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1121 lines
47 KiB
C++
1121 lines
47 KiB
C++
/* ************************************************************************
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* Copyright (C) 2020-2025 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 cop-
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* ies of the Software, and to permit persons to whom the Software is furnished
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* 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 all
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* 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 IM-
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* PLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS
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* FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR
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* COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER
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* IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNE-
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* CTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
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*
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*
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* ************************************************************************ */
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#pragma once
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#include "clientcommon.hpp"
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template <testAPI_t API, bool STRIDED, typename T, typename TT, typename W, typename U>
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void gesvda_checkBadArgs(const hipsolverHandle_t handle,
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hipsolverEigMode_t jobz,
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const int rank,
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const int m,
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const int n,
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W dA,
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const int lda,
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const int stA,
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TT dS,
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const int stS,
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T dU,
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const int ldu,
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const int stU,
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T dV,
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const int ldv,
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const int stV,
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T dWork,
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const int lwork,
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U dinfo,
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double* hRnrmF,
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const int bc)
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{
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// handle
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EXPECT_ROCBLAS_STATUS(hipsolver_gesvda(API,
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STRIDED,
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nullptr,
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jobz,
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rank,
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m,
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n,
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dA,
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lda,
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stA,
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dS,
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stS,
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dU,
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ldu,
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stU,
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dV,
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ldv,
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stV,
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dWork,
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lwork,
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dinfo,
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hRnrmF,
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bc),
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HIPSOLVER_STATUS_NOT_INITIALIZED);
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// values
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EXPECT_ROCBLAS_STATUS(hipsolver_gesvda(API,
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STRIDED,
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handle,
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hipsolverEigMode_t(-1),
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rank,
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m,
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n,
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dA,
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lda,
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stA,
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dS,
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stS,
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dU,
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ldu,
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stU,
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dV,
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ldv,
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stV,
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dWork,
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lwork,
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dinfo,
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hRnrmF,
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bc),
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HIPSOLVER_STATUS_INVALID_ENUM);
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#if defined(__HIP_PLATFORM_HCC__) || defined(__HIP_PLATFORM_AMD__)
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// pointers
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EXPECT_ROCBLAS_STATUS(hipsolver_gesvda(API,
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STRIDED,
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handle,
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jobz,
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rank,
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m,
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n,
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(W) nullptr,
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lda,
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stA,
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dS,
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stS,
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dU,
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ldu,
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stU,
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dV,
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ldv,
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stV,
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dWork,
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lwork,
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dinfo,
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hRnrmF,
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bc),
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HIPSOLVER_STATUS_INVALID_VALUE);
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EXPECT_ROCBLAS_STATUS(hipsolver_gesvda(API,
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STRIDED,
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handle,
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jobz,
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rank,
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m,
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n,
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dA,
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lda,
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stA,
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(TT) nullptr,
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stS,
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dU,
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ldu,
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stU,
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dV,
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ldv,
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stV,
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dWork,
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lwork,
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dinfo,
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hRnrmF,
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bc),
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HIPSOLVER_STATUS_INVALID_VALUE);
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EXPECT_ROCBLAS_STATUS(hipsolver_gesvda(API,
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STRIDED,
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handle,
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jobz,
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rank,
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m,
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n,
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dA,
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lda,
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stA,
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dS,
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stS,
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(T) nullptr,
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ldu,
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stU,
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dV,
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ldv,
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stV,
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dWork,
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lwork,
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dinfo,
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hRnrmF,
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bc),
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HIPSOLVER_STATUS_INVALID_VALUE);
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EXPECT_ROCBLAS_STATUS(hipsolver_gesvda(API,
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STRIDED,
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handle,
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jobz,
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rank,
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m,
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n,
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dA,
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lda,
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stA,
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dS,
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stS,
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dU,
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ldu,
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stU,
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(T) nullptr,
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ldv,
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stV,
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dWork,
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lwork,
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dinfo,
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hRnrmF,
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bc),
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HIPSOLVER_STATUS_INVALID_VALUE);
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EXPECT_ROCBLAS_STATUS(hipsolver_gesvda(API,
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STRIDED,
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handle,
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jobz,
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rank,
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m,
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n,
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dA,
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lda,
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stA,
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dS,
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stS,
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dU,
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ldu,
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stU,
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dV,
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ldv,
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stV,
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dWork,
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lwork,
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(U) nullptr,
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hRnrmF,
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bc),
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HIPSOLVER_STATUS_INVALID_VALUE);
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#endif
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}
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template <testAPI_t API, bool BATCHED, bool STRIDED, typename T>
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void testing_gesvda_bad_arg()
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{
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using S = decltype(std::real(T{}));
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// safe arguments
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hipsolver_local_handle handle;
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hipsolverEigMode_t jobz = HIPSOLVER_EIG_MODE_VECTOR;
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int rank = 1;
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int m = 2;
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int n = 2;
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int lda = 2;
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int ldu = 2;
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int ldv = 2;
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int stA = 2;
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int stS = 2;
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int stU = 2;
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int stV = 2;
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int bc = 1;
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if(BATCHED)
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{
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// // memory allocations
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// host_strided_batch_vector<double> hRnrmF(1, 1, 1, 1);
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// device_batch_vector<T> dA(1, 1, 1);
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// device_strided_batch_vector<S> dS(1, 1, 1, 1);
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// device_strided_batch_vector<T> dU(1, 1, 1, 1);
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// device_strided_batch_vector<T> dV(1, 1, 1, 1);
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// device_strided_batch_vector<int> dinfo(1, 1, 1, 1);
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// CHECK_HIP_ERROR(dA.memcheck());
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// CHECK_HIP_ERROR(dS.memcheck());
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// CHECK_HIP_ERROR(dU.memcheck());
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// CHECK_HIP_ERROR(dV.memcheck());
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// CHECK_HIP_ERROR(dinfo.memcheck());
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// int size_W;
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// hipsolver_gesvda_bufferSize(API,
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// STRIDED,
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// handle,
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// jobz,
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// rank,
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// m,
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// n,
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// dA.data(),
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// lda,
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// stA,
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// dS.data(),
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// stS,
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// dU.data(),
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// ldu,
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// stU,
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// dV.data(),
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// ldv,
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// stV,
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// &size_W,
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// bc);
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// size_t bytes_W = std::getenv("HIPSOLVER_BUFFERSIZE_RETURN_BYTES") != nullptr ? size_W : sizeof(T) * size_W;
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// device_strided_batch_vector<T> dWork(bytes_W, 1, bytes_W, 1);
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// if(size_W)
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// CHECK_HIP_ERROR(dWork.memcheck());
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// // check bad arguments
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// gesvda_checkBadArgs<API, STRIDED>(handle,
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// jobz,
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// rank,
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// m,
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// n,
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// dA.data(),
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// lda,
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// stA,
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// dS.data(),
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// stS,
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// dU.data(),
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// ldu,
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// stU,
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// dV.data(),
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// ldv,
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// stV,
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// dWork.data(),
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// size_W,
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// dinfo.data(),
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// hRnrmF.data(),
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// bc);
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}
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else
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{
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// memory allocations
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host_strided_batch_vector<double> hRnrmF(1, 1, 1, 1);
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device_strided_batch_vector<T> dA(1, 1, 1, 1);
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device_strided_batch_vector<S> dS(1, 1, 1, 1);
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device_strided_batch_vector<T> dU(1, 1, 1, 1);
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device_strided_batch_vector<T> dV(1, 1, 1, 1);
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device_strided_batch_vector<int> dinfo(1, 1, 1, 1);
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CHECK_HIP_ERROR(dA.memcheck());
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CHECK_HIP_ERROR(dS.memcheck());
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CHECK_HIP_ERROR(dU.memcheck());
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CHECK_HIP_ERROR(dV.memcheck());
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CHECK_HIP_ERROR(dinfo.memcheck());
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int size_W;
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hipsolver_gesvda_bufferSize(API,
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STRIDED,
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handle,
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jobz,
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rank,
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m,
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n,
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dA.data(),
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lda,
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stA,
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dS.data(),
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stS,
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dU.data(),
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ldu,
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stU,
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dV.data(),
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ldv,
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stV,
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&size_W,
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bc);
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size_t bytes_W = std::getenv("HIPSOLVER_BUFFERSIZE_RETURN_BYTES") != nullptr
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? size_W
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: sizeof(T) * size_W;
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device_strided_batch_vector<T> dWork(bytes_W, 1, bytes_W, 1);
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if(size_W)
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CHECK_HIP_ERROR(dWork.memcheck());
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// check bad arguments
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gesvda_checkBadArgs<API, STRIDED>(handle,
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jobz,
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rank,
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m,
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n,
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dA.data(),
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lda,
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stA,
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dS.data(),
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stS,
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dU.data(),
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ldu,
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stU,
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dV.data(),
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ldv,
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stV,
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dWork.data(),
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size_W,
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dinfo.data(),
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hRnrmF,
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bc);
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}
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}
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template <bool CPU, bool GPU, typename T, typename Td, typename Th>
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void gesvda_initData(const hipsolverHandle_t handle,
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hipsolverEigMode_t jobz,
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const int m,
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const int n,
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Td& dA,
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const int lda,
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const int bc,
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Th& hA,
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std::vector<T>& A,
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bool test = true)
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{
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if(CPU)
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{
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rocblas_init<T>(hA, true);
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for(int b = 0; b < bc; ++b)
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{
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// scale A to avoid singularities
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for(int i = 0; i < m; i++)
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{
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for(int j = 0; j < n; j++)
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{
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if(i == j)
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hA[b][i + j * lda] += 400;
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else
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hA[b][i + j * lda] -= 4;
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}
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}
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// make copy of original data to test vectors if required
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if(test && jobz != HIPSOLVER_EIG_MODE_NOVECTOR)
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{
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for(int i = 0; i < m; i++)
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{
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for(int j = 0; j < n; j++)
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A[b * lda * n + i + j * lda] = hA[b][i + j * lda];
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}
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}
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}
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}
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if(GPU)
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{
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// now copy to the GPU
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CHECK_HIP_ERROR(dA.transfer_from(hA));
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}
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}
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template <testAPI_t API,
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bool STRIDED,
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typename T,
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typename Wd,
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typename Td,
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typename Ud,
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typename Id,
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typename Wh,
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typename Th,
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typename Uh,
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typename Ih>
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void gesvda_getError(const hipsolverHandle_t handle,
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hipsolverEigMode_t jobz,
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const int rank,
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const int m,
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const int n,
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Wd& dA,
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const int lda,
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const int stA,
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Td& dS,
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const int stS,
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Ud& dU,
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const int ldu,
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const int stU,
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Ud& dV,
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const int ldv,
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const int stV,
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Ud& dWork,
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const int lwork,
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Id& dinfo,
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double* hRnrmF,
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const int bc,
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Wh& hA,
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Th& hS,
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Th& hSres,
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Uh& hUres,
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Uh& hVres,
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Ih& hinfo,
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Ih& hinfoRes,
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double* max_err,
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double* max_errv)
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{
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/** WORKAROUND: Due to errors in gesvdx, we will call gesvd to get all the singular values on the CPU side
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and use a subset of them for comparison. This approach has 2 disadvantages:
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1. singular values are not computed to the same accuracy by gesvd and gesvda. So, comparison maybe more sensitive.
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2. info cannot be tested as it has a different meaning in gesvd
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3. we cannot provide timing for CPU execution using gesvd when testing gesvda **/
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// (TODO: We may revisit the entire approach in the future: change to another solution,
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// or wait for problems with gesvdx_ to be fixed)
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using S = decltype(std::real(T{}));
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int size_W = 5 * max(m, n);
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std::vector<S> hE(size_W);
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std::vector<T> hWork(size_W);
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std::vector<T> A(lda * n * bc);
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// input data initialization
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gesvda_initData<true, true, T>(handle, jobz, m, n, dA, lda, bc, hA, A);
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|
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// GPU lapack
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CHECK_ROCBLAS_ERROR(hipsolver_gesvda(API,
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STRIDED,
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handle,
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jobz,
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rank,
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m,
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n,
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|
dA.data(),
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|
lda,
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stA,
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dS.data(),
|
|
stS,
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|
dU.data(),
|
|
ldu,
|
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stU,
|
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dV.data(),
|
|
ldv,
|
|
stV,
|
|
dWork.data(),
|
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lwork,
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dinfo.data(),
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hRnrmF,
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bc));
|
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CHECK_HIP_ERROR(hSres.transfer_from(dS));
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CHECK_HIP_ERROR(hinfoRes.transfer_from(dinfo));
|
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if(jobz != HIPSOLVER_EIG_MODE_NOVECTOR)
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{
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CHECK_HIP_ERROR(hUres.transfer_from(dU));
|
|
CHECK_HIP_ERROR(hVres.transfer_from(dV));
|
|
}
|
|
|
|
// CPU lapack
|
|
// Only singular values needed
|
|
for(int b = 0; b < bc; ++b)
|
|
cpu_gesvd<T>('N',
|
|
'N',
|
|
m,
|
|
n,
|
|
hA[b],
|
|
lda,
|
|
hS[b],
|
|
nullptr,
|
|
ldu,
|
|
nullptr,
|
|
ldv,
|
|
hWork.data(),
|
|
size_W,
|
|
hE.data(),
|
|
hinfo[b]);
|
|
|
|
// // Check info for non-convergence
|
|
*max_err = 0;
|
|
// for(int b = 0; b < bc; ++b)
|
|
// {
|
|
// EXPECT_EQ(hinfo[b][0], hinfoRes[b][0]) << "where b = " << b;
|
|
// if(hinfo[b][0] != hinfoRes[b][0])
|
|
// *max_err += 1;
|
|
// }
|
|
|
|
double err;
|
|
*max_errv = 0;
|
|
|
|
for(int b = 0; b < bc; ++b)
|
|
{
|
|
// error is ||hS - hSres||
|
|
err = norm_error('F', 1, rank, 1, hS[b], hSres[b]);
|
|
*max_err = err > *max_err ? err : *max_err;
|
|
|
|
// Check the singular vectors if required
|
|
if(hinfoRes[b][0] == 0 && jobz != HIPSOLVER_EIG_MODE_NOVECTOR)
|
|
{
|
|
err = 0;
|
|
// check singular vectors implicitly (A*v_k = s_k*u_k)
|
|
for(int k = 0; k < rank; ++k)
|
|
{
|
|
T tmp = 0;
|
|
double tmp2 = 0;
|
|
|
|
// (Comparing absolute values to deal with the fact that the pair of singular vectors (u,-v) or (-u,v) are
|
|
// both ok and we could get either one with the complementary or main executions when only
|
|
// one side set of vectors is required. May be revisited in the future.)
|
|
for(int i = 0; i < m; ++i)
|
|
{
|
|
tmp = 0;
|
|
for(rocblas_int j = 0; j < n; ++j)
|
|
tmp += A[b * lda * n + i + j * lda] * hVres[b][j + k * ldv];
|
|
tmp2 = std::abs(tmp) - std::abs(hSres[b][k] * hUres[b][i + k * ldu]);
|
|
err += tmp2 * tmp2;
|
|
}
|
|
}
|
|
err = std::sqrt(err) / double(snorm('F', m, n, A.data() + b * lda * n, lda));
|
|
*max_errv = err > *max_errv ? err : *max_errv;
|
|
}
|
|
}
|
|
}
|
|
|
|
template <testAPI_t API,
|
|
bool STRIDED,
|
|
typename T,
|
|
typename Wd,
|
|
typename Td,
|
|
typename Ud,
|
|
typename Id,
|
|
typename Wh,
|
|
typename Th,
|
|
typename Uh,
|
|
typename Ih>
|
|
void gesvda_getPerfData(const hipsolverHandle_t handle,
|
|
hipsolverEigMode_t jobz,
|
|
const int rank,
|
|
const int m,
|
|
const int n,
|
|
Wd& dA,
|
|
const int lda,
|
|
const int stA,
|
|
Td& dS,
|
|
const int stS,
|
|
Ud& dU,
|
|
const int ldu,
|
|
const int stU,
|
|
Ud& dV,
|
|
const int ldv,
|
|
const int stV,
|
|
Ud& dWork,
|
|
const int lwork,
|
|
Id& dinfo,
|
|
double* hRnrmF,
|
|
const int bc,
|
|
Wh& hA,
|
|
Th& hS,
|
|
Uh& hU,
|
|
Uh& hV,
|
|
Ih& hinfo,
|
|
double* gpu_time_used,
|
|
double* cpu_time_used,
|
|
const int hot_calls,
|
|
const bool perf)
|
|
{
|
|
std::vector<T> A;
|
|
|
|
if(!perf)
|
|
{
|
|
// For now we cannot report cpu time due to errors in LAPACK's gesvdx
|
|
*cpu_time_used = nan("");
|
|
}
|
|
|
|
gesvda_initData<true, false, T>(handle, jobz, m, n, dA, lda, bc, hA, A, 0);
|
|
|
|
// cold calls
|
|
for(int iter = 0; iter < 2; iter++)
|
|
{
|
|
gesvda_initData<false, true, T>(handle, jobz, m, n, dA, lda, bc, hA, A, 0);
|
|
|
|
CHECK_ROCBLAS_ERROR(hipsolver_gesvda(API,
|
|
STRIDED,
|
|
handle,
|
|
jobz,
|
|
rank,
|
|
m,
|
|
n,
|
|
dA.data(),
|
|
lda,
|
|
stA,
|
|
dS.data(),
|
|
stS,
|
|
dU.data(),
|
|
ldu,
|
|
stU,
|
|
dV.data(),
|
|
ldv,
|
|
stV,
|
|
dWork.data(),
|
|
lwork,
|
|
dinfo.data(),
|
|
hRnrmF,
|
|
bc));
|
|
}
|
|
|
|
// gpu-lapack performance
|
|
hipStream_t stream;
|
|
CHECK_ROCBLAS_ERROR(hipsolverGetStream(handle, &stream));
|
|
double start;
|
|
|
|
for(int iter = 0; iter < hot_calls; iter++)
|
|
{
|
|
gesvda_initData<false, true, T>(handle, jobz, m, n, dA, lda, bc, hA, A, 0);
|
|
|
|
start = get_time_us_sync(stream);
|
|
hipsolver_gesvda(API,
|
|
STRIDED,
|
|
handle,
|
|
jobz,
|
|
rank,
|
|
m,
|
|
n,
|
|
dA.data(),
|
|
lda,
|
|
stA,
|
|
dS.data(),
|
|
stS,
|
|
dU.data(),
|
|
ldu,
|
|
stU,
|
|
dV.data(),
|
|
ldv,
|
|
stV,
|
|
dWork.data(),
|
|
lwork,
|
|
dinfo.data(),
|
|
hRnrmF,
|
|
bc);
|
|
*gpu_time_used += get_time_us_sync(stream) - start;
|
|
}
|
|
*gpu_time_used /= hot_calls;
|
|
}
|
|
|
|
template <testAPI_t API, bool BATCHED, bool STRIDED, typename T>
|
|
void testing_gesvda(Arguments& argus)
|
|
{
|
|
using S = decltype(std::real(T{}));
|
|
|
|
// get arguments
|
|
hipsolver_local_handle handle;
|
|
char jobzC = argus.get<char>("jobz");
|
|
int rank = argus.get<int>("rank", 1);
|
|
int m = argus.get<int>("m");
|
|
int n = argus.get<int>("n", m);
|
|
int lda = argus.get<int>("lda", m);
|
|
int ldu = argus.get<int>("ldu", m);
|
|
int ldv = argus.get<int>("ldv", n);
|
|
rocblas_stride stA = argus.get<rocblas_stride>("strideA", lda * n);
|
|
rocblas_stride stS = argus.get<rocblas_stride>("strideS", rank);
|
|
rocblas_stride stU = argus.get<rocblas_stride>("strideU", ldu * rank);
|
|
rocblas_stride stV = argus.get<rocblas_stride>("strideV", ldv * rank);
|
|
|
|
hipsolverEigMode_t jobz = char2hipsolver_evect(jobzC);
|
|
int bc = argus.batch_count;
|
|
int hot_calls = argus.iters;
|
|
|
|
rocblas_stride stUres = 0;
|
|
rocblas_stride stVres = 0;
|
|
|
|
// determine sizes
|
|
size_t size_A = size_t(lda) * n;
|
|
size_t size_S = size_t(rank);
|
|
size_t size_S_cpu = size_t(min(m, n));
|
|
size_t size_V = 0;
|
|
size_t size_U = 0;
|
|
|
|
size_t size_Sres = 0;
|
|
size_t size_hUres = 0;
|
|
size_t size_hVres = 0;
|
|
|
|
if(jobz != HIPSOLVER_EIG_MODE_NOVECTOR)
|
|
{
|
|
size_U = size_t(ldu) * rank;
|
|
size_V = size_t(ldv) * rank;
|
|
}
|
|
|
|
if(argus.unit_check || argus.norm_check)
|
|
{
|
|
size_Sres = size_S;
|
|
size_hUres = size_U;
|
|
size_hVres = size_V;
|
|
stUres = stU;
|
|
stVres = stV;
|
|
}
|
|
|
|
double max_error = 0, gpu_time_used = 0, cpu_time_used = 0, max_errorv = 0;
|
|
|
|
// check invalid sizes
|
|
bool invalid_size = (rank <= 0 || rank > min(m, n) || n < 0 || m < 0 || lda < m || ldu < 1
|
|
|| ldv < 1 || bc < 0)
|
|
|| (jobz != HIPSOLVER_EIG_MODE_NOVECTOR && (ldu < m || ldv < n));
|
|
|
|
if(invalid_size)
|
|
{
|
|
if(BATCHED)
|
|
{
|
|
// EXPECT_ROCBLAS_STATUS(hipsolver_gesvda(API,
|
|
// STRIDED,
|
|
// handle,
|
|
// jobz,
|
|
// rank,
|
|
// m,
|
|
// n,
|
|
// (T* const*)nullptr,
|
|
// lda,
|
|
// stA,
|
|
// (S*)nullptr,
|
|
// stS,
|
|
// (T*)nullptr,
|
|
// ldu,
|
|
// stU,
|
|
// (T*)nullptr,
|
|
// ldv,
|
|
// stV,
|
|
// (T*)nullptr,
|
|
// 0,
|
|
// (int*)nullptr,
|
|
// (double*)nullptr,
|
|
// bc),
|
|
// HIPSOLVER_STATUS_INVALID_VALUE);
|
|
}
|
|
else
|
|
{
|
|
EXPECT_ROCBLAS_STATUS(hipsolver_gesvda(API,
|
|
STRIDED,
|
|
handle,
|
|
jobz,
|
|
rank,
|
|
m,
|
|
n,
|
|
(T*)nullptr,
|
|
lda,
|
|
stA,
|
|
(S*)nullptr,
|
|
stS,
|
|
(T*)nullptr,
|
|
ldu,
|
|
stU,
|
|
(T*)nullptr,
|
|
ldv,
|
|
stV,
|
|
(T*)nullptr,
|
|
0,
|
|
(int*)nullptr,
|
|
(double*)nullptr,
|
|
bc),
|
|
HIPSOLVER_STATUS_INVALID_VALUE);
|
|
}
|
|
|
|
if(argus.timing)
|
|
rocsolver_bench_inform(inform_invalid_size);
|
|
|
|
return;
|
|
}
|
|
|
|
// memory size query is necessary
|
|
int size_W;
|
|
hipsolver_gesvda_bufferSize(API,
|
|
STRIDED,
|
|
handle,
|
|
jobz,
|
|
rank,
|
|
m,
|
|
n,
|
|
(T*)nullptr,
|
|
lda,
|
|
stA,
|
|
(S*)nullptr,
|
|
stS,
|
|
(T*)nullptr,
|
|
ldu,
|
|
stU,
|
|
(T*)nullptr,
|
|
ldv,
|
|
stV,
|
|
&size_W,
|
|
bc);
|
|
size_t bytes_W
|
|
= std::getenv("HIPSOLVER_BUFFERSIZE_RETURN_BYTES") != nullptr ? size_W : sizeof(T) * size_W;
|
|
|
|
if(argus.mem_query)
|
|
{
|
|
rocsolver_bench_inform(inform_mem_query, bytes_W);
|
|
return;
|
|
}
|
|
|
|
// memory allocations (all cases)
|
|
// host
|
|
host_strided_batch_vector<S> hS(
|
|
size_S_cpu, 1, size_S_cpu, bc); // extra space for cpu_gesvd call
|
|
host_strided_batch_vector<T> hV(size_V, 1, stV, bc);
|
|
host_strided_batch_vector<T> hU(size_U, 1, stU, bc);
|
|
host_strided_batch_vector<double> hRnrmF(1, 1, 1, bc);
|
|
host_strided_batch_vector<int> hinfo(1, 1, 1, bc);
|
|
host_strided_batch_vector<int> hinfoRes(1, 1, 1, bc);
|
|
host_strided_batch_vector<S> hSres(size_Sres, 1, stS, bc);
|
|
host_strided_batch_vector<T> hVres(size_hVres, 1, stVres, bc);
|
|
host_strided_batch_vector<T> hUres(size_hUres, 1, stUres, bc);
|
|
// device
|
|
device_strided_batch_vector<S> dS(size_S, 1, stS, bc);
|
|
device_strided_batch_vector<T> dV(size_V, 1, stV, bc);
|
|
device_strided_batch_vector<T> dU(size_U, 1, stU, bc);
|
|
device_strided_batch_vector<int> dinfo(1, 1, 1, bc);
|
|
device_strided_batch_vector<T> dWork(bytes_W, 1, bytes_W, 1); // bytes_W accounts for bc
|
|
if(size_S)
|
|
CHECK_HIP_ERROR(dS.memcheck());
|
|
if(size_V)
|
|
CHECK_HIP_ERROR(dV.memcheck());
|
|
if(size_U)
|
|
CHECK_HIP_ERROR(dU.memcheck());
|
|
CHECK_HIP_ERROR(dinfo.memcheck());
|
|
if(size_W)
|
|
CHECK_HIP_ERROR(dWork.memcheck());
|
|
|
|
if(BATCHED)
|
|
{
|
|
// // memory allocations
|
|
// host_batch_vector<T> hA(size_A, 1, bc);
|
|
// device_batch_vector<T> dA(size_A, 1, bc);
|
|
// if(size_A)
|
|
// CHECK_HIP_ERROR(dA.memcheck());
|
|
|
|
// // check computations
|
|
// if(argus.unit_check || argus.norm_check)
|
|
// {
|
|
// gesvda_getError<API, STRIDED, T>(handle,
|
|
// jobz,
|
|
// rank,
|
|
// m,
|
|
// n,
|
|
// dA,
|
|
// lda,
|
|
// stA,
|
|
// dS,
|
|
// stS,
|
|
// dU,
|
|
// ldu,
|
|
// stU,
|
|
// dV,
|
|
// ldv,
|
|
// stV,
|
|
// dWork,
|
|
// size_W,
|
|
// dinfo,
|
|
// hRnrmF,
|
|
// bc,
|
|
// hA,
|
|
// hS,
|
|
// hSres,
|
|
// hUres,
|
|
// hVres,
|
|
// hinfo,
|
|
// hinfoRes,
|
|
// &max_error,
|
|
// &max_errorv);
|
|
// }
|
|
|
|
// // collect performance data
|
|
// if(argus.timing)
|
|
// {
|
|
// gesvda_getPerfData<API, STRIDED, T>(handle,
|
|
// jobz,
|
|
// rank,
|
|
// m,
|
|
// n,
|
|
// dA,
|
|
// lda,
|
|
// stA,
|
|
// dS,
|
|
// stS,
|
|
// dU,
|
|
// ldu,
|
|
// stU,
|
|
// dV,
|
|
// ldv,
|
|
// stV,
|
|
// dWork,
|
|
// size_W,
|
|
// dinfo,
|
|
// hRnrmF,
|
|
// bc,
|
|
// hA,
|
|
// hS,
|
|
// hU,
|
|
// hV,
|
|
// hinfo,
|
|
// &gpu_time_used,
|
|
// &cpu_time_used,
|
|
// hot_calls,
|
|
// argus.perf);
|
|
// }
|
|
}
|
|
|
|
else
|
|
{
|
|
// memory allocations
|
|
host_strided_batch_vector<T> hA(size_A, 1, stA, bc);
|
|
device_strided_batch_vector<T> dA(size_A, 1, stA, bc);
|
|
if(size_A)
|
|
CHECK_HIP_ERROR(dA.memcheck());
|
|
|
|
// check computations
|
|
if(argus.unit_check || argus.norm_check)
|
|
{
|
|
gesvda_getError<API, STRIDED, T>(handle,
|
|
jobz,
|
|
rank,
|
|
m,
|
|
n,
|
|
dA,
|
|
lda,
|
|
stA,
|
|
dS,
|
|
stS,
|
|
dU,
|
|
ldu,
|
|
stU,
|
|
dV,
|
|
ldv,
|
|
stV,
|
|
dWork,
|
|
size_W,
|
|
dinfo,
|
|
hRnrmF,
|
|
bc,
|
|
hA,
|
|
hS,
|
|
hSres,
|
|
hUres,
|
|
hVres,
|
|
hinfo,
|
|
hinfoRes,
|
|
&max_error,
|
|
&max_errorv);
|
|
}
|
|
|
|
// collect performance data
|
|
if(argus.timing)
|
|
{
|
|
gesvda_getPerfData<API, STRIDED, T>(handle,
|
|
jobz,
|
|
rank,
|
|
m,
|
|
n,
|
|
dA,
|
|
lda,
|
|
stA,
|
|
dS,
|
|
stS,
|
|
dU,
|
|
ldu,
|
|
stU,
|
|
dV,
|
|
ldv,
|
|
stV,
|
|
dWork,
|
|
size_W,
|
|
dinfo,
|
|
hRnrmF,
|
|
bc,
|
|
hA,
|
|
hS,
|
|
hU,
|
|
hV,
|
|
hinfo,
|
|
&gpu_time_used,
|
|
&cpu_time_used,
|
|
hot_calls,
|
|
argus.perf);
|
|
}
|
|
}
|
|
|
|
// validate results for rocsolver-test
|
|
// using 3 * min(m, n) * machine_precision as tolerance
|
|
if(argus.unit_check)
|
|
{
|
|
ROCSOLVER_TEST_CHECK(T, max_error, 3 * min(m, n));
|
|
if(jobz != HIPSOLVER_EIG_MODE_NOVECTOR)
|
|
ROCSOLVER_TEST_CHECK(T, max_errorv, 3 * min(m, n));
|
|
}
|
|
|
|
// output results for rocsolver-bench
|
|
if(argus.timing)
|
|
{
|
|
if(jobz != HIPSOLVER_EIG_MODE_NOVECTOR)
|
|
max_error = (max_error >= max_errorv) ? max_error : max_errorv;
|
|
|
|
if(!argus.perf)
|
|
{
|
|
std::cerr << "\n============================================\n";
|
|
std::cerr << "Arguments:\n";
|
|
std::cerr << "============================================\n";
|
|
if(BATCHED)
|
|
{
|
|
rocsolver_bench_output("jobz", "rank", "m", "n", "lda", "ldu", "ldv", "batch_c");
|
|
rocsolver_bench_output(jobz, rank, m, n, lda, ldu, ldv, bc);
|
|
}
|
|
else if(STRIDED)
|
|
{
|
|
rocsolver_bench_output("jobz",
|
|
"rank",
|
|
"m",
|
|
"n",
|
|
"lda",
|
|
"strideA",
|
|
"strideS",
|
|
"ldu",
|
|
"strideU",
|
|
"ldv",
|
|
"strideV",
|
|
"batch_c");
|
|
rocsolver_bench_output(jobz, rank, m, n, lda, stA, stS, ldu, stU, ldv, stV, bc);
|
|
}
|
|
else
|
|
{
|
|
rocsolver_bench_output("jobz", "rank", "m", "n", "lda", "ldu", "ldv");
|
|
rocsolver_bench_output(jobz, rank, m, n, lda, ldu, ldv);
|
|
}
|
|
std::cerr << "\n============================================\n";
|
|
std::cerr << "Results:\n";
|
|
std::cerr << "============================================\n";
|
|
if(argus.norm_check)
|
|
{
|
|
rocsolver_bench_output("cpu_time", "gpu_time", "error");
|
|
rocsolver_bench_output(cpu_time_used, gpu_time_used, max_error);
|
|
}
|
|
else
|
|
{
|
|
rocsolver_bench_output("cpu_time", "gpu_time");
|
|
rocsolver_bench_output(cpu_time_used, gpu_time_used);
|
|
}
|
|
std::cerr << std::endl;
|
|
}
|
|
else
|
|
{
|
|
if(argus.norm_check)
|
|
rocsolver_bench_output(gpu_time_used, max_error);
|
|
else
|
|
rocsolver_bench_output(gpu_time_used);
|
|
}
|
|
}
|
|
|
|
// ensure all arguments were consumed
|
|
argus.validate_consumed();
|
|
}
|