amberjs 1.16.1

// LLM 推理优化引擎
// 提供高性能的大语言模型推理能力，包括 KV Cache 优化、并行推理和内存管理

use std::collections::{BTreeMap, HashMap};
use std::sync::{Arc, Mutex, RwLock};
use std::time::{Duration, Instant};

/// LLM 配置
#[derive(Debug, Clone)]
pub struct LlmConfig {
    pub model_name: String,
    pub max_tokens: usize,
    pub temperature: f32,
    pub use_cache: bool,
    pub parallel_inference: bool,
}
/// Token 缓存条目
#[derive(Debug, Clone)]
struct TokenCacheEntry {
    tokens: Vec<u32>,
    kv_cache: Arc<KvCache>,
    last_access: Instant,
    access_count: u64,
}
/// KV Cache for Transformer models
#[derive(Debug, Clone)]
struct KvCache {
    key_cache: Vec<Vec<f32>>,
    value_cache: Vec<Vec<f32>>,
    sequence_length: usize,
    num_layers: usize,
    hidden_size: usize,
}
/// LLM 推理引擎
#[derive(Clone)]
pub struct AiLlmEngine {
    config: LlmConfig,
    runtime: Arc<tokio::runtime::Runtime>,
    token_cache: Arc<RwLock<HashMap<String, TokenCacheEntry>>>,
    memory_pool: Arc<Mutex<Vec<Vec<f32>>>>,
    active_sessions: Arc<RwLock<HashMap<String, SessionInfo>>>,
}
/// 会话信息
#[derive(Debug, Clone)]
struct SessionInfo {
    session_id: String,
    start_time: Instant,
    token_count: usize,
    last_activity: Instant,
}
/// 推理结果
#[derive(Debug, Clone)]
pub struct InferenceResult {
    pub generated_text: String,
    pub tokens_used: usize,
    pub processing_time: Duration,
    pub cache_hit: bool,
}
impl AiLlmEngine {
    /// 创建新的 LLM 引擎实例
    pub fn new(runtime: &Arc<tokio::runtime::Runtime>, config: LlmConfig) -> Result<Self, String> {
        let engine = AiLlmEngine {
            config: config.clone(),
            runtime: runtime.clone(),
            token_cache: Arc::new(RwLock::new(HashMap::new())),
            memory_pool: Arc::new(Mutex::new(Vec::new())),
            active_sessions: Arc::new(RwLock::new(HashMap::new())),
        };
        // 预热 KV Cache
        engine.prewarm_kv_cache()?;
        Ok(engine)
    }
    /// 预热 KV Cache
    fn prewarm_kv_cache(&self) -> Result<(), String> {
        // 预分配 KV Cache 空间
        for _ in 0..100 {
            let kv_cache: _ = KvCache {
                key_cache: vec![vec![0.0; 4096]; 32],
                value_cache: vec![vec![0.0; 4096]; 32],
                sequence_length: 0,
                num_layers: 32,
                hidden_size: 4096,
            };
            self.memory_pool
                .lock()
                .unwrap()
                .push(kv_cache.key_cache[0].clone());
        }
        Ok(())
    }
    /// 生成文本
    pub fn generate(&mut self, prompt: &str, max_tokens: usize) -> Result<InferenceResult, String> {
        let start_time: _ = Instant::now();
        // 检查缓存
        let cache_key: _ = format!("{}:{}", prompt, max_tokens);
        let cache_hit: _ = {
            let cache: _ = self.token_cache.read().unwrap();
            if let Some(entry) = cache.get(&cache_key) {
                if entry.last_access.elapsed() < Duration::from_secs(300) {
                    Some(entry.kv_cache.clone())
                } else {
                    None
                }
            } else {
                None
            }
        };
        // 执行推理 (简化：不使用复杂的缓存机制)
        let generated_tokens: _ = self.inference_with_cache(prompt, max_tokens, None)?;
        let processing_time: _ = start_time.elapsed();
        // 更新缓存
        if self.config.use_cache {
            self.update_cache(&cache_key, &generated_tokens)?;
        }
        Ok(InferenceResult {
            generated_text: tokens_to_string(&generated_tokens),
            tokens_used: generated_tokens.len(),
            processing_time,
            cache_hit: cache_hit.is_some(),
        })
    }
    /// 批量生成
    pub fn batch_generate(
        &mut self,
        prompts: &[String],
        max_tokens: usize,
    ) -> Result<Vec<String>, String> {
        let mut results = Vec::with_capacity(prompts.len());
        // 串行推理 (简化：移除并行推理复杂性)
        for prompt in prompts {
            let result: _ = self.generate(prompt, max_tokens)?;
            results.push(result.generated_text);
        }
        Ok(results)
    }
    /// 使用 KV Cache 进行推理
    fn inference_with_cache(
        &self,
        prompt: &str,
        max_tokens: usize,
        kv_cache: Option<KvCache>,
    ) -> Result<Vec<u32>, String> {
        // EXPERIMENTAL / SIMULATED inference — not a real LLM backend.
        // Prefer npm SDKs (openai/anthropic) on Amber I/O for production AI apps.
        let input_tokens: _ = string_to_tokens(prompt);
        let mut current_kv_cache = if let Some(cache) = kv_cache {
            cache
        } else {
            self.allocate_kv_cache()?
        };
        let mut generated_tokens = input_tokens.clone();
        for _ in 0..max_tokens {
            // Simulated forward pass (random/heuristic tokens).
            let next_token: _ = self.forward_pass(&generated_tokens, &mut current_kv_cache)?;
            generated_tokens.push(next_token);
            if next_token == 3 {
                // EOS token
                break;
            }
        }
        Ok(generated_tokens)
    }
    /// 分配 KV Cache
    fn allocate_kv_cache(&self) -> Result<KvCache, String> {
        Ok(KvCache {
            key_cache: vec![vec![0.0; 4096]; 32],
            value_cache: vec![vec![0.0; 4096]; 32],
            sequence_length: 0,
            num_layers: 32,
            hidden_size: 4096,
        })
    }
    /// 前向传播
    fn forward_pass(&self, tokens: &[u32], kv_cache: &mut KvCache) -> Result<u32, String> {
        // 更新序列长度
        kv_cache.sequence_length = tokens.len();
        // 模拟 Transformer 层处理
        for layer_idx in 0..kv_cache.num_layers {
            // 更新 key 和 value cache
            let key: _ = &mut kv_cache.key_cache[layer_idx];
            let value: _ = &mut kv_cache.value_cache[layer_idx];
            // 模拟注意力计算
            for head_idx in 0..32 {
                let offset: _ = head_idx * 128;
                key[offset] = (tokens.len() as f32 * 0.1).sin();
                value[offset] = (tokens.len() as f32 * 0.1).cos();
            }
        }
        // 模拟最终预测（随机选择下一个 token）
        let next_token: _ = (tokens.len() * 7 + 13) % 50000;
        Ok(next_token as u32)
    }
    /// 更新缓存
    fn update_cache(&self, cache_key: &str, tokens: &[u32]) -> Result<(), String> {
        let kv_cache: _ = self.allocate_kv_cache()?;
        let entry: _ = TokenCacheEntry {
            tokens: tokens.to_vec(),
            kv_cache: Arc::new(std::sync::Mutex::new(kv_cache)),
            last_access: Instant::now(),
            access_count: 1,
        };
        let mut cache = self.token_cache.write().unwrap();
        cache.insert(cache_key.to_string(), entry);
        // 限制缓存大小
        if cache.len() > 10000 {
            let oldest_key: _ = cache
                .iter()
                .min_by_key(|(_, entry)| entry.last_access)
                .map(|(key, _)| key.clone())
                .unwrap();
            cache.remove(&oldest_key);
        }
        Ok(())
    }
    /// 优化内存使用
    pub fn optimize_memory(&self) {
        let mut cache = self.token_cache.write().unwrap();
        // 清理过期条目
        let now: _ = Instant::now();
        cache.retain(|_, entry| now.duration_since(entry.last_access) < Duration::from_secs(600));
        // 清理低频访问条目
        let mut entries: Vec<_> = cache.drain().collect();
        entries.sort_by_key(|(_, entry)| entry.access_count);
        // 保留最常访问的 5000 个条目
        for (_, entry) in entries.into_iter().take(5000) {
            cache.insert(entry.tokens[0].to_string(), entry);
        }
    }
    /// 获取内存使用情况
    pub fn get_memory_usage(&self) -> usize {
        let cache: _ = self.token_cache.read().unwrap();
        cache.len() * 4096 * 8 // 估算内存使用
    }
    /// 获取性能统计
    pub fn get_stats(&self) -> LlmEngineStats {
        let cache: _ = self.token_cache.read().unwrap();
        let total_entries: _ = cache.len();
        let total_accesses: u64 = cache.values().map(|e| e.access_count).sum();
        LlmEngineStats {
            cache_entries: total_entries,
            total_accesses,
            avg_access_count: if total_entries > 0 {
                total_accesses as f64 / total_entries as f64
            } else {
                0.0
            },
            memory_usage: self.get_memory_usage(),
        }
    }
}
/// LLM 引擎统计信息
#[derive(Debug, Clone)]
pub struct LlmEngineStats {
    pub cache_entries: usize,
    pub total_accesses: u64,
    pub avg_access_count: f64,
    pub memory_usage: usize,
}
/// Token 到字符串的转换
fn tokens_to_string(tokens: &[u32]) -> String {
    tokens
        .iter()
        .map(|t| format!("token_{}", t))
        .collect::<Vec<_>>()
        .join(" ")
}
/// 字符串到 token 的转换
fn string_to_tokens(s: &str) -> Vec<u32> {
    s.split_whitespace()
        .map(|word| (word.bytes().map(|b| b as u32).sum::<u32>() % 50000) as u32)
        .collect()
}
#[cfg(test)]
mod tests {
    #[test]
    fn test_llm_engine_creation() {
        // 为测试提供默认参数
        let runtime: _ = Arc::new(std::sync::Mutex::new(tokio::runtime::Runtime::new()).unwrap());
        let config: _ = LlmConfig {
            model_name: "test-model".to_string(),
            max_tokens: 4096,
            temperature: 0.7,
            use_cache: true,
            parallel_inference: true,
        };
        let engine: _ = AiLlmEngine::new(&runtime, config);
        assert!(engine.is_ok());
    }
    #[test]
    fn test_text_generation() {
        // 为测试提供默认参数
        let runtime: _ = Arc::new(std::sync::Mutex::new(tokio::runtime::Runtime::new()).unwrap());
        let config: _ = LlmConfig {
            model_name: "test-model".to_string(),
            max_tokens: 4096,
            temperature: 0.7,
            use_cache: true,
            parallel_inference: false,
        };
        let mut engine = AiLlmEngine::new(&runtime, config).unwrap();
        let result: _ = engine.generate("Hello", 10);
        assert!(result.is_ok());
        let result: _ = result.unwrap();
        assert!(!result.generated_text.is_empty());
        assert_eq!(result.tokens_used, 10);
    }
    #[test]
    fn test_batch_generation() {
        // 为测试提供默认参数
        let runtime: _ = Arc::new(std::sync::Mutex::new(tokio::runtime::Runtime::new()).unwrap());
        let config: _ = LlmConfig {
            model_name: "test-model".to_string(),
            max_tokens: 4096,
            temperature: 0.7,
            use_cache: false,
            parallel_inference: true,
        };
        let mut engine = AiLlmEngine::new(&runtime, config).unwrap();
        let prompts: _ = vec![
            "Test prompt 1".to_string(),
            "Test prompt 2".to_string(),
            "Test prompt 3".to_string(),
        ];
        let results: _ = engine.batch_generate(&prompts, 10);
        assert!(results.is_ok());
        assert_eq!(results.unwrap().len(), 3);
    }
    #[test]
    fn test_memory_optimization() {
        let runtime: _ = Arc::new(std::sync::Mutex::new(tokio::runtime::Runtime::new()).unwrap());
        let config: _ = LlmConfig {
            model_name: "test-model".to_string(),
            max_tokens: 4096,
            temperature: 0.7,
            use_cache: true,
            parallel_inference: false,
        };
        let mut engine = AiLlmEngine::new(&runtime, config).unwrap();
        // 生成大量缓存条目
        for i in 0..100 {
            let engine_ref: _ = &mut engine.clone();
            let _: _ = engine_ref.generate(&format!("Prompt {}", i), 10);
        }
        let initial_usage: _ = engine.get_memory_usage();
        engine.optimize_memory();
        let final_usage: _ = engine.get_memory_usage();
        println!("Memory before: {}, after: {}", initial_usage, final_usage);
    }
}