derek.holloway
7502bf64a4
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780 lines
31 KiB
C++
780 lines
31 KiB
C++
/* ************************************************************************
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* Copyright (C) 2020-2024 Advanced Micro Devices, Inc. All rights reserved.
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*
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* Permission is hereby granted, free of charge, to any person obtaining a copy
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* of this software and associated documentation files (the "Software"), to deal
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* in the Software without restriction, including without limitation the rights
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* to use, copy, modify, merge, publish, distribute, sublicense, and/or sell cop-
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* ies of the Software, and to permit persons to whom the Software is furnished
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* to do so, subject to the following conditions:
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*
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* The above copyright notice and this permission notice shall be included in all
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* copies or substantial portions of the Software.
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*
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* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IM-
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* PLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS
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* FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR
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* COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER
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* IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNE-
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* CTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
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*
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*
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* ************************************************************************ */
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#include "clientcommon.hpp"
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template <testAPI_t API,
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typename I,
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typename SIZE,
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typename Td,
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typename Id,
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typename INTd,
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typename Th>
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void getrf_checkBadArgs(const hipsolverHandle_t handle,
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const hipsolverDnParams_t params,
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const I m,
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const I n,
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Td dA,
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const I lda,
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const I stA,
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Id dIpiv,
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const I stP,
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Td dWork,
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const SIZE dlwork,
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Th hWork,
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const SIZE hlwork,
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INTd dinfo,
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const int bc)
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{
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// handle
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EXPECT_ROCBLAS_STATUS(hipsolver_getrf(API,
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false,
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nullptr,
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params,
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m,
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n,
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dA,
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lda,
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stA,
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dIpiv,
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stP,
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dWork,
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dlwork,
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hWork,
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hlwork,
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dinfo,
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bc),
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HIPSOLVER_STATUS_NOT_INITIALIZED);
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// values
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// N/A
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#if defined(__HIP_PLATFORM_HCC__) || defined(__HIP_PLATFORM_AMD__)
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// pointers
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if constexpr(!std::is_same<I, int>::value)
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EXPECT_ROCBLAS_STATUS(hipsolver_getrf(API,
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false,
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handle,
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(hipsolverDnParams_t) nullptr,
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m,
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n,
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dA,
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lda,
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stA,
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dIpiv,
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stP,
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dWork,
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dlwork,
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hWork,
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hlwork,
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dinfo,
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bc),
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HIPSOLVER_STATUS_INVALID_VALUE);
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EXPECT_ROCBLAS_STATUS(hipsolver_getrf(API,
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false,
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handle,
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params,
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m,
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n,
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(Td) nullptr,
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lda,
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stA,
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dIpiv,
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stP,
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dWork,
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dlwork,
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hWork,
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hlwork,
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dinfo,
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bc),
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HIPSOLVER_STATUS_INVALID_VALUE);
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EXPECT_ROCBLAS_STATUS(hipsolver_getrf(API,
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false,
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handle,
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params,
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m,
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n,
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dA,
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lda,
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stA,
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dIpiv,
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stP,
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dWork,
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dlwork,
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hWork,
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hlwork,
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(INTd) nullptr,
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bc),
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HIPSOLVER_STATUS_INVALID_VALUE);
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#endif
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}
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template <testAPI_t API, bool BATCHED, bool STRIDED, typename T, typename I, typename SIZE>
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void testing_getrf_bad_arg()
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{
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// safe arguments
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hipsolver_local_handle handle;
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hipsolver_local_params params;
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I m = 1;
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I n = 1;
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I lda = 1;
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I stA = 1;
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I stP = 1;
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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_strided_batch_vector<I> dIpiv(1, 1, 1, 1);
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// device_strided_batch_vector<int> dInfo(1, 1, 1, 1);
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// CHECK_HIP_ERROR(dA.memcheck());
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// CHECK_HIP_ERROR(dIpiv.memcheck());
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// CHECK_HIP_ERROR(dInfo.memcheck());
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// SIZE size_dW, size_hW;
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// hipsolver_getrf_bufferSize(API, handle, params, m, n, dA.data(), lda, &size_dW, &size_hW);
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// host_strided_batch_vector<T> hWork(size_hW, 1, size_hW, 1);
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// device_strided_batch_vector<T> dWork(size_dW, 1, size_dW, 1);
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// if(size_dW)
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// CHECK_HIP_ERROR(dWork.memcheck());
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// // check bad arguments
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// getrf_checkBadArgs<API>(
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// handle, params, m, n, dA.data(), lda, stA, dIpiv.data(), stP, dWork.data(), size_dW, hWork.data(), size_hW, 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<I> dIpiv(1, 1, 1, 1);
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device_strided_batch_vector<int> dInfo(1, 1, 1, 1);
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CHECK_HIP_ERROR(dA.memcheck());
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CHECK_HIP_ERROR(dIpiv.memcheck());
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CHECK_HIP_ERROR(dInfo.memcheck());
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SIZE size_dW, size_hW;
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hipsolver_getrf_bufferSize(API, handle, params, m, n, dA.data(), lda, &size_dW, &size_hW);
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host_strided_batch_vector<T> hWork(size_hW, 1, size_hW, 1);
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device_strided_batch_vector<T> dWork(size_dW, 1, size_dW, 1);
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if(size_dW)
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CHECK_HIP_ERROR(dWork.memcheck());
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// check bad arguments
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getrf_checkBadArgs<API>(handle,
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params,
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m,
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n,
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dA.data(),
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lda,
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stA,
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dIpiv.data(),
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stP,
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dWork.data(),
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size_dW,
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hWork.data(),
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size_hW,
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dInfo.data(),
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bc);
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}
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}
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template <bool NPVT,
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bool CPU,
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bool GPU,
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typename T,
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typename I,
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typename Td,
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typename Id,
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typename INTd,
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typename Th,
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typename Ih,
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typename INTh>
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void getrf_initData(const hipsolverHandle_t handle,
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const hipsolverDnParams_t params,
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const I m,
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const I n,
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Td& dA,
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const I lda,
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const I stA,
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Id& dIpiv,
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const I stP,
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INTd& dInfo,
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const int bc,
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Th& hA,
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Ih& hIpiv,
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INTh& hInfo)
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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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for(int b = 0; b < bc; ++b)
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{
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// scale A to avoid singularities
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for(I i = 0; i < m; i++)
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{
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for(I 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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if(!NPVT)
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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(I i = 0; i < m / 2; i++)
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{
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for(I 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][m - 1 - i + j * lda];
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hA[b][m - 1 - i + j * lda] = tmp;
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}
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}
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}
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}
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}
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if(GPU)
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{
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// now copy data to the GPU
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CHECK_HIP_ERROR(dA.transfer_from(hA));
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}
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}
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template <testAPI_t API,
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bool NPVT,
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typename T,
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typename I,
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typename SIZE,
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typename Td,
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typename Id,
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typename INTd,
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typename Th,
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typename Ih,
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typename INTh>
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void getrf_getError(const hipsolverHandle_t handle,
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const hipsolverDnParams_t params,
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const I m,
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const I n,
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Td& dA,
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const I lda,
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const I stA,
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Id& dIpiv,
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const I stP,
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Td& dWork,
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const SIZE dlwork,
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Th& hWork,
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const SIZE hlwork,
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INTd& dInfo,
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const int bc,
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Th& hA,
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Th& hARes,
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INTh& hIpiv,
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Ih& hIpivRes,
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INTh& hInfo,
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INTh& hInfoRes,
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double* max_err)
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{
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// input data initialization
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getrf_initData<NPVT, true, true, T>(
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handle, params, m, n, dA, lda, stA, dIpiv, stP, dInfo, bc, hA, hIpiv, hInfo);
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// execute computations
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// GPU lapack
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CHECK_ROCBLAS_ERROR(hipsolver_getrf(API,
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NPVT,
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handle,
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params,
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m,
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n,
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dA.data(),
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lda,
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stA,
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dIpiv.data(),
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stP,
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dWork.data(),
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dlwork,
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hWork.data(),
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hlwork,
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dInfo.data(),
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bc));
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CHECK_HIP_ERROR(hARes.transfer_from(dA));
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CHECK_HIP_ERROR(hIpivRes.transfer_from(dIpiv));
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CHECK_HIP_ERROR(hInfoRes.transfer_from(dInfo));
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// CPU lapack
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for(int b = 0; b < bc; ++b)
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cpu_getrf(m, n, hA[b], lda, hIpiv[b], hInfo[b]);
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// expecting original matrix to be non-singular
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// error is ||hA - hARes|| / ||hA|| (ideally ||LU - Lres Ures|| / ||LU||)
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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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double err;
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*max_err = 0;
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for(int b = 0; b < bc; ++b)
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{
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err = norm_error('F', m, n, lda, hA[b], hARes[b]);
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*max_err = err > *max_err ? err : *max_err;
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// also check pivoting (count the number of incorrect pivots)
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if(!NPVT)
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{
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err = 0;
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for(I i = 0; i < min(m, n); ++i)
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{
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EXPECT_EQ(hIpiv[b][i], hIpivRes[b][i]) << "where b = " << b << ", i = " << i;
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if(hIpiv[b][i] != hIpivRes[b][i])
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err++;
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}
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*max_err = err > *max_err ? err : *max_err;
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}
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}
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// also check info for singularities
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err = 0;
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for(int b = 0; b < bc; ++b)
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{
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EXPECT_EQ(hInfo[b][0], hInfoRes[b][0]) << "where b = " << b;
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if(hInfo[b][0] != hInfoRes[b][0])
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err++;
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}
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*max_err += err;
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}
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template <testAPI_t API,
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bool NPVT,
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typename T,
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typename I,
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typename SIZE,
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typename Td,
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typename Id,
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typename INTd,
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typename Th,
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typename INTh>
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void getrf_getPerfData(const hipsolverHandle_t handle,
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const hipsolverDnParams_t params,
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const I m,
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const I n,
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Td& dA,
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const I lda,
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const I stA,
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Id& dIpiv,
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const I stP,
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Td& dWork,
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const SIZE dlwork,
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Th& hWork,
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const SIZE hlwork,
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INTd& dInfo,
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const int bc,
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Th& hA,
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INTh& hIpiv,
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INTh& hInfo,
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double* gpu_time_used,
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double* cpu_time_used,
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const int hot_calls,
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const bool perf)
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{
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if(!perf)
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{
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getrf_initData<NPVT, true, false, T>(
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handle, params, m, n, dA, lda, stA, dIpiv, stP, dInfo, bc, hA, hIpiv, hInfo);
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// cpu-lapack performance (only if not in perf mode)
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*cpu_time_used = get_time_us_no_sync();
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for(int b = 0; b < bc; ++b)
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cpu_getrf(m, n, hA[b], lda, hIpiv[b], hInfo[b]);
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*cpu_time_used = get_time_us_no_sync() - *cpu_time_used;
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}
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getrf_initData<NPVT, true, false, T>(
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handle, params, m, n, dA, lda, stA, dIpiv, stP, dInfo, bc, hA, hIpiv, hInfo);
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// cold calls
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for(int iter = 0; iter < 2; iter++)
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{
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getrf_initData<NPVT, false, true, T>(
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handle, params, m, n, dA, lda, stA, dIpiv, stP, dInfo, bc, hA, hIpiv, hInfo);
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CHECK_ROCBLAS_ERROR(hipsolver_getrf(API,
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NPVT,
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handle,
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params,
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m,
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n,
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dA.data(),
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lda,
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stA,
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dIpiv.data(),
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stP,
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dWork.data(),
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dlwork,
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hWork.data(),
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hlwork,
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dInfo.data(),
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bc));
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}
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// gpu-lapack performance
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hipStream_t stream;
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CHECK_ROCBLAS_ERROR(hipsolverGetStream(handle, &stream));
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double start;
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for(int iter = 0; iter < hot_calls; iter++)
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{
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getrf_initData<NPVT, false, true, T>(
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handle, params, m, n, dA, lda, stA, dIpiv, stP, dInfo, bc, hA, hIpiv, hInfo);
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start = get_time_us_sync(stream);
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hipsolver_getrf(API,
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NPVT,
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handle,
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params,
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m,
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n,
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dA.data(),
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lda,
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stA,
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dIpiv.data(),
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stP,
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dWork.data(),
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dlwork,
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hWork.data(),
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hlwork,
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dInfo.data(),
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bc);
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*gpu_time_used += get_time_us_sync(stream) - start;
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}
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*gpu_time_used /= hot_calls;
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}
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template <testAPI_t API,
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bool BATCHED,
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bool STRIDED,
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bool NPVT,
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typename T,
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typename I,
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typename SIZE>
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void testing_getrf(Arguments& argus)
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{
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// get arguments
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hipsolver_local_handle handle;
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hipsolver_local_params params;
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I m = argus.get<int>("m");
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I n = argus.get<int>("n", m);
|
|
I lda = argus.get<int>("lda", m);
|
|
I stA = argus.get<int>("strideA", lda * n);
|
|
I stP = argus.get<int>("strideP", min(m, n));
|
|
|
|
int bc = argus.batch_count;
|
|
int hot_calls = argus.iters;
|
|
|
|
I stARes = (argus.unit_check || argus.norm_check) ? stA : 0;
|
|
I stPRes = (argus.unit_check || argus.norm_check) ? stP : 0;
|
|
|
|
// check non-supported values
|
|
// N/A
|
|
|
|
// determine sizes
|
|
size_t size_A = size_t(lda) * n;
|
|
size_t size_P = size_t(min(m, n));
|
|
double max_error = 0, gpu_time_used = 0, cpu_time_used = 0;
|
|
|
|
size_t size_ARes = (argus.unit_check || argus.norm_check) ? size_A : 0;
|
|
size_t size_PRes = (argus.unit_check || argus.norm_check) ? size_P : 0;
|
|
|
|
// check invalid sizes
|
|
bool invalid_size = (m <= 0 || n <= 0 || lda < m || bc <= 0);
|
|
if(invalid_size)
|
|
{
|
|
#if defined(__HIP_PLATFORM_HCC__) || defined(__HIP_PLATFORM_AMD__)
|
|
if(BATCHED)
|
|
{
|
|
// EXPECT_ROCBLAS_STATUS(hipsolver_getrf(API,
|
|
// NPVT,
|
|
// handle,
|
|
// params,
|
|
// m,
|
|
// n,
|
|
// (T* const*)nullptr,
|
|
// lda,
|
|
// stA,
|
|
// (I*)nullptr,
|
|
// stP,
|
|
// (T*)nullptr,
|
|
// 0,
|
|
// (T*)nullptr,
|
|
// 0,
|
|
// (int*)nullptr,
|
|
// bc),
|
|
// HIPSOLVER_STATUS_INVALID_VALUE);
|
|
}
|
|
else
|
|
{
|
|
EXPECT_ROCBLAS_STATUS(hipsolver_getrf(API,
|
|
NPVT,
|
|
handle,
|
|
params,
|
|
m,
|
|
n,
|
|
(T*)nullptr,
|
|
lda,
|
|
stA,
|
|
(I*)nullptr,
|
|
stP,
|
|
(T*)nullptr,
|
|
0,
|
|
(T*)nullptr,
|
|
0,
|
|
(int*)nullptr,
|
|
bc),
|
|
HIPSOLVER_STATUS_INVALID_VALUE);
|
|
}
|
|
#endif
|
|
|
|
if(argus.timing)
|
|
rocsolver_bench_inform(inform_invalid_size);
|
|
|
|
return;
|
|
}
|
|
|
|
// memory size query is necessary
|
|
SIZE size_dW, size_hW;
|
|
hipsolver_getrf_bufferSize(API, handle, params, m, n, (T*)nullptr, lda, &size_dW, &size_hW);
|
|
|
|
if(argus.mem_query)
|
|
{
|
|
rocsolver_bench_inform(inform_mem_query, size_dW);
|
|
return;
|
|
}
|
|
|
|
if(BATCHED)
|
|
{
|
|
// // memory allocations
|
|
// host_batch_vector<T> hA(size_A, 1, bc);
|
|
// host_batch_vector<T> hARes(size_ARes, 1, bc);
|
|
// host_strided_batch_vector<int> hIpiv(size_P, 1, stP, bc);
|
|
// host_strided_batch_vector<I> hIpivRes(size_PRes, 1, stPRes, bc);
|
|
// host_strided_batch_vector<int> hInfo(1, 1, 1, bc);
|
|
// host_strided_batch_vector<int> hInfoRes(1, 1, 1, bc);
|
|
// host_strided_batch_vector<T> hWork(size_hW, 1, size_hW, 1); // size_hW accounts for bc
|
|
// device_batch_vector<T> dA(size_A, 1, bc);
|
|
// device_strided_batch_vector<I> dIpiv(size_P, 1, stP, bc);
|
|
// device_strided_batch_vector<int> dInfo(1, 1, 1, bc);
|
|
// device_strided_batch_vector<T> dWork(size_dW, 1, size_dW, 1); // size_dW accounts for bc
|
|
// if(size_A)
|
|
// CHECK_HIP_ERROR(dA.memcheck());
|
|
// CHECK_HIP_ERROR(dInfo.memcheck());
|
|
// if(size_P)
|
|
// CHECK_HIP_ERROR(dIpiv.memcheck());
|
|
// if(size_dW)
|
|
// CHECK_HIP_ERROR(dWork.memcheck());
|
|
|
|
// // check computations
|
|
// if(argus.unit_check || argus.norm_check)
|
|
// getrf_getError<API, NPVT, T>(handle,
|
|
// params,
|
|
// m,
|
|
// n,
|
|
// dA,
|
|
// lda,
|
|
// stA,
|
|
// dIpiv,
|
|
// stP,
|
|
// dWork,
|
|
// size_dW,
|
|
// hWork,
|
|
// size_hW,
|
|
// dInfo,
|
|
// bc,
|
|
// hA,
|
|
// hARes,
|
|
// hIpiv,
|
|
// hIpivRes,
|
|
// hInfo,
|
|
// hInfoRes,
|
|
// &max_error);
|
|
|
|
// // collect performance data
|
|
// if(argus.timing)
|
|
// getrf_getPerfData<API, NPVT, T>(handle,
|
|
// params,
|
|
// m,
|
|
// n,
|
|
// dA,
|
|
// lda,
|
|
// stA,
|
|
// dIpiv,
|
|
// stP,
|
|
// dWork,
|
|
// size_dW,
|
|
// hWork,
|
|
// size_hW,
|
|
// dInfo,
|
|
// bc,
|
|
// hA,
|
|
// hIpiv,
|
|
// hInfo,
|
|
// &gpu_time_used,
|
|
// &cpu_time_used,
|
|
// hot_calls,
|
|
// argus.perf);
|
|
}
|
|
|
|
else
|
|
{
|
|
// memory allocations
|
|
host_strided_batch_vector<T> hA(size_A, 1, stA, bc);
|
|
host_strided_batch_vector<T> hARes(size_ARes, 1, stARes, bc);
|
|
host_strided_batch_vector<int> hIpiv(size_P, 1, stP, bc);
|
|
host_strided_batch_vector<I> hIpivRes(size_PRes, 1, stPRes, bc);
|
|
host_strided_batch_vector<int> hInfo(1, 1, 1, bc);
|
|
host_strided_batch_vector<int> hInfoRes(1, 1, 1, bc);
|
|
host_strided_batch_vector<T> hWork(size_hW, 1, size_hW, 1); // size_hW accounts for bc
|
|
device_strided_batch_vector<T> dA(size_A, 1, stA, bc);
|
|
device_strided_batch_vector<I> dIpiv(size_P, 1, stP, bc);
|
|
device_strided_batch_vector<int> dInfo(1, 1, 1, bc);
|
|
device_strided_batch_vector<T> dWork(size_dW, 1, size_dW, 1); // size_dW accounts for bc
|
|
if(size_A)
|
|
CHECK_HIP_ERROR(dA.memcheck());
|
|
CHECK_HIP_ERROR(dInfo.memcheck());
|
|
if(size_P)
|
|
CHECK_HIP_ERROR(dIpiv.memcheck());
|
|
if(size_dW)
|
|
CHECK_HIP_ERROR(dWork.memcheck());
|
|
|
|
// check computations
|
|
if(argus.unit_check || argus.norm_check)
|
|
getrf_getError<API, NPVT, T>(handle,
|
|
params,
|
|
m,
|
|
n,
|
|
dA,
|
|
lda,
|
|
stA,
|
|
dIpiv,
|
|
stP,
|
|
dWork,
|
|
size_dW,
|
|
hWork,
|
|
size_hW,
|
|
dInfo,
|
|
bc,
|
|
hA,
|
|
hARes,
|
|
hIpiv,
|
|
hIpivRes,
|
|
hInfo,
|
|
hInfoRes,
|
|
&max_error);
|
|
|
|
// collect performance data
|
|
if(argus.timing)
|
|
getrf_getPerfData<API, NPVT, T>(handle,
|
|
params,
|
|
m,
|
|
n,
|
|
dA,
|
|
lda,
|
|
stA,
|
|
dIpiv,
|
|
stP,
|
|
dWork,
|
|
size_dW,
|
|
hWork,
|
|
size_hW,
|
|
dInfo,
|
|
bc,
|
|
hA,
|
|
hIpiv,
|
|
hInfo,
|
|
&gpu_time_used,
|
|
&cpu_time_used,
|
|
hot_calls,
|
|
argus.perf);
|
|
}
|
|
|
|
// validate results for rocsolver-test
|
|
// using min(m,n) * machine_precision as tolerance
|
|
if(argus.unit_check)
|
|
ROCSOLVER_TEST_CHECK(T, max_error, min(m, n));
|
|
|
|
// output results for rocsolver-bench
|
|
if(argus.timing)
|
|
{
|
|
if(!argus.perf)
|
|
{
|
|
std::cerr << "\n============================================\n";
|
|
std::cerr << "Arguments:\n";
|
|
std::cerr << "============================================\n";
|
|
if(BATCHED)
|
|
{
|
|
rocsolver_bench_output("m", "n", "lda", "strideP", "batch_c");
|
|
rocsolver_bench_output(m, n, lda, stP, bc);
|
|
}
|
|
else if(STRIDED)
|
|
{
|
|
rocsolver_bench_output("m", "n", "lda", "strideA", "strideP", "batch_c");
|
|
rocsolver_bench_output(m, n, lda, stA, stP, bc);
|
|
}
|
|
else
|
|
{
|
|
rocsolver_bench_output("m", "n", "lda");
|
|
rocsolver_bench_output(m, n, lda);
|
|
}
|
|
std::cerr << "\n============================================\n";
|
|
std::cerr << "Results:\n";
|
|
std::cerr << "============================================\n";
|
|
if(argus.norm_check)
|
|
{
|
|
rocsolver_bench_output("cpu_time", "gpu_time", "error");
|
|
rocsolver_bench_output(cpu_time_used, gpu_time_used, max_error);
|
|
}
|
|
else
|
|
{
|
|
rocsolver_bench_output("cpu_time", "gpu_time");
|
|
rocsolver_bench_output(cpu_time_used, gpu_time_used);
|
|
}
|
|
std::cerr << std::endl;
|
|
}
|
|
else
|
|
{
|
|
if(argus.norm_check)
|
|
rocsolver_bench_output(gpu_time_used, max_error);
|
|
else
|
|
rocsolver_bench_output(gpu_time_used);
|
|
}
|
|
}
|
|
|
|
// ensure all arguments were consumed
|
|
argus.validate_consumed();
|
|
}
|