Transformers Bug: Double Loss Rescaling With Accumulated Gradients

by Viktoria Ivanova 67 views

Hey everyone,

We've got a critical issue to discuss regarding a potential bug introduced in the Transformers library, specifically affecting version 4.54.1. This bug seems to stem from a recent pull request and impacts how accumulated gradients are handled, leading to a double rescaling of the loss. This can significantly skew your training results, so let's dive into the details and figure out how to address it.

The Issue: Double Loss Rescaling

At the heart of the matter is a double rescaling of the loss when using gradient accumulation. Gradient accumulation is a fantastic technique, guys, especially when dealing with large models or limited GPU memory. It allows you to effectively increase your batch size by accumulating gradients over multiple smaller batches before performing a backward pass and updating the model's weights. This can lead to more stable training and better utilization of your hardware.

Here's the scenario where this bug manifests:

  • You're using gradient_accumulation_steps > 1 (which is the whole point of gradient accumulation!).
  • You're not using DeepSpeed (a popular library for large-scale training).
  • num_items_in_batch is None and self.compute_loss_func is None (this usually happens when the user chooses to ignore the gradient accumulation loss bug).

In this specific situation, the final loss gets rescaled twice, which is definitely not what we want.

The problematic code stems from this previous PR from @qgallouedec: https://github.com/huggingface/transformers/pull/35207

This PR makes the backward() call after rescaling, which inadvertently creates a double rescaling issue. The first rescaling happens within the Transformers library itself:

loss = loss / gradient_accumulation_steps

And the second rescaling occurs within the Accelerate library, a popular tool for distributed training, in this specific line of code:

https://github.com/huggingface/accelerate/blob/23cf4ef8a3b58f016f63eeb158b4aa2c3e79fe6f/src/accelerate/accelerator.py#L2724

This double rescaling can lead to underestimation of the true loss and potentially impact the convergence and performance of your models. It’s like adding salt to your food twice – the flavor just isn't right!

Why is Double Loss Rescaling a Problem?

Think of it this way: when you calculate the loss, you're essentially measuring how far off your model's predictions are from the actual targets. This loss value is then used to adjust the model's parameters during the optimization process. When you rescale the loss, you're essentially scaling the gradients that are used to update the model. If you rescale the loss twice, you're effectively reducing the magnitude of the gradients, which can lead to several issues:

  1. Slower Convergence: The model might take longer to converge to an optimal solution because the updates are smaller than they should be. It's like trying to fill a bucket with a teaspoon instead of a ladle – it'll take much longer.
  2. Suboptimal Performance: The model might get stuck in a suboptimal solution because the gradients are too small to escape local minima. Imagine trying to climb a hill, but your steps are so tiny that you keep sliding back down.
  3. Inconsistent Results: The training process might become more sensitive to the learning rate and other hyperparameters, making it harder to reproduce results. It's like trying to bake a cake without precise measurements – you might end up with a different result each time.

Key Takeaway: Double loss rescaling can significantly impact the accuracy and reliability of your training process. It’s crucial to be aware of this issue and take steps to prevent it.

Identifying the Bug: System Information and Reproduction Steps

To confirm if you're affected by this bug, here’s what you need to know. Firstly, this issue has been reported in transformers version 4.54.1. Secondly, the bug occurs under specific conditions related to gradient accumulation and the use of the Accelerate library. Finally, to diagnose and potentially fix the problem, providing detailed system information and clear reproduction steps is crucial.

System Information

To help pinpoint the root cause and ensure a proper fix, it's essential to gather detailed system information. This includes:

  • transformers version: 4.54.1 (the affected version)
  • Platform: Linux-5.15.0-131-generic-x86_64-with-glibc2.39 (This indicates a Linux system)
  • Python version: 3.12.3 (Python version in use)
  • Huggingface_hub version: 0.34.3 (Version of the Hugging Face Hub library)
  • Safetensors version: 0.5.3 (Version of the Safetensors library, used for safe tensor storage)
  • Accelerate version: 1.10.0 (Version of the Accelerate library, crucial for distributed training)
  • Accelerate config: Not found (This might indicate a default configuration is being used)
  • DeepSpeed version: 0.17.4 (Version of DeepSpeed, a deep learning optimization library)
  • PyTorch version: 2.8.0a0+5228986c39.nv25.06 (CUDA) (Specific PyTorch version with CUDA support)
  • Tensorflow version: Not installed (TensorFlow is not being used in this environment)
  • Flax version: Not installed (Flax, another deep learning framework, is not installed)
  • Jax version: Not installed (Jax, often used with Flax, is not installed)
  • JaxLib version: Not installed (JaxLib, a core component of Jax, is not installed)
  • Distributed or parallel setup: Information missing (Details about distributed training setup are needed)
  • GPU usage: Information missing (Whether a GPU is being used needs to be specified)
  • GPU type: NVIDIA H100 80GB HBM3 (A high-performance NVIDIA GPU)

This comprehensive system overview helps developers understand the environment where the bug occurs, making it easier to reproduce and fix. When reporting a bug, providing this information upfront can significantly speed up the resolution process. It's like giving a doctor a complete medical history – the more information they have, the better they can diagnose and treat the problem.

Steps to Reproduce the Bug

To reliably identify and fix the bug, providing clear and concise reproduction steps is essential. The following conditions must be met for the double loss rescaling to occur:

  1. Gradient Accumulation: gradient_accumulation_steps > 1 (Gradient accumulation is enabled)
  2. No DeepSpeed: DeepSpeed is not being used (This isolates the issue to the standard training loop)
  3. Loss Function Configuration: num_items_in_batch is None and self.compute_loss_func is None (This specific configuration triggers the bug)

When these conditions are met, the final loss is rescaled twice due to the interaction between the Transformers library and the Accelerate library.

The code snippet below illustrates the issue:

loss = loss / gradient_accumulation_steps

This line in the Transformers library rescales the loss. However, the Accelerate library also rescales the loss, leading to a double rescaling. The expected behavior is that the loss should only be rescaled once to ensure accurate gradient updates.

Key Takeaway: Providing detailed reproduction steps, like the ones above, is crucial for bug reporting. It allows developers to quickly recreate the issue, understand the context, and implement a fix. Think of it as providing a recipe for disaster – but in this case, the disaster is a bug, and the recipe helps to fix it.

The Root Cause: Interplay between Transformers and Accelerate

To truly understand this bug, we need to delve into the interaction between the Transformers library and the Accelerate library. Both libraries play crucial roles in modern deep learning workflows, especially when dealing with large models and distributed training. However, their interaction in this specific scenario leads to the unintended double loss rescaling.

Transformers: The Model Powerhouse

The Transformers library, maintained by Hugging Face, has become the go-to resource for pre-trained language models and related tools. It provides a vast collection of models, from BERT to GPT-3, along with utilities for training, fine-tuning, and deploying these models. The library's flexibility and ease of use have made it incredibly popular in the NLP community and beyond.

In the context of this bug, the Transformers library is responsible for calculating the loss and applying gradient accumulation. When gradient_accumulation_steps is greater than 1, the library accumulates gradients over multiple batches before performing a backward pass. This is a crucial technique for training large models that don't fit into GPU memory.

Accelerate: The Distributed Training Maestro

The Accelerate library is designed to simplify distributed training in PyTorch. It provides a high-level API that allows you to easily distribute your training workload across multiple GPUs or machines. Accelerate handles the complexities of data parallelism, gradient synchronization, and other distributed training tasks, freeing you to focus on your model and data.

In this case, Accelerate is responsible for managing the training loop and handling the backward pass. It also includes its own loss scaling mechanism, which is where the problem arises.

The Conflict: Double Rescaling

The issue stems from the fact that both Transformers and Accelerate have their own loss scaling mechanisms. When gradient accumulation is enabled in Transformers, the library rescales the loss by dividing it by gradient_accumulation_steps. This is done to ensure that the gradients are properly scaled when they are accumulated over multiple batches.

However, Accelerate also performs loss scaling as part of its distributed training logic. This leads to the loss being rescaled twice, which, as we discussed earlier, can have detrimental effects on training.

Key Takeaway: The double loss rescaling bug highlights the importance of understanding how different libraries interact with each other. While both Transformers and Accelerate are powerful tools, their combined use in this specific scenario requires careful attention to avoid unintended consequences. It’s like two chefs adding salt to the same dish – unless they coordinate, the dish might end up too salty!

Expected Behavior: Rescale Only Once

To resolve this bug, it's crucial to understand the expected behavior. The loss should be rescaled only once during the training process. This ensures that the gradients are properly scaled and that the model learns effectively.

The current implementation, as we've seen, leads to a double rescaling, which skews the gradients and can hinder the training process. The goal is to modify the code so that the loss is rescaled either in the Transformers library or in the Accelerate library, but not in both.

Potential Solutions

Several approaches can be taken to address this issue:

  1. Modify Transformers: The Transformers library could be modified to detect when Accelerate is being used and disable its own loss scaling mechanism in that case. This would ensure that only Accelerate performs the rescaling.
  2. Modify Accelerate: The Accelerate library could be modified to detect when Transformers is performing gradient accumulation and adjust its loss scaling accordingly. This would require careful coordination between the two libraries.
  3. User-Level Control: A more flexible approach would be to provide users with a way to control the loss scaling behavior. This could involve adding a flag or configuration option that allows users to specify whether they want Transformers or Accelerate to handle loss scaling.

Best Practices for Loss Scaling

Regardless of the specific solution, it's essential to follow best practices for loss scaling. This includes:

  • Understanding the Impact: Always be aware of the impact of loss scaling on your training process. Double rescaling, as we've seen, can lead to significant issues.
  • Choosing the Right Approach: Select the appropriate loss scaling method for your setup. If you're using Accelerate, it's generally best to let Accelerate handle the rescaling.
  • Monitoring Training: Keep a close eye on your training process to ensure that the loss is behaving as expected. If you notice any unusual behavior, investigate the loss scaling configuration.

Key Takeaway: The expected behavior is for the loss to be rescaled only once. By understanding the root cause of the double rescaling bug and following best practices for loss scaling, we can ensure more accurate and reliable training results. It’s like calibrating your instruments before a concert – you want to make sure everything is in tune!

I hope this detailed explanation helps you understand the double loss rescaling bug and its implications. Let's work together to ensure the Transformers library remains a reliable and powerful tool for the community.