[torch.compile] A simple solution to recursively compile loaded model: using phi3-small as an example

[wschin]

It's often hard to apply torch.compile directly to a model because many limitations. However, there are only a few key nn.Modules we really need to compile to get max speed. This PR presents a short solution to solve the problem. Phi3-small got 15% latency improvement with this.

This PR contains 3 major functions:

• register_module_to_compile: it registers which torch.nn.Module to compile when scanning child modules from top to bottom (e.g., from Phi3 to MLP, layernorm).
• compile_child_modules: it is called after model is loaded to visit child nn.Module's from top (e.g., GPT2) to bottom (e.g., MLP). If a nn.Module is compiled, its child nn.Module's won't be compiled again.
• unregister_module_to_compile: it undoes register_module_to_compile.
• To enable this feature set COMPILE_CHILD_NN_MODULES=1.

[github-actions]

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[wschin]

@youkaichao, did you get a chance to review?
@comaniac, it'd nice if you can help reviewing. At least, let me know if I should continue writing test. This solution is extremely simple and effective.

Thank you.