fix: Initialize ApertusMLP's xielu activation using `torch_dtype`
Author: wasertech
Pull request: yes

# What does this PR do?

I cannot infer this model architecture on Turing. This fixes the issue at the root.

See #42371, https://github.com/vllm-project/vllm/issues/29349, https://github.com/vllm-project/vllm/pull/30635 and https://huggingface.co/swiss-ai/Apertus-8B-Instruct-2509/discussions/21 for context.

Fixes `RuntimeError: expected m1 and m2 to have the same dtype, but got: float != c10::Half`

Issue reproduction:

```py

import torch
from torch import nn
from transformers.models.apertus.configuration_apertus import ApertusConfig
from transformers.models.apertus.modeling_apertus import ApertusMLP
import sys

def test_apertus_mlp_crash():
    print(f"Python executable: {sys.executable}")
    print("Testing ApertusMLP crash on float16...")
    
    # 1. Setup Config with float16 and xielu
    config = ApertusConfig(
        hidden_size=64,
        intermediate_size=128,
        hidden_act="xielu",
        torch_dtype="float16" # Simulating the runner or user choice
    )
    
    # 2. Instantiate MLP
    # We might need to manually set default dtype if the model doesn't pick it up from config immediately
    # usage in vllm often sets the torch default dtype or casts the model
    torch.set_default_dtype(torch.float16) # Simulating "float16 hardware" env
    
    try:
        mlp = ApertusMLP(config)
        
        # Check act_fn dtype
        if hasattr(mlp.act_fn, 'beta'):
             print(f"XIELU beta dtype: {mlp.act_fn.beta.dtype}")
        
        # 3. Create Input
        x = torch.randn(1, 64, dtype=torch.float16)
        
        # 4. Forward Pass
        output = mlp(x)
        print("Forward pass successful!")
        print(f"Output dtype: {output.dtype}")
        
    except RuntimeError as e:
        print("\nCaught expected RuntimeError:")
        print(e)
    except Exception as e:
        print(f"\nCaught unexpected exception: {type(e)}")
        print(e)
    finally:
         torch.set_default_dtype(torch.float32) # Cleanup

if __name__ == "__main__":
    test_apertus_mlp_crash()

```

Output Without this patch:
```
Python executable: /home/waser/Projets/Transformers/transformers/venv/bin/python
Testing ApertusMLP crash on float16...
CUDA-fused xIELU not available (No module named 'xielu') – falling back to a Python version.
For CUDA xIELU (experimental), `pip install git+https://github.com/nickjbrowning/XIELU`
XIELU beta dtype: torch.bfloat16

Caught expected RuntimeError:
expected m1 and m2 to have the same dtype, but got: float != c10::Half
```

Output with this patch:
```
Python executable: /home/waser/Projets/Transformers/transformers/venv/bin/python
Testing ApertusMLP crash on float16...
CUDA-fused xIELU not available (No module named 'xielu') – falling back to a Python version.
For CUDA xIELU (experimental), `pip install git+https://github.com/nickjbrowning/XIELU`
`torch_dtype` is deprecated! Use `dtype` instead!
XIELU beta dtype: torch.float16
Forward pass successful!
Output dtype: torch.float16
```

Big thanks to everyone who helped to shape this patch into light.

Comments:
run-slow: apertus

This comment contains `run-slow`, running the specified jobs: 

models: ["models/apertus"]
quantizations: []

The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/transformers/pr_42864). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.

The test runner is very slow so I checked this out locally and the slow tests seem fine, so I'm happy to approve this! 

Imagine adding another test for f16 precision crazy ahaha thanks!

**[For maintainers]** Suggested jobs to run (before merge)

run-slow: apertus

Thanks for the quick iteration, merging!

## CI Results
[Workflow Run ⚙️](https://github.com/huggingface/transformers/actions/runs/20235389117)

✅ No failing test specific to this PR 🎉 !