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743 lines
30 KiB
C++
743 lines
30 KiB
C++
/* ************************************************************************
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* Copyright (C) 2024-2026 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, bool STRIDED, typename T, typename S, typename U>
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void syev_heev_checkBadArgs(const hipsolverHandle_t handle,
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const hipsolverDnParams_t params,
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const hipsolverEigMode_t jobz,
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const hipsolverFillMode_t uplo,
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const int64_t n,
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T dA,
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const int64_t lda,
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S dW,
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T dWork,
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const size_t lworkOnDevice,
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T hWork,
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const size_t lworkOnHost,
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U dinfo,
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const int64_t batchSize)
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{
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// handle
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EXPECT_ROCBLAS_STATUS(hipsolver_syev_heev(API,
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STRIDED,
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nullptr,
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params,
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jobz,
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uplo,
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n,
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dA,
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lda,
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dW,
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dWork,
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lworkOnDevice,
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hWork,
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lworkOnHost,
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dinfo,
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batchSize),
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HIPSOLVER_STATUS_NOT_INITIALIZED);
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// values
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EXPECT_ROCBLAS_STATUS(hipsolver_syev_heev(API,
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STRIDED,
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handle,
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params,
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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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dW,
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dWork,
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lworkOnDevice,
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hWork,
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lworkOnHost,
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dinfo,
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batchSize),
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HIPSOLVER_STATUS_INVALID_ENUM);
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EXPECT_ROCBLAS_STATUS(hipsolver_syev_heev(API,
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STRIDED,
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handle,
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params,
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jobz,
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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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dW,
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dWork,
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lworkOnDevice,
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hWork,
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lworkOnHost,
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dinfo,
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batchSize),
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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_syev_heev(API,
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STRIDED,
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handle,
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(hipsolverDnParams_t) nullptr,
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jobz,
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uplo,
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n,
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dA,
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lda,
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dW,
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dWork,
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lworkOnDevice,
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hWork,
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lworkOnHost,
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dinfo,
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batchSize),
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HIPSOLVER_STATUS_INVALID_VALUE);
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EXPECT_ROCBLAS_STATUS(hipsolver_syev_heev(API,
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STRIDED,
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handle,
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params,
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jobz,
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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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dW,
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dWork,
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lworkOnDevice,
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hWork,
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lworkOnHost,
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dinfo,
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batchSize),
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HIPSOLVER_STATUS_INVALID_VALUE);
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EXPECT_ROCBLAS_STATUS(hipsolver_syev_heev(API,
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STRIDED,
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handle,
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params,
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jobz,
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uplo,
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n,
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dA,
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lda,
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(S) nullptr,
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dWork,
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lworkOnDevice,
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hWork,
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lworkOnHost,
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dinfo,
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batchSize),
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HIPSOLVER_STATUS_INVALID_VALUE);
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EXPECT_ROCBLAS_STATUS(hipsolver_syev_heev(API,
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STRIDED,
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handle,
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params,
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jobz,
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uplo,
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n,
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dA,
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lda,
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dW,
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dWork,
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lworkOnDevice,
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hWork,
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lworkOnHost,
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(U) nullptr,
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batchSize),
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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_syev_heev_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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hipsolver_local_params params;
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hipsolverEigMode_t jobz = HIPSOLVER_EIG_MODE_NOVECTOR;
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hipsolverFillMode_t uplo = HIPSOLVER_FILL_MODE_LOWER;
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int64_t n = 1;
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int64_t lda = 1;
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int64_t batchSize = 1;
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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<S> dW(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(dW.memcheck());
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CHECK_HIP_ERROR(dinfo.memcheck());
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SIZE size_dW, size_hW;
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hipsolver_syev_heev_bufferSize(API,
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STRIDED,
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handle,
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params,
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jobz,
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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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dW.data(),
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&size_dW,
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&size_hW,
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batchSize);
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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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syev_heev_checkBadArgs<API, STRIDED>(handle,
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params,
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jobz,
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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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dW.data(),
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dWork.data(),
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size_dW,
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hWork.data(),
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size_hW,
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dinfo.data(),
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batchSize);
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}
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template <bool CPU, bool GPU, typename T, typename Td, typename Th>
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void syev_heev_initData(const hipsolverHandle_t handle,
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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 bc,
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Th& hA,
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std::vector<T>& A,
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bool test = true)
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{
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if(CPU)
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{
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rocblas_init<T>(hA, true);
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// scale A to avoid singularities
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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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hA[b][i + j * lda] = std::real(hA[b][i + j * lda]) + 400;
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else
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hA[b][i + j * lda] -= 4;
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}
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}
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// make copy of original data to test vectors if required
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if(test && evect == HIPSOLVER_EIG_MODE_VECTOR)
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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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A[b * lda * n + i + j * lda] = hA[b][i + j * lda];
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}
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}
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}
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}
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if(GPU)
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{
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// now copy to the GPU
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CHECK_HIP_ERROR(dA.transfer_from(hA));
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}
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}
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template <testAPI_t API,
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bool STRIDED,
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typename T,
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typename I,
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typename SIZE,
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typename Sd,
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typename Td,
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typename Id,
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typename Sh,
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typename Th,
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typename Ih>
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void syev_heev_getError(const hipsolverHandle_t handle,
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const hipsolverDnParams_t params,
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const hipsolverEigMode_t evect,
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const hipsolverFillMode_t uplo,
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const I n,
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Td& dA,
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const I lda,
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Sd& dW,
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Td& dWork,
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const SIZE lworkOnDevice,
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Th& hWork,
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const SIZE lworkOnHost,
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Id& dinfo,
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const int bc,
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Th& hA,
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Th& hAres,
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Sh& hW,
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Sh& hWres,
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Ih& hinfo,
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Ih& hinfoRes,
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double* max_err)
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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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I lrwork, ltwork;
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if(!COMPLEX)
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{
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ltwork = std::max(I(1), 3 * n - 1);
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lrwork = 0;
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}
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else
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{
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lrwork = std::max(I(1), 3 * n - 2);
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ltwork = std::max(I(1), 2 * n - 1);
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}
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std::vector<T> work(ltwork);
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std::vector<S> hE(lrwork);
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std::vector<T> A(lda * n * bc);
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// input data initialization
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syev_heev_initData<true, true, T>(handle, evect, n, dA, lda, bc, hA, A);
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// execute computations
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// GPU lapack
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CHECK_ROCBLAS_ERROR(hipsolver_syev_heev(API,
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STRIDED,
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handle,
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params,
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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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dW.data(),
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dWork.data(),
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lworkOnDevice,
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hWork.data(),
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lworkOnHost,
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dinfo.data(),
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bc));
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CHECK_HIP_ERROR(hWres.transfer_from(dW));
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CHECK_HIP_ERROR(hinfoRes.transfer_from(dinfo));
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if(evect == HIPSOLVER_EIG_MODE_VECTOR)
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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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cpu_syev_heev(evect, uplo, n, hA[b], lda, hW[b], work.data(), ltwork, hE.data(), hinfo[b]);
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// Check info for non-convergence
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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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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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{
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*max_err += 1;
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}
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}
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double err = 0;
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for(int b = 0; b < bc; ++b)
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{
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if(evect != HIPSOLVER_EIG_MODE_VECTOR)
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{
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// only eigenvalues needed; can compare with LAPACK
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// error is ||hW - hWRes|| / ||hW||
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// using frobenius norm
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if(hinfo[b][0] == 0)
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{
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err = norm_error('F', 1, n, 1, hW[b], hWres[b]);
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}
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*max_err = err > *max_err ? err : *max_err;
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}
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else
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{
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// both eigenvalues and eigenvectors needed; need to implicitly test
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// eigenvectors due to non-uniqueness of eigenvectors under scaling
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if(hinfo[b][0] == 0)
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{
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// multiply A with each of the n eigenvectors and divide by corresponding
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// eigenvalues
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T alpha;
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T beta = 0;
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for(int j = 0; j < n; j++)
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{
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alpha = T(1) / hWres[b][j];
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cpu_symv_hemv(uplo,
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n,
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alpha,
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A.data() + b * lda * n,
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lda,
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hAres[b] + j * lda,
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1,
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beta,
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hA[b] + j * lda,
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1);
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}
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// error is ||hA - hARes|| / ||hA||
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// using frobenius norm
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err = norm_error('F', n, n, lda, hA[b], hAres[b]);
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*max_err = err > *max_err ? err : *max_err;
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}
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}
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}
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}
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template <testAPI_t API,
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bool STRIDED,
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typename T,
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typename I,
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typename SIZE,
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typename Sd,
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typename Td,
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typename Id,
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typename Sh,
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typename Th,
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typename Ih>
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void syev_heev_getPerfData(const hipsolverHandle_t handle,
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const hipsolverDnParams_t params,
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const hipsolverEigMode_t evect,
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const hipsolverFillMode_t uplo,
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const I n,
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Td& dA,
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const I lda,
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Sd& dW,
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Td& dWork,
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const SIZE lworkOnDevice,
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Th& hWork,
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const SIZE lworkOnHost,
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Id& dinfo,
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const int bc,
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Th& hA,
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Sh& hW,
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Ih& 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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constexpr bool COMPLEX = is_complex<T>;
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using S = decltype(std::real(T{}));
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I lrwork, ltwork;
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if(!COMPLEX)
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{
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ltwork = std::max(I(1), 3 * n - 1);
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lrwork = 0;
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}
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else
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{
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lrwork = std::max(I(1), 3 * n - 2);
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ltwork = std::max(I(1), 2 * n - 1);
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}
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std::vector<T> work(ltwork);
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std::vector<S> hE(lrwork);
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std::vector<T> A;
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if(!perf)
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{
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syev_heev_initData<true, false, T>(handle, evect, n, dA, lda, bc, hA, A, 0);
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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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cpu_syev_heev(
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evect, uplo, n, hA[b], lda, hW[b], work.data(), ltwork, hE.data(), hinfo[b]);
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*cpu_time_used = get_time_us_no_sync() - *cpu_time_used;
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}
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syev_heev_initData<true, false, T>(handle, evect, n, dA, lda, bc, hA, A, 0);
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|
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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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syev_heev_initData<false, true, T>(handle, evect, n, dA, lda, bc, hA, A, 0);
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CHECK_ROCBLAS_ERROR(hipsolver_syev_heev(API,
|
|
STRIDED,
|
|
handle,
|
|
params,
|
|
evect,
|
|
uplo,
|
|
n,
|
|
dA.data(),
|
|
lda,
|
|
dW.data(),
|
|
dWork.data(),
|
|
lworkOnDevice,
|
|
hWork.data(),
|
|
lworkOnHost,
|
|
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++)
|
|
{
|
|
syev_heev_initData<false, true, T>(handle, evect, n, dA, lda, bc, hA, A, 0);
|
|
|
|
start = get_time_us_sync(stream);
|
|
hipsolver_syev_heev(API,
|
|
STRIDED,
|
|
handle,
|
|
params,
|
|
evect,
|
|
uplo,
|
|
n,
|
|
dA.data(),
|
|
lda,
|
|
dW.data(),
|
|
dWork.data(),
|
|
lworkOnDevice,
|
|
hWork.data(),
|
|
lworkOnHost,
|
|
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,
|
|
typename I = int64_t,
|
|
typename SIZE = size_t>
|
|
void testing_syev_heev(Arguments& argus)
|
|
{
|
|
using S = decltype(std::real(T{}));
|
|
|
|
// get arguments
|
|
hipsolver_local_handle handle;
|
|
hipsolver_local_params params;
|
|
char evectC = argus.get<char>("jobz");
|
|
char uploC = argus.get<char>("uplo");
|
|
I n = argus.get<int>("n");
|
|
I lda = argus.get<int>("lda", n);
|
|
I stA = lda * n;
|
|
I stW = n;
|
|
|
|
hipsolverEigMode_t evect = char2hipsolver_evect(evectC);
|
|
hipsolverFillMode_t uplo = char2hipsolver_fill(uploC);
|
|
int bc = argus.batch_count;
|
|
int hot_calls = argus.iters;
|
|
|
|
// determine sizes
|
|
size_t size_A = size_t(lda) * n;
|
|
size_t size_W = n;
|
|
size_t size_Ares = (argus.unit_check || argus.norm_check) ? size_A : 0;
|
|
size_t size_Wres = (argus.unit_check || argus.norm_check) ? size_W : 0;
|
|
|
|
double max_error = 0, gpu_time_used = 0, cpu_time_used = 0;
|
|
|
|
// check invalid sizes
|
|
bool invalid_size = (n < 0 || lda < n || bc < 0);
|
|
if(invalid_size)
|
|
{
|
|
EXPECT_ROCBLAS_STATUS(hipsolver_syev_heev(API,
|
|
STRIDED,
|
|
handle,
|
|
params,
|
|
evect,
|
|
uplo,
|
|
n,
|
|
(T*)nullptr,
|
|
lda,
|
|
(S*)nullptr,
|
|
(T*)nullptr,
|
|
(SIZE)0,
|
|
(T*)nullptr,
|
|
(SIZE)0,
|
|
(int*)nullptr,
|
|
bc),
|
|
HIPSOLVER_STATUS_INVALID_VALUE);
|
|
|
|
if(argus.timing)
|
|
rocsolver_bench_inform(inform_invalid_size);
|
|
|
|
return;
|
|
}
|
|
|
|
// memory size query is necessary
|
|
SIZE size_dW, size_hW;
|
|
hipsolver_syev_heev_bufferSize(API,
|
|
STRIDED,
|
|
handle,
|
|
params,
|
|
evect,
|
|
uplo,
|
|
n,
|
|
(T*)nullptr,
|
|
lda,
|
|
(S*)nullptr,
|
|
&size_dW,
|
|
&size_hW,
|
|
bc);
|
|
|
|
if(argus.mem_query)
|
|
{
|
|
rocsolver_bench_inform(inform_mem_query, size_dW);
|
|
return;
|
|
}
|
|
|
|
// memory allocations (all cases)
|
|
// host
|
|
host_strided_batch_vector<S> hW(size_W, 1, stW, bc);
|
|
host_strided_batch_vector<int> hinfo(1, 1, 1, bc);
|
|
host_strided_batch_vector<int> hinfoRes(1, 1, 1, bc);
|
|
host_strided_batch_vector<S> hWres(size_Wres, 1, stW, bc);
|
|
// device
|
|
device_strided_batch_vector<S> dW(size_W, 1, stW, bc);
|
|
device_strided_batch_vector<int> dinfo(1, 1, 1, bc);
|
|
if(size_W)
|
|
CHECK_HIP_ERROR(dW.memcheck());
|
|
CHECK_HIP_ERROR(dinfo.memcheck());
|
|
|
|
// memory allocations
|
|
host_strided_batch_vector<T> hA(size_A, 1, stA, bc);
|
|
host_strided_batch_vector<T> hAres(size_Ares, 1, stA, bc);
|
|
host_strided_batch_vector<T> hWork(size_hW, 1, size_hW, 1); // size_hW accounts for bc
|
|
device_strided_batch_vector<T> dA(size_A, 1, stA, bc);
|
|
device_strided_batch_vector<T> dWork(size_dW, 1, size_dW, 1); // size_dW accounts for bc
|
|
if(size_A)
|
|
CHECK_HIP_ERROR(dA.memcheck());
|
|
if(size_dW)
|
|
CHECK_HIP_ERROR(dWork.memcheck());
|
|
|
|
// check computations
|
|
if(argus.unit_check || argus.norm_check)
|
|
{
|
|
syev_heev_getError<API, STRIDED, T>(handle,
|
|
params,
|
|
evect,
|
|
uplo,
|
|
n,
|
|
dA,
|
|
lda,
|
|
dW,
|
|
dWork,
|
|
size_dW,
|
|
hWork,
|
|
size_hW,
|
|
dinfo,
|
|
bc,
|
|
hA,
|
|
hAres,
|
|
hW,
|
|
hWres,
|
|
hinfo,
|
|
hinfoRes,
|
|
&max_error);
|
|
}
|
|
|
|
// collect performance data
|
|
if(argus.timing)
|
|
{
|
|
syev_heev_getPerfData<API, STRIDED, T>(handle,
|
|
params,
|
|
evect,
|
|
uplo,
|
|
n,
|
|
dA,
|
|
lda,
|
|
dW,
|
|
dWork,
|
|
size_dW,
|
|
hWork,
|
|
size_hW,
|
|
dinfo,
|
|
bc,
|
|
hA,
|
|
hW,
|
|
hinfo,
|
|
&gpu_time_used,
|
|
&cpu_time_used,
|
|
hot_calls,
|
|
argus.perf);
|
|
}
|
|
|
|
// validate results for rocsolver-test
|
|
// using 4 * n * machine_precision as tolerance
|
|
if(argus.unit_check)
|
|
ROCSOLVER_TEST_CHECK(T, max_error, 4 * n);
|
|
|
|
// output results for rocsolver-bench
|
|
if(argus.timing)
|
|
{
|
|
if(!argus.perf)
|
|
{
|
|
std::cerr << "\n============================================\n";
|
|
std::cerr << "Arguments:\n";
|
|
std::cerr << "============================================\n";
|
|
rocsolver_bench_output("jobz", "uplo", "n", "lda", "batch_c");
|
|
rocsolver_bench_output(evectC, uploC, n, lda, bc);
|
|
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();
|
|
}
|