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/*
* Copyright (c) 2017-2018 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/CL/kernels/CLL2NormalizeLayerKernel.h"
#include "arm_compute/core/CL/CLHelpers.h"
#include "arm_compute/core/CL/CLKernelLibrary.h"
#include "arm_compute/core/CL/ICLTensor.h"
#include "arm_compute/core/Helpers.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 "support/ToolchainSupport.h"
using namespace arm_compute;
CLL2NormalizeLayerKernel::CLL2NormalizeLayerKernel()
: _input(nullptr), _sum(nullptr), _output(nullptr), _axis(0), _epsilon(1e-12)
{
}
namespace
{
Status validate_arguments(const ITensorInfo *input, const ITensorInfo *sum, const ITensorInfo *output, unsigned int axis, float epsilon)
{
ARM_COMPUTE_UNUSED(epsilon);
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(axis > 2, "Axis greater than 2 is not supported");
ARM_COMPUTE_RETURN_ERROR_ON_MSG(axis >= TensorShape::num_max_dimensions, "Reduction axis greater than max number of dimensions");
// Reduce shape on axis
TensorShape sum_shape = input->tensor_shape();
sum_shape.set(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_LAYOUT(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());
}
return Status{};
}
std::tuple<Status, Window> validate_and_configure_window(ITensorInfo *input, ITensorInfo *output)
{
const unsigned int num_elems_processed_per_iteration = 16;
Window win = calculate_max_window(*input, Steps(num_elems_processed_per_iteration));
// Output tensor auto initialization if not yet initialized
auto_init_if_empty(*output, input->tensor_shape(), 1, input->data_type());
AccessWindowHorizontal input_access(input, 0, num_elems_processed_per_iteration);
AccessWindowHorizontal output_access(output, 0, num_elems_processed_per_iteration);
bool window_changed = update_window_and_padding(win, input_access, output_access);
output_access.set_valid_region(win, input->valid_region());
Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{};
return std::make_tuple(err, win);
}
} // namespace
void CLL2NormalizeLayerKernel::configure(const ICLTensor *input, const ICLTensor *sum, ICLTensor *output, unsigned 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;
_axis = axis;
_epsilon = epsilon;
const unsigned int num_elems_processed_per_iteration = 16;
// Set build options
std::set<std::string> build_opts;
build_opts.emplace(("-DDATA_TYPE=" + get_cl_type_from_data_type(input->info()->data_type())));
build_opts.emplace(("-DVEC_SIZE=" + support::cpp11::to_string(num_elems_processed_per_iteration)));
// Create kernel
std::string kernel_name;
unsigned int idx = 0;
switch(axis)
{
case 0:
kernel_name = "x";
idx = num_arguments_per_1D_tensor() * 3;
break;
case 1:
kernel_name = "y";
idx = num_arguments_per_2D_tensor() * 3;
break;
case 2:
kernel_name = "z";
idx = num_arguments_per_3D_tensor() * 3;
break;
default:
ARM_COMPUTE_ERROR("Not supported");
}
_kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel("l2_normalize_" + kernel_name, build_opts));
// Set epsilon argument
if(input->info()->data_type() == DataType::F32)
{
_kernel.setArg<cl_uint>(idx, _epsilon);
}
else
{
_kernel.setArg<cl_ushort>(idx, _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));
ICLKernel::configure_internal(std::get<1>(win_config));
}
Status CLL2NormalizeLayerKernel::validate(const ITensorInfo *input, const ITensorInfo *sum, const ITensorInfo *output, unsigned 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 CLL2NormalizeLayerKernel::run(const Window &window, cl::CommandQueue &queue)
{
ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(IKernel::window(), window);
Window window_sum(window);
switch(_axis)
{
case 0:
{
window_sum.set(Window::DimX, Window::Dimension(0, 0, 0));
Window in_slice = window.first_slice_window_1D();
Window sum_slice = window_sum.first_slice_window_1D();
do
{
unsigned int idx = 0;
add_1D_tensor_argument(idx, _input, in_slice);
add_1D_tensor_argument(idx, _sum, sum_slice);
add_1D_tensor_argument(idx, _output, in_slice);
enqueue(queue, *this, in_slice);
}
while(window.slide_window_slice_1D(in_slice) && window.slide_window_slice_1D(sum_slice));
}
break;
case 1:
{
window_sum.set(Window::DimY, Window::Dimension(0, 0, 0));
Window in_slice = window.first_slice_window_2D();
Window sum_slice = window_sum.first_slice_window_2D();
do
{
unsigned int idx = 0;
add_2D_tensor_argument(idx, _input, in_slice);
add_2D_tensor_argument(idx, _sum, sum_slice);
add_2D_tensor_argument(idx, _output, in_slice);
enqueue(queue, *this, in_slice);
}
while(window.slide_window_slice_2D(in_slice) && window.slide_window_slice_2D(sum_slice));
}
break;
case 2:
{
window_sum.set(Window::DimZ, Window::Dimension(0, 0, 0));
Window in_slice = window.first_slice_window_3D();
Window sum_slice = window_sum.first_slice_window_3D();
do
{
unsigned int idx = 0;
add_3D_tensor_argument(idx, _input, in_slice);
add_3D_tensor_argument(idx, _sum, sum_slice);
add_3D_tensor_argument(idx, _output, in_slice);
enqueue(queue, *this, in_slice);
}
while(window.slide_window_slice_3D(in_slice) && window.slide_window_slice_3D(sum_slice));
}
break;
default:
ARM_COMPUTE_ERROR("Not supported");
}
}
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