convert_to_fragmented_sparse.hpp

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#ifndef TATAMI_CONVERT_TO_FRAGMENTED_SPARSE_H
#define TATAMI_CONVERT_TO_FRAGMENTED_SPARSE_H
 
#include <memory>
#include <vector>
#include <cstddef>
#include <optional>
 
#include "FragmentedSparseMatrix.hpp"
#include "convert_to_sparse_utils.hpp"
 
#include "../utils/parallelize.hpp"
#include "../utils/copy.hpp"
#include "../utils/consecutive_extractor.hpp"
#include "../utils/Index_to_container.hpp"
 
namespace tatami {
 
template<typename Value_, typename Index_>
struct FragmentedSparseContents {
    FragmentedSparseContents(Index_ n) :
        value(cast_Index_to_container_size<I<decltype(value)> >(n)),
        index(cast_Index_to_container_size<I<decltype(index)> >(n))
    {}
    std::vector<std::vector<Value_> > value;
 
    std::vector<std::vector<Index_> > index;
};
 
struct RetrieveFragmentedSparseContentsOptions {
    bool two_pass = false;
 
    int num_threads = 1;
};
 
template<typename StoredValue_, typename StoredIndex_, typename InputValue_, typename InputIndex_>
FragmentedSparseContents<StoredValue_, StoredIndex_> retrieve_fragmented_sparse_contents_consistent(
    const Matrix<InputValue_, InputIndex_>& matrix,
    const bool row,
    const RetrieveFragmentedSparseContentsOptions& options
) {
    const InputIndex_ NR = matrix.nrow();
    const InputIndex_ NC = matrix.ncol();
    const InputIndex_ primary = (row ? NR : NC);
    const InputIndex_ secondary = (row ? NC : NR);
 
    FragmentedSparseContents<StoredValue_, StoredIndex_> output(primary);
    auto& store_v = output.value;
    auto& store_i = output.index;
 
    if (matrix.is_sparse()) {
        parallelize([&](const int, const InputIndex_ start, const InputIndex_ length) -> void {
            auto wrk = consecutive_extractor<true>(matrix, row, start, length);
            auto buffer_v = create_container_of_Index_size<std::vector<InputValue_> >(secondary);
            auto buffer_i = create_container_of_Index_size<std::vector<InputIndex_> >(secondary);
 
            for (InputIndex_ p = start, pe = start + length; p < pe; ++p) {
                const auto range = wrk->fetch(buffer_v.data(), buffer_i.data());
                auto& sv = store_v[p];
                auto& si = store_i[p];
                sv.reserve(range.number);
                si.reserve(range.number);
 
                // We don't filter out structural non-zeros that have values of zero, for consistency with convert_to_compressed_sparse().
                for (InputIndex_ i = 0; i < range.number; ++i) {
                    sv.push_back(range.value[i]);
                    si.push_back(range.index[i]);
                }
            }
        }, primary, options.num_threads);
 
    } else {
        parallelize([&](const int, const InputIndex_ start, const InputIndex_ length) -> void {
            auto wrk = consecutive_extractor<false>(matrix, row, start, length);
            auto buffer_v = create_container_of_Index_size<std::vector<InputValue_> >(secondary);
 
            // Special conversion from dense to save ourselves from having to make
            // indices that we aren't really interested in.
            for (InputIndex_ p = start, pe = start + length; p < pe; ++p) {
                const auto ptr = wrk->fetch(buffer_v.data());
                auto& sv = store_v[p];
                auto& si = store_i[p];
 
                for (InputIndex_ s = 0; s < secondary; ++s) {
                    const auto val = ptr[s];
                    if (val) {
                        sv.push_back(val);
                        si.push_back(s);
                    }
                }
            }
        }, primary, options.num_threads);
    }
 
    return output;
}
template<typename StoredValue_, typename StoredIndex_, typename InputValue_, typename InputIndex_>
FragmentedSparseContents<StoredValue_, StoredIndex_> retrieve_fragmented_sparse_contents(
    const Matrix<InputValue_, InputIndex_>& matrix,
    const bool row,
    const RetrieveFragmentedSparseContentsOptions& options
) {
    if (row == matrix.prefer_rows()) {
        return retrieve_fragmented_sparse_contents_consistent<StoredValue_, StoredIndex_>(matrix, row, options);
    }
 
    const InputIndex_ NR = matrix.nrow();
    const InputIndex_ NC = matrix.ncol();
    const InputIndex_ primary = (row ? NR : NC);
    const InputIndex_ secondary = (row ? NC : NR);
 
    if (!options.two_pass) {
        // In the one-pass strategy, we load everything in a nice format first, then we transpose it in serial.
        // This avoids messy reallocations when trying to expand vectors on an inconsistent dimension.
        auto tmp = retrieve_fragmented_sparse_contents_consistent<StoredValue_, StoredIndex_>(matrix, !row, options);
        auto primary_counts = create_container_of_Index_size<std::vector<InputIndex_> >(primary);
        for (I<decltype(secondary)> s = 0; s < secondary; ++s) {
            const auto& sec_indices = tmp.index[s];
            const auto num = sec_indices.size();
            for (I<decltype(num)> n = 0; n < num; ++n) {
                primary_counts[sec_indices[n]] += 1; // addition must be space, this cannot exceed dimension extents.
            }
        }
 
        FragmentedSparseContents<StoredValue_, StoredIndex_> output(primary);
        for (InputIndex_ p = 0; p < primary; ++p) {
            output.index[p].reserve(primary_counts[p]);
            output.value[p].reserve(primary_counts[p]);
        }
 
        for (I<decltype(secondary)> s = 0; s < secondary; ++s) {
            const auto& sec_values = tmp.value[s];
            const auto& sec_indices = tmp.index[s];
            const auto num = sec_indices.size();
            for (I<decltype(num)> n = 0; n < num; ++n) {
                const auto curp = sec_indices[n];
                output.value[curp].push_back(sec_values[n]);
                output.index[curp].push_back(s);
            }
        }
 
        return output;
    }
 
    // In the two-pass strategy, we count the number of non-zeros first, then we fill it up in the second pass.
    std::optional<std::vector<InputIndex_> > nnz_consistent;
    auto nnz_inconsistent = create_container_of_Index_size<std::vector<InputIndex_> >(primary);
    count_sparse_non_zeros_inconsistent(matrix, primary, secondary, row, nnz_inconsistent.data(), nnz_consistent, options.num_threads);
 
    FragmentedSparseContents<StoredValue_, StoredIndex_> output(primary);
    for (InputIndex_ p = 0; p < primary; ++p) {
        output.index[p].reserve(nnz_inconsistent[p]);
        output.value[p].reserve(nnz_inconsistent[p]);
    }
 
    fill_sparse_matrix_inconsistent(
        matrix,
        primary,
        secondary,
        row,
        nnz_consistent,
        /* sparse_main = */ [&](const InputIndex_ s, const SparseRange<InputValue_, InputIndex_>& range) -> void {
            for (InputIndex_ i = 0; i < range.number; ++i) {
                output.value[range.index[i]].push_back(range.value[i]);
                output.index[range.index[i]].push_back(s);
            }
        },
        /* dense_main = */ [&](const InputIndex_ s, const InputValue_* const ptr) -> void {
            for (InputIndex_ p = 0; p < primary; ++p) {
                const auto val = ptr[p]; 
                if (val != 0) {
                    output.value[p].push_back(val);
                    output.index[p].push_back(s);
                }
            }
        },
        /* reduce = */ [&](const InputIndex_ s, const std::vector<InputValue_>& cur_values, const std::vector<InputIndex_>& cur_primary_indices) {
            const auto cur_count = cur_values.size();
            for (I<decltype(cur_count)> i = 0; i < cur_count; ++i) {
                const auto primary = cur_primary_indices[i];
                output.value[primary].push_back(cur_values[i]);
                output.index[primary].push_back(s); 
            }
        },
        options.num_threads
    );
 
    return output;
}
 
struct ConvertToFragmentedSparseOptions {
    bool two_pass = false;
 
    int num_threads = 1;
};
 
template<
    typename Value_,
    typename Index_,
    typename StoredValue_ = Value_,
    typename StoredIndex_ = Index_,
    typename InputValue_,
    typename InputIndex_
>
std::shared_ptr<Matrix<Value_, Index_> > convert_to_fragmented_sparse(
    const Matrix<InputValue_, InputIndex_>& matrix,
    const bool row,
    const ConvertToFragmentedSparseOptions& options)
{
    auto frag = retrieve_fragmented_sparse_contents<StoredValue_, StoredIndex_>(
        matrix,
        row,
        [&]{
            RetrieveFragmentedSparseContentsOptions ropt;
            ropt.two_pass = options.two_pass;
            ropt.num_threads = options.num_threads;
            return ropt;
        }()
    );
    return std::shared_ptr<Matrix<Value_, Index_> >(
        new FragmentedSparseMatrix<
            Value_, 
            Index_,
            std::vector<std::vector<StoredValue_> >,
            std::vector<std::vector<StoredIndex_> >
        >(
            matrix.nrow(), 
            matrix.ncol(), 
            std::move(frag.value), 
            std::move(frag.index),
            row, 
            []{
                FragmentedSparseMatrixOptions fopt;
                fopt.check = false; // no need for checks, as we guarantee correctness.
                return fopt;
            }()
        )
    );
}
 
// Backwards compatbility.
template<typename Value_, typename Index_, typename StoredValue_ = Value_, typename StoredIndex_ = Index_, typename InputValue_, typename InputIndex_>
std::shared_ptr<Matrix<Value_, Index_> > convert_to_fragmented_sparse(const Matrix<InputValue_, InputIndex_>* matrix, bool row, int threads = 1) {
    return convert_to_fragmented_sparse<Value_, Index_, StoredValue_, StoredIndex_>(
        *matrix,
        row,
        [&]{
            ConvertToFragmentedSparseOptions opt;
            opt.num_threads = threads;
            return opt;
        }()
    );
}
 
template<typename StoredValue_, typename StoredIndex_, typename InputValue_, typename InputIndex_>
FragmentedSparseContents<StoredValue_, StoredIndex_> retrieve_fragmented_sparse_contents(const Matrix<InputValue_, InputIndex_>* matrix, bool row, int threads = 1) {
    return retrieve_fragmented_sparse_contents<StoredValue_, StoredIndex_>(
        *matrix,
        row,
        [&]{
            RetrieveFragmentedSparseContentsOptions opt;
            opt.num_threads = threads;
            return opt;
        }()
    );
}
 
template <bool row_, typename StoredValue_, typename StoredIndex_, typename InputValue_, typename InputIndex_>
FragmentedSparseContents<StoredValue_, StoredIndex_> retrieve_fragmented_sparse_contents(const Matrix<InputValue_, InputIndex_>* matrix, int threads = 1) {
    return retrieve_fragmented_sparse_contents<StoredValue_, StoredIndex_>(matrix, row_, threads);
}
 
template <bool row_, typename Value_, typename Index_, typename StoredValue_ = Value_, typename StoredIndex_ = Index_, typename InputValue_, typename InputIndex_>
std::shared_ptr<Matrix<Value_, Index_> > convert_to_fragmented_sparse(const Matrix<InputValue_, InputIndex_>* matrix, int threads = 1) {
    return convert_to_fragmented_sparse<Value_, Index_, StoredValue_, StoredIndex_>(matrix, row_, threads);
}
}
 
#endif