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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/NEBatchNormalizationLayerKernel.h"
#include "arm_compute/core/Helpers.h"
#include "arm_compute/core/NEON/NEFixedPoint.h"
#include "arm_compute/core/NEON/NEMath.h"
#include "arm_compute/core/TensorInfo.h"
#include "arm_compute/core/Utils.h"
#include "arm_compute/core/Validate.h"
#include "arm_compute/core/Window.h"
using namespace arm_compute;
NEBatchNormalizationLayerKernel::NEBatchNormalizationLayerKernel()
: _func(nullptr), _input(nullptr), _output(nullptr), _mean(nullptr), _var(nullptr), _gamma(nullptr), _beta(nullptr), _epsilon()
{
}
void batch_normalization_q8(const ITensor *in, ITensor *out, const ITensor *mean, const ITensor *var, const ITensor *beta, const ITensor *gamma, float epsilon, const Window &window)
{
Iterator input(in, window);
Iterator output(out, window);
// Hold information about the current feature map we are iterating.
// Only compute denominator and NEON vectors once per feature map.
int slice = -1;
int fixed_point_position = in->info()->fixed_point_position();
const auto input_mean = reinterpret_cast<const qint8_t *>(mean->ptr_to_element(Coordinates(0, 0)));
const auto input_var = reinterpret_cast<const qint8_t *>(var->ptr_to_element(Coordinates(0, 0)));
const auto input_gamma = reinterpret_cast<const qint8_t *>(gamma->ptr_to_element(Coordinates(0, 0)));
const auto input_beta = reinterpret_cast<const qint8_t *>(beta->ptr_to_element(Coordinates(0, 0)));
qint8x16_t mean_vec = vdupq_n_qs8(0);
qint8x16_t var_vec = vdupq_n_qs8(0);
qint8x16_t gamma_vec = vdupq_n_qs8(0);
qint8x16_t beta_vec = vdupq_n_qs8(0);
qint8x16_t denominator = vdupq_n_qs8(0);
const qint8x16_t epsilon_vec = vdupq_n_qs8(scvt_qs8_f32(epsilon, fixed_point_position));
execute_window_loop(window, [&](const Coordinates & id)
{
if(slice != id.z())
{
// Conctruct vectors
mean_vec = vdupq_n_qs8(*(input_mean + id.z()));
var_vec = vdupq_n_qs8(*(input_var + id.z()));
gamma_vec = vdupq_n_qs8(*(input_gamma + id.z()));
beta_vec = vdupq_n_qs8(*(input_beta + id.z()));
// Calculate denominator
denominator = vqinvsqrtq_qs8(vqaddq_qs8(var_vec, epsilon_vec), fixed_point_position);
slice = id.z();
}
// Calculate x bar and store results
const qint8x16_t numerator = vqsubq_qs8(vld1q_qs8(reinterpret_cast<const qint8_t *>(input.ptr())), mean_vec);
const qint8x16_t x_bar = vqmulq_qs8(numerator, denominator, fixed_point_position);
vst1q_qs8(reinterpret_cast<qint8_t *>(output.ptr()), vqmlaq_qs8(beta_vec, x_bar, gamma_vec, fixed_point_position));
},
input, output);
}
void batch_normalization_fp32(const ITensor *in, ITensor *out, const ITensor *mean, const ITensor *var, const ITensor *beta, const ITensor *gamma, float epsilon, const Window &window)
{
Iterator input(in, window);
Iterator output(out, window);
// Hold information about the current feature map we are iterating.
// Only compute denominator and NEON vectors once per feature map.
int slice = -1;
const auto input_mean = reinterpret_cast<const float *>(mean->ptr_to_element(Coordinates(0, 0)));
const auto input_var = reinterpret_cast<const float *>(var->ptr_to_element(Coordinates(0, 0)));
const auto input_gamma = reinterpret_cast<const float *>(gamma->ptr_to_element(Coordinates(0, 0)));
const auto input_beta = reinterpret_cast<const float *>(beta->ptr_to_element(Coordinates(0, 0)));
float32x4_t mean_vec = vdupq_n_f32(0.0);
float32x4_t var_vec = vdupq_n_f32(0.0);
float32x4_t gamma_vec = vdupq_n_f32(0.0);
float32x4_t beta_vec = vdupq_n_f32(0.0);
float32x4_t denominator = vdupq_n_f32(0.0);
const float32x4_t epsilon_vec = vdupq_n_f32(epsilon);
execute_window_loop(window, [&](const Coordinates & id)
{
if(slice != id.z())
{
// Conctruct vectors
mean_vec = vdupq_n_f32(*(input_mean + id.z()));
var_vec = vdupq_n_f32(*(input_var + id.z()));
gamma_vec = vdupq_n_f32(*(input_gamma + id.z()));
beta_vec = vdupq_n_f32(*(input_beta + id.z()));
// Calculate denominator
denominator = vinvsqrtq_f32(vaddq_f32(var_vec, epsilon_vec));
slice = id.z();
}
// Calculate x bar and store results
const float32x4_t numerator = vsubq_f32(vld1q_f32(reinterpret_cast<const float *>(input.ptr())), mean_vec);
const float32x4_t x_bar = vmulq_f32(numerator, denominator);
vst1q_f32(reinterpret_cast<float *>(output.ptr()), vmlaq_f32(beta_vec, x_bar, gamma_vec));
},
input, output);
}
void NEBatchNormalizationLayerKernel::configure(const ITensor *input, ITensor *output, const ITensor *mean, const ITensor *var, const ITensor *beta, const ITensor *gamma, float epsilon)
{
ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QS8, DataType::F32);
ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::QS8, DataType::F32);
ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(mean, 1, DataType::QS8, DataType::F32);
ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(var, 1, DataType::QS8, DataType::F32);
ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(gamma, 1, DataType::QS8, DataType::F32);
ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(beta, 1, DataType::QS8, DataType::F32);
ARM_COMPUTE_ERROR_ON_MISMATCHING_SHAPES(input, output);
ARM_COMPUTE_ERROR_ON_MISMATCHING_SHAPES(mean, var);
ARM_COMPUTE_ERROR_ON_MISMATCHING_SHAPES(mean, beta);
ARM_COMPUTE_ERROR_ON_MISMATCHING_SHAPES(mean, gamma);
ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
ARM_COMPUTE_ERROR_ON_MISMATCHING_FIXED_POINT(input, output);
_input = input;
_output = output;
_mean = mean;
_var = var;
_gamma = gamma;
_beta = beta;
_epsilon = epsilon;
unsigned int num_elems_processed_per_iteration = 0;
switch(input->info()->data_type())
{
case DataType::QS8:
_func = &batch_normalization_q8;
num_elems_processed_per_iteration = 16;
break;
case DataType::F32:
_func = &batch_normalization_fp32;
num_elems_processed_per_iteration = 4;
break;
default:
ARM_COMPUTE_ERROR("Element size not supported");
break;
}
Window win = calculate_max_window(*input->info(), Steps(num_elems_processed_per_iteration));
AccessWindowHorizontal input_access(input->info(), 0, num_elems_processed_per_iteration);
AccessWindowHorizontal output_access(output->info(), 0, num_elems_processed_per_iteration);
update_window_and_padding(win, input_access, output_access);
output_access.set_valid_region(win, input->info()->valid_region());
INEKernel::configure(win);
}
void NEBatchNormalizationLayerKernel::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, _output, _mean, _var, _beta, _gamma, _epsilon, window);
}
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