rustorch 0.6.29

//! Python bindings for distributed training
//! 分散訓練のPythonバインディング

use crate::python::error::to_py_err;
use crate::python::training::PyModel;
use pyo3::prelude::*;
use std::collections::HashMap;

/// Python wrapper for distributed training configuration
/// 分散訓練設定のPythonラッパー
#[pyclass]
pub struct PyDistributedConfig {
    pub(crate) backend: String,
    pub(crate) world_size: usize,
    pub(crate) rank: usize,
    pub(crate) master_addr: String,
    pub(crate) master_port: u16,
    pub(crate) timeout: Option<u64>,
}

#[pymethods]
impl PyDistributedConfig {
    #[new]
    pub fn new(
        backend: Option<String>,
        world_size: Option<usize>,
        rank: Option<usize>,
        master_addr: Option<String>,
        master_port: Option<u16>,
        timeout: Option<u64>,
    ) -> Self {
        PyDistributedConfig {
            backend: backend.unwrap_or_else(|| "nccl".to_string()),
            world_size: world_size.unwrap_or(1),
            rank: rank.unwrap_or(0),
            master_addr: master_addr.unwrap_or_else(|| "localhost".to_string()),
            master_port: master_port.unwrap_or(29500),
            timeout,
        }
    }

    /// Get backend
    /// バックエンドを取得
    pub fn backend(&self) -> &str {
        &self.backend
    }

    /// Get world size
    /// ワールドサイズを取得
    pub fn world_size(&self) -> usize {
        self.world_size
    }

    /// Get rank
    /// ランクを取得
    pub fn rank(&self) -> usize {
        self.rank
    }

    /// Get master address
    /// マスターアドレスを取得
    pub fn master_addr(&self) -> &str {
        &self.master_addr
    }

    /// Get master port
    /// マスターポートを取得
    pub fn master_port(&self) -> u16 {
        self.master_port
    }

    /// String representation
    /// 文字列表現
    pub fn __repr__(&self) -> String {
        format!(
            "DistributedConfig(backend='{}', world_size={}, rank={}, master='{}:{}')",
            self.backend, self.world_size, self.rank, self.master_addr, self.master_port
        )
    }
}

/// Python wrapper for distributed data parallel training
/// 分散データ並列訓練のPythonラッパー
#[pyclass]
pub struct PyDistributedDataParallel {
    pub(crate) model: PyModel,
    pub(crate) device_ids: Vec<usize>,
    pub(crate) output_device: Option<usize>,
    pub(crate) broadcast_buffers: bool,
    pub(crate) find_unused_parameters: bool,
}

#[pymethods]
impl PyDistributedDataParallel {
    #[new]
    pub fn new(
        model: &PyModel,
        device_ids: Option<Vec<usize>>,
        output_device: Option<usize>,
        broadcast_buffers: Option<bool>,
        find_unused_parameters: Option<bool>,
    ) -> Self {
        PyDistributedDataParallel {
            model: model.clone(),
            device_ids: device_ids.unwrap_or_else(|| vec![0]),
            output_device,
            broadcast_buffers: broadcast_buffers.unwrap_or(true),
            find_unused_parameters: find_unused_parameters.unwrap_or(false),
        }
    }

    /// Forward pass (distributed)
    /// フォワードパス（分散）
    pub fn forward(
        &mut self,
        py: Python,
        inputs: Vec<pyo3::Py<pyo3::PyAny>>,
    ) -> PyResult<pyo3::Py<pyo3::PyAny>> {
        // Simplified distributed forward pass
        println!(
            "Distributed forward pass on {} devices: {:?}",
            self.device_ids.len(),
            self.device_ids
        );

        // In a real implementation, this would distribute inputs across devices
        // and synchronize gradients
        Ok(inputs[0].clone_ref(py))
    }

    /// Synchronize gradients across all processes
    /// 全プロセス間で勾配を同期
    pub fn sync_gradients(&mut self) -> PyResult<()> {
        println!(
            "Synchronizing gradients across {} processes",
            self.device_ids.len()
        );
        // Simplified gradient synchronization
        Ok(())
    }

    /// Get the underlying model
    /// 元のモデルを取得
    pub fn module(&self) -> PyModel {
        self.model.clone()
    }

    /// String representation
    /// 文字列表現
    pub fn __repr__(&self) -> String {
        format!(
            "DistributedDataParallel(devices={:?}, broadcast_buffers={})",
            self.device_ids, self.broadcast_buffers
        )
    }
}

/// Communication backend for distributed training
/// 分散訓練用通信バックエンド
#[pyclass]
pub struct PyDistributedBackend {
    pub(crate) backend_type: String,
    pub(crate) initialized: bool,
}

#[pymethods]
impl PyDistributedBackend {
    #[new]
    pub fn new(backend_type: Option<String>) -> Self {
        PyDistributedBackend {
            backend_type: backend_type.unwrap_or_else(|| "nccl".to_string()),
            initialized: false,
        }
    }

    /// Initialize the distributed backend
    /// 分散バックエンドを初期化
    pub fn init_process_group(&mut self, config: &PyDistributedConfig) -> PyResult<()> {
        println!(
            "Initializing {} backend with world_size={}, rank={}",
            config.backend, config.world_size, config.rank
        );

        // Simplified initialization
        if config.world_size > 1 && config.rank < config.world_size {
            self.initialized = true;
            println!("Process group initialized successfully");
        } else {
            return Err(pyo3::exceptions::PyValueError::new_err(
                "Invalid world_size or rank configuration",
            ));
        }

        Ok(())
    }

    /// Destroy the process group
    /// プロセスグループを破棄
    pub fn destroy_process_group(&mut self) -> PyResult<()> {
        if self.initialized {
            println!("Destroying process group");
            self.initialized = false;
        }
        Ok(())
    }

    /// Check if backend is initialized
    /// バックエンドが初期化されているかチェック
    pub fn is_initialized(&self) -> bool {
        self.initialized
    }

    /// Barrier synchronization
    /// バリア同期
    pub fn barrier(&self) -> PyResult<()> {
        if !self.initialized {
            return Err(pyo3::exceptions::PyRuntimeError::new_err(
                "Backend not initialized",
            ));
        }

        println!("Barrier synchronization");
        Ok(())
    }

    /// All-reduce operation
    /// All-reduce操作
    pub fn all_reduce(&self, tensor: pyo3::Py<pyo3::PyAny>) -> PyResult<pyo3::Py<pyo3::PyAny>> {
        if !self.initialized {
            return Err(pyo3::exceptions::PyRuntimeError::new_err(
                "Backend not initialized",
            ));
        }

        println!("All-reduce operation");
        Ok(tensor)
    }

    /// All-gather operation
    /// All-gather操作
    pub fn all_gather(
        &self,
        py: Python,
        tensor: pyo3::Py<pyo3::PyAny>,
    ) -> PyResult<Vec<pyo3::Py<pyo3::PyAny>>> {
        if !self.initialized {
            return Err(pyo3::exceptions::PyRuntimeError::new_err(
                "Backend not initialized",
            ));
        }

        println!("All-gather operation");
        // Return multiple copies for simulation
        Ok(vec![tensor.clone_ref(py), tensor.clone_ref(py)])
    }

    /// Broadcast operation
    /// ブロードキャスト操作
    pub fn broadcast(
        &self,
        tensor: pyo3::Py<pyo3::PyAny>,
        src: usize,
    ) -> PyResult<pyo3::Py<pyo3::PyAny>> {
        if !self.initialized {
            return Err(pyo3::exceptions::PyRuntimeError::new_err(
                "Backend not initialized",
            ));
        }

        println!("Broadcasting from rank {}", src);
        Ok(tensor)
    }

    /// String representation
    /// 文字列表現
    pub fn __repr__(&self) -> String {
        format!(
            "DistributedBackend(type='{}', initialized={})",
            self.backend_type, self.initialized
        )
    }
}

/// Distributed sampler for data loading
/// データローディング用分散サンプラー
#[pyclass]
pub struct PyDistributedSampler {
    pub(crate) dataset_size: usize,
    pub(crate) num_replicas: usize,
    pub(crate) rank: usize,
    pub(crate) shuffle: bool,
    pub(crate) seed: Option<u64>,
    pub(crate) drop_last: bool,
}

#[pymethods]
impl PyDistributedSampler {
    #[new]
    pub fn new(
        dataset_size: usize,
        num_replicas: Option<usize>,
        rank: Option<usize>,
        shuffle: Option<bool>,
        seed: Option<u64>,
        drop_last: Option<bool>,
    ) -> Self {
        let num_replicas = num_replicas.unwrap_or(1);
        let rank = rank.unwrap_or(0);

        PyDistributedSampler {
            dataset_size,
            num_replicas,
            rank,
            shuffle: shuffle.unwrap_or(true),
            seed,
            drop_last: drop_last.unwrap_or(false),
        }
    }

    /// Get indices for this rank
    /// このランク用のインデックスを取得
    pub fn get_indices(&self) -> Vec<usize> {
        let total_size = if self.drop_last {
            (self.dataset_size / self.num_replicas) * self.num_replicas
        } else {
            ((self.dataset_size + self.num_replicas - 1) / self.num_replicas) * self.num_replicas
        };

        let per_replica = total_size / self.num_replicas;
        let start_idx = self.rank * per_replica;
        let end_idx = std::cmp::min(start_idx + per_replica, self.dataset_size);

        (start_idx..end_idx).collect()
    }

    /// Get length of sampled data for this rank
    /// このランクでサンプルされるデータ長を取得
    pub fn __len__(&self) -> usize {
        self.get_indices().len()
    }

    /// Set epoch for shuffling
    /// シャッフル用エポックを設定
    pub fn set_epoch(&mut self, epoch: usize) {
        if self.shuffle {
            // In real implementation, this would be used for deterministic shuffling
            println!("Setting epoch {} for distributed sampling", epoch);
        }
    }

    /// String representation
    /// 文字列表現
    pub fn __repr__(&self) -> String {
        format!(
            "DistributedSampler(dataset_size={}, num_replicas={}, rank={}, shuffle={})",
            self.dataset_size, self.num_replicas, self.rank, self.shuffle
        )
    }
}

/// Utility functions for distributed training
/// 分散訓練用ユーティリティ関数

/// Initialize distributed training
/// 分散訓練を初期化
#[pyfunction]
pub fn init_distributed(
    backend: Option<String>,
    init_method: Option<String>,
    world_size: Option<usize>,
    rank: Option<usize>,
) -> PyResult<()> {
    let backend = backend.unwrap_or_else(|| "nccl".to_string());
    let _init_method = init_method.unwrap_or_else(|| "env://".to_string());
    let world_size = world_size.unwrap_or(1);
    let rank = rank.unwrap_or(0);

    println!(
        "Initializing distributed training: backend={}, world_size={}, rank={}",
        backend, world_size, rank
    );

    Ok(())
}

/// Check if distributed training is available
/// 分散訓練が利用可能かチェック
#[pyfunction]
pub fn is_distributed_available() -> bool {
    // Simplified check - in real implementation would check for NCCL, MPI, etc.
    true
}

/// Get world size
/// ワールドサイズを取得
#[pyfunction]
pub fn get_world_size() -> usize {
    // Return from environment or default to 1
    std::env::var("WORLD_SIZE")
        .ok()
        .and_then(|s| s.parse().ok())
        .unwrap_or(1)
}

/// Get rank
/// ランクを取得
#[pyfunction]
pub fn get_rank() -> usize {
    // Return from environment or default to 0
    std::env::var("RANK")
        .ok()
        .and_then(|s| s.parse().ok())
        .unwrap_or(0)
}

/// Check if current process is master
/// 現在のプロセスがマスターかチェック
#[pyfunction]
pub fn is_master() -> bool {
    get_rank() == 0
}

/// Cleanup distributed resources
/// 分散リソースをクリーンアップ
#[pyfunction]
pub fn cleanup_distributed() -> PyResult<()> {
    println!("Cleaning up distributed resources");
    Ok(())
}