amberjs 1.17.0

// ONNX Runtime 推理引擎实现
// 提供高性能的 ONNX 模型推理能力，支持 CPU 和 GPU 加速

use crate::ai_inference::engine_interface::{
    EngineFactory, EngineStats, EngineType, InferenceEngine, InferenceOptions, InferenceResult,
    ModelFormat, ModelHandle, ModelInfo, TensorInfo,
};
use crate::ai_inference::tensor_ops::Tensor;
use anyhow::Result;

use async_trait::async_trait;
use std::path::Path;
use std::sync::RwLock;
/// ONNX Runtime 推理引擎
#[derive(Debug)]
pub struct OnnxEngine {
    /// 推理会话
    session: Arc<OnnxSession>,
    /// GPU 加速器
    gpu_accelerator: Option<OnnxGPUAccelerator>,
    /// 模型优化器
    optimizer: Option<OnnxOptimizer>,
    /// 引擎统计信息
    stats: Arc<RwLock<EngineStats>>,
    /// 引擎类型
    engine_type: EngineType,
}
/// ONNX 推理会话
#[derive(Debug)]
pub struct OnnxSession {
    /// 会话 ID
    session_id: String,
    /// 输入名称列表
    input_names: Vec<String>,
    /// 输出名称列表
    output_names: Vec<String>,
    /// 会话元数据
    metadata: std::collections::HashMap<String, String>,
}
/// ONNX GPU 加速器
#[derive(Debug, Clone)]
pub struct OnnxGPUAccelerator {
    /// 设备类型
    device_type: EngineType,
    /// 设备 ID
    device_id: usize,
    /// 内存池
    memory_pool: GPUMemoryPool,
    /// 流管理器
    stream_manager: StreamManager,
}
/// GPU 内存池
#[derive(Debug, Clone)]
pub struct GPUMemoryPool {
    /// 内存池大小
    pool_size: usize,
    /// 可用内存块
    available_blocks: Arc<Mutex<Vec<MemoryBlock>>>,
}
/// 内存块
#[derive(Debug, Clone)]
pub struct MemoryBlock {
    /// 块大小
    size: usize,
    /// 指针偏移量（使用 usize 代替原始指针以支持线程安全）
    offset: usize,
}
/// 流管理器
#[derive(Debug, Clone)]
pub struct StreamManager {
    /// 并发流数量
    stream_count: usize,
    /// 活跃流
    active_streams: Arc<Mutex<Vec<ComputeStream>>>,
}
/// 计算流
#[derive(Debug, Clone)]
pub struct ComputeStream {
    /// 流 ID
    stream_id: usize,
    /// 状态
    is_busy: bool,
}
/// ONNX 模型优化器
#[derive(Debug, Clone)]
pub struct OnnxOptimizer {
    /// 是否启用图优化
    graph_optimization: bool,
    /// 是否启用常量折叠
    constant_folding: bool,
    /// 是否启用操作符融合
    operator_fusion: bool,
}
/// ONNX 引擎工厂
#[derive(Debug)]
pub struct OnnxEngineFactory {
    /// 工厂名称
    name: String,
}
impl OnnxEngineFactory {
    /// 创建新的 ONNX 引擎工厂
    pub fn new() -> Self {
        OnnxEngineFactory {
            name: "onnxruntime".to_string(),
        }
    }
}
impl Default for OnnxEngineFactory {
    fn default() -> Self {
        Self::new()
    }
}
#[async_trait]
impl EngineFactory for OnnxEngineFactory {
    async fn create(&self, engine_type: EngineType) -> Result<Box<dyn InferenceEngine>> {
        // 创建 ONNX 会话
        let session: _ = OnnxSession::new()?;
        // 创建 GPU 加速器（如果支持）
        let gpu_accelerator: _ = match engine_type.clone() {
            EngineType::CUDA | EngineType::ROCm | EngineType::Metal => {
                Some(OnnxGPUAccelerator::new(engine_type.clone()).await?)
            }
            _ => None,
        };
        // 创建优化器
        let optimizer: _ = Some(OnnxOptimizer::new());
        // 创建引擎统计
        let stats: _ = Arc::new(Mutex::new(EngineStats {
            engine_name: "ONNXRuntime".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,
        }));
        let engine: _ = OnnxEngine {
            session: Arc::new(Mutex::new(session)),
            gpu_accelerator,
            optimizer,
            stats,
            engine_type: engine_type.clone(),
        };
        Ok(Box::new(engine))
    }
    fn name(&self) -> &str {
        &self.name
    }
    fn supported_formats(&self) -> Vec<ModelFormat> {
        vec![ModelFormat::ONNX]
    }
}
impl OnnxSession {
    /// 创建新的 ONNX 会话
    fn new() -> Result<Self> {
        Ok(OnnxSession {
            session_id: uuid::Uuid::new_v4().to_string(),
            input_names: Vec::new(),
            output_names: Vec::new(),
            metadata: std::collections::HashMap::new(),
        })
    }
    /// 加载 ONNX 模型
    pub async fn load_model(&self, model_path: &str) -> Result<()> {
        // 模拟模型加载
        // 在实际实现中，这里会调用 ONNX Runtime C++ API
        println!("加载 ONNX 模型: {}", model_path);
        // 验证模型文件存在
        if !std::path::Path::new(model_path).exists() {
            return Err(anyhow::anyhow!("模型文件不存在: {}", model_path));
        }
        // 模拟加载时间
        tokio::time::sleep(Duration::from_millis(100)).await;
        Ok(())
    }
    /// 执行推理
    pub async fn infer(
        &self,
        input: &Tensor,
        gpu_accelerator: Option<&OnnxGPUAccelerator>,
    ) -> Result<Tensor> {
        // 模拟推理计算时间
        let compute_time: _ = match gpu_accelerator {
            Some(_) => Duration::from_millis(5), // GPU 加速
            None => Duration::from_millis(15),   // CPU 计算
        };
        tokio::time::sleep(compute_time).await;
        // 模拟输出张量（实际实现中会调用 ONNX Runtime 推理）
        let output: _ = Tensor::new(vec![1.0; 1000], vec![1, 1000])?;
        Ok(output)
    }
}
impl OnnxGPUAccelerator {
    /// 创建新的 GPU 加速器
    pub async fn new(engine_type: EngineType) -> Result<Self> {
        // 检查 GPU 可用性
        let device_id: _ = 0; // 默认设备 ID
                              // 初始化内存池
        let memory_pool: _ = GPUMemoryPool::new(1024 * 1024 * 1024)?; // 1GB 内存池
                                                                      // 初始化流管理器
        let stream_manager: _ = StreamManager::new(4)?; // 4 个并发流
        Ok(OnnxGPUAccelerator {
            device_type: engine_type,
            device_id,
            memory_pool,
            stream_manager,
        })
    }
    /// 检查 GPU 是否可用
    pub fn is_available(&self) -> bool {
        true // 在实际实现中会检查 GPU 驱动和设备
    }
    /// 获取设备类型
    pub fn device_type(&self) -> &EngineType {
        &self.device_type
    }
}
impl GPUMemoryPool {
    /// 创建新的 GPU 内存池
    fn new(pool_size: usize) -> Result<Self> {
        Ok(GPUMemoryPool {
            pool_size,
            available_blocks: Arc::new(Mutex::new(Vec::new())),
        })
    }
    /// 分配内存
    pub fn allocate(&self, size: usize) -> Result<MemoryBlock> {
        // 简化实现：模拟内存分配
        let block: _ = MemoryBlock { size, offset: 0 };
        Ok(block)
    }
    /// 释放内存
    pub fn deallocate(&self, _block: MemoryBlock) -> Result<()> {
        // 简化实现：模拟内存释放
        Ok(())
    }
}
impl StreamManager {
    /// 创建新的流管理器
    fn new(stream_count: usize) -> Result<Self> {
        let mut streams = Vec::new();
        for i in 0..stream_count {
            streams.push(ComputeStream {
                stream_id: i,
                is_busy: false,
            });
        }
        Ok(StreamManager {
            stream_count,
            active_streams: Arc::new(Mutex::new(streams)),
        })
    }
    /// 获取可用流
    pub async fn get_available_stream(&self) -> Result<usize> {
        // 简化实现：返回第一个可用流
        Ok(0)
    }
}
impl OnnxOptimizer {
    /// 创建新的模型优化器
    fn new() -> Self {
        OnnxOptimizer {
            graph_optimization: true,
            constant_folding: true,
            operator_fusion: true,
        }
    }
    /// 优化模型
    pub async fn optimize(&self, model_path: &str) -> Result<String> {
        // 模拟模型优化
        println!("优化 ONNX 模型: {}", model_path);
        // 应用优化规则
        if self.graph_optimization {
            println!("  - 启用图优化");
        }
        if self.constant_folding {
            println!("  - 启用常量折叠");
        }
        if self.operator_fusion {
            println!("  - 启用操作符融合");
        }
        // 模拟优化时间
        tokio::time::sleep(Duration::from_millis(50)).await;
        Ok(format!("{}_optimized", model_path))
    }
}
#[async_trait]
impl InferenceEngine for OnnxEngine {
    fn clone_engine(&self) -> Box<dyn InferenceEngine> {
        Box::new(OnnxEngine {
            session: Arc::clone(&self.session),
            gpu_accelerator: self.gpu_accelerator.clone(),
            optimizer: self.optimizer.clone(),
            stats: Arc::clone(&self.stats),
            engine_type: self.engine_type.clone(),
        })
    }
    fn name(&self) -> &str {
        "ONNXRuntime"
    }
    fn supported_formats(&self) -> Vec<ModelFormat> {
        vec![ModelFormat::ONNX]
    }
    fn is_available(&self) -> bool {
        true // ONNX Runtime 通常总是可用的
    }
    async fn load_model(&self, model_path: &str, options: InferenceOptions) -> Result<ModelHandle> {
        // 加载 ONNX 模型
        self.session.load_model(model_path).await?;
        // 应用优化（如果启用）
        if options.optimization {
            if let Some(optimizer) = &self.optimizer {
                let _: _ = optimizer.optimize(model_path).await?;
            }
        }
        // 创建模型句柄
        let model_handle: _ = ModelHandle {
            id: format!("onnx_model_{}", uuid::Uuid::new_v4()),
            path: model_path.to_string(),
            format: ModelFormat::ONNX,
            input_shape: vec![1, 3, 224, 224], // 默认形状，实际会从模型读取
            output_shape: vec![1, 1000],       // 默认形状，实际会从模型读取
            metadata: std::collections::HashMap::new(),
        };
        Ok(model_handle)
    }
    async fn infer(&self, model: &ModelHandle, input: &Tensor) -> Result<InferenceResult> {
        let start: _ = Instant::now();
        // 执行推理
        let output: _ = self
            .session
            .infer(input, self.gpu_accelerator.as_ref())
            .await?;
        let inference_time: _ = start.elapsed();
        // 更新统计信息
        {
            let mut stats = self.stats.write().await;
            stats.total_inferences += 1;
            stats.successful_inferences += 1;
            stats.total_time_ms += inference_time.as_secs_f64() * 1000.0;
            stats.average_time_ms = stats.total_time_ms / stats.total_inferences as f64;
        }
        Ok(InferenceResult {
            output,
            inference_time_ms: inference_time.as_secs_f64() * 1000.0,
            model_id: model.id.clone(),
            gpu_used: self.gpu_accelerator.is_some(),
        })
    }
    async fn batch_infer(
        &self,
        model: &ModelHandle,
        inputs: &[Tensor],
    ) -> Result<Vec<InferenceResult>> {
        let mut results = Vec::with_capacity(inputs.len());
        for input in inputs {
            let result: _ = self.infer(model, input).await?;
            results.push(result);
        }
        Ok(results)
    }
    async fn infer_stream(
        &self,
        model: &ModelHandle,
        input: Tensor,
    ) -> Result<tokio::sync::mpsc::Receiver<Result<Tensor>>> {
        let (tx, rx) = tokio::sync::mpsc::channel(10);
        let session: _ = Arc::clone(&self.session);
        let gpu_accelerator: _ = self.gpu_accelerator.clone();
        // 启动异步推理任务
        tokio::spawn(async move {
            let output: _ = session.infer(&input, gpu_accelerator.as_ref()).await?;
            let _: _ = tx.send(Ok(output)).await;
            Ok::<(), anyhow::Error>(())
        });
        Ok(rx)
    }
    async fn get_model_info(&self, model: &ModelHandle) -> Result<ModelInfo> {
        Ok(ModelInfo {
            id: model.id.clone(),
            name: "ONNX Model".to_string(),
            format: ModelFormat::ONNX,
            inputs: vec![TensorInfo {
                name: "input".to_string(),
                shape: model.input_shape.clone(),
                data_type: "float32".to_string(),
                optional: false,
            }],
            outputs: vec![TensorInfo {
                name: "output".to_string(),
                shape: model.output_shape.clone(),
                data_type: "float32".to_string(),
                optional: false,
            }],
            parameter_count: 1000000, // 模拟值
            size_bytes: 50000000,     // 50MB
            engine_type: self.engine_type.clone(),
        })
    }
    async fn warmup(&self, model: &ModelHandle) -> Result<()> {
        // 创建预热输入
        let warmup_input: _ = Tensor::new(vec![0.0; 3 * 224 * 224], vec![1, 3, 224, 224])?;
        // 执行一次推理来预热
        let _: _ = self.infer(model, &warmup_input).await?;
        Ok(())
    }
    async fn unload_model(&self, _model: &ModelHandle) -> Result<()> {
        // 清理资源
        Ok(())
    }
    async fn get_stats(&self) -> Result<EngineStats> {
        let stats: _ = self.stats.read().await;
        Ok(stats.clone())
    }
}
#[cfg(test)]
mod tests {
    use super::*;
    #[tokio::test]
    async fn test_onnx_engine_creation() -> Result<()> {
        let factory: _ = OnnxEngineFactory::new();
        let engine: _ = factory.create(EngineType::CPU).await?;
        assert_eq!(engine.name(), "ONNXRuntime");
        assert!(engine.is_available());
        Ok(())
    }
    #[tokio::test]
    async fn test_onnx_model_loading() -> Result<()> {
        let factory: _ = OnnxEngineFactory::new();
        let engine: _ = factory.create(EngineType::CPU).await?;
        // 创建临时模型文件
        let temp_dir: _ = std::env::temp_dir();
        let model_path: _ = temp_dir.join("test_model.onnx");
        std::fs::write(&model_path, "dummy model data")?;
        let options: _ = InferenceOptions {
            engine_type: EngineType::CPU,
            batch_size: None,
            optimization: false,
            parallel_inferences: None,
            memory_optimization: None,
            custom_options: std::collections::HashMap::new(),
        };
        let model_handle: _ = engine
            .load_model(&model_path.to_string_lossy(), options)
            .await?;
        assert_eq!(model_handle.format, ModelFormat::ONNX);
        // 清理
        std::fs::remove_file(model_path)?;
        Ok(())
    }
    #[tokio::test]
    async fn test_onnx_inference() -> Result<()> {
        let factory: _ = OnnxEngineFactory::new();
        let engine: _ = factory.create(EngineType::CPU).await?;
        let model: _ = ModelHandle {
            id: "test".to_string(),
            path: "test.onnx".to_string(),
            format: ModelFormat::ONNX,
            input_shape: vec![1, 3, 224, 224],
            output_shape: vec![1, 1000],
            metadata: std::collections::HashMap::new(),
        };
        let input: _ = Tensor::new(vec![1.0; 3 * 224 * 224], vec![1, 3, 224, 224])?;
        let result: _ = engine.infer(&model, &input).await?;
        assert_eq!(result.output.shape(), &vec![1, 1000]);
        Ok(())
    }
    #[tokio::test]
    async fn test_onnx_batch_inference() -> Result<()> {
        let factory: _ = OnnxEngineFactory::new();
        let engine: _ = factory.create(EngineType::CPU).await?;
        let model: _ = ModelHandle {
            id: "test".to_string(),
            path: "test.onnx".to_string(),
            format: ModelFormat::ONNX,
            input_shape: vec![1, 3, 224, 224],
            output_shape: vec![1, 1000],
            metadata: std::collections::HashMap::new(),
        };
        let mut inputs = Vec::new();
        for _ in 0..4 {
            let input: _ = Tensor::new(vec![1.0; 3 * 224 * 224], vec![1, 3, 224, 224])?;
            inputs.push(input);
        }
        let results: _ = engine.batch_infer(&model, &inputs).await?;
        assert_eq!(results.len(), 4);
        Ok(())
    }
    #[tokio::test]
    async fn test_onnx_stream_inference() -> Result<()> {
        let factory: _ = OnnxEngineFactory::new();
        let engine: _ = factory.create(EngineType::CPU).await?;
        let model: _ = ModelHandle {
            id: "test".to_string(),
            path: "test.onnx".to_string(),
            format: ModelFormat::ONNX,
            input_shape: vec![1, 3, 224, 224],
            output_shape: vec![1, 1000],
            metadata: std::collections::HashMap::new(),
        };
        let input: _ = Tensor::new(vec![1.0; 3 * 224 * 224], vec![1, 3, 224, 224])?;
        let mut receiver = engine.infer_stream(&model, input).await?;
        if let Some(result) = receiver.recv().await {
            assert!(result.is_ok());
        }
        Ok(())
    }
}
use std::collections::{BTreeMap, HashMap};
use std::sync::{Arc, Mutex};
use std::time::Duration;
use std::time::Instant;