Fix SoftmaxOps (#16049)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16049
We might see the pattern
```
if (scale_.numel() != N) {
scale_->Resize(N);
// set initial value for scale_
}
// In class:
Tensor scale_{CPU};
```
before in the code, where `scale_` is a member variable of Type `caffe2::Tensor`
This pattern actually serves two purposes, if `scale_` is partially initialized with device type but not size, this call will
initialize Tensor with the correct size, or if `scale_` is already initialized with size, it will check whether the size
matches a runtime value `N` and if not it will Resize. To rewrite this we'll do the following:
```
if (!scale_.defined() || scale_.numel() != N) {
ReinitializeTensor(&scale_, {N}, at::dtype<float>().device(CPU));
// set initial value for scale_
}
```
There are some variants, if `scale_` is resized to a constant size, we can call `ReinitializeTensor` instead
```
if (scale_.numel() != 1) {
scale_->Resize(1);
}
```
-->
```
ReinitializeTensor(&scale_, {1}, at::dtype<float>().device(CPU));
```
Normal Resize will be refactored directly into ReinitializeTensor:
```
scale_->Resize(N);
```
-->
```
ReinitializeTensor(&scale_, {N}, at::dtype<float>().device(CPU));
```
Reviewed By: dzhulgakov
Differential Revision: D13667883
fbshipit-source-id: 2c7cb61544b72765b594011b99150eb5a1b50836
