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TheRock/rocm-libraries/projects/hipblaslt/tensilelite/include/Tensile/MLPClassificationLibrary.hpp
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Flatten subprojects into a single base monorepo
2026-09-02 16:34:47 -07:00

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