src/core/NEON/kernels/convolution/winograd/output_transforms/arm_fp32_1x4_1x5.cpp

/*
 * Copyright (c) 2022-2023 Arm Limited.
 *
 * SPDX-License-Identifier: MIT
 *
 * Permission is hereby granted, free of charge, to any person obtaining a copy
 * of this software and associated documentation files (the "Software"), to
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 * 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
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 * SOFTWARE.
 */

#include <algorithm>
#include <cstddef>
#include <arm_neon.h>

namespace arm_conv {
namespace winograd {
namespace output_transform {

void arm_fp32_1x4_1x5(
  unsigned int n_channels,
  const float* inptr,
  size_t matrix_stride,
  const float* bptr,
  float *outptr,
  size_t,  // No need to stride across rows
  size_t output_col_stride,
  float output_min,
  float output_max
)
{
  constexpr auto inner_tile_cols = 8u, output_tile_cols = 4u;

  // For each channel of the output
  for (; n_channels >= 4; n_channels -= 4)
  {
    // Matrices used and computed during this transform
    float32x4_t F[inner_tile_cols], f[output_tile_cols], b = vdupq_n_f32(0.0f);

    // Read a 1x8 tile in the Winograd domain
    for (auto j = 0u; j < inner_tile_cols; j++)
    {
      F[j] = vld1q_f32(inptr + j*matrix_stride);
    }
    inptr += 4;

    f[0] = vmlaq_n_f32(vmlaq_n_f32(vmlaq_n_f32(vmlaq_n_f32(vmlaq_n_f32(vmlaq_n_f32(vmulq_n_f32(F[6], 1), F[5], 1), F[4], 1), F[3], 1), F[2], 1), F[1], 1), F[0], 1);
    f[1] = vmlaq_n_f32(vmlaq_n_f32(vmlaq_n_f32(vmlaq_n_f32(vmlaq_n_f32(vmulq_n_f32(F[2], 1), F[6], 3), F[4], 2), F[3], -2), F[5], -3), F[1], -1);
    f[2] = vmlaq_n_f32(vmlaq_n_f32(vmlaq_n_f32(vmlaq_n_f32(vmlaq_n_f32(vmulq_n_f32(F[2], 1), F[1], 1), F[6], 9), F[5], 9), F[4], 4), F[3], 4);
    f[3] = vmlaq_n_f32(vmlaq_n_f32(vmlaq_n_f32(vmlaq_n_f32(vmlaq_n_f32(vmlaq_n_f32(vmulq_n_f32(F[7], 1), F[2], 1), F[6], 27), F[4], 8), F[3], -8), F[5], -27), F[1], -1);

    // Write out the output tile
    if (bptr != 0)
    {
      b = vld1q_f32(bptr);
      bptr += 4;
    }
    for (auto j = 0u; j < output_tile_cols; j++)
    {
      const auto y =
          vmaxq_f32(vminq_f32(vaddq_f32(f[j], b), vdupq_n_f32(output_max)),
                    vdupq_n_f32(output_min));
      vst1q_f32(outptr + j*output_col_stride, y);
    }
    outptr += 4;
  }
  for (; n_channels >= 2; n_channels -= 2)
  {
    // Matrices used and computed during this transform
    float32x2_t F[inner_tile_cols], f[output_tile_cols], b = vdup_n_f32(0.0f);

    // Read a 1x8 tile in the Winograd domain
    for (auto j = 0u; j < inner_tile_cols; j++)
    {
      F[j] = vld1_f32(inptr + j*matrix_stride);
    }
    inptr += 2;

    f[0] = vmla_n_f32(vmla_n_f32(vmla_n_f32(vmla_n_f32(vmla_n_f32(vmla_n_f32(vmul_n_f32(F[6], 1), F[5], 1), F[4], 1), F[3], 1), F[2], 1), F[1], 1), F[0], 1);
    f[1] = vmla_n_f32(vmla_n_f32(vmla_n_f32(vmla_n_f32(vmla_n_f32(vmul_n_f32(F[2], 1), F[6], 3), F[4], 2), F[3], -2), F[5], -3), F[1], -1);
    f[2] = vmla_n_f32(vmla_n_f32(vmla_n_f32(vmla_n_f32(vmla_n_f32(vmul_n_f32(F[2], 1), F[1], 1), F[6], 9), F[5], 9), F[4], 4), F[3], 4);
    f[3] = vmla_n_f32(vmla_n_f32(vmla_n_f32(vmla_n_f32(vmla_n_f32(vmla_n_f32(vmul_n_f32(F[7], 1), F[2], 1), F[6], 27), F[4], 8), F[3], -8), F[5], -27), F[1], -1);

    // Write out the output tile
    if (bptr != 0)
    {
      b = vld1_f32(bptr);
      bptr += 2;
    }
    for (auto j = 0u; j < output_tile_cols; j++)
    {
      const auto y =
          vmax_f32(vmin_f32(vadd_f32(f[j], b), vdup_n_f32(output_max)),
                   vdup_n_f32(output_min));
      vst1_f32(outptr + j*output_col_stride, y);
    }
    outptr += 2;
  }
  for (; n_channels; n_channels--)
  {
    // Matrices used and computed during this transform
    float F[inner_tile_cols], f[output_tile_cols], b = 0.0f;

    // Read a 1x8 tile in the Winograd domain
    for (auto j = 0u; j < inner_tile_cols; j++)
    {
      F[j] = *(inptr + j*matrix_stride);
    }
    inptr++;

    f[0] = F[0]*1 + F[1]*1 + F[2]*1 + F[3]*1 + F[4]*1 + F[5]*1 + F[6]*1;
    f[1] = F[1]*-1 + F[5]*-3 + F[3]*-2 + F[4]*2 + F[6]*3 + F[2]*1;
    f[2] = F[3]*4 + F[4]*4 + F[5]*9 + F[6]*9 + F[1]*1 + F[2]*1;
    f[3] = F[1]*-1 + F[5]*-27 + F[3]*-8 + F[4]*8 + F[6]*27 + F[2]*1 + F[7]*1;

    // Write out the output tile
    if (bptr != 0)
    {
      b = *(bptr++);
    }
    for (auto j = 0u; j < output_tile_cols; j++)
    {
      const auto y = std::max(std::min(f[j] + b, output_max), output_min);
      *(outptr + j*output_col_stride) = y;
    }
    outptr++;
  }
}

}  // namespace output_transform
}  // namespace winograd
}  // namespace arm_conv