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!
CharlesgBegginer
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, usejax.grad
only on functions with real float outputs.Use gradient checkers – PyTorch’s
gradcheck
or JAX’scheck_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 insidejit
.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.
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 returningNone
, 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
, andvmap
work. I usually avoid jumping straight intojit
when debugging.Running without it helps catch shape or control flow issues early. Also, if gradients come back as
nan
orinf
, I check for division by zero or unstable operations like log on negative numbers. Tools likejax.debug.print()
have become more reliable recently, and I use those to inspect intermediate values insidegrad
-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.