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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/CL/kernels/CLNormalizationLayerKernel.h"
#include "arm_compute/core/AccessWindowStatic.h"
#include "arm_compute/core/CL/CLHelpers.h"
#include "arm_compute/core/CL/CLKernelLibrary.h"
#include "arm_compute/core/CL/CLValidate.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/Window.h"
#include "support/StringSupport.h"
using namespace arm_compute;
namespace
{
constexpr unsigned int num_elems_processed_per_iteration = 4;
Status validate_arguments(const ITensorInfo *input, const ITensorInfo *output, NormalizationLayerInfo norm_info)
{
ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(input);
ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F16, DataType::F32);
ARM_COMPUTE_RETURN_ERROR_ON_DATA_LAYOUT_NOT_IN(input, DataLayout::NCHW, DataLayout::NHWC);
ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(output);
ARM_COMPUTE_RETURN_ERROR_ON_MSG(!(norm_info.norm_size() % 2), "Normalization size should be odd");
// Checks performed when output is configured
if(output->total_size() != 0)
{
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_LAYOUT(input, output);
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(input, output);
}
return Status{};
}
std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input, ITensorInfo *output, NormalizationLayerInfo norm_info)
{
// Output tensor auto initialization if not yet initialized
auto_init_if_empty(*output, *input->clone());
const unsigned int norm_idx = get_normalization_dimension_index(input->data_layout(), norm_info);
const bool is_norm_accross_width = norm_idx == 0;
const unsigned int border_width = is_norm_accross_width ? num_elems_processed_per_iteration - 1 : 0;
const BorderSize border_size = BorderSize(0, border_width);
Window win = calculate_max_window(*input, Steps(num_elems_processed_per_iteration));
bool window_changed = false;
// We do not use a Rectangle window for IN_MAP_2D as we clamp the top and bottom accesses inside the kernel, avoiding padding
// Reads can occur within the valid region of the input
if(is_norm_accross_width)
{
AccessWindowStatic input_access(input, -border_size.left, 0, input->dimension(0) + border_size.right, 0);
window_changed = window_changed || update_window_and_padding(win, input_access);
}
else
{
AccessWindowHorizontal input_access(input, -border_size.left, num_elems_processed_per_iteration);
window_changed = window_changed || update_window_and_padding(win, input_access);
}
AccessWindowHorizontal output_access(output, 0, num_elems_processed_per_iteration);
window_changed = window_changed || update_window_and_padding(win, 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_pair(err, win);
}
} // namespace
CLNormalizationLayerKernel::CLNormalizationLayerKernel()
: _input(nullptr), _output(nullptr), _border_size(0), _is_norm_across_width(false)
{
}
BorderSize CLNormalizationLayerKernel::border_size() const
{
return _border_size;
}
void CLNormalizationLayerKernel::configure(const ICLTensor *input, ICLTensor *output, NormalizationLayerInfo norm_info)
{
configure(CLKernelLibrary::get().get_compile_context(), input, output, norm_info);
}
void CLNormalizationLayerKernel::configure(const CLCompileContext &compile_context, const ICLTensor *input, ICLTensor *output, NormalizationLayerInfo norm_info)
{
ARM_COMPUTE_ERROR_ON_NULLPTR(input, output);
// Output tensor auto initialization if not yet initialized
auto_init_if_empty(*output->info(), *input->info()->clone());
// Perform validation step
ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), output->info(), norm_info));
_input = input;
_output = output;
const DataLayout data_layout = input->info()->data_layout();
const unsigned int norm_idx = get_normalization_dimension_index(data_layout, norm_info);
_is_norm_across_width = norm_idx == 0;
const unsigned int border_width = _is_norm_across_width ? num_elems_processed_per_iteration - 1 : 0;
_border_size = BorderSize(0, border_width);
const bool is_in_map_2D = (norm_info.type() == NormType::IN_MAP_2D);
// Set build options
CLBuildOptions build_opts;
build_opts.add_option(("-DDATA_TYPE=" + get_cl_type_from_data_type(input->info()->data_type())));
build_opts.add_option(("-DCOEFF=" + float_to_string_with_full_precision(norm_info.scale_coeff())));
build_opts.add_option(("-DBETA=" + float_to_string_with_full_precision(norm_info.beta())));
build_opts.add_option(("-DKAPPA=" + float_to_string_with_full_precision(norm_info.kappa())));
build_opts.add_option(("-DVEC_SIZE=" + support::cpp11::to_string(num_elems_processed_per_iteration)));
build_opts.add_option(("-DRADIUS=" + support::cpp11::to_string(norm_info.norm_size() / 2)));
build_opts.add_option(("-DNUM_SLICES=" + support::cpp11::to_string(input->info()->dimension(2))));
build_opts.add_option_if(is_in_map_2D, "-DIN_MAP_2D");
build_opts.add_option_if(norm_info.is_in_map() || (data_layout == DataLayout::NHWC && norm_info.is_cross_map()), "-DWIDTH_SIZE=" + support::cpp11::to_string(input->info()->dimension(0)));
// Create kernel
std::string kernel_name;
if(norm_info.is_in_map())
{
kernel_name = "normalization_layer_in_map_" + lower_string(string_from_data_layout(data_layout));
}
else
{
if(data_layout == DataLayout::NCHW)
{
kernel_name = "normalization_layer_cross_map";
}
else
{
// 1D Cross-Map normalization in NHWC is the same as 1D In-Map normalization in NCHW
kernel_name = "normalization_layer_in_map_nchw";
}
}
_kernel = create_kernel(compile_context, kernel_name, build_opts.options());
// Configure kernel window
auto win_config = validate_and_configure_window(input->info(), output->info(), norm_info);
ARM_COMPUTE_ERROR_THROW_ON(win_config.first);
ICLKernel::configure_internal(win_config.second);
// Set config_id for enabling LWS tuning
_config_id = "normalization_layer_";
_config_id += lower_string(string_from_data_type(input->info()->data_type()));
_config_id += "_";
_config_id += support::cpp11::to_string(static_cast<std::underlying_type<NormType>::type>(norm_info.type()));
_config_id += "_";
_config_id += support::cpp11::to_string(norm_info.norm_size());
_config_id += "_";
_config_id += support::cpp11::to_string(input->info()->dimension(0));
_config_id += "_";
_config_id += support::cpp11::to_string(input->info()->dimension(1));
}
Status CLNormalizationLayerKernel::validate(const ITensorInfo *input, const ITensorInfo *output, NormalizationLayerInfo norm_info)
{
ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, output, norm_info));
ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(input->clone().get(), output->clone().get(), norm_info).first);
return Status{};
}
void CLNormalizationLayerKernel::run(const Window &window, cl::CommandQueue &queue)
{
ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(IKernel::window(), window);
const int collapsed_dimension = _is_norm_across_width ? Window::DimZ : 4;
Window window_collapsed = window.collapse_if_possible(ICLKernel::window(), collapsed_dimension);
Window slice = window_collapsed.first_slice_window_3D();
do
{
unsigned int idx = 0;
add_3D_tensor_argument(idx, _input, slice);
add_3D_tensor_argument(idx, _output, slice);
enqueue(queue, *this, slice, lws_hint());
}
while(window_collapsed.slide_window_slice_3D(slice));
}
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