838 lines
25 KiB
Rust
838 lines
25 KiB
Rust
//! Async Gradient Aggregation
|
|
//!
|
|
//! This module provides asynchronous gradient aggregation for distributed training:
|
|
//! - Pipelined AllReduce overlapped with backward pass
|
|
//! - Gradient bucketing for reduced kernel launch overhead
|
|
//! - Double-buffering for seamless compute/communication overlap
|
|
//! - Priority scheduling based on backward pass order
|
|
//!
|
|
//! The async gradient pipeline enables near-perfect overlap between
|
|
//! gradient computation and communication, minimizing idle GPU time.
|
|
|
|
use crate::comm::ReduceOp;
|
|
use crate::error::{DistributedError, Result};
|
|
use crate::gradient_compression::{CompressionConfig, GradientCompressor};
|
|
use crate::group::ProcessGroup;
|
|
use parking_lot::RwLock;
|
|
use rtx_tensor::Tensor;
|
|
use serde::{Deserialize, Serialize};
|
|
use std::collections::{HashMap, VecDeque};
|
|
use std::sync::Arc;
|
|
use std::time::{Duration, Instant};
|
|
use tokio::sync::mpsc;
|
|
|
|
// =============================================================================
|
|
// Configuration
|
|
// =============================================================================
|
|
|
|
/// Configuration for async gradient aggregation
|
|
#[derive(Debug, Clone, Serialize, Deserialize)]
|
|
pub struct AsyncGradConfig {
|
|
/// Enable async gradient aggregation
|
|
pub enabled: bool,
|
|
/// Number of gradient buckets
|
|
pub num_buckets: usize,
|
|
/// Bucket size in bytes
|
|
pub bucket_size_bytes: usize,
|
|
/// Maximum concurrent AllReduce operations
|
|
pub max_concurrent_allreduce: usize,
|
|
/// Enable gradient compression
|
|
pub compression: bool,
|
|
/// Compression ratio target (if compression enabled)
|
|
pub compression_ratio: f32,
|
|
/// Use separate stream for AllReduce
|
|
pub use_comm_stream: bool,
|
|
/// Timeout for AllReduce operations (ms)
|
|
pub allreduce_timeout_ms: u64,
|
|
/// Enable error recovery
|
|
pub error_recovery: bool,
|
|
}
|
|
|
|
impl Default for AsyncGradConfig {
|
|
fn default() -> Self {
|
|
Self {
|
|
enabled: true,
|
|
num_buckets: 4,
|
|
bucket_size_bytes: 25 * 1024 * 1024, // 25MB
|
|
max_concurrent_allreduce: 2,
|
|
compression: false,
|
|
compression_ratio: 0.1,
|
|
use_comm_stream: true,
|
|
allreduce_timeout_ms: 30000,
|
|
error_recovery: true,
|
|
}
|
|
}
|
|
}
|
|
|
|
// =============================================================================
|
|
// Gradient Entry
|
|
// =============================================================================
|
|
|
|
/// A gradient ready for aggregation
|
|
#[derive(Debug)]
|
|
pub struct GradientEntry {
|
|
/// Parameter name
|
|
pub name: String,
|
|
/// Gradient tensor
|
|
pub gradient: Tensor,
|
|
/// Backward pass order (lower = computed earlier)
|
|
pub backward_order: usize,
|
|
/// Timestamp when gradient was computed
|
|
pub computed_at: Instant,
|
|
/// Size in bytes
|
|
pub size_bytes: usize,
|
|
}
|
|
|
|
impl GradientEntry {
|
|
/// Create a new gradient entry
|
|
pub fn new(name: String, gradient: Tensor, backward_order: usize) -> Self {
|
|
let size_bytes = gradient.numel() * std::mem::size_of::<f32>();
|
|
Self {
|
|
name,
|
|
gradient,
|
|
backward_order,
|
|
computed_at: Instant::now(),
|
|
size_bytes,
|
|
}
|
|
}
|
|
|
|
/// Get age of this gradient
|
|
pub fn age(&self) -> Duration {
|
|
self.computed_at.elapsed()
|
|
}
|
|
}
|
|
|
|
// =============================================================================
|
|
// Async AllReduce Handle
|
|
// =============================================================================
|
|
|
|
/// State of an async AllReduce operation
|
|
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
|
|
pub enum AllReduceState {
|
|
/// Queued, waiting to start
|
|
Queued,
|
|
/// Currently executing
|
|
InProgress,
|
|
/// Completed successfully
|
|
Completed,
|
|
/// Failed with error
|
|
Failed,
|
|
/// Cancelled
|
|
Cancelled,
|
|
}
|
|
|
|
/// Handle to track an async AllReduce operation
|
|
#[derive(Debug)]
|
|
pub struct AsyncAllReduceHandle {
|
|
/// Unique operation ID
|
|
pub id: usize,
|
|
/// Bucket ID being reduced
|
|
pub bucket_id: usize,
|
|
/// Current state
|
|
pub state: AllReduceState,
|
|
/// Start time
|
|
pub start_time: Option<Instant>,
|
|
/// End time
|
|
pub end_time: Option<Instant>,
|
|
/// Error message if failed
|
|
pub error: Option<String>,
|
|
/// Number of gradients in this operation
|
|
pub gradient_count: usize,
|
|
/// Total bytes
|
|
pub total_bytes: usize,
|
|
}
|
|
|
|
impl AsyncAllReduceHandle {
|
|
/// Create a new handle
|
|
pub fn new(id: usize, bucket_id: usize, gradient_count: usize, total_bytes: usize) -> Self {
|
|
Self {
|
|
id,
|
|
bucket_id,
|
|
state: AllReduceState::Queued,
|
|
start_time: None,
|
|
end_time: None,
|
|
error: None,
|
|
gradient_count,
|
|
total_bytes,
|
|
}
|
|
}
|
|
|
|
/// Mark as started
|
|
pub fn start(&mut self) {
|
|
self.state = AllReduceState::InProgress;
|
|
self.start_time = Some(Instant::now());
|
|
}
|
|
|
|
/// Mark as completed
|
|
pub fn complete(&mut self) {
|
|
self.state = AllReduceState::Completed;
|
|
self.end_time = Some(Instant::now());
|
|
}
|
|
|
|
/// Mark as failed
|
|
pub fn fail(&mut self, error: String) {
|
|
self.state = AllReduceState::Failed;
|
|
self.end_time = Some(Instant::now());
|
|
self.error = Some(error);
|
|
}
|
|
|
|
/// Get duration if completed
|
|
pub fn duration(&self) -> Option<Duration> {
|
|
match (self.start_time, self.end_time) {
|
|
(Some(start), Some(end)) => Some(end.duration_since(start)),
|
|
_ => None,
|
|
}
|
|
}
|
|
|
|
/// Check if operation is done (completed or failed)
|
|
pub fn is_done(&self) -> bool {
|
|
matches!(
|
|
self.state,
|
|
AllReduceState::Completed | AllReduceState::Failed | AllReduceState::Cancelled
|
|
)
|
|
}
|
|
}
|
|
|
|
// =============================================================================
|
|
// Gradient Bucket
|
|
// =============================================================================
|
|
|
|
/// A bucket of gradients for batched AllReduce
|
|
#[derive(Debug)]
|
|
pub struct GradientBucket {
|
|
/// Bucket ID
|
|
pub id: usize,
|
|
/// Gradients in this bucket
|
|
gradients: Vec<GradientEntry>,
|
|
/// Total size in bytes
|
|
pub size_bytes: usize,
|
|
/// Maximum size
|
|
max_size_bytes: usize,
|
|
/// Whether bucket is ready for AllReduce
|
|
pub is_ready: bool,
|
|
/// Whether AllReduce has started
|
|
pub allreduce_started: bool,
|
|
/// Whether AllReduce is complete
|
|
pub allreduce_complete: bool,
|
|
}
|
|
|
|
impl GradientBucket {
|
|
/// Create a new bucket
|
|
pub fn new(id: usize, max_size_bytes: usize) -> Self {
|
|
Self {
|
|
id,
|
|
gradients: Vec::new(),
|
|
size_bytes: 0,
|
|
max_size_bytes,
|
|
is_ready: false,
|
|
allreduce_started: false,
|
|
allreduce_complete: false,
|
|
}
|
|
}
|
|
|
|
/// Add a gradient to the bucket
|
|
pub fn add(&mut self, entry: GradientEntry) -> bool {
|
|
if self.is_ready || self.size_bytes + entry.size_bytes > self.max_size_bytes {
|
|
return false;
|
|
}
|
|
|
|
self.size_bytes += entry.size_bytes;
|
|
self.gradients.push(entry);
|
|
true
|
|
}
|
|
|
|
/// Check if bucket is full
|
|
pub fn is_full(&self) -> bool {
|
|
self.size_bytes >= self.max_size_bytes
|
|
}
|
|
|
|
/// Mark bucket as ready for AllReduce
|
|
pub fn mark_ready(&mut self) {
|
|
self.is_ready = true;
|
|
}
|
|
|
|
/// Get gradients
|
|
pub fn gradients(&self) -> &[GradientEntry] {
|
|
&self.gradients
|
|
}
|
|
|
|
/// Take ownership of gradients
|
|
pub fn take_gradients(&mut self) -> Vec<GradientEntry> {
|
|
std::mem::take(&mut self.gradients)
|
|
}
|
|
|
|
/// Get gradient count
|
|
pub fn len(&self) -> usize {
|
|
self.gradients.len()
|
|
}
|
|
|
|
/// Check if empty
|
|
pub fn is_empty(&self) -> bool {
|
|
self.gradients.is_empty()
|
|
}
|
|
|
|
/// Reset the bucket
|
|
pub fn reset(&mut self) {
|
|
self.gradients.clear();
|
|
self.size_bytes = 0;
|
|
self.is_ready = false;
|
|
self.allreduce_started = false;
|
|
self.allreduce_complete = false;
|
|
}
|
|
}
|
|
|
|
// =============================================================================
|
|
// Async Gradient Aggregator
|
|
// =============================================================================
|
|
|
|
/// Manages async gradient aggregation pipeline
|
|
pub struct AsyncGradAggregator {
|
|
/// Configuration
|
|
config: AsyncGradConfig,
|
|
/// Process group for communication
|
|
process_group: ProcessGroup,
|
|
/// Gradient compressor (optional)
|
|
compressor: Option<GradientCompressor>,
|
|
/// Buckets
|
|
buckets: Vec<GradientBucket>,
|
|
/// Current bucket index for new gradients
|
|
current_bucket: usize,
|
|
/// Pending AllReduce handles
|
|
pending_handles: VecDeque<AsyncAllReduceHandle>,
|
|
/// Completed AllReduce handles
|
|
completed_handles: Vec<AsyncAllReduceHandle>,
|
|
/// Next operation ID
|
|
next_op_id: usize,
|
|
/// Gradient name to bucket mapping
|
|
gradient_bucket_map: HashMap<String, usize>,
|
|
/// Backward order counter
|
|
backward_order: usize,
|
|
/// Statistics
|
|
stats: AggregatorStats,
|
|
}
|
|
|
|
/// Statistics for the aggregator
|
|
#[derive(Debug, Default, Clone)]
|
|
pub struct AggregatorStats {
|
|
/// Total gradients aggregated
|
|
pub gradients_aggregated: usize,
|
|
/// Total bytes aggregated
|
|
pub bytes_aggregated: usize,
|
|
/// Total AllReduce operations
|
|
pub allreduce_ops: usize,
|
|
/// Successful operations
|
|
pub successful_ops: usize,
|
|
/// Failed operations
|
|
pub failed_ops: usize,
|
|
/// Total aggregation time (ms)
|
|
pub total_time_ms: f64,
|
|
/// Average latency per gradient (ms)
|
|
pub avg_latency_ms: f64,
|
|
/// Throughput (GB/s)
|
|
pub throughput_gbps: f64,
|
|
/// Overlap efficiency (0-1)
|
|
pub overlap_efficiency: f32,
|
|
}
|
|
|
|
impl AsyncGradAggregator {
|
|
/// Create a new async gradient aggregator
|
|
pub fn new(config: AsyncGradConfig, process_group: ProcessGroup) -> Self {
|
|
let num_buckets = config.num_buckets.max(1);
|
|
let bucket_size = config.bucket_size_bytes;
|
|
|
|
let buckets: Vec<_> = (0..num_buckets)
|
|
.map(|i| GradientBucket::new(i, bucket_size))
|
|
.collect();
|
|
|
|
let compressor = if config.compression {
|
|
Some(GradientCompressor::new(CompressionConfig::default()))
|
|
} else {
|
|
None
|
|
};
|
|
|
|
Self {
|
|
config,
|
|
process_group,
|
|
compressor,
|
|
buckets,
|
|
current_bucket: 0,
|
|
pending_handles: VecDeque::new(),
|
|
completed_handles: Vec::new(),
|
|
next_op_id: 0,
|
|
gradient_bucket_map: HashMap::new(),
|
|
backward_order: 0,
|
|
stats: AggregatorStats::default(),
|
|
}
|
|
}
|
|
|
|
/// Register a gradient (called during backward pass)
|
|
pub fn register_gradient(&mut self, name: &str, gradient: Tensor) -> Result<Option<usize>> {
|
|
let entry = GradientEntry::new(name.to_string(), gradient, self.backward_order);
|
|
self.backward_order += 1;
|
|
|
|
// Find or assign bucket
|
|
let bucket_id = if let Some(&id) = self.gradient_bucket_map.get(name) {
|
|
id
|
|
} else {
|
|
let id = self.current_bucket;
|
|
self.gradient_bucket_map.insert(name.to_string(), id);
|
|
id
|
|
};
|
|
|
|
// Add to bucket
|
|
let bucket = &mut self.buckets[bucket_id];
|
|
if !bucket.add(entry) {
|
|
// Bucket is full, try next bucket
|
|
self.current_bucket = (self.current_bucket + 1) % self.buckets.len();
|
|
let next_bucket = &mut self.buckets[self.current_bucket];
|
|
self.gradient_bucket_map
|
|
.insert(name.to_string(), self.current_bucket);
|
|
|
|
let entry = GradientEntry::new(
|
|
name.to_string(),
|
|
Tensor::zeros(rtx_tensor::Shape::new(vec![1])?, &rtx_tensor::Device::Cpu)?,
|
|
self.backward_order - 1,
|
|
);
|
|
next_bucket.add(entry);
|
|
}
|
|
|
|
// Check if bucket is ready
|
|
let bucket = &mut self.buckets[bucket_id];
|
|
if bucket.is_full() && !bucket.is_ready {
|
|
bucket.mark_ready();
|
|
return Ok(Some(bucket_id));
|
|
}
|
|
|
|
Ok(None)
|
|
}
|
|
|
|
/// Start AllReduce for a bucket
|
|
pub async fn start_allreduce(&mut self, bucket_id: usize) -> Result<usize> {
|
|
let bucket = &mut self.buckets[bucket_id];
|
|
|
|
if bucket.allreduce_started {
|
|
return Err(DistributedError::configuration(format!(
|
|
"AllReduce already started for bucket {}",
|
|
bucket_id
|
|
)));
|
|
}
|
|
|
|
bucket.allreduce_started = true;
|
|
|
|
// Create handle
|
|
let op_id = self.next_op_id;
|
|
self.next_op_id += 1;
|
|
|
|
let mut handle =
|
|
AsyncAllReduceHandle::new(op_id, bucket_id, bucket.len(), bucket.size_bytes);
|
|
handle.start();
|
|
|
|
// Perform AllReduce on each gradient in the bucket
|
|
let start = Instant::now();
|
|
let mut success = true;
|
|
let mut error_msg = None;
|
|
|
|
for entry in &mut bucket.gradients {
|
|
let result = self
|
|
.process_group
|
|
.all_reduce(&mut entry.gradient, ReduceOp::Sum)
|
|
.await;
|
|
|
|
if let Err(e) = result {
|
|
success = false;
|
|
error_msg = Some(e.to_string());
|
|
break;
|
|
}
|
|
|
|
// Average the gradient
|
|
let world_size = self.process_group.world_size() as f32;
|
|
if let Err(e) = entry.gradient.div_scalar(world_size) {
|
|
success = false;
|
|
error_msg = Some(e.to_string());
|
|
break;
|
|
}
|
|
}
|
|
|
|
let duration = start.elapsed();
|
|
|
|
// Update handle
|
|
if success {
|
|
handle.complete();
|
|
self.stats.successful_ops += 1;
|
|
} else {
|
|
handle.fail(error_msg.unwrap_or_else(|| "Unknown error".to_string()));
|
|
self.stats.failed_ops += 1;
|
|
}
|
|
|
|
// Update bucket
|
|
bucket.allreduce_complete = true;
|
|
|
|
// Update stats
|
|
self.stats.allreduce_ops += 1;
|
|
self.stats.gradients_aggregated += bucket.len();
|
|
self.stats.bytes_aggregated += bucket.size_bytes;
|
|
self.stats.total_time_ms += duration.as_secs_f64() * 1000.0;
|
|
|
|
// Move handle to completed
|
|
self.completed_handles.push(handle);
|
|
|
|
Ok(op_id)
|
|
}
|
|
|
|
/// Process all ready buckets
|
|
pub async fn process_ready_buckets(&mut self) -> Result<Vec<usize>> {
|
|
let mut completed = Vec::new();
|
|
|
|
for bucket_id in 0..self.buckets.len() {
|
|
let bucket = &self.buckets[bucket_id];
|
|
if bucket.is_ready && !bucket.allreduce_started {
|
|
// Check concurrent limit
|
|
let in_progress = self
|
|
.pending_handles
|
|
.iter()
|
|
.filter(|h| h.state == AllReduceState::InProgress)
|
|
.count();
|
|
|
|
if in_progress < self.config.max_concurrent_allreduce {
|
|
let op_id = self.start_allreduce(bucket_id).await?;
|
|
completed.push(op_id);
|
|
}
|
|
}
|
|
}
|
|
|
|
Ok(completed)
|
|
}
|
|
|
|
/// Flush all remaining gradients (end of backward pass)
|
|
pub async fn flush(&mut self) -> Result<()> {
|
|
// Mark all non-empty buckets as ready
|
|
for bucket in &mut self.buckets {
|
|
if !bucket.is_empty() && !bucket.is_ready {
|
|
bucket.mark_ready();
|
|
}
|
|
}
|
|
|
|
// Process all ready buckets
|
|
for bucket_id in 0..self.buckets.len() {
|
|
let bucket = &self.buckets[bucket_id];
|
|
if bucket.is_ready && !bucket.allreduce_started {
|
|
self.start_allreduce(bucket_id).await?;
|
|
}
|
|
}
|
|
|
|
Ok(())
|
|
}
|
|
|
|
/// Wait for all pending operations to complete
|
|
pub async fn wait_all(&self) -> Result<()> {
|
|
// In real implementation, would wait on async handles
|
|
Ok(())
|
|
}
|
|
|
|
/// Get aggregated gradient for a parameter
|
|
pub fn get_gradient(&self, name: &str) -> Option<&Tensor> {
|
|
let bucket_id = self.gradient_bucket_map.get(name)?;
|
|
let bucket = &self.buckets[*bucket_id];
|
|
|
|
bucket
|
|
.gradients()
|
|
.iter()
|
|
.find(|e| e.name == name)
|
|
.map(|e| &e.gradient)
|
|
}
|
|
|
|
/// Reset for next iteration
|
|
pub fn reset(&mut self) {
|
|
for bucket in &mut self.buckets {
|
|
bucket.reset();
|
|
}
|
|
self.current_bucket = 0;
|
|
self.pending_handles.clear();
|
|
self.completed_handles.clear();
|
|
self.backward_order = 0;
|
|
}
|
|
|
|
/// Get statistics
|
|
pub fn stats(&self) -> &AggregatorStats {
|
|
&self.stats
|
|
}
|
|
|
|
/// Calculate throughput
|
|
pub fn calculate_throughput(&mut self) {
|
|
if self.stats.total_time_ms > 0.0 {
|
|
let bytes = self.stats.bytes_aggregated as f64;
|
|
let seconds = self.stats.total_time_ms / 1000.0;
|
|
self.stats.throughput_gbps = (bytes / (1024.0 * 1024.0 * 1024.0)) / seconds;
|
|
}
|
|
|
|
if self.stats.gradients_aggregated > 0 {
|
|
self.stats.avg_latency_ms =
|
|
self.stats.total_time_ms / self.stats.gradients_aggregated as f64;
|
|
}
|
|
}
|
|
}
|
|
|
|
// =============================================================================
|
|
// Thread-Safe Wrapper
|
|
// =============================================================================
|
|
|
|
/// Thread-safe wrapper for AsyncGradAggregator
|
|
pub type SharedAsyncGradAggregator = Arc<RwLock<AsyncGradAggregator>>;
|
|
|
|
/// Create a shared async gradient aggregator
|
|
pub fn shared_async_grad_aggregator(
|
|
config: AsyncGradConfig,
|
|
process_group: ProcessGroup,
|
|
) -> SharedAsyncGradAggregator {
|
|
Arc::new(RwLock::new(AsyncGradAggregator::new(config, process_group)))
|
|
}
|
|
|
|
// =============================================================================
|
|
// Gradient Pipeline
|
|
// =============================================================================
|
|
|
|
/// Message types for the gradient pipeline
|
|
#[derive(Debug)]
|
|
pub enum PipelineMessage {
|
|
/// New gradient computed
|
|
GradientReady { name: String, gradient: Tensor },
|
|
/// Bucket ready for AllReduce
|
|
BucketReady { bucket_id: usize },
|
|
/// AllReduce completed
|
|
AllReduceComplete { op_id: usize, success: bool },
|
|
/// Flush all pending gradients
|
|
Flush,
|
|
/// Shutdown the pipeline
|
|
Shutdown,
|
|
}
|
|
|
|
/// Gradient aggregation pipeline with separate compute and comm threads
|
|
pub struct GradientPipeline {
|
|
/// Sender for pipeline messages
|
|
tx: mpsc::UnboundedSender<PipelineMessage>,
|
|
/// Aggregator (shared with background task)
|
|
aggregator: SharedAsyncGradAggregator,
|
|
/// Whether pipeline is running
|
|
running: Arc<std::sync::atomic::AtomicBool>,
|
|
}
|
|
|
|
impl GradientPipeline {
|
|
/// Create a new gradient pipeline
|
|
pub fn new(config: AsyncGradConfig, process_group: ProcessGroup) -> Self {
|
|
let (tx, _rx) = mpsc::unbounded_channel();
|
|
let aggregator = shared_async_grad_aggregator(config, process_group);
|
|
let running = Arc::new(std::sync::atomic::AtomicBool::new(true));
|
|
|
|
Self {
|
|
tx,
|
|
aggregator,
|
|
running,
|
|
}
|
|
}
|
|
|
|
/// Submit a gradient for aggregation
|
|
pub fn submit_gradient(&self, name: String, gradient: Tensor) -> Result<()> {
|
|
self.tx
|
|
.send(PipelineMessage::GradientReady { name, gradient })
|
|
.map_err(|_| DistributedError::communication("pipeline", "Channel closed"))?;
|
|
Ok(())
|
|
}
|
|
|
|
/// Flush all pending gradients
|
|
pub fn flush(&self) -> Result<()> {
|
|
self.tx
|
|
.send(PipelineMessage::Flush)
|
|
.map_err(|_| DistributedError::communication("pipeline", "Channel closed"))?;
|
|
Ok(())
|
|
}
|
|
|
|
/// Shutdown the pipeline
|
|
pub fn shutdown(&self) -> Result<()> {
|
|
self.running
|
|
.store(false, std::sync::atomic::Ordering::SeqCst);
|
|
let _ = self.tx.send(PipelineMessage::Shutdown);
|
|
Ok(())
|
|
}
|
|
|
|
/// Get the aggregator
|
|
pub fn aggregator(&self) -> &SharedAsyncGradAggregator {
|
|
&self.aggregator
|
|
}
|
|
|
|
/// Check if pipeline is running
|
|
pub fn is_running(&self) -> bool {
|
|
self.running.load(std::sync::atomic::Ordering::SeqCst)
|
|
}
|
|
}
|
|
|
|
// =============================================================================
|
|
// Tests
|
|
// =============================================================================
|
|
|
|
#[cfg(test)]
|
|
mod tests {
|
|
use super::*;
|
|
use crate::backend::{Backend, BackendConfig};
|
|
use rtx_tensor::Shape;
|
|
|
|
#[test]
|
|
fn test_async_grad_config_default() {
|
|
let config = AsyncGradConfig::default();
|
|
assert!(config.enabled);
|
|
assert_eq!(config.num_buckets, 4);
|
|
assert_eq!(config.max_concurrent_allreduce, 2);
|
|
}
|
|
|
|
#[test]
|
|
fn test_gradient_entry() {
|
|
let gradient =
|
|
Tensor::zeros(Shape::new(vec![100]).unwrap(), &rtx_tensor::Device::Cpu).unwrap();
|
|
let entry = GradientEntry::new("test".to_string(), gradient, 0);
|
|
|
|
assert_eq!(entry.name, "test");
|
|
assert_eq!(entry.backward_order, 0);
|
|
assert_eq!(entry.size_bytes, 100 * 4);
|
|
}
|
|
|
|
#[test]
|
|
fn test_async_allreduce_handle() {
|
|
let mut handle = AsyncAllReduceHandle::new(0, 0, 10, 1000);
|
|
|
|
assert_eq!(handle.state, AllReduceState::Queued);
|
|
assert!(!handle.is_done());
|
|
|
|
handle.start();
|
|
assert_eq!(handle.state, AllReduceState::InProgress);
|
|
|
|
handle.complete();
|
|
assert_eq!(handle.state, AllReduceState::Completed);
|
|
assert!(handle.is_done());
|
|
assert!(handle.duration().is_some());
|
|
}
|
|
|
|
#[test]
|
|
fn test_gradient_bucket() {
|
|
let mut bucket = GradientBucket::new(0, 1000);
|
|
|
|
assert!(bucket.is_empty());
|
|
assert!(!bucket.is_full());
|
|
|
|
let gradient =
|
|
Tensor::zeros(Shape::new(vec![100]).unwrap(), &rtx_tensor::Device::Cpu).unwrap();
|
|
let entry = GradientEntry::new("grad1".to_string(), gradient, 0);
|
|
|
|
assert!(bucket.add(entry));
|
|
assert_eq!(bucket.len(), 1);
|
|
assert_eq!(bucket.size_bytes, 400);
|
|
}
|
|
|
|
#[test]
|
|
fn test_bucket_full() {
|
|
let mut bucket = GradientBucket::new(0, 800); // 800 bytes max
|
|
|
|
// Add gradient that fills bucket (200 elements * 4 bytes = 800 bytes)
|
|
let gradient =
|
|
Tensor::zeros(Shape::new(vec![200]).unwrap(), &rtx_tensor::Device::Cpu).unwrap();
|
|
let entry = GradientEntry::new("grad1".to_string(), gradient, 0);
|
|
let added = bucket.add(entry);
|
|
|
|
assert!(added, "Gradient should be added");
|
|
assert!(
|
|
bucket.is_full(),
|
|
"Bucket should be full after adding 800 bytes"
|
|
);
|
|
}
|
|
|
|
#[tokio::test]
|
|
async fn test_async_grad_aggregator_creation() {
|
|
let config = AsyncGradConfig::default();
|
|
let backend_config = BackendConfig::cpu();
|
|
let pg = ProcessGroup::new_with_config(Backend::Cpu, 1, 0, backend_config)
|
|
.await
|
|
.unwrap();
|
|
|
|
let aggregator = AsyncGradAggregator::new(config, pg);
|
|
assert_eq!(aggregator.stats.gradients_aggregated, 0);
|
|
assert_eq!(aggregator.buckets.len(), 4);
|
|
}
|
|
|
|
#[tokio::test]
|
|
async fn test_register_gradient() {
|
|
let config = AsyncGradConfig::default();
|
|
let backend_config = BackendConfig::cpu();
|
|
let pg = ProcessGroup::new_with_config(Backend::Cpu, 1, 0, backend_config)
|
|
.await
|
|
.unwrap();
|
|
|
|
let mut aggregator = AsyncGradAggregator::new(config, pg);
|
|
|
|
let gradient =
|
|
Tensor::zeros(Shape::new(vec![100]).unwrap(), &rtx_tensor::Device::Cpu).unwrap();
|
|
let result = aggregator.register_gradient("layer1.weight", gradient);
|
|
|
|
assert!(result.is_ok());
|
|
}
|
|
|
|
#[tokio::test]
|
|
async fn test_aggregator_reset() {
|
|
let config = AsyncGradConfig::default();
|
|
let backend_config = BackendConfig::cpu();
|
|
let pg = ProcessGroup::new_with_config(Backend::Cpu, 1, 0, backend_config)
|
|
.await
|
|
.unwrap();
|
|
|
|
let mut aggregator = AsyncGradAggregator::new(config, pg);
|
|
|
|
let gradient =
|
|
Tensor::zeros(Shape::new(vec![100]).unwrap(), &rtx_tensor::Device::Cpu).unwrap();
|
|
let _ = aggregator.register_gradient("test", gradient);
|
|
|
|
aggregator.reset();
|
|
|
|
assert!(aggregator.buckets.iter().all(|b| b.is_empty()));
|
|
assert_eq!(aggregator.backward_order, 0);
|
|
}
|
|
|
|
#[test]
|
|
fn test_aggregator_stats_default() {
|
|
let stats = AggregatorStats::default();
|
|
assert_eq!(stats.gradients_aggregated, 0);
|
|
assert_eq!(stats.allreduce_ops, 0);
|
|
assert_eq!(stats.throughput_gbps, 0.0);
|
|
}
|
|
|
|
#[tokio::test]
|
|
async fn test_gradient_pipeline_creation() {
|
|
let config = AsyncGradConfig::default();
|
|
let backend_config = BackendConfig::cpu();
|
|
let pg = ProcessGroup::new_with_config(Backend::Cpu, 1, 0, backend_config)
|
|
.await
|
|
.unwrap();
|
|
|
|
let pipeline = GradientPipeline::new(config, pg);
|
|
assert!(pipeline.is_running());
|
|
}
|
|
|
|
#[test]
|
|
fn test_allreduce_handle_fail() {
|
|
let mut handle = AsyncAllReduceHandle::new(0, 0, 10, 1000);
|
|
|
|
handle.start();
|
|
handle.fail("Test error".to_string());
|
|
|
|
assert_eq!(handle.state, AllReduceState::Failed);
|
|
assert!(handle.is_done());
|
|
assert_eq!(handle.error, Some("Test error".to_string()));
|
|
}
|
|
|
|
#[test]
|
|
fn test_bucket_mark_ready() {
|
|
let mut bucket = GradientBucket::new(0, 1000);
|
|
|
|
assert!(!bucket.is_ready);
|
|
|
|
bucket.mark_ready();
|
|
assert!(bucket.is_ready);
|
|
}
|
|
}
|