FIX: TF32 warning (#43012)
Author: shantanugupta2004
Pull request: yes

# What does this PR do?
This PR replaces the matrix multiplication operator (@) with broadcasting element-wise multiplication (*) in the RotaryEmbedding implementation for several major models (Llama, Mistral, Mixtral, Qwen2, Gemma, Gemma2).
When compiling a model with torch.compile in bfloat16, the RoPE frequency calculation (which is intentionally kept in float32 for precision) triggers a UserWarning regarding TensorFloat32 (TF32) if it's not enabled.
Since the shapes involved in this specific operation [batch, dim/2, 1] and [batch, 1, seq_len] result in an outer product, using @ is mathematically equivalent to * with broadcasting. However, using * avoids the "matrix multiplication" code path in the compiler, effectively silencing the false-positive warning and potentially offering a minor performance optimization by avoiding a full GEMM call for a simple outer product.

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Fixes #43012 


## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
- [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#create-a-pull-request),
      Pull Request section?
- [x] Was this discussed/approved via a Github issue [link](https://github.com/huggingface/transformers/issues/43012)? 
- [ ] Did you write any new necessary tests?


## Who can review?

Anyone in the community is free to review the PR once the tests have passed. 

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Comments:
**[For maintainers]** Suggested jobs to run (before merge)

run-slow: afmoe, apertus, arcee, aria, bamba, bitnet, chameleon, csm, cwm, dbrx, deepseek_v3, dia, diffllama, doge, dots1, emu3

I did some checking and the output of `@` or `*` is completely identical in any precision, which makes me suspect the same kernel is being called in both cases. This means there should be no change in performance, but I think it's fine to use `*` to suppress the TF32 warning no? I think it will probably annoy/confuse users who are running the model in `bfloat16` and don't understand why `float32` matmuls are happening. cc @ArthurZucker