amberjs 1.17.0

// AI批量处理器
// 专为AI推理工作负载设计的高性能批量处理系统

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

/// AI任务类型
#[derive(Debug, Clone)]
#[allow(dead_code)]
pub enum AiTaskType {
    /// 文本生成任务
    TextGeneration {
        prompt: String,
        max_tokens: Option<usize>,
        temperature: f32,
    },
    /// 图像分类任务
    ImageClassification {
        image_data: Vec<u8>,
        top_k: Option<usize>,
    },
    /// 嵌入向量生成
    Embedding { text: String, model_name: String },
    /// 翻译任务
    Translation {
        text: String,
        source_lang: String,
        target_lang: String,
    },
}
/// AI任务结果
#[derive(Debug, Clone)]
#[allow(dead_code)]
pub enum AiTaskResult {
    TextGeneration {
        generated_text: String,
        tokens_used: usize,
        processing_time: Duration,
    },
    ImageClassification {
        predictions: Vec<(String, f32)>,
        processing_time: Duration,
    },
    Embedding {
        vector: Vec<f32>,
        dimensions: usize,
        processing_time: Duration,
    },
    Translation {
        translated_text: String,
        source_lang: String,
        target_lang: String,
        processing_time: Duration,
    },
}
/// 批处理配置
#[derive(Debug, Clone)]
pub struct BatchConfig {
    /// 最大批处理大小
    pub max_batch_size: usize,
    /// 批处理超时时间（毫秒）
    #[allow(dead_code)]
    pub batch_timeout_ms: u64,
    /// 最大并发批次数
    pub max_concurrent_batches: usize,
    /// 动态批处理调整是否启用
    #[allow(dead_code)]
    pub enable_dynamic_batching: bool,
    /// 预热批处理大小
    #[allow(dead_code)]
    pub warmup_batch_size: usize,
}
impl Default for BatchConfig {
    fn default() -> Self {
        Self {
            max_batch_size: 100,
            batch_timeout_ms: 100,
            max_concurrent_batches: 10,
            enable_dynamic_batching: true,
            warmup_batch_size: 10,
        }
    }
}
/// AI批量处理器
pub struct AiBatchProcessor {
    config: BatchConfig,
    pending_tasks: Arc<Mutex<VecDeque<(usize, AiTaskType)>>>,
    active_batches: Arc<Mutex<usize>>,
    batch_semaphore: Arc<Semaphore>,
    next_task_id: Arc<Mutex<usize>>,
    stats: Arc<Mutex<BatchStats>>,
    results: Arc<Mutex<Vec<(usize, AiTaskResult)>>>,
}
/// 批处理统计信息
#[derive(Debug, Clone, Default)]
pub struct BatchStats {
    pub total_tasks_processed: usize,
    pub total_batches_processed: usize,
    pub total_processing_time: Duration,
    pub average_batch_size: f64,
    pub peak_memory_usage: usize,
    #[allow(dead_code)]
    pub cache_hits: usize,
    #[allow(dead_code)]
    pub cache_misses: usize,
}
impl BatchStats {
    pub fn record_batch(
        &mut self,
        batch_size: usize,
        processing_time: Duration,
        memory_used: usize,
    ) {
        self.total_tasks_processed += batch_size;
        self.total_batches_processed += 1;
        self.total_processing_time += processing_time;
        self.average_batch_size =
            self.total_tasks_processed as f64 / self.total_batches_processed as f64;
        self.peak_memory_usage = self.peak_memory_usage.max(memory_used);
    }
    #[allow(dead_code)]
    pub fn cache_hit_rate(&self) -> f64 {
        let total: _ = self.cache_hits + self.cache_misses;
        if total > 0 {
            self.cache_hits as f64 / total as f64
        } else {
            0.0
        }
    }
}
#[allow(dead_code)]
impl AiBatchProcessor {
    /// 创建新的AI批量处理器
    pub fn new(config: BatchConfig) -> Self {
        let max_concurrent_batches = config.max_concurrent_batches;
        Self {
            config,
            pending_tasks: Arc::new(Mutex::new(VecDeque::new())),
            active_batches: Arc::new(Mutex::new(0)),
            batch_semaphore: Arc::new(Semaphore::new(max_concurrent_batches)),
            next_task_id: Arc::new(Mutex::new(0)),
            stats: Arc::new(Mutex::new(BatchStats::default())),
            results: Arc::new(Mutex::new(Vec::new())),
        }
    }
    /// 添加任务到批处理队列
    pub async fn add_task(&self, task: AiTaskType) -> usize {
        let task_id: _ = {
            let mut next_id = self.next_task_id.lock().unwrap();
            let id: _ = *next_id;
            *next_id += 1;
            id
        };
        {
            let mut tasks = self.pending_tasks.lock().unwrap();
            tasks.push_back((task_id, task));
        }
        // 尝试立即处理批次
        self.try_process_batch().await;
        task_id
    }
    /// 批量添加任务
    pub async fn add_tasks(&self, tasks: Vec<AiTaskType>) -> Vec<usize> {
        let mut task_ids = Vec::with_capacity(tasks.len());
        for task in tasks {
            let task_id: _ = self.add_task(task).await;
            task_ids.push(task_id);
        }
        task_ids
    }
    /// 尝试处理批次
    async fn try_process_batch(&self) {
        // 检查是否有可用的并发批次和待处理任务
        if *self.active_batches.lock().unwrap() >= self.config.max_concurrent_batches {
            return; // 达到最大并发限制
        }
        if self.pending_tasks_count() == 0 {
            return; // 没有待处理任务
        }
        let permit: _ = self.batch_semaphore.clone().try_acquire_owned();
        if permit.is_err() {
            return; // 无法获取信号量
        }
        let permit: _ = permit.unwrap();
        let pending_tasks: _ = self.pending_tasks.clone();
        let active_batches: _ = self.active_batches.clone();
        let stats: _ = self.stats.clone();
        let config: _ = self.config.clone();
        let results: _ = self.results.clone();
        tokio::spawn(async move {
            let _permit: _ = permit;
            *active_batches.lock().unwrap() += 1;
            let batch_results: _ = Self::run_batch(pending_tasks, stats, config).await;
            // 将批次结果添加到全局结果集合
            {
                let mut all_results = results.lock().unwrap();
                all_results.extend(batch_results);
            }
            *active_batches.lock().unwrap() -= 1;
        });
    }
    /// 运行单个批次
    async fn run_batch(
        pending_tasks: Arc<Mutex<VecDeque<(usize, AiTaskType)>>>,
        stats: Arc<Mutex<BatchStats>>,
        config: BatchConfig,
    ) -> Vec<(usize, AiTaskResult)> {
        let start_time: _ = Instant::now();
        // 收集批次任务
        let batch_size: _ = config.max_batch_size;
        let mut batch_tasks = Vec::with_capacity(batch_size);
        {
            let mut tasks = pending_tasks.lock().unwrap();
            for _ in 0..batch_size {
                if let Some(task) = tasks.pop_front() {
                    batch_tasks.push(task);
                } else {
                    break;
                }
            }
        }
        // 即使没有任务也要处理，避免无限等待
        // 处理批次
        let results: _ = Self::process_batch(&batch_tasks).await;
        // 更新统计信息
        {
            let mut stats_guard = stats.lock().unwrap();
            stats_guard.record_batch(
                batch_tasks.len(),
                start_time.elapsed(),
                batch_tasks.len() * 1024, // 估算内存使用
            );
        }
        // 记录结果（实际应用中会发送到结果通道）
        println!(
            "处理批次: {} 个任务, 耗时: {:.2}ms",
            batch_tasks.len(),
            start_time.elapsed().as_secs_f64() * 1000.0
        );
        results
    }
    /// 处理单个批次
    async fn process_batch(tasks: &[(usize, AiTaskType)]) -> Vec<(usize, AiTaskResult)> {
        let mut results = Vec::with_capacity(tasks.len());
        for (task_id, task) in tasks {
            let result: _ = Self::process_single_task(task).await;
            results.push((*task_id, result));
        }
        results
    }
    /// 处理单个AI任务
    async fn process_single_task(task: &AiTaskType) -> AiTaskResult {
        let start_time: _ = Instant::now();
        let result: _ = match task {
            AiTaskType::TextGeneration {
                prompt,
                max_tokens: _,
                temperature: _,
            } => {
                // 极优化：进一步减少模拟时间
                tokio::task::yield_now().await;
                AiTaskResult::TextGeneration {
                    generated_text: format!("Generated text for: {}", prompt),
                    tokens_used: prompt.len() / 4,
                    processing_time: start_time.elapsed(),
                }
            }
            AiTaskType::ImageClassification {
                image_data: _,
                top_k,
            } => {
                // 极优化：使用 yield_now 代替 sleep
                tokio::task::yield_now().await;
                let predictions: _ = vec![
                    ("cat".to_string(), 0.85),
                    ("dog".to_string(), 0.75),
                    ("bird".to_string(), 0.65),
                ];
                let predictions: _ = if let Some(k) = top_k {
                    predictions.into_iter().take(*k).collect()
                } else {
                    predictions
                };
                AiTaskResult::ImageClassification {
                    predictions,
                    processing_time: start_time.elapsed(),
                }
            }
            AiTaskType::Embedding {
                text: _,
                model_name: _,
            } => {
                // 极优化：使用 yield_now 代替 sleep
                tokio::task::yield_now().await;
                let dimensions: _ = 384;
                let vector: _ = vec![0.1; dimensions];
                AiTaskResult::Embedding {
                    vector,
                    dimensions,
                    processing_time: start_time.elapsed(),
                }
            }
            AiTaskType::Translation {
                text,
                source_lang,
                target_lang,
            } => {
                // 极优化：使用 yield_now 代替 sleep
                tokio::task::yield_now().await;
                let translated: _ = format!(
                    "Translated: {} from {} to {}",
                    text, source_lang, target_lang
                );
                AiTaskResult::Translation {
                    translated_text: translated,
                    source_lang: source_lang.clone(),
                    target_lang: target_lang.clone(),
                    processing_time: start_time.elapsed(),
                }
            }
        };
        result
    }
    /// 获取批处理统计信息
    pub fn get_stats(&self) -> BatchStats {
        self.stats.lock().unwrap().clone()
    }
    /// 重置统计信息
    pub fn reset_stats(&self) {
        *self.stats.lock().unwrap() = BatchStats::default();
    }
    /// 获取待处理任务数量
    pub fn pending_tasks_count(&self) -> usize {
        self.pending_tasks.lock().unwrap().len()
    }
    /// 获取活跃批次数
    pub fn active_batches_count(&self) -> usize {
        *self.active_batches.lock().unwrap()
    }
    /// 等待所有任务完成并返回结果
    pub async fn flush(&self) -> Vec<(usize, AiTaskResult)> {
        // 立即处理所有待处理任务（同步方式）
        self.process_all_tasks().await;
        // 等待活跃批次完成
        while *self.active_batches.lock().unwrap() > 0 {
            tokio::time::sleep(Duration::from_millis(5)).await;
        }
        // 收集所有结果
        let all_results: _ = {
            let results: _ = self.results.lock().unwrap();
            results.clone()
        };
        all_results
    }
    /// 同步处理所有任务
    async fn process_all_tasks(&self) {
        // 持续处理直到没有待处理任务
        loop {
            // 检查是否有待处理任务
            if self.pending_tasks_count() == 0 {
                break;
            }
            // 获取信号量
            let permit: _ = self.batch_semaphore.clone().try_acquire_owned();
            if permit.is_err() {
                tokio::time::sleep(Duration::from_millis(1)).await;
                continue;
            }
            let permit: _ = permit.unwrap();
            let pending_tasks: _ = self.pending_tasks.clone();
            let active_batches: _ = self.active_batches.clone();
            let stats: _ = self.stats.clone();
            let config: _ = self.config.clone();
            let results: _ = self.results.clone();
            // 异步处理批次
            let handle: _ = tokio::spawn(async move {
                let _permit: _ = permit;
                *active_batches.lock().unwrap() += 1;
                let batch_results: _ = Self::run_batch(pending_tasks, stats, config).await;
                // 将批次结果添加到全局结果集合
                {
                    let mut all_results = results.lock().unwrap();
                    all_results.extend(batch_results);
                }
                *active_batches.lock().unwrap() -= 1;
            });
            // 等待批次完成
            let _: _ = handle.await;
        }
    }
}
/// 便利函数：创建文本生成批量处理器
#[allow(dead_code)]
pub fn create_text_generation_processor(max_batch_size: usize) -> AiBatchProcessor {
    let config: _ = BatchConfig {
        max_batch_size,
        batch_timeout_ms: 50, // 文本生成需要快速响应
        max_concurrent_batches: 20,
        enable_dynamic_batching: true,
        warmup_batch_size: 5,
    };
    AiBatchProcessor::new(config)
}
/// 便利函数：创建图像分类批量处理器
#[allow(dead_code)]
pub fn create_image_classification_processor(max_batch_size: usize) -> AiBatchProcessor {
    let config: _ = BatchConfig {
        max_batch_size,
        batch_timeout_ms: 200, // 图像分类耗时较长
        max_concurrent_batches: 5,
        enable_dynamic_batching: false, // 图像批处理通常固定大小
        warmup_batch_size: 2,
    };
    AiBatchProcessor::new(config)
}
/// 便利函数：创建嵌入向量批量处理器
#[allow(dead_code)]
pub fn create_embedding_processor(max_batch_size: usize) -> AiBatchProcessor {
    let config: _ = BatchConfig {
        max_batch_size,
        batch_timeout_ms: 100,
        max_concurrent_batches: 15,
        enable_dynamic_batching: true,
        warmup_batch_size: 8,
    };
    AiBatchProcessor::new(config)
}
#[cfg(test)]
mod tests {
    #[tokio::test]
    async fn test_add_single_task() {
        let processor: _ = AiBatchProcessor::new(BatchConfig::default());
        let task: _ = AiTaskType::TextGeneration {
            prompt: "Hello, world!".to_string(),
            max_tokens: Some(100),
            temperature: 0.7,
        };
        let task_id: _ = processor.add_task(task).await;
        assert_eq!(task_id, 0);
        // 等待任务被处理（批次处理是异步的）
        tokio::time::sleep(Duration::from_millis(10)).await;
        assert_eq!(processor.pending_tasks_count(), 0); // 任务已被处理
    }
    #[tokio::test]
    async fn test_add_multiple_tasks() {
        let processor: _ = AiBatchProcessor::new(BatchConfig::default());
        let tasks: _ = vec![
            AiTaskType::TextGeneration {
                prompt: "Task 1".to_string(),
                max_tokens: Some(50),
                temperature: 0.5,
            },
            AiTaskType::TextGeneration {
                prompt: "Task 2".to_string(),
                max_tokens: Some(50),
                temperature: 0.5,
            },
        ];
        let task_ids: _ = processor.add_tasks(tasks).await;
        assert_eq!(task_ids.len(), 2);
        assert_eq!(task_ids, vec![0, 1]);
    }
    #[test]
    fn test_batch_stats() {
        let mut stats = BatchStats::default();
        stats.record_batch(10, Duration::from_millis(100), 10240);
        stats.record_batch(20, Duration::from_millis(200), 20480);
        assert_eq!(stats.total_tasks_processed, 30);
        assert_eq!(stats.total_batches_processed, 2);
        assert_eq!(stats.average_batch_size, 15.0);
        assert_eq!(stats.peak_memory_usage, 20480);
    }
    #[tokio::test]
    async fn test_processor_creation() {
        let processor: _ = create_text_generation_processor(50);
        assert_eq!(processor.config.max_batch_size, 50);
        assert_eq!(processor.config.batch_timeout_ms, 50);
        assert!(processor.config.enable_dynamic_batching);
    }
    #[tokio::test]
    async fn test_flush_processor() {
        let processor: _ = AiBatchProcessor::new(BatchConfig::default());
        let task: _ = AiTaskType::Embedding {
            text: "Test embedding".to_string(),
            model_name: "test-model".to_string(),
        };
        let _task_id: _ = processor.add_task(task).await;
        // 等待一小段时间让异步任务开始执行
        tokio::time::sleep(Duration::from_millis(50)).await;
        processor.flush().await;
        assert_eq!(processor.pending_tasks_count(), 0);
        assert_eq!(processor.active_batches_count(), 0);
    }
}