# 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)

```rust
/// 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)

```rust
/// 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

```rust
#[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

```rust
/// 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

```rust
/// 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

```rust
/// 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

```rust
/// 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

```rust
/// 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

```rust
#[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:

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.