void SymmetricL1DistanceMatrix(El::UpperOrLower uplo, direction_t dir, T alpha,
const El::Matrix<T> &A, T beta, El::Matrix<T> &C) {
const T *a = A.LockedBuffer();
El::Int ldA = A.LDim();
T *c = C.Buffer();
El::Int ldC = C.LDim();
El::Int n = A.Width();
El::Int d = A.Height();
/* Not the most efficient way... but mimicking BLAS is too much work! */
if (dir == base::COLUMNS) {
for (El::Int j = 0; j < n; j++)
for(El::Int i = ((uplo == El::UPPER) ? 0 : j);
i < ((uplo == El::UPPER) ? (j + 1) : n); i++)
for (El::Int i = 0; i < A.Width(); i++) {
T v = 0.0;
for (El::Int k = 0; k < d; k++)
v += std::abs(a[j * ldA + k] - a[i * ldA + k]);
c[j * ldC + i] = beta * c[j * ldC + i] + alpha * v;
}
}
// TODO the rest of the cases.
}
void L1DistanceMatrix(direction_t dirA, direction_t dirB, T alpha,
const El::Matrix<T> &A, const El::Matrix<T> &B,
T beta, El::Matrix<T> &C) {
// TODO verify sizes
const T *a = A.LockedBuffer();
El::Int ldA = A.LDim();
const T *b = B.LockedBuffer();
El::Int ldB = B.LDim();
T *c = C.Buffer();
El::Int ldC = C.LDim();
El::Int d = A.Height();
/* Not the most efficient way... but mimicking BLAS is too much work! */
if (dirA == base::COLUMNS && dirB == base::COLUMNS) {
for (El::Int j = 0; j < B.Width(); j++)
for (El::Int i = 0; i < A.Width(); i++) {
T v = 0.0;
for (El::Int k = 0; k < d; k++)
v += std::abs(b[j * ldB + k] - a[i * ldA + k]);
c[j * ldC + i] = beta * c[j * ldC + i] + alpha * v;
}
}
// TODO the rest of the cases.
}
void lbann_callback_dump_minibatch_sample_indices::dump_to_file(model *m, Layer *l, int64_t step) {
// Print minibatch sample indices of input layers
auto *input = dynamic_cast<generic_input_layer*>(l);
if (input != nullptr) {
El::Matrix<El::Int>* indices = l->get_sample_indices_per_mb();
if (indices == nullptr
|| indices->Height() == 0
|| indices->Width() == 0) {
return;
}
std::ostringstream s;
s << "mkdir -p " << m_basename;
const int dir= system(s.str().c_str());
if (dir< 0) {
LBANN_ERROR("callback_dump_minibatch_sample_indices is unable to create the target director");
}
const std::string file
= (m_basename
+ _to_string(m->get_execution_mode())
+ "-model" + std::to_string(m->get_comm()->get_trainer_rank())
+ "-rank" + std::to_string(m->get_comm()->get_rank_in_trainer())
+ "-epoch" + std::to_string(m->get_cur_epoch())
+ "-step" + std::to_string(m->get_cur_step())
+ "-" + l->get_name()
+ "-MB_Sample_Indices");
El::Write(*indices, file, El::ASCII);
}
}
void apply_inverse_impl(El::Matrix<value_type>& A,
skylark::sketch::rowwise_tag) const {
ValueType* AA = A.Buffer();
int j;
# ifdef SKYLARK_HAVE_OPENMP
# pragma omp parallel for private(j)
# endif
for (j = 0; j < A.Width(); j++)
wht_apply(_tree, A.Height(), AA + j);
// Not sure stride is used correctly here.
}
inline void ColumnView(El::Matrix<T>& A, El::Matrix<T>& B,
int j, int width) {
El::View(A, B, 0, j, B.Height(), width);
}
inline
const El::Matrix<T> ColumnView(const El::Matrix<T>& B, int j, int width) {
El::Matrix<T> A;
El::LockedView(A, B, 0, j, B.Height(), width);
return A;
}
int Height(const El::Matrix<T>& A) {
return A.Height();
}
int max(El::Matrix<double> Y) {
int k = (int) *std::max_element(Y.Buffer(), Y.Buffer() + Y.Height());
return k;
}
