Summary:
Obviously, the grads of conv weight and conv input are not relevant to the bias, but the original `convXd_input` and `convXd_weight` methods receive a `bias` parameter. What's more, while the doc says `bias` should have the shape `(out_channels,)`, one will get a `RuntimeError` if the bias != None and in_channels != out_channels, for the weight of transposed conv has the shape `(in_channels, out_channels, kH, kW)` while the weight of vanilla conv has the shape `(out_channels, in_channels, kH, kW)`
```
RuntimeError: Given transposed=1, weight of size [channel1, channel2, kH, kW], expected bias to be 1-dimensional with channel2 elements, but got bias of size [channel1] instead
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/12281
Differential Revision: D10217370
Pulled By: ezyang
fbshipit-source-id: bc00b439e5ae539276a5e678bdb92af700197bb2
* Codemod to update our codebase to 0.4 standard
* Update some of the test scri[ts
* remove Variable in test_clip_grad_value
* fix _symbolic_override_wrapper_maker
This PR addresses issue #5024
* Expose Conv2dBackward in python
* Separate interface for exposing gardients of operators
* Revert old changes
* Add tests
* Add conv1d gradients. Refactor tests for grad convolutions
* Refactor names and change examples
* Remove Varibale from tests for conv backward
