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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
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