
+#     assignments = ps.AssignmentCollection({x.center(): sum})
+#
+#     ast = ps.create_kernel(assignments)
+#     ps.show_code(ast)
+#     kernel = ast.compile()
+#
+#     array = np.zeros((10,), np.int64)
+#
+#     kernel(x=array, limit=100)
+#
+#     assert np.allclose(array, int(expanded_sum) * np.ones_like(array))

tests/test_sympyextensions.py

+import sympy
+import numpy as np
+import sympy as sp
+import pystencils
+
+from pystencils.sympyextensions import replace_second_order_products
+from pystencils.sympyextensions import remove_higher_order_terms
+from pystencils.sympyextensions import complete_the_squares_in_exp
+from pystencils.sympyextensions import extract_most_common_factor
+from pystencils.sympyextensions import simplify_by_equality
+from pystencils.sympyextensions import count_operations
+from pystencils.sympyextensions import common_denominator
+from pystencils.sympyextensions import get_symmetric_part
+from pystencils.sympyextensions import scalar_product
+from pystencils.sympyextensions import kronecker_delta
+
+from pystencils import Assignment
+from pystencils.functions import DivFunc
+from pystencils.fast_approximation import (fast_division, fast_inv_sqrt, fast_sqrt,
+                                           insert_fast_divisions, insert_fast_sqrts)
+
+
+def test_utility():
+    a = [1, 2]
+    b = (2, 3)
+
+    a_np = np.array(a)
+    b_np = np.array(b)
+    assert scalar_product(a, b) == np.dot(a_np, b_np)
+
+    a = sympy.Symbol("a")
+    b = sympy.Symbol("b")
+
+    assert kronecker_delta(a, a, a, b) == 0
+    assert kronecker_delta(a, a, a, a) == 1
+    assert kronecker_delta(3, 3, 3, 2) == 0
+    assert kronecker_delta(2, 2, 2, 2) == 1
+    assert kronecker_delta([10] * 100) == 1
+    assert kronecker_delta((0, 1), (0, 1)) == 1
+
+
+def test_replace_second_order_products():
+    x, y = sympy.symbols('x y')
+    expr = 4 * x * y
+    expected_expr_positive = 2 * ((x + y) ** 2 - x ** 2 - y ** 2)
+    expected_expr_negative = 2 * (-(x - y) ** 2 + x ** 2 + y ** 2)
+
+    result = replace_second_order_products(expr, search_symbols=[x, y], positive=True)
+    assert result == expected_expr_positive
+    assert (result - expected_expr_positive).simplify() == 0
+
+    result = replace_second_order_products(expr, search_symbols=[x, y], positive=False)
+    assert result == expected_expr_negative
+    assert (result - expected_expr_negative).simplify() == 0
+
+    result = replace_second_order_products(expr, search_symbols=[x, y], positive=None)
+    assert result == expected_expr_positive
+
+    a = [Assignment(sympy.symbols('z'), x + y)]
+    replace_second_order_products(expr, search_symbols=[x, y], positive=True, replace_mixed=a)
+    assert len(a) == 2
+
+    assert replace_second_order_products(4 + y, search_symbols=[x, y]) == y + 4
+
+
+def test_remove_higher_order_terms():
+    x, y = sympy.symbols('x y')
+
+    expr = sympy.Mul(x, y)
+
+    result = remove_higher_order_terms(expr, order=1, symbols=[x, y])
+    assert result == 0
+    result = remove_higher_order_terms(expr, order=2, symbols=[x, y])
+    assert result == expr
+
+    expr = sympy.Pow(x, 3)
+
+    result = remove_higher_order_terms(expr, order=2, symbols=[x, y])
+    assert result == 0
+    result = remove_higher_order_terms(expr, order=3, symbols=[x, y])
+    assert result == expr
+
+
+def test_complete_the_squares_in_exp():
+    a, b, c, s, n = sympy.symbols('a b c s n')
+    expr = a * s ** 2 + b * s + c
+    result = complete_the_squares_in_exp(expr, symbols_to_complete=[s])
+    assert result == expr
+
+    expr = sympy.exp(a * s ** 2 + b * s + c)
+    expected_result = sympy.exp(a*s**2 + c - b**2 / (4*a))
+    result = complete_the_squares_in_exp(expr, symbols_to_complete=[s])
+    assert result == expected_result
+
+
+def test_extract_most_common_factor():
+    x, y = sympy.symbols('x y')
+    expr = 1 / (x + y) + 3 / (x + y) + 3 / (x + y)
+    most_common_factor = extract_most_common_factor(expr)
+
+    assert most_common_factor[0] == 7
+    assert sympy.prod(most_common_factor) == expr
+
+    expr = 1 / x + 3 / (x + y) + 3 / y
+    most_common_factor = extract_most_common_factor(expr)
+
+    assert most_common_factor[0] == 3
+    assert sympy.prod(most_common_factor) == expr
+
+    expr = 1 / x
+    most_common_factor = extract_most_common_factor(expr)
+
+    assert most_common_factor[0] == 1
+    assert sympy.prod(most_common_factor) == expr
+    assert most_common_factor[1] == expr
+
+
+def test_count_operations():
+    x, y, z = sympy.symbols('x y z')
+    expr = 1/x + y * sympy.sqrt(z)
+    ops = count_operations(expr, only_type=None)
+    assert ops['adds'] == 1
+    assert ops['muls'] == 1
+    assert ops['divs'] == 1
+    assert ops['sqrts'] == 1
+
+    expr = 1 / sympy.sqrt(z)
+    ops = count_operations(expr, only_type=None)
+    assert ops['adds'] == 0
+    assert ops['muls'] == 0
+    assert ops['divs'] == 1
+    assert ops['sqrts'] == 1
+
+    expr = sympy.Rel(1 / sympy.sqrt(z), 5)
+    ops = count_operations(expr, only_type=None)
+    assert ops['adds'] == 0
+    assert ops['muls'] == 0
+    assert ops['divs'] == 1
+    assert ops['sqrts'] == 1
+
+    expr = sympy.sqrt(x + y)
+    expr = insert_fast_sqrts(expr).atoms(fast_sqrt)
+    ops = count_operations(*expr, only_type=None)
+    assert ops['fast_sqrts'] == 1
+
+    expr = sympy.sqrt(x / y)
+    expr = insert_fast_divisions(expr).atoms(fast_division)
+    ops = count_operations(*expr, only_type=None)
+    assert ops['fast_div'] == 1
+
+    expr = pystencils.Assignment(sympy.Symbol('tmp'), 3 / sympy.sqrt(x + y))
+    expr = insert_fast_sqrts(expr).atoms(fast_inv_sqrt)
+    ops = count_operations(*expr, only_type=None)
+    assert ops['fast_inv_sqrts'] == 1
+
+    expr = sympy.Piecewise((1.0, x > 0), (0.0, True)) + y * z
+    ops = count_operations(expr, only_type=None)
+    assert ops['adds'] == 1
+
+    expr = sympy.Pow(1/x + y * sympy.sqrt(z), 100)
+    ops = count_operations(expr, only_type=None)
+    assert ops['adds'] == 1
+    assert ops['muls'] == 99
+    assert ops['divs'] == 1
+    assert ops['sqrts'] == 1
+
+    expr = DivFunc(x, y)
+    ops = count_operations(expr, only_type=None)
+    assert ops['divs'] == 1
+
+    expr = DivFunc(x + z, y + z)
+    ops = count_operations(expr, only_type=None)
+    assert ops['adds'] == 2
+    assert ops['divs'] == 1
+
+    expr = sp.UnevaluatedExpr(sp.Mul(*[x]*100, evaluate=False))
+    ops = count_operations(expr, only_type=None)
+    assert ops['muls'] == 99
+
+    expr = DivFunc(1, sp.UnevaluatedExpr(sp.Mul(*[x]*100, evaluate=False)))
+    ops = count_operations(expr, only_type=None)
+    assert ops['divs'] == 1
+    assert ops['muls'] == 99
+
+    expr = DivFunc(y + z, sp.UnevaluatedExpr(sp.Mul(*[x]*100, evaluate=False)))
+    ops = count_operations(expr, only_type=None)
+    assert ops['adds'] == 1
+    assert ops['divs'] == 1
+    assert ops['muls'] == 99
+
+
+def test_common_denominator():
+    x = sympy.symbols('x')
+    expr = sympy.Rational(1, 2) + x * sympy.Rational(2, 3)
+    cm = common_denominator(expr)
+    assert cm == 6
+
+
+def test_get_symmetric_part():
+    x, y, z = sympy.symbols('x y z')
+    expr = x / 9 - y ** 2 / 6 + z ** 2 / 3 + z / 3
+    expected_result = x / 9 - y ** 2 / 6 + z ** 2 / 3
+    sym_part = get_symmetric_part(expr, sympy.symbols(f'y z'))
+
+    assert sym_part == expected_result
+
+
+def test_simplify_by_equality():
+    x, y, z = sp.symbols('x, y, z')
+    p, q = sp.symbols('p, q')
+
+    #   Let x = y + z
+    expr = x * p - y * p + z * q
+    expr = simplify_by_equality(expr, x, y, z)
+    assert expr == z * p + z * q
+
+    expr = x * (p - 2 * q) + 2 * q * z
+    expr = simplify_by_equality(expr, x, y, z)
+    assert expr == x * p - 2 * q * y
+
+    expr = x * (y + z) - y * z
+    expr = simplify_by_equality(expr, x, y, z)
+    assert expr == x*y + z**2
+
+    #   Let x = y + 2
+    expr = x * p - 2 * p
+    expr = simplify_by_equality(expr, x, y, 2)
+    assert expr == y * p

tests/test_timeloop.py

+import time
+import numpy as np
+
+from pystencils import Assignment
+from pystencils import create_kernel
+from pystencils.datahandling import create_data_handling
+from pystencils.timeloop import TimeLoop
+
+
+def test_timeloop():
+    dh = create_data_handling(domain_size=(2, 2), periodicity=True)
+
+    pre = dh.add_array('pre_run_field', values_per_cell=1)
+    dh.fill("pre_run_field", 0.0, ghost_layers=True)
+    f = dh.add_array('field', values_per_cell=1)
+    dh.fill("field", 0.0, ghost_layers=True)
+    post = dh.add_array('post_run_field', values_per_cell=1)
+    dh.fill("post_run_field", 0.0, ghost_layers=True)
+    single_step = dh.add_array('single_step_field', values_per_cell=1)
+    dh.fill("single_step_field", 0.0, ghost_layers=True)
+
+    pre_assignments = Assignment(pre.center, pre.center + 1)
+    pre_kernel = create_kernel(pre_assignments).compile()
+    assignments = Assignment(f.center, f.center + 1)
+    kernel = create_kernel(assignments).compile()
+    post_assignments = Assignment(post.center, post.center + 1)
+    post_kernel = create_kernel(post_assignments).compile()
+    single_step_assignments = Assignment(single_step.center, single_step.center + 1)
+    single_step_kernel = create_kernel(single_step_assignments).compile()
+
+    fixed_steps = 2
+    timeloop = TimeLoop(steps=fixed_steps)
+    assert timeloop.fixed_steps == fixed_steps
+
+    def pre_run():
+        dh.run_kernel(pre_kernel)
+
+    def post_run():
+        dh.run_kernel(post_kernel)
+
+    def single_step_run():
+        dh.run_kernel(single_step_kernel)
+
+    timeloop.add_pre_run_function(pre_run)
+    timeloop.add_post_run_function(post_run)
+    timeloop.add_single_step_function(single_step_run)
+    timeloop.add_call(kernel, {'field': dh.cpu_arrays["field"]})
+
+    # the timeloop is initialised with 2 steps. This means a single time step consists of two steps.
+    # Therefore, we have 2 main iterations and one single step iteration in this configuration
+    timeloop.run(time_steps=5)
+    assert np.all(dh.cpu_arrays["pre_run_field"] == 1.0)
+    assert np.all(dh.cpu_arrays["field"] == 2.0)
+    assert np.all(dh.cpu_arrays["single_step_field"] == 1.0)
+    assert np.all(dh.cpu_arrays["post_run_field"] == 1.0)
+
+    seconds = 2
+    start = time.perf_counter()
+    timeloop.run_time_span(seconds=seconds)
+    end = time.perf_counter()
+
+    # This test case fails often due to time measurements. It is not a good idea to assert here
+    # np.testing.assert_almost_equal(seconds, end - start, decimal=2)
+    print("timeloop: ", seconds, "  own meassurement: ", end - start)

tests/test_transformations.py

+import sympy as sp
+import numpy as np
+
+import pystencils as ps
+from pystencils import fields, TypedSymbol
+from pystencils.astnodes import LoopOverCoordinate, SympyAssignment
+from pystencils.typing import create_type
+from pystencils.transformations import (
+    filtered_tree_iteration, get_loop_hierarchy, get_loop_counter_symbol_hierarchy,
+    iterate_loops_by_depth, split_inner_loop, loop_blocking
+)
+
+from pystencils.cpu import add_pragmas
+
+def test_loop_information():
+    f, g = ps.fields("f, g: double[2D]")
+    update_rule = ps.Assignment(g[0, 0], f[0, 0])
+
+    ast = ps.create_kernel(update_rule)
+    inner_loops = [loop for loop in filtered_tree_iteration(ast, LoopOverCoordinate, stop_type=SympyAssignment)
+                   if loop.is_innermost_loop]
+
+    loop_order = []
+    for i in get_loop_hierarchy(inner_loops[0].args[0]):
+        loop_order.append(i)
+
+    assert loop_order == [0, 1]
+
+    loop_symbols = get_loop_counter_symbol_hierarchy(inner_loops[0].args[0])
+
+    assert loop_symbols == [TypedSymbol("ctr_1", create_type("int"), nonnegative=True),
+                            TypedSymbol("ctr_0", create_type("int"), nonnegative=True)]
+
+
+def test_iterate_loops_by_depth():
+    f, g = ps.fields("f, g: double[3D]", layout="fzyx")
+    x = ps.TypedSymbol('x', np.float64)
+    subs = [ps.Assignment(x, f[0, 0, 0])]
+    mains = [ps.Assignment(g[0, 0, 0], x)]
+    ac = ps.AssignmentCollection(mains, subexpressions=subs)
+
+    config = ps.CreateKernelConfig(cpu_blocking=(0, 16, 0))
+    ast = ps.create_kernel(ac, config=config)
+    split_inner_loop(ast, [[x], [g[0,0,0]]])
+
+    loops = list(iterate_loops_by_depth(ast, 0))
+    assert len(loops) == 1
+    assert loops[0].loop_counter_symbol.name == "_blockctr_1"
+
+    loops = list(iterate_loops_by_depth(ast, 1))
+    assert len(loops) == 1
+    assert loops[0].loop_counter_symbol.name == "ctr_2"
+
+    loops = list(iterate_loops_by_depth(ast, 2))
+    assert len(loops) == 1
+    assert loops[0].loop_counter_symbol.name == "ctr_1"
+
+    loops = list(iterate_loops_by_depth(ast, 3))
+    assert len(loops) == 2
+    assert loops[0].loop_counter_symbol.name == "ctr_0"
+    assert loops[1].loop_counter_symbol.name == "ctr_0"
+
+    innermost = list(iterate_loops_by_depth(ast, -1))
+    assert loops == innermost
+
+
+def test_split_optimisation():
+    src, dst = fields(f"src(9), dst(9): [2D]", layout='fzyx')
+
+    stencil = ((0, 0), (0, 1), (0, -1), (-1, 0), (1, 0), (-1, 1), (1, 1), (-1, -1), (1, -1))
+    w = [sp.Rational(4, 9)]
+    w += [sp.Rational(1, 9)] * 4
+    w += [sp.Rational(1, 36)] * 4
+    cs = sp.Rational(1, 3)
+
+    subexpressions = []
+    main_assignements = []
+
+    rho = sp.symbols("rho")
+    velo = sp.symbols("u_:2")
+
+    density = 0
+    velocity_x = 0
+    velocity_y = 0
+    for d in stencil:
+        density += src[d]
+        velocity_x += d[0] * src[d]
+        velocity_y += d[1] * src[d]
+
+    subexpressions.append(ps.Assignment(rho, density))
+    subexpressions.append(ps.Assignment(velo[0], velocity_x))
+    subexpressions.append(ps.Assignment(velo[1], velocity_y))
+
+    for i, d in enumerate(stencil):
+        u_d = velo[0] * d[0] + velo[1] * d[1]
+        u_2 = velo[0] * velo[0] + velo[1] * velo[1]
+
+        expr = w[i] * rho * (1 + u_d / cs + u_d ** 2 / (2 * cs ** 2) - u_2 / (2 * cs))
+
+        main_assignements.append(ps.Assignment(dst.center_vector[i], expr))
+
+    ac = ps.AssignmentCollection(main_assignments=main_assignements, subexpressions=subexpressions)
+
+    simplification_hint = {'density': rho,
+                           'velocity': (velo[0], velo[1]),
+                           'split_groups': [[rho, velo[0], velo[1], dst.center_vector[0]],
+                                            [dst.center_vector[1], dst.center_vector[2]],
+                                            [dst.center_vector[3], dst.center_vector[4]],
+                                            [dst.center_vector[5], dst.center_vector[6]],
+                                            [dst.center_vector[7], dst.center_vector[8]]]}
+
+    ac.simplification_hints = simplification_hint
+    ast = ps.create_kernel(ac)
+
+    code = ps.get_code_str(ast)
+    # after the split optimisation the two for loops are split into 6
+    assert code.count("for") == 6
+
+    print(code)
+
+def test_pragmas():
+    f, g = ps.fields("f, g: double[3D]", layout="fzyx")
+    x = ps.TypedSymbol('x', np.float64)
+    subs = [ps.Assignment(x, f[0, 0, 0])]
+    mains = [ps.Assignment(g[0, 0, 0], x)]
+    ac = ps.AssignmentCollection(mains, subexpressions=subs)
+
+    def prepend_omp_pragmas(ast):
+        add_pragmas(ast, ["#pragma omp for schedule(dynamic)"], nesting_depth=0)
+        add_pragmas(ast, ["#pragma omp simd simdlen(8)"], nesting_depth=-1)
+
+    ast_passes = [prepend_omp_pragmas]
+
+    config = ps.CreateKernelConfig(target=ps.Target.CPU, cpu_prepend_optimizations=ast_passes)
+    ast = ps.create_kernel(ac, config=config)
+    code = ps.get_code_str(ast)
+
+    assert code.find("#pragma omp for schedule(dynamic)") != -1
+    assert code.find("#pragma omp simd simdlen(8)") != -1
+
+    loops = [loop for loop in filtered_tree_iteration(ast, LoopOverCoordinate, stop_type=SympyAssignment)]
+    
+    innermost = list(filter(lambda n: n.is_innermost_loop, loops))
+    assert innermost[0].prefix_lines == ["#pragma omp simd simdlen(8)"]
+    
+    outermost = list(filter(lambda n: n.is_outermost_loop, loops))
+    assert outermost[0].prefix_lines == ["#pragma omp for schedule(dynamic)"]

tests/test_type_interference.py

+from sympy.abc import a, b, c, d, e, f, g
+
+import pystencils
+from pystencils.typing import CastFunc, create_type
+
+
+def test_type_interference():
+    x = pystencils.fields('x:  float32[3d]')
+    assignments = pystencils.AssignmentCollection({
+        a: CastFunc(10, create_type('float64')),
+        b: CastFunc(10, create_type('uint16')),
+        e: 11,
+        c: b,
+        f: c + b,
+        d: c + b + x.center + e,
+        x.center: c + b + x.center,
+        g: a + b + d
+    })
+
+    ast = pystencils.create_kernel(assignments)
+    code = pystencils.get_code_str(ast)
+    # print(code)
+
+    assert 'const double a' in code
+    assert 'const uint16_t b' in code
+    assert 'const uint16_t f' in code
+    assert 'const int64_t e' in code
+
+    assert 'const float d = ((float)(b)) + ((float)(c)) + ((float)(e)) + ' \
+           '_data_x[_stride_x_0*ctr_0 + _stride_x_1*ctr_1 + _stride_x_2*ctr_2];' in code
+    assert '_data_x[_stride_x_0*ctr_0 + _stride_x_1*ctr_1 + _stride_x_2*ctr_2] = (' \
+           '(float)(b)) + ((float)(c)) + _data_x[_stride_x_0*ctr_0 + _stride_x_1*ctr_1 + _stride_x_2*ctr_2];' in code
+    assert 'const double g = a + ((double)(b)) + ((double)(d));' in code

tests/test_types.py

+import pytest
+
+import pystencils.config
+import sympy as sp
+import numpy as np
+
+import pystencils as ps
+from pystencils.typing import TypedSymbol, get_type_of_expression, VectorType, collate_types, \
+    typed_symbols, CastFunc, PointerArithmeticFunc, PointerType, result_type, BasicType
+
+
+def test_result_type():
+    i = np.dtype('int32')
+    l = np.dtype('int64')
+    ui = np.dtype('uint32')
+    ul = np.dtype('uint64')
+    f = np.dtype('float32')
+    d = np.dtype('float64')
+    b = np.dtype('bool')
+
+    assert result_type(i, l) == l
+    assert result_type(l, i) == l
+    assert result_type(ui, i) == i
+    assert result_type(ui, l) == l
+    assert result_type(ul, i) == i
+    assert result_type(ul, l) == l
+    assert result_type(d, f) == d
+    assert result_type(f, d) == d
+    assert result_type(i, f) == f
+    assert result_type(l, f) == f
+    assert result_type(ui, f) == f
+    assert result_type(ul, f) == f
+    assert result_type(i, d) == d
+    assert result_type(l, d) == d
+    assert result_type(ui, d) == d
+    assert result_type(ul, d) == d
+    assert result_type(b, i) == i
+    assert result_type(b, l) == l
+    assert result_type(b, ui) == ui
+    assert result_type(b, ul) == ul
+    assert result_type(b, f) == f
+    assert result_type(b, d) == d
+
+
+@pytest.mark.parametrize('dtype', ('float64', 'float32', 'int64', 'int32', 'uint32', 'uint64'))
+def test_simple_add(dtype):
+    constant = 1.0
+    if dtype[0] in 'ui':
+        constant = 1
+    f = ps.fields(f"f: {dtype}[1D]")
+    d = TypedSymbol("d", dtype)
+
+    test_arr = np.array([constant], dtype=dtype)
+
+    ur = ps.Assignment(f[0], f[0] + d)
+
+    ast = ps.create_kernel(ur)
+    code = ps.get_code_str(ast)
+    kernel = ast.compile()
+    kernel(f=test_arr, d=constant)
+
+    assert test_arr[0] == constant+constant
+
+
+@pytest.mark.parametrize('dtype1', ('float64', 'float32', 'int64', 'int32', 'uint32', 'uint64'))
+@pytest.mark.parametrize('dtype2', ('float64', 'float32', 'int64', 'int32', 'uint32', 'uint64'))
+def test_mixed_add(dtype1, dtype2):
+
+    constant = 1
+    f = ps.fields(f"f: {dtype1}[1D]")
+    g = ps.fields(f"g: {dtype2}[1D]")
+
+    test_f = np.array([constant], dtype=dtype1)
+    test_g = np.array([constant], dtype=dtype2)
+
+    ur = ps.Assignment(f[0], f[0] + g[0])
+
+    # TODO Markus: check for the logging if colate_types(dtype1, dtype2) != dtype1
+    ast = ps.create_kernel(ur)
+    code = ps.get_code_str(ast)
+    kernel = ast.compile()
+    kernel(f=test_f, g=test_g)
+
+    assert test_f[0] == constant+constant
+
+
+def test_collation():
+    double_type = BasicType('float64')
+    float_type = BasicType('float32')
+    double4_type = VectorType(double_type, 4)
+    float4_type = VectorType(float_type, 4)
+    assert collate_types([double_type, float_type]) == double_type
+    assert collate_types([double4_type, float_type]) == double4_type
+    assert collate_types([double4_type, float4_type]) == double4_type
+
+
+def test_vector_type():
+    double_type = BasicType('float64')
+    float_type = BasicType('float32')
+    double4_type = VectorType(double_type, 4)
+    float4_type = VectorType(float_type, 4)
+
+    assert double4_type.item_size == 4
+    assert float4_type.item_size == 4
+
+    double4_type2 = VectorType(double_type, 4)
+    assert double4_type == double4_type2
+    assert double4_type != 4
+    assert double4_type != float4_type
+
+
+def test_pointer_type():
+    double_type = BasicType('float64')
+    float_type = BasicType('float32')
+    double4_type = PointerType(double_type, restrict=True)
+    float4_type = PointerType(float_type, restrict=False)
+
+    assert double4_type.item_size == 1
+    assert float4_type.item_size == 1
+
+    assert not double4_type == 4
+
+    assert not double4_type.alias
+    assert float4_type.alias
+
+
+def test_dtype_of_constants():
+    # Some come constants are neither of type Integer,Float,Rational and don't have args
+    # >>> isinstance(pi, Integer)
+    # False
+    # >>> isinstance(pi, Float)
+    # False
+    # >>> isinstance(pi, Rational)
+    # False
+    # >>> pi.args
+    # ()
+    get_type_of_expression(sp.pi)
+
+
+def test_assumptions():
+    x = ps.fields('x:  float32[3d]')
+
+    assert x.shape[0].is_nonnegative
+    assert (2 * x.shape[0]).is_nonnegative
+    assert (2 * x.shape[0]).is_integer
+    assert (TypedSymbol('a', BasicType('uint64'))).is_nonnegative
+    assert (TypedSymbol('a', BasicType('uint64'))).is_positive is None
+    assert (TypedSymbol('a', BasicType('uint64')) + 1).is_positive
+    assert (x.shape[0] + 1).is_real
+
+
+@pytest.mark.parametrize('dtype', ('float64', 'float32'))
+def test_sqrt_of_integer(dtype):
+    """Regression test for bug where sqrt(3) was classified as integer"""
+    f = ps.fields(f'f: {dtype}[1D]')
+    tmp = sp.symbols('tmp')
+
+    assignments = [ps.Assignment(tmp, sp.sqrt(3)),
+                   ps.Assignment(f[0], tmp)]
+    arr = np.array([1], dtype=dtype)
+    config = pystencils.config.CreateKernelConfig(data_type=dtype, default_number_float=dtype)
+
+    ast = ps.create_kernel(assignments, config=config)
+    kernel = ast.compile()
+    kernel(f=arr)
+    assert 1.7 < arr[0] < 1.8
+
+    code = ps.get_code_str(ast)
+    constant = '1.7320508075688772f'
+    if dtype == 'float32':
+        assert constant in code
+    else:
+        assert constant not in code
+
+
+@pytest.mark.parametrize('dtype', ('float64', 'float32'))
+def test_integer_comparision(dtype):
+    f = ps.fields(f"f: {dtype}[2D]")
+    d = TypedSymbol("dir", "int64")
+
+    ur = ps.Assignment(f[0, 0], sp.Piecewise((0, sp.Equality(d, 1)), (f[0, 0], True)))
+
+    ast = ps.create_kernel(ur)
+    code = ps.get_code_str(ast)
+
+    # There should be an explicit cast for the integer zero to the type of the field on the rhs
+    if dtype == 'float64':
+        t = "_data_f[_stride_f_0*ctr_0 + _stride_f_1*ctr_1] = " \
+            "((((dir) == (1))) ? (0.0): (_data_f[_stride_f_0*ctr_0 + _stride_f_1*ctr_1]));"
+    else:
+        t = "_data_f[_stride_f_0*ctr_0 + _stride_f_1*ctr_1] = " \
+            "((((dir) == (1))) ? (0.0f): (_data_f[_stride_f_0*ctr_0 + _stride_f_1*ctr_1]));"
+    assert t in code
+
+
+def test_typed_symbols_dtype():
+    assert typed_symbols(("s", "f"), np.uint) == typed_symbols("s, f", np.uint)
+    t_symbols = typed_symbols(("s", "f"), np.uint)
+    s = t_symbols[0]
+
+    assert t_symbols[0] == TypedSymbol("s", np.uint)
+    assert s.dtype.is_uint()
+
+    assert typed_symbols("s", np.float64).dtype.c_name == 'double'
+    assert typed_symbols("s", np.float32).dtype.c_name == 'float'
+
+    assert TypedSymbol("s", np.uint).canonical == TypedSymbol("s", np.uint)
+    assert TypedSymbol("s", np.uint).reversed == TypedSymbol("s", np.uint)
+
+
+def test_cast_func():
+    assert CastFunc(TypedSymbol("s", np.uint), np.int64).canonical == TypedSymbol("s", np.uint).canonical
+
+    a = CastFunc(5, np.uint)
+    assert a.is_negative is False
+    assert a.is_nonnegative
+
+
+def test_pointer_arithmetic_func():
+    assert PointerArithmeticFunc(TypedSymbol("s", np.uint), 1).canonical == TypedSymbol("s", np.uint).canonical
+
+
+def test_division():
+    f = ps.fields('f(10): float32[2D]')
+    m, tau = sp.symbols("m, tau")
+
+    up = [ps.Assignment(tau, 1 / (0.5 + (3.0 * m))),
+          ps.Assignment(f.center, tau)]
+    config = pystencils.config.CreateKernelConfig(data_type='float32', default_number_float='float32')
+    ast = ps.create_kernel(up, config=config)
+    code = ps.get_code_str(ast)
+
+    assert "((1.0f) / (m*3.0f + 0.5f))" in code
+
+
+def test_pow():
+    f = ps.fields('f(10): float32[2D]')
+    m, tau = sp.symbols("m, tau")
+
+    up = [ps.Assignment(tau, m ** 1.5),
+          ps.Assignment(f.center, tau)]
+
+    config = pystencils.config.CreateKernelConfig(data_type="float32", default_number_float='float32')
+    ast = ps.create_kernel(up, config=config)
+    code = ps.get_code_str(ast)
+
+    assert "1.5f" in code

pystencils_tests/test_fd_derivation.py → tests/test_utils.py

 import pytest
 import sympy as sp
-
 from pystencils.utils import LinearEquationSystem
+from pystencils.utils import DotDict
 
 
 def test_linear_equation_system():
@@ -31,4 +31,51 @@ def test_linear_equation_system():
 
     with pytest.raises(ValueError) as e:
         m.add_equation(x**2 - 1)
-    assert 'Not a linear equation' in str(e)
+    assert 'Not a linear equation' in str(e.value)
+
+    x, y, z = sp.symbols("x, y, z")
+    les = LinearEquationSystem([x, y, z])
+    les.add_equation(1 * x + 2 * y - 1 * z + 4)
+    les.add_equation(2 * x + 1 * y + 1 * z - 2)
+    les.add_equation(1 * x + 2 * y + 1 * z + 2)
+
+    # usually reduce is not necessary since almost every function of LinearEquationSystem calls reduce beforehand
+    les.reduce()
+
+    expected_matrix = sp.Matrix([[1, 0, 0, sp.Rational(5, 3)],
+                                 [0, 1, 0, sp.Rational(-7, 3)],
+                                 [0, 0, 1, sp.Integer(1)]])
+    assert les.matrix == expected_matrix
+    assert les.rank == 3
+
+    sol = les.solution()
+    assert sol[x] == sp.Rational(5, 3)
+    assert sol[y] == sp.Rational(-7, 3)
+    assert sol[z] == sp.Integer(1)
+
+    les = LinearEquationSystem([x, y])
+    assert les.solution_structure() == 'multiple'
+
+    les.add_equation(x + 1)
+    assert les.solution_structure() == 'multiple'
+
+    les.add_equation(y + 2)
+    assert les.solution_structure() == 'single'
+
+    les.add_equation(x + y + 5)
+    assert les.solution_structure() == 'none'
+
+
+def test_dot_dict():
+    d = {'a': {'c': 7}, 'b': 6}
+    t = DotDict(d)
+    assert t.a.c == 7
+    assert t.b == 6
+    assert len(t) == 2
+
+    delattr(t, 'b')
+    assert len(t) == 1
+
+    t.b = 6
+    assert len(t) == 2
+    assert t.b == 6

tests/test_vectorization.py

+import numpy as np
+
+import pytest
+
+import pystencils.config
+import sympy as sp
+
+import pystencils as ps
+import pystencils.astnodes as ast
+from pystencils.backends.simd_instruction_sets import get_supported_instruction_sets, get_vector_instruction_set
+from pystencils.cpu.vectorization import vectorize
+from pystencils.enums import Target
+from pystencils.transformations import replace_inner_stride_with_one
+
+supported_instruction_sets = get_supported_instruction_sets()
+if supported_instruction_sets:
+    instruction_set = supported_instruction_sets[-1]
+else:
+    instruction_set = None
+
+
+# TODO: Skip tests if no instruction set is available and check all codes if they are really vectorised !
+def test_vector_type_propagation1(instruction_set=instruction_set):
+    a, b, c, d, e = sp.symbols("a b c d e")
+    arr = np.ones((2 ** 2 + 2, 2 ** 3 + 2))
+    arr *= 10.0
+
+    f, g = ps.fields(f=arr, g=arr)
+    update_rule = [ps.Assignment(a, f[1, 0]),
+                   ps.Assignment(b, a),
+                   ps.Assignment(g[0, 0], b + 3 + f[0, 1])]
+
+    ast = ps.create_kernel(update_rule)
+    vectorize(ast, instruction_set=instruction_set)
+
+    # ps.show_code(ast)
+
+    func = ast.compile()
+    dst = np.zeros_like(arr)
+    func(g=dst, f=arr)
+    np.testing.assert_equal(dst[1:-1, 1:-1], 2 * 10.0 + 3)
+
+
+def test_vector_type_propagation2(instruction_set=instruction_set):
+    data_type = 'float32'
+    assume_aligned = True
+    assume_inner_stride_one = True
+    assume_sufficient_line_padding = True
+
+    domain_size = (24, 24)
+    dh = ps.create_data_handling(domain_size, default_target=Target.CPU)
+
+    # fields
+    g = dh.add_array("g", values_per_cell=1, dtype=data_type, alignment=True, ghost_layers=0)
+    f = dh.add_array("f", values_per_cell=1, dtype=data_type, alignment=True, ghost_layers=0)
+    dh.fill("g", 1.0, ghost_layers=True)
+    dh.fill("f", 0.5, ghost_layers=True)
+
+    collision = [
+        ps.Assignment(sp.Symbol("b"), sp.sqrt(sp.Symbol("a") * (1 - (1 - f.center)**2))),
+        ps.Assignment(g.center, sp.Symbol("b"))
+    ]
+
+    config = ps.CreateKernelConfig(
+        target=ps.Target.CPU,
+        data_type=data_type,
+        default_number_float=data_type,
+        cpu_vectorize_info={
+            'assume_aligned': assume_aligned,
+            'assume_inner_stride_one': assume_inner_stride_one,
+            'assume_sufficient_line_padding': assume_sufficient_line_padding,
+        },
+        ghost_layers=0
+    )
+
+    ast = ps.create_kernel(collision, config=config)
+    print(ast)
+    code = ps.get_code_str(ast)
+    print(code)
+
+    kernel = ast.compile()
+    dh.run_kernel(kernel, a=4)
+    g_arr = dh.cpu_arrays['g']
+    np.testing.assert_allclose(g_arr, np.full_like(g_arr, np.sqrt(3)))
+
+
+def test_vectorize_moved_constants1(instruction_set=instruction_set):
+    opt = {'instruction_set': instruction_set, 'assume_inner_stride_one': True}
+
+    f = ps.fields("f: [1D]")
+    x = ast.TypedSymbol("x", np.float64)
+
+    kernel_func = ps.create_kernel(
+        [ast.SympyAssignment(x, 2.0), ast.SympyAssignment(f[0], x)],
+        cpu_prepend_optimizations=[ps.transformations.move_constants_before_loop],  # explicitly move constants
+        cpu_vectorize_info=opt,
+    )
+    ps.show_code(kernel_func)  # fails if `x` on rhs was not correctly vectorized
+    kernel = kernel_func.compile()
+
+    f_arr = np.zeros(9)
+    kernel(f=f_arr)
+
+    assert(np.all(f_arr == 2))
+
+
+def test_vectorize_moved_constants2(instruction_set=instruction_set):
+    opt = {'instruction_set': instruction_set, 'assume_inner_stride_one': True}
+
+    f = ps.fields("f: [1D]")
+    x = ast.TypedSymbol("x", np.float64)
+    y = ast.TypedSymbol("y", np.float64)
+
+    kernel_func = ps.create_kernel(
+        [ast.SympyAssignment(x, 2.0), ast.SympyAssignment(y, 3.0), ast.SympyAssignment(f[0], x + y)],
+        cpu_prepend_optimizations=[ps.transformations.move_constants_before_loop],  # explicitly move constants
+        cpu_vectorize_info=opt,
+    )
+    ps.show_code(kernel_func)  # fails if `x` on rhs was not correctly vectorized
+    kernel = kernel_func.compile()
+
+    f_arr = np.zeros(9)
+    kernel(f=f_arr)
+
+    assert(np.all(f_arr == 5))
+
+
+@pytest.mark.parametrize('openmp', [True, False])
+def test_aligned_and_nt_stores(openmp, instruction_set=instruction_set):
+    domain_size = (24, 24)
+    # create a datahandling object
+    dh = ps.create_data_handling(domain_size, periodicity=(True, True), parallel=False, default_target=Target.CPU)
+
+    # fields
+    alignment = 'cacheline' if openmp else True
+    g = dh.add_array("g", values_per_cell=1, alignment=alignment)
+    dh.fill("g", 1.0, ghost_layers=True)
+    f = dh.add_array("f", values_per_cell=1, alignment=alignment)
+    dh.fill("f", 0.0, ghost_layers=True)
+    opt = {'instruction_set': instruction_set, 'assume_aligned': True, 'nontemporal': True,
+           'assume_inner_stride_one': True}
+    update_rule = [ps.Assignment(f.center(), 0.25 * (g[-1, 0] + g[1, 0] + g[0, -1] + g[0, 1]))]
+    # Without the base pointer spec, the inner store is not aligned
+    config = pystencils.config.CreateKernelConfig(target=dh.default_target, cpu_vectorize_info=opt, cpu_openmp=openmp)
+    ast = ps.create_kernel(update_rule, config=config)
+    if instruction_set in ['sse'] or instruction_set.startswith('avx') or instruction_set.startswith('sve'):
+        assert 'stream' in ast.instruction_set
+        assert 'streamFence' in ast.instruction_set
+    if instruction_set in ['neon', 'vsx', 'rvv']:
+        assert 'cachelineZero' in ast.instruction_set
+    if instruction_set in ['vsx']:
+        assert 'storeAAndFlushCacheline' in ast.instruction_set
+    for instruction in ['stream', 'streamFence', 'cachelineZero', 'storeAAndFlushCacheline', 'flushCacheline']:
+        if instruction in ast.instruction_set:
+            assert ast.instruction_set[instruction].split('{')[0] in ps.get_code_str(ast)
+    kernel = ast.compile()
+
+    # ps.show_code(ast)
+
+    dh.run_kernel(kernel)
+    np.testing.assert_equal(np.sum(dh.cpu_arrays['f']), np.prod(domain_size))
+
+
+def test_nt_stores_symbolic_size(instruction_set=instruction_set):
+    f, g = ps.fields('f, g: [2D]', layout='fzyx')
+    update_rule = [ps.Assignment(f.center(), 0.0), ps.Assignment(g.center(), 0.0)]
+    opt = {'instruction_set': instruction_set, 'assume_aligned': True, 'nontemporal': True,
+           'assume_inner_stride_one': True}
+    config = pystencils.config.CreateKernelConfig(target=Target.CPU, cpu_vectorize_info=opt)
+    ast = ps.create_kernel(update_rule, config=config)
+    # ps.show_code(ast)
+    ast.compile()
+
+
+def test_inplace_update(instruction_set=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
+
+    config = pystencils.config.CreateKernelConfig(cpu_vectorize_info={'instruction_set': instruction_set})
+    ast = ps.create_kernel(update_rule, config=config)
+    kernel = ast.compile()
+    kernel(f=arr)
+    np.testing.assert_equal(arr, 2)
+
+
+def test_vectorization_fixed_size(instruction_set=instruction_set):
+    instructions = get_vector_instruction_set(instruction_set=instruction_set)
+    configurations = []
+    # Fixed size - multiple of four
+    arr = np.ones((20 + 2, 24 + 2)) * 5.0
+    f, g = ps.fields(f=arr, g=arr)
+    configurations.append((arr, f, g))
+    # Fixed size - no multiple of four
+    arr = np.ones((21 + 2, 25 + 2)) * 5.0
+    f, g = ps.fields(f=arr, g=arr)
+    configurations.append((arr, f, g))
+    # Fixed size - different remainder
+    arr = np.ones((23 + 2, 17 + 2)) * 5.0
+    f, g = ps.fields(f=arr, g=arr)
+    configurations.append((arr, f, g))
+
+    for arr, f, g in configurations:
+        update_rule = [ps.Assignment(g[0, 0], f[0, 0] + f[-1, 0] + f[1, 0] + f[0, 1] + f[0, -1] + 42.0)]
+
+        ast = ps.create_kernel(update_rule)
+        vectorize(ast, instruction_set=instruction_set)
+        code = ps.get_code_str(ast)
+        add_instruction = instructions["+"][:instructions["+"].find("(")]
+        assert add_instruction in code
+        # print(code)
+
+        func = ast.compile()
+        dst = np.zeros_like(arr)
+        func(g=dst, f=arr)
+        np.testing.assert_equal(dst[1:-1, 1:-1], 5 * 5.0 + 42.0)
+
+
+def test_vectorization_variable_size(instruction_set=instruction_set):
+    f, g = ps.fields("f, g : double[2D]")
+    update_rule = [ps.Assignment(g[0, 0], f[0, 0] + f[-1, 0] + f[1, 0] + f[0, 1] + f[0, -1] + 42.0)]
+    ast = ps.create_kernel(update_rule)
+
+    replace_inner_stride_with_one(ast)
+    vectorize(ast, instruction_set=instruction_set)
+    func = ast.compile()
+
+    arr = np.ones((23 + 2, 17 + 2)) * 5.0
+    dst = np.zeros_like(arr)
+
+    func(g=dst, f=arr)
+    np.testing.assert_equal(dst[1:-1, 1:-1], 5 * 5.0 + 42.0)
+
+
+def test_piecewise1(instruction_set=instruction_set):
+    a, b, c, d, e = sp.symbols("a b c d e")
+    arr = np.ones((2 ** 3 + 2, 2 ** 4 + 2)) * 5.0
+
+    f, g = ps.fields(f=arr, g=arr)
+    update_rule = [ps.Assignment(a, f[1, 0]),
+                   ps.Assignment(b, a),
+                   ps.Assignment(c, f[0, 0] > 0.0),
+                   ps.Assignment(g[0, 0], sp.Piecewise((b + 3 + f[0, 1], c), (0.0, True)))]
+
+    ast = ps.create_kernel(update_rule)
+    vectorize(ast, instruction_set=instruction_set)
+    func = ast.compile()
+    dst = np.zeros_like(arr)
+    func(g=dst, f=arr)
+    np.testing.assert_equal(dst[1:-1, 1:-1], 5 + 3 + 5.0)
+
+
+def test_piecewise2(instruction_set=instruction_set):
+    arr = np.zeros((20, 20))
+
+    @ps.kernel
+    def test_kernel(s):
+        f, g = ps.fields(f=arr, g=arr)
+
+        s.condition @= f[0, 0] > 1
+        s.result    @= 0.0 if s.condition else 1.0
+        g[0, 0]     @= s.result
+
+    ast = ps.create_kernel(test_kernel)
+    # ps.show_code(ast)
+    vectorize(ast, instruction_set=instruction_set)
+    # ps.show_code(ast)
+    func = ast.compile()
+    func(f=arr, g=arr)
+    np.testing.assert_equal(arr, np.ones_like(arr))
+
+
+def test_piecewise3(instruction_set=instruction_set):
+    arr = np.zeros((22, 22))
+
+    @ps.kernel
+    def test_kernel(s):
+        f, g = ps.fields(f=arr, g=arr)
+        s.b     @= f[0, 1]
+        g[0, 0] @= 1.0 / (s.b + s.k) if f[0, 0] > 0.0 else 1.0
+
+    ast = ps.create_kernel(test_kernel)
+    # ps.show_code(ast)
+    vectorize(ast, instruction_set=instruction_set)
+    # ps.show_code(ast)
+    ast.compile()
+
+
+def test_logical_operators(instruction_set=instruction_set):
+    arr = np.zeros((22, 22))
+
+    @ps.kernel
+    def kernel_and(s):
+        f, g = ps.fields(f=arr, g=arr)
+        s.c @= sp.And(f[0, 1] < 0.0, f[1, 0] < 0.0)
+        g[0, 0] @= sp.Piecewise([1.0 / f[1, 0], s.c], [1.0, True])
+
+    ast = ps.create_kernel(kernel_and)
+    vectorize(ast, instruction_set=instruction_set)
+    ast.compile()
+
+    @ps.kernel
+    def kernel_or(s):
+        f, g = ps.fields(f=arr, g=arr)
+        s.c @= sp.Or(f[0, 1] < 0.0, f[1, 0] < 0.0)
+        g[0, 0] @= sp.Piecewise([1.0 / f[1, 0], s.c], [1.0, True])
+
+    ast = ps.create_kernel(kernel_or)
+    vectorize(ast, instruction_set=instruction_set)
+    ast.compile()
+
+    @ps.kernel
+    def kernel_equal(s):
+        f, g = ps.fields(f=arr, g=arr)
+        s.c @= sp.Eq(f[0, 1], 2.0)
+        g[0, 0] @= sp.Piecewise([1.0 / f[1, 0], s.c], [1.0, True])
+
+    ast = ps.create_kernel(kernel_equal)
+    vectorize(ast, instruction_set=instruction_set)
+    ast.compile()
+
+
+def test_hardware_query():
+    assert {'sse', 'neon', 'sve', 'sve2', 'sme', 'vsx', 'rvv'}.intersection(supported_instruction_sets)
+
+
+def test_vectorised_pow(instruction_set=instruction_set):
+    arr = np.zeros((24, 24))
+    f, g = ps.fields(f=arr, g=arr)
+
+    as1 = ps.Assignment(g[0, 0], sp.Pow(f[0, 0], 2))
+    as2 = ps.Assignment(g[0, 0], sp.Pow(f[0, 0], 0.5))
+    as3 = ps.Assignment(g[0, 0], sp.Pow(f[0, 0], -0.5))
+    as4 = ps.Assignment(g[0, 0], sp.Pow(f[0, 0], 4))
+    as5 = ps.Assignment(g[0, 0], sp.Pow(f[0, 0], -4))
+    as6 = ps.Assignment(g[0, 0], sp.Pow(f[0, 0], -1))
+
+    ast = ps.create_kernel(as1)
+    vectorize(ast, instruction_set=instruction_set)
+    ast.compile()
+
+    ast = ps.create_kernel(as2)
+    vectorize(ast, instruction_set=instruction_set)
+    ast.compile()
+
+    ast = ps.create_kernel(as3)
+    vectorize(ast, instruction_set=instruction_set)
+    ast.compile()
+
+    ast = ps.create_kernel(as4)
+    vectorize(ast, instruction_set=instruction_set)
+    ast.compile()
+
+    ast = ps.create_kernel(as5)
+    vectorize(ast, instruction_set=instruction_set)
+    ast.compile()
+
+    ast = ps.create_kernel(as6)
+    vectorize(ast, instruction_set=instruction_set)
+    ast.compile()
+
+
+def test_issue40(*_):
+    """https://i10git.cs.fau.de/pycodegen/pystencils/-/issues/40"""
+    opt = {'instruction_set': "avx512", 'assume_aligned': False,
+           'nontemporal': False, 'assume_inner_stride_one': True}
+
+    src = ps.fields("src(1): double[2D]", layout='fzyx')
+    eq = [ps.Assignment(sp.Symbol('rho'), 1.0),
+          ps.Assignment(src[0, 0](0), sp.Rational(4, 9) * sp.Symbol('rho'))]
+
+    config = pystencils.config.CreateKernelConfig(target=Target.CPU, cpu_vectorize_info=opt, data_type='float64')
+    ast = ps.create_kernel(eq, config=config)
+
+    code = ps.get_code_str(ast)
+    assert 'epi32' not in code

tests/test_vectorization_specific.py

+import pytest
+
+import numpy as np
+
+import pystencils.config
+import sympy as sp
+
+import pystencils as ps
+from pystencils.backends.simd_instruction_sets import (get_cacheline_size, get_supported_instruction_sets,
+                                                       get_vector_instruction_set)
+from pystencils.enums import Target
+from pystencils.typing import CFunction
+from . import test_vectorization
+
+
+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
+
+    config = pystencils.config.CreateKernelConfig(cpu_vectorize_info={'instruction_set': instruction_set})
+    ast = ps.create_kernel(update_rule, config=config)
+    kernel = ast.compile()
+    kernel(f=arr)
+    np.testing.assert_equal(arr, 2)
+
+
+@pytest.mark.parametrize('dtype', ('float32', 'float64'))
+@pytest.mark.parametrize('instruction_set', supported_instruction_sets)
+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.
+    """
+    arr = np.ones((2 ** 2 + 2, 2 ** 3 + 2), dtype=dtype)
+    arr[-3:, :] = -1
+
+    f, g = ps.fields(f=arr, g=arr)
+    update_rule = [ps.Assignment(g.center(), sp.Abs(f.center()))]
+
+    config = pystencils.config.CreateKernelConfig(cpu_vectorize_info={'instruction_set': instruction_set})
+    ast = ps.create_kernel(update_rule, config=config)
+
+    func = ast.compile()
+    dst = np.zeros_like(arr)
+    func(g=dst, f=arr)
+    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])
+def test_strided(instruction_set, dtype, nontemporal):
+    f, g = ps.fields(f"f, g : {dtype}[2D]")
+    update_rule = [ps.Assignment(g[0, 0], f[0, 0] + f[-1, 0] + f[1, 0] + f[0, 1] + f[0, -1] + 42.0)]
+    config = pystencils.config.CreateKernelConfig(cpu_vectorize_info={'instruction_set': instruction_set,
+                                                                      'nontemporal': nontemporal},
+                                                  default_number_float=dtype)
+    if 'storeS' not in get_vector_instruction_set(dtype, instruction_set) \
+            and instruction_set not in ['avx512', 'avx512vl', 'rvv'] and not instruction_set.startswith('sve'):
+        with pytest.warns(UserWarning) as warn:
+            ast = ps.create_kernel(update_rule, config=config)
+            assert 'Could not vectorize loop' in warn[0].message.args[0]
+    else:
+        with pytest.warns(None) as warn:
+            ast = ps.create_kernel(update_rule, config=config)
+            assert len(warn) == 0
+        instruction = 'streamS' if nontemporal and 'streamS' in ast.instruction_set else 'storeS'
+        assert ast.instruction_set[instruction].split('{')[0] in ps.get_code_str(ast)
+    instruction = 'cachelineZero'
+    if instruction in ast.instruction_set:
+        assert ast.instruction_set[instruction] not in ps.get_code_str(ast)
+
+    # ps.show_code(ast)
+    func = ast.compile()
+    ref_config = pystencils.config.CreateKernelConfig(default_number_float=dtype)
+    ref_func = ps.create_kernel(update_rule, config=ref_config).compile()
+
+    # For some reason other array creations fail on the emulated ppc pipeline
+    size = (25, 19)
+    arr = np.zeros(size).astype(dtype)
+    for i in range(size[0]):
+        for j in range(size[1]):
+            arr[i, j] = i * j
+
+    dst = np.zeros_like(arr, dtype=dtype)
+    ref = np.zeros_like(arr, dtype=dtype)
+
+    func(g=dst, f=arr)
+    ref_func(g=ref, f=arr)
+
+    # print("dst: ", dst)
+    # print("np array: ", arr)
+
+    np.testing.assert_almost_equal(dst[1:-1, 1:-1], ref[1:-1, 1:-1], 13 if dtype == 'float64' else 5)
+
+
+@pytest.mark.parametrize('dtype', ('float32', 'float64'))
+@pytest.mark.parametrize('instruction_set', supported_instruction_sets)
+@pytest.mark.parametrize('gl_field, gl_kernel', [(1, 0), (0, 1), (1, 1)])
+def test_alignment_and_correct_ghost_layers(gl_field, gl_kernel, instruction_set, dtype):
+    domain_size = (128, 128)
+    dh = ps.create_data_handling(domain_size, periodicity=(True, True), default_target=Target.CPU)
+    src = dh.add_array("src", values_per_cell=1, dtype=dtype, ghost_layers=gl_field, alignment=True)
+    dh.fill(src.name, 1.0, ghost_layers=True)
+    dst = dh.add_array("dst", values_per_cell=1, dtype=dtype, ghost_layers=gl_field, alignment=True)
+    dh.fill(dst.name, 1.0, ghost_layers=True)
+
+    update_rule = ps.Assignment(dst[0, 0], src[0, 0])
+    opt = {'instruction_set': instruction_set, 'assume_aligned': True,
+           'nontemporal': True, 'assume_inner_stride_one': True}
+    config = pystencils.config.CreateKernelConfig(target=dh.default_target,
+                                                  cpu_vectorize_info=opt, ghost_layers=gl_kernel)
+    ast = ps.create_kernel(update_rule, config=config)
+    kernel = ast.compile()
+    if ('loadA' in ast.instruction_set or 'storeA' in ast.instruction_set) and gl_kernel != gl_field:
+        with pytest.raises(ValueError):
+            dh.run_kernel(kernel)
+    else:
+        dh.run_kernel(kernel)
+
+
+@pytest.mark.parametrize('instruction_set', supported_instruction_sets)
+def test_cacheline_size(instruction_set):
+    cacheline_size = get_cacheline_size(instruction_set)
+    if cacheline_size is None and instruction_set in ['sse', 'avx', 'avx512', 'avx512vl', 'rvv']:
+        pytest.skip()
+    instruction_set = get_vector_instruction_set('double', instruction_set)
+    vector_size = instruction_set['bytes']
+    assert 8 < cacheline_size < 0x100000, "Cache line size is implausible"
+    if type(vector_size) is int:
+        assert cacheline_size % vector_size == 0, "Cache line size should be multiple of vector size"
+    assert cacheline_size & (cacheline_size - 1) == 0, "Cache line size is not a power of 2"
+
+
+# TODO move to vectorise
+@pytest.mark.parametrize('instruction_set',
+                         sorted(set(supported_instruction_sets) - {test_vectorization.instruction_set}))
+@pytest.mark.parametrize('function',
+                         [f for f in test_vectorization.__dict__ if f.startswith('test_') and f not in ['test_hardware_query', 'test_aligned_and_nt_stores']])
+def test_vectorization_other(instruction_set, function):
+    test_vectorization.__dict__[function](instruction_set)
+
+
+@pytest.mark.parametrize('dtype', ('float32', 'float64'))
+@pytest.mark.parametrize('instruction_set', supported_instruction_sets)
+@pytest.mark.parametrize('field_layout', ('fzyx', 'zyxf'))
+def test_square_root(dtype, instruction_set, field_layout):
+    config = pystencils.config.CreateKernelConfig(data_type=dtype,
+                                                  default_number_float=dtype,
+                                                  cpu_vectorize_info={'instruction_set': instruction_set,
+                                                                      'assume_inner_stride_one': True,
+                                                                      'assume_aligned': False,
+                                                                      'nontemporal': False})
+
+    src_field = ps.Field.create_generic('pdfs', 2, dtype, index_dimensions=1, layout=field_layout, index_shape=(9,))
+
+    eq = [ps.Assignment(sp.Symbol("xi"), sum(src_field.center_vector)),
+          ps.Assignment(sp.Symbol("xi_2"), sp.Symbol("xi") * sp.sqrt(src_field.center))]
+
+    ast = ps.create_kernel(eq, config=config)
+    ast.compile()
+    code = ps.get_code_str(ast)
+    print(code)
+
+
+@pytest.mark.parametrize('dtype', ('float32', 'float64'))
+@pytest.mark.parametrize('instruction_set', supported_instruction_sets)
+@pytest.mark.parametrize('padding', (True, False))
+def test_square_root_2(dtype, instruction_set, padding):
+    x, y = sp.symbols("x y")
+    src = ps.fields(f"src: {dtype}[2D]", layout='fzyx')
+
+    up = ps.Assignment(src[0, 0], 1 / x + sp.sqrt(y * 0.52 + x ** 2))
+
+    cpu_vec = {'instruction_set': instruction_set, 'assume_inner_stride_one': True,
+               'assume_sufficient_line_padding': padding,
+               'assume_aligned': True}
+
+    config = ps.CreateKernelConfig(data_type=dtype, default_number_float=dtype, cpu_vectorize_info=cpu_vec)
+    ast = ps.create_kernel(up, config=config)
+    ast.compile()
+
+    code = ps.get_code_str(ast)
+    print(code)
+
+
+@pytest.mark.parametrize('dtype', ('float32', 'float64'))
+@pytest.mark.parametrize('instruction_set', supported_instruction_sets)
+@pytest.mark.parametrize('padding', (True, False))
+def test_pow(dtype, instruction_set, padding):
+    config = pystencils.config.CreateKernelConfig(data_type=dtype,
+                                                  default_number_float=dtype,
+                                                  cpu_vectorize_info={'instruction_set': instruction_set,
+                                                                      'assume_inner_stride_one': True,
+                                                                      'assume_sufficient_line_padding': padding,
+                                                                      'assume_aligned': False, 'nontemporal': False})
+
+    src_field = ps.Field.create_generic('pdfs', 2, dtype, index_dimensions=1, layout='fzyx', index_shape=(9,))
+
+    eq = [ps.Assignment(sp.Symbol("xi"), sum(src_field.center_vector)),
+          ps.Assignment(sp.Symbol("xi_2"), sp.Symbol("xi") * sp.Pow(src_field.center, 0.5))]
+
+    ast = ps.create_kernel(eq, config=config)
+    ast.compile()
+    code = ps.get_code_str(ast)
+
+    print(code)
+
+
+@pytest.mark.parametrize('dtype', ('float32', 'float64'))
+@pytest.mark.parametrize('instruction_set', supported_instruction_sets)
+@pytest.mark.parametrize('padding', (True, False))
+def test_issue62(dtype, instruction_set, padding):
+    opt = {'instruction_set': instruction_set, 'assume_aligned': True,
+           'assume_inner_stride_one': True,
+           'assume_sufficient_line_padding': padding}
+
+    dx = sp.Symbol("dx")
+    dy = sp.Symbol("dy")
+    src, dst, rhs = ps.fields(f"src, src_tmp, rhs: {dtype}[2D]", layout='fzyx')
+
+    up = ps.Assignment(dst[0, 0], ((dy ** 2 * (src[1, 0] + src[-1, 0]))
+                                   + (dx ** 2 * (src[0, 1] + src[0, -1]))
+                                   - (rhs[0, 0] * dx ** 2 * dy ** 2)) / (2 * (dx ** 2 + dy ** 2)))
+
+    config = ps.CreateKernelConfig(data_type=dtype,
+                                   default_number_float=dtype,
+                                   cpu_vectorize_info=opt)
+
+    ast = ps.create_kernel(up, config=config)
+    ast.compile()
+    code = ps.get_code_str(ast)
+
+    print(code)
+
+    assert 'pow' not in code
+
+
+@pytest.mark.parametrize('dtype', ('float32', 'float64'))
+@pytest.mark.parametrize('instruction_set', supported_instruction_sets)
+def test_div_and_unevaluated_expr(dtype, instruction_set):
+    opt = {'instruction_set': instruction_set, 'assume_aligned': True, 'assume_inner_stride_one': True,
+           'assume_sufficient_line_padding': False}
+
+    x, y, z = sp.symbols("x y z")
+    rhs = (-4 * x ** 4 * y ** 2 * z ** 2 + (x ** 4 * y ** 2 / 3) + (x ** 4 * z ** 2 / 3)) / x ** 3
+
+    src = ps.fields(f"src: {dtype}[2D]", layout='fzyx')
+
+    up = ps.Assignment(src[0, 0], rhs)
+
+    config = ps.CreateKernelConfig(data_type=dtype,
+                                   default_number_float=dtype,
+                                   cpu_vectorize_info=opt)
+
+    ast = ps.create_kernel(up, config=config)
+    code = ps.get_code_str(ast)
+    # print(code)
+
+    ast.compile()
+
+    assert 'pow' not in code
+
+
+@pytest.mark.parametrize('dtype', ('float32', 'float64'))
+@pytest.mark.parametrize('instruction_set', ('sve', 'sve2', 'sme', 'rvv'))
+def test_check_ast_parameters_sizeless(dtype, instruction_set):
+    f, g = ps.fields(f"f, g: {dtype}[3D]", layout='fzyx')
+
+    update_rule = [ps.Assignment(g.center(), 2 * f.center())]
+
+    config = pystencils.config.CreateKernelConfig(data_type=dtype,
+                                                  cpu_vectorize_info={'instruction_set': instruction_set})
+    ast = ps.create_kernel(update_rule, config=config)
+    ast_symbols = [p.symbol for p in ast.get_parameters()]
+    assert ast.instruction_set['width'] not in ast_symbols
+    assert ast.instruction_set['intwidth'] not in ast_symbols
+
+
+
+# TODO this test case needs a complete rework of the vectoriser. The reason is that the vectoriser does not
+# TODO vectorise symbols at the moment because they could be strides or field sizes, thus involved in pointer arithmetic
+# TODO This means that the vectoriser only works if fields are involved on the rhs.
+# @pytest.mark.parametrize('dtype', ('float32', 'float64'))
+# @pytest.mark.parametrize('instruction_set', supported_instruction_sets)
+# def test_vectorised_symbols(dtype, instruction_set):
+#     opt = {'instruction_set': instruction_set, 'assume_aligned': True, 'assume_inner_stride_one': True,
+#            'assume_sufficient_line_padding': False}
+#
+#     x, y, z = sp.symbols("x y z")
+#     rhs = 1 / x ** 2 * (x + y)
+#
+#     src = ps.fields(f"src: {dtype}[2D]", layout='fzyx')
+#
+#     up = ps.Assignment(src[0, 0], rhs)
+#
+#     config = ps.CreateKernelConfig(data_type=dtype,
+#                                    default_number_float=dtype,
+#                                    cpu_vectorize_info=opt)
+#
+#     ast = ps.create_kernel(up, config=config)
+#     code = ps.get_code_str(ast)
+#     print(code)
+#
+#     ast.compile()
+#
+#     assert 'pow' not in code

tests/test_version_string.py

+import pystencils as ps
+
+
+def test_version_string():
+    version = ps.__version__
+    print(version)
+
+    numeric_version = [int(x, 10) for x in version.split('.')[0:1]]
+    test_version = sum(x * (100 ** i) for i, x in enumerate(numeric_version))
+
+    assert test_version >= 1