derek.holloway
7502bf64a4
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987 lines
35 KiB
C++
987 lines
35 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/matrix_utils/matrix_utils.hpp"
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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 <typename T, typename S, typename U>
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void stedc_checkBadArgs(const rocblas_handle handle,
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const rocblas_evect evect,
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const rocblas_int n,
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S dD,
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S dE,
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T dC,
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const rocblas_int ldc,
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U dInfo)
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{
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// handle
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EXPECT_ROCBLAS_STATUS(rocsolver_stedc(nullptr, evect, n, dD, dE, dC, ldc, dInfo),
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rocblas_status_invalid_handle);
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// values
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EXPECT_ROCBLAS_STATUS(rocsolver_stedc(handle, rocblas_evect(0), n, dD, dE, dC, ldc, dInfo),
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rocblas_status_invalid_value);
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// pointers
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EXPECT_ROCBLAS_STATUS(rocsolver_stedc(handle, evect, n, (S) nullptr, dE, dC, ldc, dInfo),
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rocblas_status_invalid_pointer);
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EXPECT_ROCBLAS_STATUS(rocsolver_stedc(handle, evect, n, dD, (S) nullptr, dC, ldc, dInfo),
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rocblas_status_invalid_pointer);
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EXPECT_ROCBLAS_STATUS(rocsolver_stedc(handle, evect, n, dD, dE, (T) nullptr, ldc, dInfo),
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rocblas_status_invalid_pointer);
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EXPECT_ROCBLAS_STATUS(rocsolver_stedc(handle, evect, n, dD, dE, dC, ldc, (U) nullptr),
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rocblas_status_invalid_pointer);
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// quick return with invalid pointers
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EXPECT_ROCBLAS_STATUS(
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rocsolver_stedc(handle, evect, 0, (S) nullptr, (S) nullptr, (T) nullptr, ldc, dInfo),
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rocblas_status_success);
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}
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template <typename T>
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void testing_stedc_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_evect evect = rocblas_evect_original;
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rocblas_int n = 2;
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rocblas_int ldc = 2;
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// memory allocations
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device_strided_batch_vector<S> dD(1, 1, 1, 1);
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device_strided_batch_vector<S> dE(1, 1, 1, 1);
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device_strided_batch_vector<T> dC(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(dD.memcheck());
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CHECK_HIP_ERROR(dE.memcheck());
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CHECK_HIP_ERROR(dC.memcheck());
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CHECK_HIP_ERROR(dInfo.memcheck());
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// check bad arguments
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stedc_checkBadArgs(handle, evect, n, dD.data(), dE.data(), dC.data(), ldc, dInfo.data());
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}
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// For `n > 1`, creates a symmetrized `n` by `n` tridiagonal, Clement matrix T of the following form:
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//
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// ( sqrt(n - 1) sqrt(2(n - 2)) sqrt((n - 2)2) sqrt(n - 1) )
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// T = tridiag ( 0 0 ... 0 0 )
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// ( sqrt(n - 1) sqrt(2(n - 2)) sqrt((n - 2)2) sqrt(n - 1) )
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//
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// were the `i-th` off-diagonal entry is sqrt(i(n - i)), 1 <= i < n.
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//
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template <bool CPU, bool GPU, typename T, typename Sd, typename Td, typename Ud, typename Sh, typename Th, typename Uh>
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void stedc_clement_initData(const rocblas_handle handle,
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const rocblas_evect evect,
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const rocblas_int n,
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Sd& dD,
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Sd& dE,
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Td& dC,
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const rocblas_int ldc,
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Ud& /* dInfo */,
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Sh& hD,
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Sh& hE,
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Th& hC,
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Uh& /* hInfo */)
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{
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using S = decltype(std::real(T{}));
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rocblas_int bc = 1;
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if(CPU)
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{
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rocblas_init<T>(hC, true);
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for(rocblas_int b = 0; b < bc; ++b)
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{
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// New matrix initialization
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using HMatT = HostMatrix<T, rocblas_int>;
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using HMatS = HostMatrix<S, rocblas_int>;
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using BDesc = typename HMatT::BlockDescriptor;
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auto hCw = HMatT::Wrap(hC[b], ldc, n);
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hCw->set_to_zero();
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auto hDw = HMatS::Wrap(hD[b], n, 1);
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hDw->set_to_zero();
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auto hEw = HMatS::Wrap(hE[b], n, 1);
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hEw->set_to_zero();
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if(hCw && hDw && hEw) // update matrices if n >= 1
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{
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auto C = HMatT::Eye(n, n);
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auto D = HMatS::Zeros(n, 1);
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auto E = HMatS::Ones(n - 1, 1);
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for(rocblas_int i = 1; i < n; ++i)
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{
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E[i - 1] = std::sqrt(i * (n - i));
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}
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hCw->copy_data_from(C);
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hDw->copy_data_from(D);
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hEw->copy_data_from(E);
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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(dD.transfer_from(hD));
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CHECK_HIP_ERROR(dE.transfer_from(hE));
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if(evect == rocblas_evect_original)
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CHECK_HIP_ERROR(dC.transfer_from(hC));
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}
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}
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// Creates an `n` by `n` tridiagonal, Toeplitz matrix T of the following form:
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//
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// ( 1 1 1 )
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// T = tridiag ( 2 2 ... 2 2 ... 2 2 )
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// ( 1 1 1 )
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//
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template <bool CPU, bool GPU, typename T, typename Sd, typename Td, typename Ud, typename Sh, typename Th, typename Uh>
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void stedc_toeplitz_initData(const rocblas_handle handle,
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const rocblas_evect evect,
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const rocblas_int n,
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Sd& dD,
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Sd& dE,
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Td& dC,
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const rocblas_int ldc,
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Ud& /* dInfo */,
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Sh& hD,
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Sh& hE,
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Th& hC,
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Uh& /* hInfo */)
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{
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using S = decltype(std::real(T{}));
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rocblas_int bc = 1;
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if(CPU)
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{
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rocblas_init<T>(hC, true);
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for(rocblas_int b = 0; b < bc; ++b)
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{
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// New matrix initialization
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using HMatT = HostMatrix<T, rocblas_int>;
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using HMatS = HostMatrix<S, rocblas_int>;
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using BDesc = typename HMatT::BlockDescriptor;
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auto hCw = HMatT::Wrap(hC[b], ldc, n);
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hCw->set_to_zero();
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auto hDw = HMatS::Wrap(hD[b], n, 1);
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hDw->set_to_zero();
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auto hEw = HMatS::Wrap(hE[b], n, 1);
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hEw->set_to_zero();
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if(hCw && hDw && hEw) // update matrices if n >= 1
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{
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auto C = HMatT::Eye(n, n);
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auto D = 2 * HMatS::Ones(n, 1);
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auto E = HMatS::Ones(n - 1, 1);
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hCw->copy_data_from(C);
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hDw->copy_data_from(D);
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hEw->copy_data_from(E);
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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(dD.transfer_from(hD));
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CHECK_HIP_ERROR(dE.transfer_from(hE));
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if(evect == rocblas_evect_original)
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CHECK_HIP_ERROR(dC.transfer_from(hC));
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}
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}
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// Creates an `n` by `n` identity matrix.
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//
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template <bool CPU, bool GPU, typename T, typename Sd, typename Td, typename Ud, typename Sh, typename Th, typename Uh>
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void stedc_identity_initData(const rocblas_handle handle,
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const rocblas_evect evect,
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const rocblas_int n,
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Sd& dD,
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Sd& dE,
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Td& dC,
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const rocblas_int ldc,
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Ud& /* dInfo */,
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Sh& hD,
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Sh& hE,
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Th& hC,
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Uh& /* hInfo */)
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{
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using S = decltype(std::real(T{}));
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rocblas_int bc = 1;
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if(CPU)
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{
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rocblas_init<T>(hC, true);
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for(rocblas_int b = 0; b < bc; ++b)
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{
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// New matrix initialization
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using HMatT = HostMatrix<T, rocblas_int>;
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using HMatS = HostMatrix<S, rocblas_int>;
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using BDesc = typename HMatT::BlockDescriptor;
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auto hCw = HMatT::Wrap(hC[b], ldc, n);
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hCw->set_to_zero();
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auto hDw = HMatS::Wrap(hD[b], n, 1);
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hDw->set_to_zero();
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auto hEw = HMatS::Wrap(hE[b], n, 1);
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hEw->set_to_zero();
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if(hCw && hDw && hEw) // update matrices if n >= 1
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{
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auto C = HMatT::Eye(n, n);
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auto D = HMatS::Ones(n, 1);
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auto E = HMatS::Zeros(n - 1, 1);
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hCw->copy_data_from(C);
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hDw->copy_data_from(D);
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hEw->copy_data_from(E);
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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(dD.transfer_from(hD));
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CHECK_HIP_ERROR(dE.transfer_from(hE));
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if(evect == rocblas_evect_original)
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CHECK_HIP_ERROR(dC.transfer_from(hC));
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}
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}
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// Creates an `n` by `n` tridiagonal, Wilkinson matrix, which is formed as follows:
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//
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// 1. If `n` is even:
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// ( 1 1 1 1 )
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// W_{2m + 1} = tridiag ( m (m - 1) ... 0.5 0.5 ... (m - 1) m )
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// ( 1 1 1 1 )
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//
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// 2. If `n` is odd:
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// ( 1 1 1 1 )
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// W_{2m + 1} = tridiag ( m (m - 1) ... 1 0 1 ... (m - 1) m )
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// ( 1 1 1 1 )
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//
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// where `n = 2m + 1`.
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//
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template <bool CPU, bool GPU, typename T, typename Sd, typename Td, typename Ud, typename Sh, typename Th, typename Uh>
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void stedc_wilkinson_initData(const rocblas_handle handle,
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const rocblas_evect evect,
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const rocblas_int n,
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Sd& dD,
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Sd& dE,
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Td& dC,
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const rocblas_int ldc,
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Ud& /* dInfo */,
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Sh& hD,
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Sh& hE,
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Th& hC,
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Uh& /* hInfo */)
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{
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using S = decltype(std::real(T{}));
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rocblas_int bc = 1;
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if(CPU)
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{
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rocblas_init<T>(hC, true);
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for(rocblas_int b = 0; b < bc; ++b)
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{
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// New matrix initialization
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using HMatT = HostMatrix<T, rocblas_int>;
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using HMatS = HostMatrix<S, rocblas_int>;
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using BDesc = typename HMatT::BlockDescriptor;
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auto hCw = HMatT::Wrap(hC[b], ldc, n);
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hCw->set_to_zero();
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auto hDw = HMatS::Wrap(hD[b], n, 1);
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hDw->set_to_zero();
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auto hEw = HMatS::Wrap(hE[b], n, 1);
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hEw->set_to_zero();
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if(hCw && hDw && hEw) // update matrices if n >= 1
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{
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S m = (n - 1) / S(2);
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auto C = HMatT::Eye(n, n);
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auto D = HMatS::Zeros(n, 1);
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auto E = HMatS::Ones(n - 1, 1);
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for(rocblas_int i = 0; i < n / 2; ++i)
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{
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D[i] = m - i;
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D[n - 1 - i] = m - i;
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}
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hCw->copy_data_from(C);
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hDw->copy_data_from(D);
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hEw->copy_data_from(E);
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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(dD.transfer_from(hD));
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CHECK_HIP_ERROR(dE.transfer_from(hE));
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if(evect == rocblas_evect_original)
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CHECK_HIP_ERROR(dC.transfer_from(hC));
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}
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}
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template <bool CPU, bool GPU, typename T, typename Sd, typename Td, typename Ud, typename Sh, typename Th, typename Uh>
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void stedc_random_initData(const rocblas_handle handle,
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const rocblas_evect evect,
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const rocblas_int n,
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Sd& dD,
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Sd& dE,
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Td& dC,
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const rocblas_int ldc,
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Ud& dInfo,
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Sh& hD,
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Sh& hE,
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Th& hC,
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Uh& hInfo)
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{
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if(CPU)
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{
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using S = decltype(std::real(T{}));
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rocblas_init<S>(hD, true);
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rocblas_init<S>(hE, true);
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for(int i = 0; i < n - 1; ++i)
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hE[0][i] -= 4;
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// initialize C to the identity matrix
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if(evect == rocblas_evect_original)
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{
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for(rocblas_int j = 0; j < n; j++)
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{
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for(rocblas_int i = 0; i < n; i++)
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{
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if(i == j)
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hC[0][i + j * ldc] = 1;
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else
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hC[0][i + j * ldc] = 0;
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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(dD.transfer_from(hD));
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CHECK_HIP_ERROR(dE.transfer_from(hE));
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if(evect == rocblas_evect_original)
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CHECK_HIP_ERROR(dC.transfer_from(hC));
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}
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}
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template <bool CPU, bool GPU, typename T, typename Sd, typename Td, typename Ud, typename Sh, typename Th, typename Uh>
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void stedc_default_initData(const rocblas_handle handle,
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const rocblas_evect evect,
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const rocblas_int n,
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Sd& dD,
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Sd& dE,
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Td& dC,
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const rocblas_int ldc,
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Ud& /* dInfo */,
|
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Sh& hD,
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Sh& hE,
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Th& hC,
|
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Uh& /* hInfo */)
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{
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if(CPU)
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|
{
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using S = decltype(std::real(T{}));
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|
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// if the matrix is too small (n < 4), simply initialize D and E
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if(n < 4)
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{
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rocblas_init<S>(hD, true);
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rocblas_init<S>(hE, true);
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}
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|
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// otherwise, the marix will be divided in exactly 2 independent blocks, if the size is even,
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// or 3 if the size is odd. The 2 main independent blocks will have the same eigenvalues.
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// The last block, when the size is odd, will have eigenvalue equal 1.
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else
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{
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rocblas_int N1 = n / 2;
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rocblas_int E = n - 2 * N1;
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|
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// a. initialize the eigenvalues for the uppermost sub-blocks of the main independent blocks.
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// The second sub-block will have some repeated eigenvalues in order to test the deflation process
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S d;
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rocblas_int NN1 = N1 / 2;
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rocblas_int NN2 = N1 - NN1;
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rocblas_int s1 = NN1 * NN1;
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rocblas_int s2 = NN2 * NN2;
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rocblas_int sw = NN2 * 32;
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std::vector<S> A1(s1);
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std::vector<S> A2(s2);
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for(rocblas_int i = 0; i < NN1; ++i)
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{
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for(rocblas_int j = 0; j < NN1; ++j)
|
|
{
|
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if(i == j)
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{
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d = (i + 1) / S(NN1);
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A1[i + i * NN1] = d;
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A2[i + i * NN2] = (i % 2 == 0) ? d : -d;
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}
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else
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{
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A1[i + j * NN1] = 0;
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A2[i + j * NN2] = 0;
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}
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}
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}
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if(NN2 > NN1)
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{
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for(rocblas_int i = 0; i < NN1; ++i)
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{
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A2[NN1 + i * NN2] = 0;
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A2[i + NN1 * NN2] = 0;
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}
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A2[NN1 + NN1 * NN2] = 0;
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}
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|
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// b. find the corresponding tridiagonal matrices containing the setup eigenvalues of each sub-block
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// first find random orthogonal matrices Q1 and Q2
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Sh Q1(s1, 1, s1, 1);
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Sh Q2(s2, 1, s2, 1);
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rocblas_init<S>(Q1, true);
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rocblas_init<S>(Q2, true);
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std::vector<S> hW(sw);
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std::vector<S> ipiv1(NN1);
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std::vector<S> ipiv2(NN2);
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cpu_geqrf<S>(NN1, NN1, Q1.data(), NN1, ipiv1.data(), hW.data(), sw);
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cpu_geqrf<S>(NN2, NN2, Q2.data(), NN2, ipiv2.data(), hW.data(), sw);
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// now multiply the orthogonal matrices by the diagonals A1 and A2 to hide the eigenvalues
|
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cpu_ormqr_unmqr<S>(rocblas_side_left, rocblas_operation_transpose, NN1, NN1, NN1,
|
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Q1.data(), NN1, ipiv1.data(), A1.data(), NN1, hW.data(), sw);
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cpu_ormqr_unmqr<S>(rocblas_side_right, rocblas_operation_none, NN1, NN1, NN1, Q1.data(),
|
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NN1, ipiv1.data(), A1.data(), NN1, hW.data(), sw);
|
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cpu_ormqr_unmqr<S>(rocblas_side_left, rocblas_operation_transpose, NN2, NN2, NN2,
|
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Q2.data(), NN2, ipiv2.data(), A2.data(), NN2, hW.data(), sw);
|
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cpu_ormqr_unmqr<S>(rocblas_side_right, rocblas_operation_none, NN2, NN2, NN2, Q2.data(),
|
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NN2, ipiv2.data(), A2.data(), NN2, hW.data(), sw);
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// finally, perform tridiagonalization
|
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cpu_sytrd_hetrd<S>(rocblas_fill_upper, NN1, A1.data(), NN1, hD[0], hE[0], ipiv1.data(),
|
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hW.data(), sw);
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cpu_sytrd_hetrd<S>(rocblas_fill_upper, NN2, A2.data(), NN2, hD[0] + NN1, hE[0] + NN1,
|
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ipiv2.data(), hW.data(), sw);
|
|
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// c. integrate blocks into final matrix
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// integrate the 2 sub-blocks into the first independent block
|
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hE[0][NN1 - 1] = 1;
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hD[0][NN1 - 1] += 1;
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hD[0][NN1] += 1;
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// copy the independent block over
|
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for(rocblas_int i = 0; i < N1; ++i)
|
|
{
|
|
hD[0][N1 + i] = hD[0][i];
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hE[0][N1 + i] = hE[0][i];
|
|
}
|
|
hE[0][N1 - 1] = 0;
|
|
hE[0][2 * N1 - 1] = 0;
|
|
// integrate the 2 sub-blocks into the second independent block
|
|
// (using negative p to test secular eqn algorithm)
|
|
hE[0][N1 + NN1 - 1] = -1;
|
|
hD[0][N1 + NN1 - 1] -= 2;
|
|
hD[0][N1 + NN1] -= 2;
|
|
// if there is a third independent block, initialize it with 1
|
|
if(E == 1)
|
|
hD[0][n - 1] = 1;
|
|
}
|
|
|
|
// initialize C to the identity matrix
|
|
if(evect == rocblas_evect_original)
|
|
{
|
|
for(rocblas_int j = 0; j < n; j++)
|
|
{
|
|
for(rocblas_int i = 0; i < n; i++)
|
|
{
|
|
if(i == j)
|
|
hC[0][i + j * ldc] = 1;
|
|
else
|
|
hC[0][i + j * ldc] = 0;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
if(GPU)
|
|
{
|
|
// now copy to the GPU
|
|
CHECK_HIP_ERROR(dD.transfer_from(hD));
|
|
CHECK_HIP_ERROR(dE.transfer_from(hE));
|
|
|
|
if(evect == rocblas_evect_original)
|
|
CHECK_HIP_ERROR(dC.transfer_from(hC));
|
|
}
|
|
}
|
|
|
|
template <bool CPU, bool GPU, typename T, typename Sd, typename Td, typename Ud, typename Sh, typename Th, typename Uh>
|
|
void stedc_initData(const rocblas_handle handle,
|
|
const rocblas_evect evect,
|
|
const rocblas_int n,
|
|
Sd& dD,
|
|
Sd& dE,
|
|
Td& dC,
|
|
const rocblas_int ldc,
|
|
Ud& dInfo,
|
|
Sh& hD,
|
|
Sh& hE,
|
|
Th& hC,
|
|
Uh& hInfo)
|
|
{
|
|
if((std::getenv("TEST_WILKINSON") != nullptr) || (std::getenv("STEDC_TEST_WILKINSON") != nullptr))
|
|
{
|
|
stedc_wilkinson_initData<CPU, GPU, T>(handle, evect, n, dD, dE, dC, ldc, dInfo, hD, hE, hC,
|
|
hInfo);
|
|
}
|
|
else if((std::getenv("TEST_CLEMENT") != nullptr)
|
|
|| (std::getenv("STEDC_TEST_CLEMENT") != nullptr))
|
|
{
|
|
stedc_clement_initData<CPU, GPU, T>(handle, evect, n, dD, dE, dC, ldc, dInfo, hD, hE, hC,
|
|
hInfo);
|
|
}
|
|
else if((std::getenv("TEST_TOEPLITZ") != nullptr)
|
|
|| (std::getenv("STEDC_TEST_TOEPLITZ") != nullptr))
|
|
{
|
|
stedc_toeplitz_initData<CPU, GPU, T>(handle, evect, n, dD, dE, dC, ldc, dInfo, hD, hE, hC,
|
|
hInfo);
|
|
}
|
|
else if((std::getenv("TEST_IDENTITY") != nullptr)
|
|
|| (std::getenv("STEDC_TEST_IDENTITY") != nullptr))
|
|
{
|
|
stedc_identity_initData<CPU, GPU, T>(handle, evect, n, dD, dE, dC, ldc, dInfo, hD, hE, hC,
|
|
hInfo);
|
|
}
|
|
else if((std::getenv("TEST_RANDOM") != nullptr) || (std::getenv("STEDC_TEST_RANDOM") != nullptr))
|
|
{
|
|
stedc_random_initData<CPU, GPU, T>(handle, evect, n, dD, dE, dC, ldc, dInfo, hD, hE, hC,
|
|
hInfo);
|
|
}
|
|
else
|
|
{
|
|
stedc_default_initData<CPU, GPU, T>(handle, evect, n, dD, dE, dC, ldc, dInfo, hD, hE, hC,
|
|
hInfo);
|
|
}
|
|
|
|
return;
|
|
}
|
|
|
|
template <typename T, typename Sd, typename Td, typename Ud, typename Sh, typename Th, typename Uh>
|
|
void stedc_getError(const rocblas_handle handle,
|
|
const rocblas_evect evect,
|
|
const rocblas_int n,
|
|
Sd& dD,
|
|
Sd& dE,
|
|
Td& dC,
|
|
const rocblas_int ldc,
|
|
Ud& dInfo,
|
|
Sh& hD,
|
|
Sh& hDRes,
|
|
Sh& hE,
|
|
Sh& hERes,
|
|
Th& hC,
|
|
Th& hCRes,
|
|
Uh& hInfo,
|
|
Uh& hInfoRes,
|
|
double* max_err,
|
|
double* max_errv)
|
|
{
|
|
constexpr bool COMPLEX = rocblas_is_complex<T>;
|
|
using S = decltype(std::real(T{}));
|
|
|
|
using HMatT = HostMatrix<T, rocblas_int>;
|
|
using HMatS = HostMatrix<S, rocblas_int>;
|
|
using BDescT = typename HMatT::BlockDescriptor;
|
|
using BDescS = typename HMatS::BlockDescriptor;
|
|
|
|
int lgn = floor(log(n - 1) / log(2)) + 1;
|
|
size_t lwork = (COMPLEX) ? n * n : 0;
|
|
size_t lrwork = (evect == rocblas_evect_none || n <= 1) ? 1 : 1 + 3 * n + 4 * n * n + 2 * n * lgn;
|
|
size_t liwork = (evect == rocblas_evect_none || n <= 1) ? 1 : 6 + 6 * n + 5 * n * lgn;
|
|
std::vector<T> work(lwork);
|
|
std::vector<S> rwork(lrwork);
|
|
std::vector<rocblas_int> iwork(liwork);
|
|
|
|
// input data initialization
|
|
stedc_initData<true, true, T>(handle, evect, n, dD, dE, dC, ldc, dInfo, hD, hE, hC, hInfo);
|
|
|
|
// execute computations
|
|
// GPU lapack
|
|
CHECK_ROCBLAS_ERROR(
|
|
rocsolver_stedc(handle, evect, n, dD.data(), dE.data(), dC.data(), ldc, dInfo.data()));
|
|
CHECK_HIP_ERROR(hDRes.transfer_from(dD));
|
|
CHECK_HIP_ERROR(hERes.transfer_from(dE));
|
|
CHECK_HIP_ERROR(hInfoRes.transfer_from(dInfo));
|
|
if(evect != rocblas_evect_none)
|
|
CHECK_HIP_ERROR(hCRes.transfer_from(dC));
|
|
|
|
// if eigenvectors were required, prepare matrix A (upper triangular) for implicit tests
|
|
rocblas_int lda = n;
|
|
size_t size_A = lda * n;
|
|
host_strided_batch_vector<T> hA(size_A, 1, size_A, 1);
|
|
if(evect != rocblas_evect_none)
|
|
{
|
|
for(rocblas_int i = 0; i < n; i++)
|
|
{
|
|
for(rocblas_int j = i; j < n; j++)
|
|
{
|
|
if(i == j)
|
|
hA[0][i + j * lda] = hD[0][i];
|
|
else if(i + 1 == j)
|
|
hA[0][i + j * lda] = hE[0][i];
|
|
else
|
|
hA[0][i + j * lda] = 0;
|
|
}
|
|
}
|
|
}
|
|
|
|
// CPU lapack
|
|
cpu_stedc(evect, n, hD[0], hE[0], hC[0], ldc, work.data(), lwork, rwork.data(), lrwork,
|
|
iwork.data(), liwork, hInfo[0]);
|
|
|
|
// Depending on `evect`, stedc can return the eigenvectors of the original
|
|
// matrix (A) or the eigenvectors of the tridiagonal matrix (T). Thus,
|
|
// given a pair U, D of eigenvectors and eigenvalues computed by stedc the
|
|
// reconstructed matrix
|
|
//
|
|
// U * D * adjoint(U)
|
|
//
|
|
// can be either A or T. The following code uses lapack to compute
|
|
//
|
|
// AorT = U * D * adjoint(U),
|
|
//
|
|
// which will be later used to compare with the eigenvectors and
|
|
// eigenvalues computed by rocSOLVER.
|
|
//
|
|
auto AorT = HMatT::Empty();
|
|
if((evect != rocblas_evect_none) && (n > 0))
|
|
{
|
|
auto C = HMatT::Wrap(hC[0], ldc, n)->block(BDescT().nrows(n).ncols(n));
|
|
auto d = HMatT::Convert(hD[0], 1, n)->block(BDescT().nrows(1).ncols(n));
|
|
auto D = HMatT::Zeros(n, n).diag(d);
|
|
AorT = C * D * adjoint(C);
|
|
}
|
|
|
|
// check info
|
|
EXPECT_EQ(hInfo[0][0], hInfoRes[0][0]);
|
|
if(hInfo[0][0] != hInfoRes[0][0])
|
|
*max_err = 1;
|
|
else
|
|
*max_err = 0;
|
|
|
|
double err;
|
|
*max_errv = 0;
|
|
|
|
if((hInfo[0][0] == 0) && (n > 0))
|
|
{
|
|
// check that eigenvalues are correct and in order
|
|
// error is ||hD - hDRes|| / ||hD||
|
|
// using frobenius norm
|
|
err = norm_error('F', 1, n, 1, hD[0], hDRes[0]);
|
|
*max_err = err > *max_err ? err : *max_err;
|
|
|
|
// check eigenvectors if required
|
|
if(evect != rocblas_evect_none)
|
|
{
|
|
// Input matrix
|
|
auto C = HMatT::Wrap(hCRes[0], ldc, n)->block(BDescT().nrows(n).ncols(n));
|
|
// Computed eigenvalues
|
|
auto d = HMatT::Convert(hDRes[0], 1, n)->block(BDescT().nrows(1).ncols(n));
|
|
// Diagonal matrix of size n by n with computed eigenvalues
|
|
auto D = HMatT::Zeros(n, n).diag(d);
|
|
|
|
// Orthogonal error
|
|
auto OE = C * adjoint(C) - HMatT::Eye(n, n);
|
|
err = OE.max_col_norm();
|
|
*max_errv = err > *max_err ? err : *max_err;
|
|
|
|
// Residual error
|
|
auto RE = AorT - C * D * adjoint(C);
|
|
err = RE.norm() / AorT.norm();
|
|
*max_err = err > *max_err ? err : *max_err;
|
|
}
|
|
}
|
|
}
|
|
|
|
template <typename T, typename Sd, typename Td, typename Ud, typename Sh, typename Th, typename Uh>
|
|
void stedc_getPerfData(const rocblas_handle handle,
|
|
const rocblas_evect evect,
|
|
const rocblas_int n,
|
|
Sd& dD,
|
|
Sd& dE,
|
|
Td& dC,
|
|
const rocblas_int ldc,
|
|
Ud& dInfo,
|
|
Sh& hD,
|
|
Sh& hE,
|
|
Th& hC,
|
|
Uh& hInfo,
|
|
double* gpu_time_used,
|
|
double* cpu_time_used,
|
|
const rocblas_int hot_calls,
|
|
const int profile,
|
|
const bool profile_kernels,
|
|
const bool perf)
|
|
{
|
|
constexpr bool COMPLEX = rocblas_is_complex<T>;
|
|
using S = decltype(std::real(T{}));
|
|
|
|
int lgn = floor(log(n - 1) / log(2)) + 1;
|
|
size_t lwork = (COMPLEX) ? n * n : 0;
|
|
size_t lrwork = (evect == rocblas_evect_none || n <= 1) ? 1 : 1 + 3 * n + 4 * n * n + 2 * n * lgn;
|
|
size_t liwork = (evect == rocblas_evect_none || n <= 1) ? 1 : 6 + 6 * n + 5 * n * lgn;
|
|
std::vector<T> work(lwork);
|
|
std::vector<S> rwork(lrwork);
|
|
std::vector<rocblas_int> iwork(liwork);
|
|
|
|
if(!perf)
|
|
{
|
|
stedc_initData<true, false, T>(handle, evect, n, dD, dE, dC, ldc, dInfo, hD, hE, hC, hInfo);
|
|
|
|
// cpu-lapack performance (only if not in perf mode)
|
|
*cpu_time_used = get_time_us_no_sync();
|
|
cpu_stedc(evect, n, hD[0], hE[0], hC[0], ldc, work.data(), lwork, rwork.data(), lrwork,
|
|
iwork.data(), liwork, hInfo[0]);
|
|
*cpu_time_used = get_time_us_no_sync() - *cpu_time_used;
|
|
}
|
|
|
|
stedc_initData<true, false, T>(handle, evect, n, dD, dE, dC, ldc, dInfo, hD, hE, hC, hInfo);
|
|
|
|
// cold calls
|
|
for(int iter = 0; iter < 2; iter++)
|
|
{
|
|
stedc_initData<false, true, T>(handle, evect, n, dD, dE, dC, ldc, dInfo, hD, hE, hC, hInfo);
|
|
|
|
CHECK_ROCBLAS_ERROR(
|
|
rocsolver_stedc(handle, evect, n, dD.data(), dE.data(), dC.data(), ldc, dInfo.data()));
|
|
}
|
|
|
|
// 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++)
|
|
{
|
|
stedc_initData<false, true, T>(handle, evect, n, dD, dE, dC, ldc, dInfo, hD, hE, hC, hInfo);
|
|
|
|
timer.start(stream);
|
|
rocsolver_stedc(handle, evect, n, dD.data(), dE.data(), dC.data(), ldc, dInfo.data());
|
|
timer.end(stream);
|
|
}
|
|
*gpu_time_used = timer.get_combined();
|
|
}
|
|
|
|
template <typename T>
|
|
void testing_stedc(Arguments& argus)
|
|
{
|
|
using S = decltype(std::real(T{}));
|
|
|
|
// get arguments
|
|
rocblas_local_handle handle;
|
|
char evectC = argus.get<char>("evect");
|
|
rocblas_int n = argus.get<rocblas_int>("n");
|
|
rocblas_int ldc = argus.get<rocblas_int>("ldc", n);
|
|
|
|
rocblas_evect evect = char2rocblas_evect(evectC);
|
|
rocblas_int hot_calls = argus.iters;
|
|
|
|
// check non-supported values
|
|
// N/A
|
|
|
|
// determine sizes
|
|
size_t size_D = n;
|
|
size_t size_E = n;
|
|
size_t size_C = ldc * n;
|
|
double max_err = 0, max_errv = 0, gpu_time_used = 0, cpu_time_used = 0;
|
|
|
|
size_t size_DRes = (argus.unit_check || argus.norm_check) ? size_D : 0;
|
|
size_t size_ERes = (argus.unit_check || argus.norm_check) ? size_E : 0;
|
|
size_t size_CRes = (argus.unit_check || argus.norm_check) ? size_C : 0;
|
|
|
|
// check invalid sizes
|
|
bool invalid_size = (n < 0 || (evect != rocblas_evect_none && ldc < n));
|
|
if(invalid_size)
|
|
{
|
|
EXPECT_ROCBLAS_STATUS(rocsolver_stedc(handle, evect, n, (S*)nullptr, (S*)nullptr,
|
|
(T*)nullptr, ldc, (rocblas_int*)nullptr),
|
|
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));
|
|
CHECK_ALLOC_QUERY(rocsolver_stedc(handle, evect, n, (S*)nullptr, (S*)nullptr, (T*)nullptr,
|
|
ldc, (rocblas_int*)nullptr));
|
|
|
|
size_t size;
|
|
CHECK_ROCBLAS_ERROR(rocblas_stop_device_memory_size_query(handle, &size));
|
|
|
|
rocsolver_bench_inform(inform_mem_query, size);
|
|
return;
|
|
}
|
|
|
|
// memory allocations
|
|
host_strided_batch_vector<S> hD(size_D, 1, size_D, 1);
|
|
host_strided_batch_vector<S> hDRes(size_DRes, 1, size_DRes, 1);
|
|
host_strided_batch_vector<S> hE(size_E, 1, size_E, 1);
|
|
host_strided_batch_vector<S> hERes(size_ERes, 1, size_ERes, 1);
|
|
host_strided_batch_vector<T> hC(size_C, 1, size_C, 1);
|
|
host_strided_batch_vector<T> hCRes(size_CRes, 1, size_CRes, 1);
|
|
host_strided_batch_vector<rocblas_int> hInfo(1, 1, 1, 1);
|
|
host_strided_batch_vector<rocblas_int> hInfoRes(1, 1, 1, 1);
|
|
device_strided_batch_vector<S> dD(size_D, 1, size_D, 1);
|
|
device_strided_batch_vector<S> dE(size_E, 1, size_E, 1);
|
|
device_strided_batch_vector<T> dC(size_C, 1, size_C, 1);
|
|
device_strided_batch_vector<rocblas_int> dInfo(1, 1, 1, 1);
|
|
if(size_D)
|
|
CHECK_HIP_ERROR(dD.memcheck());
|
|
if(size_E)
|
|
CHECK_HIP_ERROR(dE.memcheck());
|
|
if(size_C)
|
|
CHECK_HIP_ERROR(dC.memcheck());
|
|
CHECK_HIP_ERROR(dInfo.memcheck());
|
|
|
|
// check quick return
|
|
if(n == 0)
|
|
{
|
|
EXPECT_ROCBLAS_STATUS(
|
|
rocsolver_stedc(handle, evect, n, dD.data(), dE.data(), dC.data(), ldc, dInfo.data()),
|
|
rocblas_status_success);
|
|
if(argus.timing)
|
|
rocsolver_bench_inform(inform_quick_return);
|
|
|
|
return;
|
|
}
|
|
|
|
// check computations
|
|
if(argus.unit_check || argus.norm_check)
|
|
stedc_getError<T>(handle, evect, n, dD, dE, dC, ldc, dInfo, hD, hDRes, hE, hERes, hC, hCRes,
|
|
hInfo, hInfoRes, &max_err, &max_errv);
|
|
|
|
// collect performance data
|
|
if(argus.timing && hot_calls > 0)
|
|
stedc_getPerfData<T>(handle, evect, n, dD, dE, dC, ldc, dInfo, hD, hE, hC, hInfo,
|
|
&gpu_time_used, &cpu_time_used, hot_calls, argus.profile,
|
|
argus.profile_kernels, argus.perf);
|
|
|
|
// validate results for rocsolver-test
|
|
// using n * machine_precision as tolerance
|
|
if(argus.unit_check)
|
|
{
|
|
ROCSOLVER_TEST_CHECK(T, max_err, n);
|
|
if(evect != rocblas_evect_none)
|
|
ROCSOLVER_TEST_CHECK(T, max_errv, n);
|
|
}
|
|
|
|
// output results for rocsolver-bench
|
|
if(argus.timing)
|
|
{
|
|
if(!argus.perf)
|
|
{
|
|
rocsolver_bench_header("Arguments:");
|
|
rocsolver_bench_output("evect", "n", "ldc");
|
|
rocsolver_bench_output(evectC, n, ldc);
|
|
|
|
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, std::max(max_err, max_errv));
|
|
}
|
|
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, std::max(max_err, max_errv));
|
|
else
|
|
rocsolver_bench_output(gpu_time_used);
|
|
}
|
|
}
|
|
|
|
// ensure all arguments were consumed
|
|
argus.validate_consumed();
|
|
}
|
|
|
|
#define EXTERN_TESTING_STEDC(...) extern template void testing_stedc<__VA_ARGS__>(Arguments&);
|
|
|
|
INSTANTIATE(EXTERN_TESTING_STEDC, FOREACH_SCALAR_TYPE, APPLY_STAMP)
|