Feat: add se_atten_v2to PyTorch and DP (#3840)

Solve #3831 and #3139
- add `se_atten_v2` to PyTorch and DP
- add document equation for `se_attn_v2`

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

- **New Features**
- Introduced a new descriptor class with enhanced configuration options
and methods for serialization and deserialization.
- Added new configurable parameters to the descriptor setup for improved
flexibility.

- **Documentation**
- Updated function documentation to reflect new arguments and usage
instructions.

- **Bug Fixes**
- Refined serialization logic to handle new parameters and class types
more accurately.
- Improved error messages for better clarity during serialization
processes.
<!-- end of auto-generated comment: release notes by coderabbit.ai -->

---------

Signed-off-by: Chenqqian Zhang <100290172+Chengqian-Zhang@users.noreply.github.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Duo <50307526+iProzd@users.noreply.github.com>

deepmd/dpmodel/descriptor/__init__.py

@@ -11,6 +11,9 @@
 from .make_base_descriptor import (
     make_base_descriptor,
 )
+from .se_atten_v2 import (
+    DescrptSeAttenV2,
+)
 from .se_e2_a import (
     DescrptSeA,
 )
@@ -26,6 +29,7 @@
     "DescrptSeR",
     "DescrptSeT",
     "DescrptDPA1",
+    "DescrptSeAttenV2",
     "DescrptDPA2",
     "DescrptHybrid",
     "make_base_descriptor",

deepmd/dpmodel/descriptor/se_atten_v2.py

@@ -0,0 +1,180 @@
+# SPDX-License-Identifier: LGPL-3.0-or-later
+from typing import (
+    Any,
+    List,
+    Optional,
+    Tuple,
+    Union,
+)
+
+import numpy as np
+
+from deepmd.dpmodel import (
+    DEFAULT_PRECISION,
+    PRECISION_DICT,
+)
+from deepmd.dpmodel.utils import (
+    NetworkCollection,
+)
+from deepmd.dpmodel.utils.type_embed import (
+    TypeEmbedNet,
+)
+from deepmd.utils.version import (
+    check_version_compatibility,
+)
+
+from .base_descriptor import (
+    BaseDescriptor,
+)
+from .dpa1 import (
+    DescrptDPA1,
+    NeighborGatedAttention,
+)
+
+
+@BaseDescriptor.register("se_atten_v2")
+class DescrptSeAttenV2(DescrptDPA1):
+    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,
+        trainable: bool = True,
+        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,
+        concat_output_tebd: bool = True,
+        spin: Optional[Any] = None,
+        stripped_type_embedding: Optional[bool] = None,
+        use_econf_tebd: bool = False,
+        type_map: Optional[List[str]] = None,
+        # consistent with argcheck, not used though
+        seed: Optional[int] = None,
+    ) -> None:
+        DescrptDPA1.__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,
+            trainable=trainable,
+            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_type_embedding=True,
+            concat_output_tebd=concat_output_tebd,
+            spin=spin,
+            stripped_type_embedding=stripped_type_embedding,
+            use_econf_tebd=use_econf_tebd,
+            type_map=type_map,
+            # consistent with argcheck, not used though
+            seed=seed,
+        )
+
+    def serialize(self) -> dict:
+        """Serialize the descriptor to dict."""
+        obj = self.se_atten
+        data = {
+            "@class": "Descriptor",
+            "type": "se_atten_v2",
+            "@version": 1,
+            "rcut": obj.rcut,
+            "rcut_smth": obj.rcut_smth,
+            "sel": obj.sel,
+            "ntypes": obj.ntypes,
+            "neuron": obj.neuron,
+            "axis_neuron": obj.axis_neuron,
+            "tebd_dim": obj.tebd_dim,
+            "set_davg_zero": obj.set_davg_zero,
+            "attn": obj.attn,
+            "attn_layer": obj.attn_layer,
+            "attn_dotr": obj.attn_dotr,
+            "attn_mask": False,
+            "activation_function": obj.activation_function,
+            "resnet_dt": obj.resnet_dt,
+            "scaling_factor": obj.scaling_factor,
+            "normalize": obj.normalize,
+            "temperature": obj.temperature,
+            "trainable_ln": obj.trainable_ln,
+            "ln_eps": obj.ln_eps,
+            "type_one_side": obj.type_one_side,
+            "concat_output_tebd": self.concat_output_tebd,
+            "use_econf_tebd": self.use_econf_tebd,
+            "type_map": self.type_map,
+            # make deterministic
+            "precision": np.dtype(PRECISION_DICT[obj.precision]).name,
+            "embeddings": obj.embeddings.serialize(),
+            "embeddings_strip": obj.embeddings_strip.serialize(),
+            "attention_layers": obj.dpa1_attention.serialize(),
+            "env_mat": obj.env_mat.serialize(),
+            "type_embedding": self.type_embedding.serialize(),
+            "exclude_types": obj.exclude_types,
+            "env_protection": obj.env_protection,
+            "@variables": {
+                "davg": obj["davg"],
+                "dstd": obj["dstd"],
+            },
+            ## to be updated when the options are supported.
+            "trainable": self.trainable,
+            "spin": None,
+        }
+        return data
+
+    @classmethod
+    def deserialize(cls, data: dict) -> "DescrptSeAttenV2":
+        """Deserialize from dict."""
+        data = data.copy()
+        check_version_compatibility(data.pop("@version"), 1, 1)
+        data.pop("@class")
+        data.pop("type")
+        variables = data.pop("@variables")
+        embeddings = data.pop("embeddings")
+        type_embedding = data.pop("type_embedding")
+        attention_layers = data.pop("attention_layers")
+        data.pop("env_mat")
+        embeddings_strip = data.pop("embeddings_strip")
+        obj = cls(**data)
+
+        obj.se_atten["davg"] = variables["davg"]
+        obj.se_atten["dstd"] = variables["dstd"]
+        obj.se_atten.embeddings = NetworkCollection.deserialize(embeddings)
+        obj.se_atten.embeddings_strip = NetworkCollection.deserialize(embeddings_strip)
+        obj.type_embedding = TypeEmbedNet.deserialize(type_embedding)
+        obj.se_atten.dpa1_attention = NeighborGatedAttention.deserialize(
+            attention_layers
+        )
+        return obj

deepmd/pt/model/descriptor/__init__.py

@@ -29,6 +29,9 @@
     DescrptBlockSeA,
     DescrptSeA,
 )
+from .se_atten_v2 import (
+    DescrptSeAttenV2,
+)
 from .se_r import (
     DescrptSeR,
 )
@@ -42,6 +45,7 @@
     "make_default_type_embedding",
     "DescrptBlockSeA",
     "DescrptBlockSeAtten",
+    "DescrptSeAttenV2",
     "DescrptSeA",
     "DescrptSeR",
     "DescrptSeT",