docs: move arg docs from Variant to sub Arguments (#4369)

See deepmodeling/dargs#82

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

## Summary by CodeRabbit

- **Documentation**
- Enhanced documentation for various descriptor arguments and fitting
configurations, improving clarity and usability.
- Added detailed descriptions for descriptors related to the smooth
edition of Deep Potential.
- Updated registration of descriptor arguments to include new
documentation strings for better guidance on functionality and expected
parameters.

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

Signed-off-by: Jinzhe Zeng <jinzhe.zeng@rutgers.edu>

Author: njzjz

deepmd/utils/argcheck.py

@@ -40,6 +40,21 @@
 
 doc_only_tf_supported = "(Supported Backend: TensorFlow) "
 doc_only_pt_supported = "(Supported Backend: PyTorch) "
+# descriptors
+doc_loc_frame = "Defines a local frame at each atom, and the compute the descriptor as local coordinates under this frame."
+doc_se_e2_a = "Used by the smooth edition of Deep Potential. The full relative coordinates are used to construct the descriptor."
+doc_se_e2_r = "Used by the smooth edition of Deep Potential. Only the distance between atoms is used to construct the descriptor."
+doc_se_e3 = "Used by the smooth edition of Deep Potential. The full relative coordinates are used to construct the descriptor. Three-body embedding will be used by this descriptor."
+doc_se_a_tpe = "Used by the smooth edition of Deep Potential. The full relative coordinates are used to construct the descriptor. Type embedding will be used by this descriptor."
+doc_se_atten = "Used by the smooth edition of Deep Potential. The full relative coordinates are used to construct the descriptor. Attention mechanism will be used by this descriptor."
+doc_se_atten_v2 = "Used by the smooth edition of Deep Potential. The full relative coordinates are used to construct the descriptor. Attention mechanism with new modifications will be used by this descriptor."
+doc_se_a_mask = "Used by the smooth edition of Deep Potential. It can accept a variable number of atoms in a frame (Non-PBC system). *aparam* are required as an indicator matrix for the real/virtual sign of input atoms."
+doc_hybrid = "Concatenate of a list of descriptors as a new descriptor."
+# fitting
+doc_ener = "Fit an energy model (potential energy surface)."
+doc_dos = "Fit a density of states model. The total density of states / site-projected density of states labels should be provided by `dos.npy` or `atom_dos.npy` in each data system. The file has number of frames lines and number of energy grid columns (times number of atoms in `atom_dos.npy`). See `loss` parameter."
+doc_dipole = "Fit an atomic dipole model. Global dipole labels or atomic dipole labels for all the selected atoms (see `sel_type`) should be provided by `dipole.npy` in each data system. The file either has number of frames lines and 3 times of number of selected atoms columns, or has number of frames lines and 3 columns. See `loss` parameter."
+doc_polar = "Fit an atomic polarizability model. Global polarizazbility labels or atomic polarizability labels for all the selected atoms (see `sel_type`) should be provided by `polarizability.npy` in each data system. The file with has number of frames lines and 9 times of number of selected atoms columns, or has number of frames lines and 9 columns. See `loss` parameter."
 
 
 def list_to_doc(xx):
@@ -229,7 +244,7 @@ def get_all_argument(self, exclude_hybrid: bool = False) -> list[Argument]:
 descrpt_args_plugin = ArgsPlugin()
 
 
-@descrpt_args_plugin.register("loc_frame", doc=doc_only_tf_supported)
+@descrpt_args_plugin.register("loc_frame", doc=doc_only_tf_supported + doc_loc_frame)
 def descrpt_local_frame_args():
     doc_sel_a = "A list of integers. The length of the list should be the same as the number of atom types in the system. `sel_a[i]` gives the selected number of type-i neighbors. The full relative coordinates of the neighbors are used by the descriptor."
     doc_sel_r = "A list of integers. The length of the list should be the same as the number of atom types in the system. `sel_r[i]` gives the selected number of type-i neighbors. Only relative distance of the neighbors are used by the descriptor. sel_a[i] + sel_r[i] is recommended to be larger than the maximally possible number of type-i neighbors in the cut-off radius."
@@ -250,7 +265,7 @@ def descrpt_local_frame_args():
     ]
 
 
-@descrpt_args_plugin.register("se_e2_a", alias=["se_a"])
+@descrpt_args_plugin.register("se_e2_a", alias=["se_a"], doc=doc_se_e2_a)
 def descrpt_se_a_args():
     doc_sel = 'This parameter set the number of selected neighbors for each type of atom. It can be:\n\n\
     - `list[int]`. The length of the list should be the same as the number of atom types in the system. `sel[i]` gives the selected number of type-i neighbors. `sel[i]` is recommended to be larger than the maximally possible number of type-i neighbors in the cut-off radius. It is noted that the total sel value must be less than 4096 in a GPU environment.\n\n\
@@ -318,7 +333,9 @@ def descrpt_se_a_args():
     ]
 
 
-@descrpt_args_plugin.register("se_e3", alias=["se_at", "se_a_3be", "se_t"])
+@descrpt_args_plugin.register(
+    "se_e3", alias=["se_at", "se_a_3be", "se_t"], doc=doc_se_e3
+)
 def descrpt_se_t_args():
     doc_sel = 'This parameter set the number of selected neighbors for each type of atom. It can be:\n\n\
     - `list[int]`. The length of the list should be the same as the number of atom types in the system. `sel[i]` gives the selected number of type-i neighbors. `sel[i]` is recommended to be larger than the maximally possible number of type-i neighbors in the cut-off radius. It is noted that the total sel value must be less than 4096 in a GPU environment.\n\n\
@@ -373,7 +390,9 @@ def descrpt_se_t_args():
     ]
 
 
-@descrpt_args_plugin.register("se_a_tpe", alias=["se_a_ebd"], doc=doc_only_tf_supported)
+@descrpt_args_plugin.register(
+    "se_a_tpe", alias=["se_a_ebd"], doc=doc_only_tf_supported + doc_se_a_tpe
+)
 def descrpt_se_a_tpe_args():
     doc_type_nchanl = "number of channels for type embedding"
     doc_type_nlayer = "number of hidden layers of type embedding net"
@@ -387,7 +406,7 @@ def descrpt_se_a_tpe_args():
     ]
 
 
-@descrpt_args_plugin.register("se_e2_r", alias=["se_r"])
+@descrpt_args_plugin.register("se_e2_r", alias=["se_r"], doc=doc_se_e2_r)
 def descrpt_se_r_args():
     doc_sel = 'This parameter set the number of selected neighbors for each type of atom. It can be:\n\n\
     - `list[int]`. The length of the list should be the same as the number of atom types in the system. `sel[i]` gives the selected number of type-i neighbors. `sel[i]` is recommended to be larger than the maximally possible number of type-i neighbors in the cut-off radius. It is noted that the total sel value must be less than 4096 in a GPU environment.\n\n\
@@ -446,7 +465,7 @@ def descrpt_se_r_args():
     ]
 
 
-@descrpt_args_plugin.register("hybrid")
+@descrpt_args_plugin.register("hybrid", doc=doc_hybrid)
 def descrpt_hybrid_args():
     doc_list = "A list of descriptor definitions"
 
@@ -538,7 +557,7 @@ def descrpt_se_atten_common_args():
     ]
 
 
-@descrpt_args_plugin.register("se_atten", alias=["dpa1"])
+@descrpt_args_plugin.register("se_atten", alias=["dpa1"], doc=doc_se_atten)
 def descrpt_se_atten_args():
     doc_smooth_type_embedding = f"Whether to use smooth process in attention weights calculation. {doc_only_tf_supported} 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"
@@ -774,7 +793,7 @@ def descrpt_se_e3_tebd_args():
     ]
 
 
-@descrpt_args_plugin.register("se_atten_v2")
+@descrpt_args_plugin.register("se_atten_v2", doc=doc_se_atten_v2)
 def descrpt_se_atten_v2_args():
     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_trainable_ln = (
@@ -1343,7 +1362,7 @@ def descrpt_se_a_ebd_v2_args():
     return descrpt_se_a_args()
 
 
-@descrpt_args_plugin.register("se_a_mask", doc=doc_only_tf_supported)
+@descrpt_args_plugin.register("se_a_mask", doc=doc_only_tf_supported + doc_se_a_mask)
 def descrpt_se_a_mask_args():
     doc_sel = 'This parameter sets the number of selected neighbors for each type of atom. It can be:\n\n\
     - `list[int]`. The length of the list should be the same as the number of atom types in the system. `sel[i]` gives the selected number of type-i neighbors. `sel[i]` is recommended to be larger than the maximally possible number of type-i neighbors in the cut-off radius. It is noted that the total sel value must be less than 4096 in a GPU environment.\n\n\
@@ -1397,27 +1416,7 @@ def descrpt_se_a_mask_args():
 
 
 def descrpt_variant_type_args(exclude_hybrid: bool = False) -> Variant:
-    link_lf = make_link("loc_frame", "model[standard]/descriptor[loc_frame]")
-    link_se_e2_a = make_link("se_e2_a", "model[standard]/descriptor[se_e2_a]")
-    link_se_e2_r = make_link("se_e2_r", "model[standard]/descriptor[se_e2_r]")
-    link_se_e3 = make_link("se_e3", "model[standard]/descriptor[se_e3]")
-    link_se_a_tpe = make_link("se_a_tpe", "model[standard]/descriptor[se_a_tpe]")
-    link_hybrid = make_link("hybrid", "model[standard]/descriptor[hybrid]")
-    link_se_atten = make_link("se_atten", "model[standard]/descriptor[se_atten]")
-    link_se_atten_v2 = make_link(
-        "se_atten_v2", "model[standard]/descriptor[se_atten_v2]"
-    )
-    link_se_a_mask = make_link("se_a_mask", "model[standard]/descriptor[se_a_mask]")
-    doc_descrpt_type = f"The type of the descriptor. See explanation below. \n\n\
-- {link_lf}: Defines a local frame at each atom, and the compute the descriptor as local coordinates under this frame.\n\n\
-- {link_se_e2_a}: Used by the smooth edition of Deep Potential. The full relative coordinates are used to construct the descriptor.\n\n\
-- {link_se_e2_r}: Used by the smooth edition of Deep Potential. Only the distance between atoms is used to construct the descriptor.\n\n\
-- {link_se_e3}: Used by the smooth edition of Deep Potential. The full relative coordinates are used to construct the descriptor. Three-body embedding will be used by this descriptor.\n\n\
-- {link_se_a_tpe}: Used by the smooth edition of Deep Potential. The full relative coordinates are used to construct the descriptor. Type embedding will be used by this descriptor.\n\n\
-- {link_se_atten}: Used by the smooth edition of Deep Potential. The full relative coordinates are used to construct the descriptor. Attention mechanism will be used by this descriptor.\n\n\
-- {link_se_atten_v2}: Used by the smooth edition of Deep Potential. The full relative coordinates are used to construct the descriptor. Attention mechanism with new modifications will be used by this descriptor.\n\n\
-- {link_se_a_mask}: Used by the smooth edition of Deep Potential. It can accept a variable number of atoms in a frame (Non-PBC system). *aparam* are required as an indicator matrix for the real/virtual sign of input atoms. \n\n\
-- {link_hybrid}: Concatenate of a list of descriptors as a new descriptor."
+    doc_descrpt_type = "The type of the descriptor."
 
     return Variant(
         "type",
@@ -1430,7 +1429,7 @@ def descrpt_variant_type_args(exclude_hybrid: bool = False) -> Variant:
 fitting_args_plugin = ArgsPlugin()
 
 
-@fitting_args_plugin.register("ener")
+@fitting_args_plugin.register("ener", doc=doc_ener)
 def fitting_ener():
     doc_numb_fparam = "The dimension of the frame parameter. If set to >0, file `fparam.npy` should be included to provided the input fparams."
     doc_numb_aparam = "The dimension of the atomic parameter. If set to >0, file `aparam.npy` should be included to provided the input aparams."
@@ -1506,7 +1505,7 @@ def fitting_ener():
     ]
 
 
-@fitting_args_plugin.register("dos")
+@fitting_args_plugin.register("dos", doc=doc_dos)
 def fitting_dos():
     doc_numb_fparam = "The dimension of the frame parameter. If set to >0, file `fparam.npy` should be included to provided the input fparams."
     doc_numb_aparam = "The dimension of the atomic parameter. If set to >0, file `aparam.npy` should be included to provided the input aparams."
@@ -1594,7 +1593,7 @@ def fitting_property():
     ]
 
 
-@fitting_args_plugin.register("polar")
+@fitting_args_plugin.register("polar", doc=doc_polar)
 def fitting_polar():
     doc_neuron = "The number of neurons in each hidden layers of the fitting net. When two hidden layers are of the same size, a skip connection is built."
     doc_activation_function = f'The activation function in the fitting net. Supported activation functions are {list_to_doc(ACTIVATION_FN_DICT.keys())} Note that "gelu" denotes the custom operator version, and "gelu_tf" denotes the TF standard version. If you set "None" or "none" here, no activation function will be used.'
@@ -1648,7 +1647,7 @@ def fitting_polar():
 #    return fitting_polar()
 
 
-@fitting_args_plugin.register("dipole")
+@fitting_args_plugin.register("dipole", doc=doc_dipole)
 def fitting_dipole():
     doc_neuron = "The number of neurons in each hidden layers of the fitting net. When two hidden layers are of the same size, a skip connection is built."
     doc_activation_function = f'The activation function in the fitting net. Supported activation functions are {list_to_doc(ACTIVATION_FN_DICT.keys())} Note that "gelu" denotes the custom operator version, and "gelu_tf" denotes the TF standard version. If you set "None" or "none" here, no activation function will be used.'
@@ -1687,11 +1686,7 @@ def fitting_dipole():
 
 #   YWolfeee: Delete global polar mode, merge it into polar mode and use loss setting to support.
 def fitting_variant_type_args():
-    doc_descrpt_type = "The type of the fitting. See explanation below. \n\n\
-- `ener`: Fit an energy model (potential energy surface).\n\n\
-- `dos` : Fit a density of states model. The total density of states / site-projected density of states labels should be provided by `dos.npy` or `atom_dos.npy` in each data system. The file has number of frames lines and number of energy grid columns (times number of atoms in `atom_dos.npy`). See `loss` parameter. \n\n\
-- `dipole`: Fit an atomic dipole model. Global dipole labels or atomic dipole labels for all the selected atoms (see `sel_type`) should be provided by `dipole.npy` in each data system. The file either has number of frames lines and 3 times of number of selected atoms columns, or has number of frames lines and 3 columns. See `loss` parameter.\n\n\
-- `polar`: Fit an atomic polarizability model. Global polarizazbility labels or atomic polarizability labels for all the selected atoms (see `sel_type`) should be provided by `polarizability.npy` in each data system. The file with has number of frames lines and 9 times of number of selected atoms columns, or has number of frames lines and 9 columns. See `loss` parameter.\n\n"
+    doc_descrpt_type = "The type of the fitting."
 
     return Variant(
         "type",
@@ -1722,12 +1717,14 @@ def modifier_dipole_charge():
 
 
 def modifier_variant_type_args():
-    doc_modifier_type = "The type of modifier. See explanation below.\n\n\
--`dipole_charge`: Use WFCC to model the electronic structure of the system. Correct the long-range interaction"
+    doc_modifier_type = "The type of modifier."
+    doc_dipole_charge = "Use WFCC to model the electronic structure of the system. Correct the long-range interaction."
     return Variant(
         "type",
         [
-            Argument("dipole_charge", dict, modifier_dipole_charge()),
+            Argument(
+                "dipole_charge", dict, modifier_dipole_charge(), doc=doc_dipole_charge
+            ),
         ],
         optional=False,
         doc=doc_modifier_type,