src/f32-spmm/scalar.c.in

// Copyright 2019 Google LLC
//
// This source code is licensed under the BSD-style license found in the
// LICENSE file in the root directory of this source tree.

$ABC = "0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZ"
#include <assert.h>

#include <xnnpack/math.h>
#include <xnnpack/spmm.h>


void xnn_f32_spmm_ukernel_${MR}x${NR}__scalar${"_unroll" + str(UNROLL) if UNROLL > 1 else ""}(
    uint32_t m,
    uint32_t n,
    const float*restrict a,
    const float*restrict weights,
    const int32_t*restrict widx_dmap,
    const uint32_t*restrict nidx_nnzmap,
    float*restrict c,
    const union xnn_f32_minmax_params params[restrict static 1])
{
  assert(m != 0);

  const float vmin = params->scalar.min;
  const float vmax = params->scalar.max;
  size_t i = m;
  while (i >= ${MR}) {
    const float*restrict w = weights;
    const int32_t* dmap = widx_dmap;
    const uint32_t* nnzmap = nidx_nnzmap;
    size_t j = n;
    while (j >= ${NR}) {
      uint32_t nnz = *nnzmap++;
      $for N in range(0, NR, 1):
        float vacc0x${N} = *w++;
        $for M in range(1, MR):
          float vacc${ABC[M]}x${N} = vacc0x${N};
      if XNN_LIKELY(nnz != 0) {
        do {
          const intptr_t diff = *dmap++;
          $for M in range(MR):
            const float va${ABC[M]} = a[${M}];
          a = (const float*restrict) ((uintptr_t) a + (uintptr_t) diff);
          $for N in range(0, NR, 1):
            const float vb${N} = *w++;
          $for N in range(0, NR, 1):
            $for M in range(MR):
              vacc${ABC[M]}x${N} += va${ABC[M]} * vb${N};
        } while (--nnz != 0);
      }
      $for N in range(NR):
        $for M in range(MR):
          float vout${ABC[M]}x${N} = math_min_f32(vacc${ABC[M]}x${N}, vmax);
      $for N in range(NR):
        $for M in range(MR):
          vout${ABC[M]}x${N} = math_max_f32(vout${ABC[M]}x${N}, vmin);
      $for N in range(NR):
        $for M in range(MR):
          c[${N} * m + ${M}] = vout${ABC[M]}x${N};
      c += ${NR} * m;
      j -= ${NR};
    }
    if XNN_UNLIKELY(j != 0) {
      do {
        uint32_t nnz = *nnzmap++;
        float vacc0 = *w++;
        $for M in range(1, MR):
          float vacc${ABC[M]} = vacc0;
        if XNN_LIKELY(nnz != 0) {
          do {
            const intptr_t diff = *dmap++;
            $for M in range(MR):
              const float va${ABC[M]} = a[${M}];
            a = (const float*restrict) ((uintptr_t) a + (uintptr_t) diff);
            const float vb = *w++;
            $for M in range(MR):
              vacc${ABC[M]} += va${ABC[M]} * vb;
          } while (--nnz != 0);
        }
        $for M in range(MR):
          float vout${ABC[M]} = math_min_f32(vacc${ABC[M]}, vmax);
        $for M in range(MR):
          vout${ABC[M]} = math_max_f32(vout${ABC[M]}, vmin);
        $for M in range(MR):
          c[${M}] = vout${ABC[M]};
        c += m;
        j -= 1;
      } while (j != 0);
    }
    c -= m * n;
    c += ${MR};
    a += ${MR};
    i -= ${MR};
  }
  if XNN_UNLIKELY(i != 0) {
    $for LOG2M in reversed(range((MR - 1).bit_length())):
      $SUBMR = 1 << LOG2M
      if (i & ${SUBMR}) {
        const float*restrict w = weights;
        const int32_t* dmap = widx_dmap;
        const uint32_t* nnzmap = nidx_nnzmap;
        size_t j = n;
        while (j >= ${NR}) {
          uint32_t nnz = *nnzmap++;
          $for N in range(0, NR, 1):
            float vacc0x${N} = *w++;
            $for M in range(1, SUBMR):
              float vacc${ABC[M]}x${N} = vacc0x${N};
          if XNN_LIKELY(nnz != 0) {
            do {
              const intptr_t diff = *dmap++;
              $for M in range(SUBMR):
                const float va${ABC[M]} = a[${M}];
              a = (const float*restrict) ((uintptr_t) a + (uintptr_t) diff);
              $for N in range(0, NR, 1):
                const float vb${N} = *w++;
              $for N in range(0, NR, 1):
                $for M in range(SUBMR):
                  vacc${ABC[M]}x${N} += va${ABC[M]} * vb${N};
            } while (--nnz != 0);
          }
          $for N in range(0, NR, 1):
            $for M in range(SUBMR):
              float vout${ABC[M]}x${N} = math_min_f32(vacc${ABC[M]}x${N}, vmax);
          $for N in range(0, NR, 1):
            $for M in range(SUBMR):
              vout${ABC[M]}x${N} = math_max_f32(vout${ABC[M]}x${N}, vmin);
          $for N in range(0, NR, 1):
            $for M in range(SUBMR):
              c[${N} * m + ${M}] = vout${ABC[M]}x${N};
          c += ${NR} * m;
          j -= ${NR};
        }
        if XNN_UNLIKELY(j != 0) {
          do {
            uint32_t nnz = *nnzmap++;
            float vacc0 = *w++;
            $for M in range(1, SUBMR):
              float vacc${ABC[M]} = vacc0;
            if XNN_LIKELY(nnz != 0) {
              do {
                const intptr_t diff = *dmap++;
                $for M in range(SUBMR):
                  const float va${ABC[M]} = a[${M}];
                a = (const float*restrict) ((uintptr_t) a + (uintptr_t) diff);
                const float vb = *w++;
                $for M in range(SUBMR):
                  vacc${ABC[M]} += va${ABC[M]} * vb;
              } while (--nnz != 0);
            }
            $for M in range(SUBMR):
              float vout${ABC[M]} = math_min_f32(vacc${ABC[M]}, vmax);
            $for M in range(SUBMR):
              vout${ABC[M]} = math_max_f32(vout${ABC[M]}, vmin);
            $for M in range(SUBMR):
              c[${M}] = vout${ABC[M]};
            c += m;
            j -= 1;
          } while (j != 0);
        }
        c -= m * n;
        c += ${SUBMR};
        a += ${SUBMR};
      }
  }
}