cosmetic changes

cosmetic changes

struct state
{
    float V;
    float h;
    float n;
    float z;
    float s_AMPA;
    float x_NMDA;
    float s_NMDA;
    float s_GABAA;
    float I_app;
};

inline float _f_I_Na_m_inf(const float V)
{
    const float theta_m = -30;
    const float sigma_m = 9.5;

    return pow((1 + exp(-(V - theta_m) / sigma_m)), -1);
}

inline float _f_I_Na(const float V, const float h)
{
    const float g_Na = 35;
    const float V_Na = 55;

    return g_Na * pow(_f_I_Na_m_inf(V), 3) * h * (V - V_Na);
}

inline float _f_p_inf(const float V)
{
    const float theta_p = -47;
    const float sigma_p = 3;

    return pow((1 + exp(-(V - theta_p) / sigma_p)), -1);
}

inline float _f_I_NaP(const float V)
{
    const float g_NaP = 0.2;
    const float V_Na  = 55;

    return g_NaP * _f_p_inf(V) * (V - V_Na);
}

inline float _f_I_Kdr(const float V, const float n)
{
    const float g_Kdr = 3;
    const float V_K   = -90;

    return g_Kdr * pow(n, 4) * (V - V_K);
}

inline float _f_I_Leak(const float V)
{
    const float g_L = 0.05;
    const float V_L = -70;

    return g_L * (V - V_L);
}

inline float _f_I_Kslow(const float V, const float z)
{
    const float g_Kslow = 1.8;
    const float V_K     = -90;

    return g_Kslow * z * (V - V_K);
}

inline float _f_I_AMPA(const float V, const float sumFootprintAMPA)
{
    const float g_AMPA = 0.08;
    const float V_Glu  = 0;

    return g_AMPA * (V - V_Glu) * sumFootprintAMPA;
}

inline float _f_f_NMDA(const float V)
{
    const float theta_NMDA = 0;

    // TODO: theta_NMDA = -inf for [Mg2+]_0 = 0
    // and increases logarithmically with [Mg2+]_0
    const float sigma_NMDA = 10;

    return pow(1 + exp(-(V - theta_NMDA) / sigma_NMDA), -1);
}

inline float _f_I_NMDA(const float V, const float sumFootprintNMDA)
{
    const float g_NMDA = 0.07;
    const float V_Glu  = 0;

    return g_NMDA * _f_f_NMDA(V) * (V - V_Glu) * sumFootprintNMDA;
}

inline float _f_I_GABAA(const float V, const float sumFootprintGABAA)
{
    const float g_GABAA = 0.05;
    const float V_GABAA = -70;

    return g_GABAA * (V - V_GABAA) * sumFootprintGABAA;
}

inline float _f_dV_dt(const float V,
                      const float h,
                      const float n,
                      const float z,
                      const float I_app,
                      const float sumFootprintAMPA,
                      const float sumFootprintNMDA,
                      const float sumFootprintGABAA)
{
    return - _f_I_Na(V, h)
           - _f_I_NaP(V)
           - _f_I_Kdr(V, n)
           - _f_I_Kslow(V, z)
           - _f_I_Leak(V)
           - _f_I_AMPA(V, sumFootprintAMPA)
           - _f_I_NMDA(V, sumFootprintNMDA)
           - _f_I_GABAA(V, sumFootprintGABAA)
           + I_app;
}

__kernel void f_dV_dt(__global struct state* states,
                      __global const float* sumFootprintAMPA,
                      __global const float* sumFootprintNMDA,
                      __global const float* sumFootprintGABAA,
                      const unsigned int numNeurons,
                      const unsigned int ind_old,
                      const float dt)
{
    const unsigned int idx = get_global_id(0);
    const unsigned int ind_new = 1 - ind_old;

    const struct state state_0 = states[ind_old*numNeurons+idx];
    const float sumFootprintAMPA_loc = sumFootprintAMPA[idx];
    const float sumFootprintNMDA_loc = sumFootprintNMDA[idx];
    const float sumFootprintGABAA_loc = sumFootprintGABAA[idx];

    float f1, f2, f3, f4;

    f1 = _f_dV_dt(state_0.V, state_0.h, state_0.n, state_0.z, state_0.I_app, sumFootprintAMPA_loc, sumFootprintNMDA_loc, sumFootprintGABAA_loc);
    f2 = _f_dV_dt(state_0.V + dt * f1 / 2.0, state_0.h, state_0.n, state_0.z, state_0.I_app, sumFootprintAMPA_loc, sumFootprintNMDA_loc, sumFootprintGABAA_loc);
    f3 = _f_dV_dt(state_0.V + dt * f2 / 2.0, state_0.h, state_0.n, state_0.z, state_0.I_app, sumFootprintAMPA_loc, sumFootprintNMDA_loc, sumFootprintGABAA_loc);
    f4 = _f_dV_dt(state_0.V + dt * f3, state_0.h, state_0.n, state_0.z, state_0.I_app, sumFootprintAMPA_loc, sumFootprintNMDA_loc, sumFootprintGABAA_loc);

    states[ind_new*numNeurons+idx].V = state_0.V + dt * ( f1 + 2.0 * f2 + 2.0 * f3 + f4 ) / 6.0;
}

inline float _f_I_Na_h_inf(const float V)
{
    const float theta_h = -45;
    const float sigma_h = -7;

    return pow((1 + exp(-(V - theta_h) / sigma_h)), -1);
}

inline float _f_I_Na_tau_h(const float V)
{
    const float theta_th = -40.5;
    const float sigma_th = -6;

    return 0.1 + 0.75 * pow((1 + exp(-(V - theta_th) / sigma_th)), -1);
}

inline float _f_I_Na_dh_dt(const float h, const float V)
{
    return (_f_I_Na_h_inf(V) - h) / _f_I_Na_tau_h(V);
}

__kernel void f_I_Na_dh_dt(__global struct state* states, const unsigned int numNeurons, const unsigned int ind_old, const float dt)
{
    const unsigned int idx = get_global_id(0);
    const unsigned int ind_new = 1 - ind_old;

    struct state state_0 = states[ind_old*numNeurons+idx];

    float f1, f2, f3, f4;

    f1 = _f_I_Na_dh_dt(state_0.h, state_0.V);
    f2 = _f_I_Na_dh_dt(state_0.h + dt * f1 / 2.0, state_0.V);
    f3 = _f_I_Na_dh_dt(state_0.h + dt * f2 / 2.0, state_0.V);
    f4 = _f_I_Na_dh_dt(state_0.h + dt * f3, state_0.V);

    states[ind_new*numNeurons+idx].h = state_0.h + dt * ( f1 + 2.0 * f2 + 2.0 * f3 + f4 ) / 6.0;
}

inline float _f_n_inf(const float V)
{
    const float theta_n = -33;
    const float sigma_n = 10;

    return pow(1 + exp(-(V - theta_n) / sigma_n), -1);
}

inline float _f_tau_n(const float V)
{
    const float theta_tn = -33;
    const float sigma_tn = -15;

    return 0.1 + 0.5 * pow(1 + exp(-(V - theta_tn) / sigma_tn), -1);
}

inline float _f_dn_dt(const float n, const float V)
{
    return (_f_n_inf(V) - n) / _f_tau_n(V);
}

__kernel void f_dn_dt(__global struct state* states, const unsigned int numNeurons, const unsigned int ind_old, const float dt)
{
    const unsigned int idx = get_global_id(0);
    const unsigned int ind_new = 1 - ind_old;

    struct state state_0 = states[ind_old*numNeurons+idx];

    float f1, f2, f3, f4;

    f1 = _f_dn_dt(state_0.n, state_0.V);
    f2 = _f_dn_dt(state_0.n + dt * f1 / 2.0, state_0.V);
    f3 = _f_dn_dt(state_0.n + dt * f2 / 2.0, state_0.V);
    f4 = _f_dn_dt(state_0.n + dt * f3, state_0.V);

    states[ind_new*numNeurons+idx].n = state_0.n + dt * ( f1 + 2.0 * f2 + 2.0 * f3 + f4 ) / 6.0;
}

inline float _f_z_inf(const float V)
{
    const float theta_z = -39;
    const float sigma_z = 5;

    return pow(1 + exp(-(V - theta_z) / sigma_z), -1);
}

inline float _f_dz_dt(const float z, const float V)
{
    const float tau_z = 75;

    return (_f_z_inf(V) - z) / tau_z;
}

__kernel void f_dz_dt(__global struct state* states, const unsigned int numNeurons, const unsigned int ind_old, const float dt)
{
    const unsigned int idx = get_global_id(0);
    const unsigned int ind_new = 1 - ind_old;

    struct state state_0 = states[ind_old*numNeurons+idx];

    float f1, f2, f3, f4;

    f1 = _f_dz_dt(state_0.z, state_0.V);
    f2 = _f_dz_dt(state_0.z + dt * f1 / 2.0, state_0.V);
    f3 = _f_dz_dt(state_0.z + dt * f2 / 2.0, state_0.V);
    f4 = _f_dz_dt(state_0.z + dt * f3, state_0.V);

    states[ind_new*numNeurons+idx].z = state_0.z + dt * ( f1 + 2.0 * f2 + 2.0 * f3 + f4 ) / 6.0;
}

inline float _f_s_inf(const float V)
{
    const float theta_s = -20;
    const float sigma_s = 2;

    return pow(1 + exp(-(V - theta_s) / sigma_s), -1);
}

inline float _f_dsAMPA_dt(const float s_AMPA, const float V)
{
    const float k_fP     = 1;
    const float tau_AMPA = 5;

    return k_fP * _f_s_inf(V) * (1 - s_AMPA)
           - (s_AMPA / tau_AMPA);
}

__kernel void f_dsAMPA_dt(__global struct state* states, const unsigned int numNeurons, const unsigned int ind_old, const float dt)
{
    const unsigned int idx = get_global_id(0);
    const unsigned int ind_new = 1 - ind_old;

    struct state state_0 = states[ind_old*numNeurons+idx];

    float f1, f2, f3, f4;

    f1 = _f_dsAMPA_dt(state_0.s_AMPA, state_0.V);
    f2 = _f_dsAMPA_dt(state_0.s_AMPA + dt * f1 / 2.0, state_0.V);
    f3 = _f_dsAMPA_dt(state_0.s_AMPA + dt * f2 / 2.0, state_0.V);
    f4 = _f_dsAMPA_dt(state_0.s_AMPA + dt * f3, state_0.V);

    states[ind_new*numNeurons+idx].s_AMPA = state_0.s_AMPA + dt * ( f1 + 2.0 * f2 + 2.0 * f3 + f4 ) / 6.0;
}

inline float _f_dxNMDA_dt(const float x_NMDA, const float V)
{
    const float k_xN      = 1;
    const float tau2_NMDA = 14.3;

    return k_xN * _f_s_inf(V) * (1 - x_NMDA)
           - (1 - _f_s_inf(V)) * x_NMDA / tau2_NMDA;
}

__kernel void f_dxNMDA_dt(__global struct state* states, const unsigned int numNeurons, const unsigned int ind_old, const float dt)
{
    const unsigned int idx = get_global_id(0);
    const unsigned int ind_new = 1 - ind_old;

    struct state state_0 = states[ind_old*numNeurons+idx];

    float f1, f2, f3, f4;

    f1 = _f_dxNMDA_dt(state_0.x_NMDA, state_0.V);
    f2 = _f_dxNMDA_dt(state_0.x_NMDA + dt * f1 / 2.0, state_0.V);
    f3 = _f_dxNMDA_dt(state_0.x_NMDA + dt * f2 / 2.0, state_0.V);
    f4 = _f_dxNMDA_dt(state_0.x_NMDA + dt * f3, state_0.V);

    states[ind_new*numNeurons+idx].x_NMDA = state_0.x_NMDA + dt * ( f1 + 2.0 * f2 + 2.0 * f3 + f4 ) / 6.0;
}

inline float _f_dsNMDA_dt(const float s_NMDA, const float x_NMDA, const float V)
{
    const float k_fN     = 1;
    const float tau_NMDA = 14.3;

    return k_fN * x_NMDA * (1 - s_NMDA)
                     - s_NMDA / tau_NMDA;
}

__kernel void f_dsNMDA_dt(__global struct state* states, const unsigned int numNeurons, const unsigned int ind_old, const float dt)
{
    const unsigned int idx = get_global_id(0);
    const unsigned int ind_new = 1 - ind_old;

    struct state state_0 = states[ind_old*numNeurons+idx];

    float f1, f2, f3, f4;

    f1 = _f_dsNMDA_dt(state_0.s_NMDA, state_0.x_NMDA, state_0.V);
    f2 = _f_dsNMDA_dt(state_0.s_NMDA + dt * f1 / 2.0, state_0.x_NMDA, state_0.V);
    f3 = _f_dsNMDA_dt(state_0.s_NMDA + dt * f2 / 2.0, state_0.x_NMDA, state_0.V);
    f4 = _f_dsNMDA_dt(state_0.s_NMDA + dt * f3, state_0.x_NMDA, state_0.V);

    states[ind_new*numNeurons+idx].s_NMDA = state_0.s_NMDA + dt * ( f1 + 2.0 * f2 + 2.0 * f3 + f4 ) / 6.0;
}

struct state
{
    float V;
    float h;
    float n;
    float z;
    float s_AMPA;
    float x_NMDA;
    float s_NMDA;
    float s_GABAA;
    float I_app;
};

//__kernel void stepKernel(__global struct state* states, const unsigned int numNeurons)
//{
//   const int idx = get_global_id(0);
//
//   states[1*numNeurons+idx].V = states[0*numNeurons+idx].V + 1;
//}

inline float _f_I_Na_m_inf(const float V)
{
    const float theta_m = -30;
    const float sigma_m = 9.5;

    return pow((1 + exp(-(V - theta_m) / sigma_m)), -1);
}

inline float _f_I_Na(const float V, const float h)
{
    const float g_Na = 35;
    const float V_Na = 55;

    return g_Na * pow(_f_I_Na_m_inf(V), 3) * h * (V - V_Na);
}

inline float _f_p_inf(const float V)
{
    const float theta_p = -47;
    const float sigma_p = 3;

    return pow((1 + exp(-(V - theta_p) / sigma_p)), -1);
}

inline float _f_I_NaP(const float V)
{
    const float g_NaP = 0.2;
    const float V_Na  = 55;

    return g_NaP * _f_p_inf(V) * (V - V_Na);
}

inline float _f_I_Kdr(const float V, const float n)
{
    const float g_Kdr = 3;
    const float V_K   = -90;

    return g_Kdr * pow(n, 4) * (V - V_K);
}

inline float _f_I_Leak(const float V)
{
    const float g_L = 0.05;
    const float V_L = -70;

    return g_L * (V - V_L);
}

inline float _f_I_Kslow(const float V, const float z)
{
    const float g_Kslow = 1.8;
    const float V_K     = -90;

    return g_Kslow * z * (V - V_K);
}

inline float _f_I_AMPA(const float V, const float sumFootprintNMDA)
{
    const float g_AMPA = 0.08;
    const float V_Glu  = 0;

    return g_AMPA * (V - V_Glu) * sumFootprintNMDA;
}

inline float _f_f_NMDA(const float V)
{
    const float theta_NMDA = 0;

    // TODO: theta_NMDA = -inf for [Mg2+]_0 = 0
    // and increases logarithmically with [Mg2+]_0
    const float sigma_NMDA = 10;

    return pow(1 + exp(-(V - theta_NMDA) / sigma_NMDA), -1);
}

inline float _f_I_NMDA(const float V, const float sumFootprintAMPA)
{
    const float g_NMDA = 0.07;
    const float V_Glu  = 0;

    return g_NMDA * _f_f_NMDA(V) * (V - V_Glu) *sumFootprintAMPA;
}

inline float _f_I_GABAA(const float V, const float sumFootprintGABAA)
{
    const float g_GABAA = 0.05;
    const float V_GABAA = -70;

    return g_GABAA * (V - V_GABAA) * sumFootprintGABAA;
}

inline float _f_dV_dt(const float V,
                      const float h,
                      const float n,
                      const float z,
                      const float I_app,
                      const float sumFootprintAMPA,
                      const float sumFootprintNMDA,
                      const float sumFootprintGABAA)
{
    return - _f_I_Na(V, h)
           - _f_I_NaP(V)
           - _f_I_Kdr(V, n)
           - _f_I_Kslow(V, z)
           - _f_I_Leak(V)
           - _f_I_AMPA(V, sumFootprintAMPA)
           - _f_I_NMDA(V, sumFootprintNMDA)
           - _f_I_GABAA(V, sumFootprintGABAA)
           + I_app;
}

__kernel void f_dV_dt(__global struct state* states,
                      __global const float* sumFootprintAMPA,
                      __global const float* sumFootprintNMDA,
                      __global const float* sumFootprintGABAA,
                      const unsigned int numNeurons,
                      const unsigned int ind_old,
                      const float dt)
{
    const unsigned int idx = get_global_id(0);
    const unsigned int ind_new = 1 - ind_old;

    struct state state_0 = states[ind_old*numNeurons+idx];

    float f1, f2, f3, f4;

    f1 = _f_dV_dt(state_0.V, state_0.h, state_0.n, state_0.z, state_0.I_app, sumFootprintAMPA[idx], sumFootprintNMDA[idx], sumFootprintGABAA[idx]);
    f2 = _f_dV_dt(state_0.V + dt * f1 / 2.0, state_0.h, state_0.n, state_0.z, state_0.I_app, sumFootprintAMPA[idx], sumFootprintNMDA[idx], sumFootprintGABAA[idx]);
    f3 = _f_dV_dt(state_0.V + dt * f2 / 2.0, state_0.h, state_0.n, state_0.z, state_0.I_app, sumFootprintAMPA[idx], sumFootprintNMDA[idx], sumFootprintGABAA[idx]);
    f4 = _f_dV_dt(state_0.V + dt * f3, state_0.h, state_0.n, state_0.z, state_0.I_app, sumFootprintAMPA[idx], sumFootprintNMDA[idx], sumFootprintGABAA[idx]);

    states[ind_new*numNeurons+idx].V = state_0.V + dt * ( f1 + 2.0 * f2 + 2.0 * f3 + f4 ) / 6.0;
}

inline float _f_I_Na_h_inf(const float V)
{
    const float theta_h = -45;
    const float sigma_h = -7;

    return pow((1 + exp(-(V - theta_h) / sigma_h)), -1);
}

inline float _f_I_Na_tau_h(const float V)
{
    const float theta_th = -40.5;
    const float sigma_th = -6;

    return 0.1 + 0.75 * pow((1 + exp(-(V - theta_th) / sigma_th)), -1);
}

inline float _f_I_Na_dh_dt(const float h, const float V)
{
    return (_f_I_Na_h_inf(V) - h) / _f_I_Na_tau_h(V);
}

__kernel void f_I_Na_dh_dt(__global struct state* states, const unsigned int numNeurons, const unsigned int ind_old, const float dt)
{
    const unsigned int idx = get_global_id(0);
    const unsigned int ind_new = 1 - ind_old;

    struct state state_0 = states[ind_old*numNeurons+idx];

    float f1, f2, f3, f4;

    f1 = _f_I_Na_dh_dt(state_0.h, state_0.V);
    f2 = _f_I_Na_dh_dt(state_0.h + dt * f1 / 2.0, state_0.V);
    f3 = _f_I_Na_dh_dt(state_0.h + dt * f2 / 2.0, state_0.V);
    f4 = _f_I_Na_dh_dt(state_0.h + dt * f3, state_0.V);

    states[ind_new*numNeurons+idx].h = state_0.h + dt * ( f1 + 2.0 * f2 + 2.0 * f3 + f4 ) / 6.0;
}

inline float _f_n_inf(const float V)
{
    const float theta_n = -33;
    const float sigma_n = 10;

    return pow(1 + exp(-(V - theta_n) / sigma_n), -1);
}

inline float _f_tau_n(const float V)
{
    const float theta_tn = -33;
    const float sigma_tn = -15;

    return 0.1 + 0.5 * pow(1 + exp(-(V - theta_tn) / sigma_tn), -1);
}

inline float _f_dn_dt(const float n, const float V)
{
    return (_f_n_inf(V) - n) / _f_tau_n(V);
}

__kernel void f_dn_dt(__global struct state* states, const unsigned int numNeurons, const unsigned int ind_old, const float dt)
{
    const unsigned int idx = get_global_id(0);
    const unsigned int ind_new = 1 - ind_old;

    struct state state_0 = states[ind_old*numNeurons+idx];

    float f1, f2, f3, f4;

    f1 = _f_dn_dt(state_0.n, state_0.V);
    f2 = _f_dn_dt(state_0.n + dt * f1 / 2.0, state_0.V);
    f3 = _f_dn_dt(state_0.n + dt * f2 / 2.0, state_0.V);
    f4 = _f_dn_dt(state_0.n + dt * f3, state_0.V);

    states[ind_new*numNeurons+idx].n = state_0.n + dt * ( f1 + 2.0 * f2 + 2.0 * f3 + f4 ) / 6.0;
}

inline float _f_z_inf(const float V)
{
    const float theta_z = -39;
    const float sigma_z = 5;

    return pow(1 + exp(-(V - theta_z) / sigma_z), -1);
}

inline float _f_dz_dt(const float z, const float V)
{
    const float tau_z = 75;

    return (_f_z_inf(V) - z) / tau_z;
}

__kernel void f_dz_dt(__global struct state* states, const unsigned int numNeurons, const unsigned int ind_old, const float dt)
{
    const unsigned int idx = get_global_id(0);
    const unsigned int ind_new = 1 - ind_old;

    struct state state_0 = states[ind_old*numNeurons+idx];

    float f1, f2, f3, f4;

    f1 = _f_dz_dt(state_0.z, state_0.V);
    f2 = _f_dz_dt(state_0.z + dt * f1 / 2.0, state_0.V);
    f3 = _f_dz_dt(state_0.z + dt * f2 / 2.0, state_0.V);
    f4 = _f_dz_dt(state_0.z + dt * f3, state_0.V);

    states[ind_new*numNeurons+idx].z = state_0.z + dt * ( f1 + 2.0 * f2 + 2.0 * f3 + f4 ) / 6.0;
}

inline float _f_s_inf(const float V)
{
    const float theta_s = -20;
    const float sigma_s = 2;

    return pow(1 + exp(-(V - theta_s) / sigma_s), -1);
}

inline float _f_dsAMPA_dt(const float s_AMPA, const float V)
{
    const float k_fP     = 1;
    const float tau_AMPA = 5;

    return k_fP * _f_s_inf(V) * (1 - s_AMPA)
           - (s_AMPA / tau_AMPA);
}

__kernel void f_dsAMPA_dt(__global struct state* states, const unsigned int numNeurons, const unsigned int ind_old, const float dt)
{
    const unsigned int idx = get_global_id(0);
    const unsigned int ind_new = 1 - ind_old;

    struct state state_0 = states[ind_old*numNeurons+idx];

    float f1, f2, f3, f4;

    f1 = _f_dsAMPA_dt(state_0.s_AMPA, state_0.V);
    f2 = _f_dsAMPA_dt(state_0.s_AMPA + dt * f1 / 2.0, state_0.V);
    f3 = _f_dsAMPA_dt(state_0.s_AMPA + dt * f2 / 2.0, state_0.V);
    f4 = _f_dsAMPA_dt(state_0.s_AMPA + dt * f3, state_0.V);

    states[ind_new*numNeurons+idx].s_AMPA = state_0.s_AMPA + dt * ( f1 + 2.0 * f2 + 2.0 * f3 + f4 ) / 6.0;
}

inline float _f_dxNMDA_dt(const float x_NMDA, const float V)
{
    const float k_xN      = 1;
    const float tau2_NMDA = 14.3;

    return k_xN * _f_s_inf(V) * (1 - x_NMDA)
           - (1 - _f_s_inf(V)) * x_NMDA / tau2_NMDA;
}

__kernel void f_dxNMDA_dt(__global struct state* states, const unsigned int numNeurons, const unsigned int ind_old, const float dt)
{
    const unsigned int idx = get_global_id(0);
    const unsigned int ind_new = 1 - ind_old;

    struct state state_0 = states[ind_old*numNeurons+idx];

    float f1, f2, f3, f4;

    f1 = _f_dxNMDA_dt(state_0.x_NMDA, state_0.V);
    f2 = _f_dxNMDA_dt(state_0.x_NMDA + dt * f1 / 2.0, state_0.V);
    f3 = _f_dxNMDA_dt(state_0.x_NMDA + dt * f2 / 2.0, state_0.V);
    f4 = _f_dxNMDA_dt(state_0.x_NMDA + dt * f3, state_0.V);

    states[ind_new*numNeurons+idx].x_NMDA = state_0.x_NMDA + dt * ( f1 + 2.0 * f2 + 2.0 * f3 + f4 ) / 6.0;
}

inline float _f_dsNMDA_dt(const float s_NMDA, const float x_NMDA, const float V)
{
    const float k_fN     = 1;
    const float tau_NMDA = 14.3;

    return k_fN * x_NMDA * (1 - s_NMDA)
                     - s_NMDA / tau_NMDA;
}

__kernel void f_dsNMDA_dt(__global struct state* states, const unsigned int numNeurons, const unsigned int ind_old, const float dt)
{
    const unsigned int idx = get_global_id(0);
    const unsigned int ind_new = 1 - ind_old;

    struct state state_0 = states[ind_old*numNeurons+idx];

    float f1, f2, f3, f4;

    f1 = _f_dsNMDA_dt(state_0.s_NMDA, state_0.x_NMDA, state_0.V);
    f2 = _f_dsNMDA_dt(state_0.s_NMDA + dt * f1 / 2.0, state_0.x_NMDA, state_0.V);
    f3 = _f_dsNMDA_dt(state_0.s_NMDA + dt * f2 / 2.0, state_0.x_NMDA, state_0.V);
    f4 = _f_dsNMDA_dt(state_0.s_NMDA + dt * f3, state_0.x_NMDA, state_0.V);

    states[ind_new*numNeurons+idx].s_NMDA = state_0.s_NMDA + dt * ( f1 + 2.0 * f2 + 2.0 * f3 + f4 ) / 6.0;
}


use fp32 as accumulator instead of fp16

use fp32 as accumulator instead of fp16

/*******************************************************************************
 *
 * MIT License
 *
 * Copyright (c) 2017 Advanced Micro Devices, Inc.
 *
 * Permission is hereby granted, free of charge, to any person obtaining a copy
 * of this software and associated documentation files (the "Software"), to deal
 * in the Software without restriction, including without limitation the rights
 * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
 * copies of the Software, and to permit persons to whom the Software is
 * furnished to do so, subject to the following conditions:
 *
 * The above copyright notice and this permission notice shall be included in all
 * copies or substantial portions of the Software.
 *
 * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
 * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
 * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
 * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
 * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
 * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
 * SOFTWARE.
 *
 *******************************************************************************/

#define PPCAT_NX(A, B) A##B
#define PPCAT(A, B) PPCAT_NX(A, B)
#define TWO 2
#define FOUR 4
#define EIGHT 8

#if MIOPEN_USE_FP16 == 1
#pragma OPENCL EXTENSION cl_khr_fp16 : enable
#define _FLOAT half
#define _FLOAT_ACCUM float
#ifndef HALF_MAX
#define MAX_VAL 65504 /* max value */
#else
#define MAX_VAL HALF_MAX
#endif

#endif
#if MIOPEN_USE_FP32 == 1
#define _FLOAT float
#define _FLOAT_ACCUM float
#ifndef FLT_MAX
#define MAX_VAL 3.402823466e+38F /* max value */
#else
#define MAX_VAL FLT_MAX
#endif
#endif

#define _FLOAT2 PPCAT(_FLOAT, TWO)
#define _FLOAT4 PPCAT(_FLOAT, FOUR)
#define _FLOAT8 PPCAT(_FLOAT, EIGHT)

#define UNUSED __attribute__((__unused__))

#define DBG_OUT_OF_RNGE 0

#define MLO_N_OUT_HORIZ_READS ((MLO_OUT_WIDTH + MLO_IN_TILE0 - 1) / MLO_IN_TILE0)
#define MLO_N_SPANS_PER_SCAN (MLO_N_OUT_HORIZ_READS)
#define MLO_N_OUT_HORIZ_PIX_READS (MLO_N_OUT_HORIZ_READS * MLO_IN_TILE0)
#define MLO_OUT_N_PIXS_OFF (MLO_OUT_WIDTH - ((MLO_OUT_WIDTH / MLO_IN_TILE0) * MLO_IN_TILE0))
#define MLO_N_OUT_VERTICAL_READS (MLO_FILTER_SIZE1)
// won't run non-border blocks if  MLO_IN_N_VERT_LOOPS < 2
//

#define MLO_IN_VERT_READS MLO_IN_EXTENT1

#if MLO_IN_N_VERT_LOOPS >= 2
#define MLO_N_GENERIC_LOOPS (MLO_IN_N_VERT_LOOPS - 2)
#else
#define MLO_N_GENERIC_LOOPS 0
#endif

// there is an assumption that the scanline fits into LDS
#define MLO_N_IN_HORIZ_PIX_READS (MLO_IN_WIDTH)
#define MLO_N_IN_HORIZ_READS ((MLO_N_IN_HORIZ_PIX_READS + MLO_READ_UNIT - 1) / MLO_READ_UNIT)
#define MLO_IN_N_PIXS_OFF \
    (MLO_N_IN_HORIZ_PIX_READS - (MLO_N_IN_HORIZ_PIX_READS / MLO_READ_UNIT) * MLO_READ_UNIT)
#define MLO_IN_LCL_WIDTH (MLO_N_IN_HORIZ_READS * MLO_READ_UNIT + 2 * MLO_FILTER_PAD0)
#define MLO_IN_LCL_HEIGHT MLO_IN_VERT_READS
#define MLO_IN_LCL_SZ (MLO_IN_LCL_WIDTH * MLO_IN_LCL_HEIGHT)
#define MLO_TOTAL_IN_LCL_SZ (MLO_N_LCL_BATCHS * MLO_N_LCL_IN_MAPS * MLO_IN_LCL_SZ)

#define MLO_WEI_LCL_SZ (MLO_GRP_SZ * MLO_FILTER_SIZE0)
#if MLO_TOTAL_IN_LCL_SZ > MLO_WEI_LCL_SZ
#define MLO_LCL_SZ (MLO_TOTAL_IN_LCL_SZ)
#else
#define MLO_LCL_SZ (MLO_WEI_LCL_SZ)
#endif

__attribute__((always_inline)) uint iDiv(uint v, uint d)
{
    uint r = v / d; //(uint)((float)v * (1.0f / (float)d) + 0.00001f);
    return (r);
}

__attribute__((always_inline)) uint iMod(uint v, uint u, uint d)
{
    uint r = v - mul24((uint)u, (uint)d);
    return (r);
}

#if 0
// This function is for log-reduction of content in LDS,
// it's supposed to be called by MIOpenCvBwdWrW,
// However, it's no longer used and not tested.
__attribute__((always_inline)) void ReduceKernel(__local _FLOAT* lcl_blob,
                                                 __private _FLOAT* weights_accum,
                                                 uint lcl_id,
                                                 uint scan_lcl,
                                                 uint sum_stride,
                                                 uint unit_len,
                                                 UNUSED bool debug)
{
    // read first half
    if(scan_lcl < (sum_stride >> 1))
    {
        for(uint i = 0; i < unit_len; ++i)
        {
            weights_accum[i] = lcl_blob[(lcl_id + scan_lcl) * unit_len + i];
        }
    }
    // add second half
    // appload accumulated value so far
    for(uint j = (sum_stride >> 1); j > 0; j >>= 1)
    {
        barrier(CLK_LOCAL_MEM_FENCE);
        if(scan_lcl < j)
        {
            for(uint i = 0; i < unit_len; ++i)
            {
                weights_accum[i] += lcl_blob[(lcl_id + j) * unit_len + i];

                lcl_blob[lcl_id * unit_len + i] = weights_accum[i];
            }
        }
    }
}
#endif

/*
        group cooperative read
        read by MLO_READ_UNIT
        handle out of range both horizontally and vertically (by fixed number of veryical reads)

        no guard against number of inputs
*/
__attribute__((always_inline)) void readInput(uint lcl_id,
                                              uint gbl_in_scan_off,
                                              uint n_v_reads,
                                              const __global _FLOAT* __restrict bot,
                                              __local _FLOAT* __restrict lcl_bot)
{
    for(uint p4 = lcl_id; p4 < MLO_N_LCL_IN_MAPS * MLO_N_IN_HORIZ_READS * n_v_reads;
        p4 += MLO_GRP_SZ)
    {
        __private _FLOAT in_rd_data[MLO_READ_UNIT];
        // TODO : more than 1 input
        uint c    = 0;
        uint t_p4 = p4;
#if MLO_N_LCL_IN_MAPS > 1
        c    = p4 / (MLO_N_IN_HORIZ_READS * n_v_reads);
        t_p4 = iMod(p4, c, (MLO_N_IN_HORIZ_READS * n_v_reads));
#endif

        uint c_scan = t_p4 / (MLO_N_IN_HORIZ_READS);

#if MLO_N_IN_HORIZ_READS & (MLO_N_IN_HORIZ_READS - 1)
        uint c_pix4 = iMod(t_p4, c_scan, (MLO_N_IN_HORIZ_READS));
#else
        uint c_pix4 = t_p4 & (MLO_N_IN_HORIZ_READS - 1);
#endif

        //		if (c < MLO_N_INPUTS)

        {
            uint bot_off = gbl_in_scan_off + c * MLO_IN_CHANNEL_STRIDE + c_scan * MLO_IN_STRIDE +
                           c_pix4 * MLO_READ_UNIT;
            const __global _FLOAT* bot_p = &bot[bot_off];
#if MLO_IN_N_PIXS_OFF > 0

            if(c_pix4 == MLO_N_IN_HORIZ_READS - 1)
            {
                for(uint i = 0; i < MLO_IN_N_PIXS_OFF; ++i)
                {
                    in_rd_data[i] = bot_p[i];

#if DBG_OUT_OF_RNGE
                    if(bot_off + i >= MLO_IN_BATCH_STRIDE * MLO_BATCH_SZ)
                    {
                        printf("k:err:out-of-range\n");
                    }
#endif
                }

                for(uint i = MLO_IN_N_PIXS_OFF; i < MLO_READ_UNIT; ++i)
                {
                    in_rd_data[i] = 0;
                }
            }
            else
#endif
            {

                for(uint i = 0; i < MLO_READ_UNIT; ++i)
                {
                    in_rd_data[i] = bot_p[i];

#if DBG_OUT_OF_RNGE
                    if(bot_off + i >= MLO_IN_BATCH_STRIDE * MLO_BATCH_SZ)
                    {
                        printf("k:err:out-of-range\n");
                    }
#endif
                }
            }

            // MLO_N_LCL_IN_MAPS inputs
            for(uint i = 0; i < MLO_READ_UNIT; ++i)
            {
                int lcl_in_off = c * MLO_IN_LCL_SZ + c_scan * MLO_IN_LCL_WIDTH + MLO_FILTER_PAD0 +
                                 c_pix4 * MLO_READ_UNIT + i;
                lcl_bot[lcl_in_off] = in_rd_data[i];
            }
        }

    } // for (int p4 = lcl_id; p4 < MLO_N_LCL_IN_MAPS * MLO_N_IN_HORIZ_READS * MLO_IN_VERT_READS;

    barrier(CLK_LOCAL_MEM_FENCE);
}

/*
        core processing loop
        bot - input, from local (1 span)
        top - output diff, from global (array of spans, filters vertical size)

        loop over filter vertical size

*/
__attribute__((always_inline)) void Processing(UNUSED uint sc,
                                               uint sc_lcl_off,
                                               uint top_lim,
                                               int bot_lim,
                                               __private _FLOAT_ACCUM* __restrict pvt_accum,
                                               __local _FLOAT* __restrict lcl_bot,
                                               __private _FLOAT* __restrict top_dat)
{
    for(int l = top_lim; l >= bot_lim; --l)
    {
        for(uint m = 0; m < MLO_IN_TILE0; ++m)
        {
            for(uint n = 0; n < MLO_FILTER_SIZE0; ++n)
            {
                for(uint c = 0; c < MLO_N_LCL_IN_MAPS; ++c)
                {
                    int bot_off = sc_lcl_off + c * MLO_IN_LCL_SZ + n + m;

                    _FLOAT bot_val = lcl_bot[bot_off];

                    for(uint k = 0; k < MLO_N_LCL_OUT_MAPS; ++k)
                    {
                        uint pvt_top_off =
                            k * MLO_IN_TILE0 * MLO_FILTER_SIZE1 + (top_lim - l) * MLO_IN_TILE0 + m;
                        uint pvt_accum_off =
                            (k * MLO_N_LCL_IN_MAPS + c) * MLO_FILTER_SIZE1 * MLO_FILTER_SIZE0 +
                            l * MLO_FILTER_SIZE0 + n;

                        _FLOAT top_val = top_dat[pvt_top_off];

                        pvt_accum[pvt_accum_off]
                            // each wk-it process an input
                            += bot_val * top_val;

                    }
                }
            }
        }
    }
}

__attribute__((always_inline)) void moveOutputUp(__private _FLOAT* __restrict top_dat)
{
    // move up output to reduce overfetch
    for(uint k = 0; k < MLO_N_LCL_OUT_MAPS; ++k)
    {
        for(uint j = 0; j < MLO_FILTER_SIZE1 - 1; ++j)
        {
            for(uint i = 0; i < MLO_IN_TILE0; ++i)
            {
                uint pvt_off_n = k * MLO_IN_TILE0 * MLO_FILTER_SIZE1 + j * MLO_IN_TILE0 + i;
                uint pvt_off_o = k * MLO_IN_TILE0 * MLO_FILTER_SIZE1 + (j + 1) * MLO_IN_TILE0 + i;
                top_dat[pvt_off_n] = top_dat[pvt_off_o];
            }
        }
    }
}

__attribute__((always_inline)) void spanReadingOutput(int spn,
                                                      int k,
                                                      int j,
                                                      int top_df_off,
                                                      _FLOAT mask,
                                                      __private _FLOAT* __restrict top_dat,
                                                      const __global _FLOAT* __restrict top_df)
{
    int pvt_off                     = k * MLO_IN_TILE0 * MLO_FILTER_SIZE1 + j * MLO_IN_TILE0;
    const __global _FLOAT* top_df_p = &top_df[top_df_off];
#if MLO_OUT_N_PIXS_OFF > 0
    if(spn == MLO_N_SPANS_PER_SCAN - 1)
    {
        uint i = 0;
        for(; i < MLO_OUT_N_PIXS_OFF; ++i)
        {
            top_dat[pvt_off + i] = top_df_p[i] * mask;

#if DBG_OUT_OF_RNGE
            if(top_df_off + i >= MLO_OUT_BATCH_STRIDE * MLO_BATCH_SZ)
            {
                printf("k:err:out-of-range\n");
            }
#endif
        }
        for(; i < MLO_IN_TILE0; ++i)
        {
            top_dat[pvt_off + i] = 0;
        }
    }
    else
#else
    (void)spn;
#endif
    {
        for(uint i = 0; i < MLO_IN_TILE0; ++i)
        {
            top_dat[pvt_off + i] = top_df_p[i] * mask;

#if DBG_OUT_OF_RNGE
            if(top_df_off + i >= MLO_OUT_BATCH_STRIDE * MLO_BATCH_SZ)
            {
                printf("k:err:out-of-range\n");
            }
#endif
        }
    }
}

/*********************************************************************************************************
// wrw algorithm for large filters
// idea:
// split output scan-line on number of spans by the  MLO_IN_TILE0 (2 for example)
// 1 scan-line has ((MLO_OUT_WIDTH + MLO_IN_TILE0 - 1/MLO_IN_TILE0) spans
// group will process MLO_GRP_SZ/((MLO_OUT_WIDTH + MLO_IN_TILE0 - 1/MLO_IN_TILE0) output maps

// alg
// load a block of input map (or full map) into LDS
// loop
// read MLO_FILTER_SIZE1 number of spans from output map into VGPRs (for example 5 *2 = 10)
// read 1 input line for  maps into LDS
// accumulate

// accumulate all spans at the end
// start new loop for the next batch (if defined)
// write out

// kerenl handles 5x5, 3x3 with padding
// small images in 1 short- MLO_N_GENERIC_LOOPS == 0
// big images  in 2 blocks - MLO_IN_N_VERT_LOOPS == 2 or multiple blocks - MLO_IN_N_VERT_LOOPS > 2
// there are prolog and apilog that deal with top/bottom padding.
// left/right padding handles as a LDS border pixels zeroed at the beginning.

**********************************************************************************************************/

__attribute__((reqd_work_group_size(MLO_GRP_SZ0, MLO_GRP_SZ1, MLO_GRP_SZ2))) __kernel void
MIOpenCvBwdWrW(const __global _FLOAT* __restrict top_df,
               const __global _FLOAT* __restrict bot,
               __global _FLOAT* __restrict weights_df,
#if MLO_CONV_BIAS
               __global _FLOAT* __restrict bias_df,
#endif
               UNUSED _FLOAT padding_val)
{

    // input/output tiles + reduce buffer

    __local _FLOAT lcl[(MLO_LCL_SZ) + 1];
    __local _FLOAT* lcl_bot = lcl;

    uint lcl_id = get_local_id(0);

    uint c_idx_base = get_group_id(0); // input map index base

    uint o_idx_base = get_group_id(1); // output map index base

    uint ib_base = get_group_id(2);

    uint ib = ib_base * (MLO_N_BATCH_LOOPS * MLO_N_LCL_BATCHS);

    uint c_idx = c_idx_base * MLO_N_LCL_IN_MAPS; // input map index

    uint o_idx = o_idx_base * (MLO_N_LCL_OUT_MAPS * MLO_OUT_STACKS); // output map index

    uint gbl_in_off  = c_idx * MLO_IN_CHANNEL_STRIDE + ib * MLO_IN_BATCH_STRIDE;
    uint gbl_out_off = o_idx * MLO_OUT_CHANNEL_STRIDE + ib * MLO_OUT_BATCH_STRIDE;
    // 1 span per wk_item, total scanline with MLO_N_SPANS_PER_SCAN spans
    // TODO: more than 1 input
    uint o = lcl_id / MLO_N_SPANS_PER_SCAN;
#if MLO_N_SPANS_PER_SCAN & (MLO_N_SPANS_PER_SCAN - 1)
    uint spn = iMod(lcl_id, o, MLO_N_SPANS_PER_SCAN);
#else
    uint spn         = lcl_id & (MLO_N_SPANS_PER_SCAN - 1);
#endif
    //	bool scan_lead = (o*MLO_N_SPANS_PER_SCAN == lcl_id);

    uint lcl_bot_off     = spn * MLO_IN_TILE0;
    uint out_wk_item_off = o * MLO_OUT_CHANNEL_STRIDE + lcl_bot_off;
    gbl_out_off += out_wk_item_off;
    // no output out of range
    gbl_out_off = (o_idx + o < MLO_N_OUTPUTS && o < MLO_OUT_STACKS) ? gbl_out_off : 0;

#define MLO_TOP_DAT_SZ (MLO_N_LCL_OUT_MAPS * MLO_IN_TILE0 * MLO_FILTER_SIZE1)

    __private _FLOAT top_dat[MLO_TOP_DAT_SZ];

    for(uint i = 0; i < MLO_TOP_DAT_SZ; ++i)
    {
        top_dat[i] = 0;
    }

#define MLO_ACCUM_SZ (MLO_N_LCL_OUT_MAPS * MLO_N_LCL_IN_MAPS * MLO_FILTER_SIZE1 * MLO_FILTER_SIZE0)

    __private _FLOAT_ACCUM pvt_accum[MLO_ACCUM_SZ];

    for(uint i = 0; i < MLO_ACCUM_SZ; ++i)
    {
        pvt_accum[i] = 0;
    }

    // zero out LDS
    for(uint i = lcl_id; i < (MLO_LCL_SZ); i += MLO_GRP_SZ)
    {
        lcl[i] = 0;
    }

    // over all batches
    uint bend = ib + MLO_N_BATCH_LOOPS * MLO_N_LCL_BATCHS;
    bend      = bend > MLO_BATCH_SZ ? MLO_BATCH_SZ : bend;

    for(uint b = ib; b < bend; ++b,
             gbl_in_off += MLO_N_LCL_BATCHS * MLO_IN_BATCH_STRIDE,
             gbl_out_off += MLO_N_LCL_BATCHS * MLO_OUT_BATCH_STRIDE)
    {
        barrier(CLK_LOCAL_MEM_FENCE);

        // top border input block
        uint gbl_in_scan_off  = gbl_in_off;
        uint gbl_out_scan_off = gbl_out_off;

        // read input map
        readInput(lcl_id, gbl_in_scan_off, MLO_IN_VERT_READS, bot, lcl_bot);

        // move input pointer
        gbl_in_scan_off += MLO_IN_STRIDE * MLO_IN_EXTENT1;

        for(uint i = 0; i < MLO_TOP_DAT_SZ; ++i)
        {
            top_dat[i] = 0;
        }

        // prefetch output
        int gbl_out_scan_off1 = gbl_out_scan_off;
        for(uint k = 0; k < MLO_N_LCL_OUT_MAPS;
            ++k, gbl_out_scan_off1 += MLO_OUT_STACKS * MLO_OUT_CHANNEL_STRIDE)
        {
            for(uint j = 0; j < MLO_FILTER_SIZE1 - 1; ++j)
            {
                // loop around all output maps
                uint top_df_off = gbl_out_scan_off1 + j * MLO_OUT_STRIDE;
                _FLOAT mask     = 1;
#if MLO_IN_HEIGHT != MLO_OUT_HEIGHT || MLO_FILTER_SIZE1 - 1 > MLO_OUT_HEIGHT
                top_df_off = (j < MLO_OUT_HEIGHT) ? top_df_off : 0;
                mask       = (j < MLO_OUT_HEIGHT) ? 1 : 0;
#endif

                spanReadingOutput(spn, k, j, top_df_off, mask, top_dat, top_df);
            }
        }

        gbl_out_scan_off += (MLO_FILTER_SIZE1 - 1) * MLO_OUT_STRIDE;

        uint sc         = 0;
        uint sc_lcl_off = lcl_bot_off;

        // prolog
        // handling padding

        // top padding
        for(; sc < MLO_FILTER_SIZE1 - MLO_FILTER_PAD1 - 1; ++sc, sc_lcl_off += MLO_IN_LCL_WIDTH)
        {
            Processing(sc, sc_lcl_off, sc + MLO_FILTER_PAD1, 0, pvt_accum, lcl_bot, top_dat);
        }

#ifdef __AMDGCN__
#pragma unroll 2
#endif
        for(; sc < MLO_IN_EXTENT1
#if MLO_IN_N_VERT_LOOPS == 1
                       -
                       MLO_FILTER_PAD1
#endif
            ;
            ++sc, gbl_out_scan_off += MLO_OUT_STRIDE, sc_lcl_off += MLO_IN_LCL_WIDTH)
        {

            for(uint k = 0; k < MLO_N_LCL_OUT_MAPS; ++k)
            {
                uint top_df_off = gbl_out_scan_off + k * MLO_OUT_STACKS * MLO_OUT_CHANNEL_STRIDE;
                _FLOAT mask     = 1;

#if MLO_IN_HEIGHT != MLO_OUT_HEIGHT || MLO_FILTER_SIZE1 - 1 > MLO_OUT_HEIGHT
                top_df_off = ((sc + MLO_FILTER_PAD1) < MLO_OUT_HEIGHT) ? top_df_off : 0;
                mask       = ((sc + MLO_FILTER_PAD1) < MLO_OUT_HEIGHT) ? 1 : 0;
#endif

                spanReadingOutput(
                    spn, k, (MLO_FILTER_SIZE1 - 1), top_df_off, mask, top_dat, top_df);
            }

            // processing
            Processing(sc, sc_lcl_off, MLO_FILTER_SIZE1 - 1, 0, pvt_accum, lcl_bot, top_dat);

            // move up output to reduce overfetch
            moveOutputUp(top_dat);
        }

        // non-border input blocks
        for(uint i_loop = 0; i_loop < MLO_N_GENERIC_LOOPS;
            ++i_loop, gbl_in_scan_off += MLO_IN_STRIDE * MLO_IN_EXTENT1)
        {
            barrier(CLK_LOCAL_MEM_FENCE);

            readInput(lcl_id, gbl_in_scan_off, MLO_IN_VERT_READS, bot, lcl_bot);

            // point to the start of the local buffer

            sc_lcl_off = lcl_bot_off;

            for(; sc < (i_loop + 2) * MLO_IN_EXTENT1;
                ++sc, gbl_out_scan_off += MLO_OUT_STRIDE, sc_lcl_off += MLO_IN_LCL_WIDTH)
            {

                for(uint k = 0; k < MLO_N_LCL_OUT_MAPS; ++k)
                {
                    uint top_df_off =
                        gbl_out_scan_off + k * MLO_OUT_STACKS * MLO_OUT_CHANNEL_STRIDE;
                    _FLOAT mask = 1;

#if MLO_IN_HEIGHT != MLO_OUT_HEIGHT
                    top_df_off = ((sc + MLO_FILTER_PAD1) < MLO_OUT_HEIGHT) ? top_df_off : 0;
                    mask       = ((sc + MLO_FILTER_PAD1) < MLO_OUT_HEIGHT) ? 1 : 0;
#endif

                    spanReadingOutput(
                        spn, k, (MLO_FILTER_SIZE1 - 1), top_df_off, mask, top_dat, top_df);
                }

                // processing
                Processing(sc, sc_lcl_off, MLO_FILTER_SIZE1 - 1, 0, pvt_accum, lcl_bot, top_dat);

                // move up output to reduce overfetch
                moveOutputUp(top_dat);
            }
        }

        // bottom border block

        for(int i_loop = 0; i_loop < (MLO_IN_N_VERT_LOOPS - MLO_N_GENERIC_LOOPS - 1);
            ++i_loop, gbl_in_scan_off += MLO_IN_STRIDE * MLO_IN_EXTENT1)
        {
            barrier(CLK_LOCAL_MEM_FENCE);

// read 1 scan line less
// padding processing takes care of the bottom border.

#define MLO_LAST_VERT_READS (MLO_IN_HEIGHT - MLO_IN_EXTENT1 * (MLO_IN_N_VERT_LOOPS - 1))

            readInput(lcl_id, gbl_in_scan_off, MLO_LAST_VERT_READS, bot, lcl_bot);

            // point to the start of the local buffer
            sc_lcl_off = lcl_bot_off;

#pragma unroll 3
            for(; sc < MLO_OUT_HEIGHT - MLO_FILTER_PAD1;
                ++sc, gbl_out_scan_off += MLO_OUT_STRIDE, sc_lcl_off += MLO_IN_LCL_WIDTH)
            {

                for(uint k = 0; k < MLO_N_LCL_OUT_MAPS; ++k)
                {
                    uint top_df_off =
                        gbl_out_scan_off + k * MLO_OUT_STACKS * MLO_OUT_CHANNEL_STRIDE;
                    _FLOAT mask = 1;

                    spanReadingOutput(
                        spn, k, (MLO_FILTER_SIZE1 - 1), top_df_off, mask, top_dat, top_df);
                }

                // processing
                Processing(sc, sc_lcl_off, MLO_FILTER_SIZE1 - 1, 0, pvt_accum, lcl_bot, top_dat);

                // move up output to reduce overfetch
                moveOutputUp(top_dat);
            }
        }

        // epilog
        // handling padding

        for(; sc < MLO_IN_HEIGHT; ++sc, sc_lcl_off += MLO_IN_LCL_WIDTH)
        {

            // processing
            Processing(sc,
                       sc_lcl_off,
                       MLO_FILTER_SIZE1 - 1,
                       MLO_FILTER_SIZE1 - (MLO_IN_HEIGHT + MLO_FILTER_PAD1 - sc),
                       pvt_accum,
                       lcl_bot,
                       top_dat);
            // move up output to reduce overfetch
            moveOutputUp(top_dat);

        } // for (; sc < MLO_OUT_HEIGHT - MLO_FILTER_PAD1 + 2; ++sc, gbl_out_scan_off +=
          // MLO_OUT_CHANNEL_STRIDE, gbl_in_scan_off += MLO_IN_CHANNEL_STRIDE)
    } // 	for (int b = 0;

    // final summation over all output maps and each filter row
    // this coudl be done with log but it negligeble anyway
    for(uint k = 0; k < MLO_N_LCL_OUT_MAPS; ++k)
    {
        for(uint c = 0; c < MLO_N_LCL_IN_MAPS; ++c)
        {

            for(uint l = 0; l < MLO_FILTER_SIZE1; ++l)
            {
                barrier(CLK_LOCAL_MEM_FENCE);

                for(uint n = 0; n < MLO_FILTER_SIZE0; ++n)
                {
                    uint pvt_off =
                        (k * MLO_N_LCL_IN_MAPS + c) * MLO_FILTER_SIZE1 * MLO_FILTER_SIZE0 +
                        l * MLO_FILTER_SIZE0 + n;

                    lcl[lcl_id * MLO_FILTER_SIZE0 + n] = pvt_accum[pvt_off];
                }

                barrier(CLK_LOCAL_MEM_FENCE);

                if(spn == 0)
                {
                    for(uint s = 0; s < MLO_N_SPANS_PER_SCAN - 1; ++s)
                    {

                        for(uint n = 0; n < MLO_FILTER_SIZE0; ++n)
                        {
                            uint pvt_off =
                                (k * MLO_N_LCL_IN_MAPS + c) * MLO_FILTER_SIZE1 * MLO_FILTER_SIZE0 +
                                l * MLO_FILTER_SIZE0 + n;
                            pvt_accum[pvt_off] += lcl[(lcl_id + s + 1) * MLO_FILTER_SIZE0 + n];
                        }
                    }
                }
            }
        }
    }

    // output
    // inputs are outputs
    // TODO : for more than 1 input

    uint wei_df_off =
        (((ib / MLO_N_BATCH_LOOPS) * MLO_N_OUTPUTS + o_idx + o) * (uint)MLO_WEI_BATCH_STRIDE)
        // this input channel
        + mul24(c_idx, (uint)MLO_WEI_CHANNEL_STRIDE);
    for(uint k = 0; k < MLO_N_LCL_OUT_MAPS; ++k)
    {
        if(spn == 0 && o_idx + o + k * MLO_OUT_STACKS < MLO_N_OUTPUTS && o < MLO_OUT_STACKS)
        {
            for(uint c = 0; c < MLO_N_LCL_IN_MAPS; ++c)
            {
                for(uint i = 0; i < (MLO_FILTER_SIZE1 * MLO_FILTER_SIZE0); ++i)
                {
                    weights_df[wei_df_off + k * MLO_OUT_STACKS * MLO_WEI_BATCH_STRIDE +
                               c * MLO_WEI_CHANNEL_STRIDE + i] =
                        pvt_accum[(k * MLO_N_LCL_IN_MAPS + c) * MLO_FILTER_SIZE1 *
                                      MLO_FILTER_SIZE0 +
                                  i];
                }
            }
        }
    }
}

// final reduction kernel
// add filters over batches
__attribute__((reqd_work_group_size(MLO_UT_GRP_SZ0, 1, 1))) __kernel void
MIOpenCvBwdWrW_rdc(const __global _FLOAT* __restrict weight_df_tmp,
                   __global _FLOAT* __restrict weights_df)
{
    uint gbl_id   = get_global_id(0);
    uint wei_idx0 = gbl_id * MLO_UT_READ_UNIT;

#if MLO_WEI_CHANNEL_STRIDE & (MLO_WEI_CHANNEL_STRIDE - 1)
    uint wei_blk_idx = iDiv(wei_idx0, MLO_WEI_CHANNEL_STRIDE);
    uint wei_idx     = iMod(wei_idx0, wei_blk_idx, MLO_WEI_CHANNEL_STRIDE);
#else
    uint wei_blk_idx = wei_idx0 / MLO_WEI_CHANNEL_STRIDE;
    uint wei_idx     = wei_idx0 & (MLO_WEI_CHANNEL_STRIDE - 1);
#endif

    _FLOAT pvt_accum_wei[MLO_UT_READ_UNIT] = {MLO_UT_READ_UNIT * (_FLOAT)0};
    //	for (uint i = 0; i < MLO_UT_READ_UNIT; ++i)
    //	{
    //		pvt_accum_wei[i] = 0;
    //	}

    int batch_loop = (MLO_BATCH_SZ + (MLO_N_BATCH_LOOPS * MLO_N_LCL_BATCHS) - 1) /
                     (MLO_N_BATCH_LOOPS * MLO_N_LCL_BATCHS);

    for(uint i = 0; i < batch_loop; ++i)
    {
        for(uint j = 0; j < MLO_UT_READ_UNIT; ++j)
        {
            pvt_accum_wei[j] += weight_df_tmp[(wei_blk_idx * MLO_WEI_CHANNEL_STRIDE +
                                               i * MLO_N_OUTPUTS * MLO_WEI_BATCH_STRIDE) +
                                              wei_idx + j];
        }
    }

    for(uint j = 0; j < MLO_UT_READ_UNIT; ++j)
    {
        weights_df[wei_idx0 + j] = pvt_accum_wei[j];
    }
}

/*******************************************************************************
 *
 * MIT License
 *
 * Copyright (c) 2017 Advanced Micro Devices, Inc.
 *
 * Permission is hereby granted, free of charge, to any person obtaining a copy
 * of this software and associated documentation files (the "Software"), to deal
 * in the Software without restriction, including without limitation the rights
 * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
 * copies of the Software, and to permit persons to whom the Software is
 * furnished to do so, subject to the following conditions:
 *
 * The above copyright notice and this permission notice shall be included in all
 * copies or substantial portions of the Software.
 *
 * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
 * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
 * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
 * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
 * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
 * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
 * SOFTWARE.
 *
 *******************************************************************************/

#define PPCAT_NX(A, B) A##B
#define PPCAT(A, B) PPCAT_NX(A, B)
#define TWO 2
#define FOUR 4
#define EIGHT 8

#if MIOPEN_USE_FP16 == 1
#pragma OPENCL EXTENSION cl_khr_fp16 : enable
#define _FLOAT half
#ifndef HALF_MAX
#define MAX_VAL 65504 /* max value */
#else
#define MAX_VAL HALF_MAX
#endif
#endif
#if MIOPEN_USE_FP32 == 1
#define _FLOAT float
#ifndef FLT_MAX
#define MAX_VAL 3.402823466e+38F /* max value */
#else
#define MAX_VAL FLT_MAX
#endif
#endif

#define _FLOAT2 PPCAT(_FLOAT, TWO)
#define _FLOAT4 PPCAT(_FLOAT, FOUR)
#define _FLOAT8 PPCAT(_FLOAT, EIGHT)

#define UNUSED __attribute__((__unused__))

#define DBG_OUT_OF_RNGE 0

#define MLO_N_OUT_HORIZ_READS ((MLO_OUT_WIDTH + MLO_IN_TILE0 - 1) / MLO_IN_TILE0)
#define MLO_N_SPANS_PER_SCAN (MLO_N_OUT_HORIZ_READS)
#define MLO_N_OUT_HORIZ_PIX_READS (MLO_N_OUT_HORIZ_READS * MLO_IN_TILE0)
#define MLO_OUT_N_PIXS_OFF (MLO_OUT_WIDTH - ((MLO_OUT_WIDTH / MLO_IN_TILE0) * MLO_IN_TILE0))
#define MLO_N_OUT_VERTICAL_READS (MLO_FILTER_SIZE1)
// won't run non-border blocks if  MLO_IN_N_VERT_LOOPS < 2
//

#define MLO_IN_VERT_READS MLO_IN_EXTENT1

#if MLO_IN_N_VERT_LOOPS >= 2
#define MLO_N_GENERIC_LOOPS (MLO_IN_N_VERT_LOOPS - 2)
#else
#define MLO_N_GENERIC_LOOPS 0
#endif

// there is an assumption that the scanline fits into LDS
#define MLO_N_IN_HORIZ_PIX_READS (MLO_IN_WIDTH)
#define MLO_N_IN_HORIZ_READS ((MLO_N_IN_HORIZ_PIX_READS + MLO_READ_UNIT - 1) / MLO_READ_UNIT)
#define MLO_IN_N_PIXS_OFF \
    (MLO_N_IN_HORIZ_PIX_READS - (MLO_N_IN_HORIZ_PIX_READS / MLO_READ_UNIT) * MLO_READ_UNIT)
#define MLO_IN_LCL_WIDTH (MLO_N_IN_HORIZ_READS * MLO_READ_UNIT + 2 * MLO_FILTER_PAD0)
#define MLO_IN_LCL_HEIGHT MLO_IN_VERT_READS
#define MLO_IN_LCL_SZ (MLO_IN_LCL_WIDTH * MLO_IN_LCL_HEIGHT)
#define MLO_TOTAL_IN_LCL_SZ (MLO_N_LCL_BATCHS * MLO_N_LCL_IN_MAPS * MLO_IN_LCL_SZ)

#define MLO_WEI_LCL_SZ (MLO_GRP_SZ * MLO_FILTER_SIZE0)
#if MLO_TOTAL_IN_LCL_SZ > MLO_WEI_LCL_SZ
#define MLO_LCL_SZ (MLO_TOTAL_IN_LCL_SZ)
#else
#define MLO_LCL_SZ (MLO_WEI_LCL_SZ)
#endif

__attribute__((always_inline)) uint iDiv(uint v, uint d)
{
    uint r = v / d; //(uint)((float)v * (1.0f / (float)d) + 0.00001f);
    return (r);
}

__attribute__((always_inline)) uint iMod(uint v, uint u, uint d)
{
    uint r = v - mul24((uint)u, (uint)d);
    return (r);
}

#if 0
// This function is for log-reduction of content in LDS,
// it's supposed to be called by MIOpenCvBwdWrW,
// However, it's no longer used and not tested.
__attribute__((always_inline)) void ReduceKernel(__local _FLOAT* lcl_blob,
                                                 __private _FLOAT* weights_accum,
                                                 uint lcl_id,
                                                 uint scan_lcl,
                                                 uint sum_stride,
                                                 uint unit_len,
                                                 UNUSED bool debug)
{
    // read first half
    if(scan_lcl < (sum_stride >> 1))
    {
        for(uint i = 0; i < unit_len; ++i)
        {
            weights_accum[i] = lcl_blob[(lcl_id + scan_lcl) * unit_len + i];
        }
    }
    // add second half
    // appload accumulated value so far
    for(uint j = (sum_stride >> 1); j > 0; j >>= 1)
    {
        barrier(CLK_LOCAL_MEM_FENCE);
        if(scan_lcl < j)
        {
            for(uint i = 0; i < unit_len; ++i)
            {
                weights_accum[i] += lcl_blob[(lcl_id + j) * unit_len + i];

                lcl_blob[lcl_id * unit_len + i] = weights_accum[i];
            }
        }
    }
}
#endif

/*
        group cooperative read
        read by MLO_READ_UNIT
        handle out of range both horizontally and vertically (by fixed number of veryical reads)

        no guard against number of inputs
*/
__attribute__((always_inline)) void readInput(uint lcl_id,
                                              uint gbl_in_scan_off,
                                              uint n_v_reads,
                                              const __global _FLOAT* __restrict bot,
                                              __local _FLOAT* __restrict lcl_bot)
{
    for(uint p4 = lcl_id; p4 < MLO_N_LCL_IN_MAPS * MLO_N_IN_HORIZ_READS * n_v_reads;
        p4 += MLO_GRP_SZ)
    {
        __private _FLOAT in_rd_data[MLO_READ_UNIT];
        // TODO : more than 1 input
        uint c    = 0;
        uint t_p4 = p4;
#if MLO_N_LCL_IN_MAPS > 1
        c    = p4 / (MLO_N_IN_HORIZ_READS * n_v_reads);
        t_p4 = iMod(p4, c, (MLO_N_IN_HORIZ_READS * n_v_reads));
#endif

        uint c_scan = t_p4 / (MLO_N_IN_HORIZ_READS);

#if MLO_N_IN_HORIZ_READS & (MLO_N_IN_HORIZ_READS - 1)
        uint c_pix4 = iMod(t_p4, c_scan, (MLO_N_IN_HORIZ_READS));
#else
        uint c_pix4 = t_p4 & (MLO_N_IN_HORIZ_READS - 1);
#endif

        //		if (c < MLO_N_INPUTS)

        {
            uint bot_off = gbl_in_scan_off + c * MLO_IN_CHANNEL_STRIDE + c_scan * MLO_IN_STRIDE +
                           c_pix4 * MLO_READ_UNIT;
            const __global _FLOAT* bot_p = &bot[bot_off];
#if MLO_IN_N_PIXS_OFF > 0

            if(c_pix4 == MLO_N_IN_HORIZ_READS - 1)
            {
                for(uint i = 0; i < MLO_IN_N_PIXS_OFF; ++i)
                {
                    in_rd_data[i] = bot_p[i];

#if DBG_OUT_OF_RNGE
                    if(bot_off + i >= MLO_IN_BATCH_STRIDE * MLO_BATCH_SZ)
                    {
                        printf("k:err:out-of-range\n");
                    }
#endif
                }

                for(uint i = MLO_IN_N_PIXS_OFF; i < MLO_READ_UNIT; ++i)
                {
                    in_rd_data[i] = 0;
                }
            }
            else
#endif
            {

                for(uint i = 0; i < MLO_READ_UNIT; ++i)
                {
                    in_rd_data[i] = bot_p[i];

#if DBG_OUT_OF_RNGE
                    if(bot_off + i >= MLO_IN_BATCH_STRIDE * MLO_BATCH_SZ)
                    {
                        printf("k:err:out-of-range\n");
                    }
#endif
                }
            }

            // MLO_N_LCL_IN_MAPS inputs
            for(uint i = 0; i < MLO_READ_UNIT; ++i)
            {
                int lcl_in_off = c * MLO_IN_LCL_SZ + c_scan * MLO_IN_LCL_WIDTH + MLO_FILTER_PAD0 +
                                 c_pix4 * MLO_READ_UNIT + i;
                lcl_bot[lcl_in_off] = in_rd_data[i];
            }
        }

    } // for (int p4 = lcl_id; p4 < MLO_N_LCL_IN_MAPS * MLO_N_IN_HORIZ_READS * MLO_IN_VERT_READS;

    barrier(CLK_LOCAL_MEM_FENCE);
}

/*
        core processing loop
        bot - input, from local (1 span)
        top - output diff, from global (array of spans, filters vertical size)

        loop over filter vertical size

*/
__attribute__((always_inline)) void Processing(UNUSED uint sc,
                                               uint sc_lcl_off,
                                               uint top_lim,
                                               int bot_lim,
                                               __private _FLOAT* __restrict pvt_accum,
                                               __local _FLOAT* __restrict lcl_bot,
                                               __private _FLOAT* __restrict top_dat)
{
    for(int l = top_lim; l >= bot_lim; --l)
    {
        for(uint m = 0; m < MLO_IN_TILE0; ++m)
        {
            for(uint n = 0; n < MLO_FILTER_SIZE0; ++n)
            {
                for(uint c = 0; c < MLO_N_LCL_IN_MAPS; ++c)
                {
                    int bot_off = sc_lcl_off + c * MLO_IN_LCL_SZ + n + m;

                    _FLOAT bot_val = lcl_bot[bot_off];

                    for(uint k = 0; k < MLO_N_LCL_OUT_MAPS; ++k)
                    {
                        uint pvt_top_off =
                            k * MLO_IN_TILE0 * MLO_FILTER_SIZE1 + (top_lim - l) * MLO_IN_TILE0 + m;
                        uint pvt_accum_off =
                            (k * MLO_N_LCL_IN_MAPS + c) * MLO_FILTER_SIZE1 * MLO_FILTER_SIZE0 +
                            l * MLO_FILTER_SIZE0 + n;

                        _FLOAT top_val = top_dat[pvt_top_off];

                        pvt_accum[pvt_accum_off]
                            // each wk-it process an input
                            += bot_val * top_val;
                    }
                }
            }
        }
    }
}

__attribute__((always_inline)) void moveOutputUp(__private _FLOAT* __restrict top_dat)
{
    // move up output to reduce overfetch
    for(uint k = 0; k < MLO_N_LCL_OUT_MAPS; ++k)
    {
        for(uint j = 0; j < MLO_FILTER_SIZE1 - 1; ++j)
        {
            for(uint i = 0; i < MLO_IN_TILE0; ++i)
            {
                uint pvt_off_n = k * MLO_IN_TILE0 * MLO_FILTER_SIZE1 + j * MLO_IN_TILE0 + i;
                uint pvt_off_o = k * MLO_IN_TILE0 * MLO_FILTER_SIZE1 + (j + 1) * MLO_IN_TILE0 + i;
                top_dat[pvt_off_n] = top_dat[pvt_off_o];
            }
        }
    }
}

__attribute__((always_inline)) void spanReadingOutput(int spn,
                                                      int k,
                                                      int j,
                                                      int top_df_off,
                                                      _FLOAT mask,
                                                      __private _FLOAT* __restrict top_dat,
                                                      const __global _FLOAT* __restrict top_df)
{
    int pvt_off                     = k * MLO_IN_TILE0 * MLO_FILTER_SIZE1 + j * MLO_IN_TILE0;
    const __global _FLOAT* top_df_p = &top_df[top_df_off];
#if MLO_OUT_N_PIXS_OFF > 0
    if(spn == MLO_N_SPANS_PER_SCAN - 1)
    {
        uint i = 0;
        for(; i < MLO_OUT_N_PIXS_OFF; ++i)
        {
            top_dat[pvt_off + i] = top_df_p[i] * mask;

#if DBG_OUT_OF_RNGE
            if(top_df_off + i >= MLO_OUT_BATCH_STRIDE * MLO_BATCH_SZ)
            {
                printf("k:err:out-of-range\n");
            }
#endif
        }
        for(; i < MLO_IN_TILE0; ++i)
        {
            top_dat[pvt_off + i] = 0;
        }
    }
    else
#else
    (void)spn;
#endif
    {
        for(uint i = 0; i < MLO_IN_TILE0; ++i)
        {
            top_dat[pvt_off + i] = top_df_p[i] * mask;

#if DBG_OUT_OF_RNGE
            if(top_df_off + i >= MLO_OUT_BATCH_STRIDE * MLO_BATCH_SZ)
            {
                printf("k:err:out-of-range\n");
            }
#endif
        }
    }
}

/*********************************************************************************************************
// wrw algorithm for large filters
// idea:
// split output scan-line on number of spans by the  MLO_IN_TILE0 (2 for example)
// 1 scan-line has ((MLO_OUT_WIDTH + MLO_IN_TILE0 - 1/MLO_IN_TILE0) spans
// group will process MLO_GRP_SZ/((MLO_OUT_WIDTH + MLO_IN_TILE0 - 1/MLO_IN_TILE0) output maps

// alg
// load a block of input map (or full map) into LDS
// loop
// read MLO_FILTER_SIZE1 number of spans from output map into VGPRs (for example 5 *2 = 10)
// read 1 input line for  maps into LDS
// accumulate

// accumulate all spans at the end
// start new loop for the next batch (if defined)
// write out

// kerenl handles 5x5, 3x3 with padding
// small images in 1 short- MLO_N_GENERIC_LOOPS == 0
// big images  in 2 blocks - MLO_IN_N_VERT_LOOPS == 2 or multiple blocks - MLO_IN_N_VERT_LOOPS > 2
// there are prolog and apilog that deal with top/bottom padding.
// left/right padding handles as a LDS border pixels zeroed at the beginning.

**********************************************************************************************************/

__attribute__((reqd_work_group_size(MLO_GRP_SZ0, MLO_GRP_SZ1, MLO_GRP_SZ2))) __kernel void
MIOpenCvBwdWrW(const __global _FLOAT* __restrict top_df,
               const __global _FLOAT* __restrict bot,
               __global _FLOAT* __restrict weights_df,
#if MLO_CONV_BIAS
               __global _FLOAT* __restrict bias_df,
#endif
               UNUSED _FLOAT padding_val)
{

    // input/output tiles + reduce buffer

    __local _FLOAT lcl[(MLO_LCL_SZ) + 1];
    __local _FLOAT* lcl_bot = lcl;

    uint lcl_id = get_local_id(0);

    uint c_idx_base = get_group_id(0); // input map index base

    uint o_idx_base = get_group_id(1); // output map index base

    uint ib_base = get_group_id(2);

    uint ib = ib_base * (MLO_N_BATCH_LOOPS * MLO_N_LCL_BATCHS);

    uint c_idx = c_idx_base * MLO_N_LCL_IN_MAPS; // input map index

    uint o_idx = o_idx_base * (MLO_N_LCL_OUT_MAPS * MLO_OUT_STACKS); // output map index

    uint gbl_in_off  = c_idx * MLO_IN_CHANNEL_STRIDE + ib * MLO_IN_BATCH_STRIDE;
    uint gbl_out_off = o_idx * MLO_OUT_CHANNEL_STRIDE + ib * MLO_OUT_BATCH_STRIDE;
    // 1 span per wk_item, total scanline with MLO_N_SPANS_PER_SCAN spans
    // TODO: more than 1 input
    uint o = lcl_id / MLO_N_SPANS_PER_SCAN;
#if MLO_N_SPANS_PER_SCAN & (MLO_N_SPANS_PER_SCAN - 1)
    uint spn = iMod(lcl_id, o, MLO_N_SPANS_PER_SCAN);
#else
    uint spn         = lcl_id & (MLO_N_SPANS_PER_SCAN - 1);
#endif
    //	bool scan_lead = (o*MLO_N_SPANS_PER_SCAN == lcl_id);

    uint lcl_bot_off     = spn * MLO_IN_TILE0;
    uint out_wk_item_off = o * MLO_OUT_CHANNEL_STRIDE + lcl_bot_off;
    gbl_out_off += out_wk_item_off;
    // no output out of range
    gbl_out_off = (o_idx + o < MLO_N_OUTPUTS && o < MLO_OUT_STACKS) ? gbl_out_off : 0;

#define MLO_TOP_DAT_SZ (MLO_N_LCL_OUT_MAPS * MLO_IN_TILE0 * MLO_FILTER_SIZE1)

    __private _FLOAT top_dat[MLO_TOP_DAT_SZ];

    for(uint i = 0; i < MLO_TOP_DAT_SZ; ++i)
    {
        top_dat[i] = 0;
    }

#define MLO_ACCUM_SZ (MLO_N_LCL_OUT_MAPS * MLO_N_LCL_IN_MAPS * MLO_FILTER_SIZE1 * MLO_FILTER_SIZE0)

    __private _FLOAT pvt_accum[MLO_ACCUM_SZ];

    for(uint i = 0; i < MLO_ACCUM_SZ; ++i)
    {
        pvt_accum[i] = 0;
    }

    // zero out LDS
    for(uint i = lcl_id; i < (MLO_LCL_SZ); i += MLO_GRP_SZ)
    {
        lcl[i] = 0;
    }

    // over all batches
    uint bend = ib + MLO_N_BATCH_LOOPS * MLO_N_LCL_BATCHS;
    bend      = bend > MLO_BATCH_SZ ? MLO_BATCH_SZ : bend;

    for(uint b = ib; b < bend; ++b,
             gbl_in_off += MLO_N_LCL_BATCHS * MLO_IN_BATCH_STRIDE,
             gbl_out_off += MLO_N_LCL_BATCHS * MLO_OUT_BATCH_STRIDE)
    {
        barrier(CLK_LOCAL_MEM_FENCE);

        // top border input block
        uint gbl_in_scan_off  = gbl_in_off;
        uint gbl_out_scan_off = gbl_out_off;

        // read input map
        readInput(lcl_id, gbl_in_scan_off, MLO_IN_VERT_READS, bot, lcl_bot);

        // move input pointer
        gbl_in_scan_off += MLO_IN_STRIDE * MLO_IN_EXTENT1;

        for(uint i = 0; i < MLO_TOP_DAT_SZ; ++i)
        {
            top_dat[i] = 0;
        }

        // prefetch output
        int gbl_out_scan_off1 = gbl_out_scan_off;
        for(uint k = 0; k < MLO_N_LCL_OUT_MAPS;
            ++k, gbl_out_scan_off1 += MLO_OUT_STACKS * MLO_OUT_CHANNEL_STRIDE)
        {
            for(uint j = 0; j < MLO_FILTER_SIZE1 - 1; ++j)
            {
                // loop around all output maps
                uint top_df_off = gbl_out_scan_off1 + j * MLO_OUT_STRIDE;
                _FLOAT mask     = 1;
#if MLO_IN_HEIGHT != MLO_OUT_HEIGHT || MLO_FILTER_SIZE1 - 1 > MLO_OUT_HEIGHT
                top_df_off = (j < MLO_OUT_HEIGHT) ? top_df_off : 0;
                mask       = (j < MLO_OUT_HEIGHT) ? 1 : 0;
#endif

                spanReadingOutput(spn, k, j, top_df_off, mask, top_dat, top_df);
            }
        }

        gbl_out_scan_off += (MLO_FILTER_SIZE1 - 1) * MLO_OUT_STRIDE;

        uint sc         = 0;
        uint sc_lcl_off = lcl_bot_off;

        // prolog
        // handling padding

        // top padding
        for(; sc < MLO_FILTER_SIZE1 - MLO_FILTER_PAD1 - 1; ++sc, sc_lcl_off += MLO_IN_LCL_WIDTH)
        {
            Processing(sc, sc_lcl_off, sc + MLO_FILTER_PAD1, 0, pvt_accum, lcl_bot, top_dat);
        }

#ifdef __AMDGCN__
#pragma unroll 2
#endif
        for(; sc < MLO_IN_EXTENT1
#if MLO_IN_N_VERT_LOOPS == 1
                       -
                       MLO_FILTER_PAD1
#endif
            ;
            ++sc, gbl_out_scan_off += MLO_OUT_STRIDE, sc_lcl_off += MLO_IN_LCL_WIDTH)
        {

            for(uint k = 0; k < MLO_N_LCL_OUT_MAPS; ++k)
            {
                uint top_df_off = gbl_out_scan_off + k * MLO_OUT_STACKS * MLO_OUT_CHANNEL_STRIDE;
                _FLOAT mask     = 1;

#if MLO_IN_HEIGHT != MLO_OUT_HEIGHT || MLO_FILTER_SIZE1 - 1 > MLO_OUT_HEIGHT
                top_df_off = ((sc + MLO_FILTER_PAD1) < MLO_OUT_HEIGHT) ? top_df_off : 0;
                mask       = ((sc + MLO_FILTER_PAD1) < MLO_OUT_HEIGHT) ? 1 : 0;
#endif

                spanReadingOutput(
                    spn, k, (MLO_FILTER_SIZE1 - 1), top_df_off, mask, top_dat, top_df);
            }

            // processing
            Processing(sc, sc_lcl_off, MLO_FILTER_SIZE1 - 1, 0, pvt_accum, lcl_bot, top_dat);

            // move up output to reduce overfetch
            moveOutputUp(top_dat);
        }

        // non-border input blocks
        for(uint i_loop = 0; i_loop < MLO_N_GENERIC_LOOPS;
            ++i_loop, gbl_in_scan_off += MLO_IN_STRIDE * MLO_IN_EXTENT1)
        {
            barrier(CLK_LOCAL_MEM_FENCE);

            readInput(lcl_id, gbl_in_scan_off, MLO_IN_VERT_READS, bot, lcl_bot);

            // point to the start of the local buffer

            sc_lcl_off = lcl_bot_off;

            for(; sc < (i_loop + 2) * MLO_IN_EXTENT1;
                ++sc, gbl_out_scan_off += MLO_OUT_STRIDE, sc_lcl_off += MLO_IN_LCL_WIDTH)
            {

                for(uint k = 0; k < MLO_N_LCL_OUT_MAPS; ++k)
                {
                    uint top_df_off =
                        gbl_out_scan_off + k * MLO_OUT_STACKS * MLO_OUT_CHANNEL_STRIDE;
                    _FLOAT mask = 1;

#if MLO_IN_HEIGHT != MLO_OUT_HEIGHT
                    top_df_off = ((sc + MLO_FILTER_PAD1) < MLO_OUT_HEIGHT) ? top_df_off : 0;
                    mask       = ((sc + MLO_FILTER_PAD1) < MLO_OUT_HEIGHT) ? 1 : 0;
#endif

                    spanReadingOutput(
                        spn, k, (MLO_FILTER_SIZE1 - 1), top_df_off, mask, top_dat, top_df);
                }

                // processing
                Processing(sc, sc_lcl_off, MLO_FILTER_SIZE1 - 1, 0, pvt_accum, lcl_bot, top_dat);

                // move up output to reduce overfetch
                moveOutputUp(top_dat);
            }
        }

        // bottom border block

        for(int i_loop = 0; i_loop < (MLO_IN_N_VERT_LOOPS - MLO_N_GENERIC_LOOPS - 1);
            ++i_loop, gbl_in_scan_off += MLO_IN_STRIDE * MLO_IN_EXTENT1)
        {
            barrier(CLK_LOCAL_MEM_FENCE);

// read 1 scan line less
// padding processing takes care of the bottom border.

#define MLO_LAST_VERT_READS (MLO_IN_HEIGHT - MLO_IN_EXTENT1 * (MLO_IN_N_VERT_LOOPS - 1))

            readInput(lcl_id, gbl_in_scan_off, MLO_LAST_VERT_READS, bot, lcl_bot);

            // point to the start of the local buffer
            sc_lcl_off = lcl_bot_off;

#pragma unroll 3
            for(; sc < MLO_OUT_HEIGHT - MLO_FILTER_PAD1;
                ++sc, gbl_out_scan_off += MLO_OUT_STRIDE, sc_lcl_off += MLO_IN_LCL_WIDTH)
            {

                for(uint k = 0; k < MLO_N_LCL_OUT_MAPS; ++k)
                {
                    uint top_df_off =
                        gbl_out_scan_off + k * MLO_OUT_STACKS * MLO_OUT_CHANNEL_STRIDE;
                    _FLOAT mask = 1;

                    spanReadingOutput(
                        spn, k, (MLO_FILTER_SIZE1 - 1), top_df_off, mask, top_dat, top_df);
                }

                // processing
                Processing(sc, sc_lcl_off, MLO_FILTER_SIZE1 - 1, 0, pvt_accum, lcl_bot, top_dat);

                // move up output to reduce overfetch
                moveOutputUp(top_dat);
            }
        }

        // epilog
        // handling padding

        for(; sc < MLO_IN_HEIGHT; ++sc, sc_lcl_off += MLO_IN_LCL_WIDTH)
        {

            // processing
            Processing(sc,
                       sc_lcl_off,
                       MLO_FILTER_SIZE1 - 1,
                       MLO_FILTER_SIZE1 - (MLO_IN_HEIGHT + MLO_FILTER_PAD1 - sc),
                       pvt_accum,
                       lcl_bot,
                       top_dat);
            // move up output to reduce overfetch
            moveOutputUp(top_dat);

        } // for (; sc < MLO_OUT_HEIGHT - MLO_FILTER_PAD1 + 2; ++sc, gbl_out_scan_off +=
          // MLO_OUT_CHANNEL_STRIDE, gbl_in_scan_off += MLO_IN_CHANNEL_STRIDE)

    } // 	for (int b = 0;

    // final summation over all output maps and each filter row
    // this coudl be done with log but it negligeble anyway
    for(uint k = 0; k < MLO_N_LCL_OUT_MAPS; ++k)
    {
        for(uint c = 0; c < MLO_N_LCL_IN_MAPS; ++c)
        {

            for(uint l = 0; l < MLO_FILTER_SIZE1; ++l)
            {
                barrier(CLK_LOCAL_MEM_FENCE);

                for(uint n = 0; n < MLO_FILTER_SIZE0; ++n)
                {
                    uint pvt_off =
                        (k * MLO_N_LCL_IN_MAPS + c) * MLO_FILTER_SIZE1 * MLO_FILTER_SIZE0 +
                        l * MLO_FILTER_SIZE0 + n;

                    lcl[lcl_id * MLO_FILTER_SIZE0 + n] = pvt_accum[pvt_off];
                }

                barrier(CLK_LOCAL_MEM_FENCE);

                if(spn == 0)
                {
                    for(uint s = 0; s < MLO_N_SPANS_PER_SCAN - 1; ++s)
                    {

                        for(uint n = 0; n < MLO_FILTER_SIZE0; ++n)
                        {
                            uint pvt_off =
                                (k * MLO_N_LCL_IN_MAPS + c) * MLO_FILTER_SIZE1 * MLO_FILTER_SIZE0 +
                                l * MLO_FILTER_SIZE0 + n;
                            pvt_accum[pvt_off] += lcl[(lcl_id + s + 1) * MLO_FILTER_SIZE0 + n];
                        }
                    }
                }
            }
        }
    }

    // output
    // inputs are outputs
    // TODO : for more than 1 input

    uint wei_df_off =
        (((ib / MLO_N_BATCH_LOOPS) * MLO_N_OUTPUTS + o_idx + o) * (uint)MLO_WEI_BATCH_STRIDE)
        // this input channel
        + mul24(c_idx, (uint)MLO_WEI_CHANNEL_STRIDE);
    for(uint k = 0; k < MLO_N_LCL_OUT_MAPS; ++k)
    {
        if(spn == 0 && o_idx + o + k * MLO_OUT_STACKS < MLO_N_OUTPUTS && o < MLO_OUT_STACKS)
        {
            for(uint c = 0; c < MLO_N_LCL_IN_MAPS; ++c)
            {
                for(uint i = 0; i < (MLO_FILTER_SIZE1 * MLO_FILTER_SIZE0); ++i)
                {
                    weights_df[wei_df_off + k * MLO_OUT_STACKS * MLO_WEI_BATCH_STRIDE +
                               c * MLO_WEI_CHANNEL_STRIDE + i] =
                        pvt_accum[(k * MLO_N_LCL_IN_MAPS + c) * MLO_FILTER_SIZE1 *
                                      MLO_FILTER_SIZE0 +
                                  i];
                }
            }
        }
    }
}

// final reduction kernel
// add filters over batches
__attribute__((reqd_work_group_size(MLO_UT_GRP_SZ0, 1, 1))) __kernel void
MIOpenCvBwdWrW_rdc(const __global _FLOAT* __restrict weight_df_tmp,
                   __global _FLOAT* __restrict weights_df)
{
    uint gbl_id   = get_global_id(0);
    uint wei_idx0 = gbl_id * MLO_UT_READ_UNIT;

#if MLO_WEI_CHANNEL_STRIDE & (MLO_WEI_CHANNEL_STRIDE - 1)
    uint wei_blk_idx = iDiv(wei_idx0, MLO_WEI_CHANNEL_STRIDE);
    uint wei_idx     = iMod(wei_idx0, wei_blk_idx, MLO_WEI_CHANNEL_STRIDE);
#else
    uint wei_blk_idx = wei_idx0 / MLO_WEI_CHANNEL_STRIDE;
    uint wei_idx     = wei_idx0 & (MLO_WEI_CHANNEL_STRIDE - 1);
#endif

    _FLOAT pvt_accum_wei[MLO_UT_READ_UNIT] = {MLO_UT_READ_UNIT * (_FLOAT)0};
    //	for (uint i = 0; i < MLO_UT_READ_UNIT; ++i)
    //	{
    //		pvt_accum_wei[i] = 0;
    //	}

    int batch_loop = (MLO_BATCH_SZ + (MLO_N_BATCH_LOOPS * MLO_N_LCL_BATCHS) - 1) /
                     (MLO_N_BATCH_LOOPS * MLO_N_LCL_BATCHS);

    for(uint i = 0; i < batch_loop; ++i)
    {
        for(uint j = 0; j < MLO_UT_READ_UNIT; ++j)
        {
            pvt_accum_wei[j] += weight_df_tmp[(wei_blk_idx * MLO_WEI_CHANNEL_STRIDE +
                                               i * MLO_N_OUTPUTS * MLO_WEI_BATCH_STRIDE) +
                                              wei_idx + j];
        }
    }

    for(uint j = 0; j < MLO_UT_READ_UNIT; ++j)
    {
        weights_df[wei_idx0 + j] = pvt_accum_wei[j];
    }
}