| /* |
| * Copyright (c) 2022 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 <cstddef> |
| #include <arm_neon.h> |
| |
| namespace arm_conv { |
| namespace winograd { |
| namespace weight_transform { |
| |
| void arm_fp32_4x4_3x3( |
| unsigned int n_channels, |
| const float *inptr, const size_t ld_weight_row, const size_t ld_weight_col, |
| float *outptr, const size_t matrix_stride |
| ) |
| { |
| #ifdef __aarch64__ |
| for (; n_channels >= 4; n_channels -= 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(inptr + i*ld_weight_row + j*ld_weight_col); |
| } |
| } |
| |
| // 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]); |
| } |
| } |
| |
| inptr += 4; |
| outptr += 4; |
| } |
| #endif // __aarch64__ |
| for (; n_channels >= 2; n_channels -= 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(inptr + i*ld_weight_row + j*ld_weight_col); |
| } |
| } |
| |
| // 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]); |
| } |
| } |
| |
| inptr += 2; |
| outptr += 2; |
| } |
| for (; n_channels; n_channels--) |
| { |
| // 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] = *(inptr + i*ld_weight_row + j*ld_weight_col); |
| } |
| } |
| |
| // 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]; |
| } |
| } |
| |
| inptr++; |
| outptr++; |
| } |
| } |
| |
| } // namespace weight_transform |
| } // namespace winograd |
| } // namespace arm_conv |