pystencils_tests/test_conditional_vec.py → tests/test_conditional_vec.py

@@ -3,37 +3,39 @@ import sympy as sp
 import pytest
 
 import pystencils as ps
-from pystencils.astnodes import Block, Conditional
+from pystencils.alignedarray import aligned_zeros
+from pystencils.astnodes import Block, Conditional, SympyAssignment
 from pystencils.backends.simd_instruction_sets import get_supported_instruction_sets, get_vector_instruction_set
 from pystencils.enums import Target
 from pystencils.cpu.vectorization import vec_all, vec_any
+from pystencils.node_collection import NodeCollection
 
 supported_instruction_sets = get_supported_instruction_sets() if get_supported_instruction_sets() else []
 
 
 @pytest.mark.parametrize('instruction_set', supported_instruction_sets)
-@pytest.mark.parametrize('dtype', ('float', 'double'))
+@pytest.mark.parametrize('dtype', ('float32', 'float64'))
 def test_vec_any(instruction_set, dtype):
-    if instruction_set in ['sve', 'rvv']:
+    if instruction_set in ['sve', 'sve2', 'sme', 'rvv']:
         width = 4  # we don't know the actual value
     else:
         width = get_vector_instruction_set(dtype, instruction_set)['width']
-    data_arr = np.zeros((4 * width, 4 * width), dtype=np.float64 if dtype == 'double' else np.float32)
+    data_arr = np.zeros((4 * width, 4 * width), dtype=dtype)
 
     data_arr[3:9, 1:3 * width - 1] = 1.0
     data = ps.fields(f"data: {dtype}[2D]", data=data_arr)
 
     c = [
-        ps.Assignment(sp.Symbol("t1"), vec_any(data.center() > 0.0)),
-        Conditional(vec_any(data.center() > 0.0), Block([
-            ps.Assignment(data.center(), 2.0)
-        ]))
+        SympyAssignment(sp.Symbol("t1"), vec_any(data.center() > 0.0)),
+        Conditional(vec_any(data.center() > 0.0), Block([SympyAssignment(data.center(), 2.0)]))
     ]
-    ast = ps.create_kernel(c, target=ps.Target.CPU,
+
+    assignmets = NodeCollection(c)
+    ast = ps.create_kernel(assignments=assignmets, target=ps.Target.CPU,
                            cpu_vectorize_info={'instruction_set': instruction_set})
     kernel = ast.compile()
     kernel(data=data_arr)
-    if instruction_set in ['sve', 'rvv']:
+    if instruction_set in ['sve', 'sve2', 'sme', 'rvv']:
         # we only know that the first value has changed
         np.testing.assert_equal(data_arr[3:9, :3 * width - 1], 2.0)
     else:
@@ -41,27 +43,24 @@ def test_vec_any(instruction_set, dtype):
 
 
 @pytest.mark.parametrize('instruction_set', supported_instruction_sets)
-@pytest.mark.parametrize('dtype', ('float', 'double'))
+@pytest.mark.parametrize('dtype', ('float32', 'float64'))
 def test_vec_all(instruction_set, dtype):
-    if instruction_set in ['sve', 'rvv']:
+    if instruction_set in ['sve', 'sve2', 'sme', 'rvv']:
         width = 1000  # we don't know the actual value, need something guaranteed larger than vector
     else:
         width = get_vector_instruction_set(dtype, instruction_set)['width']
-    data_arr = np.zeros((4 * width, 4 * width), dtype=np.float64 if dtype == 'double' else np.float32)
+    data_arr = np.zeros((4 * width, 4 * width), dtype=dtype)
 
     data_arr[3:9, 1:3 * width - 1] = 1.0
     data = ps.fields(f"data: {dtype}[2D]", data=data_arr)
 
-    c = [
-        Conditional(vec_all(data.center() > 0.0), Block([
-            ps.Assignment(data.center(), 2.0)
-        ]))
-    ]
-    ast = ps.create_kernel(c, target=Target.CPU,
+    c = [Conditional(vec_all(data.center() > 0.0), Block([SympyAssignment(data.center(), 2.0)]))]
+    assignmets = NodeCollection(c)
+    ast = ps.create_kernel(assignmets, target=Target.CPU,
                            cpu_vectorize_info={'instruction_set': instruction_set})
     kernel = ast.compile()
     kernel(data=data_arr)
-    if instruction_set in ['sve', 'rvv']:
+    if instruction_set in ['sve', 'sve2', 'sme', 'rvv']:
         # we only know that some values in the middle have been replaced
         assert np.all(data_arr[3:9, :2] <= 1.0)
         assert np.any(data_arr[3:9, 2:] == 2.0)
@@ -88,26 +87,69 @@ def test_boolean_before_loop():
     ast = ps.create_kernel(a, cpu_vectorize_info={'instruction_set': supported_instruction_sets[-1]})
     kernel = ast.compile()
     kernel(f=f_arr, g=g_arr, t2=1.0)
-    print(g)
+    # print(g)
     np.testing.assert_array_equal(g_arr, 1.0)
     kernel(f=f_arr, g=g_arr, t2=-1.0)
     np.testing.assert_array_equal(g_arr, 42.0)
 
 
 @pytest.mark.parametrize('instruction_set', supported_instruction_sets)
-@pytest.mark.parametrize('dtype', ('float', 'double'))
-def test_vec_maskstore(instruction_set, dtype):
-    data_arr = np.zeros((16, 16), dtype=np.float64 if dtype == 'double' else np.float32)
+@pytest.mark.parametrize('dtype', ('float32', 'float64'))
+@pytest.mark.parametrize('nontemporal', [False, True])
+@pytest.mark.parametrize('aligned', [False, True])
+def test_vec_maskstore(instruction_set, dtype, nontemporal, aligned):
+    data_arr = (aligned_zeros if aligned else np.zeros)((16, 16), dtype=dtype)
     data_arr[3:-3, 3:-3] = 1.0
     data = ps.fields(f"data: {dtype}[2D]", data=data_arr)
 
-    c = [
-        Conditional(data.center() < 1.0, Block([
-            ps.Assignment(data.center(), 2.0)
-        ]))
-    ]
-    ast = ps.create_kernel(c, target=Target.CPU,
-                           cpu_vectorize_info={'instruction_set': instruction_set})
+    c = [Conditional(data.center() < 1.0, Block([SympyAssignment(data.center(), 2.0)]))]
+
+    assignmets = NodeCollection(c)
+    config = ps.CreateKernelConfig(cpu_vectorize_info={'instruction_set': instruction_set,
+                                                       'nontemporal': nontemporal,
+                                                       'assume_aligned': aligned},
+                                   default_number_float=dtype)
+    ast = ps.create_kernel(assignmets, config=config)
+    if 'maskStore' in ast.instruction_set:
+        instruction = 'maskStream' if nontemporal and 'maskStream' in ast.instruction_set else (
+                      'maskStoreA' if aligned and 'maskStoreA' in ast.instruction_set else 'maskStore')
+        assert ast.instruction_set[instruction].split('{')[0] in ps.get_code_str(ast)
+    print(ps.get_code_str(ast))
+    kernel = ast.compile()
+    kernel(data=data_arr)
+    np.testing.assert_equal(data_arr[:3, :], 2.0)
+    np.testing.assert_equal(data_arr[-3:, :], 2.0)
+    np.testing.assert_equal(data_arr[:, :3], 2.0)
+    np.testing.assert_equal(data_arr[:, -3:], 2.0)
+    np.testing.assert_equal(data_arr[3:-3, 3:-3], 1.0)
+
+
+@pytest.mark.parametrize('instruction_set', supported_instruction_sets)
+@pytest.mark.parametrize('dtype', ('float32', 'float64'))
+@pytest.mark.parametrize('nontemporal', [False, True])
+def test_vec_maskscatter(instruction_set, dtype, nontemporal):
+    data_arr = np.zeros((16, 16), dtype=dtype)
+    data_arr[3:-3, 3:-3] = 1.0
+    data = ps.fields(f"data: {dtype}[2D]")
+
+    c = [Conditional(data.center() < 1.0, Block([SympyAssignment(data.center(), 2.0)]))]
+
+    assignmets = NodeCollection(c)
+    config = ps.CreateKernelConfig(cpu_vectorize_info={'instruction_set': instruction_set,
+                                                       'nontemporal': nontemporal},
+                                   default_number_float=dtype)
+    if 'maskStoreS' not in get_vector_instruction_set(dtype, instruction_set) \
+            and not instruction_set.startswith('sve'):
+        with pytest.warns(UserWarning) as warn:
+            ast = ps.create_kernel(assignmets, config=config)
+            assert 'Could not vectorize loop' in warn[0].message.args[0]
+    else:
+        with pytest.warns(None) as warn:
+            ast = ps.create_kernel(assignmets, config=config)
+            assert len(warn) == 0
+        instruction = 'maskStreamS' if nontemporal and 'maskStreamS' in ast.instruction_set else 'maskStoreS'
+        assert ast.instruction_set[instruction].split('{')[0] in ps.get_code_str(ast)
+    print(ps.get_code_str(ast))
     kernel = ast.compile()
     kernel(data=data_arr)
     np.testing.assert_equal(data_arr[:3, :], 2.0)

tests/test_config.py

+from collections import defaultdict
+import numpy as np
+import pytest
+
+from pystencils import CreateKernelConfig, Target, Backend
+from pystencils.typing import BasicType
+
+
+def test_config():
+    # targets
+    config = CreateKernelConfig(target=Target.CPU)
+    assert config.target == Target.CPU
+    assert config.backend == Backend.C
+
+    config = CreateKernelConfig(target=Target.GPU)
+    assert config.target == Target.GPU
+    assert config.backend == Backend.CUDA
+
+    # typing
+    config = CreateKernelConfig(data_type=np.float64)
+    assert isinstance(config.data_type, defaultdict)
+    assert config.data_type.default_factory() == BasicType('float64')
+    assert config.default_number_float == BasicType('float64')
+    assert config.default_number_int == BasicType('int64')
+
+    config = CreateKernelConfig(data_type=np.float32)
+    assert isinstance(config.data_type, defaultdict)
+    assert config.data_type.default_factory() == BasicType('float32')
+    assert config.default_number_float == BasicType('float32')
+    assert config.default_number_int == BasicType('int64')
+
+    config = CreateKernelConfig(data_type=np.float32, default_number_float=np.float64)
+    assert isinstance(config.data_type, defaultdict)
+    assert config.data_type.default_factory() == BasicType('float32')
+    assert config.default_number_float == BasicType('float64')
+    assert config.default_number_int == BasicType('int64')
+
+    config = CreateKernelConfig(data_type=np.float32, default_number_float=np.float64, default_number_int=np.int16)
+    assert isinstance(config.data_type, defaultdict)
+    assert config.data_type.default_factory() == BasicType('float32')
+    assert config.default_number_float == BasicType('float64')
+    assert config.default_number_int == BasicType('int16')
+
+    config = CreateKernelConfig(data_type='float64')
+    assert isinstance(config.data_type, defaultdict)
+    assert config.data_type.default_factory() == BasicType('float64')
+    assert config.default_number_float == BasicType('float64')
+    assert config.default_number_int == BasicType('int64')
+
+    config = CreateKernelConfig(data_type={'a': np.float64, 'b': np.float32})
+    assert isinstance(config.data_type, defaultdict)
+    assert config.data_type.default_factory() == BasicType('float64')
+    assert config.default_number_float == BasicType('float64')
+    assert config.default_number_int == BasicType('int64')
+
+    config = CreateKernelConfig(data_type={'a': np.float32, 'b': np.int32})
+    assert isinstance(config.data_type, defaultdict)
+    assert config.data_type.default_factory() == BasicType('float32')
+    assert config.default_number_float == BasicType('float32')
+    assert config.default_number_int == BasicType('int64')
+
+
+def test_config_target_as_string():
+    with pytest.raises(ValueError):
+        CreateKernelConfig(target='cpu')
+
+
+def test_config_backend_as_string():
+    with pytest.raises(ValueError):
+        CreateKernelConfig(backend='C')
+
+
+def test_config_python_types():
+    with pytest.raises(ValueError):
+        CreateKernelConfig(data_type=float)
+
+
+def test_config_python_types2():
+    with pytest.raises(ValueError):
+        CreateKernelConfig(data_type={'a': float})
+
+
+def test_config_python_types3():
+    with pytest.raises(ValueError):
+        CreateKernelConfig(default_number_float=float)
+
+
+def test_config_python_types4():
+    with pytest.raises(ValueError):
+        CreateKernelConfig(default_number_int=int)
+
+
+def test_config_python_types5():
+    with pytest.raises(ValueError):
+        CreateKernelConfig(data_type="float")
+
+
+def test_config_python_types6():
+    with pytest.raises(ValueError):
+        CreateKernelConfig(default_number_float="float")
+
+
+def test_config_python_types7():
+    dtype = defaultdict(lambda: 'float', {'a': np.float64, 'b': np.int64})
+    with pytest.raises(ValueError):
+        CreateKernelConfig(data_type=dtype)
+
+
+def test_config_python_types8():
+    dtype = defaultdict(lambda: float, {'a': np.float64, 'b': np.int64})
+    with pytest.raises(ValueError):
+        CreateKernelConfig(data_type=dtype)
+
+
+def test_config_python_types9():
+    dtype = defaultdict(lambda: 'float32', {'a': 'float', 'b': np.int64})
+    with pytest.raises(ValueError):
+        CreateKernelConfig(data_type=dtype)
+
+
+def test_config_python_types10():
+    dtype = defaultdict(lambda: 'float32', {'a': float, 'b': np.int64})
+    with pytest.raises(ValueError):
+        CreateKernelConfig(data_type=dtype)

pystencils_tests/test_create_kernel_config.py → tests/test_create_kernel_config.py

 import numpy as np
+import sympy as sp
 import pystencils as ps
+import pystencils.config
 
 
 def test_create_kernel_config():
-    c = ps.CreateKernelConfig()
+    c = pystencils.config.CreateKernelConfig()
     assert c.backend == ps.Backend.C
     assert c.target == ps.Target.CPU
 
-    c = ps.CreateKernelConfig(target=ps.Target.GPU)
+    c = pystencils.config.CreateKernelConfig(target=ps.Target.GPU)
     assert c.backend == ps.Backend.CUDA
 
-    c = ps.CreateKernelConfig(target=ps.Target.OPENCL)
-    assert c.backend == ps.Backend.OPENCL
-
-    c = ps.CreateKernelConfig(backend=ps.Backend.CUDA)
+    c = pystencils.config.CreateKernelConfig(backend=ps.Backend.CUDA)
     assert c.target == ps.Target.CPU
     assert c.backend == ps.Backend.CUDA
 
 
 def test_kernel_decorator_config():
-    config = ps.CreateKernelConfig()
+    config = pystencils.config.CreateKernelConfig()
     a, b, c = ps.fields(a=np.ones(100), b=np.ones(100), c=np.ones(100))
 
     @ps.kernel_config(config)
@@ -27,3 +26,21 @@ def test_kernel_decorator_config():
         a[0] @= b[0] + c[0]
 
     ps.create_kernel(**test)
+
+
+def test_kernel_decorator2():
+    h = sp.symbols("h")
+    dtype = "float64"
+
+    src, dst = ps.fields(f"src, src_tmp: {dtype}[3D]")
+
+    @ps.kernel
+    def kernel_func():
+        dst[0, 0, 0] @= (src[1, 0, 0] + src[-1, 0, 0]
+                         + src[0, 1, 0] + src[0, -1, 0]
+                         + src[0, 0, 1] + src[0, 0, -1]) / (6 * h ** 2)
+
+    # assignments = ps.assignment_from_stencil(stencil, src, dst, normalization_factor=2)
+    ast = ps.create_kernel(kernel_func)
+
+    code = ps.get_code_str(ast)

pystencils_tests/test_custom_backends.py → tests/test_custom_backends.py

 from subprocess import CalledProcessError
 
 import pytest
-import sympy
 
 import pystencils
 import pystencils.cpu.cpujit
@@ -25,10 +24,10 @@ class ScreamingGpuBackend(CudaBackend):
 
 
 def test_custom_backends_cpu():
-    z, x, y = pystencils.fields("z, y, x: [2d]")
+    z, y, x = pystencils.fields("z, y, x: [2d]")
 
     normal_assignments = pystencils.AssignmentCollection([pystencils.Assignment(
-        z[0, 0], x[0, 0] * sympy.log(x[0, 0] * y[0, 0]))], [])
+        z[0, 0], x[0, 0] * x[0, 0] * y[0, 0])], [])
 
     ast = pystencils.create_kernel(normal_assignments, target=Target.CPU)
     pystencils.show_code(ast, ScreamingBackend())
@@ -37,16 +36,16 @@ def test_custom_backends_cpu():
 
 
 def test_custom_backends_gpu():
-    pytest.importorskip('pycuda')
-    import pycuda.driver
-    import pystencils.gpucuda.cudajit
+    pytest.importorskip('cupy')
+    import cupy
+    import pystencils.gpu.gpujit
 
     z, x, y = pystencils.fields("z, y, x: [2d]")
 
     normal_assignments = pystencils.AssignmentCollection([pystencils.Assignment(
-        z[0, 0], x[0, 0] * sympy.log(x[0, 0] * y[0, 0]))], [])
+        z[0, 0], x[0, 0] * x[0, 0] * y[0, 0])], [])
 
     ast = pystencils.create_kernel(normal_assignments, target=Target.GPU)
     pystencils.show_code(ast, ScreamingGpuBackend())
-    with pytest.raises(pycuda.driver.CompileError):
-        pystencils.gpucuda.cudajit.make_python_function(ast, custom_backend=ScreamingGpuBackend())
+    with pytest.raises((cupy.cuda.compiler.JitifyException, cupy.cuda.compiler.CompileException)):
+        pystencils.gpu.gpujit.make_python_function(ast, custom_backend=ScreamingGpuBackend())

pystencils_tests/test_datahandling.py → tests/test_datahandling.py

@@ -6,8 +6,7 @@ import numpy as np
 
 import pystencils as ps
 from pystencils import create_data_handling, create_kernel
-from pystencils.datahandling.pycuda import PyCudaArrayHandler
-from pystencils.datahandling.pyopencl import PyOpenClArrayHandler
+from pystencils.gpu.gpu_array_handler import GPUArrayHandler
 from pystencils.enums import Target
 
 try:
@@ -16,6 +15,12 @@ except ImportError:
     import unittest.mock
     pytest = unittest.mock.MagicMock()
 
+try:
+    import cupy.cuda.runtime
+    device_numbers = range(cupy.cuda.runtime.getDeviceCount())
+except ImportError:
+    device_numbers = []
+
 SCRIPT_FOLDER = Path(__file__).parent.absolute()
 INPUT_FOLDER = SCRIPT_FOLDER / "test_data"
 
@@ -86,11 +91,7 @@ def access_and_gather(dh, domain_size):
 def synchronization(dh, test_gpu=False):
     field_name = 'comm_field_test'
     if test_gpu:
-        try:
-            from pycuda import driver
-            import pycuda.autoinit
-        except ImportError:
-            return
+        pytest.importorskip("cupy")
         field_name += 'Gpu'
 
     dh.add_array(field_name, ghost_layers=1, dtype=np.int8, cpu=True, gpu=test_gpu)
@@ -117,7 +118,7 @@ def synchronization(dh, test_gpu=False):
 
 def kernel_execution_jacobi(dh, target):
 
-    test_gpu = target == Target.GPU or target == Target.OPENCL
+    test_gpu = target == Target.GPU
     dh.add_array('f', gpu=test_gpu)
     dh.add_array('tmp', gpu=test_gpu)
 
@@ -133,7 +134,7 @@ def kernel_execution_jacobi(dh, target):
     def jacobi():
         dh.fields.tmp.center @= sum(dh.fields.f.neighbors(stencil)) / len(stencil)
 
-    kernel = create_kernel(jacobi, target=target).compile()
+    kernel = create_kernel(jacobi, config=ps.CreateKernelConfig(target=target)).compile()
     for b in dh.iterate(ghost_layers=1):
         b['f'].fill(42)
     dh.run_kernel(kernel)
@@ -216,22 +217,18 @@ def test_kernel():
         reduction(dh)
 
         try:
-            import pycuda
+            import cupy
             dh = create_data_handling(domain_size=domain_shape, periodicity=True)
             kernel_execution_jacobi(dh, Target.GPU)
         except ImportError:
             pass
 
 
-@pytest.mark.parametrize('target', (Target.CPU, Target.GPU, Target.OPENCL))
+@pytest.mark.parametrize('target', (Target.CPU, Target.GPU))
 def test_kernel_param(target):
     for domain_shape in [(4, 5), (3, 4, 5)]:
         if target == Target.GPU:
-            pytest.importorskip('pycuda')
-        if target == Target.OPENCL:
-            pytest.importorskip('pyopencl')
-            from pystencils.opencl.opencljit import init_globally
-            init_globally()
+            pytest.importorskip('cupy')
 
         dh = create_data_handling(domain_size=domain_shape, periodicity=True, default_target=target)
         kernel_execution_jacobi(dh, target)
@@ -260,6 +257,20 @@ def test_add_arrays():
     assert y == dh.fields['y']
 
 
+@pytest.mark.parametrize('shape', [(17, 12), (7, 11, 18)])
+@pytest.mark.parametrize('layout', ['zyxf', 'fzyx'])
+def test_add_arrays_with_layout(shape, layout):
+    pytest.importorskip('cupy')
+
+    dh = create_data_handling(domain_size=shape, default_layout=layout, default_target=ps.Target.GPU)
+    f1 = dh.add_array("f1", values_per_cell=19)
+    dh.fill(f1.name, 1.0)
+
+    assert dh.cpu_arrays[f1.name].shape == dh.gpu_arrays[f1.name].shape
+    assert dh.cpu_arrays[f1.name].strides == dh.gpu_arrays[f1.name].strides
+    assert dh.cpu_arrays[f1.name].dtype == dh.gpu_arrays[f1.name].dtype
+
+
 def test_get_kwarg():
     domain_shape = (10, 10)
     field_description = 'src, dst'
@@ -270,7 +281,7 @@ def test_get_kwarg():
     dh.fill("dst", 0.0, ghost_layers=True)
 
     with pytest.raises(ValueError):
-        dh.add_array('src')
+        dh.add_array('src', values_per_cell=1)
 
     ur = ps.Assignment(src.center, dst.center)
     kernel = ps.create_kernel(ur).compile()
@@ -281,22 +292,20 @@ def test_get_kwarg():
 
 
 def test_add_custom_data():
-    pytest.importorskip('pycuda')
-
-    import pycuda.gpuarray as gpuarray
-    import pycuda.autoinit  # noqa
+    pytest.importorskip('cupy')
+    import cupy as cp
 
     def cpu_data_create_func():
         return np.ones((2, 2), dtype=np.float64)
 
     def gpu_data_create_func():
-        return gpuarray.zeros((2, 2), dtype=np.float64)
+        return cp.zeros((2, 2), dtype=np.float64)
 
     def cpu_to_gpu_transfer_func(gpuarr, cpuarray):
         gpuarr.set(cpuarray)
 
     def gpu_to_cpu_transfer_func(gpuarr, cpuarray):
-        gpuarr.get(cpuarray)
+        cpuarray[:] = gpuarr.get()
 
     dh = create_data_handling(domain_size=(10, 10))
     dh.add_custom_data('custom_data',
@@ -362,20 +371,11 @@ def test_load_data():
     assert np.all(dh.cpu_arrays['dst2']) == 0
 
 
-@pytest.mark.parametrize('target', (Target.GPU, Target.OPENCL))
-def test_array_handler(target):
+@pytest.mark.parametrize("device_number", device_numbers)
+def test_array_handler(device_number):
     size = (2, 2)
-    if target == Target.GPU:
-        pytest.importorskip('pycuda')
-        array_handler = PyCudaArrayHandler()
-    if target == Target.OPENCL:
-        pytest.importorskip('pyopencl')
-        import pyopencl as cl
-        from pystencils.opencl.opencljit import init_globally
-        init_globally()
-        ctx = cl.create_some_context(0)
-        queue = cl.CommandQueue(ctx)
-        array_handler = PyOpenClArrayHandler(queue)
+    pytest.importorskip('cupy')
+    array_handler = GPUArrayHandler(device_number)
 
     zero_array = array_handler.zeros(size)
     cpu_array = np.empty(size)
@@ -389,8 +389,23 @@ def test_array_handler(target):
 
     empty = array_handler.empty(size)
     assert empty.strides == (16, 8)
-    empty = array_handler.empty(shape=size, layout=(1, 0))
+    empty = array_handler.empty(shape=size, order="F")
     assert empty.strides == (8, 16)
 
     random_array = array_handler.randn(size)
 
+    cpu_array = np.empty((20, 40), dtype=np.float64)
+    gpu_array = array_handler.to_gpu(cpu_array)
+
+    assert cpu_array.base is None
+    assert gpu_array.base is None
+    assert gpu_array.strides == cpu_array.strides
+
+    cpu_array2 = np.empty((20, 40), dtype=np.float64)
+    cpu_array2 = cpu_array2.swapaxes(0, 1)
+    gpu_array2 = array_handler.to_gpu(cpu_array2)
+
+    assert cpu_array2.base is not None
+    assert gpu_array2.base is not None
+    assert gpu_array2.strides == cpu_array2.strides
+

pystencils_tests/test_datahandling_parallel.py → tests/test_datahandling_parallel.py

@@ -11,7 +11,7 @@ from pystencils.slicing import slice_from_direction
 
 from pystencils.datahandling.parallel_datahandling import ParallelDataHandling
 from pystencils.datahandling import create_data_handling
-from pystencils_tests.test_datahandling import (
+from tests.test_datahandling import (
     access_and_gather, kernel_execution_jacobi, reduction, synchronization, vtk_output)
 
 SCRIPT_FOLDER = Path(__file__).parent.absolute()
@@ -33,12 +33,12 @@ def test_access_and_gather():
     dh = ParallelDataHandling(blocks, default_ghost_layers=2)
     access_and_gather(dh, cells)
     synchronization(dh, test_gpu=False)
-    if hasattr(wlb, 'cuda'):
+    if hasattr(wlb, 'gpu'):
         synchronization(dh, test_gpu=True)
 
 
 def test_gpu():
-    pytest.importorskip('waLBerla.cuda')
+    pytest.importorskip('waLBerla.gpu')
 
     block_size = (4, 7, 1)
     num_blocks = (3, 2, 1)
@@ -59,7 +59,7 @@ def test_gpu():
 @pytest.mark.parametrize('target', (pystencils.Target.CPU, pystencils.Target.GPU))
 def test_kernel(target):
     if target == pystencils.Target.GPU:
-        pytest.importorskip('waLBerla.cuda')
+        pytest.importorskip('waLBerla.gpu')
 
     # 3D
     blocks = wlb.createUniformBlockGrid(blocks=(3, 2, 4), cellsPerBlock=(3, 2, 5), oneBlockPerProcess=False)
@@ -108,7 +108,7 @@ def test_block_iteration():
 
 
 def test_getter_setter():
-    pytest.importorskip('waLBerla.cuda')
+    pytest.importorskip('waLBerla.gpu')
 
     block_size = (2, 2, 2)
     num_blocks = (2, 2, 2)
@@ -131,7 +131,7 @@ def test_getter_setter():
 
 
 def test_parallel_datahandling_boundary_conditions():
-    pytest.importorskip('waLBerla.cuda')
+    pytest.importorskip('waLBerla.gpu')
 
     dh = create_data_handling(domain_size=(7, 7), periodicity=True, parallel=True,
                               default_target=pystencils.Target.GPU)

tests/test_dot_printer.ipynb

+%% Cell type:code id: tags:
+
+``` python
+from pystencils.session import *
+from pystencils.astnodes import Block, Conditional, SympyAssignment
+```
+
+%% Cell type:code id: tags:
+
+``` python
+src, dst = ps.fields("src, dst: double[2D]", layout='c')
+
+true_block = Block([SympyAssignment(dst[0, 0], src[-1, 0])])
+false_block = Block([SympyAssignment(dst[0, 0], src[1, 0])])
+ur = [true_block, Conditional(dst.center() > 0.0, true_block, false_block)]
+
+ast = ps.create_kernel(ur)
+```
+
+%% Cell type:code id: tags:
+
+``` python
+ps.show_code(ast)
+```
+
+%% Output
+
+
+
+%% Cell type:code id: tags:
+
+``` python
+```

tests/test_dot_printer.py

+import pystencils as ps
+
+from pystencils.astnodes import Block, Conditional, SympyAssignment
+
+
+def test_dot_print():
+    src, dst = ps.fields("src, dst: double[2D]", layout='c')
+
+    true_block = Block([SympyAssignment(dst[0, 0], src[-1, 0])])
+    false_block = Block([SympyAssignment(dst[0, 0], src[1, 0])])
+    ur = [true_block, Conditional(dst.center() > 0.0, true_block, false_block)]
+
+    ast = ps.create_kernel(ur)

pystencils_tests/test_fast_approximation.py → tests/test_fast_approximation.py

@@ -7,7 +7,7 @@ from pystencils.fast_approximation import (
 
 
 def test_fast_sqrt():
-    pytest.importorskip('pycuda')
+    pytest.importorskip('cupy')
     f, g = ps.fields("f, g: double[2D]")
     expr = sp.sqrt(f[0, 0] + f[1, 0])
 
@@ -30,7 +30,7 @@ def test_fast_sqrt():
 
 
 def test_fast_divisions():
-    pytest.importorskip('pycuda')
+    pytest.importorskip('cupy')
     f, g = ps.fields("f, g: double[2D]")
     expr = f[0, 0] / f[1, 0]
     assert len(insert_fast_divisions(expr).atoms(fast_division)) == 1