/* * Copyright (c) 2023 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 "ClTemplateReshape.h" #include "arm_compute/core/utils/helpers/AdjustVecSize.h" #include "arm_compute/core/utils/StringUtils.h" #include "src/core/helpers/WindowHelpers.h" #include "src/dynamic_fusion/sketch/gpu/GpuKernelComponentGroup.h" namespace arm_compute { namespace experimental { namespace dynamic_fusion { constexpr unsigned int vector_size_byte_opencl = 16; ClTemplateReshape::ClTemplateReshape(ComponentId id, const ArgumentPack &tensors) : IGpuTemplateComponentWriter{ id, tensors }, _src{}, _dst{} { _src = this->tensors().get_const_tensor(TensorType::ACL_SRC_0); _dst = this->tensors().get_const_tensor(TensorType::ACL_DST_0); ARM_COMPUTE_ERROR_ON_NULLPTR(_src, _dst); } std::string ClTemplateReshape::get_name() const { return "reshape"; } std::string ClTemplateReshape::get_component_code(const ComponentGroup &comp_group) const { ARM_COMPUTE_UNUSED(comp_group); std::string code; code = R"_( //------------------ START KERNEL {{meta_kernel_id}} --------------------- // IN(src) {{src}} // OUT(dst, accum) {{dst}} TILE(uint, M0, 1, g_dst_indirect_y); { __global uchar * base_src_ptr = {{src}}_ptr + {{src}}_offset_first_element_in_bytes; const int tile_vertical_idx = g_ind_1 * {{arg_dst}}_c + g_ind_2 * {{arg_dst}}_c * {{arg_dst}}_w; LOOP_UNROLLING(int, _m0, 0, 1, M0, { const int row_idx = _m0 * {{arg_dst}}_c + tile_vertical_idx; const int tile_horizontal_idx = g_ind_0 + row_idx; LOOP_UNROLLING(int, _n0, 0, 1, N0, { {{src}}_ptr = base_src_ptr; const int linear_idx = tile_horizontal_idx + _n0; const int in_id_x = linear_idx % {{src}}_c; const int in_id_y = (linear_idx / {{src}}_c) % {{src}}_w; const int in_id_z = linear_idx / ({{src}}_c * {{src}}_w); {{src}}_ptr += in_id_x * sizeof({{DATA_TYPE}}) + in_id_y * {{src}}_stride_y + in_id_z * {{src}}_stride_z; {{dst}}[_m0].s[_n0] = *((__global {{DATA_TYPE}} *){{src}}_ptr); }) }) LOOP_UNROLLING(int, i, 0, 1, M0, { g_dst_indirect_y[i].v = (uint)min((int)(g_ind_1 + i), (int)({{arg_dst}}_w) - 1); g_dst_indirect_y[i].v += (int)(g_ind_2 % {{arg_dst}}_h) * (int)({{arg_dst}}_w); g_dst_indirect_y[i].v += (int)(g_ind_2 / {{arg_dst}}_h) * (int)({{arg_dst}}_w * {{arg_dst}}_h); }) } //------------------ END KERNEL {{meta_kernel_id}} --------------------- )_"; return code; } void ClTemplateReshape::declare_variables(GpuKernelVariableTable &vtable, const ComponentGroup &comp_group) const { vtable.declare_variable( comp_group, _src, GpuKernelArgumentInfo(common_tensor_type), // GpuKernelArgumentInfo::Type::Image_3D "src"); vtable.declare_variable( comp_group, _dst, GpuKernelArgumentInfo(common_tensor_type), "dst"); } TagLUT ClTemplateReshape::get_tag_lut(const GpuKernelVariableTable &vtable, const ComponentGroup &comp_group) const { ARM_COMPUTE_UNUSED(comp_group); TagLUT lut{}; // Arguments and global shared variables lut["src"] = vtable.get_variable(_src); lut["dst"] = vtable.get_variable(_dst); lut["arg_dst"] = vtable.get_variable(comp_group.get_any_dst_tensor()); lut["meta_kernel_id"] = id(); lut["DATA_TYPE"] = get_cl_type_from_data_type(_dst->data_type()); return lut; } CLBuildOptions ClTemplateReshape::get_build_options(const ComponentGroup &comp_group) const { CLBuildOptions build_opts{}; 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("-DN0=" + support::cpp11::to_string(n0)); build_opts.add_option("-DM0=" + support::cpp11::to_string(m0)); build_opts.add_option("-DPARTIAL_N0=" + support::cpp11::to_string(partial_store_n0)); return build_opts; } std::string ClTemplateReshape::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)); return config_id; } std::set ClTemplateReshape::get_headers_list() const { return std::set{ "helpers.h", "tile_helpers.h" }; } Window ClTemplateReshape::get_window() const { ARM_COMPUTE_ERROR_ON_MSG(_dst->tensor_shape().total_size() == 0U, "Destination tensor is not initialized"); const unsigned int n0 = adjust_vec_size(vector_size_byte_opencl / _dst->element_size(), _dst->dimension(0)); Window win = calculate_max_window(*_dst, Steps(n0)); return win.collapse(win, Window::DimZ); } } // namespace dynamic_fusion } // namespace experimental } // namespace arm_compute