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chore: universal model compatible #273

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094a3e5
chore: universal model compatible
Franklalalala Sep 6, 2025
d1283fc
0.5.3 e3nn to be compatible with 2.0.0 torch
Franklalalala Sep 6, 2025
69ca22f
feat: add ABACUS CSR file output feature
Franklalalala Sep 12, 2025
a24366c
feat: add MACE-H no-back-grad scale
Franklalalala Sep 17, 2025
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46 changes: 36 additions & 10 deletions dptb/data/AtomicData.py
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Original file line number Diff line number Diff line change
Expand Up @@ -15,6 +15,9 @@
from ase.calculators.singlepoint import SinglePointCalculator, SinglePointDFTCalculator
from ase.calculators.calculator import all_properties as ase_all_properties
from ase.stress import voigt_6_to_full_3x3_stress, full_3x3_to_voigt_6_stress
from ase.neighborlist import NewPrimitiveNeighborList
from ase.data import chemical_symbols
import itertools

import torch
import e3nn.o3
Expand Down Expand Up @@ -971,17 +974,40 @@ def neighbor_list_and_relative_vec(

# ASE dependent part
temp_cell = ase.geometry.complete_cell(temp_cell)

first_idex, second_idex, shifts = ase.neighborlist.primitive_neighbor_list(
"ijS",
pbc,
temp_cell,
temp_pos,
cutoff=float(_r_max),
self_interaction=self_interaction, # we want edges from atom to itself in different periodic images!
use_scaled_positions=False,
##################################################################################
###################################### 新代码 ########################################
elements = np.unique(atomic_numbers).tolist()
pair_cutoffs = {}
for elem1, elem2 in itertools.combinations_with_replacement(elements, 2):
pair_cutoffs[(elem1, elem2)] = max(r_max[chemical_symbols[elem1]], r_max[chemical_symbols[elem2]])

nl = NewPrimitiveNeighborList(
cutoffs=10,
skin=0.0,
self_interaction=self_interaction,
bothways=True,
use_scaled_positions=False
)

nl.cutoffs = pair_cutoffs
nl.update(pbc, temp_cell, temp_pos, atomic_numbers)
first_idex, second_idex, shifts = nl.pair_first, nl.pair_second, nl.offset_vec
mask_r = False
_r_max = max(pair_cutoffs.values())

##################################################################################
##################################### 老代码 #########################################
# first_idex, second_idex, shifts = ase.neighborlist.primitive_neighbor_list(
# "ijS",
# pbc,
# temp_cell,
# temp_pos,
# cutoff=float(_r_max),
# self_interaction=self_interaction, # we want edges from atom to itself in different periodic images!
# use_scaled_positions=False,
# )

##################################################################################
##################################################################################
# Eliminate true self-edges that don't cross periodic boundaries
# if not self_interaction:
# bad_edge = first_idex == second_idex
Expand Down
11 changes: 7 additions & 4 deletions dptb/nn/deeptb.py
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Original file line number Diff line number Diff line change
Expand Up @@ -68,6 +68,7 @@ def __init__(
dtype: Union[str, torch.dtype] = torch.float32,
device: Union[str, torch.device] = torch.device("cpu"),
transform: bool = True,
scale_type: str = 'scale_w_back_grad',
**kwargs,
):

Expand Down Expand Up @@ -103,7 +104,7 @@ def __init__(
self.device = device
self.model_options = {"embedding": embedding.copy(), "prediction": prediction.copy()}
self.transform = transform

self.scale_type = scale_type

self.method = prediction.get("method", "e3tb")
# self.soc = prediction.get("soc", False)
Expand Down Expand Up @@ -298,9 +299,11 @@ def forward(self, data: AtomicDataDict.Type):
data = self.embedding(data)
if hasattr(self, "overlap") and self.method == "sktb":
data[AtomicDataDict.EDGE_OVERLAP_KEY] = data[AtomicDataDict.EDGE_FEATURES_KEY]

data = self.node_prediction_h(data)
data = self.edge_prediction_h(data)

if self.scale_type != 'no_scale':
data = self.node_prediction_h(data)
data = self.edge_prediction_h(data)

if hasattr(self, "overlap"):
data = self.edge_prediction_s(data)
data[AtomicDataDict.NODE_OVERLAP_KEY] = self.overlaponsite_param[data[AtomicDataDict.ATOM_TYPE_KEY].flatten()]
Expand Down
20 changes: 14 additions & 6 deletions dptb/nn/embedding/lem.py
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Original file line number Diff line number Diff line change
Expand Up @@ -39,6 +39,7 @@ def __init__(
avg_num_neighbors: Optional[float] = None,
# cutoffs
r_start_cos_ratio: float = 0.8,
norm_eps: float = 1e-8,
PolynomialCutoff_p: float = 6,
cutoff_type: str = "polynomial",
# general hyperparameters:
Expand Down Expand Up @@ -131,6 +132,7 @@ def __init__(
cutoff_type=cutoff_type,
device=device,
dtype=dtype,
norm_eps=norm_eps
)

self.layers = torch.nn.ModuleList()
Expand Down Expand Up @@ -235,6 +237,7 @@ def __init__(
latent_dim: int=128,
# cutoffs
r_start_cos_ratio: float = 0.8,
norm_eps: float = 1e-8,
PolynomialCutoff_p: float = 6,
cutoff_type: str = "polynomial",
device: Union[str, torch.device] = torch.device("cpu"),
Expand Down Expand Up @@ -290,7 +293,7 @@ def __init__(

self.sln_n = SeperableLayerNorm(
irreps=self.irreps_out,
eps=1e-5,
eps=norm_eps,
affine=True,
normalization='component',
std_balance_degrees=True,
Expand All @@ -300,7 +303,7 @@ def __init__(

self.sln_e = SeperableLayerNorm(
irreps=self.irreps_out,
eps=1e-5,
eps=norm_eps,
affine=True,
normalization='component',
std_balance_degrees=True,
Expand Down Expand Up @@ -438,6 +441,7 @@ def __init__(
irreps_in: o3.Irreps,
irreps_out: o3.Irreps,
latent_dim: int,
norm_eps: float = 1e-8,
radial_emb: bool=False,
radial_channels: list=[128, 128],
res_update: bool = True,
Expand Down Expand Up @@ -470,7 +474,7 @@ def __init__(

self.sln = SeperableLayerNorm(
irreps=self.irreps_in,
eps=1e-5,
eps=norm_eps,
affine=True,
normalization='component',
std_balance_degrees=True,
Expand All @@ -480,7 +484,7 @@ def __init__(

self.sln_e = SeperableLayerNorm(
irreps=self.edge_irreps_in,
eps=1e-5,
eps=norm_eps,
affine=True,
normalization='component',
std_balance_degrees=True,
Expand Down Expand Up @@ -614,6 +618,7 @@ def __init__(
irreps_in: o3.Irreps,
irreps_out: o3.Irreps,
latent_dim: int,
norm_eps: float = 1e-8,
latent_channels: list=[128, 128],
radial_emb: bool=False,
radial_channels: list=[128, 128],
Expand Down Expand Up @@ -675,7 +680,7 @@ def __init__(

self.sln_e = SeperableLayerNorm(
irreps=self.irreps_in,
eps=1e-5,
eps=norm_eps,
affine=True,
normalization='component',
std_balance_degrees=True,
Expand All @@ -685,7 +690,7 @@ def __init__(

self.sln_n = SeperableLayerNorm(
irreps=self.irreps_in,
eps=1e-5,
eps=norm_eps,
affine=True,
normalization='component',
std_balance_degrees=True,
Expand Down Expand Up @@ -806,6 +811,7 @@ def __init__(
tp_radial_emb: bool=False,
tp_radial_channels: list=[128, 128],
# MLP parameters:
norm_eps: float = 1e-8,
latent_channels: list=[128, 128],
latent_dim: int=128,
res_update: bool = True,
Expand Down Expand Up @@ -842,6 +848,7 @@ def __init__(
res_update_ratios_learnable=res_update_ratios_learnable,
dtype=dtype,
device=device,
norm_eps=norm_eps
)

self.node_update = UpdateNode(
Expand All @@ -857,6 +864,7 @@ def __init__(
avg_num_neighbors=avg_num_neighbors,
dtype=dtype,
device=device,
norm_eps=norm_eps
)

def forward(self, latents, node_features, edge_features, node_onehot, edge_index, edge_vector, atom_type, cutoff_coeffs, active_edges):
Expand Down
35 changes: 29 additions & 6 deletions dptb/nn/rescale.py
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Original file line number Diff line number Diff line change
Expand Up @@ -220,6 +220,7 @@ def __init__(
shifts_trainable: bool = False,
dtype: Union[str, torch.dtype] = torch.float32,
device: Union[str, torch.device] = torch.device("cpu"),
scale_type: str = 'scale_w_back_grad',
**kwargs,
):
"""Sum edges into nodes."""
Expand All @@ -233,6 +234,8 @@ def __init__(
self.dtype = dtype
self.shift_index = []
self.scale_index = []
self.scale_type = scale_type
self.scales_trainable = scales_trainable

start = 0
start_scalar = 0
Expand Down Expand Up @@ -293,7 +296,6 @@ def set_scale_shift(self, scales: torch.Tensor=None, shifts: torch.Tensor=None):
self.register_buffer("shifts", shifts)



def forward(self, data: AtomicDataDict.Type) -> AtomicDataDict.Type:

if not (self.has_scales or self.has_shifts):
Expand All @@ -305,22 +307,31 @@ def forward(self, data: AtomicDataDict.Type) -> AtomicDataDict.Type:
in_field = data[self.field][mask]
species_idx = data[AtomicDataDict.EDGE_TYPE_KEY].flatten()[mask]



assert len(in_field) == len(
edge_center[mask]
), "in_field doesnt seem to have correct per-edge shape"

if self.has_scales:
in_field = self.scales[species_idx][:,self.scale_index].view(-1, self.irreps_in.dim) * in_field
scales = self.scales[species_idx][:, self.scale_index].view(-1, self.irreps_in.dim)
if self.scale_type == 'scale_w_back_grad':
in_field = scales * in_field
elif self.scale_type == 'scale_wo_back_grad':
if self.scales_trainable:
in_field = in_field + in_field.detach() * (scales - 1.0)
else:
in_field = in_field + (in_field * (scales - 1.0)).detach()
else:
raise NotImplementedError

if self.has_shifts:
shifts = self.shifts[species_idx][:,self.shift_index[self.shift_index>=0]].view(-1, self.num_scalar)
in_field[:, self.shift_index>=0] = shifts + in_field[:, self.shift_index>=0]

data[self.out_field][mask] = in_field

return data


class E3PerSpeciesScaleShift(torch.nn.Module):
"""Scale and/or shift a predicted per-atom property based on (learnable) per-species/type parameters.

Expand Down Expand Up @@ -358,6 +369,7 @@ def __init__(
shifts_trainable: bool = False,
dtype: Union[str, torch.dtype] = torch.float32,
device: Union[str, torch.device] = torch.device("cpu"),
scale_type: str = 'scale_w_back_grad',
**kwargs,
):
super().__init__()
Expand All @@ -370,6 +382,8 @@ def __init__(
self.scale_index = []
self.dtype = dtype
self.device = device
self.scale_type = scale_type
self.scales_trainable = scales_trainable

start = 0
start_scalar = 0
Expand Down Expand Up @@ -442,7 +456,16 @@ def forward(self, data: AtomicDataDict.Type) -> AtomicDataDict.Type:
species_idx
), "in_field doesnt seem to have correct per-atom shape"
if self.has_scales:
in_field = self.scales[species_idx][:,self.scale_index].view(-1, self.irreps_in.dim) * in_field
scales = self.scales[species_idx][:, self.scale_index].view(-1, self.irreps_in.dim)
if self.scale_type == 'scale_w_back_grad':
in_field = scales * in_field
elif self.scale_type == 'scale_wo_back_grad':
if self.scales_trainable:
in_field = in_field + in_field.detach() * (scales - 1.0)
else:
in_field = in_field + (in_field * (scales - 1.0)).detach()
else:
raise NotImplementedError
if self.has_shifts:
shifts = self.shifts[species_idx][:,self.shift_index[self.shift_index>=0]].view(-1, self.num_scalar)
in_field[:, self.shift_index>=0] = shifts + in_field[:, self.shift_index>=0]
Expand Down
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