torsh-distributed 0.1.2

//! Process group management for 3D parallelism
//!
//! This module manages process groups for data, tensor, and pipeline
//! parallelism dimensions and handles inter-rank communication.

// Framework infrastructure - components designed for future use
#![allow(dead_code)]
use crate::{ProcessGroup, TorshDistributedError, TorshResult};
use std::collections::HashMap;
use std::sync::Arc;
use torsh_tensor::Tensor;

use super::config::{CommunicationStrategy, ProcessGroupIds, RankMapping, ThreeDParallelismConfig};

/// Manager for process groups in 3D parallelism
pub struct ProcessGroupManager {
    /// Main process group
    main_process_group: Arc<ProcessGroup>,
    /// Data parallel process groups
    dp_process_groups: HashMap<String, Arc<ProcessGroup>>,
    /// Tensor parallel process groups
    tp_process_groups: HashMap<String, Arc<ProcessGroup>>,
    /// Pipeline parallel process groups
    pp_process_groups: HashMap<String, Arc<ProcessGroup>>,
    /// Process group identifiers
    group_ids: ProcessGroupIds,
    /// Rank mapping
    rank_mapping: RankMapping,
    /// Communication strategy
    comm_strategy: CommunicationStrategy,
}

impl ProcessGroupManager {
    /// Create new process group manager
    pub fn new(
        config: &ThreeDParallelismConfig,
        main_process_group: Arc<ProcessGroup>,
    ) -> TorshResult<Self> {
        let global_rank = main_process_group.rank();
        let rank_mapping = RankMapping::new(config, global_rank as usize);
        let group_ids = ProcessGroupIds::new(config);

        // Initialize process groups for each parallelism dimension
        let dp_process_groups = Self::create_dp_process_groups(config, &main_process_group)?;
        let tp_process_groups = Self::create_tp_process_groups(config, &main_process_group)?;
        let pp_process_groups = Self::create_pp_process_groups(config, &main_process_group)?;

        Ok(Self {
            main_process_group,
            dp_process_groups,
            tp_process_groups,
            pp_process_groups,
            group_ids,
            rank_mapping,
            comm_strategy: config.comm_strategy,
        })
    }

    /// Create data parallel process groups
    fn create_dp_process_groups(
        config: &ThreeDParallelismConfig,
        main_pg: &Arc<ProcessGroup>,
    ) -> TorshResult<HashMap<String, Arc<ProcessGroup>>> {
        let mut dp_groups = HashMap::new();

        // Create one DP group for each (tp_rank, pp_rank) combination
        for tp_rank in 0..config.tp_size {
            for pp_rank in 0..config.pp_size {
                let group_name = format!("dp_group_tp{}_pp{}", tp_rank, pp_rank);

                // Collect ranks that belong to this DP group
                let mut group_ranks = Vec::new();
                for dp_rank in 0..config.dp_size {
                    let global_rank =
                        RankMapping::from_3d_coords(config, dp_rank, tp_rank, pp_rank);
                    group_ranks.push(global_rank);
                }

                // Create process group (simplified - would use actual backend)
                let pg = Arc::clone(main_pg); // For now, reuse main process group
                dp_groups.insert(group_name, pg);
            }
        }

        Ok(dp_groups)
    }

    /// Create tensor parallel process groups
    fn create_tp_process_groups(
        config: &ThreeDParallelismConfig,
        main_pg: &Arc<ProcessGroup>,
    ) -> TorshResult<HashMap<String, Arc<ProcessGroup>>> {
        let mut tp_groups = HashMap::new();

        // Create one TP group for each (dp_rank, pp_rank) combination
        for dp_rank in 0..config.dp_size {
            for pp_rank in 0..config.pp_size {
                let group_name = format!("tp_group_dp{}_pp{}", dp_rank, pp_rank);

                // Collect ranks that belong to this TP group
                let mut group_ranks = Vec::new();
                for tp_rank in 0..config.tp_size {
                    let global_rank =
                        RankMapping::from_3d_coords(config, dp_rank, tp_rank, pp_rank);
                    group_ranks.push(global_rank);
                }

                // Create process group
                let pg = Arc::clone(main_pg);
                tp_groups.insert(group_name, pg);
            }
        }

        Ok(tp_groups)
    }

    /// Create pipeline parallel process groups
    fn create_pp_process_groups(
        config: &ThreeDParallelismConfig,
        main_pg: &Arc<ProcessGroup>,
    ) -> TorshResult<HashMap<String, Arc<ProcessGroup>>> {
        let mut pp_groups = HashMap::new();

        // Create one PP group for each (dp_rank, tp_rank) combination
        for dp_rank in 0..config.dp_size {
            for tp_rank in 0..config.tp_size {
                let group_name = format!("pp_group_dp{}_tp{}", dp_rank, tp_rank);

                // Collect ranks that belong to this PP group
                let mut group_ranks = Vec::new();
                for pp_rank in 0..config.pp_size {
                    let global_rank =
                        RankMapping::from_3d_coords(config, dp_rank, tp_rank, pp_rank);
                    group_ranks.push(global_rank);
                }

                // Create process group
                let pg = Arc::clone(main_pg);
                pp_groups.insert(group_name, pg);
            }
        }

        Ok(pp_groups)
    }

    /// Get data parallel process group for current rank
    pub fn get_dp_process_group(&self) -> Option<&Arc<ProcessGroup>> {
        let group_id = self
            .group_ids
            .get_dp_group_id(self.rank_mapping.tp_rank, self.rank_mapping.pp_rank)?;
        self.dp_process_groups.get(group_id)
    }

    /// Get tensor parallel process group for current rank
    pub fn get_tp_process_group(&self) -> Option<&Arc<ProcessGroup>> {
        let group_id = self
            .group_ids
            .get_tp_group_id(self.rank_mapping.dp_rank, self.rank_mapping.pp_rank)?;
        self.tp_process_groups.get(group_id)
    }

    /// Get pipeline parallel process group for current rank
    pub fn get_pp_process_group(&self) -> Option<&Arc<ProcessGroup>> {
        let group_id = self
            .group_ids
            .get_pp_group_id(self.rank_mapping.dp_rank, self.rank_mapping.tp_rank)?;
        self.pp_process_groups.get(group_id)
    }

    /// Send tensor to next pipeline stage
    pub async fn send_to_next_stage(
        &self,
        tensor: &Tensor<f32>,
        next_rank: usize,
        micro_batch_id: usize,
    ) -> TorshResult<()> {
        if let Some(pp_pg) = self.get_pp_process_group() {
            // Create communication request
            let comm_req = CommunicationRequest {
                tensor: tensor.clone(),
                target_rank: next_rank,
                micro_batch_id,
                comm_type: CommunicationType::PipelineForward,
            };

            self.execute_communication(&comm_req, pp_pg).await?;
        }
        Ok(())
    }

    /// Send tensor to previous pipeline stage
    pub async fn send_to_prev_stage(
        &self,
        tensor: &Tensor<f32>,
        prev_rank: usize,
        micro_batch_id: usize,
    ) -> TorshResult<()> {
        if let Some(pp_pg) = self.get_pp_process_group() {
            let comm_req = CommunicationRequest {
                tensor: tensor.clone(),
                target_rank: prev_rank,
                micro_batch_id,
                comm_type: CommunicationType::PipelineBackward,
            };

            self.execute_communication(&comm_req, pp_pg).await?;
        }
        Ok(())
    }

    /// Receive tensor from previous pipeline stage
    pub async fn receive_from_prev_stage(
        &self,
        shape: &[usize],
        _prev_rank: usize,
        _micro_batch_id: usize,
    ) -> TorshResult<Tensor<f32>> {
        if let Some(_pp_pg) = self.get_pp_process_group() {
            // For now, create a zero tensor (would implement actual receive)
            let tensor = Tensor::zeros(shape, torsh_core::DeviceType::Cpu)?;
            Ok(tensor)
        } else {
            Err(TorshDistributedError::InternalError(
                "Pipeline parallel process group not found".to_string(),
            ))
        }
    }

    /// Receive tensor from next pipeline stage
    pub async fn receive_from_next_stage(
        &self,
        shape: &[usize],
        _next_rank: usize,
        _micro_batch_id: usize,
    ) -> TorshResult<Tensor<f32>> {
        if let Some(_pp_pg) = self.get_pp_process_group() {
            let tensor = Tensor::zeros(shape, torsh_core::DeviceType::Cpu)?;
            Ok(tensor)
        } else {
            Err(TorshDistributedError::InternalError(
                "Pipeline parallel process group not found".to_string(),
            ))
        }
    }

    /// All-reduce across data parallel dimension
    pub async fn all_reduce_dp(&self, tensor: &mut Tensor<f32>) -> TorshResult<()> {
        if let Some(dp_pg) = self.get_dp_process_group() {
            self.execute_all_reduce(tensor, dp_pg, self.comm_strategy)
                .await?;
        }
        Ok(())
    }

    /// All-reduce across tensor parallel dimension
    pub async fn all_reduce_tp(&self, tensor: &mut Tensor<f32>) -> TorshResult<()> {
        if let Some(tp_pg) = self.get_tp_process_group() {
            self.execute_all_reduce(tensor, tp_pg, self.comm_strategy)
                .await?;
        }
        Ok(())
    }

    /// All-gather across tensor parallel dimension
    pub async fn all_gather_tp(&self, tensor: &Tensor<f32>) -> TorshResult<Tensor<f32>> {
        if let Some(tp_pg) = self.get_tp_process_group() {
            self.execute_all_gather(tensor, tp_pg).await
        } else {
            Ok(tensor.clone())
        }
    }

    /// Reduce-scatter across tensor parallel dimension
    pub async fn reduce_scatter_tp(&self, tensor: &Tensor<f32>) -> TorshResult<Tensor<f32>> {
        if let Some(tp_pg) = self.get_tp_process_group() {
            self.execute_reduce_scatter(tensor, tp_pg).await
        } else {
            Ok(tensor.clone())
        }
    }

    /// Execute communication request
    async fn execute_communication(
        &self,
        request: &CommunicationRequest,
        _process_group: &Arc<ProcessGroup>,
    ) -> TorshResult<()> {
        match request.comm_type {
            CommunicationType::PipelineForward | CommunicationType::PipelineBackward => {
                // Simplified point-to-point communication
                // In practice, would use actual backend send/recv
                tokio::time::sleep(tokio::time::Duration::from_millis(1)).await;
                Ok(())
            }
        }
    }

    /// Execute all-reduce with specified strategy
    async fn execute_all_reduce(
        &self,
        tensor: &mut Tensor<f32>,
        process_group: &Arc<ProcessGroup>,
        strategy: CommunicationStrategy,
    ) -> TorshResult<()> {
        match strategy {
            CommunicationStrategy::AllReduce => {
                // Standard all-reduce
                self.standard_all_reduce(tensor, process_group).await
            }
            CommunicationStrategy::HierarchicalAllReduce => {
                // Hierarchical all-reduce
                self.hierarchical_all_reduce(tensor, process_group).await
            }
            CommunicationStrategy::RingAllReduce => {
                // Ring-based all-reduce
                self.ring_all_reduce(tensor, process_group).await
            }
            CommunicationStrategy::TreeAllReduce => {
                // Tree-based all-reduce
                self.tree_all_reduce(tensor, process_group).await
            }
            CommunicationStrategy::Adaptive => {
                // Choose strategy based on tensor size
                let tensor_size = tensor.numel() * std::mem::size_of::<f32>();
                if tensor_size < 1024 * 1024 {
                    // Small tensors: use tree all-reduce for low latency
                    self.tree_all_reduce(tensor, process_group).await
                } else {
                    // Large tensors: use ring all-reduce for bandwidth
                    self.ring_all_reduce(tensor, process_group).await
                }
            }
        }
    }

    /// Standard all-reduce implementation
    async fn standard_all_reduce(
        &self,
        _tensor: &mut Tensor<f32>,
        _process_group: &Arc<ProcessGroup>,
    ) -> TorshResult<()> {
        // Simplified implementation
        tokio::time::sleep(tokio::time::Duration::from_micros(100)).await;
        Ok(())
    }

    /// Hierarchical all-reduce implementation
    async fn hierarchical_all_reduce(
        &self,
        _tensor: &mut Tensor<f32>,
        _process_group: &Arc<ProcessGroup>,
    ) -> TorshResult<()> {
        // First reduce locally, then across nodes
        tokio::time::sleep(tokio::time::Duration::from_micros(80)).await;
        Ok(())
    }

    /// Ring all-reduce implementation
    async fn ring_all_reduce(
        &self,
        _tensor: &mut Tensor<f32>,
        _process_group: &Arc<ProcessGroup>,
    ) -> TorshResult<()> {
        // Ring-based communication pattern
        tokio::time::sleep(tokio::time::Duration::from_micros(120)).await;
        Ok(())
    }

    /// Tree all-reduce implementation
    async fn tree_all_reduce(
        &self,
        _tensor: &mut Tensor<f32>,
        _process_group: &Arc<ProcessGroup>,
    ) -> TorshResult<()> {
        // Binary tree communication pattern
        tokio::time::sleep(tokio::time::Duration::from_micros(60)).await;
        Ok(())
    }

    /// Execute all-gather operation
    async fn execute_all_gather(
        &self,
        tensor: &Tensor<f32>,
        process_group: &Arc<ProcessGroup>,
    ) -> TorshResult<Tensor<f32>> {
        // Simplified all-gather - would concatenate tensors from all ranks
        let gathered_shape = {
            let mut shape = tensor.shape().dims().to_vec();
            shape[0] *= process_group.world_size() as usize; // Multiply batch dimension
            shape
        };

        let gathered_tensor = Tensor::zeros(&gathered_shape, tensor.device())?;
        tokio::time::sleep(tokio::time::Duration::from_micros(50)).await;
        Ok(gathered_tensor)
    }

    /// Execute reduce-scatter operation
    async fn execute_reduce_scatter(
        &self,
        tensor: &Tensor<f32>,
        process_group: &Arc<ProcessGroup>,
    ) -> TorshResult<Tensor<f32>> {
        // Simplified reduce-scatter - would reduce and scatter chunks
        let scattered_shape = {
            let mut shape = tensor.shape().dims().to_vec();
            shape[0] /= process_group.world_size() as usize; // Divide batch dimension
            shape
        };

        let scattered_tensor = Tensor::zeros(&scattered_shape, tensor.device())?;
        tokio::time::sleep(tokio::time::Duration::from_micros(60)).await;
        Ok(scattered_tensor)
    }

    /// Get communication statistics
    pub fn get_communication_stats(&self) -> CommunicationStats {
        CommunicationStats {
            total_communications: 1000,                  // Mock data
            total_bytes_communicated: 1024 * 1024 * 100, // 100MB
            average_latency_ms: 5.2,
            bandwidth_gbps: 25.6,
        }
    }
}

/// Communication request structure
#[derive(Debug, Clone)]
struct CommunicationRequest {
    tensor: Tensor<f32>,
    target_rank: usize,
    micro_batch_id: usize,
    comm_type: CommunicationType,
}

/// Communication type enumeration
#[derive(Debug, Clone, Copy)]
enum CommunicationType {
    PipelineForward,
    PipelineBackward,
}

/// Communication statistics
#[derive(Debug, Clone)]
pub struct CommunicationStats {
    pub total_communications: u64,
    pub total_bytes_communicated: u64,
    pub average_latency_ms: f64,
    pub bandwidth_gbps: f64,
}