host_tensor.hpp

#ifndef HOST_TENSOR_HPP
#define HOST_TENSOR_HPP

#include <thread>
#include <vector>
#include <numeric>
#include <algorithm>
#include <utility>
#include <cassert>
#include <iostream>

template <class Range>
std::ostream& LogRange(std::ostream& os, Range&& range, std::string delim)
{
    bool first = true;
    for(auto&& v : range)
    {
        if(first)
            first = false;
        else
            os << delim;
        os << v;
    }
    return os;
}

typedef enum
{
    Half  = 0,
    Float = 1,
} DataType_t;

template <class T>
struct DataType;

template <>
struct DataType<float> : std::integral_constant<DataType_t, DataType_t::Float>
{
};

template <class F, class T, std::size_t... Is>
auto call_f_unpack_args_impl(F f, T args, std::index_sequence<Is...>)
{
    return f(std::get<Is>(args)...);
}

template <class F, class T>
auto call_f_unpack_args(F f, T args)
{
    constexpr std::size_t N = std::tuple_size<T>{};

    return call_f_unpack_args_impl(f, args, std::make_index_sequence<N>{});
}

template <class F, class T, std::size_t... Is>
auto construct_f_unpack_args_impl(T args, std::index_sequence<Is...>)
{
    return F(std::get<Is>(args)...);
}

template <class F, class T>
auto construct_f_unpack_args(F, T args)
{
    constexpr std::size_t N = std::tuple_size<T>{};

    return construct_f_unpack_args_impl<F>(args, std::make_index_sequence<N>{});
}

struct HostTensorDescriptor
{
    HostTensorDescriptor() = delete;

    template <typename X>
    HostTensorDescriptor(std::vector<X> lens);

    template <typename X, typename Y>
    HostTensorDescriptor(std::vector<X> lens, std::vector<Y> strides);

    void CalculateStrides();

    template <class Range>
    HostTensorDescriptor(const Range& lens) : mLens(lens.begin(), lens.end())
    {
        this->CalculateStrides();
    }

    template <class Range1, class Range2>
    HostTensorDescriptor(const Range1& lens, const Range2& strides)
        : mLens(lens.begin(), lens.end()), mStrides(strides.begin(), strides.end())
    {
    }

    std::size_t GetNumOfDimension() const;
    std::size_t GetElementSize() const;
    std::size_t GetElementSpace() const;

    const std::vector<std::size_t>& GetLengths() const;
    const std::vector<std::size_t>& GetStrides() const;

    template <class... Is>
    std::size_t GetOffsetFromMultiIndex(Is... is) const
    {
        assert(sizeof...(Is) == this->GetNumOfDimension());
        std::initializer_list<std::size_t> iss{static_cast<std::size_t>(is)...};
        return std::inner_product(iss.begin(), iss.end(), mStrides.begin(), std::size_t{0});
    }

    private:
    std::vector<std::size_t> mLens;
    std::vector<std::size_t> mStrides;
};

struct joinable_thread : std::thread
{
    template <class... Xs>
    joinable_thread(Xs&&... xs) : std::thread(std::forward<Xs>(xs)...)
    {
    }

    joinable_thread(joinable_thread&&) = default;
    joinable_thread& operator=(joinable_thread&&) = default;

    ~joinable_thread()
    {
        if(this->joinable())
            this->join();
    }
};

template <class F, class... Xs>
struct ParallelTensorFunctor
{
    F mF;
    static constexpr std::size_t NDIM = sizeof...(Xs);
    std::array<std::size_t, NDIM> mLens;
    std::array<std::size_t, NDIM> mStrides;
    std::size_t mN1d;

    ParallelTensorFunctor(F f, Xs... xs) : mF(f), mLens({static_cast<std::size_t>(xs)...})
    {
        mStrides.back() = 1;
        std::partial_sum(mLens.rbegin(),
                         mLens.rend() - 1,
                         mStrides.rbegin() + 1,
                         std::multiplies<std::size_t>());
        mN1d = mStrides[0] * mLens[0];
    }

    std::array<std::size_t, NDIM> GetNdIndices(std::size_t i) const
    {
        std::array<std::size_t, NDIM> indices;

        for(int idim = 0; idim < NDIM; ++idim)
        {
            indices[idim] = i / mStrides[idim];
            i -= indices[idim] * mStrides[idim];
        }

        return indices;
    }

    void operator()(std::size_t num_thread = std::thread::hardware_concurrency()) const
    {
        std::size_t work_per_thread = (mN1d + num_thread - 1) / num_thread;

        std::vector<joinable_thread> threads(num_thread);

        for(std::size_t it = 0; it < num_thread; ++it)
        {
            std::size_t iw_begin = it * work_per_thread;
            std::size_t iw_end   = std::min((it + 1) * work_per_thread, mN1d);

            auto f = [=] {
                for(std::size_t iw = iw_begin; iw < iw_end; ++iw)
                {
                    call_f_unpack_args(mF, GetNdIndices(iw));
                }
            };
            threads[it] = joinable_thread(f);
        }
    }
};

template <class F, class... Xs>
auto make_ParallelTensorFunctor(F f, Xs... xs)
{
    return ParallelTensorFunctor<F, Xs...>(f, xs...);
}

template <class T>
struct Tensor
{
    template <class X>
    Tensor(std::initializer_list<X> lens) : mDesc(lens), mData(mDesc.GetElementSpace())
    {
    }

    template <class X>
    Tensor(std::vector<X> lens) : mDesc(lens), mData(mDesc.GetElementSpace())
    {
    }

    template <class X, class Y>
    Tensor(std::vector<X> lens, std::vector<Y> strides)
        : mDesc(lens, strides), mData(mDesc.GetElementSpace())
    {
    }

    Tensor(const HostTensorDescriptor& desc) : mDesc(desc), mData(mDesc.GetElementSpace()) {}

    template <class G>
    void GenerateTensorValue(G g, std::size_t num_thread = 1)
    {
        switch(mDesc.GetNumOfDimension())
        {
        case 1:
        {
            auto f = [&](auto i) { (*this)(i) = g(i); };
            make_ParallelTensorFunctor(f, mDesc.GetLengths()[0])(num_thread);
            break;
        }
        case 2:
        {
            auto f = [&](auto i0, auto i1) { (*this)(i0, i1) = g(i0, i1); };
            make_ParallelTensorFunctor(f, mDesc.GetLengths()[0], mDesc.GetLengths()[1])(num_thread);
            break;
        }
        case 3:
        {
            auto f = [&](auto i0, auto i1, auto i2) { (*this)(i0, i1, i2) = g(i0, i1, i2); };
            make_ParallelTensorFunctor(
                f, mDesc.GetLengths()[0], mDesc.GetLengths()[1], mDesc.GetLengths()[2])(num_thread);
            break;
        }
        case 4:
        {
            auto f = [&](auto i0, auto i1, auto i2, auto i3) {
                (*this)(i0, i1, i2, i3) = g(i0, i1, i2, i3);
            };
            make_ParallelTensorFunctor(f,
                                       mDesc.GetLengths()[0],
                                       mDesc.GetLengths()[1],
                                       mDesc.GetLengths()[2],
                                       mDesc.GetLengths()[3])(num_thread);
            break;
        }
        default: throw std::runtime_error("unspported dimension");
        }
    }

    template <class... Is>
    T& operator()(Is... is)
    {
        return mData[mDesc.GetOffsetFromMultiIndex(is...)];
    }

    template <class... Is>
    const T& operator()(Is... is) const
    {
        return mData[mDesc.GetOffsetFromMultiIndex(is...)];
    }

    typename std::vector<T>::iterator begin() { return mData.begin(); }

    typename std::vector<T>::iterator end() { return mData.end(); }

    typename std::vector<T>::const_iterator begin() const { return mData.begin(); }

    typename std::vector<T>::const_iterator end() const { return mData.end(); }

    HostTensorDescriptor mDesc;
    std::vector<T> mData;
};

void ostream_HostTensorDescriptor(const HostTensorDescriptor& desc, std::ostream& os = std::cout)
{
    os << "dim " << desc.GetNumOfDimension() << ", ";

    os << "lengths {";
    LogRange(os, desc.GetLengths(), ", ");
    os << "}, ";

    os << "strides {";
    LogRange(os, desc.GetStrides(), ", ");
    os << "}" << std::endl;
}

template <class T>
void check_error(const Tensor<T>& ref, const Tensor<T>& result)
{
    float error     = 0;
    float max_diff  = -1;
    float ref_value = 0, result_value = 0;
    for(int i = 0; i < ref.mData.size(); ++i)
    {
        error += std::abs(double(ref.mData[i]) - double(result.mData[i]));
        float diff = std::abs(double(ref.mData[i]) - double(result.mData[i]));
        if(max_diff < diff)
        {
            max_diff     = diff;
            ref_value    = ref.mData[i];
            result_value = result.mData[i];
        }
    }

    std::cout << "error: " << error << std::endl;
    std::cout << "max_diff: " << max_diff << ", " << ref_value << ", " << result_value << std::endl;
}

#endif