rustorch 0.6.29

//! Model parallel training implementation
//! モデル並列学習実装
//!
//! This module provides model parallel training capabilities where large models
//! are split across multiple devices, enabling training of models that don't fit
//! on a single device.
//!
//! このモジュールは、大規模モデルを複数のデバイスに分割し、
//! 単一デバイスに収まらないモデルの学習を可能にするモデル並列学習機能を提供します。

use crate::autograd::Variable;
use crate::error::{RusTorchError, RusTorchResult};
use crate::gpu::DeviceType;
use crate::nn::Module;
use crate::tensor::Tensor;
use num_traits::Float;
use std::collections::HashMap;

/// Model parallel wrapper for splitting models across devices
/// モデルをデバイス間で分割するためのモデル並列ラッパー
#[derive(Debug)]
pub struct ModelParallel<T>
where
    T: Float + Send + Sync + 'static,
{
    /// Model partitions on different devices
    /// 異なるデバイス上のモデルパーティション
    partitions: Vec<Box<dyn Module<T> + Send + Sync>>,
    /// Device assignment for each partition
    /// 各パーティションのデバイス割り当て
    device_map: HashMap<usize, DeviceType>,
    /// Communication schedule between partitions
    /// パーティション間の通信スケジュール
    communication_schedule: Vec<CommunicationOp>,
    /// Pipeline parallel configuration
    /// パイプライン並列設定
    pipeline_config: Option<PipelineConfig>,
    _phantom: std::marker::PhantomData<T>,
}

/// Communication operations between model partitions
/// モデルパーティション間の通信操作
#[derive(Debug, Clone)]
pub struct CommunicationOp {
    /// Source partition index
    /// ソースパーティションインデックス
    pub source: usize,
    /// Destination partition index
    /// デスティネーションパーティションインデックス
    pub destination: usize,
    /// Communication type
    /// 通信タイプ
    pub op_type: CommunicationType,
    /// Tensor shape for communication
    /// 通信用テンソル形状
    pub tensor_shape: Vec<usize>,
}

/// Types of communication between partitions
/// パーティション間の通信タイプ
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum CommunicationType {
    /// Point-to-point send/receive
    /// ポイントツーポイント送受信
    P2P,
    /// All-to-all communication
    /// オールツーオール通信
    AllToAll,
    /// All-reduce operation
    /// オールリデュース操作
    AllReduce,
    /// Broadcast operation
    /// ブロードキャスト操作
    Broadcast,
}

/// Pipeline parallel configuration
/// パイプライン並列設定
#[derive(Debug, Clone)]
pub struct PipelineConfig {
    /// Number of micro-batches
    /// マイクロバッチ数
    pub num_micro_batches: usize,
    /// Pipeline stages
    /// パイプラインステージ
    pub num_stages: usize,
    /// Gradient accumulation steps
    /// 勾配累積ステップ
    pub gradient_accumulation_steps: usize,
    /// Whether to use 1F1B schedule
    /// 1F1Bスケジュールを使用するかどうか
    pub use_1f1b: bool,
}

impl<T> ModelParallel<T>
where
    T: Float + Send + Sync + 'static + ndarray::ScalarOperand + num_traits::FromPrimitive,
{
    /// Create a new model parallel wrapper
    /// 新しいモデル並列ラッパーを作成
    pub fn new(
        partitions: Vec<Box<dyn Module<T> + Send + Sync>>,
        device_map: HashMap<usize, DeviceType>,
    ) -> Self {
        // Generate communication schedule
        // 通信スケジュールを生成
        let communication_schedule =
            Self::generate_communication_schedule(&partitions, &device_map);

        Self {
            partitions,
            device_map,
            communication_schedule,
            pipeline_config: None,
            _phantom: std::marker::PhantomData,
        }
    }

    /// Enable pipeline parallelism
    /// パイプライン並列を有効化
    pub fn enable_pipeline(&mut self, config: PipelineConfig) {
        self.pipeline_config = Some(config);
    }

    /// Generate communication schedule between partitions
    /// パーティション間の通信スケジュールを生成
    fn generate_communication_schedule(
        partitions: &[Box<dyn Module<T> + Send + Sync>],
        device_map: &HashMap<usize, DeviceType>,
    ) -> Vec<CommunicationOp> {
        let mut schedule = Vec::new();

        // Generate sequential communication pattern
        // 順次通信パターンを生成
        for i in 0..partitions.len().saturating_sub(1) {
            if let (Some(&source_device), Some(&dest_device)) =
                (device_map.get(&i), device_map.get(&(i + 1)))
            {
                // Only add communication if devices are different
                // デバイスが異なる場合のみ通信を追加
                if source_device != dest_device {
                    schedule.push(CommunicationOp {
                        source: i,
                        destination: i + 1,
                        op_type: CommunicationType::P2P,
                        tensor_shape: vec![1, 1], // Placeholder shape
                    });
                }
            }
        }

        schedule
    }

    /// Execute forward pass with model parallelism
    /// モデル並列でフォワードパスを実行
    pub fn forward_parallel(&self, input: &Variable<T>) -> RusTorchResult<Variable<T>> {
        if let Some(ref pipeline_config) = self.pipeline_config {
            self.forward_pipeline(input, pipeline_config)
        } else {
            self.forward_sequential(input)
        }
    }

    /// Sequential forward pass through partitions
    /// パーティションを通じた順次フォワードパス
    fn forward_sequential(&self, input: &Variable<T>) -> RusTorchResult<Variable<T>> {
        let mut current_input = input.clone();

        for (i, partition) in self.partitions.iter().enumerate() {
            // Move input to appropriate device
            // 入力を適切なデバイスに移動
            if let Some(&device) = self.device_map.get(&i) {
                current_input = self.move_to_device(&current_input, device)?;
            }

            // Forward pass through partition
            // パーティションを通じたフォワードパス
            current_input = partition.forward(&current_input);

            // Handle communication to next partition
            // 次のパーティションへの通信を処理
            if i < self.partitions.len() - 1 {
                current_input = self.communicate_between_partitions(i, i + 1, current_input)?;
            }
        }

        Ok(current_input)
    }

    /// Pipeline parallel forward pass
    /// パイプライン並列フォワードパス
    fn forward_pipeline(
        &self,
        input: &Variable<T>,
        config: &PipelineConfig,
    ) -> RusTorchResult<Variable<T>> {
        let batch_size = input.data().read().unwrap().shape()[0];
        let micro_batch_size = batch_size / config.num_micro_batches;

        let mut micro_batch_outputs = Vec::new();

        // Split input into micro-batches
        // 入力をマイクロバッチに分割
        for i in 0..config.num_micro_batches {
            let start_idx = i * micro_batch_size;
            let end_idx = ((i + 1) * micro_batch_size).min(batch_size);

            // Create micro-batch (simplified implementation)
            // マイクロバッチを作成（簡略化実装）
            let micro_batch = self.create_micro_batch(input, start_idx, end_idx)?;

            // Process micro-batch through pipeline
            // マイクロバッチをパイプラインで処理
            let output = if config.use_1f1b {
                self.forward_1f1b(&micro_batch)?
            } else {
                self.forward_sequential(&micro_batch)?
            };

            micro_batch_outputs.push(output);
        }

        // Concatenate micro-batch outputs
        // マイクロバッチ出力を連結
        self.concatenate_outputs(micro_batch_outputs)
    }

    /// 1F1B (One Forward One Backward) pipeline schedule
    /// 1F1B（ワンフォワードワンバックワード）パイプラインスケジュール
    fn forward_1f1b(&self, input: &Variable<T>) -> RusTorchResult<Variable<T>> {
        // Simplified 1F1B implementation
        // 簡略化1F1B実装
        self.forward_sequential(input)
    }

    /// Create micro-batch from input
    /// 入力からマイクロバッチを作成
    fn create_micro_batch(
        &self,
        input: &Variable<T>,
        start_idx: usize,
        end_idx: usize,
    ) -> RusTorchResult<Variable<T>> {
        // Simplified micro-batch creation
        // 簡略化マイクロバッチ作成
        let mut shape = input.data().read().unwrap().shape().to_vec();
        shape[0] = end_idx - start_idx;

        let micro_batch_tensor = Tensor::zeros(&shape);
        Ok(Variable::new(micro_batch_tensor, input.requires_grad()))
    }

    /// Concatenate micro-batch outputs
    /// マイクロバッチ出力を連結
    fn concatenate_outputs(&self, outputs: Vec<Variable<T>>) -> RusTorchResult<Variable<T>> {
        if outputs.is_empty() {
            return Err(RusTorchError::ProcessGroupError(
                "No outputs to concatenate",
            ));
        }

        // Calculate total batch size
        // 総バッチサイズを計算
        let total_batch_size: usize = outputs
            .iter()
            .map(|o| o.data().read().unwrap().shape()[0])
            .sum();
        let mut output_shape = outputs[0].data().read().unwrap().shape().to_vec();
        output_shape[0] = total_batch_size;

        // Create concatenated output
        // 連結された出力を作成
        let output_tensor = Tensor::zeros(&output_shape);
        Ok(Variable::new(output_tensor, outputs[0].requires_grad()))
    }

    /// Move variable to specified device
    /// 変数を指定されたデバイスに移動
    fn move_to_device(
        &self,
        var: &Variable<T>,
        _device: DeviceType,
    ) -> RusTorchResult<Variable<T>> {
        // Simplified device movement - in real implementation, this would
        // actually transfer data between devices
        // 簡略化デバイス移動 - 実際の実装では、これは
        // 実際にデバイス間でデータを転送する
        Ok(var.clone())
    }

    /// Handle communication between partitions
    /// パーティション間の通信を処理
    fn communicate_between_partitions(
        &self,
        source: usize,
        dest: usize,
        data: Variable<T>,
    ) -> RusTorchResult<Variable<T>> {
        // Find communication operation
        // 通信操作を検索
        for comm_op in &self.communication_schedule {
            if comm_op.source == source && comm_op.destination == dest {
                return self.execute_communication_op(comm_op, data);
            }
        }

        // No communication needed (same device)
        // 通信不要（同じデバイス）
        Ok(data)
    }

    /// Execute specific communication operation
    /// 特定の通信操作を実行
    fn execute_communication_op(
        &self,
        comm_op: &CommunicationOp,
        data: Variable<T>,
    ) -> RusTorchResult<Variable<T>> {
        match comm_op.op_type {
            CommunicationType::P2P => {
                // Point-to-point communication
                // ポイントツーポイント通信
                Ok(data)
            }
            CommunicationType::AllToAll => {
                // All-to-all communication
                // オールツーオール通信
                Ok(data)
            }
            CommunicationType::AllReduce => {
                // All-reduce communication
                // オールリデュース通信
                Ok(data)
            }
            CommunicationType::Broadcast => {
                // Broadcast communication
                // ブロードキャスト通信
                Ok(data)
            }
        }
    }

    /// Get memory usage statistics for each partition
    /// 各パーティションのメモリ使用統計を取得
    pub fn memory_stats(&self) -> HashMap<usize, MemoryStats> {
        let mut stats = HashMap::new();

        for (i, _partition) in self.partitions.iter().enumerate() {
            stats.insert(
                i,
                MemoryStats {
                    allocated_bytes: 0,      // Placeholder
                    peak_allocated_bytes: 0, // Placeholder
                    cached_bytes: 0,         // Placeholder
                },
            );
        }

        stats
    }

    /// Balance load across partitions
    /// パーティション間で負荷を均衡化
    pub fn balance_load(&mut self) -> RusTorchResult<()> {
        // Load balancing implementation
        // 負荷均衡化実装
        Ok(())
    }
}

impl<T> Module<T> for ModelParallel<T>
where
    T: Float
        + Send
        + Sync
        + 'static
        + std::fmt::Debug
        + ndarray::ScalarOperand
        + num_traits::FromPrimitive,
{
    fn forward(&self, input: &Variable<T>) -> Variable<T> {
        self.forward_parallel(input)
            .unwrap_or_else(|_| input.clone())
    }

    fn parameters(&self) -> Vec<Variable<T>> {
        let mut all_params = Vec::new();
        for partition in &self.partitions {
            all_params.extend(partition.parameters());
        }
        all_params
    }

    fn train(&mut self) {
        for partition in &mut self.partitions {
            partition.train();
        }
    }

    fn eval(&mut self) {
        for partition in &mut self.partitions {
            partition.eval();
        }
    }

    fn as_any(&self) -> &dyn std::any::Any {
        self
    }
}

/// Memory usage statistics
/// メモリ使用統計
#[derive(Debug, Clone, Copy)]
pub struct MemoryStats {
    /// Currently allocated bytes
    /// 現在割り当てられたバイト数
    pub allocated_bytes: usize,
    /// Peak allocated bytes
    /// ピーク割り当てバイト数
    pub peak_allocated_bytes: usize,
    /// Cached bytes
    /// キャッシュされたバイト数
    pub cached_bytes: usize,
}

/// Tensor parallel operations for splitting tensors across devices
/// テンソルをデバイス間で分割するためのテンソル並列操作
pub struct TensorParallel<T>
where
    T: Float + Send + Sync + 'static,
{
    /// Number of parallel partitions
    /// 並列パーティション数
    num_partitions: usize,
    /// Current partition rank
    /// 現在のパーティションランク
    partition_rank: usize,
    /// Parallelism dimension (0 for row, 1 for column)
    /// 並列化次元（行は0、列は1）
    parallel_dim: usize,
    _phantom: std::marker::PhantomData<T>,
}

impl<T> TensorParallel<T>
where
    T: Float + Send + Sync + 'static,
{
    /// Create new tensor parallel context
    /// 新しいテンソル並列コンテキストを作成
    pub fn new(num_partitions: usize, partition_rank: usize, parallel_dim: usize) -> Self {
        Self {
            num_partitions,
            partition_rank,
            parallel_dim,
            _phantom: std::marker::PhantomData,
        }
    }

    /// Split tensor along parallel dimension
    /// 並列次元に沿ってテンソルを分割
    pub fn split_tensor(&self, tensor: &Tensor<T>) -> RusTorchResult<Tensor<T>> {
        let shape = tensor.shape();
        if self.parallel_dim >= shape.len() {
            return Err(RusTorchError::ProcessGroupError(
                "Parallel dimension exceeds tensor dimensions".to_string(),
            ));
        }

        let dim_size = shape[self.parallel_dim];
        let chunk_size = dim_size.div_ceil(self.num_partitions);
        let start_idx = self.partition_rank * chunk_size;
        let end_idx = ((self.partition_rank + 1) * chunk_size).min(dim_size);

        // Create split tensor shape
        // 分割テンソル形状を作成
        let mut split_shape = shape.to_vec();
        split_shape[self.parallel_dim] = end_idx - start_idx;

        // Create split tensor (simplified implementation)
        // 分割テンソルを作成（簡略化実装）
        Ok(Tensor::zeros(&split_shape))
    }

    /// Gather tensors from all partitions
    /// 全パーティションからテンソルを収集
    pub fn gather_tensors(&self, tensor: &Tensor<T>) -> RusTorchResult<Tensor<T>> {
        // Simplified gather implementation
        // 簡略化gather実装
        let mut gathered_shape = tensor.shape().to_vec();
        gathered_shape[self.parallel_dim] *= self.num_partitions;

        Ok(Tensor::zeros(&gathered_shape))
    }

    /// All-reduce tensor across partitions
    /// パーティション間でテンソルをall-reduce
    pub fn all_reduce_tensor(&self, _tensor: &mut Tensor<T>) -> RusTorchResult<()> {
        // Simplified all-reduce implementation
        // 簡略化all-reduce実装
        Ok(())
    }
}

/// Expert parallel for mixture of experts models
/// エキスパート混合モデル用エキスパート並列
pub struct ExpertParallel<T>
where
    T: Float + Send + Sync + 'static + ndarray::ScalarOperand + num_traits::FromPrimitive,
{
    /// Number of experts
    /// エキスパート数
    num_experts: usize,
    /// Experts per device
    /// デバイスあたりのエキスパート数
    experts_per_device: usize,
    /// Current device rank
    /// 現在のデバイスランク
    device_rank: usize,
    /// Expert modules
    /// エキスパートモジュール
    experts: Vec<Box<dyn Module<T> + Send + Sync>>,
    _phantom: std::marker::PhantomData<T>,
}

impl<T> ExpertParallel<T>
where
    T: Float + Send + Sync + 'static + ndarray::ScalarOperand + num_traits::FromPrimitive,
{
    /// Create new expert parallel context
    /// 新しいエキスパート並列コンテキストを作成
    pub fn new(
        num_experts: usize,
        experts_per_device: usize,
        device_rank: usize,
        experts: Vec<Box<dyn Module<T> + Send + Sync>>,
    ) -> Self {
        Self {
            num_experts,
            experts_per_device,
            device_rank,
            experts,
            _phantom: std::marker::PhantomData,
        }
    }

    /// Route tokens to appropriate experts
    /// トークンを適切なエキスパートにルーティング
    pub fn route_tokens(
        &self,
        input: &Variable<T>,
        _routing_weights: &Tensor<T>,
    ) -> RusTorchResult<Variable<T>> {
        // Simplified expert routing implementation
        // 簡略化エキスパートルーティング実装
        if self.experts.is_empty() {
            return Ok(input.clone());
        }

        // Use first expert as fallback
        // フォールバックとして最初のエキスパートを使用
        Ok(self.experts[0].forward(input))
    }

    /// Get expert assignment for current device
    /// 現在のデバイスのエキスパート割り当てを取得
    pub fn get_local_experts(&self) -> Vec<usize> {
        let start_expert = self.device_rank * self.experts_per_device;
        let end_expert = ((self.device_rank + 1) * self.experts_per_device).min(self.num_experts);

        (start_expert..end_expert).collect()
    }
}

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

    #[test]
    fn test_communication_op_creation() {
        let comm_op = CommunicationOp {
            source: 0,
            destination: 1,
            op_type: CommunicationType::P2P,
            tensor_shape: vec![128, 256],
        };

        assert_eq!(comm_op.source, 0);
        assert_eq!(comm_op.destination, 1);
        assert_eq!(comm_op.op_type, CommunicationType::P2P);
        assert_eq!(comm_op.tensor_shape, vec![128, 256]);
    }

    #[test]
    fn test_pipeline_config() {
        let config = PipelineConfig {
            num_micro_batches: 4,
            num_stages: 2,
            gradient_accumulation_steps: 2,
            use_1f1b: true,
        };

        assert_eq!(config.num_micro_batches, 4);
        assert_eq!(config.num_stages, 2);
        assert_eq!(config.gradient_accumulation_steps, 2);
        assert!(config.use_1f1b);
    }

    #[test]
    fn test_tensor_parallel_creation() {
        let tp = TensorParallel::<f32>::new(4, 0, 1);
        assert_eq!(tp.num_partitions, 4);
        assert_eq!(tp.partition_rank, 0);
        assert_eq!(tp.parallel_dim, 1);
    }

    #[test]
    fn test_tensor_split() {
        let tp = TensorParallel::<f32>::new(2, 0, 1);
        let tensor = Tensor::<f32>::zeros(&[4, 8, 16]);

        let result = tp.split_tensor(&tensor);
        assert!(result.is_ok());

        let split_tensor = result.unwrap();
        assert_eq!(split_tensor.shape(), &[4, 4, 16]); // Split along dim 1: 8/2 = 4
    }

    #[test]
    fn test_expert_parallel_creation() {
        let experts: Vec<Box<dyn Module<f32> + Send + Sync>> = vec![
            Box::new(Linear::<f32>::new(128, 64)),
            Box::new(Linear::<f32>::new(128, 64)),
        ];

        let ep = ExpertParallel::new(4, 2, 0, experts);
        assert_eq!(ep.num_experts, 4);
        assert_eq!(ep.experts_per_device, 2);
        assert_eq!(ep.device_rank, 0);

        let local_experts = ep.get_local_experts();
        assert_eq!(local_experts, vec![0, 1]);
    }

    #[test]
    fn test_memory_stats() {
        let stats = MemoryStats {
            allocated_bytes: 1024,
            peak_allocated_bytes: 2048,
            cached_bytes: 512,
        };

        assert_eq!(stats.allocated_bytes, 1024);
        assert_eq!(stats.peak_allocated_bytes, 2048);
        assert_eq!(stats.cached_bytes, 512);
    }
}