17 KiB
17 KiB
SONA LoRA-Ultra: Sub-100μs Adaptive Fine-Tuning
Ultra-Low Latency LoRA for Real-Time Self-Improvement
1. Architecture Overview
Traditional LoRA vs SONA LoRA-Ultra
TRADITIONAL LoRA SONA LoRA-ULTRA
───────────────── ─────────────────
• Offline training • Online per-request adaptation
• Full batch updates • Single-sample micro-updates
• GPU required • CPU SIMD optimized
• Minutes to hours • <100 microseconds
• Periodic deployment • Continuous integration
Core Formula
Standard LoRA:
W_adapted = W_frozen + ΔW
ΔW = α · (A @ B)
where A ∈ ℝ^(d×r), B ∈ ℝ^(r×k), r << min(d,k)
SONA LoRA-Ultra Extension:
W_adapted = W_frozen + α · (A @ B) + β · (A_micro @ B_micro)
└─────────┘ └───────────────────┘
Base LoRA Instant Micro-LoRA
(rank 4-16) (rank 1-2)
2. Two-Tier LoRA Architecture
Tier 1: Base LoRA (Updated Hourly)
/// Base LoRA adapter for major capability shifts
pub struct BaseLoRA {
/// Low-rank matrix A: d_model × rank
pub a: Array2<f32>,
/// Low-rank matrix B: rank × d_out
pub b: Array2<f32>,
/// Scaling factor
pub alpha: f32,
/// Rank (typically 4-16)
pub rank: usize,
/// Target layer indices
pub target_layers: Vec<usize>,
}
impl BaseLoRA {
/// Compute adapted weights (cached for inference)
#[inline]
pub fn delta_w(&self) -> Array2<f32> {
let scale = self.alpha / self.rank as f32;
scale * self.a.dot(&self.b)
}
/// Update from accumulated gradients (hourly)
pub fn update(&mut self, grad_a: &Array2<f32>, grad_b: &Array2<f32>, lr: f32) {
// SGD with momentum
self.a = &self.a - lr * grad_a;
self.b = &self.b - lr * grad_b;
}
}
Tier 2: Micro-LoRA (Updated Per-Request)
/// Ultra-fast micro-adapter for instant learning
pub struct MicroLoRA {
/// Micro A: d_model × micro_rank (typically 1-2)
pub a_micro: Array2<f32>,
/// Micro B: micro_rank × d_out
pub b_micro: Array2<f32>,
/// Micro scaling (smaller than base)
pub beta: f32,
/// Micro rank (1-2 for speed)
pub micro_rank: usize,
/// Decay factor for temporal smoothing
pub decay: f32,
/// Momentum buffer
momentum_a: Array2<f32>,
momentum_b: Array2<f32>,
}
impl MicroLoRA {
/// Ultra-fast single-sample update (<50μs target)
#[inline]
pub fn micro_update(&mut self, signal: &LearningSignal) {
// Rank-1 outer product update
let grad_direction = signal.to_gradient_direction();
// Exponential moving average for stability
self.momentum_a = self.decay * &self.momentum_a
+ (1.0 - self.decay) * &grad_direction.a_component;
self.momentum_b = self.decay * &self.momentum_b
+ (1.0 - self.decay) * &grad_direction.b_component;
// Apply micro-update
self.a_micro = &self.a_micro + self.beta * &self.momentum_a;
self.b_micro = &self.b_micro + self.beta * &self.momentum_b;
}
/// Periodic consolidation into base LoRA
pub fn consolidate_to_base(&mut self, base: &mut BaseLoRA) {
// Merge micro adaptations into base
// Then reset micro to zero
base.a = &base.a + &self.a_micro;
base.b = &base.b + &self.b_micro;
self.a_micro.fill(0.0);
self.b_micro.fill(0.0);
}
}
3. SIMD-Optimized LoRA Computation
AVX2 Accelerated Forward Pass
#[cfg(target_arch = "x86_64")]
mod simd {
use std::arch::x86_64::*;
/// SIMD-optimized LoRA forward: x @ (W + A @ B)
/// Fuses base weight multiplication with LoRA delta
#[target_feature(enable = "avx2", enable = "fma")]
pub unsafe fn lora_forward_avx2(
x: &[f32], // Input: [batch, d_in]
w_base: &[f32], // Base weights: [d_in, d_out]
lora_a: &[f32], // LoRA A: [d_in, rank]
lora_b: &[f32], // LoRA B: [rank, d_out]
alpha: f32,
d_in: usize,
d_out: usize,
rank: usize,
output: &mut [f32], // Output: [batch, d_out]
) {
let scale = alpha / rank as f32;
let scale_vec = _mm256_set1_ps(scale);
// Step 1: Compute x @ A (input projection to rank space)
let mut x_projected = vec![0.0f32; rank];
for r in 0..rank {
let mut sum = _mm256_setzero_ps();
let mut i = 0;
while i + 8 <= d_in {
let x_vec = _mm256_loadu_ps(x.as_ptr().add(i));
let a_vec = _mm256_loadu_ps(lora_a.as_ptr().add(r * d_in + i));
sum = _mm256_fmadd_ps(x_vec, a_vec, sum);
i += 8;
}
x_projected[r] = horizontal_sum_avx2(sum);
// Handle remainder
while i < d_in {
x_projected[r] += x[i] * lora_a[r * d_in + i];
i += 1;
}
}
// Step 2: Compute (x @ W_base) + scale * (x_projected @ B)
for j in 0..d_out {
// Base weight contribution
let mut sum = _mm256_setzero_ps();
let mut i = 0;
while i + 8 <= d_in {
let x_vec = _mm256_loadu_ps(x.as_ptr().add(i));
let w_vec = _mm256_loadu_ps(w_base.as_ptr().add(j * d_in + i));
sum = _mm256_fmadd_ps(x_vec, w_vec, sum);
i += 8;
}
let mut base_result = horizontal_sum_avx2(sum);
while i < d_in {
base_result += x[i] * w_base[j * d_in + i];
i += 1;
}
// LoRA contribution
let mut lora_result = 0.0f32;
for r in 0..rank {
lora_result += x_projected[r] * lora_b[j * rank + r];
}
output[j] = base_result + scale * lora_result;
}
}
#[inline]
unsafe fn horizontal_sum_avx2(v: __m256) -> f32 {
let high = _mm256_extractf128_ps(v, 1);
let low = _mm256_castps256_ps128(v);
let sum128 = _mm_add_ps(high, low);
let sum64 = _mm_add_ps(sum128, _mm_movehl_ps(sum128, sum128));
let sum32 = _mm_add_ss(sum64, _mm_shuffle_ps(sum64, sum64, 1));
_mm_cvtss_f32(sum32)
}
}
4. Learning Signal Extraction
From Query Feedback to Gradient Direction
/// Learning signal extracted from each interaction
#[derive(Clone)]
pub struct LearningSignal {
/// Query embedding
pub query_embedding: Vec<f32>,
/// Response quality score (0-1)
pub quality_score: f32,
/// User feedback (explicit)
pub explicit_feedback: Option<FeedbackType>,
/// Latency deviation from target
pub latency_ratio: f32,
/// Model tier used
pub model_tier: ModelTier,
/// Context tokens used
pub context_tokens: usize,
}
impl LearningSignal {
/// Convert signal to gradient direction for micro-LoRA
pub fn to_gradient_direction(&self) -> GradientDirection {
// Reward = quality * (1 - latency_penalty)
let reward = self.quality_score * (2.0 - self.latency_ratio).max(0.0);
// Direction = embedding * reward_sign
let direction = if reward > 0.5 {
// Reinforce current behavior
1.0
} else {
// Explore alternative
-0.1
};
// Scale by uncertainty (more learning when uncertain)
let uncertainty = 1.0 - self.quality_score.abs();
let learning_rate = 0.001 * (1.0 + uncertainty);
GradientDirection {
a_component: self.compute_a_gradient(direction, learning_rate),
b_component: self.compute_b_gradient(direction, learning_rate),
}
}
fn compute_a_gradient(&self, direction: f32, lr: f32) -> Array2<f32> {
// Outer product of query embedding with hidden state
// Approximated via reservoir-sampled historical embeddings
let emb = Array1::from_vec(self.query_embedding.clone());
let grad = direction * lr * outer_product(&emb, &self.get_hidden_direction());
grad
}
fn compute_b_gradient(&self, direction: f32, lr: f32) -> Array2<f32> {
// Output gradient based on prediction error
let output_error = self.compute_output_error();
direction * lr * output_error
}
}
5. Target Layer Selection
Which Layers to Apply LoRA
/// Layer selection strategy for LoRA application
pub enum LoRATargetStrategy {
/// Apply to all attention layers (Q, K, V, O projections)
AllAttention,
/// Apply to FFN layers only
AllFFN,
/// Apply to output heads only (fastest, good for routing)
OutputHeadsOnly,
/// Apply to specific layers by index
SpecificLayers(Vec<usize>),
/// Adaptive: select based on gradient magnitude
AdaptiveTopK(usize),
}
impl LoRATargetStrategy {
/// For ultra-low latency: output heads only
pub fn ultra_fast() -> Self {
Self::OutputHeadsOnly
}
/// For moderate adaptation: attention Q and V
pub fn attention_qv() -> Self {
Self::SpecificLayers(vec![0, 2]) // Q and V typically
}
/// Select layers with highest gradient magnitude
pub fn adaptive_top_k(k: usize) -> Self {
Self::AdaptiveTopK(k)
}
}
/// SONA default: Output heads for micro, attention for base
pub const SONA_DEFAULT_TARGETS: [LoRATargetStrategy; 2] = [
LoRATargetStrategy::OutputHeadsOnly, // Micro-LoRA
LoRATargetStrategy::AllAttention, // Base LoRA
];
6. Memory-Efficient Storage
Quantized LoRA Matrices
/// Q4-quantized LoRA for memory efficiency
pub struct QuantizedLoRA {
/// Quantized A matrix (4-bit)
pub a_q4: Q4Matrix,
/// Quantized B matrix (4-bit)
pub b_q4: Q4Matrix,
/// Full-precision alpha
pub alpha: f32,
/// Full-precision scaling factors
pub a_scales: Vec<f32>,
pub b_scales: Vec<f32>,
}
impl QuantizedLoRA {
/// Memory usage comparison
///
/// FP32 LoRA (rank 8, 768 dim):
/// A: 768 × 8 × 4 bytes = 24.6 KB
/// B: 8 × 768 × 4 bytes = 24.6 KB
/// Total: ~50 KB per layer
///
/// Q4 LoRA (rank 8, 768 dim):
/// A: 768 × 8 × 0.5 bytes = 3.1 KB
/// B: 8 × 768 × 0.5 bytes = 3.1 KB
/// Scales: 2 × 768 × 4 bytes = 6.1 KB
/// Total: ~12 KB per layer (4x reduction)
pub fn from_fp32(lora: &BaseLoRA) -> Self {
Self {
a_q4: Q4Matrix::quantize(&lora.a),
b_q4: Q4Matrix::quantize(&lora.b),
alpha: lora.alpha,
a_scales: compute_scales(&lora.a),
b_scales: compute_scales(&lora.b),
}
}
/// Dequantize on-the-fly during forward pass
#[inline]
pub fn forward(&self, x: &[f32]) -> Vec<f32> {
// Dequantize A, compute x @ A
let projected = self.a_q4.matmul_dequant(x, &self.a_scales);
// Dequantize B, compute projected @ B
let output = self.b_q4.matmul_dequant(&projected, &self.b_scales);
// Scale by alpha
output.iter().map(|v| v * self.alpha).collect()
}
}
7. Latency Breakdown
Target: <100μs Total LoRA Overhead
┌─────────────────────────────────────────────────────────────┐
│ LoRA-ULTRA LATENCY BUDGET │
├─────────────────────────────────────────────────────────────┤
│ │
│ Signal Extraction: 10μs ████░░░░░░░░░░░░░░░░░░░░░░░░ │
│ Gradient Direction: 15μs ██████░░░░░░░░░░░░░░░░░░░░░░ │
│ Micro-LoRA Update: 25μs ██████████░░░░░░░░░░░░░░░░░░ │
│ Forward Pass Delta: 30μs ████████████░░░░░░░░░░░░░░░░ │
│ Momentum Averaging: 10μs ████░░░░░░░░░░░░░░░░░░░░░░░░ │
│ Memory Bookkeeping: 10μs ████░░░░░░░░░░░░░░░░░░░░░░░░ │
│ ───── │
│ TOTAL: ~100μs │
│ │
│ Amortized (batched): ~30μs per query │
└─────────────────────────────────────────────────────────────┘
8. Integration with FastGRNN Router
Router-Specific LoRA Configuration
/// LoRA configuration for FastGRNN router
pub struct RouterLoRAConfig {
/// Base LoRA for hidden state transformations
pub hidden_lora: BaseLoRA,
/// Micro LoRA for gate adjustments
pub gate_micro_lora: MicroLoRA,
/// Per-output-head LoRA adapters
pub head_loras: Vec<BaseLoRA>,
}
impl RouterLoRAConfig {
pub fn new(hidden_dim: usize, output_dims: &[usize]) -> Self {
Self {
hidden_lora: BaseLoRA::new(hidden_dim, hidden_dim, 8), // rank 8
gate_micro_lora: MicroLoRA::new(hidden_dim, hidden_dim, 2), // rank 2
head_loras: output_dims.iter()
.map(|&dim| BaseLoRA::new(hidden_dim, dim, 4)) // rank 4
.collect(),
}
}
/// Apply LoRA to FastGRNN forward pass
pub fn apply(&self, base_output: &FastGRNNOutput) -> FastGRNNOutput {
let mut output = base_output.clone();
// Apply hidden state LoRA
output.hidden = self.hidden_lora.apply(&output.hidden);
// Apply micro-LoRA to gates
output.update_gate = self.gate_micro_lora.apply(&output.update_gate);
// Apply per-head LoRA
for (i, head_lora) in self.head_loras.iter().enumerate() {
output.heads[i] = head_lora.apply(&output.heads[i]);
}
output
}
}
9. Checkpointing and Recovery
Efficient LoRA State Management
/// LoRA checkpoint for persistence and recovery
#[derive(Serialize, Deserialize)]
pub struct LoRACheckpoint {
/// Base LoRA matrices (serialized as FP16 for space)
pub base_lora: SerializedLoRA,
/// Micro LoRA state
pub micro_lora: SerializedLoRA,
/// Momentum buffers
pub momentum_state: MomentumState,
/// Training statistics
pub stats: LoRAStats,
/// Checkpoint version
pub version: u32,
/// Timestamp
pub timestamp: i64,
}
impl LoRACheckpoint {
/// Save checkpoint (async, non-blocking)
pub async fn save_async(&self, path: &Path) -> Result<()> {
let bytes = bincode::serialize(self)?;
tokio::fs::write(path, &bytes).await?;
Ok(())
}
/// Load checkpoint
pub fn load(path: &Path) -> Result<Self> {
let bytes = std::fs::read(path)?;
Ok(bincode::deserialize(&bytes)?)
}
/// Incremental checkpoint (only changed matrices)
pub fn save_incremental(&self, previous: &Self, path: &Path) -> Result<()> {
let delta = self.compute_delta(previous);
// Only save changed blocks
delta.save(path)
}
}
10. Benchmark Targets
Performance Validation
#[cfg(test)]
mod benchmarks {
use super::*;
use criterion::{black_box, Criterion};
/// Target: <50μs for micro-LoRA update
fn bench_micro_lora_update(c: &mut Criterion) {
let mut micro = MicroLoRA::new(768, 768, 2);
let signal = LearningSignal::random();
c.bench_function("micro_lora_update", |b| {
b.iter(|| {
micro.micro_update(black_box(&signal));
})
});
}
/// Target: <30μs for LoRA forward pass
fn bench_lora_forward(c: &mut Criterion) {
let lora = BaseLoRA::new(768, 768, 8);
let input = vec![0.0f32; 768];
c.bench_function("lora_forward", |b| {
b.iter(|| {
lora.forward(black_box(&input))
})
});
}
/// Target: <10μs for signal extraction
fn bench_signal_extraction(c: &mut Criterion) {
let query = "test query".to_string();
let response = "test response".to_string();
c.bench_function("signal_extraction", |b| {
b.iter(|| {
LearningSignal::extract(black_box(&query), black_box(&response))
})
});
}
}
Summary
SONA LoRA-Ultra achieves sub-100μs adaptive fine-tuning through:
- Two-Tier Architecture: Base LoRA (hourly) + Micro-LoRA (per-request)
- SIMD Optimization: AVX2-accelerated forward pass
- Quantized Storage: Q4 matrices for 4x memory reduction
- Smart Targeting: Output heads for speed, attention for capability
- Momentum Smoothing: Stable micro-updates with EMA
- Async Checkpointing: Non-blocking persistence
This enables true real-time self-improvement where every query makes the model incrementally smarter.