text: /*
  // Copyright (c) 2019 Intel Corporation
  //
  // 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 "eltwise_inst.h"
  #include "primitive_gpu_base.h"
  #include "implementation_map.h"
  #include "error_handler.h"
  #include "kernel_selector_helper.h"
  #include "eltwise/eltwise_kernel_selector.h"
  #include "eltwise/eltwise_kernel_base.h"
  #include <vector>
  
  namespace cldnn {
  namespace gpu {
  
  namespace {
  inline kernel_selector::eltwise_mode convert_to_eltwise_mode(eltwise_mode mode) {
      switch (mode) {
          case eltwise_mode::sum:
              return kernel_selector::eltwise_mode::ADD;
          case eltwise_mode::sub:
              return kernel_selector::eltwise_mode::SUB;
          case eltwise_mode::max:
              return kernel_selector::eltwise_mode::MAX;
          case eltwise_mode::prod:
              return kernel_selector::eltwise_mode::MUL;
          case eltwise_mode::div:
              return kernel_selector::eltwise_mode::DIV;
          case eltwise_mode::min:
              return kernel_selector::eltwise_mode::MIN;
          case eltwise_mode::pow:
              return kernel_selector::eltwise_mode::POW;
          case eltwise_mode::mod:
              return kernel_selector::eltwise_mode::MODULU;
          case eltwise_mode::eq:
              return kernel_selector::eltwise_mode::EQ;
          case eltwise_mode::ne:
              return kernel_selector::eltwise_mode::NE;
          case eltwise_mode::lt:
              return kernel_selector::eltwise_mode::LT;
          case eltwise_mode::le:
              return kernel_selector::eltwise_mode::LE;
          case eltwise_mode::gt:
              return kernel_selector::eltwise_mode::GT;
          case eltwise_mode::ge:
              return kernel_selector::eltwise_mode::GE;
          case eltwise_mode::logic_and:
              return kernel_selector::eltwise_mode::LOGIC_AND;
          case eltwise_mode::logic_or:
              return kernel_selector::eltwise_mode::LOGIC_OR;
          case eltwise_mode::logic_xor:
              return kernel_selector::eltwise_mode::LOGIC_XOR;
          case eltwise_mode::squared_diff:
              return kernel_selector::eltwise_mode::SQUARED_DIFF;
          case eltwise_mode::floor_mod:
              return kernel_selector::eltwise_mode::FLOOR_MOD;
          default:
              return kernel_selector::eltwise_mode::ADD;
      }
  }
  }  // namespace
  
  struct eltwise_gpu : typed_primitive_gpu_impl<eltwise> {
      using parent = typed_primitive_gpu_impl<eltwise>;
      using parent::parent;
  
  protected:
      kernel::kernel_arguments_data get_arguments(typed_primitive_inst<eltwise>& instance,
                                                          int32_t split) const override {
          kernel::kernel_arguments_data args = parent::get_arguments(instance, split);
  
          args.output_calibration_factors =
              (memory_impl::cptr) (instance.output_calibration_factors_term() ? &instance.output_calibration_factors_memory() : nullptr);
          // TODO Inputs calibration factors - skipping for now as currently they should never be used in eltwise, create
          // will throw
  
          return args;
      }
  
  public:
      static primitive_impl* create(const eltwise_node& arg) {
          auto ew_params = get_default_params<kernel_selector::eltwise_params>(arg);
          auto ew_optional_params =
              get_default_optional_params<kernel_selector::eltwise_optional_params>(arg.get_program());
  
          for (size_t i = 1; i < arg.inputs_count(); i++) {
              ew_params.inputs.push_back(convert_data_tensor(arg.input(i).get_output_layout()));
          }
  
          const auto& primitive = arg.get_primitive();
  
          ew_params.operations.push_back({{kernel_selector::eltwise_params::InputType::Buffer(0),
                                           kernel_selector::eltwise_params::InputType::Buffer(1)},
                                          convert_to_eltwise_mode(primitive->mode)});
  
          for (uint32_t i = 2; i < static_cast<uint32_t>(arg.inputs_count()); i++) {
              ew_params.operations.push_back({{kernel_selector::eltwise_params::InputType::Intermediate(i - 2),
                                               kernel_selector::eltwise_params::InputType::Buffer(i)},
                                              convert_to_eltwise_mode(primitive->mode)});
          }
  
          if (primitive->mode == eltwise_mode::sum) {
              ew_params.coefficients = primitive->coefficients;
          }
  
          for (size_t i = 0; i < ew_params.inputs.size(); i++) {
              if (!ew_params.inputs[i].SameDims(ew_params.output)) {
                  std::vector<int32_t> input_size = arg.input(i).get_output_layout().size.raw.vector();
                  std::vector<int32_t> output_size = arg.get_output_layout().size.raw.vector();
                  bool broadcast = false;
                  for (size_t d = 0; d < output_size.size(); d++) {
                      if (output_size[d] != 1 && input_size[d] == 1)
                          broadcast = true;
                  }
                  if (broadcast) {
                      ew_params.broadcast = true;
                      break;
                  } else {
                      ew_params.layoutBased = true;
                      break;
                  }
              }
          }
  
          // stride
          if (!primitive->stride.empty()) {
              const auto& stride = primitive->stride;
              ew_params.stride.resize(stride.size());
              for (size_t i = 0; i < primitive->stride.size(); i++) {
                  ew_params.stride[i] = {(uint32_t)stride[i].spatial[0],
                                         (uint32_t)stride[i].spatial[1],
                                         (uint32_t)stride[i].spatial[2]};
              }
          }
  
          // check if strides are the same
          if (!ew_params.stride.empty()) {
              const auto& stride = ew_params.stride[0];
              for (size_t i = 1; i < ew_params.stride.size(); i++) {
                  if (stride.x != ew_params.stride[i].x || stride.y != ew_params.stride[i].y)
                      ew_params.layoutBased = true;
              }
          } else if (!ew_params.inputs[0].SameDimsSizes(ew_params.inputs[1])) {
              ew_params.broadcast = true;
          }
  
          if (primitive->output_calibration_factors.size() > 0 || primitive->output_quantization_factor != 1.0f) {
              ew_params.int8_quantization = true;
  
              if (primitive->output_calibration_factors.size() > 0) {
                  ew_params.output_calibration = true;
                  ew_params.output_calibration_factors.push_back(
                      convert_data_tensor(arg.output_calibration_factors().get_output_layout())
                          .FlattenFeatureAndSpatials());
              } else {
                  ew_params.output_quantization_factor = arg.get_output_qf();
              }
          }
  
          CLDNN_ERROR_BOOL(arg.id(),
                           "Eltwise inputs calibration term",
                           arg.inputs_calibration_term(),
                           "Eltwise does not yet support inputs calibration, it should be fused with convolution");
  
          if (arg.inputs_calibration_term()) {
              ew_params.int8_quantization = true;
              ew_params.inputs_calibration = true;
  
              for (size_t i = 0; i < primitive->inputs_calibration_factors.size(); ++i) {
                  auto icf_layout = arg.input_calibration_factors(i).get_output_layout();
                  ew_params.inputs_calibration_factors.push_back(
                      convert_data_tensor(icf_layout).FlattenFeatureAndSpatials());
              }
          }
  
          CLDNN_ERROR_BOOL(arg.id(),
                           "Eltwise inputs quantization term",
                           arg.inputs_quantization_term(),
                           "Eltwise does not yet support inputs quantization, it should be fused with convolution");
  
          if (arg.inputs_quantization_term()) {
              ew_params.int8_quantization = true;
  
              for (const auto& iqf : primitive->input_quantization_factors) {
                  ew_params.input_quantization_factors.push_back(iqf);
              }
          }
  
          auto& kernel_selector = kernel_selector::eltwise_kernel_selector::Instance();
          auto best_kernels = kernel_selector.GetBestKernels(ew_params, ew_optional_params);
  
          CLDNN_ERROR_BOOL(arg.id(),
                           "Best_kernel.empty()",
                           best_kernels.empty(),
                           "Cannot find a proper kernel with this arguments");
  
          auto eltwise = new eltwise_gpu(arg, best_kernels[0]);
  
          return eltwise;
      }
  };
  
  namespace detail {
  
  attach_eltwise_gpu::attach_eltwise_gpu() {
      implementation_map<eltwise>::add(
          {{std::make_tuple(engine_types::ocl, data_types::f32, format::yxfb), eltwise_gpu::create},
           {std::make_tuple(engine_types::ocl, data_types::f16, format::yxfb), eltwise_gpu::create},
           {std::make_tuple(engine_types::ocl, data_types::i8, format::yxfb), eltwise_gpu::create},
           {std::make_tuple(engine_types::ocl, data_types::i32, format::yxfb), eltwise_gpu::create},
           {std::make_tuple(engine_types::ocl, data_types::i64, format::yxfb), eltwise_gpu::create},
           {std::make_tuple(engine_types::ocl, data_types::f32, format::bfyx), eltwise_gpu::create},
           {std::make_tuple(engine_types::ocl, data_types::f16, format::bfyx), eltwise_gpu::create},
           {std::make_tuple(engine_types::ocl, data_types::i8, format::bfyx), eltwise_gpu::create},
           {std::make_tuple(engine_types::ocl, data_types::i32, format::bfyx), eltwise_gpu::create},
           {std::make_tuple(engine_types::ocl, data_types::i64, format::bfyx), eltwise_gpu::create},
           {std::make_tuple(engine_types::ocl, data_types::f32, format::byxf), eltwise_gpu::create},
           {std::make_tuple(engine_types::ocl, data_types::f16, format::byxf), eltwise_gpu::create},
           {std::make_tuple(engine_types::ocl, data_types::i8, format::byxf), eltwise_gpu::create},
           {std::make_tuple(engine_types::ocl, data_types::i32, format::byxf), eltwise_gpu::create},
           {std::make_tuple(engine_types::ocl, data_types::i64, format::byxf), eltwise_gpu::create},
           // block f16
           {std::make_tuple(engine_types::ocl, data_types::f16, format::bfyx_f16), eltwise_gpu::create},
           {std::make_tuple(engine_types::ocl, data_types::f32, format::bfyx_f16), eltwise_gpu::create},
           // 3D
           {std::make_tuple(engine_types::ocl, data_types::f32, format::bfzyx), eltwise_gpu::create},
           {std::make_tuple(engine_types::ocl, data_types::f16, format::bfzyx), eltwise_gpu::create},
           {std::make_tuple(engine_types::ocl, data_types::i8, format::bfzyx), eltwise_gpu::create},
           {std::make_tuple(engine_types::ocl, data_types::i32, format::bfzyx), eltwise_gpu::create},
           {std::make_tuple(engine_types::ocl, data_types::i64, format::bfzyx), eltwise_gpu::create},
           {std::make_tuple(engine_types::ocl, data_types::f32, format::bfzyx_f16), eltwise_gpu::create},
           {std::make_tuple(engine_types::ocl, data_types::f16, format::bfzyx_f16), eltwise_gpu::create},
           {std::make_tuple(engine_types::ocl, data_types::i8, format::bfzyx_f16), eltwise_gpu::create},
           {std::make_tuple(engine_types::ocl, data_types::i32, format::bfzyx_f16), eltwise_gpu::create},
           {std::make_tuple(engine_types::ocl, data_types::i64, format::bfzyx_f16), eltwise_gpu::create},
           // MMAD
           {std::make_tuple(engine_types::ocl, data_types::i8, format::byxf_af32), eltwise_gpu::create},
           {std::make_tuple(engine_types::ocl, data_types::u8, format::byxf_af32), eltwise_gpu::create},
           {std::make_tuple(engine_types::ocl, data_types::i8, format::fs_bs_yx_bsv4_fsv32), eltwise_gpu::create},
           {std::make_tuple(engine_types::ocl, data_types::i8, format::b_fs_yx_fsv4), eltwise_gpu::create},
           {std::make_tuple(engine_types::ocl, data_types::u8, format::b_fs_yx_fsv4), eltwise_gpu::create},
           {std::make_tuple(engine_types::ocl, data_types::f32, format::b_fs_yx_fsv4), eltwise_gpu::create},
           //
           {std::make_tuple(engine_types::ocl, data_types::f16, format::fs_b_yx_fsv32), eltwise_gpu::create}});
  }
  
  }  // namespace detail
  }  // namespace gpu
  }  // namespace cldnn
  