## Coverage Report

#### Created: 2026-05-04 15:30

next uncovered line (L), next uncovered region (R), next uncovered branch (B)

    /home/liu/actions-runner/_work/ccv/ccv/lib/nnc/cmd/lamb/ccv_nnc_lamb_cpu_ref.c

    Line
    Count
    Source
    1
    #include "ccv.h"
    2
    #include "ccv_internal.h"
    3
    #include "nnc/ccv_nnc.h"
    4
    #include "nnc/ccv_nnc_easy.h"
    5
    #include "nnc/ccv_nnc_internal.h"
    6
    #ifdef USE_OPENMP
    7
    #include <omp.h>
    8
    #endif
    9
    #ifdef USE_DISPATCH
    10
    #include <dispatch/dispatch.h>
    11
    #endif
    12
    13
    // Shared methods.
    14
    #include "../_ccv_nnc_cpu_ref.h"
    15
    16
    static int _ccv_nnc_lamb_forw(const ccv_nnc_cmd_t cmd, const ccv_nnc_hint_t hint, const int flags, ccv_nnc_tensor_t* const* const inputs, const int input_size, ccv_nnc_tensor_t* const* const outputs, const int output_size, ccv_nnc_stream_context_t* const stream_context)
    17
    3
    {
    18
    3
      assert(input_size == 4);
    19
    3
      assert(output_size == 3);
    20
    3
      ccv_nnc_tensor_view_t* const g = (ccv_nnc_tensor_view_t*)inputs[0];
    21
    3
      ccv_nnc_tensor_view_t* const a = (ccv_nnc_tensor_view_t*)inputs[1];
    22
    3
      ccv_nnc_tensor_view_t* const m = (ccv_nnc_tensor_view_t*)inputs[2];
    23
    3
      ccv_nnc_tensor_view_t* const v = (ccv_nnc_tensor_view_t*)inputs[3];
    24
    3
      ccv_nnc_tensor_view_t* const b = (ccv_nnc_tensor_view_t*)outputs[0];
    25
    3
      ccv_nnc_tensor_view_t* const n = (ccv_nnc_tensor_view_t*)outputs[1];
    26
    3
      ccv_nnc_tensor_view_t* const u = (ccv_nnc_tensor_view_t*)outputs[2];
    27
    3
      assert(ccv_nnc_tensor_nd(a->info.dim) <= CCV_NNC_MAX_DIM + 2);
    28
    3
      assert(ccv_nnc_tensor_nd(b->info.dim) <= CCV_NNC_MAX_DIM + 2);
    29
      // Assuming this is float 32.
    30
    3
      int adim[CCV_NNC_MAX_DIM_ALLOC];
    31
    3
      ccv_nnc_tensor_view_get_dim(a, adim);
    32
    3
      assert(ccv_nnc_tensor_view_check_dim(g, adim));
    33
    3
      assert(ccv_nnc_tensor_view_check_dim(m, adim));
    34
    3
      assert(ccv_nnc_tensor_view_check_dim(v, adim));
    35
    3
      assert(ccv_nnc_tensor_view_check_dim(b, adim));
    36
    3
      assert(ccv_nnc_tensor_view_check_dim(n, adim));
    37
    3
      assert(ccv_nnc_tensor_view_check_dim(u, adim));
    38
    3
      assert(CCV_NNC_MAX_DIM == 2); // Need to change this logic for CCV_NNC_MAX_DIM == other number.
    39
    3
      int gstride[CCV_NNC_MAX_DIM_ALLOC];
    40
    3
      int astride[CCV_NNC_MAX_DIM_ALLOC];
    41
    3
      int mstride[CCV_NNC_MAX_DIM_ALLOC];
    42
    3
      int vstride[CCV_NNC_MAX_DIM_ALLOC];
    43
    3
      int bstride[CCV_NNC_MAX_DIM_ALLOC];
    44
    3
      int nstride[CCV_NNC_MAX_DIM_ALLOC];
    45
    3
      int ustride[CCV_NNC_MAX_DIM_ALLOC];
    46
    3
      ccv_nnc_tensor_view_get_stride(g, gstride);
    47
    3
      ccv_nnc_tensor_view_get_stride(a, astride);
    48
    3
      ccv_nnc_tensor_view_get_stride(m, mstride);
    49
    3
      ccv_nnc_tensor_view_get_stride(v, vstride);
    50
    3
      ccv_nnc_tensor_view_get_stride(b, bstride);
    51
    3
      ccv_nnc_tensor_view_get_stride(n, nstride);
    52
    3
      ccv_nnc_tensor_view_get_stride(u, ustride);
    53
    3
      const int step = cmd.info.lamb.step;
    54
    3
      const float rate = cmd.info.lamb.rate;
    55
    3
      const float scale = cmd.info.lamb.scale;
    56
    3
      const float beta1 = cmd.info.lamb.beta1;
    57
    3
      const float beta2 = cmd.info.lamb.beta2;
    58
    3
      const float decay = cmd.info.lamb.decay;
    59
    3
      const float epsilon = cmd.info.lamb.epsilon;
    60
    3
      assert(step >= 1);
    61
    3
      const float inv_bias_correction1 = 1. / (1 - powf(beta1, step));
    62
    3
      const float inv_bias_correction2 = 1. / (1 - powf(beta2, step));
    63
    3
      int i[CCV_NNC_MAX_DIM + 1];
    64
    3
      int x;
    65
    3
      float* const gp = g->data.f32;
    66
    3
      float* const ap = a->data.f32;
    67
    3
      float* const mp = m->data.f32;
    68
    3
      float* const vp = v->data.f32;
    69
    3
      float* const bp = b->data.f32;
    70
    3
      float* const np = n->data.f32;
    71
    3
      float* const up = u->data.f32;
    72
    3
      float* const update = (float*)ccv_nnc_stream_context_get_workspace(stream_context, sizeof(float) * adim[0] * adim[1] * adim[2] * adim[3], CCV_TENSOR_CPU_MEMORY);
    73
    3
      float* updatep = update;
    74
    3
      double update_norm = 0;
    75
    3
      double w_norm = 0;
    76
    6
      for (i[0] = 0; i[0] < adim[0]; i[0]++3)
    77
    3
      {
    78
    3
        float* const gp0 = gp + i[0] * gstride[0];
    79
    3
        float* const ap0 = ap + i[0] * astride[0];
    80
    3
        float* const mp0 = mp + i[0] * mstride[0];
    81
    3
        float* const vp0 = vp + i[0] * vstride[0];
    82
    3
        float* const np0 = np + i[0] * nstride[0];
    83
    3
        float* const up0 = up + i[0] * ustride[0];
    84
    6
        for (i[1] = 0; i[1] < adim[1]; i[1]++3)
    85
    3
        {
    86
    3
          float* gp1 = gp0 + i[1] * gstride[1];
    87
    3
          float* ap1 = ap0 + i[1] * astride[1];
    88
    3
          float* mp1 = mp0 + i[1] * mstride[1];
    89
    3
          float* vp1 = vp0 + i[1] * vstride[1];
    90
    3
          float* np1 = np0 + i[1] * nstride[1];
    91
    3
          float* up1 = up0 + i[1] * ustride[1];
    92
    6
          for (i[2] = 0; i[2] < adim[2]; i[2]++3)
    93
    3
          {
    94
    33
            for (x = 0; x < adim[3]; x++30)
    95
    30
            {
    96
    30
              const float grad = scale * gp1[x];
    97
    30
              const float w = ap1[x];
    98
    30
              const float mom = np1[x] = beta1 * mp1[x] + (1 - beta1) * grad;
    99
    30
              const float vel = up1[x] = beta2 * vp1[x] + (1 - beta2) * grad * grad;
    100
    30
              const float update = updatep[x] = (mom * inv_bias_correction1) / (sqrtf(vel * inv_bias_correction2) + epsilon) + w * decay;
    101
    30
              w_norm += w * w;
    102
    30
              update_norm += update * update;
    103
    30
            }
    104
    3
            gp1 += gstride[2];
    105
    3
            ap1 += astride[2];
    106
    3
            mp1 += mstride[2];
    107
    3
            vp1 += vstride[2];
    108
    3
            np1 += nstride[2];
    109
    3
            up1 += ustride[2];
    110
    3
            updatep += adim[3];
    111
    3
          }
    112
    3
        }
    113
    3
      }
    114
    3
      w_norm = sqrt(w_norm);
    115
    3
      update_norm = sqrt(update_norm);
    116
    3
      const float trust_ratio = w_norm > 0 && update_norm > 0 ? w_norm / update_norm : 1.0;
    117
    3
      const float rate_trust_ratio = rate * trust_ratio;
    118
    3
      updatep = update;
    119
    6
      for (i[0] = 0; i[0] < adim[0]; i[0]++3)
    120
    3
      {
    121
    3
        float* const ap0 = ap + i[0] * astride[0];
    122
    3
        float* const bp0 = bp + i[0] * bstride[0];
    123
    6
        for (i[1] = 0; i[1] < adim[1]; i[1]++3)
    124
    3
        {
    125
    3
          float* ap1 = ap0 + i[1] * astride[1];
    126
    3
          float* bp1 = bp0 + i[1] * bstride[1];
    127
    6
          for (i[2] = 0; i[2] < adim[2]; i[2]++3)
    128
    3
          {
    129
    33
            for (x = 0; x < adim[3]; x++30)
    130
    30
              bp1[x] = ap1[x] - rate_trust_ratio * updatep[x];
    131
    3
            ap1 += astride[2];
    132
    3
            bp1 += bstride[2];
    133
    3
            updatep += adim[3];
    134
    3
          }
    135
    3
        }
    136
    3
      }
    137
    3
      return CCV_NNC_EXEC_SUCCESS;
    138
    3
    }
    139
    140
    static int _ccv_nnc_lamb_back(const ccv_nnc_cmd_t cmd, const ccv_nnc_hint_t hint, const int flags, ccv_nnc_tensor_t* const* const inputs, const int input_size, ccv_nnc_tensor_t* const* const outputs, const int output_size, ccv_nnc_stream_context_t* const stream_context)
    141
    0
    {
    142
    0
      return CCV_NNC_EXEC_INVALID;
    143
    0
    }
    144
    145
    REGISTER_COMMAND_BACKEND(CCV_NNC_LAMB_FORWARD, CCV_NNC_BACKEND_CPU_REF)(ccv_nnc_cmd_backend_registry_t* const registry)
    146
    1
    {
    147
    1
      registry->tensor_formats = CCV_TENSOR_FORMAT_NHWC | CCV_TENSOR_FORMAT_NCHW | CCV_TENSOR_FORMAT_CHWN;
    148
    1
      registry->tensor_datatypes = CCV_32F;
    149
    1
      registry->tensor_memory = CCV_TENSOR_CPU_MEMORY;
    150
    1
      registry->algorithms = 1;
    151
    1
      registry->exec = _ccv_nnc_lamb_forw;
    152
    1
    }
    153
    154
    REGISTER_COMMAND_BACKEND(CCV_NNC_LAMB_BACKWARD, CCV_NNC_BACKEND_CPU_REF)(ccv_nnc_cmd_backend_registry_t* const registry)
    155
    1
    {
    156
    1
      registry->tensor_formats = CCV_TENSOR_FORMAT_NHWC | CCV_TENSOR_FORMAT_NCHW | CCV_TENSOR_FORMAT_CHWN;
    157
    1
      registry->tensor_datatypes = CCV_32F;
    158
    1
      registry->tensor_memory = CCV_TENSOR_CPU_MEMORY;
    159
    1
      registry->algorithms = 1;
    160
    1
      registry->exec = _ccv_nnc_lamb_back;
    161
    1
    }