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/*
* Copyright (c) 2017 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
* 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.
*/
#include "arm_compute/core/NEON/kernels/convolution/winograd/transforms/input.hpp"
#include "arm_compute/core/NEON/kernels/convolution/winograd/winograd_gemm.hpp"
#include "arm_compute/core/NEON/kernels/convolution/common/arm.hpp"
namespace
{
template <bool Specialized, int PadTop=0, int PadLeft=0, int PadBottom=0, int PadRight=0>
void winograd_input_transform_1x8_fp32_process_tile(
int n_channels,
const float* const input_base,
const int input_row_stride,
const int input_col_stride,
float* const matrix_base,
const int matrix_stride,
const int _pad_top,
const int _pad_left,
const int _pad_bottom,
const int _pad_right
)
{
(void) input_row_stride; // No rows over which to stride
(void) _pad_top; // Never any top padding
(void) _pad_bottom; // Never any bottom padding
// Extract padding arguments
const int pad_left = Specialized ? PadLeft : _pad_left;
const int pad_right = Specialized ? PadRight : _pad_right;
constexpr int inner_tile_cols = 8;
const int cells_j = inner_tile_cols - pad_right;
float *outptr = matrix_base;
// Get pointers into the input tile
const float *x_ptrs[inner_tile_cols];
for (int j = pad_left, xj = 0; j < cells_j; j++, xj++)
{
x_ptrs[j] = input_base + xj*input_col_stride;
}
// Vectors used/computed in this kernel.
float x[inner_tile_cols];
float U[inner_tile_cols];
for (int j = 0; j < inner_tile_cols; j++)
{
x[j] = 0.0f;
}
// Perform the Winograd input transformation for each channel in the input
// tensor.
int channels_remaining = n_channels;
#ifdef __arm_any__
for (; channels_remaining >= 4; channels_remaining -= 4)
{
float32x4_t x[inner_tile_cols], U[inner_tile_cols];
for (int j = 0; j < inner_tile_cols; j++)
{
x[j] = vdupq_n_f32(0.0f);
}
// Load x
for (int j = pad_left; j < cells_j; j++)
{
x[j] = vld1q_f32(x_ptrs[j]);
x_ptrs[j] += 4;
}
// Compute U = x . X
U[0] = vmlaq_n_f32(vmlaq_n_f32(vmlaq_n_f32(vmulq_n_f32(x[6], 1), x[2], 49), x[4], -14), x[0], -36);
U[1] = vmlaq_n_f32(vmlaq_n_f32(vmlaq_n_f32(vmlaq_n_f32(vmlaq_n_f32(vmulq_n_f32(x[6], 1), x[2], 36), x[3], 13), x[4], -13), x[1], -36), x[5], -1);
U[2] = vmlaq_n_f32(vmlaq_n_f32(vmlaq_n_f32(vmlaq_n_f32(vmlaq_n_f32(vmulq_n_f32(x[6], 1), x[5], 1), x[2], 36), x[1], 36), x[4], -13), x[3], -13);
U[3] = vmlaq_n_f32(vmlaq_n_f32(vmlaq_n_f32(vmlaq_n_f32(vmlaq_n_f32(vmulq_n_f32(x[6], 1), x[3], 20), x[2], 9), x[5], -2), x[4], -10), x[1], -18);
U[4] = vmlaq_n_f32(vmlaq_n_f32(vmlaq_n_f32(vmlaq_n_f32(vmlaq_n_f32(vmulq_n_f32(x[6], 1), x[1], 18), x[2], 9), x[5], 2), x[4], -10), x[3], -20);
U[5] = vmlaq_n_f32(vmlaq_n_f32(vmlaq_n_f32(vmlaq_n_f32(vmlaq_n_f32(vmulq_n_f32(x[6], 1), x[3], 15), x[2], 4), x[5], -3), x[4], -5), x[1], -12);
U[6] = vmlaq_n_f32(vmlaq_n_f32(vmlaq_n_f32(vmlaq_n_f32(vmlaq_n_f32(vmulq_n_f32(x[6], 1), x[1], 12), x[2], 4), x[5], 3), x[4], -5), x[3], -15);
U[7] = vmlaq_n_f32(vmlaq_n_f32(vmlaq_n_f32(vmulq_n_f32(x[7], 1), x[3], 49), x[5], -14), x[1], -36);
// Store the transformed vector
for (int j = 0; j < inner_tile_cols; j++)
{
vst1q_f32(outptr + j*matrix_stride, U[j]);
}
outptr += 4;
}
for (; channels_remaining >= 2; channels_remaining -= 2)
{
float32x2_t x[inner_tile_cols], U[inner_tile_cols];
for (int j = 0; j < inner_tile_cols; j++)
{
x[j] = vdup_n_f32(0.0f);
}
// Load x
for (int j = pad_left; j < cells_j; j++)
{
x[j] = vld1_f32(x_ptrs[j]);
x_ptrs[j] += 2;
}
// Compute U = x . X
U[0] = vmla_n_f32(vmla_n_f32(vmla_n_f32(vmul_n_f32(x[6], 1), x[2], 49), x[4], -14), x[0], -36);
U[1] = vmla_n_f32(vmla_n_f32(vmla_n_f32(vmla_n_f32(vmla_n_f32(vmul_n_f32(x[6], 1), x[2], 36), x[3], 13), x[4], -13), x[1], -36), x[5], -1);
U[2] = vmla_n_f32(vmla_n_f32(vmla_n_f32(vmla_n_f32(vmla_n_f32(vmul_n_f32(x[6], 1), x[5], 1), x[2], 36), x[1], 36), x[4], -13), x[3], -13);
U[3] = vmla_n_f32(vmla_n_f32(vmla_n_f32(vmla_n_f32(vmla_n_f32(vmul_n_f32(x[6], 1), x[3], 20), x[2], 9), x[5], -2), x[4], -10), x[1], -18);
U[4] = vmla_n_f32(vmla_n_f32(vmla_n_f32(vmla_n_f32(vmla_n_f32(vmul_n_f32(x[6], 1), x[1], 18), x[2], 9), x[5], 2), x[4], -10), x[3], -20);
U[5] = vmla_n_f32(vmla_n_f32(vmla_n_f32(vmla_n_f32(vmla_n_f32(vmul_n_f32(x[6], 1), x[3], 15), x[2], 4), x[5], -3), x[4], -5), x[1], -12);
U[6] = vmla_n_f32(vmla_n_f32(vmla_n_f32(vmla_n_f32(vmla_n_f32(vmul_n_f32(x[6], 1), x[1], 12), x[2], 4), x[5], 3), x[4], -5), x[3], -15);
U[7] = vmla_n_f32(vmla_n_f32(vmla_n_f32(vmul_n_f32(x[7], 1), x[3], 49), x[5], -14), x[1], -36);
// Store the transformed vector
for (int j = 0; j < inner_tile_cols; j++)
{
vst1_f32(outptr + j*matrix_stride, U[j]);
}
outptr += 2;
}
#endif // __arm_any__
for (; channels_remaining; channels_remaining--)
{
// Load x
for (int j = pad_left; j < cells_j; j++)
{
x[j] = *(x_ptrs[j]++);
}
// Compute U = x . X
U[0] = x[0]*-36 + x[4]*-14 + x[2]*49 + x[6]*1;
U[1] = x[5]*-1 + x[1]*-36 + x[4]*-13 + x[3]*13 + x[2]*36 + x[6]*1;
U[2] = x[3]*-13 + x[4]*-13 + x[1]*36 + x[2]*36 + x[5]*1 + x[6]*1;
U[3] = x[1]*-18 + x[4]*-10 + x[5]*-2 + x[2]*9 + x[3]*20 + x[6]*1;
U[4] = x[3]*-20 + x[4]*-10 + x[5]*2 + x[2]*9 + x[1]*18 + x[6]*1;
U[5] = x[1]*-12 + x[4]*-5 + x[5]*-3 + x[2]*4 + x[3]*15 + x[6]*1;
U[6] = x[3]*-15 + x[4]*-5 + x[5]*3 + x[2]*4 + x[1]*12 + x[6]*1;
U[7] = x[1]*-36 + x[5]*-14 + x[3]*49 + x[7]*1;
// Store the transformed vector
for (int j = 0; j < inner_tile_cols; j++)
{
*(outptr + j*matrix_stride) = U[j];
}
outptr++;
}
}
}
namespace winograd
{
template <int x>
using Tiles = InputTransformImplTiles<1, x, 1, 8, float>;
/*****************************************************************************/
// 1x3 specialisations
template <>
const Tiles<3>::TileFn Tiles<3>::tilefn_generic = winograd_input_transform_1x8_fp32_process_tile<false>;
template <>
const Tiles<3>::TileFn Tiles<3>::tilefn_unpadded = winograd_input_transform_1x8_fp32_process_tile<true>;
template <>
const Tiles<3>::TileFn Tiles<3>::tilefn_left_padded[n_pad_left] = {
winograd_input_transform_1x8_fp32_process_tile<true, 0, 1, 0, 0>,
};
template <>
const Tiles<3>::TileFn Tiles<3>::tilefn_right_padded[n_pad_right] = {
winograd_input_transform_1x8_fp32_process_tile<true, 0, 0, 0, 1>,
winograd_input_transform_1x8_fp32_process_tile<true, 0, 0, 0, 2>,
winograd_input_transform_1x8_fp32_process_tile<true, 0, 0, 0, 3>,
winograd_input_transform_1x8_fp32_process_tile<true, 0, 0, 0, 4>,
winograd_input_transform_1x8_fp32_process_tile<true, 0, 0, 0, 5>,
winograd_input_transform_1x8_fp32_process_tile<true, 0, 0, 0, 6>,
winograd_input_transform_1x8_fp32_process_tile<true, 0, 0, 0, 7>,
};
/*****************************************************************************/
/*****************************************************************************/
// 1x5 specialisations
template <>
const Tiles<5>::TileFn Tiles<5>::tilefn_generic = winograd_input_transform_1x8_fp32_process_tile<false>;
template <>
const Tiles<5>::TileFn Tiles<5>::tilefn_unpadded = winograd_input_transform_1x8_fp32_process_tile<true>;
template <>
const Tiles<5>::TileFn Tiles<5>::tilefn_left_padded[n_pad_left] = {
winograd_input_transform_1x8_fp32_process_tile<true, 0, 2, 0, 0>,
};
template <>
const Tiles<5>::TileFn Tiles<5>::tilefn_right_padded[n_pad_right] = {
winograd_input_transform_1x8_fp32_process_tile<true, 0, 0, 0, 1>,
winograd_input_transform_1x8_fp32_process_tile<true, 0, 0, 0, 2>,
winograd_input_transform_1x8_fp32_process_tile<true, 0, 0, 0, 3>,
winograd_input_transform_1x8_fp32_process_tile<true, 0, 0, 0, 4>,
winograd_input_transform_1x8_fp32_process_tile<true, 0, 0, 0, 5>,
winograd_input_transform_1x8_fp32_process_tile<true, 0, 0, 0, 6>,
winograd_input_transform_1x8_fp32_process_tile<true, 0, 0, 0, 7>,
};
/*****************************************************************************/
/*****************************************************************************/
// 1x7 specialisations
template <>
const Tiles<7>::TileFn Tiles<7>::tilefn_generic = winograd_input_transform_1x8_fp32_process_tile<false>;
template <>
const Tiles<7>::TileFn Tiles<7>::tilefn_unpadded = winograd_input_transform_1x8_fp32_process_tile<true>;
template <>
const Tiles<7>::TileFn Tiles<7>::tilefn_left_padded[n_pad_left] = {
winograd_input_transform_1x8_fp32_process_tile<true, 0, 1, 0, 0>,
winograd_input_transform_1x8_fp32_process_tile<true, 0, 3, 0, 0>,
};
template <>
const Tiles<7>::TileFn Tiles<7>::tilefn_right_padded[n_pad_right] = {
winograd_input_transform_1x8_fp32_process_tile<true, 0, 0, 0, 1>,
winograd_input_transform_1x8_fp32_process_tile<true, 0, 0, 0, 2>,
winograd_input_transform_1x8_fp32_process_tile<true, 0, 0, 0, 3>,
winograd_input_transform_1x8_fp32_process_tile<true, 0, 0, 0, 4>,
winograd_input_transform_1x8_fp32_process_tile<true, 0, 0, 0, 5>,
winograd_input_transform_1x8_fp32_process_tile<true, 0, 0, 0, 6>,
winograd_input_transform_1x8_fp32_process_tile<true, 0, 0, 0, 7>,
};
/*****************************************************************************/
template class InputTransform<1, 3, 1, 8, float>;
template class InputTransform<3, 1, 8, 1, float>;
template class InputTransform<1, 5, 1, 8, float>;
template class InputTransform<5, 1, 8, 1, float>;
template class InputTransform<1, 7, 1, 8, float>;
template class InputTransform<7, 1, 8, 1, float>;
} // namespace winograd
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