Perf: use fused Adam optimizer (#4463)

This PR sets the Adam optimizer to use the `fused=True` parameter. 
For the profiling result shown below, this modification brings an 2.75x
improvement on optimizer update (22ms vs. 8ms) and ~3% improvement for
total speed up (922ms vs. 892ms). The benchmark case is training a DPA-2
Q3 release model. Please note that the absolute time may differs between
steps.

<details><summary>Before</summary>
<p>


![image](https://github.com/user-attachments/assets/d6b05a1d-6e6c-478d-921f-c497718bc551)


</p>
</details> 

<details><summary>After</summary>
<p>


![image](https://github.com/user-attachments/assets/b216b919-094c-441f-96a7-146e1e3db483)


</p>
</details> 


[Ref](https://pytorch.org/docs/stable/generated/torch.optim.Adam.html):
> The foreach and fused implementations are typically faster than the
for-loop, single-tensor implementation, with **fused being theoretically
fastest** with both vertical and horizontal fusion. As such, if the user
has not specified either flag (i.e., when foreach = fused = None), we
will attempt defaulting to the foreach implementation when the tensors
are all on CUDA. Why not fused? Since the fused implementation is
relatively new, we want to give it sufficient bake-in time.

<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->

## Summary by CodeRabbit

- **Bug Fixes**
- Improved optimizer performance during training by modifying the
initialization of the Adam optimizer.

- **Documentation**
	- Updated method signature for clarity in the `Trainer` class.

<!-- end of auto-generated comment: release notes by coderabbit.ai -->

deepmd/pt/train/training.py

@@ -579,7 +579,7 @@ def warm_up_linear(step, warmup_steps):
         # author: iProzd
         if self.opt_type == "Adam":
             self.optimizer = torch.optim.Adam(
-                self.wrapper.parameters(), lr=self.lr_exp.start_lr
+                self.wrapper.parameters(), lr=self.lr_exp.start_lr, fused=True
             )
             if optimizer_state_dict is not None and self.restart_training:
                 self.optimizer.load_state_dict(optimizer_state_dict)