src/core/NEON/kernels/convolution/winograd/winograd_transforms/weights_4x4_3x3_fp16_fp16_integers.cpp

/*
 * Copyright (c) 2020 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
 * 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
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 * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
 * SOFTWARE.
 */
#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC

#include "arm.hpp"
#include "kernel.hpp"

namespace winograd
{

template <>
void WeightTransform<3, 3, 6, 6, __fp16, __fp16, WinogradRoots::Integers>::execute(
    const int n_output_channels,
    const int n_input_channels,
    const __fp16* const input,  // NOTE: Data in HWIO order
    __fp16* const output,
    const int matrix_stride,
    const int matrix_row_stride
)
{
    // Get pointers to each cell of the weight tensor
    const auto weight_col_stride = n_input_channels * n_output_channels;
    const auto weight_row_stride = 3 * weight_col_stride;
    const __fp16 *inptrs[3][3];
    for (int i = 0; i < 3; i++)
    {
        for (int j = 0; j < 3; j++)
        {
            inptrs[i][j] = input + i*weight_row_stride + j*weight_col_stride;
        }
    }

    // For each input channel
    for (int ic = 0; ic < n_input_channels; ic++)
    {
        __fp16 *outptr = output + ic * matrix_row_stride;

        // For each output channel
        int channels_remaining = n_output_channels;
#ifdef __aarch64__
    for (; channels_remaining >= 8; channels_remaining -= 8)
    {
      // Matrices used and computed in this kernel
      float16x8_t w[3][3], Ww[6][3], V[6][6];

      // Read weights
      for (int i = 0; i < 3; i++)
      {
        for (int j = 0; j < 3; j++)
        {
          w[i][j] = vld1q_f16(inptrs[i][j]);
          inptrs[i][j] += 8;
        }
      }

      // Compute the matrix W w
      for (int j = 0; j < 3; j++)
      {
        // Ww[0][j] =  6*w[0][j];
        Ww[0][j] = vmulq_n_f16(w[0][j], 6.0);

        // Ww[1][j] = -4*w[0][j] + -4*w[1][j] + -4*w[2][j];
        Ww[1][j] = vmulq_n_f16(vaddq_f16(vaddq_f16(w[0][j], w[1][j]), w[2][j]), -4.0);

        // Ww[2][j] = -4*w[0][j] +  4*w[1][j] + -4*w[2][j];
        Ww[2][j] = vmulq_n_f16(vsubq_f16(vsubq_f16(w[1][j], w[0][j]), w[2][j]), 4.0);

        // Ww[3][j] =  1*w[0][j] +  2*w[1][j] +  4*w[2][j];
        Ww[3][j] = vaddq_f16(vaddq_f16(w[0][j], vmulq_f16(w[1][j], vdupq_n_f16(2.0f))), vmulq_f16(w[2][j], vdupq_n_f16(4.0f)));

        // Ww[4][j] =  1*w[0][j] + -2*w[1][j] +  4*w[2][j];
        Ww[4][j] = vaddq_f16(vsubq_f16(w[0][j], vmulq_f16(w[1][j], vdupq_n_f16(2.0f))), vmulq_f16(w[2][j], vdupq_n_f16(4.0f)));

        // Ww[5][j] = 24*w[2][j];
        Ww[5][j] = vmulq_n_f16(w[2][j], 24.0f);
      }

      // Compute V = W w WT
      for (int i = 0; i < 6; i++)
      {
        const float recip576 = 1.0f / 576.0f;

        // V[i][0] =  6*Ww[i][0];
        V[i][0] = vmulq_n_f16(vmulq_n_f16(Ww[i][0], 6.0), recip576);

        // V[i][1] = -4*Ww[i][0] + -4*Ww[i][1] + -4*Ww[i][2];
        V[i][1] = vmulq_n_f16(vmulq_n_f16(vaddq_f16(vaddq_f16(Ww[i][0], Ww[i][1]), Ww[i][2]), -4.0), recip576);

        // V[i][2] = -4*Ww[i][0] +  4*Ww[i][1] + -4*Ww[i][2];
        V[i][2] = vmulq_n_f16(vmulq_n_f16(vsubq_f16(vsubq_f16(Ww[i][1], Ww[i][0]), Ww[i][2]), 4.0), recip576);

        // V[i][3] =  1*Ww[i][0] +  2*Ww[i][1] +  4*Ww[i][2];
        V[i][3] = vmulq_n_f16(vaddq_f16(vaddq_f16(Ww[i][0], vmulq_f16(Ww[i][1], vdupq_n_f16(2.0f))), vmulq_f16(Ww[i][2], vdupq_n_f16(4.0f))), recip576);

        // V[i][4] =  1*Ww[i][0] + -2*Ww[i][1] +  4*Ww[i][2];
        V[i][4] = vmulq_n_f16(vaddq_f16(vsubq_f16(Ww[i][0], vmulq_f16(Ww[i][1], vdupq_n_f16(2.0f))), vmulq_f16(Ww[i][2], vdupq_n_f16(4.0f))), recip576);

        // V[i][5] = 24*Ww[i][2];
        V[i][5] = vmulq_n_f16(vmulq_n_f16(Ww[i][2], 24.0f), recip576);
      }

      // Store the transformed weights
      for (int i = 0, m = 0; i < 6; i++)
      {
        for (int j = 0; j < 6; j++, m++)
        {
          vst1q_f16(outptr + m*matrix_stride, V[i][j]);
        }
      }
      outptr += 8;
    }
#endif  // __aarch64__
#ifdef __arm_any__
        for (; channels_remaining >= 4; channels_remaining -= 4)
    {
      // Matrices used and computed in this kernel
      float16x4_t w[3][3], Ww[6][3], V[6][6];

      // Read weights
      for (int i = 0; i < 3; i++)
      {
        for (int j = 0; j < 3; j++)
        {
          w[i][j] = vld1_f16(inptrs[i][j]);
          inptrs[i][j] += 4;
        }
      }

      // Compute the matrix W w
      for (int j = 0; j < 3; j++)
      {
        // Ww[0][j] =  6*w[0][j];
        Ww[0][j] = vmul_n_f16(w[0][j], 6.0);

        // Ww[1][j] = -4*w[0][j] + -4*w[1][j] + -4*w[2][j];
        Ww[1][j] = vmul_n_f16(vadd_f16(vadd_f16(w[0][j], w[1][j]), w[2][j]), -4.0);

        // Ww[2][j] = -4*w[0][j] +  4*w[1][j] + -4*w[2][j];
        Ww[2][j] = vmul_n_f16(vsub_f16(vsub_f16(w[1][j], w[0][j]), w[2][j]), 4.0);

        // Ww[3][j] =  1*w[0][j] +  2*w[1][j] +  4*w[2][j];
        Ww[3][j] = vadd_f16(vadd_f16(w[0][j], vmul_f16(w[1][j], vdup_n_f16(2.0f))), vmul_f16(w[2][j], vdup_n_f16(4.0f)));

        // Ww[4][j] =  1*w[0][j] + -2*w[1][j] +  4*w[2][j];
        Ww[4][j] = vadd_f16(vsub_f16(w[0][j], vmul_f16(w[1][j], vdup_n_f16(2.0f))), vmul_f16(w[2][j], vdup_n_f16(4.0f)));

        // Ww[5][j] = 24*w[2][j];
        Ww[5][j] = vmul_n_f16(w[2][j], 24.0f);
      }

      // Compute V = W w WT
      for (int i = 0; i < 6; i++)
      {
        const float recip576 = 1.0f / 576.0f;

        // V[i][0] =  6*Ww[i][0];
        V[i][0] = vmul_n_f16(vmul_n_f16(Ww[i][0], 6.0), recip576);

        // V[i][1] = -4*Ww[i][0] + -4*Ww[i][1] + -4*Ww[i][2];
        V[i][1] = vmul_n_f16(vmul_n_f16(vadd_f16(vadd_f16(Ww[i][0], Ww[i][1]), Ww[i][2]), -4.0), recip576);

        // V[i][2] = -4*Ww[i][0] +  4*Ww[i][1] + -4*Ww[i][2];
        V[i][2] = vmul_n_f16(vmul_n_f16(vsub_f16(vsub_f16(Ww[i][1], Ww[i][0]), Ww[i][2]), 4.0), recip576);

        // V[i][3] =  1*Ww[i][0] +  2*Ww[i][1] +  4*Ww[i][2];
        V[i][3] = vmul_n_f16(vadd_f16(vadd_f16(Ww[i][0], vmul_f16(Ww[i][1], vdup_n_f16(2.0f))), vmul_f16(Ww[i][2], vdup_n_f16(4.0f))), recip576);

        // V[i][4] =  1*Ww[i][0] + -2*Ww[i][1] +  4*Ww[i][2];
        V[i][4] = vmul_n_f16(vadd_f16(vsub_f16(Ww[i][0], vmul_f16(Ww[i][1], vdup_n_f16(2.0f))), vmul_f16(Ww[i][2], vdup_n_f16(4.0f))), recip576);

        // V[i][5] = 24*Ww[i][2];
        V[i][5] = vmul_n_f16(vmul_n_f16(Ww[i][2], 24.0f), recip576);
      }

      // Store the transformed weights
      for (int i = 0, m = 0; i < 6; i++)
      {
        for (int j = 0; j < 6; j++, m++)
        {
          vst1_f16(outptr + m*matrix_stride, V[i][j]);
        }
      }
      outptr += 4;
    }
#endif  // __arm_any__
        for (; channels_remaining; channels_remaining--)
        {
            // Matrices used and computed in this kernel
            __fp16 w[3][3], Ww[6][3], V[6][6];

            // Read weights
            for (int i = 0; i < 3; i++)
            {
                for (int j = 0; j < 3; j++)
                {
                    w[i][j] = *(inptrs[i][j]++);
                }
            }

            // Compute the matrix W w
            for (int j = 0; j < 3; j++)
            {
                Ww[0][j] =  6*w[0][j];
                Ww[1][j] = -4*w[0][j] + -4*w[1][j] + -4*w[2][j];
                Ww[2][j] = -4*w[0][j] +  4*w[1][j] + -4*w[2][j];
                Ww[3][j] =  1*w[0][j] +  2*w[1][j] +  4*w[2][j];
                Ww[4][j] =  1*w[0][j] + -2*w[1][j] +  4*w[2][j];
                Ww[5][j] = 24*w[2][j];
            }

            // Compute V = W w WT
            for (int i = 0; i < 6; i++)
            {
                V[i][0] = ( 6*Ww[i][0]) / 576.0;
                V[i][1] = (-4*Ww[i][0] + -4*Ww[i][1] + -4*Ww[i][2]) / 576.0;
                V[i][2] = (-4*Ww[i][0] +  4*Ww[i][1] + -4*Ww[i][2]) / 576.0;
                V[i][3] = ( 1*Ww[i][0] +  2*Ww[i][1] +  4*Ww[i][2]) / 576.0;
                V[i][4] = ( 1*Ww[i][0] + -2*Ww[i][1] +  4*Ww[i][2]) / 576.0;
                V[i][5] = (24*Ww[i][2]) / 576.0;
            }

            // Store the transformed weights
            for (int i = 0, m = 0; i < 6; i++)
            {
                for (int j = 0; j < 6; j++, m++)
                {
                    *(outptr + m*matrix_stride) = V[i][j];
                }
            }
            outptr++;
        }
    }
}

template class WeightTransform<3, 3, 6, 6, __fp16, __fp16, WinogradRoots::Integers>;

}  // namespace
#endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC