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315 lines
14 KiB
C
315 lines
14 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_SDDMM_H
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#define HIPSPARSE_SDDMM_H
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#ifdef __cplusplus
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extern "C" {
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#endif
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/*! \ingroup generic_module
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* \details
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* \p hipsparseSDDMM_bufferSize returns the size of the buffer required when computing the sampled
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* dense-dense matrix multiplication:
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* \f[
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* C := \alpha (op(A) \cdot op(B)) \circ spy(C) + \beta \cdot C,
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* \f]
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* where \f$C\f$ is a sparse matrix and \f$A\f$ and \f$B\f$ are dense matrices. This routine is used in
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* conjunction with \ref hipsparseSDDMM_preprocess() and \ref hipsparseSDDMM().
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*
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* \p hipsparseSDDMM_bufferSize supports multiple combinations of data types and compute types. See \ref hipsparseSDDMM
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* for a complete listing of all the data type and compute type combinations available.
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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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* opA dense matrix \f$A\f$ operation type.
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* @param[in]
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* opB dense matrix \f$B\f$ operation type.
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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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* A dense matrix \f$A\f$ descriptor.
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* @param[in]
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* B dense matrix \f$B\f$ descriptor.
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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 sparse matrix \f$C\f$ descriptor.
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* @param[in]
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* computeType floating point precision for the SDDMM computation.
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* @param[in]
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* alg specification of the algorithm to use.
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* @param[out]
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* pBufferSizeInBytes number of bytes of the temporary storage buffer.
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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 alpha, \p beta, \p A, \p B, \p C or
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* \p pBufferSizeInBytes is nullptr, or \p opA or \p opB is invalid.
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* \retval HIPSPARSE_STATUS_NOT_SUPPORTED \p opA == \ref HIPSPARSE_OPERATION_CONJUGATE_TRANSPOSE or
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* \p opB == \ref HIPSPARSE_OPERATION_CONJUGATE_TRANSPOSE.
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*/
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#if(!defined(CUDART_VERSION) || CUDART_VERSION >= 12000)
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HIPSPARSE_EXPORT
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hipsparseStatus_t hipsparseSDDMM_bufferSize(hipsparseHandle_t handle,
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hipsparseOperation_t opA,
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hipsparseOperation_t opB,
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const void* alpha,
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hipsparseConstDnMatDescr_t A,
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hipsparseConstDnMatDescr_t B,
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const void* beta,
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hipsparseSpMatDescr_t C,
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hipDataType computeType,
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hipsparseSDDMMAlg_t alg,
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size_t* pBufferSizeInBytes);
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#elif(CUDART_VERSION >= 11022)
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HIPSPARSE_EXPORT
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hipsparseStatus_t hipsparseSDDMM_bufferSize(hipsparseHandle_t handle,
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hipsparseOperation_t opA,
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hipsparseOperation_t opB,
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const void* alpha,
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const hipsparseDnMatDescr_t A,
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const hipsparseDnMatDescr_t B,
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const void* beta,
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hipsparseSpMatDescr_t C,
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hipDataType computeType,
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hipsparseSDDMMAlg_t alg,
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size_t* pBufferSizeInBytes);
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#endif
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/*! \ingroup generic_module
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* \details
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* \p hipsparseSDDMM_preprocess performs the required preprocessing used when computing the
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* sampled dense dense matrix multiplication:
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* \f[
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* C := \alpha (op(A) \cdot op(B)) \circ spy(C) + \beta \cdot C,
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* \f]
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* where \f$C\f$ is a sparse matrix and \f$A\f$ and \f$B\f$ are dense matrices. This routine is
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* used in conjunction with \ref hipsparseSDDMM().
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*
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* \p hipsparseSDDMM_preprocess supports multiple combinations of data types and compute types. See \ref hipsparseSDDMM
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* for a complete listing of all the data type and compute type combinations available.
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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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* opA dense matrix \f$A\f$ operation type.
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* @param[in]
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* opB dense matrix \f$B\f$ operation type.
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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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* A dense matrix \f$A\f$ descriptor.
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* @param[in]
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* B dense matrix \f$B\f$ descriptor.
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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 sparse matrix \f$C\f$ descriptor.
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* @param[in]
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* computeType floating point precision for the SDDMM computation.
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* @param[in]
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* alg specification of the algorithm to use.
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* @param[in]
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* tempBuffer temporary storage buffer allocated by the user. The size must be greater or equal to
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* the size obtained with \ref hipsparseSDDMM_bufferSize.
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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 alpha, \p beta, \p A, \p B, \p C or
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* \p tempBuffer pointer is invalid or the value of \p opA or \p opB is incorrect.
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* \retval HIPSPARSE_STATUS_NOT_SUPPORTED
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* \p opA == \ref HIPSPARSE_OPERATION_CONJUGATE_TRANSPOSE or
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* \p opB == \ref HIPSPARSE_OPERATION_CONJUGATE_TRANSPOSE.
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*/
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#if(!defined(CUDART_VERSION) || CUDART_VERSION >= 12000)
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HIPSPARSE_EXPORT
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hipsparseStatus_t hipsparseSDDMM_preprocess(hipsparseHandle_t handle,
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hipsparseOperation_t opA,
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hipsparseOperation_t opB,
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const void* alpha,
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hipsparseConstDnMatDescr_t A,
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hipsparseConstDnMatDescr_t B,
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const void* beta,
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hipsparseSpMatDescr_t C,
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hipDataType computeType,
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hipsparseSDDMMAlg_t alg,
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void* tempBuffer);
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#elif(CUDART_VERSION >= 11022)
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HIPSPARSE_EXPORT
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hipsparseStatus_t hipsparseSDDMM_preprocess(hipsparseHandle_t handle,
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hipsparseOperation_t opA,
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hipsparseOperation_t opB,
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const void* alpha,
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const hipsparseDnMatDescr_t A,
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const hipsparseDnMatDescr_t B,
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const void* beta,
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hipsparseSpMatDescr_t C,
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hipDataType computeType,
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hipsparseSDDMMAlg_t alg,
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void* tempBuffer);
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#endif
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/*! \ingroup generic_module
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* \brief Sampled dense-dense matrix multiplication.
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*
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* \details
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* \p hipsparseSDDMM multiplies the scalar \f$\alpha\f$ with the dense
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* \f$m \times k\f$ matrix \f$op(A)\f$, the dense \f$k \times n\f$ matrix \f$op(B)\f$, filtered by the sparsity pattern
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* of the \f$m \times n\f$ sparse matrix \f$C\f$, and adds the result to \f$C\f$ scaled by
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* \f$\beta\f$. The final result is stored in the sparse \f$m \times n\f$ matrix \f$C\f$,
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* such that
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* \f[
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* C := \alpha ( op(A) \cdot op(B) ) \circ spy(C) + \beta 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 op(A) == HIPSPARSE_OPERATION_NON_TRANSPOSE} \\
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* A^T, & \text{if op(A) == HIPSPARSE_OPERATION_TRANSPOSE} \\
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* \end{array}
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* \right.
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* \f],
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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 op(B) == HIPSPARSE_OPERATION_NON_TRANSPOSE} \\
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* B^T, & \text{if op(B) == HIPSPARSE_OPERATION_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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* spy(C)_{ij} = \left\{
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* \begin{array}{ll}
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* 1, & \text{ if C_{ij} != 0} \\
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* 0, & \text{ otherwise} \\
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* \end{array}
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* \right.
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* \f]
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*
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* Computing the above sampled dense-dense multiplication requires three steps to complete. First, the user calls
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* \ref hipsparseSDDMM_bufferSize to determine the size of the required temporary storage buffer. Next, the user
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* allocates this buffer and calls \ref hipsparseSDDMM_preprocess, which performs any analysis of the input matrices
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* that might be required. Finally, the user calls \p hipsparseSDDMM to complete the computation. After all calls to
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* \p hipsparseSDDMM are complete, the temporary buffer can be deallocated.
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*
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* \p hipsparseSDDMM supports different algorithms, which can provide better performance for different matrices.
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*
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* <table>
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* <caption id="sddmm_algorithms">Algorithms</caption>
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* <tr><th>CSR/CSC Algorithms
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* <tr><td>HIPSPARSE_SDDMM_ALG_DEFAULT</td>
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* </table>
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*
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* Currently, \p hipsparseSDDMM only supports the uniform precisions indicated in the table below. For the sparse matrix
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* \f$C\f$, \p hipsparseSDDMM supports the index types \ref HIPSPARSE_INDEX_32I and \ref HIPSPARSE_INDEX_64I.
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*
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* \par Uniform Precisions:
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* <table>
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* <caption id="sddmm_uniform">Uniform Precisions</caption>
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* <tr><th>A / B / C / compute_type
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* <tr><td>HIP_R_16F
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* <tr><td>HIP_R_32F
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* <tr><td>HIP_R_64F
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* <tr><td>HIP_C_32F
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* <tr><td>HIP_C_64F
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* </table>
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*
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* \par Mixed Precisions:
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* <table>
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* <caption id="sddmm_mixed">Mixed Precisions</caption>
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* <tr><th>A / B <th>C <th>compute_type
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* <tr><td>HIP_R_16F <td>HIP_R_32F <td>HIP_R_32F
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* <tr><td>HIP_R_16F <td>HIP_R_16F <td>HIP_R_32F
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* <tr><td>HIP_R_16BF <td>HIP_R_32F <td>HIP_R_32F
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* <tr><td>HIP_R_16BF <td>HIP_R_16BF <td>HIP_R_32F
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* </table>
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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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* opA dense matrix \f$A\f$ operation type.
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* @param[in]
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* opB dense matrix \f$B\f$ operation type.
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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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* A dense matrix \f$A\f$ descriptor.
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* @param[in]
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* B dense matrix \f$B\f$ descriptor.
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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 sparse matrix \f$C\f$ descriptor.
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* @param[in]
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* computeType floating point precision for the SDDMM computation.
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* @param[in]
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* alg specification of the algorithm to use.
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* @param[in]
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* tempBuffer temporary storage buffer allocated by the user. The size must be greater or equal to
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* the size obtained with \ref hipsparseSDDMM_bufferSize.
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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 alpha, \p beta, \p A, \p B, \p C or
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* \p tempBuffer pointer is invalid or the value of \p opA or \p opB is incorrect.
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* \retval HIPSPARSE_STATUS_NOT_SUPPORTED
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* \p opA == \ref HIPSPARSE_OPERATION_CONJUGATE_TRANSPOSE or
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* \p opB == \ref HIPSPARSE_OPERATION_CONJUGATE_TRANSPOSE.
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*/
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#if(!defined(CUDART_VERSION) || CUDART_VERSION >= 12000)
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HIPSPARSE_EXPORT
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hipsparseStatus_t hipsparseSDDMM(hipsparseHandle_t handle,
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hipsparseOperation_t opA,
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hipsparseOperation_t opB,
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const void* alpha,
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hipsparseConstDnMatDescr_t A,
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hipsparseConstDnMatDescr_t B,
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const void* beta,
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hipsparseSpMatDescr_t C,
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hipDataType computeType,
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hipsparseSDDMMAlg_t alg,
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void* tempBuffer);
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#elif(CUDART_VERSION >= 11022)
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HIPSPARSE_EXPORT
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hipsparseStatus_t hipsparseSDDMM(hipsparseHandle_t handle,
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hipsparseOperation_t opA,
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hipsparseOperation_t opB,
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const void* alpha,
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const hipsparseDnMatDescr_t A,
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const hipsparseDnMatDescr_t B,
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const void* beta,
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hipsparseSpMatDescr_t C,
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hipDataType computeType,
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hipsparseSDDMMAlg_t alg,
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void* tempBuffer);
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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_SDDMM_H */
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