#include #include #include #include #include #include namespace migraphx { inline namespace MIGRAPHX_INLINE_NS { namespace onnx { struct parse_loop : op_parser { std::vector operators() const { return {{"Loop"}}; } std::vector parse(const op_desc& /*opd*/, onnx_parser& parser, const onnx_parser::node_info& info, std::vector args) const { // default value of the max_iter_num int64_t max_iterations = parser.max_loop_iterations; // iteration input is empty if(args.at(0)->name() == "undefined") { shape iter_s{shape::int64_type}; args[0] = info.add_literal(literal(iter_s, {max_iterations})); } else { auto arg_iters = args.at(0)->eval(); if(not arg_iters.empty()) { max_iterations = arg_iters.at(); } } // condition input is empty if(args.at(1)->name() == "undefined") { shape cond_s{shape::bool_type}; args[1] = info.add_literal(literal(cond_s, {true})); } // retrieve the subgraph const auto& sub_graph = info.attributes.at("body").g(); std::string mod_name = info.name + "_loop"; module_ref sub_mod = parser.prog.create_module(mod_name); // parse the sub_graph parser.parse_graph(sub_mod, sub_graph); auto ret = info.add_instruction( make_op("loop", {{"max_iterations", max_iterations}}), args, {sub_mod}); auto out_s = ret->get_shape(); assert(out_s.type() == shape::tuple_type); const auto& vec_shapes = out_s.sub_shapes(); std::vector out_inss; for(std::size_t i = 0; i < vec_shapes.size(); ++i) { auto r = info.add_instruction(make_op("get_tuple_elem", {{"index", i}}), ret); out_inss.push_back(r); } return out_inss; } }; } // namespace onnx } // namespace MIGRAPHX_INLINE_NS } // namespace migraphx