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/*
* Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
/*
* Copyright (c) 2017-2019 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/CL/kernels/CLMultiplyScaleFactorKernel.h"
#include "arm_compute/core/AccessWindowStatic.h"
#include "arm_compute/core/CL/CLHelpers.h"
#include "arm_compute/core/CL/CLKernelLibraryEx.h"
#include "arm_compute/core/CL/CLValidate.h"
#include "arm_compute/core/CL/ICLTensor.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/StringSupport.h"
using namespace arm_compute;
namespace
{
Status validate_arguments(const ITensorInfo *input, const ITensorInfo *scale_factor,
const ITensorInfo *output)
{
ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, output);
ARM_COMPUTE_RETURN_ERROR_ON(input->num_dimensions() > 2);
ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::S32);
ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(scale_factor, 1, DataType::F16,
DataType::F32);
ARM_COMPUTE_RETURN_ERROR_ON(scale_factor->tensor_shape().total_size() == 0);
ARM_COMPUTE_RETURN_ERROR_ON(scale_factor->num_dimensions() > 1);
ARM_COMPUTE_RETURN_ERROR_ON(scale_factor->dimension(0) != input->dimension(1));
ARM_COMPUTE_RETURN_ERROR_ON(output->tensor_shape().total_size() == 0);
ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::F16, DataType::F32);
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(input, output);
// Checks performed when output is configured
if ((output->total_size() != 0))
{
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(input, output);
}
return Status{};
}
std::tuple<Status, Window> validate_and_configure_window(const ITensorInfo *input,
ITensorInfo *output)
{
// Configure kernel window
Window win = calculate_max_window(*input, Steps());
// Output tensor auto initialization if not yet initialized
auto_init_if_empty(*output, input->tensor_shape(), 1, DataType::F32);
// CLMultiplyScaleFactorKernel 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
CLMultiplyScaleFactorKernel::CLMultiplyScaleFactorKernel()
: _input(nullptr), _scale_factor(nullptr), _output(nullptr), _multiplier(1.f)
{
}
void CLMultiplyScaleFactorKernel::configure(const ICLTensor *input, const ICLTensor *scale_factor,
ICLTensor *output, float multiplier)
{
ARM_COMPUTE_ERROR_ON_NULLPTR(input, output);
ARM_COMPUTE_ERROR_THROW_ON(
validate_arguments(input->info(), scale_factor->info(), output->info()));
_input = input;
_scale_factor = scale_factor;
_output = output;
_multiplier = multiplier;
const int vec_size_x = 16 / output->info()->element_size();
const int output_width_x = output->info()->tensor_shape().x();
const bool multi_access_x = (output_width_x / vec_size_x > 0);
// Create and update the window (if needed)
Window win = calculate_max_window(*output->info());
if (multi_access_x)
{
win.set(Window::DimX,
Window::Dimension(win.x().start(), ceil_to_multiple(win.x().end(), vec_size_x),
vec_size_x));
}
ICLKernel::configure_internal(win);
// Create kernel
CLBuildOptions build_opts;
build_opts.add_option("-DVEC_SIZE=" + support::cpp11::to_string(vec_size_x));
build_opts.add_option("-DDATA_TYPE=" + get_cl_type_from_data_type(output->info()->data_type()));
build_opts.add_option_if(
multi_access_x, "-DLAST_ACCESSED_X=" +
support::cpp11::to_string(std::max<int>(output_width_x - vec_size_x, 0)));
_kernel = static_cast<cl::Kernel>(
CLKernelLibraryEx::get().create_kernel("multiply_scale_factor", build_opts.options()));
}
Status CLMultiplyScaleFactorKernel::validate(const ITensorInfo *input,
const ITensorInfo *scale_factor,
const ITensorInfo *output)
{
ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, scale_factor, output));
ARM_COMPUTE_RETURN_ON_ERROR(
std::get<0>(validate_and_configure_window(input->clone().get(), output->clone().get())));
return Status{};
}
void CLMultiplyScaleFactorKernel::run(const Window &window, cl::CommandQueue &queue)
{
ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(ICLKernel::window(), window);
Window window_collapsed = window.collapse_if_possible(ICLKernel::window(), Window::DimZ);
Window slice = window_collapsed.first_slice_window_2D();
// Set scale_factor window
Window win_scale = calculate_max_window(*_scale_factor->info(), Steps());
do
{
unsigned int idx = 0;
add_2D_tensor_argument(idx, _input, slice);
add_1D_tensor_argument(idx, _scale_factor, win_scale);
add_2D_tensor_argument(idx, _output, slice);
_kernel.setArg<float>(idx++, _multiplier);
enqueue(queue, *this, slice, lws_hint());
} while (window_collapsed.slide_window_slice_2D(slice));
}
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