447 lines
14 KiB
Rust
447 lines
14 KiB
Rust
//! Comprehensive model compression example
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//!
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//! This example demonstrates how to use the rtx-compress crate to compress
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//! a neural network model using various techniques:
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//! - Post-training quantization
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//! - Structured and unstructured pruning
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//! - Knowledge distillation
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//! - Integrated compression pipeline
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use rtx_compress::{
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Result,
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// Distillation
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distillation::{
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DistillationConfig, DistillationLoss, DistillationMethod, FeatureMatchingConfig,
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KnowledgeDistiller,
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},
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// Pipeline
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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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pruning::{
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GradualPruningSchedule, ImportanceMetric, PruningCriterion,
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PruningMethod as StructuredPruningMethod, StructuredPruner, StructuredPruningConfig,
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UnstructuredPruner, UnstructuredPruningConfig,
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},
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// Quantization
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quantization::{
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CalibrationConfig, CalibrationMethod, PostTrainingQuantizer, QuantizationConfig,
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QuantizationScheme,
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},
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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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/// Create a simple test model with realistic parameter distributions
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fn create_test_model() -> Result<HashMap<String, Tensor>> {
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let device = Device::try_default()?;
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let mut model = HashMap::new();
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// Convolutional layers
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model.insert(
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"conv1.weight".to_string(),
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create_conv_weights(&device, 32, 3, 5, 5)?,
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);
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model.insert("conv1.bias".to_string(), create_bias(&device, 32)?);
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model.insert(
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"conv2.weight".to_string(),
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create_conv_weights(&device, 64, 32, 5, 5)?,
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);
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model.insert("conv2.bias".to_string(), create_bias(&device, 64)?);
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model.insert(
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"conv3.weight".to_string(),
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create_conv_weights(&device, 128, 64, 3, 3)?,
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);
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model.insert("conv3.bias".to_string(), create_bias(&device, 128)?);
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// Fully connected layers
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model.insert(
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"fc1.weight".to_string(),
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create_fc_weights(&device, 512, 2048)?,
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);
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model.insert("fc1.bias".to_string(), create_bias(&device, 512)?);
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model.insert(
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"fc2.weight".to_string(),
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create_fc_weights(&device, 256, 512)?,
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);
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model.insert("fc2.bias".to_string(), create_bias(&device, 256)?);
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model.insert(
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"fc3.weight".to_string(),
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create_fc_weights(&device, 10, 256)?,
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);
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model.insert("fc3.bias".to_string(), create_bias(&device, 10)?);
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Ok(model)
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}
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fn create_conv_weights(
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device: &Device,
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out_channels: usize,
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in_channels: usize,
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h: usize,
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w: usize,
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) -> Result<Tensor> {
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let total = out_channels * in_channels * h * w;
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let mut weights = Vec::with_capacity(total);
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for i in 0..total {
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// Create realistic weight distribution (Xavier initialization)
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let fan_in = in_channels * h * w;
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let scale = (2.0 / fan_in as f32).sqrt();
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let val = scale * ((i % 1000) as f32 / 500.0 - 1.0);
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weights.push(val);
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}
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Tensor::from_slice(&weights, &[out_channels, in_channels, h, w], device)
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}
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fn create_fc_weights(device: &Device, out_features: usize, in_features: usize) -> Result<Tensor> {
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let total = out_features * in_features;
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let mut weights = Vec::with_capacity(total);
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for i in 0..total {
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// Xavier initialization for fully connected
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let scale = (2.0 / in_features as f32).sqrt();
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let val = scale * ((i % 1000) as f32 / 500.0 - 1.0);
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weights.push(val);
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}
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Tensor::from_slice(&weights, &[out_features, in_features], device)
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}
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fn create_bias(device: &Device, size: usize) -> Result<Tensor> {
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let bias = vec![0.01; size]; // Small positive bias
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Tensor::from_slice(&bias, &[size], device)
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}
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fn calculate_model_size(model: &HashMap<String, Tensor>) -> (usize, f32) {
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let total_params: usize = model.values().map(|t| t.numel()).sum();
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let size_mb = (total_params * 4) as f32 / (1024.0 * 1024.0);
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(total_params, size_mb)
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}
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fn print_model_info(name: &str, model: &HashMap<String, Tensor>) {
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let (params, size_mb) = calculate_model_size(model);
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println!("{}: {} parameters, {:.2} MB", name, params, size_mb);
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}
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fn demonstrate_post_training_quantization() -> Result<()> {
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println!("\n=== Post-Training Quantization Demo ===");
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let model = create_test_model()?;
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print_model_info("Original Model", &model);
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// Create calibration dataset
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let device = Device::try_default()?;
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let mut calibration_data = Vec::new();
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for _ in 0..100 {
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calibration_data.push(Tensor::randn(&[32, 64], &device)?);
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}
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// INT8 quantization
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let int8_config =
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QuantizationConfig::new(QuantizationScheme::INT8, CalibrationConfig::new(100, 0.99));
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let mut int8_quantizer = PostTrainingQuantizer::new(int8_config)?;
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int8_quantizer.calibrate(&calibration_data)?;
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println!("INT8 Quantization Statistics:");
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let stats = int8_quantizer.get_statistics();
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println!(" Scale: {:.6}", stats.scale);
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println!(" Zero Point: {}", stats.zero_point);
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println!(" Bit Width: {}", stats.bit_width);
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// INT4 quantization for more aggressive compression
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let int4_config =
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QuantizationConfig::new(QuantizationScheme::INT4, CalibrationConfig::new(100, 0.95));
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let mut int4_quantizer = PostTrainingQuantizer::new(int4_config)?;
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int4_quantizer.calibrate(&calibration_data)?;
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let int4_stats = int4_quantizer.get_statistics();
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println!("INT4 Quantization Statistics:");
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println!(" Scale: {:.6}", int4_stats.scale);
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println!(" Zero Point: {}", int4_stats.zero_point);
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println!(" Expected compression: ~8x");
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Ok(())
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}
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fn demonstrate_structured_pruning() -> Result<()> {
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println!("\n=== Structured Pruning Demo ===");
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let model = create_test_model()?;
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print_model_info("Original Model", &model);
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// Channel pruning with L2 importance
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let config = StructuredPruningConfig::new(
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StructuredPruningMethod::ChannelPruning,
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ImportanceMetric::L2Norm,
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0.5, // 50% sparsity
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);
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let pruner = StructuredPruner::new(config)?;
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let mut pruned_model = HashMap::new();
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for (name, tensor) in &model {
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let pruned_tensor = pruner.prune_tensor(tensor, name)?;
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pruned_model.insert(name.clone(), pruned_tensor);
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}
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print_model_info("Structured Pruned Model", &pruned_model);
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let (orig_params, _) = calculate_model_size(&model);
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let (pruned_params, _) = calculate_model_size(&pruned_model);
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let compression_ratio = orig_params as f32 / pruned_params as f32;
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println!("Structured Pruning Results:");
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println!(" Compression Ratio: {:.2}x", compression_ratio);
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println!(" Parameters Removed: {}", orig_params - pruned_params);
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Ok(())
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}
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fn demonstrate_unstructured_pruning() -> Result<()> {
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println!("\n=== Unstructured Pruning Demo ===");
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let model = create_test_model()?;
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// Global magnitude pruning
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let global_config = UnstructuredPruningConfig::new(
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PruningCriterion::GlobalMagnitude,
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0.8, // 80% sparsity
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);
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let global_pruner = UnstructuredPruner::new(global_config)?;
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let globally_pruned = global_pruner.prune_model(&model)?;
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print_model_info("Globally Pruned Model (80% sparse)", &globally_pruned);
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// Gradual pruning demonstration
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let schedule = GradualPruningSchedule::polynomial(
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0.0, // Start with no pruning
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0.9, // End with 90% sparsity
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100, // Over 100 steps
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3.0, // Cubic schedule
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);
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let gradual_config =
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UnstructuredPruningConfig::new_with_schedule(PruningCriterion::GlobalMagnitude, schedule);
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let mut gradual_pruner = UnstructuredPruner::new(gradual_config)?;
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// Simulate gradual pruning over training
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let mut current_model = model.clone();
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let steps = [0, 25, 50, 75, 99];
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println!("Gradual Pruning Progress:");
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for &step in &steps {
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gradual_pruner.set_current_step(step);
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current_model = gradual_pruner.prune_model(¤t_model)?;
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let (params, _) = calculate_model_size(¤t_model);
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let (orig_params, _) = calculate_model_size(&model);
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let sparsity = 1.0 - (params as f32 / orig_params as f32);
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println!(" Step {}: {:.1}% sparse", step, sparsity * 100.0);
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}
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Ok(())
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}
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fn demonstrate_knowledge_distillation() -> Result<()> {
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println!("\n=== Knowledge Distillation Demo ===");
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let device = Device::try_default()?;
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// Simulate teacher and student logits
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let batch_size = 32;
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let num_classes = 10;
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let teacher_logits = Tensor::randn(&[batch_size, num_classes], &device)?;
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let student_logits = Tensor::randn(&[batch_size, num_classes], &device)?;
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let targets = Tensor::from_slice(
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&(0..batch_size)
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.map(|i| (i % num_classes) as i64)
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.collect::<Vec<_>>(),
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&[batch_size],
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&device,
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)?;
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// Response-based distillation
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let response_config = DistillationConfig::new(
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DistillationMethod::ResponseBased {
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temperature: 4.0,
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alpha: 0.7,
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},
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DistillationLoss::KullbackLeibler,
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);
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let distiller = KnowledgeDistiller::new(response_config)?;
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let result = distiller.compute_distillation_loss(
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&student_logits,
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&teacher_logits,
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Some(&targets),
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None,
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)?;
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println!("Response-based Distillation:");
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println!(" Total Loss: {:.4}", result.total_loss);
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println!(" Distillation Loss: {:.4}", result.distillation_loss);
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if let Some(task_loss) = result.task_loss {
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println!(" Task Loss: {:.4}", task_loss);
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}
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// Feature-based distillation
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let feature_config = FeatureMatchingConfig::new(
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vec![128, 256, 512], // Feature dimensions
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vec![0.2, 0.3, 0.5], // Layer weights
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"mse".to_string(),
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);
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let feature_method = DistillationMethod::FeatureBased {
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feature_config,
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intermediate_matching: true,
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};
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let feature_distiller = KnowledgeDistiller::new(DistillationConfig::new(
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feature_method,
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DistillationLoss::MeanSquaredError,
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))?;
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// Simulate intermediate features
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let teacher_features = vec![
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Tensor::randn(&[batch_size, 128], &device)?,
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Tensor::randn(&[batch_size, 256], &device)?,
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Tensor::randn(&[batch_size, 512], &device)?,
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];
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let student_features = vec![
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Tensor::randn(&[batch_size, 128], &device)?,
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Tensor::randn(&[batch_size, 256], &device)?,
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Tensor::randn(&[batch_size, 512], &device)?,
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];
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let feature_result = feature_distiller
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.compute_feature_distillation_loss(&student_features, &teacher_features)?;
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println!("Feature-based Distillation:");
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println!(" Total Loss: {:.4}", feature_result.total_loss);
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println!(" Layer Losses: {:?}", feature_result.feature_losses);
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Ok(())
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}
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fn demonstrate_compression_pipeline() -> Result<()> {
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println!("\n=== Comprehensive Compression Pipeline Demo ===");
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let model = create_test_model()?;
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print_model_info("Original Model", &model);
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// Automatic pipeline for mobile deployment
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let mut mobile_pipeline = CompressionPipeline::auto(
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4.0, // 4x compression target
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0.95, // 95% accuracy retention
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HardwareTarget::Mobile,
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)?;
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let compressed_result = mobile_pipeline.compress(&model, None, None)?;
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print_model_info("Compressed Model", &compressed_result.compressed_model);
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println!("Compression Results:");
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println!(
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" Compression Ratio: {:.2}x",
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compressed_result.statistics.compression_ratio
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);
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println!(
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" Size Reduction: {:.2} MB",
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compressed_result.statistics.size_reduction_mb
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);
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println!(
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" Estimated Speedup: {:.2}x",
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compressed_result.statistics.estimated_speedup
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);
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// Custom strategy for high compression
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let custom_strategy = CompressionStrategy::Custom {
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techniques: vec![
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rtx_compress::pipeline::CompressionTechnique::UnstructuredPruning,
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rtx_compress::pipeline::CompressionTechnique::StructuredPruning,
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rtx_compress::pipeline::CompressionTechnique::Quantization,
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],
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priorities: [
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(
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rtx_compress::pipeline::CompressionTechnique::UnstructuredPruning,
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0.4,
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),
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(
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rtx_compress::pipeline::CompressionTechnique::StructuredPruning,
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0.3,
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),
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(
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rtx_compress::pipeline::CompressionTechnique::Quantization,
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0.3,
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),
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]
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.iter()
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.cloned()
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.collect(),
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};
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let custom_config = CompressionPipelineConfig {
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strategy: custom_strategy,
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target_compression_ratio: 8.0,
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target_accuracy_retention: 0.90,
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progressive: true,
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num_stages: 3,
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validation_size: 1000,
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hardware_target: HardwareTarget::Generic,
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};
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let mut custom_pipeline = CompressionPipeline::new(custom_config)?;
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let custom_result = custom_pipeline.compress(&model, None, None)?;
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print_model_info("Custom Compressed Model", &custom_result.compressed_model);
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println!("Custom Compression Results:");
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println!(
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" Compression Ratio: {:.2}x",
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custom_result.statistics.compression_ratio
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);
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println!(
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" Size Reduction: {:.2} MB",
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custom_result.statistics.size_reduction_mb
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);
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Ok(())
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}
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fn main() -> Result<()> {
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println!("RustyTorch++ Comprehensive Model Compression Demo");
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println!("================================================");
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demonstrate_post_training_quantization()?;
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demonstrate_structured_pruning()?;
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demonstrate_unstructured_pruning()?;
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demonstrate_knowledge_distillation()?;
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demonstrate_compression_pipeline()?;
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println!("\n=== Summary ===");
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println!("This demo showcased comprehensive model compression techniques:");
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println!("1. Post-training quantization (INT8/INT4) for memory and speed");
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println!("2. Structured pruning for hardware-friendly compression");
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println!("3. Unstructured pruning for maximum compression ratios");
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println!("4. Knowledge distillation for accuracy preservation");
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println!("5. Integrated pipeline for automatic compression strategy");
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println!("\nAll techniques can be combined and customized for specific deployment needs.");
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Ok(())
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}
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