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Diffstat (limited to 'src/core/NEON/kernels/convolution/winograd/transforms/output_2x2_5x5_fp32.cpp')
| -rw-r--r-- | src/core/NEON/kernels/convolution/winograd/transforms/output_2x2_5x5_fp32.cpp | 242 |
1 files changed, 242 insertions, 0 deletions
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 new file mode 100644 index 0000000000..262f71118c --- /dev/null +++ b/src/core/NEON/kernels/convolution/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 "arm_compute/core/NEON/kernels/convolution/winograd/transforms/output.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 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 |