# SPDX-License-Identifier: LGPL-3.0-or-later
from typing import (
Callable,
NoReturn,
Optional,
Union,
)
import array_api_compat
import numpy as np
from deepmd.dpmodel import (
PRECISION_DICT,
NativeOP,
)
from deepmd.dpmodel.array_api import (
Array,
xp_take_along_axis,
)
from deepmd.dpmodel.common import (
cast_precision,
to_numpy_array,
)
from deepmd.dpmodel.utils import (
EmbeddingNet,
EnvMat,
NetworkCollection,
PairExcludeMask,
)
from deepmd.dpmodel.utils.env_mat_stat import (
EnvMatStatSe,
)
from deepmd.dpmodel.utils.seed import (
child_seed,
)
from deepmd.dpmodel.utils.type_embed import (
TypeEmbedNet,
)
from deepmd.dpmodel.utils.update_sel import (
UpdateSel,
)
from deepmd.utils.data_system import (
DeepmdDataSystem,
)
from deepmd.utils.env_mat_stat import (
StatItem,
)
from deepmd.utils.finetune import (
get_index_between_two_maps,
map_pair_exclude_types,
)
from deepmd.utils.path import (
DPPath,
)
from deepmd.utils.version import (
check_version_compatibility,
)
from .base_descriptor import (
BaseDescriptor,
)
from .descriptor import (
DescriptorBlock,
extend_descrpt_stat,
)
@BaseDescriptor.register("se_e3_tebd")
[docs]
class DescrptSeTTebd(NativeOP, BaseDescriptor):
r"""Construct an embedding net that takes angles between two neighboring atoms and type embeddings as input.
Parameters
----------
rcut
The cut-off radius
rcut_smth
From where the environment matrix should be smoothed
sel : Union[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
tebd_dim : int
Dimension of the type embedding
tebd_input_mode : str
The input mode of the type embedding. Supported modes are ["concat", "strip"].
- "concat": Concatenate the type embedding with the smoothed angular information as the union input for the embedding network.
- "strip": Use a separated embedding network for the type embedding and combine the output with the angular embedding network output.
resnet_dt
Time-step `dt` in the resnet construction:
y = x + dt * \phi (Wx + b)
set_davg_zero
Set the shift of embedding net input to zero.
activation_function
The activation function in the embedding net. Supported options are |ACTIVATION_FN|
env_protection: float
Protection parameter to prevent division by zero errors during environment matrix calculations.
exclude_types : list[tuple[int, 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.
precision
The precision of the embedding net parameters. Supported options are |PRECISION|
trainable
If the weights of embedding net are trainable.
seed
Random seed for initializing the network parameters.
type_map: list[str], Optional
A list of strings. Give the name to each type of atoms.
concat_output_tebd: bool
Whether to concat type embedding at the output of the descriptor.
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.
smooth: bool
Whether to use smooth process in calculation.
"""
def __init__(
self,
rcut: float,
rcut_smth: float,
sel: Union[list[int], int],
ntypes: int,
neuron: list = [2, 4, 8],
tebd_dim: int = 8,
tebd_input_mode: str = "concat",
resnet_dt: bool = False,
set_davg_zero: bool = True,
activation_function: str = "tanh",
env_protection: float = 0.0,
exclude_types: list[tuple[int, int]] = [],
precision: str = "float64",
trainable: bool = True,
seed: Optional[Union[int, list[int]]] = None,
type_map: Optional[list[str]] = None,
concat_output_tebd: bool = True,
use_econf_tebd: bool = False,
use_tebd_bias: bool = False,
smooth: bool = True,
) -> None:
[docs]
self.se_ttebd = DescrptBlockSeTTebd(
rcut,
rcut_smth,
sel,
ntypes,
neuron=neuron,
tebd_dim=tebd_dim,
tebd_input_mode=tebd_input_mode,
set_davg_zero=set_davg_zero,
activation_function=activation_function,
precision=precision,
resnet_dt=resnet_dt,
exclude_types=exclude_types,
env_protection=env_protection,
smooth=smooth,
seed=child_seed(seed, 0),
trainable=trainable,
)
[docs]
self.use_econf_tebd = use_econf_tebd
[docs]
self.type_map = type_map
[docs]
self.type_embedding = TypeEmbedNet(
ntypes=ntypes,
neuron=[tebd_dim],
padding=True,
activation_function="Linear",
precision=precision,
use_econf_tebd=use_econf_tebd,
use_tebd_bias=use_tebd_bias,
type_map=type_map,
seed=child_seed(seed, 1),
trainable=trainable,
)
[docs]
self.tebd_dim = tebd_dim
[docs]
self.concat_output_tebd = concat_output_tebd
[docs]
self.trainable = trainable
[docs]
self.precision = precision
[docs]
def get_rcut(self) -> float:
"""Returns the cut-off radius."""
return self.se_ttebd.get_rcut()
[docs]
def get_rcut_smth(self) -> float:
"""Returns the radius where the neighbor information starts to smoothly decay to 0."""
return self.se_ttebd.get_rcut_smth()
[docs]
def get_nsel(self) -> int:
"""Returns the number of selected atoms in the cut-off radius."""
return self.se_ttebd.get_nsel()
[docs]
def get_sel(self) -> list[int]:
"""Returns the number of selected atoms for each type."""
return self.se_ttebd.get_sel()
[docs]
def get_ntypes(self) -> int:
"""Returns the number of element types."""
return self.se_ttebd.get_ntypes()
[docs]
def get_type_map(self) -> list[str]:
"""Get the name to each type of atoms."""
return self.type_map
[docs]
def get_dim_out(self) -> int:
"""Returns the output dimension."""
ret = self.se_ttebd.get_dim_out()
if self.concat_output_tebd:
ret += self.tebd_dim
return ret
[docs]
def get_dim_emb(self) -> int:
return self.se_ttebd.dim_emb
[docs]
def mixed_types(self) -> bool:
"""If true, the descriptor
1. assumes total number of atoms aligned across frames;
2. requires a neighbor list that does not distinguish different atomic types.
If false, the descriptor
1. assumes total number of atoms of each atom type aligned across frames;
2. requires a neighbor list that distinguishes different atomic types.
"""
return self.se_ttebd.mixed_types()
[docs]
def has_message_passing(self) -> bool:
"""Returns whether the descriptor has message passing."""
return self.se_ttebd.has_message_passing()
[docs]
def need_sorted_nlist_for_lower(self) -> bool:
"""Returns whether the descriptor needs sorted nlist when using `forward_lower`."""
return self.se_ttebd.need_sorted_nlist_for_lower()
[docs]
def get_env_protection(self) -> float:
"""Returns the protection of building environment matrix."""
return self.se_ttebd.get_env_protection()
[docs]
def share_params(
self, base_class: "DescrptSeTTebd", shared_level: int, resume: bool = False
) -> NoReturn:
"""
Share the parameters of self to the base_class with shared_level during multitask training.
If not start from checkpoint (resume is False),
some separated parameters (e.g. mean and stddev) will be re-calculated across different classes.
"""
raise NotImplementedError
@property
[docs]
def dim_out(self) -> int:
return self.get_dim_out()
@property
[docs]
def dim_emb(self) -> int:
return self.get_dim_emb()
[docs]
def set_stat_mean_and_stddev(
self,
mean: Array,
stddev: Array,
) -> None:
"""Update mean and stddev for descriptor."""
self.se_ttebd.mean = mean
self.se_ttebd.stddev = stddev
[docs]
def get_stat_mean_and_stddev(self) -> tuple[Array, Array]:
"""Get mean and stddev for descriptor."""
return self.se_ttebd.mean, self.se_ttebd.stddev
[docs]
def change_type_map(
self,
type_map: list[str],
model_with_new_type_stat: Optional["DescrptSeTTebd"] = None,
) -> None:
"""Change the type related params to new ones, according to `type_map` and the original one in the model.
If there are new types in `type_map`, statistics will be updated accordingly to `model_with_new_type_stat` for these new types.
"""
assert self.type_map is not None, (
"'type_map' must be defined when performing type changing!"
)
remap_index, has_new_type = get_index_between_two_maps(self.type_map, type_map)
obj = self.se_ttebd
obj.ntypes = len(type_map)
self.type_map = type_map
self.type_embedding.change_type_map(type_map=type_map)
obj.reinit_exclude(map_pair_exclude_types(obj.exclude_types, remap_index))
if has_new_type:
# the avg and std of new types need to be updated
extend_descrpt_stat(
obj,
type_map,
des_with_stat=model_with_new_type_stat.se_ttebd
if model_with_new_type_stat is not None
else None,
)
obj["davg"] = obj["davg"][remap_index]
obj["dstd"] = obj["dstd"][remap_index]
@cast_precision
[docs]
def call(
self,
coord_ext: Array,
atype_ext: Array,
nlist: Array,
mapping: Optional[Array] = None,
) -> tuple[Array, Array]:
"""Compute the descriptor.
Parameters
----------
coord_ext
The extended coordinates of atoms. shape: nf x (nallx3)
atype_ext
The extended aotm types. shape: nf x nall
nlist
The neighbor list. shape: nf x nloc x nnei
mapping
The index mapping from extended to local region. not used by this descriptor.
Returns
-------
descriptor
The descriptor. shape: nf x nloc x (ng x axis_neuron)
gr
The rotationally equivariant and permutationally invariant single particle
representation. shape: nf x nloc x ng x 3
g2
The rotationally invariant pair-partical representation.
this descriptor returns None
h2
The rotationally equivariant pair-partical representation.
this descriptor returns None
sw
The smooth switch function.
"""
xp = array_api_compat.array_namespace(nlist, coord_ext, atype_ext)
del mapping
nf, nloc, nnei = nlist.shape
nall = xp.reshape(coord_ext, (nf, -1)).shape[1] // 3
type_embedding = self.type_embedding.call()
# nf x nall x tebd_dim
atype_embd_ext = xp.reshape(
xp.take(type_embedding, xp.reshape(atype_ext, (-1,)), axis=0),
(nf, nall, self.tebd_dim),
)
# nfnl x tebd_dim
atype_embd = atype_embd_ext[:, :nloc, :]
grrg, g2, h2, rot_mat, sw = self.se_ttebd(
nlist,
coord_ext,
atype_ext,
atype_embd_ext,
mapping=None,
type_embedding=type_embedding,
)
# nf x nloc x (ng + tebd_dim)
if self.concat_output_tebd:
grrg = xp.concat(
[grrg, xp.reshape(atype_embd, (nf, nloc, self.tebd_dim))], axis=-1
)
return grrg, rot_mat, None, None, sw
[docs]
def serialize(self) -> dict:
"""Serialize the descriptor to dict."""
obj = self.se_ttebd
data = {
"@class": "Descriptor",
"type": "se_e3_tebd",
"@version": 1,
"rcut": obj.rcut,
"rcut_smth": obj.rcut_smth,
"sel": obj.sel,
"ntypes": obj.ntypes,
"neuron": obj.neuron,
"tebd_dim": obj.tebd_dim,
"tebd_input_mode": obj.tebd_input_mode,
"set_davg_zero": obj.set_davg_zero,
"activation_function": obj.activation_function,
"resnet_dt": obj.resnet_dt,
"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(),
"env_mat": obj.env_mat.serialize(),
"type_embedding": self.type_embedding.serialize(),
"exclude_types": obj.exclude_types,
"env_protection": obj.env_protection,
"smooth": self.smooth,
"@variables": {
"davg": to_numpy_array(obj["davg"]),
"dstd": to_numpy_array(obj["dstd"]),
},
"trainable": self.trainable,
}
if obj.tebd_input_mode in ["strip"]:
data.update({"embeddings_strip": obj.embeddings_strip.serialize()})
return data
@classmethod
[docs]
def deserialize(cls, data: dict) -> "DescrptSeTTebd":
"""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")
env_mat = data.pop("env_mat")
tebd_input_mode = data["tebd_input_mode"]
if tebd_input_mode in ["strip"]:
embeddings_strip = data.pop("embeddings_strip")
else:
embeddings_strip = None
obj = cls(**data)
obj.type_embedding = TypeEmbedNet.deserialize(type_embedding)
obj.se_ttebd["davg"] = variables["davg"]
obj.se_ttebd["dstd"] = variables["dstd"]
obj.se_ttebd.embeddings = NetworkCollection.deserialize(embeddings)
if tebd_input_mode in ["strip"]:
obj.se_ttebd.embeddings_strip = NetworkCollection.deserialize(
embeddings_strip
)
return obj
@classmethod
[docs]
def update_sel(
cls,
train_data: DeepmdDataSystem,
type_map: Optional[list[str]],
local_jdata: dict,
) -> tuple[Array, Array]:
"""Update the selection and perform neighbor statistics.
Parameters
----------
train_data : DeepmdDataSystem
data used to do neighbor statistics
type_map : list[str], optional
The name of each type of atoms
local_jdata : dict
The local data refer to the current class
Returns
-------
dict
The updated local data
float
The minimum distance between two atoms
"""
local_jdata_cpy = local_jdata.copy()
min_nbor_dist, sel = UpdateSel().update_one_sel(
train_data, type_map, local_jdata_cpy["rcut"], local_jdata_cpy["sel"], True
)
local_jdata_cpy["sel"] = sel[0]
return local_jdata_cpy, min_nbor_dist
@DescriptorBlock.register("se_ttebd")
[docs]
class DescrptBlockSeTTebd(NativeOP, DescriptorBlock):
def __init__(
self,
rcut: float,
rcut_smth: float,
sel: Union[list[int], int],
ntypes: int,
neuron: list = [25, 50, 100],
tebd_dim: int = 8,
tebd_input_mode: str = "concat",
set_davg_zero: bool = True,
activation_function: str = "tanh",
precision: str = "float64",
resnet_dt: bool = False,
exclude_types: list[tuple[int, int]] = [],
env_protection: float = 0.0,
smooth: bool = True,
seed: Optional[Union[int, list[int]]] = None,
trainable: bool = True,
) -> None:
[docs]
self.rcut_smth = rcut_smth
[docs]
self.filter_neuron = self.neuron
[docs]
self.tebd_dim = tebd_dim
[docs]
self.set_davg_zero = set_davg_zero
[docs]
self.activation_function = activation_function
[docs]
self.precision = precision
[docs]
self.resnet_dt = resnet_dt
[docs]
self.env_protection = env_protection
if isinstance(sel, int):
sel = [sel]
[docs]
self.split_sel = self.sel
[docs]
self.ndescrpt = self.nnei * 4
# order matters, placed after the assignment of self.ntypes
self.reinit_exclude(exclude_types)
if self.tebd_input_mode in ["concat"]:
self.embd_input_dim = 1 + self.tebd_dim_input
else:
self.embd_input_dim = 1
embeddings = NetworkCollection(
ndim=0,
ntypes=self.ntypes,
network_type="embedding_network",
)
embeddings[0] = EmbeddingNet(
self.embd_input_dim,
self.neuron,
self.activation_function,
self.resnet_dt,
self.precision,
seed=child_seed(seed, 0),
trainable=trainable,
)
[docs]
self.embeddings = embeddings
if self.tebd_input_mode in ["strip"]:
embeddings_strip = NetworkCollection(
ndim=0,
ntypes=self.ntypes,
network_type="embedding_network",
)
embeddings_strip[0] = EmbeddingNet(
self.tebd_dim_input,
self.neuron,
self.activation_function,
self.resnet_dt,
self.precision,
seed=child_seed(seed, 1),
trainable=trainable,
)
self.embeddings_strip = embeddings_strip
else:
self.embeddings_strip = None
wanted_shape = (self.ntypes, self.nnei, 4)
[docs]
self.env_mat = EnvMat(self.rcut, self.rcut_smth, protection=self.env_protection)
[docs]
self.mean = np.zeros(wanted_shape, dtype=PRECISION_DICT[self.precision])
[docs]
self.stddev = np.ones(wanted_shape, dtype=PRECISION_DICT[self.precision])
[docs]
self.orig_sel = self.sel
[docs]
def get_rcut(self) -> float:
"""Returns the cut-off radius."""
return self.rcut
[docs]
def get_rcut_smth(self) -> float:
"""Returns the radius where the neighbor information starts to smoothly decay to 0."""
return self.rcut_smth
[docs]
def get_nsel(self) -> int:
"""Returns the number of selected atoms in the cut-off radius."""
return sum(self.sel)
[docs]
def get_sel(self) -> list[int]:
"""Returns the number of selected atoms for each type."""
return self.sel
[docs]
def get_ntypes(self) -> int:
"""Returns the number of element types."""
return self.ntypes
[docs]
def get_dim_in(self) -> int:
"""Returns the input dimension."""
return self.dim_in
[docs]
def get_dim_out(self) -> int:
"""Returns the output dimension."""
return self.dim_out
[docs]
def get_dim_emb(self) -> int:
"""Returns the output dimension of embedding."""
return self.filter_neuron[-1]
[docs]
def __setitem__(self, key: str, value: Array) -> None:
if key in ("avg", "data_avg", "davg"):
self.mean = value
elif key in ("std", "data_std", "dstd"):
self.stddev = value
else:
raise KeyError(key)
[docs]
def __getitem__(self, key: str) -> Array:
if key in ("avg", "data_avg", "davg"):
return self.mean
elif key in ("std", "data_std", "dstd"):
return self.stddev
else:
raise KeyError(key)
[docs]
def mixed_types(self) -> bool:
"""If true, the descriptor
1. assumes total number of atoms aligned across frames;
2. requires a neighbor list that does not distinguish different atomic types.
If false, the descriptor
1. assumes total number of atoms of each atom type aligned across frames;
2. requires a neighbor list that distinguishes different atomic types.
"""
return True
[docs]
def get_env_protection(self) -> float:
"""Returns the protection of building environment matrix."""
return self.env_protection
@property
[docs]
def dim_out(self) -> int:
"""Returns the output dimension of this descriptor."""
return self.filter_neuron[-1]
@property
[docs]
def dim_in(self) -> int:
"""Returns the atomic input dimension of this descriptor."""
return self.tebd_dim
@property
[docs]
def dim_emb(self) -> int:
"""Returns the output dimension of embedding."""
return self.get_dim_emb()
[docs]
def get_stats(self) -> dict[str, StatItem]:
"""Get the statistics of the descriptor."""
if self.stats is None:
raise RuntimeError(
"The statistics of the descriptor has not been computed."
)
return self.stats
[docs]
def reinit_exclude(
self,
exclude_types: list[tuple[int, int]] = [],
) -> None:
self.exclude_types = exclude_types
self.emask = PairExcludeMask(self.ntypes, exclude_types=exclude_types)
[docs]
def cal_g(
self,
ss: Array,
embedding_idx: int,
) -> Array:
# nfnl x nt_i x nt_j x ng
gg = self.embeddings[embedding_idx].call(ss)
return gg
[docs]
def cal_g_strip(
self,
ss: Array,
embedding_idx: int,
) -> Array:
assert self.embeddings_strip is not None
# nfnl x nt_i x nt_j x ng
gg = self.embeddings_strip[embedding_idx].call(ss)
return gg
[docs]
def call(
self,
nlist: Array,
coord_ext: Array,
atype_ext: Array,
atype_embd_ext: Optional[Array] = None,
mapping: Optional[Array] = None,
type_embedding: Optional[Array] = None,
) -> tuple[Array, Array]:
xp = array_api_compat.array_namespace(nlist, coord_ext, atype_ext)
# nf x nloc x nnei x 4
dmatrix, diff, sw = self.env_mat.call(
coord_ext,
atype_ext,
nlist,
self.mean[...],
self.stddev[...],
)
nf, nloc, nnei, _ = dmatrix.shape
exclude_mask = self.emask.build_type_exclude_mask(nlist, atype_ext)
# nfnl x nnei
exclude_mask = xp.reshape(exclude_mask, (nf * nloc, nnei))
# nfnl x nnei
nlist = xp.reshape(nlist, (nf * nloc, nnei))
exclude_mask = xp.astype(exclude_mask, xp.bool)
nlist = xp.where(exclude_mask, nlist, xp.full_like(nlist, -1))
# nfnl x nnei
nlist_mask = nlist != -1
# nfnl x nnei x 1
sw = xp.where(
nlist_mask[:, :, None],
xp.reshape(sw, (nf * nloc, nnei, 1)),
xp.zeros((nf * nloc, nnei, 1), dtype=sw.dtype),
)
# nfnl x nnei x 4
dmatrix = xp.reshape(dmatrix, (nf * nloc, nnei, 4))
# nfnl x nnei x 4
rr = dmatrix
rr = rr * xp.astype(exclude_mask[:, :, None], rr.dtype)
# nfnl x nt_i x 3
rr_i = rr[:, :, 1:]
# nfnl x nt_j x 3
rr_j = rr[:, :, 1:]
# nfnl x nt_i x nt_j
# env_ij = np.einsum("ijm,ikm->ijk", rr_i, rr_j)
env_ij = xp.sum(rr_i[:, :, None, :] * rr_j[:, None, :, :], axis=-1)
# nfnl x nt_i x nt_j x 1
ss = env_ij[..., None]
nlist_masked = xp.where(nlist_mask, nlist, xp.zeros_like(nlist))
ng = self.neuron[-1]
nt = self.tebd_dim
if self.tebd_input_mode in ["concat"]:
index = xp.tile(
xp.reshape(nlist_masked, (nf, -1, 1)), (1, 1, self.tebd_dim)
)
# nfnl x nnei x tebd_dim
atype_embd_nlist = xp_take_along_axis(atype_embd_ext, index, axis=1)
atype_embd_nlist = xp.reshape(
atype_embd_nlist, (nf * nloc, nnei, self.tebd_dim)
)
# nfnl x nt_i x nt_j x tebd_dim
nlist_tebd_i = xp.tile(
atype_embd_nlist[:, :, None, :], (1, 1, self.nnei, 1)
)
nlist_tebd_j = xp.tile(
atype_embd_nlist[:, None, :, :], (1, self.nnei, 1, 1)
)
# nfnl x nt_i x nt_j x (1 + tebd_dim * 2)
ss = xp.concat([ss, nlist_tebd_i, nlist_tebd_j], axis=-1)
# nfnl x nt_i x nt_j x ng
gg = self.cal_g(ss, 0)
elif self.tebd_input_mode in ["strip"]:
# nfnl x nt_i x nt_j x ng
gg_s = self.cal_g(ss, 0)
assert self.embeddings_strip is not None
assert type_embedding is not None
ntypes_with_padding = type_embedding.shape[0]
# nf x (nl x nnei)
nlist_index = xp.reshape(nlist_masked, (nf, nloc * nnei))
# nf x (nl x nnei)
nei_type = xp_take_along_axis(atype_ext, nlist_index, axis=1)
# nfnl x nnei
nei_type = xp.reshape(nei_type, (nf * nloc, nnei))
# nfnl x nnei x nnei
nei_type_i = xp.tile(nei_type[:, :, np.newaxis], (1, 1, nnei))
nei_type_j = xp.tile(nei_type[:, np.newaxis, :], (1, nnei, 1))
idx_i = nei_type_i * ntypes_with_padding
idx_j = nei_type_j
# (nf x nl x nt_i x nt_j) x ng
idx = xp.tile(xp.reshape((idx_i + idx_j), (-1, 1)), (1, ng))
# ntypes * (ntypes) * nt
type_embedding_i = xp.tile(
xp.reshape(type_embedding, (ntypes_with_padding, 1, nt)),
(1, ntypes_with_padding, 1),
)
# (ntypes) * ntypes * nt
type_embedding_j = xp.tile(
xp.reshape(type_embedding, (1, ntypes_with_padding, nt)),
(ntypes_with_padding, 1, 1),
)
# (ntypes * ntypes) * (nt+nt)
two_side_type_embedding = xp.reshape(
xp.concat([type_embedding_i, type_embedding_j], axis=-1), (-1, nt * 2)
)
tt_full = self.cal_g_strip(two_side_type_embedding, 0)
# (nfnl x nt_i x nt_j) x ng
gg_t = xp_take_along_axis(tt_full, idx, axis=0)
# (nfnl x nt_i x nt_j) x ng
gg_t = xp.reshape(gg_t, (nf * nloc, nnei, nnei, ng))
if self.smooth:
gg_t = (
gg_t
* xp.reshape(sw, (nf * nloc, self.nnei, 1, 1))
* xp.reshape(sw, (nf * nloc, 1, self.nnei, 1))
)
# nfnl x nt_i x nt_j x ng
gg = gg_s * gg_t + gg_s
else:
raise NotImplementedError
# nfnl x ng
# res_ij = np.einsum("ijk,ijkm->im", env_ij, gg)
res_ij = xp.sum(env_ij[:, :, :, None] * gg[:, :, :, :], axis=(1, 2))
res_ij = res_ij * (1.0 / float(self.nnei) / float(self.nnei))
# nf x nl x ng
result = xp.reshape(res_ij, (nf, nloc, self.filter_neuron[-1]))
return (
result,
None,
None,
None,
sw,
)
[docs]
def has_message_passing(self) -> bool:
"""Returns whether the descriptor block has message passing."""
return False
[docs]
def need_sorted_nlist_for_lower(self) -> bool:
"""Returns whether the descriptor block needs sorted nlist when using `forward_lower`."""
return False
[docs]
def serialize(self) -> dict:
"""Serialize the descriptor to dict."""
obj = self
data = {
"@class": "Descriptor",
"type": "se_e3_tebd",
"@version": 1,
"rcut": obj.rcut,
"rcut_smth": obj.rcut_smth,
"sel": obj.sel,
"ntypes": obj.ntypes,
"neuron": obj.neuron,
"tebd_dim": obj.tebd_dim,
"tebd_input_mode": obj.tebd_input_mode,
"set_davg_zero": obj.set_davg_zero,
"activation_function": obj.activation_function,
"resnet_dt": obj.resnet_dt,
# make deterministic
"precision": np.dtype(PRECISION_DICT[obj.precision]).name,
"embeddings": obj.embeddings.serialize(),
"env_mat": obj.env_mat.serialize(),
"exclude_types": obj.exclude_types,
"env_protection": obj.env_protection,
"smooth": obj.smooth,
"@variables": {
"davg": to_numpy_array(obj["davg"]),
"dstd": to_numpy_array(obj["dstd"]),
},
}
if obj.tebd_input_mode in ["strip"]:
data.update({"embeddings_strip": obj.embeddings_strip.serialize()})
return data
@classmethod
[docs]
def deserialize(cls, data: dict) -> "DescrptSeTTebd":
"""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")
env_mat = data.pop("env_mat")
tebd_input_mode = data["tebd_input_mode"]
if tebd_input_mode in ["strip"]:
embeddings_strip = data.pop("embeddings_strip")
else:
embeddings_strip = None
se_ttebd = cls(**data)
se_ttebd["davg"] = variables["davg"]
se_ttebd["dstd"] = variables["dstd"]
se_ttebd.embeddings = NetworkCollection.deserialize(embeddings)
if tebd_input_mode in ["strip"]:
se_ttebd.embeddings_strip = NetworkCollection.deserialize(embeddings_strip)
return se_ttebd
