LinearAlgebraUtils.h

#include "ATen/ATen.h"
#include <limits>

namespace at { namespace native {

/*
 * Clones a Tensor so that the following conditions hold:
 * If we think of a Tensor of having size (B, M, N), where B is any number
 * of batch dimensions, then:
 * - Each (M, N) matrix is in column major form
 * - Let Tensor P have size (B, M, N) and Q have size (B, M', N').
 *   Then when laid out in memory, the M by N matrix starting at
 *   P.data_ptr()[b * M * N] is of the same corresponding batch as the M' by N'
 *   matrix starting at Q.data_ptr()[b * M' * N'].
 */
static inline Tensor cloneBatchedColumnMajor(const Tensor& src) {
  // If src is already in batched column major format, then
  // this will be efficient (no reordering of the data will occur)
  // because the first transpose will make the tensor contiguous,
  // and cloning a contiguous tensor is fast.
  auto result = src.transpose(-2, -1).clone();
  result.transpose_(-2, -1);
  return result;
}

/*
 * Given batches of matrices with arbitrary batch dim,
 * computes the number of batches.
 */
static inline int64_t batchCount(const Tensor& batched_matrices) {
  int64_t result = 1;
  for (int64_t i = 0; i < batched_matrices.ndimension() - 2; i++) {
    result *= batched_matrices.size(i);
  }
  return result;
}

// Computes the number of elements of a matrix in a batched matrix tensor
static inline int64_t matrixStride(const Tensor& batched_matrices) {
  return batched_matrices.size(-1) * batched_matrices.size(-2);
}

// Returns the epsilon value for floating types except half
static inline double _get_epsilon(const ScalarType& sc_type) {
  switch (sc_type) {
    case at::ScalarType::Float:
      return static_cast<double>(std::numeric_limits<float>::epsilon());
    case at::ScalarType::Double:
      return std::numeric_limits<double>::epsilon();
    default:
      AT_ERROR("This function doesn't handle types other than float and double");
  }
}

// Checks if all the Tensors in a TensorList are of the same dimensions
static inline void checkAllSameDim(TensorList tensors, int64_t dim) {
  for (auto &t : tensors) {
    AT_CHECK(t.dim() == dim, "Tensor dimension is ", t.dim(), ", expected ", dim, " instead.");
  }
}

}}  // namespace at::native