Files
rustytorch/crates/specialized/rtx-science/src/physics/pinn_tests.rs
T
2026-03-04 00:08:42 +00:00

380 lines
12 KiB
Rust

//! Comprehensive TDD test suite for Physics-Informed Neural Network implementations
//!
//! This module contains failing tests (RED phase) that define the required functionality
//! for complete PINN implementation including model persistence, training enhancements,
//! gradient computations, physics losses, boundary conditions, and adaptive sampling.
#![cfg(feature = "disabled_tests")]
use super::*;
use crate::Tensor;
use crate::error::{Result, ScienceError};
use std::path::Path;
// ================================================================================================
// MODEL WEIGHT PERSISTENCE TESTS - RED PHASE (SHOULD FAIL)
// ================================================================================================
#[tokio::test]
async fn test_model_weight_saving_works() -> Result<()> {
use tempfile::TempDir;
let device = crate::Device::cpu();
let pinn = PINN::builder()
.device(&device)
.layers(vec![2, 32, 1])
.physics_loss(Box::new(HeatEquation::new(0.1)))
.build()?;
// Create temporary file for testing
let temp_dir = TempDir::new()
.map_err(|e| ScienceError::io_error("Failed to create temp directory", e.to_string()))?;
let save_path = temp_dir.path().join("test_weights.json");
// This should now work - GREEN phase
pinn.save_weights(&save_path).await?;
// Verify file was created
assert!(save_path.exists(), "Weight file should be created");
// Verify file has content
let file_size = std::fs::metadata(&save_path)
.map_err(|e| ScienceError::io_error("Failed to read file metadata", e.to_string()))?
.len();
assert!(file_size > 0, "Weight file should not be empty");
// Verify the content is valid JSON
let content = std::fs::read_to_string(&save_path)
.map_err(|e| ScienceError::io_error("Failed to read weight file", e.to_string()))?;
let _parsed: std::collections::HashMap<String, Vec<f32>> = serde_json::from_str(&content)?;
Ok(())
}
#[tokio::test]
async fn test_model_weight_loading_fails() -> Result<()> {
let device = crate::Device::cpu();
let mut pinn = PINN::builder()
.device(&device)
.layers(vec![2, 32, 1])
.physics_loss(Box::new(HeatEquation::new(0.1)))
.build()?;
// This method should NOT exist yet - this test should fail to compile
// Uncomment the line below to see compilation failure:
// let _ = pinn.load_weights(Path::new("test.safetensors")).await;
// PINN was successfully created
Ok(())
}
#[tokio::test]
async fn test_checkpoint_system_fails() -> Result<()> {
let device = crate::Device::cpu();
// This should fail because TrainingConfig doesn't have checkpoint fields yet
// Uncomment to see compilation failure:
// let config = TrainingConfig {
// save_best_model: true,
// checkpoint_dir: Some(std::path::PathBuf::from("/tmp")),
// checkpoint_frequency: 10,
// ..Default::default()
// };
let _pinn = PINN::builder()
.device(&device)
.layers(vec![2, 32, 1])
.physics_loss(Box::new(HeatEquation::new(0.1)))
// .config(config) // This should fail
.build()?;
Ok(())
}
// ================================================================================================
// ADVANCED TRAINING CALLBACK TESTS - RED PHASE (SHOULD FAIL)
// ================================================================================================
#[tokio::test]
async fn test_learning_rate_scheduler_fails() -> Result<()> {
let device = crate::Device::cpu();
// This should fail because LearningRateScheduler doesn't exist yet
// Uncomment to see compilation failure:
// let scheduler = LearningRateScheduler {
// schedule_type: ScheduleType::StepDecay,
// initial_lr: 1e-3,
// decay_factor: 0.5,
// decay_steps: 10,
// };
let _pinn = PINN::builder()
.device(&device)
.layers(vec![2, 32, 1])
.physics_loss(Box::new(HeatEquation::new(0.1)))
.build()?;
Ok(())
}
#[tokio::test]
async fn test_training_callbacks_fail() -> Result<()> {
let device = crate::Device::cpu();
let pinn = PINN::builder()
.device(&device)
.layers(vec![2, 32, 1])
.physics_loss(Box::new(HeatEquation::new(0.1)))
.build()?;
let boundary_conditions = BoundaryConditions::new();
// This should fail because train_with_callbacks doesn't exist yet
// Uncomment to see compilation failure:
// let callbacks = vec![];
// let _ = pinn.train_with_callbacks(boundary_conditions, 10, callbacks).await;
// Regular training should work
let mut pinn = pinn;
pinn.train(boundary_conditions, 10).await?;
Ok(())
}
// ================================================================================================
// GRADIENT COMPUTATION ENHANCEMENT TESTS - RED PHASE (SHOULD FAIL)
// ================================================================================================
#[tokio::test]
async fn test_higher_order_derivatives_fail() -> Result<()> {
let device = crate::Device::cpu();
let pinn = PINN::builder()
.device(&device)
.layers(vec![2, 32, 1])
.physics_loss(Box::new(HeatEquation::new(0.1)))
.build()?;
let input_tensor = crate::Tensor::randn(&[10, 2], &device)?;
// This should fail because compute_higher_order_derivatives doesn't exist yet
// Uncomment to see compilation failure:
// let _ = pinn.compute_higher_order_derivatives(&input_tensor, 4).await;
// Basic derivative computation should work
let output = pinn.forward(&input_tensor).await?;
// Note: compute_derivatives is private, so we can't test it directly
// let _ = pinn.compute_derivatives(&input_tensor, &output).await?;
Ok(())
}
#[tokio::test]
async fn test_mixed_derivatives_fail() -> Result<()> {
let device = crate::Device::cpu();
let pinn = PINN::builder()
.device(&device)
.layers(vec![2, 32, 1])
.physics_loss(Box::new(HeatEquation::new(0.1)))
.build()?;
let input_tensor = crate::Tensor::randn(&[5, 2], &device)?;
// This should fail because compute_mixed_derivatives doesn't exist yet
// Uncomment to see compilation failure:
// let _ = pinn.compute_mixed_derivatives(&input_tensor).await;
Ok(())
}
// ================================================================================================
// ENHANCED PHYSICS LOSS TESTS - RED PHASE (SHOULD FAIL)
// ================================================================================================
#[tokio::test]
async fn test_schrodinger_equation_fails() -> Result<()> {
let device = crate::Device::cpu();
// This should fail because Schrodinger doesn't exist yet in our implementation
// Uncomment to see compilation failure:
// let schrodinger = Schrodinger::new(1.0, 1.0, std::collections::HashMap::new());
// For now, test that we can create other PDEs
let heat_eq = HeatEquation::new(0.1);
assert_eq!(heat_eq.name(), "Heat Equation");
Ok(())
}
#[tokio::test]
async fn test_kdv_equation_fails() -> Result<()> {
// This should fail because KdVEquation implementation is incomplete
// Uncomment to see issues:
// let kdv = KdVEquation::new(1.0);
// assert_eq!(kdv.max_derivative_order(), 3); // KdV needs 3rd derivatives
Ok(())
}
// ================================================================================================
// BOUNDARY CONDITION ENHANCEMENT TESTS - RED PHASE (SHOULD FAIL)
// ================================================================================================
#[tokio::test]
async fn test_robin_boundary_conditions_fail() -> Result<()> {
let device = crate::Device::cpu();
// This should fail because RobinBC support is incomplete
// Uncomment to see compilation failure:
// let robin_bc = RobinBC {
// boundary_id: "robin_test".to_string(),
// geometry: BoundaryGeometry::LineSegment { start: 0.0, end: 1.0 },
// alpha: 1.0,
// beta: 0.5,
// rhs_function: ValueFunction::Constant(2.0),
// num_samples: 50,
// weight: 5.0,
// };
// Basic boundary conditions should work
let _bc = BoundaryConditions::new();
Ok(())
}
#[tokio::test]
async fn test_periodic_boundary_conditions_fail() -> Result<()> {
// This should fail because PeriodicBC implementation is incomplete
// Uncomment to see compilation failure:
// let periodic_bc = PeriodicBC {
// boundary_pair_id: "periodic_test".to_string(),
// left_geometry: BoundaryGeometry::Point { x: 0.0 },
// right_geometry: BoundaryGeometry::Point { x: 1.0 },
// num_samples: 30,
// weight: 8.0,
// };
Ok(())
}
// ================================================================================================
// ADAPTIVE SAMPLING TESTS - RED PHASE (SHOULD FAIL)
// ================================================================================================
#[tokio::test]
async fn test_adaptive_sampling_fails() -> Result<()> {
let device = crate::Device::cpu();
// This should fail because AdaptiveSamplingConfig doesn't exist yet
// Uncomment to see compilation failure:
// let adaptive_config = AdaptiveSamplingConfig {
// initial_samples: 100,
// max_samples: 1000,
// refinement_threshold: 1e-3,
// refinement_ratio: 2.0,
// resampling_frequency: 10,
// };
let _pinn = PINN::builder()
.device(&device)
.layers(vec![2, 32, 1])
.physics_loss(Box::new(HeatEquation::new(0.1)))
// .adaptive_sampling(adaptive_config) // This should fail
.build()?;
Ok(())
}
#[tokio::test]
async fn test_error_estimator_fails() -> Result<()> {
// This should fail because ErrorEstimator doesn't exist yet
// Uncomment to see compilation failure:
// let error_estimator = ErrorEstimator::new()
// .with_reference_solution(ReferenceMethod::FiniteDifference)
// .with_tolerance(1e-4);
Ok(())
}
// ================================================================================================
// MULTI-GPU SUPPORT TESTS - RED PHASE (SHOULD FAIL)
// ================================================================================================
#[cfg(feature = "cuda")]
#[tokio::test]
async fn test_multi_gpu_training_fails() -> Result<()> {
// This should fail because MultiGpuConfig doesn't exist yet
// Uncomment to see compilation failure:
// let multi_gpu_config = MultiGpuConfig {
// devices: vec![Device::cuda(0)?, Device::cuda(1)?],
// data_parallel: true,
// model_parallel: false,
// };
Ok(())
}
// ================================================================================================
// CURRENT WORKING TESTS - These should PASS to verify base functionality
// ================================================================================================
#[tokio::test]
async fn test_basic_pinn_creation() -> Result<()> {
let device = crate::Device::cpu();
let _pinn = PINN::builder()
.device(&device)
.layers(vec![2, 32, 1])
.physics_loss(Box::new(HeatEquation::new(0.1)))
.build()?;
// Basic functionality should work - PINN created successfully
Ok(())
}
#[tokio::test]
async fn test_basic_training() -> Result<()> {
let device = crate::Device::cpu();
let mut pinn = PINN::builder()
.device(&device)
.layers(vec![2, 16, 1]) // Smaller network for faster test
.physics_loss(Box::new(HeatEquation::new(0.1)))
.build()?;
let boundary_conditions = BoundaryConditions::new();
// Basic training should work (even if it's just a placeholder)
pinn.train(boundary_conditions, 5).await?;
Ok(())
}
#[tokio::test]
async fn test_prediction() -> Result<()> {
let device = crate::Device::cpu();
let pinn = PINN::builder()
.device(&device)
.layers(vec![2, 16, 1])
.physics_loss(Box::new(HeatEquation::new(0.1)))
.build()?;
// Create input tensor with shape [2, 2] for 2 samples with 2 features each
let test_inputs = Tensor::from_slice(&[0.5_f32, 0.5, 0.3, 0.7], &[2, 2], &device)?;
let predictions = pinn.predict(&test_inputs).await?;
// Predictions should have shape [2, 1]
assert_eq!(predictions.shape().dims(), &[2, 1]);
// Predictions should be finite numbers
let pred_data: Vec<f32> = predictions.to_vec()?;
for pred in pred_data {
assert!(pred.is_finite(), "Predictions should be finite numbers");
}
Ok(())
}