Feat: add se_atten_v2to PyTorch and DP

deepmd/dpmodel/descriptor/dpa1.py

@@ -64,7 +64,7 @@
     return x / np.linalg.norm(x, axis=axis, keepdims=True)
 
 
-@BaseDescriptor.register("se_atten")
+@BaseDescriptor.register("se_atten_v2")
 @BaseDescriptor.register("dpa1")
 class DescrptDPA1(NativeOP, BaseDescriptor):
     r"""Attention-based descriptor which is proposed in the pretrainable DPA-1[1] model.
@@ -226,7 +226,6 @@
         neuron: List[int] = [25, 50, 100],
         axis_neuron: int = 8,
         tebd_dim: int = 8,
-        tebd_input_mode: str = "concat",
         resnet_dt: bool = False,
         trainable: bool = True,
         type_one_side: bool = False,
@@ -244,7 +243,6 @@
         temperature: Optional[float] = None,
         trainable_ln: bool = True,
         ln_eps: Optional[float] = 1e-5,
-        smooth_type_embedding: bool = True,
         concat_output_tebd: bool = True,
         spin: Optional[Any] = None,
         stripped_type_embedding: Optional[bool] = None,
@@ -268,15 +266,14 @@
         if ln_eps is None:
             ln_eps = 1e-5
 
-        self.se_atten = DescrptBlockSeAtten(
+        self.se_atten = DescrptBlockSeAttenV2(
             rcut,
             rcut_smth,
             sel,
             ntypes,
             neuron=neuron,
             axis_neuron=axis_neuron,
             tebd_dim=tebd_dim,
-            tebd_input_mode=tebd_input_mode,
             set_davg_zero=set_davg_zero,
             attn=attn,
             attn_layer=attn_layer,
@@ -288,7 +285,6 @@
             scaling_factor=scaling_factor,
             normalize=normalize,
             temperature=temperature,
-            smooth=smooth_type_embedding,
             type_one_side=type_one_side,
             exclude_types=exclude_types,
             env_protection=env_protection,
@@ -871,6 +867,64 @@
         )
 
 
+@DescriptorBlock.register("se_atten_v2")
+class DescrptBlockSeAttenV2(DescrptBlockSeAtten):
+    def __init__(
+        self,
+        rcut: float,
+        rcut_smth: float,
+        sel: Union[List[int], int],
+        ntypes: int,
+        neuron: List[int] = [25, 50, 100],
+        axis_neuron: int = 8,
+        tebd_dim: int = 8,
+        resnet_dt: bool = False,
+        type_one_side: bool = False,
+        attn: int = 128,
+        attn_layer: int = 2,
+        attn_dotr: bool = True,
+        attn_mask: bool = False,
+        exclude_types: List[Tuple[int, int]] = [],
+        env_protection: float = 0.0,
+        set_davg_zero: bool = False,
+        activation_function: str = "tanh",
+        precision: str = DEFAULT_PRECISION,
+        scaling_factor=1.0,
+        normalize: bool = True,
+        temperature: Optional[float] = None,
+        trainable_ln: bool = True,
+        ln_eps: Optional[float] = 1e-5,
+    ) -> None:
+        DescrptBlockSeAtten.__init__(
+            self,
+            rcut,
+            rcut_smth,
+            sel,
+            ntypes,
+            neuron=neuron,
+            axis_neuron=axis_neuron,
+            tebd_dim=tebd_dim,
+            tebd_input_mode="strip",
+            resnet_dt=resnet_dt,
+            type_one_side=type_one_side,
+            attn=attn,
+            attn_layer=attn_layer,
+            attn_dotr=attn_dotr,
+            attn_mask=attn_mask,
+            exclude_types=exclude_types,
+            env_protection=env_protection,
+            set_davg_zero=set_davg_zero,
+            activation_function=activation_function,
+            precision=precision,
+            scaling_factor=scaling_factor,
+            normalize=normalize,
+            temperature=temperature,
+            trainable_ln=trainable_ln,
+            ln_eps=ln_eps,
+            smooth=True,
+        )
+
+
 class NeighborGatedAttention(NativeOP):
     def __init__(
         self,

deepmd/pt/model/descriptor/dpa1.py

@@ -38,13 +38,15 @@
     BaseDescriptor,
 )
 from .se_atten import (
-    DescrptBlockSeAtten,
     NeighborGatedAttention,
 )
+from .se_atten_v2 import (
+    DescrptBlockSeAttenV2,
+)
 
 
 @BaseDescriptor.register("dpa1")
-@BaseDescriptor.register("se_atten")
+@BaseDescriptor.register("se_atten_v2")
 class DescrptDPA1(BaseDescriptor, torch.nn.Module):
     r"""Attention-based descriptor which is proposed in the pretrainable DPA-1[1] model.
 
@@ -206,7 +208,6 @@
         neuron: list = [25, 50, 100],
         axis_neuron: int = 16,
         tebd_dim: int = 8,
-        tebd_input_mode: str = "concat",
         set_davg_zero: bool = True,
         attn: int = 128,
         attn_layer: int = 2,
@@ -224,7 +225,6 @@
         trainable: bool = True,
         trainable_ln: bool = True,
         ln_eps: Optional[float] = 1e-5,
-        smooth_type_embedding: bool = True,
         type_one_side: bool = False,
         stripped_type_embedding: Optional[bool] = None,
         seed: Optional[int] = None,
@@ -251,15 +251,14 @@
             ln_eps = 1e-5
 
         del type, spin, attn_mask
-        self.se_atten = DescrptBlockSeAtten(
+        self.se_atten = DescrptBlockSeAttenV2(
             rcut,
             rcut_smth,
             sel,
             ntypes,
             neuron=neuron,
             axis_neuron=axis_neuron,
             tebd_dim=tebd_dim,
-            tebd_input_mode=tebd_input_mode,
             set_davg_zero=set_davg_zero,
             attn=attn,
             attn_layer=attn_layer,
@@ -271,7 +270,6 @@
             scaling_factor=scaling_factor,
             normalize=normalize,
             temperature=temperature,
-            smooth=smooth_type_embedding,
             type_one_side=type_one_side,
             exclude_types=exclude_types,
             env_protection=env_protection,

deepmd/pt/model/descriptor/se_atten_v2.py

@@ -0,0 +1,139 @@
+# SPDX-License-Identifier: LGPL-3.0-or-later
+from typing import (
+    List,
+    Optional,
+    Tuple,
+    Union,
+)
+
+from deepmd.pt.model.descriptor.descriptor import (
+    DescriptorBlock,
+)
+from deepmd.pt.model.descriptor.se_atten import (
+    DescrptBlockSeAtten,
+)
+
+
+@DescriptorBlock.register("se_atten_v2")
+class DescrptBlockSeAttenV2(DescrptBlockSeAtten):
+    def __init__(
+        self,
+        rcut: float,
+        rcut_smth: float,
+        sel: Union[List[int], int],
+        ntypes: int,
+        neuron: list = [25, 50, 100],
+        axis_neuron: int = 16,
+        tebd_dim: int = 8,
+        set_davg_zero: bool = True,
+        attn: int = 128,
+        attn_layer: int = 2,
+        attn_dotr: bool = True,
+        attn_mask: bool = False,
+        activation_function="tanh",
+        precision: str = "float64",
+        resnet_dt: bool = False,
+        scaling_factor=1.0,
+        normalize=True,
+        temperature=None,
+        type_one_side: bool = False,
+        exclude_types: List[Tuple[int, int]] = [],
+        env_protection: float = 0.0,
+        trainable_ln: bool = True,
+        ln_eps: Optional[float] = 1e-5,
+        seed: Optional[int] = None,
+        type: Optional[str] = None,
+        old_impl: bool = False,
+    ) -> None:
+        r"""Construct an embedding net of type `se_atten`.
+
+        Parameters
+        ----------
+        rcut : float
+            The cut-off radius :math:`r_c`
+        rcut_smth : float
+            From where the environment matrix should be smoothed :math:`r_s`
+        sel : list[int], int
+            list[int]: sel[i] specifies the maxmum number of type i atoms in the cut-off radius
+            int: the total maxmum number of atoms in the cut-off radius
+        ntypes : int
+            Number of element types
+        neuron : list[int]
+            Number of neurons in each hidden layers of the embedding net :math:`\mathcal{N}`
+        axis_neuron : int
+            Number of the axis neuron :math:`M_2` (number of columns of the sub-matrix of the embedding matrix)
+        tebd_dim : int
+            Dimension of the type embedding
+        resnet_dt : bool
+            Time-step `dt` in the resnet construction:
+            y = x + dt * \phi (Wx + b)
+        trainable_ln : bool
+            Whether to use trainable shift and scale weights in layer normalization.
+        ln_eps : float, Optional
+            The epsilon value for layer normalization.
+        type_one_side : bool
+            If 'False', type embeddings of both neighbor and central atoms are considered.
+            If 'True', only type embeddings of neighbor atoms are considered.
+            Default is 'False'.
+        attn : int
+            Hidden dimension of the attention vectors
+        attn_layer : int
+            Number of attention layers
+        attn_dotr : bool
+            If dot the angular gate to the attention weights
+        attn_mask : bool
+            (Only support False to keep consistent with other backend references.)
+            (Not used in this version.)
+            If mask the diagonal of attention weights
+        exclude_types : List[List[int]]
+            The excluded pairs of types which have no interaction with each other.
+            For example, `[[0, 1]]` means no interaction between type 0 and type 1.
+        env_protection : float
+            Protection parameter to prevent division by zero errors during environment matrix calculations.
+        set_davg_zero : bool
+            Set the shift of embedding net input to zero.
+        activation_function : str
+            The activation function in the embedding net. Supported options are |ACTIVATION_FN|
+        precision : str
+            The precision of the embedding net parameters. Supported options are |PRECISION|
+        scaling_factor : float
+            The scaling factor of normalization in calculations of attention weights.
+            If `temperature` is None, the scaling of attention weights is (N_dim * scaling_factor)**0.5
+        normalize : bool
+            Whether to normalize the hidden vectors in attention weights calculation.
+        temperature : float
+            If not None, the scaling of attention weights is `temperature` itself.
+        seed : int, Optional
+            Random seed for parameter initialization.
+        """
+        DescrptBlockSeAtten.__init__(
+            self,
+            rcut,
+            rcut_smth,
+            sel,
+            ntypes,
+            neuron=neuron,
+            axis_neuron=axis_neuron,
+            tebd_dim=tebd_dim,
+            tebd_input_mode="strip",
+            set_davg_zero=set_davg_zero,
+            attn=attn,
+            attn_layer=attn_layer,
+            attn_dotr=attn_dotr,
+            attn_mask=attn_mask,
+            activation_function=activation_function,
+            precision=precision,
+            resnet_dt=resnet_dt,
+            scaling_factor=scaling_factor,
+            normalize=normalize,
+            temperature=temperature,
+            smooth=True,
+            type_one_side=type_one_side,
+            exclude_types=exclude_types,
+            env_protection=env_protection,
+            trainable_ln=trainable_ln,
+            ln_eps=ln_eps,
+            seed=seed,
+            type=type,
+            old_impl=old_impl,
+        )