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 *notice, this list of conditions and the following disclaimer in the
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#include <complex>

#include "../common/cutlass_unit_test.h"

#include "cutlass/layout/matrix.h"
#include "cutlass/layout/tensor.h"

#include "cutlass/util/reference/device/tensor_reduce.h"
#include "cutlass/util/reference/host/tensor_norm.h"
#include "cutlass/util/host_tensor.h"

////////////////////////////////////////////////////////////////////////////////////////////////////

TEST(TensorReduce, norm_rowmajor_f32) {
    int const kM = 129;
    int const kN = 91;

    cutlass::HostTensor<float, cutlass::layout::RowMajor> tensor({kM, kN});

    for (int m = 0; m < kM; ++m) {
        for (int n = 0; n < kN; ++n) {
            float x = float(((m * kN + m + 7) % 8) - 4);

            tensor.at({m, n}) = x;
        }
    }

    tensor.sync_device();

    double device_norm = cutlass::reference::device::TensorNorm(
            tensor.device_view(), double());
    double host_norm =
            cutlass::reference::host::TensorNorm(tensor.host_view(), double());

    EXPECT_TRUE(std::abs(host_norm - device_norm) < 0.001);
}

TEST(TensorReduce, norm_nhwc_f32) {
    int const kN = 19;
    int const kH = 18;
    int const kW = 17;
    int const kC = 16;

    cutlass::HostTensor<float, cutlass::layout::TensorNHWC> tensor(
            {kN, kH, kW, kC});

    int idx = 0;

    double computed_norm = double();

    for (int n = 0; n < kN; ++n) {
        for (int h = 0; h < kH; ++h) {
            for (int w = 0; w < kW; ++w) {
                for (int c = 0; c < kC; ++c, ++idx) {
                    float x = float(((idx + 7) % 8) - 4);

                    computed_norm += double(x) * double(x);

                    tensor.at({n, h, w, c}) = x;
                }
            }
        }
    }

    computed_norm = std::sqrt(computed_norm);

    tensor.sync_device();

    double device_norm = cutlass::reference::device::TensorNorm(
            tensor.device_view(), double());
    double host_norm =
            cutlass::reference::host::TensorNorm(tensor.host_view(), double());

    EXPECT_TRUE(std::abs(host_norm - device_norm) < 0.001 &&
                std::abs(computed_norm - host_norm) < 0.001)
            << "computed norm: " << computed_norm << "\n"
            << " host norm: " << host_norm << "\n"
            << "device norm: " << device_norm << "\n";
}

TEST(TensorReduce, norm_nhwc_f16) {
    int const kN = 69;
    int const kH = 68;
    int const kW = 67;
    int const kC = 66;

    cutlass::HostTensor<cutlass::half_t, cutlass::layout::TensorNHWC> tensor(
            {kN, kH, kW, kC});

    int idx = 0;

    double computed_norm = double();

    for (int n = 0; n < kN; ++n) {
        for (int h = 0; h < kH; ++h) {
            for (int w = 0; w < kW; ++w) {
                for (int c = 0; c < kC; ++c, ++idx) {
                    float x = float(((idx + 7) % 8) - 4);
                    computed_norm += double(x) * double(x);

                    tensor.at({n, h, w, c}) = cutlass::half_t(x);
                }
            }
        }
    }

    computed_norm = std::sqrt(computed_norm);

    tensor.sync_device();

    double device_norm = cutlass::reference::device::TensorNorm(
            tensor.device_view(), double());
    double host_norm =
            cutlass::reference::host::TensorNorm(tensor.host_view(), double());

    EXPECT_TRUE(std::abs(host_norm - device_norm) < 0.001 &&
                std::abs(computed_norm - host_norm) < 0.001)
            << "computed norm: " << computed_norm << "\n"
            << " host norm: " << host_norm << "\n"
            << "device norm: " << device_norm << "\n";
}

TEST(TensorReduce, norm_diff_nhwc_f32) {
    int const kN = 59;
    int const kH = 24;
    int const kW = 57;
    int const kC = 78;

    using Layout = cutlass::layout::TensorNHWC;

    cutlass::HostTensor<float, Layout> tensor_A({kN, kH, kW, kC});
    cutlass::HostTensor<float, Layout> tensor_B({kN, kH, kW, kC});

    int idx = 0;

    double sum_sq_diff = 0;

    for (int n = 0; n < kN; ++n) {
        for (int h = 0; h < kH; ++h) {
            for (int w = 0; w < kW; ++w) {
                for (int c = 0; c < kC; ++c, ++idx) {
                    float a = float(((idx * 5 + 7) % 8) - 4);
                    float b = float(((idx * 3 + 7) % 8) - 4);

                    sum_sq_diff += double(a - b) * double(a - b);

                    tensor_A.at({n, h, w, c}) = a;
                    tensor_B.at({n, h, w, c}) = b;
                }
            }
        }
    }

    tensor_A.sync_device();
    tensor_B.sync_device();

    double device_norm = cutlass::reference::device::TensorNormDiff(
            tensor_A.device_view(), tensor_B.device_view(), double());

    double host_norm = std::sqrt(sum_sq_diff);

    EXPECT_TRUE(std::abs(host_norm - device_norm) < 0.001f)
            << "  host norm: " << host_norm << "\n"
            << "device norm: " << device_norm;
}

TEST(TensorReduce, norm_diff_nhwc_f16) {
    int const kN = 59;
    int const kH = 24;
    int const kW = 57;
    int const kC = 78;

    using Layout = cutlass::layout::TensorNHWC;

    cutlass::HostTensor<cutlass::half_t, Layout> tensor_A({kN, kH, kW, kC});
    cutlass::HostTensor<cutlass::half_t, Layout> tensor_B({kN, kH, kW, kC});

    int idx = 0;

    double sum_sq_diff = 0;

    for (int n = 0; n < kN; ++n) {
        for (int h = 0; h < kH; ++h) {
            for (int w = 0; w < kW; ++w) {
                for (int c = 0; c < kC; ++c, ++idx) {
                    float a = float(((idx * 5 + 7) % 8) - 4);
                    float b = float(((idx * 3 + 7) % 8) - 4);

                    sum_sq_diff += double(a - b) * double(a - b);

                    tensor_A.at({n, h, w, c}) = cutlass::half_t(a);
                    tensor_B.at({n, h, w, c}) = cutlass::half_t(b);
                }
            }
        }
    }

    tensor_A.sync_device();
    tensor_B.sync_device();

    double device_norm = cutlass::reference::device::TensorNormDiff(
            tensor_A.device_view(), tensor_B.device_view(), double());

    double host_norm = std::sqrt(sum_sq_diff);

    EXPECT_TRUE(std::abs(host_norm - device_norm) < 0.001f)
            << "  host norm: " << host_norm << "\n"
            << "device norm: " << device_norm;
}

////////////////////////////////////////////////////////////////////////////////////////////////////
