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Commit 61ffa6ec authored by Markus Holzer's avatar Markus Holzer
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Solved merge conflict

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...@@ -61,6 +61,7 @@ def test_vectorized_abs(instruction_set, dtype): ...@@ -61,6 +61,7 @@ def test_vectorized_abs(instruction_set, dtype):
@pytest.mark.parametrize('dtype', ('float', 'double')) @pytest.mark.parametrize('dtype', ('float', 'double'))
@pytest.mark.parametrize('instruction_set', supported_instruction_sets) @pytest.mark.parametrize('instruction_set', supported_instruction_sets)
def test_strided(instruction_set, dtype): def test_strided(instruction_set, dtype):
npdtype = np.float64 if dtype == 'double' else np.float32
type_string = "float64" if dtype == 'double' else "float32" type_string = "float64" if dtype == 'double' else "float32"
f, g = ps.fields(f"f, g : {type_string}[2D]") f, g = ps.fields(f"f, g : {type_string}[2D]")
...@@ -77,30 +78,22 @@ def test_strided(instruction_set, dtype): ...@@ -77,30 +78,22 @@ def test_strided(instruction_set, dtype):
default_number_float=type_string) default_number_float=type_string)
ast = ps.create_kernel(update_rule, config=config) ast = ps.create_kernel(update_rule, config=config)
assert len(warn) == 0 assert len(warn) == 0
# ps.show_code(ast)
ps.show_code(ast)
func = ast.compile() func = ast.compile()
ref_config = pystencils.config.CreateKernelConfig(default_number_float=type_string) ref_func = ps.create_kernel(update_rule).compile()
ref_func = ps.create_kernel(update_rule, config=ref_config).compile()
# For some reason other array creations fail on the emulated ppc pipeline arr = np.random.random((23 + 2, 17 + 2)).astype(npdtype)
size = (25, 19) # print("sum arr: ", np.sum(arr))
arr = np.zeros(size).astype(type_string) # print("arr type: ", arr.dtype)
for i in range(size[0]): dst = np.zeros_like(arr, dtype=npdtype)
for j in range(size[1]): ref = np.zeros_like(arr, dtype=npdtype)
arr[i, j] = i * j
dst = np.zeros_like(arr, dtype=type_string)
ref = np.zeros_like(arr, dtype=type_string)
func(g=dst, f=arr) func(g=dst, f=arr)
ref_func(g=ref, f=arr) ref_func(g=ref, f=arr)
print("dst: ", dst) # print("dst sum: ", np.sum(dst))
print("np array: ", arr) # print("reference sum: ", np.sum(ref))
np.testing.assert_almost_equal(dst, ref, 13 if dtype == 'double' else 5)
np.testing.assert_almost_equal(dst[1:-1, 1:-1], ref[1:-1, 1:-1], 13 if dtype == 'double' else 5)
@pytest.mark.parametrize('dtype', ('float', 'double')) @pytest.mark.parametrize('dtype', ('float', 'double'))
......
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