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745 lines
29 KiB
C++
745 lines
29 KiB
C++
/* ************************************************************************
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* Copyright (C) 2025-2026 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,
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typename I,
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typename SIZE,
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typename Td,
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typename Id,
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typename Ud,
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typename INTd>
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void sytrs_checkBadArgs(const hipsolverHandle_t handle,
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const hipsolverFillMode_t uplo,
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const I n,
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const I nrhs,
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Td dA,
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const I lda,
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Id dIpiv,
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Td dB,
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const I ldb,
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Ud dWork,
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const SIZE dlwork,
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Ud hWork,
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const SIZE hlwork,
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INTd 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_sytrs(API,
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nullptr,
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uplo,
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n,
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nrhs,
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dA,
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lda,
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dIpiv,
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dB,
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ldb,
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dWork,
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dlwork,
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hWork,
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hlwork,
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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_sytrs(API,
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handle,
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hipsolverFillMode_t(-1),
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n,
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nrhs,
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dA,
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lda,
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dIpiv,
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dB,
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ldb,
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dWork,
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dlwork,
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hWork,
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hlwork,
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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_sytrs(API,
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handle,
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uplo,
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n,
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nrhs,
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(Td) nullptr,
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lda,
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dIpiv,
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dB,
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ldb,
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dWork,
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dlwork,
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hWork,
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hlwork,
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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_sytrs(API,
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handle,
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uplo,
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n,
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nrhs,
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dA,
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lda,
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(Id) nullptr,
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dB,
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ldb,
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dWork,
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dlwork,
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hWork,
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hlwork,
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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_sytrs(API,
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handle,
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uplo,
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n,
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nrhs,
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dA,
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lda,
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dIpiv,
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(Td) nullptr,
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ldb,
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dWork,
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dlwork,
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hWork,
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hlwork,
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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_sytrs(API,
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handle,
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uplo,
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n,
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nrhs,
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dA,
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lda,
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dIpiv,
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dB,
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ldb,
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dWork,
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dlwork,
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hWork,
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hlwork,
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(INTd) 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, typename I, typename SIZE>
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void testing_sytrs_bad_arg()
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{
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// safe arguments
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hipsolver_local_handle handle;
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hipsolverFillMode_t uplo = HIPSOLVER_FILL_MODE_UPPER;
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I n = 1;
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I nrhs = 1;
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I lda = 1;
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I ldb = 1;
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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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// (no batched DnX API for sytrs)
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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<I> dIpiv(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(dIpiv.memcheck());
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CHECK_HIP_ERROR(dInfo.memcheck());
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SIZE bytes_dW, bytes_hW;
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hipsolver_sytrs_bufferSize(API,
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handle,
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uplo,
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n,
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nrhs,
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dA.data(),
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lda,
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dIpiv.data(),
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dB.data(),
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ldb,
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&bytes_dW,
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&bytes_hW);
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SIZE size_dW = (bytes_dW + sizeof(T) - 1) / sizeof(T);
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SIZE size_hW = (bytes_hW + sizeof(T) - 1) / sizeof(T);
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host_strided_batch_vector<T> hWork(size_hW, 1, size_hW, 1);
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device_strided_batch_vector<T> dWork(size_dW, 1, size_dW, 1);
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if(size_dW)
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CHECK_HIP_ERROR(dWork.memcheck());
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// check bad arguments
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sytrs_checkBadArgs<API>(handle,
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uplo,
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n,
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nrhs,
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dA.data(),
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lda,
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dIpiv.data(),
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dB.data(),
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ldb,
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dWork.data(),
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bytes_dW,
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hWork.data(),
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bytes_hW,
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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,
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bool GPU,
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typename T,
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typename I,
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typename Td,
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typename Id,
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typename Th,
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typename Ih,
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typename INTh>
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void sytrs_initData(const hipsolverHandle_t handle,
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const hipsolverFillMode_t uplo,
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const I n,
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const I nrhs,
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Td& dA,
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const I lda,
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Id& dIpiv,
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Td& dB,
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const I ldb,
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const int bc,
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Th& hA,
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Ih& hIpiv,
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INTh& hIpiv_cpu,
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Th& hB)
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{
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if(CPU)
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{
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T tmp;
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rocblas_init<T>(hA, true);
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rocblas_init<T>(hB, 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(I i = 0; i < n; i++)
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{
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for(I 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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// shuffle rows to test pivoting
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// always the same permutation for debugging purposes
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for(I i = 0; i < n / 2; i++)
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{
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for(I j = 0; j < n; j++)
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{
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std::swap(hA[b][i + j * lda], hA[b][n - 1 - i + j * lda]);
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}
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}
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// do the LDL^T factorization of matrix A w/ the reference LAPACK routine
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int sytrf_lwork = lda * n;
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std::vector<T> hWork(sytrf_lwork);
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int info = 0;
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cpu_sytrf(uplo, n, hA[b], lda, hIpiv_cpu[b], hWork.data(), sytrf_lwork, &info);
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// copy ipiv from int to int64_t
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for(I i = 0; i < n; i++)
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hIpiv[b][i] = hIpiv_cpu[b][i];
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}
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}
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if(GPU)
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{
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// now copy pivoting indices and matrices 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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CHECK_HIP_ERROR(dIpiv.transfer_from(hIpiv));
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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 I,
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typename SIZE,
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typename Td,
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typename Id,
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typename INTd,
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typename Th,
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typename Ih,
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typename INTh>
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void sytrs_getError(const hipsolverHandle_t handle,
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const hipsolverFillMode_t uplo,
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const I n,
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const I nrhs,
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Td& dA,
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const I lda,
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Id& dIpiv,
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Td& dB,
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const I ldb,
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void* dWork,
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const SIZE dlwork,
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void* hWork,
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const SIZE hlwork,
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INTd& dInfo,
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const int bc,
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Th& hA,
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Ih& hIpiv,
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INTh& hIpiv_cpu,
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Th& hB,
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Th& hBRes,
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INTh& hInfo,
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INTh& hInfoRes,
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double* max_err)
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{
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// input data initialization
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sytrs_initData<true, true, T>(
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handle, uplo, n, nrhs, dA, lda, dIpiv, dB, ldb, bc, hA, hIpiv, hIpiv_cpu, hB);
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// execute computations
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// GPU lapack
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CHECK_ROCBLAS_ERROR(hipsolver_sytrs(API,
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handle,
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uplo,
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n,
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nrhs,
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dA.data(),
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lda,
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dIpiv.data(),
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dB.data(),
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ldb,
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dWork,
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dlwork,
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hWork,
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hlwork,
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dInfo.data(),
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bc));
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CHECK_HIP_ERROR(hBRes.transfer_from(dB));
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CHECK_HIP_ERROR(hInfoRes.transfer_from(dInfo));
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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_sytrs(uplo, n, nrhs, hA[b], lda, hIpiv_cpu[b], hB[b], ldb, hInfo[b]);
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}
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// error is ||hB - hBRes|| / ||hB||
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// (THIS DOES NOT ACCOUNT FOR NUMERICAL REPRODUCIBILITY ISSUES.
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// IT MIGHT BE REVISITED IN THE FUTURE)
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// using vector-induced infinity norm
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double err;
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*max_err = 0;
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for(int b = 0; b < bc; ++b)
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{
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err = norm_error('I', n, nrhs, ldb, hB[b], hBRes[b]);
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*max_err = err > *max_err ? err : *max_err;
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}
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// check info
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err = 0;
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for(int b = 0; b < bc; ++b)
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{
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EXPECT_EQ(hInfo[b][0], hInfoRes[b][0]) << "where b = " << b;
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if(hInfo[b][0] != hInfoRes[b][0])
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err++;
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}
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*max_err += err;
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}
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template <testAPI_t API,
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typename T,
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typename I,
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typename SIZE,
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typename Td,
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typename Id,
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typename INTd,
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typename Th,
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typename Ih,
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typename INTh>
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void sytrs_getPerfData(const hipsolverHandle_t handle,
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const hipsolverFillMode_t uplo,
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const I n,
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const I nrhs,
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Td& dA,
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const I lda,
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Id& dIpiv,
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Td& dB,
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const I ldb,
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void* dWork,
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const SIZE dlwork,
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void* hWork,
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const SIZE hlwork,
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INTd& dInfo,
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const int bc,
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Th& hA,
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Ih& hIpiv,
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INTh& hIpiv_cpu,
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Th& hB,
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INTh& hInfo,
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double* gpu_time_used,
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double* cpu_time_used,
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const int hot_calls,
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const bool perf)
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{
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if(!perf)
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{
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sytrs_initData<true, false, T>(
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handle, uplo, n, nrhs, dA, lda, dIpiv, dB, ldb, bc, hA, hIpiv, hIpiv_cpu, hB);
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// cpu-lapack performance (only if not in perf mode)
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*cpu_time_used = get_time_us_no_sync();
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for(int b = 0; b < bc; ++b)
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{
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cpu_sytrs(uplo, n, nrhs, hA[b], lda, hIpiv_cpu[b], hB[b], ldb, hInfo[b]);
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}
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*cpu_time_used = get_time_us_no_sync() - *cpu_time_used;
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}
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sytrs_initData<true, false, T>(
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handle, uplo, n, nrhs, dA, lda, dIpiv, dB, ldb, bc, hA, hIpiv, hIpiv_cpu, hB);
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// cold calls
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for(int iter = 0; iter < 2; iter++)
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{
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sytrs_initData<false, true, T>(
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handle, uplo, n, nrhs, dA, lda, dIpiv, dB, ldb, bc, hA, hIpiv, hIpiv_cpu, hB);
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CHECK_ROCBLAS_ERROR(hipsolver_sytrs(API,
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handle,
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uplo,
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n,
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nrhs,
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dA.data(),
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lda,
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dIpiv.data(),
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dB.data(),
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ldb,
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dWork,
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dlwork,
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hWork,
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hlwork,
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dInfo.data(),
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bc));
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}
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// gpu-lapack performance
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hipStream_t stream;
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CHECK_ROCBLAS_ERROR(hipsolverGetStream(handle, &stream));
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double start;
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for(int iter = 0; iter < hot_calls; iter++)
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{
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sytrs_initData<false, true, T>(
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handle, uplo, n, nrhs, dA, lda, dIpiv, dB, ldb, bc, hA, hIpiv, hIpiv_cpu, hB);
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start = get_time_us_sync(stream);
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hipsolver_sytrs(API,
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handle,
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uplo,
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n,
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nrhs,
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dA.data(),
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lda,
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dIpiv.data(),
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dB.data(),
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ldb,
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dWork,
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dlwork,
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hWork,
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hlwork,
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dInfo.data(),
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bc);
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*gpu_time_used += get_time_us_sync(stream) - start;
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}
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*gpu_time_used /= hot_calls;
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}
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template <testAPI_t API, bool BATCHED, bool STRIDED, typename T, typename I, typename SIZE>
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void testing_sytrs(Arguments& argus)
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{
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// get arguments
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hipsolver_local_handle handle;
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char uploC = argus.get<char>("uplo");
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I n = argus.get<int>("n");
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I nrhs = argus.get<int>("nrhs", n);
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I lda = argus.get<int>("lda", n);
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I ldb = argus.get<int>("ldb", n);
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hipsolverFillMode_t uplo = char2hipsolver_fill(uploC);
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int bc = argus.batch_count;
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int hot_calls = argus.iters;
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// check non-supported values
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if(uplo != HIPSOLVER_FILL_MODE_UPPER && uplo != HIPSOLVER_FILL_MODE_LOWER)
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{
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if(BATCHED)
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{
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// (no batched DnX API for sytrs)
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}
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else
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{
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EXPECT_ROCBLAS_STATUS(hipsolver_sytrs(API,
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handle,
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uplo,
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n,
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nrhs,
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(T*)nullptr,
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lda,
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(I*)nullptr,
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(T*)nullptr,
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ldb,
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(void*)nullptr,
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(SIZE)0,
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(void*)nullptr,
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(SIZE)0,
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(int*)nullptr,
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bc),
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HIPSOLVER_STATUS_INVALID_ENUM);
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}
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if(argus.timing)
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rocsolver_bench_inform(inform_invalid_args);
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return;
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}
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// determine sizes
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size_t size_A = size_t(lda) * n;
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size_t size_B = size_t(ldb) * nrhs;
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size_t size_P = size_t(n);
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double max_error = 0, gpu_time_used = 0, cpu_time_used = 0;
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size_t size_BRes = (argus.unit_check || argus.norm_check) ? size_B : 0;
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// check invalid sizes
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bool invalid_size = (n < 0 || nrhs < 0 || lda < n || ldb < n || bc < 0);
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if(invalid_size)
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{
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if(BATCHED)
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{
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// (no batched DnX API for sytrs)
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}
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else
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{
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EXPECT_ROCBLAS_STATUS(hipsolver_sytrs(API,
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handle,
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uplo,
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n,
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nrhs,
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(T*)nullptr,
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lda,
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(I*)nullptr,
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(T*)nullptr,
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ldb,
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(void*)nullptr,
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(SIZE)0,
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(void*)nullptr,
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(SIZE)0,
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(int*)nullptr,
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bc),
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HIPSOLVER_STATUS_INVALID_VALUE);
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}
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if(argus.timing)
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rocsolver_bench_inform(inform_invalid_size);
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return;
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}
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// memory size query is necessary
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SIZE bytes_dW, bytes_hW;
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hipsolver_sytrs_bufferSize(API,
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handle,
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uplo,
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n,
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nrhs,
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(T*)nullptr,
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lda,
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(I*)nullptr,
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(T*)nullptr,
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ldb,
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&bytes_dW,
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&bytes_hW);
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SIZE size_dW = (bytes_dW + sizeof(T) - 1) / sizeof(T);
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SIZE size_hW = (bytes_hW + sizeof(T) - 1) / sizeof(T);
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if(argus.mem_query)
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{
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rocsolver_bench_inform(inform_mem_query, bytes_dW);
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return;
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}
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if(BATCHED)
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{
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// (no batched DnX API for sytrs)
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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<T> hA(size_A, 1, size_A, bc);
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host_strided_batch_vector<T> hB(size_B, 1, size_B, bc);
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host_strided_batch_vector<T> hBRes(size_BRes, 1, size_BRes, bc);
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host_strided_batch_vector<I> hIpiv(size_P, 1, size_P, bc);
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host_strided_batch_vector<int> hIpiv_cpu(size_P, 1, size_P, bc);
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host_strided_batch_vector<int> hInfo(1, 1, 1, bc);
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host_strided_batch_vector<int> hInfoRes(1, 1, 1, bc);
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host_strided_batch_vector<T> hWork(size_hW, 1, size_hW, 1);
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device_strided_batch_vector<T> dA(size_A, 1, size_A, bc);
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device_strided_batch_vector<T> dB(size_B, 1, size_B, bc);
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device_strided_batch_vector<I> dIpiv(size_P, 1, size_P, bc);
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device_strided_batch_vector<int> dInfo(1, 1, 1, bc);
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device_strided_batch_vector<T> dWork(size_dW, 1, size_dW, 1);
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if(size_A)
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CHECK_HIP_ERROR(dA.memcheck());
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if(size_B)
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CHECK_HIP_ERROR(dB.memcheck());
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if(size_P)
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CHECK_HIP_ERROR(dIpiv.memcheck());
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CHECK_HIP_ERROR(dInfo.memcheck());
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if(size_dW)
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CHECK_HIP_ERROR(dWork.memcheck());
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// check computations
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if(argus.unit_check || argus.norm_check)
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sytrs_getError<API, T>(handle,
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uplo,
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n,
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nrhs,
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dA,
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lda,
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dIpiv,
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dB,
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ldb,
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dWork.data(),
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bytes_dW,
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hWork.data(),
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bytes_hW,
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dInfo,
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bc,
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hA,
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hIpiv,
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hIpiv_cpu,
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hB,
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hBRes,
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hInfo,
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hInfoRes,
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&max_error);
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// collect performance data
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if(argus.timing)
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sytrs_getPerfData<API, T>(handle,
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uplo,
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n,
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nrhs,
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dA,
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lda,
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dIpiv,
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dB,
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ldb,
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dWork.data(),
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bytes_dW,
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hWork.data(),
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bytes_hW,
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dInfo,
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bc,
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hA,
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hIpiv,
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hIpiv_cpu,
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hB,
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hInfo,
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&gpu_time_used,
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&cpu_time_used,
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hot_calls,
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argus.perf);
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}
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// validate results for rocsolver-test
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// using n * machine_precision as tolerance
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if(argus.unit_check)
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ROCSOLVER_TEST_CHECK(T, max_error, n);
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// output results for rocsolver-bench
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if(argus.timing)
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{
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if(!argus.perf)
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{
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std::cerr << "\n============================================\n";
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std::cerr << "Arguments:\n";
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std::cerr << "============================================\n";
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rocsolver_bench_output("uplo", "n", "nrhs", "lda", "ldb");
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rocsolver_bench_output(uploC, n, nrhs, lda, ldb);
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std::cerr << "\n============================================\n";
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std::cerr << "Results:\n";
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std::cerr << "============================================\n";
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if(argus.norm_check)
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{
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rocsolver_bench_output("cpu_time", "gpu_time", "error");
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rocsolver_bench_output(cpu_time_used, gpu_time_used, max_error);
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}
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else
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{
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rocsolver_bench_output("cpu_time", "gpu_time");
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rocsolver_bench_output(cpu_time_used, gpu_time_used);
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}
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std::cerr << std::endl;
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}
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else
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{
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if(argus.norm_check)
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rocsolver_bench_output(gpu_time_used, max_error);
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else
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rocsolver_bench_output(gpu_time_used);
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}
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}
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// ensure all arguments were consumed
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argus.validate_consumed();
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}
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