rustorch 0.6.29

//! Multi-GPU validation and benchmarking for distributed training
//! 分散学習用マルチGPU検証とベンチマーキング
//!
//! This module provides comprehensive multi-GPU validation capabilities including:
//! - GPU device discovery and capability checking
//! - Multi-GPU performance benchmarking
//! - Distributed validation across multiple GPUs
//! - Memory usage monitoring and optimization
//! - Communication overhead measurement
//!
//! 包括的なマルチGPU検証機能を提供：
//! - GPUデバイスの検出と能力チェック
//! - マルチGPUパフォーマンスベンチマーキング
//! - 複数GPU間での分散検証
//! - メモリ使用量の監視と最適化
//! - 通信オーバーヘッドの測定

use crate::error::{RusTorchError, RusTorchResult};
use crate::gpu::DeviceType;
use crate::nn::Module;
use crate::tensor::Tensor;
use num_traits::{Float, FromPrimitive};
use std::collections::HashMap;
use std::time::{Duration, Instant};

/// Process group for distributed operations
/// 分散操作用プロセスグループ
#[derive(Debug, Clone)]
pub struct ProcessGroup {
    /// Process rank in the distributed group
    /// 分散グループ内のプロセスランク
    pub rank: usize,
    /// Total number of processes in the group
    /// グループ内の総プロセス数
    pub world_size: usize,
    /// Backend type for distributed communication
    /// 分散通信用バックエンドタイプ
    pub backend: String,
}

/// GPU device information
/// GPUデバイス情報
#[derive(Debug, Clone)]
pub struct GpuDeviceInfo {
    /// Device ID
    /// デバイスID
    pub device_id: usize,
    /// Device name
    /// デバイス名
    pub name: String,
    /// Total memory in bytes
    /// 総メモリ（バイト）
    pub total_memory: usize,
    /// Available memory in bytes
    /// 利用可能メモリ（バイト）
    pub available_memory: usize,
    /// Compute capability (for CUDA)
    /// 計算能力（CUDA用）
    pub compute_capability: Option<(u32, u32)>,
    /// Device type
    /// デバイスタイプ
    pub device_type: DeviceType,
    /// Is device available for training
    /// 学習に利用可能かどうか
    pub is_available: bool,
}

/// Multi-GPU validation metrics
/// マルチGPU検証メトリクス
#[derive(Debug, Clone)]
pub struct ValidationMetrics {
    /// Total validation loss
    /// 総検証損失
    pub total_loss: f64,
    /// Per-device losses
    /// デバイスごとの損失
    pub device_losses: HashMap<usize, f64>,
    /// Total accuracy
    /// 総精度
    pub accuracy: f64,
    /// Per-device accuracies
    /// デバイスごとの精度
    pub device_accuracies: HashMap<usize, f64>,
    /// Validation time per device
    /// デバイスごとの検証時間
    pub device_times: HashMap<usize, Duration>,
    /// Communication time
    /// 通信時間
    pub communication_time: Duration,
    /// Total validation time
    /// 総検証時間
    pub total_time: Duration,
    /// Samples processed per second
    /// 1秒あたりの処理サンプル数
    pub throughput: f64,
}

/// Performance benchmark results
/// パフォーマンスベンチマーク結果
#[derive(Debug, Clone)]
pub struct BenchmarkResults {
    /// Single GPU baseline performance
    /// シングルGPUベースライン性能
    pub single_gpu_throughput: f64,
    /// Multi-GPU throughput
    /// マルチGPUスループット
    pub multi_gpu_throughput: f64,
    /// Scaling efficiency (multi/single)
    /// スケーリング効率（マルチ/シングル）
    pub scaling_efficiency: f64,
    /// Communication overhead percentage
    /// 通信オーバーヘッドパーセンテージ
    pub communication_overhead: f64,
    /// Memory usage per device
    /// デバイスごとのメモリ使用量
    pub memory_usage: HashMap<usize, MemoryUsage>,
    /// Optimal batch size per GPU
    /// GPU当たりの最適バッチサイズ
    pub optimal_batch_size: usize,
}

/// Memory usage statistics
/// メモリ使用統計
#[derive(Debug, Clone)]
pub struct MemoryUsage {
    /// Peak memory usage
    /// ピークメモリ使用量
    pub peak_usage: usize,
    /// Current usage
    /// 現在の使用量
    pub current_usage: usize,
    /// Memory fragmentation percentage
    /// メモリフラグメンテーションパーセンテージ
    pub fragmentation: f64,
}

/// Multi-GPU validator
/// マルチGPUバリデータ
pub struct MultiGpuValidator<
    T: Float + Send + Sync + 'static + ndarray::ScalarOperand + num_traits::FromPrimitive,
> {
    /// Available GPU devices
    /// 利用可能なGPUデバイス
    devices: Vec<GpuDeviceInfo>,
    /// Process group for distributed operations
    /// 分散操作用プロセスグループ
    process_group: Option<ProcessGroup>,
    /// Validation metrics history
    /// 検証メトリクス履歴
    metrics_history: Vec<ValidationMetrics>,
    /// Benchmark results cache
    /// ベンチマーク結果キャッシュ
    benchmark_cache: Option<BenchmarkResults>,
    _phantom: std::marker::PhantomData<T>,
}

impl<T> MultiGpuValidator<T>
where
    T: Float + FromPrimitive + Send + Sync + 'static + ndarray::ScalarOperand,
{
    /// Create a new multi-GPU validator
    /// 新しいマルチGPUバリデータを作成
    pub fn new() -> RusTorchResult<Self> {
        let devices = Self::discover_devices()?;

        Ok(Self {
            devices,
            process_group: None,
            metrics_history: Vec::new(),
            benchmark_cache: None,
            _phantom: std::marker::PhantomData,
        })
    }

    /// Discover available GPU devices
    /// 利用可能なGPUデバイスを検出
    fn discover_devices() -> RusTorchResult<Vec<GpuDeviceInfo>> {
        let mut devices = Vec::new();

        // Check for CUDA devices
        #[cfg(feature = "cuda")]
        {
            if let Ok(cuda_count) = Self::get_cuda_device_count() {
                for i in 0..cuda_count {
                    if let Ok(info) = Self::get_cuda_device_info(i) {
                        devices.push(info);
                    }
                }
            }
        }

        // Check for Metal devices (macOS)
        #[cfg(target_os = "macos")]
        {
            if let Ok(metal_info) = Self::get_metal_device_info() {
                devices.push(metal_info);
            }
        }

        // Check for OpenCL devices
        #[cfg(feature = "opencl")]
        {
            if let Ok(opencl_devices) = Self::get_opencl_devices() {
                devices.extend(opencl_devices);
            }
        }

        if devices.is_empty() {
            // Fallback to CPU for testing
            devices.push(GpuDeviceInfo {
                device_id: 0,
                name: "CPU".to_string(),
                total_memory: 8 * 1_073_741_824, // 8GB (8 * 1024^3)
                available_memory: 4 * 1_073_741_824, // 4GB (4 * 1024^3)
                compute_capability: None,
                device_type: DeviceType::Cpu,
                is_available: true,
            });
        }

        Ok(devices)
    }

    /// Get CUDA device count
    /// CUDAデバイス数を取得
    #[cfg(feature = "cuda")]
    fn get_cuda_device_count() -> Result<usize, RusTorchError> {
        // Simplified implementation
        Ok(0)
    }

    /// Get CUDA device information
    /// CUDAデバイス情報を取得
    #[cfg(feature = "cuda")]
    fn get_cuda_device_info(device_id: usize) -> Result<GpuDeviceInfo, RusTorchError> {
        Ok(GpuDeviceInfo {
            device_id,
            name: format!("CUDA Device {}", device_id),
            total_memory: 8 * 1024 * 1024 * 1024,
            available_memory: 6 * 1024 * 1024 * 1024,
            compute_capability: Some((7, 5)),
            device_type: DeviceType::Cuda(device_id),
            is_available: true,
        })
    }

    /// Get Metal device information
    /// Metalデバイス情報を取得
    #[cfg(target_os = "macos")]
    fn get_metal_device_info() -> Result<GpuDeviceInfo, RusTorchError> {
        Ok(GpuDeviceInfo {
            device_id: 0,
            name: "Apple Metal GPU".to_string(),
            total_memory: 32 * 1024 * 1024 * 1024, // Example: 32GB for M1 Max
            available_memory: 24 * 1024 * 1024 * 1024,
            compute_capability: None,
            device_type: DeviceType::Metal(0),
            is_available: true,
        })
    }

    /// Get OpenCL devices
    /// OpenCLデバイスを取得
    #[cfg(feature = "opencl")]
    fn get_opencl_devices() -> Result<Vec<GpuDeviceInfo>, RusTorchError> {
        // Simplified implementation
        Ok(Vec::new())
    }

    /// Initialize multi-GPU environment
    /// マルチGPU環境を初期化
    pub fn initialize(&mut self, process_group: ProcessGroup) -> RusTorchResult<()> {
        // Validate that we have enough devices
        if self.devices.len() < 2 {
            return Err(RusTorchError::ConfigurationError(
                "Multi-GPU validation requires at least 2 devices".to_string(),
            ));
        }

        self.process_group = Some(process_group);
        Ok(())
    }

    /// Validate model across multiple GPUs
    /// 複数GPU間でモデルを検証
    pub fn validate_distributed<M>(
        &mut self,
        model: &M,
        validation_data: Vec<(Tensor<T>, Tensor<T>)>,
        batch_size: usize,
    ) -> RusTorchResult<ValidationMetrics>
    where
        M: Module<T> + Send + Sync,
    {
        let start_time = Instant::now();
        let mut device_losses = HashMap::new();
        let mut device_accuracies = HashMap::new();
        let mut device_times = HashMap::new();

        // Split validation data across devices
        let chunks_per_device = validation_data.len() / self.devices.len();

        for (device_idx, device) in self.devices.iter().enumerate() {
            if !device.is_available {
                continue;
            }

            let device_start = Instant::now();

            // Get data chunk for this device
            let start_idx = device_idx * chunks_per_device;
            let end_idx = if device_idx == self.devices.len() - 1 {
                validation_data.len()
            } else {
                (device_idx + 1) * chunks_per_device
            };

            let device_data = &validation_data[start_idx..end_idx];

            // Validate on this device
            let (loss, accuracy) = self.validate_on_device(model, device_data, batch_size)?;

            device_losses.insert(device.device_id, loss);
            device_accuracies.insert(device.device_id, accuracy);
            device_times.insert(device.device_id, device_start.elapsed());
        }

        // Synchronize results across devices
        let comm_start = Instant::now();
        let (total_loss, total_accuracy) =
            self.synchronize_metrics(&device_losses, &device_accuracies)?;
        let comm_time = comm_start.elapsed();

        let total_time = start_time.elapsed();
        let total_samples = validation_data.len();
        let throughput = total_samples as f64 / total_time.as_secs_f64();

        let metrics = ValidationMetrics {
            total_loss,
            device_losses,
            accuracy: total_accuracy,
            device_accuracies,
            device_times,
            communication_time: comm_time,
            total_time,
            throughput,
        };

        self.metrics_history.push(metrics.clone());

        Ok(metrics)
    }

    /// Validate on a single device
    /// 単一デバイスで検証
    fn validate_on_device<M>(
        &self,
        _model: &M,
        data: &[(Tensor<T>, Tensor<T>)],
        batch_size: usize,
    ) -> RusTorchResult<(f64, f64)>
    where
        M: Module<T>,
    {
        let mut total_loss = 0.0;
        let mut correct = 0;
        let mut total = 0;

        // Process in batches
        for batch in data.chunks(batch_size) {
            for (input, _target) in batch {
                // Forward pass (simplified)
                // In real implementation, this would use the actual model forward
                let _output = input.clone(); // Placeholder

                // Calculate loss (simplified)
                let loss = T::from_f64(0.1).unwrap(); // Placeholder
                total_loss += loss.to_f64().unwrap_or(0.0);

                // Calculate accuracy (simplified)
                correct += 1; // Placeholder
                total += 1;
            }
        }

        let accuracy = if total > 0 {
            correct as f64 / total as f64
        } else {
            0.0
        };

        Ok((total_loss / data.len() as f64, accuracy))
    }

    /// Synchronize metrics across devices
    /// デバイス間でメトリクスを同期
    fn synchronize_metrics(
        &self,
        device_losses: &HashMap<usize, f64>,
        device_accuracies: &HashMap<usize, f64>,
    ) -> RusTorchResult<(f64, f64)> {
        // Calculate averages (in real implementation, would use all-reduce)
        let total_loss: f64 = device_losses.values().sum::<f64>() / device_losses.len() as f64;
        let total_accuracy: f64 =
            device_accuracies.values().sum::<f64>() / device_accuracies.len() as f64;

        Ok((total_loss, total_accuracy))
    }

    /// Run performance benchmark
    /// パフォーマンスベンチマークを実行
    pub fn benchmark<M>(
        &mut self,
        model: &M,
        sample_data: Tensor<T>,
        iterations: usize,
    ) -> RusTorchResult<BenchmarkResults>
    where
        M: Module<T> + Send + Sync,
    {
        // Benchmark single GPU
        let single_gpu_throughput = self.benchmark_single_gpu(model, &sample_data, iterations)?;

        // Benchmark multi-GPU
        let multi_gpu_throughput = self.benchmark_multi_gpu(model, &sample_data, iterations)?;

        // Calculate scaling efficiency
        let scaling_efficiency =
            multi_gpu_throughput / (single_gpu_throughput * self.devices.len() as f64);

        // Measure communication overhead
        let communication_overhead = self.measure_communication_overhead(&sample_data)?;

        // Get memory usage
        let memory_usage = self.get_memory_usage()?;

        // Find optimal batch size
        let optimal_batch_size = self.find_optimal_batch_size(model, &sample_data)?;

        let results = BenchmarkResults {
            single_gpu_throughput,
            multi_gpu_throughput,
            scaling_efficiency,
            communication_overhead,
            memory_usage,
            optimal_batch_size,
        };

        self.benchmark_cache = Some(results.clone());

        Ok(results)
    }

    /// Benchmark single GPU performance
    /// シングルGPUパフォーマンスをベンチマーク
    fn benchmark_single_gpu<M>(
        &self,
        _model: &M,
        sample_data: &Tensor<T>,
        iterations: usize,
    ) -> RusTorchResult<f64>
    where
        M: Module<T>,
    {
        let start = Instant::now();

        for _ in 0..iterations {
            // Simulate forward pass
            let _ = sample_data.clone();
        }

        let elapsed = start.elapsed();
        let throughput = iterations as f64 / elapsed.as_secs_f64();

        Ok(throughput)
    }

    /// Benchmark multi-GPU performance
    /// マルチGPUパフォーマンスをベンチマーク
    fn benchmark_multi_gpu<M>(
        &self,
        _model: &M,
        sample_data: &Tensor<T>,
        iterations: usize,
    ) -> RusTorchResult<f64>
    where
        M: Module<T>,
    {
        let start = Instant::now();
        let num_devices = self.devices.len();

        for _ in 0..iterations {
            // Simulate distributed forward pass
            for _ in 0..num_devices {
                let _ = sample_data.clone();
            }
        }

        let elapsed = start.elapsed();
        let throughput = (iterations * num_devices) as f64 / elapsed.as_secs_f64();

        Ok(throughput)
    }

    /// Measure communication overhead
    /// 通信オーバーヘッドを測定
    fn measure_communication_overhead(&self, data: &Tensor<T>) -> RusTorchResult<f64> {
        let iterations = 100;
        let data_size = data.shape().iter().product::<usize>() * std::mem::size_of::<T>();

        // Measure computation time
        let comp_start = Instant::now();
        for _ in 0..iterations {
            let _ = data.clone();
        }
        let comp_time = comp_start.elapsed();

        // Measure communication time (simulated)
        let comm_start = Instant::now();
        for _ in 0..iterations {
            // Simulate all-reduce
            std::thread::sleep(Duration::from_micros(data_size as u64 / 1000));
        }
        let comm_time = comm_start.elapsed();

        let overhead =
            comm_time.as_secs_f64() / (comp_time.as_secs_f64() + comm_time.as_secs_f64());

        Ok(overhead * 100.0) // Return as percentage
    }

    /// Get memory usage for all devices
    /// 全デバイスのメモリ使用量を取得
    fn get_memory_usage(&self) -> RusTorchResult<HashMap<usize, MemoryUsage>> {
        let mut usage_map = HashMap::new();

        for device in &self.devices {
            let usage = MemoryUsage {
                peak_usage: device.total_memory / 2,    // Simulated
                current_usage: device.total_memory / 3, // Simulated
                fragmentation: 5.0,                     // 5% fragmentation simulated
            };

            usage_map.insert(device.device_id, usage);
        }

        Ok(usage_map)
    }

    /// Find optimal batch size per GPU
    /// GPU当たりの最適バッチサイズを見つける
    fn find_optimal_batch_size<M>(
        &self,
        _model: &M,
        sample_data: &Tensor<T>,
    ) -> RusTorchResult<usize>
    where
        M: Module<T>,
    {
        let batch_sizes = vec![8, 16, 32, 64, 128, 256];
        let mut best_batch_size = 32;
        let mut best_throughput = 0.0;

        for &batch_size in &batch_sizes {
            // Try this batch size
            let throughput = self.test_batch_size(batch_size, sample_data)?;

            if throughput > best_throughput {
                best_throughput = throughput;
                best_batch_size = batch_size;
            }
        }

        Ok(best_batch_size)
    }

    /// Test a specific batch size
    /// 特定のバッチサイズをテスト
    fn test_batch_size(&self, batch_size: usize, data: &Tensor<T>) -> RusTorchResult<f64> {
        let start = Instant::now();
        let iterations = 10;

        for _ in 0..iterations {
            // Simulate processing batch
            for _ in 0..batch_size {
                let _ = data.clone();
            }
        }

        let elapsed = start.elapsed();
        let throughput = (iterations * batch_size) as f64 / elapsed.as_secs_f64();

        Ok(throughput)
    }

    /// Get device information
    /// デバイス情報を取得
    pub fn get_devices(&self) -> &[GpuDeviceInfo] {
        &self.devices
    }

    /// Get validation history
    /// 検証履歴を取得
    pub fn get_metrics_history(&self) -> &[ValidationMetrics] {
        &self.metrics_history
    }

    /// Get cached benchmark results
    /// キャッシュされたベンチマーク結果を取得
    pub fn get_benchmark_results(&self) -> Option<&BenchmarkResults> {
        self.benchmark_cache.as_ref()
    }

    /// Clear metrics history
    /// メトリクス履歴をクリア
    pub fn clear_history(&mut self) {
        self.metrics_history.clear();
    }
}

#[cfg(test)]
mod tests {
    use super::*;

    #[test]
    fn test_multi_gpu_validator_creation() {
        let validator = MultiGpuValidator::<f32>::new();
        assert!(validator.is_ok());

        let validator = validator.unwrap();
        assert!(!validator.devices.is_empty());
    }

    #[test]
    fn test_gpu_device_info() {
        let device = GpuDeviceInfo {
            device_id: 0,
            name: "Test GPU".to_string(),
            total_memory: 8 * 1024 * 1024 * 1024,
            available_memory: 6 * 1024 * 1024 * 1024,
            compute_capability: Some((7, 5)),
            device_type: DeviceType::Cuda(0),
            is_available: true,
        };

        assert_eq!(device.device_id, 0);
        assert_eq!(device.name, "Test GPU");
        assert!(device.is_available);
    }

    #[test]
    fn test_validation_metrics() {
        let mut device_losses = HashMap::new();
        device_losses.insert(0, 0.5);
        device_losses.insert(1, 0.6);

        let mut device_accuracies = HashMap::new();
        device_accuracies.insert(0, 0.95);
        device_accuracies.insert(1, 0.94);

        let metrics = ValidationMetrics {
            total_loss: 0.55,
            device_losses,
            accuracy: 0.945,
            device_accuracies,
            device_times: HashMap::new(),
            communication_time: Duration::from_millis(100),
            total_time: Duration::from_secs(10),
            throughput: 1000.0,
        };

        assert_eq!(metrics.total_loss, 0.55);
        assert_eq!(metrics.accuracy, 0.945);
        assert_eq!(metrics.throughput, 1000.0);
    }

    #[test]
    fn test_benchmark_results() {
        let mut memory_usage = HashMap::new();
        memory_usage.insert(
            0,
            MemoryUsage {
                peak_usage: 4 * 1024 * 1024 * 1024,
                current_usage: 2 * 1024 * 1024 * 1024,
                fragmentation: 5.0,
            },
        );

        let results = BenchmarkResults {
            single_gpu_throughput: 1000.0,
            multi_gpu_throughput: 3800.0,
            scaling_efficiency: 0.95,
            communication_overhead: 5.0,
            memory_usage,
            optimal_batch_size: 64,
        };

        assert_eq!(results.single_gpu_throughput, 1000.0);
        assert_eq!(results.multi_gpu_throughput, 3800.0);
        assert_eq!(results.scaling_efficiency, 0.95);
        assert_eq!(results.optimal_batch_size, 64);
    }
}