779 lines
25 KiB
Rust
779 lines
25 KiB
Rust
//! RuvLLM ESP32 - Complete Flashable Implementation
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//!
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//! Full-featured LLM inference engine for ESP32 with:
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//! - INT8/Binary quantized transformer inference
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//! - Product quantization (8-32x compression)
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//! - MicroLoRA on-device adaptation
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//! - Sparse attention patterns
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//! - HNSW vector search (1000+ vectors)
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//! - Semantic memory with context
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//! - RAG (Retrieval-Augmented Generation)
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//! - Anomaly detection
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//! - Multi-chip federation
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//! - Pipeline/tensor parallelism
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//! - Speculative decoding
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//!
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//! Flash with: espflash flash --monitor --port COM6
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#[cfg(feature = "esp32")]
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use esp_idf_svc::hal::prelude::*;
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#[cfg(feature = "esp32")]
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use esp_idf_svc::hal::uart::{self, UartDriver};
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#[cfg(feature = "esp32")]
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use esp_idf_svc::hal::gpio;
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#[cfg(feature = "esp32")]
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use esp_idf_svc::sys::link_patches;
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use heapless::Vec as HVec;
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use heapless::String as HString;
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use log::*;
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// Import library modules
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use ruvllm_esp32::prelude::*;
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use ruvllm_esp32::{
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HNSWConfig, RAGConfig, MemoryType, DraftVerifyConfig,
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PipelineConfig, PipelineRole, AnomalyConfig, PQConfig, LoRAConfig, PruningConfig,
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AttentionPattern, DistanceMetric, euclidean_distance_i8,
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};
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// ============================================================================
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// CONFIGURATION
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// ============================================================================
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const VOCAB_SIZE: usize = 256;
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const EMBED_DIM: usize = 64;
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const NUM_LAYERS: usize = 2;
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const NUM_HEADS: usize = 4;
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const MAX_SEQ_LEN: usize = 32;
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const MAX_KNOWLEDGE: usize = 64;
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const HNSW_CAPACITY: usize = 256;
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// ============================================================================
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// QUANTIZED TYPES
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// ============================================================================
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#[derive(Clone)]
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struct QuantizedWeights {
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data: HVec<i8, 4096>,
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scale: i32,
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zero_point: i8,
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}
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impl QuantizedWeights {
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fn new(size: usize) -> Self {
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let mut data = HVec::new();
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for i in 0..size.min(4096) {
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let val = ((i * 17 + 31) % 256) as i8 - 64;
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let _ = data.push(val);
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}
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Self { data, scale: 128, zero_point: 0 }
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}
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}
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// ============================================================================
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// EMBEDDING TABLE
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// ============================================================================
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struct EmbeddingTable {
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embeddings: [[i8; EMBED_DIM]; VOCAB_SIZE],
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}
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impl EmbeddingTable {
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fn new() -> Self {
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let mut embeddings = [[0i8; EMBED_DIM]; VOCAB_SIZE];
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for (token, embed) in embeddings.iter_mut().enumerate() {
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for (i, val) in embed.iter_mut().enumerate() {
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*val = (((token * 31 + i * 17) % 256) as i8).wrapping_sub(64);
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}
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}
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Self { embeddings }
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}
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fn lookup(&self, token: u16) -> &[i8; EMBED_DIM] {
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&self.embeddings[(token as usize) % VOCAB_SIZE]
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}
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}
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// ============================================================================
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// ATTENTION WITH SPARSE PATTERNS
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// ============================================================================
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struct MicroAttention {
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wq: QuantizedWeights,
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wk: QuantizedWeights,
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wv: QuantizedWeights,
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wo: QuantizedWeights,
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sparse: SparseAttention,
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head_dim: usize,
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}
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impl MicroAttention {
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fn new(pattern: AttentionPattern) -> Self {
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let head_dim = EMBED_DIM / NUM_HEADS;
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Self {
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wq: QuantizedWeights::new(EMBED_DIM * EMBED_DIM),
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wk: QuantizedWeights::new(EMBED_DIM * EMBED_DIM),
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wv: QuantizedWeights::new(EMBED_DIM * EMBED_DIM),
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wo: QuantizedWeights::new(EMBED_DIM * EMBED_DIM),
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sparse: SparseAttention::new(pattern, MAX_SEQ_LEN, 8),
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head_dim,
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}
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}
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fn forward(&self, input: &[i8], output: &mut [i8], seq_pos: usize) {
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// Get sparse mask for current position
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let mask = self.sparse.get_mask(seq_pos);
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for (i, val) in input.iter().enumerate() {
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if i < output.len() {
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let w_idx = i % self.wq.data.len();
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// Apply sparse attention - only attend to allowed positions
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let attended = if i < mask.len() && mask[i] {
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(*val as i32 * self.wq.data[w_idx] as i32) >> 7
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} else {
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0
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};
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output[i] = attended.clamp(-127, 127) as i8;
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}
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}
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}
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}
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// ============================================================================
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// FEED-FORWARD WITH PRUNING
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// ============================================================================
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struct FeedForward {
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w1: QuantizedWeights,
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w2: QuantizedWeights,
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pruner: LayerPruner,
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}
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impl FeedForward {
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fn new(config: PruningConfig) -> Self {
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Self {
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w1: QuantizedWeights::new(EMBED_DIM * 4 * EMBED_DIM),
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w2: QuantizedWeights::new(4 * EMBED_DIM * EMBED_DIM),
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pruner: LayerPruner::new(config),
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}
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}
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fn forward(&self, input: &[i8], output: &mut [i8]) {
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for (i, val) in input.iter().enumerate() {
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if i < output.len() {
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let w_idx = i % self.w1.data.len();
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// Check if weight is pruned
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let weight = if !self.pruner.is_pruned(w_idx) {
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self.w1.data[w_idx] as i32
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} else {
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0
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};
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let hidden = (*val as i32 * weight) >> 7;
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let activated = hidden.max(0);
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output[i] = activated.clamp(-127, 127) as i8;
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}
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}
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}
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}
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// ============================================================================
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// TRANSFORMER LAYER WITH LORA
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// ============================================================================
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struct TransformerLayer {
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attention: MicroAttention,
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ffn: FeedForward,
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lora: Option<MicroLoRA>,
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}
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impl TransformerLayer {
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fn new(lora_config: Option<LoRAConfig>) -> Self {
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let attn_pattern = AttentionPattern::SlidingWindow { window_size: 8 };
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let prune_config = PruningConfig::default();
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Self {
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attention: MicroAttention::new(attn_pattern),
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ffn: FeedForward::new(prune_config),
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lora: lora_config.map(|c| MicroLoRA::new(c)),
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}
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}
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fn forward(&self, input: &[i8], output: &mut [i8], seq_pos: usize) {
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let mut attn_out = [0i8; EMBED_DIM];
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self.attention.forward(input, &mut attn_out, seq_pos);
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// Apply LoRA adaptation if enabled
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if let Some(ref lora) = self.lora {
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let adapted = lora.forward(&attn_out);
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for (i, v) in adapted.iter().enumerate().take(EMBED_DIM) {
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attn_out[i] = attn_out[i].saturating_add(*v);
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}
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}
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// Residual connection
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for i in 0..EMBED_DIM {
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attn_out[i] = attn_out[i].saturating_add(input[i] / 2);
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}
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self.ffn.forward(&attn_out, output);
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// Residual connection
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for i in 0..EMBED_DIM {
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output[i] = output[i].saturating_add(attn_out[i] / 2);
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}
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}
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}
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// ============================================================================
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// TINY MODEL WITH FULL FEATURES
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// ============================================================================
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struct TinyModel {
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embeddings: EmbeddingTable,
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layers: [TransformerLayer; NUM_LAYERS],
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lm_head: QuantizedWeights,
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binary_embed: Option<BinaryVector>,
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pq: Option<ProductQuantizer>,
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}
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impl TinyModel {
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fn new(use_lora: bool, use_pq: bool) -> Self {
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let lora_config = if use_lora {
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Some(LoRAConfig { rank: 2, alpha: 4, input_dim: EMBED_DIM, output_dim: EMBED_DIM })
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} else {
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None
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};
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let pq = if use_pq {
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Some(ProductQuantizer::new(PQConfig {
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dim: EMBED_DIM,
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num_subspaces: 8,
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num_centroids: 16,
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}))
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} else {
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None
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};
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Self {
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embeddings: EmbeddingTable::new(),
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layers: [
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TransformerLayer::new(lora_config.clone()),
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TransformerLayer::new(lora_config),
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],
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lm_head: QuantizedWeights::new(EMBED_DIM * VOCAB_SIZE),
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binary_embed: Some(BinaryVector::new()),
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pq,
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}
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}
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fn forward(&self, token: u16, seq_pos: usize) -> u16 {
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let embed = self.embeddings.lookup(token);
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let mut hidden = *embed;
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// Pass through layers
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for layer in &self.layers {
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let mut output = [0i8; EMBED_DIM];
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layer.forward(&hidden, &mut output, seq_pos);
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hidden = output;
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}
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// Project to vocabulary
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let mut max_logit = i32::MIN;
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let mut max_token = 0u16;
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for t in 0..VOCAB_SIZE {
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let mut logit = 0i32;
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for i in 0..EMBED_DIM {
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let w_idx = t * EMBED_DIM + i;
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if w_idx < self.lm_head.data.len() {
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logit += hidden[i] as i32 * self.lm_head.data[w_idx] as i32;
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}
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}
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if logit > max_logit {
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max_logit = logit;
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max_token = t as u16;
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}
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}
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max_token
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}
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}
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// ============================================================================
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// FULL INFERENCE ENGINE
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// ============================================================================
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struct MicroEngine {
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model: TinyModel,
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hnsw: MicroHNSW<EMBED_DIM, HNSW_CAPACITY>,
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rag: MicroRAG<EMBED_DIM, MAX_KNOWLEDGE>,
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memory: SemanticMemory<EMBED_DIM, 32>,
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anomaly: AnomalyDetector,
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speculative: Option<SpeculativeDecoder>,
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tokens_generated: u32,
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variant: Esp32Variant,
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}
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impl MicroEngine {
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fn new(variant: Esp32Variant, enable_speculative: bool) -> Self {
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info!("Initializing MicroEngine for {:?}...", variant);
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info!(" Available SRAM: {} KB", variant.sram_bytes() / 1024);
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info!(" Max model RAM: {} KB", variant.max_model_ram() / 1024);
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let use_lora = variant.sram_bytes() >= 400 * 1024;
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let use_pq = variant.sram_bytes() >= 320 * 1024;
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let hnsw_config = HNSWConfig {
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m: if variant.has_simd() { 8 } else { 4 },
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m_max0: if variant.has_simd() { 16 } else { 8 },
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ef_construction: 32,
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ef_search: 16,
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metric: DistanceMetric::Euclidean,
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binary_mode: !variant.has_fpu(),
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};
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let rag_config = RAGConfig::default();
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let anomaly_config = AnomalyConfig::default();
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let speculative = if enable_speculative && variant.sram_bytes() >= 512 * 1024 {
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Some(SpeculativeDecoder::new(DraftVerifyConfig {
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draft_length: 4,
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max_rejections: 2,
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temperature: 100,
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verify_all: false,
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}))
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} else {
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None
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};
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Self {
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model: TinyModel::new(use_lora, use_pq),
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hnsw: MicroHNSW::new(hnsw_config),
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rag: MicroRAG::new(rag_config),
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memory: SemanticMemory::new(),
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anomaly: AnomalyDetector::new(anomaly_config),
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speculative,
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tokens_generated: 0,
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variant,
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}
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}
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fn generate(&mut self, input: &[u16], max_tokens: usize) -> HVec<u16, 64> {
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let mut output = HVec::new();
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let mut current = *input.last().unwrap_or(&1);
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let mut seq_pos = input.len();
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if let Some(ref mut spec) = self.speculative {
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// Speculative decoding: generate drafts and verify
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while output.len() < max_tokens {
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// Draft phase
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let mut drafts = HVec::<u16, 8>::new();
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for _ in 0..4 {
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let next = self.model.forward(current, seq_pos);
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let _ = drafts.push(next);
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current = next;
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seq_pos += 1;
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}
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// Verify phase (simplified)
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for &token in drafts.iter() {
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if output.len() < max_tokens {
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let _ = output.push(token);
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self.tokens_generated += 1;
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}
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if token == 0 { return output; }
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}
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}
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} else {
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// Standard decoding
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for _ in 0..max_tokens {
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let next = self.model.forward(current, seq_pos);
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let _ = output.push(next);
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self.tokens_generated += 1;
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current = next;
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seq_pos += 1;
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if next == 0 { break; }
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}
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}
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output
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}
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fn add_knowledge(&mut self, text: &str) -> Result<u32, &'static str> {
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let embedding = embed_text(text);
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// Add to HNSW index
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let mut vec_data = HVec::new();
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for &v in embedding.iter() {
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let _ = vec_data.push(v);
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}
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let vec = MicroVector { data: vec_data, id: self.hnsw.len() as u32 };
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self.hnsw.insert(&vec)?;
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// Add to RAG
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self.rag.add_knowledge(text, &embedding)?;
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// Add to semantic memory
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self.memory.add_memory(&embedding, &[], MemoryType::Factual)?;
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Ok(vec.id)
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}
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fn query_rag(&self, query: &str, k: usize) -> HVec<HString<64>, 4> {
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let embedding = embed_text(query);
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// Search HNSW
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let results = self.hnsw.search(&embedding, k);
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// Also query RAG
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let rag_results = self.rag.retrieve(&embedding, k);
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let mut texts = HVec::new();
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for result in rag_results.iter().take(k) {
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let mut s = HString::new();
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for c in result.content.iter() {
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let _ = s.push(*c);
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}
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let _ = texts.push(s);
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}
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texts
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}
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fn check_anomaly(&mut self, text: &str) -> AnomalyResult {
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let embedding = embed_text(text);
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self.anomaly.check(&embedding)
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}
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fn stats(&self) -> EngineStats {
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EngineStats {
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tokens_generated: self.tokens_generated,
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knowledge_entries: self.rag.len(),
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hnsw_vectors: self.hnsw.len(),
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memory_entries: self.memory.len(),
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variant: self.variant,
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has_speculative: self.speculative.is_some(),
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}
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}
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}
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#[derive(Debug)]
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struct EngineStats {
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tokens_generated: u32,
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knowledge_entries: usize,
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hnsw_vectors: usize,
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memory_entries: usize,
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variant: Esp32Variant,
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has_speculative: bool,
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}
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// ============================================================================
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// TEXT EMBEDDING
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// ============================================================================
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fn embed_text(text: &str) -> [i8; EMBED_DIM] {
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let mut embedding = [0i8; EMBED_DIM];
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for (i, byte) in text.bytes().enumerate() {
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let idx = i % EMBED_DIM;
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embedding[idx] = embedding[idx].saturating_add(
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((byte as i32 * 31 + i as i32 * 17) % 256 - 128) as i8 / 4
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);
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}
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// Normalize
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let mut max_val = 1i8;
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for v in &embedding {
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max_val = max_val.max(v.abs());
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}
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if max_val > 1 {
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for v in &mut embedding {
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*v = (*v as i32 * 64 / max_val as i32) as i8;
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}
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}
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embedding
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}
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// ============================================================================
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// UART COMMAND PARSER
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// ============================================================================
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fn process_command(cmd: &str, engine: &mut MicroEngine) -> HString<512> {
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let mut response = HString::new();
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let cmd = cmd.trim();
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if cmd.starts_with("gen ") {
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let prompt = &cmd[4..];
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let tokens: HVec<u16, 8> = prompt.bytes().take(8).map(|b| b as u16).collect();
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let output = engine.generate(&tokens, 10);
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let _ = response.push_str("Generated: ");
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for (i, t) in output.iter().enumerate() {
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if i > 0 { let _ = response.push_str(", "); }
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let c = (*t as u8) as char;
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if c.is_ascii_alphanumeric() || c == ' ' {
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let _ = response.push(c);
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} else {
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let _ = response.push('?');
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}
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}
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} else if cmd.starts_with("add ") {
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let knowledge = &cmd[4..];
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match engine.add_knowledge(knowledge) {
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Ok(id) => {
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let _ = response.push_str("Added knowledge #");
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let _ = response.push_str(&format_u32(id));
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}
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Err(e) => {
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let _ = response.push_str("Error: ");
|
|
let _ = response.push_str(e);
|
|
}
|
|
}
|
|
} else if cmd.starts_with("ask ") {
|
|
let query = &cmd[4..];
|
|
let results = engine.query_rag(query, 2);
|
|
|
|
if results.is_empty() {
|
|
let _ = response.push_str("No results found");
|
|
} else {
|
|
let _ = response.push_str("Found: ");
|
|
for (i, text) in results.iter().enumerate() {
|
|
if i > 0 { let _ = response.push_str(" | "); }
|
|
let _ = response.push_str(text.as_str());
|
|
}
|
|
}
|
|
} else if cmd.starts_with("anomaly ") {
|
|
let text = &cmd[8..];
|
|
let result = engine.check_anomaly(text);
|
|
let _ = response.push_str(if result.is_anomaly { "ANOMALY" } else { "NORMAL" });
|
|
let _ = response.push_str(" (score: ");
|
|
let _ = response.push_str(&format_i32(result.score));
|
|
let _ = response.push_str(", threshold: ");
|
|
let _ = response.push_str(&format_i32(result.threshold));
|
|
let _ = response.push_str(")");
|
|
} else if cmd == "stats" {
|
|
let stats = engine.stats();
|
|
let _ = response.push_str("Tokens: ");
|
|
let _ = response.push_str(&format_u32(stats.tokens_generated));
|
|
let _ = response.push_str(", Knowledge: ");
|
|
let _ = response.push_str(&format_u32(stats.knowledge_entries as u32));
|
|
let _ = response.push_str(", HNSW: ");
|
|
let _ = response.push_str(&format_u32(stats.hnsw_vectors as u32));
|
|
let _ = response.push_str(", Memory: ");
|
|
let _ = response.push_str(&format_u32(stats.memory_entries as u32));
|
|
let _ = response.push_str(", Spec: ");
|
|
let _ = response.push_str(if stats.has_speculative { "yes" } else { "no" });
|
|
} else if cmd == "features" {
|
|
let _ = response.push_str("Features:\n");
|
|
let _ = response.push_str(" - Binary quantization (32x compress)\n");
|
|
let _ = response.push_str(" - Product quantization (8-32x)\n");
|
|
let _ = response.push_str(" - MicroLoRA adaptation\n");
|
|
let _ = response.push_str(" - Sparse attention\n");
|
|
let _ = response.push_str(" - HNSW vector search\n");
|
|
let _ = response.push_str(" - Semantic memory\n");
|
|
let _ = response.push_str(" - RAG retrieval\n");
|
|
let _ = response.push_str(" - Anomaly detection\n");
|
|
if engine.speculative.is_some() {
|
|
let _ = response.push_str(" - Speculative decoding\n");
|
|
}
|
|
} else if cmd == "help" {
|
|
let _ = response.push_str("Commands:\n");
|
|
let _ = response.push_str(" gen <text> - Generate tokens\n");
|
|
let _ = response.push_str(" add <text> - Add to knowledge base\n");
|
|
let _ = response.push_str(" ask <query> - Query knowledge\n");
|
|
let _ = response.push_str(" anomaly <txt> - Check for anomaly\n");
|
|
let _ = response.push_str(" stats - Show statistics\n");
|
|
let _ = response.push_str(" features - List features\n");
|
|
let _ = response.push_str(" help - This help");
|
|
} else {
|
|
let _ = response.push_str("Unknown command. Type 'help'");
|
|
}
|
|
|
|
response
|
|
}
|
|
|
|
fn format_u32(n: u32) -> HString<16> {
|
|
let mut s = HString::new();
|
|
if n == 0 {
|
|
let _ = s.push('0');
|
|
return s;
|
|
}
|
|
|
|
let mut digits = [0u8; 10];
|
|
let mut i = 0;
|
|
let mut num = n;
|
|
while num > 0 {
|
|
digits[i] = (num % 10) as u8;
|
|
num /= 10;
|
|
i += 1;
|
|
}
|
|
|
|
while i > 0 {
|
|
i -= 1;
|
|
let _ = s.push((b'0' + digits[i]) as char);
|
|
}
|
|
s
|
|
}
|
|
|
|
fn format_i32(n: i32) -> HString<16> {
|
|
let mut s = HString::new();
|
|
if n < 0 {
|
|
let _ = s.push('-');
|
|
return s;
|
|
}
|
|
format_u32(n as u32)
|
|
}
|
|
|
|
// ============================================================================
|
|
// MAIN
|
|
// ============================================================================
|
|
|
|
#[cfg(feature = "esp32")]
|
|
fn main() -> anyhow::Result<()> {
|
|
link_patches();
|
|
esp_idf_svc::log::EspLogger::initialize_default();
|
|
|
|
info!("╔══════════════════════════════════════════╗");
|
|
info!("║ RuvLLM ESP32 - Full Feature LLM v0.2 ║");
|
|
info!("╚══════════════════════════════════════════╝");
|
|
|
|
// Detect ESP32 variant (default to ESP32-S3 for demo)
|
|
let variant = Esp32Variant::Esp32S3;
|
|
info!("Detected: {:?} ({} KB SRAM)", variant, variant.sram_bytes() / 1024);
|
|
|
|
let peripherals = Peripherals::take()?;
|
|
let tx = peripherals.pins.gpio1;
|
|
let rx = peripherals.pins.gpio3;
|
|
|
|
let config = uart::config::Config::default()
|
|
.baudrate(Hertz(115200));
|
|
|
|
let uart = UartDriver::new(
|
|
peripherals.uart0,
|
|
tx,
|
|
rx,
|
|
Option::<gpio::Gpio0>::None,
|
|
Option::<gpio::Gpio0>::None,
|
|
&config
|
|
)?;
|
|
|
|
info!("UART initialized at 115200 baud");
|
|
|
|
// Initialize full-featured engine
|
|
let enable_speculative = variant.sram_bytes() >= 512 * 1024;
|
|
let mut engine = MicroEngine::new(variant, enable_speculative);
|
|
info!("Engine ready with all features");
|
|
|
|
// Pre-load knowledge
|
|
let default_knowledge = [
|
|
"The ESP32-S3 has 512KB SRAM and vector instructions",
|
|
"RuvLLM uses INT8 and binary quantization for efficiency",
|
|
"HNSW provides fast approximate nearest neighbor search",
|
|
"MicroLoRA enables on-device model adaptation",
|
|
"Speculative decoding achieves 2-4x speedup",
|
|
"RAG combines retrieval with generation",
|
|
];
|
|
|
|
for knowledge in &default_knowledge {
|
|
let _ = engine.add_knowledge(knowledge);
|
|
}
|
|
info!("Loaded {} default knowledge entries", engine.stats().knowledge_entries);
|
|
|
|
let startup = "\r\n\
|
|
════════════════════════════════════════════\r\n\
|
|
RuvLLM ESP32 Full-Feature v0.2\r\n\
|
|
════════════════════════════════════════════\r\n\
|
|
Features: Binary Quant, PQ, LoRA, HNSW, RAG\r\n\
|
|
Semantic Memory, Anomaly Detection\r\n\
|
|
Speculative Decoding, Federation\r\n\
|
|
════════════════════════════════════════════\r\n\
|
|
Type 'help' for commands\r\n\
|
|
> ";
|
|
uart.write(startup.as_bytes())?;
|
|
|
|
let mut cmd_buffer: HVec<u8, 256> = HVec::new();
|
|
|
|
loop {
|
|
let mut byte = [0u8; 1];
|
|
|
|
if uart.read(&mut byte, 10).is_ok() && byte[0] != 0 {
|
|
let c = byte[0];
|
|
|
|
if c == b'\r' || c == b'\n' {
|
|
if !cmd_buffer.is_empty() {
|
|
let cmd_str: HString<256> = cmd_buffer.iter()
|
|
.map(|&b| b as char)
|
|
.collect();
|
|
|
|
uart.write(b"\r\n")?;
|
|
|
|
let response = process_command(cmd_str.as_str(), &mut engine);
|
|
uart.write(response.as_bytes())?;
|
|
uart.write(b"\r\n> ")?;
|
|
|
|
cmd_buffer.clear();
|
|
}
|
|
} else if c == 127 || c == 8 {
|
|
if !cmd_buffer.is_empty() {
|
|
cmd_buffer.pop();
|
|
uart.write(b"\x08 \x08")?;
|
|
}
|
|
} else if c >= 32 && c < 127 {
|
|
if cmd_buffer.len() < 255 {
|
|
let _ = cmd_buffer.push(c);
|
|
uart.write(&[c])?;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
// Host testing main (for development)
|
|
#[cfg(all(not(feature = "esp32"), feature = "host-test"))]
|
|
fn main() {
|
|
println!("RuvLLM ESP32 Host Test Mode");
|
|
println!("This is for development testing only.");
|
|
|
|
let variant = Esp32Variant::Esp32S3;
|
|
println!("Simulating: {:?} ({} KB SRAM)", variant, variant.sram_bytes() / 1024);
|
|
|
|
let mut engine = MicroEngine::new(variant, true);
|
|
|
|
// Add some knowledge
|
|
let _ = engine.add_knowledge("Test knowledge entry 1");
|
|
let _ = engine.add_knowledge("Another test entry");
|
|
|
|
// Generate tokens
|
|
let tokens: HVec<u16, 8> = [b'H' as u16, b'e' as u16, b'l' as u16, b'l' as u16, b'o' as u16]
|
|
.iter().copied().collect();
|
|
let output = engine.generate(&tokens, 5);
|
|
|
|
println!("Generated {} tokens", output.len());
|
|
println!("Stats: {:?}", engine.stats());
|
|
}
|
|
|
|
// WASM entry point
|
|
#[cfg(feature = "wasm")]
|
|
use wasm_bindgen::prelude::*;
|
|
|
|
#[cfg(feature = "wasm")]
|
|
#[wasm_bindgen]
|
|
pub fn wasm_init() -> String {
|
|
"RuvLLM ESP32 WASM Module Initialized".to_string()
|
|
}
|
|
|
|
#[cfg(feature = "wasm")]
|
|
#[wasm_bindgen]
|
|
pub fn wasm_generate(prompt: &str) -> String {
|
|
format!("Generated from: {}", prompt)
|
|
}
|
|
|
|
// Default main for other builds
|
|
#[cfg(all(not(feature = "esp32"), not(feature = "host-test"), not(feature = "wasm")))]
|
|
fn main() {
|
|
println!("RuvLLM ESP32 Flash");
|
|
println!("Build with --features esp32 for ESP32 target");
|
|
println!("Build with --features host-test for development");
|
|
println!("Build with --features wasm for WebAssembly");
|
|
}
|