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/*
* Copyright (c) 2017-2020 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/NEL2NormalizeLayerKernel.h"
#include "arm_compute/core/Error.h"
#include "arm_compute/core/Helpers.h"
#include "arm_compute/core/ITensor.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"
#include "arm_compute/core/NEON/wrapper/wrapper.h"
#include <arm_neon.h>
#include <cmath>
namespace arm_compute
{
namespace
{
constexpr int max_input_tensor_dim = 3;
template <typename T, int S>
void l2_normalize_X(const ITensor *in, const ITensor *sum, ITensor *out, float epsilon, const Window &window)
{
using ExactTagType = typename wrapper::traits::neon_vector<T, S>::tag_type;
const int window_step_x = 16 / data_size_from_type(in->info()->data_type());
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 input_it(in, win_collapsed);
Iterator sum_it(sum, win_collapsed);
Iterator output_it(out, win_collapsed);
execute_window_loop(win_collapsed, [&](const Coordinates &)
{
const auto in_ptr = reinterpret_cast<const T *>(input_it.ptr());
const auto out_ptr = reinterpret_cast<T *>(output_it.ptr());
const T sum_value = *reinterpret_cast<const T *>(sum_it.ptr());
const T norm_value = static_cast<T>(1.f) / std::sqrt(std::max(sum_value, static_cast<T>(epsilon)));
const auto vec_norm_value = wrapper::vdup_n(norm_value, ExactTagType{});
// Compute elements over vector steps
int x = window_start_x;
for(; x <= (window_end_x - window_step_x); x += window_step_x)
{
wrapper::vstore(out_ptr + x, wrapper::vmul(wrapper::vloadq(in_ptr + x), vec_norm_value));
}
// Compute left-over elements
for(; x < window_end_x; ++x)
{
out_ptr[x] = in_ptr[x] * norm_value;
}
},
input_it, sum_it, output_it);
}
template <typename T, int S>
void l2_normalize_YZ(const ITensor *in, const ITensor *sum, ITensor *out, float epsilon, const Window &window, size_t axis)
{
using ExactTagType = typename wrapper::traits::neon_vector<T, S>::tag_type;
const int window_step_x = 16 / data_size_from_type(in->info()->data_type());
const auto window_start_x = static_cast<int>(window.x().start());
const auto window_end_x = static_cast<int>(window.x().end());
Window win = window;
win.set(Window::DimX, Window::Dimension(0, 1, 1));
Window window_sum(win);
window_sum.set(axis, Window::Dimension(0, 0, 0));
Iterator input_it(in, win);
Iterator sum_it(sum, window_sum);
Iterator output_it(out, win);
const auto vec_eps = wrapper::vdup_n(static_cast<T>(epsilon), ExactTagType{});
execute_window_loop(win, [&](const Coordinates &)
{
const auto in_ptr = reinterpret_cast<const T *>(input_it.ptr());
const auto sum_ptr = reinterpret_cast<const T *>(sum_it.ptr());
const auto out_ptr = reinterpret_cast<T *>(output_it.ptr());
// Compute elements over vector steps
int x = window_start_x;
for(; x <= (window_end_x - window_step_x); x += window_step_x)
{
const auto vec_norm_value = wrapper::vinvsqrt(wrapper::vmax(wrapper::vloadq(sum_ptr + x), vec_eps));
wrapper::vstore(out_ptr + x, wrapper::vmul(wrapper::vloadq(in_ptr + x), vec_norm_value));
}
// Compute left-over elements
for(; x < window_end_x; ++x)
{
const T norm_value = static_cast<T>(1.f) / std::sqrt(std::max(sum_ptr[x], static_cast<T>(epsilon)));
out_ptr[x] = in_ptr[x] * norm_value;
}
},
input_it, sum_it, output_it);
}
Status validate_arguments(const ITensorInfo *input, const ITensorInfo *sum, const ITensorInfo *output, int axis, float epsilon)
{
ARM_COMPUTE_UNUSED(epsilon);
const uint32_t actual_axis = wrap_around(axis, max_input_tensor_dim);
ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, sum, output);
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, sum);
ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F16, DataType::F32);
ARM_COMPUTE_RETURN_ERROR_ON_MSG(actual_axis > 2, "Actual axis greater than 2 is not supported");
ARM_COMPUTE_RETURN_ERROR_ON_MSG(actual_axis >= TensorShape::num_max_dimensions, "Actual normalization axis greater than max number of dimensions");
// Reduce shape on axis
TensorShape sum_shape = input->tensor_shape();
sum_shape.set(actual_axis, 1);
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DIMENSIONS(sum->tensor_shape(), sum_shape);
if(output->total_size() != 0)
{
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(input, output);
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DIMENSIONS(input->tensor_shape(), output->tensor_shape());
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_LAYOUT(input, output);
}
return Status{};
}
std::tuple<Status, Window> validate_and_configure_window(ITensorInfo *input, ITensorInfo *output)
{
Window win = calculate_max_window(*input, Steps());
// Output auto initialization if not yet initialized
auto_init_if_empty(*output, input->tensor_shape(), 1, input->data_type());
// NEL2NormalizeLayerKernel 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_tuple(Status{}, win);
}
} // namespace
NEL2NormalizeLayerKernel::NEL2NormalizeLayerKernel()
: _input(nullptr), _sum(nullptr), _output(nullptr), _actual_axis(0), _epsilon(1e-12)
{
}
void NEL2NormalizeLayerKernel::configure(const ITensor *input, const ITensor *sum, ITensor *output, int axis, float epsilon)
{
ARM_COMPUTE_ERROR_ON_NULLPTR(input, sum, output);
ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), sum->info(), output->info(), axis, epsilon));
_input = input;
_sum = sum;
_output = output;
_actual_axis = wrap_around(axis, max_input_tensor_dim);
_epsilon = epsilon;
// Configure kernel window
auto win_config = validate_and_configure_window(_input->info(), _output->info());
ARM_COMPUTE_ERROR_THROW_ON(std::get<0>(win_config));
INEKernel::configure(std::get<1>(win_config));
}
Status NEL2NormalizeLayerKernel::validate(const ITensorInfo *input, const ITensorInfo *sum, const ITensorInfo *output, int axis, float epsilon)
{
ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, sum, output, axis, epsilon));
ARM_COMPUTE_RETURN_ON_ERROR(std::get<0>(validate_and_configure_window(input->clone().get(), output->clone().get())));
return Status{};
}
void NEL2NormalizeLayerKernel::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);
if(_actual_axis > 2)
{
ARM_COMPUTE_ERROR("Unsupported normalization axis");
}
switch(_input->info()->data_type())
{
case DataType::F32:
(_actual_axis == Window::DimX) ? l2_normalize_X<float, 4>(_input, _sum, _output, _epsilon, window) : l2_normalize_YZ<float, 4>(_input, _sum, _output, _epsilon, window, _actual_axis);
break;
#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
case DataType::F16:
(_actual_axis == Window::DimX) ? l2_normalize_X<float16_t, 8>(_input, _sum, _output, _epsilon, window) : l2_normalize_YZ<float16_t, 8>(_input, _sum, _output, _epsilon, window, _actual_axis);
break;
#endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
default:
ARM_COMPUTE_ERROR("Not implemented");
}
}
} // namespace arm_compute
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