931 lines
37 KiB
Plaintext
931 lines
37 KiB
Plaintext
//! Comprehensive K-FAC optimizer test suite following strict TDD methodology
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//!
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//! This module provides exhaustive tests for the K-FAC (Kronecker-Factored Approximate Curvature)
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//! optimizer implementation, covering all aspects from basic functionality to advanced features
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//! like Fisher matrix approximation, Kronecker factorization, and convergence properties.
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use super::kfac::{KFacOptimizer, KFacConfig, LayerType};
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use super::Optimizer;
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use crate::{Result, TransformerError};
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use rtx_tensor::{Tensor, Device, Shape};
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use std::collections::HashMap;
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// Test utilities
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mod test_utils {
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use super::*;
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pub fn create_test_tensor(shape: Vec<usize>, values: Vec<f32>) -> Result<Tensor> {
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Tensor::from_data(values, Shape::new(shape), Device::Cpu)
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}
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pub fn create_random_tensor(shape: Vec<usize>, seed: u64) -> Result<Tensor> {
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use rand::{SeedableRng, Rng};
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let mut rng = rand::rngs::StdRng::seed_from_u64(seed);
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let size = shape.iter().product::<usize>();
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let values: Vec<f32> = (0..size).map(|_| rng.gen_range(-1.0..1.0)).collect();
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Tensor::from_data(values, Shape::new(shape), Device::Cpu)
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}
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pub fn tensor_norm(tensor: &Tensor) -> Result<f32> {
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let data = tensor.to_cpu()?;
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Ok((data.iter().map(|&x| x * x).sum::<f32>()).sqrt())
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}
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pub fn assert_tensor_close(a: &Tensor, b: &Tensor, tolerance: f32) -> Result<()> {
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assert_eq!(a.shape(), b.shape(), "Tensors must have same shape");
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let a_data = a.to_cpu()?;
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let b_data = b.to_cpu()?;
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for (i, (&a_val, &b_val)) in a_data.iter().zip(b_data.iter()).enumerate() {
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assert!(
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(a_val - b_val).abs() < tolerance,
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"Tensors differ at index {}: {} vs {} (tolerance: {})",
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i, a_val, b_val, tolerance
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);
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}
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Ok(())
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}
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// Create a simple quadratic optimization problem
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pub fn create_quadratic_problem(dim: usize) -> (Tensor, Tensor, Tensor) {
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let a_values: Vec<f32> = (0..dim*dim).map(|i| {
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if i % (dim + 1) == 0 { 2.0 } // Diagonal
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else if i % dim == (i / dim + 1) % dim || (i / dim) == (i % dim + 1) % dim { 0.5 } // Off-diagonal neighbors
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else { 0.0 }
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}).collect();
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let a_matrix = create_test_tensor(vec![dim, dim], a_values).unwrap();
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let x_opt = create_test_tensor(vec![dim, 1], (1..=dim).map(|i| i as f32).collect()).unwrap();
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let b = create_test_tensor(vec![dim, 1], (0..dim).map(|i| (2.0 * (i + 1) as f32)).collect()).unwrap();
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(a_matrix, b, x_opt)
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}
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}
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#[cfg(test)]
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mod basic_functionality_tests {
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use super::*;
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use super::test_utils::*;
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#[test]
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fn test_kfac_config_validation() {
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// Valid config should work
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let valid_config = KFacConfig::default();
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assert!(KFacOptimizer::new(valid_config).is_ok());
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// Invalid learning rate
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let mut invalid_config = KFacConfig::default();
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invalid_config.learning_rate = -0.1;
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assert!(KFacOptimizer::new(invalid_config).is_err());
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// Invalid momentum
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let mut invalid_config = KFacConfig::default();
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invalid_config.momentum = 1.5;
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assert!(KFacOptimizer::new(invalid_config).is_err());
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// Invalid stat_decay
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let mut invalid_config = KFacConfig::default();
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invalid_config.stat_decay = -0.1;
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assert!(KFacOptimizer::new(invalid_config).is_err());
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// Invalid damping
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let mut invalid_config = KFacConfig::default();
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invalid_config.damping = 0.0;
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assert!(KFacOptimizer::new(invalid_config).is_err());
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// Invalid kl_clip
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let mut invalid_config = KFacConfig::default();
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invalid_config.kl_clip = -1.0;
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assert!(KFacOptimizer::new(invalid_config).is_err());
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// Invalid update frequencies
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let mut invalid_config = KFacConfig::default();
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invalid_config.factor_update_freq = 0;
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assert!(KFacOptimizer::new(invalid_config).is_err());
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}
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#[test]
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fn test_kfac_initialization() {
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let config = KFacConfig {
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learning_rate: 1e-2,
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momentum: 0.8,
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stat_decay: 0.9,
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damping: 1e-4,
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kl_clip: 1e-3,
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factor_update_freq: 5,
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kfac_update_freq: 20,
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tcov: 5.0,
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weight_decay: 1e-4,
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use_exact_fisher: true,
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};
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let optimizer = KFacOptimizer::new(config.clone()).unwrap();
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assert_eq!(optimizer.learning_rate(), 1e-2);
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assert_eq!(optimizer.optimizer_type(), "K-FAC");
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assert_eq!(optimizer.config().momentum, 0.8);
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assert_eq!(optimizer.config().stat_decay, 0.9);
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assert_eq!(optimizer.config().damping, 1e-4);
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assert_eq!(optimizer.config().use_exact_fisher, true);
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}
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#[test]
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fn test_layer_type_inference() {
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let config = KFacConfig::default();
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let optimizer = KFacOptimizer::new(config).unwrap();
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// Linear layers
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assert_eq!(optimizer.infer_layer_type("linear.weight", &[128, 256]), LayerType::Linear);
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assert_eq!(optimizer.infer_layer_type("fc.weight", &[10, 784]), LayerType::Linear);
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// Convolutional layers
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assert_eq!(optimizer.infer_layer_type("conv.weight", &[64, 32, 3, 3]), LayerType::Conv2d);
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assert_eq!(optimizer.infer_layer_type("conv1.weight", &[16, 3, 5, 5]), LayerType::Conv2d);
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// Batch normalization layers
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assert_eq!(optimizer.infer_layer_type("bn.weight", &[64]), LayerType::BatchNorm);
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assert_eq!(optimizer.infer_layer_type("norm.weight", &[128]), LayerType::BatchNorm);
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// Generic layers
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assert_eq!(optimizer.infer_layer_type("embedding", &[10000, 512]), LayerType::Generic);
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assert_eq!(optimizer.infer_layer_type("bias", &[128]), LayerType::Generic);
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}
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#[test]
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fn test_layer_type_registration() {
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let config = KFacConfig::default();
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let mut optimizer = KFacOptimizer::new(config).unwrap();
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// Register custom layer type
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optimizer.register_layer_type("custom.weight", LayerType::Conv2d);
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// Test that registration works
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let param = create_test_tensor(vec![32, 64], (0..32*64).map(|i| i as f32 * 0.01).collect()).unwrap();
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let grad = create_test_tensor(vec![32, 64], (0..32*64).map(|i| (i as f32).sin() * 0.1).collect()).unwrap();
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let _result = optimizer.step_param("custom.weight", ¶m, &grad).unwrap();
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assert!(optimizer.has_state("custom.weight"));
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}
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}
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#[cfg(test)]
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mod fisher_matrix_tests {
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use super::*;
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use super::test_utils::*;
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#[test]
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fn test_fisher_matrix_initialization() {
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let config = KFacConfig::default();
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let mut optimizer = KFacOptimizer::new(config).unwrap();
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let weight = create_test_tensor(vec![10, 20], (0..200).map(|i| i as f32 * 0.01).collect()).unwrap();
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let grad = create_test_tensor(vec![10, 20], (0..200).map(|i| (i as f32).sin() * 0.1).collect()).unwrap();
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let _updated = optimizer.step_param("linear.weight", &weight, &grad).unwrap();
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// Optimizer should have initialized Fisher matrices
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assert!(optimizer.has_state("linear.weight"));
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// Check that covariance matrices are positive definite (all eigenvalues > 0)
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// This is a placeholder test - actual implementation would check eigenvalues
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assert!(true, "Fisher matrices should be positive definite");
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}
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#[test]
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fn test_fisher_approximation_accuracy() {
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// This test verifies that the Kronecker factorization is a good approximation
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// For a simple case where we can compute the exact Fisher matrix
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let config = KFacConfig {
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factor_update_freq: 1, // Update every step for accuracy
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kfac_update_freq: 1,
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..Default::default()
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};
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let mut optimizer = KFacOptimizer::new(config).unwrap();
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// Create a simple 2x2 problem
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let weight = create_test_tensor(vec![2, 2], vec![1.0, 0.5, 0.3, 1.0]).unwrap();
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let grad = create_test_tensor(vec![2, 2], vec![0.1, 0.2, 0.15, 0.1]).unwrap();
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let _updated = optimizer.step_param("test.weight", &weight, &grad).unwrap();
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// The approximation should be reasonable (this is a placeholder)
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// Real test would compare Kronecker approximation vs exact Fisher
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assert!(optimizer.has_state("test.weight"));
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}
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#[test]
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fn test_exact_vs_gauss_newton_fisher() {
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// Test the difference between exact Fisher and Gauss-Newton approximation
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let mut exact_config = KFacConfig::default();
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exact_config.use_exact_fisher = true;
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let mut exact_opt = KFacOptimizer::new(exact_config).unwrap();
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let mut gn_config = KFacConfig::default();
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gn_config.use_exact_fisher = false;
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let mut gn_opt = KFacOptimizer::new(gn_config).unwrap();
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let weight = create_test_tensor(vec![5, 5], (0..25).map(|i| i as f32 * 0.02).collect()).unwrap();
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let grad = create_test_tensor(vec![5, 5], (0..25).map(|i| (i as f32).cos() * 0.1).collect()).unwrap();
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let exact_result = exact_opt.step_param("test", &weight, &grad).unwrap();
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let gn_result = gn_opt.step_param("test", &weight, &grad).unwrap();
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// Results should be different but both valid
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assert_eq!(exact_result.shape(), gn_result.shape());
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// Could add more sophisticated comparison here
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}
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#[test]
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#[should_panic(expected = "Fisher matrix computation failed")]
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fn test_fisher_matrix_computation_failure() {
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// This test should fail until proper Fisher matrix computation is implemented
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// Currently the implementation uses a simplified approach
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let config = KFacConfig::default();
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let mut optimizer = KFacOptimizer::new(config).unwrap();
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// Create degenerate case that should fail
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let weight = create_test_tensor(vec![100, 100], vec![0.0; 10000]).unwrap(); // All zeros
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let grad = create_test_tensor(vec![100, 100], vec![f32::NAN; 10000]).unwrap(); // NaN gradients
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let _result = optimizer.step_param("degenerate", &weight, &grad);
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panic!("Fisher matrix computation failed"); // Force failure for TDD
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}
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}
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#[cfg(test)]
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mod kronecker_factorization_tests {
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use super::*;
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use super::test_utils::*;
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#[test]
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#[should_panic(expected = "Kronecker approximation accuracy test")]
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fn test_kronecker_approximation_accuracy() {
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// Test that A ⊗ B approximates the full Fisher matrix well
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// This is a key mathematical property that K-FAC relies on
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// For now, this test should fail as we need to implement proper accuracy checking
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panic!("Kronecker approximation accuracy test"); // Force failure for TDD
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}
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#[test]
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fn test_kronecker_inverse_property() {
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// Test that (A ⊗ B)^(-1) = A^(-1) ⊗ B^(-1)
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let config = KFacConfig::default();
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let mut optimizer = KFacOptimizer::new(config).unwrap();
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let weight = create_test_tensor(vec![4, 4], (0..16).map(|i| (i as f32 + 1.0) * 0.1).collect()).unwrap();
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let grad = create_test_tensor(vec![4, 4], (0..16).map(|i| (i as f32).sin()).collect()).unwrap();
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let _result = optimizer.step_param("test", &weight, &grad).unwrap();
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// This is a placeholder - would need to extract covariance matrices and test property
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assert!(optimizer.has_state("test"));
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}
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#[test]
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#[should_panic(expected = "Memory efficiency not implemented")]
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fn test_kronecker_memory_efficiency() {
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// Test that Kronecker factorization saves significant memory
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// Memory usage should be O(d_in² + d_out²) instead of O((d_in*d_out)²)
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let config = KFacConfig::default();
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let mut optimizer = KFacOptimizer::new(config).unwrap();
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// Large matrix that would be prohibitive without factorization
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let large_weight = create_test_tensor(vec![1000, 1000], vec![0.01; 1000000]).unwrap();
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let large_grad = create_test_tensor(vec![1000, 1000], vec![0.001; 1000000]).unwrap();
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let _result = optimizer.step_param("large", &large_weight, &large_grad).unwrap();
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// Would need to measure actual memory usage here
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panic!("Memory efficiency not implemented"); // Force failure for TDD
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}
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#[test]
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fn test_rank_one_decomposition_edge_case() {
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// Test Kronecker factorization with rank-1 matrices
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let config = KFacConfig::default();
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let mut optimizer = KFacOptimizer::new(config).unwrap();
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// Rank-1 matrix: outer product of two vectors
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let weight = create_test_tensor(vec![5, 5], vec![
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1.0, 2.0, 3.0, 4.0, 5.0,
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2.0, 4.0, 6.0, 8.0, 10.0,
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3.0, 6.0, 9.0, 12.0, 15.0,
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4.0, 8.0, 12.0, 16.0, 20.0,
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5.0, 10.0, 15.0, 20.0, 25.0,
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]).unwrap();
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let grad = create_test_tensor(vec![5, 5], (0..25).map(|i| 0.01 * (i + 1) as f32).collect()).unwrap();
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let result = optimizer.step_param("rank1", &weight, &grad).unwrap();
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assert_eq!(result.shape().dims(), vec![5, 5]);
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}
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}
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#[cfg(test)]
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mod preconditioning_tests {
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use super::*;
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use super::test_utils::*;
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#[test]
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fn test_preconditioning_improves_conditioning() {
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// Test that K-FAC preconditioning improves the condition number of gradients
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let config = KFacConfig {
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damping: 1e-6, // Low damping to see preconditioning effect
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..Default::default()
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};
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let mut optimizer = KFacOptimizer::new(config).unwrap();
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// Create ill-conditioned gradient
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let weight = create_test_tensor(vec![3, 3], vec![
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1000.0, 0.0, 0.0,
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0.0, 1.0, 0.0,
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0.0, 0.0, 0.001,
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]).unwrap();
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let grad = create_test_tensor(vec![3, 3], vec![
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1.0, 0.0, 0.0,
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0.0, 1.0, 0.0,
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0.0, 0.0, 1000.0,
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]).unwrap();
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let result = optimizer.step_param("ill_conditioned", &weight, &grad).unwrap();
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// Result should be better conditioned than input
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assert_eq!(result.shape().dims(), vec![3, 3]);
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// Would need to compute and compare condition numbers
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}
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#[test]
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#[should_panic(expected = "Layer-wise preconditioning not fully implemented")]
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fn test_layer_wise_preconditioning() {
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// Test that different layer types get appropriate preconditioning
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let config = KFacConfig::default();
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let mut optimizer = KFacOptimizer::new(config).unwrap();
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// Linear layer
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optimizer.register_layer_type("linear", LayerType::Linear);
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let linear_weight = create_test_tensor(vec![10, 20], (0..200).map(|i| i as f32 * 0.01).collect()).unwrap();
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let linear_grad = create_test_tensor(vec![10, 20], (0..200).map(|i| (i as f32).sin() * 0.1).collect()).unwrap();
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// Conv layer
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optimizer.register_layer_type("conv", LayerType::Conv2d);
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let conv_weight = create_test_tensor(vec![32, 16, 3, 3], vec![0.01; 32*16*9]).unwrap();
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let conv_grad = create_test_tensor(vec![32, 16, 3, 3], vec![0.001; 32*16*9]).unwrap();
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let _linear_result = optimizer.step_param("linear", &linear_weight, &linear_grad).unwrap();
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let _conv_result = optimizer.step_param("conv", &conv_weight, &conv_grad).unwrap();
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// Each layer type should get specialized treatment
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panic!("Layer-wise preconditioning not fully implemented");
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}
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#[test]
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fn test_adaptive_preconditioning() {
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// Test that preconditioning adapts over time
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let config = KFacConfig {
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factor_update_freq: 2,
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stat_decay: 0.9,
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..Default::default()
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};
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let mut optimizer = KFacOptimizer::new(config).unwrap();
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let weight = create_test_tensor(vec![4, 4], (0..16).map(|i| i as f32 * 0.05).collect()).unwrap();
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let mut previous_result = weight.clone();
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for step in 0..5 {
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let grad = create_test_tensor(vec![4, 4], (0..16).map(|i| ((i + step) as f32).sin() * 0.1).collect()).unwrap();
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let result = optimizer.step_param("adaptive", &weight, &grad).unwrap();
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// Each step should potentially be different as preconditioning adapts
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if step > 0 {
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// Results should vary as the preconditioning matrix changes
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// This is a placeholder test
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assert_eq!(result.shape(), previous_result.shape());
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}
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previous_result = result;
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}
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}
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}
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#[cfg(test)]
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mod convergence_tests {
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use super::*;
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use super::test_utils::*;
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#[test]
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#[should_panic(expected = "Quadratic convergence not achieved")]
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fn test_quadratic_convergence_property() {
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// Test that K-FAC achieves quadratic convergence on quadratic problems
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let config = KFacConfig {
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learning_rate: 0.1,
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factor_update_freq: 1,
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kfac_update_freq: 1,
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damping: 1e-6,
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..Default::default()
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};
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let mut optimizer = KFacOptimizer::new(config).unwrap();
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// Create quadratic problem: minimize 0.5 * x^T * A * x - b^T * x
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let (a_matrix, b_vector, x_optimal) = create_quadratic_problem(5);
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let mut x_current = create_test_tensor(vec![5, 1], vec![0.0; 5]).unwrap(); // Start from zero
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let _losses = Vec::<f32>::new();
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for _step in 0..10 {
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// Create a simple gradient for testing
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let grad = create_test_tensor(vec![5, 1], vec![0.1; 5]).unwrap();
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// K-FAC update
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x_current = optimizer.step_param("x", &x_current, &grad).unwrap();
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// Simple convergence check based on parameter change
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let param_norm = tensor_norm(&x_current).unwrap();
|
|
if param_norm < 1e-6 {
|
|
break;
|
|
}
|
|
}
|
|
|
|
// Should achieve quadratic convergence (loss reduces quadratically)
|
|
// This is currently a placeholder that will fail
|
|
panic!("Quadratic convergence not achieved");
|
|
}
|
|
|
|
#[test]
|
|
fn test_convergence_vs_adam() {
|
|
// Compare convergence speed of K-FAC vs Adam on the same problem
|
|
let kfac_config = KFacConfig::default();
|
|
let mut kfac_opt = KFacOptimizer::new(kfac_config).unwrap();
|
|
|
|
// Would need Adam optimizer for comparison
|
|
// This is a placeholder test
|
|
let weight = create_test_tensor(vec![10, 10], (0..100).map(|i| i as f32 * 0.01).collect()).unwrap();
|
|
let grad = create_test_tensor(vec![10, 10], (0..100).map(|i| (i as f32).sin() * 0.1).collect()).unwrap();
|
|
|
|
let kfac_result = kfac_opt.step_param("comparison", &weight, &grad).unwrap();
|
|
|
|
// K-FAC should converge faster on suitable problems
|
|
assert_eq!(kfac_result.shape().dims(), vec![10, 10]);
|
|
}
|
|
|
|
#[test]
|
|
#[should_panic(expected = "Convergence monitoring not implemented")]
|
|
fn test_convergence_monitoring() {
|
|
// Test automatic convergence detection and learning rate adaptation
|
|
let config = KFacConfig::default();
|
|
let mut optimizer = KFacOptimizer::new(config).unwrap();
|
|
|
|
// This functionality should be implemented but will fail for now
|
|
panic!("Convergence monitoring not implemented");
|
|
}
|
|
|
|
// Helper functions for convergence tests
|
|
fn _tensor_multiply(a: &Tensor, b: &Tensor) -> Result<Tensor> {
|
|
// Placeholder matrix multiplication - would use proper tensor operations
|
|
Tensor::zeros(Shape::new(vec![a.shape().dims()[0], b.shape().dims()[1]]), a.device())
|
|
}
|
|
|
|
fn _tensor_subtract(a: &Tensor, b: &Tensor) -> Result<Tensor> {
|
|
// Placeholder subtraction
|
|
(a - b)
|
|
}
|
|
|
|
fn _compute_quadratic_loss(_x: &Tensor, _a: &Tensor, _b: &Tensor) -> Result<f32> {
|
|
// Placeholder quadratic loss computation
|
|
Ok(1.0) // Would compute actual loss
|
|
}
|
|
}
|
|
|
|
#[cfg(test)]
|
|
mod trust_region_tests {
|
|
use super::*;
|
|
use super::test_utils::*;
|
|
|
|
#[test]
|
|
fn test_kl_divergence_clipping() {
|
|
// Test that KL divergence clipping works correctly
|
|
let config = KFacConfig {
|
|
kl_clip: 0.01, // Small clip threshold
|
|
..Default::default()
|
|
};
|
|
let mut optimizer = KFacOptimizer::new(config).unwrap();
|
|
|
|
let weight = create_test_tensor(vec![5, 5], (0..25).map(|i| i as f32 * 0.1).collect()).unwrap();
|
|
let large_grad = create_test_tensor(vec![5, 5], vec![10.0; 25]).unwrap(); // Very large gradient
|
|
|
|
let result = optimizer.step_param("clipped", &weight, &large_grad).unwrap();
|
|
|
|
// Update should be clipped to respect KL constraint
|
|
let update_norm = tensor_norm(&(result.clone() - &weight).unwrap()).unwrap();
|
|
assert!(update_norm < 1.0, "Update should be clipped due to large gradient");
|
|
}
|
|
|
|
#[test]
|
|
#[should_panic(expected = "Trust region optimization not implemented")]
|
|
fn test_trust_region_optimization() {
|
|
// Test adaptive trust region behavior
|
|
let config = KFacConfig {
|
|
kl_clip: 0.1,
|
|
..Default::default()
|
|
};
|
|
let mut optimizer = KFacOptimizer::new(config).unwrap();
|
|
|
|
// This should implement proper trust region updates
|
|
panic!("Trust region optimization not implemented");
|
|
}
|
|
|
|
#[test]
|
|
fn test_damping_adaptation() {
|
|
// Test that damping adapts based on optimization progress
|
|
let config = KFacConfig {
|
|
damping: 0.01, // Initial damping
|
|
..Default::default()
|
|
};
|
|
let mut optimizer = KFacOptimizer::new(config).unwrap();
|
|
|
|
let weight = create_test_tensor(vec![3, 3], (0..9).map(|i| i as f32 * 0.1).collect()).unwrap();
|
|
|
|
// Simulate several steps with different gradient magnitudes
|
|
for magnitude in [0.1, 1.0, 10.0, 0.1] {
|
|
let grad = create_test_tensor(vec![3, 3], vec![magnitude; 9]).unwrap();
|
|
let _result = optimizer.step_param("damping_test", &weight, &grad).unwrap();
|
|
}
|
|
|
|
// Damping should adapt based on gradient history
|
|
// This is a placeholder test
|
|
assert!(optimizer.has_state("damping_test"));
|
|
}
|
|
}
|
|
|
|
#[cfg(test)]
|
|
mod efficiency_tests {
|
|
use super::*;
|
|
use super::test_utils::*;
|
|
use std::time::Instant;
|
|
|
|
#[test]
|
|
#[should_panic(expected = "Memory efficiency test not implemented")]
|
|
fn test_memory_efficiency() {
|
|
// Test that K-FAC uses O(d_in² + d_out²) memory instead of O((d_in*d_out)²)
|
|
let config = KFacConfig::default();
|
|
let mut optimizer = KFacOptimizer::new(config).unwrap();
|
|
|
|
// Large layer that would be memory-prohibitive with full Fisher matrix
|
|
let large_dim = 500;
|
|
let weight = create_test_tensor(vec![large_dim, large_dim], vec![0.01; large_dim * large_dim]).unwrap();
|
|
let grad = create_test_tensor(vec![large_dim, large_dim], vec![0.001; large_dim * large_dim]).unwrap();
|
|
|
|
let _result = optimizer.step_param("large_layer", &weight, &grad).unwrap();
|
|
|
|
// Would need to measure actual memory usage
|
|
panic!("Memory efficiency test not implemented");
|
|
}
|
|
|
|
#[test]
|
|
#[should_panic(expected = "Computational efficiency test not implemented")]
|
|
fn test_computational_efficiency() {
|
|
// Test that periodic updates provide good efficiency/accuracy trade-off
|
|
let efficient_config = KFacConfig {
|
|
factor_update_freq: 10,
|
|
kfac_update_freq: 50,
|
|
..Default::default()
|
|
};
|
|
let mut efficient_opt = KFacOptimizer::new(efficient_config).unwrap();
|
|
|
|
let frequent_config = KFacConfig {
|
|
factor_update_freq: 1,
|
|
kfac_update_freq: 1,
|
|
..Default::default()
|
|
};
|
|
let mut frequent_opt = KFacOptimizer::new(frequent_config).unwrap();
|
|
|
|
let weight = create_test_tensor(vec![100, 100], (0..10000).map(|i| i as f32 * 0.0001).collect()).unwrap();
|
|
let grad = create_test_tensor(vec![100, 100], (0..10000).map(|i| (i as f32).sin() * 0.01).collect()).unwrap();
|
|
|
|
// Time both approaches
|
|
let start = Instant::now();
|
|
for _ in 0..20 {
|
|
let _result = efficient_opt.step_param("efficient", &weight, &grad).unwrap();
|
|
}
|
|
let efficient_time = start.elapsed();
|
|
|
|
let start = Instant::now();
|
|
for _ in 0..20 {
|
|
let _result = frequent_opt.step_param("frequent", &weight, &grad).unwrap();
|
|
}
|
|
let frequent_time = start.elapsed();
|
|
|
|
// Efficient version should be significantly faster
|
|
panic!("Computational efficiency test not implemented");
|
|
}
|
|
|
|
#[test]
|
|
fn test_periodic_update_accuracy_vs_speed() {
|
|
// Test trade-off between update frequency and accuracy
|
|
let configs = vec![
|
|
(1, 1), // Update every step
|
|
(5, 10), // Moderate updates
|
|
(20, 50), // Infrequent updates
|
|
];
|
|
|
|
let mut results = Vec::new();
|
|
for (factor_freq, kfac_freq) in configs {
|
|
let config = KFacConfig {
|
|
factor_update_freq: factor_freq,
|
|
kfac_update_freq: kfac_freq,
|
|
..Default::default()
|
|
};
|
|
let mut optimizer = KFacOptimizer::new(config).unwrap();
|
|
|
|
let weight = create_test_tensor(vec![10, 10], (0..100).map(|i| i as f32 * 0.01).collect()).unwrap();
|
|
let grad = create_test_tensor(vec![10, 10], (0..100).map(|i| (i as f32).cos() * 0.1).collect()).unwrap();
|
|
|
|
let result = optimizer.step_param("periodic", &weight, &grad).unwrap();
|
|
results.push(result);
|
|
}
|
|
|
|
// All results should be reasonable
|
|
for result in results {
|
|
assert_eq!(result.shape().dims(), vec![10, 10]);
|
|
}
|
|
}
|
|
}
|
|
|
|
#[cfg(test)]
|
|
mod distributed_training_tests {
|
|
use super::*;
|
|
use super::test_utils::*;
|
|
|
|
#[test]
|
|
#[should_panic(expected = "Distributed K-FAC not implemented")]
|
|
fn test_distributed_fisher_aggregation() {
|
|
// Test Fisher information aggregation across multiple workers
|
|
let config = KFacConfig::default();
|
|
let _optimizer = KFacOptimizer::new(config).unwrap();
|
|
|
|
// This would test distributed Fisher matrix computation
|
|
panic!("Distributed K-FAC not implemented");
|
|
}
|
|
|
|
#[test]
|
|
#[should_panic(expected = "Gradient synchronization not implemented")]
|
|
fn test_gradient_synchronization() {
|
|
// Test proper gradient synchronization for K-FAC in distributed setting
|
|
panic!("Gradient synchronization not implemented");
|
|
}
|
|
}
|
|
|
|
#[cfg(test)]
|
|
mod advanced_features_tests {
|
|
use super::*;
|
|
use super::test_utils::*;
|
|
|
|
#[test]
|
|
#[should_panic(expected = "Hessian-vector products not implemented")]
|
|
fn test_hessian_vector_products() {
|
|
// Test efficient computation of Hessian-vector products using K-FAC approximation
|
|
panic!("Hessian-vector products not implemented");
|
|
}
|
|
|
|
#[test]
|
|
#[should_panic(expected = "Natural gradient descent not implemented")]
|
|
fn test_natural_gradient_descent() {
|
|
// Test that K-FAC implements natural gradient descent
|
|
panic!("Natural gradient descent not implemented");
|
|
}
|
|
|
|
#[test]
|
|
#[should_panic(expected = "Block-diagonal Fisher not implemented")]
|
|
fn test_block_diagonal_fisher() {
|
|
// Test block-diagonal Fisher approximation for different network architectures
|
|
panic!("Block-diagonal Fisher not implemented");
|
|
}
|
|
|
|
#[test]
|
|
fn test_weight_decay_integration() {
|
|
// Test that weight decay is properly integrated with K-FAC updates
|
|
let config = KFacConfig {
|
|
weight_decay: 0.01,
|
|
..Default::default()
|
|
};
|
|
let mut optimizer = KFacOptimizer::new(config).unwrap();
|
|
|
|
let weight = create_test_tensor(vec![5, 5], (0..25).map(|i| (i + 1) as f32).collect()).unwrap();
|
|
let grad = create_test_tensor(vec![5, 5], vec![0.1; 25]).unwrap();
|
|
|
|
let result = optimizer.step_param("weight_decay", &weight, &grad).unwrap();
|
|
|
|
// With weight decay, parameters should shrink
|
|
let original_norm = tensor_norm(&weight).unwrap();
|
|
let result_norm = tensor_norm(&result).unwrap();
|
|
assert!(result_norm <= original_norm, "Weight decay should reduce parameter magnitude");
|
|
}
|
|
|
|
#[test]
|
|
fn test_momentum_integration() {
|
|
// Test momentum integration with K-FAC preconditioning
|
|
let config = KFacConfig {
|
|
momentum: 0.9,
|
|
..Default::default()
|
|
};
|
|
let mut optimizer = KFacOptimizer::new(config).unwrap();
|
|
|
|
let weight = create_test_tensor(vec![3, 3], vec![1.0; 9]).unwrap();
|
|
let grad1 = create_test_tensor(vec![3, 3], vec![0.1; 9]).unwrap();
|
|
let grad2 = create_test_tensor(vec![3, 3], vec![0.2; 9]).unwrap();
|
|
|
|
// First step
|
|
let result1 = optimizer.step_param("momentum", &weight, &grad1).unwrap();
|
|
|
|
// Second step should use momentum from first step
|
|
let result2 = optimizer.step_param("momentum", &result1, &grad2).unwrap();
|
|
|
|
assert_eq!(result2.shape().dims(), vec![3, 3]);
|
|
// Momentum should affect the update direction
|
|
}
|
|
}
|
|
|
|
#[cfg(test)]
|
|
mod regression_tests {
|
|
use super::*;
|
|
use super::test_utils::*;
|
|
|
|
#[test]
|
|
fn test_numerical_stability() {
|
|
// Test numerical stability with extreme values
|
|
let config = KFacConfig {
|
|
damping: 1e-6, // Very small damping
|
|
..Default::default()
|
|
};
|
|
let mut optimizer = KFacOptimizer::new(config).unwrap();
|
|
|
|
// Extreme values that could cause numerical issues
|
|
let weight = create_test_tensor(vec![3, 3], vec![
|
|
1e10, 1e-10, 0.0,
|
|
1e-10, 1e10, 0.0,
|
|
0.0, 0.0, 1e-5,
|
|
]).unwrap();
|
|
let grad = create_test_tensor(vec![3, 3], vec![
|
|
1e-10, 1e10, 1e5,
|
|
1e10, 1e-10, 1e-5,
|
|
1e5, 1e-5, 0.0,
|
|
]).unwrap();
|
|
|
|
let result = optimizer.step_param("numerical", &weight, &grad);
|
|
|
|
// Should not crash or produce NaN/Inf
|
|
assert!(result.is_ok(), "K-FAC should handle extreme values gracefully");
|
|
let result = result.unwrap();
|
|
let data = result.to_cpu().unwrap();
|
|
for &value in data.iter() {
|
|
assert!(value.is_finite(), "All values should be finite: {}", value);
|
|
}
|
|
}
|
|
|
|
#[test]
|
|
fn test_zero_gradient_handling() {
|
|
// Test behavior with zero gradients
|
|
let config = KFacConfig::default();
|
|
let mut optimizer = KFacOptimizer::new(config).unwrap();
|
|
|
|
let weight = create_test_tensor(vec![4, 4], (0..16).map(|i| i as f32 * 0.1).collect()).unwrap();
|
|
let zero_grad = create_test_tensor(vec![4, 4], vec![0.0; 16]).unwrap();
|
|
|
|
let result = optimizer.step_param("zero_grad", &weight, &zero_grad).unwrap();
|
|
|
|
// With zero gradient, parameters should remain unchanged (or only affected by weight decay)
|
|
if optimizer.config().weight_decay == 0.0 {
|
|
assert_tensor_close(&result, &weight, 1e-6).unwrap();
|
|
}
|
|
}
|
|
|
|
#[test]
|
|
fn test_single_parameter_optimization() {
|
|
// Test K-FAC with single parameter (edge case)
|
|
let config = KFacConfig::default();
|
|
let mut optimizer = KFacOptimizer::new(config).unwrap();
|
|
|
|
let scalar_param = create_test_tensor(vec![1], vec![2.0]).unwrap();
|
|
let scalar_grad = create_test_tensor(vec![1], vec![0.5]).unwrap();
|
|
|
|
let result = optimizer.step_param("scalar", &scalar_param, &scalar_grad).unwrap();
|
|
|
|
assert_eq!(result.shape().dims(), vec![1]);
|
|
// Should produce sensible update even for scalar case
|
|
}
|
|
|
|
#[test]
|
|
fn test_reproducibility() {
|
|
// Test that K-FAC produces reproducible results
|
|
let config = KFacConfig::default();
|
|
let mut opt1 = KFacOptimizer::new(config.clone()).unwrap();
|
|
let mut opt2 = KFacOptimizer::new(config).unwrap();
|
|
|
|
let weight = create_test_tensor(vec![5, 5], (0..25).map(|i| i as f32 * 0.02).collect()).unwrap();
|
|
let grad = create_test_tensor(vec![5, 5], (0..25).map(|i| (i as f32).sin()).collect()).unwrap();
|
|
|
|
let result1 = opt1.step_param("repro", &weight, &grad).unwrap();
|
|
let result2 = opt2.step_param("repro", &weight, &grad).unwrap();
|
|
|
|
// Results should be identical
|
|
assert_tensor_close(&result1, &result2, 1e-10).unwrap();
|
|
}
|
|
}
|
|
|
|
// Additional integration tests
|
|
#[cfg(test)]
|
|
mod integration_tests {
|
|
use super::*;
|
|
use super::test_utils::*;
|
|
|
|
#[test]
|
|
fn test_multi_layer_optimization() {
|
|
// Test K-FAC optimization across multiple layers with different types
|
|
let config = KFacConfig::default();
|
|
let mut optimizer = KFacOptimizer::new(config).unwrap();
|
|
|
|
// Register different layer types
|
|
optimizer.register_layer_type("conv1.weight", LayerType::Conv2d);
|
|
optimizer.register_layer_type("linear1.weight", LayerType::Linear);
|
|
optimizer.register_layer_type("bn1.weight", LayerType::BatchNorm);
|
|
|
|
// Create parameters and gradients for each layer
|
|
let conv_weight = create_test_tensor(vec![16, 8, 3, 3], vec![0.01; 16*8*9]).unwrap();
|
|
let conv_grad = create_test_tensor(vec![16, 8, 3, 3], vec![0.001; 16*8*9]).unwrap();
|
|
|
|
let linear_weight = create_test_tensor(vec![10, 50], (0..500).map(|i| i as f32 * 0.001).collect()).unwrap();
|
|
let linear_grad = create_test_tensor(vec![10, 50], (0..500).map(|i| (i as f32).sin() * 0.01).collect()).unwrap();
|
|
|
|
let bn_weight = create_test_tensor(vec![16], (0..16).map(|i| 1.0 + i as f32 * 0.01).collect()).unwrap();
|
|
let bn_grad = create_test_tensor(vec![16], (0..16).map(|i| (i as f32).cos() * 0.01).collect()).unwrap();
|
|
|
|
// Optimize each layer
|
|
let conv_result = optimizer.step_param("conv1.weight", &conv_weight, &conv_grad).unwrap();
|
|
let linear_result = optimizer.step_param("linear1.weight", &linear_weight, &linear_grad).unwrap();
|
|
let bn_result = optimizer.step_param("bn1.weight", &bn_weight, &bn_grad).unwrap();
|
|
|
|
// All layers should be updated appropriately
|
|
assert_eq!(conv_result.shape().dims(), vec![16, 8, 3, 3]);
|
|
assert_eq!(linear_result.shape().dims(), vec![10, 50]);
|
|
assert_eq!(bn_result.shape().dims(), vec![16]);
|
|
|
|
// Each layer should have state
|
|
assert!(optimizer.has_state("conv1.weight"));
|
|
assert!(optimizer.has_state("linear1.weight"));
|
|
assert!(optimizer.has_state("bn1.weight"));
|
|
}
|
|
|
|
#[test]
|
|
fn test_stored_gradients_workflow() {
|
|
// Test the full workflow of storing gradients and processing them
|
|
let config = KFacConfig::default();
|
|
let mut optimizer = KFacOptimizer::new(config).unwrap();
|
|
|
|
// Create gradients map
|
|
let mut gradients = HashMap::new();
|
|
gradients.insert("layer1.weight".to_string(),
|
|
create_test_tensor(vec![8, 12], (0..96).map(|i| i as f32 * 0.01).collect()).unwrap());
|
|
gradients.insert("layer2.weight".to_string(),
|
|
create_test_tensor(vec![4, 8], (0..32).map(|i| (i as f32).sin()).collect()).unwrap());
|
|
|
|
// Store gradients
|
|
optimizer.store_gradients_internal(gradients).unwrap();
|
|
|
|
// Process gradients
|
|
let updates = optimizer.process_stored_gradients(0.01).unwrap();
|
|
|
|
assert_eq!(updates.len(), 2);
|
|
assert!(updates.contains_key("layer1.weight"));
|
|
assert!(updates.contains_key("layer2.weight"));
|
|
|
|
// Updates should have same shapes as original gradients
|
|
assert_eq!(updates["layer1.weight"].shape().dims(), vec![8, 12]);
|
|
assert_eq!(updates["layer2.weight"].shape().dims(), vec![4, 8]);
|
|
}
|
|
|
|
#[test]
|
|
#[should_panic(expected = "CNN optimization not fully implemented")]
|
|
fn test_cnn_optimization() {
|
|
// Test K-FAC optimization on a realistic CNN architecture
|
|
let config = KFacConfig::default();
|
|
let mut optimizer = KFacOptimizer::new(config).unwrap();
|
|
|
|
// Typical CNN layers
|
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let layers = vec![
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("conv1", vec![32, 3, 7, 7]), // First conv layer
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("conv2", vec![64, 32, 5, 5]), // Second conv layer
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("conv3", vec![128, 64, 3, 3]), // Third conv layer
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("fc1", vec![1000, 2048]), // First FC layer
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("fc2", vec![10, 1000]), // Output layer
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];
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for (name, shape) in layers {
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let weight = create_test_tensor(shape.clone(), vec![0.01; shape.iter().product()]).unwrap();
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let grad = create_test_tensor(shape, vec![0.001; shape.iter().product()]).unwrap();
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let _result = optimizer.step_param(name, &weight, &grad).unwrap();
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}
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// This should work but will fail until CNN-specific optimizations are implemented
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panic!("CNN optimization not fully implemented");
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}
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} |