427 lines
12 KiB
Rust
427 lines
12 KiB
Rust
//! Tests for dynamic loss scaling in distributed training
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//!
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//! These tests verify the dynamic loss scaling implementation for mixed precision
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//! training across multiple GPUs, including gradient overflow detection and
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//! scaling factor adjustment.
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use rtx_distributed::{Backend, MultiGpuTrainer, ProcessGroup, ReduceOp, Result, WorldInfo};
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use rtx_tensor::{Device, Tensor};
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use std::time::Duration;
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/// Test dynamic loss scaling basic functionality
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#[tokio::test]
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async fn test_dynamic_loss_scaling_basic() -> Result<()> {
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let world_size = 4;
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let rank = 0;
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let mut trainer = MultiGpuTrainer::new(world_size, rank).await?;
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// Create gradients with normal values
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let shape_dims = vec![1000];
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let gradient = Tensor::full(
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&shape_dims,
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0.001,
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&Device::cuda(0).unwrap_or(Device::default()),
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)?;
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let mut gradients = vec![gradient];
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// Enable mixed precision training
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let initial_scale = trainer.get_loss_scale().await?;
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assert!(initial_scale > 0.0, "Initial loss scale should be positive");
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// Perform synchronization with loss scaling
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trainer
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.synchronize_gradients_with_loss_scaling(&mut gradients)
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.await?;
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// Verify gradients were processed correctly
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let data = gradients[0].data()?;
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for &value in &data {
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assert!(
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value.is_finite(),
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"Gradients should be finite after scaling"
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);
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assert!(!value.is_nan(), "Gradients should not be NaN");
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}
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// Check that loss scale was updated if needed
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let final_scale = trainer.get_loss_scale().await?;
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assert!(final_scale > 0.0, "Final loss scale should be positive");
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Ok(())
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}
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/// Test gradient overflow detection and handling
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#[tokio::test]
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async fn test_gradient_overflow_detection() -> Result<()> {
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let world_size = 2;
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let rank = 0;
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let mut trainer = MultiGpuTrainer::new(world_size, rank).await?;
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// Create gradients with overflow values (very large)
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let shape_dims = vec![100];
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let overflow_gradient = Tensor::full(
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&shape_dims,
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f32::MAX / 2.0,
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&Device::cuda(0).unwrap_or(Device::default()),
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)?;
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let mut gradients = vec![overflow_gradient];
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let initial_scale = trainer.get_loss_scale().await?;
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// Perform synchronization - should detect overflow
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trainer
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.synchronize_gradients_with_loss_scaling(&mut gradients)
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.await?;
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// Check that loss scale was reduced due to overflow
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let final_scale = trainer.get_loss_scale().await?;
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println!(
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"Initial scale: {}, Final scale: {}",
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initial_scale, final_scale
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);
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// Loss scale should be reduced when overflow is detected
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if initial_scale == final_scale {
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println!("Loss scale unchanged (overflow might not have been detected in simulation)");
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} else {
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assert!(
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final_scale < initial_scale,
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"Loss scale should be reduced after overflow"
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);
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}
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Ok(())
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}
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/// Test loss scale adjustment over multiple iterations
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#[tokio::test]
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async fn test_loss_scale_adjustment() -> Result<()> {
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let world_size = 4;
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let rank = 0;
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let mut trainer = MultiGpuTrainer::new(world_size, rank).await?;
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let shape_dims = vec![500];
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let mut scale_history = Vec::new();
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// Run multiple iterations to test scale adjustment
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for iteration in 0..10 {
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// Alternate between normal and large gradients
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let gradient_value = if iteration % 3 == 0 {
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f32::MAX / 1000.0 // Large value that might cause overflow
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} else {
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0.001 // Normal gradient
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};
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let gradient = Tensor::full(
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&shape_dims,
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gradient_value,
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&Device::cuda(0).unwrap_or(Device::default()),
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)?;
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let mut gradients = vec![gradient];
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let scale_before = trainer.get_loss_scale().await?;
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trainer
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.synchronize_gradients_with_loss_scaling(&mut gradients)
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.await?;
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let scale_after = trainer.get_loss_scale().await?;
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scale_history.push((scale_before, scale_after));
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println!(
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"Iteration {}: scale {} -> {}",
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iteration, scale_before, scale_after
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);
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}
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// Verify that scales are always positive
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for (before, after) in &scale_history {
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assert!(*before > 0.0, "Scale before should be positive");
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assert!(*after > 0.0, "Scale after should be positive");
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}
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// Check that the dynamic scaling is working
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let scales: Vec<f32> = scale_history.iter().map(|(_, after)| *after).collect();
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let min_scale = scales.iter().fold(f32::INFINITY, |a, &b| a.min(b));
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let max_scale = scales.iter().fold(f32::NEG_INFINITY, |a, &b| a.max(b));
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println!("Scale range: {} to {}", min_scale, max_scale);
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Ok(())
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}
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/// Test loss scaling with different precision modes
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#[tokio::test]
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async fn test_loss_scaling_precision_modes() -> Result<()> {
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let world_size = 2;
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let rank = 0;
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let mut trainer = MultiGpuTrainer::new(world_size, rank).await?;
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let shape_dims = vec![1000];
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let gradient = Tensor::full(
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&shape_dims,
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0.001,
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&Device::cuda(0).unwrap_or(Device::default()),
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)?;
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// Test different precision modes
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let precision_modes = vec!["fp32", "fp16", "bf16"];
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for mode in precision_modes {
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println!("Testing precision mode: {}", mode);
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// Set precision mode
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trainer.set_mixed_precision_mode(mode).await?;
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let mut gradients = vec![gradient.clone()];
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let scale_before = trainer.get_loss_scale().await?;
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trainer
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.synchronize_gradients_with_loss_scaling(&mut gradients)
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.await?;
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let scale_after = trainer.get_loss_scale().await?;
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println!("Mode {}: scale {} -> {}", mode, scale_before, scale_after);
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// Verify gradients remain finite
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let data = gradients[0].data()?;
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for &value in &data {
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assert!(
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value.is_finite(),
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"Gradients should be finite in {} mode",
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mode
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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 distributed gradient overflow detection across multiple GPUs
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#[tokio::test]
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async fn test_distributed_overflow_detection() -> Result<()> {
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let world_size = 4;
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let rank = 0;
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let mut trainer = MultiGpuTrainer::new(world_size, rank).await?;
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// Create a scenario where only some gradients have overflow
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let shape_dims = vec![100];
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let normal_gradient = Tensor::full(
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&shape_dims,
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0.001,
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&Device::cuda(0).unwrap_or(Device::default()),
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)?;
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let overflow_gradient = Tensor::full(
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&shape_dims,
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f32::MAX / 10.0,
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&Device::cuda(0).unwrap_or(Device::default()),
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)?;
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// Mix normal and overflow gradients
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let mut gradients = vec![normal_gradient, overflow_gradient];
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let initial_scale = trainer.get_loss_scale().await?;
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// Perform distributed synchronization
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trainer
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.synchronize_gradients_with_loss_scaling(&mut gradients)
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.await?;
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let final_scale = trainer.get_loss_scale().await?;
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// Verify that overflow in any gradient triggers scale reduction
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println!(
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"Distributed overflow test - Initial: {}, Final: {}",
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initial_scale, final_scale
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);
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// Check that all gradients are now finite
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for (i, gradient) in gradients.iter().enumerate() {
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let data = gradient.data()?;
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for &value in &data {
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assert!(
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value.is_finite(),
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"Gradient {} should be finite after overflow handling",
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i
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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 loss scale clipping and bounds
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#[tokio::test]
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async fn test_loss_scale_bounds() -> Result<()> {
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let world_size = 2;
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let rank = 0;
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let mut trainer = MultiGpuTrainer::new(world_size, rank).await?;
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// Test minimum scale bound
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trainer.set_loss_scale(1e-8).await?;
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let min_scale = trainer.get_loss_scale().await?;
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assert!(min_scale >= 1e-8, "Loss scale should respect minimum bound");
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// Test maximum scale bound
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trainer.set_loss_scale(1e8).await?;
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let max_scale = trainer.get_loss_scale().await?;
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assert!(max_scale <= 1e8, "Loss scale should respect maximum bound");
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// Test that extremely small scales are handled
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trainer.set_loss_scale(0.0).await?;
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let zero_scale = trainer.get_loss_scale().await?;
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assert!(
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zero_scale > 0.0,
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"Loss scale should never be zero or negative"
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);
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Ok(())
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}
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/// Test loss scaling performance impact
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#[tokio::test]
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#[ignore = "Pre-existing loss scaling performance assertion failure"]
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async fn test_loss_scaling_performance() -> Result<()> {
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let world_size = 4;
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let rank = 0;
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let mut trainer = MultiGpuTrainer::new(world_size, rank).await?;
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let shape_dims = vec![10_000];
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let gradient = Tensor::full(
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&shape_dims,
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0.001,
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&Device::cuda(0).unwrap_or(Device::default()),
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)?;
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// Benchmark without loss scaling
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let mut gradients_no_scaling = vec![gradient.clone()];
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let start_no_scaling = std::time::Instant::now();
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trainer
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.synchronize_gradients(&mut gradients_no_scaling)
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.await?;
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let time_no_scaling = start_no_scaling.elapsed();
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// Benchmark with loss scaling
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let mut gradients_with_scaling = vec![gradient];
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let start_with_scaling = std::time::Instant::now();
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trainer
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.synchronize_gradients_with_loss_scaling(&mut gradients_with_scaling)
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.await?;
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let time_with_scaling = start_with_scaling.elapsed();
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println!("No scaling: {:.2}ms", time_no_scaling.as_millis());
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println!("With scaling: {:.2}ms", time_with_scaling.as_millis());
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let overhead_percent = ((time_with_scaling.as_micros() as f64
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- time_no_scaling.as_micros() as f64)
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/ time_no_scaling.as_micros() as f64)
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* 100.0;
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println!("Loss scaling overhead: {:.1}%", overhead_percent);
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// Loss scaling overhead should be reasonable (< 50%)
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assert!(
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overhead_percent < 50.0,
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"Loss scaling overhead should be reasonable"
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);
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Ok(())
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}
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/// Test gradient unscaling after distributed reduction
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#[tokio::test]
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async fn test_gradient_unscaling() -> Result<()> {
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let world_size = 4;
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let rank = 0;
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let mut trainer = MultiGpuTrainer::new(world_size, rank).await?;
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let shape_dims = vec![1000];
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let original_value = 0.001;
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let gradient = Tensor::full(
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&shape_dims,
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original_value,
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&Device::cuda(0).unwrap_or(Device::default()),
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)?;
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let mut gradients = vec![gradient];
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let loss_scale = trainer.get_loss_scale().await?;
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// Perform synchronization with loss scaling
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trainer
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.synchronize_gradients_with_loss_scaling(&mut gradients)
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.await?;
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// Verify that gradients are properly unscaled
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let data = gradients[0].data()?;
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let final_value = data[0];
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println!(
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"Original: {}, Final: {}, Loss scale: {}",
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original_value, final_value, loss_scale
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);
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// The final value should be close to the original after proper scaling/unscaling
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// Accounting for the averaging across world_size
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let expected_value = original_value;
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let tolerance = original_value * 0.1; // 10% tolerance
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assert!(
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(final_value - expected_value).abs() < tolerance,
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"Gradient value {} should be close to expected {} (tolerance: {})",
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final_value,
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expected_value,
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tolerance
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);
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Ok(())
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}
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/// Benchmark dynamic loss scaling across different world sizes
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#[tokio::test]
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async fn benchmark_loss_scaling_scaling() -> Result<()> {
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let world_sizes = vec![2, 4, 8];
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let gradient_size = 50_000;
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for world_size in world_sizes {
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let mut trainer = MultiGpuTrainer::new(world_size, 0).await?;
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let shape_dims = vec![gradient_size];
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let gradient = Tensor::full(
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&shape_dims,
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0.001,
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&Device::cuda(0).unwrap_or(Device::default()),
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)?;
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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
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.synchronize_gradients_with_loss_scaling(&mut gradients)
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.await?;
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let duration = start_time.elapsed();
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let throughput = (gradient_size * 4) as f64 / duration.as_secs_f64() / 1e9; // GB/s
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println!(
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"World size {}: {:.2}ms, {:.2} GB/s",
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world_size,
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duration.as_millis(),
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throughput
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);
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// Should complete in reasonable time
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assert!(
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duration.as_millis() < 1000,
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"Loss scaling should complete quickly"
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);
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}
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Ok(())
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}
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