BitNet Inference

High-performance inference engine for BitNet neural networks, providing optimized model execution, batch processing, and streaming inference capabilities.

🎯 Purpose

bitnet-inference provides the runtime engine for executing BitNet models:

• Model Loading: Load and manage BitNet models from various formats
• Batch Processing: Efficient batched inference for high throughput
• Streaming Inference: Real-time streaming inference for interactive applications
• Dynamic Quantization: Runtime quantization optimization
• Multi-Device Support: Seamless execution across CPU, GPU, and Neural Engine

🔴 Current Status: PLACEHOLDER ONLY

⚠️ This crate is currently a placeholder and contains no implementation.

The current src/lib.rs contains only:

//! BitNet Inference Library
//! 
//! This crate provides inference utilities for BitNet models.

// Placeholder for future inference implementation

✅ What Needs to be Implemented

🔴 Model Management (Not Implemented)

Model Loading and Serialization

• Model Format Support: Load models from SafeTensors, ONNX, and custom formats
• Model Validation: Validate model structure and compatibility
• Version Management: Handle different model versions and migrations
• Compression: Support for compressed model storage and loading

Model Optimization

• Graph Optimization: Optimize computation graphs for inference
• Operator Fusion: Fuse compatible operations for better performance
• Memory Layout: Optimize tensor layouts for target hardware
• Quantization Optimization: Apply runtime quantization optimizations

Model Registry

• Model Caching: Cache loaded models for reuse
• Model Versioning: Track and manage model versions
• Model Metadata: Store and retrieve model metadata
• Model Discovery: Automatic discovery of available models

🔴 Inference Engine (Not Implemented)

Core Inference Runtime

• Forward Pass: Execute model forward pass with BitNet operations
• Dynamic Shapes: Support for dynamic input shapes
• Memory Management: Efficient memory allocation during inference
• Error Handling: Robust error handling and recovery

Batch Processing

• Batch Optimization: Optimize operations for batched inputs
• Dynamic Batching: Automatically batch requests for efficiency
• Memory Pooling: Reuse memory across batch operations
• Load Balancing: Balance load across available compute resources

Streaming Inference

• Real-time Processing: Low-latency streaming inference
• Pipeline Processing: Pipeline multiple inference stages
• Asynchronous Execution: Non-blocking inference operations
• Resource Management: Manage resources for concurrent streams

🔴 Performance Optimization (Not Implemented)

Hardware Acceleration

• Multi-Device Execution: Distribute computation across devices
• GPU Acceleration: Leverage GPU for compute-intensive operations
• Neural Engine: Utilize Apple Neural Engine when available
• SIMD Optimization: Vectorized operations for CPU execution

Memory Optimization

• Memory Reuse: Reuse intermediate tensors across operations
• Memory Prefetching: Prefetch data for upcoming operations
• Garbage Collection: Efficient cleanup of temporary allocations
• Memory Pressure: Handle memory pressure gracefully

Compute Optimization

• Kernel Fusion: Fuse operations to reduce memory bandwidth
• Loop Optimization: Optimize loops for better cache utilization
• Parallel Execution: Parallelize independent operations
• Pipeline Optimization: Optimize execution pipelines

🔴 Text Generation (Not Implemented)

Generation Strategies

• Greedy Decoding: Simple greedy text generation
• Beam Search: Beam search for higher quality generation
• Sampling Methods: Top-k, top-p, and temperature sampling
• Custom Strategies: Pluggable generation strategies

Generation Control

• Length Control: Control generation length and stopping criteria
• Content Filtering: Filter generated content for safety
• Prompt Engineering: Advanced prompt processing and engineering
• Context Management: Manage long contexts efficiently

Streaming Generation

• Token Streaming: Stream generated tokens in real-time
• Incremental Generation: Generate text incrementally
• Interactive Generation: Support for interactive text generation
• Cancellation: Cancel generation requests gracefully

🚀 Planned API Design

Basic Model Inference

use bitnet_inference::{InferenceEngine, ModelLoader, InferenceConfig};
use bitnet_core::{Tensor, Device};

// Load model
let model = ModelLoader::from_file("model.safetensors")?;

// Create inference engine
let config = InferenceConfig {
    batch_size: 32,
    max_sequence_length: 2048,
    device: Device::Auto,
    ..Default::default()
};

let engine = InferenceEngine::new(model, config)?;

// Run inference
let input = Tensor::from_slice(&[1, 2, 3, 4], &[1, 4])?;
let output = engine.forward(&input)?;

println!("Output shape: {:?}", output.shape());

Batch Processing

use bitnet_inference::{BatchProcessor, BatchConfig};

// Create batch processor
let batch_config = BatchConfig {
    max_batch_size: 64,
    timeout_ms: 100,
    dynamic_batching: true,
};

let processor = BatchProcessor::new(engine, batch_config)?;

// Process multiple requests
let requests = vec![
    InferenceRequest::new(input1),
    InferenceRequest::new(input2),
    InferenceRequest::new(input3),
];

let results = processor.process_batch(requests).await?;

Streaming Inference

use bitnet_inference::{StreamingEngine, StreamConfig};
use tokio_stream::StreamExt;

// Create streaming engine
let stream_config = StreamConfig {
    max_concurrent_streams: 10,
    buffer_size: 1024,
    low_latency: true,
};

let streaming_engine = StreamingEngine::new(engine, stream_config)?;

// Process streaming requests
let mut stream = streaming_engine.create_stream(input).await?;

while let Some(result) = stream.next().await {
    match result {
        Ok(output) => println!("Received output: {:?}", output),
        Err(e) => eprintln!("Stream error: {}", e),
    }
}

Text Generation

use bitnet_inference::{TextGenerator, GenerationConfig, SamplingStrategy};

// Create text generator
let generation_config = GenerationConfig {
    max_length: 1024,
    temperature: 0.8,
    top_k: 50,
    top_p: 0.9,
    strategy: SamplingStrategy::TopP,
    stop_tokens: vec!["<|endoftext|>".to_string()],
};

let generator = TextGenerator::new(engine, generation_config)?;

// Generate text
let prompt = "The future of AI is";
let generated = generator.generate(prompt).await?;

println!("Generated: {}", generated);

Advanced Features

use bitnet_inference::{
    ModelOptimizer, QuantizationConfig, DeviceManager,
    PerformanceMonitor
};

// Optimize model for inference
let optimizer = ModelOptimizer::new();
let optimized_model = optimizer
    .fuse_operations(true)
    .optimize_memory_layout(true)
    .apply_quantization(QuantizationConfig::default())
    .optimize(model)?;

// Multi-device execution
let device_manager = DeviceManager::new();
let devices = device_manager.available_devices();

let distributed_engine = InferenceEngine::distributed(
    optimized_model,
    devices,
    DistributionStrategy::DataParallel
)?;

// Performance monitoring
let monitor = PerformanceMonitor::new();
monitor.start_monitoring(&engine);

let output = engine.forward(&input)?;

let metrics = monitor.get_metrics();
println!("Inference time: {:?}", metrics.inference_time);
println!("Memory usage: {} MB", metrics.peak_memory_mb);

🏗️ Planned Architecture

Core Components

bitnet-inference/src/
├── lib.rs                   # Main library interface
├── engine/                  # Core inference engine
│   ├── mod.rs              # Engine interface
│   ├── inference_engine.rs # Main inference engine
│   ├── executor.rs         # Operation executor
│   ├── scheduler.rs        # Operation scheduler
│   └── context.rs          # Execution context
├── model/                   # Model management
│   ├── mod.rs              # Model interface
│   ├── loader.rs           # Model loading and parsing
│   ├── optimizer.rs        # Model optimization
│   ├── registry.rs         # Model registry and caching
│   ├── validation.rs       # Model validation
│   └── formats/            # Support for different formats
│       ├── safetensors.rs  # SafeTensors format
│       ├── onnx.rs         # ONNX format support
│       └── custom.rs       # Custom BitNet format
├── batch/                   # Batch processing
│   ├── mod.rs              # Batch interface
│   ├── processor.rs        # Batch processor
│   ├── scheduler.rs        # Batch scheduler
│   ├── dynamic.rs          # Dynamic batching
│   └── memory.rs           # Batch memory management
├── streaming/               # Streaming inference
│   ├── mod.rs              # Streaming interface
│   ├── engine.rs           # Streaming engine
│   ├── pipeline.rs         # Processing pipeline
│   ├── buffer.rs           # Stream buffering
│   └── async_runtime.rs    # Async runtime support
├── generation/              # Text generation
│   ├── mod.rs              # Generation interface
│   ├── generator.rs        # Text generator
│   ├── strategies.rs       # Generation strategies
│   ├── sampling.rs         # Sampling methods
│   ├── beam_search.rs      # Beam search implementation
│   └── streaming_gen.rs    # Streaming generation
├── optimization/            # Performance optimization
│   ├── mod.rs              # Optimization interface
│   ├── graph.rs            # Graph optimization
│   ├── fusion.rs           # Operation fusion
│   ├── memory.rs           # Memory optimization
│   ├── quantization.rs     # Runtime quantization
│   └── device.rs           # Device-specific optimizations
├── device/                  # Device management
│   ├── mod.rs              # Device interface
│   ├── manager.rs          # Device manager
│   ├── scheduler.rs        # Device scheduler
│   ├── load_balancer.rs    # Load balancing
│   └── migration.rs        # Data migration
├── monitoring/              # Performance monitoring
│   ├── mod.rs              # Monitoring interface
│   ├── profiler.rs         # Performance profiler
│   ├── metrics.rs          # Metrics collection
│   ├── telemetry.rs        # Telemetry and logging
│   └── dashboard.rs        # Performance dashboard
└── utils/                   # Utilities and helpers
    ├── mod.rs              # Utility interface
    ├── tokenizer.rs        # Tokenization utilities
    ├── preprocessing.rs    # Input preprocessing
    ├── postprocessing.rs   # Output postprocessing
    └── validation.rs       # Input/output validation

Integration Architecture

// Integration with other BitNet crates
use bitnet_core::memory::HybridMemoryPool;
use bitnet_quant::BitNetQuantizer;
use bitnet_metal::MetalDevice;

// Unified inference pipeline
let pool = HybridMemoryPool::new()?;
let quantizer = BitNetQuantizer::new(config.quantization)?;
let metal_device = MetalDevice::default()?;

let engine = InferenceEngine::builder()
    .memory_pool(pool)
    .quantizer(quantizer)
    .device(metal_device)
    .build()?;

📊 Expected Performance Characteristics

Inference Performance (Projected)

Memory Usage (Projected)

Throughput Scaling

🧪 Planned Testing Strategy

Unit Tests

# Test inference engine
cargo test --package bitnet-inference engine

# Test model loading
cargo test --package bitnet-inference model

# Test batch processing
cargo test --package bitnet-inference batch

# Test text generation
cargo test --package bitnet-inference generation

Integration Tests

# Test end-to-end inference
cargo test --package bitnet-inference --test e2e_inference

# Test multi-device execution
cargo test --package bitnet-inference --test multi_device

# Test streaming inference
cargo test --package bitnet-inference --test streaming

Performance Tests

# Benchmark inference performance
cargo bench --package bitnet-inference -- inference

# Benchmark batch processing
cargo bench --package bitnet-inference -- batch

# Memory usage benchmarks
cargo bench --package bitnet-inference -- memory

Model Compatibility Tests

# Test with different model formats
cargo test --package bitnet-inference --test model_formats

# Test with various model sizes
cargo test --package bitnet-inference --test model_sizes

# Accuracy validation tests
cargo test --package bitnet-inference --test accuracy

🔧 Configuration

Inference Configuration

use bitnet_inference::{InferenceConfig, DeviceConfig, MemoryConfig};

let config = InferenceConfig {
    // Model configuration
    model_path: "path/to/model.safetensors".into(),
    model_format: ModelFormat::SafeTensors,
    
    // Device configuration
    device: DeviceConfig {
        primary: Device::Auto,
        fallback: vec![Device::Cpu],
        memory_fraction: 0.8,
    },
    
    // Memory configuration
    memory: MemoryConfig {
        pool_size: 8 * 1024 * 1024 * 1024, // 8GB
        enable_memory_mapping: true,
        prefetch_size: 1024 * 1024, // 1MB
    },
    
    // Performance configuration
    batch_size: 32,
    max_sequence_length: 2048,
    enable_kv_cache: true,
    enable_graph_optimization: true,
    
    // Generation configuration
    generation: GenerationConfig {
        max_length: 1024,
        temperature: 0.8,
        top_k: 50,
        top_p: 0.9,
        repetition_penalty: 1.1,
    },
};

Advanced Configuration

use bitnet_inference::{OptimizationConfig, MonitoringConfig};

let advanced_config = InferenceConfig {
    // Optimization settings
    optimization: OptimizationConfig {
        enable_operator_fusion: true,
        enable_memory_optimization: true,
        enable_quantization_optimization: true,
        optimization_level: OptimizationLevel::Aggressive,
    },
    
    // Monitoring settings
    monitoring: MonitoringConfig {
        enable_profiling: true,
        enable_telemetry: true,
        metrics_interval: Duration::from_secs(1),
        log_level: LogLevel::Info,
    },
    
    // Streaming settings
    streaming: StreamingConfig {
        max_concurrent_streams: 10,
        buffer_size: 1024,
        timeout: Duration::from_secs(30),
        enable_backpressure: true,
    },
    
    ..Default::default()
};

🚀 Performance Optimization

Memory Optimization

• KV Cache: Efficient key-value cache for transformer models
• Memory Pooling: Reuse memory allocations across requests
• Memory Mapping: Use memory-mapped files for large models
• Garbage Collection: Intelligent cleanup of unused tensors

Compute Optimization

• Graph Fusion: Fuse compatible operations for better performance
• Kernel Optimization: Use optimized kernels for common operations
• Pipeline Parallelism: Pipeline different stages of inference
• Data Parallelism: Distribute computation across devices

I/O Optimization

• Model Caching: Cache frequently used models in memory
• Prefetching: Prefetch model weights and data
• Compression: Use compressed model formats
• Streaming: Stream large models from storage

🤝 Contributing

This crate needs complete implementation! Priority areas:

1. Core Engine: Implement the basic inference engine
2. Model Loading: Build model loading and management system
3. Batch Processing: Implement efficient batch processing
4. Text Generation: Add text generation capabilities

Getting Started

1. Study transformer architecture and inference patterns
2. Implement basic forward pass execution
3. Add model loading from SafeTensors format
4. Implement batch processing for efficiency
5. Add comprehensive benchmarks and tests

Development Priorities

1. Phase 1: Basic inference engine and model loading
2. Phase 2: Batch processing and memory optimization
3. Phase 3: Streaming inference and text generation
4. Phase 4: Advanced optimizations and multi-device support

📚 References

• Transformer Architecture: Attention Is All You Need
• BitNet Paper: BitNet: Scaling 1-bit Transformers
• Inference Optimization: Efficient Transformers: A Survey
• SafeTensors Format: SafeTensors Documentation

📄 License

Licensed under the MIT License. See LICENSE for details.