[`Mamba2`] Fix slow path
Author: vasqu
Pull request: yes
Labels: name: run-slow

# What does this PR do?
- Fixes a wrong contraction in the mamba2 slow path discovered by @HanGuo97 
- Simplifying one step (4 vs 1 permutations) 
- Cleaning up some comments to follow the og ssd minimal more closely again 

Verified it locally, see the test over [here](https://github.com/vasqu/mamba2-torch/blob/main/tests/TestSSDMinimal.py)

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Fixes #34817


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@ArthurZucker @molbap 

Comments:
Thanks @vasqu for the investigation and fix - and @HanGuo97 for reporting!
Indeed, looks like this is a better reduction - pen&paper helped me understand it. One thing, could you move the test you did locally to an actual test, with a dependency check to protect the `mamba_chunk_scan_combined` import? that way, either with a test image with this dep installed or locally, one can verify that this is always true  

Also can you please push this empty commit to trigger the slow tests workflow?
`git commit --allow-empty -m "[run-slow] mamba2"`

@molbap I think there might be a missing cache initialization for the conv in the cuda forward, i.e. see https://github.com/huggingface/transformers/blob/0044dabbc01f287e791de43d68c26b8f4de6484f/src/transformers/models/mamba2/modeling_mamba2.py#L368-L373 where some copy mises. Not sure when I'll get to it, but I would leave that to a separate PR.

Added a test now. It takes 3-4s on my machine due to the warming of the triton kernel - should I make it a slow test?

@vasqu 3-4 s is ok, no need to flag it as slow I think!
and for sure, ping me on the separate PR when you have time :saluting_face: 
Slow tests have one failure that seems to be independent from this PR
```python
             # Discretize x into dB
            # [bsz, intermediate_size] -> [bsz, num_heads, head_dim]
            hidden_states = hidden_states.reshape(batch_size, -1, self.head_dim)
            dBx = dB * hidden_states[..., None]
    
            # State calculation
            cache_params.ssm_states[self.layer_idx].copy_(
>               cache_params.ssm_states[self.layer_idx] * dA + dBx
            )
E           RuntimeError: Expected all tensors to be on the same device, but found at least two devices, cuda:0 and cuda:1!
```
it's related to multi-GPU runs, it's separate from this issue, I think we're ok to go here! cc @ArthurZucker 

I found an issue for the failure, so yea seems like a separate issue ^^ #33567

gentle ping @ArthurZucker 

Closing in favor of #35154