feat(pt): add AdamW for pt training (#4757)

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

## Summary by CodeRabbit

- **New Features**
  - Added support for the "AdamW" optimizer in training configurations.
- Introduced a "weight_decay" parameter for optimizer settings, with a
default value of 0.001.

- **Chores**
- Updated configuration options to allow selection of "AdamW" as an
optimizer type.

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

Author: iProzd

deepmd/pt/train/training.py

@@ -158,6 +158,7 @@ def get_opt_param(params):
                 "kf_limit_pref_e": params.get("kf_limit_pref_e", 1),
                 "kf_start_pref_f": params.get("kf_start_pref_f", 1),
                 "kf_limit_pref_f": params.get("kf_limit_pref_f", 1),
+                "weight_decay": params.get("weight_decay", 0.001),
             }
             return opt_type, opt_param
 
@@ -609,12 +610,20 @@ def warm_up_linear(step, warmup_steps):
 
         # TODO add optimizers for multitask
         # author: iProzd
-        if self.opt_type == "Adam":
-            self.optimizer = torch.optim.Adam(
-                self.wrapper.parameters(),
-                lr=self.lr_exp.start_lr,
-                fused=False if DEVICE.type == "cpu" else True,
-            )
+        if self.opt_type in ["Adam", "AdamW"]:
+            if self.opt_type == "Adam":
+                self.optimizer = torch.optim.Adam(
+                    self.wrapper.parameters(),
+                    lr=self.lr_exp.start_lr,
+                    fused=False if DEVICE.type == "cpu" else True,
+                )
+            else:
+                self.optimizer = torch.optim.AdamW(
+                    self.wrapper.parameters(),
+                    lr=self.lr_exp.start_lr,
+                    weight_decay=float(self.opt_param["weight_decay"]),
+                    fused=False if DEVICE.type == "cpu" else True,
+                )
             if optimizer_state_dict is not None and self.restart_training:
                 self.optimizer.load_state_dict(optimizer_state_dict)
             self.scheduler = torch.optim.lr_scheduler.LambdaLR(
@@ -710,7 +719,7 @@ def step(_step_id, task_key="Default") -> None:
                 print_str = f"Step {_step_id}: sample system{log_dict['sid']}  frame{log_dict['fid']}\n"
                 fout1.write(print_str)
                 fout1.flush()
-            if self.opt_type == "Adam":
+            if self.opt_type in ["Adam", "AdamW"]:
                 cur_lr = self.scheduler.get_last_lr()[0]
                 if _step_id < self.warmup_steps:
                     pref_lr = _lr.start_lr

deepmd/utils/argcheck.py

@@ -3177,6 +3177,7 @@ def training_args(
             "opt_type",
             choices=[
                 Argument("Adam", dict, [], [], optional=True),
+                Argument("AdamW", dict, [], [], optional=True),
                 Argument(
                     "LKF",
                     dict,