Slice.cc

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/*
 * Copyright (c) 2025 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 "../KernelGenerator.h"
#include "../Validator.h"
 
#include <AclKernelGen.h>
 
namespace onert::backend::acl_neon
{
 
void Validator::visit(const ir::operation::Slice &) { _supported = true; }
 
void KernelGenerator::visit(const ir::operation::Slice &node)
{
  const auto output_index{node.getOutputs().at(0)};
  const auto input_index{node.getInputs().at(ir::operation::Slice::Input::INPUT)};
  const auto begins_index{node.getInputs().at(ir::operation::Slice::Input::BEGINS)};
  const auto sizes_index{node.getInputs().at(ir::operation::Slice::Input::SIZES)};
 
  auto outputData_tensor = _tensor_reg->getAclTensor(output_index);
  auto inputData_tensor = _tensor_reg->getAclTensor(input_index);
 
  // Set initializers for indices data such as order of inputData
  int input_rank = _ctx.at(input_index).shape().rank();
  std::vector<int32_t> starts;
  std::vector<int32_t> ends;
  starts.resize(input_rank, 0);
  ends.resize(input_rank, 0);
  {
    auto beginData_base = _ctx.at(begins_index).data()->base();
    auto sizeData_base = _ctx.at(sizes_index).data()->base();
    [[maybe_unused]] const int beginData_size = _ctx.at(begins_index).shape().num_elements();
    [[maybe_unused]] const int sizeData_size = _ctx.at(sizes_index).shape().num_elements();
 
    using ir::DataType;
 
    assert(_ctx.at(begins_index).typeInfo().type() == DataType::INT32);
    assert(_ctx.at(sizes_index).typeInfo().type() == DataType::INT32);
    assert(beginData_size == input_rank);
    assert(sizeData_size == input_rank);
 
    assert(beginData_base != nullptr);
    for (int n = 0; n < input_rank; ++n)
    {
      auto axis = ::onert::backend::acl_common::ToARMComputeAxis(input_rank, n).value();
 
      int32_t begin_value = *(reinterpret_cast<const int32_t *>(beginData_base) + n);
      starts[axis] = begin_value;
 
      int32_t size_value = *(reinterpret_cast<const int32_t *>(sizeData_base) + n);
      ends[axis] = begin_value + size_value;
    }
  }
 
  ::arm_compute::Coordinates starts_set;
  ::arm_compute::Coordinates ends_set;
 
  for (size_t i = 0; i < starts.size(); ++i)
  {
    starts_set.set(i, starts[i]);
    ends_set.set(i, ends[i]);
  }
 
  auto fn = acl_common::generateLayer<arm_compute::NESlice>(
    inputData_tensor->handle(), outputData_tensor->handle(), starts_set, ends_set);
 
  _return_fn = acl_common::asAclFunction(std::move(fn));
}
 
} // namespace onert::backend::acl_neon