/* * 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 ClElementwiseKernelComponent::get_headers_list() const { return std::set { "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 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 */