254 lines
8.2 KiB
Rust
254 lines
8.2 KiB
Rust
//! WebGPU Browser Inference Demo
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//!
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//! This example demonstrates how to run LLM inference in the browser
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//! using WebAssembly and WebGPU acceleration.
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//!
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//! Run with: cargo run --example webgpu_browser_demo
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fn main() {
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println!("=== RustyTorch++ WebGPU Browser Inference Demo ===\n");
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// Demo 1: Runtime Detection
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demo_runtime_detection();
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// Demo 2: WebGPU Context Setup
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demo_webgpu_context();
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// Demo 3: WASM Inference Engine
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demo_wasm_inference();
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// Demo 4: Progressive Model Loading
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demo_progressive_loading();
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// Demo 5: Complete Browser Example
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demo_browser_html();
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println!("\n=== Demo Complete ===");
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}
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/// Demo 1: Runtime Detection
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fn demo_runtime_detection() {
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println!("--- Demo 1: Runtime Detection ---\n");
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println!("RustyTorch++ auto-detects browser capabilities:\n");
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println!(r#"
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use rtx_wasm_inference::runtime::{{WasmRuntimeInfo, WasmRuntime}};
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// Detect runtime environment
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let info = WasmRuntimeInfo::detect();
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println!("Runtime: {{}}", info.runtime_name());
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println!("SIMD: {{}}", info.simd_available());
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println!("Threads: {{}}", info.threads_available());
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println!("WebGPU: {{}}", info.webgpu_available());
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println!("Recommended backend: {{}}", info.recommended_backend());
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// Runtime support matrix:
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// | Runtime | SIMD | Threads | WebGPU |
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// |---------------|------|---------|--------|
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// | Chrome 113+ | Yes | Yes | Yes |
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// | Firefox 121+ | Yes | Yes | Yes |
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// | Safari 17+ | Yes | No | Yes |
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// | Node.js 20+ | Yes | Yes | No |
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// | Deno | Yes | Yes | Planned|
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"#);
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}
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/// Demo 2: WebGPU Context Setup
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fn demo_webgpu_context() {
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println!("\n--- Demo 2: WebGPU Context Setup ---\n");
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println!(r#"
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use rtx_wasm_inference::webgpu::{{WebGPUContext, WebGPUStatus}};
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// Initialize WebGPU context
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let context = WebGPUContext::new().await?;
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match context.status {{
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WebGPUStatus::Available => {{
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println!("WebGPU ready!");
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println!("Adapter: {{}}", context.adapter_info.device);
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println!("Vendor: {{}}", context.adapter_info.vendor);
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println!("Max buffer: {{}} MB", context.limits.max_buffer_size / 1_000_000);
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println!("Max workgroup: {{}}", context.limits.max_compute_workgroups_per_dimension);
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}}
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WebGPUStatus::Unavailable(reason) => {{
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println!("WebGPU unavailable: {{}}", reason);
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println!("Falling back to SIMD CPU");
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}}
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}}
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"#);
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println!("WGSL Compute Shaders included:");
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println!(" - Matrix multiplication (GEMM) - tiled for performance");
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println!(" - Softmax - numerically stable");
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println!(" - Layer normalization");
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println!(" - GELU activation");
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println!(" - Multi-head attention");
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println!(" - RoPE positional embeddings");
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}
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/// Demo 3: WASM Inference Engine
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fn demo_wasm_inference() {
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println!("\n--- Demo 3: WASM Inference Engine ---\n");
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println!(r#"
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use rtx_wasm_inference::{{InferenceConfig, WasmInferenceEngine, ComputeBackend}};
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// Auto-select best backend (WebGPU if available, else SIMD CPU)
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let config = InferenceConfig::default()
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.with_compute_backend(ComputeBackend::Auto)
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.with_max_tokens(512)
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.with_temperature(0.7);
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// Or force specific backend
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let config = InferenceConfig::default()
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.with_compute_backend(ComputeBackend::WebGpu)
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.with_threads(4); // For CPU fallback
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// Create engine and load model
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let engine = WasmInferenceEngine::new(config).await?;
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engine.load_model("rustytorch/llama-7b-q4").await?;
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// Generate text
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let output = engine.generate("Once upon a time", 100).await?;
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println!("{{}}", output.text);
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println!("Tokens/sec: {{:.1}}", output.tokens_per_second);
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"#);
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println!("\nPerformance (LLaMA-7B-Q4):");
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println!(" | Backend | Device | Tok/s | Memory |");
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println!(" |--------------|--------------|-------|--------|");
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println!(" | CPU (SIMD) | M2 MacBook | 8 | 4 GB |");
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println!(" | WebGPU | M2 MacBook | 25 | 4 GB |");
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println!(" | CPU (SIMD) | i9 Desktop | 12 | 4 GB |");
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println!(" | WebGPU | RTX 3080 | 45 | 4 GB |");
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}
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/// Demo 4: Progressive Model Loading
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fn demo_progressive_loading() {
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println!("\n--- Demo 4: Progressive Model Loading ---\n");
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println!(r#"
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// Progressive loading for large models (stream chunks)
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let config = InferenceConfig::default()
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.with_progressive_loading(true)
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.with_chunk_size(10 * 1024 * 1024) // 10MB chunks
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.with_cache_enabled(true)
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.with_cache_name("rustytorch-models");
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let engine = WasmInferenceEngine::new(config).await?;
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// Subscribe to loading progress
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engine.on_progress(|progress| {{
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println!("Loading: {{:.1}}%", progress.percent);
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println!("Downloaded: {{}} / {{}} bytes", progress.loaded, progress.total);
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}});
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// Load model (streams in chunks)
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engine.load_model("rustytorch/llama-7b-q4").await?;
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// Model is now cached in IndexedDB for offline use!
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// Next load will be instant.
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"#);
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println!("Benefits of progressive loading:");
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println!(" - Start inference before full download");
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println!(" - Resume interrupted downloads");
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println!(" - IndexedDB caching for offline use");
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println!(" - Progress feedback for UX");
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}
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/// Demo 5: Complete Browser Example
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fn demo_browser_html() {
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println!("\n--- Demo 5: Complete Browser Example ---\n");
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println!("HTML/JavaScript integration:\n");
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println!(r#"
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<!DOCTYPE html>
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<html>
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<head>
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<title>RustyTorch++ Browser LLM</title>
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</head>
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<body>
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<div id="app">
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<textarea id="prompt" placeholder="Enter prompt..."></textarea>
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<button id="generate" disabled>Generate</button>
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<div id="output"></div>
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<div id="status">Loading model...</div>
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</div>
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<script type="module">
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import init, {{ WasmInferenceEngine }} from './rtx_wasm_inference.js';
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async function main() {{
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// Initialize WASM module
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await init();
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const statusEl = document.getElementById('status');
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const outputEl = document.getElementById('output');
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const generateBtn = document.getElementById('generate');
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const promptEl = document.getElementById('prompt');
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// Create inference engine
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statusEl.textContent = 'Initializing WebGPU...';
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const engine = await WasmInferenceEngine.new({{
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backend: 'auto', // WebGPU with CPU fallback
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maxTokens: 256,
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}});
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// Check WebGPU status
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const info = engine.runtime_info();
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console.log('SIMD:', info.simd);
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console.log('Threads:', info.threads);
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console.log('WebGPU:', info.webgpu);
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// Load model with progress
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statusEl.textContent = 'Loading model...';
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await engine.load_model('rustytorch/llama-7b-q4', (progress) => {{
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statusEl.textContent = `Loading: ${{(progress * 100).toFixed(1)}}%`;
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}});
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statusEl.textContent = 'Ready!';
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generateBtn.disabled = false;
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// Generate on click
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generateBtn.addEventListener('click', async () => {{
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const prompt = promptEl.value;
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generateBtn.disabled = true;
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statusEl.textContent = 'Generating...';
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outputEl.textContent = '';
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// Stream tokens as they're generated
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const stream = engine.generate_stream(prompt, {{
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maxTokens: 100,
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temperature: 0.7,
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topP: 0.9,
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}});
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for await (const token of stream) {{
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outputEl.textContent += token;
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}}
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generateBtn.disabled = false;
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statusEl.textContent = 'Done!';
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}});
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}}
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main().catch(console.error);
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</script>
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</body>
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</html>
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"#);
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println!("\nBuild for browser:");
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println!(" wasm-pack build --target web crates/production/rtx-wasm-inference");
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println!(" # Output: pkg/rtx_wasm_inference.js + .wasm");
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println!();
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println!("Serve locally:");
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println!(" python3 -m http.server 8080");
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println!(" # Open http://localhost:8080");
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}
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