Add a wrapper to wrap all optimization for mobile.

[kimishpatel]

Stack from ghstack:

• Add a wrapper to wrap all optimization for mobile. #35227 Add a wrapper to wrap all optimization for mobile.
• Refactor code to move const prop to convolution 2d replacer. #35226 Refactor code to move const prop to convolution 2d replacer.

Summary:
This wraps.

1. Conv BN folding (not mobile specific)
2. insert XNNPACK conv2d/Linear ops
3. Remove prepacking ops.

Test Plan:
Checked via model transformation of one of the internal model.

Reviewers:

Subscribers:

Tasks:

Tags:

Differential Revision: D20603562

[dr-ci]

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This comment has been revised 19 times.

[dreiss]

Is there a strong reason to write this wrapper in C++? Putting it in Python would make it easier to experiment with.

[kimishpatel]

Is there a strong reason to write this wrapper in C++? Putting it in Python would make it easier to experiment with.

So each individual step of the wrapper pass is also available as a python binding. BatchNormConv2d fusion, freezing, insertPrePackedOp, FoldPrePackedOps.

This is just sugar around it.

[dreiss]

My vote, then, would be to just do the wrapper in Python as well, to make further work on it easier.

[kimishpatel]

My vote, then, would be to just do the wrapper in Python as well, to make further work on it easier.

My preference is to keep in the _jit_pass_* space, since it is a JIT pass, wrapper around JIT passes to be precise, and should bleong there. If you want to keep it in python, I need to find an appropriate place for it (maybe torch.nn.utils..?), but in what ways, keeping it in python makes further work on it easier (not having to recompile)?

[facebook-github-bot]

This pull request has been merged in dc2c4d0.