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/*
* Copyright (c) 2023 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.
*/
#include "Builders.h"
#include "kernels/Sum.h"
namespace luci_interpreter
{
std::unique_ptr<Kernel> build_kernel_CircleSum(const luci::CircleNode *circle_node,
KernelBuilderHelper &helper)
{
const auto *node = loco::must_cast<const luci::CircleSum *>(circle_node);
assert(node->arity() == 2);
const Tensor *input = helper.getInputTensor(node->input());
const Tensor *axes = helper.getInputTensor(node->reduction_indices());
Tensor *output = helper.getOutputTensor(node);
auto temp_index_unique =
std::make_unique<Tensor>(DataType::S32, Shape({}), AffineQuantization{}, "");
temp_index_unique->set_observable(false);
temp_index_unique->set_data_buffer(nullptr);
Tensor *temp_index =
helper.getRuntimeGraph(node->graph())->addTensor(std::move(temp_index_unique));
auto resolved_axes_unique =
std::make_unique<Tensor>(DataType::S32, Shape({}), AffineQuantization{}, "");
resolved_axes_unique->set_observable(false);
resolved_axes_unique->set_data_buffer(nullptr);
Tensor *resolved_axes =
helper.getRuntimeGraph(node->graph())->addTensor(std::move(resolved_axes_unique));
ReducerParams params{};
params.keep_dims = node->keep_dims();
return std::make_unique<kernels::Sum>(input, axes, output, temp_index, resolved_axes, params);
}
} // namespace luci_interpreter
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