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Diffstat (limited to 'src/core/experimental/dynamic_fusion/ClKernelBuildingImpl/components/ClElementwiseKernelComponent.cpp')
| -rw-r--r-- | src/core/experimental/dynamic_fusion/ClKernelBuildingImpl/components/ClElementwiseKernelComponent.cpp | 266 |
1 files changed, 0 insertions, 266 deletions
diff --git a/src/core/experimental/dynamic_fusion/ClKernelBuildingImpl/components/ClElementwiseKernelComponent.cpp b/src/core/experimental/dynamic_fusion/ClKernelBuildingImpl/components/ClElementwiseKernelComponent.cpp deleted file mode 100644 index e2eba68a63..0000000000 --- a/src/core/experimental/dynamic_fusion/ClKernelBuildingImpl/components/ClElementwiseKernelComponent.cpp +++ /dev/null @@ -1,266 +0,0 @@ -/* - * 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 */
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