BitNet Metal

Metal GPU acceleration for BitNet neural networks, providing high-performance compute shaders and optimized memory management for Apple Silicon devices.

🎯 Purpose

bitnet-metal provides GPU acceleration for BitNet operations on Apple Silicon:

• Metal Compute Shaders: Optimized GPU kernels for BitNet operations
• Unified Memory Management: Efficient GPU memory allocation and transfers
• Apple Silicon Optimization: Leverages unique Apple Silicon architecture features
• Neural Engine Integration: Future integration with Apple's Neural Engine
• Performance Monitoring: GPU utilization and performance metrics

🔴 Current Status: PLACEHOLDER ONLY

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

The current src/lib.rs contains only:

//! BitNet Metal Library
//! 
//! This crate provides Metal GPU acceleration for BitNet models.

// Placeholder for future Metal implementation

✅ What Needs to be Implemented

🔴 Metal Compute Shaders (Not Implemented)

Core BitNet Operations

• 1.58-bit Matrix Multiplication: GPU kernels for quantized matrix operations
• Quantization Kernels: GPU-accelerated weight and activation quantization
• Dequantization Kernels: Fast GPU dequantization operations
• Element-wise Operations: Vectorized element-wise operations on GPU

Optimized Kernels

• Tiled Matrix Multiplication: Memory-efficient tiled implementations
• Fused Operations: Combined operations to reduce memory bandwidth
• Batch Processing: Efficient batched operations for inference
• Mixed Precision: Support for different precision levels

Memory-Efficient Operations

• In-place Operations: Minimize memory allocations during computation
• Streaming Operations: Process large tensors in chunks
• Memory Coalescing: Optimize memory access patterns
• Shared Memory Usage: Leverage GPU shared memory effectively

🔴 GPU Memory Management (Not Implemented)

Unified Memory Architecture

• Shared Memory Pools: Leverage Apple Silicon unified memory
• Zero-Copy Operations: Minimize CPU-GPU memory transfers
• Memory Mapping: Efficient memory mapping between CPU and GPU
• Automatic Migration: Intelligent data placement and migration

Metal Buffer Management

• Buffer Pooling: Reuse Metal buffers to reduce allocation overhead
• Memory Alignment: Ensure optimal memory alignment for GPU operations
• Resource Management: Automatic cleanup of GPU resources
• Memory Pressure Handling: Graceful degradation under memory pressure

Device-Specific Optimizations

• M1/M2/M3 Optimizations: Leverage specific Apple Silicon features
• Memory Bandwidth Optimization: Maximize memory bandwidth utilization
• Cache-Friendly Layouts: Optimize data layouts for GPU caches
• Thermal Management: Monitor and respond to thermal constraints

🔴 Metal Performance Shaders Integration (Not Implemented)

MPS Neural Network Support

• MPS Graph Integration: Use Metal Performance Shaders graph API
• Optimized Primitives: Leverage Apple's optimized neural network primitives
• Custom Operations: Implement BitNet-specific operations as MPS nodes
• Graph Optimization: Automatic graph optimization and fusion

Advanced MPS Features

• Dynamic Shapes: Support for dynamic tensor shapes
• Control Flow: Conditional execution and loops in MPS graphs
• Memory Planning: Automatic memory planning and optimization
• Multi-GPU Support: Future support for multiple GPU devices

🔴 Neural Engine Integration (Not Implemented)

ANE Acceleration

• Neural Engine Kernels: Implement BitNet operations for Apple Neural Engine
• Model Compilation: Compile BitNet models for Neural Engine execution
• Hybrid Execution: Combine GPU and Neural Engine for optimal performance
• Power Efficiency: Leverage Neural Engine for power-efficient inference

🚀 Planned API Design

Basic Metal Operations

use bitnet_metal::{MetalDevice, MetalTensor, MetalKernel};
use bitnet_core::{Tensor, Device};

// Create Metal device
let metal_device = MetalDevice::default()?;

// Create Metal tensors
let a = MetalTensor::from_tensor(&tensor_a, &metal_device)?;
let b = MetalTensor::from_tensor(&tensor_b, &metal_device)?;

// Perform quantized matrix multiplication
let kernel = MetalKernel::quantized_matmul(&metal_device)?;
let result = kernel.execute(&a, &b)?;

// Convert back to CPU tensor
let cpu_result = result.to_cpu_tensor()?;

Advanced GPU Operations

use bitnet_metal::{MetalCommandBuffer, MetalComputeEncoder};

// Create command buffer for batched operations
let command_buffer = metal_device.new_command_buffer()?;
let encoder = command_buffer.new_compute_encoder()?;

// Encode multiple operations
encoder.encode_quantization(&weights, &quantized_weights)?;
encoder.encode_matmul(&quantized_weights, &activations, &output)?;
encoder.encode_dequantization(&output, &final_output)?;

// Execute all operations
encoder.end_encoding();
command_buffer.commit();
command_buffer.wait_until_completed()?;

Memory Management Integration

use bitnet_metal::{MetalMemoryPool, MetalBuffer};
use bitnet_core::memory::HybridMemoryPool;

// Create Metal memory pool integrated with core memory management
let core_pool = HybridMemoryPool::new()?;
let metal_pool = MetalMemoryPool::new(&metal_device, &core_pool)?;

// Allocate GPU memory
let gpu_buffer = metal_pool.allocate_buffer(size, &metal_device)?;

// Zero-copy tensor creation
let metal_tensor = MetalTensor::from_buffer(gpu_buffer, shape, dtype)?;

MPS Integration

use bitnet_metal::{MPSGraph, MPSGraphTensor, BitNetMPSOperations};

// Create MPS graph for BitNet model
let graph = MPSGraph::new();

// Add BitNet operations to graph
let input = graph.placeholder(&[batch_size, input_dim], dtype)?;
let weights = graph.constant(&quantized_weights)?;
let output = graph.bitnet_linear(&input, &weights)?;

// Compile and execute graph
let executable = graph.compile(&metal_device)?;
let result = executable.execute(&[input_data])?;

🏗️ Planned Architecture

Core Components

bitnet-metal/src/
├── lib.rs                   # Main library interface
├── device/                  # Metal device management
│   ├── mod.rs              # Device interface
│   ├── metal_device.rs     # Metal device wrapper
│   ├── capabilities.rs     # Device capability detection
│   └── selection.rs        # Automatic device selection
├── memory/                  # GPU memory management
│   ├── mod.rs              # Memory interface
│   ├── buffer_pool.rs      # Metal buffer pooling
│   ├── unified_memory.rs   # Unified memory management
│   ├── allocator.rs        # GPU memory allocator
│   └── migration.rs        # CPU-GPU memory migration
├── kernels/                 # Metal compute shaders
│   ├── mod.rs              # Kernel interface
│   ├── quantization.rs     # Quantization kernels
│   ├── matmul.rs           # Matrix multiplication kernels
│   ├── elementwise.rs      # Element-wise operation kernels
│   └── reduction.rs        # Reduction operation kernels
├── shaders/                 # Metal shader source files
│   ├── quantization.metal  # Quantization compute shaders
│   ├── matmul.metal        # Matrix multiplication shaders
│   ├── bitnet_ops.metal    # BitNet-specific operations
│   └── utils.metal         # Utility functions
├── mps/                     # Metal Performance Shaders integration
│   ├── mod.rs              # MPS interface
│   ├── graph.rs            # MPS graph operations
│   ├── operations.rs       # BitNet MPS operations
│   └── optimization.rs     # Graph optimization
├── tensor/                  # Metal tensor operations
│   ├── mod.rs              # Tensor interface
│   ├── metal_tensor.rs     # Metal tensor implementation
│   ├── operations.rs       # Tensor operations
│   └── conversion.rs       # CPU-GPU tensor conversion
├── ane/                     # Apple Neural Engine integration
│   ├── mod.rs              # ANE interface
│   ├── compilation.rs      # Model compilation for ANE
│   ├── execution.rs        # ANE execution engine
│   └── optimization.rs     # ANE-specific optimizations
└── utils/                   # Utilities and helpers
    ├── mod.rs              # Utility interface
    ├── profiling.rs        # GPU performance profiling
    ├── debugging.rs        # Metal debugging utilities
    └── validation.rs       # GPU operation validation

Metal Shader Architecture

// Example quantization shader
#include <metal_stdlib>
using namespace metal;

kernel void quantize_weights_1_58bit(
    device const float* input [[buffer(0)]],
    device char* output [[buffer(1)]],
    device float* scale [[buffer(2)]],
    constant uint& size [[buffer(3)]],
    uint index [[thread_position_in_grid]]
) {
    if (index >= size) return;
    
    // 1.58-bit quantization logic
    float value = input[index];
    float s = scale[0];
    
    // Quantize to {-1, 0, +1}
    if (value > s/2) {
        output[index] = 1;
    } else if (value < -s/2) {
        output[index] = -1;
    } else {
        output[index] = 0;
    }
}

📊 Expected Performance Characteristics

GPU Performance (Apple M1 Pro, Projected)

Memory Bandwidth Utilization

Power Efficiency

🧪 Planned Testing Strategy

Unit Tests

# Test Metal device management
cargo test --package bitnet-metal device

# Test GPU memory management
cargo test --package bitnet-metal memory

# Test Metal kernels
cargo test --package bitnet-metal kernels

Performance Tests

# Benchmark GPU operations
cargo bench --package bitnet-metal

# Compare CPU vs GPU performance
cargo bench --package bitnet-metal -- comparison

# Memory bandwidth tests
cargo bench --package bitnet-metal -- bandwidth

Integration Tests

# Test with bitnet-core integration
cargo test --package bitnet-metal --test core_integration

# Test MPS integration
cargo test --package bitnet-metal --test mps_integration

# Test end-to-end model execution
cargo test --package bitnet-metal --test model_execution

🔧 Platform Requirements

Hardware Requirements

• Apple Silicon: M1, M1 Pro, M1 Max, M2, M2 Pro, M2 Max, or newer
• Memory: 8GB+ unified memory (16GB+ recommended)
• macOS: 12.0+ (Monterey or newer)

Software Requirements

• Xcode: 13.0+ with Metal development tools
• Metal: Metal 2.4+ support
• Rust: 1.70+ with Metal bindings

Development Setup

# Install Xcode command line tools
xcode-select --install

# Verify Metal support
system_profiler SPDisplaysDataType | grep Metal

# Build with Metal features
cargo build --package bitnet-metal --features metal

🚀 Performance Optimization Strategies

Memory Optimization

• Unified Memory: Leverage Apple Silicon's unified memory architecture
• Zero-Copy: Minimize data transfers between CPU and GPU
• Memory Pooling: Reuse GPU buffers to reduce allocation overhead
• Prefetching: Intelligent data prefetching for GPU operations

Compute Optimization

• Kernel Fusion: Combine multiple operations into single kernels
• Tiling: Optimize memory access patterns with tiling strategies
• Occupancy: Maximize GPU occupancy with optimal thread configurations
• Pipeline: Pipeline CPU and GPU operations for maximum throughput

Apple Silicon Specific

• AMX Integration: Leverage Apple Matrix coprocessor when available
• Thermal Awareness: Monitor and respond to thermal constraints
• Power Management: Balance performance and power consumption
• Cache Optimization: Optimize for Apple Silicon cache hierarchy

🤝 Contributing

This crate needs complete implementation! Priority areas:

1. Metal Kernels: Implement core BitNet compute shaders
2. Memory Management: Build GPU memory management system
3. MPS Integration: Integrate with Metal Performance Shaders
4. Performance: Optimize for Apple Silicon architecture

Getting Started

1. Set up Metal development environment on macOS
2. Study Metal compute shader programming
3. Implement basic quantization kernels
4. Add comprehensive benchmarks
5. Integrate with bitnet-core memory management

Metal Shader Development

# Compile Metal shaders
xcrun -sdk macosx metal -c shaders/quantization.metal -o quantization.air
xcrun -sdk macosx metallib quantization.air -o quantization.metallib

# Debug Metal shaders
xcrun -sdk macosx metal-objdump -disassemble quantization.air

📚 References

• Metal Programming Guide: Apple Metal Documentation
• Metal Performance Shaders: MPS Framework
• Apple Silicon Architecture: Apple Silicon Technical Overview
• BitNet Paper: BitNet: Scaling 1-bit Transformers

📄 License

Licensed under the MIT License. See LICENSE for details.