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/*
* Copyright (c) 2017-2021 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 "src/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 "src/core/CL/CLValidate.h"
#include "src/core/helpers/AutoConfiguration.h"
#include "src/core/helpers/WindowHelpers.h"
#include "support/StringSupport.h"
namespace arm_compute
{
namespace
{
constexpr int max_input_tensor_dim = 3;
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_F16_UNSUPPORTED(input);
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_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{};
}
} // namespace
CLL2NormalizeLayerKernel::CLL2NormalizeLayerKernel()
: _input(nullptr), _sum(nullptr), _output(nullptr), _actual_axis(0), _epsilon(1e-12)
{
}
void CLL2NormalizeLayerKernel::configure(const ICLTensor *input, const ICLTensor *sum, ICLTensor *output, int axis, float epsilon)
{
configure(CLKernelLibrary::get().get_compile_context(), input, sum, output, axis, epsilon);
}
void CLL2NormalizeLayerKernel::configure(const CLCompileContext &compile_context, const ICLTensor *input, const ICLTensor *sum, ICLTensor *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));
auto padding_info = get_padding_info({ input, sum, output });
_input = input;
_sum = sum;
_output = output;
_actual_axis = wrap_around(axis, max_input_tensor_dim);
_epsilon = epsilon;
const unsigned int vec_size_x = adjust_vec_size(max_cl_vector_width / input->info()->element_size(), input->info()->dimension(0));
const int vec_size_x_leftovers = input->info()->dimension(0) % vec_size_x;
// 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("-DVEC_SIZE_X=" + support::cpp11::to_string(vec_size_x));
build_opts.add_option("-DVEC_SIZE_LEFTOVER_X=" + support::cpp11::to_string(vec_size_x_leftovers));
// Create kernel
std::string kernel_name;
unsigned int idx = 0;
switch(_actual_axis)
{
case 0:
kernel_name = "l2_normalize_x";
idx = num_arguments_per_2D_tensor() * 3;
break;
case 1:
kernel_name = "l2_normalize_y";
idx = num_arguments_per_2D_tensor() * 3;
break;
case 2:
kernel_name = "l2_normalize_z";
idx = num_arguments_per_3D_tensor() * 3;
break;
default:
ARM_COMPUTE_ERROR("Axis not supported");
}
_kernel = create_kernel(compile_context, kernel_name, build_opts.options());
// Set epsilon argument
if(input->info()->data_type() == DataType::F32)
{
_kernel.setArg<cl_float>(idx, _epsilon);
}
else
{
_kernel.setArg<cl_half>(idx, _epsilon);
}
// Configure kernel window
Window win = calculate_max_window(*input->info(), Steps(vec_size_x));
// Output tensor auto initialization if not yet initialized
auto_init_if_empty(*output->info(), input->info()->tensor_shape(), 1, input->info()->data_type());
ICLKernel::configure_internal(win);
ARM_COMPUTE_ERROR_ON(has_padding_changed(padding_info));
}
Status CLL2NormalizeLayerKernel::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));
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(_actual_axis)
{
case 0:
{
window_sum.set(Window::DimX, 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, lws_hint());
}
while(window.slide_window_slice_2D(in_slice) && window.slide_window_slice_2D(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, lws_hint());
}
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, lws_hint());
}
while(window.slide_window_slice_3D(in_slice) && window.slide_window_slice_3D(sum_slice));
}
break;
default:
ARM_COMPUTE_ERROR("Not supported");
}
}
} // namespace arm_compute
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