The “torch.nn.grad” package (https://github.com/pytorch/pytorch/blob/master/torch/nn/grad.py) contains functions to manually compute backwards through a convolution operation. It is unclear why it has this name but only contains this for convolutions; I was not able to find documentation for this package or these functions. My question is, is there any similar Python interface for other operations, for instance F.max_pool2d? (It is quite simple to compute the gradient backward through max pooling. But I believe I remember finding this exact backward function somewhere in PyTorch just a few days ago, but I cannot for the life of me remember where, which is why I ask about this specifically.)

# Backward for operations in torch.nn.functional

Bump.

There is a `max_pool2d_with_indices_backward`

in L5188 of `native_functions.yaml`

https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/native/native_functions.yaml#L5188

and L1145 & L1282 of `derivatives.yaml`

https://github.com/pytorch/pytorch/blob/master/tools/autograd/derivatives.yaml#L1145-L1282

“Life of a Tensor” pytorch wiki seems to imply that all the functions listed in `native_functions.yaml`

could be used as python functions:

https://github.com/pytorch/pytorch/wiki/Life-of-a-Tensor

I have no idea how to directly call `max_pool2d_with_indices_backward`

via pytorch’s python frontend though.

How can I call `max_pool2d_with_indices_backward`

(and all the other `*_backward`

functions) via pytorch’s python frontend (without using `torch.Tensor.backward`

or `torch.autograd.backward`

)?