288 lines
8.6 KiB
Rust
288 lines
8.6 KiB
Rust
//! # RuvLLM WASM - Browser-Compatible LLM Inference Runtime
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//!
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//! This crate provides WebAssembly bindings for the RuvLLM inference runtime,
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//! enabling LLM inference directly in web browsers.
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//!
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//! ## Features
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//!
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//! - **KV Cache Management**: Two-tier KV cache with FP16 tail and quantized store
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//! - **Memory Pooling**: Efficient buffer reuse for minimal allocation overhead
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//! - **Chat Templates**: Support for Llama3, Mistral, Qwen, Phi, Gemma formats
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//! - **Intelligent Learning**: HNSW Router (150x faster), MicroLoRA (<1ms adaptation), SONA loops
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//! - **TypeScript-Friendly**: All types have getter/setter methods for easy JS interop
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//!
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//! ## Quick Start (JavaScript)
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//!
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//! ```javascript
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//! import init, { RuvLLMWasm, GenerateConfig, ChatMessageWasm, ChatTemplateWasm } from 'ruvllm-wasm';
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//!
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//! async function main() {
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//! // Initialize WASM module
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//! await init();
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//!
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//! // Create inference engine
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//! const llm = new RuvLLMWasm();
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//! llm.initialize();
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//!
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//! // Format a chat conversation
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//! const template = ChatTemplateWasm.llama3();
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//! const messages = [
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//! ChatMessageWasm.system("You are a helpful assistant."),
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//! ChatMessageWasm.user("What is WebAssembly?"),
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//! ];
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//! const prompt = template.format(messages);
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//!
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//! console.log("Formatted prompt:", prompt);
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//!
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//! // KV Cache management
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//! const config = new KvCacheConfigWasm();
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//! config.tailLength = 256;
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//! const kvCache = new KvCacheWasm(config);
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//!
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//! const stats = kvCache.stats();
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//! console.log("Cache stats:", stats.toJson());
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//!
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//! // Intelligent LLM with learning
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//! const intelligentConfig = new IntelligentConfigWasm();
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//! const intelligentLLM = new IntelligentLLMWasm(intelligentConfig);
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//!
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//! // Process with routing, LoRA, and SONA learning
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//! const embedding = new Float32Array(384);
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//! const output = intelligentLLM.process(embedding, "user query", 0.9);
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//!
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//! console.log("Intelligent stats:", intelligentLLM.stats());
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//! }
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//!
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//! main();
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//! ```
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//!
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//! ## Building
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//!
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//! ```bash
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//! # Build for browser (bundler target)
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//! wasm-pack build --target bundler
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//!
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//! # Build for Node.js
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//! wasm-pack build --target nodejs
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//!
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//! # Build for web (no bundler)
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//! wasm-pack build --target web
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//! ```
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//!
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//! ## Architecture
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//!
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//! ```text
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//! +-------------------+ +-------------------+
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//! | JavaScript/TS |---->| wasm-bindgen |
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//! | Application | | Bindings |
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//! +-------------------+ +-------------------+
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//! |
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//! v
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//! +-------------------+
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//! | RuvLLM Core |
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//! | (Rust WASM) |
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//! +-------------------+
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//! |
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//! v
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//! +-------------------+
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//! | Memory Pool |
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//! | KV Cache |
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//! | Chat Templates |
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//! +-------------------+
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//! ```
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//!
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//! ## Memory Management
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//!
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//! The WASM module uses efficient memory management strategies:
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//!
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//! - **Arena Allocator**: O(1) bump allocation for inference temporaries
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//! - **Buffer Pool**: Pre-allocated buffers in size classes (1KB-256KB)
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//! - **Two-Tier KV Cache**: FP32 tail + u8 quantized store
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//!
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//! ## Browser Compatibility
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//!
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//! Requires browsers with WebAssembly support:
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//! - Chrome 57+
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//! - Firefox 52+
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//! - Safari 11+
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//! - Edge 16+
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#![warn(missing_docs)]
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#![warn(clippy::all)]
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use wasm_bindgen::prelude::*;
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pub mod bindings;
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pub mod hnsw_router;
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pub mod micro_lora;
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pub mod sona_instant;
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pub mod utils;
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pub mod workers;
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#[cfg(feature = "webgpu")]
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pub mod webgpu;
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// Re-export all bindings
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pub use bindings::*;
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pub use hnsw_router::{HnswRouterWasm, PatternWasm, RouteResultWasm};
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pub use sona_instant::{SonaAdaptResultWasm, SonaConfigWasm, SonaInstantWasm, SonaStatsWasm};
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pub use utils::{error, log, now_ms, set_panic_hook, warn, Timer};
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// Re-export workers module
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pub use workers::{
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cross_origin_isolated, detect_capability_level, feature_summary, is_atomics_available,
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is_shared_array_buffer_available, optimal_worker_count, supports_parallel_inference,
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ParallelInference,
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};
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// Re-export WebGPU module when enabled
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#[cfg(feature = "webgpu")]
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pub use webgpu::*;
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/// Initialize the WASM module.
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///
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/// This should be called once at application startup to set up
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/// panic hooks and any other initialization.
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#[wasm_bindgen(start)]
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pub fn init() {
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utils::set_panic_hook();
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}
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/// Perform a simple health check.
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///
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/// Returns true if the WASM module is functioning correctly.
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#[wasm_bindgen(js_name = healthCheck)]
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pub fn health_check() -> bool {
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// Verify we can create basic structures
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let arena = bindings::InferenceArenaWasm::new(1024);
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arena.capacity() >= 1024
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}
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// ============================================================================
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// Integrated Intelligence System
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// ============================================================================
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// Note: This integration code is currently commented out pending full implementation
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// of micro_lora and sona_instant modules. The HNSW router can be used standalone.
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/*
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/// Configuration for the intelligent LLM system (combines all components)
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#[wasm_bindgen]
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pub struct IntelligentConfigWasm {
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router_config: HnswRouterConfigWasm,
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lora_config: MicroLoraConfigWasm,
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sona_config: SonaConfigWasm,
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}
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*/
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// Full integration system temporarily commented out - uncomment when micro_lora and sona_instant
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// are fully compatible with the new HnswRouterWasm API
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/*
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#[wasm_bindgen]
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impl IntelligentConfigWasm {
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... (implementation temporarily removed)
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}
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#[wasm_bindgen]
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pub struct IntelligentLLMWasm {
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... (implementation temporarily removed)
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}
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#[wasm_bindgen]
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impl IntelligentLLMWasm {
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... (implementation temporarily removed)
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}
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*/
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#[cfg(test)]
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mod tests {
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use super::*;
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#[test]
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fn test_generate_config_defaults() {
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let config = bindings::GenerateConfig::new();
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assert_eq!(config.max_tokens, 256);
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assert!((config.temperature - 0.7).abs() < 0.01);
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}
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#[test]
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fn test_chat_message() {
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let msg = bindings::ChatMessageWasm::user("Hello");
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assert_eq!(msg.role(), "user");
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assert_eq!(msg.content(), "Hello");
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}
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#[test]
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fn test_chat_template_detection() {
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let template = bindings::ChatTemplateWasm::detect_from_model_id("meta-llama/Llama-3-8B");
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assert_eq!(template.name(), "llama3");
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}
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#[test]
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fn test_kv_cache_config() {
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let mut config = bindings::KvCacheConfigWasm::new();
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config.set_tail_length(512);
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assert_eq!(config.tail_length(), 512);
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}
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#[test]
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fn test_arena_creation() {
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let arena = bindings::InferenceArenaWasm::new(4096);
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assert!(arena.capacity() >= 4096);
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assert_eq!(arena.used(), 0);
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}
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#[test]
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fn test_buffer_pool() {
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let pool = bindings::BufferPoolWasm::new();
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pool.prewarm_all(2);
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assert!(pool.hit_rate() >= 0.0);
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}
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// RuvLLMWasm::new() calls set_panic_hook which uses wasm-bindgen,
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// so skip this test on non-wasm32 targets
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#[cfg(target_arch = "wasm32")]
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#[test]
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fn test_ruvllm_wasm() {
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let mut llm = bindings::RuvLLMWasm::new();
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assert!(!llm.is_initialized());
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llm.initialize().unwrap();
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assert!(llm.is_initialized());
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}
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// Integration tests temporarily commented out
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/*
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#[test]
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fn test_micro_lora_integration() {
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let config = micro_lora::MicroLoraConfigWasm::new();
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let adapter = micro_lora::MicroLoraWasm::new(&config);
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let stats = adapter.stats();
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assert_eq!(stats.samples_seen(), 0);
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assert!(stats.memory_bytes() > 0);
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}
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#[test]
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fn test_intelligent_llm_creation() {
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let config = IntelligentConfigWasm::new();
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let llm = IntelligentLLMWasm::new(config).unwrap();
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let stats_json = llm.stats();
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assert!(stats_json.contains("router"));
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assert!(stats_json.contains("lora"));
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assert!(stats_json.contains("sona"));
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}
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#[test]
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fn test_intelligent_llm_learn_pattern() {
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let config = IntelligentConfigWasm::new();
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let mut llm = IntelligentLLMWasm::new(config).unwrap();
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let embedding = vec![0.1; 384];
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llm.learn_pattern(&embedding, "coder", "code_generation", "implement function", 0.85)
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.unwrap();
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let stats_json = llm.stats();
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assert!(stats_json.contains("totalPatterns"));
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}
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*/
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}
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