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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 "arm_compute/core/NEON/kernels/NEDirectConvolutionLayerBiasAccumulateKernel.h"
#include "arm_compute/core/AccessWindowStatic.h"
#include "arm_compute/core/Error.h"
#include "arm_compute/core/Helpers.h"
#include "arm_compute/core/ITensor.h"
#include "arm_compute/core/NEON/NEFixedPoint.h"
#include "arm_compute/core/Types.h"
#include "arm_compute/core/Validate.h"
#include "arm_compute/core/Window.h"
#include <arm_neon.h>
#include <cstddef>
#include <cstdint>
using namespace arm_compute;
namespace
{
// Internal load
inline float32x4_t internal_vld1q(const float *in)
{
return vld1q_f32(in);
}
inline qint8x16_t internal_vld1q(const qint8_t *in)
{
return vld1q_qs8(in);
}
inline qint16x8_t internal_vld1q(const qint16_t *in)
{
return vld1q_qs16(in);
}
// Internal store
inline void internal_vst1q(float *p, const float32x4_t &v)
{
vst1q_f32(p, v);
}
inline void internal_vst1q(qint8_t *p, const qint8x16_t &v)
{
vst1q_qs8(p, v);
}
inline void internal_vst1q(qint8_t *p, const qint16x8_t &v)
{
vst1_qs8(p, vqmovn_s16(v));
}
inline void internal_vst1q(qint16_t *p, const qint16x8_t &v)
{
vst1q_qs16(p, v);
}
// Internal vdup
inline float32x4_t internal_vdupq_n(float v)
{
return vdupq_n_f32(v);
}
inline qint8x16_t internal_vdupq_n(qint8_t v)
{
return vdupq_n_qs8(v);
}
inline qint16x8_t internal_vdupq_n(qint16_t v)
{
return vdupq_n_qs16(v);
}
// Internal vadd
inline float32x4_t internal_vqaddq(const float32x4_t &x, const float32x4_t &y)
{
return vaddq_f32(x, y);
}
inline qint8x16_t internal_vqaddq(const qint8x16_t &x, const qint8x16_t &y)
{
return vqaddq_qs8(x, y);
}
inline qint16x8_t internal_vqaddq(const qint16x8_t &x, const qint16x8_t &y)
{
return vqaddq_qs16(x, y);
}
template <typename T1, typename T2, bool in_place>
void accumulate_bias(ITensor *input, const ITensor *bias, const Window window, ITensor *output)
{
Iterator in(input, window);
if(in_place) // In place accumulate
{
execute_window_loop(window, [&](const Coordinates & id)
{
// Get bias and pointer to input
const auto in_ptr = reinterpret_cast<T1 *>(in.ptr());
const auto vb = internal_vdupq_n(static_cast<T1>(*reinterpret_cast<const T2 *>(bias->ptr_to_element(Coordinates(id.z())))));
// Accumulate bias
internal_vst1q(in_ptr, internal_vqaddq(internal_vld1q(in_ptr), vb));
},
in);
}
else // Out of place accumulate
{
Iterator out(output, window);
execute_window_loop(window, [&](const Coordinates & id)
{
// Get bias and pointer to input
const auto in_ptr = reinterpret_cast<const T1 *>(in.ptr());
const auto out_ptr = reinterpret_cast<T2 *>(out.ptr());
const auto vb = internal_vdupq_n(static_cast<T1>(*reinterpret_cast<const T2 *>(bias->ptr_to_element(Coordinates(id.z())))));
// Accumulate bias
internal_vst1q(out_ptr, internal_vqaddq(internal_vld1q(in_ptr), vb));
},
in, out);
}
}
} // namespace
NEDirectConvolutionLayerBiasAccumulateKernel::NEDirectConvolutionLayerBiasAccumulateKernel()
: _func(nullptr), _input(nullptr), _bias(nullptr), _output(nullptr)
{
}
void NEDirectConvolutionLayerBiasAccumulateKernel::configure(ITensor *input, const ITensor *bias, ITensor *output)
{
ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QS8, DataType::QS16, DataType::F32);
ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(bias, 1, DataType::QS8, DataType::QS16, DataType::F32);
ARM_COMPUTE_ERROR_ON(input->info()->fixed_point_position() != bias->info()->fixed_point_position());
if(output != nullptr)
{
ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::QS8, DataType::QS16, DataType::F32);
ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(bias, output);
ARM_COMPUTE_ERROR_ON_MISMATCHING_FIXED_POINT(bias, output);
}
ARM_COMPUTE_ERROR_ON(bias->info()->num_dimensions() > 1);
_func = nullptr;
_bias = bias;
_input = input;
_output = output;
const unsigned int num_elems_processed_per_iteration = 16 / element_size_from_data_type(input->info()->data_type());
// Configure kernel window
Window win = calculate_max_window(*input->info(), Steps(num_elems_processed_per_iteration));
AccessWindowHorizontal input_access(input->info(), 0, num_elems_processed_per_iteration);
AccessWindowStatic bias_access(bias->info(), 0, 0, bias->info()->dimension(0), bias->info()->dimension(1));
if(output != nullptr)
{
AccessWindowHorizontal output_access(output->info(), 0, num_elems_processed_per_iteration);
update_window_and_padding(win, input_access, output_access, bias_access);
output_access.set_valid_region(win, ValidRegion(Coordinates(), output->info()->tensor_shape()));
}
else
{
update_window_and_padding(win, input_access, bias_access);
input_access.set_valid_region(win, ValidRegion(Coordinates(), input->info()->tensor_shape()));
}
INEKernel::configure(win);
// Set appropriate function
if(input->info()->data_type() == DataType::F32)
{
_func = (output == nullptr) ? &accumulate_bias<float, float, true> : &accumulate_bias<float, float, false>;
}
else if(input->info()->data_type() == DataType::QS8)
{
_func = (output == nullptr) ? &accumulate_bias<qint8_t, qint8_t, true> : &accumulate_bias<qint8_t, qint8_t, false>;
}
else if(input->info()->data_type() == DataType::QS16 && bias->info()->data_type() == DataType::QS8)
{
_func = (output == nullptr) ? &accumulate_bias<qint16_t, qint8_t, true> : &accumulate_bias<qint16_t, qint8_t, false>;
}
else
{
ARM_COMPUTE_ERROR("Unsupported combination of types among the inputs.");
}
}
void NEDirectConvolutionLayerBiasAccumulateKernel::run(const Window &window)
{
ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INEKernel::window(), window);
ARM_COMPUTE_ERROR_ON(_func == nullptr);
(*_func)(_input, _bias, window, _output);
}
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