Troubleshooting Temp Model Saving Issues During LoRA Finetuning

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Introduction

This article addresses a common issue encountered during LoRA (Low-Rank Adaptation) finetuning, specifically the failure to save temporary models during training, which subsequently leads to errors when accessing them. This problem often manifests as a FileNotFoundError in the error logs, indicating that a checkpoint file expected by the training process is missing. We will delve into the potential causes of this issue, primarily focusing on whether an excessively high learning rate (lr) parameter could be causing numerical instability, and provide guidance on how to diagnose and resolve this problem. This comprehensive guide is designed to help researchers and practitioners in the field of genomics and computational biology, particularly those working with single-cell data and models like scPrinter, to effectively troubleshoot and optimize their model training pipelines.

Understanding the Problem: NaN Loss and Checkpoint Loading

The core problem lies in the scenario where the training process encounters a NaN (Not a Number) loss. This typically signals a numerical instability issue, often triggered by a learning rate that is too high for the model and dataset at hand. When a NaN loss occurs, the training process might attempt to revert to a previously saved checkpoint. However, if the model hasn't been saved correctly or the checkpoint file is inaccessible, a FileNotFoundError arises. This error halts the training process and prevents the model from converging properly. Identifying and addressing the root cause of this issue is crucial for successful LoRA finetuning and achieving optimal model performance. In the following sections, we will explore the error logs, configuration files, and potential solutions to ensure a smooth and effective training process for your scPrinter models.

Analyzing the Error and Configuration

To effectively address the issue, let's dissect the error logs and configuration files provided. The error logs clearly indicate a FileNotFoundError, specifically stating that the file /projects/dscott_prj/amfong/multiome_dz/data/scprinter/temp/resilient-mountain-42 cannot be found. This suggests that the training process is trying to load a checkpoint named resilient-mountain-42 from the temporary directory, but it doesn't exist. The traceback points to the load_train_state_dict function in scprinter/seq/Models.py, which is responsible for loading the model's training state, including the optimizer, scaler, and Exponential Moving Average (EMA) if enabled. The error occurs during the torch.load(savename) call, indicating that the specified checkpoint file is missing.

Examining the Slurm Script and Singularity Execution

The Slurm script provides valuable insights into the execution environment. The script allocates resources, including CPUs, GPUs, and memory, and defines the execution environment using Singularity. The key components of the script are:

  • Resource Allocation: The script requests 1 CPU per task, utilizes the gpu3090 or gpuA6000 partition, allocates 900GB of memory, and requests 1 GPU.
  • Output and Error Logging: Standard output and standard error are redirected to log files in the /projects/dscott_scratch/amfong/slurm/logs/ directory.
  • Singularity Execution: The script uses Singularity to create a consistent and reproducible environment. It binds several directories, including /home/amfong, /projects/dscott_prj/, /projects/dscott_scratch/, /projects/rmorin, /projects/rmorin_scratch, /projects/clc, and /tmp, into the container.
  • Command Execution: Inside the Singularity container, the script executes the seq2print_train command with specific arguments:
    • --config: Specifies the path to the configuration file (e.g., /projects/dscott_prj/amfong/multiome_dz/data/scprinter/configs/all_lora_fold${i}.JSON).
    • --temp_dir: Sets the temporary directory for saving checkpoints (/projects/dscott_prj/amfong/multiome_dz/data/scprinter/temp).
    • --model_dir: Sets the model directory (/projects/dscott_prj/amfong/multiome_dz/data/scprinter/model).
    • --data_dir: Sets the data directory (/projects/dscott_prj/amfong/multiome_dz/data/scprinter).
    • --project: Sets the project name (all_lora).
    • --enable_wandb: Enables Weights & Biases (WandB) for experiment tracking.

Deep Dive into the Configuration File

The configuration file provides crucial details about the model architecture, training parameters, and data paths. Let's highlight the key parameters:

  • Data Paths: The configuration specifies paths for peaks (seq2print_all_cleaned.bed), signals (scprinter_supp/all.bw), and various supporting files like insertions, group-to-barcodes mapping, group-to-embeddings mapping, and group-to-covariates mapping.
  • Training Split: The data is split into training, validation, and test sets based on chromosome regions.
  • Model Architecture: The model architecture parameters include the number of filters (n_filters), bottleneck factor (bottleneck_factor), number of layers (n_layers), and various settings related to inception layers, activation functions, and batch normalization.
  • LoRA Configuration: The configuration enables LoRA with a specified rank (lora_rank), hidden dimension (lora_hidden_dim), and application to different CNN modules (DNA, dilated, PFF, profile, and count).
  • Training Parameters: The training parameters include batch size (batch_size), weight decay (weight_decay), learning rate (lr), scheduler settings, and the use of AMP (Automatic Mixed Precision) and EMA (Exponential Moving Average).
  • Checkpointing: The savename parameter defines the base name for saved checkpoints.
  • Pretraining: The pretrain_model parameter specifies the path to a pretrained model (model/all_fold0-playful-valley-9.pt).

Identifying the Root Cause: A High Learning Rate?

Based on the error logs and the configuration, a primary suspect for the FileNotFoundError is indeed a high learning rate (lr) causing numerical instability. The lr is set to 3e-06 in the configuration file. While this value might seem small, it's essential to consider the specific model architecture, dataset, and optimization process. A learning rate that is too high can lead to overshooting the optimal parameter values during training, resulting in large loss values and even NaN losses. When a NaN loss occurs, the training process might attempt to revert to the last saved checkpoint, but if the model saving process is interrupted due to the instability, the checkpoint file might not be available, leading to the FileNotFoundError.

Troubleshooting Steps and Solutions

To resolve the issue of the missing temporary model and FileNotFoundError during LoRA fintuning, a systematic approach is crucial. Here are several troubleshooting steps and potential solutions:

1. Lower the Learning Rate (lr)

The first and most straightforward approach is to reduce the learning rate. As suspected, a high learning rate can lead to numerical instability, causing NaN losses and interrupting the model saving process. Start by reducing the learning rate by a factor of 10. For instance, if your current learning rate is 3e-06, try setting it to 3e-07. Monitor the training process closely to see if the NaN losses are reduced and if the model starts saving checkpoints correctly. If reducing the learning rate once doesn't solve the problem, you might need to reduce it further, but do so incrementally to avoid excessively slow convergence.

2. Implement Gradient Clipping

Gradient clipping is a technique used to prevent gradients from becoming too large during training. Large gradients can cause instability and lead to the same NaN loss issue. By clipping the gradients, you ensure that they remain within a reasonable range, thus stabilizing the training process. Most deep learning frameworks, such as PyTorch, provide built-in functionalities for gradient clipping. Implement gradient clipping by setting a threshold value (e.g., 1.0) and clipping the gradients if their norm exceeds this threshold. This can be added directly into the training loop within the seq2print_lora_train.py script.

3. Check for Data Issues

Sometimes, issues in the data itself can lead to NaN losses. Look for any anomalies in the input data, such as extreme values or missing data points. Ensure that the input data is properly normalized and preprocessed. For genomic data, this might involve checking for incorrect read alignments or abnormal coverage regions. Review the data loading and preprocessing steps in your code to identify and rectify any potential issues. Data quality is paramount, and addressing any data-related problems can significantly improve the stability of your training.

4. Verify the Model Saving Mechanism

Ensure that the model saving mechanism is functioning correctly. Check the code responsible for saving the model checkpoints to ensure that it is being executed at the expected intervals and that the files are being written to the correct directory. Verify that the temporary directory specified by the --temp_dir argument has sufficient space and that the process has the necessary permissions to write to it. It's also a good practice to add logging statements around the model saving calls to confirm that they are being executed and to track any potential errors during the saving process.

5. Monitor Training with Weights & Biases (WandB)

The script is configured to use Weights & Biases (WandB), which is an excellent tool for monitoring and tracking machine learning experiments. Use WandB to monitor the loss curves, learning rate, and other training metrics in real-time. WandB can help you identify patterns or anomalies that might indicate instability or convergence issues. Pay close attention to the loss curves; if you see sharp spikes or the loss suddenly turning to NaN, it's a clear sign of numerical instability. WandB also allows you to compare different runs with different configurations, making it easier to identify the optimal training parameters.

6. Implement a Learning Rate Scheduler

Consider using a learning rate scheduler to dynamically adjust the learning rate during training. A learning rate scheduler starts with a higher learning rate and gradually reduces it as training progresses. This can help the model converge more effectively and avoid overshooting the optimal parameter values. Popular learning rate schedulers include StepLR, CosineAnnealingLR, and ReduceLROnPlateau. Experiment with different schedulers to find the one that works best for your model and dataset.

7. Check Hardware and Software Compatibility

Ensure that your hardware and software environment is correctly configured and compatible. Verify that the correct versions of PyTorch, CUDA, and other libraries are installed. GPU-related issues can sometimes lead to unexpected errors during training. If you are using a distributed training setup, make sure that the communication between the processes is working correctly. Check the NVIDIA drivers and ensure they are up to date and compatible with your CUDA version.

8. Review the LoRA Configuration

Double-check the LoRA configuration parameters, such as lora_rank and lora_hidden_dim. While LoRA is designed to reduce the number of trainable parameters and stabilize training, incorrect settings can still lead to issues. Experiment with different LoRA rank values; a higher rank might provide more flexibility but also increase the risk of overfitting, while a lower rank might limit the model's capacity. Ensure that the LoRA layers are correctly integrated into the model architecture.

9. Check the Pretrained Model

Since the configuration uses a pretrained model, ensure that the pretrained model itself is not corrupted or causing issues. Try training the model without the pretrained weights to see if the problem persists. If the issue disappears without the pretrained model, it might indicate that the pretrained weights are causing instability. In this case, you might need to retrain the pretrained model or use a different one.

10. Reduce Batch Size

If you are running out of memory or encountering CUDA-related errors, try reducing the batch size. A smaller batch size requires less memory and can sometimes stabilize training. While a smaller batch size might increase the training time, it can help you avoid out-of-memory errors and numerical instability. Experiment with different batch sizes to find the largest one that your hardware can handle without causing issues.

Revisiting the Configuration File

Based on the troubleshooting steps, let's revisit the configuration file and consider the changes that might help resolve the issue. Here's a summary of the key parameters to adjust:

  • lr (Learning Rate): Reduce the learning rate from 3e-06 to a lower value, such as 3e-07 or even 1e-07. Monitor the training loss closely to determine the optimal learning rate.
  • weight_decay: While a weight decay of 0.001 is generally a good starting point, you might want to experiment with slightly lower values (e.g., 0.0001) to see if it helps stabilize training.
  • batch_size: If you encounter memory issues, reduce the batch size. A batch size of 4 might be small enough to avoid memory problems, but experiment with larger values if your hardware allows.
  • accumulate_grad_batches: This parameter allows you to effectively increase the batch size without increasing memory consumption. A value of 8 means that gradients are accumulated over 8 batches before updating the model weights.
  • scheduler: Consider enabling a learning rate scheduler. You can implement a scheduler directly in your training script or use a built-in PyTorch scheduler. Popular choices include StepLR, CosineAnnealingLR, and ReduceLROnPlateau.

Conclusion

Troubleshooting model training issues, such as the FileNotFoundError during LoRA fintuning, requires a systematic approach. By carefully analyzing the error logs, configuration files, and training behavior, you can identify the root cause of the problem and implement effective solutions. In this article, we have explored several potential causes, with a primary focus on a high learning rate leading to numerical instability. We have also discussed various troubleshooting steps, including lowering the learning rate, implementing gradient clipping, checking for data issues, verifying the model saving mechanism, monitoring training with WandB, and implementing a learning rate scheduler.

By following these guidelines and iteratively adjusting your training parameters, you can overcome the FileNotFoundError and successfully finetune your scPrinter models for optimal performance. Remember that machine learning model training is often an iterative process, and careful experimentation and monitoring are key to achieving the desired results. The ability to diagnose and resolve issues like this is crucial for advancing research in genomics, computational biology, and other fields that rely on sophisticated machine learning models. By addressing these challenges effectively, you can unlock the full potential of your models and make significant contributions to your field. This comprehensive guide aims to equip you with the knowledge and tools necessary to tackle these challenges and ensure the success of your model training endeavors.