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701 lines
28 KiB
C++
701 lines
28 KiB
C++
/* **************************************************************************
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* Copyright (C) 2021-2025 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 U>
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void geblttrf_npvt_checkBadArgs(const rocblas_handle handle,
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const rocblas_int nb,
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const rocblas_int nblocks,
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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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T dC,
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const rocblas_int ldc,
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const rocblas_stride stC,
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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_geblttrf_npvt(STRIDED, nullptr, nb, nblocks, dA, lda, stA, dB,
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ldb, stB, dC, ldc, stC, dInfo, bc),
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rocblas_status_invalid_handle);
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// values
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// N/A
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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_geblttrf_npvt(STRIDED, handle, nb, nblocks, dA, lda, stA,
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dB, ldb, stB, dC, ldc, stC, dInfo, -1),
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rocblas_status_invalid_size);
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// pointers
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EXPECT_ROCBLAS_STATUS(rocsolver_geblttrf_npvt(STRIDED, handle, nb, nblocks, (T) nullptr, lda,
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stA, dB, ldb, stB, dC, ldc, stC, dInfo, bc),
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rocblas_status_invalid_pointer);
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EXPECT_ROCBLAS_STATUS(rocsolver_geblttrf_npvt(STRIDED, handle, nb, nblocks, dA, lda, stA,
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(T) nullptr, ldb, stB, dC, ldc, stC, dInfo, bc),
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rocblas_status_invalid_pointer);
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EXPECT_ROCBLAS_STATUS(rocsolver_geblttrf_npvt(STRIDED, handle, nb, nblocks, dA, lda, stA, dB,
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ldb, stB, (T) nullptr, ldc, stC, dInfo, bc),
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rocblas_status_invalid_pointer);
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EXPECT_ROCBLAS_STATUS(rocsolver_geblttrf_npvt(STRIDED, handle, nb, nblocks, dA, lda, stA, dB,
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ldb, stB, dC, ldc, stC, (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_geblttrf_npvt(STRIDED, handle, 0, nblocks, (T) nullptr, lda,
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stA, (T) nullptr, ldb, stB, (T) nullptr, ldc, stC,
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dInfo, bc),
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rocblas_status_success);
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EXPECT_ROCBLAS_STATUS(rocsolver_geblttrf_npvt(STRIDED, handle, nb, 0, (T) nullptr, lda, stA,
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(T) nullptr, ldb, stB, (T) nullptr, ldc, stC,
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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_geblttrf_npvt(STRIDED, handle, nb, nblocks, dA, lda, stA,
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dB, ldb, stB, dC, ldc, stC, (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_geblttrf_npvt_bad_arg()
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{
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// safe arguments
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rocblas_local_handle handle;
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rocblas_int nb = 1;
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rocblas_int nblocks = 2;
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rocblas_int lda = 1;
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rocblas_int ldb = 1;
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rocblas_int ldc = 1;
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rocblas_stride stA = 2;
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rocblas_stride stB = 2;
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rocblas_stride stC = 2;
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rocblas_int bc = 1;
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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_batch_vector<T> dC(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(dC.memcheck());
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CHECK_HIP_ERROR(dInfo.memcheck());
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// check bad arguments
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geblttrf_npvt_checkBadArgs<STRIDED>(handle, nb, nblocks, dA.data(), lda, stA, dB.data(),
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ldb, stB, dC.data(), ldc, stC, 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<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(dA.memcheck());
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CHECK_HIP_ERROR(dB.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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geblttrf_npvt_checkBadArgs<STRIDED>(handle, nb, nblocks, dA.data(), lda, stA, dB.data(),
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ldb, stB, dC.data(), ldc, stC, 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 geblttrf_npvt_initData(const rocblas_handle handle,
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const rocblas_int nb,
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const rocblas_int nblocks,
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Td& dA,
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const rocblas_int lda,
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Td& dB,
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const rocblas_int ldb,
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Td& dC,
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const rocblas_int ldc,
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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& hC,
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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_init<T>(hA, true);
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rocblas_init<T>(hB, false);
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rocblas_init<T>(hC, false);
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rocblas_int n = nb * nblocks;
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for(rocblas_int b = 0; b < bc; ++b)
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{
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// scale to avoid singularities
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// leaving matrix as diagonal dominant so that pivoting is not required
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for(rocblas_int i = 0; i < nb; i++)
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{
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for(rocblas_int j = 0; j < nb; j++)
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{
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for(rocblas_int k = 0; k < nblocks; k++)
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{
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if(i == j)
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hB[b][i + j * ldb + k * ldb * nb] += 400;
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else
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hB[b][i + j * ldb + k * ldb * nb] -= 4;
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}
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for(rocblas_int k = 0; k < nblocks - 1; k++)
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{
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hA[b][i + j * lda + k * lda * nb] -= 4;
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hC[b][i + j * ldc + k * ldc * nb] -= 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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// When required, add some singularities
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// (always the same elements for debugging purposes)
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rocblas_int jj = n / 4 + b;
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jj -= (jj / n) * n;
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rocblas_int j = jj % nb;
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rocblas_int k = jj / nb;
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for(rocblas_int i = 0; i < nb; i++)
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{
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// zero the jj-th column
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hB[b][i + j * ldb + k * ldb * nb] = 0;
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if(k < nblocks - 1)
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hA[b][i + j * lda + k * lda * nb] = 0;
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if(k > 0)
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hC[b][i + j * ldc + (k - 1) * ldc * nb] = 0;
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}
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jj = n / 2 + b;
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jj -= (jj / n) * n;
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j = jj % nb;
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k = jj / nb;
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for(rocblas_int i = 0; i < nb; i++)
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{
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// zero the jj-th column
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hB[b][i + j * ldb + k * ldb * nb] = 0;
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if(k < nblocks - 1)
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hA[b][i + j * lda + k * lda * nb] = 0;
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if(k > 0)
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hC[b][i + j * ldc + (k - 1) * ldc * nb] = 0;
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}
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jj = n - 1 + b;
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jj -= (jj / n) * n;
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j = jj % nb;
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k = jj / nb;
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for(rocblas_int i = 0; i < nb; i++)
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{
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// zero the jj-th column
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hB[b][i + j * ldb + k * ldb * nb] = 0;
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if(k < nblocks - 1)
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hA[b][i + j * lda + k * lda * nb] = 0;
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if(k > 0)
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hC[b][i + j * ldc + (k - 1) * ldc * nb] = 0;
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}
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}
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}
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}
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// now copy data to the GPU
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if(GPU)
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{
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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(dC.transfer_from(hC));
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}
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}
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template <bool STRIDED, typename T, typename Td, typename Ud, typename Th, typename Uh>
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void geblttrf_npvt_getError(const rocblas_handle handle,
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const rocblas_int nb,
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const rocblas_int nblocks,
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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& dC,
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const rocblas_int ldc,
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const rocblas_stride stC,
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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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Th& hC,
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Th& hCRes,
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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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int n = nb * nblocks;
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std::vector<T> L(n * n);
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std::vector<T> U(n * n);
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std::vector<T> M(n * n);
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std::vector<T> MRes(n * n);
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// input data initialization
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geblttrf_npvt_initData<true, true, T>(handle, nb, nblocks, dA, lda, dB, ldb, dC, ldc, bc, hA,
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hB, hC, singular);
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// execute computations
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// GPU lapack
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CHECK_ROCBLAS_ERROR(rocsolver_geblttrf_npvt(STRIDED, handle, nb, nblocks, dA.data(), lda, stA,
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dB.data(), ldb, stB, dC.data(), ldc, stC,
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dInfo.data(), bc));
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CHECK_HIP_ERROR(hBRes.transfer_from(dB));
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CHECK_HIP_ERROR(hCRes.transfer_from(dC));
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CHECK_HIP_ERROR(hInfoRes.transfer_from(dInfo));
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// check info for singularities
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double err = 0;
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*max_err = 0;
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for(rocblas_int b = 0; b < bc; ++b)
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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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EXPECT_GT(hInfoRes[b][0], 0) << "where b = " << b;
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if(hInfoRes[b][0] <= 0)
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err++;
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}
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else
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{
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EXPECT_EQ(hInfoRes[b][0], 0) << "where b = " << b;
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if(hInfoRes[b][0] != 0)
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err++;
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}
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}
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*max_err += err;
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for(rocblas_int b = 0; b < bc; ++b)
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{
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if(hInfoRes[b][0] == 0)
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{
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// compute diagonal blocks and store in full matrix L
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for(rocblas_int k = 0; k < nblocks; k++)
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{
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for(rocblas_int i = 0; i < nb; i++)
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{
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for(rocblas_int j = 0; j < nb; j++)
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{
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if(i <= j)
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L[i + j * n + k * (n + 1) * nb] = hBRes[b][i + j * ldb + k * ldb * nb];
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else
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L[i + j * n + k * (n + 1) * nb] = 0;
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}
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}
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cpu_trmm(rocblas_side_left, rocblas_fill_lower, rocblas_operation_none,
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rocblas_diagonal_unit, nb, nb, T(1), hBRes[b] + k * ldb * nb, ldb,
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L.data() + k * (n + 1) * nb, n);
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}
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// move blocks A, updated C, and I into full matrices L and U
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for(rocblas_int k = 0; k < nblocks; k++)
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{
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for(rocblas_int i = 0; i < nb; i++)
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{
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if(k < nblocks - 1)
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{
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for(rocblas_int j = 0; j < nb; j++)
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{
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U[i + (j + nb) * n + k * (n + 1) * nb]
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= hCRes[b][i + j * ldc + k * ldc * nb];
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L[(i + nb) + j * n + k * (n + 1) * nb]
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= hA[b][i + j * lda + k * lda * nb];
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}
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}
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U[i + i * n + k * (n + 1) * nb] = 1;
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}
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}
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// reconstruct input matrix from factors and store it in MRes
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cpu_gemm(rocblas_operation_none, rocblas_operation_none, n, n, n, T(1), L.data(), n,
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U.data(), n, T(0), MRes.data(), n);
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// form original matrix from original blocks
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for(rocblas_int k = 0; k < nblocks; k++)
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{
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for(rocblas_int i = 0; i < nb; i++)
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{
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for(rocblas_int j = 0; j < nb; j++)
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{
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M[i + j * n + k * (n + 1) * nb] = hB[b][i + j * ldb + k * ldb * nb];
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if(k < nblocks - 1)
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{
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M[(i + nb) + j * n + k * (n + 1) * nb]
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= hA[b][i + j * lda + k * lda * nb];
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M[i + (j + nb) * n + k * (n + 1) * nb]
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= hC[b][i + j * ldc + k * ldc * nb];
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}
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}
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}
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}
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// error is ||M - MRes|| / ||M||
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// (THIS DOES NOT ACCOUNT FOR NUMERICAL REPRODUCIBILITY ISSUES.
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// IT MIGHT BE REVISITED IN THE FUTURE)
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// using frobenius norm
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err = norm_error('F', n, n, n, M.data(), MRes.data());
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*max_err = err > *max_err ? err : *max_err;
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}
|
|
}
|
|
}
|
|
|
|
template <bool STRIDED, typename T, typename Td, typename Ud, typename Th>
|
|
void geblttrf_npvt_getPerfData(const rocblas_handle handle,
|
|
const rocblas_int nb,
|
|
const rocblas_int nblocks,
|
|
Td& dA,
|
|
const rocblas_int lda,
|
|
const rocblas_stride stA,
|
|
Td& dB,
|
|
const rocblas_int ldb,
|
|
const rocblas_stride stB,
|
|
Td& dC,
|
|
const rocblas_int ldc,
|
|
const rocblas_stride stC,
|
|
Ud& dInfo,
|
|
const rocblas_int bc,
|
|
Th& hA,
|
|
Th& hB,
|
|
Th& hC,
|
|
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)
|
|
{
|
|
if(!perf)
|
|
{
|
|
// there is no direct CPU/LAPACK equivalent for this function, therefore
|
|
// we return an invalid CPU time
|
|
*cpu_time_used = nan("");
|
|
}
|
|
|
|
geblttrf_npvt_initData<true, false, T>(handle, nb, nblocks, dA, lda, dB, ldb, dC, ldc, bc, hA,
|
|
hB, hC, singular);
|
|
|
|
// cold calls
|
|
for(int iter = 0; iter < 2; iter++)
|
|
{
|
|
geblttrf_npvt_initData<false, true, T>(handle, nb, nblocks, dA, lda, dB, ldb, dC, ldc, bc,
|
|
hA, hB, hC, singular);
|
|
|
|
CHECK_ROCBLAS_ERROR(rocsolver_geblttrf_npvt(STRIDED, handle, nb, nblocks, dA.data(), lda,
|
|
stA, dB.data(), ldb, stB, dC.data(), ldc, stC,
|
|
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++)
|
|
{
|
|
geblttrf_npvt_initData<false, true, T>(handle, nb, nblocks, dA, lda, dB, ldb, dC, ldc, bc,
|
|
hA, hB, hC, singular);
|
|
|
|
timer.start(stream);
|
|
rocsolver_geblttrf_npvt(STRIDED, handle, nb, nblocks, dA.data(), lda, stA, dB.data(), ldb,
|
|
stB, dC.data(), ldc, stC, dInfo.data(), bc);
|
|
timer.end(stream);
|
|
}
|
|
*gpu_time_used = timer.get_combined();
|
|
}
|
|
|
|
template <bool BATCHED, bool STRIDED, typename T>
|
|
void testing_geblttrf_npvt(Arguments& argus)
|
|
{
|
|
// get arguments
|
|
rocblas_local_handle handle;
|
|
rocblas_int nb = argus.get<rocblas_int>("nb");
|
|
rocblas_int nblocks = argus.get<rocblas_int>("nblocks");
|
|
rocblas_int lda = argus.get<rocblas_int>("lda", nb);
|
|
rocblas_int ldb = argus.get<rocblas_int>("ldb", nb);
|
|
rocblas_int ldc = argus.get<rocblas_int>("ldc", nb);
|
|
rocblas_stride stA = argus.get<rocblas_stride>("strideA", lda * nb * nblocks);
|
|
rocblas_stride stB = argus.get<rocblas_stride>("strideB", ldb * nb * nblocks);
|
|
rocblas_stride stC = argus.get<rocblas_stride>("strideC", ldc * nb * nblocks);
|
|
|
|
rocblas_int bc = argus.batch_count;
|
|
rocblas_int hot_calls = argus.iters;
|
|
|
|
rocblas_stride stBRes = (argus.unit_check || argus.norm_check) ? stB : 0;
|
|
rocblas_stride stCRes = (argus.unit_check || argus.norm_check) ? stC : 0;
|
|
|
|
// check non-supported values
|
|
// N/A
|
|
|
|
// determine sizes
|
|
size_t size_A = size_t(lda) * nb * nblocks;
|
|
size_t size_B = size_t(ldb) * nb * nblocks;
|
|
size_t size_C = size_t(ldc) * nb * nblocks;
|
|
double max_error = 0, gpu_time_used = 0, cpu_time_used = 0;
|
|
|
|
size_t size_BRes = (argus.unit_check || argus.norm_check) ? size_B : 0;
|
|
size_t size_CRes = (argus.unit_check || argus.norm_check) ? size_C : 0;
|
|
|
|
// check invalid sizes
|
|
bool invalid_size = (nb < 0 || nblocks < 0 || lda < nb || ldb < nb || ldc < nb || bc < 0);
|
|
if(invalid_size)
|
|
{
|
|
if(BATCHED)
|
|
EXPECT_ROCBLAS_STATUS(
|
|
rocsolver_geblttrf_npvt(STRIDED, handle, nb, nblocks, (T* const*)nullptr, lda, stA,
|
|
(T* const*)nullptr, ldb, stB, (T* const*)nullptr, ldc, stC,
|
|
(rocblas_int*)nullptr, bc),
|
|
rocblas_status_invalid_size);
|
|
else
|
|
EXPECT_ROCBLAS_STATUS(rocsolver_geblttrf_npvt(STRIDED, handle, nb, nblocks, (T*)nullptr,
|
|
lda, stA, (T*)nullptr, ldb, stB, (T*)nullptr,
|
|
ldc, stC, (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_geblttrf_npvt(
|
|
STRIDED, handle, nb, nblocks, (T* const*)nullptr, lda, stA, (T* const*)nullptr, ldb,
|
|
stB, (T* const*)nullptr, ldc, stC, (rocblas_int*)nullptr, bc));
|
|
else
|
|
CHECK_ALLOC_QUERY(rocsolver_geblttrf_npvt(STRIDED, handle, nb, nblocks, (T*)nullptr,
|
|
lda, stA, (T*)nullptr, ldb, stB, (T*)nullptr,
|
|
ldc, stC, (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;
|
|
}
|
|
|
|
if(BATCHED)
|
|
{
|
|
// memory allocations
|
|
host_batch_vector<T> hA(size_A, 1, bc);
|
|
host_batch_vector<T> hB(size_B, 1, bc);
|
|
host_batch_vector<T> hC(size_C, 1, bc);
|
|
host_batch_vector<T> hBRes(size_BRes, 1, bc);
|
|
host_batch_vector<T> hCRes(size_CRes, 1, bc);
|
|
host_strided_batch_vector<rocblas_int> hInfo(1, 1, 1, bc);
|
|
host_strided_batch_vector<rocblas_int> hInfoRes(1, 1, 1, bc);
|
|
device_batch_vector<T> dA(size_A, 1, bc);
|
|
device_batch_vector<T> dB(size_B, 1, bc);
|
|
device_batch_vector<T> dC(size_C, 1, bc);
|
|
device_strided_batch_vector<rocblas_int> dInfo(1, 1, 1, bc);
|
|
if(size_A)
|
|
CHECK_HIP_ERROR(dA.memcheck());
|
|
if(size_B)
|
|
CHECK_HIP_ERROR(dB.memcheck());
|
|
if(size_C)
|
|
CHECK_HIP_ERROR(dC.memcheck());
|
|
CHECK_HIP_ERROR(dInfo.memcheck());
|
|
|
|
// check quick return
|
|
if(nb == 0 || nblocks == 0 || bc == 0)
|
|
{
|
|
EXPECT_ROCBLAS_STATUS(rocsolver_geblttrf_npvt(STRIDED, handle, nb, nblocks, dA.data(),
|
|
lda, stA, dB.data(), ldb, stB, dC.data(),
|
|
ldc, stC, 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)
|
|
geblttrf_npvt_getError<STRIDED, T>(handle, nb, nblocks, dA, lda, stA, dB, ldb, stB, dC,
|
|
ldc, stC, dInfo, bc, hA, hB, hBRes, hC, hCRes, hInfo,
|
|
hInfoRes, &max_error, argus.singular);
|
|
|
|
// collect performance data
|
|
if(argus.timing && hot_calls > 0)
|
|
geblttrf_npvt_getPerfData<STRIDED, T>(
|
|
handle, nb, nblocks, dA, lda, stA, dB, ldb, stB, dC, ldc, stC, dInfo, bc, hA, hB,
|
|
hC, &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> hC(size_C, 1, stC, bc);
|
|
host_strided_batch_vector<T> hBRes(size_BRes, 1, stBRes, bc);
|
|
host_strided_batch_vector<T> hCRes(size_CRes, 1, stCRes, 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> dC(size_C, 1, stC, bc);
|
|
device_strided_batch_vector<rocblas_int> dInfo(1, 1, 1, bc);
|
|
if(size_A)
|
|
CHECK_HIP_ERROR(dA.memcheck());
|
|
if(size_B)
|
|
CHECK_HIP_ERROR(dB.memcheck());
|
|
if(size_C)
|
|
CHECK_HIP_ERROR(dC.memcheck());
|
|
CHECK_HIP_ERROR(dInfo.memcheck());
|
|
|
|
// check quick return
|
|
if(nb == 0 || nblocks == 0 || bc == 0)
|
|
{
|
|
EXPECT_ROCBLAS_STATUS(rocsolver_geblttrf_npvt(STRIDED, handle, nb, nblocks, dA.data(),
|
|
lda, stA, dB.data(), ldb, stB, dC.data(),
|
|
ldc, stC, 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)
|
|
geblttrf_npvt_getError<STRIDED, T>(handle, nb, nblocks, dA, lda, stA, dB, ldb, stB, dC,
|
|
ldc, stC, dInfo, bc, hA, hB, hBRes, hC, hCRes, hInfo,
|
|
hInfoRes, &max_error, argus.singular);
|
|
|
|
// collect performance data
|
|
if(argus.timing && hot_calls > 0)
|
|
geblttrf_npvt_getPerfData<STRIDED, T>(
|
|
handle, nb, nblocks, dA, lda, stA, dB, ldb, stB, dC, ldc, stC, dInfo, bc, hA, hB,
|
|
hC, &gpu_time_used, &cpu_time_used, hot_calls, argus.profile, argus.profile_kernels,
|
|
argus.perf, argus.singular);
|
|
}
|
|
|
|
// validate results for rocsolver-test
|
|
// using nb * machine_precision as tolerance
|
|
if(argus.unit_check)
|
|
ROCSOLVER_TEST_CHECK(T, max_error, nb);
|
|
|
|
// output results for rocsolver-bench
|
|
if(argus.timing)
|
|
{
|
|
if(!argus.perf)
|
|
{
|
|
rocsolver_bench_header("Arguments:");
|
|
if(BATCHED)
|
|
{
|
|
rocsolver_bench_output("nb", "nblocks", "lda", "ldb", "ldc", "batch_c");
|
|
rocsolver_bench_output(nb, nblocks, lda, ldb, ldc, bc);
|
|
}
|
|
else if(STRIDED)
|
|
{
|
|
rocsolver_bench_output("nb", "nblocks", "lda", "strideA", "ldb", "strideB", "ldc",
|
|
"strideC", "batch_c");
|
|
rocsolver_bench_output(nb, nblocks, lda, stA, ldb, stB, ldc, stC, bc);
|
|
}
|
|
else
|
|
{
|
|
rocsolver_bench_output("nb", "nblocks", "lda", "ldb", "ldc");
|
|
rocsolver_bench_output(nb, nblocks, lda, ldb, 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, 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_GEBLTTRF_NPVT(...) \
|
|
extern template void testing_geblttrf_npvt<__VA_ARGS__>(Arguments&);
|
|
|
|
INSTANTIATE(EXTERN_TESTING_GEBLTTRF_NPVT, FOREACH_MATRIX_DATA_LAYOUT, FOREACH_SCALAR_TYPE, APPLY_STAMP)
|