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826 lines
36 KiB
C++
826 lines
36 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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* Redistribution and use in source and binary forms, with or without
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* modification, are permitted provided that the following conditions
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* are met:
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*
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* 1. Redistributions of source code must retain the above copyright
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* notice, this list of conditions and the following disclaimer.
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*
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* 2. Redistributions in binary form must reproduce the above copyright
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* notice, this list of conditions and the following disclaimer in the
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* documentation and/or other materials provided with the distribution.
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*
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* THIS SOFTWARE IS PROVIDED BY THE AUTHOR AND CONTRIBUTORS ``AS IS'' AND
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* ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
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* IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
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* ARE DISCLAIMED. IN NO EVENT SHALL THE AUTHOR OR CONTRIBUTORS BE LIABLE
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* FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
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* DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS
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* OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION)
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* HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
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* LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY
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* OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF
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* SUCH DAMAGE.
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* *************************************************************************/
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#pragma once
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#include "common/misc/client_util.hpp"
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#include "common/misc/clientcommon.hpp"
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#include "common/misc/lapack_host_reference.hpp"
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#include "common/misc/norm.hpp"
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#include "common/misc/rocsolver.hpp"
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#include "common/misc/rocsolver_arguments.hpp"
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#include "common/misc/rocsolver_test.hpp"
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#include "common/misc/rocsolver_timer.hpp"
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template <bool STRIDED, typename T, typename S, typename SS, typename U>
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void sygvj_hegvj_checkBadArgs(const rocblas_handle handle,
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const rocblas_eform itype,
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const rocblas_evect evect,
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const rocblas_fill uplo,
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const rocblas_int n,
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T dA,
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const rocblas_int lda,
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const rocblas_stride stA,
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T dB,
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const rocblas_int ldb,
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const rocblas_stride stB,
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const SS abstol,
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S dResidual,
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const rocblas_int max_sweeps,
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U dSweeps,
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S dW,
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const rocblas_stride stW,
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U dInfo,
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const rocblas_int bc)
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{
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// handle
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EXPECT_ROCBLAS_STATUS(rocsolver_sygvj_hegvj(STRIDED, nullptr, itype, evect, uplo, n, dA, lda,
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stA, dB, ldb, stB, abstol, dResidual, max_sweeps,
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dSweeps, dW, stW, dInfo, bc),
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rocblas_status_invalid_handle);
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// values
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EXPECT_ROCBLAS_STATUS(rocsolver_sygvj_hegvj(STRIDED, handle, rocblas_eform(0), evect, uplo, n,
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dA, lda, stA, dB, ldb, stB, abstol, dResidual,
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max_sweeps, dSweeps, dW, stW, dInfo, bc),
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rocblas_status_invalid_value);
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EXPECT_ROCBLAS_STATUS(rocsolver_sygvj_hegvj(STRIDED, handle, itype, rocblas_evect(0), uplo, n,
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dA, lda, stA, dB, ldb, stB, abstol, dResidual,
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max_sweeps, dSweeps, dW, stW, dInfo, bc),
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rocblas_status_invalid_value);
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EXPECT_ROCBLAS_STATUS(rocsolver_sygvj_hegvj(STRIDED, handle, itype, rocblas_evect_tridiagonal,
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uplo, n, dA, lda, stA, dB, ldb, stB, abstol,
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dResidual, max_sweeps, dSweeps, dW, stW, dInfo, bc),
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rocblas_status_invalid_value);
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EXPECT_ROCBLAS_STATUS(rocsolver_sygvj_hegvj(STRIDED, handle, itype, evect, rocblas_fill_full, n,
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dA, lda, stA, dB, ldb, stB, abstol, dResidual,
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max_sweeps, dSweeps, dW, stW, dInfo, bc),
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rocblas_status_invalid_value);
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// sizes (only check batch_count if applicable)
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if(STRIDED)
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EXPECT_ROCBLAS_STATUS(rocsolver_sygvj_hegvj(STRIDED, handle, itype, evect, uplo, n, dA, lda,
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stA, dB, ldb, stB, abstol, dResidual,
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max_sweeps, dSweeps, dW, stW, dInfo, -1),
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rocblas_status_invalid_size);
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// pointers
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EXPECT_ROCBLAS_STATUS(rocsolver_sygvj_hegvj(STRIDED, handle, itype, evect, uplo, n, (T) nullptr,
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lda, stA, dB, ldb, stB, abstol, dResidual,
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max_sweeps, dSweeps, dW, stW, dInfo, bc),
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rocblas_status_invalid_pointer);
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EXPECT_ROCBLAS_STATUS(rocsolver_sygvj_hegvj(STRIDED, handle, itype, evect, uplo, n, dA, lda,
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stA, (T) nullptr, ldb, stB, abstol, dResidual,
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max_sweeps, dSweeps, dW, stW, dInfo, bc),
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rocblas_status_invalid_pointer);
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EXPECT_ROCBLAS_STATUS(rocsolver_sygvj_hegvj(STRIDED, handle, itype, evect, uplo, n, dA, lda,
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stA, dB, ldb, stB, abstol, (S) nullptr, max_sweeps,
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dSweeps, dW, stW, dInfo, bc),
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rocblas_status_invalid_pointer);
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EXPECT_ROCBLAS_STATUS(rocsolver_sygvj_hegvj(STRIDED, handle, itype, evect, uplo, n, dA, lda,
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stA, dB, ldb, stB, abstol, dResidual, max_sweeps,
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(U) nullptr, dW, stW, dInfo, bc),
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rocblas_status_invalid_pointer);
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EXPECT_ROCBLAS_STATUS(rocsolver_sygvj_hegvj(STRIDED, handle, itype, evect, uplo, n, dA, lda,
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stA, dB, ldb, stB, abstol, dResidual, max_sweeps,
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dSweeps, (S) nullptr, stW, dInfo, bc),
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rocblas_status_invalid_pointer);
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EXPECT_ROCBLAS_STATUS(rocsolver_sygvj_hegvj(STRIDED, handle, itype, evect, uplo, n, dA, lda,
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stA, dB, ldb, stB, abstol, dResidual, max_sweeps,
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dSweeps, dW, stW, (U) nullptr, bc),
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rocblas_status_invalid_pointer);
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// quick return with invalid pointers
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EXPECT_ROCBLAS_STATUS(rocsolver_sygvj_hegvj(STRIDED, handle, itype, evect, uplo, 0, (T) nullptr,
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lda, stA, (T) nullptr, ldb, stB, abstol, dResidual,
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max_sweeps, dSweeps, (S) nullptr, stW, dInfo, bc),
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rocblas_status_success);
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// quick return with zero batch_count if applicable
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if(STRIDED)
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EXPECT_ROCBLAS_STATUS(rocsolver_sygvj_hegvj(STRIDED, handle, itype, evect, uplo, n, dA, lda,
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stA, dB, ldb, stB, abstol, (S) nullptr,
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max_sweeps, (U) nullptr, dW, stW, (U) nullptr, 0),
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rocblas_status_success);
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}
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template <bool BATCHED, bool STRIDED, typename T>
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void testing_sygvj_hegvj_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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rocblas_local_handle handle;
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rocblas_int n = 1;
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rocblas_int lda = 1;
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rocblas_int ldb = 1;
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rocblas_stride stA = 1;
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rocblas_stride stB = 1;
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rocblas_stride stW = 1;
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rocblas_stride stE = 1;
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rocblas_int bc = 1;
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rocblas_eform itype = rocblas_eform_ax;
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rocblas_evect evect = rocblas_evect_none;
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rocblas_fill uplo = rocblas_fill_upper;
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S abstol = 0;
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rocblas_int max_sweeps = 100;
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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> dResidual(1, 1, 1, 1);
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device_strided_batch_vector<rocblas_int> dSweeps(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<rocblas_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(dResidual.memcheck());
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CHECK_HIP_ERROR(dSweeps.memcheck());
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CHECK_HIP_ERROR(dW.memcheck());
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CHECK_HIP_ERROR(dInfo.memcheck());
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// check bad arguments
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sygvj_hegvj_checkBadArgs<STRIDED>(handle, itype, evect, uplo, n, dA.data(), lda, stA,
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dB.data(), ldb, stB, abstol, dResidual.data(), max_sweeps,
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dSweeps.data(), dW.data(), stW, dInfo.data(), 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> dResidual(1, 1, 1, 1);
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device_strided_batch_vector<rocblas_int> dSweeps(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<rocblas_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(dResidual.memcheck());
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CHECK_HIP_ERROR(dSweeps.memcheck());
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CHECK_HIP_ERROR(dW.memcheck());
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CHECK_HIP_ERROR(dInfo.memcheck());
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// check bad arguments
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sygvj_hegvj_checkBadArgs<STRIDED>(handle, itype, evect, uplo, n, dA.data(), lda, stA,
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dB.data(), ldb, stB, abstol, dResidual.data(), max_sweeps,
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dSweeps.data(), dW.data(), stW, dInfo.data(), 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 sygvj_hegvj_initData(const rocblas_handle handle,
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const rocblas_eform itype,
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const rocblas_evect evect,
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const rocblas_int n,
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Td& dA,
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const rocblas_int lda,
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const rocblas_stride stA,
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Td& dB,
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const rocblas_int ldb,
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const rocblas_stride stB,
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const rocblas_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_int info;
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rocblas_init<T>(hA, true);
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rocblas_init<T>(hB, false);
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for(rocblas_int b = 0; b < bc; ++b)
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{
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for(rocblas_int i = 0; i < n; i++)
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{
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for(rocblas_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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if(singular && (b == bc / 4 || b == bc / 2 || b == bc - 1))
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{
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// make some matrices B not positive definite
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// always the same elements for debugging purposes
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// the algorithm must detect the lower order of the principal minors <= 0
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// in those matrices in the batch that are non positive definite
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rocblas_int i = n / 4 + b;
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i -= (i / n) * n;
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hB[b][i + i * ldb] = 0;
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i = n / 2 + b;
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i -= (i / n) * n;
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hB[b][i + i * ldb] = 0;
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i = n - 1 + b;
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i -= (i / n) * n;
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hB[b][i + i * ldb] = 0;
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}
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// store A and B for testing purposes
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if(test && evect != rocblas_evect_none)
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{
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for(rocblas_int i = 0; i < n; i++)
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{
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for(rocblas_int j = 0; j < n; j++)
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{
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if(itype != rocblas_eform_bax)
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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 <bool STRIDED, typename T, typename S, typename Sd, typename Td, typename Id, typename Sh, typename Th, typename Ih>
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void sygvj_hegvj_getError(const rocblas_handle handle,
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const rocblas_eform itype,
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const rocblas_evect evect,
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const rocblas_fill uplo,
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const rocblas_int n,
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Td& dA,
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const rocblas_int lda,
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const rocblas_stride stA,
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Td& dB,
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const rocblas_int ldb,
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const rocblas_stride stB,
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const S abstol,
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Sd& dResidual,
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const rocblas_int max_sweeps,
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Id& dSweeps,
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Sd& dW,
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const rocblas_stride stW,
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Id& dInfo,
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const rocblas_int bc,
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Th& hA,
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Th& hARes,
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Th& hB,
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Sh& hResidualRes,
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Ih& hSweepsRes,
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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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const bool singular)
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{
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constexpr bool COMPLEX = rocblas_is_complex<T>;
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S atol = (abstol <= 0) ? get_epsilon<S>() : abstol;
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rocblas_int lwork = (COMPLEX ? 2 * n - 1 : 3 * n - 1);
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rocblas_int lrwork = (COMPLEX ? 3 * n - 2 : 0);
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std::vector<T> work(lwork);
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std::vector<S> rwork(lrwork);
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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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sygvj_hegvj_initData<true, true, T>(handle, itype, evect, n, dA, lda, stA, dB, ldb, stB, bc, hA,
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hB, A, B, true, singular);
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// execute computations
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// GPU lapack
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CHECK_ROCBLAS_ERROR(rocsolver_sygvj_hegvj(
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STRIDED, handle, itype, evect, uplo, n, dA.data(), lda, stA, dB.data(), ldb, stB, abstol,
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dResidual.data(), max_sweeps, dSweeps.data(), dW.data(), stW, dInfo.data(), bc));
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CHECK_HIP_ERROR(hResidualRes.transfer_from(dResidual));
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CHECK_HIP_ERROR(hSweepsRes.transfer_from(dSweeps));
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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 != rocblas_evect_none)
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CHECK_HIP_ERROR(hARes.transfer_from(dA));
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// CPU lapack
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for(rocblas_int b = 0; b < bc; ++b)
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{
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cpu_sygv_hegv(itype, evect, uplo, n, hA[b], lda, hB[b], ldb, hW[b], work.data(), lwork,
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rwork.data(), hInfo[b]);
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}
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// (We expect the used input matrices to always converge)
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// check info for non-convergence and/or positive-definiteness
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*max_err = 0;
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for(rocblas_int b = 0; b < bc; ++b)
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{
|
|
EXPECT_EQ(hInfo[b][0], hInfoRes[b][0]) << "where b = " << b;
|
|
if(hInfo[b][0] != hInfoRes[b][0])
|
|
*max_err += 1;
|
|
}
|
|
|
|
// Also check validity of residual
|
|
for(rocblas_int b = 0; b < bc; ++b)
|
|
if(hInfoRes[b][0] == 0)
|
|
{
|
|
EXPECT_GE(hResidualRes[b][0], 0) << "where b = " << b;
|
|
if(hResidualRes[b][0] < 0)
|
|
*max_err += 1;
|
|
}
|
|
|
|
// Also check validity of sweeps
|
|
for(rocblas_int b = 0; b < bc; ++b)
|
|
if(hInfoRes[b][0] == 0)
|
|
{
|
|
EXPECT_GE(hSweepsRes[b][0], 0) << "where b = " << b;
|
|
EXPECT_LE(hSweepsRes[b][0], max_sweeps) << "where b = " << b;
|
|
if(hSweepsRes[b][0] < 0 || hSweepsRes[b][0] > max_sweeps)
|
|
*max_err += 1;
|
|
}
|
|
|
|
double err;
|
|
|
|
for(rocblas_int b = 0; b < bc; ++b)
|
|
{
|
|
if(evect == rocblas_evect_none)
|
|
{
|
|
// only eigenvalues needed; can compare with LAPACK
|
|
|
|
// error is ||hW - hWRes|| / ||hW||
|
|
// using frobenius norm
|
|
if(hInfoRes[b][0] == 0)
|
|
{
|
|
err = norm_error('F', 1, n, 1, hW[b], hWRes[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
|
|
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(rocblas_side_left, uplo, n, n, alpha, B[b], ldb, hARes[b], lda, beta,
|
|
hB[b], ldb);
|
|
|
|
if(itype == rocblas_eform_ax)
|
|
{
|
|
// 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) / hWRes[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(rocblas_int i = 0; i < n; i++)
|
|
for(rocblas_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) / hWRes[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 <bool STRIDED, typename T, typename S, typename Sd, typename Td, typename Id, typename Sh, typename Th, typename Ih>
|
|
void sygvj_hegvj_getPerfData(const rocblas_handle handle,
|
|
const rocblas_eform itype,
|
|
const rocblas_evect evect,
|
|
const rocblas_fill uplo,
|
|
const rocblas_int n,
|
|
Td& dA,
|
|
const rocblas_int lda,
|
|
const rocblas_stride stA,
|
|
Td& dB,
|
|
const rocblas_int ldb,
|
|
const rocblas_stride stB,
|
|
const S abstol,
|
|
Sd& dResidual,
|
|
const rocblas_int max_sweeps,
|
|
Id& dSweeps,
|
|
Sd& dW,
|
|
const rocblas_stride stW,
|
|
Id& dInfo,
|
|
const rocblas_int bc,
|
|
Th& hA,
|
|
Th& hB,
|
|
Sh& hW,
|
|
Ih& hInfo,
|
|
double* gpu_time_used,
|
|
double* cpu_time_used,
|
|
const rocblas_int hot_calls,
|
|
const int profile,
|
|
const bool profile_kernels,
|
|
const bool perf,
|
|
const bool singular)
|
|
{
|
|
constexpr bool COMPLEX = rocblas_is_complex<T>;
|
|
|
|
rocblas_int lwork = (COMPLEX ? 2 * n - 1 : 3 * n - 1);
|
|
rocblas_int lrwork = (COMPLEX ? 3 * n - 2 : 0);
|
|
std::vector<T> work(lwork);
|
|
std::vector<S> rwork(lrwork);
|
|
host_strided_batch_vector<T> A(1, 1, 1, 1);
|
|
host_strided_batch_vector<T> B(1, 1, 1, 1);
|
|
|
|
if(!perf)
|
|
{
|
|
sygvj_hegvj_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(rocblas_int b = 0; b < bc; ++b)
|
|
{
|
|
cpu_sygv_hegv(itype, evect, uplo, n, hA[b], lda, hB[b], ldb, hW[b], work.data(), lwork,
|
|
rwork.data(), hInfo[b]);
|
|
}
|
|
*cpu_time_used = get_time_us_no_sync() - *cpu_time_used;
|
|
}
|
|
|
|
sygvj_hegvj_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++)
|
|
{
|
|
sygvj_hegvj_initData<false, true, T>(handle, itype, evect, n, dA, lda, stA, dB, ldb, stB,
|
|
bc, hA, hB, A, B, false, singular);
|
|
|
|
CHECK_ROCBLAS_ERROR(rocsolver_sygvj_hegvj(
|
|
STRIDED, handle, itype, evect, uplo, n, dA.data(), lda, stA, dB.data(), ldb, stB,
|
|
abstol, dResidual.data(), max_sweeps, dSweeps.data(), dW.data(), stW, dInfo.data(), bc));
|
|
}
|
|
|
|
// gpu-lapack performance
|
|
hipStream_t stream;
|
|
CHECK_ROCBLAS_ERROR(rocblas_get_stream(handle, &stream));
|
|
rocsolver_timer timer;
|
|
|
|
if(profile > 0)
|
|
{
|
|
if(profile_kernels)
|
|
rocsolver_log_set_layer_mode(rocblas_layer_mode_log_profile
|
|
| rocblas_layer_mode_ex_log_kernel);
|
|
else
|
|
rocsolver_log_set_layer_mode(rocblas_layer_mode_log_profile);
|
|
rocsolver_log_set_max_levels(profile);
|
|
}
|
|
|
|
for(rocblas_int iter = 0; iter < hot_calls; iter++)
|
|
{
|
|
sygvj_hegvj_initData<false, true, T>(handle, itype, evect, n, dA, lda, stA, dB, ldb, stB,
|
|
bc, hA, hB, A, B, false, singular);
|
|
|
|
timer.start(stream);
|
|
rocsolver_sygvj_hegvj(STRIDED, handle, itype, evect, uplo, n, dA.data(), lda, stA,
|
|
dB.data(), ldb, stB, abstol, dResidual.data(), max_sweeps,
|
|
dSweeps.data(), dW.data(), stW, dInfo.data(), bc);
|
|
timer.end(stream);
|
|
}
|
|
*gpu_time_used = timer.get_combined();
|
|
}
|
|
|
|
template <bool BATCHED, bool STRIDED, typename T>
|
|
void testing_sygvj_hegvj(Arguments& argus)
|
|
{
|
|
using S = decltype(std::real(T{}));
|
|
|
|
// get arguments
|
|
rocblas_local_handle handle;
|
|
char itypeC = argus.get<char>("itype");
|
|
char evectC = argus.get<char>("evect");
|
|
char uploC = argus.get<char>("uplo");
|
|
rocblas_int n = argus.get<rocblas_int>("n");
|
|
rocblas_int lda = argus.get<rocblas_int>("lda", n);
|
|
rocblas_int ldb = argus.get<rocblas_int>("ldb", n);
|
|
rocblas_stride stA = argus.get<rocblas_stride>("strideA", lda * n);
|
|
rocblas_stride stB = argus.get<rocblas_stride>("strideB", ldb * n);
|
|
rocblas_stride stW = argus.get<rocblas_stride>("strideD", n);
|
|
|
|
S abstol = S(argus.get<double>("abstol", 0));
|
|
rocblas_int max_sweeps = argus.get<rocblas_int>("max_sweeps", 100);
|
|
|
|
rocblas_eform itype = char2rocblas_eform(itypeC);
|
|
rocblas_evect evect = char2rocblas_evect(evectC);
|
|
rocblas_fill uplo = char2rocblas_fill(uploC);
|
|
rocblas_int bc = argus.batch_count;
|
|
rocblas_int hot_calls = argus.iters;
|
|
|
|
rocblas_stride stARes = (argus.unit_check || argus.norm_check) ? stA : 0;
|
|
rocblas_stride stWRes = (argus.unit_check || argus.norm_check) ? stW : 0;
|
|
|
|
// check non-supported values
|
|
if(uplo == rocblas_fill_full || evect == rocblas_evect_tridiagonal)
|
|
{
|
|
if(BATCHED)
|
|
EXPECT_ROCBLAS_STATUS(
|
|
rocsolver_sygvj_hegvj(STRIDED, handle, itype, evect, uplo, n, (T* const*)nullptr,
|
|
lda, stA, (T* const*)nullptr, ldb, stB, abstol, (S*)nullptr,
|
|
max_sweeps, (rocblas_int*)nullptr, (S*)nullptr, stW,
|
|
(rocblas_int*)nullptr, bc),
|
|
rocblas_status_invalid_value);
|
|
else
|
|
EXPECT_ROCBLAS_STATUS(rocsolver_sygvj_hegvj(STRIDED, handle, itype, evect, uplo, n,
|
|
(T*)nullptr, lda, stA, (T*)nullptr, ldb,
|
|
stB, abstol, (S*)nullptr, max_sweeps,
|
|
(rocblas_int*)nullptr, (S*)nullptr, stW,
|
|
(rocblas_int*)nullptr, bc),
|
|
rocblas_status_invalid_value);
|
|
|
|
if(argus.timing)
|
|
rocsolver_bench_inform(inform_invalid_args);
|
|
|
|
return;
|
|
}
|
|
|
|
// determine sizes
|
|
size_t size_A = size_t(lda) * n;
|
|
size_t size_B = size_t(ldb) * n;
|
|
size_t size_W = 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_WRes = (argus.unit_check || argus.norm_check) ? size_W : 0;
|
|
|
|
// check invalid sizes
|
|
bool invalid_size = (n < 0 || lda < n || ldb < n || bc < 0);
|
|
if(invalid_size)
|
|
{
|
|
if(BATCHED)
|
|
EXPECT_ROCBLAS_STATUS(
|
|
rocsolver_sygvj_hegvj(STRIDED, handle, itype, evect, uplo, n, (T* const*)nullptr,
|
|
lda, stA, (T* const*)nullptr, ldb, stB, abstol, (S*)nullptr,
|
|
max_sweeps, (rocblas_int*)nullptr, (S*)nullptr, stW,
|
|
(rocblas_int*)nullptr, bc),
|
|
rocblas_status_invalid_size);
|
|
else
|
|
EXPECT_ROCBLAS_STATUS(rocsolver_sygvj_hegvj(STRIDED, handle, itype, evect, uplo, n,
|
|
(T*)nullptr, lda, stA, (T*)nullptr, ldb,
|
|
stB, abstol, (S*)nullptr, max_sweeps,
|
|
(rocblas_int*)nullptr, (S*)nullptr, stW,
|
|
(rocblas_int*)nullptr, bc),
|
|
rocblas_status_invalid_size);
|
|
|
|
if(argus.timing)
|
|
rocsolver_bench_inform(inform_invalid_size);
|
|
|
|
return;
|
|
}
|
|
|
|
// memory size query is necessary
|
|
if(argus.mem_query)
|
|
{
|
|
CHECK_ROCBLAS_ERROR(rocblas_start_device_memory_size_query(handle));
|
|
if(BATCHED)
|
|
CHECK_ALLOC_QUERY(rocsolver_sygvj_hegvj(
|
|
STRIDED, handle, itype, evect, uplo, n, (T* const*)nullptr, lda, stA,
|
|
(T* const*)nullptr, ldb, stB, abstol, (S*)nullptr, max_sweeps,
|
|
(rocblas_int*)nullptr, (S*)nullptr, stW, (rocblas_int*)nullptr, bc));
|
|
else
|
|
CHECK_ALLOC_QUERY(rocsolver_sygvj_hegvj(
|
|
STRIDED, handle, itype, evect, uplo, n, (T*)nullptr, lda, stA, (T*)nullptr, ldb,
|
|
stB, abstol, (S*)nullptr, max_sweeps, (rocblas_int*)nullptr, (S*)nullptr, stW,
|
|
(rocblas_int*)nullptr, bc));
|
|
|
|
size_t size;
|
|
CHECK_ROCBLAS_ERROR(rocblas_stop_device_memory_size_query(handle, &size));
|
|
|
|
rocsolver_bench_inform(inform_mem_query, size);
|
|
return;
|
|
}
|
|
|
|
// memory allocations (all cases)
|
|
// host
|
|
host_strided_batch_vector<S> hResidualRes(1, 1, 1, bc);
|
|
host_strided_batch_vector<rocblas_int> hSweepsRes(1, 1, 1, bc);
|
|
host_strided_batch_vector<S> hW(size_W, 1, stW, bc);
|
|
host_strided_batch_vector<S> hWRes(size_WRes, 1, stWRes, bc);
|
|
host_strided_batch_vector<rocblas_int> hInfo(1, 1, 1, bc);
|
|
host_strided_batch_vector<rocblas_int> hInfoRes(1, 1, 1, bc);
|
|
// device
|
|
device_strided_batch_vector<S> dResidual(1, 1, 1, bc);
|
|
device_strided_batch_vector<rocblas_int> dSweeps(1, 1, 1, bc);
|
|
device_strided_batch_vector<S> dW(size_W, 1, stW, bc);
|
|
device_strided_batch_vector<rocblas_int> dInfo(1, 1, 1, bc);
|
|
CHECK_HIP_ERROR(dResidual.memcheck());
|
|
CHECK_HIP_ERROR(dSweeps.memcheck());
|
|
if(size_W)
|
|
CHECK_HIP_ERROR(dW.memcheck());
|
|
CHECK_HIP_ERROR(dInfo.memcheck());
|
|
|
|
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);
|
|
device_batch_vector<T> dA(size_A, 1, bc);
|
|
device_batch_vector<T> dB(size_B, 1, bc);
|
|
if(size_A)
|
|
CHECK_HIP_ERROR(dA.memcheck());
|
|
if(size_B)
|
|
CHECK_HIP_ERROR(dB.memcheck());
|
|
|
|
// check quick return
|
|
if(n == 0 || bc == 0)
|
|
{
|
|
EXPECT_ROCBLAS_STATUS(
|
|
rocsolver_sygvj_hegvj(STRIDED, handle, itype, evect, uplo, n, dA.data(), lda, stA,
|
|
dB.data(), ldb, stB, abstol, dResidual.data(), max_sweeps,
|
|
dSweeps.data(), dW.data(), stW, dInfo.data(), bc),
|
|
rocblas_status_success);
|
|
if(argus.timing)
|
|
rocsolver_bench_inform(inform_quick_return);
|
|
|
|
return;
|
|
}
|
|
|
|
// check computations
|
|
if(argus.unit_check || argus.norm_check)
|
|
sygvj_hegvj_getError<STRIDED, T>(handle, itype, evect, uplo, n, dA, lda, stA, dB, ldb,
|
|
stB, abstol, dResidual, max_sweeps, dSweeps, dW, stW,
|
|
dInfo, bc, hA, hARes, hB, hResidualRes, hSweepsRes, hW,
|
|
hWRes, hInfo, hInfoRes, &max_error, argus.singular);
|
|
|
|
// collect performance data
|
|
if(argus.timing && hot_calls > 0)
|
|
sygvj_hegvj_getPerfData<STRIDED, T>(handle, itype, evect, uplo, n, dA, lda, stA, dB,
|
|
ldb, stB, abstol, dResidual, max_sweeps, dSweeps, dW,
|
|
stW, dInfo, bc, hA, hB, hW, hInfo, &gpu_time_used,
|
|
&cpu_time_used, hot_calls, argus.profile,
|
|
argus.profile_kernels, 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);
|
|
device_strided_batch_vector<T> dA(size_A, 1, stA, bc);
|
|
device_strided_batch_vector<T> dB(size_B, 1, stB, bc);
|
|
if(size_A)
|
|
CHECK_HIP_ERROR(dA.memcheck());
|
|
if(size_B)
|
|
CHECK_HIP_ERROR(dB.memcheck());
|
|
|
|
// check quick return
|
|
if(n == 0 || bc == 0)
|
|
{
|
|
EXPECT_ROCBLAS_STATUS(
|
|
rocsolver_sygvj_hegvj(STRIDED, handle, itype, evect, uplo, n, dA.data(), lda, stA,
|
|
dB.data(), ldb, stB, abstol, dResidual.data(), max_sweeps,
|
|
dSweeps.data(), dW.data(), stW, dInfo.data(), bc),
|
|
rocblas_status_success);
|
|
if(argus.timing)
|
|
rocsolver_bench_inform(inform_quick_return);
|
|
|
|
return;
|
|
}
|
|
|
|
// check computations
|
|
if(argus.unit_check || argus.norm_check)
|
|
sygvj_hegvj_getError<STRIDED, T>(handle, itype, evect, uplo, n, dA, lda, stA, dB, ldb,
|
|
stB, abstol, dResidual, max_sweeps, dSweeps, dW, stW,
|
|
dInfo, bc, hA, hARes, hB, hResidualRes, hSweepsRes, hW,
|
|
hWRes, hInfo, hInfoRes, &max_error, argus.singular);
|
|
|
|
// collect performance data
|
|
if(argus.timing && hot_calls > 0)
|
|
sygvj_hegvj_getPerfData<STRIDED, T>(handle, itype, evect, uplo, n, dA, lda, stA, dB,
|
|
ldb, stB, abstol, dResidual, max_sweeps, dSweeps, dW,
|
|
stW, dInfo, bc, hA, hB, hW, hInfo, &gpu_time_used,
|
|
&cpu_time_used, hot_calls, argus.profile,
|
|
argus.profile_kernels, argus.perf, argus.singular);
|
|
}
|
|
|
|
// validate results for rocsolver-test
|
|
// using 2 * n * machine_precision as tolerance
|
|
if(argus.unit_check)
|
|
ROCSOLVER_TEST_CHECK(T, max_error, 2 * n);
|
|
|
|
// output results for rocsolver-bench
|
|
if(argus.timing)
|
|
{
|
|
if(!argus.perf)
|
|
{
|
|
rocsolver_bench_header("Arguments:");
|
|
if(BATCHED)
|
|
{
|
|
rocsolver_bench_output("itype", "evect", "uplo", "n", "lda", "ldb", "abstol",
|
|
"max_sweeps", "strideW", "batch_c");
|
|
rocsolver_bench_output(itypeC, evectC, uploC, n, lda, ldb, abstol, max_sweeps, stW,
|
|
bc);
|
|
}
|
|
else if(STRIDED)
|
|
{
|
|
rocsolver_bench_output("itype", "evect", "uplo", "n", "lda", "ldb", "strideA",
|
|
"strideB", "abstol", "max_sweeps", "strideW", "batch_c");
|
|
rocsolver_bench_output(itypeC, evectC, uploC, n, lda, ldb, stA, stB, abstol,
|
|
max_sweeps, stW, bc);
|
|
}
|
|
else
|
|
{
|
|
rocsolver_bench_output("itype", "evect", "uplo", "n", "lda", "ldb", "abstol",
|
|
"max_sweeps");
|
|
rocsolver_bench_output(itypeC, evectC, uploC, n, lda, ldb, abstol, max_sweeps);
|
|
}
|
|
rocsolver_bench_header("Results:");
|
|
if(argus.norm_check)
|
|
{
|
|
rocsolver_bench_output("cpu_time_us", "gpu_time_us", "error");
|
|
rocsolver_bench_output(cpu_time_used, gpu_time_used, max_error);
|
|
}
|
|
else
|
|
{
|
|
rocsolver_bench_output("cpu_time_us", "gpu_time_us");
|
|
rocsolver_bench_output(cpu_time_used, gpu_time_used);
|
|
}
|
|
rocsolver_bench_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();
|
|
}
|
|
|
|
#define EXTERN_TESTING_SYGVJ_HEGVJ(...) \
|
|
extern template void testing_sygvj_hegvj<__VA_ARGS__>(Arguments&);
|
|
|
|
INSTANTIATE(EXTERN_TESTING_SYGVJ_HEGVJ, FOREACH_MATRIX_DATA_LAYOUT, FOREACH_SCALAR_TYPE, APPLY_STAMP)
|