TP support for Quark quantized model 
Author: amd-xiaoyu12
Pull request: yes

# What does this PR do?

After integrating the AMD Quark quantizer, we aim to leverage the HF tensor parallelism feature to accelerate the inference of the Quark-quantized model. I have identified several areas where modifications are necessary to support model TP. The changes made serve as a demonstration of the required adjustments. Please review and provide feedback.

For the shard_and_distribute_module() function in tensor_parallel.py, please consider adding quantizer awareness. There are several unique Quark parameters that need to be addressed. Refer to the updated version for details. I suggest adding a quantizer parameter to this function and allowing a quantizer-specific function to be used in column-wise and row-wise partition functions.

The modification for src/transformers/modeling_utils.py is temporary changes to make the test code work.

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> Hey! Nice idea to extend TP support! Just a thought on this: I don't think the way to go is to add all kind of possible exceptions for all quantizers in `tensor_parallel.py`. I believe, this would be much cleaner to add this kind of code directlly in a specific method of the quantizer itself, or maybe to simply extends the tp-plan there if it's enough. That way, we clearly separate quantizer stuff from core loading, and each quantizer has the liberty to do it its own way!
> 
> Then `tensor_parallel` can call the quantizer method if a quantizer is provided!

Thanks for the comments, is there an example for extend the tp-plan to handle the special parameter like scale and zero_point?

cc @MekkCyber 

We need to handle the TP  logic on the quantization side, because each quantization method has its own quirks. For example, some methods use not only `scales` and `zero_points`, but also other parameters.

In the PR, it looks like we're replicating the `scales` and `zero_points`, but I don't think this should be done for all cases. Specifically:

- For `gate_proj` and `up_proj`, we should **shard** the `scales` because these projections are column-wise.
- For `down_proj`, we should **replicate** the `scales` if the quantization is done per output channel, since `down_proj` is row-wise.

So the handling of `scales` and `zero_points` should depend on the quantization granularity (e.g., per-channel vs per-tensor) and the projection type (column-wise vs row-wise).

For now we are just updating the tp_plan for some methods on the quantization side using : `update_tp_plan` and we are thinking of a way to automate that if possible