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1107 lines
47 KiB
C++
1107 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, typename T, typename U>
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void sygvd_hegvd_checkBadArgs(const hipsolverHandle_t handle,
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const hipsolverEigType_t itype,
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const hipsolverEigMode_t evect,
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const hipsolverFillMode_t uplo,
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const int n,
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T dA,
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const int lda,
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const int stA,
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T dB,
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const int ldb,
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const int stB,
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U dD,
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const int stD,
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T dWork,
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const int lwork,
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int* dInfo,
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const int bc)
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{
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// handle
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EXPECT_ROCBLAS_STATUS(hipsolver_sygvd_hegvd(API,
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nullptr,
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itype,
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evect,
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uplo,
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n,
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dA,
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lda,
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stA,
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dB,
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ldb,
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stB,
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dD,
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stD,
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dWork,
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lwork,
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dInfo,
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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_sygvd_hegvd(API,
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handle,
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hipsolverEigType_t(-1),
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evect,
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uplo,
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n,
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dA,
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lda,
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stA,
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dB,
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ldb,
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stB,
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dD,
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stD,
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dWork,
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lwork,
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dInfo,
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bc),
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HIPSOLVER_STATUS_INVALID_ENUM);
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EXPECT_ROCBLAS_STATUS(hipsolver_sygvd_hegvd(API,
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handle,
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itype,
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hipsolverEigMode_t(-1),
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uplo,
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n,
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dA,
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lda,
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stA,
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dB,
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ldb,
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stB,
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dD,
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stD,
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dWork,
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lwork,
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dInfo,
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bc),
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HIPSOLVER_STATUS_INVALID_ENUM);
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EXPECT_ROCBLAS_STATUS(hipsolver_sygvd_hegvd(API,
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handle,
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itype,
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evect,
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hipsolverFillMode_t(-1),
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n,
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dA,
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lda,
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stA,
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dB,
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ldb,
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stB,
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dD,
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stD,
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dWork,
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lwork,
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dInfo,
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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_sygvd_hegvd(API,
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handle,
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itype,
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evect,
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uplo,
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n,
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(T) nullptr,
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lda,
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stA,
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dB,
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ldb,
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stB,
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dD,
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stD,
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dWork,
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lwork,
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dInfo,
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bc),
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HIPSOLVER_STATUS_INVALID_VALUE);
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EXPECT_ROCBLAS_STATUS(hipsolver_sygvd_hegvd(API,
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handle,
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itype,
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evect,
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uplo,
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n,
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dA,
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lda,
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stA,
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(T) nullptr,
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ldb,
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stB,
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dD,
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stD,
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dWork,
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lwork,
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dInfo,
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bc),
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HIPSOLVER_STATUS_INVALID_VALUE);
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EXPECT_ROCBLAS_STATUS(hipsolver_sygvd_hegvd(API,
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handle,
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itype,
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evect,
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uplo,
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n,
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dA,
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lda,
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stA,
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dB,
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ldb,
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stB,
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(U) nullptr,
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stD,
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dWork,
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lwork,
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dInfo,
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bc),
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HIPSOLVER_STATUS_INVALID_VALUE);
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EXPECT_ROCBLAS_STATUS(hipsolver_sygvd_hegvd(API,
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handle,
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itype,
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evect,
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uplo,
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n,
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dA,
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lda,
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stA,
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dB,
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ldb,
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stB,
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dD,
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stD,
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dWork,
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lwork,
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(int*)nullptr,
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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_sygvd_hegvd_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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int n = 1;
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int lda = 1;
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int ldb = 1;
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int stA = 1;
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int stB = 1;
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int stD = 1;
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int bc = 1;
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hipsolverEigType_t itype = HIPSOLVER_EIG_TYPE_1;
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hipsolverEigMode_t evect = HIPSOLVER_EIG_MODE_NOVECTOR;
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hipsolverFillMode_t uplo = HIPSOLVER_FILL_MODE_UPPER;
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if(BATCHED)
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{
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// // memory allocations
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// device_batch_vector<T> dA(1, 1, 1);
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// device_batch_vector<T> dB(1, 1, 1);
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// device_strided_batch_vector<S> dD(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(dB.memcheck());
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// CHECK_HIP_ERROR(dD.memcheck());
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// CHECK_HIP_ERROR(dInfo.memcheck());
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// int size_W;
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// hipsolver_sygvd_hegvd_bufferSize(API,
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// handle,
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// itype,
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// evect,
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// uplo,
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// n,
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// dA.data(),
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// lda,
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// dB.data(),
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// ldb,
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// dD.data(),
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// &size_W);
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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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// sygvd_hegvd_checkBadArgs<API>(handle,
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// itype,
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// evect,
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// uplo,
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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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// dB.data(),
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// ldb,
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// stB,
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// dD.data(),
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// stD,
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// dWork.data(),
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// size_W,
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// dInfo.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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device_strided_batch_vector<T> dA(1, 1, 1, 1);
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device_strided_batch_vector<T> dB(1, 1, 1, 1);
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device_strided_batch_vector<S> dD(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(dB.memcheck());
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CHECK_HIP_ERROR(dD.memcheck());
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CHECK_HIP_ERROR(dInfo.memcheck());
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int size_W;
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hipsolver_sygvd_hegvd_bufferSize(
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API, handle, itype, evect, uplo, n, dA.data(), lda, dB.data(), ldb, dD.data(), &size_W);
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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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sygvd_hegvd_checkBadArgs<API>(handle,
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itype,
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evect,
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uplo,
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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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dB.data(),
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ldb,
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stB,
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dD.data(),
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stD,
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dWork.data(),
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size_W,
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dInfo.data(),
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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 sygvd_hegvd_initData(const hipsolverHandle_t handle,
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const hipsolverEigType_t itype,
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const hipsolverEigMode_t evect,
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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 stA,
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Td& dB,
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const int ldb,
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const int stB,
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const int bc,
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Th& hA,
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Th& hB,
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host_strided_batch_vector<T>& A,
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host_strided_batch_vector<T>& B,
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const bool test,
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const bool singular)
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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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rocblas_init<T>(hB, false);
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for(int b = 0; b < bc; ++b)
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{
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for(int i = 0; i < n; 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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{
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hA[b][i + j * lda] = std::real(hA[b][i + j * lda]) + 400;
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hB[b][i + j * ldb] = std::real(hB[b][i + j * ldb]) + 400;
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}
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else
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{
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hA[b][i + j * lda] -= 4;
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}
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}
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}
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// store A and B for testing purposes
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if(test && evect != HIPSOLVER_EIG_MODE_NOVECTOR)
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{
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for(int i = 0; i < n; 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(itype != HIPSOLVER_EIG_TYPE_3)
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{
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A[b][i + j * lda] = hA[b][i + j * lda];
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B[b][i + j * ldb] = hB[b][i + j * ldb];
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}
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else
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{
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A[b][i + j * lda] = hB[b][i + j * ldb];
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B[b][i + j * ldb] = 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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}
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}
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if(GPU)
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{
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// now copy data to the GPU
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CHECK_HIP_ERROR(dA.transfer_from(hA));
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CHECK_HIP_ERROR(dB.transfer_from(hB));
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}
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}
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template <testAPI_t API,
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typename T,
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typename Td,
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typename Ud,
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typename Vd,
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typename Th,
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typename Uh,
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typename Vh>
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void sygvd_hegvd_getError(const hipsolverHandle_t handle,
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const hipsolverEigType_t itype,
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const hipsolverEigMode_t evect,
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const hipsolverFillMode_t uplo,
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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 stA,
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Td& dB,
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const int ldb,
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const int stB,
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Ud& dD,
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const int stD,
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Td& dWork,
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const int lwork,
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Vd& dInfo,
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const int bc,
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Th& hA,
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Th& hARes,
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Th& hB,
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Uh& hD,
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Uh& hDRes,
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Vh& hInfo,
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Vh& hInfoRes,
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double* max_err,
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const bool singular)
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{
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constexpr bool COMPLEX = is_complex<T>;
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using S = decltype(std::real(T{}));
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int lrwork, ltwork;
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if(!COMPLEX)
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{
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lrwork = (evect == HIPSOLVER_EIG_MODE_NOVECTOR ? 2 * n + 1 : 1 + 6 * n + 2 * n * n);
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ltwork = 0;
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}
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else
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{
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lrwork = (evect == HIPSOLVER_EIG_MODE_NOVECTOR ? n : 1 + 5 * n + 2 * n * n);
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ltwork = (evect == HIPSOLVER_EIG_MODE_NOVECTOR ? n + 1 : 2 * n + n * n);
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}
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int liwork = (evect == HIPSOLVER_EIG_MODE_NOVECTOR ? 1 : 3 + 5 * n);
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std::vector<T> work(ltwork);
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std::vector<S> rwork(lrwork);
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std::vector<int> iwork(liwork);
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host_strided_batch_vector<T> A(lda * n, 1, lda * n, bc);
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host_strided_batch_vector<T> B(ldb * n, 1, ldb * n, bc);
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// input data initialization
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sygvd_hegvd_initData<true, true, T>(
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handle, itype, evect, n, dA, lda, stA, dB, ldb, stB, bc, hA, hB, A, B, true, singular);
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// execute computations
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// GPU lapack
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CHECK_ROCBLAS_ERROR(hipsolver_sygvd_hegvd(API,
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handle,
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itype,
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evect,
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uplo,
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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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dB.data(),
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ldb,
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stB,
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dD.data(),
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stD,
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dWork.data(),
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lwork,
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dInfo.data(),
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bc));
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CHECK_HIP_ERROR(hDRes.transfer_from(dD));
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CHECK_HIP_ERROR(hInfoRes.transfer_from(dInfo));
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if(evect != HIPSOLVER_EIG_MODE_NOVECTOR)
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CHECK_HIP_ERROR(hARes.transfer_from(dA));
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// CPU lapack
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for(int b = 0; b < bc; ++b)
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{
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cpu_sygvd_hegvd(itype,
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evect,
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uplo,
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n,
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hA[b],
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lda,
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hB[b],
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ldb,
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hD[b],
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work.data(),
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ltwork,
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rwork.data(),
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lrwork,
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iwork.data(),
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liwork,
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hInfo[b]);
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}
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|
|
// (We expect the used input matrices to always converge. Testing
|
|
// implicitly the equivalent non-converged matrix is very complicated and it boils
|
|
// down to essentially run the algorithm again and until convergence is achieved.
|
|
// We do test with indefinite matrices B).
|
|
|
|
// check info for non-convergence and/or positive-definiteness
|
|
*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;
|
|
|
|
if(evect == HIPSOLVER_EIG_MODE_NOVECTOR)
|
|
{
|
|
// only eigenvalues needed; can compare with LAPACK
|
|
|
|
// error is ||hD - hDRes|| / ||hD||
|
|
// using frobenius norm
|
|
for(int b = 0; b < bc; ++b)
|
|
{
|
|
if(hInfoRes[b][0] == 0)
|
|
{
|
|
err = norm_error('F', 1, n, 1, hD[b], hDRes[b]);
|
|
*max_err = err > *max_err ? err : *max_err;
|
|
}
|
|
}
|
|
}
|
|
else
|
|
{
|
|
// both eigenvalues and eigenvectors needed; need to implicitly test
|
|
// eigenvectors due to non-uniqueness of eigenvectors under scaling
|
|
|
|
for(int b = 0; b < bc; ++b)
|
|
{
|
|
if(hInfoRes[b][0] == 0)
|
|
{
|
|
T alpha = 1;
|
|
T beta = 0;
|
|
|
|
// hARes contains eigenvectors x
|
|
// compute B*x (or A*x) and store in hB
|
|
cpu_symm_hemm(HIPSOLVER_SIDE_LEFT,
|
|
uplo,
|
|
n,
|
|
n,
|
|
alpha,
|
|
B[b],
|
|
ldb,
|
|
hARes[b],
|
|
lda,
|
|
beta,
|
|
hB[b],
|
|
ldb);
|
|
|
|
if(itype == HIPSOLVER_EIG_TYPE_1)
|
|
{
|
|
// problem is A*x = (lambda)*B*x
|
|
|
|
// compute (1/lambda)*A*x and store in hA
|
|
for(int j = 0; j < n; j++)
|
|
{
|
|
alpha = T(1) / hDRes[b][j];
|
|
cpu_symv_hemv(uplo,
|
|
n,
|
|
alpha,
|
|
A[b],
|
|
lda,
|
|
hARes[b] + j * lda,
|
|
1,
|
|
beta,
|
|
hA[b] + j * lda,
|
|
1);
|
|
}
|
|
|
|
// move B*x into hARes
|
|
for(int i = 0; i < n; i++)
|
|
for(int j = 0; j < n; j++)
|
|
hARes[b][i + j * lda] = hB[b][i + j * ldb];
|
|
}
|
|
else
|
|
{
|
|
// problem is A*B*x = (lambda)*x or B*A*x = (lambda)*x
|
|
|
|
// compute (1/lambda)*A*B*x or (1/lambda)*B*A*x and store in hA
|
|
for(int j = 0; j < n; j++)
|
|
{
|
|
alpha = T(1) / hDRes[b][j];
|
|
cpu_symv_hemv(uplo,
|
|
n,
|
|
alpha,
|
|
A[b],
|
|
lda,
|
|
hB[b] + j * ldb,
|
|
1,
|
|
beta,
|
|
hA[b] + j * lda,
|
|
1);
|
|
}
|
|
}
|
|
|
|
// error is ||hA - hARes|| / ||hA||
|
|
// using frobenius norm
|
|
err = norm_error('F', n, n, lda, hA[b], hARes[b]);
|
|
*max_err = err > *max_err ? err : *max_err;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
template <testAPI_t API,
|
|
typename T,
|
|
typename Td,
|
|
typename Ud,
|
|
typename Vd,
|
|
typename Th,
|
|
typename Uh,
|
|
typename Vh>
|
|
void sygvd_hegvd_getPerfData(const hipsolverHandle_t handle,
|
|
const hipsolverEigType_t itype,
|
|
const hipsolverEigMode_t evect,
|
|
const hipsolverFillMode_t uplo,
|
|
const int n,
|
|
Td& dA,
|
|
const int lda,
|
|
const int stA,
|
|
Td& dB,
|
|
const int ldb,
|
|
const int stB,
|
|
Ud& dD,
|
|
const int stD,
|
|
Td& dWork,
|
|
const int lwork,
|
|
Vd& dInfo,
|
|
const int bc,
|
|
Th& hA,
|
|
Th& hB,
|
|
Uh& hD,
|
|
Vh& hInfo,
|
|
double* gpu_time_used,
|
|
double* cpu_time_used,
|
|
const int hot_calls,
|
|
const bool perf,
|
|
const bool singular)
|
|
{
|
|
constexpr bool COMPLEX = is_complex<T>;
|
|
using S = decltype(std::real(T{}));
|
|
|
|
int lrwork, ltwork;
|
|
if(!COMPLEX)
|
|
{
|
|
lrwork = (evect == HIPSOLVER_EIG_MODE_NOVECTOR ? 2 * n + 1 : 1 + 6 * n + 2 * n * n);
|
|
ltwork = 0;
|
|
}
|
|
else
|
|
{
|
|
lrwork = (evect == HIPSOLVER_EIG_MODE_NOVECTOR ? n : 1 + 5 * n + 2 * n * n);
|
|
ltwork = (evect == HIPSOLVER_EIG_MODE_NOVECTOR ? n + 1 : 2 * n + n * n);
|
|
}
|
|
int liwork = (evect == HIPSOLVER_EIG_MODE_NOVECTOR ? 1 : 3 + 5 * n);
|
|
|
|
std::vector<T> work(ltwork);
|
|
std::vector<S> rwork(lrwork);
|
|
std::vector<int> iwork(liwork);
|
|
host_strided_batch_vector<T> A(1, 1, 1, 1);
|
|
host_strided_batch_vector<T> B(1, 1, 1, 1);
|
|
|
|
if(!perf)
|
|
{
|
|
sygvd_hegvd_initData<true, false, T>(
|
|
handle, itype, evect, n, dA, lda, stA, dB, ldb, stB, bc, hA, hB, A, B, false, singular);
|
|
|
|
// cpu-lapack performance (only if not in perf mode)
|
|
*cpu_time_used = get_time_us_no_sync();
|
|
for(int b = 0; b < bc; ++b)
|
|
{
|
|
cpu_sygvd_hegvd(itype,
|
|
evect,
|
|
uplo,
|
|
n,
|
|
hA[b],
|
|
lda,
|
|
hB[b],
|
|
ldb,
|
|
hD[b],
|
|
work.data(),
|
|
ltwork,
|
|
rwork.data(),
|
|
lrwork,
|
|
iwork.data(),
|
|
liwork,
|
|
hInfo[b]);
|
|
}
|
|
*cpu_time_used = get_time_us_no_sync() - *cpu_time_used;
|
|
}
|
|
|
|
sygvd_hegvd_initData<true, false, T>(
|
|
handle, itype, evect, n, dA, lda, stA, dB, ldb, stB, bc, hA, hB, A, B, false, singular);
|
|
|
|
// cold calls
|
|
for(int iter = 0; iter < 2; iter++)
|
|
{
|
|
sygvd_hegvd_initData<false, true, T>(
|
|
handle, itype, evect, n, dA, lda, stA, dB, ldb, stB, bc, hA, hB, A, B, false, singular);
|
|
|
|
CHECK_ROCBLAS_ERROR(hipsolver_sygvd_hegvd(API,
|
|
handle,
|
|
itype,
|
|
evect,
|
|
uplo,
|
|
n,
|
|
dA.data(),
|
|
lda,
|
|
stA,
|
|
dB.data(),
|
|
ldb,
|
|
stB,
|
|
dD.data(),
|
|
stD,
|
|
dWork.data(),
|
|
lwork,
|
|
dInfo.data(),
|
|
bc));
|
|
}
|
|
|
|
// gpu-lapack performance
|
|
hipStream_t stream;
|
|
CHECK_ROCBLAS_ERROR(hipsolverGetStream(handle, &stream));
|
|
double start;
|
|
|
|
for(int iter = 0; iter < hot_calls; iter++)
|
|
{
|
|
sygvd_hegvd_initData<false, true, T>(
|
|
handle, itype, evect, n, dA, lda, stA, dB, ldb, stB, bc, hA, hB, A, B, false, singular);
|
|
|
|
start = get_time_us_sync(stream);
|
|
hipsolver_sygvd_hegvd(API,
|
|
handle,
|
|
itype,
|
|
evect,
|
|
uplo,
|
|
n,
|
|
dA.data(),
|
|
lda,
|
|
stA,
|
|
dB.data(),
|
|
ldb,
|
|
stB,
|
|
dD.data(),
|
|
stD,
|
|
dWork.data(),
|
|
lwork,
|
|
dInfo.data(),
|
|
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_sygvd_hegvd(Arguments& argus)
|
|
{
|
|
using S = decltype(std::real(T{}));
|
|
|
|
// get arguments
|
|
hipsolver_local_handle handle;
|
|
char itypeC = argus.get<char>("itype");
|
|
char evectC = argus.get<char>("jobz");
|
|
char uploC = argus.get<char>("uplo");
|
|
int n = argus.get<int>("n");
|
|
int lda = argus.get<int>("lda", n);
|
|
int ldb = argus.get<int>("ldb", n);
|
|
int stA = argus.get<int>("strideA", lda * n);
|
|
int stB = argus.get<int>("strideB", ldb * n);
|
|
int stD = argus.get<int>("strideD", n);
|
|
|
|
hipsolverEigType_t itype = char2hipsolver_eform(itypeC);
|
|
hipsolverEigMode_t evect = char2hipsolver_evect(evectC);
|
|
hipsolverFillMode_t uplo = char2hipsolver_fill(uploC);
|
|
int bc = argus.batch_count;
|
|
int hot_calls = argus.iters;
|
|
|
|
int stARes = (argus.unit_check || argus.norm_check) ? stA : 0;
|
|
int stDRes = (argus.unit_check || argus.norm_check) ? stD : 0;
|
|
|
|
// determine sizes
|
|
size_t size_A = size_t(lda) * n;
|
|
size_t size_B = size_t(ldb) * n;
|
|
size_t size_D = size_t(n);
|
|
double max_error = 0, gpu_time_used = 0, cpu_time_used = 0;
|
|
|
|
size_t size_ARes = (argus.unit_check || argus.norm_check) ? size_A : 0;
|
|
size_t size_DRes = (argus.unit_check || argus.norm_check) ? size_D : 0;
|
|
|
|
// check invalid sizes
|
|
bool invalid_size = (n < 0 || lda < n || ldb < n || bc < 0);
|
|
if(invalid_size)
|
|
{
|
|
if(BATCHED)
|
|
{
|
|
// EXPECT_ROCBLAS_STATUS(hipsolver_sygvd_hegvd(API,
|
|
// handle,
|
|
// itype,
|
|
// evect,
|
|
// uplo,
|
|
// n,
|
|
// (T* const*)nullptr,
|
|
// lda,
|
|
// stA,
|
|
// (T* const*)nullptr,
|
|
// ldb,
|
|
// stB,
|
|
// (S*)nullptr,
|
|
// stD,
|
|
// (T*)nullptr,
|
|
// 0,
|
|
// (int*)nullptr,
|
|
// bc),
|
|
// HIPSOLVER_STATUS_INVALID_VALUE);
|
|
}
|
|
else
|
|
{
|
|
EXPECT_ROCBLAS_STATUS(hipsolver_sygvd_hegvd(API,
|
|
handle,
|
|
itype,
|
|
evect,
|
|
uplo,
|
|
n,
|
|
(T*)nullptr,
|
|
lda,
|
|
stA,
|
|
(T*)nullptr,
|
|
ldb,
|
|
stB,
|
|
(S*)nullptr,
|
|
stD,
|
|
(T*)nullptr,
|
|
0,
|
|
(int*)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_sygvd_hegvd_bufferSize(API,
|
|
handle,
|
|
itype,
|
|
evect,
|
|
uplo,
|
|
n,
|
|
(T*)nullptr,
|
|
lda,
|
|
(T*)nullptr,
|
|
ldb,
|
|
(S*)nullptr,
|
|
&size_W);
|
|
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;
|
|
}
|
|
|
|
if(BATCHED)
|
|
{
|
|
// // memory allocations
|
|
// host_batch_vector<T> hA(size_A, 1, bc);
|
|
// host_batch_vector<T> hARes(size_ARes, 1, bc);
|
|
// host_batch_vector<T> hB(size_B, 1, bc);
|
|
// host_strided_batch_vector<S> hD(size_D, 1, stD, bc);
|
|
// host_strided_batch_vector<S> hDRes(size_DRes, 1, stDRes, bc);
|
|
// host_strided_batch_vector<int> hInfo(1, 1, 1, bc);
|
|
// host_strided_batch_vector<int> hInfoRes(1, 1, 1, bc);
|
|
// device_batch_vector<T> dA(size_A, 1, bc);
|
|
// device_batch_vector<T> dB(size_B, 1, bc);
|
|
// device_strided_batch_vector<S> dD(size_D, 1, stD, 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_A)
|
|
// CHECK_HIP_ERROR(dA.memcheck());
|
|
// if(size_B)
|
|
// CHECK_HIP_ERROR(dB.memcheck());
|
|
// if(size_D)
|
|
// CHECK_HIP_ERROR(dD.memcheck());
|
|
// CHECK_HIP_ERROR(dInfo.memcheck());
|
|
// if(size_W)
|
|
// CHECK_HIP_ERROR(dWork.memcheck());
|
|
|
|
// // check computations
|
|
// if(argus.unit_check || argus.norm_check)
|
|
// sygvd_hegvd_getError<API, T>(handle,
|
|
// itype,
|
|
// evect,
|
|
// uplo,
|
|
// n,
|
|
// dA,
|
|
// lda,
|
|
// stA,
|
|
// dB,
|
|
// ldb,
|
|
// stB,
|
|
// dD,
|
|
// stD,
|
|
// dWork,
|
|
// size_W,
|
|
// dInfo,
|
|
// bc,
|
|
// hA,
|
|
// hARes,
|
|
// hB,
|
|
// hD,
|
|
// hDRes,
|
|
// hInfo,
|
|
// hInfoRes,
|
|
// &max_error,
|
|
// argus.singular);
|
|
|
|
// // collect performance data
|
|
// if(argus.timing)
|
|
// sygvd_hegvd_getPerfData<API, T>(handle,
|
|
// itype,
|
|
// evect,
|
|
// uplo,
|
|
// n,
|
|
// dA,
|
|
// lda,
|
|
// stA,
|
|
// dB,
|
|
// ldb,
|
|
// stB,
|
|
// dD,
|
|
// stD,
|
|
// dWork,
|
|
// size_W,
|
|
// dInfo,
|
|
// bc,
|
|
// hA,
|
|
// hB,
|
|
// hD,
|
|
// hInfo,
|
|
// &gpu_time_used,
|
|
// &cpu_time_used,
|
|
// hot_calls,
|
|
// argus.perf,
|
|
// argus.singular);
|
|
}
|
|
|
|
else
|
|
{
|
|
// memory allocations
|
|
host_strided_batch_vector<T> hA(size_A, 1, stA, bc);
|
|
host_strided_batch_vector<T> hARes(size_ARes, 1, stARes, bc);
|
|
host_strided_batch_vector<T> hB(size_B, 1, stB, bc);
|
|
host_strided_batch_vector<S> hD(size_D, 1, stD, bc);
|
|
host_strided_batch_vector<S> hDRes(size_DRes, 1, stDRes, bc);
|
|
host_strided_batch_vector<int> hInfo(1, 1, 1, bc);
|
|
host_strided_batch_vector<int> hInfoRes(1, 1, 1, bc);
|
|
device_strided_batch_vector<T> dA(size_A, 1, stA, bc);
|
|
device_strided_batch_vector<T> dB(size_B, 1, stB, bc);
|
|
device_strided_batch_vector<S> dD(size_D, 1, stD, 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_A)
|
|
CHECK_HIP_ERROR(dA.memcheck());
|
|
if(size_B)
|
|
CHECK_HIP_ERROR(dB.memcheck());
|
|
if(size_D)
|
|
CHECK_HIP_ERROR(dD.memcheck());
|
|
CHECK_HIP_ERROR(dInfo.memcheck());
|
|
if(size_W)
|
|
CHECK_HIP_ERROR(dWork.memcheck());
|
|
|
|
// check computations
|
|
if(argus.unit_check || argus.norm_check)
|
|
sygvd_hegvd_getError<API, T>(handle,
|
|
itype,
|
|
evect,
|
|
uplo,
|
|
n,
|
|
dA,
|
|
lda,
|
|
stA,
|
|
dB,
|
|
ldb,
|
|
stB,
|
|
dD,
|
|
stD,
|
|
dWork,
|
|
size_W,
|
|
dInfo,
|
|
bc,
|
|
hA,
|
|
hARes,
|
|
hB,
|
|
hD,
|
|
hDRes,
|
|
hInfo,
|
|
hInfoRes,
|
|
&max_error,
|
|
argus.singular);
|
|
|
|
// collect performance data
|
|
if(argus.timing)
|
|
sygvd_hegvd_getPerfData<API, T>(handle,
|
|
itype,
|
|
evect,
|
|
uplo,
|
|
n,
|
|
dA,
|
|
lda,
|
|
stA,
|
|
dB,
|
|
ldb,
|
|
stB,
|
|
dD,
|
|
stD,
|
|
dWork,
|
|
size_W,
|
|
dInfo,
|
|
bc,
|
|
hA,
|
|
hB,
|
|
hD,
|
|
hInfo,
|
|
&gpu_time_used,
|
|
&cpu_time_used,
|
|
hot_calls,
|
|
argus.perf,
|
|
argus.singular);
|
|
}
|
|
|
|
// validate results for rocsolver-test
|
|
// using n * machine_precision as tolerance
|
|
if(argus.unit_check)
|
|
ROCSOLVER_TEST_CHECK(T, max_error, n);
|
|
|
|
// output results for rocsolver-bench
|
|
if(argus.timing)
|
|
{
|
|
if(!argus.perf)
|
|
{
|
|
std::cerr << "\n============================================\n";
|
|
std::cerr << "Arguments:\n";
|
|
std::cerr << "============================================\n";
|
|
if(BATCHED)
|
|
{
|
|
rocsolver_bench_output(
|
|
"itype", "evect", "uplo", "n", "lda", "ldb", "strideD", "batch_c");
|
|
rocsolver_bench_output(itypeC, evectC, uploC, n, lda, ldb, stD, bc);
|
|
}
|
|
else if(STRIDED)
|
|
{
|
|
rocsolver_bench_output("itype",
|
|
"evect",
|
|
"uplo",
|
|
"n",
|
|
"lda",
|
|
"ldb",
|
|
"strideA",
|
|
"strideB",
|
|
"strideD",
|
|
"batch_c");
|
|
rocsolver_bench_output(itypeC, evectC, uploC, n, lda, ldb, stA, stB, stD, bc);
|
|
}
|
|
else
|
|
{
|
|
rocsolver_bench_output("itype", "evect", "uplo", "n", "lda", "ldb");
|
|
rocsolver_bench_output(itypeC, evectC, uploC, n, lda, ldb);
|
|
}
|
|
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();
|
|
}
|