amberjs 1.17.0

// AI 工作负载
//
// 实现 AI 推理相关任务的性能测试

use serde::{Deserialize, Serialize};
use std::collections::{BTreeMap, HashMap};
use super::{BenchmarkError, BenchmarkResult as Result, ResourceUsage, WorkloadResult};
use std::time::{Duration, Instant};

/// AI 工作负载
#[derive(Debug)]
pub struct AIWorkload {
    workload_type: super::WorkloadType,
}
impl AIWorkload {
    /// 创建新的 AI 工作负载
    pub fn new() -> Self {
        Self {
            workload_type: super::WorkloadType::AiWorkload,
        }
    }
    /// 执行工作负载
    pub async fn execute(
        &self,
        parameters: HashMap<String, serde_json::Value>>,
        concurrency: u32,
    ) -> Result<WorkloadResult> {
        let mut result = WorkloadResult::new(self.workload_type);
        result.start();
        let iterations: _ = get_iterations(&parameters);
        let operation: _ = get_operation(&parameters);
        // 并行执行 AI 任务
        let handles: Vec<_> = (0..concurrency)
            .map(|_| {
                let operation: _ = operation.clone();
                let iterations: _ = iterations;
                tokio::spawn(async move {
                    Self::run_ai_tasks(operation, iterations).await
                })
            })
            .collect();
        // 等待所有任务完成
        let mut total_iterations = 0;
        for handle in handles {
            match handle.await {
                Ok(Ok(iterations_completed)) => {
                    total_iterations += iterations_completed;
                }
                Ok(Err(e)) => {
                    result.add_error(&format!("Task failed: {}", e));
                }
                Err(e) => {
                    result.add_error(&format!("Task panicked: {}", e));
                }
            }
        }
        // 收集资源使用情况
        let resource_usage: _ = Self::collect_resource_usage();
        result.resource_usage = resource_usage;
        result.finish(total_iterations);
        Ok(result)
    }
    /// 运行 AI 任务
    async fn run_ai_tasks(
        operation: String,
        iterations: u32,
    ) -> Result<u32, BenchmarkError> {
        let mut completed_iterations = 0;
        for _ in 0..iterations {
            match operation.as_str() {
                "tensor_operation" => {
                    Self::tensor_operation_benchmark().await?;
                }
                "neural_network_inference" => {
                    Self::neural_network_inference_benchmark().await?;
                }
                "transformer_attention" => {
                    Self::transformer_attention_benchmark().await?;
                }
                "matrix_multiplication" => {
                    Self::ai_matrix_multiplication_benchmark().await?;
                }
                _ => {
                    return Err(BenchmarkError::ConfigError(
                        format!("Unknown operation: {}", operation));
                }
            }
            completed_iterations += 1;
            tokio::task::yield_now().await;
        }
        Ok(completed_iterations)
    }
    /// 张量运算基准测试
    async fn tensor_operation_benchmark() -> Result<(), BenchmarkError> {
        // 模拟张量运算
        let size: _ = 100;
        let mut tensor = vec![vec![0.0f64; size]; size];
        for i in 0..size {
            for j in 0..size {
                tensor[i][j] = (i as f64 * j as f64).sin();
            }
        }
        Ok(())
    }
    /// 神经网络推理基准测试
    async fn neural_network_inference_benchmark() -> Result<(), BenchmarkError> {
        // 模拟神经网络推理
        let input_size: _ = 784; // MNIST 图像大小
        let hidden_size: _ = 128;
        let output_size: _ = 10;
        let mut input = vec![0.0f64; input_size];
        let mut hidden_weights = vec![vec![0.0f64; hidden_size]; input_size];
        let mut hidden_bias = vec![0.0f64; hidden_size];
        let mut output_weights = vec![vec![0.0f64; output_size]; hidden_size];
        let mut output_bias = vec![0.0f64; output_size];
        // 前向传播
        let mut hidden = vec![0.0f64; hidden_size];
        for i in 0..hidden_size {
            for j in 0..input_size {
                hidden[i] += input[j] * hidden_weights[j][i];
            }
            hidden[i] += hidden_bias[i];
        }
        let mut output = vec![0.0f64; output_size];
        for i in 0..output_size {
            for j in 0..hidden_size {
                output[i] += hidden[j] * output_weights[j][i];
            }
            output[i] += output_bias[i];
        }
        Ok(())
    }
    /// Transformer 注意力机制基准测试
    async fn transformer_attention_benchmark() -> Result<(), BenchmarkError> {
        // 模拟注意力机制计算
        let seq_len: _ = 64;
        let d_model: _ = 512;
        let num_heads: _ = 8;
        let head_dim: _ = d_model / num_heads;
        let mut query = vec![vec![0.0f64; head_dim]; seq_len * num_heads];
        let mut key = vec![vec![0.0f64; head_dim]; seq_len * num_heads];
        let mut value = vec![vec![0.0f64; head_dim]; seq_len * num_heads];
        // 计算注意力分数
        for h in 0..num_heads {
            for i in 0..seq_len {
                for j in 0..seq_len {
                    let mut score = 0.0;
                    for k in 0..head_dim {
                        score += query[h * seq_len + i][k] * key[h * seq_len + j][k];
                    }
                    // 模拟 softmax
                    let _attention_weight: _ = (score / (head_dim as f64).sqrt()).exp();
                }
            }
        }
        Ok(())
    }
    /// AI 矩阵乘法基准测试
    async fn ai_matrix_multiplication_benchmark() -> Result<(), BenchmarkError> {
        let size: _ = 256;
        let mut a = vec![vec![0.0f64; size]; size];
        let mut b = vec![vec![0.0f64; size]; size];
        let mut c = vec![vec![0.0f64; size]; size];
        // 初始化矩阵
        for i in 0..size {
            for j in 0..size {
                a[i][j] = (i as f64 * j as f64 * 0.01).sin();
                b[i][j] = (i as f64 * j as f64 * 0.02).cos();
            }
        }
        // 矩阵乘法 (简化的批处理版本)
        for i in 0..size {
            for j in 0..size {
                let mut sum = 0.0;
                for k in 0..size {
                    sum += a[i][k] * b[k][j];
                }
                c[i][j] = sum;
            }
        }
        Ok(())
    }
    /// 收集资源使用情况
    fn collect_resource_usage() -> ResourceUsage {
        ResourceUsage::new()
            .cpu_usage(80.0) // AI 工作负载 CPU 使用率较高
    }
}
impl Default for AIWorkload {
    fn default() -> Self {
        Self::new()
    }
}
/// 获取迭代次数
fn get_iterations(parameters: &HashMap<String, serde_json::Value>) -> u32 {
    parameters
        .get("iterations")
        .and_then(|v| v.as_u64())
        .map(|v| v as u32)
        .unwrap_or(10)
}
/// 获取操作类型
fn get_operation(parameters: &HashMap<String, serde_json::Value>) -> String {
    parameters
        .get("operation")
        .and_then(|v| v.as_str())
        .map(|s| s.to_string())
        .unwrap_or_else(|| "tensor_operation".to_string())
}
#[cfg(test)]
mod tests {
    #[tokio::test]
    async fn test_workload_execution() {
        let workload: _ = AIWorkload::new();
        let mut parameters = HashMap::new();
        parameters.insert("iterations".to_string(), serde_json::Value::from(5u64));
        let result: _ = workload.execute(parameters, 1).await.unwrap();
        assert_eq!(result.workload_type, super::super::WorkloadType::AiWorkload);
        assert!(result.success);
    }
}