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 *
 * MIT License
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 * Copyright (c) 2023 Advanced Micro Devices, Inc.
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#ifndef GUARD_CPU_GROUPNORM_HPP
#define GUARD_CPU_GROUPNORM_HPP

#include "tensor_holder.hpp"

template <class T>
void cpu_groupnorm_forward(tensor<T> input,
                           tensor<T> weight,
                           tensor<T> bias,
                           tensor<T>& ref_output,
                           tensor<T>& ref_mean,
                           tensor<T>& ref_rstd,
                           uint64_t num_groups,
                           float eps,
                           miopenNormMode_t mode)
{
    auto dims = input.desc.GetLengths();

    size_t numel             = input.desc.GetElementSize();
    size_t numel_per_channel = numel / dims[0] / dims[1];
    size_t num_channels      = dims[1];

    size_t outer_size = dims[0] * num_groups;
    size_t inner_size = numel / outer_size;

    miopen::par_ford(outer_size)([&](int32_t o) {
        T mean_v = 0.0f;
        T var_v  = 0.0f;

        miopen::ford(inner_size)([&](int32_t i) {
            T tmp = input[o * inner_size + i];
            mean_v += tmp;
            var_v += tmp * tmp;
        });

        mean_v   = mean_v / inner_size;
        var_v    = var_v / inner_size - mean_v * mean_v;
        T rstd_v = 1.0f / sqrt(var_v + eps);

        ref_mean[o] = mean_v;
        ref_rstd[o] = rstd_v;

        miopen::ford(inner_size)([&](int32_t i) {
            size_t idx = o * inner_size + i;
            size_t c   = (idx / numel_per_channel) % num_channels;
            T weight_v = mode ? weight[c] : 1;
            T bias_v   = mode ? bias[c] : 0;

            ref_output[idx] = (input[idx] - mean_v) * rstd_v * weight_v + bias_v;
        });
    });
}
#endif