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+/*
+ * Copyright (c) 2023 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 "Reorder.h"
+#include "src/core/NEON/kernels/arm_gemm/utils.hpp"
+
+namespace arm_compute
+{
+namespace test
+{
+namespace validation
+{
+namespace reference
+{
+
+/*
+ * Generic transform.
+ *
+ * Assuming the untransposed case, this works by first reading <BlockBy>
+ * consecutive values from the first input row. This same number of values
+ * are then read from the next <IntBy-1> rows. Now return to the first
+ * input row and repeat.
+ *
+ * Need to cope with the work requested in either dimension not actually
+ * being a multiple of the block sizes.
+ */
+template <unsigned int tIntBy, unsigned int BlockBy, bool Transposed, size_t TOutSize, size_t TInSize, typename d_type, arm_gemm::VLType vlt>
+struct Transform_ref
+{
+ template <typename TOut, typename TIn>
+ static void Transform(TOut &out, const TIn in, const int stride,
+ const int y0, const int ymax, const int x0, const int xmax)
+ {
+ // NOTE: This code is disabled to avoid the call to get_vector_length(), so templated transforms will not be
+ // correct for SVE. This is not an issue as we have specializations for all SVE cases.
+ // For SVE cases we multiply the interleave factor by the vector length.
+ // const unsigned int IntBy = tIntBy * (vlt == VLType::SVE ? get_vector_length<TOut>() / BlockBy : 1);
+ const unsigned int IntBy = tIntBy;
+ int out_index = 0;
+
+ const int n_whole_y_blocks = (ymax - y0) / IntBy;
+ const int y_remainders = (ymax - y0) % IntBy;
+ const int n_y_blocks = n_whole_y_blocks + (y_remainders ? 1 : 0);
+
+ const int n_whole_x_blocks = (xmax - x0) / BlockBy;
+ const int x_remainders = (xmax - x0) % BlockBy;
+ const int n_x_blocks = n_whole_x_blocks + (x_remainders ? 1 : 0);
+
+ // "Y" loop: advance down the rows of the source IntBy rows at a time.
+ // Set up fill_rows to show the number rows to copy from, and blank_rows
+ // for the number of blank rows to add.
+ for(int y_block = 0; y_block < n_y_blocks; y_block++)
+ {
+ const int fill_rows = (y_block < n_whole_y_blocks) ? IntBy : y_remainders;
+ const int blank_rows = IntBy - fill_rows;
+
+ const int y_base = y0 + (y_block * IntBy);
+
+ // So now advance along this block of rows, BlockBy columns at a time.
+ for(int x_block = 0; x_block < n_x_blocks; x_block++)
+ {
+ const int fill_cols = (x_block < n_whole_x_blocks) ? BlockBy : x_remainders;
+ const int blank_cols = BlockBy - fill_cols;
+
+ const int x_base = x0 + (x_block * BlockBy);
+
+ for(int row = 0; row < fill_rows; row++)
+ {
+ for(int col = 0; col < fill_cols; col++)
+ {
+ // In-range copy. If it's transposed, we reverse the sense of rows and columns here.
+ if(Transposed)
+ {
+ out[out_index] = in[(x_base + col) * stride + y_base + row];
+ out_index++;
+ }
+ else
+ {
+ out[out_index] = in[(y_base + row) * stride + x_base + col];
+ out_index++;
+ }
+ }
+ // "col" tail - row is in range but column is out of range.
+ for(int col = 0; col < blank_cols; col++)
+ {
+ out[out_index] = 0;
+ out_index++;
+ }
+ }
+ // "row" tail - row is out of range so fill with zeros always.
+ const d_type zeroval = 0;
+ const int pads = blank_rows * (fill_cols + blank_cols);
+
+ for(int i = 0; i < pads; i++)
+ {
+ out[out_index] = zeroval;
+ }
+
+ out_index += pads;
+ }
+ }
+ }
+};
+
+template <typename T>
+SimpleTensor<T> reorder_layer(const SimpleTensor<T> &src, const TensorShape &output_shape, WeightFormat output_wf)
+{
+ SimpleTensor<T> dst{ output_shape, src.data_type() };
+ const int cols = src.shape()[0];
+ const int rows = src.shape()[1];
+
+ switch(output_wf)
+ {
+ case WeightFormat::OHWIo4:
+ {
+ Transform_ref<4, 1, true, sizeof(float), sizeof(float), float, arm_gemm::VLType::None>::Transform<SimpleTensor<T> &, SimpleTensor<T>>(dst, src, rows, 0, rows, 0, cols);
+ break;
+ }
+ case WeightFormat::OHWIo8:
+ {
+ Transform_ref<8, 1, true, sizeof(float), sizeof(float), float, arm_gemm::VLType::None>::Transform<SimpleTensor<T> &, SimpleTensor<T>>(dst, src, rows, 0, rows, 0, cols);
+ break;
+ }
+ default:
+ break;
+ }
+
+ return dst;
+}
+
+template SimpleTensor<float> reorder_layer(const SimpleTensor<float> &src, const TensorShape &output_shape, WeightFormat output_wf);
+
+} // namespace reference
+} // namespace validation
+} // namespace test
+} // namespace arm_compute