JIT-compiling the extension results in non-functional Python module.

[mrTsjolder]

When submitting a bug report, please include the following information (where relevant):

• OS: Arch Linux
• PyTorch version: 2.3.0
• How you installed PyTorch (conda, pip, source): conda
• Python version: 3.12
• CUDA/cuDNN version: 12.1 (runtime) / 8.9.2
• GPU models and configuration: 1070 Ti
• GCC version (if compiling from source): /

When using torch.utils.cpp_extension.load to JIT compile the extension, the returned module does not provide access to the exported functions.
Code to reproduce (in interactive Python session with working directory extension-cpp):

>>> import torch
>>> from torch.utils.cpp_extension import load
>>> extension_cpp = load(name="extension_cpp", sources=["extension_cpp/csrc/muladd.cpp"])
>>> extension_cpp.mymuladd
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
AttributeError: module 'extension_cpp' has no attribute 'mymuladd'

Note that after the loading, the following does work:

>>> torch.ops.extension_cpp.mymuladd
<OpOverloadPacket(op='extension_cpp.mymuladd')>

I.e., load does seem to have the side-effect of loading the PyTorch op (similar to setting is_python_module=False), but does not provide the functions in the Python module.
I would expect to be able to use extension_cpp in the same way as torch.ops.extension_cpp.

I suspect that this issue is due to the empty PYBIND11_MODULE block.
This seems to be confirmed by the fact that the following modification in muladd.cpp makes extension_cpp.mymuladd available:

PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
  m.def("mymuladd", &mymuladd_cpu, "custom pytorch operation 'muladd'");
  m.def("mymul", &mymuladd_cpu, "custom pytorch operation 'mul'");
  m.def("myadd_out", &mymuladd_cpu, "custom pytorch operation 'add_out'");
}

However, I am not sure if this is just a workaround or the actual bug-fix.