diff options
Diffstat (limited to 'src/core/NEON/kernels/convolution/winograd/transforms')
15 files changed, 0 insertions, 3908 deletions
diff --git a/src/core/NEON/kernels/convolution/winograd/transforms/input_1x8_fp32.cpp b/src/core/NEON/kernels/convolution/winograd/transforms/input_1x8_fp32.cpp deleted file mode 100644 index e66300d39a..0000000000 --- a/src/core/NEON/kernels/convolution/winograd/transforms/input_1x8_fp32.cpp +++ /dev/null @@ -1,261 +0,0 @@ -/* - * 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 diff --git a/src/core/NEON/kernels/convolution/winograd/transforms/input_2x2_3x3_fp32.cpp b/src/core/NEON/kernels/convolution/winograd/transforms/input_2x2_3x3_fp32.cpp deleted file mode 100644 index 4203945dd3..0000000000 --- a/src/core/NEON/kernels/convolution/winograd/transforms/input_2x2_3x3_fp32.cpp +++ /dev/null @@ -1,311 +0,0 @@ -/* - * 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 winograd -{ - -using Tiles = InputTransformImplTiles<3, 3, 4, 4, float>; - -namespace -{ - - -template <bool Specialized, int PadTop=0, int PadLeft=0, int PadBottom=0, int PadRight=0> -void winograd_input_transform_4x4_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 - ) -{ -const int pad_top = Specialized ? PadTop : _pad_top; - const int pad_left = Specialized ? PadLeft : _pad_left; - const int pad_bottom = Specialized ? PadBottom : _pad_bottom; - const int pad_right = Specialized ? PadRight : _pad_right; - - constexpr int inner_tile_i = 4, inner_tile_j = 4; - const int cells_i = inner_tile_i - pad_bottom; - const int cells_j = inner_tile_i - pad_right; - - - - float *outptr = matrix_base; - - // Get pointers into the input tile - const float *x_ptrs[inner_tile_i][inner_tile_j]; - for (int i = pad_top, xi = 0; i < cells_i; i++, xi++) - { - // Get a pointer into the row - const float* const row_ptr = input_base + xi*input_row_stride; - - for (int j = pad_left, xj = 0; j < cells_j; j++, xj++) - { - x_ptrs[i][j] = row_ptr + xj*input_col_stride; - } - } - - // Matrices used/computed in this kernel. - float x[inner_tile_i][inner_tile_j]; - float XTx[inner_tile_i][inner_tile_j]; - float U[inner_tile_i][inner_tile_j]; - - for (int i = 0; i < inner_tile_i; i++) - { - for (int j = 0; j < inner_tile_j; j++) - { - x[i][j] = XTx[i][j] = 0.0f; - } - } - - // Perform the Winograd input transformation for each channel in the input - // tensor. - int channels_remaining = n_channels; -#ifdef __aarch64__ - for (; channels_remaining >= 4; channels_remaining -= 4) - { - // Matrices used/computed in this kernel. - float32x4_t x[inner_tile_i][inner_tile_j]; - float32x4_t XTx[inner_tile_i][inner_tile_j]; - float32x4_t U[inner_tile_i][inner_tile_j]; - - for (int i = 0; i < inner_tile_i; i++) - { - for (int j = 0; j < inner_tile_j; j++) - { - x[i][j] = vdupq_n_f32(0.0f); - XTx[i][j] = vdupq_n_f32(0.0f); - } - } - - // Load x - for (int i = pad_top; i < cells_i; i++) - { - for (int j = pad_left; j < cells_j; j++) - { - x[i][j] = vld1q_f32(x_ptrs[i][j]); - x_ptrs[i][j] += 4; - } - } - - // Compute XT . x - for (int j = pad_left; j < cells_j; j++) - { - // XTx[0][j] = x[0][j] - x[2][j]; - XTx[0][j] = vsubq_f32(x[0][j], x[2][j]); - - // XTx[1][j] = x[1][j] + x[2][j]; - XTx[1][j] = vaddq_f32(x[1][j], x[2][j]); - - // XTx[2][j] = x[2][j] - x[1][j]; - XTx[2][j] = vsubq_f32(x[2][j], x[1][j]); - - // XTx[3][j] = x[1][j] - x[3][j]; - XTx[3][j] = vsubq_f32(x[1][j], x[3][j]); - } - - // Compute U = XT . x . X - for (int i = 0; i < inner_tile_i; i++) - { - // U[i][0] = XTx[i][0] - XTx[i][2]; - U[i][0] = vsubq_f32(XTx[i][0], XTx[i][2]); - - // U[i][1] = XTx[i][1] + XTx[i][2]; - U[i][1] = vaddq_f32(XTx[i][1], XTx[i][2]); - - // U[i][2] = XTx[i][2] - XTx[i][1]; - U[i][2] = vsubq_f32(XTx[i][2], XTx[i][1]); - - // U[i][3] = XTx[i][1] - XTx[i][3]; - U[i][3] = vsubq_f32(XTx[i][1], XTx[i][3]); - } - - // Store the transformed matrix - for (int i = 0, m = 0; i < inner_tile_i; i++) - { - for (int j = 0; j < inner_tile_j; j++, m++) - { - vst1q_f32(outptr + m*matrix_stride, U[i][j]); - } - } - outptr += 4; - } -#endif // __aarch64__ -#ifdef __arm_any__ - for (; channels_remaining >= 2; channels_remaining -= 2) - { - // Matrices used/computed in this kernel. - float32x2_t x[inner_tile_i][inner_tile_j]; - float32x2_t XTx[inner_tile_i][inner_tile_j]; - float32x2_t U[inner_tile_i][inner_tile_j]; - - for (int i = 0; i < inner_tile_i; i++) - { - for (int j = 0; j < inner_tile_j; j++) - { - x[i][j] = vdup_n_f32(0.0f); - XTx[i][j] = vdup_n_f32(0.0f); - } - } - - // Load x - for (int i = pad_top; i < cells_i; i++) - { - for (int j = pad_left; j < cells_j; j++) - { - x[i][j] = vld1_f32(x_ptrs[i][j]); - x_ptrs[i][j] += 2; - } - } - - // Compute XT . x - for (int j = pad_left; j < cells_j; j++) - { - // XTx[0][j] = x[0][j] - x[2][j]; - XTx[0][j] = vsub_f32(x[0][j], x[2][j]); - - // XTx[1][j] = x[1][j] + x[2][j]; - XTx[1][j] = vadd_f32(x[1][j], x[2][j]); - - // XTx[2][j] = x[2][j] - x[1][j]; - XTx[2][j] = vsub_f32(x[2][j], x[1][j]); - - // XTx[3][j] = x[1][j] - x[3][j]; - XTx[3][j] = vsub_f32(x[1][j], x[3][j]); - } - - // Compute U = XT . x . X - for (int i = 0; i < inner_tile_i; i++) - { - // U[i][0] = XTx[i][0] - XTx[i][2]; - U[i][0] = vsub_f32(XTx[i][0], XTx[i][2]); - - // U[i][1] = XTx[i][1] + XTx[i][2]; - U[i][1] = vadd_f32(XTx[i][1], XTx[i][2]); - - // U[i][2] = XTx[i][2] - XTx[i][1]; - U[i][2] = vsub_f32(XTx[i][2], XTx[i][1]); - - // U[i][3] = XTx[i][1] - XTx[i][3]; - U[i][3] = vsub_f32(XTx[i][1], XTx[i][3]); - } - - // Store the transformed matrix - for (int i = 0, m = 0; i < inner_tile_i; i++) - { - for (int j = 0; j < inner_tile_j; j++, m++) - { - vst1_f32(outptr + m*matrix_stride, U[i][j]); - } - } - outptr += 2; - } -#endif // __arm_any__ - for (; channels_remaining; channels_remaining--) - { - // Load x - for (int i = pad_top; i < cells_i; i++) - { - for (int j = pad_left; j < cells_j; j++) - { - x[i][j] = *(x_ptrs[i][j]++); - } - } - - // Compute XT . x - for (int j = pad_left; j < cells_j; j++) - { - XTx[0][j] = x[0][j] - x[2][j]; - XTx[1][j] = x[1][j] + x[2][j]; - XTx[2][j] = x[2][j] - x[1][j]; - XTx[3][j] = x[1][j] - x[3][j]; - } - - // Compute U = XT . x . X - for (int i = 0; i < inner_tile_i; i++) - { - U[i][0] = XTx[i][0] - XTx[i][2]; - U[i][1] = XTx[i][1] + XTx[i][2]; - U[i][2] = XTx[i][2] - XTx[i][1]; - U[i][3] = XTx[i][1] - XTx[i][3]; - } - - // Store the transformed matrix - for (int i = 0, m = 0; i < inner_tile_i; i++) - { - for (int j = 0; j < inner_tile_j; j++, m++) - { - *(outptr + m*matrix_stride) = U[i][j]; - } - } - outptr++; - } -} - -} // namespace (anonymous) - -template <> -const Tiles::TileFn Tiles::tilefn_generic = winograd_input_transform_4x4_fp32_process_tile<false>; - -template <> -const Tiles::TileFn Tiles::tilefn_unpadded = winograd_input_transform_4x4_fp32_process_tile<true>; - - -template <> -const Tiles::TileFn Tiles::tilefn_top_padded[n_pad_top] = { - winograd_input_transform_4x4_fp32_process_tile<true, 1, 0, 0, 0>, -}; - -template <> -const Tiles::TileFn Tiles::tilefn_left_padded[n_pad_left] = { - winograd_input_transform_4x4_fp32_process_tile<true, 0, 1, 0, 0>, -}; - -template <> -const Tiles::TileFn Tiles::tilefn_bottom_padded[n_pad_bottom] = { - winograd_input_transform_4x4_fp32_process_tile<true, 0, 0, 1, 0>, - winograd_input_transform_4x4_fp32_process_tile<true, 0, 0, 2, 0>, - winograd_input_transform_4x4_fp32_process_tile<true, 0, 0, 3, 0>, - winograd_input_transform_4x4_fp32_process_tile<true, 0, 0, 4, 0>, -}; - -template <> -const Tiles::TileFn Tiles::tilefn_right_padded[n_pad_right] = { - winograd_input_transform_4x4_fp32_process_tile<true, 0, 0, 0, 1>, - winograd_input_transform_4x4_fp32_process_tile<true, 0, 0, 0, 2>, - winograd_input_transform_4x4_fp32_process_tile<true, 0, 0, 0, 3>, - winograd_input_transform_4x4_fp32_process_tile<true, 0, 0, 0, 4>, -}; - -template class InputTransform<3, 3, 4, 4, float>; -} // namespace winograd diff --git a/src/core/NEON/kernels/convolution/winograd/transforms/input_6x6_fp32.cpp b/src/core/NEON/kernels/convolution/winograd/transforms/input_6x6_fp32.cpp deleted file mode 100644 index 893122cc45..0000000000 --- a/src/core/NEON/kernels/convolution/winograd/transforms/input_6x6_fp32.cpp +++ /dev/null @@ -1,376 +0,0 @@ -/* - * 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_6x6_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 -) -{ - const int pad_top = Specialized ? PadTop : _pad_top; - const int pad_left = Specialized ? PadLeft : _pad_left; - const int pad_bottom = Specialized ? PadBottom : _pad_bottom; - const int pad_right = Specialized ? PadRight : _pad_right; - - constexpr int inner_tile_rows = 6; - constexpr int inner_tile_cols = 6; - - const int cells_i = inner_tile_rows - pad_bottom; - 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_rows][inner_tile_cols]; - for (int i = pad_top, xi = 0; i < cells_i; i++, xi++) - { - // Get a pointer into the row - const float* const row_ptr = input_base + xi*input_row_stride; - - for (int j = pad_left, xj = 0; j < cells_j; j++, xj++) - { - x_ptrs[i][j] = row_ptr + xj*input_col_stride; - } - } - - // Matrices used/computed in this kernel. - float x[inner_tile_rows][inner_tile_cols]; - float XTx[inner_tile_rows][inner_tile_cols]; - float U[inner_tile_rows][inner_tile_cols]; - for (int i = 0; i < inner_tile_rows; i++) - { - for (int j = 0; j < inner_tile_cols; j++) - { - x[i][j] = XTx[i][j] = 0.0f; - } - } - - // Perform the Winograd input transformation for each channel in the input - // tensor. - int channels_remaining = n_channels; -#ifdef __aarch64__ - for (; channels_remaining >= 4; channels_remaining -= 4) - { - // Matrices used/computed in this kernel - float32x4_t x[inner_tile_rows][inner_tile_cols]; - float32x4_t XTx[inner_tile_rows][inner_tile_cols]; - float32x4_t U[inner_tile_rows][inner_tile_cols]; - for (int i = 0; i < inner_tile_rows; i++) - { - for (int j = 0; j < inner_tile_cols; j++) - { - x[i][j] = vdupq_n_f32(0.0f); - XTx[i][j] = vdupq_n_f32(0.0f); - } - } - - // Read a 6x6 tile in the Winograd domain - for (int i = pad_top; i < cells_i; i++) - { - for (int j = pad_left; j < cells_j; j++) - { - x[i][j] = vld1q_f32(x_ptrs[i][j]); - x_ptrs[i][j] += 4; - } - } - - // Compute XT . x - for (int j = pad_left; j < cells_j; j++) - { - // XTx[0][j] = 4*x[0][j] + -5*x[2][j] + 1*x[4][j]; - XTx[0][j] = vmlsq_n_f32(vmlaq_n_f32(x[4][j], x[0][j], 4.0f), x[2][j], 5.0f); - - // XTx[1][j] = -4*x[1][j] + -4*x[2][j] + 1*x[3][j] + 1*x[4][j]; - XTx[1][j] = vmlsq_n_f32(vaddq_f32(x[3][j], x[4][j]), vaddq_f32(x[1][j], x[2][j]), 4.0f); - - // XTx[2][j] = 4*x[1][j] + -4*x[2][j] + -1*x[3][j] + 1*x[4][j]; - XTx[2][j] = vmlaq_n_f32(vsubq_f32(x[4][j], x[3][j]), vsubq_f32(x[1][j], x[2][j]), 4.0f); - - // XTx[3][j] = -2*x[1][j] + -1*x[2][j] + 2*x[3][j] + 1*x[4][j]; - XTx[3][j] = vmlaq_n_f32(vsubq_f32(x[4][j], x[2][j]), vsubq_f32(x[3][j], x[1][j]), 2.0f); - - // XTx[4][j] = 2*x[1][j] + -1*x[2][j] + -2*x[3][j] + 1*x[4][j]; - XTx[4][j] = vmlaq_n_f32(vsubq_f32(x[4][j], x[2][j]), vsubq_f32(x[1][j], x[3][j]), 2.0f); - - // XTx[5][j] = 4*x[1][j] + -5*x[3][j] + 1*x[5][j]; - XTx[5][j] = vmlsq_n_f32(vmlaq_n_f32(x[5][j], x[1][j], 4.0f), x[3][j], 5.0f); - } - - // Compute U = XT . x . X - for (int i = 0; i < inner_tile_rows; i++) - { - // U[i][0] = 4*XTx[i][0] + -5*XTx[i][2] + 1*XTx[i][4]; - U[i][0] = vmlsq_n_f32(vmlaq_n_f32(XTx[i][4], XTx[i][0], 4.0f), XTx[i][2], 5.0f); - - // U[i][1] = -4*XTx[i][1] + -4*XTx[i][2] + 1*XTx[i][3] + 1*XTx[i][4]; - U[i][1] = vmlsq_n_f32(vaddq_f32(XTx[i][3], XTx[i][4]), vaddq_f32(XTx[i][1], XTx[i][2]), 4.0f); - - // U[i][2] = 4*XTx[i][1] + -4*XTx[i][2] + -1*XTx[i][3] + 1*XTx[i][4]; - U[i][2] = vmlaq_n_f32(vsubq_f32(XTx[i][4], XTx[i][3]), vsubq_f32(XTx[i][1], XTx[i][2]), 4.0f); - - // U[i][3] = -2*XTx[i][1] + -1*XTx[i][2] + 2*XTx[i][3] + 1*XTx[i][4]; - U[i][3] = vmlaq_n_f32(vsubq_f32(XTx[i][4], XTx[i][2]), vsubq_f32(XTx[i][3], XTx[i][1]), 2.0f); - - // U[i][4] = 2*XTx[i][1] + -1*XTx[i][2] + -2*XTx[i][3] + 1*XTx[i][4]; - U[i][4] = vmlaq_n_f32(vsubq_f32(XTx[i][4], XTx[i][2]), vsubq_f32(XTx[i][1], XTx[i][3]), 2.0f); - - // U[i][5] = 4*XTx[i][1] + -5*XTx[i][3] + 1*XTx[i][5]; - U[i][5] = vmlsq_n_f32(vmlaq_n_f32(XTx[i][5], XTx[i][1], 4.0f), XTx[i][3], 5.0f); - } - - // Store the transformed matrix - for (int i = 0, m = 0; i < inner_tile_rows; i++) - { - for (int j = 0; j < inner_tile_cols; j++, m++) - { - vst1q_f32(outptr + m*matrix_stride, U[i][j]); - } - } - outptr += 4; - } -#endif // __aarch64__ -#ifdef __arm_any__ - for (; channels_remaining >= 2; channels_remaining -= 2) - { - // Matrices used/computed in this kernel - float32x2_t x[inner_tile_rows][inner_tile_cols]; - float32x2_t XTx[inner_tile_rows][inner_tile_cols]; - float32x2_t U[inner_tile_rows][inner_tile_cols]; - for (int i = 0; i < inner_tile_rows; i++) - { - for (int j = 0; j < inner_tile_cols; j++) - { - x[i][j] = vdup_n_f32(0.0f); - XTx[i][j] = vdup_n_f32(0.0f); - } - } - - // Read a 6x6 tile in the Winograd domain - for (int i = pad_top; i < cells_i; i++) - { - for (int j = pad_left; j < cells_j; j++) - { - x[i][j] = vld1_f32(x_ptrs[i][j]); - x_ptrs[i][j] += 2; - } - } - - // Compute XT . x - for (int j = pad_left; j < cells_j; j++) - { - // XTx[0][j] = 4*x[0][j] + -5*x[2][j] + 1*x[4][j]; - XTx[0][j] = vmls_n_f32(vmla_n_f32(x[4][j], x[0][j], 4.0f), x[2][j], 5.0f); - - // XTx[1][j] = -4*x[1][j] + -4*x[2][j] + 1*x[3][j] + 1*x[4][j]; - XTx[1][j] = vmls_n_f32(vadd_f32(x[3][j], x[4][j]), vadd_f32(x[1][j], x[2][j]), 4.0f); - - // XTx[2][j] = 4*x[1][j] + -4*x[2][j] + -1*x[3][j] + 1*x[4][j]; - XTx[2][j] = vmla_n_f32(vsub_f32(x[4][j], x[3][j]), vsub_f32(x[1][j], x[2][j]), 4.0f); - - // XTx[3][j] = -2*x[1][j] + -1*x[2][j] + 2*x[3][j] + 1*x[4][j]; - XTx[3][j] = vmla_n_f32(vsub_f32(x[4][j], x[2][j]), vsub_f32(x[3][j], x[1][j]), 2.0f); - - // XTx[4][j] = 2*x[1][j] + -1*x[2][j] + -2*x[3][j] + 1*x[4][j]; - XTx[4][j] = vmla_n_f32(vsub_f32(x[4][j], x[2][j]), vsub_f32(x[1][j], x[3][j]), 2.0f); - - // XTx[5][j] = 4*x[1][j] + -5*x[3][j] + 1*x[5][j]; - XTx[5][j] = vmls_n_f32(vmla_n_f32(x[5][j], x[1][j], 4.0f), x[3][j], 5.0f); - } - - // Compute U = XT . x . X - for (int i = 0; i < inner_tile_rows; i++) - { - // U[i][0] = 4*XTx[i][0] + -5*XTx[i][2] + 1*XTx[i][4]; - U[i][0] = vmls_n_f32(vmla_n_f32(XTx[i][4], XTx[i][0], 4.0f), XTx[i][2], 5.0f); - - // U[i][1] = -4*XTx[i][1] + -4*XTx[i][2] + 1*XTx[i][3] + 1*XTx[i][4]; - U[i][1] = vmls_n_f32(vadd_f32(XTx[i][3], XTx[i][4]), vadd_f32(XTx[i][1], XTx[i][2]), 4.0f); - - // U[i][2] = 4*XTx[i][1] + -4*XTx[i][2] + -1*XTx[i][3] + 1*XTx[i][4]; - U[i][2] = vmla_n_f32(vsub_f32(XTx[i][4], XTx[i][3]), vsub_f32(XTx[i][1], XTx[i][2]), 4.0f); - - // U[i][3] = -2*XTx[i][1] + -1*XTx[i][2] + 2*XTx[i][3] + 1*XTx[i][4]; - U[i][3] = vmla_n_f32(vsub_f32(XTx[i][4], XTx[i][2]), vsub_f32(XTx[i][3], XTx[i][1]), 2.0f); - - // U[i][4] = 2*XTx[i][1] + -1*XTx[i][2] + -2*XTx[i][3] + 1*XTx[i][4]; - U[i][4] = vmla_n_f32(vsub_f32(XTx[i][4], XTx[i][2]), vsub_f32(XTx[i][1], XTx[i][3]), 2.0f); - - // U[i][5] = 4*XTx[i][1] + -5*XTx[i][3] + 1*XTx[i][5]; - U[i][5] = vmls_n_f32(vmla_n_f32(XTx[i][5], XTx[i][1], 4.0f), XTx[i][3], 5.0f); - } - - // Store the transformed matrix - for (int i = 0, m = 0; i < inner_tile_rows; i++) - { - for (int j = 0; j < inner_tile_cols; j++, m++) - { - vst1_f32(outptr + m*matrix_stride, U[i][j]); - } - } - outptr += 2; - } -#endif // __arm_any__ - for (; channels_remaining; channels_remaining--) - { - // Load x - for (int i = pad_top; i < cells_i; i++) - { - for (int j = pad_left; j < cells_j; j++) - { - x[i][j] = *(x_ptrs[i][j]++); - } - } - - // Compute XT . x - for (int j = pad_left; j < cells_j; j++) - { - XTx[0][j] = 4*x[0][j] + -5*x[2][j] + 1*x[4][j]; - XTx[1][j] = -4*x[1][j] + -4*x[2][j] + 1*x[3][j] + 1*x[4][j]; - XTx[2][j] = 4*x[1][j] + -4*x[2][j] + -1*x[3][j] + 1*x[4][j]; - XTx[3][j] = -2*x[1][j] + -1*x[2][j] + 2*x[3][j] + 1*x[4][j]; - XTx[4][j] = 2*x[1][j] + -1*x[2][j] + -2*x[3][j] + 1*x[4][j]; - XTx[5][j] = 4*x[1][j] + -5*x[3][j] + 1*x[5][j]; - } - - // Compute U = XT . x . X - for (int i = 0; i < inner_tile_rows; i++) - { - U[i][0] = 4*XTx[i][0] + -5*XTx[i][2] + 1*XTx[i][4]; - U[i][1] = -4*XTx[i][1] + -4*XTx[i][2] + 1*XTx[i][3] + 1*XTx[i][4]; - U[i][2] = 4*XTx[i][1] + -4*XTx[i][2] + -1*XTx[i][3] + 1*XTx[i][4]; - U[i][3] = -2*XTx[i][1] + -1*XTx[i][2] + 2*XTx[i][3] + 1*XTx[i][4]; - U[i][4] = 2*XTx[i][1] + -1*XTx[i][2] + -2*XTx[i][3] + 1*XTx[i][4]; - U[i][5] = 4*XTx[i][1] + -5*XTx[i][3] + 1*XTx[i][5]; - } - - // Store the transformed matrix - for (int i = 0, m = 0; i < inner_tile_rows; i++) - { - for (int j = 0; j < inner_tile_cols; j++, m++) - { - *(outptr + m*matrix_stride) = U[i][j]; - } - } - outptr++; - } -} -} - -namespace winograd -{ -template <int k> -using Tiles = InputTransformImplTiles<k, k, 6, 6, float>; - -template <> -const Tiles<3>::TileFn Tiles<3>::tilefn_generic = winograd_input_transform_6x6_fp32_process_tile<false>; - -template <> -const Tiles<3>::TileFn Tiles<3>::tilefn_unpadded = winograd_input_transform_6x6_fp32_process_tile<true>; - -template <> -const Tiles<3>::TileFn Tiles<3>::tilefn_top_padded[n_pad_top] = { - winograd_input_transform_6x6_fp32_process_tile<true, 1, 0, 0, 0>, -}; - -template <> -const Tiles<3>::TileFn Tiles<3>::tilefn_left_padded[n_pad_left] = { - winograd_input_transform_6x6_fp32_process_tile<true, 0, 1, 0, 0>, -}; - -template <> -const Tiles<3>::TileFn Tiles<3>::tilefn_bottom_padded[n_pad_bottom] = { - winograd_input_transform_6x6_fp32_process_tile<true, 0, 0, 1, 0>, - winograd_input_transform_6x6_fp32_process_tile<true, 0, 0, 2, 0>, - winograd_input_transform_6x6_fp32_process_tile<true, 0, 0, 3, 0>, - winograd_input_transform_6x6_fp32_process_tile<true, 0, 0, 4, 0>, - winograd_input_transform_6x6_fp32_process_tile<true, 0, 0, 5, 0>, - winograd_input_transform_6x6_fp32_process_tile<true, 0, 0, 6, 0>, -}; - -template <> -const Tiles<3>::TileFn Tiles<3>::tilefn_right_padded[n_pad_right] = { - winograd_input_transform_6x6_fp32_process_tile<true, 0, 0, 0, 1>, - winograd_input_transform_6x6_fp32_process_tile<true, 0, 0, 0, 2>, - winograd_input_transform_6x6_fp32_process_tile<true, 0, 0, 0, 3>, - winograd_input_transform_6x6_fp32_process_tile<true, 0, 0, 0, 4>, - winograd_input_transform_6x6_fp32_process_tile<true, 0, 0, 0, 5>, - winograd_input_transform_6x6_fp32_process_tile<true, 0, 0, 0, 6>, -}; - -template <> -const Tiles<5>::TileFn Tiles<5>::tilefn_generic = winograd_input_transform_6x6_fp32_process_tile<false>; - - -template <> -const Tiles<5>::TileFn Tiles<5>::tilefn_unpadded = winograd_input_transform_6x6_fp32_process_tile<true>; - - -template <> -const Tiles<5>::TileFn Tiles<5>::tilefn_top_padded[n_pad_top] = { - winograd_input_transform_6x6_fp32_process_tile<true, 2, 0, 0, 0>, -}; - -template <> -const Tiles<5>::TileFn Tiles<5>::tilefn_left_padded[n_pad_left] = { - winograd_input_transform_6x6_fp32_process_tile<true, 0, 2, 0, 0>, -}; - -template <> -const Tiles<5>::TileFn Tiles<5>::tilefn_bottom_padded[n_pad_bottom] = { - winograd_input_transform_6x6_fp32_process_tile<true, 0, 0, 1, 0>, - winograd_input_transform_6x6_fp32_process_tile<true, 0, 0, 2, 0>, - winograd_input_transform_6x6_fp32_process_tile<true, 0, 0, 3, 0>, - winograd_input_transform_6x6_fp32_process_tile<true, 0, 0, 4, 0>, - winograd_input_transform_6x6_fp32_process_tile<true, 0, 0, 5, 0>, - winograd_input_transform_6x6_fp32_process_tile<true, 0, 0, 6, 0>, -}; - -template <> -const Tiles<5>::TileFn Tiles<5>::tilefn_right_padded[n_pad_right] = { - winograd_input_transform_6x6_fp32_process_tile<true, 0, 0, 0, 1>, - winograd_input_transform_6x6_fp32_process_tile<true, 0, 0, 0, 2>, - winograd_input_transform_6x6_fp32_process_tile<true, 0, 0, 0, 3>, - winograd_input_transform_6x6_fp32_process_tile<true, 0, 0, 0, 4>, - winograd_input_transform_6x6_fp32_process_tile<true, 0, 0, 0, 5>, - winograd_input_transform_6x6_fp32_process_tile<true, 0, 0, 0, 6>, -}; - -template class InputTransform<3, 3, 6, 6, float>; -template class InputTransform<5, 5, 6, 6, float>; -} diff --git a/src/core/NEON/kernels/convolution/winograd/transforms/output_2_7_fp32.cpp b/src/core/NEON/kernels/convolution/winograd/transforms/output_2_7_fp32.cpp deleted file mode 100644 index ea842a45ee..0000000000 --- a/src/core/NEON/kernels/convolution/winograd/transforms/output_2_7_fp32.cpp +++ /dev/null @@ -1,163 +0,0 @@ -/* - * 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/output.hpp" -#include "arm_compute/core/NEON/kernels/convolution/winograd/winograd_output_transform.hpp" -#include "arm_compute/core/NEON/kernels/convolution/common/arm.hpp" - -namespace -{ - -template <bool Specialized, int PadRight=0> -void winograd_output_transform_2_7_fp32_process_tile( - const int n_channels, - const float* const matrix_base, - const int matrix_stride, - const float* const biases, - float* const output, - const int output_row_stride, - const int output_col_stride, - const int _pad_bottom, - const int _pad_right -) -{ - (void) output_row_stride; - (void) _pad_bottom; - constexpr int output_tile_cols = 2; - constexpr int inner_tile_cols = 8; - - const int pad_right = Specialized ? PadRight : _pad_right; - const int cells_j = output_tile_cols - pad_right; - - - // Construct a map to the output cells - float *outptrs[cells_j]; - for (int j = 0; j < cells_j; j++) - { - outptrs[j] = output + j*output_col_stride; - } - const float *inptr = matrix_base; - const float *bptr = biases; - - // For each channel of the output - int channels_remaining = n_channels; -#ifdef __arm_any__ - for (; channels_remaining >= 4; channels_remaining -= 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 (int j = 0; 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(vmlaq_n_f32(vmulq_n_f32(F[7], 1), F[2], 1), F[6], 3), F[4], 2), F[3], -2), F[5], -3), F[1], -1); - - // Write out the output tile - if (bptr != 0) - { - b = vld1q_f32(bptr); - bptr += 4; - } - for (int j = 0; j < cells_j; j++) - { - vst1q_f32(outptrs[j], f[j] + b); - outptrs[j] += 4; - } - } - for (; channels_remaining >= 2; channels_remaining -= 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 (int j = 0; 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(vmla_n_f32(vmul_n_f32(F[7], 1), F[2], 1), F[6], 3), F[4], 2), F[3], -2), F[5], -3), F[1], -1); - - // Write out the output tile - if (bptr != 0) - { - b = vld1_f32(bptr); - bptr += 2; - } - for (int j = 0; j < cells_j; j++) - { - vst1_f32(outptrs[j], f[j] + b); - outptrs[j] += 2; - } - } -#endif // __arm_any__ - for (; channels_remaining; channels_remaining--) - { - // 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 (int j = 0; 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[7]*1; - - // Write out the output tile - if (bptr != 0) - { - b = *(bptr++); - } - for (int j = 0; j < cells_j; j++) - { - *(outptrs[j]++) = f[j] + b; - } - } -} -} // namespace (anonymous) - -namespace winograd -{ -using Tiles = OutputTransformImplTiles<1, 7, 1, 8, float>; - -template <> -const Tiles::TileFn Tiles::tilefn_unpadded = winograd_output_transform_2_7_fp32_process_tile<true>; - -template <> -const Tiles::TileFn Tiles::tilefn_right_padded[n_pad_right] = { - winograd_output_transform_2_7_fp32_process_tile<true, 1> -}; - -template class OutputTransform<1, 7, 1, 8, float>; -template class OutputTransform<7, 1, 8, 1, float>; -} // namespace winograd diff --git a/src/core/NEON/kernels/convolution/winograd/transforms/output_2x2_3x3_fp32.cpp b/src/core/NEON/kernels/convolution/winograd/transforms/output_2x2_3x3_fp32.cpp deleted file mode 100644 index 597b074026..0000000000 --- a/src/core/NEON/kernels/convolution/winograd/transforms/output_2x2_3x3_fp32.cpp +++ /dev/null @@ -1,375 +0,0 @@ -/* - * 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/output.hpp" -#include "arm_compute/core/NEON/kernels/convolution/winograd/winograd_output_transform.hpp" -#include "arm_compute/core/NEON/kernels/convolution/common/arm.hpp" - -namespace -{ - -template <bool Specialized, int PadBottom=0, int PadRight=0> -void winograd_output_transform_2x2_3x3_fp32_process_tile( - const int n_channels, - const float* const matrix_base, - const int matrix_stride, - const float* const biases, - float* const output, - const int output_row_stride, - const int output_col_stride, - const int _pad_bottom, - const int _pad_right -) -{ - constexpr int OutputTileRows = 2, OutputTileCols = 2; - const int pad_bottom = Specialized ? PadBottom : _pad_bottom; - const int pad_right = Specialized ? PadRight : _pad_right; - - const int cells_i = OutputTileRows - pad_bottom; - const int cells_j = OutputTileCols - pad_right; - - // Construct a map to the output cells - float *outptrs[OutputTileRows][OutputTileCols]; - for (int i = 0; i < cells_i; i++) - { - for (int j = 0; j < cells_j; j++) - { - outptrs[i][j] = output + i*output_row_stride + j*output_col_stride; - } - } - const float *inptr = matrix_base; - const float *bptr = biases; - - if (bptr) - { - // For each channel of the output - int channels_remaining = n_channels; -#ifdef __aarch64__ - for (; channels_remaining >= 4; channels_remaining -= 4) - { - // Matrices used and computed during this transform - float32x4_t F[4][4], FZ[4][2], f[2][2], b; - - // Read a 4x4 tile in the Winograd domain - for (int i = 0, m = 0; i < 4; i++) - { - for (int j = 0; j < 4; j++, m++) - { - F[i][j] = vld1q_f32(inptr + m*matrix_stride); - } - } - inptr += 4; - - // Compute the matrix F Z - for (int i = 0; i < 4; i++) - { - // FZ[i][0] = F[i][0] + F[i][1] + F[i][2]; - FZ[i][0] = vaddq_f32(vaddq_f32(F[i][0], F[i][1]), F[i][2]); - - // FZ[i][1] = F[i][1] - F[i][2] - F[i][3]; - FZ[i][1] = vsubq_f32(vsubq_f32(F[i][1], F[i][2]), F[i][3]); - } - - // Compute the output tile f = ZT F Z - for (int j = 0; j < 2; j++) - { - // f[0][j] = FZ[0][j] + FZ[1][j] + FZ[2][j]; - f[0][j] = vaddq_f32(vaddq_f32(FZ[0][j], FZ[1][j]), FZ[2][j]); - - // f[1][j] = FZ[1][j] - FZ[2][j] - FZ[3][j]; - f[1][j] = vsubq_f32(vsubq_f32(FZ[1][j], FZ[2][j]), FZ[3][j]); - } - - // Load the bias vector - b = vld1q_f32(bptr); - bptr += 4; - - // Write out the output tile - for (int i = 0; i < cells_i; i++) - { - for (int j = 0; j < cells_j; j++) - { - vst1q_f32(outptrs[i][j], vaddq_f32(f[i][j], b)); - outptrs[i][j] += 4; - } - } - } -#endif // __aarch64__ -#ifdef __arm_any__ - for (; channels_remaining >= 2; channels_remaining -= 2) - { - // Matrices used and computed during this transform - float32x2_t F[4][4], FZ[4][2], f[2][2], b; - - // Read a 4x4 tile in the Winograd domain - for (int i = 0, m = 0; i < 4; i++) - { - for (int j = 0; j < 4; j++, m++) - { - F[i][j] = vld1_f32(inptr + m*matrix_stride); - } - } - inptr += 2; - - // Compute the matrix F Z - for (int i = 0; i < 4; i++) - { - // FZ[i][0] = F[i][0] + F[i][1] + F[i][2]; - FZ[i][0] = vadd_f32(vadd_f32(F[i][0], F[i][1]), F[i][2]); - - // FZ[i][1] = F[i][1] - F[i][2] - F[i][3]; - FZ[i][1] = vsub_f32(vsub_f32(F[i][1], F[i][2]), F[i][3]); - } - - // Compute the output tile f = ZT F Z - for (int j = 0; j < 2; j++) - { - // f[0][j] = FZ[0][j] + FZ[1][j] + FZ[2][j]; - f[0][j] = vadd_f32(vadd_f32(FZ[0][j], FZ[1][j]), FZ[2][j]); - - // f[1][j] = FZ[1][j] - FZ[2][j] - FZ[3][j]; - f[1][j] = vsub_f32(vsub_f32(FZ[1][j], FZ[2][j]), FZ[3][j]); - } - - // Load the bias vector - b = vld1_f32(bptr); - bptr += 2; - - // Write out the output tile - for (int i = 0; i < cells_i; i++) - { - for (int j = 0; j < cells_j; j++) - { - vst1_f32(outptrs[i][j], vadd_f32(f[i][j], b)); - outptrs[i][j] += 2; - } - } - } -#endif // __arm_any__ - for (; channels_remaining; channels_remaining--) - { - // Matrices used and computed during this transform - float F[4][4], FZ[4][2], f[2][2], b; - - // Read a 4x4 tile in the Winograd domain - for (int i = 0, m = 0; i < 4; i++) - { - for (int j = 0; j < 4; j++, m++) - { - F[i][j] = *(inptr + m*matrix_stride); - } - } - inptr++; - - // Compute the matrix F Z - for (int i = 0; i < 4; i++) - { - FZ[i][0] = F[i][0] + F[i][1] + F[i][2]; - FZ[i][1] = F[i][1] - F[i][2] - F[i][3]; - } - - // Compute the output tile f = ZT F Z - for (int j = 0; j < 2; j++) - { - f[0][j] = FZ[0][j] + FZ[1][j] + FZ[2][j]; - f[1][j] = FZ[1][j] - FZ[2][j] - FZ[3][j]; - } - - // Load the bias - b = *(bptr++); - - // Write out the output tile - for (int i = 0; i < cells_i; i++) - { - for (int j = 0; j < cells_j; j++) - { - *(outptrs[i][j]++) = f[i][j] + b; - } - } - } - } - else - { - // For each channel of the output - int channels_remaining = n_channels; -#ifdef __aarch64__ - for (; channels_remaining >= 4; channels_remaining -= 4) - { - // Matrices used and computed during this transform - float32x4_t F[4][4], FZ[4][2], f[2][2]; - - // Read a 4x4 tile in the Winograd domain - for (int i = 0, m = 0; i < 4; i++) - { - for (int j = 0; j < 4; j++, m++) - { - F[i][j] = vld1q_f32(inptr + m*matrix_stride); - } - } - inptr += 4; - - // Compute the matrix F Z - for (int i = 0; i < 4; i++) - { - // FZ[i][0] = F[i][0] + F[i][1] + F[i][2]; - FZ[i][0] = vaddq_f32(vaddq_f32(F[i][0], F[i][1]), F[i][2]); - - // FZ[i][1] = F[i][1] - F[i][2] - F[i][3]; - FZ[i][1] = vsubq_f32(vsubq_f32(F[i][1], F[i][2]), F[i][3]); - } - - // Compute the output tile f = ZT F Z - for (int j = 0; j < 2; j++) - { - // f[0][j] = FZ[0][j] + FZ[1][j] + FZ[2][j]; - f[0][j] = vaddq_f32(vaddq_f32(FZ[0][j], FZ[1][j]), FZ[2][j]); - - // f[1][j] = FZ[1][j] - FZ[2][j] - FZ[3][j]; - f[1][j] = vsubq_f32(vsubq_f32(FZ[1][j], FZ[2][j]), FZ[3][j]); - } - - // Write out the output tile - for (int i = 0; i < cells_i; i++) - { - for (int j = 0; j < cells_j; j++) - { - vst1q_f32(outptrs[i][j], f[i][j]); - outptrs[i][j] += 4; - } - } - } -#endif // __aarch64__ -#ifdef __arm_any__ - for (; channels_remaining >= 2; channels_remaining -= 2) - { - // Matrices used and computed during this transform - float32x2_t F[4][4], FZ[4][2], f[2][2]; - - // Read a 4x4 tile in the Winograd domain - for (int i = 0, m = 0; i < 4; i++) - { - for (int j = 0; j < 4; j++, m++) - { - F[i][j] = vld1_f32(inptr + m*matrix_stride); - } - } - inptr += 2; - - // Compute the matrix F Z - for (int i = 0; i < 4; i++) - { - // FZ[i][0] = F[i][0] + F[i][1] + F[i][2]; - FZ[i][0] = vadd_f32(vadd_f32(F[i][0], F[i][1]), F[i][2]); - - // FZ[i][1] = F[i][1] - F[i][2] - F[i][3]; - FZ[i][1] = vsub_f32(vsub_f32(F[i][1], F[i][2]), F[i][3]); - } - - // Compute the output tile f = ZT F Z - for (int j = 0; j < 2; j++) - { - // f[0][j] = FZ[0][j] + FZ[1][j] + FZ[2][j]; - f[0][j] = vadd_f32(vadd_f32(FZ[0][j], FZ[1][j]), FZ[2][j]); - - // f[1][j] = FZ[1][j] - FZ[2][j] - FZ[3][j]; - f[1][j] = vsub_f32(vsub_f32(FZ[1][j], FZ[2][j]), FZ[3][j]); - } - - // Write out the output tile - for (int i = 0; i < cells_i; i++) - { - for (int j = 0; j < cells_j; j++) - { - vst1_f32(outptrs[i][j], f[i][j]); - outptrs[i][j] += 2; - } - } - } -#endif // __arm_any__ - for (; channels_remaining; channels_remaining--) - { - // Matrices used and computed during this transform - float F[4][4], FZ[4][2], f[2][2]; - - // Read a 4x4 tile in the Winograd domain - for (int i = 0, m = 0; i < 4; i++) - { - for (int j = 0; j < 4; j++, m++) - { - F[i][j] = *(inptr + m*matrix_stride); - } - } - inptr++; - - // Compute the matrix F Z - for (int i = 0; i < 4; i++) - { - FZ[i][0] = F[i][0] + F[i][1] + F[i][2]; - FZ[i][1] = F[i][1] - F[i][2] - F[i][3]; - } - - // Compute the output tile f = ZT F Z - for (int j = 0; j < 2; j++) - { - f[0][j] = FZ[0][j] + FZ[1][j] + FZ[2][j]; - f[1][j] = FZ[1][j] - FZ[2][j] - FZ[3][j]; - } - - // Write out the output tile - for (int i = 0; i < cells_i; i++) - { - for (int j = 0; j < cells_j; j++) - { - *(outptrs[i][j]++) = f[i][j]; - } - } - } - } -} - -} // namespace (anonymous) - -namespace winograd -{ -using Tiles = OutputTransformImplTiles<3, 3, 4, 4, float>; - -template <> -const Tiles::TileFn Tiles::tilefn_generic = winograd_output_transform_2x2_3x3_fp32_process_tile<false>; - -template <> -const Tiles::TileFn Tiles::tilefn_unpadded = winograd_output_transform_2x2_3x3_fp32_process_tile<true>; - -template <> -const Tiles::TileFn Tiles::tilefn_bottom_padded[n_pad_bottom] = { - winograd_output_transform_2x2_3x3_fp32_process_tile<true, 1, 0> -}; - -template <> -const Tiles::TileFn Tiles::tilefn_right_padded[n_pad_right] = { - winograd_output_transform_2x2_3x3_fp32_process_tile<true, 0, 1> -}; - -template class OutputTransform<3, 3, 4, 4, float>; -} // namespace winograd - diff --git a/src/core/NEON/kernels/convolution/winograd/transforms/output_2x2_5x5_fp32.cpp b/src/core/NEON/kernels/convolution/winograd/transforms/output_2x2_5x5_fp32.cpp deleted file mode 100644 index 60d7181d97..0000000000 --- a/src/core/NEON/kernels/convolution/winograd/transforms/output_2x2_5x5_fp32.cpp +++ /dev/null @@ -1,369 +0,0 @@ -/* - * 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/output.hpp" -#include "arm_compute/core/NEON/kernels/convolution/winograd/winograd_output_transform.hpp" -#include "arm_compute/core/NEON/kernels/convolution/common/arm.hpp" - -namespace -{ - -template <bool Specialized, int PadBottom=0, int PadRight=0> -void winograd_output_transform_2x2_5x5_fp32_process_tile( - const int n_channels, - const float* const matrix_base, - const int matrix_stride, - const float* const biases, - float* const output, - const int output_row_stride, - const int output_col_stride, - const int _pad_bottom, - const int _pad_right -) -{ - constexpr int OutputTileRows = 2, OutputTileCols = 2; - const int pad_bottom = Specialized ? PadBottom : _pad_bottom; - const int pad_right = Specialized ? PadRight : _pad_right; - - const int cells_i = 2 - pad_bottom; - const int cells_j = 2 - pad_right; - - // Construct a map to the output cells - float *outptrs[OutputTileRows][OutputTileCols]; - for (int i = 0; i < cells_i; i++) - { - for (int j = 0; j < cells_j; j++) - { - outptrs[i][j] = output + i*output_row_stride + j*output_col_stride; - } - } - const float *inptr = matrix_base; - const float *bptr = biases; - - if (bptr) - { - // For each channel of the output - int channels_remaining = n_channels; -#ifdef __aarch64__ - for (; channels_remaining >= 4; channels_remaining -= 4) - { - // Matrices used and computed during this transform - float32x4_t F[6][6], FZ[6][2], f[2][2], b; - - // Read a 6x6 tile in the Winograd domain - for (int i = 0, m = 0; i < 6; i++) - { - for (int j = 0; j < 6; j++, m++) - { - F[i][j] = vld1q_f32(inptr + m*matrix_stride); - } - } - inptr += 4; - - // Compute the matrix F Z - for (int i = 0; i < 6; i++) - { - // FZ[i][0] = 1*F[i][0] + 1*F[i][1] + 1*F[i][2] + 1*F[i][3] + 1*F[i][4]; - FZ[i][0] = vaddq_f32(vaddq_f32(vaddq_f32(F[i][0], F[i][1]), vaddq_f32(F[i][2], F[i][3])), F[i][4]); - - // FZ[i][1] = 1*F[i][1] + -1*F[i][2] + 2*F[i][3] + -2*F[i][4] + 1*F[i][5]; - FZ[i][1] = vaddq_f32(vmlaq_n_f32(vsubq_f32(F[i][1], F[i][2]), vsubq_f32(F[i][3], F[i][4]), 2.0f), F[i][5]); - } - - // Compute the output tile f = ZT F Z - for (int j = 0; j < 2; j++) - { - // f[0][j] = 1*FZ[0][j] + 1*FZ[1][j] + 1*FZ[2][j] + 1*FZ[3][j] + 1*FZ[4][j]; - f[0][j] = vaddq_f32(vaddq_f32(vaddq_f32(FZ[0][j], FZ[1][j]), vaddq_f32(FZ[2][j], FZ[3][j])), FZ[4][j]); - - // f[1][j] = 1*FZ[1][j] + -1*FZ[2][j] + 2*FZ[3][j] + -2*FZ[4][j] + 1*FZ[5][j]; - f[1][j] = vaddq_f32(vmlaq_n_f32(vsubq_f32(FZ[1][j], FZ[2][j]), vsubq_f32(FZ[3][j], FZ[4][j]), 2.0f), FZ[5][j]); - } - - // Write out the output tile - b = vld1q_f32(bptr); - bptr += 4; - for (int i = 0; i < cells_i; i++) - { - for (int j = 0; j < cells_j; j++) - { - vst1q_f32(outptrs[i][j], vaddq_f32(f[i][j], b)); - outptrs[i][j] += 4; - } - } - } -#endif // __aarch64__ -#ifdef __arm_any__ - for (; channels_remaining >= 2; channels_remaining -= 2) - { - // Matrices used and computed during this transform - float32x2_t F[6][6], FZ[6][2], f[2][2], b; - - // Read a 6x6 tile in the Winograd domain - for (int i = 0, m = 0; i < 6; i++) - { - for (int j = 0; j < 6; j++, m++) - { - F[i][j] = vld1_f32(inptr + m*matrix_stride); - } - } - inptr += 2; - - // Compute the matrix F Z - for (int i = 0; i < 6; i++) - { - // FZ[i][0] = 1*F[i][0] + 1*F[i][1] + 1*F[i][2] + 1*F[i][3] + 1*F[i][4]; - FZ[i][0] = vadd_f32(vadd_f32(vadd_f32(F[i][0], F[i][1]), vadd_f32(F[i][2], F[i][3])), F[i][4]); - - // FZ[i][1] = 1*F[i][1] + -1*F[i][2] + 2*F[i][3] + -2*F[i][4] + 1*F[i][5]; - FZ[i][1] = vadd_f32(vmla_n_f32(vsub_f32(F[i][1], F[i][2]), vsub_f32(F[i][3], F[i][4]), 2.0f), F[i][5]); - } - - // Compute the output tile f = ZT F Z - for (int j = 0; j < 2; j++) - { - // f[0][j] = 1*FZ[0][j] + 1*FZ[1][j] + 1*FZ[2][j] + 1*FZ[3][j] + 1*FZ[4][j]; - f[0][j] = vadd_f32(vadd_f32(vadd_f32(FZ[0][j], FZ[1][j]), vadd_f32(FZ[2][j], FZ[3][j])), FZ[4][j]); - - // f[1][j] = 1*FZ[1][j] + -1*FZ[2][j] + 2*FZ[3][j] + -2*FZ[4][j] + 1*FZ[5][j]; - f[1][j] = vadd_f32(vmla_n_f32(vsub_f32(FZ[1][j], FZ[2][j]), vsub_f32(FZ[3][j], FZ[4][j]), 2.0f), FZ[5][j]); - } - - // Write out the output tile - b = vld1_f32(bptr); - bptr += 2; - for (int i = 0; i < cells_i; i++) - { - for (int j = 0; j < cells_j; j++) - { - vst1_f32(outptrs[i][j], vadd_f32(f[i][j], b)); - outptrs[i][j] += 2; - } - } - } -#endif // __arm_any__ - for (; channels_remaining; channels_remaining--) - { - // Matrices used and computed during this transform - float F[6][6], FZ[6][2], f[2][2], b; - - // Read a 6x6 tile in the Winograd domain - for (int i = 0, m = 0; i < 6; i++) - { - for (int j = 0; j < 6; j++, m++) - { - F[i][j] = *(inptr + m*matrix_stride); - } - } - inptr++; - - // Compute the matrix F Z - for (int i = 0; i < 6; i++) - { - FZ[i][0] = 1*F[i][0] + 1*F[i][1] + 1*F[i][2] + 1*F[i][3] + 1*F[i][4]; - FZ[i][1] = 1*F[i][1] + -1*F[i][2] + 2*F[i][3] + -2*F[i][4] + 1*F[i][5]; - } - - // Compute the output tile f = ZT F Z - for (int j = 0; j < 2; j++) - { - f[0][j] = 1*FZ[0][j] + 1*FZ[1][j] + 1*FZ[2][j] + 1*FZ[3][j] + 1*FZ[4][j]; - f[1][j] = 1*FZ[1][j] + -1*FZ[2][j] + 2*FZ[3][j] + -2*FZ[4][j] + 1*FZ[5][j]; - } - - // Write out the output tile - b = *(bptr++); - for (int i = 0; i < cells_i; i++) - { - for (int j = 0; j < cells_j; j++) - { - *(outptrs[i][j]++) = f[i][j] + b; - } - } - } - } - else - { - // For each channel of the output - int channels_remaining = n_channels; -#ifdef __aarch64__ - for (; channels_remaining >= 4; channels_remaining -= 4) - { - // Matrices used and computed during this transform - float32x4_t F[6][6], FZ[6][2], f[2][2]; - - // Read a 6x6 tile in the Winograd domain - for (int i = 0, m = 0; i < 6; i++) - { - for (int j = 0; j < 6; j++, m++) - { - F[i][j] = vld1q_f32(inptr + m*matrix_stride); - } - } - inptr += 4; - - // Compute the matrix F Z - for (int i = 0; i < 6; i++) - { - // FZ[i][0] = 1*F[i][0] + 1*F[i][1] + 1*F[i][2] + 1*F[i][3] + 1*F[i][4]; - FZ[i][0] = vaddq_f32(vaddq_f32(vaddq_f32(F[i][0], F[i][1]), vaddq_f32(F[i][2], F[i][3])), F[i][4]); - - // FZ[i][1] = 1*F[i][1] + -1*F[i][2] + 2*F[i][3] + -2*F[i][4] + 1*F[i][5]; - FZ[i][1] = vaddq_f32(vmlaq_n_f32(vsubq_f32(F[i][1], F[i][2]), vsubq_f32(F[i][3], F[i][4]), 2.0f), F[i][5]); - } - - // Compute the output tile f = ZT F Z - for (int j = 0; j < 2; j++) - { - // f[0][j] = 1*FZ[0][j] + 1*FZ[1][j] + 1*FZ[2][j] + 1*FZ[3][j] + 1*FZ[4][j]; - f[0][j] = vaddq_f32(vaddq_f32(vaddq_f32(FZ[0][j], FZ[1][j]), vaddq_f32(FZ[2][j], FZ[3][j])), FZ[4][j]); - - // f[1][j] = 1*FZ[1][j] + -1*FZ[2][j] + 2*FZ[3][j] + -2*FZ[4][j] + 1*FZ[5][j]; - f[1][j] = vaddq_f32(vmlaq_n_f32(vsubq_f32(FZ[1][j], FZ[2][j]), vsubq_f32(FZ[3][j], FZ[4][j]), 2.0f), FZ[5][j]); - } - - // Write out the output tile - for (int i = 0; i < cells_i; i++) - { - for (int j = 0; j < cells_j; j++) - { - vst1q_f32(outptrs[i][j], f[i][j]); - outptrs[i][j] += 4; - } - } - } -#endif // __aarch64__ -#ifdef __arm_any__ - for (; channels_remaining >= 2; channels_remaining -= 2) - { - // Matrices used and computed during this transform - float32x2_t F[6][6], FZ[6][2], f[2][2]; - - // Read a 6x6 tile in the Winograd domain - for (int i = 0, m = 0; i < 6; i++) - { - for (int j = 0; j < 6; j++, m++) - { - F[i][j] = vld1_f32(inptr + m*matrix_stride); - } - } - inptr += 2; - - // Compute the matrix F Z - for (int i = 0; i < 6; i++) - { - // FZ[i][0] = 1*F[i][0] + 1*F[i][1] + 1*F[i][2] + 1*F[i][3] + 1*F[i][4]; - FZ[i][0] = vadd_f32(vadd_f32(vadd_f32(F[i][0], F[i][1]), vadd_f32(F[i][2], F[i][3])), F[i][4]); - - // FZ[i][1] = 1*F[i][1] + -1*F[i][2] + 2*F[i][3] + -2*F[i][4] + 1*F[i][5]; - FZ[i][1] = vadd_f32(vmla_n_f32(vsub_f32(F[i][1], F[i][2]), vsub_f32(F[i][3], F[i][4]), 2.0f), F[i][5]); - } - - // Compute the output tile f = ZT F Z - for (int j = 0; j < 2; j++) - { - // f[0][j] = 1*FZ[0][j] + 1*FZ[1][j] + 1*FZ[2][j] + 1*FZ[3][j] + 1*FZ[4][j]; - f[0][j] = vadd_f32(vadd_f32(vadd_f32(FZ[0][j], FZ[1][j]), vadd_f32(FZ[2][j], FZ[3][j])), FZ[4][j]); - - // f[1][j] = 1*FZ[1][j] + -1*FZ[2][j] + 2*FZ[3][j] + -2*FZ[4][j] + 1*FZ[5][j]; - f[1][j] = vadd_f32(vmla_n_f32(vsub_f32(FZ[1][j], FZ[2][j]), vsub_f32(FZ[3][j], FZ[4][j]), 2.0f), FZ[5][j]); - } - - // Write out the output tile - for (int i = 0; i < cells_i; i++) - { - for (int j = 0; j < cells_j; j++) - { - vst1_f32(outptrs[i][j], f[i][j]); - outptrs[i][j] += 2; - } - } - } -#endif // __arm_any__ - for (; channels_remaining; channels_remaining--) - { - // Matrices used and computed during this transform - float F[6][6], FZ[6][2], f[2][2]; - - // Read a 6x6 tile in the Winograd domain - for (int i = 0, m = 0; i < 6; i++) - { - for (int j = 0; j < 6; j++, m++) - { - F[i][j] = *(inptr + m*matrix_stride); - } - } - inptr++; - - // Compute the matrix F Z - for (int i = 0; i < 6; i++) - { - FZ[i][0] = 1*F[i][0] + 1*F[i][1] + 1*F[i][2] + 1*F[i][3] + 1*F[i][4]; - FZ[i][1] = 1*F[i][1] + -1*F[i][2] + 2*F[i][3] + -2*F[i][4] + 1*F[i][5]; - } - - // Compute the output tile f = ZT F Z - for (int j = 0; j < 2; j++) - { - f[0][j] = 1*FZ[0][j] + 1*FZ[1][j] + 1*FZ[2][j] + 1*FZ[3][j] + 1*FZ[4][j]; - f[1][j] = 1*FZ[1][j] + -1*FZ[2][j] + 2*FZ[3][j] + -2*FZ[4][j] + 1*FZ[5][j]; - } - - // Write out the output tile - for (int i = 0; i < cells_i; i++) - { - for (int j = 0; j < cells_j; j++) - { - *(outptrs[i][j]++) = f[i][j]; - } - } - } - } -} - -} // namespace (anonymous) - -namespace winograd -{ -using Tiles = OutputTransformImplTiles<5, 5, 6, 6, float>; - -template <> -const Tiles::TileFn Tiles::tilefn_generic = winograd_output_transform_2x2_5x5_fp32_process_tile<false>; - -template <> -const Tiles::TileFn Tiles::tilefn_unpadded = winograd_output_transform_2x2_5x5_fp32_process_tile<true>; - -template <> -const Tiles::TileFn Tiles::tilefn_bottom_padded[n_pad_bottom] = { - winograd_output_transform_2x2_5x5_fp32_process_tile<true, 1, 0> -}; - -template <> -const Tiles::TileFn Tiles::tilefn_right_padded[n_pad_right] = { - winograd_output_transform_2x2_5x5_fp32_process_tile<true, 0, 1> -}; - -template class OutputTransform<5, 5, 6, 6, float>; -} // namespace winograd - diff --git a/src/core/NEON/kernels/convolution/winograd/transforms/output_4_5_fp32.cpp b/src/core/NEON/kernels/convolution/winograd/transforms/output_4_5_fp32.cpp deleted file mode 100644 index 911759b128..0000000000 --- a/src/core/NEON/kernels/convolution/winograd/transforms/output_4_5_fp32.cpp +++ /dev/null @@ -1,171 +0,0 @@ -/* - * 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/output.hpp" -#include "arm_compute/core/NEON/kernels/convolution/winograd/winograd_output_transform.hpp" -#include "arm_compute/core/NEON/kernels/convolution/common/arm.hpp" - -namespace -{ - -template <bool Specialized, int PadRight=0> -void winograd_output_transform_4_5_fp32_process_tile( - const int n_channels, - const float* const matrix_base, - const int matrix_stride, - const float* const biases, - float* const output, - const int output_row_stride, - const int output_col_stride, - const int _pad_bottom, - const int _pad_right -) -{ - (void) output_row_stride; - (void) _pad_bottom; - constexpr int output_tile_cols = 4; - constexpr int inner_tile_cols = 8; - - const int pad_right = Specialized ? PadRight : _pad_right; - const int cells_j = output_tile_cols - pad_right; - - // Construct a map to the output cells - float *outptrs[cells_j]; - for (int j = 0; j < cells_j; j++) - { - outptrs[j] = output + j*output_col_stride; - } - const float *inptr = matrix_base; - const float *bptr = biases; - - // For each channel of the output - int channels_remaining = n_channels; -#ifdef __arm_any__ - for (; channels_remaining >= 4; channels_remaining -= 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 (int j = 0; 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 (int j = 0; j < cells_j; j++) - { - vst1q_f32(outptrs[j], f[j] + b); - outptrs[j] += 4; - } - } - for (; channels_remaining >= 2; channels_remaining -= 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 (int j = 0; 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 (int j = 0; j < cells_j; j++) - { - vst1_f32(outptrs[j], f[j] + b); - outptrs[j] += 2; - } - } -#endif // __arm_any__ - for (; channels_remaining; channels_remaining--) - { - // 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 (int j = 0; 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 (int j = 0; j < cells_j; j++) - { - *(outptrs[j]++) = f[j] + b; - } - } -} - -} // namespace (anonymous) - -namespace winograd -{ -using Tiles = OutputTransformImplTiles<1, 5, 1, 8, float>; - -template <> -const Tiles::TileFn Tiles::tilefn_unpadded = winograd_output_transform_4_5_fp32_process_tile<true>; - -template <> -const Tiles::TileFn Tiles::tilefn_right_padded[n_pad_right] = { - winograd_output_transform_4_5_fp32_process_tile<true, 1>, - winograd_output_transform_4_5_fp32_process_tile<true, 2>, - winograd_output_transform_4_5_fp32_process_tile<true, 3> -}; - -template class OutputTransform<1, 5, 1, 8, float>; -template class OutputTransform<5, 1, 8, 1, float>; -} // namespace winograd diff --git a/src/core/NEON/kernels/convolution/winograd/transforms/output_4x4_3x3_fp32.cpp b/src/core/NEON/kernels/convolution/winograd/transforms/output_4x4_3x3_fp32.cpp deleted file mode 100644 index 15cc04b352..0000000000 --- a/src/core/NEON/kernels/convolution/winograd/transforms/output_4x4_3x3_fp32.cpp +++ /dev/null @@ -1,428 +0,0 @@ -/* - * 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/output.hpp" -#include "arm_compute/core/NEON/kernels/convolution/winograd/winograd_output_transform.hpp" -#include "arm_compute/core/NEON/kernels/convolution/common/arm.hpp" - -namespace -{ - -template <bool Specialized, int PadBottom=0, int PadRight=0> -void winograd_output_transform_4x4_3x3_fp32_process_tile( - const int n_channels, - const float* const matrix_base, - const int matrix_stride, - const float* const biases, - float* const output, - const int output_row_stride, - const int output_col_stride, - const int _pad_bottom, - const int _pad_right -) -{ - const int pad_bottom = Specialized ? PadBottom : _pad_bottom; - const int pad_right = Specialized ? PadRight : _pad_right; - constexpr int TileRows = 4, TileCols = 4; - - const int cells_i = TileRows - pad_bottom; - const int cells_j = TileCols - pad_right; - - // Construct a map to the output cells - float *outptrs[TileRows][TileCols]; - for (int i = 0; i < cells_i; i++) - { - for (int j = 0; j < cells_j; j++) - { - outptrs[i][j] = output + i*output_row_stride + j*output_col_stride; - } - } - const float *inptr = matrix_base; - const float *bptr = biases; - - if (bptr) - { - // For each channel of the output - int channels_remaining = n_channels; -#ifdef __aarch64__ - for (; channels_remaining >= 4; channels_remaining -= 4) - { - // Matrices used and computed during this transform - float32x4_t F[6][6], FZ[6][4], f[4][4], b; - - // Read a 6x6 tile in the Winograd domain - for (int i = 0, m = 0; i < 6; i++) - { - for (int j = 0; j < 6; j++, m++) - { - F[i][j] = vld1q_f32(inptr + m*matrix_stride); - } - } - inptr += 4; - - // Compute the matrix F Z - for (int i = 0; i < 6; i++) - { - // FZ[i][0] = 1*F[i][0] + 1*F[i][1] + 1*F[i][2] + 1*F[i][3] + 1*F[i][4]; - FZ[i][0] = vaddq_f32(vaddq_f32(vaddq_f32(F[i][0], F[i][1]), vaddq_f32(F[i][2], F[i][3])), F[i][4]); - - // FZ[i][1] = 1*F[i][1] + -1*F[i][2] + 2*F[i][3] + -2*F[i][4]; - FZ[i][1] = vmlaq_n_f32(vsubq_f32(F[i][1], F[i][2]), vsubq_f32(F[i][3], F[i][4]), 2.0f); - - // FZ[i][2] = 1*F[i][1] + 1*F[i][2] + 4*F[i][3] + 4*F[i][4]; - FZ[i][2] = vmlaq_n_f32(vaddq_f32(F[i][1], F[i][2]), vaddq_f32(F[i][3], F[i][4]), 4.0f); - - // FZ[i][3] = 1*F[i][1] + -1*F[i][2] + 8*F[i][3] + -8*F[i][4] + 1*F[i][5]; - FZ[i][3] = vaddq_f32(vmlaq_n_f32(vsubq_f32(F[i][1], F[i][2]), vsubq_f32(F[i][3], F[i][4]), 8.0f), F[i][5]); - } - - // Compute the output tile f = ZT F Z - for (int j = 0; j < 4; j++) - { - // f[0][j] = 1*FZ[0][j] + 1*FZ[1][j] + 1*FZ[2][j] + 1*FZ[3][j] + 1*FZ[4][j]; - f[0][j] = vaddq_f32(vaddq_f32(vaddq_f32(FZ[0][j], FZ[1][j]), vaddq_f32(FZ[2][j], FZ[3][j])), FZ[4][j]); - - // f[1][j] = 1*FZ[1][j] + -1*FZ[2][j] + 2*FZ[3][j] + -2*FZ[4][j]; - f[1][j] = vmlaq_n_f32(vsubq_f32(FZ[1][j], FZ[2][j]), vsubq_f32(FZ[3][j], FZ[4][j]), 2.0f); - - // f[2][j] = 1*FZ[1][j] + 1*FZ[2][j] + 4*FZ[3][j] + 4*FZ[4][j]; - f[2][j] = vmlaq_n_f32(vaddq_f32(FZ[1][j], FZ[2][j]), vaddq_f32(FZ[3][j], FZ[4][j]), 4.0f); - - // f[3][j] = 1*FZ[1][j] + -1*FZ[2][j] + 8*FZ[3][j] + -8*FZ[4][j] + 1*FZ[5][j]; - f[3][j] = vaddq_f32(vmlaq_n_f32(vsubq_f32(FZ[1][j], FZ[2][j]), vsubq_f32(FZ[3][j], FZ[4][j]), 8.0f), FZ[5][j]); - } - - // Write out the output tile - b = vld1q_f32(bptr); - bptr += 4; - for (int i = 0; i < cells_i; i++) - { - for (int j = 0; j < cells_j; j++) - { - vst1q_f32(outptrs[i][j], vaddq_f32(f[i][j], b)); - outptrs[i][j] += 4; - } - } - } -#endif // __aarch64__ -#ifdef __arm_any__ - for (; channels_remaining >= 2; channels_remaining -= 2) - { - // Matrices used and computed during this transform - float32x2_t F[6][6], FZ[6][4], f[4][4], b; - - // Read a 6x6 tile in the Winograd domain - for (int i = 0, m = 0; i < 6; i++) - { - for (int j = 0; j < 6; j++, m++) - { - F[i][j] = vld1_f32(inptr + m*matrix_stride); - } - } - inptr += 2; - - // Compute the matrix F Z - for (int i = 0; i < 6; i++) - { - // FZ[i][0] = 1*F[i][0] + 1*F[i][1] + 1*F[i][2] + 1*F[i][3] + 1*F[i][4]; - FZ[i][0] = vadd_f32(vadd_f32(vadd_f32(F[i][0], F[i][1]), vadd_f32(F[i][2], F[i][3])), F[i][4]); - - // FZ[i][1] = 1*F[i][1] + -1*F[i][2] + 2*F[i][3] + -2*F[i][4]; - FZ[i][1] = vmla_n_f32(vsub_f32(F[i][1], F[i][2]), vsub_f32(F[i][3], F[i][4]), 2.0f); - - // FZ[i][2] = 1*F[i][1] + 1*F[i][2] + 4*F[i][3] + 4*F[i][4]; - FZ[i][2] = vmla_n_f32(vadd_f32(F[i][1], F[i][2]), vadd_f32(F[i][3], F[i][4]), 4.0f); - - // FZ[i][3] = 1*F[i][1] + -1*F[i][2] + 8*F[i][3] + -8*F[i][4] + 1*F[i][5]; - FZ[i][3] = vadd_f32(vmla_n_f32(vsub_f32(F[i][1], F[i][2]), vsub_f32(F[i][3], F[i][4]), 8.0f), F[i][5]); - } - - // Compute the output tile f = ZT F Z - for (int j = 0; j < 4; j++) - { - // f[0][j] = 1*FZ[0][j] + 1*FZ[1][j] + 1*FZ[2][j] + 1*FZ[3][j] + 1*FZ[4][j]; - f[0][j] = vadd_f32(vadd_f32(vadd_f32(FZ[0][j], FZ[1][j]), vadd_f32(FZ[2][j], FZ[3][j])), FZ[4][j]); - - // f[1][j] = 1*FZ[1][j] + -1*FZ[2][j] + 2*FZ[3][j] + -2*FZ[4][j]; - f[1][j] = vmla_n_f32(vsub_f32(FZ[1][j], FZ[2][j]), vsub_f32(FZ[3][j], FZ[4][j]), 2.0f); - - // f[2][j] = 1*FZ[1][j] + 1*FZ[2][j] + 4*FZ[3][j] + 4*FZ[4][j]; - f[2][j] = vmla_n_f32(vadd_f32(FZ[1][j], FZ[2][j]), vadd_f32(FZ[3][j], FZ[4][j]), 4.0f); - - // f[3][j] = 1*FZ[1][j] + -1*FZ[2][j] + 8*FZ[3][j] + -8*FZ[4][j] + 1*FZ[5][j]; - f[3][j] = vadd_f32(vmla_n_f32(vsub_f32(FZ[1][j], FZ[2][j]), vsub_f32(FZ[3][j], FZ[4][j]), 8.0f), FZ[5][j]); - } - - // Write out the output tile - b = vld1_f32(bptr); - bptr += 2; - for (int i = 0; i < cells_i; i++) - { - for (int j = 0; j < cells_j; j++) - { - vst1_f32(outptrs[i][j], vadd_f32(f[i][j], b)); - outptrs[i][j] += 2; - } - } - } -#endif - for (; channels_remaining; channels_remaining--) - { - // Matrices used and computed during this transform - float F[6][6], FZ[6][4], f[4][4], b; - - // Read a 6x6 tile in the Winograd domain - for (int i = 0, m = 0; i < 6; i++) - { - for (int j = 0; j < 6; j++, m++) - { - F[i][j] = *(inptr + m*matrix_stride); - } - } - inptr++; - - // Compute the matrix F Z - for (int i = 0; i < 6; i++) - { - FZ[i][0] = 1*F[i][0] + 1*F[i][1] + 1*F[i][2] + 1*F[i][3] + 1*F[i][4]; - FZ[i][1] = 1*F[i][1] + -1*F[i][2] + 2*F[i][3] + -2*F[i][4]; - FZ[i][2] = 1*F[i][1] + 1*F[i][2] + 4*F[i][3] + 4*F[i][4]; - FZ[i][3] = 1*F[i][1] + -1*F[i][2] + 8*F[i][3] + -8*F[i][4] + 1*F[i][5]; - } - - // Compute the output tile f = ZT F Z - for (int j = 0; j < 4; j++) - { - f[0][j] = 1*FZ[0][j] + 1*FZ[1][j] + 1*FZ[2][j] + 1*FZ[3][j] + 1*FZ[4][j]; - f[1][j] = 1*FZ[1][j] + -1*FZ[2][j] + 2*FZ[3][j] + -2*FZ[4][j]; - f[2][j] = 1*FZ[1][j] + 1*FZ[2][j] + 4*FZ[3][j] + 4*FZ[4][j]; - f[3][j] = 1*FZ[1][j] + -1*FZ[2][j] + 8*FZ[3][j] + -8*FZ[4][j] + 1*FZ[5][j]; - } - - // Write out the output tile - b = *(bptr++); - for (int i = 0; i < cells_i; i++) - { - for (int j = 0; j < cells_j; j++) - { - *(outptrs[i][j]++) = f[i][j] + b; - } - } - } - } - else - { - // For each channel of the output - int channels_remaining = n_channels; -#ifdef __aarch64__ - for (; channels_remaining >= 4; channels_remaining -= 4) - { - // Matrices used and computed during this transform - float32x4_t F[6][6], FZ[6][4], f[4][4]; - - // Read a 6x6 tile in the Winograd domain - for (int i = 0, m = 0; i < 6; i++) - { - for (int j = 0; j < 6; j++, m++) - { - F[i][j] = vld1q_f32(inptr + m*matrix_stride); - } - } - inptr += 4; - - // Compute the matrix F Z - for (int i = 0; i < 6; i++) - { - // FZ[i][0] = 1*F[i][0] + 1*F[i][1] + 1*F[i][2] + 1*F[i][3] + 1*F[i][4]; - FZ[i][0] = vaddq_f32(vaddq_f32(vaddq_f32(F[i][0], F[i][1]), vaddq_f32(F[i][2], F[i][3])), F[i][4]); - - // FZ[i][1] = 1*F[i][1] + -1*F[i][2] + 2*F[i][3] + -2*F[i][4]; - FZ[i][1] = vmlaq_n_f32(vsubq_f32(F[i][1], F[i][2]), vsubq_f32(F[i][3], F[i][4]), 2.0f); - - // FZ[i][2] = 1*F[i][1] + 1*F[i][2] + 4*F[i][3] + 4*F[i][4]; - FZ[i][2] = vmlaq_n_f32(vaddq_f32(F[i][1], F[i][2]), vaddq_f32(F[i][3], F[i][4]), 4.0f); - - // FZ[i][3] = 1*F[i][1] + -1*F[i][2] + 8*F[i][3] + -8*F[i][4] + 1*F[i][5]; - FZ[i][3] = vaddq_f32(vmlaq_n_f32(vsubq_f32(F[i][1], F[i][2]), vsubq_f32(F[i][3], F[i][4]), 8.0f), F[i][5]); - } - - // Compute the output tile f = ZT F Z - for (int j = 0; j < 4; j++) - { - // f[0][j] = 1*FZ[0][j] + 1*FZ[1][j] + 1*FZ[2][j] + 1*FZ[3][j] + 1*FZ[4][j]; - f[0][j] = vaddq_f32(vaddq_f32(vaddq_f32(FZ[0][j], FZ[1][j]), vaddq_f32(FZ[2][j], FZ[3][j])), FZ[4][j]); - - // f[1][j] = 1*FZ[1][j] + -1*FZ[2][j] + 2*FZ[3][j] + -2*FZ[4][j]; - f[1][j] = vmlaq_n_f32(vsubq_f32(FZ[1][j], FZ[2][j]), vsubq_f32(FZ[3][j], FZ[4][j]), 2.0f); - - // f[2][j] = 1*FZ[1][j] + 1*FZ[2][j] + 4*FZ[3][j] + 4*FZ[4][j]; - f[2][j] = vmlaq_n_f32(vaddq_f32(FZ[1][j], FZ[2][j]), vaddq_f32(FZ[3][j], FZ[4][j]), 4.0f); - - // f[3][j] = 1*FZ[1][j] + -1*FZ[2][j] + 8*FZ[3][j] + -8*FZ[4][j] + 1*FZ[5][j]; - f[3][j] = vaddq_f32(vmlaq_n_f32(vsubq_f32(FZ[1][j], FZ[2][j]), vsubq_f32(FZ[3][j], FZ[4][j]), 8.0f), FZ[5][j]); - } - - // Write out the output tile - for (int i = 0; i < cells_i; i++) - { - for (int j = 0; j < cells_j; j++) - { - vst1q_f32(outptrs[i][j], f[i][j]); - outptrs[i][j] += 4; - } - } - } -#endif // __aarch64__ -#ifdef __arm_any__ - for (; channels_remaining >= 2; channels_remaining -= 2) - { - // Matrices used and computed during this transform - float32x2_t F[6][6], FZ[6][4], f[4][4]; - - // Read a 6x6 tile in the Winograd domain - for (int i = 0, m = 0; i < 6; i++) - { - for (int j = 0; j < 6; j++, m++) - { - F[i][j] = vld1_f32(inptr + m*matrix_stride); - } - } - inptr += 2; - - // Compute the matrix F Z - for (int i = 0; i < 6; i++) - { - // FZ[i][0] = 1*F[i][0] + 1*F[i][1] + 1*F[i][2] + 1*F[i][3] + 1*F[i][4]; - FZ[i][0] = vadd_f32(vadd_f32(vadd_f32(F[i][0], F[i][1]), vadd_f32(F[i][2], F[i][3])), F[i][4]); - - // FZ[i][1] = 1*F[i][1] + -1*F[i][2] + 2*F[i][3] + -2*F[i][4]; - FZ[i][1] = vmla_n_f32(vsub_f32(F[i][1], F[i][2]), vsub_f32(F[i][3], F[i][4]), 2.0f); - - // FZ[i][2] = 1*F[i][1] + 1*F[i][2] + 4*F[i][3] + 4*F[i][4]; - FZ[i][2] = vmla_n_f32(vadd_f32(F[i][1], F[i][2]), vadd_f32(F[i][3], F[i][4]), 4.0f); - - // FZ[i][3] = 1*F[i][1] + -1*F[i][2] + 8*F[i][3] + -8*F[i][4] + 1*F[i][5]; - FZ[i][3] = vadd_f32(vmla_n_f32(vsub_f32(F[i][1], F[i][2]), vsub_f32(F[i][3], F[i][4]), 8.0f), F[i][5]); - } - - // Compute the output tile f = ZT F Z - for (int j = 0; j < 4; j++) - { - // f[0][j] = 1*FZ[0][j] + 1*FZ[1][j] + 1*FZ[2][j] + 1*FZ[3][j] + 1*FZ[4][j]; - f[0][j] = vadd_f32(vadd_f32(vadd_f32(FZ[0][j], FZ[1][j]), vadd_f32(FZ[2][j], FZ[3][j])), FZ[4][j]); - - // f[1][j] = 1*FZ[1][j] + -1*FZ[2][j] + 2*FZ[3][j] + -2*FZ[4][j]; - f[1][j] = vmla_n_f32(vsub_f32(FZ[1][j], FZ[2][j]), vsub_f32(FZ[3][j], FZ[4][j]), 2.0f); - - // f[2][j] = 1*FZ[1][j] + 1*FZ[2][j] + 4*FZ[3][j] + 4*FZ[4][j]; - f[2][j] = vmla_n_f32(vadd_f32(FZ[1][j], FZ[2][j]), vadd_f32(FZ[3][j], FZ[4][j]), 4.0f); - - // f[3][j] = 1*FZ[1][j] + -1*FZ[2][j] + 8*FZ[3][j] + -8*FZ[4][j] + 1*FZ[5][j]; - f[3][j] = vadd_f32(vmla_n_f32(vsub_f32(FZ[1][j], FZ[2][j]), vsub_f32(FZ[3][j], FZ[4][j]), 8.0f), FZ[5][j]); - } - - // Write out the output tile - for (int i = 0; i < cells_i; i++) - { - for (int j = 0; j < cells_j; j++) - { - vst1_f32(outptrs[i][j], f[i][j]); - outptrs[i][j] += 2; - } - } - } -#endif - for (; channels_remaining; channels_remaining--) - { - // Matrices used and computed during this transform - float F[6][6], FZ[6][4], f[4][4]; - - // Read a 6x6 tile in the Winograd domain - for (int i = 0, m = 0; i < 6; i++) - { - for (int j = 0; j < 6; j++, m++) - { - F[i][j] = *(inptr + m*matrix_stride); - } - } - inptr++; - - // Compute the matrix F Z - for (int i = 0; i < 6; i++) - { - FZ[i][0] = 1*F[i][0] + 1*F[i][1] + 1*F[i][2] + 1*F[i][3] + 1*F[i][4]; - FZ[i][1] = 1*F[i][1] + -1*F[i][2] + 2*F[i][3] + -2*F[i][4]; - FZ[i][2] = 1*F[i][1] + 1*F[i][2] + 4*F[i][3] + 4*F[i][4]; - FZ[i][3] = 1*F[i][1] + -1*F[i][2] + 8*F[i][3] + -8*F[i][4] + 1*F[i][5]; - } - - // Compute the output tile f = ZT F Z - for (int j = 0; j < 4; j++) - { - f[0][j] = 1*FZ[0][j] + 1*FZ[1][j] + 1*FZ[2][j] + 1*FZ[3][j] + 1*FZ[4][j]; - f[1][j] = 1*FZ[1][j] + -1*FZ[2][j] + 2*FZ[3][j] + -2*FZ[4][j]; - f[2][j] = 1*FZ[1][j] + 1*FZ[2][j] + 4*FZ[3][j] + 4*FZ[4][j]; - f[3][j] = 1*FZ[1][j] + -1*FZ[2][j] + 8*FZ[3][j] + -8*FZ[4][j] + 1*FZ[5][j]; - } - - // Write out the output tile - for (int i = 0; i < cells_i; i++) - { - for (int j = 0; j < cells_j; j++) - { - *(outptrs[i][j]++) = f[i][j]; - } - } - } - } -} - -} // namespace (anonymous) - -namespace winograd -{ -using Tiles = OutputTransformImplTiles<3, 3, 6, 6, float>; - -template <> -const Tiles::TileFn Tiles::tilefn_generic = winograd_output_transform_4x4_3x3_fp32_process_tile<false>; - -template <> -const Tiles::TileFn Tiles::tilefn_unpadded = winograd_output_transform_4x4_3x3_fp32_process_tile<true>; - -template <> -const Tiles::TileFn Tiles::tilefn_bottom_padded[n_pad_bottom] = { - winograd_output_transform_4x4_3x3_fp32_process_tile<true, 1, 0>, - winograd_output_transform_4x4_3x3_fp32_process_tile<true, 2, 0>, - winograd_output_transform_4x4_3x3_fp32_process_tile<true, 3, 0>, -}; - -template <> -const Tiles::TileFn Tiles::tilefn_right_padded[n_pad_right] = { - winograd_output_transform_4x4_3x3_fp32_process_tile<true, 0, 1>, - winograd_output_transform_4x4_3x3_fp32_process_tile<true, 0, 2>, - winograd_output_transform_4x4_3x3_fp32_process_tile<true, 0, 3>, -}; - -template class OutputTransform<3, 3, 6, 6, float>; -} // namespace winograd diff --git a/src/core/NEON/kernels/convolution/winograd/transforms/output_6_3_fp32.cpp b/src/core/NEON/kernels/convolution/winograd/transforms/output_6_3_fp32.cpp deleted file mode 100644 index 58bed71a47..0000000000 --- a/src/core/NEON/kernels/convolution/winograd/transforms/output_6_3_fp32.cpp +++ /dev/null @@ -1,179 +0,0 @@ -/* - * 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/output.hpp" -#include "arm_compute/core/NEON/kernels/convolution/winograd/winograd_output_transform.hpp" -#include "arm_compute/core/NEON/kernels/convolution/common/arm.hpp" - -namespace -{ - -template <bool Specialized, int PadRight=0> -void winograd_output_transform_6_3_fp32_process_tile( - const int n_channels, - const float* const matrix_base, - const int matrix_stride, - const float* const biases, - float* const output, - const int output_row_stride, - const int output_col_stride, - const int _pad_bottom, - const int _pad_right -) -{ - (void) output_row_stride; - (void) _pad_bottom; - constexpr int output_tile_cols = 6; - constexpr int inner_tile_cols = 8; - - const int pad_right = Specialized ? PadRight : _pad_right; - const int cells_j = output_tile_cols - pad_right; - - // Construct a map to the output cells - float *outptrs[cells_j]; - for (int j = 0; j < cells_j; j++) - { - outptrs[j] = output + j*output_col_stride; - } - const float *inptr = matrix_base; - const float *bptr = biases; - - // For each channel of the output - int channels_remaining = n_channels; -#ifdef __arm_any__ - for (; channels_remaining >= 4; channels_remaining -= 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 (int j = 0; 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(vmulq_n_f32(F[2], 1), F[6], 27), F[4], 8), F[3], -8), F[5], -27), F[1], -1); - f[4] = 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], 81), F[5], 81), F[4], 16), F[3], 16); - f[5] = 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], 243), F[4], 32), F[3], -32), F[5], -243), F[1], -1); - - // Write out the output tile - if (bptr != 0) - { - b = vld1q_f32(bptr); - bptr += 4; - } - for (int j = 0; j < cells_j; j++) - { - vst1q_f32(outptrs[j], f[j] + b); - outptrs[j] += 4; - } - } - for (; channels_remaining >= 2; channels_remaining -= 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 (int j = 0; 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(vmul_n_f32(F[2], 1), F[6], 27), F[4], 8), F[3], -8), F[5], -27), F[1], -1); - f[4] = 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], 81), F[5], 81), F[4], 16), F[3], 16); - f[5] = 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], 243), F[4], 32), F[3], -32), F[5], -243), F[1], -1); - - // Write out the output tile - if (bptr != 0) - { - b = vld1_f32(bptr); - bptr += 2; - } - for (int j = 0; j < cells_j; j++) - { - vst1_f32(outptrs[j], f[j] + b); - outptrs[j] += 2; - } - } -#endif // __arm_any__ - for (; channels_remaining; channels_remaining--) - { - // 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 (int j = 0; 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[4] = F[3]*16 + F[4]*16 + F[5]*81 + F[6]*81 + F[1]*1 + F[2]*1; - f[5] = F[1]*-1 + F[5]*-243 + F[3]*-32 + F[4]*32 + F[6]*243 + F[2]*1 + F[7]*1; - - // Write out the output tile - if (bptr != 0) - { - b = *(bptr++); - } - for (int j = 0; j < cells_j; j++) - { - *(outptrs[j]++) = f[j] + b; - } - } -} - -} // namespace (anonymous) - -namespace winograd -{ -using Tiles = OutputTransformImplTiles<1, 3, 1, 8, float>; - -template <> -const Tiles::TileFn Tiles::tilefn_unpadded = winograd_output_transform_6_3_fp32_process_tile<true>; - -template <> -const Tiles::TileFn Tiles::tilefn_right_padded[n_pad_right] = { - winograd_output_transform_6_3_fp32_process_tile<true, 1>, - winograd_output_transform_6_3_fp32_process_tile<true, 2>, - winograd_output_transform_6_3_fp32_process_tile<true, 3>, - winograd_output_transform_6_3_fp32_process_tile<true, 4>, - winograd_output_transform_6_3_fp32_process_tile<true, 5>, -}; - -template class OutputTransform<1, 3, 1, 8, float>; -template class OutputTransform<3, 1, 8, 1, float>; -} // namespace winograd diff --git a/src/core/NEON/kernels/convolution/winograd/transforms/weights_2_7_fp32.cpp b/src/core/NEON/kernels/convolution/winograd/transforms/weights_2_7_fp32.cpp deleted file mode 100644 index 85cf418656..0000000000 --- a/src/core/NEON/kernels/convolution/winograd/transforms/weights_2_7_fp32.cpp +++ /dev/null @@ -1,124 +0,0 @@ -/* - * 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/common/arm.hpp" -#include "arm_compute/core/NEON/kernels/convolution/winograd/winograd_gemm.hpp" -#include "arm_compute/core/NEON/kernels/convolution/winograd/transforms/kernel.hpp" - -namespace winograd -{ - template <> - template <> - void WinogradGEMM<1, 2, 1, 7>::WeightsTransform<float>::execute( - const int n_output_channels, - const int n_input_channels, - const float* const input, // NOTE: Data in HWIO order - float* 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 float *inptrs[kernel_cols]; - for (int j = 0; j < kernel_cols; j++) - { - inptrs[j] = input + j*weight_col_stride; - } - - // For each input channel - for (int ic = 0; ic < n_input_channels; ic++) - { - float *outptr = output + ic * matrix_row_stride; - - // For each output channel - int channels_remaining = n_output_channels; - for (; channels_remaining; channels_remaining--) - { - // Matrices used and computed in this kernel - float w[kernel_cols], V[inner_tile_cols]; - - // Read weights - for (int j = 0; j < kernel_cols; j++) - { - w[j] = *(inptrs[j]++); - } - - // Compute V = w WT - V[0] = (w[0]*-1) / 36.0f; - V[1] = (w[1]*-1 + w[3]*-1 + w[5]*-1 + w[0]*1 + w[2]*1 + w[4]*1 + w[6]*1) / 48.0f; - V[2] = (w[0]*1 + w[1]*1 + w[2]*1 + w[3]*1 + w[4]*1 + w[5]*1 + w[6]*1) / 48.0f; - V[3] = (w[0]*-1 + w[6]*-64 + w[4]*-16 + w[2]*-4 + w[1]*2 + w[3]*8 + w[5]*32) / 120.0f; - V[4] = (w[0]*-1 + w[6]*-64 + w[5]*-32 + w[4]*-16 + w[3]*-8 + w[2]*-4 + w[1]*-2) / 120.0f; - V[5] = (w[5]*-243 + w[3]*-27 + w[1]*-3 + w[2]*9 + w[4]*81 + w[6]*729 + w[0]*1) / 720.0f; - V[6] = (w[1]*3 + w[2]*9 + w[3]*27 + w[4]*81 + w[5]*243 + w[6]*729 + w[0]*1) / 720.0f; - V[7] = (w[6]*1) / 1.0f; - - // Store the transformed weights - for (int j = 0; j < inner_tile_cols; j++) - { - *(outptr + j*matrix_stride) = V[j]; - } - outptr++; - } - } - } - - template <> - template <> - int WinogradGEMM<1, 2, 1, 7>::WeightsTransform<float>::ops_performed(const KernelShape &shape) - { - (void) shape; - return 0; // TODO - } - - template <> - template <> - void WinogradGEMM<2, 1, 7, 1>::WeightsTransform<float>::execute( - const int n_output_channels, - const int n_input_channels, - const float* const input, // NOTE: Data in HWIO order - float* const output, - const int matrix_stride, - const int matrix_row_stride - ) - { - // Redirect to the 1xN implementation - WinogradGEMM<1, 2, 1, 7>::template WeightsTransform<float>::execute( - n_output_channels, n_input_channels, input, output, matrix_stride, - matrix_row_stride - ); - } - - template <> - template <> - int WinogradGEMM<2, 1, 7, 1>::WeightsTransform<float>::ops_performed(const KernelShape &shape) - { - (void) shape; - return 0; // TODO - } - - template struct WinogradGEMM<1, 2, 1, 7>::WeightsTransform<float>; - template struct WinogradGEMM<2, 1, 7, 1>::WeightsTransform<float>; -} diff --git a/src/core/NEON/kernels/convolution/winograd/transforms/weights_2x2_3x3_fp32.cpp b/src/core/NEON/kernels/convolution/winograd/transforms/weights_2x2_3x3_fp32.cpp deleted file mode 100644 index 6c71461f81..0000000000 --- a/src/core/NEON/kernels/convolution/winograd/transforms/weights_2x2_3x3_fp32.cpp +++ /dev/null @@ -1,228 +0,0 @@ -/* - * 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/common/arm.hpp" -#include "arm_compute/core/NEON/kernels/convolution/winograd/winograd_gemm.hpp" -#include "arm_compute/core/NEON/kernels/convolution/winograd/transforms/kernel.hpp" - -namespace winograd -{ - template <> - template <> - void WinogradGEMM<2, 2, 3, 3>::WeightsTransform<float>::execute( - const int n_output_channels, - const int n_input_channels, - const float* const input, - float* const output, - const int matrix_stride, - const int matrix_row_stride - ) - { - constexpr int inner_tile_i = 4; - constexpr int inner_tile_j = 4; - - // 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 float *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++) - { - float *outptr = output + ic * matrix_row_stride; - - // For each output channel - int channels_remaining = n_output_channels; -#ifdef __aarch64__ - for (; channels_remaining >= 4; channels_remaining -= 4) - { - // Matrices used and computed in this kernel - float32x4_t w[3][3], Ww[inner_tile_i][3], V[inner_tile_i][inner_tile_j]; - - // Read weights - for (int i = 0; i < 3; i++) - { - for (int j = 0; j < 3; j++) - { - w[i][j] = vld1q_f32(inptrs[i][j]); - inptrs[i][j] += 4; - } - } - - // Compute the matrix W w - for (int j = 0; j < 3; j++) - { - Ww[0][j] = w[0][j]; - - // Ww[1][j] = 0.5*(w[0][j] + w[1][j] + w[2][j]); - Ww[1][j] = vmulq_n_f32(vaddq_f32(vaddq_f32(w[0][j], w[1][j]), w[2][j]), 0.5f); - - // Ww[2][j] = 0.5*(w[0][j] - w[1][j] + w[2][j]); - Ww[2][j] = vmulq_n_f32(vaddq_f32(vsubq_f32(w[0][j], w[1][j]), w[2][j]), 0.5f); - - Ww[3][j] = w[2][j]; - } - - // Compute V = W w WT - for (int i = 0; i < inner_tile_i; i++) - { - V[i][0] = Ww[i][0]; - - // V[i][1] = 0.5*(Ww[i][0] + Ww[i][1] + Ww[i][2]); - V[i][1] = vmulq_n_f32(vaddq_f32(vaddq_f32(Ww[i][0], Ww[i][1]), Ww[i][2]), 0.5f); - - // V[i][2] = 0.5*(Ww[i][0] - Ww[i][1] + Ww[i][2]); - V[i][2] = vmulq_n_f32(vaddq_f32(vsubq_f32(Ww[i][0], Ww[i][1]), Ww[i][2]), 0.5f); - - V[i][3] = Ww[i][2]; - } - - // Store the transformed weights - for (int i = 0, m = 0; i < inner_tile_i; i++) - { - for (int j = 0; j < inner_tile_j; j++, m++) - { - vst1q_f32(outptr + m*matrix_stride, V[i][j]); - } - } - outptr += 4; - } -#endif // __aarch64__ -#ifdef __arm_any__ - for (; channels_remaining >= 2; channels_remaining -= 2) - { - // Matrices used and computed in this kernel - float32x2_t w[3][3], Ww[inner_tile_i][3], V[inner_tile_i][inner_tile_j]; - - // Read weights - for (int i = 0; i < 3; i++) - { - for (int j = 0; j < 3; j++) - { - w[i][j] = vld1_f32(inptrs[i][j]); - inptrs[i][j] += 2; - } - } - - // Compute the matrix W w - for (int j = 0; j < 3; j++) - { - Ww[0][j] = w[0][j]; - - // Ww[1][j] = 0.5*(w[0][j] + w[1][j] + w[2][j]); - Ww[1][j] = vmul_n_f32(vadd_f32(vadd_f32(w[0][j], w[1][j]), w[2][j]), 0.5f); - - // Ww[2][j] = 0.5*(w[0][j] - w[1][j] + w[2][j]); - Ww[2][j] = vmul_n_f32(vadd_f32(vsub_f32(w[0][j], w[1][j]), w[2][j]), 0.5f); - - Ww[3][j] = w[2][j]; - } - - // Compute V = W w WT - for (int i = 0; i < inner_tile_i; i++) - { - V[i][0] = Ww[i][0]; - - // V[i][1] = 0.5*(Ww[i][0] + Ww[i][1] + Ww[i][2]); - V[i][1] = vmul_n_f32(vadd_f32(vadd_f32(Ww[i][0], Ww[i][1]), Ww[i][2]), 0.5f); - - // V[i][2] = 0.5*(Ww[i][0] - Ww[i][1] + Ww[i][2]); - V[i][2] = vmul_n_f32(vadd_f32(vsub_f32(Ww[i][0], Ww[i][1]), Ww[i][2]), 0.5f); - - V[i][3] = Ww[i][2]; - } - - // Store the transformed weights - for (int i = 0, m = 0; i < inner_tile_i; i++) - { - for (int j = 0; j < inner_tile_j; j++, m++) - { - vst1_f32(outptr + m*matrix_stride, V[i][j]); - } - } - outptr += 2; - } -#endif // __arm_any__ - for (; channels_remaining; channels_remaining--) - { - // Matrices used and computed in this kernel - float w[3][3], Ww[inner_tile_i][3], V[inner_tile_i][inner_tile_j]; - - // 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] = w[0][j]; - Ww[1][j] = 0.5*(w[0][j] + w[1][j] + w[2][j]); - Ww[2][j] = 0.5*(w[0][j] - w[1][j] + w[2][j]); - Ww[3][j] = w[2][j]; - } - - // Compute V = W w WT - for (int i = 0; i < inner_tile_i; i++) - { - V[i][0] = Ww[i][0]; - V[i][1] = 0.5*(Ww[i][0] + Ww[i][1] + Ww[i][2]); - V[i][2] = 0.5*(Ww[i][0] - Ww[i][1] + Ww[i][2]); - V[i][3] = Ww[i][2]; - } - - // Store the transformed weights - for (int i = 0, m = 0; i < inner_tile_i; i++) - { - for (int j = 0; j < inner_tile_j; j++, m++) - { - *(outptr + m*matrix_stride) = V[i][j]; - } - } - outptr++; - } - } - } - - template <> - template <> - int WinogradGEMM<2, 2, 3, 3>::WeightsTransform<float>::ops_performed(const KernelShape &shape) - { - const int channel_prod = shape.n_input_channels * shape.n_output_channels; - return 2 * 18 * channel_prod; - } - - template struct WinogradGEMM<2, 2, 3, 3>::WeightsTransform<float>; -} // namespace winograd diff --git a/src/core/NEON/kernels/convolution/winograd/transforms/weights_2x2_5x5_fp32.cpp b/src/core/NEON/kernels/convolution/winograd/transforms/weights_2x2_5x5_fp32.cpp deleted file mode 100644 index 2f4f6e1ba2..0000000000 --- a/src/core/NEON/kernels/convolution/winograd/transforms/weights_2x2_5x5_fp32.cpp +++ /dev/null @@ -1,408 +0,0 @@ -/* - * 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/common/arm.hpp" -#include "arm_compute/core/NEON/kernels/convolution/winograd/winograd_gemm.hpp" -#include "arm_compute/core/NEON/kernels/convolution/winograd/transforms/kernel.hpp" - -namespace winograd -{ - template <> - template <> - void WinogradGEMM<2, 2, 5, 5>::WeightsTransform<float>::execute( - const int n_output_channels, - const int n_input_channels, - const float* const input, - float* 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 = 5 * weight_col_stride; - const float *inptrs[5][5]; - for (int i = 0; i < 5; i++) - { - for (int j = 0; j < 5; 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++) - { - float *outptr = output + ic * matrix_row_stride; - - // For each output channel - int channels_remaining = n_output_channels; -#ifdef __aarch64__ - for (; channels_remaining >= 4; channels_remaining -= 4) - { - // Matrices used and computed in this kernel - float32x4_t w[5][5], Ww[6][5], V[6][6]; - - // Read weights - for (int i = 0; i < 5; i++) - { - for (int j = 0; j < 5; j++) - { - w[i][j] = vld1q_f32(inptrs[i][j]); - inptrs[i][j] += 4; - } - } - - // Compute the matrix W w - for (int j = 0; j < 5; j++) - { - // Ww[0][j] = w[0][j]/4.0f; - Ww[0][j] = vmulq_n_f32(w[0][j], 1.0f/4.0f); - - // Ww[1][j] = -( w[0][j] + w[1][j] + w[2][j] + w[3][j] + w[4][j])/6.0f; - Ww[1][j] = vmulq_n_f32( - vaddq_f32( - vaddq_f32( - vaddq_f32(w[1][j], w[0][j]), - vaddq_f32(w[3][j], w[2][j]) - ), - w[4][j] - ), - -1.0f/6.0f - ); - - // Ww[2][j] = +(-w[0][j] + w[1][j] - w[2][j] + w[3][j] - w[4][j])/6.0f; - // Ww[2][j] = ((w[1][j] - w[0][j]) + (w[3][j] - w[2][j]) - w[4][j])/6.0f; - Ww[2][j] = vmulq_n_f32( - vsubq_f32( - vaddq_f32( - vsubq_f32(w[1][j], w[0][j]), - vsubq_f32(w[3][j], w[2][j]) - ), - w[4][j] - ), - 1.0f/6.0f - ); - - // Ww[3][j] = (w[0][j]/8.0f + w[1][j]/4.0f + w[2][j]/2.0f + w[3][j] + 2*w[4][j])/3.0f; - Ww[3][j] = vmulq_n_f32( - vmlaq_n_f32( - vaddq_f32( - vaddq_f32(vmulq_n_f32(w[0][j], 1.0f/8.0f), vmulq_n_f32(w[1][j], 1.0f/4.0f)), - vaddq_f32(vmulq_n_f32(w[2][j], 1.0f/2.0f), w[3][j]) - ), - w[4][j], 2.0f - ), - 1.0f/3.0f - ); - - // Ww[4][j] = (w[0][j]/8.0f - w[1][j]/4.0f + w[2][j]/2.0f - w[3][j] + 2*w[4][j])/3.0f; - Ww[4][j] = vmulq_n_f32( - vmlaq_n_f32( - vaddq_f32( - vsubq_f32(vmulq_n_f32(w[0][j], 1.0f/8.0f), vmulq_n_f32(w[1][j], 1.0f/4.0f)), - vsubq_f32(vmulq_n_f32(w[2][j], 1.0f/2.0f), w[3][j]) - ), - w[4][j], 2.0f - ), - 1.0f/3.0f - ); - - // Ww[5][j] = w[4][j]; - Ww[5][j] = w[4][j]; - } - - // Compute V = W w WT - for (int i = 0; i < 6; i++) - { - // V[i][0] = Ww[i][0]/4.0f; - V[i][0] = vmulq_n_f32(Ww[i][0], 1.0f/4.0f); - - // V[i][1] = -( Ww[i][0] + Ww[i][1] + Ww[i][2] + Ww[i][3] + Ww[i][4])/6.0f; - V[i][1] = vmulq_n_f32( - vaddq_f32( - vaddq_f32( - vaddq_f32(Ww[i][1], Ww[i][0]), - vaddq_f32(Ww[i][3], Ww[i][2]) - ), - Ww[i][4] - ), - -1.0f/6.0f - ); - - // V[i][2] = +(-Ww[i][0] + Ww[i][1] - Ww[i][2] + Ww[i][3] - Ww[i][4])/6.0f; - // V[i][2] = ((Ww[i][1] - Ww[i][0]) + (Ww[i][3] - Ww[i][2]) - Ww[i][4])/6.0f; - V[i][2] = vmulq_n_f32( - vsubq_f32( - vaddq_f32( - vsubq_f32(Ww[i][1], Ww[i][0]), - vsubq_f32(Ww[i][3], Ww[i][2]) - ), - Ww[i][4] - ), - 1.0f/6.0f - ); - - // V[i][3] = (Ww[i][0]/8.0f + Ww[i][1]/4.0f + Ww[i][2]/2.0f + Ww[i][3] + 2*Ww[i][4])/3.0f; - V[i][3] = vmulq_n_f32( - vmlaq_n_f32( - vaddq_f32( - vaddq_f32(vmulq_n_f32(Ww[i][0], 1.0f/8.0f), vmulq_n_f32(Ww[i][1], 1.0f/4.0f)), - vaddq_f32(vmulq_n_f32(Ww[i][2], 1.0f/2.0f), Ww[i][3]) - ), - Ww[i][4], 2.0f - ), - 1.0f/3.0f - ); - - // V[i][4] = (Ww[i][0]/8.0f - Ww[i][1]/4.0f + Ww[i][2]/2.0f - Ww[i][3] + 2*Ww[i][4])/3.0f; - V[i][4] = vmulq_n_f32( - vmlaq_n_f32( - vaddq_f32( - vsubq_f32(vmulq_n_f32(Ww[i][0], 1.0f/8.0f), vmulq_n_f32(Ww[i][1], 1.0f/4.0f)), - vsubq_f32(vmulq_n_f32(Ww[i][2], 1.0f/2.0f), Ww[i][3]) - ), - Ww[i][4], 2.0f - ), - 1.0f/3.0f - ); - - // V[i][5] = Ww[i][4]; - V[i][5] = Ww[i][4]; - } - - // Store the transformed weights - for (int i = 0, m = 0; i < 6; i++) - { - for (int j = 0; j < 6; j++, m++) - { - vst1q_f32(outptr + m*matrix_stride, V[i][j]); - } - } - outptr += 4; - } -#endif // __aarch64__ -#ifdef __arm_any__ - for (; channels_remaining >= 2; channels_remaining -= 2) - { - // Matrices used and computed in this kernel - float32x2_t w[5][5], Ww[6][5], V[6][6]; - - // Read weights - for (int i = 0; i < 5; i++) - { - for (int j = 0; j < 5; j++) - { - w[i][j] = vld1_f32(inptrs[i][j]); - inptrs[i][j] += 2; - } - } - - // Compute the matrix W w - for (int j = 0; j < 5; j++) - { - // Ww[0][j] = w[0][j]/4.0f; - Ww[0][j] = vmul_n_f32(w[0][j], 1.0f/4.0f); - - // Ww[1][j] = -( w[0][j] + w[1][j] + w[2][j] + w[3][j] + w[4][j])/6.0f; - Ww[1][j] = vmul_n_f32( - vadd_f32( - vadd_f32( - vadd_f32(w[1][j], w[0][j]), - vadd_f32(w[3][j], w[2][j]) - ), - w[4][j] - ), - -1.0f/6.0f - ); - - // Ww[2][j] = +(-w[0][j] + w[1][j] - w[2][j] + w[3][j] - w[4][j])/6.0f; - // Ww[2][j] = ((w[1][j] - w[0][j]) + (w[3][j] - w[2][j]) - w[4][j])/6.0f; - Ww[2][j] = vmul_n_f32( - vsub_f32( - vadd_f32( - vsub_f32(w[1][j], w[0][j]), - vsub_f32(w[3][j], w[2][j]) - ), - w[4][j] - ), - 1.0f/6.0f - ); - - // Ww[3][j] = (w[0][j]/8.0f + w[1][j]/4.0f + w[2][j]/2.0f + w[3][j] + 2*w[4][j])/3.0f; - Ww[3][j] = vmul_n_f32( - vmla_n_f32( - vadd_f32( - vadd_f32(vmul_n_f32(w[0][j], 1.0f/8.0f), vmul_n_f32(w[1][j], 1.0f/4.0f)), - vadd_f32(vmul_n_f32(w[2][j], 1.0f/2.0f), w[3][j]) - ), - w[4][j], 2.0f - ), - 1.0f/3.0f - ); - - // Ww[4][j] = (w[0][j]/8.0f - w[1][j]/4.0f + w[2][j]/2.0f - w[3][j] + 2*w[4][j])/3.0f; - Ww[4][j] = vmul_n_f32( - vmla_n_f32( - vadd_f32( - vsub_f32(vmul_n_f32(w[0][j], 1.0f/8.0f), vmul_n_f32(w[1][j], 1.0f/4.0f)), - vsub_f32(vmul_n_f32(w[2][j], 1.0f/2.0f), w[3][j]) - ), - w[4][j], 2.0f - ), - 1.0f/3.0f - ); - - // Ww[5][j] = w[4][j]; - Ww[5][j] = w[4][j]; - } - - // Compute V = W w WT - for (int i = 0; i < 6; i++) - { - // V[i][0] = Ww[i][0]/4.0f; - V[i][0] = vmul_n_f32(Ww[i][0], 1.0f/4.0f); - - // V[i][1] = -( Ww[i][0] + Ww[i][1] + Ww[i][2] + Ww[i][3] + Ww[i][4])/6.0f; - V[i][1] = vmul_n_f32( - vadd_f32( - vadd_f32( - vadd_f32(Ww[i][1], Ww[i][0]), - vadd_f32(Ww[i][3], Ww[i][2]) - ), - Ww[i][4] - ), - -1.0f/6.0f - ); - - // V[i][2] = +(-Ww[i][0] + Ww[i][1] - Ww[i][2] + Ww[i][3] - Ww[i][4])/6.0f; - // V[i][2] = ((Ww[i][1] - Ww[i][0]) + (Ww[i][3] - Ww[i][2]) - Ww[i][4])/6.0f; - V[i][2] = vmul_n_f32( - vsub_f32( - vadd_f32( - vsub_f32(Ww[i][1], Ww[i][0]), - vsub_f32(Ww[i][3], Ww[i][2]) - ), - Ww[i][4] - ), - 1.0f/6.0f - ); - - // V[i][3] = (Ww[i][0]/8.0f + Ww[i][1]/4.0f + Ww[i][2]/2.0f + Ww[i][3] + 2*Ww[i][4])/3.0f; - V[i][3] = vmul_n_f32( - vmla_n_f32( - vadd_f32( - vadd_f32(vmul_n_f32(Ww[i][0], 1.0f/8.0f), vmul_n_f32(Ww[i][1], 1.0f/4.0f)), - vadd_f32(vmul_n_f32(Ww[i][2], 1.0f/2.0f), Ww[i][3]) - ), - Ww[i][4], 2.0f - ), - 1.0f/3.0f - ); - - // V[i][4] = (Ww[i][0]/8.0f - Ww[i][1]/4.0f + Ww[i][2]/2.0f - Ww[i][3] + 2*Ww[i][4])/3.0f; - V[i][4] = vmul_n_f32( - vmla_n_f32( - vadd_f32( - vsub_f32(vmul_n_f32(Ww[i][0], 1.0f/8.0f), vmul_n_f32(Ww[i][1], 1.0f/4.0f)), - vsub_f32(vmul_n_f32(Ww[i][2], 1.0f/2.0f), Ww[i][3]) - ), - Ww[i][4], 2.0f - ), - 1.0f/3.0f - ); - - // V[i][5] = Ww[i][4]; - V[i][5] = Ww[i][4]; - } - - // Store the transformed weights - for (int i = 0, m = 0; i < 6; i++) - { - for (int j = 0; j < 6; j++, m++) - { - vst1_f32(outptr + m*matrix_stride, V[i][j]); - } - } - outptr += 2; - } -#endif // __arm_any__ - for (; channels_remaining; channels_remaining--) - { - // Matrices used and computed in this kernel - float w[5][5], Ww[6][5], V[6][6]; - - // Read weights - for (int i = 0; i < 5; i++) - { - for (int j = 0; j < 5; j++) - { - w[i][j] = *(inptrs[i][j]++); - } - } - - // Compute the matrix W w - for (int j = 0; j < 5; j++) - { - Ww[0][j] = w[0][j]/4.0f; - Ww[1][j] = -( w[0][j] + w[1][j] + w[2][j] + w[3][j] + w[4][j])/6.0f; - Ww[2][j] = +(-w[0][j] + w[1][j] - w[2][j] + w[3][j] - w[4][j])/6.0f; - Ww[3][j] = (w[0][j]/8.0f + w[1][j]/4.0f + w[2][j]/2.0f + w[3][j] + 2*w[4][j])/3.0f; - Ww[4][j] = (w[0][j]/8.0f - w[1][j]/4.0f + w[2][j]/2.0f - w[3][j] + 2*w[4][j])/3.0f; - Ww[5][j] = w[4][j]; - } - - // Compute V = W w WT - for (int i = 0; i < 6; i++) - { - V[i][0] = Ww[i][0]/4.0f; - V[i][1] = -( Ww[i][0] + Ww[i][1] + Ww[i][2] + Ww[i][3] + Ww[i][4])/6.0f; - V[i][2] = +(-Ww[i][0] + Ww[i][1] - Ww[i][2] + Ww[i][3] - Ww[i][4])/6.0f; - V[i][3] = (Ww[i][0]/8.0f + Ww[i][1]/4.0f + Ww[i][2]/2.0f + Ww[i][3] + 2*Ww[i][4])/3.0f; - V[i][4] = (Ww[i][0]/8.0f - Ww[i][1]/4.0f + Ww[i][2]/2.0f - Ww[i][3] + 2*Ww[i][4])/3.0f; - V[i][5] = Ww[i][4]; - } - - // 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 <> - template <> - int WinogradGEMM<2, 2, 5, 5>::WeightsTransform<float>::ops_performed(const KernelShape &shape) - { - return 0; // TODO - } - - template class WinogradGEMM<2, 2, 5, 5>::WeightsTransform<float>; -} // namespace winograd diff --git a/src/core/NEON/kernels/convolution/winograd/transforms/weights_4_5_fp32.cpp b/src/core/NEON/kernels/convolution/winograd/transforms/weights_4_5_fp32.cpp deleted file mode 100644 index 2f14e20142..0000000000 --- a/src/core/NEON/kernels/convolution/winograd/transforms/weights_4_5_fp32.cpp +++ /dev/null @@ -1,124 +0,0 @@ -/* - * 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/common/arm.hpp" -#include "arm_compute/core/NEON/kernels/convolution/winograd/winograd_gemm.hpp" -#include "arm_compute/core/NEON/kernels/convolution/winograd/transforms/kernel.hpp" - -namespace winograd -{ - template <> - template <> - void WinogradGEMM<1, 4, 1, 5>::WeightsTransform<float>::execute( - const int n_output_channels, - const int n_input_channels, - const float* const input, // NOTE: Data in HWIO order - float* 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 float *inptrs[kernel_cols]; - for (int j = 0; j < kernel_cols; j++) - { - inptrs[j] = input + j*weight_col_stride; - } - - // For each input channel - for (int ic = 0; ic < n_input_channels; ic++) - { - float *outptr = output + ic * matrix_row_stride; - - // For each output channel - int channels_remaining = n_output_channels; - for (; channels_remaining; channels_remaining--) - { - // Matrices used and computed in this kernel - float w[kernel_cols], V[inner_tile_cols]; - - // Read weights - for (int j = 0; j < kernel_cols; j++) - { - w[j] = *(inptrs[j]++); - } - - // Compute V = w WT - V[0] = (w[0]*-1) / 36; - V[1] = (w[1]*-1 + w[3]*-1 + w[0]*1 + w[2]*1 + w[4]*1) / 48; - V[2] = (w[0]*1 + w[1]*1 + w[2]*1 + w[3]*1 + w[4]*1) / 48; - V[3] = (w[0]*-1 + w[4]*-16 + w[2]*-4 + w[1]*2 + w[3]*8) / 120; - V[4] = (w[0]*-1 + w[4]*-16 + w[3]*-8 + w[2]*-4 + w[1]*-2) / 120; - V[5] = (w[3]*-27 + w[1]*-3 + w[2]*9 + w[4]*81 + w[0]*1) / 720; - V[6] = (w[1]*3 + w[2]*9 + w[3]*27 + w[4]*81 + w[0]*1) / 720; - V[7] = (w[4]*1) / 1; - - // Store the transformed weights - for (int j = 0; j < inner_tile_cols; j++) - { - *(outptr + j*matrix_stride) = V[j]; - } - outptr++; - } - } - } - - template <> - template <> - int WinogradGEMM<1, 4, 1, 5>::WeightsTransform<float>::ops_performed(const KernelShape &shape) - { - (void) shape; - return 0; // TODO - } - - template <> - template <> - void WinogradGEMM<4, 1, 5, 1>::WeightsTransform<float>::execute( - const int n_output_channels, - const int n_input_channels, - const float* const input, // NOTE: Data in HWIO order - float* const output, - const int matrix_stride, - const int matrix_row_stride - ) - { - // Redirect to the 1xN implementation - WinogradGEMM<1, 4, 1, 5>::template WeightsTransform<float>::execute( - n_output_channels, n_input_channels, input, output, matrix_stride, - matrix_row_stride - ); - } - - template <> - template <> - int WinogradGEMM<4, 1, 5, 1>::WeightsTransform<float>::ops_performed(const KernelShape &shape) - { - (void) shape; - return 0; // TODO - } - - template struct WinogradGEMM<1, 4, 1, 5>::WeightsTransform<float>; - template struct WinogradGEMM<4, 1, 5, 1>::WeightsTransform<float>; -} diff --git a/src/core/NEON/kernels/convolution/winograd/transforms/weights_4x4_3x3_fp32.cpp b/src/core/NEON/kernels/convolution/winograd/transforms/weights_4x4_3x3_fp32.cpp deleted file mode 100644 index a56a475fc9..0000000000 --- a/src/core/NEON/kernels/convolution/winograd/transforms/weights_4x4_3x3_fp32.cpp +++ /dev/null @@ -1,266 +0,0 @@ -/* - * 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/common/arm.hpp" -#include "arm_compute/core/NEON/kernels/convolution/winograd/winograd_gemm.hpp" -#include "arm_compute/core/NEON/kernels/convolution/winograd/transforms/kernel.hpp" - -namespace winograd -{ - /* Float implementation for kernel transform F(4x4, 3x3) */ - template <> - template <> - void WinogradGEMM<4, 4, 3, 3>::WeightsTransform<float>::execute( - const int n_output_channels, - const int n_input_channels, - const float* const input, // NOTE: Data in HWIO order - float* 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 float *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++) - { - float *outptr = output + ic * matrix_row_stride; - - // For each output channel - int channels_remaining = n_output_channels; -#ifdef __aarch64__ - for (; channels_remaining >= 4; channels_remaining -= 4) - { - // Matrices used and computed in this kernel - float32x4_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_f32(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] = vmulq_n_f32(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_f32(vaddq_f32(vaddq_f32(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_f32(vsubq_f32(vsubq_f32(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] = vmlaq_n_f32(vmlaq_n_f32(w[0][j], w[1][j], 2.0f), w[2][j], 4.0f); - - // Ww[4][j] = 1*w[0][j] + -2*w[1][j] + 4*w[2][j]; - Ww[4][j] = vmlaq_n_f32(vmlsq_n_f32(w[0][j], w[1][j], 2.0f), w[2][j], 4.0f); - - // Ww[5][j] = 24*w[2][j]; - Ww[5][j] = vmulq_n_f32(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_f32(vmulq_n_f32(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_f32(vmulq_n_f32(vaddq_f32(vaddq_f32(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_f32(vmulq_n_f32(vsubq_f32(vsubq_f32(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_f32(vmlaq_n_f32(vmlaq_n_f32(Ww[i][0], Ww[i][1], 2.0f), Ww[i][2], 4.0f), recip576); - - // V[i][4] = 1*Ww[i][0] + -2*Ww[i][1] + 4*Ww[i][2]; - V[i][4] = vmulq_n_f32(vmlaq_n_f32(vmlsq_n_f32(Ww[i][0], Ww[i][1], 2.0f), Ww[i][2], 4.0f), recip576); - - // V[i][5] = 24*Ww[i][2]; - V[i][5] = vmulq_n_f32(vmulq_n_f32(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_f32(outptr + m*matrix_stride, V[i][j]); - } - } - outptr += 4; - } -#endif // __aarch64__ -#ifdef __arm_any__ - for (; channels_remaining >= 2; channels_remaining -= 2) - { - // Matrices used and computed in this kernel - float32x2_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_f32(inptrs[i][j]); - inptrs[i][j] += 2; - } - } - - // Compute the matrix W w - for (int j = 0; j < 3; j++) - { - // Ww[0][j] = 6*w[0][j]; - Ww[0][j] = vmul_n_f32(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_f32(vadd_f32(vadd_f32(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_f32(vsub_f32(vsub_f32(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] = vmla_n_f32(vmla_n_f32(w[0][j], w[1][j], 2.0f), w[2][j], 4.0f); - - // Ww[4][j] = 1*w[0][j] + -2*w[1][j] + 4*w[2][j]; - Ww[4][j] = vmla_n_f32(vmls_n_f32(w[0][j], w[1][j], 2.0f), w[2][j], 4.0f); - - // Ww[5][j] = 24*w[2][j]; - Ww[5][j] = vmul_n_f32(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_f32(vmul_n_f32(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_f32(vmul_n_f32(vadd_f32(vadd_f32(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_f32(vmul_n_f32(vsub_f32(vsub_f32(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_f32(vmla_n_f32(vmla_n_f32(Ww[i][0], Ww[i][1], 2.0f), Ww[i][2], 4.0f), recip576); - - // V[i][4] = 1*Ww[i][0] + -2*Ww[i][1] + 4*Ww[i][2]; - V[i][4] = vmul_n_f32(vmla_n_f32(vmls_n_f32(Ww[i][0], Ww[i][1], 2.0f), Ww[i][2], 4.0f), recip576); - - // V[i][5] = 24*Ww[i][2]; - V[i][5] = vmul_n_f32(vmul_n_f32(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_f32(outptr + m*matrix_stride, V[i][j]); - } - } - outptr += 2; - } -#endif // __arm_any__ - for (; channels_remaining; channels_remaining--) - { - // Matrices used and computed in this kernel - float 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 <> - template <> - int WinogradGEMM<4, 4, 3, 3>::WeightsTransform<float>::ops_performed(const KernelShape &shape) - { - const int channel_prod = shape.n_input_channels * shape.n_output_channels; - return 9 * 16 * channel_prod; - } - - template struct WinogradGEMM<4, 4, 3, 3>::WeightsTransform<float>; -} diff --git a/src/core/NEON/kernels/convolution/winograd/transforms/weights_6_3_fp32.cpp b/src/core/NEON/kernels/convolution/winograd/transforms/weights_6_3_fp32.cpp deleted file mode 100644 index c560aa8c8f..0000000000 --- a/src/core/NEON/kernels/convolution/winograd/transforms/weights_6_3_fp32.cpp +++ /dev/null @@ -1,125 +0,0 @@ -/* - * 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/common/arm.hpp" -#include "arm_compute/core/NEON/kernels/convolution/winograd/winograd_gemm.hpp" -#include "arm_compute/core/NEON/kernels/convolution/winograd/transforms/kernel.hpp" - - -namespace winograd -{ - template <> - template <> - void WinogradGEMM<1, 6, 1, 3>::WeightsTransform<float>::execute( - const int n_output_channels, - const int n_input_channels, - const float* const input, // NOTE: Data in HWIO order - float* 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 float *inptrs[3]; - for (int j = 0; j < 3; j++) - { - inptrs[j] = input + j*weight_col_stride; - } - - // For each input channel - for (int ic = 0; ic < n_input_channels; ic++) - { - float *outptr = output + ic * matrix_row_stride; - - // For each output channel - int channels_remaining = n_output_channels; - for (; channels_remaining; channels_remaining--) - { - // Matrices used and computed in this kernel - float w[3], V[inner_tile_cols]; - - // Read weights - for (int j = 0; j < 3; j++) - { - w[j] = *(inptrs[j]++); - } - - // Compute V = w WT - V[0] = (w[0]*-1) / 36.0f; - V[1] = (w[1]*-1 + w[0]*1 + w[2]*1) / 48.0f; - V[2] = (w[0]*1 + w[1]*1 + w[2]*1) / 48.0f; - V[3] = (w[0]*-1 + w[2]*-4 + w[1]*2) / 120.0f; - V[4] = (w[0]*-1 + w[2]*-4 + w[1]*-2) / 120.0f; - V[5] = (w[1]*-3 + w[2]*9 + w[0]*1) / 720.0f; - V[6] = (w[1]*3 + w[2]*9 + w[0]*1) / 720.0f; - V[7] = (w[2]*1) / 1; - - // Store the transformed weights - for (int j = 0; j < inner_tile_cols; j++) - { - *(outptr + j*matrix_stride) = V[j]; - } - outptr++; - } - } - } - - template <> - template <> - int WinogradGEMM<1, 6, 1, 3>::WeightsTransform<float>::ops_performed(const KernelShape &shape) - { - (void) shape; - return 0; // TODO - } - - template <> - template <> - void WinogradGEMM<6, 1, 3, 1>::WeightsTransform<float>::execute( - const int n_output_channels, - const int n_input_channels, - const float* const input, // NOTE: Data in HWIO order - float* const output, - const int matrix_stride, - const int matrix_row_stride - ) - { - // Redirect to the 1xN implementation - WinogradGEMM<1, 6, 1, 3>::template WeightsTransform<float>::execute( - n_output_channels, n_input_channels, input, output, matrix_stride, - matrix_row_stride - ); - } - - template <> - template <> - int WinogradGEMM<6, 1, 3, 1>::WeightsTransform<float>::ops_performed(const KernelShape &shape) - { - (void) shape; - return 0; // TODO - } - - template struct WinogradGEMM<1, 6, 1, 3>::WeightsTransform<float>; - template struct WinogradGEMM<6, 1, 3, 1>::WeightsTransform<float>; -} |