Fix typo in smooth_type_embdding

deepmd/pt/model/descriptor/dpa1.py

@@ -65,7 +65,7 @@ def __init__(
         trainable: bool = True,
         exclude_types: List[Tuple[int, int]] = [],
         stripped_type_embedding: bool = False,
-        smooth_type_embdding: bool = False,
+        smooth_type_embedding: bool = False,
     ):
         super().__init__()
         if resnet_dt:
@@ -76,8 +76,8 @@ def __init__(
             raise NotImplementedError("precison 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.")
+        if smooth_type_embedding:
+            raise NotImplementedError("smooth_type_embedding is not supported.")
         del type
         self.se_atten = DescrptBlockSeAtten(
             rcut,

deepmd/tf/descriptor/se_atten.py

@@ -122,7 +122,7 @@ class DescrptSeAtten(DescrptSeA):
     stripped_type_embedding
             Whether to strip the type embedding into a separated embedding network.
             Default value will be True in `se_atten_v2` descriptor.
-    smooth_type_embdding
+    smooth_type_embedding
             When using stripped type embedding, whether to dot smooth factor on the network output of type embedding
             to keep the network smooth, instead of setting `set_davg_zero` to be True.
             Default value will be True in `se_atten_v2` descriptor.
@@ -156,7 +156,7 @@ def __init__(
         attn_mask: bool = False,
         multi_task: bool = False,
         stripped_type_embedding: bool = False,
-        smooth_type_embdding: bool = False,
+        smooth_type_embedding: bool = False,
         # not implemented
         post_ln=True,
         ffn=False,
@@ -169,7 +169,9 @@ def __init__(
         concat_output_tebd: bool = True,
         **kwargs,
     ) -> None:
-        if not set_davg_zero and not (stripped_type_embedding and smooth_type_embdding):
+        if not set_davg_zero and not (
+            stripped_type_embedding and smooth_type_embedding
+        ):
             warnings.warn(
                 "Set 'set_davg_zero' False in descriptor 'se_atten' "
                 "may cause unexpected incontinuity during model inference!"
@@ -220,7 +222,7 @@ def __init__(
         if ntypes == 0:
             raise ValueError("`model/type_map` is not set or empty!")
         self.stripped_type_embedding = stripped_type_embedding
-        self.smooth = smooth_type_embdding
+        self.smooth = smooth_type_embedding
         self.ntypes = ntypes
         self.att_n = attn
         self.attn_layer = attn_layer

deepmd/tf/descriptor/se_atten_v2.py

@@ -110,6 +110,6 @@ def __init__(
             attn_mask=attn_mask,
             multi_task=multi_task,
             stripped_type_embedding=True,
-            smooth_type_embdding=True,
+            smooth_type_embedding=True,
             **kwargs,
         )

deepmd/utils/argcheck.py

@@ -476,7 +476,7 @@ def descrpt_se_atten_common_args():
 @descrpt_args_plugin.register("se_atten", alias=["dpa1"])
 def descrpt_se_atten_args():
     doc_stripped_type_embedding = "Whether to strip the type embedding into a separated embedding network. Setting it to `False` will fall back to the previous version of `se_atten` which is non-compressible."
-    doc_smooth_type_embdding = "When using stripped type embedding, whether to dot smooth factor on the network output of type embedding to keep the network smooth, instead of setting `set_davg_zero` to be True."
+    doc_smooth_type_embedding = "When using stripped type embedding, whether to dot smooth factor on the network output of type embedding to keep the network smooth, instead of setting `set_davg_zero` to be True."
     doc_set_davg_zero = "Set the normalization average to zero. This option should be set when `se_atten` descriptor or `atom_ener` in the energy fitting is used"
     doc_tebd_dim = "The dimension of atom type embedding."
     doc_temperature = "The scaling factor of normalization in calculations of attention weights, which is used to scale the matmul(Q, K)."
@@ -503,11 +503,12 @@ def descrpt_se_atten_args():
             doc=doc_only_tf_supported + doc_stripped_type_embedding,
         ),
         Argument(
-            "smooth_type_embdding",
+            "smooth_type_embedding",
             bool,
             optional=True,
             default=False,
-            doc=doc_only_tf_supported + doc_smooth_type_embdding,
+            alias=["smooth_type_embdding"],
+            doc=doc_only_tf_supported + doc_smooth_type_embedding,
         ),
         Argument(
             "set_davg_zero", bool, optional=True, default=True, doc=doc_set_davg_zero

doc/model/train-se-atten.md

@@ -159,7 +159,7 @@ We highly recommend using the version 2.0 of the attention-based descriptor `"se
 
 ```json
       "stripped_type_embedding": true,
-      "smooth_type_embdding": true,
+      "smooth_type_embedding": true,
       "set_davg_zero": false
 ```
 

source/tests/tf/test_model_compression_se_atten.py

@@ -37,27 +37,27 @@ def _file_delete(file):
     {
         "se_atten precision": "float64",
         "type embedding precision": "float64",
-        "smooth_type_embdding": True,
+        "smooth_type_embedding": True,
     },
     {
         "se_atten precision": "float64",
         "type embedding precision": "float64",
-        "smooth_type_embdding": False,
+        "smooth_type_embedding": False,
     },
     {
         "se_atten precision": "float64",
         "type embedding precision": "float32",
-        "smooth_type_embdding": True,
+        "smooth_type_embedding": True,
     },
     {
         "se_atten precision": "float32",
         "type embedding precision": "float64",
-        "smooth_type_embdding": True,
+        "smooth_type_embedding": True,
     },
     {
         "se_atten precision": "float32",
         "type embedding precision": "float32",
-        "smooth_type_embdding": True,
+        "smooth_type_embedding": True,
     },
 ]
 
@@ -82,8 +82,8 @@ def _init_models():
         jdata["model"]["descriptor"]["stripped_type_embedding"] = True
         jdata["model"]["descriptor"]["sel"] = 120
         jdata["model"]["descriptor"]["attn_layer"] = 0
-        jdata["model"]["descriptor"]["smooth_type_embdding"] = tests[i][
-            "smooth_type_embdding"
+        jdata["model"]["descriptor"]["smooth_type_embedding"] = tests[i][
+            "smooth_type_embedding"
         ]
         jdata["model"]["type_embedding"] = {}
         jdata["model"]["type_embedding"]["precision"] = tests[i][

source/tests/tf/test_model_se_atten.py

@@ -764,7 +764,7 @@ def test_smoothness_of_stripped_type_embedding_smooth_model(self):
         jdata["model"]["descriptor"].pop("type", None)
         jdata["model"]["descriptor"]["ntypes"] = 2
         jdata["model"]["descriptor"]["stripped_type_embedding"] = True
-        jdata["model"]["descriptor"]["smooth_type_embdding"] = True
+        jdata["model"]["descriptor"]["smooth_type_embedding"] = True
         jdata["model"]["descriptor"]["attn_layer"] = 1
         jdata["model"]["descriptor"]["rcut"] = 6.0
         jdata["model"]["descriptor"]["rcut_smth"] = 4.0
@@ -910,7 +910,7 @@ def test_smoothness_of_stripped_type_embedding_smooth_model_excluded_types(self)
         jdata["model"]["descriptor"].pop("type", None)
         jdata["model"]["descriptor"]["ntypes"] = 2
         jdata["model"]["descriptor"]["stripped_type_embedding"] = True
-        jdata["model"]["descriptor"]["smooth_type_embdding"] = True
+        jdata["model"]["descriptor"]["smooth_type_embedding"] = True
         jdata["model"]["descriptor"]["attn_layer"] = 1
         jdata["model"]["descriptor"]["rcut"] = 6.0
         jdata["model"]["descriptor"]["rcut_smth"] = 4.0