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517 lines
20 KiB
C++
517 lines
20 KiB
C++
/* **************************************************************************
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* Copyright (C) 2023-2026 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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/*
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* ===========================================================================
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* testing_getrf_large tests the correctness of getrf, getrs, and gemm. We
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* use an implicit test that solves Ax = b for x (using getrf and getrs),
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* then computes Ax (using gemm) and compare with b. For the sizes tested,
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* this is much faster than calling getrf on the CPU.
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* ===========================================================================
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*/
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template <bool CPU,
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bool GPU,
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typename T,
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typename Td,
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typename Ud,
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typename Th,
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typename Uh,
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std::enable_if_t<!rocblas_is_complex<T>, int> = 0>
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void getrf_large_initData(const rocblas_handle handle,
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const rocblas_int n,
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const rocblas_int nrhs,
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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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Td& dX,
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Ud& dIpiv,
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const rocblas_stride stP,
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Ud& dInfo,
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const rocblas_int bc,
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Th& hA,
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Th& hB,
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Uh& hIpiv,
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Uh& hInfo,
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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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T tmp;
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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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// scale A to avoid singularities
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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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hA[b][i + j * lda] += 400;
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else
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hA[b][i + j * lda] -= 4;
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}
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}
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// shuffle rows to test pivoting
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// always the same permuation for debugging purposes
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for(rocblas_int i = 0; i < n / 2; 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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tmp = hA[b][i + j * lda];
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hA[b][i + j * lda] = hA[b][n - 1 - i + j * lda];
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hA[b][n - 1 - i + j * lda] = tmp;
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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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// When required, add some singularities
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// (always the same elements for debugging purposes).
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// The algorithm must detect the first zero pivot in those
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// matrices in the batch that are singular
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rocblas_int j = n / 4 + b;
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j -= (j / n) * n;
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for(rocblas_int i = 0; i < n; i++)
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hA[b][i + j * lda] = 0;
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j = n / 2 + b;
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j -= (j / n) * n;
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for(rocblas_int i = 0; i < n; i++)
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hA[b][i + j * lda] = 0;
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j = n - 1 + b;
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j -= (j / n) * n;
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for(rocblas_int i = 0; i < n; i++)
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hA[b][i + j * lda] = 0;
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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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CHECK_HIP_ERROR(dX.transfer_from(hB));
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}
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}
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template <bool CPU,
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bool GPU,
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typename T,
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typename Td,
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typename Ud,
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typename Th,
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typename Uh,
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std::enable_if_t<rocblas_is_complex<T>, int> = 0>
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void getrf_large_initData(const rocblas_handle handle,
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const rocblas_int n,
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const rocblas_int nrhs,
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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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Td& dX,
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Ud& dIpiv,
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const rocblas_stride stP,
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Ud& dInfo,
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const rocblas_int bc,
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Th& hA,
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Th& hB,
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Uh& hIpiv,
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Uh& hInfo,
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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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T tmp;
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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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// scale A to avoid singularities
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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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hA[b][i + j * lda] += T(400, 400);
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else
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hA[b][i + j * lda] -= T(4, 4);
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}
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}
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// shuffle rows to test pivoting
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// always the same permuation for debugging purposes
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for(rocblas_int i = 0; i < n / 2; 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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tmp = hA[b][i + j * lda];
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hA[b][i + j * lda] = hA[b][n - 1 - i + j * lda];
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hA[b][n - 1 - i + j * lda] = tmp;
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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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// When required, add some singularities
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// (always the same elements for debugging purposes).
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// The algorithm must detect the first zero pivot in those
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// matrices in the batch that are singular
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rocblas_int j = n / 4 + b;
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j -= (j / n) * n;
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for(rocblas_int i = 0; i < n; i++)
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hA[b][i + j * lda] = 0;
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j = n / 2 + b;
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j -= (j / n) * n;
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for(rocblas_int i = 0; i < n; i++)
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hA[b][i + j * lda] = 0;
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j = n - 1 + b;
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j -= (j / n) * n;
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for(rocblas_int i = 0; i < n; i++)
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hA[b][i + j * lda] = 0;
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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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CHECK_HIP_ERROR(dX.transfer_from(hB));
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}
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}
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template <bool STRIDED, bool GETRF, typename T, typename Td, typename Ud, typename Th, typename Uh>
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void getrf_large_getError(const rocblas_handle handle,
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const rocblas_int n,
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const rocblas_int nrhs,
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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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Td& dX,
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Ud& dIpiv,
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const rocblas_stride stP,
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Ud& dInfo,
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const rocblas_int bc,
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Th& hA,
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Th& hB,
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Th& hBRes,
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Uh& hIpiv,
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Uh& hInfo,
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Uh& hInfoRes,
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double* max_err,
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const bool singular)
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{
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// Input data initialization for Matrix A
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getrf_large_initData<true, true, T>(handle, n, nrhs, dA, lda, stA, dB, ldb, stB, dX, dIpiv, stP,
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dInfo, bc, hA, hB, hIpiv, hInfo, singular);
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// execute computations
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// GPU lapack
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CHECK_ROCBLAS_ALLOCATION(rocsolver_getf2_getrf(STRIDED, GETRF, handle, n, n, dA.data(), lda,
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stA, dIpiv.data(), stP, dInfo.data(), bc));
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// Solve Ax = b for x
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CHECK_ROCBLAS_ALLOCATION(rocsolver_getrs(STRIDED, handle, rocblas_operation_none, n, nrhs, dA,
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lda, stA, dIpiv, stP, dX, ldb, stB, bc));
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// Resetting the value of dA.
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CHECK_HIP_ERROR(dA.transfer_from(hA));
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// Compute Ax
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T alpha = T(1), beta = T(0);
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CHECK_ROCBLAS_ALLOCATION(rocsolver_gemm(STRIDED, handle, rocblas_operation_none,
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rocblas_operation_none, n, nrhs, n, &alpha, dA, lda,
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stA, dX, ldb, stB, &beta, dB, ldb, stB, bc));
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CHECK_HIP_ERROR(hBRes.transfer_from(dB));
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double err;
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*max_err = 0;
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for(rocblas_int b = 0; b < bc; ++b)
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{ // Pass the matrices here
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err = norm_error('F', n, nrhs, ldb, hB[b], hBRes[b]);
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*max_err = err > *max_err ? err : *max_err;
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}
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}
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// Function for perfromance data.
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template <bool STRIDED, bool GETRF, typename T, typename Td, typename Ud, typename Th, typename Uh>
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void getrf_large_getPerfData(const rocblas_handle handle,
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const rocblas_int m,
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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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Ud& dIpiv,
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const rocblas_stride stP,
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Ud& dInfo,
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const rocblas_int bc,
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Th& hA,
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Uh& hIpiv,
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Uh& hInfo,
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double* gpu_time_used,
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double* cpu_time_used,
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const rocblas_int hot_calls,
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const int profile,
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const bool profile_kernels,
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const bool perf,
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const bool singular)
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{
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*cpu_time_used = nan(""); // no timing on cpu-lapack execution
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*gpu_time_used = nan(""); // no timing on gpu-lapack execution
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}
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template <bool BATCHED, bool STRIDED, bool GETRF, typename T>
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void testing_getrf_large(Arguments& argus)
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{
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// get arguments
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rocblas_local_handle handle;
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rocblas_int n = argus.get<rocblas_int>("n");
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rocblas_int nrhs = argus.get<rocblas_int>("nrhs", n);
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rocblas_int lda = argus.get<rocblas_int>("lda", n);
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rocblas_int ldb = argus.get<rocblas_int>("ldb", n);
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rocblas_stride stA = argus.get<rocblas_stride>("strideA", lda * n);
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rocblas_stride stB = argus.get<rocblas_stride>("strideB", ldb * nrhs);
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rocblas_stride stP = argus.get<rocblas_stride>("strideP", n);
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rocblas_int bc = argus.batch_count;
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rocblas_int hot_calls = argus.iters;
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rocblas_stride stARes = (argus.unit_check || argus.norm_check) ? stA : 0;
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rocblas_stride stBRes = (argus.unit_check || argus.norm_check) ? stB : 0;
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rocblas_stride stPRes = (argus.unit_check || argus.norm_check) ? stP : 0;
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// determine sizes using the leading Dimensions, which are typically greater than the rows
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size_t size_A = size_t(lda) * n;
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size_t size_B = size_t(ldb) * nrhs;
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size_t size_P = size_t(n);
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double max_error = 0, gpu_time_used = 0, cpu_time_used = 0;
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size_t size_ARes = (argus.unit_check || argus.norm_check) ? size_A : 0;
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size_t size_BRes = (argus.unit_check || argus.norm_check) ? size_B : 0;
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size_t size_PRes = (argus.unit_check || argus.norm_check) ? size_P : 0;
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// check invalid sizes
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bool invalid_size = (n < 0 || nrhs < 0 || lda < n || ldb < n || bc < 0);
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if(invalid_size)
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{
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// if(BATCHED)
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// EXPECT_ROCBLAS_STATUS(
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// rocsolver_getf2_getrf(STRIDED, GETRF, handle, n, n, (T* const*)nullptr, lda, stA,
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// (rocblas_int*)nullptr, stP, (rocblas_int*)nullptr, bc),
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// rocblas_status_invalid_size);
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// else
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// EXPECT_ROCBLAS_STATUS(rocsolver_getf2_getrf(STRIDED, GETRF, handle, n, n, (T*)nullptr,
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// lda, stA, (rocblas_int*)nullptr, stP,
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// (rocblas_int*)nullptr, bc),
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// rocblas_status_invalid_size);
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if(argus.timing)
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rocsolver_bench_inform(inform_invalid_size);
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return;
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}
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// memory size query is necessary
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if(argus.mem_query)
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{
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CHECK_ROCBLAS_ERROR(rocblas_start_device_memory_size_query(handle));
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if(BATCHED)
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CHECK_ALLOC_QUERY(rocsolver_getf2_getrf(STRIDED, GETRF, handle, n, n, (T* const*)nullptr,
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lda, stA, (rocblas_int*)nullptr, stP,
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(rocblas_int*)nullptr, bc));
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else
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CHECK_ALLOC_QUERY(rocsolver_getf2_getrf(STRIDED, GETRF, handle, n, n, (T*)nullptr, lda,
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stA, (rocblas_int*)nullptr, stP,
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(rocblas_int*)nullptr, bc));
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size_t size;
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CHECK_ROCBLAS_ERROR(rocblas_stop_device_memory_size_query(handle, &size));
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rocsolver_bench_inform(inform_mem_query, size);
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return;
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}
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if(BATCHED)
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{
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// memory allocations
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host_batch_vector<T> hA(size_A, 1, bc);
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host_batch_vector<T> hB(size_B, 1, bc);
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host_batch_vector<T> hBRes(size_BRes, 1, bc);
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host_strided_batch_vector<rocblas_int> hIpiv(size_P, 1, stP, bc);
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host_strided_batch_vector<rocblas_int> hInfo(1, 1, 1, bc);
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host_strided_batch_vector<rocblas_int> hInfoRes(1, 1, 1, bc);
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device_batch_vector<T> dA(size_A, 1, bc);
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device_batch_vector<T> dB(size_B, 1, bc);
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device_batch_vector<T> dX(size_B, 1, bc);
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device_strided_batch_vector<rocblas_int> dIpiv(size_P, 1, stP, bc);
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device_strided_batch_vector<rocblas_int> dInfo(1, 1, 1, bc);
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if(size_A)
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CHECK_DEVICE_ALLOCATION(dA.memcheck());
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if(size_B)
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{
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CHECK_DEVICE_ALLOCATION(dB.memcheck());
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CHECK_DEVICE_ALLOCATION(dX.memcheck());
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}
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if(size_P)
|
|
CHECK_DEVICE_ALLOCATION(dIpiv.memcheck());
|
|
CHECK_DEVICE_ALLOCATION(dInfo.memcheck());
|
|
|
|
// check computations
|
|
if(argus.unit_check || argus.norm_check)
|
|
// Modify the parameters passed.
|
|
getrf_large_getError<STRIDED, GETRF, T>(handle, n, nrhs, dA, lda, stA, dB, ldb, stB, dX,
|
|
dIpiv, stP, dInfo, bc, hA, hB, hBRes, hIpiv,
|
|
hInfo, hInfoRes, &max_error, argus.singular);
|
|
|
|
// collect performance data
|
|
if(argus.timing && hot_calls > 0)
|
|
getrf_large_getPerfData<STRIDED, GETRF, T>(
|
|
handle, n, n, dA, lda, stA, dIpiv, stP, dInfo, bc, hA, hIpiv, 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> hB(size_B, 1, stB, bc);
|
|
host_strided_batch_vector<T> hBRes(size_BRes, 1, stBRes, bc);
|
|
host_strided_batch_vector<rocblas_int> hIpiv(size_P, 1, stP, bc);
|
|
host_strided_batch_vector<rocblas_int> hInfo(1, 1, 1, bc);
|
|
host_strided_batch_vector<rocblas_int> hInfoRes(1, 1, 1, bc);
|
|
device_strided_batch_vector<T> dA(size_A, 1, stA, bc);
|
|
device_strided_batch_vector<T> dB(size_B, 1, stB, bc);
|
|
device_strided_batch_vector<T> dX(size_B, 1, stB, bc);
|
|
device_strided_batch_vector<rocblas_int> dIpiv(size_P, 1, stP, bc);
|
|
device_strided_batch_vector<rocblas_int> dInfo(1, 1, 1, bc);
|
|
|
|
if(size_A)
|
|
CHECK_DEVICE_ALLOCATION(dA.memcheck());
|
|
if(size_B)
|
|
{
|
|
CHECK_DEVICE_ALLOCATION(dB.memcheck());
|
|
CHECK_DEVICE_ALLOCATION(dX.memcheck());
|
|
}
|
|
if(size_P)
|
|
CHECK_DEVICE_ALLOCATION(dIpiv.memcheck());
|
|
CHECK_DEVICE_ALLOCATION(dInfo.memcheck());
|
|
|
|
// check computations
|
|
if(argus.unit_check || argus.norm_check)
|
|
getrf_large_getError<STRIDED, GETRF, T>(handle, n, nrhs, dA, lda, stA, dB, ldb, stB, dX,
|
|
dIpiv, stP, dInfo, bc, hA, hB, hBRes, hIpiv,
|
|
hInfo, hInfoRes, &max_error, argus.singular);
|
|
|
|
// The perf function must return NAN
|
|
// collect performance data
|
|
if(argus.timing && hot_calls > 0)
|
|
getrf_large_getPerfData<STRIDED, GETRF, T>(
|
|
handle, n, n, dA, lda, stA, dIpiv, stP, dInfo, bc, hA, hIpiv, hInfo, &gpu_time_used,
|
|
&cpu_time_used, hot_calls, argus.profile, argus.profile_kernels, argus.perf,
|
|
argus.singular);
|
|
}
|
|
|
|
// validate results for rocsolver-test
|
|
// using min(m,n) * machine_precision as tolerance
|
|
if(argus.unit_check)
|
|
ROCSOLVER_TEST_CHECK(T, max_error, min(n, n));
|
|
// output results for rocsolver-bench
|
|
if(argus.timing)
|
|
{
|
|
if(!argus.perf)
|
|
{
|
|
rocsolver_bench_header("Arguments:");
|
|
if(BATCHED)
|
|
{
|
|
rocsolver_bench_output("n", "nrhs", "lda", "ldb", "strideP", "batch_c");
|
|
rocsolver_bench_output(n, nrhs, lda, ldb, stP, bc);
|
|
}
|
|
else if(STRIDED)
|
|
{
|
|
rocsolver_bench_output("n", "nrhs", "lda", "strideA", "ldb", "strideB", "strideP",
|
|
"batch_c");
|
|
rocsolver_bench_output(n, nrhs, lda, stA, ldb, stB, stP, bc);
|
|
}
|
|
else
|
|
{
|
|
rocsolver_bench_output("n", "nrhs", "lda", "ldb");
|
|
rocsolver_bench_output(n, nrhs, lda, ldb);
|
|
}
|
|
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();
|
|
}
|