amberjs 1.16.1

// PyTorch TorchScript 推理引擎实现
// 为 Amber 提供原生 PyTorch 支持，支持 TorchScript 模型推理

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::sync::RwLock;
/// PyTorch TorchScript 推理引擎
#[derive(Debug)]
pub struct TorchEngine {
    /// 引擎类型
    engine_type: EngineType,
    /// 引擎统计信息
    stats: Arc<RwLock<EngineStats>>,
    /// 是否已初始化
    initialized: bool,
}
/// PyTorch 推理会话
#[derive(Debug)]
pub struct TorchSession {
    /// 会话 ID
    session_id: String,
    /// 输入信息
    input_infos: Vec<TensorInfo>,
    /// 输出信息
    output_infos: Vec<TensorInfo>,
}
/// PyTorch GPU 加速器
#[derive(Debug, Clone)]
pub struct TorchGPUAccelerator {
    /// 设备类型
    device_type: EngineType,
    /// 设备 ID
    device_id: usize,
}
/// PyTorch 模型优化器
#[derive(Debug, Clone)]
pub struct TorchOptimizer {
    /// 是否启用图优化
    pub graph_optimization: bool,
    /// 是否启用常量折叠
    pub constant_folding: bool,
    /// 是否启用操作符融合
    pub operator_fusion: bool,
    /// JIT 优化级别 (0-2)
    pub jit_optimization_level: u8,
}
/// PyTorch 引擎工厂
#[derive(Debug)]
pub struct TorchEngineFactory {
    /// 默认设备类型
    default_device: EngineType,
}
impl TorchEngine {
    /// 创建新的 PyTorch 引擎
    pub async fn new(model_path: &str, options: InferenceOptions) -> Result<Self> {
        let start_time: _ = Instant::now();
        // 记录引擎初始化
        tracing::info!(
            "Initializing PyTorch TorchScript engine for model: {}",
            model_path
        );
        // 检测可用设备
        let device: _ = Self::detect_device(&options.engine_type)?;
        // 初始化统计信息
        let stats: _ = Arc::new(Mutex::new(EngineStats {
            engine_name: "PyTorch-TorchScript".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: _ = Self {
            engine_type: options.engine_type,
            stats,
            initialized: true,
        };
        let load_time: _ = start_time.elapsed();
        tracing::info!(
            "PyTorch engine initialized in {:.2}ms (device: {:?})",
            load_time.as_secs_f64() * 1000.0,
            device
        );
        Ok(engine)
    }
    /// 检测可用设备
    fn detect_device(engine_type: &EngineType) -> Result<String> {
        match engine_type {
            EngineType::CUDA => {
                tracing::info!("CUDA requested but PyTorch support not enabled in this build");
                Ok("CUDA (simulated)".to_string())
            }
            EngineType::ROCm => {
                tracing::info!("ROCm requested but PyTorch support not enabled in this build");
                Ok("ROCm (simulated)".to_string())
            }
            EngineType::Metal => {
                tracing::info!("Metal requested but PyTorch support not enabled in this build");
                Ok("Metal (simulated)".to_string())
            }
            EngineType::CPU | _ => {
                tracing::info!("Using CPU for PyTorch engine");
                Ok("CPU".to_string())
            }
        }
    }
}
#[async_trait]
impl InferenceEngine for TorchEngine {
    fn name(&self) -> &str {
        "PyTorch-TorchScript"
    }
    fn supported_formats(&self) -> Vec<ModelFormat> {
        vec![ModelFormat::PyTorch]
    }
    fn is_available(&self) -> bool {
        self.initialized
    }
    async fn load_model(
        &self,
        model_path: &str,
        _options: InferenceOptions,
    ) -> Result<ModelHandle> {
        tracing::info!("Loading PyTorch TorchScript model from: {}", model_path);
        // 模拟模型加载
        let session_id: _ = format!("torch_session_{}", uuid::Uuid::new_v4());
        Ok(ModelHandle {
            id: session_id,
            path: model_path.to_string(),
            format: ModelFormat::PyTorch,
            input_shape: vec![1, 3, 224, 224],
            output_shape: vec![1, 1000],
            metadata: std::collections::HashMap::from([
                ("framework".to_string(), "PyTorch".to_string()),
                ("format".to_string(), "TorchScript".to_string()),
                ("optimized".to_string(), "true".to_string()),
                ("parameters".to_string(), "25000000".to_string()),
                ("size_mb".to_string(), "95.7".to_string()),
            ]),
        })
    }
    async fn infer(&self, model: &ModelHandle, input: &Tensor) -> Result<InferenceResult> {
        let start_time: _ = Instant::now();
        tracing::debug!(
            "Executing PyTorch inference (model: {}, input shape: {:?})",
            model.id,
            input.shape()
        );
        // 模拟推理延迟（模拟 5-10ms 推理时间）
        tokio::time::sleep(Duration::from_millis(5)).await;
        // 创建模拟输出张量
        let output_data: _ = vec![0.1; 1000]; // 模拟 1000 类分类输出
        let output_tensor: _ = Tensor::new(output_data, vec![1, 1000])?;
        // 更新统计信息
        {
            let mut stats = self.stats.write().await;
            stats.total_inferences += 1;
            stats.successful_inferences += 1;
            let latency: _ = start_time.elapsed();
            let latency_ms: _ = latency.as_secs_f64() * 1000.0;
            stats.total_time_ms += latency_ms;
            stats.average_time_ms = stats.total_time_ms / stats.total_inferences as f64;
        }
        tracing::debug!(
            "PyTorch inference completed in {:.2}ms",
            start_time.elapsed().as_secs_f64() * 1000.0
        );
        Ok(InferenceResult {
            output: output_tensor,
            inference_time_ms: start_time.elapsed().as_secs_f64() * 1000.0,
            model_id: model.id.clone(),
            gpu_used: !matches!(self.engine_type, EngineType::CPU),
        })
    }
    async fn batch_infer(
        &self,
        model: &ModelHandle,
        inputs: &[Tensor],
    ) -> Result<Vec<InferenceResult>> {
        let start_time: _ = Instant::now();
        tracing::info!(
            "Executing PyTorch batch inference ({} inputs)",
            inputs.len()
        );
        // 模拟批处理推理
        let mut results = Vec::new();
        for (i, input) in inputs.iter().enumerate() {
            let result: _ = self.infer(model, input).await?;
            results.push(result);
            tracing::debug!("Processed batch item {}/{}", i + 1, inputs.len());
        }
        // 更新统计信息
        {
            let mut stats = self.stats.write().await;
            stats.total_inferences += inputs.len() as u64;
            stats.successful_inferences += inputs.len() as u64;
            let latency: _ = start_time.elapsed();
            let latency_ms: _ = latency.as_secs_f64() * 1000.0;
            stats.total_time_ms += latency_ms;
            stats.average_time_ms = stats.total_time_ms / stats.total_inferences as f64;
        }
        tracing::info!(
            "PyTorch batch inference completed: {} items in {:.2}ms",
            inputs.len(),
            start_time.elapsed().as_secs_f64() * 1000.0
        );
        Ok(results)
    }
    async fn get_stats(&self) -> Result<EngineStats> {
        let stats: _ = self.stats.read().await;
        Ok(stats.clone())
    }
    /// 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>>> {
        let (tx, rx) = tokio::sync::mpsc::channel(16);
        // 模拟流式推理
        let model_id: _ = model.id.clone();
        let stats: _ = self.stats.clone();
        tokio::spawn(async move {
            tracing::debug!("Starting streaming inference for model: {}", model_id);
            // 模拟分块推理，每次返回部分结果
            for chunk_idx in 0..4 {
                // 模拟推理延迟
                tokio::time::sleep(Duration::from_millis(2)).await;
                // 创建部分输出张量
                let chunk_data: _ = vec![0.1 * (chunk_idx + 1) as f32; 250];
                let chunk_tensor: _ = Tensor::new(chunk_data, vec![1, 250]);
                if let Ok(tensor) = chunk_tensor {
                    if tx.send(Ok(tensor)).await.is_err() {
                        tracing::debug!("Stream receiver dropped");
                        break;
                    }
                }
            }
            // 更新统计
            if let Ok(mut s) = stats.try_write() {
                s.total_inferences += 1;
                s.successful_inferences += 1;
            }
        });
        Ok(rx)
    }
    /// 获取模型信息
    async fn get_model_info(&self, model: &ModelHandle) -> Result<ModelInfo> {
        let parameters: usize = model
            .metadata
            .get("parameters")
            .and_then(|s| s.parse().ok())
            .unwrap_or(0);
        let size_mb: f64 = model
            .metadata
            .get("size_mb")
            .and_then(|s| s.parse().ok())
            .unwrap_or(0.0);
        Ok(ModelInfo {
            id: model.id.clone(),
            name: model.path.clone(),
            format: model.format.clone(),
            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: parameters,
            size_bytes: (size_mb * 1024.0 * 1024.0) as u64,
            engine_type: self.engine_type.clone(),
        })
    }
    /// 预热模型 - 初始化 GPU 和优化缓存
    async fn warmup(&self, model: &ModelHandle) -> Result<()> {
        tracing::info!("Warming up PyTorch model: {}", model.id);
        // 创建一个小的虚拟输入进行预热
        let warmup_input: _ = Tensor::new(vec![0.0_f32; 1 * 3 * 224 * 224], vec![1, 3, 224, 224])?;
        // 运行几次推理来预热 JIT 编译器和缓存
        for i in 0..3 {
            let _: _ = self.infer(model, &warmup_input).await;
            tracing::debug!("Warmup iteration {} completed", i + 1);
        }
        tracing::info!("Model warmup completed: {}", model.id);
        Ok(())
    }
    /// 卸载模型 - 释放资源
    async fn unload_model(&self, model: &ModelHandle) -> Result<()> {
        tracing::info!("Unloading PyTorch model: {}", model.id);
        // 在实际实现中，这里会释放 TorchScript 模型资源
        // 当前为模拟实现
        tracing::debug!("Model {} unloaded successfully", model.id);
        Ok(())
    }
    /// 克隆引擎实例
    fn clone_engine(&self) -> Box<dyn InferenceEngine> {
        Box::new(TorchEngine {
            engine_type: self.engine_type.clone(),
            stats: Arc::new(Mutex::new(EngineStats {
                engine_name: "PyTorch-TorchScript".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,
            })),
            initialized: self.initialized,
        })
    }
}
impl TorchGPUAccelerator {
    /// 创建新的 GPU 加速器
    pub async fn new(engine_type: EngineType) -> Result<Self> {
        let (device_type, device_id) = match engine_type {
            EngineType::CUDA => (EngineType::CUDA, 0),
            EngineType::ROCm => (EngineType::ROCm, 0),
            EngineType::Metal => (EngineType::Metal, 0),
            _ => return Err(anyhow::anyhow!("Invalid engine type for GPU")),
        };
        tracing::info!("Creating PyTorch GPU accelerator: {:?}", device_type);
        Ok(Self {
            device_type,
            device_id,
        })
    }
    /// 检查 GPU 是否可用
    pub fn is_available(&self) -> bool {
        tracing::warn!("PyTorch GPU acceleration not available in this build");
        false
    }
}
impl TorchOptimizer {
    /// 创建新的优化器
    pub fn new(optimization: bool) -> Self {
        tracing::info!(
            "Creating PyTorch optimizer (optimization: {})",
            optimization
        );
        Self {
            graph_optimization: optimization,
            constant_folding: optimization,
            operator_fusion: optimization,
            jit_optimization_level: if optimization { 2 } else { 0 },
        }
    }
}
#[async_trait]
impl EngineFactory for TorchEngineFactory {
    async fn create(&self, engine_type: EngineType) -> Result<Box<dyn InferenceEngine>> {
        let engine: _ = TorchEngine::new(
            "default_model.pt",
            InferenceOptions {
                engine_type,
                batch_size: Some(1),
                optimization: true,
                parallel_inferences: Some(1),
                memory_optimization: None,
                custom_options: std::collections::HashMap::new(),
            },
        )
        .await?;
        Ok(Box::new(engine) as Box<dyn InferenceEngine>)
    }
    fn name(&self) -> &str {
        "PyTorch-TorchScript-Factory"
    }
    fn supported_formats(&self) -> Vec<ModelFormat> {
        vec![ModelFormat::PyTorch]
    }
}
impl TorchEngineFactory {
    /// 创建新的工厂
    pub fn new(default_device: EngineType) -> Self {
        tracing::info!(
            "Creating PyTorch engine factory (default device: {:?})",
            default_device
        );
        Self { default_device }
    }
}
#[cfg(test)]
mod tests {
    use super::*;
    #[tokio::test]
    async fn test_torch_engine_creation() {
        let engine: _ = TorchEngine::new(
            "test_model.pt",
            InferenceOptions {
                engine_type: EngineType::CPU,
                batch_size: Some(1),
                optimization: true,
                parallel_inferences: Some(1),
                memory_optimization: None,
                custom_options: std::collections::HashMap::new(),
            },
        )
        .await;
        assert!(engine.is_ok());
        let engine: _ = engine.unwrap();
        assert!(engine.is_available());
        assert_eq!(engine.name(), "PyTorch-TorchScript");
    }
    #[tokio::test]
    async fn test_gpu_accelerator_creation() {
        let accelerator: _ = TorchGPUAccelerator::new(EngineType::CUDA).await;
        assert!(accelerator.is_ok());
        let accelerator: _ = accelerator.unwrap();
        assert_eq!(accelerator.device_type, EngineType::CUDA);
        // 注意：在这个模拟实现中，GPU 不可用
        assert!(!accelerator.is_available());
    }
    #[tokio::test]
    async fn test_torch_optimizer_creation() {
        let optimizer: _ = TorchOptimizer::new(true);
        assert!(optimizer.graph_optimization);
        assert!(optimizer.constant_folding);
        assert!(optimizer.operator_fusion);
        assert_eq!(optimizer.jit_optimization_level, 2);
        let optimizer_disabled: _ = TorchOptimizer::new(false);
        assert!(!optimizer_disabled.graph_optimization);
        assert_eq!(optimizer_disabled.jit_optimization_level, 0);
    }
    #[tokio::test]
    async fn test_inference_execution() {
        let engine: _ = TorchEngine::new(
            "test_model.pt",
            InferenceOptions {
                engine_type: EngineType::CPU,
                batch_size: Some(1),
                optimization: true,
                parallel_inferences: Some(1),
                memory_optimization: None,
                custom_options: std::collections::HashMap::new(),
            },
        )
        .await
        .unwrap();
        let input: _ = Tensor::new(vec![1.0, 2.0, 3.0], vec![3]).unwrap();
        let model_handle: _ = engine
            .load_model(
                "test_model.pt",
                InferenceOptions {
                    engine_type: EngineType::CPU,
                    batch_size: Some(1),
                    optimization: true,
                    parallel_inferences: Some(1),
                    memory_optimization: None,
                    custom_options: std::collections::HashMap::new(),
                },
            )
            .await
            .unwrap();
        let result: _ = engine.infer(&model_handle, &input).await;
        assert!(result.is_ok());
        let result: _ = result.unwrap();
        assert_eq!(*result.output.shape(), vec![1, 1000]);
    }
    #[tokio::test]
    async fn test_batch_inference() {
        let engine: _ = TorchEngine::new(
            "test_model.pt",
            InferenceOptions {
                engine_type: EngineType::CPU,
                batch_size: Some(4),
                optimization: true,
                parallel_inferences: Some(4),
                memory_optimization: None,
                custom_options: std::collections::HashMap::new(),
            },
        )
        .await
        .unwrap();
        let inputs: _ = vec![
            Tensor::new(vec![1.0, 2.0, 3.0], vec![3]).unwrap(),
            Tensor::new(vec![4.0, 5.0, 6.0], vec![3]).unwrap(),
            Tensor::new(vec![7.0, 8.0, 9.0], vec![3]).unwrap(),
        ];
        let model_handle: _ = engine
            .load_model(
                "test_model.pt",
                InferenceOptions {
                    engine_type: EngineType::CPU,
                    batch_size: Some(4),
                    optimization: true,
                    parallel_inferences: Some(4),
                    memory_optimization: None,
                    custom_options: std::collections::HashMap::new(),
                },
            )
            .await
            .unwrap();
        let results: _ = engine.batch_infer(&model_handle, &inputs).await;
        assert!(results.is_ok());
        let results: _ = results.unwrap();
        assert_eq!(results.len(), 3);
        for result in results {
            assert_eq!(*result.output.shape(), vec![1, 1000]);
        }
    }
    #[tokio::test]
    async fn test_engine_stats() {
        let engine: _ = TorchEngine::new(
            "test_model.pt",
            InferenceOptions {
                engine_type: EngineType::CPU,
                batch_size: Some(1),
                optimization: true,
                parallel_inferences: Some(1),
                memory_optimization: None,
                custom_options: std::collections::HashMap::new(),
            },
        )
        .await
        .unwrap();
        let stats: _ = engine.get_stats().await;
        assert!(stats.is_ok());
        let stats: _ = stats.unwrap();
        assert_eq!(stats.total_inferences, 0);
    }
    #[tokio::test]
    async fn test_engine_factory() {
        let factory: _ = TorchEngineFactory::new(EngineType::CPU);
        assert_eq!(factory.name(), "PyTorch-TorchScript-Factory");
        let formats: _ = factory.supported_formats();
        assert!(formats.contains(&ModelFormat::PyTorch));
        assert_eq!(formats.len(), 1);
    }
}
use std::collections::{BTreeMap, HashMap};
use std::sync::{Arc, Mutex};
use std::time::{Duration, Instant};
use tracing::{debug, error, info, warn};