compile_ops.cpp

/*
 * The MIT License (MIT)
 *
 * Copyright (c) 2015-2022 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
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 * 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
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 * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
 * THE SOFTWARE.
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#include <migraphx/gpu/compile_ops.hpp>
#include <migraphx/gpu/context.hpp>
#include <migraphx/module.hpp>
#include <migraphx/iterator_for.hpp>
#include <migraphx/instruction.hpp>
#include <migraphx/par_for.hpp>
#include <migraphx/register_op.hpp>
#include <migraphx/op/identity.hpp>
#include <migraphx/gpu/compiler.hpp>
#include <migraphx/gpu/time_op.hpp>

namespace migraphx {
inline namespace MIGRAPHX_INLINE_NS {
namespace gpu {

MIGRAPHX_DECLARE_ENV_VAR(MIGRAPHX_GPU_COMPILE_PARALLEL);

struct precompile_op
{
    operation op                = op::identity{};
    std::size_t additional_args = 1;
    bool ignore_modules         = false;

    template <class Self, class F>
    static auto reflect(Self& self, F f)
    {
        return pack(f(self.op, "op"),
                    f(self.additional_args, "additional_args"),
                    f(self.ignore_modules, "ignore_modules"));
    }

    std::string name() const { return "gpu::precompile_op"; }

    shape compute_shape(std::vector<shape> inputs, const std::vector<module_ref>& mods) const
    {
        // Pop off additional args
        inputs.resize(inputs.size() - additional_args);
        if(ignore_modules)
            return op.compute_shape(inputs);
        return op.compute_shape(inputs, mods);
    }

    std::ptrdiff_t output_alias(const std::vector<shape>& shapes) const
    {
        return shapes.size() - 1;
    }
};

MIGRAPHX_REGISTER_OP(precompile_op);

struct compiled_result
{
    compiler_replace replace;
    instruction_ref ins;
};

struct problem_cache
{
    bool has(const std::string& name, const value& problem) const
    {
        return contains(cache, create_key(name, problem));
    }
    void insert(const std::string& name, const value& problem, const value& solution)
    {
        cache[create_key(name, problem)] = solution;
    }
    optional<value> get(const std::string& name, const value& problem) const
    {
        auto it = cache.find(create_key(name, problem));
        if (it == cache.end())
            return nullopt;
        return it->second;
    }
    static value create_key(const std::string& name, const value& problem)
    {
        return {{"name", name}, {"problem", problem}};
    }
    std::unordered_map<value, value> cache;
};

struct compile_plan
{
    context* ctx;
    operation preop;
    instruction_ref ins;
    optional<tuning_config> config       = nullopt;
    std::vector<compiled_result> results = {};
    void update_config() { config = get_tuning_config(*ctx, ins, preop); }
    template <class Vector>
    void add_compiles(Vector& compiles, problem_cache& pc)
    {
        if(config.has_value())
        {
            const auto& problem = config.value().problem;
            if(auto sol = pc.get(preop.name(), problem))
            {
                auto solution = sol.value();
                // No solution yet until benchmarked so skip for now
                if(solution.empty())
                    return;
                compiles.emplace_back([=] {
                    results[0] = compiled_result{compile(*ctx, ins, preop, solution), ins};
                });
            }
            else
            {
                const auto& solutions = config.value().solutions;
                results.resize(solutions.size());
                for(auto i : range(solutions.size()))
                {
                    auto solution = solutions[i];
                    compiles.emplace_back([=] {
                        results[i] = compiled_result{compile(*ctx, ins, preop, solution), ins};
                    });
                }
            }
        }
        else
        {
            results.resize(1);
            compiles.emplace_back([=] {
                results[0] = compiled_result{compile(*ctx, ins, preop, value{}), ins};
            });
        }
    }
    const compiled_result& benchmark(problem_cache& pc) const
    {
        if(results.empty())
            MIGRAPHX_THROW("No configs to tune");
        if(results.size() == 1)
            return results.front();
        std::cout << "Benchmarking " << preop.name() << ": " << results.size() << " configs"
                  << std::endl;
        std::vector<double> times;
        for(const auto& cr : results)
        {
            times.push_back(
                time_op(*ctx, cr.replace.code_object, to_shapes(cr.ins->inputs()), 20).first);
        }
        auto i = std::distance(times.begin(), std::min_element(times.begin(), times.end()));
        pc.insert(preop.name(), config.value().problem, config.value().solutions[i]);
        return results[i];
    }
    void replace(module& m, problem_cache& pc) const
    {
        const auto& cr = benchmark(pc);
        cr.replace.replace(m, cr.ins);
    }
};

template <class F>
void par_compile(std::size_t n, F f)
{
    if(n == 0)
        return;
    par_for(n, n / value_of(MIGRAPHX_GPU_COMPILE_PARALLEL{}, n), f);
}

struct compile_manager
{
    problem_cache pc;
    std::vector<compile_plan> cps;

    template<class... Ts>
    void add_plan(Ts&&... xs)
    {
        cps.push_back({std::forward<Ts>(xs)...});
    }

    void update_configs()
    {
        par_compile(cps.size(), [&](auto i) { cps[i].update_config(); });
    }

    void compile(module& m)
    {
        std::vector<std::function<void()>> compiles;
        for(auto& cp : cps)
        {
            cp.add_compiles(compiles, pc);
        }
        par_compile(compiles.size(), [&](auto i) { compiles[i](); });

        // Replace and/or benchmark
        for(const auto& cp : cps)
        {
            if(cp.results.empty())
                continue;
            cp.replace(m, pc);
        }

        // Remove compile_plan already executed
        cps.erase(std::remove_if(cps.begin(), cps.end(), [](const auto& cp) {
            return not cp.results.empty();
        }), cps.end());
    }
};

void compile_ops::apply(module& m) const
{
    compile_manager cm;
    problem_cache pc;
    std::vector<compile_plan> cps;
    // Find all precompile opes
    for(auto ins : iterator_for(m))
    {
        if(ins->name() != "gpu::precompile_op")
            continue;
        operation preop = any_cast<precompile_op>(ins->get_operator()).op;
        cm.add_plan(ctx, preop, ins);
    }
    cm.update_configs();
    cm.compile(m);
    // Compile already tuned configs
    cm.compile(m);
}

} // namespace gpu

} // namespace MIGRAPHX_INLINE_NS
} // namespace migraphx