Per Channel in brack-propagation function (#97475)
Summary:
Supporting Per Channel quantization in the gradient computation function.
One workaround that I have added here is
Current QNNPACK is not designed to process [transposed weight](https://fb.workplace.com/groups/pytorch.edge.users/permalink/1283737025829921/)
Here we are simply replacing Per Channel to Per Tensor to compute a gradient (Some slow learning curve or WER degradation might be expected - We don't know, nothing is guaranteed)
Test Plan:
You can create your own synthetic model,
FP32 layer -> INT8 layer with Per Channel and see if loss is decreasing
Differential Revision: D43898794
Pull Request resolved: https://github.com/pytorch/pytorch/pull/97475
Approved by: https://github.com/weiwangmeta
