amberjs 1.17.0

// 统一的 AI 推理引擎接口
// 支持多种 AI 框架和模型格式的通用接口

use crate::ai_inference::Tensor;
use anyhow::Result;
use async_trait::async_trait;
use std::sync::RwLock;
/// AI 模型格式枚举
#[derive(Debug, Clone, PartialEq)]
pub enum ModelFormat {
    /// ONNX 模型格式
    ONNX,
    /// PyTorch 模型格式
    PyTorch,
    /// TensorFlow Lite 模型格式
    TensorFlowLite,
    /// TensorFlow SavedModel 格式
    TensorFlow,
    /// 自定义模型格式
    Custom(String),
}
/// AI 推理引擎类型
#[derive(Debug, Clone, PartialEq)]
pub enum EngineType {
    /// CPU 推理引擎
    CPU,
    /// CUDA GPU 推理引擎
    CUDA,
    /// ROCm GPU 推理引擎
    ROCm,
    /// Metal GPU 推理引擎（Apple Silicon）
    Metal,
    /// WebGPU 推理引擎
    WebGPU,
    /// 专用 AI 加速器
    NPU,
}
/// 推理选项配置
#[derive(Debug, Clone)]
pub struct InferenceOptions {
    /// 推理引擎类型
    pub engine_type: EngineType,
    /// 批处理大小
    pub batch_size: Option<usize>,
    /// 是否启用优化
    pub optimization: bool,
    /// 并发推理数量
    pub parallel_inferences: Option<usize>,
    /// 内存优化级别
    pub memory_optimization: Option<MemoryOptimization>,
    /// 自定义配置参数
    pub custom_options: std::collections::HashMap<String, String>,
}
/// 内存优化级别
#[derive(Debug, Clone)]
pub enum MemoryOptimization {
    /// 无优化
    None,
    /// 低级优化
    Low,
    /// 中级优化
    Medium,
    /// 高级优化
    High,
    /// 极致优化
    Aggressive,
}
/// 推理引擎特征 - 统一的 AI 推理接口
#[async_trait]
pub trait InferenceEngine: Send + Sync {
    /// 获取引擎名称
    fn name(&self) -> &str;
    /// 获取支持的模型格式
    fn supported_formats(&self) -> Vec<ModelFormat>;
    /// 检查引擎是否可用
    fn is_available(&self) -> bool;
    /// 加载模型
    async fn load_model(&self, model_path: &str, options: InferenceOptions) -> Result<ModelHandle>;
    /// 执行单次推理
    async fn infer(&self, model: &ModelHandle, input: &Tensor) -> Result<InferenceResult>;
    /// 执行批量推理
    async fn batch_infer(
        &self,
        model: &ModelHandle,
        inputs: &[Tensor],
    ) -> Result<Vec<InferenceResult>>;
    /// 流式推理 - 支持实时推理结果
    async fn infer_stream(
        &self,
        model: &ModelHandle,
        input: Tensor,
    ) -> Result<tokio::sync::mpsc::Receiver<Result<Tensor>>>;
    /// 获取模型信息
    async fn get_model_info(&self, model: &ModelHandle) -> Result<ModelInfo>;
    /// 预热模型 - 初始化 GPU 和优化缓存
    async fn warmup(&self, model: &ModelHandle) -> Result<()>;
    /// 卸载模型 - 释放资源
    async fn unload_model(&self, model: &ModelHandle) -> Result<()>;
    /// 获取引擎统计信息
    async fn get_stats(&self) -> Result<EngineStats>;
    /// 克隆引擎实例
    fn clone_engine(&self) -> Box<dyn InferenceEngine>;
}
/// AI 推理结果
#[derive(Debug, Clone)]
pub struct InferenceResult {
    pub output: Tensor,
    pub inference_time_ms: f64,
    pub model_id: String,
    pub gpu_used: bool,
}
/// 模型句柄 - 代表已加载的模型
#[derive(Debug, Clone)]
pub struct ModelHandle {
    /// 内部模型标识符
    pub id: String,
    /// 模型路径
    pub path: String,
    /// 模型格式
    pub format: ModelFormat,
    /// 输入形状
    pub input_shape: Vec<usize>,
    /// 输出形状
    pub output_shape: Vec<usize>,
    /// 模型元数据
    pub metadata: std::collections::HashMap<String, String>,
}
/// 模型信息
#[derive(Debug, Clone)]
pub struct ModelInfo {
    /// 模型 ID
    pub id: String,
    /// 模型名称
    pub name: String,
    /// 模型格式
    pub format: ModelFormat,
    /// 输入张量信息
    pub inputs: Vec<TensorInfo>,
    /// 输出张量信息
    pub outputs: Vec<TensorInfo>,
    /// 模型参数数量
    pub parameter_count: usize,
    /// 模型大小（字节）
    pub size_bytes: u64,
    /// 引擎类型
    pub engine_type: EngineType,
}
/// 张量信息
#[derive(Debug, Clone)]
pub struct TensorInfo {
    /// 张量名称
    pub name: String,
    /// 张量形状
    pub shape: Vec<usize>,
    /// 张量数据类型
    pub data_type: String,
    /// 是否为可选张量
    pub optional: bool,
}
/// 引擎统计信息
#[derive(Debug, Clone)]
pub struct EngineStats {
    /// 引擎名称
    pub engine_name: String,
    /// 总推理次数
    pub total_inferences: u64,
    /// 成功推理次数
    pub successful_inferences: u64,
    /// 失败推理次数
    pub failed_inferences: u64,
    /// 总推理时间（毫秒）
    pub total_time_ms: f64,
    /// 平均推理时间（毫秒）
    pub average_time_ms: f64,
    /// GPU 使用率（0.0-1.0）
    pub gpu_utilization: f64,
    /// 内存使用量（字节）
    pub memory_usage_bytes: u64,
    /// 缓存命中率（0.0-1.0）
    pub cache_hit_rate: f64,
}
/// 引擎工厂特征 - 用于创建不同类型的推理引擎
#[async_trait]
pub trait EngineFactory: Send + Sync {
    /// 创建引擎实例
    async fn create(&self, engine_type: EngineType) -> Result<Box<dyn InferenceEngine>>;
    /// 获取工厂名称
    fn name(&self) -> &str;
    /// 获取支持的模型格式
    fn supported_formats(&self) -> Vec<ModelFormat>;
}
/// 引擎管理器 - 管理多个推理引擎实例
pub struct EngineManager {
    /// 引擎工厂注册表
    factories: std::collections::HashMap<String, Box<dyn EngineFactory>>,
    /// 活跃的引擎实例
    active_engines: Arc<RwLock<std::collections::HashMap<String, Box<dyn InferenceEngine>>>>,
    /// 默认引擎类型
    default_engine_type: EngineType,
}
impl EngineManager {
    /// 创建新的引擎管理器
    pub fn new(default_engine_type: EngineType) -> Self {
        EngineManager {
            factories: std::collections::HashMap::new(),
            active_engines: Arc::new(Mutex::new(std::collections::HashMap::new())),
            default_engine_type,
        }
    }
    /// 注册引擎工厂
    pub fn register_factory(&mut self, name: String, factory: Box<dyn EngineFactory>) {
        self.factories.insert(name, factory);
    }
    /// 获取或创建引擎实例
    pub async fn get_or_create_engine(
        &self,
        factory_name: &str,
        engine_type: Option<EngineType>,
    ) -> Result<Box<dyn InferenceEngine>> {
        let engine_type: _ = engine_type
            .clone()
            .unwrap_or_else(|| self.default_engine_type.clone());
        // 检查缓存的引擎
        let cache_key: _ = format!("{}_{:?}", engine_type, factory_name);
        {
            let active_engines: _ = self.active_engines.read().await;
            if let Some(engine) = active_engines.get(&cache_key) {
                return Ok(engine.boxed_clone());
            }
        }
        // 创建新引擎
        let factory: _ = self
            .factories
            .get(factory_name)
            .ok_or_else(|| anyhow::anyhow!("Factory '{}' not found", factory_name))?;
        let engine: _ = factory.create(engine_type).await?;
        // 缓存引擎
        {
            let mut active_engines = self.active_engines.write().await;
            active_engines.insert(cache_key, engine.boxed_clone());
        }
        Ok(engine)
    }
    /// 获取所有可用的引擎名称
    pub fn available_engines(&self) -> Vec<String> {
        self.factories.keys().cloned().collect()
    }
    /// 检查引擎是否可用
    pub async fn is_engine_available(
        &self,
        factory_name: &str,
        engine_type: Option<EngineType>,
    ) -> bool {
        let engine_type: _ = engine_type
            .clone()
            .unwrap_or_else(|| self.default_engine_type.clone());
        let cache_key: _ = format!("{}_{:?}", engine_type, factory_name);
        {
            let active_engines: _ = self.active_engines.read().await;
            if let Some(engine) = active_engines.get(&cache_key) {
                return engine.is_available();
            }
        }
        if let Some(factory) = self.factories.get(factory_name) {
            if let Ok(engine) = factory.create(engine_type).await {
                return engine.is_available();
            }
        }
        false
    }
}
/// 扩展 trait - 为 Box<dyn InferenceEngine> 添加便利方法
pub trait InferenceEngineExt {
    fn boxed_clone(&self) -> Box<dyn InferenceEngine>;
}
impl InferenceEngineExt for Box<dyn InferenceEngine> {
    fn boxed_clone(&self) -> Box<dyn InferenceEngine> {
        // 使用引擎自身的克隆方法
        self.as_ref().clone_engine()
    }
}
#[cfg(test)]
mod tests {
    use super::*;
    /// 测试引擎管理器的基本功能
    #[tokio::test]
    async fn test_engine_manager_basic() {
        let mut manager = EngineManager::new(EngineType::CPU);
        // 创建模拟工厂
        struct MockFactory;
        #[async_trait]
        impl EngineFactory for MockFactory {
            async fn create(&self, _engine_type: EngineType) -> Result<Box<dyn InferenceEngine>> {
                Ok(Box::new(MockEngine))
            }
            fn name(&self) -> &str {
                "mock"
            }
            fn supported_formats(&self) -> Vec<ModelFormat> {
                vec![ModelFormat::ONNX]
            }
        }
        struct MockEngine;
        #[async_trait]
        impl InferenceEngine for MockEngine {
            fn clone_engine(&self) -> Box<dyn InferenceEngine> {
                Box::new(MockEngine)
            }
            fn name(&self) -> &str {
                "MockEngine"
            }
            fn supported_formats(&self) -> Vec<ModelFormat> {
                vec![ModelFormat::ONNX]
            }
            fn is_available(&self) -> bool {
                true
            }
            async fn load_model(
                &self,
                _model_path: &str,
                _options: InferenceOptions,
            ) -> Result<ModelHandle> {
                Ok(ModelHandle {
                    id: "test".to_string(),
                    path: _model_path.to_string(),
                    format: ModelFormat::ONNX,
                    input_shape: vec![1, 3, 224, 224],
                    output_shape: vec![1, 1000],
                    metadata: std::collections::HashMap::new(),
                })
            }
            async fn infer(
                &self,
                _model: &ModelHandle,
                _input: &Tensor,
            ) -> Result<InferenceResult> {
                todo!()
            }
            async fn batch_infer(
                &self,
                _model: &ModelHandle,
                _inputs: &[Tensor],
            ) -> Result<Vec<InferenceResult>> {
                todo!()
            }
            async fn infer_stream(
                &self,
                _model: &ModelHandle,
                _input: Tensor,
            ) -> Result<tokio::sync::mpsc::Receiver<Result<Tensor>>> {
                todo!()
            }
            async fn get_model_info(&self, _model: &ModelHandle) -> Result<ModelInfo> {
                todo!()
            }
            async fn warmup(&self, _model: &ModelHandle) -> Result<()> {
                Ok(())
            }
            async fn unload_model(&self, _model: &ModelHandle) -> Result<()> {
                Ok(())
            }
            async fn get_stats(&self) -> Result<EngineStats> {
                Ok(EngineStats {
                    engine_name: "MockEngine".to_string(),
                    total_inferences: 0,
                    successful_inferences: 0,
                    failed_inferences: 0,
                    total_time_ms: 0.0,
                    average_time_ms: 0.0,
                    gpu_utilization: 0.0,
                    memory_usage_bytes: 0,
                    cache_hit_rate: 0.0,
                })
            }
        }
        manager.register_factory("mock".to_string(), Box::new(MockFactory));
        let engines: _ = manager.available_engines();
        assert_eq!(engines, vec!["mock"]);
        let is_available: _ = manager.is_engine_available("mock", None).await;
        assert!(is_available);
    }
}
use std::collections::{BTreeMap, HashMap};
use std::sync::{Arc, Mutex};