# SPDX-License-Identifier: LGPL-3.0-or-later
from abc import (
abstractmethod,
)
from collections.abc import (
Callable,
)
from typing import (
Any,
)
import array_api_compat
import numpy as np
from deepmd.dpmodel import (
DEFAULT_PRECISION,
PRECISION_DICT,
NativeOP,
)
from deepmd.dpmodel.array_api import (
Array,
)
from deepmd.dpmodel.common import (
get_xp_precision,
to_numpy_array,
)
from deepmd.dpmodel.utils import (
AtomExcludeMask,
FittingNet,
NetworkCollection,
)
from deepmd.dpmodel.utils.seed import (
child_seed,
)
from deepmd.env import (
GLOBAL_NP_FLOAT_PRECISION,
)
from deepmd.utils.env_mat_stat import (
StatItem,
)
from deepmd.utils.finetune import (
get_index_between_two_maps,
map_atom_exclude_types,
)
from deepmd.utils.path import (
DPPath,
)
from .base_fitting import (
BaseFitting,
)
[docs]
class GeneralFitting(NativeOP, BaseFitting):
r"""General fitting class.
Parameters
----------
var_name
The name of the output variable.
ntypes
The number of atom types.
dim_descrpt
The dimension of the input descriptor.
neuron
Number of neurons :math:`N` in each hidden layer of the fitting net
bias_atom_e
Average energy per atom for each element.
resnet_dt
Time-step `dt` in the resnet construction:
:math:`y = x + dt * \phi (Wx + b)`
numb_fparam
Number of frame parameter
numb_aparam
Number of atomic parameter
rcond
The condition number for the regression of atomic energy.
tot_ener_zero
Force the total energy to zero. Useful for the charge fitting.
trainable
If the weights of fitting net are trainable.
Suppose that we have :math:`N_l` hidden layers in the fitting net,
this list is of length :math:`N_l + 1`, specifying if the hidden layers and the output layer are trainable.
activation_function
The activation function :math:`\boldsymbol{\phi}` in the embedding net. Supported options are |ACTIVATION_FN|
precision
The precision of the embedding net parameters. Supported options are |PRECISION|
layer_name : list[Optional[str]], optional
The name of the each layer. If two layers, either in the same fitting or different fittings,
have the same name, they will share the same neural network parameters.
use_aparam_as_mask: bool, optional
If True, the atomic parameters will be used as a mask that determines the atom is real/virtual.
And the aparam will not be used as the atomic parameters for embedding.
mixed_types
If true, use a uniform fitting net for all atom types, otherwise use
different fitting nets for different atom types.
exclude_types: list[int]
Atomic contributions of the excluded atom types are set zero.
remove_vaccum_contribution: list[bool], optional
Remove vacuum contribution before the bias is added. The list assigned each
type. For `mixed_types` provide `[True]`, otherwise it should be a list of the same
length as `ntypes` signaling if or not removing the vacuum contribution for the atom types in the list.
type_map: list[str], Optional
A list of strings. Give the name to each type of atoms.
seed: Optional[Union[int, list[int]]]
Random seed for initializing the network parameters.
default_fparam: list[float], optional
The default frame parameter. If set, when `fparam.npy` files are not included in the data system,
this value will be used as the default value for the frame parameter in the fitting net.
"""
def __init__(
self,
var_name: str,
ntypes: int,
dim_descrpt: int,
neuron: list[int] = [120, 120, 120],
resnet_dt: bool = True,
numb_fparam: int = 0,
numb_aparam: int = 0,
dim_case_embd: int = 0,
bias_atom_e: Array | None = None,
rcond: float | None = None,
tot_ener_zero: bool = False,
trainable: list[bool] | None = None,
activation_function: str = "tanh",
precision: str = DEFAULT_PRECISION,
layer_name: list[str | None] | None = None,
use_aparam_as_mask: bool = False,
spin: Any = None,
mixed_types: bool = True,
exclude_types: list[int] = [],
remove_vaccum_contribution: list[bool] | None = None,
type_map: list[str] | None = None,
seed: int | list[int] | None = None,
default_fparam: list[float] | None = None,
) -> None:
[docs]
self.var_name = var_name
[docs]
self.dim_descrpt = dim_descrpt
[docs]
self.resnet_dt = resnet_dt
[docs]
self.numb_fparam = numb_fparam
[docs]
self.numb_aparam = numb_aparam
[docs]
self.dim_case_embd = dim_case_embd
[docs]
self.default_fparam = default_fparam
[docs]
self.tot_ener_zero = tot_ener_zero
[docs]
self.trainable = trainable
[docs]
self.type_map = type_map
if self.trainable is None:
self.trainable = [True for ii in range(len(self.neuron) + 1)]
if isinstance(self.trainable, bool):
self.trainable = [self.trainable] * (len(self.neuron) + 1)
[docs]
self.activation_function = activation_function
[docs]
self.precision = precision
if self.precision.lower() not in PRECISION_DICT:
raise ValueError(
f"Unsupported precision '{self.precision}'. Supported options are: {list(PRECISION_DICT.keys())}"
)
[docs]
self.prec = PRECISION_DICT[self.precision.lower()]
[docs]
self.layer_name = layer_name
[docs]
self.use_aparam_as_mask = use_aparam_as_mask
[docs]
self.mixed_types = mixed_types
# order matters, should be place after the assignment of ntypes
self.reinit_exclude(exclude_types)
if self.spin is not None:
raise NotImplementedError("spin is not supported")
[docs]
self.remove_vaccum_contribution = remove_vaccum_contribution
net_dim_out = self._net_out_dim()
# init constants
if bias_atom_e is None:
self.bias_atom_e = np.zeros(
[self.ntypes, net_dim_out], dtype=GLOBAL_NP_FLOAT_PRECISION
)
else:
assert bias_atom_e.shape == (self.ntypes, net_dim_out)
self.bias_atom_e = bias_atom_e.astype(GLOBAL_NP_FLOAT_PRECISION)
if self.numb_fparam > 0:
self.fparam_avg = np.zeros(self.numb_fparam, dtype=self.prec)
self.fparam_inv_std = np.ones(self.numb_fparam, dtype=self.prec)
else:
self.fparam_avg, self.fparam_inv_std = None, None
if self.numb_aparam > 0:
self.aparam_avg = np.zeros(self.numb_aparam, dtype=self.prec)
self.aparam_inv_std = np.ones(self.numb_aparam, dtype=self.prec)
else:
self.aparam_avg, self.aparam_inv_std = None, None
if self.dim_case_embd > 0:
self.case_embd = np.zeros(self.dim_case_embd, dtype=self.prec)
else:
self.case_embd = None
if self.default_fparam is not None:
if self.numb_fparam > 0:
assert len(self.default_fparam) == self.numb_fparam, (
"default_fparam length mismatch!"
)
self.default_fparam_tensor = np.array(self.default_fparam, dtype=self.prec)
else:
self.default_fparam_tensor = None
# init networks
in_dim = (
self.dim_descrpt
+ self.numb_fparam
+ (0 if self.use_aparam_as_mask else self.numb_aparam)
+ self.dim_case_embd
)
[docs]
self.nets = NetworkCollection(
1 if not self.mixed_types else 0,
self.ntypes,
network_type="fitting_network",
networks=[
FittingNet(
in_dim,
net_dim_out,
self.neuron,
self.activation_function,
self.resnet_dt,
self.precision,
bias_out=True,
seed=child_seed(seed, ii),
trainable=trainable,
)
for ii in range(self.ntypes if not self.mixed_types else 1)
],
)
@staticmethod
[docs]
def _save_param_stats_to_file(
stat_file_path: DPPath,
name: str,
stats: list[StatItem],
) -> None:
stat_file_path.mkdir(exist_ok=True, parents=True)
fp = stat_file_path / name
arr = np.array([[s.number, s.sum, s.squared_sum] for s in stats])
fp.save_numpy(arr)
@staticmethod
[docs]
def _load_param_stats_from_file(
stat_file_path: DPPath,
name: str,
numb: int,
) -> list[StatItem]:
fp = stat_file_path / name
arr = fp.load_numpy()
assert arr.shape == (numb, 3)
return [
StatItem(number=arr[ii][0], sum=arr[ii][1], squared_sum=arr[ii][2])
for ii in range(numb)
]
@abstractmethod
[docs]
def _net_out_dim(self) -> int:
"""Set the FittingNet output dim."""
pass
[docs]
def get_dim_fparam(self) -> int:
"""Get the number (dimension) of frame parameters of this atomic model."""
return self.numb_fparam
[docs]
def get_dim_aparam(self) -> int:
"""Get the number (dimension) of atomic parameters of this atomic model."""
return self.numb_aparam
[docs]
def has_default_fparam(self) -> bool:
"""Check if the fitting has default frame parameters."""
return self.default_fparam is not None
[docs]
def get_default_fparam(self) -> list[float] | None:
"""Get the default frame parameters."""
return self.default_fparam
[docs]
def get_sel_type(self) -> list[int]:
"""Get the selected atom types of this model.
Only atoms with selected atom types have atomic contribution
to the result of the model.
If returning an empty list, all atom types are selected.
"""
return [ii for ii in range(self.ntypes) if ii not in self.exclude_types]
[docs]
def get_type_map(self) -> list[str]:
"""Get the name to each type of atoms."""
return self.type_map
[docs]
def set_case_embd(self, case_idx: int) -> None:
"""
Set the case embedding of this fitting net by the given case_idx,
typically concatenated with the output of the descriptor and fed into the fitting net.
"""
self.case_embd = np.eye(self.dim_case_embd, dtype=self.prec)[case_idx]
[docs]
def change_type_map(
self, type_map: list[str], model_with_new_type_stat: Any | None = 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!"
)
assert self.mixed_types, "Only models in mixed types can perform type changing!"
remap_index, has_new_type = get_index_between_two_maps(self.type_map, type_map)
self.type_map = type_map
self.ntypes = len(type_map)
self.reinit_exclude(map_atom_exclude_types(self.exclude_types, remap_index))
if has_new_type:
xp = array_api_compat.array_namespace(self.bias_atom_e)
extend_shape = [len(type_map), *list(self.bias_atom_e.shape[1:])]
extend_bias_atom_e = xp.zeros(
extend_shape,
dtype=self.bias_atom_e.dtype,
device=array_api_compat.device(self.bias_atom_e),
)
self.bias_atom_e = xp.concat([self.bias_atom_e, extend_bias_atom_e], axis=0)
self.bias_atom_e = self.bias_atom_e[remap_index]
[docs]
def __setitem__(self, key: str, value: Any) -> None:
if key in ["bias_atom_e"]:
self.bias_atom_e = value
elif key in ["fparam_avg"]:
self.fparam_avg = value
elif key in ["fparam_inv_std"]:
self.fparam_inv_std = value
elif key in ["aparam_avg"]:
self.aparam_avg = value
elif key in ["aparam_inv_std"]:
self.aparam_inv_std = value
elif key in ["case_embd"]:
self.case_embd = value
elif key in ["scale"]:
self.scale = value
elif key in ["default_fparam_tensor"]:
self.default_fparam_tensor = value
else:
raise KeyError(key)
[docs]
def __getitem__(self, key: str) -> Any:
if key in ["bias_atom_e"]:
return self.bias_atom_e
elif key in ["fparam_avg"]:
return self.fparam_avg
elif key in ["fparam_inv_std"]:
return self.fparam_inv_std
elif key in ["aparam_avg"]:
return self.aparam_avg
elif key in ["aparam_inv_std"]:
return self.aparam_inv_std
elif key in ["case_embd"]:
return self.case_embd
elif key in ["scale"]:
return self.scale
elif key in ["default_fparam_tensor"]:
return self.default_fparam_tensor
else:
raise KeyError(key)
[docs]
def reinit_exclude(
self,
exclude_types: list[int] = [],
) -> None:
self.exclude_types = exclude_types
self.emask = AtomExcludeMask(self.ntypes, self.exclude_types)
[docs]
def serialize(self) -> dict:
"""Serialize the fitting to dict."""
return {
"@class": "Fitting",
"@version": 4,
"var_name": self.var_name,
"ntypes": self.ntypes,
"dim_descrpt": self.dim_descrpt,
"neuron": self.neuron,
"resnet_dt": self.resnet_dt,
"numb_fparam": self.numb_fparam,
"numb_aparam": self.numb_aparam,
"dim_case_embd": self.dim_case_embd,
"default_fparam": self.default_fparam,
"rcond": self.rcond,
"activation_function": self.activation_function,
"precision": self.precision,
"mixed_types": self.mixed_types,
"exclude_types": self.exclude_types,
"nets": self.nets.serialize(),
"@variables": {
"bias_atom_e": to_numpy_array(self.bias_atom_e),
"case_embd": to_numpy_array(self.case_embd),
"fparam_avg": to_numpy_array(self.fparam_avg),
"fparam_inv_std": to_numpy_array(self.fparam_inv_std),
"aparam_avg": to_numpy_array(self.aparam_avg),
"aparam_inv_std": to_numpy_array(self.aparam_inv_std),
},
"type_map": self.type_map,
# not supported
"tot_ener_zero": self.tot_ener_zero,
"trainable": self.trainable,
"layer_name": self.layer_name,
"use_aparam_as_mask": self.use_aparam_as_mask,
"spin": self.spin,
}
@classmethod
[docs]
def deserialize(cls, data: dict) -> "GeneralFitting":
data = data.copy()
data.pop("@class")
data.pop("type")
variables = data.pop("@variables")
nets = data.pop("nets")
obj = cls(**data)
for kk in variables.keys():
obj[kk] = variables[kk]
obj.nets = NetworkCollection.deserialize(nets)
return obj
[docs]
def _call_common(
self,
descriptor: Array,
atype: Array,
gr: Array | None = None,
g2: Array | None = None,
h2: Array | None = None,
fparam: Array | None = None,
aparam: Array | None = None,
) -> dict[str, Array]:
"""Calculate the fitting.
Parameters
----------
descriptor
input descriptor. shape: nf x nloc x nd
atype
the atom type. shape: nf x nloc
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.
shape: nf x nloc x nnei x ng
h2
The rotationally equivariant pair-partical representation.
shape: nf x nloc x nnei x 3
fparam
The frame parameter. shape: nf x nfp. nfp being `numb_fparam`
aparam
The atomic parameter. shape: nf x nloc x nap. nap being `numb_aparam`
"""
xp = array_api_compat.array_namespace(descriptor, atype)
nf, nloc, nd = descriptor.shape
net_dim_out = self._net_out_dim()
# check input dim
if nd != self.dim_descrpt:
raise ValueError(
"get an input descriptor of dim {nd},"
"which is not consistent with {self.dim_descrpt}."
)
xx = descriptor
if self.remove_vaccum_contribution is not None:
# TODO: comput the input for vacuum when setting remove_vaccum_contribution
# Ideally, the input for vacuum should be computed;
# we consider it as always zero for convenience.
# Needs a compute_input_stats for vacuum passed from the
# descriptor.
xx_zeros = xp.zeros_like(xx)
else:
xx_zeros = None
if self.numb_fparam > 0 and fparam is None:
# use default fparam
assert self.default_fparam_tensor is not None
fparam = xp.tile(
xp.reshape(self.default_fparam_tensor, (1, self.numb_fparam)), (nf, 1)
)
# check fparam dim, concate to input descriptor
if self.numb_fparam > 0:
assert fparam is not None, "fparam should not be None"
if fparam.shape[-1] != self.numb_fparam:
raise ValueError(
f"get an input fparam of dim {fparam.shape[-1]}, "
f"which is not consistent with {self.numb_fparam}."
)
fparam = (fparam - self.fparam_avg[...]) * self.fparam_inv_std[...]
fparam = xp.tile(
xp.reshape(fparam, (nf, 1, self.numb_fparam)), (1, nloc, 1)
)
xx = xp.concat(
[xx, fparam],
axis=-1,
)
if xx_zeros is not None:
xx_zeros = xp.concat(
[xx_zeros, fparam],
axis=-1,
)
# check aparam dim, concate to input descriptor
if self.numb_aparam > 0 and not self.use_aparam_as_mask:
assert aparam is not None, "aparam should not be None"
if aparam.shape[-1] != self.numb_aparam:
raise ValueError(
f"get an input aparam of dim {aparam.shape[-1]}, "
f"which is not consistent with {self.numb_aparam}."
)
aparam = xp.reshape(aparam, (nf, nloc, self.numb_aparam))
aparam = (aparam - self.aparam_avg[...]) * self.aparam_inv_std[...]
xx = xp.concat(
[xx, aparam],
axis=-1,
)
if xx_zeros is not None:
xx_zeros = xp.concat(
[xx_zeros, aparam],
axis=-1,
)
if self.dim_case_embd > 0:
assert self.case_embd is not None
case_embd = xp.tile(
xp.reshape(self.case_embd[...], (1, 1, -1)), (nf, nloc, 1)
)
xx = xp.concat(
[xx, case_embd],
axis=-1,
)
if xx_zeros is not None:
xx_zeros = xp.concat(
[xx_zeros, case_embd],
axis=-1,
)
# calculate the prediction
results: dict[str, Array] = {}
if not self.mixed_types:
outs = xp.zeros(
[nf, nloc, net_dim_out],
dtype=get_xp_precision(xp, self.precision),
device=array_api_compat.device(descriptor),
)
for type_i in range(self.ntypes):
mask = xp.tile(
xp.reshape((atype == type_i), (nf, nloc, 1)), (1, 1, net_dim_out)
)
atom_property = self.nets[(type_i,)](xx)
if self.remove_vaccum_contribution is not None and not (
len(self.remove_vaccum_contribution) > type_i
and not self.remove_vaccum_contribution[type_i]
):
assert xx_zeros is not None
atom_property -= self.nets[(type_i,)](xx_zeros)
atom_property = xp.where(
mask, atom_property, xp.zeros_like(atom_property)
)
outs = outs + atom_property # Shape is [nframes, natoms[0], 1]
else:
outs = self.nets[()](xx)
if xx_zeros is not None:
outs -= self.nets[()](xx_zeros)
outs += xp.reshape(
xp.take(
xp.astype(self.bias_atom_e[...], outs.dtype),
xp.reshape(atype, (-1,)),
axis=0,
),
(nf, nloc, net_dim_out),
)
# nf x nloc
exclude_mask = self.emask.build_type_exclude_mask(atype)
exclude_mask = xp.astype(exclude_mask, xp.bool)
# nf x nloc x nod
outs = xp.where(exclude_mask[:, :, None], outs, xp.zeros_like(outs))
results[self.var_name] = outs
return results
