Flax debugging: making a hash of things (www.gilesthomas.com)

In a recent exploration of debugging techniques for a JAX/Flax training loop, a developer encountered persistent issues with loss values that indicated a fault in the model's parameter updates. Upon implementing a basic language model (LLM) structure, they found that the loss remained stagnantly high, resembling predictions from a random model. The challenge was to determine if gradients were properly propagating and if the parameters were effectively updating during training. To tackle this, the developer devised an innovative solution using parameter hashing to monitor changes. By hashing the parameters' values, they could quickly ascertain if updates were occurring, as even minuscule adjustments would result in drastically different hashes. Ultimately, the issue was traced back to an incorrect application of the JIT decorator, highlighting the importance of automatic state propagation in Flax’s NNX API. This debugging method not only resolved the problem, allowing the loss to decrease during training, but also introduced a valuable technique for future troubleshooting in machine learning projects—a reminder of the intricate relationships between model architecture, training algorithms, and framework specifics in the AI/ML community.