COMPMID-2063: New Winograd implementation
Refactoring of winograd code reducing the size of the binaries
about 8X.
Change-Id: If8845bda324573e1a5cf436f354ac8603e88a92e
Signed-off-by: Pablo Tello <pablo.tello@arm.com>
Reviewed-on: https://review.mlplatform.org/c/959
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
Tested-by: Anthony Barbier <Anthony.barbier@arm.com>
Reviewed-by: Georgios Pinitas <georgios.pinitas@arm.com>
diff --git a/src/core/NEON/kernels/convolution/winograd/winograd_transforms/weights_2x2_3x3_fp32_fp32_integers.cpp b/src/core/NEON/kernels/convolution/winograd/winograd_transforms/weights_2x2_3x3_fp32_fp32_integers.cpp
new file mode 100644
index 0000000..8fab6db
--- /dev/null
+++ b/src/core/NEON/kernels/convolution/winograd/winograd_transforms/weights_2x2_3x3_fp32_fp32_integers.cpp
@@ -0,0 +1,220 @@
+/*
+ * Copyright (c) 2019 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 "kernel.hpp"
+
+namespace winograd
+{
+
+template <>
+void WeightTransform<3, 3, 4, 4, float, float, WinogradRoots::Integers>::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 class WeightTransform<3, 3, 4, 4, float, float, WinogradRoots::Integers>;
+
+} // namespace