/* ************************************************************************ * Copyright 2021-2022 Advanced Micro Devices, Inc. * ************************************************************************ */ #pragma once #include "clientcommon.hpp" template void syevj_heevj_checkBadArgs(const hipsolverHandle_t handle, const hipsolverEigMode_t evect, const hipsolverFillMode_t uplo, const int n, T dA, const int lda, const int stA, S dD, const int stD, T dWork, const int lwork, U dinfo, const hipsolverSyevjInfo_t params, const int bc) { #if defined(__HIP_PLATFORM_HCC__) || defined(__HIP_PLATFORM_AMD__) // handle EXPECT_ROCBLAS_STATUS(hipsolver_syevj_heevj(API, STRIDED, nullptr, evect, uplo, n, dA, lda, stA, dD, stD, dWork, lwork, dinfo, params, bc), HIPSOLVER_STATUS_NOT_INITIALIZED); // values EXPECT_ROCBLAS_STATUS(hipsolver_syevj_heevj(API, STRIDED, handle, hipsolverEigMode_t(-1), uplo, n, dA, lda, stA, dD, stD, dWork, lwork, dinfo, params, bc), HIPSOLVER_STATUS_INVALID_ENUM); EXPECT_ROCBLAS_STATUS(hipsolver_syevj_heevj(API, STRIDED, handle, evect, hipsolverFillMode_t(-1), n, dA, lda, stA, dD, stD, dWork, lwork, dinfo, params, bc), HIPSOLVER_STATUS_INVALID_ENUM); // pointers EXPECT_ROCBLAS_STATUS(hipsolver_syevj_heevj(API, STRIDED, handle, evect, uplo, n, (T) nullptr, lda, stA, dD, stD, dWork, lwork, dinfo, params, bc), HIPSOLVER_STATUS_INVALID_VALUE); EXPECT_ROCBLAS_STATUS(hipsolver_syevj_heevj(API, STRIDED, handle, evect, uplo, n, dA, lda, stA, (S) nullptr, stD, dWork, lwork, dinfo, params, bc), HIPSOLVER_STATUS_INVALID_VALUE); EXPECT_ROCBLAS_STATUS(hipsolver_syevj_heevj(API, STRIDED, handle, evect, uplo, n, dA, lda, stA, dD, stD, dWork, lwork, (U) nullptr, params, bc), HIPSOLVER_STATUS_INVALID_VALUE); #endif } template void testing_syevj_heevj_bad_arg() { using S = decltype(std::real(T{})); // safe arguments hipsolver_local_handle handle; hipsolver_local_syevj_info params; hipsolverEigMode_t evect = HIPSOLVER_EIG_MODE_NOVECTOR; hipsolverFillMode_t uplo = HIPSOLVER_FILL_MODE_LOWER; int n = 1; int lda = 1; int stA = 1; int stD = 1; int bc = 1; if(BATCHED) { // // memory allocations // device_batch_vector dA(1, 1, 1); // device_strided_batch_vector dD(1, 1, 1, 1); // device_strided_batch_vector dinfo(1, 1, 1, 1); // CHECK_HIP_ERROR(dA.memcheck()); // CHECK_HIP_ERROR(dD.memcheck()); // CHECK_HIP_ERROR(dinfo.memcheck()); // int size_W; // hipsolver_syevj_heevj_bufferSize( // API, handle, evect, uplo, n, dA.data(), lda, dD.data(), &size_W, params, bc); // device_strided_batch_vector dWork(size_W, 1, size_W, bc); // if(size_W) // CHECK_HIP_ERROR(dWork.memcheck()); // // check bad arguments // syevj_heevj_checkBadArgs(handle, // evect, // uplo, // n, // dA.data(), // lda, // stA, // dD.data(), // stD, // dWork.data(), // size_W, // dinfo.data(), // params, // bc); } else { // memory allocations device_strided_batch_vector dA(1, 1, 1, 1); device_strided_batch_vector dD(1, 1, 1, 1); device_strided_batch_vector dinfo(1, 1, 1, 1); CHECK_HIP_ERROR(dA.memcheck()); CHECK_HIP_ERROR(dD.memcheck()); CHECK_HIP_ERROR(dinfo.memcheck()); int size_W; hipsolver_syevj_heevj_bufferSize( API, STRIDED, handle, evect, uplo, n, dA.data(), lda, dD.data(), &size_W, params, bc); device_strided_batch_vector dWork(size_W, 1, size_W, bc); if(size_W) CHECK_HIP_ERROR(dWork.memcheck()); // check bad arguments syevj_heevj_checkBadArgs(handle, evect, uplo, n, dA.data(), lda, stA, dD.data(), stD, dWork.data(), size_W, dinfo.data(), params, bc); } } template void syevj_heevj_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 = 0; j < n; j++) { if(i == j) hA[b][i + j * lda] += 400; else hA[b][i + j * lda] -= 4; } } // 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 syevj_heevj_getError(const hipsolverHandle_t handle, const hipsolverEigMode_t evect, const hipsolverFillMode_t uplo, const int n, Td& dA, const int lda, const int stA, Sd& dD, const int stD, Td& dWork, const int lwork, Id& dinfo, hipsolverSyevjInfo_t params, const int bc, Th& hA, Th& hAres, Sh& hD, Sh& hDres, Ih& hinfo, Ih& hinfoRes, double* max_err) { constexpr bool COMPLEX = is_complex; using S = decltype(std::real(T{})); int sizeE, ltwork; if(!COMPLEX) { sizeE = (evect == HIPSOLVER_EIG_MODE_NOVECTOR ? 2 * n + 1 : 1 + 6 * n + 2 * n * n); ltwork = 0; } else { sizeE = (evect == HIPSOLVER_EIG_MODE_NOVECTOR ? n : 1 + 5 * n + 2 * n * n); ltwork = (evect == HIPSOLVER_EIG_MODE_NOVECTOR ? n + 1 : 2 * n + n * n); } int liwork = (evect == HIPSOLVER_EIG_MODE_NOVECTOR ? 1 : 3 + 5 * n); std::vector work(ltwork); std::vector hE(sizeE); std::vector iwork(liwork); std::vector A(lda * n * bc); // input data initialization syevj_heevj_initData(handle, evect, n, dA, lda, bc, hA, A); // execute computations // GPU lapack CHECK_ROCBLAS_ERROR(hipsolver_syevj_heevj(API, STRIDED, handle, evect, uplo, n, dA.data(), lda, stA, dD.data(), stD, dWork.data(), lwork, dinfo.data(), params, bc)); CHECK_HIP_ERROR(hDres.transfer_from(dD)); 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) cblas_syevd_heevd(evect, uplo, n, hA[b], lda, hD[b], work.data(), ltwork, hE.data(), sizeE, iwork.data(), liwork, hinfo[b]); // Check info for non-convergence *max_err = 0; for(int b = 0; b < bc; ++b) if(hinfo[b][0] != hinfoRes[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 ||hD - hDRes|| / ||hD|| // using frobenius norm if(hinfo[b][0] == 0) err = norm_error('F', 1, n, 1, hD[b], hDres[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 n eigenvectors and divide by corresponding // eigenvalues T alpha; T beta = 0; for(int j = 0; j < n; j++) { alpha = T(1) / hDres[b][j]; cblas_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, n, lda, hA[b], hAres[b]); *max_err = err > *max_err ? err : *max_err; } } } } template void syevj_heevj_getPerfData(const hipsolverHandle_t handle, const hipsolverEigMode_t evect, const hipsolverFillMode_t uplo, const int n, Td& dA, const int lda, const int stA, Sd& dD, const int stD, Td& dWork, const int lwork, Id& dinfo, hipsolverSyevjInfo_t params, const int bc, Th& hA, Sh& hD, Ih& hinfo, double* gpu_time_used, double* cpu_time_used, const int hot_calls, const bool perf) { constexpr bool COMPLEX = is_complex; using S = decltype(std::real(T{})); int sizeE, ltwork; if(!COMPLEX) { sizeE = (evect == HIPSOLVER_EIG_MODE_NOVECTOR ? 2 * n + 1 : 1 + 6 * n + 2 * n * n); ltwork = 0; } else { sizeE = (evect == HIPSOLVER_EIG_MODE_NOVECTOR ? n : 1 + 5 * n + 2 * n * n); ltwork = (evect == HIPSOLVER_EIG_MODE_NOVECTOR ? n + 1 : 2 * n + n * n); } int liwork = (evect == HIPSOLVER_EIG_MODE_NOVECTOR ? 1 : 3 + 5 * n); std::vector work(ltwork); std::vector hE(sizeE); std::vector iwork(liwork); std::vector A; if(!perf) { syevj_heevj_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) cblas_syevd_heevd(evect, uplo, n, hA[b], lda, hD[b], work.data(), ltwork, hE.data(), sizeE, iwork.data(), liwork, hinfo[b]); *cpu_time_used = get_time_us_no_sync() - *cpu_time_used; } syevj_heevj_initData(handle, evect, n, dA, lda, bc, hA, A, 0); // cold calls for(int iter = 0; iter < 2; iter++) { syevj_heevj_initData(handle, evect, n, dA, lda, bc, hA, A, 0); CHECK_ROCBLAS_ERROR(hipsolver_syevj_heevj(API, STRIDED, handle, evect, uplo, n, dA.data(), lda, stA, dD.data(), stD, dWork.data(), lwork, dinfo.data(), params, bc)); } // gpu-lapack performance hipStream_t stream; CHECK_ROCBLAS_ERROR(hipsolverGetStream(handle, &stream)); double start; for(int iter = 0; iter < hot_calls; iter++) { syevj_heevj_initData(handle, evect, n, dA, lda, bc, hA, A, 0); start = get_time_us_sync(stream); hipsolver_syevj_heevj(API, STRIDED, handle, evect, uplo, n, dA.data(), lda, stA, dD.data(), stD, dWork.data(), lwork, dinfo.data(), params, bc); *gpu_time_used += get_time_us_sync(stream) - start; } *gpu_time_used /= hot_calls; } template void testing_syevj_heevj(Arguments& argus) { using S = decltype(std::real(T{})); // get arguments hipsolver_local_handle handle; hipsolver_local_syevj_info params; char evectC = argus.get("jobz"); char uploC = argus.get("uplo"); int n = argus.get("n"); int lda = argus.get("lda", n); int stA = lda * n; int stD = n; hipsolverEigMode_t evect = char2hipsolver_evect(evectC); 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_D = n; size_t size_Ares = (argus.unit_check || argus.norm_check) ? size_A : 0; size_t size_Dres = (argus.unit_check || argus.norm_check) ? size_D : 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); if(invalid_size) { if(BATCHED) { // EXPECT_ROCBLAS_STATUS(hipsolver_syevj_heevj(API, // STRIDED, // handle, // evect, // uplo, // n, // (T* const*)nullptr, // lda, // stA, // (S*)nullptr, // stD, // (T*)nullptr, // 0, // (int*)nullptr, // params, // bc), // HIPSOLVER_STATUS_INVALID_VALUE); } else { EXPECT_ROCBLAS_STATUS(hipsolver_syevj_heevj(API, STRIDED, handle, evect, uplo, n, (T*)nullptr, lda, stA, (S*)nullptr, stD, (T*)nullptr, 0, (int*)nullptr, params, bc), HIPSOLVER_STATUS_INVALID_VALUE); } if(argus.timing) ROCSOLVER_BENCH_INFORM(1); return; } // memory allocations (all cases) // host host_strided_batch_vector hD(size_D, 1, stD, bc); host_strided_batch_vector hinfo(1, 1, 1, bc); host_strided_batch_vector hinfoRes(1, 1, 1, bc); host_strided_batch_vector hDres(size_Dres, 1, stD, bc); // device device_strided_batch_vector dD(size_D, 1, stD, bc); device_strided_batch_vector dinfo(1, 1, 1, bc); if(size_D) CHECK_HIP_ERROR(dD.memcheck()); CHECK_HIP_ERROR(dinfo.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()); // int size_W; // hipsolver_syevj_heevj_bufferSize( // API, STRIDED, handle, evect, uplo, n, dA.data(), lda, dD.data(), &size_W, params, bc); // device_strided_batch_vector dWork(size_W, 1, size_W, bc); // if(size_W) // CHECK_HIP_ERROR(dWork.memcheck()); // // check computations // if(argus.unit_check || argus.norm_check) // { // syevj_heevj_getError(handle, // evect, // uplo, // n, // dA, // lda, // stA, // dD, // stD, // dWork, // size_W, // dinfo, // params, // bc, // hA, // hAres, // hD, // hDres, // hinfo, // hinfoRes, // &max_error); // } // // collect performance data // if(argus.timing) // { // syevj_heevj_getPerfData(handle, // evect, // uplo, // n, // dA, // lda, // stA, // dD, // stD, // dWork, // size_W, // dinfo, // params, // bc, // hA, // hD, // 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()); int size_W; hipsolver_syevj_heevj_bufferSize( API, STRIDED, handle, evect, uplo, n, dA.data(), lda, dD.data(), &size_W, params, bc); device_strided_batch_vector dWork(size_W, 1, size_W, bc); if(size_W) CHECK_HIP_ERROR(dWork.memcheck()); // check computations if(argus.unit_check || argus.norm_check) { syevj_heevj_getError(handle, evect, uplo, n, dA, lda, stA, dD, stD, dWork, size_W, dinfo, params, bc, hA, hAres, hD, hDres, hinfo, hinfoRes, &max_error); } // collect performance data if(argus.timing) { syevj_heevj_getPerfData(handle, evect, uplo, n, dA, lda, stA, dD, stD, dWork, size_W, dinfo, params, bc, hA, hD, hinfo, &gpu_time_used, &cpu_time_used, hot_calls, argus.perf); } } // validate results for rocsolver-test // using 4 * n * machine_precision as tolerance if(argus.unit_check) ROCSOLVER_TEST_CHECK(T, max_error, 4 * 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", "uplo", "n", "lda", "strideD", "batch_c"); rocsolver_bench_output(evectC, uploC, n, lda, stD, bc); } else if(STRIDED) { rocsolver_bench_output("jobz", "uplo", "n", "lda", "strideA", "strideD", "batch_c"); rocsolver_bench_output(evectC, uploC, n, lda, stA, stD, bc); } else { rocsolver_bench_output("jobz", "uplo", "n", "lda"); rocsolver_bench_output(evectC, uploC, 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(); }