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387 lines
19 KiB
C
387 lines
19 KiB
C
/*! \file */
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/* ************************************************************************
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* Copyright (C) 2025 Advanced Micro Devices, Inc. All rights Reserved.
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*
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* Permission is hereby granted, free of charge, to any person obtaining a copy
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* of this software and associated documentation files (the "Software"), to deal
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* in the Software without restriction, including without limitation the rights
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* to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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* copies of the Software, and to permit persons to whom the Software is
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* furnished to do so, subject to the following conditions:
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*
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* The above copyright notice and this permission notice shall be included in
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* all copies or substantial portions of the Software.
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*
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* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
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* THE SOFTWARE.
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*
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* ************************************************************************ */
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#ifndef HIPSPARSE_CSRMM_H
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#define HIPSPARSE_CSRMM_H
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#ifdef __cplusplus
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extern "C" {
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#endif
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#if(!defined(CUDART_VERSION) || CUDART_VERSION < 11000)
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/*! \ingroup level3_module
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* \brief Sparse matrix dense matrix multiplication using the CSR storage format.
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*
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* \details
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* \p hipsparseXcsrmm multiplies the scalar \f$\alpha\f$ with a sparse \f$m \times k\f$
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* matrix \f$A\f$, defined in CSR storage format, and the column-oriented dense \f$k \times n\f$
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* matrix \f$B\f$ and adds the result to the column-oriented dense \f$m \times n\f$ matrix \f$C\f$ that
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* is multiplied by the scalar \f$\beta\f$, such that
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* \f[
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* C := \alpha \cdot op(A) \cdot B + \beta \cdot C,
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* \f]
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* with
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* \f[
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* op(A) = \left\{
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* \begin{array}{ll}
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* A, & \text{if transA == HIPSPARSE_OPERATION_NON_TRANSPOSE} \\
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* A^T, & \text{if transA == HIPSPARSE_OPERATION_TRANSPOSE} \\
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* A^H, & \text{if transA == HIPSPARSE_OPERATION_CONJUGATE_TRANSPOSE}
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* \end{array}
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* \right.
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* \f]
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*
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* \code{.c}
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* for(i = 0; i < ldc; ++i)
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* {
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* for(j = 0; j < n; ++j)
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* {
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* C[i][j] = beta * C[i][j];
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*
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* for(k = csrRowPtr[i]; k < csrRowPtr[i + 1]; ++k)
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* {
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* C[i][j] += alpha * csrVal[k] * B[csrColInd[k]][j];
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* }
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* }
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* }
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* \endcode
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*
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* \note
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* This function is non-blocking and executed asynchronously with respect to the host.
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* It can return before the actual computation has finished.
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*
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* \deprecated
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* This function is deprecated when using the CUDA backend (CUDA 10.0+) and will be
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* removed in CUDA 11.0. This deprecation does not apply to the ROCm backend.
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*
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* @param[in]
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* handle handle to the hipSPARSE library context queue.
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* @param[in]
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* transA matrix \f$A\f$ operation type.
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* @param[in]
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* m number of rows of the sparse CSR matrix \f$A\f$. Must be non-negative.
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* @param[in]
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* n number of columns of the dense matrix \f$op(B)\f$ and \f$C\f$. Must be non-negative.
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* @param[in]
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* k number of columns of the sparse CSR matrix \f$A\f$. Must be non-negative.
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* @param[in]
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* nnz number of non-zero entries of the sparse CSR matrix \f$A\f$. Must be non-negative.
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* @param[in]
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* alpha scalar \f$\alpha\f$.
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* @param[in]
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* descrA descriptor of the sparse CSR matrix \f$A\f$. Currently, only
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* \ref HIPSPARSE_MATRIX_TYPE_GENERAL is supported.
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* @param[in]
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* csrSortedValA array of \p nnz elements of the sparse CSR matrix \f$A\f$.
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* @param[in]
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* csrSortedRowPtrA array of \p m+1 elements that point to the start of every row of the
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* sparse CSR matrix \f$A\f$.
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* @param[in]
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* csrSortedColIndA array of \p nnz elements containing the column indices of the sparse
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* CSR matrix \f$A\f$.
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* @param[in]
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* B array of dimension \p ldb*n (\f$op(B) == B\f$),
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* \p ldb*k otherwise.
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* @param[in]
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* ldb leading dimension of \f$B\f$, must be at least \f$\max{(1, k)}\f$
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* (\f$op(B) == B\f$), \f$\max{(1, n)}\f$ otherwise.
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* @param[in]
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* beta scalar \f$\beta\f$.
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* @param[inout]
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* C array of dimension \p ldc*n.
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* @param[in]
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* ldc leading dimension of \f$C\f$, must be at least \f$\max{(1, m)}\f$
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* (\f$op(A) == A\f$), \f$\max{(1, k)}\f$ otherwise.
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*
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* \retval HIPSPARSE_STATUS_SUCCESS the operation completed successfully.
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* \retval HIPSPARSE_STATUS_NOT_INITIALIZED \p handle is not initialized.
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* \retval HIPSPARSE_STATUS_INVALID_VALUE \p handle, \p descrA, \p alpha, or \p beta is nullptr,
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* \p m, \p n, \p k, or \p nnz is negative, \p ldb or \p ldc is invalid, or
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* \p csrSortedValA, \p csrSortedRowPtrA, \p csrSortedColIndA, \p B, or \p C is nullptr.
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* \retval HIPSPARSE_STATUS_ARCH_MISMATCH the device is not supported.
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* \retval HIPSPARSE_STATUS_NOT_SUPPORTED \ref hipsparseMatrixType_t is not
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* \ref HIPSPARSE_MATRIX_TYPE_GENERAL.
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*/
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/**@{*/
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DEPRECATED_CUDA_10000("The routine will be removed in CUDA 11")
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HIPSPARSE_EXPORT
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hipsparseStatus_t hipsparseScsrmm(hipsparseHandle_t handle,
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hipsparseOperation_t transA,
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int m,
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int n,
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int k,
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int nnz,
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const float* alpha,
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const hipsparseMatDescr_t descrA,
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const float* csrSortedValA,
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const int* csrSortedRowPtrA,
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const int* csrSortedColIndA,
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const float* B,
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int ldb,
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const float* beta,
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float* C,
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int ldc);
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DEPRECATED_CUDA_10000("The routine will be removed in CUDA 11")
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HIPSPARSE_EXPORT
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hipsparseStatus_t hipsparseDcsrmm(hipsparseHandle_t handle,
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hipsparseOperation_t transA,
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int m,
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int n,
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int k,
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int nnz,
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const double* alpha,
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const hipsparseMatDescr_t descrA,
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const double* csrSortedValA,
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const int* csrSortedRowPtrA,
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const int* csrSortedColIndA,
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const double* B,
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int ldb,
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const double* beta,
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double* C,
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int ldc);
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DEPRECATED_CUDA_10000("The routine will be removed in CUDA 11")
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HIPSPARSE_EXPORT
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hipsparseStatus_t hipsparseCcsrmm(hipsparseHandle_t handle,
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hipsparseOperation_t transA,
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int m,
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int n,
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int k,
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int nnz,
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const hipComplex* alpha,
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const hipsparseMatDescr_t descrA,
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const hipComplex* csrSortedValA,
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const int* csrSortedRowPtrA,
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const int* csrSortedColIndA,
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const hipComplex* B,
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int ldb,
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const hipComplex* beta,
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hipComplex* C,
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int ldc);
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DEPRECATED_CUDA_10000("The routine will be removed in CUDA 11")
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HIPSPARSE_EXPORT
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hipsparseStatus_t hipsparseZcsrmm(hipsparseHandle_t handle,
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hipsparseOperation_t transA,
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int m,
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int n,
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int k,
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int nnz,
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const hipDoubleComplex* alpha,
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const hipsparseMatDescr_t descrA,
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const hipDoubleComplex* csrSortedValA,
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const int* csrSortedRowPtrA,
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const int* csrSortedColIndA,
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const hipDoubleComplex* B,
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int ldb,
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const hipDoubleComplex* beta,
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hipDoubleComplex* C,
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int ldc);
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/**@}*/
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#endif
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#if(!defined(CUDART_VERSION) || CUDART_VERSION < 11000)
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/*! \ingroup level3_module
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* \brief Sparse matrix dense matrix multiplication using the CSR storage format.
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*
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* \details
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* \p hipsparseXcsrmm2 multiplies the scalar \f$\alpha\f$ with a sparse \f$m \times k\f$
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* matrix \f$A\f$, defined in CSR storage format, and the column-oriented dense \f$k \times n\f$
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* matrix \f$B\f$ and adds the result to the column-oriented dense \f$m \times n\f$ matrix \f$C\f$ that
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* is multiplied by the scalar \f$\beta\f$, such that
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* \f[
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* C := \alpha \cdot op(A) \cdot op(B) + \beta \cdot C,
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* \f]
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* with
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* \f[
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* op(A) = \left\{
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* \begin{array}{ll}
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* A, & \text{if transA == HIPSPARSE_OPERATION_NON_TRANSPOSE} \\
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* A^T, & \text{if transA == HIPSPARSE_OPERATION_TRANSPOSE} \\
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* A^H, & \text{if transA == HIPSPARSE_OPERATION_CONJUGATE_TRANSPOSE}
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* \end{array}
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* \right.
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* \f]
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* and
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* \f[
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* op(B) = \left\{
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* \begin{array}{ll}
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* B, & \text{if transB == HIPSPARSE_OPERATION_NON_TRANSPOSE} \\
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* B^T, & \text{if transB == HIPSPARSE_OPERATION_TRANSPOSE} \\
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* B^H, & \text{if transB == HIPSPARSE_OPERATION_CONJUGATE_TRANSPOSE}
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* \end{array}
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* \right.
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* \f]
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*
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* \code{.c}
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* for(i = 0; i < ldc; ++i)
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* {
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* for(j = 0; j < n; ++j)
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* {
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* C[i][j] = beta * C[i][j];
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*
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* for(k = csrRowPtr[i]; k < csrRowPtr[i + 1]; ++k)
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* {
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* C[i][j] += alpha * csrVal[k] * B[csrColInd[k]][j];
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* }
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* }
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* }
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* \endcode
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*
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* \note
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* This function is non-blocking and executed asynchronously with respect to the host.
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* It can return before the actual computation has finished.
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*
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* @param[in]
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* handle handle to the hipSPARSE library context queue.
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* @param[in]
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* transA matrix \f$A\f$ operation type.
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* @param[in]
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* transB matrix \f$B\f$ operation type.
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* @param[in]
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* m number of rows of the sparse CSR matrix \f$A\f$.
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* @param[in]
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* n number of columns of the dense matrix \f$op(B)\f$ and \f$C\f$.
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* @param[in]
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* k number of columns of the sparse CSR matrix \f$A\f$.
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* @param[in]
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* nnz number of non-zero entries of the sparse CSR matrix \f$A\f$.
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* @param[in]
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* alpha scalar \f$\alpha\f$.
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* @param[in]
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* descrA descriptor of the sparse CSR matrix \f$A\f$. Currently, only
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* \ref HIPSPARSE_MATRIX_TYPE_GENERAL is supported.
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* @param[in]
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* csrSortedValA array of \p nnz elements of the sparse CSR matrix \f$A\f$.
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* @param[in]
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* csrSortedRowPtrA array of \p m+1 elements that point to the start of every row of the
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* sparse CSR matrix \f$A\f$.
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* @param[in]
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* csrSortedColIndA array of \p nnz elements containing the column indices of the sparse
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* CSR matrix \f$A\f$.
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* @param[in]
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* B array of dimension \p ldb*n (\f$op(B) == B\f$),
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* \p ldb*k otherwise.
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* @param[in]
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* ldb leading dimension of \f$B\f$. Must be at least \f$\max{(1, k)}\f$
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* (\f$op(B) == B\f$), \f$\max{(1, n)}\f$ otherwise.
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* @param[in]
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* beta scalar \f$\beta\f$.
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* @param[inout]
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* C array of dimension \p ldc*n.
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* @param[in]
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* ldc leading dimension of \f$C\f$. Must be at least \f$\max{(1, m)}\f$
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* (\f$op(A) == A\f$), \f$\max{(1, k)}\f$ otherwise.
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*
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* \retval HIPSPARSE_STATUS_SUCCESS the operation completed successfully.
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* \retval HIPSPARSE_STATUS_INVALID_VALUE \p handle, \p m, \p n, \p k, \p nnz, \p ldb, \p ldc,
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* \p descrA, \p alpha, \p csrSortedValA, \p csrSortedRowPtrA, \p csrSortedColIndA,
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* \p B, \p beta, or \p C is invalid.
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* \retval HIPSPARSE_STATUS_ARCH_MISMATCH the device is not supported.
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* \retval HIPSPARSE_STATUS_NOT_SUPPORTED
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* \ref hipsparseMatrixType_t != \ref HIPSPARSE_MATRIX_TYPE_GENERAL.
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*/
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/**@{*/
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DEPRECATED_CUDA_10000("The routine will be removed in CUDA 11")
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HIPSPARSE_EXPORT
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hipsparseStatus_t hipsparseScsrmm2(hipsparseHandle_t handle,
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hipsparseOperation_t transA,
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hipsparseOperation_t transB,
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int m,
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int n,
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int k,
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int nnz,
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const float* alpha,
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const hipsparseMatDescr_t descrA,
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const float* csrSortedValA,
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const int* csrSortedRowPtrA,
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const int* csrSortedColIndA,
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const float* B,
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int ldb,
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const float* beta,
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float* C,
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int ldc);
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DEPRECATED_CUDA_10000("The routine will be removed in CUDA 11")
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HIPSPARSE_EXPORT
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hipsparseStatus_t hipsparseDcsrmm2(hipsparseHandle_t handle,
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hipsparseOperation_t transA,
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hipsparseOperation_t transB,
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int m,
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int n,
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int k,
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int nnz,
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const double* alpha,
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const hipsparseMatDescr_t descrA,
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const double* csrSortedValA,
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const int* csrSortedRowPtrA,
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const int* csrSortedColIndA,
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const double* B,
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int ldb,
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const double* beta,
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double* C,
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int ldc);
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DEPRECATED_CUDA_10000("The routine will be removed in CUDA 11")
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HIPSPARSE_EXPORT
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hipsparseStatus_t hipsparseCcsrmm2(hipsparseHandle_t handle,
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hipsparseOperation_t transA,
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hipsparseOperation_t transB,
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int m,
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int n,
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int k,
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int nnz,
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const hipComplex* alpha,
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const hipsparseMatDescr_t descrA,
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const hipComplex* csrSortedValA,
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const int* csrSortedRowPtrA,
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const int* csrSortedColIndA,
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const hipComplex* B,
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int ldb,
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const hipComplex* beta,
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hipComplex* C,
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int ldc);
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DEPRECATED_CUDA_10000("The routine will be removed in CUDA 11")
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HIPSPARSE_EXPORT
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hipsparseStatus_t hipsparseZcsrmm2(hipsparseHandle_t handle,
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hipsparseOperation_t transA,
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hipsparseOperation_t transB,
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int m,
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int n,
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int k,
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int nnz,
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const hipDoubleComplex* alpha,
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const hipsparseMatDescr_t descrA,
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const hipDoubleComplex* csrSortedValA,
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const int* csrSortedRowPtrA,
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const int* csrSortedColIndA,
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const hipDoubleComplex* B,
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int ldb,
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const hipDoubleComplex* beta,
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hipDoubleComplex* C,
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int ldc);
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/**@}*/
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#endif
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#ifdef __cplusplus
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}
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#endif
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#endif /* HIPSPARSE_CSRMM_H */
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