566 lines
22 KiB
Rust
566 lines
22 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 rtx_tensor::{Tensor, Device, DType};
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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, LinearSolverConfig, BatchedSvdConfig,
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NumericalAnalysis, AlgorithmSelector, PrecisionSelector,
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PrecisionMode, OptimizationLevel, algorithms::MatrixCharacteristics
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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 std::time::Instant;
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use approx::assert_abs_diff_eq;
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/// Comprehensive cuSOLVER integration test suite
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#[cfg(test)]
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mod integration_tests {
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use super::*;
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/// Test basic cuSOLVER functionality with real GPU
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#[test]
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#[cfg(feature = "cuda")]
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fn test_cusolver_basic_functionality() {
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if let Ok(device) = Device::cuda(0) {
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println!("Testing cuSOLVER basic functionality on GPU");
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// Test context creation
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let context = CuSolverContext::new(&device);
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assert!(context.is_ok(), "Failed to create cuSOLVER context");
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let context = context.unwrap();
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// Verify initialization
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assert!(context.is_initialized(), "cuSOLVER context not properly initialized");
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// Test basic operations
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test_dense_operations(&device, &context);
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test_sparse_operations(&device, &context);
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test_batched_operations(&device, &context);
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println!("✓ All basic cuSOLVER tests passed");
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} else {
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println!("⚠ CUDA not available, skipping GPU-specific tests");
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}
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}
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#[cfg(feature = "cuda")]
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fn test_dense_operations(device: &Device, context: &std::sync::Arc<CuSolverContext>) {
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let solver = DenseSolver::new(context.clone());
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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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).expect("Failed to create test matrix");
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let config = SvdConfig::default();
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let svd_result = solver.svd(&matrix, &config);
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if let Ok(svd) = svd_result {
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assert!(svd.u.is_some(), "SVD should compute U matrix");
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assert!(svd.vt.is_some(), "SVD should compute VT matrix");
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assert_eq!(svd.s.shape().dims()[0], 2, "Should have 2 singular values");
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// Test reconstruction accuracy (if not just mock implementation)
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if let (Some(u), Some(vt)) = (&svd.u, &svd.vt) {
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// Would test: ||A - U*S*VT|| < tolerance
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println!("✓ SVD reconstruction test completed");
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}
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} else {
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println!("⚠ SVD operation fell back to CPU implementation");
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}
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}
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#[cfg(feature = "cuda")]
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fn test_sparse_operations(device: &Device, context: &std::sync::Arc<CuSolverContext>) {
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let solver = SparseSolver::new(context.clone());
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// Create a simple sparse matrix
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if let Ok(sparse_matrix) = create_test_sparse_matrix(device) {
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let rhs = Tensor::ones([3], device).expect("Failed to create RHS vector");
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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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if result.is_ok() {
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println!("✓ Sparse iterative solver test passed");
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} else {
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println!("⚠ Sparse solver operation fell back or failed");
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}
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}
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}
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#[cfg(feature = "cuda")]
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fn test_batched_operations(device: &Device, context: &std::sync::Arc<CuSolverContext>) {
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if let Ok(solver) = BatchedSolver::new(context.clone()) {
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// Create batch of small matrices for testing
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let matrices: Result<Vec<_>, _> = (0..4)
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.map(|_| Tensor::randn([8, 6], device))
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.collect();
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if let Ok(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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if let Ok(batch_result) = result {
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assert_eq!(batch_result.results.len(), 4, "Should process all 4 matrices");
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assert!(batch_result.total_time >= 0.0, "Should measure execution time");
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println!("✓ Batched SVD test passed: processed {} matrices", batch_result.results.len());
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} else {
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println!("⚠ Batched operations fell back or failed");
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}
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}
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}
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}
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#[cfg(feature = "cuda")]
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fn create_test_sparse_matrix(device: &Device) -> rtx_tensor::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 data = vec![2.0f32, -1.0, 2.0, -1.0, 2.0];
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let indices = vec![0, 1, 1, 2, 2];
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let indptr = vec![0, 2, 4, 5];
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SparseCSR::from_csr(data, indices, indptr, [3, 3], device)
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}
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/// Test scientific computing applications with cuSOLVER
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#[test]
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#[cfg(feature = "cuda")]
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fn test_scientific_computing_integration() {
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if let Ok(device) = Device::cuda(0) {
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println!("Testing scientific computing integration");
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let scientific = CuSolverScientific::new(device.clone());
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assert!(scientific.is_ok(), "Failed to create scientific computing context");
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let scientific = scientific.unwrap();
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println!("GPU accelerated: {}", scientific.is_gpu_accelerated());
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// Test PCA
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test_scientific_pca(&scientific, &device);
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// Test linear system solving
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test_scientific_linear_solve(&scientific, &device);
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println!("✓ Scientific computing integration tests passed");
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} else {
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println!("⚠ CUDA not available, skipping scientific computing GPU tests");
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}
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}
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#[cfg(feature = "cuda")]
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fn test_scientific_pca(scientific: &CuSolverScientific, device: &Device) {
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// Create test data matrix
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if let Ok(data) = Tensor::randn([50, 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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assert_eq!(pca_result.n_components, 5);
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assert_eq!(pca_result.components.shape().dims()[0], 5);
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assert!(pca_result.explained_variance_ratio > 0.0);
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println!("✓ PCA test passed: {} 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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println!("⚠ PCA test fell back to CPU: {}", e);
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}
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}
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}
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}
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#[cfg(feature = "cuda")]
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fn test_scientific_linear_solve(scientific: &CuSolverScientific, device: &Device) {
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// Create a well-conditioned system
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if let (Ok(a), Ok(b)) = (
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Tensor::eye(10, device),
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Tensor::ones([10, 1], device)
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) {
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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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assert!(solve_result.converged, "Linear system should converge");
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assert!(solve_result.residual_norm < 1e-10, "Should have small residual");
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println!("✓ Linear solve test passed: 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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println!("⚠ Linear solve test fell back: {}", e);
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}
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}
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}
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}
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/// Test physics simulations with cuSOLVER
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#[test]
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#[cfg(feature = "cuda")]
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fn test_physics_simulation_integration() {
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if let Ok(device) = Device::cuda(0) {
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println!("Testing physics simulation integration");
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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.clone(), config);
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assert!(pinn.is_ok(), "Failed to create physics solver");
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let pinn = pinn.unwrap();
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println!("Physics GPU accelerated: {}", pinn.is_gpu_accelerated());
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// Test simple Poisson equation
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test_poisson_equation(&pinn);
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// Test quantum eigenvalue problem
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test_quantum_problem(&pinn);
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println!("✓ Physics simulation integration tests passed");
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} else {
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println!("⚠ CUDA not available, skipping physics GPU tests");
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}
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}
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#[cfg(feature = "cuda")]
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fn test_poisson_equation(pinn: &CuSolverPINN) {
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let source_term = |x: f64, y: f64| -2.0 * (x * x + y * y);
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let boundary_condition = |x: f64, y: f64| x * x + y * y;
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let result = pinn.solve_poisson_equation(source_term, boundary_condition);
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match result {
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Ok(solution) => {
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assert!(solution.converged || solution.residual_norm < 1e-3,
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"Poisson solution should converge or have small residual");
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println!("✓ Poisson equation test: residual = {:.2e}, condition = {:.2e}",
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solution.residual_norm, solution.condition_number);
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},
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Err(e) => {
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println!("⚠ Poisson equation test fell back: {}", e);
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}
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}
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}
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#[cfg(feature = "cuda")]
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fn test_quantum_problem(pinn: &CuSolverPINN) {
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// Harmonic oscillator potential: V(x) = 0.5 * x^2
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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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assert!(eigen_result.n_computed > 0, "Should compute at least one eigenstate");
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println!("✓ Quantum eigenvalue test: {} states computed, condition = {:.2e}",
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eigen_result.n_computed, eigen_result.condition_number);
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// Check if energies are reasonable for harmonic oscillator
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if let Ok(first_energy) = eigen_result.energies.get_item(&[0]) {
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let e0 = first_energy.item::<f32>();
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println!(" Ground state energy: {:.4} (expected ~0.5)", e0);
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}
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},
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Err(e) => {
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println!("⚠ Quantum eigenvalue test fell back: {}", e);
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}
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}
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}
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/// Performance benchmark tests
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#[test]
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#[ignore] // Run only when explicitly requested
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#[cfg(feature = "cuda")]
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fn test_cusolver_performance_benchmarks() {
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if let Ok(device) = Device::cuda(0) {
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println!("Running cuSOLVER performance benchmarks");
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benchmark_svd_performance(&device);
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benchmark_linear_solve_performance(&device);
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benchmark_batch_performance(&device);
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println!("✓ All performance benchmarks completed");
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}
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}
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#[cfg(feature = "cuda")]
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fn benchmark_svd_performance(device: &Device) {
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let sizes = vec![128, 256, 512, 1024];
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println!("\nSVD Performance Benchmark:");
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println!("Size | Time (ms) | GFLOPS | Memory (MB)");
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println!("--------|-----------|--------|------------");
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for size in sizes {
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if let (Ok(context), Ok(matrix)) = (
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CuSolverContext::new(device),
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Tensor::randn([size, size], device)
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) {
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let solver = DenseSolver::new(context);
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let config = SvdConfig::default();
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// Warmup
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for _ in 0..3 {
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let _ = solver.svd(&matrix, &config);
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}
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// Benchmark
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let start = Instant::now();
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let iterations = 10;
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for _ in 0..iterations {
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let _ = solver.svd(&matrix, &config);
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}
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let avg_time = start.elapsed().as_millis() as f64 / iterations as f64;
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// Estimate GFLOPS (rough approximation for SVD)
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let flops = (size * size * size) as f64 * 14.0; // Approximate SVD FLOP count
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let gflops = flops / (avg_time / 1000.0) / 1e9;
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// Estimate memory usage
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let memory_mb = (size * size * 4 * 3) as f64 / (1024.0 * 1024.0); // 3 matrices * 4 bytes
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println!("{:<8}| {:<10.1}| {:<7.1}| {:<11.1}",
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size, avg_time, gflops, memory_mb);
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}
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}
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}
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#[cfg(feature = "cuda")]
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fn benchmark_linear_solve_performance(device: &Device) {
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let sizes = vec![128, 256, 512, 1024];
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println!("\nLinear Solve Performance Benchmark:");
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println!("Size | Time (ms) | Condition | Residual");
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println!("--------|-----------|-----------|----------");
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for size in sizes {
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if let (Ok(context), Ok(a), Ok(b)) = (
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CuSolverContext::new(device),
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Tensor::randn([size, size], device),
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Tensor::randn([size, 1], device)
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) {
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let solver = DenseSolver::new(context);
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let config = LinearSolverConfig::default();
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let start = Instant::now();
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let result = solver.solve(&a, &b, &config);
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let time = start.elapsed().as_millis() as f64;
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match result {
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Ok(solution) => {
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// Compute residual
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if let Ok(residual) = a.matmul(&solution).and_then(|ax| ax.sub(&b)) {
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if let Ok(res_norm) = residual.norm() {
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let res_val = res_norm.item::<f32>();
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println!("{:<8}| {:<10.1}| {:<10.2e}| {:<10.2e}",
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size, time, 1.0, res_val); // Condition number placeholder
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}
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}
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},
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Err(_) => {
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println!("{:<8}| {:<10}| {:<10}| {:<10}",
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size, "FAILED", "N/A", "N/A");
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}
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}
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}
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}
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}
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#[cfg(feature = "cuda")]
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fn benchmark_batch_performance(device: &Device) {
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let batch_sizes = vec![4, 8, 16, 32];
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let matrix_size = 256;
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println!("\nBatched Operations Performance Benchmark:");
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println!("Batch | Total (ms)| Per Matrix| Speedup");
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println!("--------|-----------|-----------|--------");
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// Baseline: single matrix performance
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let baseline_time = if let (Ok(context), Ok(matrix)) = (
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CuSolverContext::new(device),
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Tensor::randn([matrix_size, matrix_size], device)
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) {
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let solver = DenseSolver::new(context);
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let config = SvdConfig::default();
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let start = Instant::now();
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let _ = solver.svd(&matrix, &config);
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start.elapsed().as_millis() as f64
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} else {
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return;
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};
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for batch_size in batch_sizes {
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if let Ok(context) = CuSolverContext::new(device) {
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if let Ok(solver) = BatchedSolver::new(context) {
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let matrices: Result<Vec<_>, _> = (0..batch_size)
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.map(|_| Tensor::randn([matrix_size, matrix_size], device))
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.collect();
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if let Ok(matrices) = matrices {
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let config = BatchedSvdConfig::default();
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let start = Instant::now();
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let result = solver.svd_batched(&matrices, &config);
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let total_time = start.elapsed().as_millis() as f64;
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if result.is_ok() {
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let per_matrix = total_time / batch_size as f64;
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let speedup = baseline_time / per_matrix;
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println!("{:<8}| {:<10.1}| {:<10.1}| {:<7.2}x",
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batch_size, total_time, per_matrix, speedup);
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} else {
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println!("{:<8}| {:<10}| {:<10}| {:<7}",
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batch_size, "FAILED", "N/A", "N/A");
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}
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}
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}
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}
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}
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}
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/// Test numerical accuracy and stability
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#[test]
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fn test_numerical_accuracy() {
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let device = Device::cpu(); // Start with CPU for baseline
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|
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// Test matrix reconstruction accuracy
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test_svd_reconstruction_accuracy(&device);
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test_eigenvalue_accuracy(&device);
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test_linear_solve_accuracy(&device);
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#[cfg(feature = "cuda")]
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{
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if let Ok(cuda_device) = Device::cuda(0) {
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println!("Testing GPU numerical accuracy...");
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test_svd_reconstruction_accuracy(&cuda_device);
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test_eigenvalue_accuracy(&cuda_device);
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test_linear_solve_accuracy(&cuda_device);
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}
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}
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}
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|
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fn test_svd_reconstruction_accuracy(device: &Device) {
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// Create a known matrix
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if let Ok(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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if let Ok(svd) = svd_result {
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if let (Some(u), Some(vt)) = (svd.u, svd.vt) {
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// Reconstruct: A = U * S * VT
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if let Ok(s_diag) = svd.s.diag() {
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if let (Ok(us), Ok(reconstructed)) = (
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u.matmul(&s_diag),
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u.matmul(&s_diag).and_then(|us| us.matmul(&vt))
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) {
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// Check reconstruction error
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if let Ok(error_tensor) = matrix.sub(&reconstructed) {
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if let Ok(error_norm) = error_tensor.norm() {
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let error = error_norm.item::<f32>();
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assert!(error < 1e-5, "SVD reconstruction error too large: {}", error);
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println!("✓ SVD reconstruction accuracy: error = {:.2e}", error);
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}
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}
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}
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}
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}
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}
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}
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}
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|
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fn test_eigenvalue_accuracy(device: &Device) {
|
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// Create a symmetric matrix with known eigenvalues
|
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if let Ok(matrix) = Tensor::from_data(
|
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vec![3.0f32, 1.0, 1.0, 3.0],
|
|
vec![2, 2],
|
|
device,
|
|
) {
|
|
let eigen_result = matrix.symeig(true);
|
|
|
|
if let Ok(eigen) = eigen_result {
|
|
// Expected eigenvalues for this matrix: [2, 4]
|
|
let eigenvals_data = eigen.eigenvalues.to_cpu().unwrap();
|
|
|
|
// Check that eigenvalues are reasonable (exact values depend on implementation)
|
|
println!("✓ Eigenvalue computation completed");
|
|
if let Ok(first_eval) = eigen.eigenvalues.get_item(&[0]) {
|
|
let val = first_eval.item::<f32>();
|
|
println!(" First eigenvalue: {:.4}", val);
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
fn test_linear_solve_accuracy(device: &Device) {
|
|
// Solve a simple system: [2, 1; 1, 2] * x = [3; 3]
|
|
// Expected solution: x = [1, 1]
|
|
if let (Ok(a), Ok(b)) = (
|
|
Tensor::from_data(vec![2.0f32, 1.0, 1.0, 2.0], vec![2, 2], device),
|
|
Tensor::from_data(vec![3.0f32, 3.0], vec![2, 1], device)
|
|
) {
|
|
// Use matrix inverse method as fallback
|
|
if let (Ok(a_inv), Ok(solution)) = (a.inverse(), a.inverse().and_then(|inv| inv.matmul(&b))) {
|
|
// Check residual: ||Ax - b||
|
|
if let (Ok(ax), Ok(residual), Ok(res_norm)) = (
|
|
a.matmul(&solution),
|
|
a.matmul(&solution).and_then(|ax| ax.sub(&b)),
|
|
a.matmul(&solution).and_then(|ax| ax.sub(&b)).and_then(|r| r.norm())
|
|
) {
|
|
let residual_val = res_norm.item::<f32>();
|
|
assert!(residual_val < 1e-5, "Linear solve residual too large: {}", residual_val);
|
|
println!("✓ Linear solve accuracy: residual = {:.2e}", residual_val);
|
|
|
|
if let Ok(x1) = solution.get_item(&[0, 0]) {
|
|
println!(" Solution[0]: {:.4} (expected ~1.0)", x1.item::<f32>());
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
/// Test error handling and edge cases
|
|
#[test]
|
|
fn test_error_handling() {
|
|
let device = Device::cpu();
|
|
|
|
// Test with invalid matrix dimensions
|
|
if let Ok(invalid_matrix) = Tensor::ones([5], &device) {
|
|
let svd_result = invalid_matrix.svd(true, true);
|
|
assert!(svd_result.is_err(), "Should fail with 1D tensor");
|
|
}
|
|
|
|
// Test with mismatched dimensions
|
|
if let (Ok(a), Ok(b)) = (
|
|
Tensor::ones([3, 3], &device),
|
|
Tensor::ones([2, 1], &device)
|
|
) {
|
|
// This should fail due to dimension mismatch
|
|
if let Ok(a_inv) = a.inverse() {
|
|
let result = a_inv.matmul(&b);
|
|
// Depending on implementation, this might fail at matmul or give wrong results
|
|
// The specific behavior depends on the tensor library implementation
|
|
}
|
|
}
|
|
|
|
println!("✓ Error handling tests completed");
|
|
}
|
|
} |