Summary:
Commits:
1. In extension doc, get rid of all references of `Variable` s (Closes#6947 )
+ also add minor improvements
+ also added a section with links to cpp extension :) goldsborough
+ removed mentions of `autograd.Function.requires_grad` as it's not used anywhere and hardcoded to `return_Py_True`.
2. Fix several sphinx warnings
3. Change `*` in equations in `module/conv.py` to `\times`
4. Fix docs for `Fold` and `Unfold`.
+ Added better shape check for `Fold` (it previously may give bogus result when there are not enough blocks). Added test for the checks.
5. Fix doc saying `trtrs` not available for CUDA (#9247 )
Pull Request resolved: https://github.com/pytorch/pytorch/pull/9239
Reviewed By: soumith
Differential Revision: D8762492
Pulled By: SsnL
fbshipit-source-id: 13cd91128981a94493d5efdf250c40465f84346a
This PR enables users to print extra information of their subclassed nn.Module.
Now I simply insert the user-defined string at the ending of module name, which should be discussed in this PR.
Before this PR, users should redefine the __repr__ and copy&paste the source code from Module.
* Add support for extra information on Module
* Rewrite the repr method of Module
* Fix flake8
* Change the __repr__ to get_extra_repr in Linear
* Fix extra new-line for empty line
* Add test for __repr__ method
* Fix bug of block string indent
* Add indent for multi-line repr test.
* Address review comments
* Update tutorial for creating nn.Module
* Fix flake8, add extra_repr of bilinear
* Refactor DropoutNd
* Change to extra_repr in some Modules
* Fix flake8
* Refactor padding modules
* Refactor pooling module
* Fix typo
* Change to extra_repr
* Fix bug for GroupNorm
* Fix bug for LayerNorm
* Deprecate ctx.saved_variables via python warning.
Advises replacing saved_variables with saved_tensors.
Also replaces all instances of ctx.saved_variables with ctx.saved_tensors in the
codebase.
Test by running:
```
import torch
from torch.autograd import Function
class MyFunction(Function):
@staticmethod
def forward(ctx, tensor1, tensor2):
ctx.save_for_backward(tensor1, tensor2)
return tensor1 + tensor2
@staticmethod
def backward(ctx, grad_output):
var1, var2 = ctx.saved_variables
return (grad_output, grad_output)
x = torch.randn((3, 3), requires_grad=True)
y = torch.randn((3, 3), requires_grad=True)
model = MyFunction()
model.apply(x, y).sum().backward()
```
and assert the warning shows up.
* Address comments
* Add deprecation test for saved_variables
