| /* |
| * 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<ITensorInfo> &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_dst_tensors().front()); |
| } |
| 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<std::string> ClTemplateElementwiseBinary::get_headers_list() const |
| { |
| return std::set<std::string>{ "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 |