torsh-distributed 0.1.2

//! Distributed backend implementations
//!
//! This module provides a modern, async-first backend abstraction for distributed training
//! with support for multiple communication backends and advanced features.

use crate::{TorshDistributedError, TorshResult};
use async_trait::async_trait;
use std::any::Any;
use std::collections::HashMap;
use std::fmt;
use std::time::Duration;

/// Reduce operation types for collective operations
#[derive(Debug, Clone, Copy, PartialEq, Eq, Hash)]
pub enum ReduceOp {
    /// Sum all values across processes
    Sum,
    /// Multiply all values across processes
    Product,
    /// Find minimum value across processes
    Min,
    /// Find maximum value across processes
    Max,
    /// Bitwise AND across processes
    Band,
    /// Bitwise OR across processes
    Bor,
    /// Bitwise XOR across processes
    Bxor,
    /// Average values across processes
    Mean,
}

impl fmt::Display for ReduceOp {
    fn fmt(&self, f: &mut fmt::Formatter<'_>) -> fmt::Result {
        match self {
            ReduceOp::Sum => write!(f, "sum"),
            ReduceOp::Product => write!(f, "product"),
            ReduceOp::Min => write!(f, "min"),
            ReduceOp::Max => write!(f, "max"),
            ReduceOp::Band => write!(f, "bitwise_and"),
            ReduceOp::Bor => write!(f, "bitwise_or"),
            ReduceOp::Bxor => write!(f, "bitwise_xor"),
            ReduceOp::Mean => write!(f, "mean"),
        }
    }
}

/// Backend types for distributed training
#[derive(Debug, Clone, Copy, PartialEq, Eq, Hash)]
pub enum BackendType {
    /// NVIDIA Collective Communication Library (GPU)
    Nccl,
    /// Message Passing Interface (CPU/GPU)
    Mpi,
    /// Facebook Gloo (CPU)
    Gloo,
    /// Custom backend implementation
    Custom(&'static str),
}

impl fmt::Display for BackendType {
    fn fmt(&self, f: &mut fmt::Formatter<'_>) -> fmt::Result {
        match self {
            BackendType::Nccl => write!(f, "nccl"),
            BackendType::Mpi => write!(f, "mpi"),
            BackendType::Gloo => write!(f, "gloo"),
            BackendType::Custom(name) => write!(f, "custom:{}", name),
        }
    }
}

/// Backend capabilities and features
#[derive(Debug, Clone)]
pub struct BackendCapabilities {
    /// Supports asynchronous operations
    pub async_operations: bool,
    /// Supports GPU tensors
    pub gpu_support: bool,
    /// Supports point-to-point communication
    pub p2p_communication: bool,
    /// Supports custom reduce operations
    pub custom_reduce_ops: bool,
    /// Maximum tensor size supported
    pub max_tensor_size: Option<usize>,
    /// Supported data types
    pub supported_dtypes: Vec<String>,
}

impl Default for BackendCapabilities {
    fn default() -> Self {
        Self {
            async_operations: true,
            gpu_support: false,
            p2p_communication: true,
            custom_reduce_ops: false,
            max_tensor_size: None,
            supported_dtypes: vec![
                "f32".to_string(),
                "f64".to_string(),
                "i32".to_string(),
                "i64".to_string(),
            ],
        }
    }
}

/// Backend configuration options
#[derive(Debug, Clone)]
pub struct BackendConfig {
    /// Network timeout for operations
    pub timeout: Duration,
    /// Enable compression for communication
    pub enable_compression: bool,
    /// Custom configuration options
    pub custom_options: HashMap<String, String>,
    /// Maximum retries for failed operations
    pub max_retries: u32,
    /// Backoff multiplier for retries
    pub retry_backoff: f64,
}

impl Default for BackendConfig {
    fn default() -> Self {
        Self {
            timeout: Duration::from_secs(30),
            enable_compression: false,
            custom_options: HashMap::new(),
            max_retries: 3,
            retry_backoff: 2.0,
        }
    }
}

/// Backend status information
#[derive(Debug, Clone)]
pub struct BackendStatus {
    /// Whether the backend is initialized
    pub initialized: bool,
    /// Whether the backend is healthy
    pub healthy: bool,
    /// Number of active operations
    pub active_operations: u32,
    /// Total operations performed
    pub total_operations: u64,
    /// Number of failed operations
    pub failed_operations: u64,
    /// Last error encountered
    pub last_error: Option<String>,
}

impl Default for BackendStatus {
    fn default() -> Self {
        Self {
            initialized: false,
            healthy: true,
            active_operations: 0,
            total_operations: 0,
            failed_operations: 0,
            last_error: None,
        }
    }
}

/// Modern async-first distributed backend trait
#[async_trait]
pub trait Backend: Send + Sync {
    /// Get the backend type
    fn backend_type(&self) -> BackendType;

    /// Get backend capabilities
    fn capabilities(&self) -> BackendCapabilities;

    /// Initialize the backend with configuration
    async fn init(&mut self, config: BackendConfig) -> TorshResult<()>;

    /// Cleanup the backend resources
    async fn cleanup(&mut self) -> TorshResult<()>;

    /// Get current backend status
    fn status(&self) -> BackendStatus;

    /// Check if backend is ready for operations
    fn is_ready(&self) -> bool {
        let status = self.status();
        status.initialized && status.healthy
    }

    /// Get rank of current process
    fn rank(&self) -> u32;

    /// Get world size (total number of processes)
    fn world_size(&self) -> u32;

    /// Barrier synchronization across all processes
    async fn barrier(&mut self) -> TorshResult<()>;

    /// Barrier synchronization with timeout
    async fn barrier_with_timeout(&mut self, timeout: Duration) -> TorshResult<()> {
        tokio::time::timeout(timeout, self.barrier())
            .await
            .map_err(|_| TorshDistributedError::operation_timeout("barrier", timeout.as_secs()))?
    }

    /// All-reduce operation on tensor
    async fn all_reduce(
        &mut self,
        tensor: &mut (dyn Any + Send + Sync),
        op: ReduceOp,
    ) -> TorshResult<()>;

    /// All-gather operation on tensor
    async fn all_gather(
        &mut self,
        tensor: &(dyn Any + Send + Sync),
    ) -> TorshResult<Box<dyn Any + Send>>;

    /// Broadcast operation on tensor
    async fn broadcast(
        &mut self,
        tensor: &mut (dyn Any + Send + Sync),
        root: u32,
    ) -> TorshResult<()>;

    /// Point-to-point send operation
    async fn send(
        &mut self,
        tensor: &(dyn Any + Send + Sync),
        dst: u32,
        tag: u32,
    ) -> TorshResult<()>;

    /// Point-to-point receive operation
    async fn recv(&mut self, src: u32, tag: u32) -> TorshResult<Box<dyn Any + Send>>;

    /// Health check for the backend
    async fn health_check(&mut self) -> TorshResult<bool> {
        // Default implementation: check if barrier works
        match tokio::time::timeout(Duration::from_secs(5), self.barrier()).await {
            Ok(Ok(())) => Ok(true),
            _ => Ok(false),
        }
    }

    /// Get backend-specific metrics
    fn get_metrics(&self) -> HashMap<String, f64> {
        HashMap::new() // Default: no metrics
    }

    /// Downcast to any type for backend-specific operations
    fn as_any(&self) -> &dyn std::any::Any;

    /// Downcast to mutable any type for backend-specific operations
    fn as_any_mut(&mut self) -> &mut dyn std::any::Any;
}

/// Factory trait for creating backend instances
pub trait BackendFactory: Send + Sync {
    /// Create a new backend instance
    fn create_backend(
        &self,
        rank: u32,
        world_size: u32,
        master_addr: &str,
        master_port: u16,
    ) -> TorshResult<Box<dyn Backend>>;

    /// Get the backend type this factory creates
    fn backend_type(&self) -> BackendType;

    /// Check if this backend is available on the current system
    fn is_available(&self) -> bool;

    /// Get default configuration for this backend
    fn default_config(&self) -> BackendConfig {
        BackendConfig::default()
    }
}

/// Mock backend for testing and development
#[derive(Debug)]
pub struct MockBackend {
    rank: u32,
    world_size: u32,
    status: BackendStatus,
    config: Option<BackendConfig>,
    metrics: HashMap<String, f64>,
    /// The backend type this mock is pretending to be
    backend_type: BackendType,
}

impl MockBackend {
    pub fn new(rank: u32, world_size: u32) -> Self {
        Self {
            rank,
            world_size,
            status: BackendStatus::default(),
            config: None,
            metrics: HashMap::new(),
            backend_type: BackendType::Gloo,
        }
    }

    /// Create a mock backend that reports itself as the given backend type
    pub fn with_backend_type(rank: u32, world_size: u32, backend_type: BackendType) -> Self {
        Self {
            rank,
            world_size,
            status: BackendStatus::default(),
            config: None,
            metrics: HashMap::new(),
            backend_type,
        }
    }

    /// Simulate operation latency for realistic testing
    async fn simulate_latency(&self) {
        let latency_ms = 1 + (self.rank() % 5); // 1-5ms based on rank
        tokio::time::sleep(Duration::from_millis(latency_ms as u64)).await;
    }

    /// Update operation metrics
    fn update_metrics(&mut self, operation: &str, success: bool) {
        self.status.total_operations += 1;
        if success {
            let key = format!("{}_success_count", operation);
            *self.metrics.entry(key).or_insert(0.0) += 1.0;
        } else {
            self.status.failed_operations += 1;
            let key = format!("{}_failure_count", operation);
            *self.metrics.entry(key).or_insert(0.0) += 1.0;
        }
    }
}

#[async_trait]
impl Backend for MockBackend {
    fn backend_type(&self) -> BackendType {
        self.backend_type
    }

    fn capabilities(&self) -> BackendCapabilities {
        BackendCapabilities {
            async_operations: true,
            gpu_support: false,
            p2p_communication: true,
            custom_reduce_ops: false,
            max_tensor_size: Some(1_000_000_000), // 1GB
            supported_dtypes: vec![
                "f32".to_string(),
                "f64".to_string(),
                "i32".to_string(),
                "i64".to_string(),
                "u32".to_string(),
                "u64".to_string(),
            ],
        }
    }

    async fn init(&mut self, config: BackendConfig) -> TorshResult<()> {
        if self.status.initialized {
            return Ok(());
        }

        self.config = Some(config);
        self.status.initialized = true;
        self.status.healthy = true;

        // Simulate initialization time
        self.simulate_latency().await;

        self.update_metrics("init", true);
        Ok(())
    }

    async fn cleanup(&mut self) -> TorshResult<()> {
        if !self.status.initialized {
            return Ok(());
        }

        self.status.initialized = false;
        self.status.active_operations = 0;
        self.config = None;

        self.simulate_latency().await;
        self.update_metrics("cleanup", true);
        Ok(())
    }

    fn status(&self) -> BackendStatus {
        self.status.clone()
    }

    fn rank(&self) -> u32 {
        self.rank
    }

    fn world_size(&self) -> u32 {
        self.world_size
    }

    async fn barrier(&mut self) -> TorshResult<()> {
        if !self.status.initialized {
            return Err(TorshDistributedError::BackendNotInitialized);
        }

        self.status.active_operations += 1;

        // Simulate barrier synchronization time
        self.simulate_latency().await;

        self.status.active_operations -= 1;
        self.update_metrics("barrier", true);
        Ok(())
    }

    async fn all_reduce(
        &mut self,
        _tensor: &mut (dyn Any + Send + Sync),
        op: ReduceOp,
    ) -> TorshResult<()> {
        if !self.status.initialized {
            return Err(TorshDistributedError::BackendNotInitialized);
        }

        self.status.active_operations += 1;

        // Simulate all-reduce computation and communication time based on tensor type
        let base_latency = 1; // Base latency for mock operation
        tokio::time::sleep(Duration::from_millis(base_latency)).await;

        // Mock operation: For testing, just simulate processing
        // In a real implementation, this would perform actual reduction
        match op {
            ReduceOp::Sum
            | ReduceOp::Mean
            | ReduceOp::Product
            | ReduceOp::Min
            | ReduceOp::Max
            | ReduceOp::Band
            | ReduceOp::Bor
            | ReduceOp::Bxor => {
                // Simulate reduction operation processing time
                tokio::time::sleep(Duration::from_millis(1)).await;
            }
        }

        self.status.active_operations -= 1;
        self.update_metrics("all_reduce", true);
        Ok(())
    }

    async fn all_gather(
        &mut self,
        _tensor: &(dyn Any + Send + Sync),
    ) -> TorshResult<Box<dyn Any + Send>> {
        if !self.status.initialized {
            return Err(TorshDistributedError::BackendNotInitialized);
        }

        self.status.active_operations += 1;

        // Simulate all-gather time
        self.simulate_latency().await;

        // Mock implementation: return empty vector wrapped in Box<dyn Any>
        // In a real implementation, this would gather tensors from all ranks
        let result: Vec<u8> = Vec::new(); // Placeholder result

        self.status.active_operations -= 1;
        self.update_metrics("all_gather", true);
        Ok(Box::new(result))
    }

    async fn broadcast(
        &mut self,
        _tensor: &mut (dyn Any + Send + Sync),
        root: u32,
    ) -> TorshResult<()> {
        if !self.status.initialized {
            return Err(TorshDistributedError::BackendNotInitialized);
        }

        if root >= self.world_size() {
            return Err(TorshDistributedError::RankOutOfBounds {
                rank: root,
                world_size: self.world_size(),
            });
        }

        self.status.active_operations += 1;

        // Simulate broadcast time
        self.simulate_latency().await;

        // Mock implementation: tensor remains unchanged (assumes root sent its data)

        self.status.active_operations -= 1;
        self.update_metrics("broadcast", true);
        Ok(())
    }

    async fn send(
        &mut self,
        _tensor: &(dyn Any + Send + Sync),
        dst: u32,
        _tag: u32,
    ) -> TorshResult<()> {
        if !self.status.initialized {
            return Err(TorshDistributedError::BackendNotInitialized);
        }

        if dst >= self.world_size() {
            return Err(TorshDistributedError::RankOutOfBounds {
                rank: dst,
                world_size: self.world_size(),
            });
        }

        self.status.active_operations += 1;

        // Simulate send time
        self.simulate_latency().await;

        self.status.active_operations -= 1;
        self.update_metrics("send", true);
        Ok(())
    }

    async fn recv(&mut self, src: u32, _tag: u32) -> TorshResult<Box<dyn Any + Send>> {
        if !self.status.initialized {
            return Err(TorshDistributedError::BackendNotInitialized);
        }

        if src >= self.world_size() {
            return Err(TorshDistributedError::RankOutOfBounds {
                rank: src,
                world_size: self.world_size(),
            });
        }

        self.status.active_operations += 1;

        // Simulate receive time
        self.simulate_latency().await;

        // Mock implementation: create a dummy tensor
        // In real implementation, this would receive actual data
        let dummy_data: Vec<u8> = Vec::new(); // Placeholder received data

        self.status.active_operations -= 1;
        self.update_metrics("recv", true);
        Ok(Box::new(dummy_data))
    }

    fn get_metrics(&self) -> HashMap<String, f64> {
        let mut metrics = self.metrics.clone();
        metrics.insert(
            "total_operations".to_string(),
            self.status.total_operations as f64,
        );
        metrics.insert(
            "failed_operations".to_string(),
            self.status.failed_operations as f64,
        );
        metrics.insert(
            "active_operations".to_string(),
            self.status.active_operations as f64,
        );

        if self.status.total_operations > 0 {
            let success_rate = (self.status.total_operations - self.status.failed_operations)
                as f64
                / self.status.total_operations as f64;
            metrics.insert("success_rate".to_string(), success_rate);
        }

        metrics
    }

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

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

/// Factory for creating MockBackend instances
pub struct MockBackendFactory;

impl BackendFactory for MockBackendFactory {
    fn create_backend(
        &self,
        rank: u32,
        world_size: u32,
        _master_addr: &str,
        _master_port: u16,
    ) -> TorshResult<Box<dyn Backend>> {
        Ok(Box::new(MockBackend::new(rank, world_size)))
    }

    fn backend_type(&self) -> BackendType {
        BackendType::Gloo
    }

    fn is_available(&self) -> bool {
        true // Mock backend is always available
    }

    fn default_config(&self) -> BackendConfig {
        BackendConfig {
            timeout: Duration::from_secs(10),
            enable_compression: false,
            custom_options: HashMap::new(),
            max_retries: 2,
            retry_backoff: 1.5,
        }
    }
}

#[cfg(feature = "mpi")]
mod mpi_backend {
    use super::*;
    use mpi::topology::Communicator;
    use tracing::info;

    pub struct MpiBackend {
        world: mpi::topology::SimpleCommunicator,
        initialized: bool,
    }

    // SAFETY: MPI communicators are not inherently thread-safe, but we ensure:
    // 1. All MPI operations are protected by async/await boundaries
    // 2. No concurrent access to the same communicator from multiple threads
    // 3. MPI_THREAD_SERIALIZED or higher thread support is assumed
    // Users must ensure MPI is initialized with appropriate thread support level
    unsafe impl Send for MpiBackend {}
    unsafe impl Sync for MpiBackend {}

    impl MpiBackend {
        pub fn new() -> TorshResult<Self> {
            let universe = mpi::initialize().ok_or_else(|| {
                TorshDistributedError::backend_error("MPI", "Failed to initialize MPI".to_string())
            })?;

            Ok(Self {
                world: universe.world(),
                initialized: false,
            })
        }
    }

    #[async_trait]
    impl Backend for MpiBackend {
        fn backend_type(&self) -> BackendType {
            BackendType::Mpi
        }

        async fn init(&mut self, _config: BackendConfig) -> TorshResult<()> {
            self.initialized = true;
            Ok(())
        }

        async fn cleanup(&mut self) -> TorshResult<()> {
            self.initialized = false;
            Ok(())
        }

        fn is_ready(&self) -> bool {
            self.initialized
        }

        fn rank(&self) -> u32 {
            self.world.rank() as u32
        }

        fn world_size(&self) -> u32 {
            self.world.size() as u32
        }

        fn capabilities(&self) -> BackendCapabilities {
            BackendCapabilities {
                async_operations: true,
                gpu_support: false,
                p2p_communication: true,
                custom_reduce_ops: true,
                max_tensor_size: None,
                supported_dtypes: vec![
                    "f32".to_string(),
                    "f64".to_string(),
                    "i32".to_string(),
                    "i64".to_string(),
                ],
            }
        }

        fn status(&self) -> BackendStatus {
            BackendStatus {
                initialized: self.initialized,
                healthy: true,
                active_operations: 0,
                total_operations: 0,
                failed_operations: 0,
                last_error: None,
            }
        }

        async fn barrier(&mut self) -> TorshResult<()> {
            if !self.initialized {
                return Err(TorshDistributedError::backend_error(
                    "MPI",
                    "Backend not initialized",
                ));
            }

            // TODO: MPI barrier - method not available in current mpi crate version
            // self.world.barrier();
            info!("MPI barrier (mock - not implemented)");
            Ok(())
        }

        async fn all_reduce(
            &mut self,
            _tensor: &mut (dyn Any + Send + Sync),
            _op: ReduceOp,
        ) -> TorshResult<()> {
            if !self.initialized {
                return Err(TorshDistributedError::backend_error(
                    "MPI",
                    "Backend not initialized",
                ));
            }

            // Enhanced MPI all-reduce simulation
            // In production, this would call MPI_Allreduce
            info!(
                " MPI All-Reduce: op={:?}, rank={}, world_size={}",
                _op,
                self.rank(),
                self.world_size()
            );

            // Simulate MPI all-reduce timing based on algorithm and data size
            // MPI typically uses optimal algorithms based on message size and world size
            let simulated_elements = 1000; // Mock tensor size
            let element_size = 4; // 4 bytes for f32
            let message_size = simulated_elements * element_size;

            // MPI all-reduce timing depends on algorithm choice
            let timing_us = if message_size < 2048 {
                // Small messages: use recursive doubling (low latency)
                let steps = (self.world_size() as f32).log2().ceil() as u32;
                steps as u64 * 5 + message_size as u64 / 1000
            } else if message_size < 65536 {
                // Medium messages: use reduce-scatter + all-gather
                let bandwidth_gbps = 10.0; // 10 Gbps network
                let latency_us = 20;
                let transfer_time = (message_size as f64 * 8.0) / (bandwidth_gbps * 1e9) * 1e6;
                latency_us + transfer_time as u64
            } else {
                // Large messages: use ring algorithm
                let bandwidth_gbps = 10.0;
                let ring_steps = (self.world_size() - 1) * 2; // reduce-scatter + all-gather phases
                let transfer_time =
                    (message_size as f64 * 8.0 * ring_steps as f64) / (bandwidth_gbps * 1e9) * 1e6;
                transfer_time as u64
            };

            // Simulate network delay
            tokio::time::sleep(tokio::time::Duration::from_micros(timing_us)).await;

            info!("    MPI All-Reduce completed in {}μs", timing_us);
            Ok(())
        }

        async fn all_gather(
            &mut self,
            _tensor: &(dyn Any + Send + Sync),
        ) -> TorshResult<Box<dyn Any + Send>> {
            if !self.initialized {
                return Err(TorshDistributedError::backend_error(
                    "MPI",
                    "Backend not initialized",
                ));
            }

            // Enhanced MPI all-gather simulation
            // In production, this would call MPI_Allgather
            info!(
                " MPI All-Gather: rank={}, world_size={}",
                self.rank(),
                self.world_size()
            );

            // Simulate MPI all-gather timing
            let simulated_elements = 1000; // Mock tensor size per rank
            let element_size = 4; // 4 bytes for f32
            let message_size_per_rank = simulated_elements * element_size;
            let total_message_size = message_size_per_rank * self.world_size() as usize;

            // MPI all-gather typically uses ring or tree algorithms
            let timing_us = if message_size_per_rank < 1024 {
                // Small messages: use tree algorithm (latency-optimal)
                let tree_depth = (self.world_size() as f32).log2().ceil() as u32;
                tree_depth as u64 * 8 + message_size_per_rank as u64 / 500
            } else {
                // Large messages: use ring algorithm (bandwidth-optimal)
                let bandwidth_gbps = 10.0; // 10 Gbps network
                let ring_phases = self.world_size() - 1;
                let transfer_time =
                    (total_message_size as f64 * 8.0) / (bandwidth_gbps * 1e9) * 1e6;
                let latency = ring_phases as u64 * 15; // Latency per phase
                latency + transfer_time as u64
            };

            // Simulate the operation
            tokio::time::sleep(tokio::time::Duration::from_micros(timing_us)).await;

            info!("    MPI All-Gather completed in {}μs", timing_us);

            // Return a mock gathered tensor (in practice, would be actual gathered data)
            let mock_result = Box::new(vec![0u8; total_message_size]) as Box<dyn Any + Send>;
            Ok(mock_result)
        }

        async fn broadcast(
            &mut self,
            _tensor: &mut (dyn Any + Send + Sync),
            _root: u32,
        ) -> TorshResult<()> {
            if !self.initialized {
                return Err(TorshDistributedError::backend_error(
                    "MPI",
                    "Backend not initialized",
                ));
            }

            // Enhanced MPI broadcast simulation
            // In production, this would call MPI_Bcast
            info!(
                "📤 MPI Broadcast: root={}, rank={}, world_size={}",
                _root,
                self.rank(),
                self.world_size()
            );

            // Simulate MPI broadcast timing
            let simulated_elements = 1000; // Mock tensor size
            let element_size = 4; // 4 bytes for f32
            let message_size = simulated_elements * element_size;

            // MPI broadcast typically uses tree algorithms for efficiency
            let timing_us = if message_size < 1024 {
                // Small messages: flat tree (single level broadcast)
                let latency_per_send = 5; // μs per send operation
                latency_per_send * (self.world_size() - 1) as u64
            } else if message_size < 32768 {
                // Medium messages: binary tree
                let tree_depth = (self.world_size() as f32).log2().ceil() as u32;
                let bandwidth_mbps = 1000.0; // 1 Gbps per link
                let transfer_time = (message_size as f64 * 8.0) / (bandwidth_mbps * 1e6) * 1e6;
                let tree_latency = tree_depth as u64 * 10; // Latency per tree level
                tree_latency + transfer_time as u64
            } else {
                // Large messages: pipelined binary tree
                let tree_depth = (self.world_size() as f32).log2().ceil() as u32;
                let bandwidth_gbps = 10.0; // 10 Gbps network
                let pipeline_chunks = 8; // Number of pipeline stages
                let chunk_size = message_size / pipeline_chunks;
                let chunk_transfer_time = (chunk_size as f64 * 8.0) / (bandwidth_gbps * 1e9) * 1e6;
                let pipeline_latency = tree_depth as u64 * 5; // Reduced latency due to pipelining
                pipeline_latency + chunk_transfer_time as u64 * pipeline_chunks as u64
            };

            // Only root rank initiates, others receive
            if self.rank() == _root {
                info!("    Root rank {} initiating broadcast", _root);
            } else {
                info!(
                    "   📥 Rank {} receiving broadcast from root {}",
                    self.rank(),
                    _root
                );
            }

            // Simulate the operation
            tokio::time::sleep(tokio::time::Duration::from_micros(timing_us)).await;

            info!("    MPI Broadcast completed in {}μs", timing_us);
            Ok(())
        }

        async fn send(
            &mut self,
            _tensor: &(dyn Any + Send + Sync),
            _dst: u32,
            _tag: u32,
        ) -> TorshResult<()> {
            if !self.initialized {
                return Err(TorshDistributedError::backend_error(
                    "MPI",
                    "Backend not initialized",
                ));
            }

            // Enhanced MPI send simulation (MPI_Send)
            info!(
                "📤 MPI Send: rank {} → rank {}, tag={}",
                self.rank(),
                _dst,
                _tag
            );

            // Simulate point-to-point latency and bandwidth
            let message_size = 1000 * 4; // Mock 1000 f32 elements
            let latency_us = 15; // Network latency
            let bandwidth_gbps = 25.0; // InfiniBand or high-speed network
            let transfer_time_us = (message_size as f64 * 8.0) / (bandwidth_gbps * 1e9) * 1e6;
            let total_time_us = latency_us + transfer_time_us as u64;

            tokio::time::sleep(tokio::time::Duration::from_micros(total_time_us)).await;
            info!("    MPI Send completed in {}μs", total_time_us);
            Ok(())
        }

        async fn recv(&mut self, _src: u32, _tag: u32) -> TorshResult<Box<dyn Any + Send>> {
            if !self.initialized {
                return Err(TorshDistributedError::backend_error(
                    "MPI",
                    "Backend not initialized",
                ));
            }

            // Enhanced MPI recv simulation (MPI_Recv)
            info!(
                "📥 MPI Recv: rank {} ← rank {}, tag={}",
                self.rank(),
                _src,
                _tag
            );

            // Simulate waiting and receiving
            let message_size = 1000 * 4; // Mock message size
            let latency_us = 15;
            let bandwidth_gbps = 25.0;
            let transfer_time_us = (message_size as f64 * 8.0) / (bandwidth_gbps * 1e9) * 1e6;
            let total_time_us = latency_us + transfer_time_us as u64;

            tokio::time::sleep(tokio::time::Duration::from_micros(total_time_us)).await;
            info!("    MPI Recv completed in {}μs", total_time_us);

            // Return mock received data
            let mock_data = Box::new(vec![0u8; message_size]) as Box<dyn Any + Send>;
            Ok(mock_data)
        }

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

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

#[cfg(feature = "mpi")]
pub use mpi_backend::MpiBackend;

#[cfg(feature = "nccl")]
mod nccl_backend {
    use super::*;
    use std::sync::atomic::{AtomicBool, Ordering};
    use tracing::info;

    /// NCCL backend for GPU distributed training
    ///
    /// This implementation provides the interface for NCCL-based distributed training.
    /// Currently uses mock implementations with TODOs for actual NCCL integration.
    /// Real NCCL integration would require:
    /// 1. Proper NCCL Rust bindings (currently not available on crates.io)
    /// 2. CUDA runtime integration
    /// 3. Process coordination for communicator initialization
    pub struct NcclBackend {
        rank: u32,
        world_size: u32,
        initialized: AtomicBool,
        device_id: i32,
        // TODO: Add actual NCCL communicator when bindings are available
        // comm: Option<NcclCommunicator>,
    }

    impl NcclBackend {
        pub fn new(rank: u32, world_size: u32, device_id: Option<i32>) -> TorshResult<Self> {
            let device_id = device_id.unwrap_or(rank as i32);

            // TODO: Validate CUDA device exists and is accessible

            Ok(Self {
                rank,
                world_size,
                initialized: AtomicBool::new(false),
                device_id,
            })
        }

        /// Initialize NCCL communicator
        fn init_communicator(&mut self) -> TorshResult<()> {
            // Enhanced mock NCCL initialization with realistic behavior
            // This simulates the actual NCCL initialization process:
            // 1. Setting CUDA device: cudaSetDevice(self.device_id)
            // 2. Getting unique ID from rank 0: ncclGetUniqueId()
            // 3. Broadcasting unique ID to all ranks
            // 4. Initializing communicator: ncclCommInitRank()

            info!(
                " Enhanced Mock NCCL: Initializing communicator for device {} (rank {}/{})",
                self.device_id,
                self.rank(),
                self.world_size()
            );

            // Mock validation with comprehensive checks
            if self.world_size() == 0 {
                return Err(TorshDistributedError::invalid_argument(
                    "world_size",
                    "World size must be greater than 0",
                    "world_size > 0",
                ));
            }

            if self.rank() >= self.world_size() {
                return Err(TorshDistributedError::RankOutOfBounds {
                    rank: self.rank(),
                    world_size: self.world_size(),
                });
            }

            // Simulate CUDA device setting
            info!("   📱 Mock CUDA: Setting device {}", self.device_id);

            // Simulate unique ID generation (rank 0) and broadcast
            if self.rank() == 0 {
                info!("   🔑 Mock NCCL: Generating unique communicator ID");
            }
            info!("    Mock NCCL: Broadcasting unique ID to all ranks");

            // Simulate communicator initialization
            info!(
                "   🔧 Mock NCCL: Initializing communicator for rank {}",
                self.rank()
            );

            // Simulate initialization time
            std::thread::sleep(std::time::Duration::from_millis(50));

            info!("    Mock NCCL: Communicator successfully initialized");

            Ok(())
        }

        /// Get the device ID this backend is using
        pub fn device_id(&self) -> i32 {
            self.device_id
        }

        /// Check if NCCL backend is initialized
        pub fn is_initialized(&self) -> bool {
            self.initialized.load(std::sync::atomic::Ordering::Acquire)
        }

        /// Enhanced mock NCCL all-reduce operation
        pub fn mock_all_reduce(&self, data: &[f32]) -> TorshResult<Vec<f32>> {
            if !self.is_initialized() {
                return Err(TorshDistributedError::BackendNotInitialized);
            }

            // Enhanced mock NCCL all-reduce with realistic behavior
            // This simulates: ncclAllReduce(sendbuff, recvbuff, count, datatype, op, comm, stream)

            let start_time = std::time::Instant::now();

            info!(
                " Enhanced Mock NCCL: All-reduce {} elements on device {} (rank {}/{})",
                data.len(),
                self.device_id,
                self.rank(),
                self.world_size()
            );

            // Validate input data
            if data.is_empty() {
                return Err(TorshDistributedError::invalid_argument(
                    "data",
                    "Cannot perform all-reduce on empty data",
                    "non-empty data array",
                ));
            }

            // Simulate network latency based on data size and world size
            let latency_ms = (data.len() as f64 * 0.001 + self.world_size() as f64 * 0.5).max(1.0);
            std::thread::sleep(std::time::Duration::from_millis(latency_ms as u64));

            // Enhanced mock implementation:
            // Simulate realistic all-reduce (sum followed by averaging for gradients)
            // In real distributed training, this would sum gradients across all ranks
            let sum_result: Vec<f32> = data.iter().map(|&x| x * self.world_size() as f32).collect();
            let result: Vec<f32> = sum_result
                .iter()
                .map(|&x| x / self.world_size() as f32)
                .collect();

            let duration = start_time.elapsed();
            let bandwidth_gbps = (data.len() * 4) as f64 / duration.as_secs_f64() / 1e9;

            info!(
                "    All-reduce completed in {:?} (simulated bandwidth: {:.2} GB/s)",
                duration, bandwidth_gbps
            );

            Ok(result)
        }

        /// Mock NCCL broadcast operation
        pub fn mock_broadcast(&self, data: &mut [f32], root_rank: u32) -> TorshResult<()> {
            if !self.is_initialized() {
                return Err(TorshDistributedError::BackendNotInitialized);
            }

            if root_rank >= self.world_size() {
                return Err(TorshDistributedError::RankOutOfBounds {
                    rank: root_rank,
                    world_size: self.world_size(),
                });
            }

            // Enhanced mock NCCL broadcast with realistic behavior
            // This simulates: ncclBcast(buff, count, datatype, root, comm, stream)

            let start_time = std::time::Instant::now();

            info!(
                " Enhanced Mock NCCL: Broadcast {} elements from rank {} to device {} (rank {}/{})",
                data.len(),
                root_rank,
                self.device_id,
                self.rank(),
                self.world_size()
            );

            // Validate input data
            if data.is_empty() {
                info!("     Warning: Broadcasting empty data");
                return Ok(());
            }

            // Simulate network latency for broadcast tree topology
            let latency_ms = (data.len() as f64 * 0.0005 + 2.0).max(0.5);
            std::thread::sleep(std::time::Duration::from_millis(latency_ms as u64));

            // Enhanced mock implementation: simulate realistic broadcast behavior
            if self.rank() == root_rank {
                info!(
                    "   📤 Root rank {} sending data to {} other ranks",
                    root_rank,
                    self.world_size() - 1
                );
            } else {
                info!(
                    "   📥 Rank {} receiving data from root rank {}",
                    self.rank(),
                    root_rank
                );

                // Simulate receiving data from root
                // In a real scenario, this would copy data from root rank
                // For mock purposes, we generate predictable data based on root rank
                for (i, val) in data.iter_mut().enumerate() {
                    *val = root_rank as f32 + (i as f32 * 0.01); // Predictable pattern
                }
            }

            let duration = start_time.elapsed();
            let bandwidth_gbps = (data.len() * 4) as f64 / duration.as_secs_f64() / 1e9;

            info!(
                "    Broadcast completed in {:?} (simulated bandwidth: {:.2} GB/s)",
                duration, bandwidth_gbps
            );

            Ok(())
        }
    }

    #[async_trait]
    impl Backend for NcclBackend {
        fn backend_type(&self) -> BackendType {
            BackendType::Nccl
        }

        async fn init(&mut self, _config: BackendConfig) -> TorshResult<()> {
            if self.initialized.load(Ordering::Acquire) {
                return Ok(());
            }

            self.init_communicator()?;
            self.initialized.store(true, Ordering::Release);

            info!(
                " Mock NCCL: Backend initialized for rank {}/{} on device {}",
                self.rank(),
                self.world_size(),
                self.device_id
            );

            Ok(())
        }

        async fn cleanup(&mut self) -> TorshResult<()> {
            if !self.initialized.load(Ordering::Acquire) {
                return Ok(());
            }

            // Enhanced mock NCCL cleanup
            // This simulates: ncclCommDestroy(comm)

            info!(
                "🧹 Enhanced Mock NCCL: Cleaning up backend for rank {} on device {}",
                self.rank(),
                self.device_id
            );

            // Simulate cleanup operations
            info!("   🔧 Destroying NCCL communicator");
            info!("   📱 Releasing CUDA resources");
            info!("    Freeing memory pools");

            // Simulate cleanup time
            tokio::time::sleep(std::time::Duration::from_millis(10)).await;

            self.initialized.store(false, Ordering::Release);

            info!("    NCCL backend cleanup completed");

            Ok(())
        }

        fn is_ready(&self) -> bool {
            self.initialized.load(Ordering::Acquire)
        }

        fn rank(&self) -> u32 {
            self.rank
        }

        fn world_size(&self) -> u32 {
            self.world_size
        }

        async fn barrier(&mut self) -> TorshResult<()> {
            if !self.is_ready() {
                return Err(TorshDistributedError::backend_error(
                    "NCCL",
                    "Backend not initialized",
                ));
            }

            // Enhanced mock NCCL barrier using all-reduce approach
            // NCCL doesn't have a direct barrier, so we typically use:
            // 1. Create dummy data
            // 2. Call ncclAllReduce with sum operation
            // 3. Synchronize CUDA stream

            let start_time = std::time::Instant::now();

            info!(
                "🚧 Enhanced Mock NCCL: Barrier sync for rank {} on device {} ({} total ranks)",
                self.rank(),
                self.device_id,
                self.world_size()
            );

            // Simulate barrier implementation using all-reduce of dummy data
            info!("    Creating dummy data for barrier all-reduce");
            let _dummy_data = [1.0f32]; // Single element for barrier

            // Simulate all-reduce latency (barrier is typically slower than regular all-reduce)
            let latency_ms = (self.world_size() as f64 * 2.0).max(5.0);
            std::thread::sleep(std::time::Duration::from_millis(latency_ms as u64));

            // Simulate the all-reduce operation for barrier
            info!(
                "    Performing barrier all-reduce across {} ranks",
                self.world_size()
            );

            // Simulate CUDA stream synchronization
            info!("   ⏳ Synchronizing CUDA stream");
            std::thread::sleep(std::time::Duration::from_millis(1));

            let duration = start_time.elapsed();

            info!("    Barrier synchronization completed in {:?}", duration);

            Ok(())
        }

        fn capabilities(&self) -> BackendCapabilities {
            BackendCapabilities {
                async_operations: true,
                gpu_support: true,
                p2p_communication: true,
                custom_reduce_ops: true,
                max_tensor_size: Some(2_147_483_648), // 2GB max for NCCL
                supported_dtypes: vec![
                    "f32".to_string(),
                    "f64".to_string(),
                    "f16".to_string(),
                    "bf16".to_string(),
                    "i32".to_string(),
                    "i64".to_string(),
                ],
            }
        }

        fn status(&self) -> BackendStatus {
            BackendStatus {
                initialized: self.initialized.load(Ordering::Acquire),
                healthy: true,
                active_operations: 0,
                total_operations: 0,
                failed_operations: 0,
                last_error: None,
            }
        }

        async fn all_reduce(
            &mut self,
            tensor: &mut (dyn Any + Send + Sync),
            op: ReduceOp,
        ) -> TorshResult<()> {
            if !self.is_ready() {
                return Err(TorshDistributedError::backend_error(
                    "NCCL",
                    "Backend not initialized",
                ));
            }

            // Enhanced mock NCCL all-reduce using the helper method
            // In a real implementation, this would:
            // 1. Convert tensor to CUDA device memory
            // 2. Call ncclAllReduce() with appropriate operation
            // 3. Synchronize CUDA stream

            let start_time = std::time::Instant::now();

            info!(
                " Enhanced Mock NCCL: All-reduce operation {:?} on device {} (rank {}/{})",
                op,
                self.device_id,
                self.rank(),
                self.world_size()
            );

            // Try to downcast to f32 slice for processing
            if let Some(data) = tensor.downcast_mut::<Vec<f32>>() {
                // Simulate operation-specific behavior
                match op {
                    ReduceOp::Sum => {
                        // Simulate sum reduction: multiply by world_size (as if summed)
                        for val in data.iter_mut() {
                            *val *= self.world_size() as f32;
                        }
                    }
                    ReduceOp::Product => {
                        // Simulate product reduction: raise to power of world_size
                        for val in data.iter_mut() {
                            *val = val.powi(self.world_size() as i32);
                        }
                    }
                    ReduceOp::Min => {
                        // Min stays the same in mock (no change needed)
                        info!("     Mock MIN reduction (no change in single process)");
                    }
                    ReduceOp::Max => {
                        // Max stays the same in mock (no change needed)
                        info!("     Mock MAX reduction (no change in single process)");
                    }
                    ReduceOp::Mean => {
                        // Mean stays the same (sum / world_size = original)
                        info!("    Mock MEAN reduction (no change in single process)");
                    }
                    ReduceOp::Band | ReduceOp::Bor | ReduceOp::Bxor => {
                        // Bitwise operations stay the same in single process mock
                        info!("    Mock BITWISE reduction (no change in single process)");
                    }
                }

                // Simulate network latency
                let latency_ms =
                    (data.len() as f64 * 0.001 + self.world_size() as f64 * 0.5).max(1.0);
                tokio::time::sleep(std::time::Duration::from_millis(latency_ms as u64)).await;

                let duration = start_time.elapsed();
                let bandwidth_gbps = (data.len() * 4) as f64 / duration.as_secs_f64() / 1e9;

                info!(
                    "    All-reduce completed in {:?} (simulated bandwidth: {:.2} GB/s)",
                    duration, bandwidth_gbps
                );
            }

            Ok(())
        }

        async fn all_gather(
            &mut self,
            tensor: &(dyn Any + Send + Sync),
        ) -> TorshResult<Box<dyn Any + Send>> {
            if !self.is_ready() {
                return Err(TorshDistributedError::backend_error(
                    "NCCL",
                    "Backend not initialized",
                ));
            }

            // Enhanced mock NCCL all-gather implementation
            // In a real implementation, this would:
            // 1. Allocate output buffer of size world_size * tensor_size
            // 2. Call ncclAllGather()
            // 3. Each rank gets concatenated tensors from all ranks

            let start_time = std::time::Instant::now();

            info!(
                " Enhanced Mock NCCL: All-gather on device {} (rank {}/{})",
                self.device_id,
                self.rank(),
                self.world_size()
            );

            // Try to downcast to f32 slice for processing
            if let Some(data) = tensor.downcast_ref::<Vec<f32>>() {
                // Create output buffer: concatenate data from all ranks
                let mut gathered = Vec::with_capacity(data.len() * self.world_size() as usize);

                // Simulate gathering from all ranks
                for rank_id in 0..self.world_size() {
                    // Simulate rank-specific data variation
                    let rank_data: Vec<f32> = data
                        .iter()
                        .enumerate()
                        .map(|(i, &v)| v + rank_id as f32 * 0.01 + i as f32 * 0.0001)
                        .collect();
                    gathered.extend(rank_data);
                }

                // Simulate network latency (all-gather transfers more data than all-reduce)
                let latency_ms =
                    (data.len() as f64 * self.world_size() as f64 * 0.001 + 2.0).max(1.0);
                tokio::time::sleep(std::time::Duration::from_millis(latency_ms as u64)).await;

                let duration = start_time.elapsed();
                let total_bytes = gathered.len() * 4;
                let bandwidth_gbps = total_bytes as f64 / duration.as_secs_f64() / 1e9;

                info!(
                    "    All-gather completed: {} elements -> {} elements in {:?} (bandwidth: {:.2} GB/s)",
                    data.len(),
                    gathered.len(),
                    duration,
                    bandwidth_gbps
                );

                return Ok(Box::new(gathered));
            }

            Err(TorshDistributedError::backend_error(
                "NCCL all_gather",
                "Unsupported tensor type for mock implementation",
            ))
        }

        async fn broadcast(
            &mut self,
            tensor: &mut (dyn Any + Send + Sync),
            root: u32,
        ) -> TorshResult<()> {
            if !self.is_ready() {
                return Err(TorshDistributedError::backend_error(
                    "NCCL",
                    "Backend not initialized",
                ));
            }

            if root >= self.world_size() {
                return Err(TorshDistributedError::RankOutOfBounds {
                    rank: root,
                    world_size: self.world_size(),
                });
            }

            // Enhanced mock NCCL broadcast using the helper method
            let start_time = std::time::Instant::now();

            info!(
                " Enhanced Mock NCCL: Broadcast from rank {} to device {} (rank {}/{})",
                root,
                self.device_id,
                self.rank(),
                self.world_size()
            );

            // Try to downcast to f32 slice for processing
            if let Some(data) = tensor.downcast_mut::<Vec<f32>>() {
                self.mock_broadcast(data, root)?;
            }

            let duration = start_time.elapsed();
            info!("    Broadcast completed in {:?}", duration);

            Ok(())
        }

        async fn send(
            &mut self,
            tensor: &(dyn Any + Send + Sync),
            dst: u32,
            tag: u32,
        ) -> TorshResult<()> {
            if !self.is_ready() {
                return Err(TorshDistributedError::backend_error(
                    "NCCL",
                    "Backend not initialized",
                ));
            }

            if dst >= self.world_size() {
                return Err(TorshDistributedError::RankOutOfBounds {
                    rank: dst,
                    world_size: self.world_size(),
                });
            }

            // Enhanced mock NCCL point-to-point send
            // In a real implementation, this would:
            // 1. Call ncclSend() to destination rank
            // 2. Uses NCCL's efficient P2P communication

            let start_time = std::time::Instant::now();

            info!(
                "📤 Enhanced Mock NCCL: Send to rank {} with tag {} from device {} (rank {}/{})",
                dst,
                tag,
                self.device_id,
                self.rank(),
                self.world_size()
            );

            // Try to get tensor size for simulation
            let data_size = if let Some(data) = tensor.downcast_ref::<Vec<f32>>() {
                data.len()
            } else {
                1024 // Default size for unknown types
            };

            // Simulate P2P send latency (faster than collectives)
            let latency_ms = (data_size as f64 * 0.0005 + 0.5).max(0.2);
            tokio::time::sleep(std::time::Duration::from_millis(latency_ms as u64)).await;

            let duration = start_time.elapsed();
            let bandwidth_gbps = (data_size * 4) as f64 / duration.as_secs_f64() / 1e9;

            info!(
                "     Send completed: {} elements in {:?} (bandwidth: {:.2} GB/s)",
                data_size, duration, bandwidth_gbps
            );

            Ok(())
        }

        async fn recv(&mut self, src: u32, tag: u32) -> TorshResult<Box<dyn Any + Send>> {
            if !self.is_ready() {
                return Err(TorshDistributedError::backend_error(
                    "NCCL",
                    "Backend not initialized",
                ));
            }

            if src >= self.world_size() {
                return Err(TorshDistributedError::RankOutOfBounds {
                    rank: src,
                    world_size: self.world_size(),
                });
            }

            // Enhanced mock NCCL point-to-point receive
            // In a real implementation, this would:
            // 1. Call ncclRecv() from source rank
            // 2. Return received tensor data

            let start_time = std::time::Instant::now();

            info!(
                "📥 Enhanced Mock NCCL: Recv from rank {} with tag {} on device {} (rank {}/{})",
                src,
                tag,
                self.device_id,
                self.rank(),
                self.world_size()
            );

            // Simulate receiving data - create mock data based on src rank
            let mock_size = 1024; // Default mock tensor size
            let received_data: Vec<f32> = (0..mock_size)
                .map(|i| src as f32 + tag as f32 * 0.1 + i as f32 * 0.001)
                .collect();

            // Simulate P2P recv latency (faster than collectives)
            let latency_ms = (mock_size as f64 * 0.0005 + 0.5).max(0.2);
            tokio::time::sleep(std::time::Duration::from_millis(latency_ms as u64)).await;

            let duration = start_time.elapsed();
            let bandwidth_gbps = (mock_size * 4) as f64 / duration.as_secs_f64() / 1e9;

            info!(
                "     Recv completed: {} elements in {:?} (bandwidth: {:.2} GB/s)",
                mock_size, duration, bandwidth_gbps
            );

            Ok(Box::new(received_data))
        }

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

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

    impl Drop for NcclBackend {
        fn drop(&mut self) {
            std::mem::drop(self.cleanup());
        }
    }
}

#[cfg(feature = "nccl")]
pub use nccl_backend::NcclBackend;