feat(pd): add se_atten_v2

deepmd/pd/model/descriptor/__init__.py

@@ -22,6 +22,9 @@
     DescrptBlockSeA,
     DescrptSeA,
 )
+from .se_atten_v2 import (
+    DescrptSeAttenV2,
+)
 from .se_t_tebd import (
     DescrptBlockSeTTebd,
     DescrptSeTTebd,
@@ -37,6 +40,7 @@
     "DescrptDPA1",
     "DescrptDPA2",
     "DescrptSeA",
+    "DescrptSeAttenV2",
     "DescrptSeTTebd",
     "prod_env_mat",
 ]

deepmd/pd/model/descriptor/se_atten_v2.py

@@ -0,0 +1,275 @@
+# SPDX-License-Identifier: LGPL-3.0-or-later
+from typing import (
+    Optional,
+    Union,
+)
+
+import paddle
+
+from deepmd.dpmodel.utils import EnvMat as DPEnvMat
+from deepmd.pd.model.descriptor.dpa1 import (
+    DescrptDPA1,
+)
+from deepmd.pd.model.network.mlp import (
+    NetworkCollection,
+)
+from deepmd.pd.model.network.network import (
+    TypeEmbedNetConsistent,
+)
+from deepmd.pd.utils import (
+    env,
+)
+from deepmd.pd.utils.env import (
+    RESERVED_PRECISION_DICT,
+)
+from deepmd.utils.version import (
+    check_version_compatibility,
+)
+
+from .base_descriptor import (
+    BaseDescriptor,
+)
+from .se_atten import (
+    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 = [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: str = "tanh",
+        precision: str = "float64",
+        resnet_dt: bool = False,
+        exclude_types: list[tuple[int, int]] = [],
+        env_protection: float = 0.0,
+        scaling_factor: int = 1.0,
+        normalize=True,
+        temperature=None,
+        concat_output_tebd: bool = True,
+        trainable: bool = True,
+        trainable_ln: bool = True,
+        ln_eps: Optional[float] = 1e-5,
+        type_one_side: bool = False,
+        stripped_type_embedding: Optional[bool] = None,
+        seed: Optional[Union[int, list[int]]] = None,
+        use_econf_tebd: bool = False,
+        use_tebd_bias: bool = False,
+        type_map: Optional[list[str]] = None,
+        # not implemented
+        spin=None,
+        type: Optional[str] = None,
+    ) -> None:
+        r"""Construct smooth version of embedding net of type `se_atten_v2`.
+
+        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
+        set_davg_zero : bool
+            Set the shift of embedding net input to zero.
+        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
+        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|
+        resnet_dt : bool
+            Time-step `dt` in the resnet construction:
+            y = x + dt * \phi (Wx + b)
+        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.
+        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.
+        concat_output_tebd : bool
+            Whether to concat type embedding at the output of the descriptor.
+        trainable : bool
+            If the weights of this descriptors are trainable.
+        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'.
+        stripped_type_embedding : bool, Optional
+            (Deprecated, kept only for compatibility.)
+            Whether to strip the type embedding into a separate embedding network.
+            Setting this parameter to `True` is equivalent to setting `tebd_input_mode` to 'strip'.
+            Setting it to `False` is equivalent to setting `tebd_input_mode` to 'concat'.
+            The default value is `None`, which means the `tebd_input_mode` setting will be used instead.
+        seed : int, Optional
+            Random seed for parameter initialization.
+        use_econf_tebd : bool, Optional
+            Whether to use electronic configuration type embedding.
+        use_tebd_bias : bool, Optional
+            Whether to use bias in the type embedding layer.
+        type_map : list[str], Optional
+            A list of strings. Give the name to each type of atoms.
+        spin
+            (Only support None to keep consistent with other backend references.)
+            (Not used in this version. Not-none option is not implemented.)
+            The old implementation of deepspin.
+        """
+        DescrptDPA1.__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,
+            exclude_types=exclude_types,
+            env_protection=env_protection,
+            scaling_factor=scaling_factor,
+            normalize=normalize,
+            temperature=temperature,
+            concat_output_tebd=concat_output_tebd,
+            trainable=trainable,
+            trainable_ln=trainable_ln,
+            ln_eps=ln_eps,
+            smooth_type_embedding=True,
+            type_one_side=type_one_side,
+            stripped_type_embedding=stripped_type_embedding,
+            seed=seed,
+            use_econf_tebd=use_econf_tebd,
+            use_tebd_bias=use_tebd_bias,
+            type_map=type_map,
+            # not implemented
+            spin=spin,
+            type=type,
+        )
+
+    def serialize(self) -> dict:
+        obj = self.se_atten
+        data = {
+            "@class": "Descriptor",
+            "type": "se_atten_v2",
+            "@version": 2,
+            "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_dim,
+            "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,
+            "use_tebd_bias": self.use_tebd_bias,
+            "type_map": self.type_map,
+            # make deterministic
+            "precision": RESERVED_PRECISION_DICT[obj.prec],
+            "embeddings": obj.filter_layers.serialize(),
+            "embeddings_strip": obj.filter_layers_strip.serialize(),
+            "attention_layers": obj.dpa1_attention.serialize(),
+            "env_mat": DPEnvMat(obj.rcut, obj.rcut_smth).serialize(),
+            "type_embedding": self.type_embedding.embedding.serialize(),
+            "exclude_types": obj.exclude_types,
+            "env_protection": obj.env_protection,
+            "@variables": {
+                "davg": obj["davg"].detach().cpu().numpy(),
+                "dstd": obj["dstd"].detach().cpu().numpy(),
+            },
+            "trainable": self.trainable,
+            "spin": None,
+        }
+        return data
+
+    @classmethod
+    def deserialize(cls, data: dict) -> "DescrptSeAttenV2":
+        data = data.copy()
+        check_version_compatibility(data.pop("@version"), 2, 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")
+        # compat with version 1
+        if "use_tebd_bias" not in data:
+            data["use_tebd_bias"] = True
+        obj = cls(**data)
+
+        def t_cvt(xx):
+            return paddle.to_tensor(xx, dtype=obj.se_atten.prec, place=env.DEVICE)
+
+        obj.type_embedding.embedding = TypeEmbedNetConsistent.deserialize(
+            type_embedding
+        )
+        obj.se_atten["davg"] = t_cvt(variables["davg"])
+        obj.se_atten["dstd"] = t_cvt(variables["dstd"])
+        obj.se_atten.filter_layers = NetworkCollection.deserialize(embeddings)
+        obj.se_atten.filter_layers_strip = NetworkCollection.deserialize(
+            embeddings_strip
+        )
+        obj.se_atten.dpa1_attention = NeighborGatedAttention.deserialize(
+            attention_layers
+        )
+        return obj

source/tests/consistent/descriptor/test_se_atten_v2.py

@@ -18,6 +18,7 @@
 from ..common import (
     INSTALLED_ARRAY_API_STRICT,
     INSTALLED_JAX,
+    INSTALLED_PD,
     INSTALLED_PT,
     CommonTest,
     parameterized,
@@ -44,6 +45,12 @@
     )
 else:
     DescrptSeAttenV2Strict = None
+if INSTALLED_PD:
+    from deepmd.pd.model.descriptor.se_atten_v2 import (
+        DescrptSeAttenV2 as DescrptSeAttenV2PD,
+    )
+else:
+    DescrptSeAttenV2PD = None
 DescrptSeAttenV2TF = None
 from deepmd.utils.argcheck import (
     descrpt_se_atten_args,
@@ -248,11 +255,40 @@ def skip_array_api_strict(self) -> bool:
             )
         )
 
+    @property
+    def skip_pd(self) -> bool:
+        (
+            tebd_dim,
+            resnet_dt,
+            type_one_side,
+            attn,
+            attn_layer,
+            attn_dotr,
+            excluded_types,
+            env_protection,
+            set_davg_zero,
+            scaling_factor,
+            normalize,
+            temperature,
+            ln_eps,
+            concat_output_tebd,
+            precision,
+            use_econf_tebd,
+            use_tebd_bias,
+        ) = self.param
+        return not INSTALLED_PD or self.is_meaningless_zero_attention_layer_tests(
+            attn_layer,
+            attn_dotr,
+            normalize,
+            temperature,
+        )
+
     tf_class = DescrptSeAttenV2TF
     dp_class = DescrptSeAttenV2DP
     pt_class = DescrptSeAttenV2PT
     jax_class = DescrptSeAttenV2JAX
     array_api_strict_class = DescrptSeAttenV2Strict
+    pd_class = DescrptSeAttenV2PD
     args = descrpt_se_atten_args().append(Argument("ntypes", int, optional=False))
 
     def setUp(self) -> None:
@@ -339,6 +375,16 @@ def eval_array_api_strict(self, array_api_strict_obj: Any) -> Any:
             mixed_types=True,
         )
 
+    def eval_pd(self, pd_obj: Any) -> Any:
+        return self.eval_pd_descriptor(
+            pd_obj,
+            self.natoms,
+            self.coords,
+            self.atype,
+            self.box,
+            mixed_types=True,
+        )
+
     def extract_ret(self, ret: Any, backend) -> tuple[np.ndarray, ...]:
         return (ret[0], ret[1])