/* * 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. */ #include "ClTemplateElementwiseBinary.h" #include "src/dynamic_fusion/sketch/gpu/GpuKernelComponentGroup.h" #include "src/dynamic_fusion/sketch/gpu/components/cl/ClComponentElementwiseBinary.h" #include "arm_compute/core/utils/misc/ShapeCalculator.h" #include "src/core/helpers/WindowHelpers.h" #include "support/StringSupport.h" namespace arm_compute { namespace experimental { namespace dynamic_fusion { constexpr unsigned int vector_size_byte_opencl = 16; ClTemplateElementwiseBinary::ClTemplateElementwiseBinary(ComponentId id, const ArgumentPack &tensors, const Attributes &attributes) : IGpuTemplateComponentWriter{ id, tensors }, _lhs{}, _rhs{}, _dst{}, _attributes{ attributes } { _lhs = this->tensors().get_const_tensor(TensorType::ACL_SRC_0); _rhs = this->tensors().get_const_tensor(TensorType::ACL_SRC_1); _dst = this->tensors().get_const_tensor(TensorType::ACL_DST_0); ARM_COMPUTE_ERROR_ON_NULLPTR(_lhs, _rhs, _dst); } std::string ClTemplateElementwiseBinary::get_name() const { return "elementwise_binary"; } std::string ClTemplateElementwiseBinary::get_component_code(const ComponentGroup &comp_group) const { ARM_COMPUTE_UNUSED(comp_group); std::string code; const bool is_broadcast = _lhs->tensor_shape() != _rhs->tensor_shape(); const bool is_root = (comp_group.get_root_component()->id() == this->id()); if(is_root) { code = 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) R"_( TILE({{DATA_TYPE}}, M0, N0, {{dst}}); TILE(uint, M0, 1, g_dst_indirect_y); { TILE({{DATA_TYPE}}, M0, N0, lhs_tile); TILE({{DATA_TYPE}}, M0, N0, rhs_tile); )_" // Assuming un-collapsed window R"_( {{lhs}}_offset_first_element_in_bytes += g_ind_2 * {{lhs}}_stride_z; {{rhs}}_offset_first_element_in_bytes += g_ind_2 * {{rhs}}_stride_z; T_LOAD({{DATA_TYPE}}, M0, N0, BUFFER, {{lhs}}, g_ind_0, g_ind_1, 1, {{lhs}}_stride_y, lhs_tile); T_LOAD({{DATA_TYPE}}, {{rhs_m0}}, {{rhs_n0}}, BUFFER, {{rhs}}, {{rhs_start_ind_0}}, {{rhs_start_ind_1}}, 1, {{rhs}}_stride_y, rhs_tile); )_"; if(is_broadcast) { code += R"_( T_ELTWISE_BROADCAST_{{ELTWISE_OP}}_X({{DATA_TYPE}}, M0, N0, lhs_tile, rhs_tile, {{dst}}); )_"; } else { code += R"_( T_ELTWISE_{{ELTWISE_OP}}({{DATA_TYPE}}, M0, N0, lhs_tile, rhs_tile, {{dst}}); )_"; } code += // Calculate the destination indirect Y R"_( LOOP_UNROLLING(int, i, 0, 1, M0, { g_dst_indirect_y[i].v = (uint)min(g_ind_1 + i, (int)({{out}}_w * {{out}}_h) - 1); g_dst_indirect_y[i].v += g_ind_2 * (int)({{out}}_w * {{out}}_h); }) } //------------------ END KERNEL {{meta_kernel_id}} ELTWISE_OP --------------------- )_"; } else // non-root { code = R"_( //------------------ START KERNEL {{meta_kernel_id}} ELTWISE_OP --------------------- )_" // IN_0/Out(Accumulator) {{acc}} // IN_1(Operand) {{operand}} // acc = operand + acc (mix-precision, broadcast, boundary aware) R"_( { TILE(DATA_TYPE, M0, N0, operand_tile); T_LOAD({{DATA_TYPE}}, {{rhs_m0}}, {{rhs_n0}}, BUFFER, {{operand}}, {{rhs_start_ind_0}}, {{rhs_start_ind_1}}, 1, {{operand}}_stride_y, operand_tile); )_"; if(is_broadcast) { code += R"_( T_ELTWISE_BROADCAST_{{ELTWISE_OP}}_X({{DATA_TYPE}}, M0, N0, {{acc}}, operand_tile, {{acc}}); )_"; } else { code += R"_( T_ELTWISE_{{ELTWISE_OP}}({{DATA_TYPE}}, M0, N0, {{acc}}, operand_tile, {{acc}}); )_"; } code += R"_( } //------------------ END KERNEL {{meta_kernel_id}} ELTWISE_OP --------------------- )_"; } return code; } void ClTemplateElementwiseBinary::declare_variables(GpuKernelVariableTable &vtable, const ComponentGroup &comp_group) const { vtable.declare_variable( _lhs, GpuKernelArgumentInfo(common_tensor_type), comp_group.is_intermediate_tensor(_lhs), "lhs"); vtable.declare_variable( _rhs, GpuKernelArgumentInfo(common_tensor_type), comp_group.is_intermediate_tensor(_rhs), "rhs"); vtable.declare_variable( _dst, GpuKernelArgumentInfo(common_tensor_type), comp_group.is_intermediate_tensor(_dst), "dst"); } TagLUT ClTemplateElementwiseBinary::get_tag_lut(const GpuKernelVariableTable &vtable, const ComponentGroup &comp_group) const { TagLUT lut{}; const ITensorInfo *accumulator = _lhs; const ITensorInfo *operand = _rhs; // Local build options lut["meta_kernel_id"] = id(); lut["DATA_TYPE"] = get_cl_type_from_data_type(_lhs->data_type()); // Arguments and global shared variables const bool is_root = (comp_group.get_root_component()->id() == this->id()); if(is_root) { lut["lhs"] = vtable.get_variable(_lhs); lut["rhs"] = vtable.get_variable(_rhs); lut["dst"] = vtable.get_variable(_dst); lut["out"] = vtable.get_variable(comp_group.get_any_dst_tensor()); } else { // Determine which tensor is the accumulator if(comp_group.is_intermediate_tensor(_lhs)) { accumulator = _lhs; operand = _rhs; } else if(comp_group.is_intermediate_tensor(_rhs)) { accumulator = _rhs; operand = _lhs; } else { ARM_COMPUTE_ERROR("Invalid elementwise component linking"); } lut["acc"] = vtable.get_variable(accumulator); lut["operand"] = vtable.get_variable(operand); } switch(_attributes.operation()) { case Attributes::ElementwiseOp::ADD: lut["ELTWISE_OP"] = "ADD"; break; default: ARM_COMPUTE_ERROR("Arithmetic Operation not supported"); } ARM_COMPUTE_ERROR_ON_MSG(detail::have_different_dimensions(accumulator->tensor_shape(), _dst->tensor_shape(), 0), "Only the operand can be broadcast to match the accumulator's shape"); const bool is_broadcast = (operand->tensor_shape() != _dst->tensor_shape()); // Set broadcast parameters // PRE: All tensors are broadcast-compatible 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(operand->dimension(0) == 1U && operand->dimension(1) == 1U && operand->dimension(2) == 1U) // Broadcast in X, Y, Z: collapsed rhs win [M0xN0] = [1x1] { lut["rhs_m0"] = "1"; lut["rhs_n0"] = "1"; lut["rhs_start_ind_1"] = "0"; lut["rhs_start_ind_0"] = "0"; } else if(operand->dimension(1) == 1U && operand->dimension(2) == 1U) // Broadcast in Y and Z: collapsed rhs win [M0xN0] = [1xN] { lut["rhs_m0"] = "1"; lut["rhs_n0"] = "N0"; lut["rhs_start_ind_1"] = "0"; lut["rhs_start_ind_0"] = "g_ind_0"; } 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_ind_1"] = "g_ind_1"; lut["rhs_start_ind_0"] = "g_ind_0"; } return lut; } CLBuildOptions ClTemplateElementwiseBinary::get_build_options(const ComponentGroup &comp_group) const { CLBuildOptions build_opts{}; /// NOTE: For now tile sizes (n0, m0) are set by the execution window. This may change in the future const auto root_window = comp_group.get_root_component()->template_writer()->get_window(); const unsigned int n0 = root_window.x().step(); const unsigned int m0 = root_window.y().step(); const unsigned int partial_store_n0 = _dst->dimension(0) % n0; build_opts.add_option("-DM0=" + support::cpp11::to_string(m0)); build_opts.add_option("-DN0=" + support::cpp11::to_string(n0)); build_opts.add_option("-DDATA_TYPE=" + get_cl_type_from_data_type(_lhs->data_type())); build_opts.add_option("-DPARTIAL_N0=" + support::cpp11::to_string(partial_store_n0)); return build_opts; } std::string ClTemplateElementwiseBinary::get_config_id() const { std::string config_id{}; config_id += lower_string(string_from_data_type(_dst->data_type())); config_id += "_"; config_id += support::cpp11::to_string(_dst->dimension(0)); config_id += "_"; config_id += support::cpp11::to_string(_dst->dimension(1)); config_id += "_"; config_id += lower_string(string_from_data_layout(_dst->data_layout())); return config_id; } std::set ClTemplateElementwiseBinary::get_headers_list() const { return std::set{ "helpers.h", "tile_helpers.h" }; } Window ClTemplateElementwiseBinary::get_window() const { ARM_COMPUTE_ERROR_ON_MSG(_dst->tensor_shape().total_size() == 0U, "Destination tensor is not initialized"); TensorShape output_shape = _dst->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 operators like Conv2d output_shape.collapse(2U, 1U); const unsigned int num_elems_processed_per_iteration = adjust_vec_size(vector_size_byte_opencl / _dst->element_size(), _dst->dimension(0)); Window win = calculate_max_window(output_shape, Steps(num_elems_processed_per_iteration)); return win; } } // namespace dynamic_fusion } // namespace experimental } // namespace arm_compute