pt: apply argcheck to pt

deepmd/pt/entrypoints/main.py

@@ -50,6 +50,12 @@
 from deepmd.pt.utils.stat import (
     make_stat_input,
 )
+from deepmd.utils.argcheck import (
+    normalize,
+)
+from deepmd.utils.compat import (
+    update_deepmd_input,
+)
 from deepmd.utils.path import (
     DPPath,
 )
@@ -67,6 +73,11 @@ def get_trainer(
     force_load=False,
     init_frz_model=None,
 ):
+    # argcheck
+    if "model_dict" not in config.get("model", {}):
+        config = update_deepmd_input(config, warning=True, dump="input_v2_compat.json")
+        config = normalize(config)
+
     # Initialize DDP
     local_rank = os.environ.get("LOCAL_RANK")
     if local_rank is not None:

deepmd/pt/model/descriptor/dpa1.py

@@ -43,16 +43,38 @@
         post_ln=True,
         ffn=False,
         ffn_embed_dim=1024,
-        activation="tanh",
+        activation_function="tanh",
         scaling_factor=1.0,
         head_num=1,
         normalize=True,
         temperature=None,
         return_rot=False,
         concat_output_tebd: bool = True,
         type: Optional[str] = None,
+        # not implemented
+        resnet_dt: bool = False,
+        type_one_side: bool = True,
+        precision: str = "default",
+        trainable: bool = True,
+        exclude_types: Optional[List[List[int]]] = None,
+        stripped_type_embedding: bool = False,
+        smooth_type_embdding: bool = False,
     ):
         super().__init__()
+        if resnet_dt:
+            raise NotImplementedError("resnet_dt is not supported.")
+        if not type_one_side:
+            raise NotImplementedError("type_one_side is not supported.")
+        if precision != "default" and precision != "float64":
+            raise NotImplementedError("precison is not supported.")
+        if not trainable:
+            raise NotImplementedError("trainable == False is not supported.")
+        if exclude_types is not None and exclude_types != []:
+            raise NotImplementedError("exclude_types is not supported.")
+        if stripped_type_embedding:
+            raise NotImplementedError("stripped_type_embedding is not supported.")
+        if smooth_type_embdding:
+            raise NotImplementedError("smooth_type_embdding is not supported.")
         del type
         self.se_atten = DescrptBlockSeAtten(
             rcut,
@@ -71,7 +93,7 @@
             post_ln=post_ln,
             ffn=ffn,
             ffn_embed_dim=ffn_embed_dim,
-            activation=activation,
+            activation_function=activation_function,
             scaling_factor=scaling_factor,
             head_num=head_num,
             normalize=normalize,

deepmd/pt/model/descriptor/dpa2.py

@@ -197,7 +197,7 @@ def __init__(
             tebd_input_mode="concat",
             # tebd_input_mode='dot_residual_s',
             set_davg_zero=repinit_set_davg_zero,
-            activation=repinit_activation,
+            activation_function=repinit_activation,
         )
         self.repformers = DescrptBlockRepformers(
             repformer_rcut,
@@ -223,7 +223,7 @@ def __init__(
             attn2_hidden=repformer_attn2_hidden,
             attn2_nhead=repformer_attn2_nhead,
             attn2_has_gate=repformer_attn2_has_gate,
-            activation=repformer_activation,
+            activation_function=repformer_activation,
             update_style=repformer_update_style,
             set_davg_zero=repformer_set_davg_zero,
             smooth=True,

deepmd/pt/model/descriptor/repformer_layer.py

@@ -313,7 +313,7 @@ def __init__(
         attn2_hidden: int = 16,
         attn2_nhead: int = 4,
         attn2_has_gate: bool = False,
-        activation: str = "tanh",
+        activation_function: str = "tanh",
         update_style: str = "res_avg",
         set_davg_zero: bool = True,  # TODO
         smooth: bool = True,
@@ -332,7 +332,7 @@ def __init__(
         self.set_davg_zero = set_davg_zero
         self.do_bn_mode = do_bn_mode
         self.bn_momentum = bn_momentum
-        self.act = get_activation_fn(activation)
+        self.act = get_activation_fn(activation_function)
         self.update_g1_has_grrg = update_g1_has_grrg
         self.update_g1_has_drrd = update_g1_has_drrd
         self.update_g1_has_conv = update_g1_has_conv

deepmd/pt/model/descriptor/repformers.py

@@ -76,7 +76,7 @@ def __init__(
         attn2_hidden: int = 16,
         attn2_nhead: int = 4,
         attn2_has_gate: bool = False,
-        activation: str = "tanh",
+        activation_function: str = "tanh",
         update_style: str = "res_avg",
         set_davg_zero: bool = True,  # TODO
         smooth: bool = True,
@@ -109,7 +109,7 @@ def __init__(
         self.set_davg_zero = set_davg_zero
         self.g1_dim = g1_dim
         self.g2_dim = g2_dim
-        self.act = get_activation_fn(activation)
+        self.act = get_activation_fn(activation_function)
         self.direct_dist = direct_dist
         self.add_type_ebd_to_seq = add_type_ebd_to_seq
 
@@ -140,7 +140,7 @@ def __init__(
                     attn2_has_gate=attn2_has_gate,
                     attn2_hidden=attn2_hidden,
                     attn2_nhead=attn2_nhead,
-                    activation=activation,
+                    activation_function=activation_function,
                     update_style=update_style,
                     smooth=smooth,
                 )

deepmd/pt/model/descriptor/se_atten.py

@@ -53,7 +53,7 @@ def __init__(
         post_ln=True,
         ffn=False,
         ffn_embed_dim=1024,
-        activation="tanh",
+        activation_function="tanh",
         scaling_factor=1.0,
         head_num=1,
         normalize=True,
@@ -86,7 +86,7 @@ def __init__(
         self.post_ln = post_ln
         self.ffn = ffn
         self.ffn_embed_dim = ffn_embed_dim
-        self.activation = activation
+        self.activation = activation_function
         # TODO: To be fixed: precision should be given from inputs
         self.prec = torch.float64
         self.scaling_factor = scaling_factor

deepmd/tf/descriptor/se_atten.py

@@ -152,13 +152,41 @@
         multi_task: bool = False,
         stripped_type_embedding: bool = False,
         smooth_type_embdding: bool = False,
+        # not implemented
+        post_ln=True,
+        ffn=False,
+        ffn_embed_dim=1024,
+        scaling_factor=1.0,
+        head_num=1,
+        normalize=True,
+        temperature=None,
+        return_rot=False,
+        concat_output_tebd: bool = True,
         **kwargs,
     ) -> None:
         if not set_davg_zero and not (stripped_type_embedding and smooth_type_embdding):
             warnings.warn(
                 "Set 'set_davg_zero' False in descriptor 'se_atten' "
                 "may cause unexpected incontinuity during model inference!"
             )
+        if not post_ln:
+            raise NotImplementedError("post_ln is not supported.")
+        if ffn:
+            raise NotImplementedError("ffn is not supported.")
+        if ffn_embed_dim != 1024:
+            raise NotImplementedError("ffn_embed_dim is not supported.")
+        if scaling_factor != 1.0:
+            raise NotImplementedError("scaling_factor is not supported.")
+        if head_num != 1:
+            raise NotImplementedError("head_num is not supported.")
+        if not normalize:
+            raise NotImplementedError("normalize is not supported.")
+        if temperature is not None:
+            raise NotImplementedError("temperature is not supported.")
+        if return_rot:
+            raise NotImplementedError("return_rot is not supported.")
+        if not concat_output_tebd:
+            raise NotImplementedError("concat_output_tebd is not supported.")
         DescrptSeA.__init__(
             self,
             rcut,