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/*
* 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/runtime/CL/functions/CLReductionOperation.h"
#include "arm_compute/core/CL/ICLTensor.h"
#include "arm_compute/core/CL/kernels/CLReductionOperationKernel.h"
#include "arm_compute/core/Error.h"
#include "arm_compute/core/PixelValue.h"
#include "arm_compute/core/TensorInfo.h"
#include "arm_compute/core/Validate.h"
#include "arm_compute/runtime/CL/CLScheduler.h"
#include "arm_compute/runtime/Tensor.h"
#include "support/ToolchainSupport.h"
using namespace arm_compute;
namespace
{
unsigned int calculate_number_of_stages(const ITensorInfo *input, unsigned int axis)
{
// We need only 1 stage for all axis except x-axis and x-axis for QASYMM8.
if(axis != 0 || (axis == 0 && is_data_type_quantized(input->data_type())))
{
return 1;
}
// Calculate number of WGs. 16 elements per thread, 8 threads per WG
const unsigned int num_of_wg = ceil(input->dimension(0) / 128.f);
// Calculate number of stages. First stage performs op and the rest reduction sum
// depending on the size of the input. Last stage should have only 1 WG.
const unsigned int num_of_stages = num_of_wg / 128 + 2;
return num_of_stages;
}
} // namespace
CLReductionOperation::CLReductionOperation(std::shared_ptr<IMemoryManager> memory_manager)
: _memory_group(std::move(memory_manager)), _results_vector(), _reduction_kernels_vector(), _border_handlers_vector(), _num_of_stages(), _reduction_axis(), _is_serial()
{
}
Status CLReductionOperation::validate(const ITensorInfo *input, const ITensorInfo *output, unsigned int axis, ReductionOperation op)
{
const unsigned int num_of_stages = calculate_number_of_stages(input, axis);
bool is_serial = is_data_type_quantized(input->data_type()) || axis != 0;
if(is_serial)
{
ARM_COMPUTE_RETURN_ON_ERROR(CLReductionOperationKernel::validate(input, output, axis, op));
}
else
{
// Create temporary tensor infos
auto sums_vector = arm_compute::support::cpp14::make_unique<TensorInfo[]>(num_of_stages - 1);
// Create intermediate tensor info
TensorShape shape{ input->tensor_shape() };
for(unsigned int i = 0; i < num_of_stages - 1; i++)
{
shape.set(0, ceil(shape.x() / 128.f));
sums_vector[i].set_data_type(input->data_type());
sums_vector[i].set_tensor_shape(shape);
sums_vector[i].set_num_channels(input->num_channels());
}
ReductionOperation first_kernel_op;
ReductionOperation intermediate_kernel_op;
ReductionOperation last_kernel_op;
switch(op)
{
case ReductionOperation::SUM:
case ReductionOperation::MEAN_SUM:
first_kernel_op = ReductionOperation::SUM;
intermediate_kernel_op = ReductionOperation::SUM;
last_kernel_op = op;
break;
case ReductionOperation::SUM_SQUARE:
first_kernel_op = ReductionOperation::SUM_SQUARE;
intermediate_kernel_op = ReductionOperation::SUM;
last_kernel_op = ReductionOperation::SUM;
break;
case ReductionOperation::PROD:
first_kernel_op = ReductionOperation::PROD;
intermediate_kernel_op = ReductionOperation::PROD;
last_kernel_op = ReductionOperation::PROD;
break;
default:
ARM_COMPUTE_ERROR("Not supported");
}
// Validate ReductionOperation only on first kernel
ARM_COMPUTE_RETURN_ON_ERROR(CLReductionOperationKernel::validate(input, sums_vector.get(), axis, first_kernel_op));
// Validate ReductionOperation on intermediate stages
for(unsigned int i = 1; i < num_of_stages - 1; ++i)
{
ARM_COMPUTE_RETURN_ON_ERROR(CLReductionOperationKernel::validate(sums_vector.get() + i - 1, sums_vector.get() + i, axis, intermediate_kernel_op));
}
// Validate ReductionOperation on the last stage
const unsigned int last_stage = num_of_stages - 1;
ARM_COMPUTE_RETURN_ON_ERROR(CLReductionOperationKernel::validate(sums_vector.get() + last_stage - 1, output, axis, last_kernel_op, input->dimension(0)));
}
return Status{};
}
void CLReductionOperation::configure(ICLTensor *input, ICLTensor *output, unsigned int axis, ReductionOperation op)
{
_num_of_stages = calculate_number_of_stages(input->info(), axis);
_reduction_axis = axis;
_is_serial = is_data_type_quantized(input->info()->data_type()) || axis != 0;
// Configure reduction operation kernels
_reduction_kernels_vector = arm_compute::support::cpp14::make_unique<CLReductionOperationKernel[]>(_num_of_stages);
// Create temporary tensors
if(_is_serial)
{
_reduction_kernels_vector[0].configure(input, output, axis, op, 0);
}
else
{
_border_handlers_vector = arm_compute::support::cpp14::make_unique<CLFillBorderKernel[]>(_num_of_stages);
_results_vector = arm_compute::support::cpp14::make_unique<CLTensor[]>(_num_of_stages - 1);
TensorShape shape{ input->info()->tensor_shape() };
for(unsigned int i = 0; i < _num_of_stages - 1; i++)
{
shape.set(0, ceil(shape.x() / 128.f));
_results_vector[i].allocator()->init(input->info()->clone()->set_tensor_shape(shape));
}
// Apply ReductionOperation only on first kernel
_memory_group.manage(_results_vector.get());
ReductionOperation first_kernel_op;
ReductionOperation intermediate_kernel_op;
ReductionOperation last_kernel_op;
PixelValue pixelValue;
switch(op)
{
case ReductionOperation::SUM:
case ReductionOperation::MEAN_SUM:
first_kernel_op = ReductionOperation::SUM;
intermediate_kernel_op = ReductionOperation::SUM;
last_kernel_op = op;
pixelValue = PixelValue();
break;
case ReductionOperation::SUM_SQUARE:
first_kernel_op = ReductionOperation::SUM_SQUARE;
intermediate_kernel_op = ReductionOperation::SUM;
last_kernel_op = ReductionOperation::SUM;
pixelValue = PixelValue();
break;
case ReductionOperation::PROD:
first_kernel_op = ReductionOperation::PROD;
intermediate_kernel_op = ReductionOperation::PROD;
last_kernel_op = ReductionOperation::PROD;
pixelValue = PixelValue(1, input->info()->data_type());
break;
default:
ARM_COMPUTE_ERROR("Not supported");
}
_reduction_kernels_vector[0].configure(input, _results_vector.get(), axis, first_kernel_op);
_border_handlers_vector[0].configure(input, _reduction_kernels_vector[0].border_size(), BorderMode::CONSTANT, pixelValue);
// Apply ReductionOperation on intermediate stages
for(unsigned int i = 1; i < _num_of_stages - 1; ++i)
{
_memory_group.manage(_results_vector.get() + i);
_reduction_kernels_vector[i].configure(_results_vector.get() + i - 1, _results_vector.get() + i, axis, intermediate_kernel_op);
_border_handlers_vector[i].configure(_results_vector.get() + i - 1, _reduction_kernels_vector[i].border_size(), BorderMode::CONSTANT, pixelValue);
_results_vector[i - 1].allocator()->allocate();
}
// Apply ReductionOperation on the last stage
const unsigned int last_stage = _num_of_stages - 1;
const unsigned int input_width = input->info()->dimension(0);
_reduction_kernels_vector[last_stage].configure(_results_vector.get() + last_stage - 1, output, axis, last_kernel_op, input_width);
_border_handlers_vector[last_stage].configure(_results_vector.get() + last_stage - 1, _reduction_kernels_vector[last_stage].border_size(), BorderMode::CONSTANT, pixelValue);
_results_vector[last_stage - 1].allocator()->allocate();
}
}
void CLReductionOperation::run()
{
MemoryGroupResourceScope scope_mg(_memory_group);
if(_is_serial)
{
CLScheduler::get().enqueue(_reduction_kernels_vector[0], false);
}
else
{
for(unsigned int i = 0; i < _num_of_stages; ++i)
{
CLScheduler::get().enqueue(_border_handlers_vector[i], false);
CLScheduler::get().enqueue(_reduction_kernels_vector[i], false);
}
}
}
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