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/*
* Copyright (c) 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/CLArithmeticDivisionKernel.h"
#include "arm_compute/core/CL/CLHelpers.h"
#include "arm_compute/core/CL/CLValidate.h"
#include "arm_compute/core/CL/ICLTensor.h"
using namespace arm_compute;
namespace
{
constexpr unsigned int num_elems_processed_per_iteration = 16;
Status validate_arguments(const ITensorInfo *input1, const ITensorInfo *input2, const ITensorInfo *output)
{
ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input1, input2, output);
ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(input1);
ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input1, 1, DataType::F16, DataType::F32);
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input1, input2);
const TensorShape out_shape = TensorShape::broadcast_shape(input1->tensor_shape(), input2->tensor_shape());
ARM_COMPUTE_RETURN_ERROR_ON_MSG(out_shape.total_size() == 0, "Inputs are not broadcast compatible");
// Validate in case of configured output
if(output->total_size() > 0)
{
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input1, output);
ARM_COMPUTE_RETURN_ERROR_ON_MSG(detail::have_different_dimensions(out_shape, output->tensor_shape(), 0),
"Wrong shape for output");
}
return Status{};
}
std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input1, ITensorInfo *input2, ITensorInfo *output)
{
const std::pair<TensorShape, ValidRegion> broadcast_pair = ITensorInfo::broadcast_shape_and_valid_region(*input1, *input2);
const TensorShape &out_shape = broadcast_pair.first;
const ValidRegion &valid_region = broadcast_pair.second;
// Auto initialize output if not initialized
{
set_shape_if_empty(*output, out_shape);
if(input1->data_type() == DataType::F16 && input2->data_type() == DataType::F16)
{
set_format_if_unknown(*output, Format::F16);
}
else if(input1->data_type() == DataType::F32 || input2->data_type() == DataType::F32)
{
set_format_if_unknown(*output, Format::F32);
}
}
Window win = calculate_max_window(valid_region, Steps(num_elems_processed_per_iteration));
Window win_input1 = win.broadcast_if_dimension_le_one(*input1);
Window win_input2 = win.broadcast_if_dimension_le_one(*input2);
AccessWindowHorizontal input1_access(input1, 0, num_elems_processed_per_iteration);
AccessWindowHorizontal input2_access(input2, 0, num_elems_processed_per_iteration);
AccessWindowHorizontal output_access(output, 0, num_elems_processed_per_iteration);
bool window_changed = update_window_and_padding(win_input1, input1_access)
|| update_window_and_padding(win_input2, input2_access)
|| update_window_and_padding(win, output_access);
output_access.set_valid_region(win, valid_region);
Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{};
return std::make_pair(err, win);
}
} // namespace
CLArithmeticDivisionKernel::CLArithmeticDivisionKernel()
: _input1(nullptr), _input2(nullptr), _output(nullptr)
{
}
void CLArithmeticDivisionKernel::configure(const ICLTensor *input1, const ICLTensor *input2, ICLTensor *output)
{
ARM_COMPUTE_ERROR_ON_NULLPTR(input1, input2, output);
ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input1->info(), input2->info(), output->info()));
// Configure kernel window
auto win_config = validate_and_configure_window(input1->info(), input2->info(), output->info());
ARM_COMPUTE_ERROR_THROW_ON(win_config.first);
_input1 = input1;
_input2 = input2;
_output = output;
// Set kernel build options
std::set<std::string> build_opts;
build_opts.emplace("-DDATA_TYPE_IN1=" + get_cl_type_from_data_type(input1->info()->data_type()));
build_opts.emplace("-DDATA_TYPE_IN2=" + get_cl_type_from_data_type(input2->info()->data_type()));
build_opts.emplace("-DDATA_TYPE_OUT=" + get_cl_type_from_data_type(output->info()->data_type()));
// Create kernel
_kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel("arithmetic_div", build_opts));
ICLKernel::configure_internal(win_config.second);
}
Status CLArithmeticDivisionKernel::validate(const ITensorInfo *input1, const ITensorInfo *input2, const ITensorInfo *output)
{
ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input1, input2, output));
ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(input1->clone().get(), input2->clone().get(), output->clone().get()).first);
return Status{};
}
void CLArithmeticDivisionKernel::run(const Window &window, cl::CommandQueue &queue)
{
ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(ICLKernel::window(), window);
const TensorShape &in_shape1 = _input1->info()->tensor_shape();
const TensorShape &in_shape2 = _input2->info()->tensor_shape();
const TensorShape &out_shape = _output->info()->tensor_shape();
bool can_collapse = true;
if(std::min(in_shape1.total_size(), in_shape2.total_size()) > 1)
{
can_collapse = (std::min(in_shape1.num_dimensions(), in_shape2.num_dimensions()) > Window::DimZ);
for(size_t d = Window::DimZ; can_collapse && (d < out_shape.num_dimensions()); d++)
{
can_collapse = (in_shape1[d] == in_shape2[d]);
}
}
bool has_collapsed = false;
Window collapsed = can_collapse ? window.collapse_if_possible(ICLKernel::window(), Window::DimZ, &has_collapsed) : window;
const TensorShape &in_shape1_collapsed = has_collapsed ? in_shape1.collapsed_from(Window::DimZ) : in_shape1;
const TensorShape &in_shape2_collapsed = has_collapsed ? in_shape2.collapsed_from(Window::DimZ) : in_shape2;
Window slice = collapsed.first_slice_window_3D();
Window slice_input1 = slice.broadcast_if_dimension_le_one(in_shape1_collapsed);
Window slice_input2 = slice.broadcast_if_dimension_le_one(in_shape2_collapsed);
do
{
unsigned int idx = 0;
add_3D_tensor_argument(idx, _input1, slice_input1);
add_3D_tensor_argument(idx, _input2, slice_input2);
add_3D_tensor_argument(idx, _output, slice);
enqueue(queue, *this, slice);
collapsed.slide_window_slice_3D(slice_input1);
collapsed.slide_window_slice_3D(slice_input2);
}
while(collapsed.slide_window_slice_3D(slice));
}
BorderSize CLArithmeticDivisionKernel::border_size() const
{
const unsigned int replicateSize = _output->info()->dimension(0) - std::min(_input1->info()->dimension(0), _input2->info()->dimension(0));
const unsigned int border = std::min<unsigned int>(num_elems_processed_per_iteration - 1U, replicateSize);
return BorderSize(0, border, 0, 0);
}
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