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/**
* RuVector Training Service
* Real WASM-accelerated neural training using @ruvector packages
*
* Features:
* - MicroLoRA: <1µs adaptation with rank-2 LoRA (2.3M ops/s)
* - SONA: Self-Optimizing Neural Architecture (624k learn/s, 60k search/s)
* - Flash Attention: 2.49x-7.47x speedup (9k ops/s)
* - Trajectory Buffer: Learning from success/failure
* - Contrastive Learning: InfoNCE loss
*
* Backward Compatible: All v1 APIs preserved, SONA adds new capabilities
*
* Created with ❤️ by ruv.io
*/
// Optional dependency — `@ruvector/learning-wasm` is not always installed in
// CI (install-safety / Build V3 configurations). A literal `import type … from
// '@ruvector/learning-wasm'` triggers TS2307 at build time when absent, even
// though every runtime callsite is behind try/catch. Alias the exported types
// to `any` locally — matches the pattern already used a few lines below for
// `@ruvector/attention` and preserves the runtime behaviour that the wasm
// bridge lazy-loads via dynamic import (#2586 / #2608 pattern).
// eslint-disable-next-line @typescript-eslint/no-explicit-any -- optional-dep type aliases
type WasmMicroLoRA = any;
// eslint-disable-next-line @typescript-eslint/no-explicit-any -- optional-dep type aliases
type WasmScopedLoRA = any;
// eslint-disable-next-line @typescript-eslint/no-explicit-any -- optional-dep type aliases
type WasmTrajectoryBuffer = any;
// @ruvector/attention types — use any since the NAPI exports vary across versions
type FlashAttention = any;
type MoEAttention = any;
type HyperbolicAttention = any;
type AdamWOptimizer = any;
type InfoNceLoss = any;
type CurriculumScheduler = any;
type HardNegativeMiner = any;
type BenchmarkResult = any;
// SONA Engine type (from @ruvector/sona)
interface SonaEngineInstance {
forceLearn(embedding: Float32Array, reward: number): void;
findPatterns(embedding: number[], k: number): unknown[];
tick(): void;
getStats(): string;
isEnabled(): boolean;
setEnabled(enabled: boolean): void;
flush(): void;
}
/**
* ESM/CJS interop helper — handles `.default` for CJS modules.
* Uses `'default' in mod` check which is safer than `mod.default || mod`.
*/
async function importWithInterop<T = any>(packageName: string): Promise<T> {
const mod = await import(packageName);
return ('default' in mod) ? (mod as any).default : mod;
}
// Lazy-loaded WASM modules
let microLoRA: WasmMicroLoRA | null = null;
let scopedLoRA: WasmScopedLoRA | null = null;
let trajectoryBuffer: WasmTrajectoryBuffer | null = null;
let flashAttention: FlashAttention | null = null;
let moeAttention: MoEAttention | null = null;
let hyperbolicAttention: HyperbolicAttention | null = null;
let optimizer: AdamWOptimizer | null = null;
let contrastiveLoss: InfoNceLoss | null = null;
let curriculum: CurriculumScheduler | null = null;
let hardMiner: HardNegativeMiner | null = null;
// SONA engine (optional enhancement)
let sonaEngine: SonaEngineInstance | null = null;
let sonaAvailable = false;
// Training state
let initialized = false;
let totalAdaptations = 0;
let totalForwards = 0;
let totalSonaLearns = 0;
let totalSonaSearches = 0;
let lastBenchmark: BenchmarkResult[] | null = null;
// Backend tracking
let activeBackend: 'wasm' | 'js-fallback' = 'js-fallback';
/**
* Get which backend is active for training
*/
export function getActiveBackend(): 'wasm' | 'js-fallback' {
return activeBackend;
}
export interface TrainingConfig {
dim?: number; // Embedding dimension (max 256)
learningRate?: number; // Learning rate
alpha?: number; // LoRA scaling factor
trajectoryCapacity?: number;
useFlashAttention?: boolean;
useMoE?: boolean;
useHyperbolic?: boolean;
totalSteps?: number; // For curriculum
warmupSteps?: number;
// SONA options (v2 enhancement)
useSona?: boolean; // Enable SONA self-optimizing neural architecture
sonaRank?: number; // SONA LoRA rank (default: 4)
}
export interface TrainingResult {
success: boolean;
adaptationCount: bigint;
forwardCount: bigint;
deltaNorm: number;
trajectoryStats?: {
successRate: number;
meanImprovement: number;
bestImprovement: number;
totalCount: bigint;
};
benchmark?: BenchmarkResult[];
}
/**
* Pure-JS fallback implementations for when WASM is unavailable.
* These provide the same API surface with basic linear algebra.
*/
class JsMicroLoRA implements Pick<WasmMicroLoRA, 'adapt_array' | 'adapt_count' | 'param_count' | 'forward_array' | 'forward_count' | 'adapt_with_reward' | 'delta_norm' | 'dim' | 'reset' | 'free'> {
private _dim: number;
private _alpha: number;
private _lr: number;
private _adaptCount = 0n;
private _forwardCount = 0n;
private _deltaNorm = 0;
private _A: Float32Array; // Low-rank A (rank x dim)
private _B: Float32Array; // Low-rank B (dim x rank)
private readonly RANK = 2;
constructor(dim: number, alpha: number, lr: number) {
this._dim = dim;
this._alpha = alpha;
this._lr = lr;
this._A = new Float32Array(this.RANK * dim);
this._B = new Float32Array(dim * this.RANK);
// Xavier initialization
const scale = Math.sqrt(2 / (dim + this.RANK));
for (let i = 0; i < this._A.length; i++) this._A[i] = (Math.random() - 0.5) * scale;
for (let i = 0; i < this._B.length; i++) this._B[i] = (Math.random() - 0.5) * scale;
}
adapt_array(gradient: Float32Array): void {
// Simple gradient update on low-rank matrices
let norm = 0;
for (let i = 0; i < Math.min(gradient.length, this._A.length); i++) {
const delta = -this._lr * gradient[i % gradient.length] * this._alpha;
this._A[i] += delta;
norm += delta * delta;
}
this._deltaNorm = Math.sqrt(norm);
this._adaptCount++;
}
adapt_count(): bigint { return this._adaptCount; }
param_count(): number { return this._A.length + this._B.length; }
forward_array(input: Float32Array): Float32Array {
const output = new Float32Array(this._dim);
// y = x + alpha * B @ A @ x (simplified low-rank)
for (let i = 0; i < this._dim; i++) {
output[i] = input[i];
let sum = 0;
for (let r = 0; r < this.RANK; r++) {
let dot = 0;
for (let j = 0; j < this._dim; j++) {
dot += this._A[r * this._dim + j] * input[j];
}
sum += this._B[i * this.RANK + r] * dot;
}
output[i] += this._alpha * sum;
}
this._forwardCount++;
return output;
}
forward_count(): bigint { return this._forwardCount; }
adapt_with_reward(improvement: number): void {
const scale = improvement * this._lr * this._alpha;
let norm = 0;
for (let i = 0; i < this._A.length; i++) {
const delta = scale * (Math.random() - 0.5);
this._A[i] += delta;
norm += delta * delta;
}
this._deltaNorm = Math.sqrt(norm);
this._adaptCount++;
}
delta_norm(): number { return this._deltaNorm; }
dim(): number { return this._dim; }
reset(): void {
this._A.fill(0);
this._B.fill(0);
this._adaptCount = 0n;
this._forwardCount = 0n;
this._deltaNorm = 0;
}
free(): void { /* no-op for JS */ }
}
class JsScopedLoRA implements Pick<WasmScopedLoRA, 'adapt_array' | 'adapt_count' | 'forward_array' | 'forward_count' | 'adapt_with_reward' | 'delta_norm' | 'total_adapt_count' | 'total_forward_count' | 'set_category_fallback' | 'reset_all' | 'reset_scope' | 'free'> {
private adapters: Map<number, JsMicroLoRA> = new Map();
private _dim: number;
private _alpha: number;
private _lr: number;
private _fallback = false;
constructor(dim: number, alpha: number, lr: number) {
this._dim = dim;
this._alpha = alpha;
this._lr = lr;
}
private getAdapter(opType: number): JsMicroLoRA {
if (!this.adapters.has(opType)) {
if (this._fallback && opType > 0 && this.adapters.has(0)) {
return this.adapters.get(0)!;
}
this.adapters.set(opType, new JsMicroLoRA(this._dim, this._alpha, this._lr));
}
return this.adapters.get(opType)!;
}
adapt_array(opType: number, gradient: Float32Array): void { this.getAdapter(opType).adapt_array(gradient); }
adapt_count(opType: number): bigint { return this.getAdapter(opType).adapt_count(); }
forward_array(opType: number, input: Float32Array): Float32Array { return this.getAdapter(opType).forward_array(input); }
forward_count(opType: number): bigint { return this.getAdapter(opType).forward_count(); }
adapt_with_reward(opType: number, improvement: number): void { this.getAdapter(opType).adapt_with_reward(improvement); }
delta_norm(opType: number): number { return this.getAdapter(opType).delta_norm(); }
set_category_fallback(enabled: boolean): void { this._fallback = enabled; }
total_adapt_count(): bigint {
let total = 0n;
for (const a of this.adapters.values()) total += a.adapt_count();
return total;
}
total_forward_count(): bigint {
let total = 0n;
for (const a of this.adapters.values()) total += a.forward_count();
return total;
}
reset_all(): void { this.adapters.clear(); }
reset_scope(opType: number): void { this.adapters.delete(opType); }
free(): void { this.adapters.clear(); }
}
class JsTrajectoryBuffer implements Pick<WasmTrajectoryBuffer, 'record' | 'is_empty' | 'total_count' | 'success_rate' | 'mean_improvement' | 'best_improvement' | 'high_quality_count' | 'variance' | 'reset' | 'free'> {
private entries: { improvement: number }[] = [];
private capacity: number;
constructor(capacity: number, _dim: number) {
this.capacity = capacity;
}
record(_embedding: Float32Array, _opType: number, _attType: number, executionMs: number, baselineMs: number): void {
const improvement = baselineMs > 0 ? (baselineMs - executionMs) / baselineMs : 0;
if (this.entries.length >= this.capacity) this.entries.shift();
this.entries.push({ improvement });
}
is_empty(): boolean { return this.entries.length === 0; }
total_count(): bigint { return BigInt(this.entries.length); }
success_rate(): number {
if (this.entries.length === 0) return 0;
return this.entries.filter(e => e.improvement > 0).length / this.entries.length;
}
mean_improvement(): number {
if (this.entries.length === 0) return 0;
return this.entries.reduce((s, e) => s + e.improvement, 0) / this.entries.length;
}
best_improvement(): number {
if (this.entries.length === 0) return 0;
return Math.max(...this.entries.map(e => e.improvement));
}
high_quality_count(threshold: number): number {
return this.entries.filter(e => e.improvement > threshold).length;
}
variance(): number {
if (this.entries.length < 2) return 0;
const mean = this.mean_improvement();
return this.entries.reduce((s, e) => s + (e.improvement - mean) ** 2, 0) / (this.entries.length - 1);
}
reset(): void { this.entries = []; }
free(): void { this.entries = []; }
}
/**
* Initialize the RuVector training system.
* Attempts to load @ruvector/learning-wasm for WASM-accelerated training.
* Falls back to a pure-JS implementation if WASM is unavailable.
*/
export async function initializeTraining(config: TrainingConfig = {}): Promise<{
success: boolean;
features: string[];
backend: 'wasm' | 'js-fallback';
error?: string;
}> {
const features: string[] = [];
const dim = Math.min(config.dim || 256, 256); // Max 256 for WASM
const lr = config.learningRate || 0.01;
const alpha = config.alpha || 0.1;
// --- Attempt WASM backend first ---
let wasmLoaded = false;
try {
const fs = await import('fs');
const { createRequire } = await import('module');
const require = createRequire(import.meta.url);
// Indirect the optional-dep specifier through a string variable so tsc
// doesn't statically resolve `@ruvector/learning-wasm` at build time
// (TS2307 when the optional dep is absent — #2586 pattern).
const learningWasmPkg: string = '@ruvector/learning-wasm';
const wasmPath = require.resolve(`${learningWasmPkg}/ruvector_learning_wasm_bg.wasm`);
const wasmBuffer = fs.readFileSync(wasmPath);
// eslint-disable-next-line @typescript-eslint/no-explicit-any -- optional-dep dynamic import
const learningWasm: any = await import(learningWasmPkg);
learningWasm.initSync({ module: wasmBuffer });
microLoRA = new learningWasm.WasmMicroLoRA(dim, alpha, lr);
features.push(`MicroLoRA/WASM (${dim}-dim, <1μs adaptation)`);
scopedLoRA = new learningWasm.WasmScopedLoRA(dim, alpha, lr);
scopedLoRA.set_category_fallback(true);
features.push('ScopedLoRA/WASM (17 operators)');
trajectoryBuffer = new learningWasm.WasmTrajectoryBuffer(
config.trajectoryCapacity || 10000,
dim
);
features.push('TrajectoryBuffer/WASM');
activeBackend = 'wasm';
wasmLoaded = true;
} catch (wasmError) {
// WASM not available - fall back to JS implementation
const reason = wasmError instanceof Error ? wasmError.message : String(wasmError);
console.warn(`[ruvector] WASM backend unavailable (${reason}), using JS fallback`);
microLoRA = new JsMicroLoRA(dim, alpha, lr) as unknown as WasmMicroLoRA;
features.push(`MicroLoRA/JS (${dim}-dim, JS fallback)`);
scopedLoRA = new JsScopedLoRA(dim, alpha, lr) as unknown as WasmScopedLoRA;
(scopedLoRA as any).set_category_fallback(true);
features.push('ScopedLoRA/JS (17 operators)');
trajectoryBuffer = new JsTrajectoryBuffer(
config.trajectoryCapacity || 10000,
dim
) as unknown as WasmTrajectoryBuffer;
features.push('TrajectoryBuffer/JS');
activeBackend = 'js-fallback';
}
// --- Attention mechanisms (optional, independent of WASM) ---
try {
const attention: any = await importWithInterop('@ruvector/attention');
if (config.useFlashAttention !== false) {
flashAttention = new attention.FlashAttention(dim, 64);
features.push('FlashAttention');
}
if (config.useMoE) {
moeAttention = attention.MoEAttention.simple(dim, 8, 2);
features.push('MoE (8 experts, top-2)');
}
if (config.useHyperbolic) {
hyperbolicAttention = new attention.HyperbolicAttention(dim, 1.0);
features.push('HyperbolicAttention');
}
optimizer = new attention.AdamWOptimizer(lr, 0.9, 0.999, 1e-8, 0.01);
features.push('AdamW Optimizer');
contrastiveLoss = new attention.InfoNceLoss(0.07);
features.push('InfoNCE Loss');
if (config.totalSteps) {
curriculum = new attention.CurriculumScheduler(
config.totalSteps,
config.warmupSteps || Math.floor(config.totalSteps * 0.1)
);
features.push('Curriculum Learning');
}
try {
hardMiner = new attention.HardNegativeMiner(5, 'semi_hard');
features.push('Hard Negative Mining');
} catch {
// Mining not available, continue without it
}
} catch (attentionError) {
// @ruvector/attention not available - attention features skipped
const reason = attentionError instanceof Error ? attentionError.message : String(attentionError);
console.warn(`[ruvector] @ruvector/attention unavailable (${reason}), attention features disabled`);
}
// --- SONA (optional, backward compatible) ---
if (config.useSona !== false) {
try {
const sona = await importWithInterop('@ruvector/sona');
const sonaRank = config.sonaRank || 4;
sonaEngine = new sona.SonaEngine(dim, sonaRank, alpha, lr) as SonaEngineInstance;
sonaAvailable = true;
features.push(`SONA (${dim}-dim, rank-${sonaRank}, 624k learn/s)`);
} catch (sonaError) {
sonaAvailable = false;
if (config.useSona === true) {
console.warn('SONA requested but not available:', sonaError);
}
}
}
initialized = true;
return { success: true, features, backend: activeBackend };
}
/**
* Operator types for scoped LoRA (0-16)
*/
export const OperatorType = {
GENERAL: 0,
ATTENTION: 1,
MLP: 2,
EMBEDDING: 3,
NORMALIZATION: 4,
PROJECTION: 5,
POOLING: 6,
CONVOLUTION: 7,
RECURRENT: 8,
ROUTING: 9,
MEMORY: 10,
REASONING: 11,
COORDINATION: 12,
OPTIMIZATION: 13,
SECURITY: 14,
TESTING: 15,
DEBUGGING: 16,
} as const;
/**
* Train a pattern with MicroLoRA
*/
export async function trainPattern(
embedding: Float32Array,
gradient: Float32Array,
operatorType?: number
): Promise<{ deltaNorm: number; adaptCount: bigint }> {
if (!initialized || !microLoRA) {
throw new Error('Training system not initialized');
}
// Use scoped LoRA if operator type specified
if (operatorType !== undefined && scopedLoRA) {
scopedLoRA.adapt_array(operatorType, gradient);
return {
deltaNorm: scopedLoRA.delta_norm(operatorType),
adaptCount: scopedLoRA.adapt_count(operatorType),
};
}
// Standard MicroLoRA adaptation
microLoRA.adapt_array(gradient);
totalAdaptations++;
return {
deltaNorm: microLoRA.delta_norm(),
adaptCount: microLoRA.adapt_count(),
};
}
/**
* Forward pass through LoRA
*/
export function forward(
input: Float32Array,
operatorType?: number
): Float32Array {
if (!initialized || !microLoRA) {
throw new Error('Training system not initialized');
}
totalForwards++;
if (operatorType !== undefined && scopedLoRA) {
return scopedLoRA.forward_array(operatorType, input);
}
return microLoRA.forward_array(input);
}
/**
* Reward-based adaptation (reinforcement learning)
*/
export function adaptWithReward(
improvement: number,
operatorType?: number
): void {
if (!initialized) {
throw new Error('Training system not initialized');
}
if (operatorType !== undefined && scopedLoRA) {
scopedLoRA.adapt_with_reward(operatorType, improvement);
} else if (microLoRA) {
microLoRA.adapt_with_reward(improvement);
}
totalAdaptations++;
}
/**
* Record a learning trajectory
*/
export function recordTrajectory(
embedding: Float32Array,
operatorType: number,
attentionType: number,
executionMs: number,
baselineMs: number
): void {
if (!trajectoryBuffer) {
throw new Error('Trajectory buffer not initialized');
}
trajectoryBuffer.record(
embedding,
operatorType,
attentionType,
executionMs,
baselineMs
);
}
/**
* Get trajectory statistics
*/
export function getTrajectoryStats(): {
successRate: number;
meanImprovement: number;
bestImprovement: number;
totalCount: bigint;
highQualityCount: number;
variance: number;
} | null {
if (!trajectoryBuffer || trajectoryBuffer.is_empty()) {
return null;
}
return {
successRate: trajectoryBuffer.success_rate(),
meanImprovement: trajectoryBuffer.mean_improvement(),
bestImprovement: trajectoryBuffer.best_improvement(),
totalCount: trajectoryBuffer.total_count(),
highQualityCount: trajectoryBuffer.high_quality_count(0.1),
variance: trajectoryBuffer.variance(),
};
}
/**
* Compute attention with Flash Attention (2.49x-7.47x faster)
*/
export function computeFlashAttention(
query: Float32Array,
keys: Float32Array[],
values: Float32Array[]
): Float32Array {
if (!flashAttention) {
throw new Error('Flash attention not initialized');
}
return flashAttention.computeRaw(query, keys, values);
}
/**
* Compute MoE routing
*/
export function computeMoEAttention(
query: Float32Array,
keys: Float32Array[],
values: Float32Array[]
): Float32Array {
if (!moeAttention) {
throw new Error('MoE attention not initialized');
}
return moeAttention.computeRaw(query, keys, values);
}
/**
* Compute hyperbolic attention (for hierarchical patterns)
*/
export function computeHyperbolicAttention(
query: Float32Array,
keys: Float32Array[],
values: Float32Array[]
): Float32Array {
if (!hyperbolicAttention) {
throw new Error('Hyperbolic attention not initialized');
}
return hyperbolicAttention.computeRaw(query, keys, values);
}
/**
* Compute contrastive loss for training
*/
export function computeContrastiveLoss(
anchor: Float32Array,
positives: Float32Array[],
negatives: Float32Array[]
): { loss: number; gradient: Float32Array } {
if (!contrastiveLoss) {
throw new Error('Contrastive loss not initialized');
}
const loss = contrastiveLoss.compute(anchor, positives, negatives);
const gradient = contrastiveLoss.backward(anchor, positives, negatives);
return { loss, gradient };
}
/**
* Optimizer step
*/
export function optimizerStep(
params: Float32Array,
gradients: Float32Array
): Float32Array {
if (!optimizer) {
throw new Error('Optimizer not initialized');
}
return optimizer.step(params, gradients);
}
/**
* Get curriculum difficulty for current step
*/
export function getCurriculumDifficulty(step: number): number {
if (!curriculum) {
return 1.0; // Full difficulty if no curriculum
}
return curriculum.getDifficulty(step);
}
/**
* Mine hard negatives for better training
*/
export function mineHardNegatives(
anchor: Float32Array,
candidates: Float32Array[]
): number[] {
if (!hardMiner) {
throw new Error('Hard negative miner not initialized');
}
return hardMiner.mine(anchor, candidates);
}
/**
* Benchmark the training system
*/
export async function benchmarkTraining(
dim?: number,
iterations?: number
): Promise<BenchmarkResult[]> {
const attention: any = await importWithInterop('@ruvector/attention');
lastBenchmark = attention.benchmarkAttention(dim || 256, 100, iterations || 1000);
return lastBenchmark ?? [];
}
// ============================================
// SONA Functions (v2 enhancement, optional)
// ============================================
/**
* Check if SONA is available
*/
export function isSonaAvailable(): boolean {
return sonaAvailable && sonaEngine !== null;
}
/**
* Force-learn a pattern with SONA (1.6μs, 624k ops/s)
* This is a one-shot learning mechanism for immediate pattern storage
*/
export function sonaForceLearn(
embedding: Float32Array,
reward: number
): void {
if (!sonaEngine) {
throw new Error('SONA not initialized. Call initializeTraining with useSona: true');
}
sonaEngine.forceLearn(embedding, reward);
totalSonaLearns++;
}
/**
* Search for similar patterns with SONA (16.7μs, 60k searches/s)
* Returns the k most similar patterns from the pattern bank
*/
export function sonaFindPatterns(
embedding: Float32Array,
k: number = 5
): unknown[] {
if (!sonaEngine) {
throw new Error('SONA not initialized. Call initializeTraining with useSona: true');
}
// SONA requires Array, not Float32Array
const embeddingArray = Array.from(embedding);
totalSonaSearches++;
return sonaEngine.findPatterns(embeddingArray, k);
}
/**
* Process SONA background tasks (0.13μs, 7.5M ticks/s)
* Call periodically to process background learning and consolidation
*/
export function sonaTick(): void {
if (!sonaEngine) {
return; // Silent no-op if SONA not available
}
sonaEngine.tick();
}
/**
* Get SONA statistics
*/
export function getSonaStats(): {
available: boolean;
enabled: boolean;
stats: Record<string, unknown> | null;
totalLearns: number;
totalSearches: number;
} {
if (!sonaEngine) {
return {
available: false,
enabled: false,
stats: null,
totalLearns: totalSonaLearns,
totalSearches: totalSonaSearches,
};
}
try {
const statsJson = sonaEngine.getStats();
const stats = JSON.parse(statsJson);
return {
available: true,
enabled: sonaEngine.isEnabled(),
stats,
totalLearns: totalSonaLearns,
totalSearches: totalSonaSearches,
};
} catch {
return {
available: true,
enabled: false,
stats: null,
totalLearns: totalSonaLearns,
totalSearches: totalSonaSearches,
};
}
}
/**
* Enable/disable SONA learning
*/
export function setSonaEnabled(enabled: boolean): void {
if (!sonaEngine) {
return;
}
sonaEngine.setEnabled(enabled);
}
/**
* Flush SONA buffers (persist any pending patterns)
*/
export function sonaFlush(): void {
if (!sonaEngine) {
return;
}
sonaEngine.flush();
}
/**
* Get training statistics
*/
export function getTrainingStats(): {
initialized: boolean;
backend: 'wasm' | 'js-fallback';
totalAdaptations: number;
totalForwards: number;
microLoraStats?: {
paramCount: number;
adaptCount: bigint;
forwardCount: bigint;
deltaNorm: number;
};
scopedLoraStats?: {
totalAdaptCount: bigint;
totalForwardCount: bigint;
};
trajectoryStats?: ReturnType<typeof getTrajectoryStats>;
sonaStats?: ReturnType<typeof getSonaStats>;
lastBenchmark?: BenchmarkResult[];
} {
const stats: ReturnType<typeof getTrainingStats> = {
initialized,
backend: activeBackend,
totalAdaptations,
totalForwards,
};
if (microLoRA) {
stats.microLoraStats = {
paramCount: microLoRA.param_count(),
adaptCount: microLoRA.adapt_count(),
forwardCount: microLoRA.forward_count(),
deltaNorm: microLoRA.delta_norm(),
};
}
if (scopedLoRA) {
stats.scopedLoraStats = {
totalAdaptCount: scopedLoRA.total_adapt_count(),
totalForwardCount: scopedLoRA.total_forward_count(),
};
}
if (trajectoryBuffer && !trajectoryBuffer.is_empty()) {
stats.trajectoryStats = getTrajectoryStats();
}
// Include SONA stats if available
if (sonaAvailable) {
stats.sonaStats = getSonaStats();
}
if (lastBenchmark) {
stats.lastBenchmark = lastBenchmark;
}
return stats;
}
/**
* Reset the training system
*/
export function resetTraining(): void {
if (microLoRA) microLoRA.reset();
if (scopedLoRA) scopedLoRA.reset_all();
if (trajectoryBuffer) trajectoryBuffer.reset();
// Reset SONA stats (engine doesn't have reset, just flush)
if (sonaEngine) {
sonaEngine.flush();
}
totalAdaptations = 0;
totalForwards = 0;
totalSonaLearns = 0;
totalSonaSearches = 0;
activeBackend = 'js-fallback';
}
/**
* Export trained weights
*/
export function exportWeights(): {
dim: number;
deltaNorm: number;
adaptCount: bigint;
trajectoryStats: ReturnType<typeof getTrajectoryStats>;
} | null {
if (!initialized || !microLoRA) {
return null;
}
return {
dim: microLoRA.dim(),
deltaNorm: microLoRA.delta_norm(),
adaptCount: microLoRA.adapt_count(),
trajectoryStats: getTrajectoryStats(),
};
}
/**
* Cleanup resources
*/
export function cleanup(): void {
if (microLoRA) {
microLoRA.free();
microLoRA = null;
}
if (scopedLoRA) {
scopedLoRA.free();
scopedLoRA = null;
}
if (trajectoryBuffer) {
trajectoryBuffer.free();
trajectoryBuffer = null;
}
// Cleanup SONA
if (sonaEngine) {
sonaEngine.flush();
sonaEngine = null;
sonaAvailable = false;
}
flashAttention = null;
moeAttention = null;
hyperbolicAttention = null;
optimizer = null;
contrastiveLoss = null;
curriculum = null;
hardMiner = null;
initialized = false;
totalAdaptations = 0;
totalForwards = 0;
totalSonaLearns = 0;
totalSonaSearches = 0;
lastBenchmark = null;
}
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