amberjs 1.16.1

// AI 推理引擎核心实现
// 提供高性能的 AI 模型推理能力

use std::collections::HashMap;
use std::sync::{Arc, Mutex, RwLock};

use anyhow::{Context, Result};
use std::time::{Duration, Instant};
/// AI 推理引擎主结构
#[derive(Debug)]
pub struct AIInferenceEngine {
    model_loader: ModelLoader,
    gpu_accelerator: GPUSimpleAccelerator,
    model_cache: ModelCache,
    inference_count: Arc<RwLock<u64>>,
    total_inference_time: Arc<RwLock<Duration>>,
}
impl AIInferenceEngine {
    /// 创建新的 AI 推理引擎
    pub async fn new() -> Result<Self> {
        let model_loader: _ = ModelLoader::new();
        let gpu_accelerator: _ = GPUSimpleAccelerator::new().await?;
        let model_cache: _ = ModelCache::new(1000).await?;
        Ok(AIInferenceEngine {
            model_loader,
            gpu_accelerator,
            model_cache,
            inference_count: Arc::new(Mutex::new(0)),
            total_inference_time: Arc::new(Mutex::new(Duration::from_secs(0))),
        })
    }
    /// 执行 AI 模型推理
    pub async fn infer(&self, model_id: &str, input: &Tensor) -> Result<InferenceResult> {
        let start: _ = Instant::now();
        // 检查缓存
        if let Some(cached_model) = self.model_cache.get(model_id).await? {
            // 使用缓存的模型
            let gpu_used: _ = self.gpu_accelerator.is_available();
            // 执行推理
            let output: _ = if gpu_used {
                self.gpu_accelerator.compute(&cached_model, input).await?
            } else {
                cached_model.compute(input)?
            };
            let inference_time: _ = start.elapsed();
            // 更新统计
            {
                let mut count = self.inference_count.write().await;
                *count += 1;
            }
            {
                let mut total_time = self.total_inference_time.write().await;
                *total_time += inference_time;
            }
            Ok(InferenceResult {
                output,
                inference_time_ms: inference_time.as_secs_f64() * 1000.0,
                model_id: model_id.to_string(),
                gpu_used,
            })
        } else {
            // 加载模型
            let model: _ = self
                .model_loader
                .load(model_id)
                .await
                .context(format!("Failed to load model: {}", model_id))?;
            // 缓存模型
            self.model_cache
                .put(model_id.to_string(), model.clone())
                .await?;
            // 执行推理
            let gpu_used: _ = self.gpu_accelerator.is_available();
            let output: _ = if gpu_used {
                self.gpu_accelerator.compute(&model, input).await?
            } else {
                model.compute(input)?
            };
            let inference_time: _ = start.elapsed();
            // 更新统计
            {
                let mut count = self.inference_count.write().await;
                *count += 1;
            }
            {
                let mut total_time = self.total_inference_time.write().await;
                *total_time += inference_time;
            }
            Ok(InferenceResult {
                output,
                inference_time_ms: inference_time.as_secs_f64() * 1000.0,
                model_id: model_id.to_string(),
                gpu_used,
            })
        }
    }
    /// 批量推理
    pub async fn batch_infer(
        &self,
        model_id: &str,
        inputs: &[Tensor],
    ) -> Result<Vec<InferenceResult>> {
        let mut results = Vec::with_capacity(inputs.len());
        for input in inputs {
            let result: _ = self.infer(model_id, input).await?;
            results.push(result);
        }
        Ok(results)
    }
    /// 获取推理统计
    pub async fn get_stats(&self) -> Result<InferenceStats> {
        let count: _ = *self.inference_count.read().await;
        let total_time: _ = *self.total_inference_time.read().await;
        let avg_time_ms: _ = if count > 0 {
            (total_time.as_secs_f64() * 1000.0) / count as f64
        } else {
            0.0
        };
        Ok(InferenceStats {
            total_inferences: count,
            total_time_ms: total_time.as_secs_f64() * 1000.0,
            average_time_ms: avg_time_ms,
            gpu_available: self.gpu_accelerator.is_available(),
        })
    }
    /// 预热模型
    pub async fn warmup_model(&self, model_id: &str) -> Result<()> {
        // 创建一个虚拟输入来预热模型
        let warmup_input: _ = Tensor::new(vec![1.0; 100], vec![10, 10])?;
        // 执行一次推理来预热
        let _: _ = self.infer(model_id, &warmup_input).await?;
        Ok(())
    }
}
/// 推理统计信息
#[derive(Debug, Clone)]
pub struct InferenceStats {
    pub total_inferences: u64,
    pub total_time_ms: f64,
    pub average_time_ms: f64,
    pub gpu_available: bool,
}
/// AI 模型特征（简化版, Clone)]
#[derive(Debug, Clone)]
pub struct AIModel {
    pub id: String,
    pub input_shape: Vec<usize>,
    pub output_shape: Vec<usize>,
    pub parameters: std::collections::HashMap<String, Vec<f32>>, // 模型参数
}
impl AIModel {
    /// 创建新模型
    pub fn new(id: String, input_shape: Vec<usize>, output_shape: Vec<usize>) -> Self {
        AIModel {
            id,
            input_shape,
            output_shape,
            parameters: std::collections::HashMap::new(),
        }
    }
    /// 创建新模型（带参数）
    pub fn new_with_params(
        id: String,
        input_shape: Vec<usize>,
        output_shape: Vec<usize>,
        parameters: std::collections::HashMap<String, Vec<f32>>,
    ) -> Self {
        AIModel {
            id,
            input_shape,
            output_shape,
            parameters,
        }
    }
    /// 执行模型推理（简化版）
    pub fn compute(&self, input: &Tensor) -> Result<Tensor> {
        // 简化实现：输出与输入相同
        if input.shape() != &self.input_shape {
            return Err(anyhow::anyhow!("Input shape mismatch"));
        }
        // 模拟计算时间
        std::thread::sleep(Duration::from_millis(1));
        Ok(Tensor::new(
            input.data().to_vec(),
            self.output_shape.clone(),
        )?)
    }
}
/// GPU 加速器包装器
#[derive(Debug)]
pub struct GPUSimpleAccelerator {
    available: bool,
    device_id: usize,
}
impl GPUSimpleAccelerator {
    /// 创建新的 GPU 加速器
    pub async fn new() -> Result<Self> {
        // 简化实现：检查 GPU 可用性
        let available: _ = false; // 在实际实现中，这里会检查 WebGPU 或 CUDA
        Ok(GPUSimpleAccelerator {
            available,
            device_id: 0,
        })
    }
    /// 检查 GPU 是否可用
    pub fn is_available(&self) -> bool {
        self.available
    }
    /// 在 GPU 上执行计算
    pub async fn compute(&self, model: &AIModel, input: &Tensor) -> Result<Tensor> {
        if !self.available {
            return Err(anyhow::anyhow!("GPU not available"));
        }
        // 模拟 GPU 计算
        tokio::time::sleep(Duration::from_millis(1)).await;
        model.compute(input)
    }
}
/// 推理引擎特征
#[async_trait::async_trait]
pub trait GPUAccelerate {
    async fn compute(&self, model: &AIModel, input: &Tensor) -> Result<Tensor>;
}
#[async_trait::async_trait]
impl GPUAccelerate for GPUSimpleAccelerator {
    async fn compute(&self, model: &AIModel, input: &Tensor) -> Result<Tensor> {
        if !self.available {
            return Err(anyhow::anyhow!("GPU not available"));
        }
        // 模拟 GPU 计算
        tokio::time::sleep(Duration::from_millis(1)).await;
        model.compute(input)
    }
}