Comments (2)
Chillee
commented on June 20, 2024
hmmmm..... does testing grad + our current opinfo vmap tests suffice?
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zou3519
commented on June 20, 2024
I'm working on this. I'll probably add testing for all pairs of { vmap, vjp, None} x { vmap, vjp } (grad is really just a special case of vjp, but we didn't implement it like that, so maybe grad needs to go in there too).
This design is not the greatest because it implies that if we have N transforms then each OpInfo test will generate N ** 2 tests which seem bad for runtime. Maybe in the future we'll decide to test fewer pairs when we have more confidence in the dynamic layering mechanism or add some "structure" to our transforms to make it so that it's impossible for them to not compose.
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Related Issues (20)
- Add pytorch 1.13.1 compatibility HOT 3
- Unit Test Error When Testing vmap With Missing Module "autograd_function_db" HOT 7
- Will pmap be supported in functorh？ HOT 2
- How to get only the last few layers' gradident? HOT 2
- [Question] Packaging policy for `functorch` and `torch.func` HOT 5
- INTERNAL_ASSERT failed HOT 4
- RuntimeError: Batching rule not implemented for aten::is_same_size. We could not generate a fallback.
- Vmap and backward hook problem HOT 1
- item() support for vmap HOT 2
- Performance drop because of not yet implemented batching rule for bincount
- Use functional models inside usual nn.Module HOT 1
- Error about using a grad transform with in-place operation is inconsistent with and without DDP HOT 1
- How to get the jacobian matrix in GCNs?
- Per-sample-gradient: Get gradient 0 when using grad(params_tograd, params) with respect to part of model's parameters HOT 1
- Can I call torch.utils.data.WeightedRandomSampler inside vmap? HOT 1
- vmap fails if your model includes full_backward_hook in pytorch2.0 HOT 1
- wrapper->level().value() <= current_level INTERNAL ASSERT FAILED at "../aten/src/ATen/functorch/ADInterpreters.cpp":39 HOT 1
- Swapping 2 columns in a 2d tensor
- vmap does not support Tensor.clone()
- Small difference between functorch grads and torch.autograd.grad
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