d803bfe2b1
git-subtree-dir: vendor/ruvector git-subtree-split: b64c21726f2bb37286d9ee36a7869fef60cc6900
560 lines
17 KiB
Markdown
560 lines
17 KiB
Markdown
# SONA LoRA-Ultra: Sub-100μs Adaptive Fine-Tuning
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## Ultra-Low Latency LoRA for Real-Time Self-Improvement
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---
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## 1. Architecture Overview
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### Traditional LoRA vs SONA LoRA-Ultra
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```
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TRADITIONAL LoRA SONA LoRA-ULTRA
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───────────────── ─────────────────
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• Offline training • Online per-request adaptation
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• Full batch updates • Single-sample micro-updates
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• GPU required • CPU SIMD optimized
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• Minutes to hours • <100 microseconds
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• Periodic deployment • Continuous integration
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```
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### Core Formula
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```
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Standard LoRA:
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W_adapted = W_frozen + ΔW
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ΔW = α · (A @ B)
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where A ∈ ℝ^(d×r), B ∈ ℝ^(r×k), r << min(d,k)
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SONA LoRA-Ultra Extension:
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W_adapted = W_frozen + α · (A @ B) + β · (A_micro @ B_micro)
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└─────────┘ └───────────────────┘
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Base LoRA Instant Micro-LoRA
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(rank 4-16) (rank 1-2)
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```
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---
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## 2. Two-Tier LoRA Architecture
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### Tier 1: Base LoRA (Updated Hourly)
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```rust
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/// Base LoRA adapter for major capability shifts
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pub struct BaseLoRA {
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/// Low-rank matrix A: d_model × rank
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pub a: Array2<f32>,
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/// Low-rank matrix B: rank × d_out
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pub b: Array2<f32>,
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/// Scaling factor
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pub alpha: f32,
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/// Rank (typically 4-16)
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pub rank: usize,
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/// Target layer indices
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pub target_layers: Vec<usize>,
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}
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impl BaseLoRA {
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/// Compute adapted weights (cached for inference)
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#[inline]
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pub fn delta_w(&self) -> Array2<f32> {
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let scale = self.alpha / self.rank as f32;
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scale * self.a.dot(&self.b)
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}
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/// Update from accumulated gradients (hourly)
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pub fn update(&mut self, grad_a: &Array2<f32>, grad_b: &Array2<f32>, lr: f32) {
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// SGD with momentum
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self.a = &self.a - lr * grad_a;
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self.b = &self.b - lr * grad_b;
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}
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}
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```
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### Tier 2: Micro-LoRA (Updated Per-Request)
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```rust
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/// Ultra-fast micro-adapter for instant learning
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pub struct MicroLoRA {
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/// Micro A: d_model × micro_rank (typically 1-2)
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pub a_micro: Array2<f32>,
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/// Micro B: micro_rank × d_out
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pub b_micro: Array2<f32>,
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/// Micro scaling (smaller than base)
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pub beta: f32,
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/// Micro rank (1-2 for speed)
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pub micro_rank: usize,
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/// Decay factor for temporal smoothing
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pub decay: f32,
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/// Momentum buffer
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momentum_a: Array2<f32>,
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momentum_b: Array2<f32>,
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}
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impl MicroLoRA {
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/// Ultra-fast single-sample update (<50μs target)
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#[inline]
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pub fn micro_update(&mut self, signal: &LearningSignal) {
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// Rank-1 outer product update
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let grad_direction = signal.to_gradient_direction();
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// Exponential moving average for stability
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self.momentum_a = self.decay * &self.momentum_a
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+ (1.0 - self.decay) * &grad_direction.a_component;
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self.momentum_b = self.decay * &self.momentum_b
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+ (1.0 - self.decay) * &grad_direction.b_component;
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// Apply micro-update
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self.a_micro = &self.a_micro + self.beta * &self.momentum_a;
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self.b_micro = &self.b_micro + self.beta * &self.momentum_b;
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}
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/// Periodic consolidation into base LoRA
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pub fn consolidate_to_base(&mut self, base: &mut BaseLoRA) {
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// Merge micro adaptations into base
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// Then reset micro to zero
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base.a = &base.a + &self.a_micro;
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base.b = &base.b + &self.b_micro;
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self.a_micro.fill(0.0);
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self.b_micro.fill(0.0);
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}
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}
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```
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---
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## 3. SIMD-Optimized LoRA Computation
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### AVX2 Accelerated Forward Pass
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```rust
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#[cfg(target_arch = "x86_64")]
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mod simd {
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use std::arch::x86_64::*;
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/// SIMD-optimized LoRA forward: x @ (W + A @ B)
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/// Fuses base weight multiplication with LoRA delta
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#[target_feature(enable = "avx2", enable = "fma")]
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pub unsafe fn lora_forward_avx2(
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x: &[f32], // Input: [batch, d_in]
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w_base: &[f32], // Base weights: [d_in, d_out]
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lora_a: &[f32], // LoRA A: [d_in, rank]
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lora_b: &[f32], // LoRA B: [rank, d_out]
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alpha: f32,
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d_in: usize,
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d_out: usize,
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rank: usize,
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output: &mut [f32], // Output: [batch, d_out]
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) {
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let scale = alpha / rank as f32;
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let scale_vec = _mm256_set1_ps(scale);
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// Step 1: Compute x @ A (input projection to rank space)
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let mut x_projected = vec![0.0f32; rank];
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for r in 0..rank {
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let mut sum = _mm256_setzero_ps();
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let mut i = 0;
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while i + 8 <= d_in {
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let x_vec = _mm256_loadu_ps(x.as_ptr().add(i));
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let a_vec = _mm256_loadu_ps(lora_a.as_ptr().add(r * d_in + i));
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sum = _mm256_fmadd_ps(x_vec, a_vec, sum);
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i += 8;
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}
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x_projected[r] = horizontal_sum_avx2(sum);
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// Handle remainder
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while i < d_in {
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x_projected[r] += x[i] * lora_a[r * d_in + i];
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i += 1;
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}
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}
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// Step 2: Compute (x @ W_base) + scale * (x_projected @ B)
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for j in 0..d_out {
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// Base weight contribution
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let mut sum = _mm256_setzero_ps();
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let mut i = 0;
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while i + 8 <= d_in {
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let x_vec = _mm256_loadu_ps(x.as_ptr().add(i));
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let w_vec = _mm256_loadu_ps(w_base.as_ptr().add(j * d_in + i));
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sum = _mm256_fmadd_ps(x_vec, w_vec, sum);
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i += 8;
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}
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let mut base_result = horizontal_sum_avx2(sum);
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while i < d_in {
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base_result += x[i] * w_base[j * d_in + i];
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i += 1;
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}
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// LoRA contribution
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let mut lora_result = 0.0f32;
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for r in 0..rank {
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lora_result += x_projected[r] * lora_b[j * rank + r];
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}
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output[j] = base_result + scale * lora_result;
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}
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}
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#[inline]
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unsafe fn horizontal_sum_avx2(v: __m256) -> f32 {
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let high = _mm256_extractf128_ps(v, 1);
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let low = _mm256_castps256_ps128(v);
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let sum128 = _mm_add_ps(high, low);
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let sum64 = _mm_add_ps(sum128, _mm_movehl_ps(sum128, sum128));
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let sum32 = _mm_add_ss(sum64, _mm_shuffle_ps(sum64, sum64, 1));
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_mm_cvtss_f32(sum32)
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}
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}
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```
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---
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## 4. Learning Signal Extraction
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### From Query Feedback to Gradient Direction
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```rust
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/// Learning signal extracted from each interaction
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#[derive(Clone)]
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pub struct LearningSignal {
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/// Query embedding
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pub query_embedding: Vec<f32>,
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/// Response quality score (0-1)
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pub quality_score: f32,
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/// User feedback (explicit)
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pub explicit_feedback: Option<FeedbackType>,
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/// Latency deviation from target
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pub latency_ratio: f32,
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/// Model tier used
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pub model_tier: ModelTier,
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/// Context tokens used
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pub context_tokens: usize,
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}
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impl LearningSignal {
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/// Convert signal to gradient direction for micro-LoRA
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pub fn to_gradient_direction(&self) -> GradientDirection {
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// Reward = quality * (1 - latency_penalty)
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let reward = self.quality_score * (2.0 - self.latency_ratio).max(0.0);
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// Direction = embedding * reward_sign
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let direction = if reward > 0.5 {
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// Reinforce current behavior
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1.0
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} else {
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// Explore alternative
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-0.1
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};
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// Scale by uncertainty (more learning when uncertain)
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let uncertainty = 1.0 - self.quality_score.abs();
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let learning_rate = 0.001 * (1.0 + uncertainty);
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GradientDirection {
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a_component: self.compute_a_gradient(direction, learning_rate),
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b_component: self.compute_b_gradient(direction, learning_rate),
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}
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}
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fn compute_a_gradient(&self, direction: f32, lr: f32) -> Array2<f32> {
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// Outer product of query embedding with hidden state
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// Approximated via reservoir-sampled historical embeddings
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let emb = Array1::from_vec(self.query_embedding.clone());
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let grad = direction * lr * outer_product(&emb, &self.get_hidden_direction());
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grad
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}
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fn compute_b_gradient(&self, direction: f32, lr: f32) -> Array2<f32> {
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// Output gradient based on prediction error
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let output_error = self.compute_output_error();
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direction * lr * output_error
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}
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}
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```
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---
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## 5. Target Layer Selection
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### Which Layers to Apply LoRA
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```rust
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/// Layer selection strategy for LoRA application
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pub enum LoRATargetStrategy {
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/// Apply to all attention layers (Q, K, V, O projections)
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AllAttention,
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/// Apply to FFN layers only
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AllFFN,
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/// Apply to output heads only (fastest, good for routing)
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OutputHeadsOnly,
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/// Apply to specific layers by index
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SpecificLayers(Vec<usize>),
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/// Adaptive: select based on gradient magnitude
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AdaptiveTopK(usize),
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}
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impl LoRATargetStrategy {
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/// For ultra-low latency: output heads only
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pub fn ultra_fast() -> Self {
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Self::OutputHeadsOnly
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}
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/// For moderate adaptation: attention Q and V
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pub fn attention_qv() -> Self {
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Self::SpecificLayers(vec![0, 2]) // Q and V typically
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}
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/// Select layers with highest gradient magnitude
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pub fn adaptive_top_k(k: usize) -> Self {
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Self::AdaptiveTopK(k)
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}
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}
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/// SONA default: Output heads for micro, attention for base
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pub const SONA_DEFAULT_TARGETS: [LoRATargetStrategy; 2] = [
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LoRATargetStrategy::OutputHeadsOnly, // Micro-LoRA
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LoRATargetStrategy::AllAttention, // Base LoRA
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];
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```
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---
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## 6. Memory-Efficient Storage
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### Quantized LoRA Matrices
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```rust
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/// Q4-quantized LoRA for memory efficiency
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pub struct QuantizedLoRA {
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/// Quantized A matrix (4-bit)
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pub a_q4: Q4Matrix,
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/// Quantized B matrix (4-bit)
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pub b_q4: Q4Matrix,
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/// Full-precision alpha
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pub alpha: f32,
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/// Full-precision scaling factors
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pub a_scales: Vec<f32>,
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pub b_scales: Vec<f32>,
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}
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impl QuantizedLoRA {
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/// Memory usage comparison
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///
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/// FP32 LoRA (rank 8, 768 dim):
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/// A: 768 × 8 × 4 bytes = 24.6 KB
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/// B: 8 × 768 × 4 bytes = 24.6 KB
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/// Total: ~50 KB per layer
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///
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/// Q4 LoRA (rank 8, 768 dim):
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/// A: 768 × 8 × 0.5 bytes = 3.1 KB
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/// B: 8 × 768 × 0.5 bytes = 3.1 KB
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/// Scales: 2 × 768 × 4 bytes = 6.1 KB
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/// Total: ~12 KB per layer (4x reduction)
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pub fn from_fp32(lora: &BaseLoRA) -> Self {
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Self {
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a_q4: Q4Matrix::quantize(&lora.a),
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b_q4: Q4Matrix::quantize(&lora.b),
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alpha: lora.alpha,
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a_scales: compute_scales(&lora.a),
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b_scales: compute_scales(&lora.b),
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}
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}
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/// Dequantize on-the-fly during forward pass
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#[inline]
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pub fn forward(&self, x: &[f32]) -> Vec<f32> {
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// Dequantize A, compute x @ A
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let projected = self.a_q4.matmul_dequant(x, &self.a_scales);
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// Dequantize B, compute projected @ B
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let output = self.b_q4.matmul_dequant(&projected, &self.b_scales);
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// Scale by alpha
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output.iter().map(|v| v * self.alpha).collect()
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}
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}
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```
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---
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## 7. Latency Breakdown
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### Target: <100μs Total LoRA Overhead
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```
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┌─────────────────────────────────────────────────────────────┐
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│ LoRA-ULTRA LATENCY BUDGET │
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├─────────────────────────────────────────────────────────────┤
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│ │
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│ Signal Extraction: 10μs ████░░░░░░░░░░░░░░░░░░░░░░░░ │
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│ Gradient Direction: 15μs ██████░░░░░░░░░░░░░░░░░░░░░░ │
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│ Micro-LoRA Update: 25μs ██████████░░░░░░░░░░░░░░░░░░ │
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│ Forward Pass Delta: 30μs ████████████░░░░░░░░░░░░░░░░ │
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│ Momentum Averaging: 10μs ████░░░░░░░░░░░░░░░░░░░░░░░░ │
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│ Memory Bookkeeping: 10μs ████░░░░░░░░░░░░░░░░░░░░░░░░ │
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│ ───── │
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│ TOTAL: ~100μs │
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│ │
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│ Amortized (batched): ~30μs per query │
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└─────────────────────────────────────────────────────────────┘
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```
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---
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## 8. Integration with FastGRNN Router
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### Router-Specific LoRA Configuration
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```rust
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/// LoRA configuration for FastGRNN router
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pub struct RouterLoRAConfig {
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/// Base LoRA for hidden state transformations
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pub hidden_lora: BaseLoRA,
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/// Micro LoRA for gate adjustments
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pub gate_micro_lora: MicroLoRA,
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/// Per-output-head LoRA adapters
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pub head_loras: Vec<BaseLoRA>,
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}
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impl RouterLoRAConfig {
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pub fn new(hidden_dim: usize, output_dims: &[usize]) -> Self {
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Self {
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hidden_lora: BaseLoRA::new(hidden_dim, hidden_dim, 8), // rank 8
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gate_micro_lora: MicroLoRA::new(hidden_dim, hidden_dim, 2), // rank 2
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head_loras: output_dims.iter()
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.map(|&dim| BaseLoRA::new(hidden_dim, dim, 4)) // rank 4
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.collect(),
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}
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}
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/// Apply LoRA to FastGRNN forward pass
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pub fn apply(&self, base_output: &FastGRNNOutput) -> FastGRNNOutput {
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let mut output = base_output.clone();
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// Apply hidden state LoRA
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output.hidden = self.hidden_lora.apply(&output.hidden);
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// Apply micro-LoRA to gates
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output.update_gate = self.gate_micro_lora.apply(&output.update_gate);
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// Apply per-head LoRA
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for (i, head_lora) in self.head_loras.iter().enumerate() {
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output.heads[i] = head_lora.apply(&output.heads[i]);
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}
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output
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}
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}
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```
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---
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## 9. Checkpointing and Recovery
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### Efficient LoRA State Management
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```rust
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/// LoRA checkpoint for persistence and recovery
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#[derive(Serialize, Deserialize)]
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pub struct LoRACheckpoint {
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/// Base LoRA matrices (serialized as FP16 for space)
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pub base_lora: SerializedLoRA,
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/// Micro LoRA state
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pub micro_lora: SerializedLoRA,
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/// Momentum buffers
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pub momentum_state: MomentumState,
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/// Training statistics
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pub stats: LoRAStats,
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/// Checkpoint version
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pub version: u32,
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/// Timestamp
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pub timestamp: i64,
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}
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impl LoRACheckpoint {
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/// Save checkpoint (async, non-blocking)
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pub async fn save_async(&self, path: &Path) -> Result<()> {
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let bytes = bincode::serialize(self)?;
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tokio::fs::write(path, &bytes).await?;
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Ok(())
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}
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/// Load checkpoint
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pub fn load(path: &Path) -> Result<Self> {
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let bytes = std::fs::read(path)?;
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Ok(bincode::deserialize(&bytes)?)
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}
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/// Incremental checkpoint (only changed matrices)
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pub fn save_incremental(&self, previous: &Self, path: &Path) -> Result<()> {
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let delta = self.compute_delta(previous);
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// Only save changed blocks
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delta.save(path)
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}
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}
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```
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---
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## 10. Benchmark Targets
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### Performance Validation
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```rust
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#[cfg(test)]
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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:
|
||
|
||
1. **Two-Tier Architecture**: Base LoRA (hourly) + Micro-LoRA (per-request)
|
||
2. **SIMD Optimization**: AVX2-accelerated forward pass
|
||
3. **Quantized Storage**: Q4 matrices for 4x memory reduction
|
||
4. **Smart Targeting**: Output heads for speed, attention for capability
|
||
5. **Momentum Smoothing**: Stable micro-updates with EMA
|
||
6. **Async Checkpointing**: Non-blocking persistence
|
||
|
||
This enables true real-time self-improvement where every query makes the model incrementally smarter.
|