mirror of
https://github.com/blakeblackshear/frigate.git
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106 lines
4.0 KiB
C++
106 lines
4.0 KiB
C++
/*
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* The MIT License (MIT)
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*
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* Copyright (c) 2015-2023 Advanced Micro Devices, Inc. All rights reserved.
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*
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* Permission is hereby granted, free of charge, to any person obtaining a copy
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* of this software and associated documentation files (the "Software"), to deal
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* in the Software without restriction, including without limitation the rights
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* to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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* copies of the Software, and to permit persons to whom the Software is
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* furnished to do so, subject to the following conditions:
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*
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* The above copyright notice and this permission notice shall be included in
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* all copies or substantial portions of the Software.
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*
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* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
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* THE SOFTWARE.
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*/
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#include <migraphx/onnx/op_parser.hpp>
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#include <migraphx/onnx/onnx_parser.hpp>
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#include <migraphx/onnx/checks.hpp>
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#include <migraphx/ranges.hpp>
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#include <migraphx/instruction.hpp>
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#include <migraphx/make_op.hpp>
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namespace migraphx {
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inline namespace MIGRAPHX_INLINE_NS {
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namespace onnx {
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struct parse_loop : op_parser<parse_loop>
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{
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std::vector<op_desc> operators() const { return {{"Loop"}}; }
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std::vector<instruction_ref> parse(const op_desc& /*opd*/,
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onnx_parser& parser,
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const onnx_parser::node_info& info,
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std::vector<instruction_ref> args) const
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{
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// default value of the max_iter_num
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int64_t max_iterations = parser.max_loop_iterations;
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// iteration input is empty
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if(args.at(0)->name() == "undefined")
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{
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shape iter_s{shape::int64_type};
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args[0] = info.add_literal(literal(iter_s, {max_iterations}));
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}
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else
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{
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auto arg_iters = args.at(0)->eval();
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if(not arg_iters.empty())
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{
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max_iterations = arg_iters.at<int64_t>();
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}
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}
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// cap max_iter because loop uses static shapes with max_iter size and huge numbers
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// here can cause overflow
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if(max_iterations > parser.limit_max_iterations)
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{
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std::cerr << "WARNING: PARSE_LOOP max_iterations exceeds the maximum loop "
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"iterations limit, it will be changed from "
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<< max_iterations << " to " << parser.limit_max_iterations << ".\n";
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max_iterations = parser.limit_max_iterations;
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}
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// condition input is empty
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if(args.at(1)->name() == "undefined")
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{
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shape cond_s{shape::bool_type};
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args[1] = info.add_literal(literal(cond_s, {true}));
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}
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// retrieve the subgraph
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const auto& sub_graph = info.attributes.at("body").g();
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std::string mod_name = info.name + "_loop";
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module_ref sub_mod = parser.prog.create_module(mod_name);
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// parse the sub_graph
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(void)parser.parse_graph(sub_mod, sub_graph);
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auto ret = info.add_instruction(
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make_op("loop", {{"max_iterations", max_iterations}}), args, {sub_mod});
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auto out_s = ret->get_shape();
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assert(out_s.type() == shape::tuple_type);
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const auto& vec_shapes = out_s.sub_shapes();
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std::vector<instruction_ref> out_inss;
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for(std::size_t i = 0; i < vec_shapes.size(); ++i)
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{
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auto r = info.add_instruction(make_op("get_tuple_elem", {{"index", i}}), ret);
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out_inss.push_back(r);
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}
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return out_inss;
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}
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};
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} // namespace onnx
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} // namespace MIGRAPHX_INLINE_NS
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} // namespace migraphx
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