BitNet Quantization

The quantization engine for BitNet neural networks, implementing 1.58-bit quantization algorithms and calibration utilities optimized for extreme compression while maintaining model accuracy.

🎯 Purpose

bitnet-quant provides the core quantization functionality for BitNet models:

• 1.58-bit Quantization: Implementation of the novel 1.58-bit quantization scheme
• Weight Quantization: Efficient algorithms for quantizing neural network weights
• Activation Quantization: Runtime quantization of activations and intermediate values
• Calibration Utilities: Tools for determining optimal quantization parameters
• Dequantization: Fast dequantization for computation and inference
• 🆕 Advanced Precision Control: Dynamic precision adjustment and monitoring
• 🆕 Enhanced Configuration System: Comprehensive configuration builders with validation
• 🆕 Mixed Precision Integration: Seamless integration with bitnet-core's mixed precision system
• 🆕 Configurable Quantization Schemes: Flexible schemes supporting 1-bit to 8-bit quantization
• 🆕 Configuration Presets: Pre-configured settings for different use cases
• 🆕 Real-time Monitoring: Performance and quality metrics tracking

✅ NEW: Advanced Features

🎉 The crate now includes comprehensive advanced quantization features!

Enhanced Configuration System

• Type-Safe Configuration Builders: Fluent API for building complex configurations
• Comprehensive Validation: Automatic validation of all configuration parameters
• Hierarchical Configuration: Base configurations with specialized extensions
• Configuration Presets: Pre-built configurations for common use cases

Advanced Precision Control System

• Dynamic Precision Adjustment: Automatically adjust precision based on performance metrics
• Precision Bounds Validation: Ensure quantization parameters stay within acceptable ranges
• Real-time Monitoring: Track quantization performance and quality metrics
• Performance Thresholds: Configurable thresholds for automatic adjustments
• Custom Metrics Support: Track application-specific performance indicators

Mixed Precision Integration

• Seamless Integration: Works with bitnet-core's mixed precision system
• Layer-wise Precision: Different precision levels for different layers
• Automatic Precision Selection: Optimal precision selection based on layer characteristics
• Performance Optimization: Automatic precision adjustment for performance targets

Configurable Quantization Schemes

• Multi-Precision Support: 1-bit, 1.58-bit, 2-bit, 4-bit, and 8-bit quantization
• Flexible Threshold Methods: Multiple threshold calculation methods
• Optimization Configurations: SIMD, lookup tables, and parallel processing options
• Custom Parameters: Extensible parameter system for specialized use cases

Quick Start with Enhanced Features

use bitnet_quant::prelude::*;
use bitnet_quant::{ConfigurationPreset, create_enhanced_config, create_precision_controller};
use candle_core::Device;

// Create a BitNet-optimized configuration
let config = ConfigurationPreset::BitNetOptimized.build()?;
let device = Device::Cpu;
let mut controller = create_precision_controller(config.precision_control, device)?;

// The controller will automatically monitor and adjust precision as needed

Configuration Presets

Choose from optimized presets for different use cases:

• BitNetOptimized: Balanced performance for 1.58-bit quantization
• PerformanceOptimized: Maximum speed with aggressive compression
• AccuracyOptimized: Maximum precision with conservative settings
• MemoryOptimized: Minimal memory footprint
• Balanced: General-purpose configuration

See the Configuration Guide for comprehensive documentation.

✅ Implementation Status: Feature Complete

✅ This crate now contains a comprehensive implementation with advanced features.

🟢 Enhanced Configuration System (Implemented)

Comprehensive Configuration Builders

• QuantizationConfigBuilder: Fluent API for base quantization configuration
• WeightQuantizationConfigBuilder: Specialized builder for weight quantization
• EnhancedQuantizationConfigBuilder: Advanced builder with precision control
• Configuration Validation: Automatic validation of all parameters with detailed error messages

Configuration Presets

• ConfigurationPreset: Pre-built configurations for common use cases
• BitNet Optimized: Balanced performance for 1.58-bit quantization
• Performance Optimized: Maximum speed with aggressive compression
• Accuracy Optimized: Maximum precision with conservative settings
• Memory Optimized: Minimal memory footprint

🟢 Advanced Precision Control System (Implemented)

Dynamic Precision Management

• PrecisionController: Comprehensive precision control manager
• PrecisionBounds: Configurable precision constraints
• DynamicAdjustmentConfig: Automatic precision adjustment
• Real-time Monitoring: Performance and quality metrics tracking

Performance Monitoring

• PerformanceMonitor: Real-time performance tracking
• MetricsHistory: Historical metrics storage
• PrecisionAdjustment: Adjustment tracking and analysis

🟢 Mixed Precision Integration (Implemented)

Seamless Integration with bitnet-core

• MixedPrecisionQuantizer: Integrated quantizer with precision management
• LayerQuantizationResult: Comprehensive layer quantization results
• Automatic Precision Selection: Optimal precision based on layer characteristics
• Performance Optimization: Automatic adjustment for performance targets

🟢 Configurable Quantization Schemes (Implemented)

Multi-Precision Support

• ConfigurableQuantizationScheme: Flexible quantization schemes
• QuantizationSchemeFactory: Factory for creating schemes
• 1-bit to 8-bit Support: Complete range of quantization precisions
• BinaryThresholdMethod: Multiple threshold calculation methods

Advanced Quantization Features

• OneBitParams: 1-bit quantization configuration
• OneFiveEightBitParams: 1.58-bit quantization configuration
• MultiBitParams: Multi-bit quantization configuration
• OptimizationConfig: SIMD and performance optimizations

🚀 API Examples

Enhanced Configuration System

use bitnet_quant::prelude::*;
use candle_core::{Tensor, Device};

// Using configuration builders
let config = QuantizationConfigBuilder::new()
    .precision(QuantizationPrecision::OneFiveFiveBit)
    .strategy(QuantizationStrategy::Symmetric)
    .per_channel(false)
    .clip_threshold(3.0)
    .qat_enabled(false)
    .build();

// Using weight quantization builder
let weight_config = WeightQuantizationConfigBuilder::new()
    .base(config)
    .group_size(128)
    .learnable_scales(true)
    .ternary_method(TernaryMethod::OptimalThreshold)
    .custom_threshold_factor(0.8)
    .packing(PackingConfig::bitnet())
    .build();

// Validate configuration
weight_config.validate()?;

Configuration Presets

use bitnet_quant::{ConfigurationPreset, create_enhanced_config};

// Use pre-built configurations
let bitnet_config = ConfigurationPreset::BitNetOptimized.build()?;
let performance_config = ConfigurationPreset::PerformanceOptimized.build()?;
let accuracy_config = ConfigurationPreset::AccuracyOptimized.build()?;

// Create custom configuration with builder
let custom_config = create_custom_enhanced_config(|builder| {
    builder
        .precision(QuantizationPrecision::TwoBit)
        .auto_optimization(true)
        .adaptive_thresholds(false)
        .real_time_monitoring(true)
})?;

Precision Control System

use bitnet_quant::{create_precision_controller, PrecisionControlConfig};
use candle_core::Device;

// Create precision controller
let precision_config = PrecisionControlConfig::conservative();
let device = Device::Cpu;
let mut controller = create_precision_controller(precision_config, device)?;

// Validate precision bounds
controller.validate_precision_bounds(
    QuantizationPrecision::OneFiveFiveBit,
    0.7, // threshold
    1.0, // scale
)?;

// Record metrics and adjust precision dynamically
let stats = QuantizationStats {
    elements_count: 1000,
    quantization_error: 0.05,
    compression_ratio: 20.0,
    min_value: -1.0,
    max_value: 1.0,
    scale_factor: 1.0,
    zero_point: None,
};

if let Some(adjustment) = controller.adjust_precision_dynamically(&stats)? {
    println!("Precision adjusted: {:?} -> {:?}",
             adjustment.from_precision, adjustment.to_precision);
}

// Get performance summary
let summary = controller.get_performance_summary();
println!("Average error: {:.4}", summary.average_error);
println!("Average compression: {:.1}x", summary.average_compression_ratio);

Configurable Quantization Schemes

use bitnet_quant::{ConfigurableQuantizationScheme, QuantizationSchemeFactory};
use bitnet_quant::{BinaryThresholdMethod, OneBitParams, OneFiveEightBitParams};

// Create 1-bit quantization scheme
let device = Device::Cpu;
let mut one_bit_scheme = QuantizationSchemeFactory::create_one_bit_scheme(device.clone());

// Create 1.58-bit quantization scheme
let mut ternary_scheme = QuantizationSchemeFactory::create_one_five_eight_bit_scheme(device.clone());

// Custom scheme configuration
let custom_config = QuantizationSchemeConfig {
    base: QuantizationConfig::new(QuantizationPrecision::OneBit),
    scheme_params: SchemeParameters {
        one_bit: OneBitParams {
            threshold_method: BinaryThresholdMethod::Optimal,
            sign_based: false,
            stochastic_prob: Some(0.1),
            ..Default::default()
        },
        ..Default::default()
    },
    adaptive_threshold: true,
    optimization: OptimizationConfig {
        enable_simd: true,
        use_lookup_tables: true,
        parallel_processing: true,
        memory_optimization_level: 2,
        cache_parameters: true,
    },
    ..Default::default()
};

let custom_scheme = QuantizationSchemeFactory::create_custom_scheme(custom_config, device);

// Quantize tensor
let input = Tensor::randn(&[64, 128], &device)?;
let quantized = custom_scheme.quantize_tensor(&input)?;
let dequantized = custom_scheme.dequantize_tensor(&quantized)?;

Mixed Precision Integration

use bitnet_quant::{MixedPrecisionQuantizationConfig, create_mixed_precision_quantizer};
use bitnet_core::mixed_precision::{LayerPrecisionSpec, LayerType, ComponentType};

// Create mixed precision configuration
let mixed_config = MixedPrecisionQuantizationConfig::bitnet()
    .with_auto_adjustment(PrecisionAdjustmentParams {
        accuracy_threshold: 0.95,
        memory_pressure_threshold: 0.8,
        performance_threshold: 0.9,
        ..Default::default()
    });

// Create mixed precision quantizer
let device = Device::Cpu;
let mut quantizer = create_mixed_precision_quantizer(mixed_config, device)?;

// Register layer specifications
let layer_spec = LayerPrecisionSpec {
    layer_id: "conv1".to_string(),
    layer_type: LayerType::Convolution,
    input_shape: vec![1, 3, 224, 224],
    output_shape: vec![1, 64, 112, 112],
    weight_shape: vec![64, 3, 7, 7],
    ..Default::default()
};
quantizer.register_layer(layer_spec)?;

// Quantize layer components
let weights = BitNetTensor::new(/* ... */);
let activations = BitNetTensor::new(/* ... */);

let result = quantizer.quantize_layer(
    "conv1",
    &weights,
    Some(&activations),
    None, // bias
)?;

println!("Layer quantization completed:");
println!("  Compression ratio: {:.1}x", result.compression_ratio);
println!("  Original size: {} bytes", result.original_size_bytes);
println!("  Quantized size: {} bytes", result.quantized_size_bytes);

Basic Weight and Activation Quantization

use bitnet_quant::prelude::*;

// Basic weight quantization
let device = Device::Cpu;
let weights = Tensor::randn(0.0, 1.0, (256, 512), &device)?;

// Quantize weights to 1.58-bit
let quantized = absmean_quantize_weights(&weights, &device)?;

println!("Compression: {:.1}x", quantized.compression_ratio());
println!("Memory saved: {:.1} MB",
         (weights.elem_count() * 4 - quantized.memory_footprint()) as f32 / 1024.0 / 1024.0);

// Basic activation quantization
let activations = Tensor::randn(0.0, 1.0, (32, 256), &device)?;
let quantized_activations = absmax_quantize_activations(&activations, &device)?;

🏗️ Architecture

Core Components

bitnet-quant/src/
├── lib.rs                           # Main library interface and re-exports
├── quantization/                    # Core quantization module
│   ├── mod.rs                      # Quantization traits and common types
│   ├── weights.rs                  # Weight quantization implementation (1,017 lines)
│   ├── activations.rs              # Activation quantization
│   ├── packing.rs                  # Ternary weight packing strategies (1,308 lines)
│   ├── simd_unpacking.rs           # SIMD-optimized unpacking (642 lines)
│   ├── corruption_detection.rs     # Advanced corruption detection (1,215 lines)
│   └── utils.rs                    # Quantization utilities and helpers
└── examples/                       # Usage examples and demos
    └── simd_unpacking_demo.rs      # SIMD unpacking demonstration

Key Traits and Types

• Quantizer: Core trait for all quantization operations
• WeightQuantizer: Specialized trait for weight quantization
• TernaryPacker: Trait for ternary weight packing strategies
• SimdUnpacker: SIMD-optimized unpacking implementation
• CorruptionDetector: Advanced corruption detection and recovery

Integration with BitNet Core

use bitnet_core::memory::{HybridMemoryPool, BitNetTensor};
use bitnet_quant::{absmean_quantize_weights, QuantizerFactory};

// Integrate with memory management
let device = Device::Cpu;
let weights = Tensor::randn(0.0, 1.0, (128, 256), &device)?;

// Quantize weights with automatic packing
let mut quantized = absmean_quantize_weights(&weights, &device)?;
quantized.pack_weights()?; // Apply optimal packing strategy

// Use in neural network layers
let dequantized = quantized.unpack_weights()?;

📊 Performance Characteristics

Enhanced Quantization Performance (Measured)

Memory Efficiency with New Precisions

Enhanced Packing Strategy Performance

SIMD Performance Gains with Enhanced Detection

Configuration System Performance

Precision Control Performance

🧪 Testing and Benchmarking

Comprehensive Test Suite

# Run all quantization tests
cargo test --package bitnet-quant

# Test specific modules
cargo test --package bitnet-quant weights
cargo test --package bitnet-quant packing
cargo test --package bitnet-quant simd_unpacking
cargo test --package bitnet-quant corruption_detection

# Run with all features
cargo test --package bitnet-quant --all-features

Performance Benchmarking

# Run comprehensive benchmarks
cd bitnet-benchmarks
cargo bench comprehensive_performance_comparison
cargo bench quantization_performance
cargo bench simd_unpacking_performance
cargo bench packing_performance

# Generate performance reports
cargo run --release -- compare --output results.json
cargo run --release -- report --input results.json --output report.html

Accuracy Validation

# Test quantization accuracy preservation
cargo test --package bitnet-quant test_ternary_quantization_preserves_signs
cargo test --package bitnet-quant test_absmean_quantize_weights_basic

# Validate packing/unpacking integrity
cargo test --package bitnet-quant test_simd_vs_scalar_consistency
cargo test --package bitnet-quant test_corruption_detector_creation

Memory and Performance Profiling

# Enable memory tracking
cargo test --package bitnet-quant --features memory

# Run energy efficiency benchmarks
cargo bench energy_efficiency_comparison

# Profile memory usage
cargo bench memory_efficiency

🔬 Research Implementation

BitNet 1.58-bit Quantization

The core innovation of BitNet is the 1.58-bit quantization scheme:

Quantization levels: {-1, 0, +1}
Effective bits per weight: log₂(3) ≈ 1.58 bits
Compression ratio: 32 bits / 1.58 bits = 20.25x

Mathematical Foundation:

• Weights are quantized to three discrete levels using optimal thresholds
• Scaling factors computed via least-squares optimization: α = (W·Q) / (Q·Q)
• Multiple threshold selection methods for different weight distributions
• Comprehensive error analysis with MSE and MAE metrics

Advanced Features Implemented

1. ✅ Complete Weight Quantization: All ternary methods with statistical analysis
2. ✅ Optimal Packing Strategies: 7 different compression algorithms with auto-selection
3. ✅ SIMD Acceleration: Hardware-optimized unpacking for major architectures
4. ✅ Corruption Detection: Production-ready integrity validation and recovery
5. ✅ Performance Benchmarking: Comprehensive testing framework with detailed metrics

Quantization Methods Comparison

🚀 Installation and Setup

Prerequisites

• Rust 1.70+ with Cargo
• Optional: SIMD-capable CPU (SSE2, AVX2, or NEON) for optimal performance
• Optional: GPU support for mixed precision operations

Basic Installation

[dependencies]
bitnet-quant = "0.2.2"
bitnet-core = ">=0.1.0, <0.3.0"
candle-core.workspace = true

Feature Flags

[dependencies]
bitnet-quant = { version = "0.2.2", features = ["calibration", "advanced", "qat"] }

Available features:

• std: Standard library support (default)
• qat: Quantization-aware training utilities with tracing support
• calibration: Calibration utilities with random sampling
• advanced: Advanced quantization methods with statistical analysis

Quick Start

use bitnet_quant::prelude::*;
use candle_core::{Tensor, Device};

fn main() -> Result<(), Box<dyn std::error::Error>> {
    let device = Device::Cpu;
    
    // Create enhanced configuration
    let config = ConfigurationPreset::BitNetOptimized.build()?;
    
    // Basic quantization
    let weights = Tensor::randn(0.0, 1.0, (256, 512), &device)?;
    let quantized = absmean_quantize_weights(&weights, &device)?;
    
    println!("Compression: {:.1}x", quantized.compression_ratio());
    println!("Memory saved: {:.1} MB",
             (weights.elem_count() * 4 - quantized.memory_footprint()) as f32 / 1024.0 / 1024.0);
    
    // Advanced precision control
    let mut controller = create_precision_controller(config.precision_control, device)?;
    
    Ok(())
}

Configuration-First Approach

The new API emphasizes configuration-first design:

use bitnet_quant::prelude::*;

// 1. Choose or build configuration
let config = WeightQuantizationConfigBuilder::new()
    .base(QuantizationConfig::bitnet_158())
    .group_size(128)
    .learnable_scales(true)
    .ternary_method(TernaryMethod::OptimalThreshold)
    .packing(PackingConfig::max_compression())
    .build();

// 2. Validate configuration
config.validate()?;

// 3. Create quantizer
let quantizer = QuantizerFactory::create_weight_quantizer(config)?;

// 4. Use quantizer
let quantized = quantizer.quantize(&weights)?;

🤝 Contributing

This crate is production-ready but welcomes contributions! Priority areas:

1. Performance Optimization: Further SIMD optimizations and GPU acceleration
2. Additional Packing Strategies: New compression algorithms for specific use cases
3. Quantization-Aware Training: Enhanced QAT support and gradient estimation
4. Hardware Support: Additional SIMD instruction sets and accelerators

Development Setup

1. Clone the repository: git clone <repo-url>
2. Install Rust 1.70+: rustup update
3. Run tests: cargo test --package bitnet-quant --all-features
4. Run benchmarks: cd bitnet-benchmarks && cargo bench
5. Check documentation: cargo doc --package bitnet-quant --open

Performance Testing

# Run comprehensive performance comparison
cd bitnet-benchmarks
cargo run --release -- compare --operations "quantization,packing,simd" --output results.json

# Generate detailed HTML report
cargo run --release -- report --input results.json --output performance_report.html --theme professional

🔧 Configuration and Tuning

Configuration Presets Guide

The new configuration system provides pre-built presets optimized for different use cases:

BitNet Optimized

use bitnet_quant::{ConfigurationPreset, create_enhanced_config};

// Balanced performance for 1.58-bit quantization
let config = ConfigurationPreset::BitNetOptimized.build()?;

// Features:
// - 1.58-bit precision with symmetric strategy
// - Adaptive thresholds enabled
// - Real-time monitoring
// - Conservative precision bounds
// - Automatic optimization

Performance Optimized

// Maximum speed with aggressive compression
let config = ConfigurationPreset::PerformanceOptimized.build()?;

// Features:
// - 1-bit precision for maximum speed
// - Aggressive dynamic adjustment
// - Tight precision bounds (1-bit to 2-bit)
// - High performance thresholds
// - Real-time monitoring enabled

Accuracy Optimized

// Maximum precision with conservative settings
let config = ConfigurationPreset::AccuracyOptimized.build()?;

// Features:
// - 4-bit precision with asymmetric strategy
// - Per-channel quantization enabled
// - Conservative dynamic adjustment
// - Wide precision bounds (2-bit to 8-bit)
// - High accuracy thresholds (98%+)

Memory Optimized

// Minimal memory footprint
let config = ConfigurationPreset::MemoryOptimized.build()?;

// Features:
// - 1-bit precision for maximum compression
// - High compression ratio requirements (20x+)
// - Monitoring disabled to reduce overhead
// - Aggressive memory optimization

Enhanced Weight Quantization Configuration

use bitnet_quant::{WeightQuantizationConfigBuilder, TernaryMethod, PackingConfig};

let config = WeightQuantizationConfigBuilder::new()
    .base(QuantizationConfig::bitnet_158())
    .group_size(128)
    .normalize_weights(true)
    .outlier_threshold(3.0)
    .learnable_scales(false)
    .block_size(64)
    .ternary_method(TernaryMethod::OptimalThreshold)
    .custom_threshold_factor(0.7)
    .packing(PackingConfig::bitnet())
    .freeze_weights(false)
    .weight_decay(1e-4)
    .gradient_clip(1.0)
    .build();

// Validate before use
config.validate()?;

SIMD Optimization Settings

use bitnet_quant::{SimdConfig, simd_unpacking::{SimdUnpacker, SimdCapabilities}};

// Aggressive SIMD configuration
let simd_config = SimdConfig::aggressive();

// Conservative SIMD configuration
let simd_config = SimdConfig::conservative();

// Force specific SIMD capabilities (for testing)
let capabilities = SimdCapabilities {
    sse2: true,
    avx2: false,
    neon: false,
};
let unpacker = SimdUnpacker::with_capabilities(capabilities);

// Or use automatic detection
let unpacker = SimdUnpacker::new();

Corruption Detection Configuration

use bitnet_quant::corruption_detection::CorruptionDetector;

let detector = CorruptionDetector::new(
    true,  // enable_checksums
    true,  // enable_deep_validation
    0.05,  // max_corruption_ratio (5%)
);

🐛 Troubleshooting

Common Issues

1. SIMD Not Available: Falls back to optimized scalar automatically
2. Memory Usage: Use packing strategies for large models
3. Quantization Accuracy: Try different ternary methods for your data distribution
4. Compilation Errors: Ensure Rust 1.70+ and compatible dependencies
5. 🆕 Configuration Validation Errors: Check parameter ranges and compatibility
6. 🆕 Precision Control Issues: Verify bounds and thresholds are reasonable
7. 🆕 Mixed Precision Errors: Ensure bitnet-core compatibility

Enhanced Performance Tips

• Use TernaryPackingStrategy::Hybrid for automatic optimization
• Enable SIMD with simd_optimized: true in packing config
• For sparse weights (>70% zeros), use CompressedSparse strategy
• Batch quantization operations when possible
• 🆕 Use Configuration Presets: Start with ConfigurationPreset::BitNetOptimized
• 🆕 Enable Precision Control: Use dynamic adjustment for optimal performance
• 🆕 Validate Configurations: Always call .validate() before use

Configuration Troubleshooting

// Validate configuration before use
let config = WeightQuantizationConfigBuilder::new()
    .base(QuantizationConfig::bitnet_158())
    .group_size(128)
    .build();

// Check for validation errors
match config.validate() {
    Ok(()) => println!("Configuration is valid"),
    Err(e) => {
        eprintln!("Configuration error: {}", e);
        // Fix the configuration based on error message
    }
}

// Use presets for known-good configurations
let safe_config = ConfigurationPreset::BitNetOptimized.build()?;

Precision Control Troubleshooting

// Check precision bounds
let controller = create_precision_controller(config.precision_control, device)?;

// Validate specific precision settings
match controller.validate_precision_bounds(
    QuantizationPrecision::OneFiveFiveBit,
    0.7, // threshold
    1.0, // scale
) {
    Ok(()) => println!("Precision settings are valid"),
    Err(e) => eprintln!("Precision error: {}", e),
}

// Monitor for adjustment issues
if let Some(adjustment) = controller.adjust_precision_dynamically(&stats)? {
    if !adjustment.success {
        eprintln!("Precision adjustment failed: {:?}", adjustment.reason);
    }
}

Mixed Precision Troubleshooting

// Validate mixed precision configuration
let mixed_config = MixedPrecisionQuantizationConfig::bitnet();
match mixed_config.validate() {
    Ok(()) => println!("Mixed precision config is valid"),
    Err(e) => eprintln!("Mixed precision error: {}", e),
}

// Check layer registration
let quantizer = create_mixed_precision_quantizer(mixed_config, device)?;
match quantizer.register_layer(layer_spec) {
    Ok(()) => println!("Layer registered successfully"),
    Err(e) => eprintln!("Layer registration failed: {}", e),
}

Debug Mode

// Enable detailed logging
env_logger::init();

// Use corruption detection for debugging
let detector = CorruptionDetector::default();
let reports = detector.detect_corruption(&packed_weights)?;
for report in reports {
    println!("Issue: {}", report.corruption_type);
}

// Enable verbose configuration
let config = QuantizationConfig::bitnet_158().with_verbose();

// Monitor precision control in debug mode
let precision_config = PrecisionControlConfig::default();
let mut controller = create_precision_controller(precision_config, device)?;
let summary = controller.get_performance_summary();
println!("Debug - Operations: {}, Avg Error: {:.4}",
         summary.operations_count, summary.average_error);

Common Error Messages and Solutions

📚 References

• BitNet Paper: BitNet: Scaling 1-bit Transformers for Large Language Models
• BitNet 1.58b: BitNet: Scaling 1-bit Transformers for Large Language Models
• Quantization Survey: A Survey of Quantization Methods for Efficient Neural Network Inference
• SIMD Optimization: Intel Intrinsics Guide

📄 License

Licensed under the MIT License. See LICENSE for details.

Performance Note: All benchmarks measured on Apple M2 Pro with 16GB RAM. Results may vary by hardware configuration. See bitnet-benchmarks for comprehensive performance testing tools.