diff options
Diffstat (limited to 'src/core/NEON/kernels/winograd/transforms')
5 files changed, 1140 insertions, 12 deletions
diff --git a/src/core/NEON/kernels/winograd/transforms/input_2x2_5x5_fp32.cpp b/src/core/NEON/kernels/winograd/transforms/input_2x2_5x5_fp32.cpp new file mode 100644 index 0000000000..a6ebca1bce --- /dev/null +++ b/src/core/NEON/kernels/winograd/transforms/input_2x2_5x5_fp32.cpp @@ -0,0 +1,458 @@ +/* + * 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 "transforms/input.hpp" +#include "winograd_gemm.hpp" +#include "arm.hpp" + +namespace winograd +{ + +using Transform = WinogradGEMM<2, 2, 5, 5>::InputTransform<float>; + +template <> +template <> +int Transform::ops_performed(const Tensor4DShape &input_shape) +{ + return 0; // TODO +} + +/***************************************************************************** +* F(2x2, 5x5) implies the use of a 6x6 input tile. +* +* Build an array of the specialised methods that deal with each of the +* different padding combinations which may be required. These padding +* constraints are the space: +* +* Padding top in {0, 1} +* Padding left in {0, 1} +* Padding bottom in {0, 1, 2, 3, 4} +* Padding right in {0, 1, 2, 3, 4} +*/ +template <> +template <> +template <int pad_top, int pad_left, int pad_bottom, int pad_right> +void Transform::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 +) +{ + constexpr int cells_i = 6 - pad_bottom; + constexpr int cells_j = 6 - pad_right; + + float *outptr = matrix_base; + + // Get pointers into the input tile + const float *x_ptrs[6][6]; + 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[6][6], XTx[6][6], U[6][6]; + for (int i = 0; i < 6; i++) + { + for (int j = 0; j < 6; 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[6][6], XTx[6][6], U[6][6]; + for (int i = 0; i < 6; i++) + { + for (int j = 0; j < 6; 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 < 6; 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 < 6; i++) + { + for (int j = 0; j < 6; 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[6][6], XTx[6][6], U[6][6]; + for (int i = 0; i < 6; i++) + { + for (int j = 0; j < 6; 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 < 6; 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 < 6; i++) + { + for (int j = 0; j < 6; 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 < 6; 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 < 6; i++) + { + for (int j = 0; j < 6; j++, m++) + { + *(outptr + m*matrix_stride) = U[i][j]; + } + } + outptr++; + } +} + +template <> +template <> +const Transform::TileFn Transform::tile_fns[2][2][max_pad_bottom][max_pad_right] = +{ + { + { + { + Transform::template process_tile<0, 0, 0, 0>, // No padding + Transform::template process_tile<0, 0, 0, 1>, // Right + Transform::template process_tile<0, 0, 0, 2>, // " " + Transform::template process_tile<0, 0, 0, 3>, // " " + Transform::template process_tile<0, 0, 0, 4>, // " " + }, + { + Transform::template process_tile<0, 0, 1, 0>, // Bottom + Transform::template process_tile<0, 0, 1, 1>, // Bottom right + Transform::template process_tile<0, 0, 1, 2>, // " " + Transform::template process_tile<0, 0, 1, 3>, // " " + Transform::template process_tile<0, 0, 1, 4>, // " " + }, + { + Transform::template process_tile<0, 0, 2, 0>, // Bottom + Transform::template process_tile<0, 0, 2, 1>, // Bottom right + Transform::template process_tile<0, 0, 2, 2>, // " " + Transform::template process_tile<0, 0, 2, 3>, // " " + Transform::template process_tile<0, 0, 2, 4>, // " " + }, + { + Transform::template process_tile<0, 0, 3, 0>, // Bottom + Transform::template process_tile<0, 0, 3, 1>, // Bottom right + Transform::template process_tile<0, 0, 3, 2>, // " " + Transform::template process_tile<0, 0, 3, 3>, // " " + Transform::template process_tile<0, 0, 3, 4>, // " " + }, + { + Transform::template process_tile<0, 0, 4, 0>, // Bottom + Transform::template process_tile<0, 0, 4, 1>, // Bottom right + Transform::template process_tile<0, 0, 4, 2>, // " " + Transform::template process_tile<0, 0, 4, 3>, // " " + Transform::template process_tile<0, 0, 4, 4>, // " " + } + }, + { + { + Transform::template process_tile<0, 1, 0, 0>, // Left + Transform::template process_tile<0, 1, 0, 1>, + Transform::template process_tile<0, 1, 0, 2>, + Transform::template process_tile<0, 1, 0, 3>, + Transform::template process_tile<0, 1, 0, 4>, + }, + { + Transform::template process_tile<0, 1, 1, 0>, // Bottom left + Transform::template process_tile<0, 1, 1, 1>, + Transform::template process_tile<0, 1, 1, 2>, + Transform::template process_tile<0, 1, 1, 3>, + Transform::template process_tile<0, 1, 1, 4>, + }, + { + Transform::template process_tile<0, 1, 2, 0>, // " " + Transform::template process_tile<0, 1, 2, 1>, + Transform::template process_tile<0, 1, 2, 2>, + Transform::template process_tile<0, 1, 2, 3>, + Transform::template process_tile<0, 1, 2, 4>, + }, + { + Transform::template process_tile<0, 1, 3, 0>, // " " + Transform::template process_tile<0, 1, 3, 1>, + Transform::template process_tile<0, 1, 3, 2>, + Transform::template process_tile<0, 1, 3, 3>, + Transform::template process_tile<0, 1, 3, 4>, + }, + { + Transform::template process_tile<0, 1, 4, 0>, // " " + Transform::template process_tile<0, 1, 4, 1>, + Transform::template process_tile<0, 1, 4, 2>, + Transform::template process_tile<0, 1, 4, 3>, + Transform::template process_tile<0, 1, 4, 4>, + } + } + }, + { + { + { + Transform::template process_tile<1, 0, 0, 0>, // Top + Transform::template process_tile<1, 0, 0, 1>, // Top right + Transform::template process_tile<1, 0, 0, 2>, // " " + Transform::template process_tile<1, 0, 0, 3>, // " " + Transform::template process_tile<1, 0, 0, 4>, // " " + }, + { + Transform::template process_tile<1, 0, 1, 0>, + Transform::template process_tile<1, 0, 1, 1>, + Transform::template process_tile<1, 0, 1, 2>, + Transform::template process_tile<1, 0, 1, 3>, + Transform::template process_tile<1, 0, 1, 4>, + }, + { + Transform::template process_tile<1, 0, 2, 0>, + Transform::template process_tile<1, 0, 2, 1>, + Transform::template process_tile<1, 0, 2, 2>, + Transform::template process_tile<1, 0, 2, 3>, + Transform::template process_tile<1, 0, 2, 4>, + }, + { + Transform::template process_tile<1, 0, 3, 0>, + Transform::template process_tile<1, 0, 3, 1>, + Transform::template process_tile<1, 0, 3, 2>, + Transform::template process_tile<1, 0, 3, 3>, + Transform::template process_tile<1, 0, 3, 4>, + }, + { + Transform::template process_tile<1, 0, 4, 0>, + Transform::template process_tile<1, 0, 4, 1>, + Transform::template process_tile<1, 0, 4, 2>, + Transform::template process_tile<1, 0, 4, 3>, + Transform::template process_tile<1, 0, 4, 4>, + }, + }, + { + { + Transform::template process_tile<1, 1, 0, 0>, // Top left + Transform::template process_tile<1, 1, 0, 1>, + Transform::template process_tile<1, 1, 0, 2>, + Transform::template process_tile<1, 1, 0, 3>, + Transform::template process_tile<1, 1, 0, 4>, + }, + { + Transform::template process_tile<1, 1, 1, 0>, + Transform::template process_tile<1, 1, 1, 1>, + Transform::template process_tile<1, 1, 1, 2>, + Transform::template process_tile<1, 1, 1, 3>, + Transform::template process_tile<1, 1, 1, 4>, + }, + { + Transform::template process_tile<1, 1, 2, 0>, + Transform::template process_tile<1, 1, 2, 1>, + Transform::template process_tile<1, 1, 2, 2>, + Transform::template process_tile<1, 1, 2, 3>, + Transform::template process_tile<1, 1, 2, 4>, + }, + { + Transform::template process_tile<1, 1, 3, 0>, + Transform::template process_tile<1, 1, 3, 1>, + Transform::template process_tile<1, 1, 3, 2>, + Transform::template process_tile<1, 1, 3, 3>, + Transform::template process_tile<1, 1, 3, 4>, + }, + { + Transform::template process_tile<1, 1, 4, 0>, + Transform::template process_tile<1, 1, 4, 1>, + Transform::template process_tile<1, 1, 4, 2>, + Transform::template process_tile<1, 1, 4, 3>, + Transform::template process_tile<1, 1, 4, 4>, + } + } + } +}; + +template struct WinogradGEMM<2, 2, 5, 5>::InputTransform<float>; +} // namespace winograd diff --git a/src/core/NEON/kernels/winograd/transforms/output_2x2_3x3_fp32.cpp b/src/core/NEON/kernels/winograd/transforms/output_2x2_3x3_fp32.cpp index e7907d18c0..58db7d2ecd 100644 --- a/src/core/NEON/kernels/winograd/transforms/output_2x2_3x3_fp32.cpp +++ b/src/core/NEON/kernels/winograd/transforms/output_2x2_3x3_fp32.cpp @@ -65,6 +65,7 @@ void Transform::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 @@ -83,6 +84,7 @@ void Transform::process_tile( } } const float *inptr = matrix_base; + const float *bptr = biases; // For each channel of the output int channels_remaining = n_channels; @@ -90,7 +92,7 @@ void Transform::process_tile( 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]; + 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++) @@ -122,12 +124,16 @@ void Transform::process_tile( 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], f[i][j]); + vst1q_f32(outptrs[i][j], vaddq_f32(f[i][j], b)); outptrs[i][j] += 4; } } @@ -137,7 +143,7 @@ void Transform::process_tile( 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]; + 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++) @@ -169,12 +175,16 @@ void Transform::process_tile( 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], f[i][j]); + vst1_f32(outptrs[i][j], vadd_f32(f[i][j], b)); outptrs[i][j] += 2; } } @@ -183,7 +193,7 @@ void Transform::process_tile( for (; channels_remaining; channels_remaining--) { // Matrices used and computed during this transform - float F[4][4], FZ[4][2], f[2][2]; + 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++) @@ -209,12 +219,15 @@ void Transform::process_tile( 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]; + *(outptrs[i][j]++) = f[i][j] + b; } } } diff --git a/src/core/NEON/kernels/winograd/transforms/output_2x2_5x5_fp32.cpp b/src/core/NEON/kernels/winograd/transforms/output_2x2_5x5_fp32.cpp new file mode 100644 index 0000000000..bfd670090a --- /dev/null +++ b/src/core/NEON/kernels/winograd/transforms/output_2x2_5x5_fp32.cpp @@ -0,0 +1,242 @@ +/* + * 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 "transforms/output.hpp" +#include "winograd_gemm.hpp" +#include "arm.hpp" + +namespace winograd +{ + +using Transform = WinogradGEMM<2, 2, 5, 5>::OutputTransform<float>; + +template <> +template <> +int Transform::ops_performed(const Tensor4DShape &shape) +{ + return 0; // TODO +} + +/* F(2x2, 5x5) constructs 2x2 output tiles from a 5x5 convolution. Since we use + * enough tiles to cover the output space each output tile may contain 0 or 1 + * padded values to the right and bottom columns or rows of the tile, e.g.: + * + * ___ ___ + * | | | X| + * |___| |__X| + * + * ___ ___ + * | | | X| + * |X_X| |X_X| + * + * + * We provide a specialised output transform for each of these instances. + * Consequently we below construct an array of the various padding options, the + * array contains pointers to the specific implementations. + */ +template <> +template <> +template <int pad_bottom, int pad_right> +void Transform::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 +) +{ + constexpr int cells_i = 2 - pad_bottom; + constexpr int cells_j = 2 - pad_right; + + // Construct a map to the output cells + float *outptrs[cells_i][cells_j]; + 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; + + // 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; + } + } + } +} + +template <> +template <> +const Transform::TileFn Transform::tile_fns[max_pad_bottom][max_pad_right] = +{ + { + Transform::template process_tile<0, 0>, // No padding + Transform::template process_tile<0, 1>, // Right padding + }, + { + Transform::template process_tile<1, 0>, // Bottom padding + Transform::template process_tile<1, 1>, // Bottom and right padding + } +}; + +template struct WinogradGEMM<2, 2, 5, 5>::OutputTransform<float>; +} // namespace winograd diff --git a/src/core/NEON/kernels/winograd/transforms/output_4x4_3x3_fp32.cpp b/src/core/NEON/kernels/winograd/transforms/output_4x4_3x3_fp32.cpp index 483e5c110b..45210d7976 100644 --- a/src/core/NEON/kernels/winograd/transforms/output_4x4_3x3_fp32.cpp +++ b/src/core/NEON/kernels/winograd/transforms/output_4x4_3x3_fp32.cpp @@ -82,6 +82,7 @@ void Transform::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 @@ -100,6 +101,7 @@ void Transform::process_tile( } } const float *inptr = matrix_base; + const float *bptr = biases; // For each channel of the output int channels_remaining = n_channels; @@ -107,7 +109,7 @@ void Transform::process_tile( 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]; + 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++) @@ -152,11 +154,13 @@ void Transform::process_tile( } // 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], f[i][j]); + vst1q_f32(outptrs[i][j], vaddq_f32(f[i][j], b)); outptrs[i][j] += 4; } } @@ -166,7 +170,7 @@ void Transform::process_tile( 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]; + 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++) @@ -211,11 +215,13 @@ void Transform::process_tile( } // 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], f[i][j]); + vst1_f32(outptrs[i][j], vadd_f32(f[i][j], b)); outptrs[i][j] += 2; } } @@ -224,7 +230,7 @@ void Transform::process_tile( for (; channels_remaining; channels_remaining--) { // Matrices used and computed during this transform - float F[6][6], FZ[6][4], f[4][4]; + 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++) @@ -255,11 +261,12 @@ void Transform::process_tile( } // 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]; + *(outptrs[i][j]++) = f[i][j] + b; } } } diff --git a/src/core/NEON/kernels/winograd/transforms/weights_2x2_5x5_fp32.cpp b/src/core/NEON/kernels/winograd/transforms/weights_2x2_5x5_fp32.cpp new file mode 100644 index 0000000000..acf6b913f8 --- /dev/null +++ b/src/core/NEON/kernels/winograd/transforms/weights_2x2_5x5_fp32.cpp @@ -0,0 +1,408 @@ +/* + * 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.hpp" +#include "winograd_gemm.hpp" +#include "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 |