amberjs 1.16.1

// ONNX 批处理优化器
// 智能批处理算法，动态调整批处理大小，优化推理性能

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

use crate::ai_inference::{
    engine_interface::{InferenceResult, ModelHandle},
    tensor_ops::Tensor,
};
use anyhow::Result;
use std::sync::RwLock;

// serde imports removed - unused
/// 批处理优化策略
#[derive(Debug, Clone, PartialEq)]
pub enum BatchStrategy {
    /// 固定批处理大小
    Fixed(usize),
    /// 动态批处理
    Dynamic(DynamicConfig),
    /// 自适应批处理
    Adaptive(AdaptiveConfig),
}
/// 动态批处理配置
#[derive(Debug, Clone, PartialEq)]
pub struct DynamicConfig {
    /// 最小批处理大小
    pub min_batch_size: usize,
    /// 最大批处理大小
    pub max_batch_size: usize,
    /// 目标延迟（毫秒）
    pub target_latency_ms: f64,
    /// 批处理超时（毫秒）
    pub batch_timeout_ms: u64,
}
/// 自适应批处理配置
#[derive(Debug, Clone, PartialEq)]
pub struct AdaptiveConfig {
    /// 初始批处理大小
    pub initial_batch_size: usize,
    /// 性能窗口大小
    pub performance_window: usize,
    /// 延迟阈值（毫秒）
    pub latency_threshold_ms: f64,
    /// 吞吐量目标
    pub throughput_target: f64,
}
/// 批处理项
#[derive(Debug)]
struct BatchItem {
    /// 输入张量
    input: Tensor,
    /// 发送者
    sender: tokio::sync::oneshot::Sender<Result<InferenceResult>>,
    /// 接收时间
    received_at: Instant,
}
/// 批处理器
#[derive(Debug)]
pub struct BatchProcessor {
    /// 优化策略
    strategy: BatchStrategy,
    /// 当前批处理
    current_batch: Arc<RwLock<Vec<BatchItem>>>,
    /// 批处理统计
    stats: Arc<Mutex<BatchStats>>,
    /// 处理任务
    processing_task: Arc<Mutex<Option<tokio::task::JoinHandle<()>>>>,
}
/// 批处理统计信息
#[derive(Debug, Clone)]
pub struct BatchStats {
    /// 总批处理数量
    pub total_batches: u64,
    /// 总处理项数量
    pub total_items: u64,
    /// 平均批处理大小
    pub average_batch_size: f64,
    /// 平均延迟（毫秒）
    pub average_latency_ms: f64,
    /// 吞吐量（items/sec）
    pub throughput: f64,
    /// GPU 利用率
    pub gpu_utilization: f64,
}
impl BatchProcessor {
    /// 创建新的批处理器
    pub fn new(strategy: BatchStrategy) -> Self {
        BatchProcessor {
            strategy,
            current_batch: Arc::new(Mutex::new(Vec::new())),
            stats: Arc::new(Mutex::new(BatchStats {
                total_batches: 0,
                total_items: 0,
                average_batch_size: 0.0,
                average_latency_ms: 0.0,
                throughput: 0.0,
                gpu_utilization: 0.0,
            })),
            processing_task: Arc::new(Mutex::new(None)),
        }
    }
    /// 添加推理请求到批处理队列
    pub async fn add_request(&self, model: &ModelHandle, input: Tensor) -> Result<InferenceResult> {
        let (sender, receiver) = tokio::sync::oneshot::channel();
        let item: _ = BatchItem {
            input,
            sender,
            received_at: Instant::now(),
        };
        // 添加到当前批处理
        {
            let mut batch = self.current_batch.write().await;
            batch.push(item);
        }
        // 检查是否需要处理批处理
        self.check_and_process_batch().await?;
        // 等待结果
        Ok(receiver.await??)
    }
    /// 检查并处理批处理
    async fn check_and_process_batch(&self) -> Result<()> {
        let batch_size: _ = {
            let batch: _ = self.current_batch.read().await;
            batch.len()
        };
        let should_process: _ = match &self.strategy {
            BatchStrategy::Fixed(size) => batch_size >= *size,
            BatchStrategy::Dynamic(config) => {
                batch_size >= config.min_batch_size
                    || self.should_timeout(config.batch_timeout_ms).await
            }
            BatchStrategy::Adaptive(config) => {
                batch_size >= config.initial_batch_size || self.should_timeout(100).await
                // 默认超时
            }
        };
        if should_process {
            self.process_batch().await?;
        }
        Ok(())
    }
    /// 检查是否超时
    async fn should_timeout(&self, timeout_ms: u64) -> bool {
        let batch: _ = self.current_batch.read().await;
        if let Some(first_item) = batch.first() {
            let elapsed: _ = first_item.received_at.elapsed();
            elapsed >= Duration::from_millis(timeout_ms)
        } else {
            false
        }
    }
    /// 处理当前批处理
    async fn process_batch(&self) -> Result<()> {
        // 获取批处理项
        let items: _ = {
            let mut batch = self.current_batch.write().await;
            let items: _ = batch.split_off(0);
            items
        };
        if items.is_empty() {
            return Ok(());
        }
        // 启动处理任务
        let items_count: _ = items.len();
        let _current_batch: _ = Arc::clone(&self.current_batch);
        let stats: _ = Arc::clone(&self.stats);
        let handle: _ = tokio::spawn(async move {
            let start: _ = Instant::now();
            // 模拟批处理推理
            // 在实际实现中，这里会调用 ONNX Runtime 批处理推理
            let outputs: _ = simulate_batch_inference(&items).await;
            let processing_time: _ = start.elapsed();
            // 发送结果
            for (item, output) in items.into_iter().zip(outputs) {
                let latency: _ = item.received_at.elapsed();
                let result: _ = InferenceResult {
                    output,
                    inference_time_ms: latency.as_secs_f64() * 1000.0,
                    model_id: "batch_model".to_string(),
                    gpu_used: true,
                };
                let _: _ = item.sender.send(Ok(result));
            }
            // 更新统计信息
            {
                let mut stats = stats.lock().unwrap();
                stats.total_batches += 1;
                stats.total_items += items_count as u64;
                stats.average_batch_size = stats.total_items as f64 / stats.total_batches as f64;
                stats.average_latency_ms =
                    (stats.average_latency_ms + processing_time.as_secs_f64() * 1000.0) / 2.0;
                stats.throughput = stats.total_items as f64
                    / (stats.total_batches as f64 * processing_time.as_secs_f64());
            }
        });
        // 保存任务句柄
        {
            let mut task = self.processing_task.lock().unwrap();
            *task = Some(handle);
        }
        Ok(())
    }
    /// 获取批处理统计信息
    pub fn get_stats(&self) -> BatchStats {
        self.stats.lock().unwrap().clone()
    }
    /// 强制处理当前批处理
    pub async fn flush(&self) -> Result<()> {
        let has_items: _ = {
            let batch: _ = self.current_batch.read().await;
            !batch.is_empty()
        };
        if has_items {
            self.process_batch().await?;
        }
        Ok(())
    }
}
/// 模拟批处理推理
async fn simulate_batch_inference(items: &[BatchItem]) -> Vec<Tensor> {
    let batch_size: _ = items.len();
    // 模拟批处理推理时间（应该比单次推理更快）
    let processing_time: _ = match batch_size {
        1 => Duration::from_millis(15),
        2..=4 => Duration::from_millis(20),
        5..=8 => Duration::from_millis(30),
        _ => Duration::from_millis(50),
    };
    tokio::time::sleep(processing_time).await;
    // 生成输出张量
    let mut outputs = Vec::new();
    for _ in 0..batch_size {
        let output: _ = Tensor::new(vec![1.0; 1000], vec![1, 1000]).unwrap();
        outputs.push(output);
    }
    outputs
}
/// 智能批处理器
#[derive(Debug)]
pub struct SmartBatchProcessor {
    /// 基础批处理器
    processor: BatchProcessor,
    /// 性能监控
    performance_monitor: PerformanceMonitor,
    /// 自适应参数 (使用 Mutex 支持内部可变性)
    adaptive_params: Arc<Mutex<AdaptiveParams>>,
}
/// 性能监控器
#[derive(Debug)]
struct PerformanceMonitor {
    /// 延迟历史
    latency_history: Arc<Mutex<Vec<f64>>>,
    /// 吞吐量历史
    throughput_history: Arc<Mutex<Vec<f64>>>,
    /// 窗口大小
    window_size: usize,
}
/// 自适应参数
#[derive(Debug, Clone)]
struct AdaptiveParams {
    /// 当前批处理大小
    current_batch_size: usize,
    /// 最小批处理大小
    min_batch_size: usize,
    /// 最大批处理大小
    max_batch_size: usize,
    /// 延迟目标
    latency_target: f64,
    /// 吞吐量目标
    throughput_target: f64,
    /// 调整因子
    adjustment_factor: f64,
}
impl SmartBatchProcessor {
    /// 创建新的智能批处理器
    pub fn new(initial_batch_size: usize) -> Self {
        let strategy: _ = BatchStrategy::Adaptive(AdaptiveConfig {
            initial_batch_size,
            performance_window: 100,
            latency_threshold_ms: 10.0,
            throughput_target: 1000.0,
        });
        SmartBatchProcessor {
            processor: BatchProcessor::new(strategy),
            performance_monitor: PerformanceMonitor::new(100),
            adaptive_params: Arc::new(Mutex::new(AdaptiveParams {
                current_batch_size: initial_batch_size,
                min_batch_size: 1,
                max_batch_size: 64,
                latency_target: 10.0,
                throughput_target: 1000.0,
                adjustment_factor: 0.1,
            })),
        }
    }
    /// 添加推理请求
    pub async fn add_request(&self, model: &ModelHandle, input: Tensor) -> Result<InferenceResult> {
        let result: _ = self.processor.add_request(model, input).await?;
        // 记录性能指标
        self.performance_monitor
            .record_latency(result.inference_time_ms);
        // 自适应调整批处理大小
        self.adapt_batch_size().await?;
        Ok(result)
    }
    /// 自适应调整批处理大小
    async fn adapt_batch_size(&self) -> Result<()> {
        let avg_latency: _ = self.performance_monitor.get_average_latency();
        let current_throughput: _ = self.performance_monitor.get_average_throughput();
        // 获取自适应参数的锁并更新
        let mut params = self.adaptive_params.lock().unwrap();
        // 根据性能指标调整批处理大小
        if avg_latency > params.latency_target {
            // 延迟过高，减少批处理大小
            if params.current_batch_size > params.min_batch_size {
                let new_size: _ =
                    (params.current_batch_size as f64 * (1.0 - params.adjustment_factor)) as usize;
                params.current_batch_size = new_size.max(params.min_batch_size);
            }
        } else if current_throughput < params.throughput_target {
            // 吞吐量不足，增加批处理大小
            if params.current_batch_size < params.max_batch_size {
                let new_size: _ =
                    (params.current_batch_size as f64 * (1.0 + params.adjustment_factor)) as usize;
                params.current_batch_size = new_size.min(params.max_batch_size);
            }
        }
        Ok(())
    }
    /// 强制刷新
    pub async fn flush(&self) -> Result<()> {
        self.processor.flush().await
    }
    /// 获取性能统计
    pub fn get_performance_stats(&self) -> PerformanceStats {
        let params: _ = self.adaptive_params.lock().unwrap();
        PerformanceStats {
            current_batch_size: params.current_batch_size,
            average_latency: self.performance_monitor.get_average_latency(),
            average_throughput: self.performance_monitor.get_average_throughput(),
            batch_stats: self.processor.get_stats(),
        }
    }
}
/// 性能统计信息
#[derive(Debug, Clone)]
pub struct PerformanceStats {
    /// 当前批处理大小
    pub current_batch_size: usize,
    /// 平均延迟
    pub average_latency: f64,
    /// 平均吞吐量
    pub average_throughput: f64,
    /// 批处理统计
    pub batch_stats: BatchStats,
}
impl PerformanceMonitor {
    /// 创建新的性能监控器
    fn new(window_size: usize) -> Self {
        PerformanceMonitor {
            latency_history: Arc::new(Mutex::new(Vec::with_capacity(window_size))),
            throughput_history: Arc::new(Mutex::new(Vec::with_capacity(window_size))),
            window_size,
        }
    }
    /// 记录延迟
    fn record_latency(&self, latency_ms: f64) {
        let mut history = self.latency_history.lock().unwrap();
        if history.len() >= self.window_size {
            history.remove(0);
        }
        history.push(latency_ms);
    }
    /// 记录吞吐量
    fn record_throughput(&self, throughput: f64) {
        let mut history = self.throughput_history.lock().unwrap();
        if history.len() >= self.window_size {
            history.remove(0);
        }
        history.push(throughput);
    }
    /// 获取平均延迟
    fn get_average_latency(&self) -> f64 {
        let history: _ = self.latency_history.lock().unwrap();
        if history.is_empty() {
            0.0
        } else {
            history.iter().sum::<f64>() / history.len() as f64
        }
    }
    /// 获取平均吞吐量
    fn get_average_throughput(&self) -> f64 {
        let history: _ = self.throughput_history.lock().unwrap();
        if history.is_empty() {
            0.0
        } else {
            history.iter().sum::<f64>() / history.len() as f64
        }
    }
}
#[cfg(test)]
mod tests {
    use super::*;
    use std::time::{Duration, Instant};
    #[tokio::test]
    async fn test_batch_processor_fixed() -> Result<()> {
        let processor: _ = BatchProcessor::new(BatchStrategy::Fixed(4));
        let model: _ = ModelHandle {
            id: "test".to_string(),
            path: "test.onnx".to_string(),
            format: crate::ai_inference::engine_interface::ModelFormat::ONNX,
            input_shape: vec![1, 3, 224, 224],
            output_shape: vec![1, 1000],
            metadata: std::collections::HashMap::new(),
        };
        // 添加 4 个请求
        for _ in 0..4 {
            let input: _ = Tensor::new(vec![1.0; 3 * 224 * 224], vec![1, 3, 224, 224])?;
            let _: _ = processor.add_request(&model, input).await?;
        }
        let stats: _ = processor.get_stats();
        assert_eq!(stats.total_batches, 1);
        assert_eq!(stats.total_items, 4);
        Ok(())
    }
    #[tokio::test]
    async fn test_batch_processor_dynamic() -> Result<()> {
        let config: _ = DynamicConfig {
            min_batch_size: 2,
            max_batch_size: 8,
            target_latency_ms: 10.0,
            batch_timeout_ms: 100,
        };
        let processor: _ = BatchProcessor::new(BatchStrategy::Dynamic(config));
        let model: _ = ModelHandle {
            id: "test".to_string(),
            path: "test.onnx".to_string(),
            format: crate::ai_inference::engine_interface::ModelFormat::ONNX,
            input_shape: vec![1, 3, 224, 224],
            output_shape: vec![1, 1000],
            metadata: std::collections::HashMap::new(),
        };
        // 添加 2 个请求（触发最小批处理大小）
        for _ in 0..2 {
            let input: _ = Tensor::new(vec![1.0; 3 * 224 * 224], vec![1, 3, 224, 224])?;
            let _: _ = processor.add_request(&model, input).await?;
        }
        let stats: _ = processor.get_stats();
        assert_eq!(stats.total_batches, 1);
        assert_eq!(stats.total_items, 2);
        Ok(())
    }
    #[tokio::test]
    async fn test_smart_batch_processor() -> Result<()> {
        let processor: _ = SmartBatchProcessor::new(4);
        let model: _ = ModelHandle {
            id: "test".to_string(),
            path: "test.onnx".to_string(),
            format: crate::ai_inference::engine_interface::ModelFormat::ONNX,
            input_shape: vec![1, 3, 224, 224],
            output_shape: vec![1, 1000],
            metadata: std::collections::HashMap::new(),
        };
        // 添加多个请求
        for _ in 0..10 {
            let input: _ = Tensor::new(vec![1.0; 3 * 224 * 224], vec![1, 3, 224, 224])?;
            let _: _ = processor.add_request(&model, input).await?;
        }
        let perf_stats: _ = processor.get_performance_stats();
        assert!(perf_stats.current_batch_size >= 1);
        assert!(perf_stats.current_batch_size <= 64);
        Ok(())
    }
}