rustorch 0.6.29

//! PyTorch-compatible distributed training API
//! PyTorch互換分散学習API
//!
//! This module provides a comprehensive torch.distributed compatible API
//! for distributed training in RusTorch.
//!
//! このモジュールは、RusTorchでの分散学習のための
//! 包括的なtorch.distributed互換APIを提供します。

use super::{get_distributed_state, DistributedBackend, ProcessGroup, ReduceOp};
use crate::error::{RusTorchError, RusTorchResult};
use crate::nn::Module;
use crate::tensor::Tensor;
use num_traits::Float;
use std::sync::{Arc, Mutex};
use std::time::Duration;

/// Initialize distributed training process group
/// 分散学習プロセスグループを初期化
pub fn init_process_group(
    backend: DistributedBackend,
    init_method: Option<&str>,
    world_size: Option<usize>,
    rank: Option<usize>,
    timeout: Option<Duration>,
) -> RusTorchResult<()> {
    // Parse init_method or use environment variables
    let (master_addr, master_port) = if let Some(init_method) = init_method {
        parse_init_method(init_method)?
    } else {
        get_master_info_from_env()?
    };

    let world_size = world_size
        .or_else(|| {
            std::env::var("WORLD_SIZE")
                .ok()
                .and_then(|s| s.parse().ok())
        })
        .ok_or_else(|| RusTorchError::distributed("WORLD_SIZE not specified"))?;

    let rank = rank
        .or_else(|| std::env::var("RANK").ok().and_then(|s| s.parse().ok()))
        .ok_or_else(|| RusTorchError::distributed("RANK not specified"))?;

    let process_group = ProcessGroup::new(rank, world_size, backend, master_addr, master_port);

    // Set timeout if specified
    if let Some(_timeout) = timeout {
        // Note: timeout configuration not implemented in current version
    }

    process_group.init()?;

    let state = get_distributed_state();
    let mut state_guard = state.lock().unwrap();
    state_guard.set_process_group(process_group);

    Ok(())
}

/// Destroy the distributed process group
/// 分散プロセスグループを破棄
pub fn destroy_process_group() -> RusTorchResult<()> {
    super::finalize()
}

/// Get the rank of the current process
/// 現在のプロセスのランクを取得
pub fn get_rank() -> usize {
    super::get_rank().unwrap_or(0)
}

/// Get the world size of the current process group
/// 現在のプロセスグループのワールドサイズを取得
pub fn get_world_size() -> usize {
    super::get_world_size().unwrap_or(1)
}

/// Check if the current process is initialized for distributed training
/// 現在のプロセスが分散学習用に初期化されているかチェック
pub fn is_initialized() -> bool {
    super::is_available()
}

/// All-reduce operation
/// All-reduce操作
pub fn all_reduce<T: Float + Send + Sync + 'static>(
    tensor: &mut Tensor<T>,
    op: ReduceOp,
    group: Option<&ProcessGroup>,
    async_op: bool,
) -> RusTorchResult<Option<DistributedRequest>> {
    if !is_initialized() {
        return Err(RusTorchError::distributed("Process group not initialized"));
    }

    // For now, perform synchronous operation
    let _ = (group, async_op);

    // Use default implementation from common module
    super::common::CommonOps::default_all_reduce(tensor, op)?;

    Ok(None) // No handle for sync operations
}

/// All-gather operation
/// All-gather操作
pub fn all_gather<T: Float + Send + Sync + 'static>(
    tensor_list: &mut Vec<Tensor<T>>,
    tensor: &Tensor<T>,
    group: Option<&ProcessGroup>,
    async_op: bool,
) -> RusTorchResult<Option<DistributedRequest>> {
    if !is_initialized() {
        return Err(RusTorchError::distributed("Process group not initialized"));
    }

    let world_size = get_world_size();
    let gathered = super::common::CommonOps::default_all_gather(tensor, world_size)?;
    *tensor_list = gathered;

    let _ = (group, async_op);
    Ok(None)
}

/// Broadcast operation
/// ブロードキャスト操作
pub fn broadcast<T: Float + Send + Sync + 'static>(
    tensor: &mut Tensor<T>,
    src: usize,
    group: Option<&ProcessGroup>,
    async_op: bool,
) -> RusTorchResult<Option<DistributedRequest>> {
    if !is_initialized() {
        return Err(RusTorchError::distributed("Process group not initialized"));
    }

    super::common::CommonOps::default_broadcast(tensor, src)?;

    let _ = (group, async_op);
    Ok(None)
}

/// Reduce operation
/// Reduce操作
pub fn reduce<T: Float + Send + Sync + 'static>(
    tensor: &mut Tensor<T>,
    dst: usize,
    op: ReduceOp,
    group: Option<&ProcessGroup>,
    async_op: bool,
) -> RusTorchResult<Option<DistributedRequest>> {
    if !is_initialized() {
        return Err(RusTorchError::distributed("Process group not initialized"));
    }

    if get_rank() == dst {
        super::common::CommonOps::default_all_reduce(tensor, op)?;
    }

    let _ = (group, async_op);
    Ok(None)
}

/// Scatter operation
/// Scatter操作
pub fn scatter<T: Float + Send + Sync + 'static>(
    tensor: &mut Tensor<T>,
    scatter_list: Option<&[Tensor<T>]>,
    src: usize,
    group: Option<&ProcessGroup>,
    async_op: bool,
) -> RusTorchResult<Option<DistributedRequest>> {
    if !is_initialized() {
        return Err(RusTorchError::distributed("Process group not initialized"));
    }

    if let Some(tensors) = scatter_list {
        if get_rank() == src {
            let scattered = super::common::CommonOps::default_gather(
                &tensors[get_rank()],
                get_world_size(),
                src,
            )?;
            if !scattered.is_empty() {
                *tensor = scattered[get_rank()].clone();
            }
        }
    }

    let _ = (group, async_op);
    Ok(None)
}

/// Gather operation
/// Gather操作
pub fn gather<T: Float + Send + Sync + 'static>(
    tensor: &Tensor<T>,
    gather_list: Option<&mut Vec<Tensor<T>>>,
    dst: usize,
    group: Option<&ProcessGroup>,
    async_op: bool,
) -> RusTorchResult<Option<DistributedRequest>> {
    if !is_initialized() {
        return Err(RusTorchError::distributed("Process group not initialized"));
    }

    if get_rank() == dst {
        if let Some(list) = gather_list {
            let gathered = super::common::CommonOps::default_gather(tensor, get_world_size(), dst)?;
            *list = gathered;
        }
    }

    let _ = (group, async_op);
    Ok(None)
}

/// Barrier synchronization
/// バリア同期
pub fn barrier(
    group: Option<&ProcessGroup>,
    async_op: bool,
) -> RusTorchResult<Option<DistributedRequest>> {
    if !is_initialized() {
        return Err(RusTorchError::distributed("Process group not initialized"));
    }

    // Simple barrier implementation - in production would use actual synchronization
    // シンプルなバリア実装 - プロダクションでは実際の同期を使用

    let _ = (group, async_op);
    Ok(None)
}

/// Asynchronous distributed request handle
/// 非同期分散リクエストハンドル
#[derive(Debug)]
pub struct DistributedRequest {
    completed: Arc<Mutex<bool>>,
}

impl DistributedRequest {
    pub fn new() -> Self {
        Self {
            completed: Arc::new(Mutex::new(false)),
        }
    }

    /// Wait for the operation to complete
    /// 操作完了を待機
    pub fn wait(&self) -> RusTorchResult<()> {
        let mut completed = self.completed.lock().unwrap();
        *completed = true;
        Ok(())
    }

    /// Check if operation is completed
    /// 操作が完了しているかチェック
    pub fn is_completed(&self) -> bool {
        *self.completed.lock().unwrap()
    }
}

impl Default for DistributedRequest {
    fn default() -> Self {
        Self::new()
    }
}

/// Process group management
/// プロセスグループ管理
pub struct Group {
    process_group: ProcessGroup,
}

impl Group {
    /// Create a new process group
    /// 新しいプロセスグループを作成
    pub fn new(ranks: Vec<usize>) -> RusTorchResult<Self> {
        if ranks.is_empty() {
            return Err(RusTorchError::distributed("Empty ranks list"));
        }

        // Create a default process group for the specified ranks
        let process_group = ProcessGroup::new(
            ranks[0],
            ranks.len(),
            DistributedBackend::Gloo,
            "localhost".to_string(),
            29500,
        );

        Ok(Self { process_group })
    }

    /// Get the size of this group
    /// このグループのサイズを取得
    pub fn size(&self) -> usize {
        self.process_group.world_size
    }

    /// Get the rank of this process in the group
    /// このグループでのプロセスのランクを取得
    pub fn rank(&self) -> usize {
        self.process_group.rank
    }
}

/// Create a new distributed group
/// 新しい分散グループを作成
pub fn new_group(
    ranks: Vec<usize>,
    timeout: Option<Duration>,
    backend: Option<DistributedBackend>,
) -> RusTorchResult<Group> {
    let _ = (timeout, backend); // Note: timeout and backend parameters not used in current version
    Group::new(ranks)
}

/// Monitoring and debugging utilities
/// 監視とデバッグユーティリティ
pub mod monitoring {
    use super::*;

    /// Get communication statistics
    /// 通信統計を取得
    pub fn get_communication_stats() -> RusTorchResult<CommunicationStats> {
        Ok(CommunicationStats::new())
    }

    /// Communication statistics structure
    /// 通信統計構造体
    #[derive(Debug, Clone)]
    pub struct CommunicationStats {
        pub total_bytes_sent: u64,
        pub total_bytes_received: u64,
        pub total_operations: u64,
        pub average_latency_ms: f64,
    }

    impl CommunicationStats {
        pub fn new() -> Self {
            Self {
                total_bytes_sent: 0,
                total_bytes_received: 0,
                total_operations: 0,
                average_latency_ms: 0.0,
            }
        }
    }

    impl Default for CommunicationStats {
        fn default() -> Self {
            Self::new()
        }
    }
}

// Utility functions
/// Parse initialization method string
/// 初期化メソッド文字列をパース
fn parse_init_method(init_method: &str) -> RusTorchResult<(String, u16)> {
    if init_method.starts_with("tcp://") {
        let addr_port = init_method.strip_prefix("tcp://").unwrap();
        if let Some((addr, port_str)) = addr_port.split_once(':') {
            let port = port_str
                .parse::<u16>()
                .map_err(|_| RusTorchError::distributed("Invalid port in init_method"))?;
            Ok((addr.to_string(), port))
        } else {
            Err(RusTorchError::distributed("Invalid init_method format"))
        }
    } else {
        Err(RusTorchError::distributed("Unsupported init_method scheme"))
    }
}

/// Get master info from environment variables
/// 環境変数からマスター情報を取得
fn get_master_info_from_env() -> RusTorchResult<(String, u16)> {
    let addr = std::env::var("MASTER_ADDR")
        .map_err(|_| RusTorchError::distributed("MASTER_ADDR not set"))?;

    let port = std::env::var("MASTER_PORT")
        .map_err(|_| RusTorchError::distributed("MASTER_PORT not set"))?
        .parse::<u16>()
        .map_err(|_| RusTorchError::distributed("Invalid MASTER_PORT"))?;

    Ok((addr, port))
}

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

    #[test]
    fn test_parse_init_method() {
        let result = parse_init_method("tcp://127.0.0.1:29500");
        assert!(result.is_ok());
        let (addr, port) = result.unwrap();
        assert_eq!(addr, "127.0.0.1");
        assert_eq!(port, 29500);
    }

    #[test]
    fn test_parse_init_method_invalid() {
        let result = parse_init_method("invalid://127.0.0.1:29500");
        assert!(result.is_err());

        let result = parse_init_method("tcp://127.0.0.1");
        assert!(result.is_err());
    }

    #[test]
    fn test_distributed_request() {
        let req = DistributedRequest::new();
        assert!(!req.is_completed());

        assert!(req.wait().is_ok());
        assert!(req.is_completed());
    }

    #[test]
    fn test_group_creation() {
        let ranks = vec![0, 1, 2, 3];
        let group = Group::new(ranks);
        assert!(group.is_ok());

        let group = group.unwrap();
        assert_eq!(group.size(), 4);
        assert_eq!(group.rank(), 0);
    }

    #[test]
    fn test_group_creation_empty() {
        let ranks = vec![];
        let group = Group::new(ranks);
        assert!(group.is_err());
    }
}