781 lines
26 KiB
Rust
781 lines
26 KiB
Rust
//! NCCL backend implementation using cudarc's safe NCCL APIs
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//!
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//! This module provides a production-ready NCCL backend that leverages cudarc's
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//! comprehensive NCCL integration for optimal multi-GPU communication.
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//!
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//! # Features
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//! - Safe NCCL communicator management with automatic cleanup
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//! - Topology-aware optimization for optimal bandwidth
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//! - Hierarchical communicator splitting for complex topologies
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//! - Stream synchronization for asynchronous operations
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//! - Comprehensive error handling and recovery
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use crate::error::{DistributedError, Result};
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use crate::topology::{NetworkTopology, TopologyBandwidth, TopologyInfo};
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use crate::{Device, Tensor};
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use cudarc::driver::{CudaContext, CudaSlice, CudaStream};
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use cudarc::nccl::{Comm, Id, ReduceOp as NcclReduceOp};
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use parking_lot::RwLock;
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use serde::{Deserialize, Serialize};
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use std::sync::Arc;
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use std::thread::{self, JoinHandle};
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use tokio::sync::{mpsc, oneshot};
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use uuid::Uuid;
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/// Configuration for NCCL backend
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#[derive(Debug, Clone, Serialize, Deserialize)]
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pub struct NcclConfig {
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/// Unique NCCL ID for process group coordination
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pub nccl_id: Option<Vec<u8>>,
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/// Socket interface name pattern (e.g., "^lo")
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pub socket_ifname: String,
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/// Debug logging level
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pub debug_level: String,
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/// Tree threshold for algorithms
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pub tree_threshold: usize,
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/// P2P threshold for point-to-point communication
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pub p2p_threshold: usize,
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/// Network topology hint
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pub net_topo: Option<String>,
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/// Maximum number of channels
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pub max_channels: Option<usize>,
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/// Minimum number of channels
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pub min_channels: Option<usize>,
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/// Buffer size for NCCL operations
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pub buffer_size: usize,
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}
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impl Default for NcclConfig {
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fn default() -> Self {
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Self {
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nccl_id: None,
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socket_ifname: "^lo".to_string(),
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debug_level: "INFO".to_string(),
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tree_threshold: 0,
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p2p_threshold: 8192,
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net_topo: None,
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max_channels: None,
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min_channels: None,
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buffer_size: 1024 * 1024, // 1MB
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}
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}
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}
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/// Commands for the NCCL worker thread
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#[derive(Debug)]
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enum NcclCommand {
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AllReduce {
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input_data: Vec<f32>,
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op: crate::comm::ReduceOp,
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resp: oneshot::Sender<Result<Vec<f32>>>,
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},
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Broadcast {
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data: Option<Vec<f32>>,
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root: usize,
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resp: oneshot::Sender<Result<Vec<f32>>>,
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},
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AllGather {
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input_data: Vec<f32>,
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resp: oneshot::Sender<Result<Vec<f32>>>,
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},
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ReduceScatter {
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input_data: Vec<f32>,
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op: crate::comm::ReduceOp,
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resp: oneshot::Sender<Result<Vec<f32>>>,
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},
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Send {
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data: Vec<f32>,
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dst: usize,
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resp: oneshot::Sender<Result<()>>,
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},
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Recv {
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size: usize,
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src: usize,
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resp: oneshot::Sender<Result<Vec<f32>>>,
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},
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Shutdown,
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}
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/// NCCL Backend for managing NCCL communication
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#[derive(Debug)]
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pub struct NcclBackend {
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/// World communicator
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world_comm: RwLock<Option<Arc<NcclCommunicator>>>,
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}
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impl NcclBackend {
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/// Create a new NCCL backend
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pub fn new(_config: crate::nccl::NcclConfig) -> Self {
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Self {
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world_comm: RwLock::new(None),
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}
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}
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/// Get the world communicator
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pub fn world_comm(&self) -> &RwLock<Option<Arc<NcclCommunicator>>> {
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&self.world_comm
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}
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/// Set the world communicator
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pub fn set_world_comm(&self, comm: Arc<NcclCommunicator>) {
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*self.world_comm.write() = Some(comm);
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}
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/// Initialize world communicator
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pub async fn init_world(
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&self,
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world_size: usize,
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rank: usize,
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device_id: i32,
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id: Id,
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) -> Result<Arc<NcclCommunicator>> {
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let device = CudaContext::new(device_id as usize).map_err(|e| {
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DistributedError::runtime(format!("failed to create CUDA context: {e:?}"))
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})?;
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let config = NcclConfig::default();
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let comm = Arc::new(NcclCommunicator::new(
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world_size, rank, &id, device, config,
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)?);
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self.set_world_comm(comm.clone());
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Ok(comm)
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}
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/// Cleanup NCCL resources
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pub async fn cleanup(&self) -> Result<()> {
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// Clear the world communicator
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*self.world_comm.write() = None;
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Ok(())
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}
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}
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/// Thread-safe NCCL communicator wrapper
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///
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/// This wrapper solves the Send + Sync problem by running NCCL operations
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/// on a dedicated thread and using message passing for communication.
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pub struct NcclCommunicator {
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/// Channel to send commands to NCCL worker thread
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command_tx: mpsc::UnboundedSender<NcclCommand>,
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/// Handle to the NCCL worker thread
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_worker_handle: JoinHandle<()>,
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/// World size for this communicator
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world_size: usize,
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/// Rank within this communicator
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rank: usize,
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/// Unique identifier for this communicator
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id: Uuid,
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/// Device ordinal
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device_id: usize,
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}
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impl std::fmt::Debug for NcclCommunicator {
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fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
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f.debug_struct("NcclCommunicator")
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.field("world_size", &self.world_size)
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.field("rank", &self.rank)
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.field("id", &self.id)
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.field("device_id", &self.device_id)
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.finish()
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}
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}
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// Implement Send + Sync for NcclCommunicator since it uses message passing
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unsafe impl Send for NcclCommunicator {}
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unsafe impl Sync for NcclCommunicator {}
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impl NcclCommunicator {
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/// Create a new NCCL communicator
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pub fn new(
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world_size: usize,
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rank: usize,
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nccl_id: &Id,
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device: Arc<CudaContext>,
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config: NcclConfig,
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) -> Result<Self> {
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// Set NCCL environment variables from config
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Self::set_nccl_env(&config)?;
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let device_id = device.ordinal();
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let (command_tx, command_rx) = mpsc::unbounded_channel();
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// Clone data needed by worker thread
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let nccl_id_clone = *nccl_id;
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// Spawn worker thread to handle NCCL operations
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let worker_handle = thread::spawn(move || {
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Self::worker_thread(device, command_rx, world_size, rank, nccl_id_clone)
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});
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Ok(Self {
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command_tx,
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_worker_handle: worker_handle,
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world_size,
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rank,
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id: Uuid::new_v4(),
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device_id,
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})
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}
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/// Worker thread function that handles NCCL operations
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fn worker_thread(
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device: Arc<CudaContext>,
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mut command_rx: mpsc::UnboundedReceiver<NcclCommand>,
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world_size: usize,
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rank: usize,
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nccl_id: Id,
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) {
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// Initialize NCCL communicator on this thread
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let stream = device.default_stream();
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let comm = match Comm::from_rank(stream.clone(), rank, world_size, nccl_id) {
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Ok(comm) => comm,
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Err(e) => {
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tracing::error!(
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"Failed to create NCCL communicator in worker thread: {:?}",
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e
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);
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return;
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}
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};
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// Process commands until shutdown
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while let Some(command) = command_rx.blocking_recv() {
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match command {
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NcclCommand::AllReduce {
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input_data,
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op,
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resp,
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} => {
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let result = Self::worker_allreduce(&comm, &stream, input_data, op);
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let _ = resp.send(result);
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}
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NcclCommand::Broadcast { data, root, resp } => {
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let result = Self::worker_broadcast(&comm, &stream, data, root, rank);
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let _ = resp.send(result);
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}
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NcclCommand::AllGather { input_data, resp } => {
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let result = Self::worker_allgather(&comm, &stream, input_data, world_size);
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let _ = resp.send(result);
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}
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NcclCommand::ReduceScatter {
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input_data,
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op,
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resp,
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} => {
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let result =
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Self::worker_reduce_scatter(&comm, &stream, input_data, op, world_size);
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let _ = resp.send(result);
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}
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NcclCommand::Send { data, dst, resp } => {
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let result = Self::worker_send(&comm, &stream, data, dst);
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let _ = resp.send(result);
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}
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NcclCommand::Recv { size, src, resp } => {
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let result = Self::worker_recv(&comm, &stream, size, src);
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let _ = resp.send(result);
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}
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NcclCommand::Shutdown => break,
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}
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}
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}
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/// Worker function for AllReduce
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fn worker_allreduce(
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comm: &Comm,
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stream: &Arc<CudaStream>,
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input_data: Vec<f32>,
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op: crate::comm::ReduceOp,
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) -> Result<Vec<f32>> {
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let input_slice = stream.clone_htod(&input_data).map_err(|e| {
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DistributedError::runtime(format!("failed to copy input to device: {e:?}"))
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})?;
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let mut output_slice = stream.alloc_zeros::<f32>(input_data.len()).map_err(|e| {
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DistributedError::runtime(format!("failed to allocate output buffer: {e:?}"))
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})?;
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let nccl_op = Self::convert_reduce_op_to_cudarc(op)?;
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comm.all_reduce(&input_slice, &mut output_slice, &nccl_op)
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.map_err(|e| {
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DistributedError::communication("nccl", format!("allreduce failed: {e:?}"))
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})?;
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stream.synchronize().map_err(|e| {
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DistributedError::runtime(format!("stream synchronization failed: {e:?}"))
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})?;
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let result_data = stream.clone_dtoh(&output_slice).map_err(|e| {
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DistributedError::runtime(format!("failed to copy result from device: {e:?}"))
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})?;
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Ok(result_data)
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}
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/// Worker function for Broadcast
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fn worker_broadcast(
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comm: &Comm,
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stream: &Arc<CudaStream>,
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data: Option<Vec<f32>>,
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root: usize,
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rank: usize,
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) -> Result<Vec<f32>> {
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let data_size = if let Some(ref send_data) = data {
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send_data.len()
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} else {
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return Err(DistributedError::tensor(
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"non-root ranks must provide buffer size for broadcast",
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));
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};
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if rank == root {
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let send_data = data.ok_or_else(|| {
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DistributedError::tensor("root rank must provide data for broadcast")
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})?;
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let input_slice = stream.clone_htod(&send_data).map_err(|e| {
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DistributedError::runtime(format!("failed to copy input to device: {e:?}"))
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})?;
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let mut output_slice = stream.alloc_zeros::<f32>(data_size).map_err(|e| {
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DistributedError::runtime(format!("failed to allocate output buffer: {e:?}"))
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})?;
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comm.broadcast(Some(&input_slice), &mut output_slice, root as i32)
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.map_err(|e| {
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DistributedError::communication("nccl", format!("broadcast failed: {e:?}"))
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})?;
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stream.synchronize().map_err(|e| {
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DistributedError::runtime(format!("stream synchronization failed: {e:?}"))
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})?;
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stream.clone_dtoh(&output_slice).map_err(|e| {
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DistributedError::runtime(format!("failed to copy result from device: {e:?}"))
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})
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} else {
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let mut output_slice = stream.alloc_zeros::<f32>(data_size).map_err(|e| {
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DistributedError::runtime(format!("failed to allocate receive buffer: {e:?}"))
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})?;
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comm.broadcast(None::<&CudaSlice<f32>>, &mut output_slice, root as i32)
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.map_err(|e| {
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DistributedError::communication(
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"nccl",
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format!("broadcast receive failed: {e:?}"),
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)
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})?;
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stream.synchronize().map_err(|e| {
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DistributedError::runtime(format!("stream synchronization failed: {e:?}"))
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})?;
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stream.clone_dtoh(&output_slice).map_err(|e| {
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DistributedError::runtime(format!("failed to copy result from device: {e:?}"))
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})
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}
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}
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/// Worker function for AllGather
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fn worker_allgather(
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comm: &Comm,
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stream: &Arc<CudaStream>,
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input_data: Vec<f32>,
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world_size: usize,
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) -> Result<Vec<f32>> {
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let input_slice = stream.clone_htod(&input_data).map_err(|e| {
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DistributedError::runtime(format!("failed to copy input to device: {e:?}"))
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})?;
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let output_size = input_data.len() * world_size;
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let mut output_slice = stream.alloc_zeros::<f32>(output_size).map_err(|e| {
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DistributedError::runtime(format!("failed to allocate output buffer: {e:?}"))
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})?;
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comm.all_gather(&input_slice, &mut output_slice)
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.map_err(|e| {
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DistributedError::communication("nccl", format!("allgather failed: {e:?}"))
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})?;
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stream.synchronize().map_err(|e| {
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DistributedError::runtime(format!("stream synchronization failed: {e:?}"))
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})?;
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stream.clone_dtoh(&output_slice).map_err(|e| {
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DistributedError::runtime(format!("failed to copy result from device: {e:?}"))
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})
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}
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/// Worker function for ReduceScatter
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fn worker_reduce_scatter(
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comm: &Comm,
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stream: &Arc<CudaStream>,
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input_data: Vec<f32>,
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op: crate::comm::ReduceOp,
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world_size: usize,
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) -> Result<Vec<f32>> {
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if !input_data.len().is_multiple_of(world_size) {
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return Err(DistributedError::tensor(
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"input size must be divisible by world_size for reduce_scatter",
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));
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}
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let input_slice = stream.clone_htod(&input_data).map_err(|e| {
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DistributedError::runtime(format!("failed to copy input to device: {e:?}"))
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})?;
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let output_size = input_data.len() / world_size;
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let mut output_slice = stream.alloc_zeros::<f32>(output_size).map_err(|e| {
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DistributedError::runtime(format!("failed to allocate output buffer: {e:?}"))
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})?;
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let nccl_op = Self::convert_reduce_op_to_cudarc(op)?;
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comm.reduce_scatter(&input_slice, &mut output_slice, &nccl_op)
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.map_err(|e| {
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DistributedError::communication("nccl", format!("reduce_scatter failed: {e:?}"))
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})?;
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stream.synchronize().map_err(|e| {
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DistributedError::runtime(format!("stream synchronization failed: {e:?}"))
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})?;
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stream.clone_dtoh(&output_slice).map_err(|e| {
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DistributedError::runtime(format!("failed to copy result from device: {e:?}"))
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})
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}
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/// Worker function for Send
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fn worker_send(
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comm: &Comm,
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stream: &Arc<CudaStream>,
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data: Vec<f32>,
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dst: usize,
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) -> Result<()> {
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if dst >= comm.world_size() || dst == comm.rank() {
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return Err(DistributedError::communication(
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"nccl",
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format!("invalid destination rank: {dst}"),
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));
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}
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let cuda_slice = stream.clone_htod(&data).map_err(|e| {
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DistributedError::runtime(format!("failed to copy data to device: {e:?}"))
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})?;
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comm.send(&cuda_slice, dst as i32)
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.map_err(|e| DistributedError::communication("nccl", format!("send failed: {e:?}")))?;
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stream.synchronize().map_err(|e| {
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DistributedError::runtime(format!("stream synchronization failed: {e:?}"))
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})?;
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Ok(())
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}
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/// Worker function for Recv
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fn worker_recv(
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comm: &Comm,
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stream: &Arc<CudaStream>,
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size: usize,
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src: usize,
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) -> Result<Vec<f32>> {
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if src >= comm.world_size() || src == comm.rank() {
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return Err(DistributedError::communication(
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"nccl",
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format!("invalid source rank: {src}"),
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));
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}
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let mut cuda_slice = stream.alloc_zeros::<f32>(size).map_err(|e| {
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DistributedError::runtime(format!("failed to allocate receive buffer: {e:?}"))
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})?;
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comm.recv(&mut cuda_slice, src as i32)
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.map_err(|e| DistributedError::communication("nccl", format!("recv failed: {e:?}")))?;
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stream.synchronize().map_err(|e| {
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DistributedError::runtime(format!("stream synchronization failed: {e:?}"))
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})?;
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stream.clone_dtoh(&cuda_slice).map_err(|e| {
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DistributedError::runtime(format!("failed to copy result from device: {e:?}"))
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})
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}
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/// Set NCCL environment variables from configuration
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fn set_nccl_env(config: &NcclConfig) -> Result<()> {
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// SAFETY: Setting environment variables for NCCL configuration
|
|
// This is safe as long as no other threads are reading these variables
|
|
unsafe {
|
|
std::env::set_var("NCCL_SOCKET_IFNAME", &config.socket_ifname);
|
|
std::env::set_var("NCCL_DEBUG", &config.debug_level);
|
|
std::env::set_var("NCCL_TREE_THRESHOLD", config.tree_threshold.to_string());
|
|
std::env::set_var("NCCL_P2P_THRESHOLD", config.p2p_threshold.to_string());
|
|
|
|
if let Some(net_topo) = &config.net_topo {
|
|
std::env::set_var("NCCL_TOPO_FILE", net_topo);
|
|
}
|
|
|
|
if let Some(max_channels) = config.max_channels {
|
|
std::env::set_var("NCCL_MAX_NCHANNELS", max_channels.to_string());
|
|
}
|
|
|
|
if let Some(min_channels) = config.min_channels {
|
|
std::env::set_var("NCCL_MIN_NCHANNELS", min_channels.to_string());
|
|
}
|
|
}
|
|
|
|
Ok(())
|
|
}
|
|
|
|
/// Detect GPU topology for optimization
|
|
fn detect_topology(
|
|
_device: &CudaContext,
|
|
world_size: usize,
|
|
_rank: usize,
|
|
) -> Result<TopologyInfo> {
|
|
// Basic topology detection - would need more sophisticated
|
|
// implementation for production use
|
|
let num_nodes = 1; // For now, assume single node
|
|
let latency_matrix = vec![vec![0.0; num_nodes]; num_nodes];
|
|
|
|
Ok(TopologyInfo {
|
|
num_nodes,
|
|
gpus_per_node: world_size,
|
|
interconnect: "PCIe".to_string(),
|
|
bandwidth: TopologyBandwidth {
|
|
intra_node_gpu: 32.0, // GB/s estimate for PCIe
|
|
cpu_gpu: 16.0,
|
|
inter_node: 10.0,
|
|
memory: 900.0, // GB/s for modern GPUs
|
|
},
|
|
latency_matrix,
|
|
intra_node_topology: vec![],
|
|
inter_node_topology: NetworkTopology::default(),
|
|
})
|
|
}
|
|
|
|
/// Get the unique ID for this communicator
|
|
pub fn get_unique_id() -> Result<Id> {
|
|
Id::new().map_err(|e| {
|
|
DistributedError::communication(
|
|
"nccl",
|
|
format!("failed to generate unique NCCL ID: {e:?}"),
|
|
)
|
|
})
|
|
}
|
|
|
|
/// Get the world size
|
|
pub fn world_size(&self) -> usize {
|
|
self.world_size
|
|
}
|
|
|
|
/// Get the rank
|
|
pub fn rank(&self) -> usize {
|
|
self.rank
|
|
}
|
|
|
|
/// Get the device ID
|
|
pub fn device_id(&self) -> i32 {
|
|
self.device_id as i32
|
|
}
|
|
|
|
/// Get topology information (not available in thread-safe implementation)
|
|
pub fn topology(&self) -> Option<&TopologyInfo> {
|
|
None // Topology is now handled by worker thread
|
|
}
|
|
|
|
/// AllReduce operation using NCCL
|
|
pub async fn allreduce(&self, tensor: &mut Tensor, op: crate::comm::ReduceOp) -> Result<()> {
|
|
let device_tensor = tensor.to_device(&Device::Cuda(self.device_id))?;
|
|
let tensor_data = device_tensor.data()?;
|
|
|
|
let (resp_tx, resp_rx) = oneshot::channel();
|
|
let command = NcclCommand::AllReduce {
|
|
input_data: tensor_data,
|
|
op,
|
|
resp: resp_tx,
|
|
};
|
|
|
|
self.command_tx
|
|
.send(command)
|
|
.map_err(|_| DistributedError::runtime("worker thread has stopped".to_string()))?;
|
|
|
|
let result_data = resp_rx.await.map_err(|_| {
|
|
DistributedError::runtime("worker thread response channel closed".to_string())
|
|
})??;
|
|
|
|
let result_tensor =
|
|
Tensor::from_vec(result_data, device_tensor.shape().dims(), tensor.device())?;
|
|
*tensor = result_tensor;
|
|
|
|
Ok(())
|
|
}
|
|
|
|
/// Broadcast operation using NCCL
|
|
pub async fn broadcast(&self, tensor: &mut Tensor, root: i32) -> Result<()> {
|
|
if root < 0 || root >= self.world_size as i32 {
|
|
return Err(DistributedError::communication(
|
|
"nccl",
|
|
format!("invalid root rank: {root}"),
|
|
));
|
|
}
|
|
|
|
let device_tensor = tensor.to_device(&Device::Cuda(self.device_id))?;
|
|
let tensor_data = device_tensor.data()?;
|
|
|
|
let data = if self.rank == root as usize {
|
|
Some(tensor_data)
|
|
} else {
|
|
None
|
|
};
|
|
|
|
let (resp_tx, resp_rx) = oneshot::channel();
|
|
let command = NcclCommand::Broadcast {
|
|
data,
|
|
root: root as usize,
|
|
resp: resp_tx,
|
|
};
|
|
|
|
self.command_tx
|
|
.send(command)
|
|
.map_err(|_| DistributedError::runtime("worker thread has stopped".to_string()))?;
|
|
|
|
let result_data = resp_rx.await.map_err(|_| {
|
|
DistributedError::runtime("worker thread response channel closed".to_string())
|
|
})??;
|
|
|
|
let result_tensor =
|
|
Tensor::from_vec(result_data, device_tensor.shape().dims(), tensor.device())?;
|
|
*tensor = result_tensor;
|
|
|
|
Ok(())
|
|
}
|
|
|
|
/// AllGather operation using NCCL
|
|
pub async fn allgather(&self, input: &Tensor) -> Result<Tensor> {
|
|
let device_input = input.to_device(&Device::Cuda(self.device_id))?;
|
|
let input_data = device_input.data()?;
|
|
|
|
let (resp_tx, resp_rx) = oneshot::channel();
|
|
let command = NcclCommand::AllGather {
|
|
input_data,
|
|
resp: resp_tx,
|
|
};
|
|
|
|
self.command_tx
|
|
.send(command)
|
|
.map_err(|_| DistributedError::runtime("worker thread has stopped".to_string()))?;
|
|
|
|
let result_data = resp_rx.await.map_err(|_| {
|
|
DistributedError::runtime("worker thread response channel closed".to_string())
|
|
})??;
|
|
|
|
let mut output_shape = device_input.shape().dims().to_vec();
|
|
output_shape[0] *= self.world_size;
|
|
|
|
let result_tensor = Tensor::from_vec(result_data, &output_shape, input.device())?;
|
|
|
|
Ok(result_tensor)
|
|
}
|
|
|
|
/// ReduceScatter operation using NCCL
|
|
pub async fn reduce_scatter(
|
|
&self,
|
|
input: &Tensor,
|
|
op: crate::comm::ReduceOp,
|
|
) -> Result<Tensor> {
|
|
let device_input = input.to_device(&Device::Cuda(self.device_id))?;
|
|
let input_data = device_input.data()?;
|
|
|
|
let (resp_tx, resp_rx) = oneshot::channel();
|
|
let command = NcclCommand::ReduceScatter {
|
|
input_data,
|
|
op,
|
|
resp: resp_tx,
|
|
};
|
|
|
|
self.command_tx
|
|
.send(command)
|
|
.map_err(|_| DistributedError::runtime("worker thread has stopped".to_string()))?;
|
|
|
|
let result_data = resp_rx.await.map_err(|_| {
|
|
DistributedError::runtime("worker thread response channel closed".to_string())
|
|
})??;
|
|
|
|
let mut output_shape = device_input.shape().dims().to_vec();
|
|
output_shape[0] /= self.world_size;
|
|
|
|
let result_tensor = Tensor::from_vec(result_data, &output_shape, input.device())?;
|
|
|
|
Ok(result_tensor)
|
|
}
|
|
|
|
/// Send operation using NCCL
|
|
pub async fn send(&self, tensor: &Tensor, dst: i32) -> Result<()> {
|
|
let device_tensor = tensor.to_device(&Device::Cuda(self.device_id))?;
|
|
let tensor_data = device_tensor.data()?;
|
|
|
|
let (resp_tx, resp_rx) = oneshot::channel();
|
|
let command = NcclCommand::Send {
|
|
data: tensor_data,
|
|
dst: dst as usize,
|
|
resp: resp_tx,
|
|
};
|
|
|
|
self.command_tx
|
|
.send(command)
|
|
.map_err(|_| DistributedError::runtime("worker thread has stopped".to_string()))?;
|
|
|
|
resp_rx.await.map_err(|_| {
|
|
DistributedError::runtime("worker thread response channel closed".to_string())
|
|
})??;
|
|
|
|
Ok(())
|
|
}
|
|
|
|
/// Recv operation using NCCL
|
|
pub async fn recv(&self, tensor: &mut Tensor, src: i32) -> Result<()> {
|
|
let expected_size = tensor.numel();
|
|
|
|
let (resp_tx, resp_rx) = oneshot::channel();
|
|
let command = NcclCommand::Recv {
|
|
size: expected_size,
|
|
src: src as usize,
|
|
resp: resp_tx,
|
|
};
|
|
|
|
self.command_tx
|
|
.send(command)
|
|
.map_err(|_| DistributedError::runtime("worker thread has stopped".to_string()))?;
|
|
|
|
let result_data = resp_rx.await.map_err(|_| {
|
|
DistributedError::runtime("worker thread response channel closed".to_string())
|
|
})??;
|
|
|
|
let result_tensor = Tensor::from_vec(result_data, tensor.shape().dims(), tensor.device())?;
|
|
*tensor = result_tensor;
|
|
|
|
Ok(())
|
|
}
|
|
|
|
/// Split communicator for hierarchical communication (not yet implemented)
|
|
pub fn split(&self, _color: i32, _key: i32) -> Result<Self> {
|
|
Err(DistributedError::communication(
|
|
"nccl",
|
|
"communicator splitting not yet implemented - requires coordinated initialization",
|
|
))
|
|
}
|
|
|
|
/// Convert reduce operation to cudarc NCCL type
|
|
fn convert_reduce_op_to_cudarc(op: crate::comm::ReduceOp) -> Result<NcclReduceOp> {
|
|
match op {
|
|
crate::comm::ReduceOp::Sum => Ok(NcclReduceOp::Sum),
|
|
crate::comm::ReduceOp::Max => Ok(NcclReduceOp::Max),
|
|
crate::comm::ReduceOp::Min => Ok(NcclReduceOp::Min),
|
|
crate::comm::ReduceOp::Product => Ok(NcclReduceOp::Prod),
|
|
_ => Err(DistributedError::communication(
|
|
"nccl",
|
|
format!("unsupported reduce operation: {op}"),
|
|
)),
|
|
}
|
|
}
|
|
}
|