feat: add seeds to dpmodel and fix seeds in tf & pt (#3880)

Fix #3799.

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

- **New Features**
- Introduced flexibility in specifying seed values, allowing either an
integer or a list of integers.
- Enhanced seed parameter usage across various initialization methods
and classes for more controlled randomization.

- **Improvements**
- Updated seed initialization logic to include additional computations
and dynamic adjustments.
- Enhanced documentation for parameters in multiple classes, providing
clearer usage guidelines.
<!-- end of auto-generated comment: release notes by coderabbit.ai -->

---------

Signed-off-by: Jinzhe Zeng <[email protected]>

deepmd/dpmodel/descriptor/dpa1.py

@@ -25,6 +25,9 @@
     LayerNorm,
     NativeLayer,
 )
+from deepmd.dpmodel.utils.seed import (
+    child_seed,
+)
 from deepmd.dpmodel.utils.type_embed import (
     TypeEmbedNet,
 )
@@ -251,7 +254,7 @@ def __init__(
         use_econf_tebd: bool = False,
         type_map: Optional[List[str]] = None,
         # consistent with argcheck, not used though
-        seed: Optional[int] = None,
+        seed: Optional[Union[int, List[int]]] = None,
     ) -> None:
         ## seed, uniform_seed, not included.
         # Ensure compatibility with the deprecated stripped_type_embedding option.
@@ -294,6 +297,7 @@ def __init__(
             env_protection=env_protection,
             trainable_ln=trainable_ln,
             ln_eps=ln_eps,
+            seed=child_seed(seed, 0),
         )
         self.use_econf_tebd = use_econf_tebd
         self.type_map = type_map
@@ -305,6 +309,7 @@ def __init__(
             precision=precision,
             use_econf_tebd=use_econf_tebd,
             type_map=type_map,
+            seed=child_seed(seed, 1),
         )
         self.tebd_dim = tebd_dim
         self.concat_output_tebd = concat_output_tebd
@@ -625,6 +630,7 @@ def __init__(
         trainable_ln: bool = True,
         ln_eps: Optional[float] = 1e-5,
         smooth: bool = True,
+        seed: Optional[Union[int, List[int]]] = None,
     ) -> None:
         self.rcut = rcut
         self.rcut_smth = rcut_smth
@@ -674,6 +680,7 @@ def __init__(
             self.activation_function,
             self.resnet_dt,
             self.precision,
+            seed=child_seed(seed, 0),
         )
         if self.tebd_input_mode in ["strip"]:
             self.embeddings_strip = NetworkCollection(
@@ -687,6 +694,7 @@ def __init__(
                 self.activation_function,
                 self.resnet_dt,
                 self.precision,
+                seed=child_seed(seed, 1),
             )
         else:
             self.embeddings_strip = None
@@ -703,6 +711,7 @@ def __init__(
             ln_eps=self.ln_eps,
             smooth=self.smooth,
             precision=self.precision,
+            seed=child_seed(seed, 2),
         )
 
         wanted_shape = (self.ntypes, self.nnei, 4)
@@ -950,6 +959,7 @@ def __init__(
         ln_eps: float = 1e-5,
         smooth: bool = True,
         precision: str = DEFAULT_PRECISION,
+        seed: Optional[Union[int, List[int]]] = None,
     ):
         """Construct a neighbor-wise attention net."""
         super().__init__()
@@ -982,8 +992,9 @@ def __init__(
                 ln_eps=ln_eps,
                 smooth=smooth,
                 precision=precision,
+                seed=child_seed(seed, ii),
             )
-            for _ in range(layer_num)
+            for ii in range(layer_num)
         ]
 
     def call(
@@ -1076,6 +1087,7 @@ def __init__(
         ln_eps: float = 1e-5,
         smooth: bool = True,
         precision: str = DEFAULT_PRECISION,
+        seed: Optional[Union[int, List[int]]] = None,
     ):
         """Construct a neighbor-wise attention layer."""
         super().__init__()
@@ -1101,9 +1113,14 @@ def __init__(
             temperature=temperature,
             smooth=smooth,
             precision=precision,
+            seed=child_seed(seed, 0),
         )
         self.attn_layer_norm = LayerNorm(
-            self.embed_dim, eps=ln_eps, trainable=self.trainable_ln, precision=precision
+            self.embed_dim,
+            eps=ln_eps,
+            trainable=self.trainable_ln,
+            precision=precision,
+            seed=child_seed(seed, 1),
         )
 
     def call(
@@ -1176,6 +1193,7 @@ def __init__(
         bias: bool = True,
         smooth: bool = True,
         precision: str = DEFAULT_PRECISION,
+        seed: Optional[Union[int, List[int]]] = None,
     ):
         """Construct a multi-head neighbor-wise attention net."""
         super().__init__()
@@ -1204,13 +1222,15 @@ def __init__(
             bias=bias,
             use_timestep=False,
             precision=precision,
+            seed=child_seed(seed, 0),
         )
         self.out_proj = NativeLayer(
             hidden_dim,
             embed_dim,
             bias=bias,
             use_timestep=False,
             precision=precision,
+            seed=child_seed(seed, 1),
         )
 
     def call(self, query, nei_mask, input_r=None, sw=None, attnw_shift=20.0):

deepmd/dpmodel/descriptor/dpa2.py

@@ -23,6 +23,9 @@
     build_multiple_neighbor_list,
     get_multiple_nlist_key,
 )
+from deepmd.dpmodel.utils.seed import (
+    child_seed,
+)
 from deepmd.dpmodel.utils.type_embed import (
     TypeEmbedNet,
 )
@@ -325,7 +328,7 @@ def __init__(
         exclude_types: List[Tuple[int, int]] = [],
         env_protection: float = 0.0,
         trainable: bool = True,
-        seed: Optional[int] = None,
+        seed: Optional[Union[int, List[int]]] = None,
         add_tebd_to_repinit_out: bool = False,
         use_econf_tebd: bool = False,
         type_map: Optional[List[str]] = None,
@@ -408,6 +411,7 @@ def init_subclass_params(sub_data, sub_class):
             resnet_dt=self.repinit_args.resnet_dt,
             smooth=smooth,
             type_one_side=self.repinit_args.type_one_side,
+            seed=child_seed(seed, 0),
         )
         self.repformers = DescrptBlockRepformers(
             self.repformer_args.rcut,
@@ -442,6 +446,7 @@ def init_subclass_params(sub_data, sub_class):
             precision=precision,
             trainable_ln=self.repformer_args.trainable_ln,
             ln_eps=self.repformer_args.ln_eps,
+            seed=child_seed(seed, 1),
         )
         self.use_econf_tebd = use_econf_tebd
         self.type_map = type_map
@@ -453,6 +458,7 @@ def init_subclass_params(sub_data, sub_class):
             precision=precision,
             use_econf_tebd=use_econf_tebd,
             type_map=type_map,
+            seed=child_seed(seed, 2),
         )
         self.concat_output_tebd = concat_output_tebd
         self.precision = precision