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/*
* Copyright (c) 2019 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 <AclActivationBuilder.h>
#include <AclFunction.h>
#include <Convert.h>
#include <Swizzle.h>
#include "ConstantInitializer.h"
namespace onert
{
namespace backend
{
namespace acl_cl
{
ConstantInitializer::ConstantInitializer(const ir::Operands &operands,
const std::shared_ptr<ITensorRegistry> &tensor_reg)
: acl_common::AclConstantInitializer{operands, tensor_reg}
{
// DO NOTHING
}
void ConstantInitializer::visit(const ir::operation::EmbeddingLookup &node)
{
copyInputInitialize(node, ir::operation::EmbeddingLookup::LOOKUPS);
}
void ConstantInitializer::visit(const ir::operation::Gather &node)
{
copyInputInitialize(node, ir::operation::Gather::INDICES);
}
void ConstantInitializer::visit(const ir::operation::HashtableLookup &node)
{
copyInputInitialize(node, ir::operation::HashtableLookup::LOOKUPS);
copyInputInitialize(node, ir::operation::HashtableLookup::KEYS);
}
void ConstantInitializer::visit(const ir::operation::SpaceToBatchND &node)
{
const auto &block_size_index = node.getInputs().at(ir::operation::SpaceToBatchND::BLOCK_SIZE);
const auto &block_size_obj = _operands.at(block_size_index);
if (block_size_obj.isConstant())
{
_init_map[block_size_index] = acl_common::initReverseOrder<int32_t>;
}
const auto &paddings_index = node.getInputs().at(ir::operation::SpaceToBatchND::PADDINGS);
const auto &paddings_obj = _operands.at(paddings_index);
if (paddings_obj.isConstant())
{
_init_map[paddings_index] = [](const ir::Operand &model_obj, backend::ITensor &obj) {
assert(model_obj.data());
const auto &shape = model_obj.shape();
const auto base = reinterpret_cast<const int32_t *>(model_obj.data()->base());
assert(model_obj.shape().rank() == 2);
assert(obj.getShape().dim(0) == 2);
obj.access([&](ITensor &tensor) {
for (auto i = 0; i < shape.dim(0); ++i)
{
for (auto j = 0; j < shape.dim(1); ++j)
{
const int32_t value = base[i * 2 + j];
int32_t *into = reinterpret_cast<int32_t *>(
tensor.buffer() + tensor.calcOffset({shape.dim(0) - i - 1, j}));
*into = value;
}
}
});
};
}
}
void ConstantInitializer::visit(const ir::operation::Reverse &node)
{
const auto &output_index = node.getOutputs().at(0);
const auto &input_index = node.getInputs().at(ir::operation::Reverse::Input::INPUT);
const auto &input_obj = _operands.at(input_index);
const auto &axis_index = node.getInputs().at(ir::operation::Reverse::Input::AXIS);
const auto &axis_obj = _operands.at(axis_index);
const auto ifm_rank = input_obj.shape().rank();
const auto frontend_layout = this->_current_layout;
auto output_tensor = this->_tensor_reg->getITensor(output_index);
const auto backend_layout = output_tensor->layout();
if (axis_obj.isConstant())
{
_init_map[axis_index] = [ifm_rank, frontend_layout, backend_layout](const ir::Operand &operand,
backend::ITensor &obj) {
assert(operand.data());
const auto axis_value = *(reinterpret_cast<const int32_t *>(operand.data()->base()));
int32_t axis_tmp = axis_value;
if (axis_tmp < 0)
{
axis_tmp = axis_tmp + ifm_rank;
}
auto axis =
acl_common::ToARMComputeAxis(ifm_rank, axis_tmp, frontend_layout, backend_layout).value();
obj.access([&](ITensor &tensor) {
int32_t *into = reinterpret_cast<int32_t *>(tensor.buffer());
*into = (int32_t)axis;
});
};
}
}
} // namespace acl_cl
} // namespace backend
} // namespace onert
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