Fix width-one iteration slices on GPU

src/pystencils/gpu/kernelcreation.py

@@ -66,7 +66,8 @@ def create_cuda_kernel(assignments: NodeCollection, config: CreateKernelConfig):
         iteration_space = normalize_slice(iteration_slice, common_shape)
     else:
         iteration_space = normalize_slice(iteration_slice, common_shape)
-    iteration_space = tuple([s if isinstance(s, slice) else slice(s, s, 1) for s in iteration_space])
+    
+    iteration_space = tuple([s if isinstance(s, slice) else slice(s, s + 1, 1) for s in iteration_space])
 
     loop_counter_symbols = [LoopOverCoordinate.get_loop_counter_symbol(i) for i in range(len(iteration_space))]
 

tests/test_sliced_iteration.py

 import numpy as np
 import sympy as sp
+import pytest
 
-from pystencils import Assignment, Field, TypedSymbol, create_kernel, make_slice
+from pystencils import (
+    Assignment,
+    Field,
+    TypedSymbol,
+    create_kernel,
+    make_slice,
+    Target,
+    create_data_handling,
+)
 from pystencils.simp import sympy_cse_on_assignment_list
 
 
-def test_sliced_iteration():
+@pytest.mark.parametrize("target", [Target.CPU, Target.GPU])
+def test_sliced_iteration(target):
+    if target == Target.GPU:
+        pytest.importorskip("cupy")
+
     size = (4, 4)
-    src_arr = np.ones(size)
-    dst_arr = np.zeros_like(src_arr)
-    src_field = Field.create_from_numpy_array('src', src_arr)
-    dst_field = Field.create_from_numpy_array('dst', dst_arr)
+
+    dh = create_data_handling(size, default_target=target, default_ghost_layers=0)
+
+    src_field = dh.add_array("src", 1)
+    dst_field = dh.add_array("dst", 1)
+
+    dh.fill(src_field.name, 1.0, ghost_layers=True)
+    dh.fill(dst_field.name, 0.0, ghost_layers=True)
 
     a, b = sp.symbols("a b")
-    update_rule = Assignment(dst_field[0, 0],
-                             (a * src_field[0, 1] + a * src_field[0, -1] +
-                              b * src_field[1, 0] + b * src_field[-1, 0]) / 4)
+    update_rule = Assignment(
+        dst_field[0, 0],
+        (
+            a * src_field[0, 1]
+            + a * src_field[0, -1]
+            + b * src_field[1, 0]
+            + b * src_field[-1, 0]
+        )
+        / 4,
+    )
+
+    s = make_slice[1:3, 1]
+    kernel = create_kernel(
+        sympy_cse_on_assignment_list([update_rule]), iteration_slice=s, target=target
+    ).compile()
+
+    if target == Target.GPU:
+        dh.all_to_gpu()
+
+    dh.run_kernel(kernel, a=1.0, b=1.0)
+
+    if target == Target.GPU:
+        dh.all_to_cpu()
+
+    expected_result = np.zeros(size)
+    expected_result[1:3, 1] = 1
+    np.testing.assert_almost_equal(dh.gather_array(dst_field.name), expected_result)
+
+
+@pytest.mark.parametrize("target", [Target.CPU, Target.GPU])
+def test_symbols_in_slice(target):
+    if target == Target.GPU:
+        pytest.xfail("Iteration slices including arbitrary symbols are currently broken on GPU")
+
+    size = (4, 4)
+
+    dh = create_data_handling(size, default_target=target, default_ghost_layers=0)
+
+    src_field = dh.add_array("src", 1)
+    dst_field = dh.add_array("dst", 1)
+
+    dh.fill(src_field.name, 1.0, ghost_layers=True)
+    dh.fill(dst_field.name, 0.0, ghost_layers=True)
+
+    a, b = sp.symbols("a b")
+    update_rule = Assignment(
+        dst_field[0, 0],
+        (
+            a * src_field[0, 1]
+            + a * src_field[0, -1]
+            + b * src_field[1, 0]
+            + b * src_field[-1, 0]
+        )
+        / 4,
+    )
 
     x_end = TypedSymbol("x_end", "int")
     s = make_slice[1:x_end, 1]
     x_end_value = size[1] - 1
-    kernel = create_kernel(sympy_cse_on_assignment_list([update_rule]), iteration_slice=s).compile()
+    kernel = create_kernel(
+        sympy_cse_on_assignment_list([update_rule]), iteration_slice=s, target=target
+    ).compile()
+
+    if target == Target.GPU:
+        dh.all_to_gpu()
+
+    dh.run_kernel(kernel, a=1.0, b=1.0, x_end=x_end_value)
 
-    kernel(src=src_arr, dst=dst_arr, a=1.0, b=1.0, x_end=x_end_value)
+    if target == Target.GPU:
+        dh.all_to_cpu()
 
     expected_result = np.zeros(size)
     expected_result[1:x_end_value, 1] = 1
-    np.testing.assert_almost_equal(expected_result, dst_arr)
+    np.testing.assert_almost_equal(dh.gather_array(dst_field.name), expected_result)