oxidizr 0.1.0-beta1

//! NCCL-based gradient synchronization backend
//!
//! This module provides high-performance gradient synchronization using NVIDIA's
//! NCCL (NVIDIA Collective Communications Library) for all-reduce operations.
//!
//! # Requirements
//!
//! - CUDA 12.x (cudarc doesn't support CUDA 13.0 yet)
//! - NCCL library installed on the system
//! - Compile with `--features nccl`
//!
//! # Performance
//!
//! NCCL provides optimal bandwidth utilization for GPU-to-GPU communication:
//! - NVLink: up to 600 GB/s bidirectional
//! - PCIe: ~12-32 GB/s depending on generation
//!
//! For 8+ GPUs, NCCL is significantly faster than CPU staging.

use candle_core::{Device, Result, Tensor};
use candle_nn::VarMap;

use super::sync::{GradientSynchronizer, SyncedGradient};

/// NCCL-based gradient synchronizer
///
/// Uses NCCL all-reduce operations for high-performance gradient synchronization
/// across multiple GPUs.
pub struct NcclSynchronizer {
    /// GPU devices participating in training
    devices: Vec<Device>,
    /// Number of GPUs
    world_size: usize,
    // TODO: Add NCCL communicator when cudarc NCCL bindings are integrated
    // comm: nccl::Communicator,
}

impl NcclSynchronizer {
    /// Create a new NCCL synchronizer for the specified GPUs
    ///
    /// # Arguments
    ///
    /// * `gpu_ids` - GPU ordinal IDs to use (e.g., [0, 1, 2, 3])
    ///
    /// # Returns
    ///
    /// New synchronizer instance or error if initialization fails
    pub fn new(gpu_ids: &[usize]) -> Result<Self> {
        let world_size = gpu_ids.len();
        if world_size < 2 {
            return Err(candle_core::Error::Msg(
                "NcclSynchronizer requires at least 2 GPUs. Use CpuStagingSynchronizer for single GPU.".to_string()
            ));
        }

        let devices: Vec<Device> = gpu_ids
            .iter()
            .map(|&id| Device::new_cuda(id))
            .collect::<Result<Vec<_>>>()?;

        // TODO: Initialize NCCL communicator
        // This requires cudarc with NCCL feature:
        //
        // use cudarc::nccl::{Comm, Id as NcclId};
        //
        // let nccl_id = NcclId::new()?;
        // let comms = Comm::from_devices(devices.clone())?;
        //
        // For now, we return an error indicating NCCL is not yet fully implemented

        eprintln!("WARNING: NCCL backend is not yet fully implemented.");
        eprintln!("         Using GPU device creation for validation only.");
        eprintln!("         Gradient synchronization will fall back to no-op.");

        Ok(Self {
            devices,
            world_size,
        })
    }

    /// Get the primary device (GPU 0)
    #[allow(dead_code)]
    pub fn primary_device(&self) -> &Device {
        &self.devices[0]
    }
}

impl GradientSynchronizer for NcclSynchronizer {
    fn synchronize_gradients(
        &self,
        grads: &candle_core::backprop::GradStore,
        varmap: &VarMap,
    ) -> Result<Vec<SyncedGradient>> {
        // TODO: Implement proper NCCL all-reduce
        //
        // The full implementation would:
        // 1. Collect all gradients into a contiguous buffer
        // 2. Call NCCL all_reduce with ReduceOp::Avg
        // 3. Scatter back to individual gradient tensors
        //
        // For now, just pass through gradients (no actual synchronization)

        let data = varmap.data().lock().unwrap();
        let mut result = Vec::with_capacity(data.len());

        for (name, var) in data.iter() {
            if let Some(grad) = grads.get(var) {
                // TODO: Replace with actual NCCL all-reduce
                // For now, just scale by 1/world_size as a placeholder
                let scale = 1.0 / self.world_size as f64;
                let scaled_grad = (grad * scale)?;
                result.push((name.clone(), var.clone(), scaled_grad));
            }
        }

        Ok(result)
    }

    fn local_rank(&self) -> usize {
        0 // Primary GPU in single-process mode
    }

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

    fn barrier(&self) -> Result<()> {
        // TODO: Implement NCCL barrier
        // For now, this is a no-op
        Ok(())
    }
}