offline_intelligence/cache_management/

llama_cache_interface.rs

//! Interface to llama.cpp's attention KV cache for infinite context management
//!
//! This module provides the bridge between our abstract KV cache management system
//! and the actual llama.cpp attention KV cache that holds the transformer's state.

use crate::cache_management::cache_extractor::KVEntry;
use crate::model_runtime::ModelRuntime;
use tracing::{debug, info};

/// Represents the actual llama.cpp KV cache state
#[derive(Debug, Clone)]
pub struct LlamaKVCacheState {
    pub layer_count: usize,
    pub head_count: usize,
    pub kv_dim: usize,
    pub context_size: usize,
    pub current_tokens: usize,
    pub used_memory_bytes: usize,
    pub capacity_percentage: f32,
}

/// Interface for interacting with llama.cpp's KV cache
pub struct LlamaKVCacheInterface {
    runtime: Option<Box<dyn ModelRuntime + Send>>,
}

impl LlamaKVCacheInterface {
    pub fn new() -> Self {
        Self { runtime: None }
    }

    pub fn set_runtime(&mut self, runtime: Box<dyn ModelRuntime + Send>) {
        self.runtime = Some(runtime);
    }
    


    /// Get current KV cache state from llama.cpp
    pub async fn get_current_cache_state(&self) -> anyhow::Result<LlamaKVCacheState> {
        // If we have a runtime, query it for actual cache stats
        if let Some(ref _runtime) = self.runtime {
            // Attempt to get cache state from the runtime
            // In a real implementation, this would call the runtime's cache management methods
            // For now, we'll use a fallback simulation
            Ok(LlamaKVCacheState {
                layer_count: 32, // Typical for larger models
                head_count: 32,
                kv_dim: 128,
                context_size: 4096,
                current_tokens: 512, // Simulated
                used_memory_bytes: 1024 * 1024, // 1MB simulated
                capacity_percentage: 0.25, // 25% used
            })
        } else {
            // Fallback simulation when no runtime is available
            Ok(LlamaKVCacheState {
                layer_count: 32, // Typical for larger models
                head_count: 32,
                kv_dim: 128,
                context_size: 4096,
                current_tokens: 512, // Simulated
                used_memory_bytes: 1024 * 1024, // 1MB simulated
                capacity_percentage: 0.25, // 25% used
            })
        }
    }

    /// Extract current KV cache entries from llama.cpp
    pub async fn extract_current_kv_entries(&self) -> anyhow::Result<Vec<KVEntry>> {
        // If we have a runtime, attempt to extract actual KV cache entries
        if let Some(ref _runtime) = self.runtime {
            // In a real implementation, this would call the runtime's KV cache extraction methods
            // For now, we'll simulate based on the runtime's actual state
        }
        
        // This is a simplified simulation - in reality, we'd need to interface
        // with llama.cpp's internal KV cache structures
        
        // In a real implementation, this would:
        // 1. Query llama.cpp for its current attention KV cache contents
        // 2. Extract the K and V tensors for each layer and head
        // 3. Convert to our KVEntry format with proper metadata
        
        let mut entries = Vec::new();
        
        // Simulate extracting some KV cache entries
        for layer_idx in 0..8 { // Only first 8 layers for simulation
            for head_idx in 0..8 { // Only first 8 heads for simulation
                let k_data = vec![0u8; 128]; // Simulated K tensor
                let v_data = vec![0u8; 128]; // Simulated V tensor
                
                // Create entry for K tensor
                entries.push(KVEntry {
                    key_hash: format!("layer{}_head{}_k", layer_idx, head_idx),
                    key_data: Some(k_data.clone()),
                    value_data: k_data,
                    key_type: "attention_key".to_string(),
                    layer_index: layer_idx as i32,
                    head_index: Some(head_idx as i32),
                    importance_score: 0.5, // Would be calculated in real implementation
                    access_count: 1,
                    last_accessed: chrono::Utc::now(),
                    token_positions: Some(vec![0, 1, 2]), // Would be actual token positions
                    embedding: None, // Would be computed in real implementation
                    size_bytes: 128,
                    is_persistent: false,
                });
                
                // Create entry for V tensor
                entries.push(KVEntry {
                    key_hash: format!("layer{}_head{}_v", layer_idx, head_idx),
                    key_data: Some(v_data.clone()),
                    value_data: v_data,
                    key_type: "attention_value".to_string(),
                    layer_index: layer_idx as i32,
                    head_index: Some(head_idx as i32),
                    importance_score: 0.5, // Would be calculated in real implementation
                    access_count: 1,
                    last_accessed: chrono::Utc::now(),
                    token_positions: Some(vec![0, 1, 2]), // Would be actual token positions
                    embedding: None, // Would be computed in real implementation
                    size_bytes: 128,
                    is_persistent: false,
                });
            }
        }
        
        debug!("Extracted {} KV cache entries from llama.cpp simulation", entries.len());
        Ok(entries)
    }

    /// Inject KV cache entries back into llama.cpp
    pub async fn inject_kv_entries(&self, entries: &[KVEntry]) -> anyhow::Result<()> {
        // If we have a runtime, attempt to inject entries into the actual cache
        if let Some(ref _runtime) = self.runtime {
            // In a real implementation, this would call the runtime's KV cache injection methods
            // For now, we'll just log that we would have injected entries
        }
        
        // In a real implementation, this would:
        // 1. Prepare the KV entries in llama.cpp's expected format
        // 2. Call llama.cpp's KV cache injection API
        // 3. Handle any necessary state updates
        
        info!("Injected {} KV cache entries into llama.cpp simulation", entries.len());
        Ok(())
    }

    /// Clear specific entries from llama.cpp's KV cache
    pub async fn clear_cache_entries(&self, layer_indices: &[i32], head_indices: &[Option<i32>]) -> anyhow::Result<()> {
        // If we have a runtime, attempt to clear entries from the actual cache
        if let Some(ref _runtime) = self.runtime {
            // In a real implementation, this would call the runtime's KV cache clearing methods
            // For now, we'll just log that we would have cleared entries
        }
        
        // In a real implementation, this would:
        // 1. Call llama.cpp's KV cache clearing API
        // 2. Specify which layers/heads to clear
        // 3. Handle any necessary state updates
        
        info!("Cleared KV cache entries for {} layers", layer_indices.len());
        Ok(())
    }

    /// Calculate memory usage of current KV cache
    pub async fn get_cache_memory_usage(&self) -> anyhow::Result<usize> {
        // If we have a runtime, query it for actual memory usage
        if let Some(ref _runtime) = self.runtime {
            // In a real implementation, this would call the runtime's memory usage methods
            // For now, return a simulated value
        }
        
        // In a real implementation, this would query llama.cpp for actual memory usage
        // For now, return a simulated value
        Ok(1024 * 1024) // 1MB
    }

    /// Estimate when the KV cache will reach capacity
    pub async fn estimate_cache_capacity(&self) -> anyhow::Result<f32> {
        // If we have a runtime, query it for actual capacity estimation
        if let Some(ref _runtime) = self.runtime {
            // In a real implementation, this would call the runtime's capacity estimation methods
            // For now, return a simulated percentage
        }
        
        // In a real implementation, this would calculate based on:
        // - Current token count
        // - Context window size
        // - Memory usage patterns
        // For now, return a simulated percentage
        Ok(0.6) // 60% capacity
    }
}

impl Default for LlamaKVCacheInterface {
    fn default() -> Self {
        Self::new()
    }
}

// Note: This trait cannot be used as a trait object with async methods
// Keeping for reference but not using in dyn context
/*
pub trait KVCacheController {
    /// Get the current state of the KV cache
    async fn get_cache_state(&self) -> anyhow::Result<LlamaKVCacheState>;
    
    /// Extract current KV entries for preservation
    async fn extract_kv_entries(&self) -> anyhow::Result<Vec<KVEntry>>;
    
    /// Inject preserved KV entries back into the cache
    async fn inject_kv_entries(&self, entries: &[KVEntry]) -> anyhow::Result<()>;
    
    /// Clear specific KV cache entries
    async fn clear_cache_entries(&self, layer_indices: &[i32], head_indices: &[Option<i32>]) -> anyhow::Result<()>;
    
    /// Get current memory usage of the KV cache
    async fn get_cache_memory_usage(&self) -> anyhow::Result<usize>;
    
    /// Estimate cache capacity percentage
    async fn estimate_cache_capacity(&self) -> anyhow::Result<f32>;
}
*/