Comments (2)
Hmm, I guess this should return a Tensor full of zeros. There's nothing wrong with the dynamic layering mechanism, it's just that we need to pass allow_unused
to autograd and if it returns us "None" as the result (which it will) we need to turn that into a tensor full of zeros.
from functorch.
There are two things that are wrong:
- We need to pass allow_unused to autograd.grad
- If the outputs are not part of any computation graph, then autograd complains even if allow_unused is True. This is the same for the grad transform, even after #5 goes in:
import torch
from functorch import grad
x = torch.tensor(1.)
y = torch.tensor(2.)
def unrelated(x):
return y
# Error!
grad(unrelated)(x)
Will try to fix if I have time later
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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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from functorch.