monolish_tensor_coo.hpp

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#pragma once
#include "monolish_matrix.hpp"
#include "monolish_tensor.hpp"
#include "monolish_vector.hpp"
 
namespace monolish {
template <typename Float> class vector;
template <typename TYPE, typename Float> class view1D;
namespace tensor {
template <typename Float> class tensor_Dense;
template <typename Float> class tensor_CRS;
template <typename Float> class tensor_COO {
private:
  std::vector<size_t> shape;
 
  mutable bool gpu_status = false;
 
public:
  std::vector<std::vector<size_t>> index;
 
  std::shared_ptr<Float> val;
 
  size_t val_nnz = 0;
 
  size_t alloc_nnz = 0;
 
  bool val_create_flag = false;
 
  tensor_COO() : shape(), gpu_status(false), index(), val_nnz(0) {
    val_create_flag = true;
  }
 
  tensor_COO(const std::vector<size_t> &shape_)
      : shape(shape_), gpu_status(false), index(), val_nnz(0) {
    val_create_flag = true;
  }
 
  tensor_COO(const std::initializer_list<size_t> &shape_)
      : shape(shape_), gpu_status(false), index(), val_nnz(0) {
    val_create_flag = true;
  }
 
  void convert(const tensor::tensor_Dense<Float> &tens);
 
  tensor_COO(const tensor::tensor_Dense<Float> &tens) {
    val_create_flag = true;
    convert(tens);
  }
 
  void convert(const tensor::tensor_CRS<Float> &tens);
 
  tensor_COO(const tensor::tensor_CRS<Float> &tens) {
    val_create_flag = true;
    convert(tens);
  }
 
  tensor_COO(const std::vector<size_t> &shape_,
             const std::vector<std::vector<size_t>> &index_,
             const Float *value);
 
  tensor_COO(const tensor_COO<Float> &coo);
 
  tensor_COO(const tensor_COO<Float> &coo, Float value);
 
  void print_all(bool force_cpu = false) const;
 
  void print_all(const std::string filename) const;
 
  // communication
  // ///////////////////////////////////////////////////////////////////////////
  void send() const;
 
  void recv();
 
  void nonfree_recv();
 
  void device_free() const;
 
  // TODO
  [[nodiscard]] double get_data_size() const {
    return get_nnz() * sizeof(Float) / 1.0e+9;
  }
 
  [[nodiscard]] Float at(const std::vector<size_t> &pos) const;
 
  [[nodiscard]] Float at(const std::vector<size_t> &pos) {
    return static_cast<const tensor_COO *>(this)->at(pos);
  };
 
  void set_ptr(const std::vector<size_t> &shape,
               const std::vector<std::vector<size_t>> &index,
               const std::vector<Float> &v);
 
  void set_ptr(const std::vector<size_t> &shape,
               const std::vector<std::vector<size_t>> &index,
               const size_t vsize, const Float *v);
 
  void set_ptr(const std::vector<size_t> &shape,
               const std::vector<std::vector<size_t>> &index,
               const size_t vsize, const Float v);
 
  [[nodiscard]] std::vector<size_t> get_shape() const { return shape; }
 
  [[nodiscard]] std::shared_ptr<Float> get_val() { return val; }
 
  [[nodiscard]] size_t get_nnz() const { return val_nnz; }
 
  void fill(Float value);
 
  void set_shape(const std::vector<size_t> &shape) { this->shape = shape; }
 
  [[nodiscard]] bool get_device_mem_stat() const { return gpu_status; }
 
  [[nodiscard]] const Float *data() const { return val.get(); }
 
  [[nodiscard]] Float *data() { return val.get(); }
 
  void resize(const size_t N, Float Val = 0) {
    if (get_device_mem_stat()) {
      throw std::runtime_error("Error, GPU matrix cant use resize");
    }
    if (val_create_flag) {
      std::shared_ptr<Float> tmp(new Float[N], std::default_delete<Float[]>());
      size_t copy_size = std::min(val_nnz, N);
      for (size_t i = 0; i < copy_size; ++i) {
        tmp.get()[i] = begin()[i];
      }
      for (size_t i = copy_size; i < N; ++i) {
        tmp.get()[i] = Val;
      }
      val = tmp;
      alloc_nnz = N;
      val_nnz = N;
 
      index.resize(N);
    } else {
      throw std::runtime_error("Error, not create vector cant use resize");
    }
  }
 
  [[nodiscard]] std::string type() const { return "tensor_COO"; }
 
  [[nodiscard]] const Float *begin() const { return data(); }
 
  [[nodiscard]] Float *begin() { return data(); }
 
  [[nodiscard]] const Float *end() const { return data() + get_nnz(); }
 
  [[nodiscard]] Float *end() { return data() + get_nnz(); }
 
  void diag(vector<Float> &vec) const;
  void diag(view1D<vector<Float>, Float> &vec) const;
  void diag(view1D<matrix::Dense<Float>, Float> &vec) const;
  void diag(view1D<tensor::tensor_Dense<Float>, Float> &vec) const;
 
  void operator=(const tensor_COO<Float> &tens);
 
  [[nodiscard]] Float &operator[](size_t i) {
    if (get_device_mem_stat()) {
      throw std::runtime_error("Error, GPU vector cant use operator[]");
    }
    return data()[i];
  }
 
  [[nodiscard]] bool equal(const tensor_COO<Float> &tens,
                           bool compare_cpu_and_device = false) const;
 
  [[nodiscard]] bool operator==(const tensor_COO<Float> &tens) const;
 
  [[nodiscard]] bool operator!=(const tensor_COO<Float> &tens) const;
 
  size_t get_index(const std::vector<size_t> &pos) {
    if (pos.size() != this->shape.size()) {
      throw std::runtime_error("pos size should be same with the shape");
    }
    size_t ind = 0;
    for (auto i = 0; i < pos.size(); ++i) {
      ind *= this->shape[i];
      ind += pos[i];
    }
    return ind;
  }
 
  std::vector<size_t> get_index(const size_t pos) {
    std::vector<size_t> ind(this->shape.size(), 0);
    auto pos_copy = pos;
    for (int i = (int)this->shape.size() - 1; i >= 0; --i) {
      ind[i] = pos_copy % this->shape[i];
      pos_copy /= this->shape[i];
    }
    return ind;
  }
 
  void insert(const std::vector<size_t> &pos, const Float val);
 
private:
  void _q_sort(int lo, int hi);
 
public:
  void sort(bool merge);
};
} // namespace tensor
} // namespace monolish