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/*
* 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 "FullyConnectedLayer.h"
#include "tests/validation/FixedPoint.h"
#include "tests/validation/half.h"
#include <numeric>
namespace arm_compute
{
namespace test
{
namespace validation
{
namespace reference
{
namespace
{
// Vector matrix multiply for floating point
template <typename T, typename std::enable_if<is_floating_point<T>::value, int>::type = 0>
void vector_matrix_multiply(const T *src, const T *weights, const T *bias, T *dst, int cols_weights, int rows_weights, uint8_t fixed_point_position)
{
ARM_COMPUTE_UNUSED(fixed_point_position);
for(int y = 0; y < rows_weights; ++y)
{
dst[y] = std::inner_product(src, src + cols_weights, weights, static_cast<T>(0)) + bias[y];
weights += cols_weights;
}
}
// Vector matrix multiply for fixed point type
template <typename T, typename std::enable_if<std::is_integral<T>::value, int>::type = 0>
void vector_matrix_multiply(const T *src, const T *weights, const T *bias, T *dst, int cols_weights, int rows_weights, uint8_t fixed_point_position)
{
using namespace fixed_point_arithmetic;
using promoted_type = fixed_point_arithmetic::traits::promote_t<T>;
for(int y = 0; y < rows_weights; ++y)
{
// Reset accumulator
fixed_point<promoted_type> acc(0, fixed_point_position);
for(int x = 0; x < cols_weights; ++x)
{
const fixed_point<promoted_type> i_value(src[x], fixed_point_position, true);
const fixed_point<promoted_type> w_value(weights[x], fixed_point_position, true);
acc = acc + i_value * w_value;
}
// Get the bias
const fixed_point<T> b(bias[y], fixed_point_position, true);
// Convert back and accumulate the bias
fixed_point<T> res(acc);
res = res + b;
// Store the result
dst[y] = res.raw();
weights += cols_weights;
}
}
} // namespace
template <typename T>
SimpleTensor<T> fully_connected_layer(const SimpleTensor<T> &src, const SimpleTensor<T> &weights, const SimpleTensor<T> &bias, const TensorShape &dst_shape)
{
// Create reference
SimpleTensor<T> dst{ TensorShape{ dst_shape }, src.data_type(), 1, src.fixed_point_position() };
// Sanity checks
const int num_batch_dimensions = std::max(0, static_cast<int>(dst_shape.num_dimensions()) - 1);
const int num_input_dimensions = src.shape().num_dimensions() - num_batch_dimensions;
const unsigned int linear_input_size = src.shape().total_size_lower(num_input_dimensions);
ARM_COMPUTE_UNUSED(num_batch_dimensions);
ARM_COMPUTE_UNUSED(num_input_dimensions);
ARM_COMPUTE_UNUSED(linear_input_size);
ARM_COMPUTE_ERROR_ON(weights.shape().x() != linear_input_size);
ARM_COMPUTE_ERROR_ON(weights.shape().y() != bias.shape().x());
ARM_COMPUTE_ERROR_ON(weights.shape().y() != dst.shape().x());
// Compute reference
const int cols_weights = weights.shape().x();
const int rows_weights = weights.shape().y();
const int num_batches = dst_shape.total_size_upper(1);
for(int k = 0; k < num_batches; ++k)
{
vector_matrix_multiply<T>(src.data() + k * cols_weights,
weights.data(),
bias.data(),
dst.data() + k * rows_weights,
cols_weights,
rows_weights,
src.fixed_point_position());
}
return dst;
}
template SimpleTensor<float> fully_connected_layer(const SimpleTensor<float> &src, const SimpleTensor<float> &weights, const SimpleTensor<float> &bias, const TensorShape &dst_shape);
template SimpleTensor<half_float::half> fully_connected_layer(const SimpleTensor<half_float::half> &src, const SimpleTensor<half_float::half> &weights, const SimpleTensor<half_float::half> &bias,
const TensorShape &dst_shape);
template SimpleTensor<qint8_t> fully_connected_layer(const SimpleTensor<qint8_t> &src, const SimpleTensor<qint8_t> &weights, const SimpleTensor<qint8_t> &bias, const TensorShape &dst_shape);
template SimpleTensor<qint16_t> fully_connected_layer(const SimpleTensor<qint16_t> &src, const SimpleTensor<qint16_t> &weights, const SimpleTensor<qint16_t> &bias, const TensorShape &dst_shape);
} // namespace reference
} // namespace validation
} // namespace test
} // namespace arm_compute
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