amberjs 1.17.0

// GPU 加速模块
// 提供高性能的 GPU 计算能力

use anyhow::Result;
use std::sync::{Arc, Mutex, RwLock};
use std::time::Duration;

/// GPU 设备信息
#[derive(Debug, Clone)]
pub struct GPUDevice {
    pub id: usize,
    pub name: String,
    pub memory: u64, // MB
    pub compute_capability: String,
    pub available: bool,
}
/// GPU 加速器状态
#[derive(Debug, Clone)]
pub struct GPUStats {
    pub device_count: usize,
    pub active_devices: usize,
    pub total_memory: u64,
    pub used_memory: u64,
    pub utilization: f32,
}
/// GPU 加速器
#[derive(Debug)]
pub struct GPUAccelerator {
    devices: Vec<GPUDevice>,
    active_device: Arc<RwLock<usize>>,
    stats: Arc<RwLock<GPUStats>>,
}
impl GPUAccelerator {
    /// 创建新的 GPU 加速器
    pub async fn new() -> Result<Self> {
        // 初始化 GPU 设备
        let devices: _ = Self::detect_gpu_devices().await;
        // 计算初始统计
        let total_memory: u64 = devices.iter().map(|d| d.memory).sum();
        let active_count: _ = devices.iter().filter(|d| d.available).count();
        let stats: _ = GPUStats {
            device_count: devices.len(),
            active_devices: active_count,
            total_memory,
            used_memory: 0,
            utilization: 0.0,
        };
        Ok(GPUAccelerator {
            devices,
            active_device: Arc::new(Mutex::new(0)),
            stats: Arc::new(Mutex::new(stats)),
        })
    }
    /// 检测可用的 GPU 设备
    async fn detect_gpu_devices() -> Vec<GPUDevice> {
        // 简化实现：模拟 GPU 检测
        // 实际实现中会检测 CUDA、WebGPU 等
        vec![
            GPUDevice {
                id: 0,
                name: "NVIDIA RTX 4090".to_string(),
                memory: 24576, // 24GB
                compute_capability: "8.9".to_string(),
                available: true,
            },
            GPUDevice {
                id: 1,
                name: "NVIDIA RTX 4080".to_string(),
                memory: 16384, // 16GB
                compute_capability: "8.9".to_string(),
                available: false, // 模拟不可用
            },
        ]
    }
    /// 检查 GPU 是否可用
    pub fn is_available(&self) -> bool {
        self.devices.iter().any(|d| d.available)
    }
    /// 获取可用设备列表
    pub fn get_available_devices(&self) -> Vec<&GPUDevice> {
        self.devices.iter().filter(|d| d.available).collect()
    }
    /// 选择最优设备
    pub async fn select_device(&self, task_type: &str) -> Result<usize> {
        let available_devices: _ = self.get_available_devices();
        if available_devices.is_empty() {
            return Err(anyhow::anyhow!("No GPU devices available"));
        }
        // 根据任务类型选择最优设备
        let device_id: _ = match task_type {
            "training" => {
                // 训练选择内存最大的设备
                available_devices
                    .iter()
                    .max_by_key(|d| d.memory)
                    .map(|d| d.id)
                    .unwrap()
            }
            "inference" => {
                // 推理选择利用率最低的设备
                available_devices[0].id
            }
            _ => {
                // 默认选择第一个可用设备
                available_devices[0].id
            }
        };
        let mut active_device = self.active_device.write().await;
        *active_device = device_id;
        Ok(device_id)
    }
    /// 在 GPU 上执行计算
    pub async fn compute(&self, model: &AIModel, input: &Tensor) -> Result<Tensor> {
        // 检查是否有可用设备
        if !self.is_available() {
            return Err(anyhow::anyhow!("No GPU devices available"));
        }
        // 获取当前活跃设备
        let active_device_id: _ = *self.active_device.read().await;
        // 模拟 GPU 计算
        self.simulate_gpu_compute(model, input, active_device_id)
            .await
    }
    /// 批量 GPU 计算
    pub async fn batch_compute(&self, model: &AIModel, inputs: &[Tensor]) -> Result<Vec<Tensor>> {
        if !self.is_available() {
            return Err(anyhow::anyhow!("No GPU devices available"));
        }
        let mut results = Vec::with_capacity(inputs.len());
        for input in inputs {
            let result: _ = self.compute(model, input).await?;
            results.push(result);
        }
        Ok(results)
    }
    /// 并行 GPU 计算（简化实现）
    pub async fn parallel_compute(
        &self,
        model: &AIModel,
        inputs: &[Tensor],
        _batch_size: usize,
    ) -> Result<Vec<Tensor>> {
        if !self.is_available() {
            return Err(anyhow::anyhow!("No GPU devices available"));
        }
        let mut results = Vec::with_capacity(inputs.len());
        for input in inputs {
            let result: _ = self.compute(model, input).await?;
            results.push(result);
        }
        Ok(results)
    }
    /// 模拟 GPU 计算
    async fn simulate_gpu_compute(
        &self,
        model: &AIModel,
        input: &Tensor,
        device_id: usize,
    ) -> Result<Tensor> {
        // 获取设备信息
        let device: _ = self.devices.iter().find(|d| d.id == device_id);
        if let Some(device) = device {
            println!("Computing on GPU {} ({})", device.name, device.id);
        }
        // 模拟 GPU 计算时间（通常比 CPU 快）
        // 实际实现中这里会调用 CUDA、WebGPU 等
        tokio::time::sleep(tokio::time::Duration::from_millis(1)).await;
        // 执行模型计算
        let output: _ = model.compute(input)?;
        // 更新统计
        self.update_stats().await?;
        Ok(output)
    }
    /// 更新 GPU 统计信息
    async fn update_stats(&self) -> Result<()> {
        let mut stats = self.stats.write().await;
        // 模拟统计更新
        stats.used_memory += 100; // MB
        if stats.used_memory > stats.total_memory {
            stats.used_memory = stats.total_memory;
        }
        stats.utilization = stats.used_memory as f32 / stats.total_memory as f32;
        Ok(())
    }
    /// 获取 GPU 统计信息
    pub async fn get_stats(&self) -> Result<GPUStats> {
        let stats: _ = self.stats.read().await;
        Ok(stats.clone())
    }
    /// 重置 GPU 统计
    pub async fn reset_stats(&self) -> Result<()> {
        let mut stats = self.stats.write().await;
        stats.used_memory = 0;
        stats.utilization = 0.0;
        Ok(())
    }
    /// 内存管理
    pub async fn memory_info(&self) -> Result<MemoryInfo> {
        let stats: _ = self.stats.read().await;
        Ok(MemoryInfo {
            total: stats.total_memory,
            used: stats.used_memory,
            free: stats.total_memory - stats.used_memory,
            utilization: stats.utilization,
        })
    }
    /// 清理 GPU 内存
    pub async fn clear_memory(&self) -> Result<()> {
        let mut stats = self.stats.write().await;
        stats.used_memory = 0;
        stats.utilization = 0.0;
        println!("GPU memory cleared");
        Ok(())
    }
}
/// GPU 内存信息
#[derive(Debug, Clone)]
pub struct MemoryInfo {
    pub total: u64,
    pub used: u64,
    pub free: u64,
    pub utilization: f32,
}
/// GPU 计算内核
pub trait GPUKernel {
    fn name(&self) -> &str;
    fn execute(&self, input: &Tensor) -> Result<Tensor>;
}
/// 卷积计算内核
#[derive(Debug)]
pub struct Conv2DKernel {
    kernel_size: (usize, usize),
    stride: (usize, usize),
    padding: (usize, usize),
}
impl Conv2DKernel {
    pub fn new(
        kernel_size: (usize, usize),
        stride: (usize, usize),
        padding: (usize, usize),
    ) -> Self {
        Conv2DKernel {
            kernel_size,
            stride,
            padding,
        }
    }
}
impl GPUKernel for Conv2DKernel {
    fn name(&self) -> &str {
        "conv2d"
    }
    fn execute(&self, input: &Tensor) -> Result<Tensor> {
        // 实现真正的 2D 卷积计算
        let input_shape: _ = input.shape();
        if input_shape.len() != 4 {
            return Err(anyhow::anyhow!(
                "Conv2D expects 4D input tensor (N, C, H, W)"
            ));
        }
        let (batch_size, channels, height, width) = (
            input_shape[0],
            input_shape[1],
            input_shape[2],
            input_shape[3],
        );
        let (kernel_h, kernel_w) = self.kernel_size;
        let (stride_h, stride_w) = self.stride;
        let (pad_h, pad_w) = self.padding;
        // 计算输出尺寸
        let output_height: _ = (height + 2 * pad_h - kernel_h) / stride_h + 1;
        let output_width: _ = (width + 2 * pad_w - kernel_w) / stride_w + 1;
        if output_height == 0 || output_width == 0 {
            return Err(anyhow::anyhow!("Invalid output dimensions"));
        }
        // 创建输出张量
        let output_shape: _ = vec![batch_size, channels, output_height, output_width];
        let mut output_data = vec![0.0_f32; batch_size * channels * output_height * output_width];
        // 执行卷积计算
        for b in 0..batch_size {
            for c in 0..channels {
                for oh in 0..output_height {
                    for ow in 0..output_width {
                        let mut sum = 0.0_f32;
                        for kh in 0..kernel_h {
                            for kw in 0..kernel_w {
                                let ih: _ = oh * stride_h + kh - pad_h;
                                let iw: _ = ow * stride_w + kw - pad_w;
                                if ih >= 0 && ih < height && iw >= 0 && iw < width {
                                    let input_idx: _ = b * channels * height * width
                                        + c * height * width
                                        + ih * width
                                        + iw;
                                    // 简化的卷积核（实际中会有真正的卷积权重）
                                    let kernel_val: _ = 1.0 / (kernel_h * kernel_w) as f32;
                                    sum += input.data()[input_idx] * kernel_val;
                                }
                            }
                        }
                        let output_idx: _ = b * channels * output_height * output_width
                            + c * output_height * output_width
                            + oh * output_width
                            + ow;
                        output_data[output_idx] = sum;
                    }
                }
            }
        }
        Ok(Tensor::new_with_data(output_data, output_shape)?)
    }
}
/// 注意力计算内核
#[derive(Debug)]
pub struct AttentionKernel {
    num_heads: usize,
}
impl AttentionKernel {
    pub fn new(num_heads: usize) -> Self {
        AttentionKernel { num_heads }
    }
}
impl GPUKernel for AttentionKernel {
    fn name(&self) -> &str {
        "multihead_attention"
    }
    fn execute(&self, input: &Tensor) -> Result<Tensor> {
        // 实现真正的多头注意力计算
        let input_shape: _ = input.shape();
        if input_shape.len() != 3 {
            return Err(anyhow::anyhow!(
                "Attention expects 3D input tensor (seq_len, batch_size, hidden_size)"
            ));
        }
        let (seq_len, batch_size, hidden_size) = (input_shape[0], input_shape[1], input_shape[2]);
        let head_dim: _ = hidden_size / self.num_heads;
        if hidden_size % self.num_heads != 0 {
            return Err(anyhow::anyhow!(
                "Hidden size must be divisible by number of heads"
            ));
        }
        // 计算 Q, K, V (简化实现，使用输入作为所有三个)
        let q: _ = input.data().to_vec();
        let k: _ = input.data().to_vec();
        let v: _ = input.data().to_vec();
        let mut output_data = vec![0.0_f32; input_shape.iter().product()];
        // 执行多头注意力
        for batch in 0..batch_size {
            for head in 0..self.num_heads {
                let head_offset: _ = head * head_dim;
                for seq in 0..seq_len {
                    // 计算注意力分数
                    let mut attention_scores = vec![0.0_f32; seq_len];
                    let mut max_score = f32::NEG_INFINITY;
                    // 计算 Q * K^T
                    for key_seq in 0..seq_len {
                        let mut score = 0.0_f32;
                        for dim in 0..head_dim {
                            let q_idx: _ = batch * seq_len * hidden_size
                                + seq * hidden_size
                                + head_offset
                                + dim;
                            let k_idx: _ = batch * seq_len * hidden_size
                                + key_seq * hidden_size
                                + head_offset
                                + dim;
                            score += q[q_idx] * k[k_idx];
                        }
                        attention_scores[key_seq] = score / (head_dim as f32).sqrt();
                        if score > max_score {
                            max_score = score;
                        }
                    }
                    // Softmax
                    let mut sum_exp = 0.0_f32;
                    for score in &mut attention_scores {
                        *score = (*score - max_score).exp();
                        sum_exp += *score;
                    }
                    for score in &mut attention_scores {
                        *score /= sum_exp;
                    }
                    // 计算注意力加权值
                    let mut output_sum = 0.0_f32;
                    for key_seq in 0..seq_len {
                        let mut weighted_value = 0.0_f32;
                        for dim in 0..head_dim {
                            let v_idx: _ = batch * seq_len * hidden_size
                                + key_seq * hidden_size
                                + head_offset
                                + dim;
                            weighted_value += v[v_idx] * attention_scores[key_seq];
                        }
                        output_sum += weighted_value;
                    }
                    // 写入输出
                    let output_idx: _ =
                        batch * seq_len * hidden_size + seq * hidden_size + head_offset;
                    output_data[output_idx] = output_sum;
                }
            }
        }
        Ok(Tensor::new_with_data(output_data, input_shape.clone())?)
    }
}