Silent Numerical instabilities in training

[qsimeon]

The torch.autograd.set_detect_anomaly(True) function in PyTorch is a diagnostic tool designed to help identify operations that produce NaNs or infinities in gradients, which are often symptoms of instability in your computational graph. It works by performing extra checks during the forward and backward passes to track down where these ill-defined values originate.

Why Anomaly Detection Might Surface Hidden Issues

When torch.autograd.set_detect_anomaly(True) is enabled and you encounter an error related to NaNs or infinities that you don't see when it's disabled, several factors could be at play:

1. Silent Failures: Without anomaly detection, small numerical instabilities that result in NaNs or infinities might be occurring silently in your gradients. These errors can propagate through your network, potentially leading to degraded performance, but they may not cause immediate or obvious failures. Over time, they can affect convergence and model accuracy.

2. Early Detection: With anomaly detection on, PyTorch halts computation at the very first sign of trouble, raising an exception as soon as a NaN or infinity is detected in any gradient. This immediate feedback is designed to help you catch and fix the root cause of the instability quickly, even though it might seem restrictive by stopping training at the first anomaly.

3. No Actual NaNs in Final Gradients: It's possible that intermediate computations produce NaNs which are somehow "resolved" by subsequent calculations (e.g., NaNs multiplied by zero may result in zero in some contexts). In such cases, final gradients might not contain NaNs, and thus training proceeds without error when anomaly detection is off. This doesn't mean the underlying issue is resolved; rather, it’s just obscured.

How to Handle This Situation

If you find that disabling anomaly detection prevents the runtime error, but you're concerned about underlying issues, consider the following steps:

• Gradual Debugging: Temporarily turn on anomaly detection to isolate which part of your model or which operation introduces the instability. Once identified, you can focus on adjusting that part of your model or computation.

• Check Model Components: Review parts of your model that are more prone to numerical issues, such as divisions, exponentiations, logarithms, and large sums. Make sure operations are safe-guarded against division by zero, taking logs of non-positive numbers, etc.

• Improve Numerical Stability: Implement practices such as adding small constants to denominators in divisions, using stabilized forms of operations (e.g., using torch.nn.functional.softmax instead of manual exponentiations and divisions), and clipping gradients to prevent exploding values.

• Floating Point Precision: Consider using higher precision for your tensors (float32 or float64 instead of float16), especially if your model is deep or involves precise calculations.

• Comprehensive Testing: Even with anomaly detection turned off, regularly check your model's outputs and loss values for unexpected behavior or values. NaNs or infinities in model outputs or loss calculations are red flags that require attention.

By integrating these practices, you can enhance the robustness of your model against numerical instabilities and ensure that silent failures are caught and addressed effectively, even if you decide to keep anomaly detection turned off for performance reasons.

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Links

• Debugging feature for "modified by an inplace operation" errors pytorch/pytorch#15803
• https://pytorch.org/docs/stable/autograd.html