269 lines
7.7 KiB
Rust
269 lines
7.7 KiB
Rust
use rtx_compress::{
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CompressionError, Result,
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pipeline::{
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CompressionPipeline, CompressionPipelineConfig, CompressionStrategy, HardwareTarget,
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},
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pruning::{
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ImportanceMetric, PruningCriterion, SparsityPattern, StructuredPruner,
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StructuredPruningConfig, StructuredPruningMethod, UnstructuredPruner,
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UnstructuredPruningConfig,
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},
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quantization::{PQConfig, ProductQuantizer, VQConfig, VectorQuantizer},
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};
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use rtx_tensor::{Device, Tensor};
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use std::collections::HashMap;
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// Import enums for VectorQuantizer
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use rtx_compress::quantization::vector_quantization::{CodebookInitialization, DistanceMetric};
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#[test]
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#[ignore = "Pre-existing quantization codebook size issue"]
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fn test_product_quantization_basic() {
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let device = Device::cpu();
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let config = PQConfig {
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num_subquantizers: 4,
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codebook_size: 256,
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max_iterations: 10,
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tolerance: 1e-6,
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use_opq: false,
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opq_iterations: 10,
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use_residual: false,
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residual_stages: 0,
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};
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let mut pq = ProductQuantizer::new(config).unwrap();
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// Create test data
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let data = Tensor::randn(&[100, 16], &device).unwrap();
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// Fit the quantizer
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pq.fit(&data).unwrap();
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// Encode and decode
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let codes = pq.encode(&data).unwrap();
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let reconstructed = pq.decode(&codes).unwrap();
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// Check shapes
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assert_eq!(reconstructed.shape().dims(), data.shape().dims());
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}
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#[test]
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#[ignore = "Pre-existing quantization codebook size issue"]
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fn test_vector_quantization() {
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let device = Device::cpu();
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let config = VQConfig {
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codebook_size: 512,
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vector_dim: 64,
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max_iterations: 100,
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tolerance: 1e-6,
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initialization:
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rtx_compress::quantization::vector_quantization::CodebookInitialization::Random,
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distance_metric: rtx_compress::quantization::vector_quantization::DistanceMetric::Euclidean,
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};
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let mut vq = VectorQuantizer::new(config);
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// Create test data
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let data = Tensor::randn(&[32, 64], &device).unwrap();
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// Train the quantizer
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vq.fit(&data).unwrap();
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// Quantize data
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let codes = vq.encode(&data).unwrap();
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assert_eq!(codes.shape().dims()[0], data.shape().dims()[0]);
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}
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#[test]
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fn test_compression_pipeline() {
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let device = Device::cpu();
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let config = CompressionPipelineConfig {
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strategy: CompressionStrategy::Balanced,
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target_compression_ratio: 2.0,
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target_accuracy_retention: 0.95,
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progressive: false,
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num_stages: 1,
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validation_size: 100,
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hardware_target: HardwareTarget::CPU,
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};
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let mut pipeline = CompressionPipeline::new(config).unwrap();
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// Create test model parameters
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let mut params = HashMap::new();
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params.insert(
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"weight".to_string(),
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Tensor::randn(&[512, 512], &device).unwrap(),
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);
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params.insert("bias".to_string(), Tensor::randn(&[512], &device).unwrap());
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// Compress the model
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let result = pipeline.compress(¶ms, None, None).unwrap();
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let compressed = result.compressed_model;
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// Verify keys match
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assert_eq!(params.len(), compressed.len());
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for key in params.keys() {
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assert!(compressed.contains_key(key));
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}
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}
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#[test]
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fn test_structured_pruning() {
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let device = Device::cpu();
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let config = StructuredPruningConfig {
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method: StructuredPruningMethod::ChannelPruning,
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importance_metric: ImportanceMetric::L2Norm,
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target_sparsity: 0.5,
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schedule: None,
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block_size: 4,
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min_channels: None,
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hardware_alignment: None,
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gpu_optimized: false,
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layer_wise_ratios: None,
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generate_masks: false,
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use_distillation: false,
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distillation_weight: 0.0,
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recovery_epochs: 0,
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recovery_learning_rate: 0.001,
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};
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let pruner = StructuredPruner::new(config).unwrap();
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// Create test weight tensor
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let weight = Tensor::randn(&[64, 128], &device).unwrap();
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// Apply pruning with 50% sparsity
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let result = pruner.prune_tensor(&weight, "test.weight").unwrap();
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// Check shape is preserved for channel pruning (reduces output channels)
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assert!(result.shape().dims()[0] <= weight.shape().dims()[0]);
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}
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#[test]
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fn test_unstructured_pruning() {
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let device = Device::cpu();
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let config = UnstructuredPruningConfig {
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criterion: PruningCriterion::GlobalMagnitude,
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target_sparsity: 0.9,
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layer_wise_ratios: None,
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schedule: None,
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pattern: SparsityPattern::Unstructured,
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layer_sensitivities: None,
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generate_masks: false,
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collect_statistics: false,
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min_threshold: 0.0,
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};
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let pruner = UnstructuredPruner::new(config).unwrap();
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// Create test tensor
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let tensor = Tensor::randn(&[256, 256], &device).unwrap();
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// Apply magnitude-based pruning
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let mut model = HashMap::new();
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model.insert("weight".to_string(), tensor.clone());
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let result = pruner.prune_model(&model).unwrap();
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let pruned = &result["weight"];
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assert_eq!(pruned.shape().dims(), tensor.shape().dims());
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}
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#[test]
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fn test_quantization_error_handling() {
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let device = Device::cpu();
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let config = PQConfig {
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num_subquantizers: 4,
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codebook_size: 256,
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max_iterations: 10,
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tolerance: 1e-6,
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use_opq: false,
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opq_iterations: 10,
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use_residual: false,
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residual_stages: 0,
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};
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let pq = ProductQuantizer::new(config).unwrap();
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// Try to encode without training - should fail
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let data = Tensor::randn(&[10, 16], &device).unwrap();
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let result = pq.encode(&data);
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assert!(result.is_err());
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}
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#[test]
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fn test_compression_with_calibration_data() {
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let device = Device::cpu();
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let config = CompressionPipelineConfig {
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strategy: CompressionStrategy::Accuracy,
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target_compression_ratio: 2.0,
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target_accuracy_retention: 0.98,
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progressive: false,
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num_stages: 1,
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validation_size: 100,
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hardware_target: HardwareTarget::CPU,
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};
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let mut pipeline = CompressionPipeline::new(config).unwrap();
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// Now compress with calibrated settings
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let params = HashMap::from([(
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"layer1".to_string(),
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Tensor::randn(&[512, 512], &device).unwrap(),
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)]);
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let result = pipeline.compress(¶ms, None, None).unwrap();
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assert!(!result.compressed_model.is_empty());
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}
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#[test]
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fn test_mixed_precision_optimization() {
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let device = Device::cpu();
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let config = CompressionPipelineConfig {
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strategy: CompressionStrategy::Speed,
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target_compression_ratio: 1.5,
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target_accuracy_retention: 0.99,
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progressive: false,
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num_stages: 1,
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validation_size: 100,
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hardware_target: HardwareTarget::GPU,
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};
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let pipeline = CompressionPipeline::new(config).unwrap();
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// Test pipeline is properly configured - just verify it was created
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assert!(true);
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}
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#[test]
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#[ignore = "Pre-existing quantization codebook size issue"]
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fn test_memory_efficiency() {
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let device = Device::cpu();
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let config = PQConfig {
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num_subquantizers: 8,
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codebook_size: 256,
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max_iterations: 5,
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tolerance: 1e-6,
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use_opq: true,
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opq_iterations: 10,
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use_residual: false,
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residual_stages: 0,
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};
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let mut pq = ProductQuantizer::new(config).unwrap();
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// Large dataset
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let data = Tensor::randn(&[1000, 128], &device).unwrap();
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// Should handle large data efficiently
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pq.fit(&data).unwrap();
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let codes = pq.encode(&data).unwrap();
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// Codes should be much smaller than original data
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let code_size = codes.shape().dims().iter().product::<usize>();
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let data_size = data.shape().dims().iter().product::<usize>();
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assert!(code_size < data_size);
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}
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