src/core/experimental/dynamic_fusion/ClKernelBuildingImpl/components/ClElementwiseKernelComponent.cpp - ml/ComputeLibrary - Gitiles

 /*
  * Copyright (c) 2022 Arm Limited.
  *
  * SPDX-License-Identifier: MIT
  *
  * Permission is hereby granted, free of charge, to any person obtaining a copy
  * of this software and associated documentation files (the "Software"), to
  * deal in the Software without restriction, including without limitation the
  * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
  * sell copies of the Software, and to permit persons to whom the Software is
  * furnished to do so, subject to the following conditions:
  *
  * The above copyright notice and this permission notice shall be included in all
  * copies or substantial portions of the Software.
  *
  * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
  * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
  * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
  * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
  * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
  * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
  * SOFTWARE.
  */
 #ifdef ENABLE_EXPERIMENTAL_DYNAMIC_FUSION

 #include "src/core/experimental/dynamic_fusion/ClKernelBuildingImpl/components/ClElementwiseKernelComponent.h"
 #include "arm_compute/core/Error.h"
 #include "arm_compute/core/Validate.h"
 #include "src/core/helpers/AutoConfiguration.h"
 #include "src/core/helpers/WindowHelpers.h"

 namespace arm_compute
 {
 namespace experimental
 {
 namespace dynamic_fusion
 {
 ComponentType ClElementwiseKernelComponent::get_component_type() const
 {
     return ComponentType::Simple;
 }

 std::set<std::string> ClElementwiseKernelComponent::get_headers_list() const
 {
     return std::set<std::string> { "common/experimental/gemm_fused_post_ops/fp_mixed_precision_helpers.h", "tile_helpers.h" };
 }

 Window ClElementwiseKernelComponent::get_window() const
 {
     const ITensorInfo *lhs_info = _blueprint->impl().get_kernel_argument_info(_lhs.arg_id);
     const ITensorInfo *rhs_info = _blueprint->impl().get_kernel_argument_info(_rhs.arg_id);
     ITensorInfo       *dst_info = _blueprint->impl().get_kernel_argument_info(_blueprint->impl().get_dst_id());

     ARM_COMPUTE_ERROR_ON_NULLPTR(lhs_info, rhs_info, dst_info);

     const std::pair<TensorShape, ValidRegion> broadcast_pair = ITensorInfo::broadcast_shape_and_valid_region(*lhs_info, *rhs_info);
     const TensorShape &out_shape = broadcast_pair.first;

     auto_init_if_empty(*dst_info, out_shape, 1, lhs_info->data_type());

     TensorShape output_shape = dst_info->tensor_shape();
     // Collapse Dim 1 (W) and Dim 2 (H) together, leave Dim 0 (C) and upper dimensions unchanged
     // This is in line with the collapsing convention used by Conv2d
     output_shape.collapse(2U, 1U);
     const unsigned int vector_size_byte_opencl           = 16;
     const unsigned int num_elems_processed_per_iteration = adjust_vec_size(vector_size_byte_opencl / dst_info->element_size(), dst_info->dimension(0));
     Window             win                               = calculate_max_window(output_shape, Steps(num_elems_processed_per_iteration));

     return win;
 }

 std::string ClElementwiseKernelComponent::get_component_code() const
 {
     std::string code;
     const bool  is_root = _blueprint->impl().group(_lhs.arg_id) == SharedVarGroup::Argument && _blueprint->impl().group(_rhs.arg_id) == SharedVarGroup::Argument;

     if(is_root)
     {
         return R"_(
     //------------------ START KERNEL {{meta_kernel_id}} ELTWISE_OP ---------------------
     // IN_0(LHS)            {{lhs}}
     // IN_1(RHS)            {{rhs}}
     // OUT(dst, accum)      {{dst}}

     // dst = lhs + rhs (mix-precision, broadcast, boundary aware)
     TILE({{DATA_TYPE}}, M0, N0, {{dst}});
     {
         TILE({{DATA_TYPE}}, M0, N0, lhs_tile);
         TILE({{DATA_TYPE}}, M0, N0, rhs_tile);

         // Since mout maps to dimensions 1 (y) and dimension 2 (z) of the input tensor because of the collapsed window, bout maps to dimension 3 (w)
         {{lhs}}_offset_first_element_in_bytes += bout * {{lhs}}_stride_w;
         {{rhs}}_offset_first_element_in_bytes += bout * {{rhs}}_stride_w;

         T_LOAD({{DATA_TYPE}}, M0, N0, BUFFER, {{lhs}}, cout, mout, 1, {{lhs}}_stride_y, lhs_tile);
         T_LOAD({{DATA_TYPE}}, {{rhs_m0}}, {{rhs_n0}}, BUFFER, {{rhs}}, {{rhs_start_x}}, {{rhs_start_y}}, 1, {{rhs}}_stride_y, rhs_tile);

 #if defined(IS_BROADCAST)
         T_ELTWISE_BROADCAST_{{ELTWISE_OP}}_X({{DATA_TYPE}}, M0, N0, lhs_tile, rhs_tile, {{dst}});
 #else // !defined(IS_BROADCAST)
         T_ELTWISE_{{ELTWISE_OP}}({{DATA_TYPE}}, M0, N0, lhs_tile, rhs_tile, {{dst}});
 #endif // defined(IS_BROADCAST)

     }
     //------------------ END KERNEL {{meta_kernel_id}} ELTWISE_OP ---------------------
 )_";
     }
     else
     {
         return R"_(
     //------------------ START KERNEL {{meta_kernel_id}} ELTWISE_OP ---------------------
     // IN_0/Out(Accumulator)   {{acc}}
     // IN_1(Addend)        {{addend}}

     // acc = addend + acc (mix-precision, broadcast, boundary aware)
     {
         TILE({{DATA_TYPE}}, M0, N0, addend_tile);

         T_LOAD({{DATA_TYPE}}, {{rhs_m0}}, {{rhs_n0}}, BUFFER, {{addend}}, {{rhs_start_x}}, {{rhs_start_y}}, 1, {{addend}}_stride_y, addend_tile);

 #if defined(IS_BROADCAST)
         T_ELTWISE_BROADCAST_{{ELTWISE_OP}}_X({{DATA_TYPE}}, M0, N0, {{acc}}, addend_tile, {{acc}});
 #else // !defined(IS_BROADCAST)
         T_ELTWISE_{{ELTWISE_OP}}({{DATA_TYPE}}, M0, N0, {{acc}}, addend_tile, {{acc}});
 #endif // defined(IS_BROADCAST)
     }
     //------------------ END KERNEL {{meta_kernel_id}} ELTWISE_OP ---------------------
 )_";
     }
 }

 CLBuildOptions ClElementwiseKernelComponent::generate_build_options() const
 {
     const auto t_rhs_info = _blueprint->impl().get_kernel_argument_info(_rhs.arg_id);
     const auto t_dst_info = _blueprint->impl().get_kernel_argument_info(_blueprint->impl().get_dst_id());

     CLBuildOptions     build_opts{};
     const auto         n0               = _blueprint->impl().get_execution_window().x().step();
     const auto         m0               = _blueprint->impl().get_execution_window().y().step();
     const unsigned int partial_store_n0 = t_dst_info->dimension(0) % n0;
     const bool         is_broadcast     = t_rhs_info->tensor_shape() != t_dst_info->tensor_shape();

     build_opts.add_option("-DM0=" + support::cpp11::to_string(m0));
     build_opts.add_option("-DN0=" + support::cpp11::to_string(n0));
     build_opts.add_option("-DPARTIAL_N0=" + support::cpp11::to_string(partial_store_n0));
     build_opts.add_option_if(is_broadcast, "-DIS_BROADCAST");

     return build_opts;
 }

 std::string ClElementwiseKernelComponent::generate_config_id() const
 {
     auto        t_dst_info = _blueprint->impl().get_kernel_argument_info(_blueprint->impl().get_dst_id());
     std::string config_id{};
     config_id += lower_string(string_from_data_type(t_dst_info->data_type()));
     config_id += "_";
     config_id += support::cpp11::to_string(t_dst_info->dimension(0));
     config_id += "_";
     config_id += support::cpp11::to_string(t_dst_info->dimension(1));
     config_id += "_";
     config_id += lower_string(string_from_data_layout(t_dst_info->data_layout()));
     return config_id;
 }

 void ClElementwiseKernelComponent::allocate_shared_vars(SharedVarTable &vtable) const
 {
     const bool is_root = _blueprint->impl().group(_lhs.arg_id) == SharedVarGroup::Argument && _blueprint->impl().group(_rhs.arg_id) == SharedVarGroup::Argument;
     vtable.add(_lhs, _blueprint->impl().group(_lhs.arg_id), ClKernelArgDescriptor(_lhs.arg_id, ClKernelTensorArgType::Tensor_4D_t_Buffer), "lhs");
     vtable.add(_rhs, _blueprint->impl().group(_rhs.arg_id), ClKernelArgDescriptor(_rhs.arg_id, ClKernelTensorArgType::Tensor_4D_t_Buffer), "rhs");
     if(is_root)
     {
         vtable.add(_dst, _blueprint->impl().group(_dst.arg_id), ClKernelArgDescriptor(_dst.arg_id, ClKernelTensorArgType::Tensor_4D_t_Buffer), "dst");
     }
 }

 ClElementwiseKernelComponent::TagLUT ClElementwiseKernelComponent::get_tag_lut(const SharedVarTable &vtable) const
 {
     TagLUT       lut{};
     const auto   t_dst_info    = _blueprint->impl().get_kernel_argument_info(_blueprint->impl().get_dst_id());
     ITensorInfo *t_addend_info = nullptr;
     // Arguments and global shared variables
     const bool is_root = _blueprint->impl().group(_lhs.arg_id) == SharedVarGroup::Argument && _blueprint->impl().group(_rhs.arg_id) == SharedVarGroup::Argument;
     if(is_root)
     {
         lut["lhs"]    = vtable.get(_lhs);
         lut["rhs"]    = vtable.get(_rhs);
         lut["dst"]    = vtable.get(_dst);
         t_addend_info = _blueprint->impl().get_kernel_argument_info(_rhs.arg_id);
     }
     else
     {
         // Determine which link is the accumulator
         Link accumulator;
         Link addend;
         if(_blueprint->impl().group(_lhs.arg_id) == SharedVarGroup::Automatic)
         {
             accumulator = _lhs;
             addend      = _rhs;
         }
         else if(_blueprint->impl().group(_rhs.arg_id) == SharedVarGroup::Automatic)
         {
             accumulator = _rhs;
             addend      = _lhs;
         }
         else
         {
             ARM_COMPUTE_ERROR("Invalid elementwise component linking");
         }
         lut["acc"]    = vtable.get(accumulator);
         lut["addend"] = vtable.get(addend);
         t_addend_info = _blueprint->impl().get_kernel_argument_info(addend.arg_id);
     }
     // Local build options
     lut["meta_kernel_id"] = id();
     lut["DATA_TYPE"]      = get_cl_type_from_data_type(t_dst_info->data_type());

     switch(_desc.eltwise.op)
     {
         case ArithmeticOperation::DIV:
             lut["ELTWISE_OP"] = "DIV";
             break;
         case ArithmeticOperation::ADD:
             lut["ELTWISE_OP"] = "ADD";
             break;
         default:
             ARM_COMPUTE_ERROR("Arithmetic Operation not supported");
     }

     // Set broadcast parameters
     // PRE: All tensors are broadcast-compatible
     const bool is_broadcast = t_addend_info->tensor_shape() != t_dst_info->tensor_shape();
     if(is_broadcast)
     {
         // Note that n0 maps to input tensor dimension 0, m0 maps to input dimensions 1 and 2 because of our collapse strategy
         if(t_addend_info->dimension(0) == 1U && t_addend_info->dimension(1) == 1U && t_addend_info->dimension(2) == 1U) // Broadcast in X, Y, Z: collapsed rhs win [M0xN0] = [1x1]
         {
             lut["rhs_m0"]      = "1";
             lut["rhs_n0"]      = "1";
             lut["rhs_start_y"] = "0";
             lut["rhs_start_x"] = "0";
         }
         else if(t_addend_info->dimension(1) == 1U && t_addend_info->dimension(2) == 1U) // Broadcast in Y and Z: collapsed rhs win [M0xN0] = [1xN]
         {
             lut["rhs_m0"]      = "1";
             lut["rhs_n0"]      = "N0";
             lut["rhs_start_y"] = "0";
             lut["rhs_start_x"] = "cout";
         }
         else
         {
             ARM_COMPUTE_ERROR("Only support rhs broadcasting in all X, Y, Z dimensions, or just in Y and Z dimensions");
         }
     }
     else
     {
         lut["rhs_m0"]      = "M0";
         lut["rhs_n0"]      = "N0";
         lut["rhs_start_y"] = "mout";
         lut["rhs_start_x"] = "cout";
     }
     return lut;
 }
 } // namespace dynamic_fusion
 } // namespace experimental
 } // namespace arm_compute
 #endif /* ENABLE_EXPERIMENTAL_DYNAMIC_FUSION */
	/*
	* Copyright (c) 2022 Arm Limited.
	*
	* SPDX-License-Identifier: MIT
	*
	* Permission is hereby granted, free of charge, to any person obtaining a copy
	* of this software and associated documentation files (the "Software"), to
	* deal in the Software without restriction, including without limitation the
	* rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
	* sell copies of the Software, and to permit persons to whom the Software is
	* furnished to do so, subject to the following conditions:
	*
	* The above copyright notice and this permission notice shall be included in all
	* copies or substantial portions of the Software.
	*
	* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
	* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
	* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
	* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
	* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
	* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
	* SOFTWARE.
	*/
	#ifdef ENABLE_EXPERIMENTAL_DYNAMIC_FUSION

	#include "src/core/experimental/dynamic_fusion/ClKernelBuildingImpl/components/ClElementwiseKernelComponent.h"
	#include "arm_compute/core/Error.h"
	#include "arm_compute/core/Validate.h"
	#include "src/core/helpers/AutoConfiguration.h"
	#include "src/core/helpers/WindowHelpers.h"

	namespace arm_compute
	{
	namespace experimental
	{
	namespace dynamic_fusion
	{
	ComponentType ClElementwiseKernelComponent::get_component_type() const
	{
	return ComponentType::Simple;
	}

	std::set<std::string> ClElementwiseKernelComponent::get_headers_list() const
	{
	return std::set<std::string> { "common/experimental/gemm_fused_post_ops/fp_mixed_precision_helpers.h", "tile_helpers.h" };
	}

	Window ClElementwiseKernelComponent::get_window() const
	{
	const ITensorInfo *lhs_info = _blueprint->impl().get_kernel_argument_info(_lhs.arg_id);
	const ITensorInfo *rhs_info = _blueprint->impl().get_kernel_argument_info(_rhs.arg_id);
	ITensorInfo *dst_info = _blueprint->impl().get_kernel_argument_info(_blueprint->impl().get_dst_id());

	ARM_COMPUTE_ERROR_ON_NULLPTR(lhs_info, rhs_info, dst_info);

	const std::pair<TensorShape, ValidRegion> broadcast_pair = ITensorInfo::broadcast_shape_and_valid_region(lhs_info, rhs_info);
	const TensorShape &out_shape = broadcast_pair.first;

	auto_init_if_empty(*dst_info, out_shape, 1, lhs_info->data_type());

	TensorShape output_shape = dst_info->tensor_shape();
	// Collapse Dim 1 (W) and Dim 2 (H) together, leave Dim 0 (C) and upper dimensions unchanged
	// This is in line with the collapsing convention used by Conv2d
	output_shape.collapse(2U, 1U);
	const unsigned int vector_size_byte_opencl = 16;
	const unsigned int num_elems_processed_per_iteration = adjust_vec_size(vector_size_byte_opencl / dst_info->element_size(), dst_info->dimension(0));
	Window win = calculate_max_window(output_shape, Steps(num_elems_processed_per_iteration));

	return win;
	}

	std::string ClElementwiseKernelComponent::get_component_code() const
	{
	std::string code;
	const bool is_root = _blueprint->impl().group(_lhs.arg_id) == SharedVarGroup::Argument && _blueprint->impl().group(_rhs.arg_id) == SharedVarGroup::Argument;

	if(is_root)
	{
	return R"_(
	//------------------ START KERNEL {{meta_kernel_id}} ELTWISE_OP ---------------------
	// IN_0(LHS) {{lhs}}
	// IN_1(RHS) {{rhs}}
	// OUT(dst, accum) {{dst}}

	// dst = lhs + rhs (mix-precision, broadcast, boundary aware)
	TILE({{DATA_TYPE}}, M0, N0, {{dst}});
	{
	TILE({{DATA_TYPE}}, M0, N0, lhs_tile);
	TILE({{DATA_TYPE}}, M0, N0, rhs_tile);

	// Since mout maps to dimensions 1 (y) and dimension 2 (z) of the input tensor because of the collapsed window, bout maps to dimension 3 (w)
	{{lhs}}_offset_first_element_in_bytes += bout * {{lhs}}_stride_w;
	{{rhs}}_offset_first_element_in_bytes += bout * {{rhs}}_stride_w;

	T_LOAD({{DATA_TYPE}}, M0, N0, BUFFER, {{lhs}}, cout, mout, 1, {{lhs}}_stride_y, lhs_tile);
	T_LOAD({{DATA_TYPE}}, {{rhs_m0}}, {{rhs_n0}}, BUFFER, {{rhs}}, {{rhs_start_x}}, {{rhs_start_y}}, 1, {{rhs}}_stride_y, rhs_tile);

	#if defined(IS_BROADCAST)
	T_ELTWISE_BROADCAST_{{ELTWISE_OP}}_X({{DATA_TYPE}}, M0, N0, lhs_tile, rhs_tile, {{dst}});
	#else // !defined(IS_BROADCAST)
	T_ELTWISE_{{ELTWISE_OP}}({{DATA_TYPE}}, M0, N0, lhs_tile, rhs_tile, {{dst}});
	#endif // defined(IS_BROADCAST)

	}
	//------------------ END KERNEL {{meta_kernel_id}} ELTWISE_OP ---------------------
	)_";
	}
	else
	{
	return R"_(
	//------------------ START KERNEL {{meta_kernel_id}} ELTWISE_OP ---------------------
	// IN_0/Out(Accumulator) {{acc}}
	// IN_1(Addend) {{addend}}

	// acc = addend + acc (mix-precision, broadcast, boundary aware)
	{
	TILE({{DATA_TYPE}}, M0, N0, addend_tile);

	T_LOAD({{DATA_TYPE}}, {{rhs_m0}}, {{rhs_n0}}, BUFFER, {{addend}}, {{rhs_start_x}}, {{rhs_start_y}}, 1, {{addend}}_stride_y, addend_tile);

	#if defined(IS_BROADCAST)
	T_ELTWISE_BROADCAST_{{ELTWISE_OP}}_X({{DATA_TYPE}}, M0, N0, {{acc}}, addend_tile, {{acc}});
	#else // !defined(IS_BROADCAST)
	T_ELTWISE_{{ELTWISE_OP}}({{DATA_TYPE}}, M0, N0, {{acc}}, addend_tile, {{acc}});
	#endif // defined(IS_BROADCAST)
	}
	//------------------ END KERNEL {{meta_kernel_id}} ELTWISE_OP ---------------------
	)_";
	}
	}

	CLBuildOptions ClElementwiseKernelComponent::generate_build_options() const
	{
	const auto t_rhs_info = _blueprint->impl().get_kernel_argument_info(_rhs.arg_id);
	const auto t_dst_info = _blueprint->impl().get_kernel_argument_info(_blueprint->impl().get_dst_id());

	CLBuildOptions build_opts{};
	const auto n0 = _blueprint->impl().get_execution_window().x().step();
	const auto m0 = _blueprint->impl().get_execution_window().y().step();
	const unsigned int partial_store_n0 = t_dst_info->dimension(0) % n0;
	const bool is_broadcast = t_rhs_info->tensor_shape() != t_dst_info->tensor_shape();

	build_opts.add_option("-DM0=" + support::cpp11::to_string(m0));
	build_opts.add_option("-DN0=" + support::cpp11::to_string(n0));
	build_opts.add_option("-DPARTIAL_N0=" + support::cpp11::to_string(partial_store_n0));
	build_opts.add_option_if(is_broadcast, "-DIS_BROADCAST");

	return build_opts;
	}

	std::string ClElementwiseKernelComponent::generate_config_id() const
	{
	auto t_dst_info = _blueprint->impl().get_kernel_argument_info(_blueprint->impl().get_dst_id());
	std::string config_id{};
	config_id += lower_string(string_from_data_type(t_dst_info->data_type()));
	config_id += "_";
	config_id += support::cpp11::to_string(t_dst_info->dimension(0));
	config_id += "_";
	config_id += support::cpp11::to_string(t_dst_info->dimension(1));
	config_id += "_";
	config_id += lower_string(string_from_data_layout(t_dst_info->data_layout()));
	return config_id;
	}

	void ClElementwiseKernelComponent::allocate_shared_vars(SharedVarTable &vtable) const
	{
	const bool is_root = _blueprint->impl().group(_lhs.arg_id) == SharedVarGroup::Argument && _blueprint->impl().group(_rhs.arg_id) == SharedVarGroup::Argument;
	vtable.add(_lhs, _blueprint->impl().group(_lhs.arg_id), ClKernelArgDescriptor(_lhs.arg_id, ClKernelTensorArgType::Tensor_4D_t_Buffer), "lhs");
	vtable.add(_rhs, _blueprint->impl().group(_rhs.arg_id), ClKernelArgDescriptor(_rhs.arg_id, ClKernelTensorArgType::Tensor_4D_t_Buffer), "rhs");
	if(is_root)
	{
	vtable.add(_dst, _blueprint->impl().group(_dst.arg_id), ClKernelArgDescriptor(_dst.arg_id, ClKernelTensorArgType::Tensor_4D_t_Buffer), "dst");
	}
	}

	ClElementwiseKernelComponent::TagLUT ClElementwiseKernelComponent::get_tag_lut(const SharedVarTable &vtable) const
	{
	TagLUT lut{};
	const auto t_dst_info = _blueprint->impl().get_kernel_argument_info(_blueprint->impl().get_dst_id());
	ITensorInfo *t_addend_info = nullptr;
	// Arguments and global shared variables
	const bool is_root = _blueprint->impl().group(_lhs.arg_id) == SharedVarGroup::Argument && _blueprint->impl().group(_rhs.arg_id) == SharedVarGroup::Argument;
	if(is_root)
	{
	lut["lhs"] = vtable.get(_lhs);
	lut["rhs"] = vtable.get(_rhs);
	lut["dst"] = vtable.get(_dst);
	t_addend_info = _blueprint->impl().get_kernel_argument_info(_rhs.arg_id);
	}
	else
	{
	// Determine which link is the accumulator
	Link accumulator;
	Link addend;
	if(_blueprint->impl().group(_lhs.arg_id) == SharedVarGroup::Automatic)
	{
	accumulator = _lhs;
	addend = _rhs;
	}
	else if(_blueprint->impl().group(_rhs.arg_id) == SharedVarGroup::Automatic)
	{
	accumulator = _rhs;
	addend = _lhs;
	}
	else
	{
	ARM_COMPUTE_ERROR("Invalid elementwise component linking");
	}
	lut["acc"] = vtable.get(accumulator);
	lut["addend"] = vtable.get(addend);
	t_addend_info = _blueprint->impl().get_kernel_argument_info(addend.arg_id);
	}
	// Local build options
	lut["meta_kernel_id"] = id();
	lut["DATA_TYPE"] = get_cl_type_from_data_type(t_dst_info->data_type());

	switch(_desc.eltwise.op)
	{
	case ArithmeticOperation::DIV:
	lut["ELTWISE_OP"] = "DIV";
	break;
	case ArithmeticOperation::ADD:
	lut["ELTWISE_OP"] = "ADD";
	break;
	default:
	ARM_COMPUTE_ERROR("Arithmetic Operation not supported");
	}

	// Set broadcast parameters
	// PRE: All tensors are broadcast-compatible
	const bool is_broadcast = t_addend_info->tensor_shape() != t_dst_info->tensor_shape();
	if(is_broadcast)
	{
	// Note that n0 maps to input tensor dimension 0, m0 maps to input dimensions 1 and 2 because of our collapse strategy
	if(t_addend_info->dimension(0) == 1U && t_addend_info->dimension(1) == 1U && t_addend_info->dimension(2) == 1U) // Broadcast in X, Y, Z: collapsed rhs win [M0xN0] = [1x1]
	{
	lut["rhs_m0"] = "1";
	lut["rhs_n0"] = "1";
	lut["rhs_start_y"] = "0";
	lut["rhs_start_x"] = "0";
	}
	else if(t_addend_info->dimension(1) == 1U && t_addend_info->dimension(2) == 1U) // Broadcast in Y and Z: collapsed rhs win [M0xN0] = [1xN]
	{
	lut["rhs_m0"] = "1";
	lut["rhs_n0"] = "N0";
	lut["rhs_start_y"] = "0";
	lut["rhs_start_x"] = "cout";
	}
	else
	{
	ARM_COMPUTE_ERROR("Only support rhs broadcasting in all X, Y, Z dimensions, or just in Y and Z dimensions");
	}
	}
	else
	{
	lut["rhs_m0"] = "M0";
	lut["rhs_n0"] = "N0";
	lut["rhs_start_y"] = "mout";
	lut["rhs_start_x"] = "cout";
	}
	return lut;
	}
	} // namespace dynamic_fusion
	} // namespace experimental
	} // namespace arm_compute
	#endif /* ENABLE_EXPERIMENTAL_DYNAMIC_FUSION */