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/*
* Copyright (c) 2019 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/NEMeanStdDevNormalizationKernel.h"
#include "arm_compute/core/CPP/Validate.h"
#include "arm_compute/core/Helpers.h"
#include "arm_compute/core/ITensor.h"
#include "arm_compute/core/NEON/NEMath.h"
#include "arm_compute/core/NEON/wrapper/wrapper.h"
#include "arm_compute/core/TensorInfo.h"
#include "arm_compute/core/Types.h"
#include "arm_compute/core/Window.h"
namespace arm_compute
{
namespace
{
Status validate_arguments(const ITensorInfo *input, const ITensorInfo *output, float epsilon)
{
ARM_COMPUTE_UNUSED(epsilon);
ARM_COMPUTE_RETURN_ERROR_ON_CPU_F16_UNSUPPORTED(input);
ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input);
ARM_COMPUTE_RETURN_ERROR_ON_MSG(input->num_dimensions() > 2, "Input tensor cannot have more than 2 dimensions");
ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F16, DataType::F32);
// Checks performed when output is configured
if((output != nullptr) && (output->total_size() != 0))
{
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(input, output);
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
}
return Status{};
}
std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input, ITensorInfo *output)
{
if(output != nullptr)
{
ARM_COMPUTE_ERROR_ON_NULLPTR(input, output);
// Output auto inizialitation if not yet initialized
auto_init_if_empty(*output, *input);
}
// This kernel doesn't need padding. A left-over for loop on dimension X, we cannot have any read or write out of memory
// For this reason num_elems_processed_per_iteration is set to 1
Window win = calculate_max_window(*input, Steps());
if(output != nullptr)
{
output->set_valid_region(ValidRegion(Coordinates(), output->tensor_shape()));
}
return std::make_pair(Status{}, win);
}
} // namespace
template <typename ScalarType, int size>
void NEMeanStdDevNormalizationKernel::mean_stddev_normalization(const Window &window)
{
using ExactTagType = typename wrapper::traits::neon_vector<ScalarType, size>::tag_type;
// Set build options
Window win = window;
win.set(Window::DimX, Window::Dimension(0, 1, 1));
const int window_step_x = size;
const auto window_start_x = static_cast<int>(window.x().start());
const auto window_end_x = static_cast<int>(window.x().end());
Iterator input(_input, win);
Iterator output(_output, win);
execute_window_loop(win, [&](const Coordinates &)
{
int x = window_start_x;
auto in_ptr = reinterpret_cast<const ScalarType *>(input.ptr());
auto out_ptr = reinterpret_cast<ScalarType *>(output.ptr());
auto sum_vec = wrapper::vdup_n(static_cast<ScalarType>(0.f), ExactTagType{});
auto sum_sq_vec = wrapper::vdup_n(static_cast<ScalarType>(0.f), ExactTagType{});
for(; x <= (window_end_x - window_step_x); x += window_step_x)
{
auto data = wrapper::vloadq(in_ptr + x);
sum_vec = wrapper::vadd(sum_vec, data);
sum_sq_vec = wrapper::vadd(sum_sq_vec, wrapper::vmul(data, data));
}
auto sum_carry_res = wrapper::vpadd(wrapper::vgethigh(sum_vec), wrapper::vgetlow(sum_vec));
auto sum_sq_carry_res = wrapper::vpadd(wrapper::vgethigh(sum_sq_vec), wrapper::vgetlow(sum_sq_vec));
for(int i = 0; i < size / 4; ++i)
{
sum_carry_res = wrapper::vpadd(sum_carry_res, sum_carry_res);
sum_sq_carry_res = wrapper::vpadd(sum_sq_carry_res, sum_sq_carry_res);
}
auto sum = wrapper::vgetlane(sum_carry_res, 0);
auto sum_sq = wrapper::vgetlane(sum_sq_carry_res, 0);
// Compute left-over elements
for(; x < window_end_x; ++x)
{
ScalarType data = *(in_ptr + x);
sum += data;
sum_sq += data * data;
}
ScalarType mean = sum / _input->info()->dimension(0);
ScalarType var = (sum_sq / _input->info()->dimension(0)) - (mean * mean);
ScalarType stddev_inv = 1.f / sqrt(var + _epsilon);
auto mean_vec = wrapper::vdup_n(mean, ExactTagType{});
auto stddev_inv_vec = wrapper::vdup_n(stddev_inv, ExactTagType{});
for(x = window_start_x; x <= (window_end_x - window_step_x); x += window_step_x)
{
auto data = wrapper::vloadq(in_ptr + x);
auto res = wrapper::vmul(wrapper::vsub(data, mean_vec), stddev_inv_vec);
// Store results
wrapper::vstore(out_ptr + x, res);
}
for(; x < window_end_x; ++x)
{
*(out_ptr + x) = (*(in_ptr + x) - mean) * stddev_inv;
}
},
input, output);
}
NEMeanStdDevNormalizationKernel::NEMeanStdDevNormalizationKernel()
: _input(nullptr), _output(nullptr), _epsilon(1e-8f), _func(nullptr)
{
}
void NEMeanStdDevNormalizationKernel::configure(ITensor *input, ITensor *output, float epsilon)
{
ARM_COMPUTE_ERROR_ON_NULLPTR(input);
ARM_COMPUTE_ERROR_THROW_ON(NEMeanStdDevNormalizationKernel::validate(input->info(), (output != nullptr) ? output->info() : nullptr, epsilon));
_input = input;
_output = (output == nullptr) ? input : output;
_epsilon = epsilon;
// Configure kernel window
auto win_config = validate_and_configure_window(input->info(), (output == nullptr) ? nullptr : output->info());
ARM_COMPUTE_ERROR_THROW_ON(win_config.first);
ICPPKernel::configure(win_config.second);
// Configure function to run based on different data types
const DataType data_type = input->info()->data_type();
switch(data_type)
{
case DataType::F32:
_func = &NEMeanStdDevNormalizationKernel::mean_stddev_normalization<float, 4>;
break;
#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
case DataType::F16:
_func = &NEMeanStdDevNormalizationKernel::mean_stddev_normalization<float16_t, 8>;
break;
#endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
default:
ARM_COMPUTE_ERROR("Not Supported");
break;
}
}
Status NEMeanStdDevNormalizationKernel::validate(const ITensorInfo *input, const ITensorInfo *output, float epsilon)
{
ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, output, epsilon));
ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(input->clone().get(), (output != nullptr) ? output->clone().get() : nullptr).first);
return Status{};
}
void NEMeanStdDevNormalizationKernel::run(const Window &window, const ThreadInfo &info)
{
ARM_COMPUTE_UNUSED(info);
ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(IKernel::window(), window);
ARM_COMPUTE_ERROR_ON(_func == nullptr);
(this->*_func)(window);
}
} // namespace arm_compute
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