amberjs 1.16.1

// 动态批处理优化器 - 简化版
// Stage 35.0 候选特性 - 动态调整批次大小以优化推理性能

use anyhow::Result;
use std::collections::{BTreeMap, HashMap};
use std::sync::{Arc, Mutex};

use super::{AIInferenceEngine, InferenceResult};
use std::collections::VecDeque;
use std::time::{Duration, Instant};

/// 动态批处理配置
#[derive(Debug, Clone)]
pub struct DynamicBatchConfig {
    /// 最小批次大小
    pub min_batch_size: usize,
    /// 最大批次大小
    pub max_batch_size: usize,
    /// 等待时间阈值 (ms)
    pub wait_timeout_ms: u64,
    /// 性能监控窗口大小
    pub performance_window: usize,
}
impl Default for DynamicBatchConfig {
    fn default() -> Self {
        Self {
            min_batch_size: 1,
            max_batch_size: 64,
            wait_timeout_ms: 10,
            performance_window: 100,
        }
    }
}
/// 批处理性能统计
#[derive(Debug, Clone, Default)]
struct BatchPerformanceStats {
    /// 总处理次数
    pub total_processed: u64,
    /// 总吞吐量 (items/sec)
    pub throughput: f64,
    /// 平均延迟 (ms)
    pub avg_latency_ms: f64,
}
/// 动态批处理器 - 简化版
pub struct DynamicBatchProcessor {
    /// AI 推理引擎
    inference_engine: Arc<AIInferenceEngine>,
    /// 配置
    config: DynamicBatchConfig,
    /// 待处理的输入队列
    input_queue: Arc<Mutex<VecDeque<Tensor>>>,
    /// 结果队列
    result_queue: Arc<Mutex<VecDeque<InferenceResult>>>,
    /// 当前批次大小
    current_batch_size: usize,
    /// 性能统计
    performance_stats: Arc<Mutex<BatchPerformanceStats>>,
    /// 是否运行中
    running: Arc<Mutex<bool>>,
}
impl DynamicBatchProcessor {
    /// 创建新的动态批处理器
    pub async fn new(
        inference_engine: Arc<AIInferenceEngine>,
        config: DynamicBatchConfig,
    ) -> Result<Self> {
        let initial_batch_size: _ = (config.min_batch_size + config.max_batch_size) / 2;
        Ok(Self {
            inference_engine,
            config,
            input_queue: Arc::new(Mutex::new(VecDeque::new())),
            result_queue: Arc::new(Mutex::new(VecDeque::new())),
            current_batch_size: initial_batch_size,
            performance_stats: Arc::new(Mutex::new(BatchPerformanceStats::default())),
            running: Arc::new(Mutex::new(false)),
        })
    }
    /// 提交推理任务
    pub async fn submit_inference(&self, input: Tensor) -> Result<usize> {
        let mut queue = self.input_queue.lock().await;
        let task_id: _ = queue.len();
        queue.push_back(input);
        Ok(task_id)
    }
    /// 启动批处理循环
    pub async fn start_processing(&self) -> Result<()> {
        {
            let mut running = self.running.lock().await;
            if *running {
                return Ok(());
            }
            *running = true;
        }
        // 提取所需数据到闭包外部，避免借用 self
        let input_queue: _ = Arc::clone(&self.input_queue);
        let result_queue: _ = Arc::clone(&self.result_queue);
        let inference_engine: _ = Arc::clone(&self.inference_engine);
        let config: _ = self.config.clone();
        let performance_stats: _ = Arc::clone(&self.performance_stats);
        let running: _ = Arc::clone(&self.running);
        // 注意：current_batch_size 将在运行时获取，而不是在启动时捕获
        // 启动批处理任务
        tokio::spawn(async move {
            let mut last_batch_time = Instant::now();
            loop {
                // 检查是否应该停止
                {
                    let running_guard: _ = running.lock().await;
                    if !*running_guard {
                        break;
                    }
                }
                // 获取当前批次大小（这里简化处理，实际可使用 Arc<Mutex<usize>>）
                let current_batch_size: _ = 8; // 默认值，可根据需要调整
                                               // 检查是否应该处理批次
                let should_process: _ = {
                    let queue: _ = input_queue.lock().await;
                    let timeout_reached: _ =
                        last_batch_time.elapsed() > Duration::from_millis(config.wait_timeout_ms);
                    let batch_full: _ = queue.len() >= current_batch_size;
                    timeout_reached || batch_full
                };
                if should_process {
                    // 收集当前批次
                    let mut batch = Vec::new();
                    {
                        let mut queue = input_queue.lock().await;
                        for _ in 0..current_batch_size {
                            if let Some(input) = queue.pop_front() {
                                batch.push(input);
                            } else {
                                break;
                            }
                        }
                    }
                    if !batch.is_empty() {
                        // 执行批次推理
                        let batch_start: _ = Instant::now();
                        let model_id: _ = "test_model".to_string();
                        let batch_size: _ = batch.len();
                        // 逐个处理
                        for (i, input) in batch.into_iter().enumerate() {
                            let result: _ = inference_engine.infer(&model_id, &input).await;
                            match result {
                                Ok(inference_result) => {
                                    let mut queue = result_queue.lock().await;
                                    queue.push_back(inference_result);
                                }
                                Err(e) => {
                                    eprintln!("推理失败 (任务 {}): {}", i, e);
                                }
                            }
                        }
                        let batch_duration: _ = batch_start.elapsed();
                        // 更新性能统计
                        {
                            let mut stats = performance_stats.lock().await;
                            stats.total_processed += batch_size as u64;
                            stats.avg_latency_ms = batch_duration.as_secs_f64() * 1000.0;
                            stats.throughput = batch_size as f64 / batch_duration.as_secs_f64();
                        }
                        last_batch_time = Instant::now();
                    }
                }
                // 短暂休眠以避免忙等待
                tokio::time::sleep(Duration::from_millis(1)).await;
            }
        });
        Ok(())
    }
    /// 停止批处理
    pub async fn stop_processing(&self) {
        let mut running = self.running.lock().await;
        *running = false;
    }
    /// 动态调整批次大小
    pub async fn adjust_batch_size(&self) -> Result<()> {
        let stats: _ = self.performance_stats.lock().await;
        // 简化的调整逻辑：如果延迟很低，增加批次大小
        if stats.avg_latency_ms < 5.0 && self.current_batch_size < self.config.max_batch_size {
            let new_size: _ = (self.current_batch_size * 2).min(self.config.max_batch_size);
            println!(
                "🔧 动态调整批次大小: {} -> {}",
                self.current_batch_size, new_size
            );
            // 注意：在实际实现中，这里需要使用 Arc<Mutex<usize>> 来安全地更新
        } else if stats.avg_latency_ms > 20.0
            && self.current_batch_size > self.config.min_batch_size
        {
            let new_size: _ = (self.current_batch_size / 2).max(self.config.min_batch_size);
            println!(
                "🔧 动态调整批次大小: {} -> {}",
                self.current_batch_size, new_size
            );
        }
        Ok(())
    }
    /// 获取性能统计
    pub async fn get_performance_stats(&self) -> BatchPerformanceStats {
        self.performance_stats.lock().await.clone()
    }
    /// 获取结果
    pub async fn get_result(&self) -> Option<InferenceResult> {
        let mut queue = self.result_queue.lock().await;
        queue.pop_front()
    }
}
#[cfg(test)]
mod tests {
    #[tokio::test]
    async fn test_dynamic_batch_processor_creation() {
        let config: _ = DynamicBatchConfig::default();
        let engine: _ = AIInferenceEngine::new().await.unwrap();
        let processor: _ = DynamicBatchProcessor::new(Arc::new(Mutex::new(engine)), config).await;
        assert!(processor.is_ok());
    }
    #[tokio::test]
    async fn test_dynamic_batch_submission() {
        let config: _ = DynamicBatchConfig::default();
        let engine: _ = AIInferenceEngine::new().await.unwrap();
        let processor: _ = DynamicBatchProcessor::new(Arc::new(Mutex::new(engine)), config)
            .await
            .unwrap();
        // 创建测试张量
        let input: _ = Tensor::new(vec![1.0, 2.0, 3.0, 4.0], vec![2, 2]).unwrap();
        let task_id: _ = processor.submit_inference(input).await.unwrap();
        assert_eq!(task_id, 0);
    }
    #[tokio::test]
    async fn test_dynamic_batch_processing() {
        let config: _ = DynamicBatchConfig {
            min_batch_size: 1,
            max_batch_size: 4,
            wait_timeout_ms: 50,
            performance_window: 10,
        };
        let engine: _ = AIInferenceEngine::new().await.unwrap();
        let processor: _ = DynamicBatchProcessor::new(Arc::new(Mutex::new(engine)), config)
            .await
            .unwrap();
        // 启动处理
        processor.start_processing().await.unwrap();
        // 提交几个任务
        for i in 0..3 {
            let input: _ = Tensor::new(
                vec![i as f32, (i + 1) as f32, (i + 2) as f32, (i + 3) as f32],
                vec![2, 2],
            )
            .unwrap();
            let _: _ = processor.submit_inference(input).await;
        }
        // 等待处理完成
        sleep(Duration::from_millis(200)).await;
        // 停止处理
        processor.stop_processing().await;
        // 获取性能统计
        let stats: _ = processor.get_performance_stats().await;
        assert!(stats.total_processed > 0);
    }
}