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198 lines
7.9 KiB
C++
198 lines
7.9 KiB
C++
/*******************************************************************************
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*
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* MIT License
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*
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* Copyright (C) 2022-2025 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 THE
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* SOFTWARE.
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*
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*******************************************************************************/
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#pragma once
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#include <atomic>
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#include <queue>
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#include <set>
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#include <vector>
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#include <Tensile/Debug.hpp>
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#include <Tensile/MLFeatures.hpp>
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#include <Tensile/MLPClassification.hpp>
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#include <Tensile/ProblemKey.hpp>
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#include <Tensile/SolutionLibrary.hpp>
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#include <Tensile/Utils.hpp>
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#include <tensilelitehost/export.h>
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namespace TensileLite
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{
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/**
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* \ingroup SolutionLibrary
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*
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* Uses a small neural network to rank solutions for a given size.
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*/
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template <typename MyProblem, typename MySolution = typename MyProblem::Solution>
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struct MLPClassificationLibrary : public SolutionLibrary<MyProblem, MySolution>
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{
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using MLPNet = MLPClassification::MLPNet;
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using SolutionFeatures = std::vector<std::shared_ptr<MLFeatures::MLFeature<MySolution>>>;
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using ProblemFeatures = std::vector<std::shared_ptr<MLFeatures::MLFeature<MyProblem>>>;
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std::map<int, std::shared_ptr<MySolution>> solutionmap;
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std::shared_ptr<MLPNet> model;
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SolutionFeatures solFeatures;
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ProblemFeatures probFeatures;
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mutable std::atomic<bool> lastFindTopRetAll = false;
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static std::string Type()
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{
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return "MLPClassification";
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}
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virtual std::string type() const override
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{
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return Type();
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}
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virtual std::string description() const override
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{
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if(model == nullptr)
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return concatenate(type(), ", MLPNet: nullptr");
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else
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return concatenate(type(), ": ", model->description());
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}
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virtual std::shared_ptr<MySolution> getSolutionByIndex(MyProblem const& problem,
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Hardware const& hardware,
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const int index) const override
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{
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const bool experimental = Debug::Instance().useExperimentalSelection();
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if(!experimental)
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{
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// If the experimental library mode is not on treat it like it asserted out
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return nullptr;
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}
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// ;
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auto indexMatch = solutionmap.find(index);
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if(indexMatch != solutionmap.end())
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return indexMatch->second;
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return nullptr;
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}
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virtual std::shared_ptr<MySolution> findBestSolution(MyProblem const& problem,
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Hardware const& hardware,
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double* fitness
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= nullptr) const override
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{
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SolutionVector<MySolution> solutions = findTopSolutions(problem, hardware, 1);
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std::shared_ptr<MySolution> solution = nullptr;
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if(solutions.size() > 0)
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solution = solutions[0];
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return solution;
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}
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virtual SolutionSet<MySolution>
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findAllSolutions(MyProblem const& problem,
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Hardware const& hardware,
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SolutionLibrarySearchType searchType
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= SolutionLibrarySearchType::DEFAULT) const override
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{
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const bool experimental = Debug::Instance().useExperimentalSelection();
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if(!experimental)
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{
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// Skip the search for solutions if the environment variable
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// that enables the experimental method is not set
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SolutionSet<MySolution> rv;
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return rv;
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}
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SolutionSet<MySolution> rv;
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for(auto const& row : solutionmap)
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rv.insert(row.second);
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return rv;
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}
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virtual SolutionVector<MySolution> findTopSolutions(MyProblem const& problem,
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Hardware const& hardware,
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int numSolutions) const override
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{
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std::vector<float> problemkey
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= ProblemKey::keyForProblem<std::vector<float>, MyProblem, float>(
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problem, this->probFeatures);
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auto logits = model->predict(problemkey);
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assert(logits.size() == solutionmap.size());
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std::vector<std::pair<decltype(logits)::value_type,
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std::shared_ptr<MySolution>*>> solution_ranking;
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solution_ranking.reserve(solutionmap.size());
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for(auto& s : solutionmap)
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solution_ranking.emplace_back(logits[s.second->libraryLogicIndex],
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(std::shared_ptr<MySolution>*)(&s.second));
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SolutionVector<MySolution> rv;
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int numToSort = std::min(numSolutions, int(solution_ranking.size()));
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rv.reserve(numToSort);
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auto it = solution_ranking.begin(), it_end = solution_ranking.end();
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while(it != it_end && numToSort)
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{
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std::partial_sort(it, it + numToSort, it_end, std::greater{});
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for(; it != it + numToSort; it++)
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if((*((*it->second)->problemPredicate))(problem))
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{
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rv.emplace_back(*it->second);
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numToSort--;
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}
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}
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// can't reach the requested number, means findTop already done its best
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lastFindTopRetAll = (rv.size() < numSolutions);
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return rv;
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}
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virtual bool lastFindTopAlreadyRetAll() const override
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{
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return lastFindTopRetAll;
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}
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virtual SolutionSet<MySolution>
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findAllSolutionsGroupedGemm(std::vector<MyProblem> const& problems,
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Hardware const& hardware,
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SolutionLibrarySearchType searchType
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= SolutionLibrarySearchType::DEFAULT) const override
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{
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const bool experimental = Debug::Instance().useExperimentalSelection();
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if(!experimental)
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{
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// Skip the search for solutions if the environment variable
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// that enables the experimental method is not set
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SolutionSet<MySolution> rv;
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return rv;
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}
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SolutionSet<MySolution> rv;
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for(auto const& row : solutionmap)
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rv.insert(row.second);
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return rv;
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}
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};
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} // namespace TensileLite
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