/* ************************************************************************** * Copyright (C) 2020-2025 Advanced Micro Devices, Inc. All rights reserved. * * Redistribution and use in source and binary forms, with or without * modification, are permitted provided that the following conditions * are met: * * 1. Redistributions of source code must retain the above copyright * notice, this list of conditions and the following disclaimer. * * 2. Redistributions in binary form must reproduce the above copyright * notice, this list of conditions and the following disclaimer in the * documentation and/or other materials provided with the distribution. * * THIS SOFTWARE IS PROVIDED BY THE AUTHOR AND CONTRIBUTORS ``AS IS'' AND * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE * ARE DISCLAIMED. IN NO EVENT SHALL THE AUTHOR OR CONTRIBUTORS BE LIABLE * FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL * DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS * OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) * HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT * LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY * OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF * SUCH DAMAGE. * *************************************************************************/ #pragma once #include "common/misc/client_util.hpp" #include "common/misc/clientcommon.hpp" #include "common/misc/lapack_host_reference.hpp" #include "common/misc/norm.hpp" #include "common/misc/rocsolver.hpp" #include "common/misc/rocsolver_arguments.hpp" #include "common/misc/rocsolver_test.hpp" #include "common/misc/rocsolver_timer.hpp" template void getf2_getrf_checkBadArgs(const rocblas_handle handle, const I m, const I n, Td dA, const I lda, const rocblas_stride stA, Id dIpiv, const rocblas_stride stP, Id dInfo, const I bc) { // handle EXPECT_ROCBLAS_STATUS( rocsolver_getf2_getrf(STRIDED, GETRF, nullptr, m, n, dA, lda, stA, dIpiv, stP, dInfo, bc), rocblas_status_invalid_handle); // values // N/A // sizes (only check batch_count if applicable) if(STRIDED) EXPECT_ROCBLAS_STATUS(rocsolver_getf2_getrf(STRIDED, GETRF, handle, m, n, dA, lda, stA, dIpiv, stP, dInfo, -1), rocblas_status_invalid_size); // pointers EXPECT_ROCBLAS_STATUS(rocsolver_getf2_getrf(STRIDED, GETRF, handle, m, n, (Td) nullptr, lda, stA, dIpiv, stP, dInfo, bc), rocblas_status_invalid_pointer); EXPECT_ROCBLAS_STATUS(rocsolver_getf2_getrf(STRIDED, GETRF, handle, m, n, dA, lda, stA, (Id) nullptr, stP, dInfo, bc), rocblas_status_invalid_pointer); EXPECT_ROCBLAS_STATUS(rocsolver_getf2_getrf(STRIDED, GETRF, handle, m, n, dA, lda, stA, dIpiv, stP, (Id) nullptr, bc), rocblas_status_invalid_pointer); // quick return with invalid pointers EXPECT_ROCBLAS_STATUS(rocsolver_getf2_getrf(STRIDED, GETRF, handle, 0, n, (Td) nullptr, lda, stA, (Id) nullptr, stP, dInfo, bc), rocblas_status_success); EXPECT_ROCBLAS_STATUS(rocsolver_getf2_getrf(STRIDED, GETRF, handle, m, 0, (Td) nullptr, lda, stA, (Id) nullptr, stP, dInfo, bc), rocblas_status_success); if(STRIDED) EXPECT_ROCBLAS_STATUS(rocsolver_getf2_getrf(STRIDED, GETRF, handle, m, n, dA, lda, stA, dIpiv, stP, (Id) nullptr, 0), rocblas_status_success); // quick return with zero batch_count if applicable if(STRIDED) EXPECT_ROCBLAS_STATUS( rocsolver_getf2_getrf(STRIDED, GETRF, handle, m, n, dA, lda, stA, dIpiv, stP, dInfo, 0), rocblas_status_success); } template void testing_getf2_getrf_bad_arg() { // safe arguments rocblas_local_handle handle; I m = 1; I n = 1; I lda = 1; rocblas_stride stA = 1; rocblas_stride stP = 1; I bc = 1; if(BATCHED) { // memory allocations device_batch_vector dA(1, 1, 1); device_strided_batch_vector dIpiv(1, 1, 1, 1); device_strided_batch_vector dInfo(1, 1, 1, 1); CHECK_HIP_ERROR(dA.memcheck()); CHECK_HIP_ERROR(dIpiv.memcheck()); CHECK_HIP_ERROR(dInfo.memcheck()); // check bad arguments getf2_getrf_checkBadArgs(handle, m, n, dA.data(), lda, stA, dIpiv.data(), stP, dInfo.data(), bc); } else { // memory allocations device_strided_batch_vector dA(1, 1, 1, 1); device_strided_batch_vector dIpiv(1, 1, 1, 1); device_strided_batch_vector dInfo(1, 1, 1, 1); CHECK_HIP_ERROR(dA.memcheck()); CHECK_HIP_ERROR(dIpiv.memcheck()); CHECK_HIP_ERROR(dInfo.memcheck()); // check bad arguments getf2_getrf_checkBadArgs(handle, m, n, dA.data(), lda, stA, dIpiv.data(), stP, dInfo.data(), bc); } } template void getf2_getrf_initData(const rocblas_handle handle, const I m, const I n, Td& dA, const I lda, const rocblas_stride stA, Id& dIpiv, const rocblas_stride stP, Id& dInfo, const I bc, Th& hA, Uh& hIpiv, const bool singular) { if(CPU) { T tmp; rocblas_init(hA, true); for(I b = 0; b < bc; ++b) { // scale A to avoid singularities for(I i = 0; i < m; i++) { for(I j = 0; j < n; j++) { if(i == j) hA[b][i + j * lda] += 400; else hA[b][i + j * lda] -= 4; } } // shuffle rows to test pivoting // always the same permuation for debugging purposes for(I i = 0; i < m / 2; i++) { for(I j = 0; j < n; j++) { tmp = hA[b][i + j * lda]; hA[b][i + j * lda] = hA[b][m - 1 - i + j * lda]; hA[b][m - 1 - i + j * lda] = tmp; } } if(singular && (b == bc / 4 || b == bc / 2 || b == bc - 1)) { // When required, add some singularities // (always the same elements for debugging purposes). // The algorithm must detect the first zero pivot in those // matrices in the batch that are singular I j = n / 4 + b; j -= (j / n) * n; for(I i = 0; i < m; i++) hA[b][i + j * lda] = 0; j = n / 2 + b; j -= (j / n) * n; for(I i = 0; i < m; i++) hA[b][i + j * lda] = 0; j = n - 1 + b; j -= (j / n) * n; for(I i = 0; i < m; i++) hA[b][i + j * lda] = 0; } } } if(GPU) { // now copy data to the GPU CHECK_HIP_ERROR(dA.transfer_from(hA)); } } template void getf2_getrf_getError(const rocblas_handle handle, const I m, const I n, Td& dA, const I lda, const rocblas_stride stA, Id& dIpiv, const rocblas_stride stP, Id& dInfo, const I bc, Th& hA, Th& hARes, Uh& hIpiv, Ih& hIpivRes, Uh& hInfo, Ih& hInfoRes, double* max_err, const bool singular, size_t& hashA, size_t& hashARes, size_t& hashIpivRes) { // input data initialization getf2_getrf_initData(handle, m, n, dA, lda, stA, dIpiv, stP, dInfo, bc, hA, hIpiv, singular); // compute input hashes hashA = deterministic_hash(hA, bc); // execute computations // GPU lapack CHECK_ROCBLAS_ERROR(rocsolver_getf2_getrf(STRIDED, GETRF, handle, m, n, dA.data(), lda, stA, dIpiv.data(), stP, dInfo.data(), bc)); CHECK_HIP_ERROR(hARes.transfer_from(dA)); CHECK_HIP_ERROR(hIpivRes.transfer_from(dIpiv)); CHECK_HIP_ERROR(hInfoRes.transfer_from(dInfo)); // CPU lapack for(I b = 0; b < bc; ++b) { GETRF ? cpu_getrf(m, n, hA[b], lda, hIpiv[b], hInfo[b]) : cpu_getf2(m, n, hA[b], lda, hIpiv[b], hInfo[b]); } // compute output hashes hashARes = deterministic_hash(hARes, bc); hashIpivRes = deterministic_hash(hIpivRes); // expecting original matrix to be non-singular // error is ||hA - hARes|| / ||hA|| (ideally ||LU - Lres Ures|| / ||LU||) // (THIS DOES NOT ACCOUNT FOR NUMERICAL REPRODUCIBILITY ISSUES. // IT MIGHT BE REVISITED IN THE FUTURE) // using frobenius norm double err; *max_err = 0; for(I b = 0; b < bc; ++b) { err = norm_error('F', m, n, lda, hA[b], hARes[b]); *max_err = err > *max_err ? err : *max_err; // also check pivoting (count the number of incorrect pivots) err = 0; for(I i = 0; i < min(m, n); ++i) { EXPECT_EQ(hIpiv[b][i], hIpivRes[b][i]) << "where b = " << b << ", i = " << i; if(hIpiv[b][i] != hIpivRes[b][i]) err++; } *max_err = err > *max_err ? err : *max_err; } // also check info for singularities err = 0; for(I b = 0; b < bc; ++b) { EXPECT_EQ(hInfo[b][0], hInfoRes[b][0]) << "where b = " << b; if(hInfo[b][0] != hInfoRes[b][0]) err++; } *max_err += err; } template void getf2_getrf_getPerfData(const rocblas_handle handle, const I m, const I n, Td& dA, const I lda, const rocblas_stride stA, Id& dIpiv, const rocblas_stride stP, Id& dInfo, const I bc, Th& hA, Uh& hIpiv, Uh& hInfo, double* gpu_time_used, double* cpu_time_used, const int hot_calls, const int profile, const bool profile_kernels, const bool perf, const bool singular) { if(!perf) { getf2_getrf_initData(handle, m, n, dA, lda, stA, dIpiv, stP, dInfo, bc, hA, hIpiv, singular); // cpu-lapack performance (only if not in perf mode) *cpu_time_used = get_time_us_no_sync(); for(I b = 0; b < bc; ++b) { GETRF ? cpu_getrf(m, n, hA[b], lda, hIpiv[b], hInfo[b]) : cpu_getf2(m, n, hA[b], lda, hIpiv[b], hInfo[b]); } *cpu_time_used = get_time_us_no_sync() - *cpu_time_used; } getf2_getrf_initData(handle, m, n, dA, lda, stA, dIpiv, stP, dInfo, bc, hA, hIpiv, singular); // cold calls for(int iter = 0; iter < 2; iter++) { getf2_getrf_initData(handle, m, n, dA, lda, stA, dIpiv, stP, dInfo, bc, hA, hIpiv, singular); CHECK_ROCBLAS_ERROR(rocsolver_getf2_getrf(STRIDED, GETRF, handle, m, n, dA.data(), lda, stA, dIpiv.data(), stP, 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(int iter = 0; iter < hot_calls; iter++) { getf2_getrf_initData(handle, m, n, dA, lda, stA, dIpiv, stP, dInfo, bc, hA, hIpiv, singular); timer.start(stream); rocsolver_getf2_getrf(STRIDED, GETRF, handle, m, n, dA.data(), lda, stA, dIpiv.data(), stP, dInfo.data(), bc); timer.end(stream); } *gpu_time_used = timer.get_combined(); } template void testing_getf2_getrf(Arguments& argus) { // get arguments rocblas_local_handle handle; I m = argus.get("m"); I n = argus.get("n", m); I lda = argus.get("lda", m); rocblas_stride stA = argus.get("strideA", lda * n); rocblas_stride stP = argus.get("strideP", min(m, n)); I bc = argus.batch_count; int hot_calls = argus.iters; rocblas_stride stARes = (argus.unit_check || argus.norm_check || argus.hash_check) ? stA : 0; rocblas_stride stPRes = (argus.unit_check || argus.norm_check || argus.hash_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 hashA = 0, hashARes = 0, hashIpivRes = 0; size_t size_ARes = (argus.unit_check || argus.norm_check || argus.hash_check) ? size_A : 0; size_t size_PRes = (argus.unit_check || argus.norm_check || argus.hash_check) ? size_P : 0; // check invalid sizes bool invalid_size = (m < 0 || n < 0 || lda < m || bc < 0); if(invalid_size) { if(BATCHED) EXPECT_ROCBLAS_STATUS(rocsolver_getf2_getrf(STRIDED, GETRF, handle, m, n, (T* const*)nullptr, lda, stA, (I*)nullptr, stP, (I*)nullptr, bc), rocblas_status_invalid_size); else EXPECT_ROCBLAS_STATUS(rocsolver_getf2_getrf(STRIDED, GETRF, handle, m, n, (T*)nullptr, lda, stA, (I*)nullptr, stP, (I*)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_getf2_getrf(STRIDED, GETRF, handle, m, n, (T* const*)nullptr, lda, stA, (I*)nullptr, stP, (I*)nullptr, bc)); else CHECK_ALLOC_QUERY(rocsolver_getf2_getrf(STRIDED, GETRF, handle, m, n, (T*)nullptr, lda, stA, (I*)nullptr, stP, (I*)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 hA(size_A, 1, bc); host_batch_vector hARes(size_ARes, 1, bc); host_strided_batch_vector hIpiv(size_P, 1, stP, bc); host_strided_batch_vector hIpivRes(size_PRes, 1, stPRes, bc); host_strided_batch_vector hInfo(1, 1, 1, bc); host_strided_batch_vector hInfoRes(1, 1, 1, bc); device_batch_vector dA(size_A, 1, bc); device_strided_batch_vector dIpiv(size_P, 1, stP, bc); device_strided_batch_vector dInfo(1, 1, 1, bc); if(size_A) CHECK_HIP_ERROR(dA.memcheck()); CHECK_HIP_ERROR(dInfo.memcheck()); if(size_P) CHECK_HIP_ERROR(dIpiv.memcheck()); // check quick return if(m == 0 || n == 0 || bc == 0) { EXPECT_ROCBLAS_STATUS(rocsolver_getf2_getrf(STRIDED, GETRF, handle, m, n, dA.data(), lda, stA, dIpiv.data(), stP, 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 || argus.hash_check) getf2_getrf_getError( handle, m, n, dA, lda, stA, dIpiv, stP, dInfo, bc, hA, hARes, hIpiv, hIpivRes, hInfo, hInfoRes, &max_error, argus.singular, hashA, hashARes, hashIpivRes); // collect performance data if(argus.timing && hot_calls > 0) getf2_getrf_getPerfData( handle, m, 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 hA(size_A, 1, stA, bc); host_strided_batch_vector hARes(size_ARes, 1, stARes, bc); host_strided_batch_vector hIpiv(size_P, 1, stP, bc); host_strided_batch_vector hIpivRes(size_PRes, 1, stPRes, bc); host_strided_batch_vector hInfo(1, 1, 1, bc); host_strided_batch_vector hInfoRes(1, 1, 1, bc); device_strided_batch_vector dA(size_A, 1, stA, bc); device_strided_batch_vector dIpiv(size_P, 1, stP, bc); device_strided_batch_vector dInfo(1, 1, 1, bc); if(size_A) CHECK_HIP_ERROR(dA.memcheck()); CHECK_HIP_ERROR(dInfo.memcheck()); if(size_P) CHECK_HIP_ERROR(dIpiv.memcheck()); // check quick return if(m == 0 || n == 0 || bc == 0) { EXPECT_ROCBLAS_STATUS(rocsolver_getf2_getrf(STRIDED, GETRF, handle, m, n, dA.data(), lda, stA, dIpiv.data(), stP, 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 || argus.hash_check) getf2_getrf_getError( handle, m, n, dA, lda, stA, dIpiv, stP, dInfo, bc, hA, hARes, hIpiv, hIpivRes, hInfo, hInfoRes, &max_error, argus.singular, hashA, hashARes, hashIpivRes); // collect performance data if(argus.timing && hot_calls > 0) getf2_getrf_getPerfData( handle, m, 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(m, n)); // output results for rocsolver-bench if(argus.timing) { if(!argus.perf) { rocsolver_bench_header("Arguments:"); 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); } 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(); if(argus.hash_check) { rocsolver_bench_output("hash(A)", "hash(ARes)", "hash(ipivRes)"); rocsolver_bench_output(ROCSOLVER_FORMAT_HASH(hashA), ROCSOLVER_FORMAT_HASH(hashARes), ROCSOLVER_FORMAT_HASH(hashIpivRes)); 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_GETF2_GETRF(...) \ extern template void testing_getf2_getrf<__VA_ARGS__>(Arguments&); INSTANTIATE(EXTERN_TESTING_GETF2_GETRF, FOREACH_MATRIX_DATA_LAYOUT, FOREACH_BLOCKED_VARIANT, FOREACH_SCALAR_TYPE, FOREACH_INT_TYPE, APPLY_STAMP)