BinaryArithmetic.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_cl
{
 
void Validator::visit(const ir::operation::BinaryArithmetic &) { _supported = true; }
 
void KernelGenerator::visit(const ir::operation::BinaryArithmetic &node)
{
  const auto ofm_index{node.getOutputs().at(0)};
  const auto lhs_index{node.getInputs().at(ir::operation::BinaryArithmetic::Input::LHS)};
  const auto rhs_index{node.getInputs().at(ir::operation::BinaryArithmetic::Input::RHS)};
 
  const auto activation = node.param().activation;
 
  auto ofm_tensor = _tensor_reg->getAclTensor(ofm_index);
  auto lhs_tensor = _tensor_reg->getAclTensor(lhs_index);
  auto rhs_tensor = _tensor_reg->getAclTensor(rhs_index);
 
  const auto act_info = acl_common::asActivationLayerInfo(activation);
 
  std::unique_ptr<arm_compute::IFunction> fn;
  switch (node.param().arithmetic_type)
  {
    case ir::operation::BinaryArithmetic::ArithmeticType::ADD:
    {
      arm_compute::CLArithmeticAddition::validate(lhs_tensor->info(), rhs_tensor->info(),
                                                  ofm_tensor->info(),
                                                  arm_compute::ConvertPolicy::SATURATE, act_info)
        .throw_if_error();
      fn = acl_common::generateLayer<arm_compute::CLArithmeticAddition>(
        lhs_tensor->handle(), rhs_tensor->handle(), ofm_tensor->handle(),
        arm_compute::ConvertPolicy::SATURATE, act_info);
      break;
    }
    case ir::operation::BinaryArithmetic::ArithmeticType::SUB:
    {
      arm_compute::CLArithmeticSubtraction::validate(lhs_tensor->info(), rhs_tensor->info(),
                                                     ofm_tensor->info(),
                                                     arm_compute::ConvertPolicy::SATURATE, act_info)
        .throw_if_error();
      fn = acl_common::generateLayer<arm_compute::CLArithmeticSubtraction>(
        lhs_tensor->handle(), rhs_tensor->handle(), ofm_tensor->handle(),
        arm_compute::ConvertPolicy::SATURATE, act_info);
      break;
    }
    case ir::operation::BinaryArithmetic::ArithmeticType::MUL:
    {
      arm_compute::CLPixelWiseMultiplication::validate(
        lhs_tensor->info(), rhs_tensor->info(), ofm_tensor->info(), 1.0,
        arm_compute::ConvertPolicy::SATURATE, arm_compute::RoundingPolicy::TO_NEAREST_EVEN,
        act_info)
        .throw_if_error();
      fn = acl_common::generateLayer<arm_compute::CLPixelWiseMultiplication>(
        lhs_tensor->handle(), rhs_tensor->handle(), ofm_tensor->handle(), 1.0, // scale
        arm_compute::ConvertPolicy::SATURATE, arm_compute::RoundingPolicy::TO_NEAREST_EVEN,
        act_info);
      break;
    }
    case ir::operation::BinaryArithmetic::ArithmeticType::DIV:
    {
      arm_compute::CLArithmeticDivision::validate(lhs_tensor->info(), rhs_tensor->info(),
                                                  ofm_tensor->info(), act_info)
        .throw_if_error();
      fn = acl_common::generateLayer<arm_compute::CLArithmeticDivision>(
        lhs_tensor->handle(), rhs_tensor->handle(), ofm_tensor->handle(), act_info);
      break;
    }
    default:
      assert(false && "The BinaryArithmetic operation supports only binary arithmetic operations");
      break;
  }
 
  _return_fn = acl_common::asAclFunction(std::move(fn));
}
 
} // namespace onert::backend::acl_cl