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/*
* Copyright (c) 2019-2020 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/NEGEMMLowpQuantizeDownInt32ToInt8ScaleByFixedPointKernel.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/NEAsymm.h"
#include "arm_compute/core/TensorInfo.h"
#include "arm_compute/core/Types.h"
#include "arm_compute/core/Utils.h"
#include "arm_compute/core/Validate.h"
#include "arm_compute/core/Window.h"
#include "arm_compute/core/utils/misc/ShapeCalculator.h"
#include <arm_neon.h>
#include <cstddef>
#include <cstdint>
namespace arm_compute
{
namespace
{
Status validate_arguments(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, int min, int max)
{
ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::S32);
ARM_COMPUTE_RETURN_ERROR_ON(min > max);
// Check biases if exist
if(bias != nullptr)
{
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, bias);
ARM_COMPUTE_RETURN_ERROR_ON(bias->num_dimensions() > 1);
ARM_COMPUTE_RETURN_ERROR_ON(input->dimension(0) != bias->dimension(0));
}
if(output->total_size() != 0)
{
ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::QASYMM8_SIGNED);
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(output, input);
}
return Status{};
}
std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input, ITensorInfo *output)
{
// Output auto initialization if not yet initialized
auto_init_if_empty(*output, input->clone()->set_data_type(DataType::QASYMM8_SIGNED));
// Configure kernel window
Window win = calculate_max_window(*input, Steps());
// NEGEMMLowpQuantizeDownInt32ToInt8ScaleByFixedPointKernel doesn't need padding so update_window_and_padding() can be skipped
Coordinates coord;
coord.set_num_dimensions(output->num_dimensions());
output->set_valid_region(ValidRegion(coord, output->tensor_shape()));
return std::make_pair(Status{}, win);
}
} // namespace
template <bool is_bounded_relu>
void NEGEMMLowpQuantizeDownInt32ToInt8ScaleByFixedPointKernel::run(const Window &window)
{
const int32x4_t result_offset_after_shift_s32 = vdupq_n_s32(_result_offset_after_shift);
const int8x16_t min_s8 = vdupq_n_s8(static_cast<int8_t>(_min));
const int8x16_t max_s8 = vdupq_n_s8(static_cast<int8_t>(_max));
ARM_COMPUTE_UNUSED(min_s8, max_s8);
const int window_step_x = 16;
const auto window_start_x = static_cast<int>(window.x().start());
const auto window_end_x = static_cast<int>(window.x().end());
Window win_collapsed = window.collapse_if_possible(window, Window::DimZ);
win_collapsed.set(Window::DimX, Window::Dimension(0, 1, 1));
Iterator in(_input, win_collapsed);
Iterator out(_output, win_collapsed);
if(_bias != nullptr)
{
Window win_biases;
win_biases.set(Window::DimX, Window::Dimension(0, 1, 1));
win_biases.set(Window::DimY, Window::Dimension(0, 1, 1));
Iterator bias(_bias, win_biases);
execute_window_loop(win_collapsed, [&](const Coordinates &)
{
// Compute 16 elements per iteration
int x = window_start_x;
for(; x <= (window_end_x - window_step_x); x += window_step_x)
{
int32x4x4_t in_s32 =
{
{
vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + x + 0),
vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + x + 4),
vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + x + 8),
vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + x + 12)
}
};
const int32x4x4_t bias_s32 =
{
{
vld1q_s32(reinterpret_cast<const int32_t *>(bias.ptr()) + x + 0),
vld1q_s32(reinterpret_cast<const int32_t *>(bias.ptr()) + x + 4),
vld1q_s32(reinterpret_cast<const int32_t *>(bias.ptr()) + x + 8),
vld1q_s32(reinterpret_cast<const int32_t *>(bias.ptr()) + x + 12)
}
};
// Add the bias to GEMM's result
in_s32.val[0] = vaddq_s32(in_s32.val[0], bias_s32.val[0]);
in_s32.val[1] = vaddq_s32(in_s32.val[1], bias_s32.val[1]);
in_s32.val[2] = vaddq_s32(in_s32.val[2], bias_s32.val[2]);
in_s32.val[3] = vaddq_s32(in_s32.val[3], bias_s32.val[3]);
vst1q_s8(reinterpret_cast<int8_t *>(out.ptr() + x),
finalize_quantization(in_s32, _result_fixedpoint_multiplier, _result_shift, result_offset_after_shift_s32, min_s8, max_s8, is_bounded_relu));
}
// Compute left-over elements
for(; x < window_end_x; ++x)
{
const int32_t bias_value = *(reinterpret_cast<const int32_t *>(bias.ptr()) + x);
int32_t in_value = *(reinterpret_cast<const int32_t *>(in.ptr()) + x);
// Add bias
in_value += bias_value;
// Finalize and store the result
*reinterpret_cast<int8_t *>(out.ptr() + x) = finalize_quantization(in_value, _result_fixedpoint_multiplier, _result_shift, _result_offset_after_shift,
static_cast<int8_t>(_min), static_cast<int8_t>(_max), is_bounded_relu);
}
},
in, out, bias);
}
else
{
execute_window_loop(win_collapsed, [&](const Coordinates &)
{
// Compute 16 elements per iteration
int x = window_start_x;
for(; x <= (window_end_x - window_step_x); x += window_step_x)
{
int32x4x4_t in_s32 =
{
{
vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + x + 0),
vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + x + 4),
vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + x + 8),
vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + x + 12)
}
};
vst1q_s8(reinterpret_cast<int8_t *>(out.ptr() + x),
finalize_quantization(in_s32, _result_fixedpoint_multiplier, _result_shift, result_offset_after_shift_s32, min_s8, max_s8, is_bounded_relu));
}
// Compute left-over elements
for(; x < window_end_x; ++x)
{
const int32_t in_value = *(reinterpret_cast<const int32_t *>(in.ptr()) + x);
// Finalize and store the result
*reinterpret_cast<int8_t *>(out.ptr() + x) = finalize_quantization(in_value, _result_fixedpoint_multiplier, _result_shift, _result_offset_after_shift,
static_cast<int8_t>(_min), static_cast<int8_t>(_max), is_bounded_relu);
}
},
in, out);
}
}
NEGEMMLowpQuantizeDownInt32ToInt8ScaleByFixedPointKernel::NEGEMMLowpQuantizeDownInt32ToInt8ScaleByFixedPointKernel()
: _func(nullptr), _input(nullptr), _bias(nullptr), _output(nullptr), _result_fixedpoint_multiplier(0), _result_shift(0), _result_offset_after_shift(0), _min(0), _max(0)
{
}
void NEGEMMLowpQuantizeDownInt32ToInt8ScaleByFixedPointKernel::configure(const ITensor *input, const ITensor *bias, ITensor *output, int result_fixedpoint_multiplier, int result_shift,
int result_offset_after_shift, int min, int max)
{
// Perform validate step
ARM_COMPUTE_ERROR_ON_NULLPTR(input, output);
ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), (bias != nullptr) ? bias->info() : nullptr, output->info(), min, max));
_input = input;
_bias = bias;
_output = output;
_result_fixedpoint_multiplier = result_fixedpoint_multiplier;
_result_shift = result_shift;
_result_offset_after_shift = result_offset_after_shift;
_min = min;
_max = max;
// Configure kernel window
auto win_config = validate_and_configure_window(input->info(), output->info());
ARM_COMPUTE_ERROR_THROW_ON(win_config.first);
INEKernel::configure(win_config.second);
// Check if we need to clamp the result using min and max
const bool is_bounded_relu = !(min <= -128 && max >= 127);
_func = is_bounded_relu ? &NEGEMMLowpQuantizeDownInt32ToInt8ScaleByFixedPointKernel::run<true> : &NEGEMMLowpQuantizeDownInt32ToInt8ScaleByFixedPointKernel::run<false>;
}
Status NEGEMMLowpQuantizeDownInt32ToInt8ScaleByFixedPointKernel::validate(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, int min, int max)
{
ARM_COMPUTE_ERROR_ON_NULLPTR(input, output);
ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, bias, output, min, max));
ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(input->clone().get(), output->clone().get()).first);
return Status{};
}
void NEGEMMLowpQuantizeDownInt32ToInt8ScaleByFixedPointKernel::run(const Window &window, const ThreadInfo &info)
{
ARM_COMPUTE_UNUSED(info);
ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INEKernel::window(), window);
(this->*_func)(window);
}
} // namespace arm_compute
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