745 lines
24 KiB
Rust
745 lines
24 KiB
Rust
//! Integration tests for cuSOLVER functionality
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//!
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//! These tests verify that cuSOLVER integration works correctly across
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//! the entire RTX tensor ecosystem and provides expected performance benefits.
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use crate::{IntegrationTestConfig, TestResults, integration_test};
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use rtx_tensor::{Tensor, Device};
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#[cfg(feature = "cuda")]
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use rtx_tensor::cusolver::{
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CuSolverContext, DenseSolver, SparseSolver, BatchedSolver,
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SvdConfig, QrConfig, BatchedSvdConfig
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};
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use rtx_science::{
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computing::CuSolverScientific,
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physics::{CuSolverPINN, PhysicsConfig}
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};
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use anyhow::Result;
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use std::time::Instant;
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use tracing::{info, warn};
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/// Run comprehensive cuSOLVER integration tests
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pub async fn run_cusolver_tests(config: &IntegrationTestConfig) -> Result<TestResults> {
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info!("Starting cuSOLVER integration tests");
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let mut results = TestResults::new();
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if config.skip_gpu_tests {
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results.add_skip("GPU tests disabled");
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return Ok(results);
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}
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// Basic functionality tests
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integration_test!("cuSOLVER Context Creation", || test_cusolver_context_creation(config), results);
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integration_test!("Dense Operations", || test_dense_operations(config), results);
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integration_test!("Sparse Operations", || test_sparse_operations(config), results);
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integration_test!("Batched Operations", || test_batched_operations(config), results);
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// Scientific computing tests
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integration_test!("Scientific Computing PCA", || test_scientific_pca(config), results);
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integration_test!("Linear System Solving", || test_linear_system_solving(config), results);
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integration_test!("Matrix Factorization", || test_matrix_factorization(config), results);
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// Physics simulation tests
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integration_test!("Physics Heat Equation", || test_physics_heat_equation(config), results);
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integration_test!("Physics Eigenvalue Problem", || test_physics_eigenvalue(config), results);
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integration_test!("Physics PCA Analysis", || test_physics_pca(config), results);
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// Numerical accuracy tests
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integration_test!("SVD Reconstruction Accuracy", || test_svd_accuracy(config), results);
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integration_test!("Linear Solve Accuracy", || test_solve_accuracy(config), results);
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integration_test!("Eigenvalue Accuracy", || test_eigenvalue_accuracy(config), results);
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// Performance benchmarks (if performance mode enabled)
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if config.performance_mode {
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integration_test!("SVD Performance Benchmark", || test_svd_performance(config), results);
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integration_test!("Batched Performance Benchmark", || test_batched_performance(config), results);
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integration_test!("Memory Usage Benchmark", || test_memory_usage(config), results);
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}
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// Error handling tests
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integration_test!("Error Handling", || test_error_handling(config), results);
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info!("cuSOLVER integration tests completed");
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results.print_summary();
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Ok(results)
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}
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#[cfg(feature = "cuda")]
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async fn test_cusolver_context_creation(_config: &IntegrationTestConfig) -> Result<()> {
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let device = Device::cuda(0)?;
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let context = CuSolverContext::new(&device)?;
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if !context.is_initialized() {
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anyhow::bail!("cuSOLVER context not properly initialized");
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}
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info!("✓ cuSOLVER context created successfully");
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Ok(())
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}
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#[cfg(not(feature = "cuda"))]
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async fn test_cusolver_context_creation(_config: &IntegrationTestConfig) -> Result<()> {
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warn!("CUDA not available, skipping cuSOLVER context test");
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Ok(())
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}
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#[cfg(feature = "cuda")]
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async fn test_dense_operations(_config: &IntegrationTestConfig) -> Result<()> {
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let device = Device::cuda(0)?;
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let context = CuSolverContext::new(&device)?;
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let solver = DenseSolver::new(context);
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// Test SVD on a known matrix
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let matrix = Tensor::from_data(
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vec![4.0f32, 1.0, 2.0, 3.0, 5.0, 6.0],
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vec![2, 3],
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&device,
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)?;
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let config = SvdConfig::default();
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let svd_result = solver.svd(&matrix, &config);
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match svd_result {
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Ok(svd) => {
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if svd.u.is_none() || svd.vt.is_none() {
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anyhow::bail!("SVD did not compute U or VT matrices");
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}
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if svd.s.shape().dims()[0] != 2 {
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anyhow::bail!("SVD produced wrong number of singular values");
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}
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info!("✓ Dense SVD operation successful");
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},
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Err(e) => {
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warn!("Dense SVD fell back to CPU: {}", e);
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}
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}
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// Test QR decomposition
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let square_matrix = Tensor::randn(&[100, 80], &device)?;
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let qr_config = QrConfig::default();
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let qr_result = solver.qr(&square_matrix, &qr_config);
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match qr_result {
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Ok(qr) => {
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if qr.q.is_none() {
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anyhow::bail!("QR did not compute Q matrix");
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}
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info!("✓ Dense QR operation successful");
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},
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Err(e) => {
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warn!("Dense QR fell back to CPU: {}", e);
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}
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}
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Ok(())
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}
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#[cfg(not(feature = "cuda"))]
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async fn test_dense_operations(_config: &IntegrationTestConfig) -> Result<()> {
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warn!("CUDA not available, skipping dense operations test");
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Ok(())
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}
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#[cfg(feature = "cuda")]
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async fn test_sparse_operations(_config: &IntegrationTestConfig) -> Result<()> {
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let device = Device::cuda(0)?;
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let context = CuSolverContext::new(&device)?;
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let solver = SparseSolver::new(context);
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// Create a test sparse matrix
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let sparse_matrix = create_test_sparse_matrix(&device)?;
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let rhs = Tensor::ones([3], &device)?;
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let config = rtx_tensor::cusolver::sparse::IterativeSolverConfig::default();
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let result = solver.solve_iterative(&sparse_matrix, &rhs, &config);
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match result {
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Ok(solve_result) => {
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if solve_result.iterations == 0 {
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anyhow::bail!("Iterative solver did not run any iterations");
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}
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info!("✓ Sparse iterative solver completed in {} iterations", solve_result.iterations);
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},
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Err(e) => {
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warn!("Sparse solver fell back or failed: {}", e);
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}
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}
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Ok(())
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}
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#[cfg(not(feature = "cuda"))]
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async fn test_sparse_operations(_config: &IntegrationTestConfig) -> Result<()> {
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warn!("CUDA not available, skipping sparse operations test");
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Ok(())
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}
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#[cfg(feature = "cuda")]
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fn create_test_sparse_matrix(device: &Device) -> Result<rtx_tensor::sparse::SparseCSR> {
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use rtx_tensor::sparse::SparseCSR;
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// Create a simple 3x3 tridiagonal matrix
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let values = vec![2.0f32, -1.0, 2.0, -1.0, 2.0];
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let col_indices = vec![0, 1, 1, 2, 2];
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let row_ptr = vec![0, 2, 4, 5];
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let shape = rtx_tensor::Shape::from([3, 3]);
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Ok(SparseCSR::from_csr_arrays(row_ptr, col_indices, values, shape, device)?)
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}
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#[cfg(feature = "cuda")]
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async fn test_batched_operations(_config: &IntegrationTestConfig) -> Result<()> {
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let device = Device::cuda(0)?;
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let context = CuSolverContext::new(&device)?;
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let solver = BatchedSolver::new(context)?;
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// Create batch of matrices
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let matrices: Result<Vec<_>, _> = (0..8)
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.map(|_| Tensor::randn(&[16, 12], &device))
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.collect();
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let matrices = matrices?;
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let config = BatchedSvdConfig::default();
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let result = solver.svd_batched(&matrices, &config);
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match result {
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Ok(batch_result) => {
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if batch_result.results.len() != 8 {
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anyhow::bail!("Batched operation processed wrong number of matrices");
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}
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if batch_result.total_time < 0.0 {
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anyhow::bail!("Invalid timing measurement");
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}
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info!("✓ Batched SVD processed {} matrices in {:.2}ms",
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batch_result.results.len(), batch_result.total_time * 1000.0);
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},
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Err(e) => {
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warn!("Batched operation fell back: {}", e);
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}
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}
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Ok(())
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}
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#[cfg(not(feature = "cuda"))]
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async fn test_batched_operations(_config: &IntegrationTestConfig) -> Result<()> {
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warn!("CUDA not available, skipping batched operations test");
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Ok(())
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}
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async fn test_scientific_pca(_config: &IntegrationTestConfig) -> Result<()> {
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let device = if cfg!(feature = "cuda") {
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Device::cuda(0).unwrap_or_else(|_| Device::cpu())
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} else {
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Device::cpu()
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};
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let scientific = CuSolverScientific::new(device.clone())?;
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// Create test data matrix
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let data = Tensor::randn(&[100, 20], &device)?;
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let result = scientific.pca(&data, 5);
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match result {
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Ok(pca_result) => {
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if pca_result.n_components != 5 {
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anyhow::bail!("PCA returned wrong number of components");
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}
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if pca_result.components.shape().dims()[0] != 5 {
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anyhow::bail!("PCA components have wrong shape");
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}
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if pca_result.explained_variance_ratio <= 0.0 {
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anyhow::bail!("PCA explained variance ratio is invalid");
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}
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info!("✓ Scientific PCA: {} components, {:.2}% variance explained",
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pca_result.n_components,
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pca_result.explained_variance_ratio * 100.0);
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},
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Err(e) => {
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warn!("Scientific PCA fell back to CPU: {}", e);
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}
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}
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Ok(())
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}
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async fn test_linear_system_solving(_config: &IntegrationTestConfig) -> Result<()> {
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let device = if cfg!(feature = "cuda") {
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Device::cuda(0).unwrap_or_else(|_| Device::cpu())
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} else {
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Device::cpu()
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};
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let scientific = CuSolverScientific::new(device.clone())?;
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// Create a well-conditioned system
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let a = Tensor::eye(10, &device)?;
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let b = Tensor::ones([10, 1], &device)?;
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let result = scientific.solve_linear_system(&a, &b);
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match result {
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Ok(solve_result) => {
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if !solve_result.converged && solve_result.residual_norm > 1e-8 {
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anyhow::bail!("Linear system did not converge adequately");
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}
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info!("✓ Linear system solved: residual = {:.2e}, condition = {:.2e}",
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solve_result.residual_norm, solve_result.condition_number);
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},
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Err(e) => {
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warn!("Linear system solving fell back: {}", e);
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}
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}
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Ok(())
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}
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async fn test_matrix_factorization(_config: &IntegrationTestConfig) -> Result<()> {
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let device = if cfg!(feature = "cuda") {
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Device::cuda(0).unwrap_or_else(|_| Device::cpu())
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} else {
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Device::cpu()
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};
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let scientific = CuSolverScientific::new(device.clone())?;
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let matrix = Tensor::eye(8, &device)?;
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// Test QR factorization
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let qr_result = scientific.matrix_factorization(&matrix, rtx_science::computing::FactorizationMethod::QR);
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if let Ok(qr_fact) = qr_result {
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if let rtx_science::computing::FactorizationResult::QR { r, .. } = qr_fact
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&& r.shape().dims() != vec![8, 8] {
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anyhow::bail!("QR factorization produced wrong R matrix shape");
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}
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info!("✓ QR factorization successful");
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}
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// Test LU factorization
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let lu_result = scientific.matrix_factorization(&matrix, rtx_science::computing::FactorizationMethod::LU);
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if let Ok(lu_fact) = lu_result {
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if let rtx_science::computing::FactorizationResult::LU { l, u, .. } = lu_fact
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&& (l.shape().dims() != vec![8, 8] || u.shape().dims() != vec![8, 8]) {
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anyhow::bail!("LU factorization produced wrong matrix shapes");
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}
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info!("✓ LU factorization successful");
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}
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Ok(())
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}
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async fn test_physics_heat_equation(_config: &IntegrationTestConfig) -> Result<()> {
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let device = if cfg!(feature = "cuda") {
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Device::cuda(0).unwrap_or_else(|_| Device::cpu())
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} else {
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Device::cpu()
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};
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let config = PhysicsConfig {
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n_collocation: 25, // Small for testing
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n_boundary: 10,
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tolerance: 1e-6,
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..Default::default()
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};
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let pinn = CuSolverPINN::new(device, config)?;
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let initial_condition = |x: f64, _y: f64| (std::f64::consts::PI * x).sin();
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let boundary_condition = |_x: f64, _y: f64, _t: f64| 0.0; // Homogeneous
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let result = pinn.solve_heat_equation(
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0.1, // alpha
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initial_condition,
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boundary_condition,
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0.5, // t_final (shorter for testing)
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);
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match result {
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Ok(solution) => {
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if solution.x_coords.shape().dims()[0] == 0 {
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anyhow::bail!("Heat equation solution has no spatial coordinates");
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}
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info!("✓ Heat equation solved: condition = {:.2e}, converged = {}",
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solution.condition_number, solution.converged);
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},
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Err(e) => {
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warn!("Heat equation solver fell back: {}", e);
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}
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}
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Ok(())
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}
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async fn test_physics_eigenvalue(_config: &IntegrationTestConfig) -> Result<()> {
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let device = if cfg!(feature = "cuda") {
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Device::cuda(0).unwrap_or_else(|_| Device::cpu())
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} else {
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Device::cpu()
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};
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let config = PhysicsConfig::default();
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let pinn = CuSolverPINN::new(device, config)?;
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// Harmonic oscillator potential
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let potential = |x: f64| 0.5 * x * x;
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let result = pinn.solve_quantum_eigenvalue_problem(potential, 3);
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match result {
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Ok(eigen_result) => {
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if eigen_result.n_computed == 0 {
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anyhow::bail!("No eigenvalues computed");
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}
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info!("✓ Quantum eigenvalue problem: {} states computed, condition = {:.2e}",
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eigen_result.n_computed, eigen_result.condition_number);
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// Check ground state energy for harmonic oscillator (should be ~0.5)
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if let Ok(e0) = eigen_result.energies.get(&[0]) {
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let energy = e0.abs();
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if energy > 10.0 {
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warn!("Ground state energy seems high: {:.4}", energy);
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} else {
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info!("Ground state energy: {:.4}", energy);
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}
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}
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},
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Err(e) => {
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warn!("Quantum eigenvalue problem fell back: {}", e);
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}
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}
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Ok(())
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}
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async fn test_physics_pca(_config: &IntegrationTestConfig) -> Result<()> {
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let device = if cfg!(feature = "cuda") {
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Device::cuda(0).unwrap_or_else(|_| Device::cpu())
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} else {
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Device::cpu()
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};
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let config = PhysicsConfig::default();
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let pinn = CuSolverPINN::new(device.clone(), config)?;
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// Generate mock physics data (could be simulation snapshots)
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let data = Tensor::randn(&[50, 10], &device)?;
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let result = pinn.physics_pca(&data, 3);
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match result {
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Ok(pca_result) => {
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if pca_result.n_components != 3 {
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anyhow::bail!("Physics PCA returned wrong number of components");
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}
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info!("✓ Physics PCA: {} components, {:.2}% variance",
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pca_result.n_components,
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pca_result.explained_variance * 100.0);
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},
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Err(e) => {
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warn!("Physics PCA fell back: {}", e);
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}
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}
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Ok(())
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}
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async fn test_svd_accuracy(_config: &IntegrationTestConfig) -> Result<()> {
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let device = if cfg!(feature = "cuda") {
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Device::cuda(0).unwrap_or_else(|_| Device::cpu())
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} else {
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Device::cpu()
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};
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// Create a known matrix for testing
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let matrix = Tensor::from_data(
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vec![4.0f32, 7.0, 2.0, 6.0],
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vec![2, 2],
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&device,
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)?;
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let svd_result = matrix.svd(true, true)?;
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// Reconstruct: A = U * S * VT
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let s_diag = Tensor::diag(&svd_result.s)?;
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let us = svd_result.u.matmul(&s_diag)?;
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let reconstructed = us.matmul(&svd_result.vt)?;
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// Check reconstruction error
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let error_tensor = matrix.sub(&reconstructed)?;
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// Compute Frobenius norm manually since norm_l2 doesn't exist
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let squared = error_tensor.mul(&error_tensor)?;
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let sum = squared.sum(None)?;
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let error = sum.item()?.sqrt();
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if error > 1e-4 {
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anyhow::bail!("SVD reconstruction error too large: {error:.2e}");
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}
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info!("✓ SVD reconstruction accuracy: error = {:.2e}", error);
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Ok(())
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}
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async fn test_solve_accuracy(_config: &IntegrationTestConfig) -> Result<()> {
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let device = if cfg!(feature = "cuda") {
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Device::cuda(0).unwrap_or_else(|_| Device::cpu())
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} else {
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Device::cpu()
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};
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// Solve: [2, 1; 1, 2] * x = [3; 3], expected solution: x = [1, 1]
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let a = Tensor::from_data(vec![2.0f32, 1.0, 1.0, 2.0], vec![2, 2], &device)?;
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let b = Tensor::from_data(vec![3.0f32, 3.0], vec![2, 1], &device)?;
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let a_inv = a.inverse()?;
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let solution = a_inv.matmul(&b)?;
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|
|
// Check residual: ||Ax - b||
|
|
let ax = a.matmul(&solution)?;
|
|
let residual = ax.sub(&b)?;
|
|
// Compute norm manually
|
|
let squared = residual.mul(&residual)?;
|
|
let sum = squared.sum(None)?;
|
|
let residual_val = sum.item()?.sqrt();
|
|
|
|
if residual_val > 1e-4 {
|
|
anyhow::bail!("Linear solve residual too large: {residual_val:.2e}");
|
|
}
|
|
|
|
// Check if solution is close to expected [1, 1]
|
|
let x1 = solution.get(&[0, 0])?;
|
|
if (x1 - 1.0).abs() > 1e-3 {
|
|
warn!("Solution component deviation: {:.4} (expected ~1.0)", x1);
|
|
}
|
|
|
|
info!("✓ Linear solve accuracy: residual = {:.2e}, solution[0] = {:.4}",
|
|
residual_val, x1);
|
|
|
|
Ok(())
|
|
}
|
|
|
|
async fn test_eigenvalue_accuracy(_config: &IntegrationTestConfig) -> Result<()> {
|
|
let device = if cfg!(feature = "cuda") {
|
|
Device::cuda(0).unwrap_or_else(|_| Device::cpu())
|
|
} else {
|
|
Device::cpu()
|
|
};
|
|
|
|
// Create symmetric matrix with known eigenvalues
|
|
let matrix = Tensor::from_data(
|
|
vec![3.0f32, 1.0, 1.0, 3.0],
|
|
vec![2, 2],
|
|
&device,
|
|
)?;
|
|
|
|
let eigen_result = matrix.symeig(true)?;
|
|
|
|
// For this matrix, eigenvalues should be [2, 4]
|
|
let eigenvals = eigen_result.eigenvalues;
|
|
let first_eval = eigenvals.get(&[0])?;
|
|
|
|
// Eigenvalues should be positive and reasonable
|
|
if first_eval <= 0.0 || first_eval > 10.0 {
|
|
warn!("Eigenvalue seems unreasonable: {:.4}", first_eval);
|
|
}
|
|
|
|
info!("✓ Eigenvalue computation: first eigenvalue = {:.4}", first_eval);
|
|
|
|
Ok(())
|
|
}
|
|
|
|
#[cfg(feature = "cuda")]
|
|
async fn test_svd_performance(_config: &IntegrationTestConfig) -> Result<()> {
|
|
let device = Device::cuda(0)?;
|
|
let context = CuSolverContext::new(&device)?;
|
|
let solver = DenseSolver::new(context);
|
|
|
|
let sizes = vec![256, 512, 1024];
|
|
info!("SVD Performance Benchmark:");
|
|
|
|
for size in sizes {
|
|
let matrix = Tensor::randn(&[size, size], &device)?;
|
|
let config = SvdConfig::default();
|
|
|
|
// Warmup
|
|
for _ in 0..3 {
|
|
let _ = solver.svd(&matrix, &config);
|
|
}
|
|
|
|
// Benchmark
|
|
let iterations = 10;
|
|
let start = Instant::now();
|
|
|
|
for _ in 0..iterations {
|
|
let _ = solver.svd(&matrix, &config);
|
|
}
|
|
|
|
let avg_time = start.elapsed().as_millis() as f64 / iterations as f64;
|
|
|
|
// Estimate GFLOPS
|
|
let flops = (size * size * size) as f64 * 14.0;
|
|
let gflops = flops / (avg_time / 1000.0) / 1e9;
|
|
|
|
info!(" {}x{}: {:.1}ms, {:.1} GFLOPS", size, size, avg_time, gflops);
|
|
|
|
// Performance assertion
|
|
if avg_time > 1000.0 && size <= 512 {
|
|
anyhow::bail!("SVD performance too slow for size {size}: {avg_time:.1}ms");
|
|
}
|
|
}
|
|
|
|
info!("✓ SVD performance benchmark completed");
|
|
Ok(())
|
|
}
|
|
|
|
#[cfg(not(feature = "cuda"))]
|
|
async fn test_svd_performance(_config: &IntegrationTestConfig) -> Result<()> {
|
|
warn!("CUDA not available, skipping SVD performance test");
|
|
Ok(())
|
|
}
|
|
|
|
#[cfg(feature = "cuda")]
|
|
async fn test_batched_performance(_config: &IntegrationTestConfig) -> Result<()> {
|
|
let device = Device::cuda(0)?;
|
|
let context = CuSolverContext::new(&device)?;
|
|
let solver = BatchedSolver::new(context)?;
|
|
|
|
let batch_sizes = vec![4, 8, 16];
|
|
let matrix_size = 128;
|
|
|
|
info!("Batched Performance Benchmark:");
|
|
|
|
for batch_size in batch_sizes {
|
|
let matrices: Result<Vec<_>, _> = (0..batch_size)
|
|
.map(|_| Tensor::randn(&[matrix_size, matrix_size], &device))
|
|
.collect();
|
|
let matrices = matrices?;
|
|
|
|
let config = BatchedSvdConfig::default();
|
|
|
|
let start = Instant::now();
|
|
let result = solver.svd_batched(&matrices, &config);
|
|
let total_time = start.elapsed().as_millis() as f64;
|
|
|
|
match result {
|
|
Ok(_) => {
|
|
let per_matrix = total_time / batch_size as f64;
|
|
info!(" Batch {}: {:.1}ms total, {:.1}ms per matrix",
|
|
batch_size, total_time, per_matrix);
|
|
|
|
// Performance assertion
|
|
if per_matrix > 100.0 {
|
|
anyhow::bail!("Batched performance too slow: {per_matrix:.1}ms per matrix");
|
|
}
|
|
},
|
|
Err(e) => {
|
|
warn!("Batched operation failed for batch size {}: {}", batch_size, e);
|
|
}
|
|
}
|
|
}
|
|
|
|
info!("✓ Batched performance benchmark completed");
|
|
Ok(())
|
|
}
|
|
|
|
#[cfg(not(feature = "cuda"))]
|
|
async fn test_batched_performance(_config: &IntegrationTestConfig) -> Result<()> {
|
|
warn!("CUDA not available, skipping batched performance test");
|
|
Ok(())
|
|
}
|
|
|
|
#[cfg(feature = "cuda")]
|
|
async fn test_memory_usage(_config: &IntegrationTestConfig) -> Result<()> {
|
|
let device = Device::cuda(0)?;
|
|
let context = CuSolverContext::new(&device)?;
|
|
|
|
// Check memory statistics
|
|
let initial_stats = context.memory_stats();
|
|
info!("Initial memory usage: {} MB", initial_stats.current_usage / (1024 * 1024));
|
|
|
|
// Perform operations and check memory usage
|
|
let solver = DenseSolver::new(context.clone());
|
|
let matrix = Tensor::randn(&[1000, 1000], &device)?;
|
|
let config = SvdConfig::default();
|
|
|
|
let _ = solver.svd(&matrix, &config);
|
|
|
|
let final_stats = context.memory_stats();
|
|
let memory_increase = final_stats.current_usage - initial_stats.current_usage;
|
|
|
|
info!("Memory increase: {} MB", memory_increase / (1024 * 1024));
|
|
info!("Peak usage: {} MB", final_stats.peak_usage / (1024 * 1024));
|
|
|
|
// Memory assertion
|
|
if memory_increase > 1024 * 1024 * 1024 { // 1GB
|
|
warn!("High memory usage detected: {} MB", memory_increase / (1024 * 1024));
|
|
}
|
|
|
|
info!("✓ Memory usage test completed");
|
|
Ok(())
|
|
}
|
|
|
|
#[cfg(not(feature = "cuda"))]
|
|
async fn test_memory_usage(_config: &IntegrationTestConfig) -> Result<()> {
|
|
warn!("CUDA not available, skipping memory usage test");
|
|
Ok(())
|
|
}
|
|
|
|
async fn test_error_handling(_config: &IntegrationTestConfig) -> Result<()> {
|
|
let device = Device::cpu();
|
|
|
|
// Test with invalid matrix dimensions
|
|
let invalid_matrix = Tensor::ones([5], &device)?;
|
|
let svd_result = invalid_matrix.svd(true, true);
|
|
|
|
if svd_result.is_ok() {
|
|
anyhow::bail!("Expected SVD to fail with 1D tensor");
|
|
}
|
|
|
|
// Test with mismatched dimensions for matrix multiplication
|
|
let a = Tensor::ones([3, 3], &device)?;
|
|
let b = Tensor::ones([2, 1], &device)?;
|
|
|
|
let matmul_result = a.matmul(&b);
|
|
if matmul_result.is_ok() {
|
|
// Some implementations might succeed with broadcasting, others might fail
|
|
// This is implementation-dependent behavior
|
|
info!("Matrix multiplication with mismatched dimensions succeeded (broadcasting?)");
|
|
} else {
|
|
info!("Matrix multiplication properly rejected mismatched dimensions");
|
|
}
|
|
|
|
// Test with empty matrix
|
|
let empty_result = Tensor::zeros([0, 0], &device);
|
|
if let Ok(empty_matrix) = empty_result {
|
|
let empty_svd = empty_matrix.svd(true, true);
|
|
if empty_svd.is_ok() {
|
|
warn!("SVD succeeded on empty matrix (unexpected)");
|
|
}
|
|
}
|
|
|
|
info!("✓ Error handling tests completed");
|
|
Ok(())
|
|
}
|
|
|
|
#[cfg(test)]
|
|
mod tests {
|
|
use super::*;
|
|
use crate::IntegrationTestConfig;
|
|
|
|
#[tokio::test]
|
|
async fn test_cusolver_integration_suite() {
|
|
let config = IntegrationTestConfig::default();
|
|
let results = run_cusolver_tests(&config).await;
|
|
|
|
match results {
|
|
Ok(test_results) => {
|
|
assert!(test_results.success_rate() > 0.5, "Success rate too low");
|
|
println!("cuSOLVER integration tests: {:.1}% success rate",
|
|
test_results.success_rate() * 100.0);
|
|
},
|
|
Err(e) => {
|
|
panic!("Integration test suite failed: {}", e);
|
|
}
|
|
}
|
|
}
|
|
} |