Enable functools/jacrev#20
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Pull request overview
This PR updates the adjoint autograd wrapper to be compatible with torch.func transforms (notably jacrev) and adds a regression test demonstrating how to wrap the solver/module so functional_call + jacrev work correctly.
Changes:
- Refactor
AdjointWrapperto use the newersetup_contextAPI soforwardno longer takesctx. - Add a
jacrev-based test that comparesjacrev(functional_call(solver))vsjacrevthroughsolve_adjointvia a wrapper module.
Reviewed changes
Copilot reviewed 2 out of 2 changed files in this pull request and generated 2 comments.
| File | Description |
|---|---|
pyzag/nonlinear.py |
Refactors the custom torch.autograd.Function to be torch.func-compatible via setup_context. |
test/test_adjoint.py |
Adds a jacrev/functional_call regression test and a wrapper nn.Module to exercise the adjoint path under torch.func. |
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Slight changes needed to get jacrev to work. Includes a rather annoying example of how you need to wrap the model to get the model to "functionalize" correctly.