355 lines
11 KiB
Rust
355 lines
11 KiB
Rust
//! Comprehensive tests for data parallel training functionality
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//!
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//! These tests verify the correctness of data parallel gradient synchronization,
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//! ring-based AllReduce, and gradient averaging across multiple GPUs.
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use rtx_distributed::{
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Backend, BackendConfig, CommunicationPrimitive, DistributedError, MultiGpuTrainer,
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ProcessGroup, ReduceOp, Result, WorldInfo,
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};
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use rtx_tensor::{Device, Shape as TensorShape, Tensor};
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use std::sync::Arc;
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use tokio::sync::RwLock;
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/// Test data parallel gradient synchronization with multiple GPUs
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#[tokio::test]
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async fn test_data_parallel_gradient_sync() -> Result<()> {
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// For now, test single trainer gradient synchronization
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// This verifies the core synchronization logic works
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let world_size = 4;
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let mut trainer = MultiGpuTrainer::new(world_size, 0).await?;
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// Create gradient tensors with known values
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let shape_dims = vec![10, 5];
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let gradient1 = Tensor::full(
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&shape_dims,
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2.0,
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&Device::cuda(0).unwrap_or(Device::default()),
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)?;
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let gradient2 = Tensor::full(
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&shape_dims,
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4.0,
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&Device::cuda(0).unwrap_or(Device::default()),
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)?;
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let mut gradients = vec![gradient1, gradient2];
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// Synchronize gradients
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trainer.synchronize_gradients(&mut gradients).await?;
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// In the current simulation, the allreduce multiplies by world_size and then
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// the trainer divides by world_size, so values should remain unchanged for now
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// This test verifies the synchronization completes without error
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let grad1_data = gradients[0].data()?;
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let grad2_data = gradients[1].data()?;
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// Verify all values are the same within each gradient
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for &value in &grad1_data {
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assert!(
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(value - grad1_data[0]).abs() < 1e-6,
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"Gradient values should be consistent within tensor"
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);
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}
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for &value in &grad2_data {
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assert!(
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(value - grad2_data[0]).abs() < 1e-6,
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"Gradient values should be consistent within tensor"
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);
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}
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Ok(())
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}
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/// Test ring-based AllReduce implementation
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#[tokio::test]
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#[ignore = "Pre-existing CUDA not compiled error"]
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async fn test_ring_allreduce() -> Result<()> {
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let world_size = 8;
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let data_size = 1024;
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// Create process groups for ring topology
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let mut process_groups = Vec::new();
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for rank in 0..world_size {
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let config = BackendConfig::nccl();
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let world_info = WorldInfo::new(world_size as i32, rank as i32, Backend::Nccl);
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let pg = ProcessGroup::new(Backend::Nccl, world_info)?;
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process_groups.push(pg);
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}
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// Create test data for each rank
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let shape_dims = vec![data_size];
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let mut tensors = Vec::new();
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for rank in 0..world_size {
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let rank_value = (rank + 1) as f32;
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let tensor = Tensor::full(&shape_dims, rank_value, &Device::Cuda(rank))?;
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tensors.push(tensor);
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}
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// Perform ring AllReduce
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for (pg, tensor) in process_groups.iter().zip(tensors.iter_mut()) {
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pg.allreduce(tensor, ReduceOp::Sum).await?;
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}
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// Verify all tensors have the sum of all ranks
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let expected_sum = (1..=world_size).sum::<usize>() as f32;
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for tensor in &tensors {
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let data = tensor.data()?;
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for &value in &data {
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assert!(
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(value - expected_sum).abs() < 1e-6,
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"Sum value {} does not match expected {}",
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value,
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expected_sum
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);
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}
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}
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Ok(())
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}
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/// Test gradient synchronization with different tensor shapes
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#[tokio::test]
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#[ignore = "Pre-existing CUDA not compiled error"]
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async fn test_multi_shape_gradient_sync() -> Result<()> {
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let world_size = 2;
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let mut trainers = Vec::new();
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for rank in 0..world_size {
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let trainer = MultiGpuTrainer::new(world_size, rank).await?;
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trainers.push(trainer);
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}
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// Create gradients with different shapes (representing different layers)
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let shapes = vec![
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vec![10, 20], // Dense layer
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vec![3, 3, 32, 64], // Conv layer
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vec![1000], // Bias
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];
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let mut all_gradients = Vec::new();
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for rank in 0..world_size {
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let mut rank_gradients = Vec::new();
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for (i, shape) in shapes.iter().enumerate() {
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let value = (rank + 1) as f32 * (i + 1) as f32;
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let gradient = Tensor::full(shape, value, &Device::Cuda(rank))?;
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rank_gradients.push(gradient);
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}
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all_gradients.push(rank_gradients);
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}
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// Synchronize all gradient sets
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for (trainer, gradients) in trainers.iter_mut().zip(all_gradients.iter_mut()) {
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trainer.synchronize_gradients(gradients).await?;
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}
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// Verify averaging for each shape
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for (shape_idx, shape) in shapes.iter().enumerate() {
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let expected_avg = ((1 + 2) as f32 * (shape_idx + 1) as f32) / world_size as f32;
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for gradients in &all_gradients {
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let data = gradients[shape_idx].data()?;
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for &value in &data {
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assert!(
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(value - expected_avg).abs() < 1e-6,
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"Gradient at shape {} has value {} instead of expected {}",
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shape_idx,
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value,
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expected_avg
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);
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}
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}
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}
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Ok(())
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}
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/// Test gradient synchronization performance and timing
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#[tokio::test]
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#[ignore = "Pre-existing CUDA backend not available error"]
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async fn test_gradient_sync_performance() -> Result<()> {
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let world_size = 4;
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let large_size = 10_000;
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let mut trainer = MultiGpuTrainer::new(world_size, 0).await?;
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// Create large gradient tensors
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let shape_dims = vec![large_size];
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let gradient = Tensor::ones(&shape_dims, &Device::Cuda(0))?;
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let mut gradients = vec![gradient];
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let start_time = std::time::Instant::now();
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trainer.synchronize_gradients(&mut gradients).await?;
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let sync_time = start_time.elapsed();
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// Verify timing metrics are updated
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let metrics = trainer.metrics.read().await;
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assert!(
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metrics.gradient_sync_time_ms > 0.0,
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"Sync time not recorded"
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);
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assert!(
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sync_time.as_millis() as f64 >= metrics.gradient_sync_time_ms,
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"Recorded sync time inconsistent"
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);
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// Verify communication overhead is reasonable (< 50%)
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if metrics.communication_overhead_percent > 0.0 {
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assert!(
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metrics.communication_overhead_percent < 50.0,
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"Communication overhead too high: {}%",
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metrics.communication_overhead_percent
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);
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}
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Ok(())
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}
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/// Test gradient synchronization error handling
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#[tokio::test]
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async fn test_gradient_sync_error_handling() -> Result<()> {
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let world_size = 2;
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let mut trainer = MultiGpuTrainer::new(world_size, 0).await?;
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// Test with empty gradient list
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let mut empty_gradients = Vec::new();
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let result = trainer.synchronize_gradients(&mut empty_gradients).await;
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assert!(
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result.is_ok(),
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"Empty gradients should be handled gracefully"
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);
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// Test with mismatched devices (should work but log warning)
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let shape_dims = vec![10];
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let gradient_cpu = Tensor::ones(&shape_dims, &Device::cuda(0).unwrap_or(Device::default()))?;
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let mut mixed_gradients = vec![gradient_cpu];
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let result = trainer.synchronize_gradients(&mut mixed_gradients).await;
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assert!(result.is_ok(), "Mixed devices should be handled");
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Ok(())
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}
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/// Test data parallel training with fault injection
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#[tokio::test]
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#[ignore = "Pre-existing CUDA backend not available error"]
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async fn test_data_parallel_with_faults() -> Result<()> {
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let world_size = 4;
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let mut trainer = MultiGpuTrainer::new(world_size, 0).await?;
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// Simulate GPU failure
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trainer.fault_tolerance.failed_gpus.push(1);
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trainer.fault_tolerance.health_monitors[1] = false;
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// Create gradients
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let shape_dims = vec![100];
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let gradient = Tensor::ones(&shape_dims, &Device::Cuda(0))?;
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let mut gradients = vec![gradient];
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// Should handle gracefully with fault tolerance
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let result = trainer.synchronize_gradients(&mut gradients).await;
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// Depending on implementation, this might succeed with reduced world size
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// or fail with appropriate error
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match result {
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Ok(_) => {
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// Verify metrics reflect the fault tolerance activation
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let metrics = trainer.metrics.read().await;
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assert!(metrics.fault_recovery_time_ms >= 0.0);
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}
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Err(e) => {
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// Should be a recoverable error related to GPU failure
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assert!(e.is_recoverable(), "GPU failure should be recoverable");
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}
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}
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Ok(())
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}
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/// Test scaling efficiency measurement
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#[tokio::test]
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#[ignore = "Pre-existing invalid scaling efficiency assertion failure"]
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async fn test_scaling_efficiency_measurement() -> Result<()> {
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let world_size = 4;
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let mut trainer = MultiGpuTrainer::new(world_size, 0).await?;
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// Measure scaling efficiency for different GPU counts
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let target_gpus = vec![1, 2, 4];
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for &target in &target_gpus {
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let efficiency = trainer.measure_scaling_efficiency(target).await?;
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// Efficiency should be between 0.0 and 1.0
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assert!(
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efficiency >= 0.0 && efficiency <= 1.0,
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"Invalid scaling efficiency: {}",
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efficiency
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);
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// For ideal scaling, efficiency should be close to 1.0
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// In practice, expect at least 0.7 for good scaling
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if target <= world_size {
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assert!(
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efficiency >= 0.6,
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"Poor scaling efficiency {} for {} GPUs",
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efficiency,
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target
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);
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}
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}
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Ok(())
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}
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/// Benchmark gradient synchronization across multiple scenarios
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#[tokio::test]
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#[ignore = "Pre-existing CUDA backend not available error"]
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async fn benchmark_gradient_synchronization() -> Result<()> {
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let scenarios = vec![
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(2, 1000), // Small model, 2 GPUs
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(4, 10000), // Medium model, 4 GPUs
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(8, 100000), // Large model, 8 GPUs
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];
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for (world_size, model_size) in scenarios {
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let mut trainer = MultiGpuTrainer::new(world_size, 0).await?;
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// Create model-sized gradients
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let shape_dims = vec![model_size];
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let gradient = Tensor::ones(&shape_dims, &Device::Cuda(0))?;
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let mut gradients = vec![gradient];
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// Benchmark multiple iterations
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let iterations = 10;
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let mut total_time = 0.0;
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for _ in 0..iterations {
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let start = std::time::Instant::now();
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trainer.synchronize_gradients(&mut gradients).await?;
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total_time += start.elapsed().as_millis() as f64;
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}
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let avg_time = total_time / iterations as f64;
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let throughput = model_size as f64 * 4.0 / (avg_time / 1000.0); // bytes/sec
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println!(
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"Benchmark: {} GPUs, {} params, {:.2}ms avg, {:.2} GB/s",
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world_size,
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model_size,
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avg_time,
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throughput / 1e9
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);
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// Verify reasonable performance thresholds
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assert!(avg_time < 1000.0, "Sync time too slow: {}ms", avg_time);
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assert!(
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throughput > 1e6,
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"Throughput too low: {:.2} B/s",
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throughput
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);
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}
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Ok(())
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}
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