trustformers-wasm 0.1.4

# TrustformeRS WASM Performance Guide

This guide covers best practices and techniques for optimizing TrustformeRS WASM performance in web and edge environments.

## Table of Contents

- [Performance Overview](#performance-overview)
- [WebGPU Acceleration](#webgpu-acceleration)
- [Memory Optimization](#memory-optimization)
- [Model Optimization](#model-optimization)
- [Browser Optimization](#browser-optimization)
- [Edge Computing](#edge-computing)
- [Profiling and Monitoring](#profiling-and-monitoring)
- [Common Bottlenecks](#common-bottlenecks)
- [Performance Tuning](#performance-tuning)

## Performance Overview

TrustformeRS WASM can achieve significant performance improvements through careful optimization:

- **WebGPU Acceleration**: 10-100x speedup over CPU
- **Model Quantization**: 50-75% memory reduction, 2-4x faster
- **Batch Processing**: 3-5x throughput improvement
- **Streaming**: Real-time text generation
- **Caching**: 90%+ reduction in loading time

## WebGPU Acceleration

### Enable WebGPU

Always use WebGPU when available for maximum performance:

```javascript
import { is_webgpu_supported, InferenceSession } from 'trustformers-wasm';

const session = new InferenceSession('text-generation');

if (is_webgpu_supported()) {
    await session.initialize_with_device('GPU');
    console.log('Using WebGPU acceleration');
} else {
    await session.initialize_with_device('CPU');
    console.log('Falling back to CPU');
}
```

### Device Selection

Choose the optimal device based on workload:

```javascript
const capabilities = session.get_device_capabilities();

// For large models (>1GB)
if (capabilities.memory_mb > 4000) {
    session.force_device_type('GPU');
}

// For small models on mobile
if (capabilities.memory_mb < 2000) {
    session.force_device_type('CPU');
}
```

### GPU Memory Management

Optimize GPU memory usage:

```javascript
import { get_memory_stats } from 'trustformers-wasm';

// Monitor GPU memory usage
function monitorGPUMemory() {
    const stats = get_memory_stats();
    console.log('GPU Memory:', stats.gpu_memory / 1024 / 1024, 'MB');
    
    // Free unused memory if approaching limit
    if (stats.gpu_memory > 0.8 * capabilities.memory_mb * 1024 * 1024) {
        session.cleanup();
    }
}
```

### Workgroup Optimization

For custom shaders, optimize workgroup sizes:

```javascript
// Optimal workgroup sizes for different operations
const WORKGROUP_SIZES = {
    'matrix_multiply': [16, 16],
    'layer_norm': [256],
    'attention': [32, 8],
    'elementwise': [256]
};
```

## Memory Optimization

### Quantization

Enable quantization to reduce memory usage:

```javascript
import { QuantizationConfig, QuantizationPrecision } from 'trustformers-wasm';

const quantConfig = new QuantizationConfig();

// For deployment (best compression)
quantConfig.precision = QuantizationPrecision.Int8;
quantConfig.calibration_samples = 100;
quantConfig.enable_dynamic = true;

// For development (balanced)
quantConfig.precision = QuantizationPrecision.FP16;
quantConfig.preserve_accuracy = true;

session.enable_quantization(quantConfig);
```

### Memory Monitoring

Track memory usage to prevent OOM:

```javascript
import { get_memory_stats } from 'trustformers-wasm';

function checkMemoryUsage() {
    const stats = get_memory_stats();
    const totalMB = stats.wasm_memory / 1024 / 1024;
    
    console.log(`Memory usage: ${totalMB.toFixed(1)} MB`);
    
    if (totalMB > 1000) {
        console.warn('High memory usage detected');
        // Consider enabling quantization or reducing batch size
    }
}

// Check every 30 seconds
setInterval(checkMemoryUsage, 30000);
```

### Model Caching

Use IndexedDB caching to avoid repeated downloads:

```javascript
// Initialize storage with size limit
await session.initialize_storage(500); // 500MB limit

// Load with automatic caching
await session.load_model_with_cache(
    'gpt2-small',
    'https://example.com/gpt2-small.bin',
    'GPT-2 Small',
    'gpt2',
    '1.0'
);
```

### Memory Cleanup

Implement proper cleanup to prevent memory leaks:

```javascript
// Clean up after inference
function cleanup() {
    session.cleanup();
    
    // Force garbage collection (if available)
    if (window.gc) {
        window.gc();
    }
}

// Clean up on page unload
window.addEventListener('beforeunload', cleanup);
```

## Model Optimization

### Model Size Selection

Choose appropriate model sizes for your use case:

```javascript
const RECOMMENDED_MODEL_SIZES = {
    'mobile': '< 100MB',
    'desktop': '< 500MB', 
    'server': '< 2GB',
    'edge': '< 50MB'
};

function getOptimalModelSize() {
    const isMobile = /Android|iPhone|iPad|iPod|BlackBerry|IEMobile|Opera Mini/i.test(navigator.userAgent);
    const memory = navigator.deviceMemory || 4; // GB
    
    if (isMobile && memory < 4) {
        return 'mobile';
    } else if (memory < 8) {
        return 'desktop';
    } else {
        return 'server';
    }
}
```

### Model Splitting

For large models, use splitting for progressive loading:

```javascript
import { ModelSplitter, LoadingStrategy } from 'trustformers-wasm';

const splitter = new ModelSplitter();

// Configure splitting
const chunkConfig = {
    max_chunk_size_mb: 50,
    strategy: LoadingStrategy.Progressive,
    priority_order: ['embeddings', 'attention', 'feedforward']
};

// Split and load progressively
const session = await splitter.load_split_model(modelUrl, chunkConfig);
```

### Weight Compression

Apply weight compression for deployment:

```javascript
import { WeightCompressor, CompressionStrategy } from 'trustformers-wasm';

const compressor = new WeightCompressor();

// Configure compression
const compressionConfig = {
    strategy: CompressionStrategy.MagnitudePruning,
    sparsity_ratio: 0.9, // Remove 90% of smallest weights
    preserve_important_layers: ['attention', 'layer_norm']
};

// Compress model
const compressedModel = await compressor.compress(modelData, compressionConfig);
console.log('Compression ratio:', compressedModel.compression_ratio);
```

## Browser Optimization

### Loading Optimization

Optimize WASM loading for faster startup:

```javascript
// Use streaming compilation
const wasmPromise = WebAssembly.compileStreaming(fetch('trustformers_wasm_bg.wasm'));

// Preload while displaying loading screen
async function initialize() {
    const module = await wasmPromise;
    const instance = await WebAssembly.instantiate(module);
    // Continue with initialization
}
```

### Web Workers

Use Web Workers for background processing:

```javascript
import { WorkerPool, WorkerPriority } from 'trustformers-wasm';

const workerPool = new WorkerPool();
await workerPool.initialize(4); // 4 workers

// Process inference in background
const result = await workerPool.submit_task(
    inputTensor,
    WorkerPriority.High
);
```

### Shared Memory

Enable SharedArrayBuffer for multi-threading:

```html
<!-- Required headers for SharedArrayBuffer -->
<meta http-equiv="Cross-Origin-Opener-Policy" content="same-origin">
<meta http-equiv="Cross-Origin-Embedder-Policy" content="require-corp">
```

```javascript
import { is_cross_origin_isolated, ThreadPool } from 'trustformers-wasm';

if (is_cross_origin_isolated()) {
    const threadPool = new ThreadPool();
    await threadPool.initialize(navigator.hardwareConcurrency);
    console.log('Multi-threading enabled');
}
```

## Edge Computing

### Cold Start Optimization

Minimize cold start latency in edge environments:

```javascript
import { EdgeRuntime, EdgeInferenceConfig } from 'trustformers-wasm';

const edgeRuntime = new EdgeRuntime();
const config = EdgeInferenceConfig.for_cold_start_optimization();

// Pre-warm the runtime
await edgeRuntime.initialize(config);

// Use smaller models for edge
const modelSize = edgeRuntime.recommended_model_size_mb();
console.log('Recommended model size:', modelSize, 'MB');
```

### Geographic Distribution

Use geo-distributed caching:

```javascript
import { GeoDistributionManager } from 'trustformers-wasm';

const geoManager = new GeoDistributionManager();

// Automatically select nearest edge location
const nearestEdge = await geoManager.get_nearest_edge_location();
console.log('Using edge location:', nearestEdge.region);

// Load model from nearest edge
const modelUrl = nearestEdge.get_model_url('gpt2-small');
```

### Edge Caching

Implement intelligent edge caching:

```javascript
import { EdgeCacheManager, EvictionPolicy } from 'trustformers-wasm';

const cacheManager = new EdgeCacheManager();

// Configure cache for edge environment
const cacheConfig = {
    max_size_mb: 100,
    eviction_policy: EvictionPolicy.LRU,
    compression_level: 'high',
    replication_factor: 2
};

await cacheManager.initialize(cacheConfig);
```

## Profiling and Monitoring

### Performance Profiler

Use the built-in profiler to identify bottlenecks:

```javascript
import { PerformanceProfiler, create_development_profiler } from 'trustformers-wasm';

const profiler = create_development_profiler();

// Profile inference
profiler.start_profiling();
const result = session.predict(inputTensor);
const profile = profiler.stop_profiling();

console.log('Profile results:', profile);
console.log('Bottlenecks:', profile.bottlenecks);
```

### Debug Logging

Enable comprehensive logging for performance analysis:

```javascript
import { DebugConfig, LogLevel } from 'trustformers-wasm';

const debugConfig = new DebugConfig();
debugConfig.level = LogLevel.Info;
debugConfig.enable_performance_monitoring = true;
debugConfig.enable_memory_tracking = true;
debugConfig.log_tensor_shapes = true;

session.enable_debug_logging(debugConfig);
```

### Benchmarking

Regular benchmarking to track performance:

```javascript
async function runBenchmark() {
    const iterations = 100;
    const times = [];
    
    for (let i = 0; i < iterations; i++) {
        const start = performance.now();
        await session.predict(testInput);
        const end = performance.now();
        times.push(end - start);
    }
    
    const avgTime = times.reduce((a, b) => a + b) / times.length;
    const p95Time = times.sort()[Math.floor(times.length * 0.95)];
    
    console.log('Average inference time:', avgTime.toFixed(2), 'ms');
    console.log('P95 inference time:', p95Time.toFixed(2), 'ms');
}
```

## Common Bottlenecks

### 1. Memory Bandwidth

**Symptoms**: High GPU usage but slow performance
**Solutions**:
- Use tensor fusion to reduce memory transfers
- Optimize data layouts (use texture storage)
- Batch operations to amortize transfer costs

```javascript
// Enable kernel fusion
session.enable_kernel_fusion(['layer_norm', 'activation']);
```

### 2. Model Loading

**Symptoms**: Long initialization times
**Solutions**:
- Use model caching
- Enable streaming compilation
- Implement progressive loading

```javascript
// Progressive model loading
const loadingProgress = await session.load_model_progressively(modelData, {
    chunk_size_mb: 10,
    on_progress: (progress) => console.log('Loading:', progress.percentage, '%')
});
```

### 3. CPU/GPU Synchronization

**Symptoms**: GPU utilization gaps
**Solutions**:
- Use asynchronous operations
- Pipeline multiple requests
- Minimize CPU-GPU data transfers

```javascript
// Asynchronous inference
const promises = [];
for (const input of inputs) {
    promises.push(session.predict_async(input));
}
const results = await Promise.all(promises);
```

### 4. JavaScript Overhead

**Symptoms**: High CPU usage in main thread
**Solutions**:
- Use Web Workers for heavy computations
- Minimize JavaScript tensor operations
- Batch API calls

```javascript
// Batch multiple predictions
const batchInput = concatenateTensors(inputs);
const batchResult = session.predict(batchInput);
const results = splitTensorResults(batchResult, inputs.length);
```

## Performance Tuning

### Device-Specific Optimizations

```javascript
function optimizeForDevice() {
    const capabilities = session.get_device_capabilities();
    
    if (capabilities.vendor.includes('NVIDIA')) {
        // NVIDIA-specific optimizations
        session.set_workgroup_size([32, 32]);
        session.enable_tensor_cores(true);
    } else if (capabilities.vendor.includes('AMD')) {
        // AMD-specific optimizations
        session.set_workgroup_size([64, 16]);
        session.enable_wave64(true);
    } else if (capabilities.vendor.includes('Intel')) {
        // Intel-specific optimizations
        session.set_workgroup_size([16, 16]);
        session.enable_subgroups(true);
    }
}
```

### Network Optimization

```javascript
import { is_low_data_mode, NetworkType } from 'trustformers-wasm';

function optimizeForNetwork() {
    const networkType = getNetworkType();
    
    if (networkType === NetworkType.Slow2G || is_low_data_mode()) {
        // Use aggressive compression
        session.enable_quantization(QuantizationPrecision.Int4);
        session.set_cache_compression('maximum');
    } else if (networkType === NetworkType.WiFi) {
        // Use balanced settings
        session.enable_quantization(QuantizationPrecision.FP16);
        session.set_cache_compression('balanced');
    }
}
```

### Battery Optimization

```javascript
import { BatteryInfo, get_battery_info } from 'trustformers-wasm';

async function optimizeForBattery() {
    const battery = await get_battery_info();
    
    if (battery.level < 0.2) {
        // Low battery: prioritize efficiency
        session.force_device_type('CPU');
        session.set_power_mode('efficiency');
    } else if (battery.charging) {
        // Charging: prioritize performance
        session.force_device_type('GPU');
        session.set_power_mode('performance');
    }
}
```

## Performance Checklist

- [ ] Enable WebGPU when available
- [ ] Use appropriate quantization settings
- [ ] Implement model caching
- [ ] Monitor memory usage
- [ ] Use batch processing for multiple requests
- [ ] Enable streaming for long generations
- [ ] Use Web Workers for background tasks
- [ ] Enable SharedArrayBuffer for multi-threading
- [ ] Optimize for target deployment environment
- [ ] Profile and benchmark regularly
- [ ] Implement proper cleanup
- [ ] Use device-specific optimizations

## Performance Targets

### Latency Targets

- **Text Generation**: < 100ms first token, < 50ms subsequent tokens
- **Classification**: < 50ms for single input
- **Image Captioning**: < 500ms for 224x224 image
- **Translation**: < 200ms for sentence-length input

### Throughput Targets

- **Batch Processing**: 100+ requests/second
- **Streaming**: 20+ tokens/second
- **Model Loading**: < 5 seconds for models under 500MB

### Memory Targets

- **Mobile**: < 500MB total memory usage
- **Desktop**: < 2GB total memory usage
- **Edge**: < 100MB total memory usage

## Conclusion

Optimal TrustformeRS WASM performance requires careful attention to:

1. **Hardware utilization** - Use WebGPU and multi-threading
2. **Memory management** - Enable quantization and caching
3. **Model optimization** - Choose appropriate sizes and compression
4. **Environment adaptation** - Optimize for deployment target
5. **Continuous monitoring** - Profile and benchmark regularly

Following these guidelines can achieve 10-100x performance improvements over naive implementations.