Attention

The vector search and clustering algorithms in RAFT are being migrated to a new library dedicated to vector search called cuVS. We will continue to support the vector search algorithms in RAFT during this move, but will no longer update them after the RAPIDS 24.06 (June) release. We plan to complete the migration by RAPIDS 24.10 (October) release and they will be removed from RAFT altogether in the 24.12 (December) release.

Sparse Linear Algebra#

template<typename T>
size_t raft::sparse::linalg::csr_add_calc_inds(const int *a_ind, const int *a_indptr, const T *a_val, int nnz1, const int *b_ind, const int *b_indptr, const T *b_val, int nnz2, int m, int *out_ind, cudaStream_t stream)#

Calculate the CSR row_ind array that would result from summing together two CSR matrices.

Parameters:

  • a_ind – left hand row_ind array

  • a_indptr – left hand index_ptr array

  • a_val – left hand data array

  • nnz1 – size of left hand index_ptr and val arrays

  • b_ind – right hand row_ind array

  • b_indptr – right hand index_ptr array

  • b_val – right hand data array

  • nnz2 – size of right hand index_ptr and val arrays

  • m – size of output array (number of rows in final matrix)

  • out_ind – output row_ind array

  • stream – cuda stream to use

template<typename T>
void raft::sparse::linalg::csr_add_finalize(const int *a_ind, const int *a_indptr, const T *a_val, int nnz1, const int *b_ind, const int *b_indptr, const T *b_val, int nnz2, int m, int *c_ind, int *c_indptr, T *c_val, cudaStream_t stream)#

Calculate the CSR row_ind array that would result from summing together two CSR matrices.

Parameters:

  • a_ind – left hand row_ind array

  • a_indptr – left hand index_ptr array

  • a_val – left hand data array

  • nnz1 – size of left hand index_ptr and val arrays

  • b_ind – right hand row_ind array

  • b_indptr – right hand index_ptr array

  • b_val – right hand data array

  • nnz2 – size of right hand index_ptr and val arrays

  • m – size of output array (number of rows in final matrix)

  • c_ind – output row_ind array

  • c_indptr – output ind_ptr array

  • c_val – output data array

  • stream – cuda stream to use

template<typename T = int>
void raft::sparse::linalg::coo_degree(const T *rows, int nnz, T *results, cudaStream_t stream)#

Count the number of values for each row.

Template Parameters:

TPB_X – number of threads to use per block

Parameters:

  • rows – rows array of the COO matrix

  • nnz – size of the rows array

  • results – output result array

  • stream – cuda stream to use

template<typename T>
void raft::sparse::linalg::coo_degree(COO<T> *in, int *results, cudaStream_t stream)#

Count the number of values for each row.

Template Parameters:

  • TPB_X – number of threads to use per block

  • T – type name of underlying values array

Parameters:

  • in – input COO object for counting rows

  • results – output array with row counts (size=in->n_rows)

  • stream – cuda stream to use

template<typename T>
void raft::sparse::linalg::coo_degree_scalar(const int *rows, const T *vals, int nnz, T scalar, int *results, cudaStream_t stream = 0)#

Count the number of values for each row that doesn’t match a particular scalar.

Template Parameters:

  • TPB_X – number of threads to use per block

  • T – the type name of the underlying value arrays

Parameters:

  • rows – Input COO row array

  • vals – Input COO val arrays

  • nnz – size of input COO arrays

  • scalar – scalar to match for counting rows

  • results – output row counts

  • stream – cuda stream to use

template<typename T>
void raft::sparse::linalg::coo_degree_scalar(COO<T> *in, T scalar, int *results, cudaStream_t stream)#

Count the number of values for each row that doesn’t match a particular scalar.

Template Parameters:

  • TPB_X – number of threads to use per block

  • T – the type name of the underlying value arrays

Parameters:

  • in – Input COO array

  • scalar – scalar to match for counting rows

  • results – output row counts

  • stream – cuda stream to use

template<typename T>
void raft::sparse::linalg::coo_degree_nz(const int *rows, const T *vals, int nnz, int *results, cudaStream_t stream)#

Count the number of nonzeros for each row.

Template Parameters:

  • TPB_X – number of threads to use per block

  • T – the type name of the underlying value arrays

Parameters:

  • rows – Input COO row array

  • vals – Input COO val arrays

  • nnz – size of input COO arrays

  • results – output row counts

  • stream – cuda stream to use

template<typename T>
void raft::sparse::linalg::coo_degree_nz(COO<T> *in, int *results, cudaStream_t stream)#

Count the number of nonzero values for each row.

Template Parameters:

  • TPB_X – number of threads to use per block

  • T – the type name of the underlying value arrays

Parameters:

  • in – Input COO array

  • results – output row counts

  • stream – cuda stream to use

template<typename T>
void raft::sparse::linalg::csr_row_normalize_l1(const int *ia, const T *vals, int nnz, int m, T *result, cudaStream_t stream)#

Perform L1 normalization on the rows of a given CSR-formatted sparse matrix.

Parameters:

  • ia – row_ind array

  • vals – data array

  • nnz – size of data array

  • m – size of row_ind array

  • result – l1 normalized data array

  • stream – cuda stream to use

template<typename T>
void raft::sparse::linalg::csr_row_normalize_max(const int *ia, const T *vals, int nnz, int m, T *result, cudaStream_t stream)#

Perform L_inf normalization on a given CSR-formatted sparse matrix.

Parameters:

  • ia – row_ind array

  • vals – data array

  • nnz – size of data array

  • m – size of row_ind array

  • result – l1 normalized data array

  • stream – cuda stream to use

template<typename Type, typename IdxType = int, typename Lambda = raft::identity_op>
void raft::sparse::linalg::rowNormCsr(raft::resources const &handle, const IdxType *ia, const Type *data, const IdxType nnz, const IdxType N, Type *norm, raft::linalg::NormType type, Lambda fin_op = raft::identity_op())#

Compute row-wise norm of the input matrix and perform fin_op lambda.

Row-wise norm is useful while computing pairwise distance matrix, for example. This is used in many clustering algos like knn, kmeans, dbscan, etc…

Template Parameters:

  • Type – the data type

  • Lambda – device final lambda

  • IdxType – Integer type used to for addressing

Parameters:

  • handle – raft handle

  • ia – the input matrix row index array

  • data – the input matrix nnz data

  • nnz – number of elements in data

  • N – number of rows

  • norm – the output vector of row-wise norm, size [N]

  • type – the type of norm to be applied

  • fin_op – the final lambda op

template<typename ValueType, typename IndexType, typename NZType, typename LayoutPolicyA, typename LayoutPolicyB, typename OutputType>
void raft::sparse::linalg::sddmm(raft::resources const &handle, raft::device_matrix_view<const ValueType, IndexType, LayoutPolicyA> A, raft::device_matrix_view<const ValueType, IndexType, LayoutPolicyB> B, raft::device_csr_matrix_view<OutputType, IndexType, IndexType, NZType> C, const raft::linalg::Operation opA, const raft::linalg::Operation opB, raft::host_scalar_view<OutputType> alpha, raft::host_scalar_view<OutputType> beta)#

This function performs the multiplication of dense matrix A and dense matrix B, followed by an element-wise multiplication with the sparsity pattern of C. It computes the following equation: C = alpha · (opA(A) * opB(B) ∘ spy(C)) + beta · C where A,B are device matrix views and C is a CSR device matrix view.

Template Parameters:

  • ValueType – Data type of input/output matrices (float/double/half)

  • IndexType – Type of C

  • NZType – Type of C

  • LayoutPolicyA – layout of A

  • LayoutPolicyB – layout of B

  • OutputType – output type, equal to ValueType by default

Parameters:
template<typename ValueType, typename IndexType, typename NZType, typename LayoutPolicyY, typename LayoutPolicyZ>
void raft::sparse::linalg::spmm(raft::resources const &handle, const bool trans_x, const bool trans_y, const ValueType *alpha, raft::device_csr_matrix_view<const ValueType, int, int, NZType> x, raft::device_matrix_view<const ValueType, IndexType, LayoutPolicyY> y, const ValueType *beta, raft::device_matrix_view<ValueType, IndexType, LayoutPolicyZ> z)#

SPMM function designed for handling all CSR * DENSE combinations of operand layouts for cuSparse. It computes the following equation: Z = alpha . X * Y + beta . Z where X is a CSR device matrix view and Y,Z are device matrix views.

Template Parameters:

  • ValueType – Data type of input/output matrices (float/double)

  • IndexType – Type of Y and Z

  • NZType – Type of X

  • LayoutPolicyY – layout of Y

  • LayoutPolicyZ – layout of Z

Parameters:
template<typename T, typename Lambda>
void raft::sparse::linalg::coo_symmetrize(COO<T> *in, COO<T> *out, Lambda reduction_op, cudaStream_t stream)#

takes a COO matrix which may not be symmetric and symmetrizes it, running a custom reduction function against the each value and its transposed value.

Parameters:

  • in – Input COO matrix

  • out – Output symmetrized COO matrix

  • reduction_op – a custom reduction function

  • stream – cuda stream to use

template<typename value_idx = int64_t, typename value_t = float> RAFT_KERNEL raft::sparse::linalg::symmetric_find_size (const value_t __restrict__ *data, const value_idx __restrict__ *indices, const value_idx n, const int k, value_idx __restrict__ *row_sizes, value_idx __restrict__ *row_sizes2)

Find how much space needed in each row. We look through all datapoints and increment the count for each row.

TODO: This isn’t generalized. Remove in place of symmetrize()

Parameters:

  • data – Input knn distances(n, k)

  • indices – Input knn indices(n, k)

  • n – Number of rows

  • k – Number of n_neighbors

  • row_sizes – Input empty row sum 1 array(n)

  • row_sizes2 – Input empty row sum 2 array(n) for faster reduction

template<typename value_idx> RAFT_KERNEL raft::sparse::linalg::reduce_find_size (const value_idx n, const int k, value_idx __restrict__ *row_sizes, const value_idx __restrict__ *row_sizes2)

Reduce sum(row_sizes) + k Reduction for symmetric_find_size kernel. Allows algo to be faster.

TODO: This isn’t generalized. Remove in place of symmetrize()

Parameters:

  • n – Number of rows

  • k – Number of n_neighbors

  • row_sizes – Input row sum 1 array(n)

  • row_sizes2 – Input row sum 2 array(n) for faster reduction

template<typename value_idx = int64_t, typename value_t = float> RAFT_KERNEL raft::sparse::linalg::symmetric_sum (value_idx *__restrict__ edges, const value_t *__restrict__ data, const value_idx *__restrict__ indices, value_t *__restrict__ VAL, value_idx *__restrict__ COL, value_idx *__restrict__ ROW, const value_idx n, const int k)

Perform data + data.T operation. Can only run once row_sizes from the CSR matrix of data + data.T has been determined.

TODO: This isn’t generalized. Remove in place of symmetrize()

Parameters:

  • edges – Input row sum array(n) after reduction

  • data – Input knn distances(n, k)

  • indices – Input knn indices(n, k)

  • VAL – Output values for data + data.T

  • COL – Output column indices for data + data.T

  • ROW – Output row indices for data + data.T

  • n – Number of rows

  • k – Number of n_neighbors

template<typename value_idx = int64_t, typename value_t = float, int TPB_X = 32, int TPB_Y = 32> void raft::sparse::linalg::from_knn_symmetrize_matrix (const value_idx *__restrict__ knn_indices, const value_t *__restrict__ knn_dists, const value_idx n, const int k, COO< value_t, value_idx > *out, cudaStream_t stream)

Perform data + data.T on raw KNN data. The following steps are invoked: (1) Find how much space needed in each row (2) Compute final space needed (n*k + sum(row_sizes)) == 2*n*k (3) Allocate new space (4) Prepare edges for each new row (5) Perform final data + data.T operation (6) Return summed up VAL, COL, ROW.

TODO: This isn’t generalized. Remove in place of symmetrize()

Parameters:

  • knn_indices – Input knn distances(n, k)

  • knn_dists – Input knn indices(n, k)

  • n – Number of rows

  • k – Number of n_neighbors

  • out – Output COO Matrix class

  • stream – Input cuda stream

template<typename value_idx, typename value_t>
void raft::sparse::linalg::symmetrize(raft::resources const &handle, const value_idx *rows, const value_idx *cols, const value_t *vals, size_t m, size_t n, size_t nnz, raft::sparse::COO<value_t, value_idx> &out)#

Symmetrizes a COO matrix

template<typename value_idx, typename value_t>
void raft::sparse::linalg::csr_transpose(raft::resources const &handle, const value_idx *csr_indptr, const value_idx *csr_indices, const value_t *csr_data, value_idx *csc_indptr, value_idx *csc_indices, value_t *csc_data, value_idx csr_nrows, value_idx csr_ncols, value_idx nnz, cudaStream_t stream)#

Transpose a set of CSR arrays into a set of CSC arrays.

Template Parameters:

  • value_idx – : data type of the CSR index arrays

  • value_t – : data type of the CSR data array

Parameters:

  • handle – [in] : used for invoking cusparse

  • csr_indptr – [in] : CSR row index array

  • csr_indices – [in] : CSR column indices array

  • csr_data – [in] : CSR data array

  • csc_indptr – [out] : CSC row index array

  • csc_indices – [out] : CSC column indices array

  • csc_data – [out] : CSC data array

  • csr_nrows – [in] : Number of rows in CSR

  • csr_ncols – [in] : Number of columns in CSR

  • nnz – [in] : Number of nonzeros of CSR

  • stream – [in] : Cuda stream for ordering events

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