/* ************************************************************************ * Copyright (C) 2021-2025 Advanced Micro Devices, Inc. All rights reserved. * * Permission is hereby granted, free of charge, to any person obtaining a copy * of this software and associated documentation files (the "Software"), to deal * in the Software without restriction, including without limitation the rights * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell cop- * ies of the Software, and to permit persons to whom the Software is furnished * to do so, subject to the following conditions: * * The above copyright notice and this permission notice shall be included in all * copies or substantial portions of the Software. * * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IM- * PLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS * FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR * COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER * IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNE- * CTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. * * * ************************************************************************ */ #pragma once #include "clientcommon.hpp" template void syevdx_heevdx_checkBadArgs(const hipsolverHandle_t handle, const hipsolverEigMode_t evect, const hipsolverEigRange_t erange, const hipsolverFillMode_t uplo, const int n, T dA, const int lda, const int stA, const SS vl, const SS vu, const int il, const int iu, U hNev, S dW, const int stW, T dWork, const int lwork, U dinfo, const int bc) { // handle EXPECT_ROCBLAS_STATUS(hipsolver_syevdx_heevdx(API, nullptr, evect, erange, uplo, n, dA, lda, stA, vl, vu, il, iu, hNev, dW, stW, dWork, lwork, dinfo, bc), HIPSOLVER_STATUS_NOT_INITIALIZED); // values EXPECT_ROCBLAS_STATUS(hipsolver_syevdx_heevdx(API, handle, hipsolverEigMode_t(-1), erange, uplo, n, dA, lda, stA, vl, vu, il, iu, hNev, dW, stW, dWork, lwork, dinfo, bc), HIPSOLVER_STATUS_INVALID_ENUM); EXPECT_ROCBLAS_STATUS(hipsolver_syevdx_heevdx(API, handle, evect, hipsolverEigRange_t(-1), uplo, n, dA, lda, stA, vl, vu, il, iu, hNev, dW, stW, dWork, lwork, dinfo, bc), HIPSOLVER_STATUS_INVALID_ENUM); EXPECT_ROCBLAS_STATUS(hipsolver_syevdx_heevdx(API, handle, evect, erange, hipsolverFillMode_t(-1), n, dA, lda, stA, vl, vu, il, iu, hNev, dW, stW, dWork, lwork, dinfo, bc), HIPSOLVER_STATUS_INVALID_ENUM); #if defined(__HIP_PLATFORM_HCC__) || defined(__HIP_PLATFORM_AMD__) // pointers EXPECT_ROCBLAS_STATUS(hipsolver_syevdx_heevdx(API, handle, evect, erange, uplo, n, (T) nullptr, lda, stA, vl, vu, il, iu, hNev, dW, stW, dWork, lwork, dinfo, bc), HIPSOLVER_STATUS_INVALID_VALUE); EXPECT_ROCBLAS_STATUS(hipsolver_syevdx_heevdx(API, handle, evect, erange, uplo, n, dA, lda, stA, vl, vu, il, iu, (U) nullptr, dW, stW, dWork, lwork, dinfo, bc), HIPSOLVER_STATUS_INVALID_VALUE); EXPECT_ROCBLAS_STATUS(hipsolver_syevdx_heevdx(API, handle, evect, erange, uplo, n, dA, lda, stA, vl, vu, il, iu, hNev, (S) nullptr, stW, dWork, lwork, dinfo, bc), HIPSOLVER_STATUS_INVALID_VALUE); EXPECT_ROCBLAS_STATUS(hipsolver_syevdx_heevdx(API, handle, evect, erange, uplo, n, dA, lda, stA, vl, vu, il, iu, hNev, dW, stW, dWork, lwork, (U) nullptr, bc), HIPSOLVER_STATUS_INVALID_VALUE); #endif } template void testing_syevdx_heevdx_bad_arg() { using S = decltype(std::real(T{})); // safe arguments hipsolver_local_handle handle; hipsolverEigMode_t evect = HIPSOLVER_EIG_MODE_VECTOR; hipsolverEigRange_t erange = HIPSOLVER_EIG_RANGE_V; hipsolverFillMode_t uplo = HIPSOLVER_FILL_MODE_LOWER; int n = 1; int lda = 1; int stA = 1; int stW = 1; int bc = 1; S vl = 0.0; S vu = 1.0; int il = 0; int iu = 0; if(BATCHED) { // // memory allocations // host_strided_batch_vector hNev(1, 1, 1, 1); // device_batch_vector dA(1, 1, 1); // device_strided_batch_vector dW(1, 1, 1, 1); // device_strided_batch_vector dinfo(1, 1, 1, 1); // CHECK_HIP_ERROR(dA.memcheck()); // CHECK_HIP_ERROR(dW.memcheck()); // CHECK_HIP_ERROR(dinfo.memcheck()); // int size_W; // hipsolver_syevdx_heevdx_bufferSize(API, // handle, // evect, // erange, // uplo, // n, // dA.data(), // lda, // vl, // vu, // il, // iu, // hNev.data(), // dW.data(), // &size_W); // size_t bytes_W = std::getenv("HIPSOLVER_BUFFERSIZE_RETURN_BYTES") != nullptr ? size_W : sizeof(T) * size_W; // device_strided_batch_vector dWork(bytes_W, 1, bytes_W, 1); // if(size_W) // CHECK_HIP_ERROR(dWork.memcheck()); // // check bad arguments // syevdx_heevdx_checkBadArgs(handle, // evect, // erange, // uplo, // n, // dA.data(), // lda, // stA, // vl, // vu, // il, // iu, // hNev.data(), // dW.data(), // stW, // dWork.data(), // size_W, // dinfo.data(), // bc); } else { // memory allocations host_strided_batch_vector hNev(1, 1, 1, 1); device_strided_batch_vector dA(1, 1, 1, 1); device_strided_batch_vector dW(1, 1, 1, 1); device_strided_batch_vector dinfo(1, 1, 1, 1); CHECK_HIP_ERROR(dA.memcheck()); CHECK_HIP_ERROR(dW.memcheck()); CHECK_HIP_ERROR(dinfo.memcheck()); int size_W; hipsolver_syevdx_heevdx_bufferSize(API, handle, evect, erange, uplo, n, dA.data(), lda, vl, vu, il, iu, hNev.data(), dW.data(), &size_W); size_t bytes_W = std::getenv("HIPSOLVER_BUFFERSIZE_RETURN_BYTES") != nullptr ? size_W : sizeof(T) * size_W; device_strided_batch_vector dWork(bytes_W, 1, bytes_W, 1); if(size_W) CHECK_HIP_ERROR(dWork.memcheck()); // check bad arguments syevdx_heevdx_checkBadArgs(handle, evect, erange, uplo, n, dA.data(), lda, stA, vl, vu, il, iu, hNev.data(), dW.data(), stW, dWork.data(), size_W, dinfo.data(), bc); } } template void syevdx_heevdx_initData(const hipsolverHandle_t handle, const hipsolverEigMode_t evect, const int n, Td& dA, const int lda, const int bc, Th& hA, std::vector& A, bool test = true) { if(CPU) { rocblas_init(hA, true); // scale A to avoid singularities for(int b = 0; b < bc; ++b) { for(int i = 0; i < n; i++) { for(int j = i; j < n; j++) { if(i == j) hA[b][i + j * lda] = std::real(hA[b][i + j * lda]) + 10; else { if(j == i + 1) { hA[b][i + j * lda] = (hA[b][i + j * lda] - 5) / 10; hA[b][j + i * lda] = conj(hA[b][i + j * lda]); } else hA[b][j + i * lda] = hA[b][i + j * lda] = 0; } } if(i == n / 4 || i == n / 2 || i == n - 1 || i == n / 7 || i == n / 5 || i == n / 3) hA[b][i + i * lda] *= -1; } // make copy of original data to test vectors if required if(test && evect == HIPSOLVER_EIG_MODE_VECTOR) { for(int i = 0; i < n; i++) { for(int j = 0; j < n; j++) A[b * lda * n + i + j * lda] = hA[b][i + j * lda]; } } } } if(GPU) { // now copy to the GPU CHECK_HIP_ERROR(dA.transfer_from(hA)); } } template void syevdx_heevdx_getError(const hipsolverHandle_t handle, const hipsolverEigMode_t evect, const hipsolverEigRange_t erange, const hipsolverFillMode_t uplo, const int n, Td& dA, const int lda, const int stA, const S vl, const S vu, const int il, const int iu, Ih& hNevRes, Sd& dW, const int stW, Td& dWork, const int lwork, Id& dinfo, const int bc, Th& hA, Th& hARes, Ih& hNev, Sh& hW, Sh& hWRes, Ih& hinfo, Ih& hinfoRes, double* max_err) { constexpr bool COMPLEX = is_complex; int size_work = !COMPLEX ? 35 * n : 33 * n; int size_rwork = !COMPLEX ? 0 : 7 * n; int size_iwork = 5 * n; std::vector work(size_work); std::vector rwork(size_rwork); std::vector iwork(size_iwork); std::vector A(lda * n * bc); std::vector Z(lda * n); std::vector ifail(n); S abstol = 2 * get_safemin(); // input data initialization syevdx_heevdx_initData(handle, evect, n, dA, lda, bc, hA, A); // execute computations // GPU lapack CHECK_ROCBLAS_ERROR(hipsolver_syevdx_heevdx(API, handle, evect, erange, uplo, n, dA.data(), lda, stA, vl, vu, il, iu, hNevRes.data(), dW.data(), stW, dWork.data(), lwork, dinfo.data(), bc)); CHECK_HIP_ERROR(hWRes.transfer_from(dW)); CHECK_HIP_ERROR(hinfoRes.transfer_from(dinfo)); if(evect == HIPSOLVER_EIG_MODE_VECTOR) CHECK_HIP_ERROR(hARes.transfer_from(dA)); // CPU lapack for(int b = 0; b < bc; ++b) cpu_syevx_heevx(evect, erange, uplo, n, hA[b], lda, vl, vu, il, iu, abstol, hNev[b], hW[b], Z.data(), lda, work.data(), size_work, rwork.data(), iwork.data(), ifail.data(), hinfo[b]); // Check info for non-convergence *max_err = 0; for(int b = 0; b < bc; ++b) { EXPECT_EQ(hinfo[b][0], hinfoRes[b][0]) << "where b = " << b; if(hinfo[b][0] != hinfoRes[b][0]) *max_err += 1; } // Check number of returned eigenvalues *max_err = 0; for(int b = 0; b < bc; ++b) { EXPECT_EQ(hNev[b][0], hNevRes[b][0]) << "where b = " << b; if(hNev[b][0] != hNevRes[b][0]) *max_err += 1; } // (We expect the used input matrices to always converge. Testing // implicitly the equivalent non-converged matrix is very complicated and it boils // down to essentially run the algorithm again and until convergence is achieved). double err = 0; for(int b = 0; b < bc; ++b) { if(evect != HIPSOLVER_EIG_MODE_VECTOR) { // only eigenvalues needed; can compare with LAPACK // error is ||hW - hWRes|| / ||hW|| // using frobenius norm if(hinfo[b][0] == 0) err = norm_error('F', 1, hNev[b][0], 1, hW[b], hWRes[b]); *max_err = err > *max_err ? err : *max_err; } else { // both eigenvalues and eigenvectors needed; need to implicitly test // eigenvectors due to non-uniqueness of eigenvectors under scaling if(hinfo[b][0] == 0) { // multiply A with each of the m eigenvectors and divide by corresponding // eigenvalues T alpha; T beta = 0; for(int j = 0; j < hNev[b][0]; j++) { alpha = T(1) / hWRes[b][j]; cpu_symv_hemv(uplo, n, alpha, A.data() + b * lda * n, lda, hARes[b] + j * lda, 1, beta, hA[b] + j * lda, 1); } // error is ||hA - hARes|| / ||hA|| // using frobenius norm err = norm_error('F', n, hNev[b][0], lda, hA[b], hARes[b]); *max_err = err > *max_err ? err : *max_err; } } } } template void syevdx_heevdx_getPerfData(const hipsolverHandle_t handle, const hipsolverEigMode_t evect, const hipsolverEigRange_t erange, const hipsolverFillMode_t uplo, const int n, Td& dA, const int lda, const int stA, const S vl, const S vu, const int il, const int iu, Ih& hNevRes, Sd& dW, const int stW, Td& dWork, const int lwork, Id& dinfo, const int bc, Th& hA, Ih& hNev, Sh& hW, Ih& hinfo, double* gpu_time_used, double* cpu_time_used, const int hot_calls, const bool perf) { constexpr bool COMPLEX = is_complex; int size_work = !COMPLEX ? 35 * n : 33 * n; int size_rwork = !COMPLEX ? 0 : 7 * n; int size_iwork = 5 * n; std::vector work(size_work); std::vector rwork(size_rwork); std::vector iwork(size_iwork); std::vector A; std::vector Z(lda * n); std::vector ifail(n); S abstol = 2 * get_safemin(); if(!perf) { syevdx_heevdx_initData(handle, evect, n, dA, lda, bc, hA, A, 0); // cpu-lapack performance (only if not in perf mode) *cpu_time_used = get_time_us_no_sync(); for(int b = 0; b < bc; ++b) cpu_syevx_heevx(evect, erange, uplo, n, hA[b], lda, vl, vu, il, iu, abstol, hNev[b], hW[b], Z.data(), lda, work.data(), size_work, rwork.data(), iwork.data(), ifail.data(), hinfo[b]); *cpu_time_used = get_time_us_no_sync() - *cpu_time_used; } syevdx_heevdx_initData(handle, evect, n, dA, lda, bc, hA, A, 0); // cold calls for(int iter = 0; iter < 2; iter++) { syevdx_heevdx_initData(handle, evect, n, dA, lda, bc, hA, A, 0); CHECK_ROCBLAS_ERROR(hipsolver_syevdx_heevdx(API, handle, evect, erange, uplo, n, dA.data(), lda, stA, vl, vu, il, iu, hNevRes.data(), dW.data(), stW, dWork.data(), lwork, dinfo.data(), bc)); } // gpu-lapack performance hipStream_t stream; CHECK_ROCBLAS_ERROR(hipsolverGetStream(handle, &stream)); double start; for(int iter = 0; iter < hot_calls; iter++) { syevdx_heevdx_initData(handle, evect, n, dA, lda, bc, hA, A, 0); start = get_time_us_sync(stream); hipsolver_syevdx_heevdx(API, handle, evect, erange, uplo, n, dA.data(), lda, stA, vl, vu, il, iu, hNevRes.data(), dW.data(), stW, dWork.data(), lwork, dinfo.data(), bc); *gpu_time_used += get_time_us_sync(stream) - start; } *gpu_time_used /= hot_calls; } template void testing_syevdx_heevdx(Arguments& argus) { using S = decltype(std::real(T{})); // get arguments hipsolver_local_handle handle; char evectC = argus.get("jobz"); char erangeC = argus.get("range"); char uploC = argus.get("uplo"); int n = argus.get("n"); int lda = argus.get("lda", n); int stA = argus.get("strideA", lda * n); int stW = argus.get("strideW", n); S vl = S(argus.get("vl", 0)); S vu = S(argus.get("vu", erangeC == 'V' ? 1 : 0)); int il = argus.get("il", erangeC == 'I' ? 1 : 0); int iu = argus.get("iu", erangeC == 'I' ? 1 : 0); hipsolverEigMode_t evect = char2hipsolver_evect(evectC); hipsolverEigRange_t erange = char2hipsolver_erange(erangeC); hipsolverFillMode_t uplo = char2hipsolver_fill(uploC); int bc = argus.batch_count; int hot_calls = argus.iters; // determine sizes size_t size_A = size_t(lda) * n; size_t size_W = n; size_t size_ARes = (argus.unit_check || argus.norm_check) ? size_A : 0; size_t size_WRes = (argus.unit_check || argus.norm_check) ? size_W : 0; double max_error = 0, gpu_time_used = 0, cpu_time_used = 0; // check invalid sizes bool invalid_size = (n < 0 || lda < n || bc < 0 || (erange == HIPSOLVER_EIG_RANGE_V && vl >= vu) || (erange == HIPSOLVER_EIG_RANGE_I && (il < 1 || iu < 0)) || (erange == HIPSOLVER_EIG_RANGE_I && (iu > n || (n > 0 && il > iu)))); if(invalid_size) { if(BATCHED) { // EXPECT_ROCBLAS_STATUS(hipsolver_syevdx_heevdx(API, // handle, // evect, // erange, // uplo, // n, // (T* const*)nullptr, // lda, // stA, // vl, // vu, // il, // iu, // (int*)nullptr, // (S*)nullptr, // stW, // (T*)nullptr, // 0, // (int*)nullptr, // bc), // HIPSOLVER_STATUS_INVALID_VALUE); } else { EXPECT_ROCBLAS_STATUS(hipsolver_syevdx_heevdx(API, handle, evect, erange, uplo, n, (T*)nullptr, lda, stA, vl, vu, il, iu, (int*)nullptr, (S*)nullptr, stW, (T*)nullptr, 0, (int*)nullptr, bc), HIPSOLVER_STATUS_INVALID_VALUE); } if(argus.timing) rocsolver_bench_inform(inform_invalid_size); return; } // memory size query is necessary int size_Work; hipsolver_syevdx_heevdx_bufferSize(API, handle, evect, erange, uplo, n, (T*)nullptr, lda, vl, vu, il, iu, (int*)nullptr, (S*)nullptr, &size_Work); size_t bytes_Work = std::getenv("HIPSOLVER_BUFFERSIZE_RETURN_BYTES") != nullptr ? size_Work : sizeof(T) * size_Work; if(argus.mem_query) { rocsolver_bench_inform(inform_mem_query, bytes_Work); return; } // memory allocations (all cases) // host host_strided_batch_vector hNev(1, 1, 1, bc); host_strided_batch_vector hNevRes(1, 1, 1, bc); host_strided_batch_vector hW(size_W, 1, stW, bc); host_strided_batch_vector hinfo(1, 1, 1, bc); host_strided_batch_vector hinfoRes(1, 1, 1, bc); host_strided_batch_vector hWres(size_WRes, 1, stW, bc); // device device_strided_batch_vector dW(size_W, 1, stW, bc); device_strided_batch_vector dinfo(1, 1, 1, bc); device_strided_batch_vector dWork( bytes_Work, 1, bytes_Work, 1); // bytes_Work accounts for bc if(size_W) CHECK_HIP_ERROR(dW.memcheck()); CHECK_HIP_ERROR(dinfo.memcheck()); if(size_Work) CHECK_HIP_ERROR(dWork.memcheck()); if(BATCHED) { // // memory allocations // host_batch_vector hA(size_A, 1, bc); // host_batch_vector hARes(size_ARes, 1, bc); // device_batch_vector dA(size_A, 1, bc); // if(size_A) // CHECK_HIP_ERROR(dA.memcheck()); // // check computations // if(argus.unit_check || argus.norm_check) // { // syevdx_heevdx_getError(handle, // evect, // erange, // uplo, // n, // dA, // lda, // stA, // vl, // vu, // il, // iu, // hNevRes, // dW, // stW, // dWork, // size_Work, // dinfo, // bc, // hA, // hARes, // hNev, // hW, // hWres, // hinfo, // hinfoRes, // &max_error); // } // // collect performance data // if(argus.timing) // { // syevdx_heevdx_getPerfData(handle, // evect, // erange, // uplo, // n, // dA, // lda, // stA, // vl, // vu, // il, // iu, // hNevRes, // dW, // stW, // dWork, // size_Work, // dinfo, // bc, // hA, // hNev, // hW, // hinfo, // &gpu_time_used, // &cpu_time_used, // hot_calls, // argus.perf); // } } else { // memory allocations host_strided_batch_vector hA(size_A, 1, stA, bc); host_strided_batch_vector hARes(size_ARes, 1, stA, bc); device_strided_batch_vector dA(size_A, 1, stA, bc); if(size_A) CHECK_HIP_ERROR(dA.memcheck()); // check computations if(argus.unit_check || argus.norm_check) { syevdx_heevdx_getError(handle, evect, erange, uplo, n, dA, lda, stA, vl, vu, il, iu, hNevRes, dW, stW, dWork, size_Work, dinfo, bc, hA, hARes, hNev, hW, hWres, hinfo, hinfoRes, &max_error); } // collect performance data if(argus.timing) { syevdx_heevdx_getPerfData(handle, evect, erange, uplo, n, dA, lda, stA, vl, vu, il, iu, hNevRes, dW, stW, dWork, size_Work, dinfo, bc, hA, hNev, hW, hinfo, &gpu_time_used, &cpu_time_used, hot_calls, argus.perf); } } // validate results for rocsolver-test // using 3 * n * machine_precision as tolerance if(argus.unit_check) ROCSOLVER_TEST_CHECK(T, max_error, 3 * 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("jobz", "range", "uplo", "n", "lda", "vl", "vu", "il", "iu", "strideW", "batch_c"); rocsolver_bench_output(evectC, erangeC, uploC, n, lda, vl, vu, il, iu, stW, bc); } else if(STRIDED) { rocsolver_bench_output("jobz", "range", "uplo", "n", "lda", "strideA", "vl", "vu", "il", "iu", "strideW", "batch_c"); rocsolver_bench_output( evectC, erangeC, uploC, n, lda, stA, vl, vu, il, iu, stW, bc); } else { rocsolver_bench_output("jobz", "range", "uplo", "n", "lda", "vl", "vu", "il", "iu"); rocsolver_bench_output(evectC, erangeC, uploC, n, lda, vl, vu, il, iu); } 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(); }