/* ************************************************************************ * Copyright 2020-2022 Advanced Micro Devices, Inc. * ************************************************************************ */ #pragma once #include "clientcommon.hpp" template void sygvj_hegvj_checkBadArgs(const hipsolverHandle_t handle, const hipsolverEigType_t itype, const hipsolverEigMode_t evect, const hipsolverFillMode_t uplo, const int n, T dA, const int lda, const int stA, T dB, const int ldb, const int stB, U dD, const int stD, T dWork, const int lwork, int* dInfo, hipsolverSyevjInfo_t params, const int bc) { // handle EXPECT_ROCBLAS_STATUS(hipsolver_sygvj_hegvj(API, nullptr, itype, evect, uplo, n, dA, lda, stA, dB, ldb, stB, dD, stD, dWork, lwork, dInfo, params, bc), HIPSOLVER_STATUS_NOT_INITIALIZED); // values EXPECT_ROCBLAS_STATUS(hipsolver_sygvj_hegvj(API, handle, hipsolverEigType_t(-1), evect, uplo, n, dA, lda, stA, dB, ldb, stB, dD, stD, dWork, lwork, dInfo, params, bc), HIPSOLVER_STATUS_INVALID_ENUM); EXPECT_ROCBLAS_STATUS(hipsolver_sygvj_hegvj(API, handle, itype, hipsolverEigMode_t(-1), uplo, n, dA, lda, stA, dB, ldb, stB, dD, stD, dWork, lwork, dInfo, params, bc), HIPSOLVER_STATUS_INVALID_ENUM); EXPECT_ROCBLAS_STATUS(hipsolver_sygvj_hegvj(API, handle, itype, evect, hipsolverFillMode_t(-1), n, dA, lda, stA, dB, ldb, stB, dD, stD, dWork, lwork, dInfo, params, bc), HIPSOLVER_STATUS_INVALID_ENUM); #if defined(__HIP_PLATFORM_HCC__) || defined(__HIP_PLATFORM_AMD__) // pointers EXPECT_ROCBLAS_STATUS(hipsolver_sygvj_hegvj(API, handle, itype, evect, uplo, n, (T) nullptr, lda, stA, dB, ldb, stB, dD, stD, dWork, lwork, dInfo, params, bc), HIPSOLVER_STATUS_INVALID_VALUE); EXPECT_ROCBLAS_STATUS(hipsolver_sygvj_hegvj(API, handle, itype, evect, uplo, n, dA, lda, stA, (T) nullptr, ldb, stB, dD, stD, dWork, lwork, dInfo, params, bc), HIPSOLVER_STATUS_INVALID_VALUE); EXPECT_ROCBLAS_STATUS(hipsolver_sygvj_hegvj(API, handle, itype, evect, uplo, n, dA, lda, stA, dB, ldb, stB, (U) nullptr, stD, dWork, lwork, dInfo, params, bc), HIPSOLVER_STATUS_INVALID_VALUE); EXPECT_ROCBLAS_STATUS(hipsolver_sygvj_hegvj(API, handle, itype, evect, uplo, n, dA, lda, stA, dB, ldb, stB, dD, stD, dWork, lwork, (int*)nullptr, params, bc), HIPSOLVER_STATUS_INVALID_VALUE); #endif } template void testing_sygvj_hegvj_bad_arg() { using S = decltype(std::real(T{})); // safe arguments hipsolver_local_handle handle; hipsolver_local_syevj_info params; int n = 1; int lda = 1; int ldb = 1; int stA = 1; int stB = 1; int stD = 1; int bc = 1; hipsolverEigType_t itype = HIPSOLVER_EIG_TYPE_1; hipsolverEigMode_t evect = HIPSOLVER_EIG_MODE_NOVECTOR; hipsolverFillMode_t uplo = HIPSOLVER_FILL_MODE_UPPER; if(BATCHED) { // // memory allocations // device_batch_vector dA(1, 1, 1); // device_batch_vector dB(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(dB.memcheck()); // CHECK_HIP_ERROR(dD.memcheck()); // CHECK_HIP_ERROR(dInfo.memcheck()); // int size_W; // hipsolver_sygvj_hegvj_bufferSize(API, // handle, // itype, // evect, // uplo, // n, // dA.data(), // lda, // dB.data(), // ldb, // dD.data(), // &size_W, // params); // device_strided_batch_vector dWork(size_W, 1, size_W, bc); // if(size_W) // CHECK_HIP_ERROR(dWork.memcheck()); // // check bad arguments // sygvj_hegvj_checkBadArgs(handle, // itype, // evect, // uplo, // n, // dA.data(), // lda, // stA, // dB.data(), // ldb, // stB, // 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 dB(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(dB.memcheck()); CHECK_HIP_ERROR(dD.memcheck()); CHECK_HIP_ERROR(dInfo.memcheck()); int size_W; hipsolver_sygvj_hegvj_bufferSize(API, handle, itype, evect, uplo, n, dA.data(), lda, dB.data(), ldb, dD.data(), &size_W, params); device_strided_batch_vector dWork(size_W, 1, size_W, bc); if(size_W) CHECK_HIP_ERROR(dWork.memcheck()); // check bad arguments sygvj_hegvj_checkBadArgs(handle, itype, evect, uplo, n, dA.data(), lda, stA, dB.data(), ldb, stB, dD.data(), stD, dWork.data(), size_W, dInfo.data(), params, bc); } } template void sygvj_hegvj_initData(const hipsolverHandle_t handle, const hipsolverEigType_t itype, const hipsolverEigMode_t evect, const int n, Td& dA, const int lda, const int stA, Td& dB, const int ldb, const int stB, const int bc, Th& hA, Th& hB, host_strided_batch_vector& A, host_strided_batch_vector& B, const bool test, const bool singular) { if(CPU) { rocblas_init(hA, true); rocblas_init(hB, false); 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] = std::real(hA[b][i + j * lda]) + 400; hB[b][i + j * ldb] = std::real(hB[b][i + j * ldb]) + 400; } else { hA[b][i + j * lda] -= 4; } } } // store A and B for testing purposes if(test && evect != HIPSOLVER_EIG_MODE_NOVECTOR) { for(int i = 0; i < n; i++) { for(int j = 0; j < n; j++) { if(itype != HIPSOLVER_EIG_TYPE_3) { A[b][i + j * lda] = hA[b][i + j * lda]; B[b][i + j * ldb] = hB[b][i + j * ldb]; } else { A[b][i + j * lda] = hB[b][i + j * ldb]; B[b][i + j * ldb] = hA[b][i + j * lda]; } } } } } } if(GPU) { // now copy data to the GPU CHECK_HIP_ERROR(dA.transfer_from(hA)); CHECK_HIP_ERROR(dB.transfer_from(hB)); } } template void sygvj_hegvj_getError(const hipsolverHandle_t handle, const hipsolverEigType_t itype, const hipsolverEigMode_t evect, const hipsolverFillMode_t uplo, const int n, Td& dA, const int lda, const int stA, Td& dB, const int ldb, const int stB, Ud& dD, const int stD, Td& dWork, const int lwork, Vd& dInfo, hipsolverSyevjInfo_t params, const int bc, Th& hA, Th& hARes, Th& hB, Uh& hD, Uh& hDRes, Vh& hInfo, Vh& hInfoRes, double* max_err, const bool singular) { constexpr bool COMPLEX = is_complex; using S = decltype(std::real(T{})); int lrwork, ltwork; if(!COMPLEX) { lrwork = (evect == HIPSOLVER_EIG_MODE_NOVECTOR ? 2 * n + 1 : 1 + 6 * n + 2 * n * n); ltwork = 0; } else { lrwork = (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 rwork(lrwork); std::vector iwork(liwork); host_strided_batch_vector A(lda * n, 1, lda * n, bc); host_strided_batch_vector B(ldb * n, 1, ldb * n, bc); // input data initialization sygvj_hegvj_initData( handle, itype, evect, n, dA, lda, stA, dB, ldb, stB, bc, hA, hB, A, B, true, singular); // execute computations // GPU lapack CHECK_ROCBLAS_ERROR(hipsolver_sygvj_hegvj(API, handle, itype, evect, uplo, n, dA.data(), lda, stA, dB.data(), ldb, stB, 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_NOVECTOR) CHECK_HIP_ERROR(hARes.transfer_from(dA)); // CPU lapack for(int b = 0; b < bc; ++b) { cblas_sygvd_hegvd(itype, evect, uplo, n, hA[b], lda, hB[b], ldb, hD[b], work.data(), ltwork, rwork.data(), lrwork, iwork.data(), liwork, hInfo[b]); } // (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. // We do test with indefinite matrices B). // check info for non-convergence and/or positive-definiteness *max_err = 0; for(int b = 0; b < bc; ++b) if(hInfo[b][0] != hInfoRes[b][0]) *max_err += 1; double err; if(evect == HIPSOLVER_EIG_MODE_NOVECTOR) { // only eigenvalues needed; can compare with LAPACK // error is ||hD - hDRes|| / ||hD|| // using frobenius norm for(int b = 0; b < bc; ++b) { if(hInfoRes[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 for(int b = 0; b < bc; ++b) { if(hInfoRes[b][0] == 0) { T alpha = 1; T beta = 0; // hARes contains eigenvectors x // compute B*x (or A*x) and store in hB cblas_symm_hemm(HIPSOLVER_SIDE_LEFT, uplo, n, n, alpha, B[b], ldb, hARes[b], lda, beta, hB[b], ldb); if(itype == HIPSOLVER_EIG_TYPE_1) { // problem is A*x = (lambda)*B*x // compute (1/lambda)*A*x and store in hA for(int j = 0; j < n; j++) { alpha = T(1) / hDRes[b][j]; cblas_symv_hemv(uplo, n, alpha, A[b], lda, hARes[b] + j * lda, 1, beta, hA[b] + j * lda, 1); } // move B*x into hARes for(int i = 0; i < n; i++) for(int j = 0; j < n; j++) hARes[b][i + j * lda] = hB[b][i + j * ldb]; } else { // problem is A*B*x = (lambda)*x or B*A*x = (lambda)*x // compute (1/lambda)*A*B*x or (1/lambda)*B*A*x and store in hA for(int j = 0; j < n; j++) { alpha = T(1) / hDRes[b][j]; cblas_symv_hemv(uplo, n, alpha, A[b], lda, hB[b] + j * ldb, 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 sygvj_hegvj_getPerfData(const hipsolverHandle_t handle, const hipsolverEigType_t itype, const hipsolverEigMode_t evect, const hipsolverFillMode_t uplo, const int n, Td& dA, const int lda, const int stA, Td& dB, const int ldb, const int stB, Ud& dD, const int stD, Td& dWork, const int lwork, Vd& dInfo, hipsolverSyevjInfo_t params, const int bc, Th& hA, Th& hB, Uh& hD, Vh& hInfo, double* gpu_time_used, double* cpu_time_used, const int hot_calls, const bool perf, const bool singular) { constexpr bool COMPLEX = is_complex; using S = decltype(std::real(T{})); int lrwork, ltwork; if(!COMPLEX) { lrwork = (evect == HIPSOLVER_EIG_MODE_NOVECTOR ? 2 * n + 1 : 1 + 6 * n + 2 * n * n); ltwork = 0; } else { lrwork = (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 rwork(lrwork); std::vector iwork(liwork); host_strided_batch_vector A(1, 1, 1, 1); host_strided_batch_vector B(1, 1, 1, 1); if(!perf) { sygvj_hegvj_initData( handle, itype, evect, n, dA, lda, stA, dB, ldb, stB, bc, hA, hB, A, B, false, singular); // 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_sygvd_hegvd(itype, evect, uplo, n, hA[b], lda, hB[b], ldb, hD[b], work.data(), ltwork, rwork.data(), lrwork, iwork.data(), liwork, hInfo[b]); } *cpu_time_used = get_time_us_no_sync() - *cpu_time_used; } sygvj_hegvj_initData( handle, itype, evect, n, dA, lda, stA, dB, ldb, stB, bc, hA, hB, A, B, false, singular); // cold calls for(int iter = 0; iter < 2; iter++) { sygvj_hegvj_initData( handle, itype, evect, n, dA, lda, stA, dB, ldb, stB, bc, hA, hB, A, B, false, singular); CHECK_ROCBLAS_ERROR(hipsolver_sygvj_hegvj(API, handle, itype, evect, uplo, n, dA.data(), lda, stA, dB.data(), ldb, stB, 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++) { sygvj_hegvj_initData( handle, itype, evect, n, dA, lda, stA, dB, ldb, stB, bc, hA, hB, A, B, false, singular); start = get_time_us_sync(stream); hipsolver_sygvj_hegvj(API, handle, itype, evect, uplo, n, dA.data(), lda, stA, dB.data(), ldb, stB, 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_sygvj_hegvj(Arguments& argus) { using S = decltype(std::real(T{})); // get arguments hipsolver_local_handle handle; hipsolver_local_syevj_info params; char itypeC = argus.get("itype"); char evectC = argus.get("jobz"); char uploC = argus.get("uplo"); int n = argus.get("n"); int lda = argus.get("lda", n); int ldb = argus.get("ldb", n); int stA = argus.get("strideA", lda * n); int stB = argus.get("strideB", ldb * n); int stD = argus.get("strideD", n); hipsolverEigType_t itype = char2hipsolver_eform(itypeC); hipsolverEigMode_t evect = char2hipsolver_evect(evectC); hipsolverFillMode_t uplo = char2hipsolver_fill(uploC); int bc = argus.batch_count; int hot_calls = argus.iters; int stARes = (argus.unit_check || argus.norm_check) ? stA : 0; int stDRes = (argus.unit_check || argus.norm_check) ? stD : 0; // determine sizes size_t size_A = size_t(lda) * n; size_t size_B = size_t(ldb) * n; size_t size_D = size_t(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_DRes = (argus.unit_check || argus.norm_check) ? size_D : 0; // check invalid sizes bool invalid_size = (n < 0 || lda < n || ldb < n || bc < 0); if(invalid_size) { if(BATCHED) { // EXPECT_ROCBLAS_STATUS(hipsolver_sygvj_hegvj(API, // handle, // itype, // evect, // uplo, // n, // (T* const*)nullptr, // lda, // stA, // (T* const*)nullptr, // ldb, // stB, // (S*)nullptr, // stD, // (T*)nullptr, // 0, // (int*)nullptr, // params, // bc), // HIPSOLVER_STATUS_INVALID_VALUE); } else { EXPECT_ROCBLAS_STATUS(hipsolver_sygvj_hegvj(API, handle, itype, evect, uplo, n, (T*)nullptr, lda, stA, (T*)nullptr, ldb, stB, (S*)nullptr, stD, (T*)nullptr, 0, (int*)nullptr, params, bc), HIPSOLVER_STATUS_INVALID_VALUE); } if(argus.timing) ROCSOLVER_BENCH_INFORM(1); return; } if(BATCHED) { // // memory allocations // host_batch_vector hA(size_A, 1, bc); // host_batch_vector hARes(size_ARes, 1, bc); // host_batch_vector hB(size_B, 1, bc); // host_strided_batch_vector hD(size_D, 1, stD, bc); // host_strided_batch_vector hDRes(size_DRes, 1, stDRes, 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_batch_vector dB(size_B, 1, bc); // device_strided_batch_vector dD(size_D, 1, stD, bc); // device_strided_batch_vector dInfo(1, 1, 1, bc); // if(size_A) // CHECK_HIP_ERROR(dA.memcheck()); // if(size_B) // CHECK_HIP_ERROR(dB.memcheck()); // if(size_D) // CHECK_HIP_ERROR(dD.memcheck()); // CHECK_HIP_ERROR(dInfo.memcheck()); // int size_W; // hipsolver_sygvj_hegvj_bufferSize(API, // handle, // itype, // evect, // uplo, // n, // dA.data(), // lda, // dB.data(), // ldb, // dD.data(), // &size_W, // params); // 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) // sygvj_hegvj_getError(handle, // itype, // evect, // uplo, // n, // dA, // lda, // stA, // dB, // ldb, // stB, // dD, // stD, // dWork, // size_W, // dInfo, // params, // bc, // hA, // hARes, // hB, // hD, // hDRes, // hInfo, // hInfoRes, // &max_error, // argus.singular); // // collect performance data // if(argus.timing) // sygvj_hegvj_getPerfData(handle, // itype, // evect, // uplo, // n, // dA, // lda, // stA, // dB, // ldb, // stB, // dD, // stD, // dWork, // size_W, // dInfo, // params, // bc, // hA, // hB, // hD, // hInfo, // &gpu_time_used, // &cpu_time_used, // hot_calls, // 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 hB(size_B, 1, stB, bc); host_strided_batch_vector hD(size_D, 1, stD, bc); host_strided_batch_vector hDRes(size_DRes, 1, stDRes, 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 dB(size_B, 1, stB, bc); device_strided_batch_vector dD(size_D, 1, stD, bc); device_strided_batch_vector dInfo(1, 1, 1, bc); if(size_A) CHECK_HIP_ERROR(dA.memcheck()); if(size_B) CHECK_HIP_ERROR(dB.memcheck()); if(size_D) CHECK_HIP_ERROR(dD.memcheck()); CHECK_HIP_ERROR(dInfo.memcheck()); int size_W; hipsolver_sygvj_hegvj_bufferSize(API, handle, itype, evect, uplo, n, dA.data(), lda, dB.data(), ldb, dD.data(), &size_W, params); 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) sygvj_hegvj_getError(handle, itype, evect, uplo, n, dA, lda, stA, dB, ldb, stB, dD, stD, dWork, size_W, dInfo, params, bc, hA, hARes, hB, hD, hDRes, hInfo, hInfoRes, &max_error, argus.singular); // collect performance data if(argus.timing) sygvj_hegvj_getPerfData(handle, itype, evect, uplo, n, dA, lda, stA, dB, ldb, stB, dD, stD, dWork, size_W, dInfo, params, bc, hA, hB, hD, hInfo, &gpu_time_used, &cpu_time_used, hot_calls, argus.perf, argus.singular); } // validate results for rocsolver-test // using 2 * n * machine_precision as tolerance if(argus.unit_check) ROCSOLVER_TEST_CHECK(T, max_error, 2 * 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( "itype", "evect", "uplo", "n", "lda", "ldb", "strideD", "batch_c"); rocsolver_bench_output(itypeC, evectC, uploC, n, lda, ldb, stD, bc); } else if(STRIDED) { rocsolver_bench_output("itype", "evect", "uplo", "n", "lda", "ldb", "strideA", "strideB", "strideD", "batch_c"); rocsolver_bench_output(itypeC, evectC, uploC, n, lda, ldb, stA, stB, stD, bc); } else { rocsolver_bench_output("itype", "evect", "uplo", "n", "lda", "ldb"); rocsolver_bench_output(itypeC, evectC, uploC, n, lda, ldb); } 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(); }