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Charlesg
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Begginer
Asked: 2025-05-29T22:44:42+00:00 In:

Anybody knows good methods to debug autograd issues in dynamic graphs, especially with JAX or PyTorch?

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I’ve been running into unexpected gradients or zero gradients in some layers, and it’s been tricky to trace where things go wrong. Any tips, tools, or workflows that help identify and fix these problems would be really appreciated!

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2 Answers

  1. Hassaan Arif
    Enlightened

    If you’re hitting autograd issues in JAX or PyTorch, here’s what works for me:

    First, check gradients are even enabled – in PyTorch, make sure requires_grad=True. In JAX, use jax.grad only on functions with real float outputs.

    Use gradient checkers – PyTorch’s gradcheck or JAX’s check_grads help spot silent failures.

    Debug with hooks or prints – PyTorch has register_hook() on tensors to inspect gradients. In JAX, jax.debug.print() is a lifesaver inside jit.

    Simplify the code – isolate the function, drop the model size, and test with dummy data. Most bugs pop up when the setup is too complex.

    In short: test small, print often, and trust the math to guide you.

  2. kalim
    Begginer

    Yeah, debugging autograd issues in dynamic graphs especially with libraries like JAX or PyTorch can get pretty tricky. One thing that’s helped me a lot is starting simple.
    I try to isolate the function that’s failing and run it with the smallest possible input. That usually makes it easier to catch shape mismatches or type errors that are silently breaking the graph construction.
    In PyTorch, one super useful trick is to use torch.autograd.set_detect_anomaly(True). This throws more informative stack traces when something breaks during backpropagation, which honestly saves a lot of time.
    Also, checking .grad values after the backward pass helps if something’s returning None, it could mean part of your graph was detached unintentionally. That’s a red flag I always look for.
    With JAX, the approach is a bit different because of how function transformations like jit, grad, and vmap work. I usually avoid jumping straight into jit when debugging.
    Running without it helps catch shape or control flow issues early. Also, if gradients come back as nan or inf, I check for division by zero or unstable operations like log on negative numbers. Tools like jax.debug.print() have become more reliable recently, and I use those to inspect intermediate values inside grad-wrapped functions.
    Lastly, I’ve found that unit testing parts of the computation graph can prevent these issues from piling up.
    Even simple tests that just check the output shape and dtype after a forward and backward pass can catch a lot. The key is: don’t assume the graph is behaving  verify it.