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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 "arm_compute/core/Types.h"
#include "tests/validation/FixedPoint.h"
#include "tests/validation/reference/UtilsQuantizedAsymm.h"
#include "arm_compute/core/utils/quantization/AsymmHelpers.h"
#include <numeric>
namespace arm_compute
{
namespace test
{
namespace validation
{
namespace reference
{
namespace
{
// Vector matrix multiply for floating point
template < typename T, typename TB, typename std::enable_if < is_floating_point<T>::value &&is_floating_point<TB>::value, int >::type = 0 >
void vector_matrix_multiply(const SimpleTensor<T> &src, const SimpleTensor<T> &weights, const SimpleTensor<TB> &bias, SimpleTensor<T> &dst, int offset_src, int offset_dst, int cols_weights,
int rows_weights, uint8_t fixed_point_position)
{
ARM_COMPUTE_UNUSED(fixed_point_position);
const T *src_ptr = src.data() + offset_src;
const T *weights_ptr = weights.data();
const TB *bias_ptr = bias.data();
T *dst_ptr = dst.data() + offset_dst;
for(int y = 0; y < rows_weights; ++y)
{
dst_ptr[y] = std::inner_product(src_ptr, src_ptr + cols_weights, weights_ptr, static_cast<T>(0)) + bias_ptr[y];
weights_ptr += cols_weights;
}
}
// Vector matrix multiply for fixed point type
template < typename T, typename TB, typename std::enable_if < std::is_integral<T>::value &&std::is_integral<TB>::value, int >::type = 0 >
void vector_matrix_multiply(const SimpleTensor<T> &src, const SimpleTensor<T> &weights, const SimpleTensor<TB> &bias, SimpleTensor<T> &dst, int offset_src, int offset_dst, int cols_weights,
int rows_weights, uint8_t fixed_point_position)
{
const T *src_ptr = src.data() + offset_src;
const T *weights_ptr = weights.data();
const TB *bias_ptr = bias.data();
T *dst_ptr = dst.data() + offset_dst;
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_ptr[x], fixed_point_position, true);
const fixed_point<promoted_type> w_value(weights_ptr[x], fixed_point_position, true);
acc = acc + i_value * w_value;
}
// Get the bias
const fixed_point<T> b(bias_ptr[y], fixed_point_position, true);
// Convert back and accumulate the bias
fixed_point<T> res(acc);
res = res + b;
// Store the result
dst_ptr[y] = res.raw();
weights_ptr += cols_weights;
}
}
// Vector matrix multiply for quantized type
template <>
void vector_matrix_multiply(const SimpleTensor<uint8_t> &src, const SimpleTensor<uint8_t> &weights, const SimpleTensor<int32_t> &bias, SimpleTensor<uint8_t> &dst, int offset_src, int offset_dst,
int cols_weights, int rows_weights, uint8_t fixed_point_position)
{
ARM_COMPUTE_UNUSED(fixed_point_position);
const uint8_t *src_ptr = src.data() + offset_src;
const uint8_t *weights_ptr = weights.data();
const int32_t *bias_ptr = bias.data();
uint8_t *dst_ptr = dst.data() + offset_dst;
const int input_offset = -src.quantization_info().offset;
const float input_scale = src.quantization_info().scale;
const int weights_offset = -weights.quantization_info().offset;
const float weights_scale = weights.quantization_info().scale;
const int output_offset = dst.quantization_info().offset;
const float output_scale = dst.quantization_info().scale;
int output_multiplier = 0;
int output_shift = 0;
const float multiplier = input_scale * weights_scale / output_scale;
arm_compute::quantization::calculate_quantized_multiplier_less_than_one(multiplier, &output_multiplier, &output_shift);
for(int y = 0; y < rows_weights; ++y)
{
// Reset accumulator
int32_t acc = 0;
for(int x = 0; x < cols_weights; ++x)
{
acc += (src_ptr[x] + input_offset) * (weights_ptr[x] + weights_offset);
}
// Accumulate the bias
acc += bias_ptr[y];
acc = asymm_rounding_divide_by_pow2(asymm_int_mult(acc, output_multiplier), output_shift);
acc += output_offset;
acc = utility::clamp<int32_t>(acc, 0, 255);
// Store the result
dst_ptr[y] = static_cast<uint8_t>(acc);
weights_ptr += cols_weights;
}
}
} // namespace
template <typename T, typename TB>
SimpleTensor<T> fully_connected_layer(const SimpleTensor<T> &src, const SimpleTensor<T> &weights, const SimpleTensor<TB> &bias, const TensorShape &dst_shape)
{
// Create reference
SimpleTensor<T> dst{ TensorShape{ dst_shape }, src.data_type(), 1, src.fixed_point_position(), src.quantization_info() };
// 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)
{
const int offset_in = k * cols_weights;
const int offset_out = k * rows_weights;
vector_matrix_multiply<T>(src,
weights,
bias,
dst,
offset_in,
offset_out,
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> fully_connected_layer(const SimpleTensor<half> &src, const SimpleTensor<half> &weights, const SimpleTensor<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);
template SimpleTensor<uint8_t> fully_connected_layer(const SimpleTensor<uint8_t> &src, const SimpleTensor<uint8_t> &weights, const SimpleTensor<int32_t> &bias, const TensorShape &dst_shape);
} // namespace reference
} // namespace validation
} // namespace test
} // namespace arm_compute
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