Fixed bug Issue, where kernel functions were missing
Author: adv-11
Pull request: yes

# What does this PR do?

Hey everyone!

In this PR, I’ve added some new CUDA kernels to handle sparse matrix operations. These include functions for index_max, mm_to_sparse, sparse_dense_mm, reduce_sum, and scatter. I’ve written them in a new folder called custom_kernels inside the kernel directory.

Here’s a quick breakdown of what I’ve done:

Added kernels.cu to implement the CUDA kernels.
Created cuda_launch.h to declare the kernel functions.
Added sparse_kernels.cpp to bind the CUDA kernels to PyTorch using the pybind11 extension.
Updated setup.py to include the new custom kernel files so they compile correctly.
These new kernels should help with sparse matrix operations, and I’ve tried to keep the code modular in case we need to expand on it later.

Would love feedback on the implementation, and I’m happy to make any adjustments! Thanks for reviewing 😊

Fixes # (  Link :  https://github.com/huggingface/transformers/issues/33856) 


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     Link :  https://github.com/huggingface/transformers/issues/33856
      
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Comments:
> Changes are up to the mark!

Thanks! this was my first PR, hope it gets approved tho


cc @ArthurZucker 

> Hey! Thanks for contributing! In general we avoid having kernels in `transformers` as our primary goal is to have general torch support for models.
> 
> We usually rely on `torch.compile` for compiling torch to graphs 🤗
> 
> Also if these are not used in any `modeling_xxx.py` then not entirely convinced of their utility to live in transformers!

haha yes i agree with using torch rather than cuda, just fixed what the issue was entailing. hoping to contribute more on the repo!


closing PR, if want to re open let me know