Repository: huggingface/transformers
Pull request: fix: XIELU act parameters not being casted to correct dtype

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Was in the process of integrating the new Swiss-ai's Apertus model into axolotl and found an error with the xielu_cuda method. Ran with a 4bit QLoRA config.

```bash
  File "/root/miniconda3/envs/py3.11/lib/python3.11/site-packages/transformers/activations.py", line 282, in forward
    return self._xielu_cuda_fn(input)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/root/miniconda3/envs/py3.11/lib/python3.11/site-packages/transformers/activations.py", line 268, in _xielu_cuda
    result = self._xielu_cuda_obj.forward(
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
RuntimeError: Data type of x (c10::BFloat16) must match data type of alpha_p (float).
```

This error is raised from: 

```cuda
  TORCH_CHECK(x.dtype() == alpha_p.dtype(), "Data type of x (", x.dtype(),
              ") must match data type of alpha_p (", alpha_p.dtype(), ").");
  TORCH_CHECK(x.dtype() == alpha_n.dtype(), "Data type of x (", x.dtype(),
              ") must match data type of alpha_n (", alpha_n.dtype(), ").");
```
https://github.com/rubber-duck-debug/xielu/blob/59d60314edf567ad6b9895a1ff5d7533a14ddc96/xielu/cuda/src/xielu_impl.cu#L395-L398

I see that it does start with `dtype=bf16` https://github.com/huggingface/transformers/blob/de01a22aff21d16532d8dd68806589ca6c73dd5c/src/transformers/activations.py#L203-L210

However, it could be due to some casting (maybe peft and FA) that caused it to end up being fp32.

This PR solves it by casting it to the hidden states. Since they are both 1D Tensor of 1element, there should be very minor cost to this cast.

Tested working downstream via monkey patching https://github.com/axolotl-ai-cloud/axolotl/pull/3144


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Changed files:
filename: src/transformers/activations.py
patch: @@ -262,8 +262,8 @@ def _xielu_cuda(self, x: Tensor) -> Tensor:
             )
         result = self._xielu_cuda_obj.forward(
             x,
-            self.alpha_p,
-            self.alpha_n,
+            self.alpha_p.to(x.dtype),
+            self.alpha_n.to(x.dtype),
             # Temporary until xIELU CUDA fully implemented -> self.{beta,eps}.item()
             self._beta_scalar,
             self._eps_scalar,