amberjs 1.17.0

// 推理加速引擎
// 实现硬件加速和并行推理引擎，包括 GPU 加速、流水线并行和动态批处理

use std::collections::{BTreeMap, HashMap, VecDeque};
use std::sync::{Arc, Mutex, atomic::Ordering};
use std::time::{Duration, Instant};
use std::sync::atomic::Ordering;
use std::sync::atomic::AtomicU64;

/// 加速配置
#[derive(Debug, Clone)]
pub struct AccelerationConfig {
    pub use_gpu: bool,
    pub use_npu: bool,
    pub batch_size: usize,
    pub pipeline_parallel: bool,
}
/// 硬件类型
#[derive(Debug, Clone, PartialEq)]
pub enum HardwareType {
    CPU,
    GPU,
    NPU,
}
/// 推理任务
#[derive(Debug, Clone)]
struct InferenceTask {
    id: u64,
    input_data: Vec<f32>,
    output_data: Option<Vec<f32>>,
    start_time: Instant,
    hardware_type: HardwareType,
}
/// 批处理配置
#[derive(Debug, Clone)]
struct BatchConfig {
    dynamic_batching: bool,
    max_batch_size: usize,
    batch_timeout_ms: u64,
}
/// 加速引擎
pub struct AccelerationEngine {
    config: AccelerationConfig,
    runtime: Arc<Runtime>,
    gpu_available: bool,
    npu_available: bool,
    active_tasks: Arc<Mutex<HashMap<u64, InferenceTask>>>,
    batch_queue: Arc<Mutex<VecDeque<InferenceTask>>>,
    performance_stats: Arc<Mutex<AccelerationStats>>,
    batch_config: Arc<Mutex<BatchConfig>>,
}
/// 加速统计信息
#[derive(Debug, Clone, Default)]
pub struct AccelerationStats {
    pub total_inferences: u64,
    pub cpu_inferences: u64,
    pub gpu_inferences: u64,
    pub npu_inferences: u64,
    pub avg_cpu_latency: Duration,
    pub avg_gpu_latency: Duration,
    pub avg_npu_latency: Duration,
    pub throughput_cpu: f64,
    pub throughput_gpu: f64,
    pub throughput_npu: f64,
}
/// 推理结果
#[derive(Debug, Clone)]
pub struct InferenceResult {
    pub output: Vec<f32>,
    pub latency: Duration,
    pub hardware_used: HardwareType,
    pub batch_size: usize,
}
impl AccelerationEngine {
    /// 创建新的加速引擎实例
    pub fn new(runtime: &Arc<Runtime>, config: AccelerationConfig) -> Result<Self, String> {
        // 检测硬件可用性
        let gpu_available: _ = config.use_gpu && AccelerationEngine::detect_gpu()?;
        let npu_available: _ = config.use_npu && AccelerationEngine::detect_npu()?;
        Ok(AccelerationEngine {
            config: config.clone(),
            runtime: runtime.clone(),
            gpu_available,
            npu_available,
            active_tasks: Arc::new(Mutex::new(HashMap::new()))
            batch_queue: Arc::new(Mutex::new(VecDeque::new()))
            performance_stats: Arc::new(Mutex::new(AccelerationStats::default()))
            batch_config: Arc::new(Mutex::new(std::sync::Mutex::new(Mutex::new(BatchConfig {),
                dynamic_batching: false,
                max_batch_size: 32,
                batch_timeout_ms: 10,
            })),
        })
    }
    /// 检测 GPU 可用性
    fn detect_gpu() -> Result<bool, String> {
        // 简化实现：模拟 GPU 检测
        Ok(true)
    }
    /// 检测 NPU 可用性
    fn detect_npu() -> Result<bool, String> {
        // 简化实现：模拟 NPU 检测
        Ok(false)
    }
    /// 执行推理
    pub fn inference(&mut self, input: &[f32]) -> Result<InferenceResult, String> {
        let task: _ = InferenceTask {
            id: self.generate_task_id(),
            input_data: input.to_vec(),
            output_data: None,
            start_time: Instant::now(),
            hardware_type: HardwareType::CPU,
        };
        // 动态批处理
        if self.batch_config.lock().unwrap().dynamic_batching {
            return self.dynamic_batch_inference(task);
        }
        // 选择硬件
        let hardware_type: _ = self.select_hardware(input.len())?;
        // 执行推理
        let result: _ = match hardware_type {
            HardwareType::GPU => self.gpu_inference(input),
            HardwareType::NPU => self.npu_inference(input),
            HardwareType::CPU => self.cpu_inference(input),
        };
        // 更新统计
        self.update_stats(hardware_type, result.as_ref().map(|r| r.latency).unwrap_or_default());
        result
    }
    /// CPU 推理
    pub fn cpu_inference(&self, input: &[f32]) -> Result<InferenceResult, String> {
        let start: _ = Instant::now();
        // 模拟 CPU 推理
        let output: _ = self.simulate_inference(input, HardwareType::CPU)?;
        let latency: _ = start.elapsed();
        Ok(InferenceResult {
            output,
            latency,
            hardware_used: HardwareType::CPU,
            batch_size: 1,
        })
    }
    /// GPU 推理
    pub fn gpu_inference(&self, input: &[f32]) -> Result<InferenceResult, String> {
        let start: _ = Instant::now();
        if !self.gpu_available {
            return Err("GPU not available".to_string());
        }
        // 模拟 GPU 推理（更快）
        let output: _ = self.simulate_inference(input, HardwareType::GPU)?;
        let latency: _ = start.elapsed();
        Ok(InferenceResult {
            output,
            latency,
            hardware_used: HardwareType::GPU,
            batch_size: 1,
        })
    }
    /// NPU 推理
    pub fn npu_inference(&self, input: &[f32]) -> Result<InferenceResult, String> {
        let start: _ = Instant::now();
        if !self.npu_available {
            return Err("NPU not available".to_string());
        }
        // 模拟 NPU 推理（最快）
        let output: _ = self.simulate_inference(input, HardwareType::NPU)?;
        let latency: _ = start.elapsed();
        Ok(InferenceResult {
            output,
            latency,
            hardware_used: HardwareType::NPU,
            batch_size: 1,
        })
    }
    /// 批量推理
    pub fn batch_inference(&mut self, inputs: &[Vec<f32>]) -> Result<Vec<InferenceResult>, String> {
        if inputs.is_empty() {
            return Ok(Vec::new());
        }
        let batch_size: _ = inputs.len();
        let hardware_type: _ = self.select_hardware(batch_size)?;
        let start: _ = Instant::now();
        // 并行处理批量输入
        let outputs: Result<Vec<_>, _> = inputs
            .iter()
            .map(|input| self.simulate_inference(input, hardware_type.clone())
            .collect();
        let outputs: _ = outputs?;
        let latency: _ = start.elapsed();
        let results: _ = outputs
            .into_iter()
            .map(|output| InferenceResult {
                output,
                latency: latency / batch_size as u32,
                hardware_used: hardware_type.clone(),
                batch_size,
            })
            .collect();
        Ok(results)
    }
    /// 流水线并行推理
    pub fn pipeline_inference(&mut self, inputs: &[Vec<f32>]) -> Result<Vec<InferenceResult>, String> {
        if !self.config.pipeline_parallel {
            return self.batch_inference(inputs);
        }
        let pipeline_stages: _ = 4; // 4 阶段流水线
        let mut results = Vec::with_capacity(inputs.len());
        // 简化的流水线实现
        for chunk in inputs.chunks(pipeline_stages) {
            let chunk_results: _ = self.batch_inference(chunk)?;
            for result in chunk_results {
                results.push(result);
            }
        }
        Ok(results)
    }
    /// 动态批处理推理
    fn dynamic_batch_inference(&mut self, task: InferenceTask) -> Result<InferenceResult, String> {
        // 添加到批处理队列
        {
            let mut queue = self.batch_queue.lock().unwrap();
            queue.push_back(task);
        }
        // 检查是否达到批处理大小
        let should_process: _ = {
            let batch_config: _ = self.batch_config.lock().unwrap();
            let queue: _ = self.batch_queue.lock().unwrap();
            queue.len() >= batch_config.max_batch_size
                || (queue.len() > 0 && self.should_timeout(&queue, batch_config.batch_timeout_ms))
        };
        if should_process {
            self.process_batch()
        } else {
            // 等待批处理
            let timeout_ms: _ = {
                let batch_config: _ = self.batch_config.lock().unwrap();
                batch_config.batch_timeout_ms
            };
            std::thread::sleep(Duration::from_millis(timeout_ms));
            self.process_batch()
        }
    }
    /// 处理批处理队列
    fn process_batch(&mut self) -> Result<InferenceResult, String> {
        let (_batch_size, tasks) = {
            let mut queue = self.batch_queue.lock().unwrap();
            if queue.is_empty() {
                return Err("No tasks in batch queue".to_string());
            }
            // 收集批次
            let batch_size: _ = {
                let batch_config: _ = self.batch_config.lock().unwrap();
                batch_config.max_batch_size.min(queue.len())
            };
            let tasks: Vec<_> = queue.drain(..batch_size).collect();
            (batch_size, tasks)
        };
        let inputs: Vec<Vec<f32> = tasks.iter().map(|t| t.input_data.clone()).collect();
        let results: _ = self.batch_inference(&inputs)?;
        // 返回第一个结果（简化）
        Ok(results[0].clone())
    }
    /// 检查批处理超时
    fn should_timeout(&self, queue: &VecDeque<InferenceTask>, timeout_ms: u64) -> bool {
        if let Some(first_task) = queue.front() {
            first_task.start_time.elapsed() > Duration::from_millis(timeout_ms)
        } else {
            false
        }
    }
    /// 选择硬件
    fn select_hardware(&self, batch_size: usize) -> Result<HardwareType, String> {
        // 根据批处理大小和配置选择硬件
        if batch_size > 16 && self.gpu_available {
            Ok(HardwareType::GPU)
        } else if batch_size > 32 && self.npu_available {
            Ok(HardwareType::NPU)
        } else {
            Ok(HardwareType::CPU)
        }
    }
    /// 模拟推理计算
    fn simulate_inference(&self, input: &[f32], hardware: HardwareType) -> Result<Vec<f32>, String> {
        // 模拟不同硬件的计算延迟
        let delay: _ = match hardware {
            HardwareType::CPU => Duration::from_millis(50),
            HardwareType::GPU => Duration::from_millis(10),
            HardwareType::NPU => Duration::from_millis(5),
        };
        std::thread::sleep(delay);
        // 模拟输出计算
        let output_size: _ = input.len();
        let mut output = vec![0.0; output_size];
        for (i, &val) in input.iter().enumerate() {
            output[i] = (val * 2.0).sin().cos() * (i as f32 * 0.1);
        }
        Ok(output)
    }
    /// 设置动态批处理
    pub fn set_dynamic_batching(&self, enable: bool) {
        let mut config = self.batch_config.lock().unwrap();
        config.dynamic_batching = enable;
    }
    /// 设置批处理大小
    pub fn set_batch_size(&mut self, batch_size: usize) {
        let mut config = self.batch_config.lock().unwrap();
        config.max_batch_size = batch_size;
    }
    /// 生成任务 ID
    fn generate_task_id(&self) -> u64 {
        static COUNTER: AtomicU64 = AtomicU64::new(0);
        COUNTER.fetch_add(1, Ordering::SeqCst)
    }
    /// 更新性能统计
    fn update_stats(&self, hardware: HardwareType, latency: Duration) {
        let mut stats = self.performance_stats.lock().unwrap();
        stats.total_inferences += 1;
        match hardware {
            HardwareType::CPU => {
                stats.cpu_inferences += 1;
                stats.avg_cpu_latency = avg_duration(stats.avg_cpu_latency, latency, stats.cpu_inferences);
                stats.throughput_cpu = stats.cpu_inferences as f64 / stats.avg_cpu_latency.as_secs_f64();
            }
            HardwareType::GPU => {
                stats.gpu_inferences += 1;
                stats.avg_gpu_latency = avg_duration(stats.avg_gpu_latency, latency, stats.gpu_inferences);
                stats.throughput_gpu = stats.gpu_inferences as f64 / stats.avg_gpu_latency.as_secs_f64();
            }
            HardwareType::NPU => {
                stats.npu_inferences += 1;
                stats.avg_npu_latency = avg_duration(stats.avg_npu_latency, latency, stats.npu_inferences);
                stats.throughput_npu = stats.npu_inferences as f64 / stats.avg_npu_latency.as_secs_f64();
            }
        }
    }
    /// 检查引擎健康状态
    pub fn is_healthy(&self) -> bool {
        let stats: _ = self.performance_stats.lock().unwrap();
        stats.total_inferences > 0
    }
    /// 获取性能统计
    pub fn get_stats(&self) -> AccelerationStats {
        self.performance_stats.lock().unwrap().clone()
    }
}
/// 计算平均持续时间
fn avg_duration(current_avg: Duration, new_value: Duration, count: u64) -> Duration {
    if count <= 1 {
        return new_value;
    }
    let total_secs: _ = current_avg.as_secs_f64() * (count - 1) as f64 + new_value.as_secs_f64();
    Duration::from_secs_f64(total_secs / count as f64)
}
#[cfg(test)]
mod tests {
    #[test]
    fn test_acceleration_engine_creation() {
        let runtime: _ = Arc::new(Mutex::new(Runtime::new(8 * 1024 * 1024, 64 * 1024 * 1024, false, false)),;
        let config: _ = AccelerationConfig {
            use_gpu: true,
            use_npu: false,
            batch_size: 32,
            pipeline_parallel: true,
        };
        let engine: _ = AccelerationEngine::new(&runtime, config);
        assert!(engine.is_ok());
    }
    #[test]
    fn test_cpu_inference() {
        let runtime: _ = Arc::new(Mutex::new(Runtime::new(8 * 1024 * 1024, 64 * 1024 * 1024, false, false)),;
        let config: _ = AccelerationConfig {
            use_gpu: false,
            use_npu: false,
            batch_size: 1,
            pipeline_parallel: false,
        };
        let engine: _ = AccelerationEngine::new(&runtime, config).unwrap();
        let input: _ = vec![1.0, 2.0, 3.0];
        let result: _ = engine.cpu_inference(&input);
        assert!(result.is_ok());
        assert_eq!(result.unwrap().output.len(), input.len());
    }
    #[test]
    fn test_gpu_inference() {
        let runtime: _ = Arc::new(Mutex::new(Runtime::new(8 * 1024 * 1024, 64 * 1024 * 1024, false, false)),;
        let config: _ = AccelerationConfig {
            use_gpu: true,
            use_npu: false,
            batch_size: 1,
            pipeline_parallel: false,
        };
        let engine: _ = AccelerationEngine::new(&runtime, config).unwrap();
        let input: _ = vec![1.0, 2.0, 3.0];
        let result: _ = engine.gpu_inference(&input);
        assert!(result.is_ok());
    }
    #[test]
    fn test_batch_inference() {
        let runtime: _ = Arc::new(Mutex::new(Runtime::new(8 * 1024 * 1024, 64 * 1024 * 1024, false, false)),;
        let config: _ = AccelerationConfig {
            use_gpu: false,
            use_npu: false,
            batch_size: 64,
            pipeline_parallel: false,
        };
        let mut engine = AccelerationEngine::new(&runtime, config).unwrap();
        let inputs: _ = vec![
            vec![1.0, 2.0],
            vec![3.0, 4.0],
            vec![5.0, 6.0],
        ];
        let results: _ = engine.batch_inference(&inputs);
        assert!(results.is_ok());
        assert_eq!(results.unwrap().len(), 3);
    }
    #[test]
    fn test_pipeline_inference() {
        let runtime: _ = Arc::new(Mutex::new(Runtime::new(8 * 1024 * 1024, 64 * 1024 * 1024, false, false)),;
        let config: _ = AccelerationConfig {
            use_gpu: false,
            use_npu: false,
            batch_size: 32,
            pipeline_parallel: true,
        };
        let mut engine = AccelerationEngine::new(&runtime, config).unwrap();
        let inputs: _ = vec![
            vec![1.0, 2.0],
            vec![3.0, 4.0],
            vec![5.0, 6.0],
            vec![7.0, 8.0],
        ];
        let results: _ = engine.pipeline_inference(&inputs);
        assert!(results.is_ok());
        assert_eq!(results.unwrap().len(), 4);
    }
    #[test]
    fn test_dynamic_batching() {
        let runtime: _ = Arc::new(Mutex::new(Runtime::new(8 * 1024 * 1024, 64 * 1024 * 1024, false, false)),;
        let config: _ = AccelerationConfig {
            use_gpu: false,
            use_npu: false,
            batch_size: 16,
            pipeline_parallel: false,
        };
        let mut engine = AccelerationEngine::new(&runtime, config).unwrap();
        engine.set_dynamic_batching(true);
        let input: _ = vec![1.0, 2.0];
        let result: _ = engine.inference(&input);
        assert!(result.is_ok());
    }
}