199 lines
7.9 KiB
C++
199 lines
7.9 KiB
C++
// Copyright (C) 2018-2021 Intel Corporation
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// SPDX-License-Identifier: Apache-2.0
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//
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#include <algorithm>
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#include <cinttypes>
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#include <cmath>
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#include <cstdlib>
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#include <random>
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#include <string>
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#include "gtest/gtest.h"
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#include "ngraph/ngraph.hpp"
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#include "ngraph/runtime/tensor.hpp"
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#include "runtime/backend.hpp"
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#include "util/all_close.hpp"
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#include "util/all_close_f.hpp"
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#include "util/ndarray.hpp"
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#include "util/random.hpp"
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#include "util/test_control.hpp"
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#include "util/test_tools.hpp"
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static std::mt19937_64 random_generator;
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using namespace std;
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using namespace ngraph;
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static string s_manifest = "${MANIFEST}";
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NGRAPH_TEST(${BACKEND_NAME}, cum_sum_default)
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{
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Shape shape{1, 4};
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auto A = make_shared<op::Parameter>(element::f32, shape);
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auto axis = make_shared<op::Parameter>(element::i32, Shape{1});
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auto f = make_shared<Function>(make_shared<op::CumSum>(A, axis), ParameterVector{A, axis});
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auto backend = runtime::Backend::create("${BACKEND_NAME}");
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// Create some tensors for input/output
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auto a = backend->create_tensor(element::f32, shape);
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copy_data(a, vector<float>{1, 2, 3, 4});
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auto axis_tensor = backend->create_tensor(axis->get_element_type(), axis->get_shape());
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copy_data(axis_tensor, vector<int32_t>{1});
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auto result = backend->create_tensor(element::f32, shape);
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auto handle = backend->compile(f);
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handle->call_with_validate({result}, {a, axis_tensor});
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EXPECT_TRUE(test::all_close_f((vector<float>{1, 3, 6, 10}), read_vector<float>(result)));
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}
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NGRAPH_TEST(${BACKEND_NAME}, cum_sum_2dim)
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{
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Shape shape{2, 4};
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auto A = make_shared<op::Parameter>(element::f32, shape);
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auto axis = make_shared<op::Parameter>(element::i64, Shape{1});
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auto f = make_shared<Function>(make_shared<op::CumSum>(A, axis), ParameterVector{A, axis});
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auto backend = runtime::Backend::create("${BACKEND_NAME}");
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// Create some tensors for input/output
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auto a = backend->create_tensor(element::f32, shape);
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copy_data(a, vector<float>{0, 1, 2, 3, 4, 5, 6, 7});
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auto axis_tensor = backend->create_tensor(axis->get_element_type(), axis->get_shape());
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copy_data(axis_tensor, vector<int64_t>{0});
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auto result = backend->create_tensor(element::f32, shape);
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auto handle = backend->compile(f);
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handle->call_with_validate({result}, {a, axis_tensor});
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EXPECT_TRUE(
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test::all_close_f((vector<float>{0, 1, 2, 3, 4, 6, 8, 10}), read_vector<float>(result)));
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}
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NGRAPH_TEST(${BACKEND_NAME}, cum_sum_2dim_default_axis)
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{
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Shape shape{2, 4};
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auto A = make_shared<op::Parameter>(element::f32, shape);
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auto f = make_shared<Function>(make_shared<op::CumSum>(A), ParameterVector{A});
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auto backend = runtime::Backend::create("${BACKEND_NAME}");
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// Create some tensors for input/output
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auto a = backend->create_tensor(element::f32, shape);
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copy_data(a, vector<float>{0, 1, 2, 3, 4, 5, 6, 7});
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auto result = backend->create_tensor(element::f32, shape);
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auto handle = backend->compile(f);
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handle->call_with_validate({result}, {a});
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EXPECT_TRUE(
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test::all_close_f((vector<float>{0, 1, 2, 3, 4, 6, 8, 10}), read_vector<float>(result)));
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}
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NGRAPH_TEST(${BACKEND_NAME}, cum_sum_3d)
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{
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auto test_cumsum_3d = [](const int32_t axis_val) -> void {
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Shape shape{3, 2, 4};
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auto A = make_shared<op::Parameter>(element::f32, shape);
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auto axis = make_shared<op::Parameter>(element::i32, Shape{1});
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auto f = make_shared<Function>(make_shared<op::CumSum>(A, axis), ParameterVector{A, axis});
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auto backend = runtime::Backend::create("${BACKEND_NAME}");
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// Create some tensors for input/output
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auto a = backend->create_tensor(element::f32, shape);
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copy_data(a, vector<float>{0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11,
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12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23});
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auto axis_tensor = backend->create_tensor(axis->get_element_type(), axis->get_shape());
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copy_data(axis_tensor, vector<int32_t>{axis_val});
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auto result = backend->create_tensor(element::f32, shape);
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auto handle = backend->compile(f);
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handle->call_with_validate({result}, {a, axis_tensor});
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if (axis_val == 0)
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{
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EXPECT_TRUE(
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test::all_close_f((vector<float>{0, 1, 2, 3, 4, 5, 6, 7, 8, 10, 12, 14,
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16, 18, 20, 22, 24, 27, 30, 33, 36, 39, 42, 45}),
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read_vector<float>(result)));
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}
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else if (axis_val == 1)
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{
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EXPECT_TRUE(
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test::all_close_f((vector<float>{0, 1, 2, 3, 4, 6, 8, 10, 8, 9, 10, 11,
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20, 22, 24, 26, 16, 17, 18, 19, 36, 38, 40, 42}),
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read_vector<float>(result)));
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}
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else if (axis_val == 2)
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{
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EXPECT_TRUE(
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test::all_close_f((vector<float>{0, 1, 3, 6, 4, 9, 15, 22, 8, 17, 27, 38,
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12, 25, 39, 54, 16, 33, 51, 70, 20, 41, 63, 86}),
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read_vector<float>(result)));
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}
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};
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test_cumsum_3d(0);
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test_cumsum_3d(1);
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test_cumsum_3d(2);
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}
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NGRAPH_TEST(${BACKEND_NAME}, cum_sum_2dim_allmodes)
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{
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auto test_cum_sum_allmodes = [](const int64_t axis_val, int exclusive, int reverse) {
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Shape shape{2, 4};
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auto A = make_shared<op::Parameter>(element::f32, shape);
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auto axis = make_shared<op::Parameter>(element::i64, Shape{1});
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auto f = make_shared<Function>(make_shared<op::CumSum>(A, axis, exclusive, reverse),
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ParameterVector{A, axis});
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auto backend = runtime::Backend::create("${BACKEND_NAME}");
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// Create some tensors for input/output
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auto a = backend->create_tensor(element::f32, shape);
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copy_data(a, vector<float>{0, 1, 2, 3, 4, 5, 6, 7});
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auto axis_tensor = backend->create_tensor(axis->get_element_type(), axis->get_shape());
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copy_data(axis_tensor, vector<int64_t>{axis_val});
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auto result = backend->create_tensor(element::f32, shape);
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auto handle = backend->compile(f);
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handle->call_with_validate({result}, {a, axis_tensor});
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if (axis_val == 1 && exclusive == 1 && reverse == 0)
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{
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EXPECT_TRUE(test::all_close_f((vector<float>{0, 0, 1, 3, 0, 4, 9, 15}),
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read_vector<float>(result)));
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}
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else if (axis_val == 1 && exclusive == 0 && reverse == 1)
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{
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EXPECT_TRUE(test::all_close_f((vector<float>{6, 6, 5, 3, 22, 18, 13, 7}),
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read_vector<float>(result)));
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}
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else if (axis_val == 1 && exclusive == 1 && reverse == 1)
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{
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EXPECT_TRUE(test::all_close_f((vector<float>{6, 5, 3, 0, 18, 13, 7, 0}),
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read_vector<float>(result)));
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}
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else if (axis_val == 0 && exclusive == 0 && reverse == 0)
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{
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EXPECT_TRUE(test::all_close_f((vector<float>{0, 1, 2, 3, 4, 6, 8, 10}),
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read_vector<float>(result)));
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}
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else if (axis_val == 0 && exclusive == 1 && reverse == 1)
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{
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EXPECT_TRUE(test::all_close_f((vector<float>{4, 5, 6, 7, 0, 0, 0, 0}),
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read_vector<float>(result)));
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}
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else if (axis_val == 0 && exclusive == 0 && reverse == 1)
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{
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EXPECT_TRUE(test::all_close_f((vector<float>{4, 6, 8, 10, 4, 5, 6, 7}),
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read_vector<float>(result)));
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}
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};
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test_cum_sum_allmodes(1, 1, 0);
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test_cum_sum_allmodes(-1, 0, 1);
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test_cum_sum_allmodes(-1, 1, 1);
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test_cum_sum_allmodes(0, 0, 0);
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test_cum_sum_allmodes(0, 1, 1);
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test_cum_sum_allmodes(0, 0, 1);
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}
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