Fixing CUDA Out-of-Memory Errors: A Troubleshooting Guide

by Admin 58 views
Troubleshooting CUDA Out-of-Memory Errors in Volcengine/VERL

Experiencing CUDA out-of-memory errors can be a real headache, especially when you suspect a memory leak. Let's dive into how to troubleshoot this issue within the Volcengine/VERL framework. In this guide, we'll break down potential causes and solutions in a friendly, conversational way. We’ll explore your provided configuration, analyze memory allocation patterns, and pinpoint potential memory leak sources.

Understanding the Configuration

Alright, let's start by dissecting your configuration. You're running a distributed training setup with 2 nodes, each equipped with 4 H100 GPUs. This is a beefy setup, but memory management is crucial even with such powerful hardware. The configuration you've shared gives us a detailed look into your actor, critic, and rollout settings. We'll pay close attention to the following key areas:

  • FSDP (Fully Sharded Data Parallel): You're using FSDP, which is great for large models as it shards the model across multiple GPUs. However, misconfigurations in FSDP can sometimes lead to memory issues. Specifically, we'll look at the fsdp_config settings within both the actor and critic configurations. Pay close attention to param_offload, optimizer_offload, and reshard_after_forward parameters. Understanding how these parameters interact is crucial for optimizing memory usage.
  • Model Configuration: You're using the Qwen3-4B-Base model, which is a sizable model. The model configuration, especially settings like enable_activation_offload and enable_gradient_checkpointing, will play a significant role in memory consumption. We need to examine these settings to ensure they're appropriately configured for your hardware.
  • Rollout Configuration: The rollout configuration dictates how data is generated and processed. Parameters like max_num_batched_tokens, max_num_seqs, and dtype (data type) can significantly impact memory usage. We’ll investigate these settings to identify potential bottlenecks.
  • Optimizer Configuration: The optimizer settings, particularly the FSDPOptimizerConfig, define how gradients are calculated and applied. Incorrect configurations can lead to excessive memory consumption. Key parameters here include lr (learning rate), weight_decay, and whether optimizer offloading is enabled.

It's important to note that CUDA out-of-memory errors often arise not from a single issue, but from a combination of factors. By methodically reviewing each component of your configuration, we can isolate the root cause.

Analyzing the Memory Allocation Progression

The Wandb log you provided gives us a visual representation of the memory allocation progression. This is super helpful! Here's what we can glean from the graph:

  • Memory Growth Over Time: The graph clearly shows memory usage increasing over time, which is a classic sign of a memory leak. Ideally, memory usage should plateau after an initial spike as the model and data are loaded.
  • Spikes and Plateaus: Are there any noticeable spikes in memory usage followed by plateaus? Spikes might indicate specific operations or phases of training that are memory-intensive. Plateaus might suggest periods where memory isn't being released properly.
  • Consistent Upward Trend: If the memory usage shows a consistent upward trend without any significant drops, it strongly suggests a leak. This means that memory is being allocated but not deallocated, eventually leading to the out-of-memory error.

When analyzing this kind of memory allocation graph, it's essential to correlate the memory usage patterns with the training steps or epochs. Are there particular stages where the memory usage jumps significantly? This can provide clues about which parts of your code might be contributing to the leak.

Potential Memory Leak Sources

Based on your configuration and the memory allocation graph, let’s brainstorm some potential memory leak culprits:

  1. Unreleased Tensors: This is a common cause. If you're creating tensors within a loop or function and not explicitly deleting them, they'll accumulate in memory. PyTorch's garbage collector should handle this, but sometimes it doesn't happen quickly enough, especially with large tensors. Ensure that any temporary tensors created during forward or backward passes are properly deallocated using del or by moving them to the CPU if they're no longer needed on the GPU.
  2. Accumulating Gradients: If you're accumulating gradients across multiple batches without zeroing them out, the memory used to store these gradients will keep growing. PyTorch accumulates gradients by default, so you need to call optimizer.zero_grad() at the beginning of each iteration or batch. This is a frequent oversight that leads to memory issues.
  3. FSDP Configuration Issues: As mentioned earlier, FSDP can be tricky. Incorrect settings for param_offload, optimizer_offload, or reshard_after_forward can lead to unexpected memory usage. For instance, if param_offload is set to True, parameters are offloaded to the CPU, but the overhead of transferring them back and forth can sometimes increase memory usage. Experiment with different FSDP configurations to see if it resolves the issue. Try setting reshard_after_forward to False as a starting point, as this might reduce memory fragmentation.
  4. Data Loading Bottlenecks: Inefficient data loading can also contribute to memory issues. If you're loading data faster than your model can process it, the data will pile up in memory. Look at your DataLoader settings, such as num_workers and batch_size. Try reducing the number of workers or the batch size to see if it alleviates the memory pressure.
  5. Caching Issues: PyTorch and other libraries often cache computations to speed up subsequent operations. However, this cache can sometimes grow too large and lead to memory exhaustion. Try clearing the cache periodically using torch.cuda.empty_cache(). This can help free up memory, especially after memory-intensive operations.
  6. Third-Party Libraries: Sometimes, memory leaks can originate from third-party libraries you're using. If you suspect this, try isolating the issue by removing or disabling these libraries one by one to see if the memory leak disappears. Libraries that perform complex operations or have custom memory management routines are more likely to cause issues.
  7. Gradient Checkpointing: While gradient checkpointing is intended to reduce memory usage, incorrect implementation or usage can sometimes lead to memory leaks. Review how gradient checkpointing is implemented in your code and ensure that it is being used correctly. Specifically, verify that the checkpointed sections are not holding onto unnecessary intermediate tensors.

Debugging Strategies

Okay, so we've identified some potential culprits. Now, let's talk about how to debug this thing. Here are some effective strategies you can use:

  1. Memory Profiling Tools: PyTorch offers built-in memory profiling tools that can help you track memory allocation and deallocation. Tools like torch.cuda.memory_summary() and torch.cuda.memory_stats() can provide detailed information about memory usage. You can also use more advanced profiling tools like Nsight Systems or PyTorch Profiler for a deeper dive into memory allocation patterns. These tools can help you pinpoint exactly where memory is being allocated and whether it's being released correctly.
  2. Garbage Collection: Explicitly triggering Python's garbage collector using gc.collect() can sometimes help release memory that's being held onto by circular references or other issues. Add gc.collect() calls at strategic points in your code, such as after completing an epoch or a significant operation.
  3. Reduce Batch Size: A simple yet effective way to reduce memory pressure is to reduce your batch size. This will decrease the amount of data processed in each iteration, lowering the memory footprint. If reducing the batch size resolves the issue, it indicates that your model or data processing pipeline is exceeding the available memory.
  4. Gradually Isolate Components: Comment out sections of your code to identify which parts are contributing to the leak. Start by commenting out large blocks and then narrow down the search to specific functions or loops. This divide-and-conquer approach can help you quickly isolate the problematic code.
  5. Checkpointing and Resuming: Implement checkpointing in your training loop. This involves saving the model's state and optimizer state periodically. If a memory error occurs, you can resume training from the last checkpoint instead of starting from scratch. Checkpointing not only helps with debugging but also prevents data loss during long training runs.

Specific Configuration Recommendations

Given your configuration, here are a few specific recommendations to try:

  • FSDP Tuning: Experiment with different FSDP configurations. Try setting reshard_after_forward to False. Also, consider using NO_SHARD strategy if appropriate for your model and data size, as it might reduce communication overhead and memory fragmentation. Monitor memory usage with each configuration change to see which settings work best.
  • Gradient Accumulation: Ensure you're calling optimizer.zero_grad() at the correct places. Double-check your training loop to make sure gradients aren't accumulating unintentionally.
  • Memory Offloading: If you're using param_offload or optimizer_offload, try disabling them temporarily to see if it resolves the issue. Offloading can sometimes introduce overhead that outweighs its benefits, especially if data transfer between CPU and GPU becomes a bottleneck.
  • Data Loading Optimization: Review your data loading pipeline. Ensure that you're not loading more data than necessary and that data is being released after it's processed. Use tools like torch.utils.data.DataLoader efficiently, and consider using techniques like prefetching to speed up data loading without excessive memory usage.

Step-by-Step Troubleshooting Guide

To help you tackle this systematically, here’s a step-by-step guide:

  1. Enable Memory Profiling: Start by enabling PyTorch's memory profiling tools. Use torch.cuda.memory_summary() at different points in your code to get a snapshot of memory usage.
  2. Check Gradient Accumulation: Verify that optimizer.zero_grad() is being called correctly before each batch.
  3. Simplify FSDP Configuration: Try disabling reshard_after_forward or experimenting with different FSDP strategies.
  4. Reduce Batch Size: Reduce your batch size to see if the memory usage decreases proportionally.
  5. Implement Checkpointing: Set up checkpointing in your training loop to save your progress periodically.
  6. Isolate Code Sections: Comment out blocks of code to isolate the source of the memory leak.
  7. Clear CUDA Cache: Periodically call torch.cuda.empty_cache() to free up cached memory.
  8. Review Third-Party Libraries: If the issue persists, investigate any third-party libraries you're using for potential memory leaks.

Final Thoughts

Dealing with CUDA out-of-memory errors can be frustrating, but with a methodical approach, you can definitely get to the bottom of it. By understanding your configuration, analyzing memory allocation patterns, and using the right debugging strategies, you'll be back to training in no time. Remember, memory management is a critical aspect of deep learning, especially when working with large models and distributed training setups. Keep experimenting, keep profiling, and you'll conquer those memory leaks!

If you've tried these steps and are still facing issues, don't hesitate to share more details about your specific scenario. Providing code snippets or specific error messages can help in diagnosing the problem more accurately. Good luck, and happy debugging!