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Diffstat (limited to 'src/dynamic_fusion/sketch/gpu/template_writer/cl/ClTemplateElementwiseBinary.cpp')
| -rw-r--r-- | src/dynamic_fusion/sketch/gpu/template_writer/cl/ClTemplateElementwiseBinary.cpp | 315 |
1 files changed, 315 insertions, 0 deletions
diff --git a/src/dynamic_fusion/sketch/gpu/template_writer/cl/ClTemplateElementwiseBinary.cpp b/src/dynamic_fusion/sketch/gpu/template_writer/cl/ClTemplateElementwiseBinary.cpp new file mode 100644 index 0000000000..996bf15d01 --- /dev/null +++ b/src/dynamic_fusion/sketch/gpu/template_writer/cl/ClTemplateElementwiseBinary.cpp @@ -0,0 +1,315 @@ +/* + * 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)({{dst}}_w * {{dst}}_h) - 1); + g_dst_indirect_y[i].v += g_ind_2 * (int)({{dst}}_w * {{dst}}_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); + } + 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 |