# SPDX-License-Identifier: LGPL-3.0-or-later
import logging
from typing import (
List,
Optional,
)
import numpy as np
from deepmd.common import (
add_data_requirement,
cast_precision,
get_activation_func,
get_precision,
)
from deepmd.env import (
GLOBAL_TF_FLOAT_PRECISION,
global_cvt_2_tf_float,
tf,
)
from deepmd.fit.fitting import (
Fitting,
)
from deepmd.infer import (
DeepPotential,
)
from deepmd.loss.ener import (
EnerDipoleLoss,
EnerSpinLoss,
EnerStdLoss,
)
from deepmd.loss.loss import (
Loss,
)
from deepmd.nvnmd.fit.ener import (
one_layer_nvnmd,
)
from deepmd.nvnmd.utils.config import (
nvnmd_cfg,
)
from deepmd.utils.errors import (
GraphWithoutTensorError,
)
from deepmd.utils.graph import (
get_fitting_net_variables_from_graph_def,
get_tensor_by_name_from_graph,
)
from deepmd.utils.network import one_layer as one_layer_deepmd
from deepmd.utils.network import (
one_layer_rand_seed_shift,
)
from deepmd.utils.spin import (
Spin,
)
log = logging.getLogger(__name__)
[docs]@Fitting.register("ener")
class EnerFitting(Fitting):
r"""Fitting the energy of the system. The force and the virial can also be trained.
The potential energy :math:`E` is a fitting network function of the descriptor :math:`\mathcal{D}`:
.. math::
E(\mathcal{D}) = \mathcal{L}^{(n)} \circ \mathcal{L}^{(n-1)}
\circ \cdots \circ \mathcal{L}^{(1)} \circ \mathcal{L}^{(0)}
The first :math:`n` hidden layers :math:`\mathcal{L}^{(0)}, \cdots, \mathcal{L}^{(n-1)}` are given by
.. math::
\mathbf{y}=\mathcal{L}(\mathbf{x};\mathbf{w},\mathbf{b})=
\boldsymbol{\phi}(\mathbf{x}^T\mathbf{w}+\mathbf{b})
where :math:`\mathbf{x} \in \mathbb{R}^{N_1}` is the input vector and :math:`\mathbf{y} \in \mathbb{R}^{N_2}`
is the output vector. :math:`\mathbf{w} \in \mathbb{R}^{N_1 \times N_2}` and
:math:`\mathbf{b} \in \mathbb{R}^{N_2}` are weights and biases, respectively,
both of which are trainable if `trainable[i]` is `True`. :math:`\boldsymbol{\phi}`
is the activation function.
The output layer :math:`\mathcal{L}^{(n)}` is given by
.. math::
\mathbf{y}=\mathcal{L}^{(n)}(\mathbf{x};\mathbf{w},\mathbf{b})=
\mathbf{x}^T\mathbf{w}+\mathbf{b}
where :math:`\mathbf{x} \in \mathbb{R}^{N_{n-1}}` is the input vector and :math:`\mathbf{y} \in \mathbb{R}`
is the output scalar. :math:`\mathbf{w} \in \mathbb{R}^{N_{n-1}}` and
:math:`\mathbf{b} \in \mathbb{R}` are weights and bias, respectively,
both of which are trainable if `trainable[n]` is `True`.
Parameters
----------
descrpt
The descrptor :math:`\mathcal{D}`
neuron
Number of neurons :math:`N` in each hidden layer of the fitting net
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.
seed
Random seed for initializing the network parameters.
atom_ener
Specifying atomic energy contribution in vacuum. The `set_davg_zero` key in the descrptor should be set.
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|
uniform_seed
Only for the purpose of backward compatibility, retrieves the old behavior of using the random seed
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.
"""
def __init__(
self,
descrpt: tf.Tensor,
neuron: List[int] = [120, 120, 120],
resnet_dt: bool = True,
numb_fparam: int = 0,
numb_aparam: int = 0,
rcond: Optional[float] = None,
tot_ener_zero: bool = False,
trainable: Optional[List[bool]] = None,
seed: Optional[int] = None,
atom_ener: List[float] = [],
activation_function: str = "tanh",
precision: str = "default",
uniform_seed: bool = False,
layer_name: Optional[List[Optional[str]]] = None,
use_aparam_as_mask: bool = False,
spin: Optional[Spin] = None,
**kwargs,
) -> None:
"""Constructor."""
# model param
self.ntypes = descrpt.get_ntypes()
self.dim_descrpt = descrpt.get_dim_out()
self.use_aparam_as_mask = use_aparam_as_mask
# args = ()\
# .add('numb_fparam', int, default = 0)\
# .add('numb_aparam', int, default = 0)\
# .add('neuron', list, default = [120,120,120], alias = 'n_neuron')\
# .add('resnet_dt', bool, default = True)\
# .add('rcond', float, default = 1e-3) \
# .add('tot_ener_zero', bool, default = False) \
# .add('seed', int) \
# .add('atom_ener', list, default = [])\
# .add("activation_function", str, default = "tanh")\
# .add("precision", str, default = "default")\
# .add("trainable", [list, bool], default = True)
self.numb_fparam = numb_fparam
self.numb_aparam = numb_aparam
self.n_neuron = neuron
self.resnet_dt = resnet_dt
self.rcond = rcond
self.seed = seed
self.uniform_seed = uniform_seed
self.spin = spin
self.ntypes_spin = self.spin.get_ntypes_spin() if self.spin is not None else 0
self.seed_shift = one_layer_rand_seed_shift()
self.tot_ener_zero = tot_ener_zero
self.fitting_activation_fn = get_activation_func(activation_function)
self.fitting_precision = get_precision(precision)
self.trainable = trainable
if self.trainable is None:
self.trainable = [True for ii in range(len(self.n_neuron) + 1)]
if isinstance(self.trainable, bool):
self.trainable = [self.trainable] * (len(self.n_neuron) + 1)
assert (
len(self.trainable) == len(self.n_neuron) + 1
), "length of trainable should be that of n_neuron + 1"
self.atom_ener = []
self.atom_ener_v = atom_ener
for at, ae in enumerate(atom_ener):
if ae is not None:
self.atom_ener.append(
tf.constant(ae, GLOBAL_TF_FLOAT_PRECISION, name="atom_%d_ener" % at)
)
else:
self.atom_ener.append(None)
self.useBN = False
self.bias_atom_e = np.zeros(self.ntypes, dtype=np.float64)
# data requirement
if self.numb_fparam > 0:
add_data_requirement(
"fparam", self.numb_fparam, atomic=False, must=True, high_prec=False
)
self.fparam_avg = None
self.fparam_std = None
self.fparam_inv_std = None
if self.numb_aparam > 0:
add_data_requirement(
"aparam", self.numb_aparam, atomic=True, must=True, high_prec=False
)
self.aparam_avg = None
self.aparam_std = None
self.aparam_inv_std = None
self.fitting_net_variables = None
self.mixed_prec = None
self.layer_name = layer_name
if self.layer_name is not None:
assert isinstance(self.layer_name, list), "layer_name should be a list"
assert (
len(self.layer_name) == len(self.n_neuron) + 1
), "length of layer_name should be that of n_neuron + 1"
[docs] def get_numb_fparam(self) -> int:
"""Get the number of frame parameters."""
return self.numb_fparam
[docs] def get_numb_aparam(self) -> int:
"""Get the number of atomic parameters."""
return self.numb_aparam
[docs] def compute_output_stats(self, all_stat: dict, mixed_type: bool = False) -> None:
"""Compute the ouput statistics.
Parameters
----------
all_stat
must have the following components:
all_stat['energy'] of shape n_sys x n_batch x n_frame
can be prepared by model.make_stat_input
mixed_type
Whether to perform the mixed_type mode.
If True, the input data has the mixed_type format (see doc/model/train_se_atten.md),
in which frames in a system may have different natoms_vec(s), with the same nloc.
"""
self.bias_atom_e = self._compute_output_stats(
all_stat, rcond=self.rcond, mixed_type=mixed_type
)
def _compute_output_stats(self, all_stat, rcond=1e-3, mixed_type=False):
data = all_stat["energy"]
# data[sys_idx][batch_idx][frame_idx]
sys_ener = []
for ss in range(len(data)):
sys_data = []
for ii in range(len(data[ss])):
for jj in range(len(data[ss][ii])):
sys_data.append(data[ss][ii][jj])
sys_data = np.concatenate(sys_data)
sys_ener.append(np.average(sys_data))
sys_ener = np.array(sys_ener)
sys_tynatom = []
if mixed_type:
data = all_stat["real_natoms_vec"]
nsys = len(data)
for ss in range(len(data)):
tmp_tynatom = []
for ii in range(len(data[ss])):
for jj in range(len(data[ss][ii])):
tmp_tynatom.append(data[ss][ii][jj].astype(np.float64))
tmp_tynatom = np.average(np.array(tmp_tynatom), axis=0)
sys_tynatom.append(tmp_tynatom)
else:
data = all_stat["natoms_vec"]
nsys = len(data)
for ss in range(len(data)):
sys_tynatom.append(data[ss][0].astype(np.float64))
sys_tynatom = np.array(sys_tynatom)
sys_tynatom = np.reshape(sys_tynatom, [nsys, -1])
sys_tynatom = sys_tynatom[:, 2:]
if len(self.atom_ener) > 0:
# Atomic energies stats are incorrect if atomic energies are assigned.
# In this situation, we directly use these assigned energies instead of computing stats.
# This will make the loss decrease quickly
assigned_atom_ener = np.array(
[ee for ee in self.atom_ener_v if ee is not None]
)
assigned_ener_idx = [
ii for ii, ee in enumerate(self.atom_ener_v) if ee is not None
]
# np.dot out size: nframe
sys_ener -= np.dot(sys_tynatom[:, assigned_ener_idx], assigned_atom_ener)
sys_tynatom[:, assigned_ener_idx] = 0.0
energy_shift, resd, rank, s_value = np.linalg.lstsq(
sys_tynatom, sys_ener, rcond=rcond
)
if len(self.atom_ener) > 0:
for ii in assigned_ener_idx:
energy_shift[ii] = self.atom_ener_v[ii]
return energy_shift
def _compute_std(self, sumv2, sumv, sumn):
return np.sqrt(sumv2 / sumn - np.multiply(sumv / sumn, sumv / sumn))
@cast_precision
def _build_lower(
self,
start_index,
natoms,
inputs,
fparam=None,
aparam=None,
bias_atom_e=0.0,
type_suffix="",
suffix="",
reuse=None,
):
# cut-out inputs
inputs_i = tf.slice(inputs, [0, start_index, 0], [-1, natoms, -1])
inputs_i = tf.reshape(inputs_i, [-1, self.dim_descrpt])
layer = inputs_i
if fparam is not None:
ext_fparam = tf.tile(fparam, [1, natoms])
ext_fparam = tf.reshape(ext_fparam, [-1, self.numb_fparam])
ext_fparam = tf.cast(ext_fparam, self.fitting_precision)
layer = tf.concat([layer, ext_fparam], axis=1)
if aparam is not None:
ext_aparam = tf.slice(
aparam,
[0, start_index * self.numb_aparam],
[-1, natoms * self.numb_aparam],
)
ext_aparam = tf.reshape(ext_aparam, [-1, self.numb_aparam])
ext_aparam = tf.cast(ext_aparam, self.fitting_precision)
layer = tf.concat([layer, ext_aparam], axis=1)
if nvnmd_cfg.enable:
one_layer = one_layer_nvnmd
else:
one_layer = one_layer_deepmd
for ii in range(0, len(self.n_neuron)):
if self.layer_name is not None and self.layer_name[ii] is not None:
layer_suffix = "share_" + self.layer_name[ii] + type_suffix
layer_reuse = tf.AUTO_REUSE
else:
layer_suffix = "layer_" + str(ii) + type_suffix + suffix
layer_reuse = reuse
if ii >= 1 and self.n_neuron[ii] == self.n_neuron[ii - 1]:
layer += one_layer(
layer,
self.n_neuron[ii],
name=layer_suffix,
reuse=layer_reuse,
seed=self.seed,
use_timestep=self.resnet_dt,
activation_fn=self.fitting_activation_fn,
precision=self.fitting_precision,
trainable=self.trainable[ii],
uniform_seed=self.uniform_seed,
initial_variables=self.fitting_net_variables,
mixed_prec=self.mixed_prec,
)
else:
layer = one_layer(
layer,
self.n_neuron[ii],
name=layer_suffix,
reuse=layer_reuse,
seed=self.seed,
activation_fn=self.fitting_activation_fn,
precision=self.fitting_precision,
trainable=self.trainable[ii],
uniform_seed=self.uniform_seed,
initial_variables=self.fitting_net_variables,
mixed_prec=self.mixed_prec,
)
if (not self.uniform_seed) and (self.seed is not None):
self.seed += self.seed_shift
if self.layer_name is not None and self.layer_name[-1] is not None:
layer_suffix = "share_" + self.layer_name[-1] + type_suffix
layer_reuse = tf.AUTO_REUSE
else:
layer_suffix = "final_layer" + type_suffix + suffix
layer_reuse = reuse
final_layer = one_layer(
layer,
1,
activation_fn=None,
bavg=bias_atom_e,
name=layer_suffix,
reuse=layer_reuse,
seed=self.seed,
precision=self.fitting_precision,
trainable=self.trainable[-1],
uniform_seed=self.uniform_seed,
initial_variables=self.fitting_net_variables,
mixed_prec=self.mixed_prec,
final_layer=True,
)
if (not self.uniform_seed) and (self.seed is not None):
self.seed += self.seed_shift
return final_layer
[docs] def build(
self,
inputs: tf.Tensor,
natoms: tf.Tensor,
input_dict: Optional[dict] = None,
reuse: Optional[bool] = None,
suffix: str = "",
) -> tf.Tensor:
"""Build the computational graph for fitting net.
Parameters
----------
inputs
The input descriptor
input_dict
Additional dict for inputs.
if numb_fparam > 0, should have input_dict['fparam']
if numb_aparam > 0, should have input_dict['aparam']
natoms
The number of atoms. This tensor has the length of Ntypes + 2
natoms[0]: number of local atoms
natoms[1]: total number of atoms held by this processor
natoms[i]: 2 <= i < Ntypes+2, number of type i atoms
reuse
The weights in the networks should be reused when get the variable.
suffix
Name suffix to identify this descriptor
Returns
-------
ener
The system energy
"""
if input_dict is None:
input_dict = {}
bias_atom_e = self.bias_atom_e
type_embedding = input_dict.get("type_embedding", None)
atype = input_dict.get("atype", None)
if self.numb_fparam > 0:
if self.fparam_avg is None:
self.fparam_avg = 0.0
if self.fparam_inv_std is None:
self.fparam_inv_std = 1.0
if self.numb_aparam > 0:
if self.aparam_avg is None:
self.aparam_avg = 0.0
if self.aparam_inv_std is None:
self.aparam_inv_std = 1.0
ntypes_atom = self.ntypes - self.ntypes_spin
if self.spin is not None:
for type_i in range(ntypes_atom):
if self.bias_atom_e.shape[0] != self.ntypes:
self.bias_atom_e = np.pad(
self.bias_atom_e,
(0, self.ntypes_spin),
"constant",
constant_values=(0, 0),
)
bias_atom_e = self.bias_atom_e
if self.spin.use_spin[type_i]:
self.bias_atom_e[type_i] = (
self.bias_atom_e[type_i]
+ self.bias_atom_e[type_i + ntypes_atom]
)
else:
self.bias_atom_e[type_i] = self.bias_atom_e[type_i]
self.bias_atom_e = self.bias_atom_e[:ntypes_atom]
if nvnmd_cfg.enable:
# fix the bug: CNN and QNN have different t_bias_atom_e.
if "t_bias_atom_e" in nvnmd_cfg.weight.keys():
self.bias_atom_e = nvnmd_cfg.weight["t_bias_atom_e"]
with tf.variable_scope("fitting_attr" + suffix, reuse=reuse):
t_dfparam = tf.constant(self.numb_fparam, name="dfparam", dtype=tf.int32)
t_daparam = tf.constant(self.numb_aparam, name="daparam", dtype=tf.int32)
self.t_bias_atom_e = tf.get_variable(
"t_bias_atom_e",
self.bias_atom_e.shape,
dtype=GLOBAL_TF_FLOAT_PRECISION,
trainable=False,
initializer=tf.constant_initializer(self.bias_atom_e),
)
if self.numb_fparam > 0:
t_fparam_avg = tf.get_variable(
"t_fparam_avg",
self.numb_fparam,
dtype=GLOBAL_TF_FLOAT_PRECISION,
trainable=False,
initializer=tf.constant_initializer(self.fparam_avg),
)
t_fparam_istd = tf.get_variable(
"t_fparam_istd",
self.numb_fparam,
dtype=GLOBAL_TF_FLOAT_PRECISION,
trainable=False,
initializer=tf.constant_initializer(self.fparam_inv_std),
)
if self.numb_aparam > 0:
t_aparam_avg = tf.get_variable(
"t_aparam_avg",
self.numb_aparam,
dtype=GLOBAL_TF_FLOAT_PRECISION,
trainable=False,
initializer=tf.constant_initializer(self.aparam_avg),
)
t_aparam_istd = tf.get_variable(
"t_aparam_istd",
self.numb_aparam,
dtype=GLOBAL_TF_FLOAT_PRECISION,
trainable=False,
initializer=tf.constant_initializer(self.aparam_inv_std),
)
inputs = tf.reshape(inputs, [-1, natoms[0], self.dim_descrpt])
if len(self.atom_ener):
# only for atom_ener
nframes = input_dict.get("nframes")
if nframes is not None:
# like inputs, but we don't want to add a dependency on inputs
inputs_zero = tf.zeros(
(nframes, natoms[0], self.dim_descrpt),
dtype=GLOBAL_TF_FLOAT_PRECISION,
)
else:
inputs_zero = tf.zeros_like(inputs, dtype=GLOBAL_TF_FLOAT_PRECISION)
if bias_atom_e is not None:
assert len(bias_atom_e) == self.ntypes
fparam = None
if self.numb_fparam > 0:
fparam = input_dict["fparam"]
fparam = tf.reshape(fparam, [-1, self.numb_fparam])
fparam = (fparam - t_fparam_avg) * t_fparam_istd
aparam = None
if not self.use_aparam_as_mask:
if self.numb_aparam > 0:
aparam = input_dict["aparam"]
aparam = tf.reshape(aparam, [-1, self.numb_aparam])
aparam = (aparam - t_aparam_avg) * t_aparam_istd
aparam = tf.reshape(aparam, [-1, self.numb_aparam * natoms[0]])
atype_nall = tf.reshape(atype, [-1, natoms[1]])
self.atype_nloc = tf.slice(
atype_nall, [0, 0], [-1, natoms[0]]
) ## lammps will make error
atype_filter = tf.cast(self.atype_nloc >= 0, GLOBAL_TF_FLOAT_PRECISION)
self.atype_nloc = tf.reshape(self.atype_nloc, [-1])
# prevent embedding_lookup error,
# but the filter will be applied anyway
self.atype_nloc = tf.clip_by_value(self.atype_nloc, 0, self.ntypes - 1)
## if spin is used
if self.spin is not None:
self.atype_nloc = tf.slice(
atype_nall, [0, 0], [-1, tf.reduce_sum(natoms[2 : 2 + ntypes_atom])]
)
atype_filter = tf.cast(self.atype_nloc >= 0, GLOBAL_TF_FLOAT_PRECISION)
self.atype_nloc = tf.reshape(self.atype_nloc, [-1])
if (
nvnmd_cfg.enable
and nvnmd_cfg.quantize_descriptor
and nvnmd_cfg.restore_descriptor
and (nvnmd_cfg.version == 1)
):
type_embedding = nvnmd_cfg.map["t_ebd"]
if type_embedding is not None:
atype_embed = tf.nn.embedding_lookup(type_embedding, self.atype_nloc)
else:
atype_embed = None
self.atype_embed = atype_embed
if atype_embed is None:
start_index = 0
outs_list = []
for type_i in range(ntypes_atom):
final_layer = self._build_lower(
start_index,
natoms[2 + type_i],
inputs,
fparam,
aparam,
bias_atom_e=0.0,
type_suffix="_type_" + str(type_i),
suffix=suffix,
reuse=reuse,
)
# concat the results
if type_i < len(self.atom_ener) and self.atom_ener[type_i] is not None:
zero_layer = self._build_lower(
start_index,
natoms[2 + type_i],
inputs_zero,
fparam,
aparam,
bias_atom_e=0.0,
type_suffix="_type_" + str(type_i),
suffix=suffix,
reuse=True,
)
final_layer -= zero_layer
final_layer = tf.reshape(
final_layer, [tf.shape(inputs)[0], natoms[2 + type_i]]
)
outs_list.append(final_layer)
start_index += natoms[2 + type_i]
# concat the results
# concat once may be faster than multiple concat
outs = tf.concat(outs_list, axis=1)
# with type embedding
else:
atype_embed = tf.cast(atype_embed, GLOBAL_TF_FLOAT_PRECISION)
type_shape = atype_embed.get_shape().as_list()
inputs = tf.concat(
[tf.reshape(inputs, [-1, self.dim_descrpt]), atype_embed], axis=1
)
original_dim_descrpt = self.dim_descrpt
self.dim_descrpt = self.dim_descrpt + type_shape[1]
inputs = tf.reshape(inputs, [-1, natoms[0], self.dim_descrpt])
final_layer = self._build_lower(
0,
natoms[0],
inputs,
fparam,
aparam,
bias_atom_e=0.0,
suffix=suffix,
reuse=reuse,
)
if len(self.atom_ener):
# remove contribution in vacuum
inputs_zero = tf.concat(
[tf.reshape(inputs_zero, [-1, original_dim_descrpt]), atype_embed],
axis=1,
)
inputs_zero = tf.reshape(inputs_zero, [-1, natoms[0], self.dim_descrpt])
zero_layer = self._build_lower(
0,
natoms[0],
inputs_zero,
fparam,
aparam,
bias_atom_e=0.0,
suffix=suffix,
reuse=True,
)
# atomic energy will be stored in `self.t_bias_atom_e` which is not trainable
final_layer -= zero_layer
outs = tf.reshape(final_layer, [tf.shape(inputs)[0], natoms[0]])
# add bias
self.atom_ener_before = outs * atype_filter
# atomic bias energy from data statistics
self.atom_bias_ener = tf.reshape(
tf.nn.embedding_lookup(self.t_bias_atom_e, self.atype_nloc),
[tf.shape(inputs)[0], tf.reduce_sum(natoms[2 : 2 + ntypes_atom])],
)
outs = outs + self.atom_bias_ener
outs *= atype_filter
self.atom_bias_ener *= atype_filter
self.atom_ener_after = outs
if self.tot_ener_zero:
force_tot_ener = 0.0
outs = tf.reshape(outs, [-1, tf.reduce_sum(natoms[2 : 2 + ntypes_atom])])
outs_mean = tf.reshape(tf.reduce_mean(outs, axis=1), [-1, 1])
outs_mean = outs_mean - tf.ones_like(
outs_mean, dtype=GLOBAL_TF_FLOAT_PRECISION
) * (
force_tot_ener
/ global_cvt_2_tf_float(tf.reduce_sum(natoms[2 : 2 + ntypes_atom]))
)
outs = outs - outs_mean
outs = tf.reshape(outs, [-1])
tf.summary.histogram("fitting_net_output", outs)
return tf.reshape(outs, [-1])
[docs] def init_variables(
self,
graph: tf.Graph,
graph_def: tf.GraphDef,
suffix: str = "",
) -> None:
"""Init the fitting net variables with the given dict.
Parameters
----------
graph : tf.Graph
The input frozen model graph
graph_def : tf.GraphDef
The input frozen model graph_def
suffix : str
suffix to name scope
"""
self.fitting_net_variables = get_fitting_net_variables_from_graph_def(
graph_def, suffix=suffix
)
if self.layer_name is not None:
# shared variables have no suffix
shared_variables = get_fitting_net_variables_from_graph_def(
graph_def, suffix=""
)
self.fitting_net_variables.update(shared_variables)
if self.numb_fparam > 0:
self.fparam_avg = get_tensor_by_name_from_graph(
graph, "fitting_attr%s/t_fparam_avg" % suffix
)
self.fparam_inv_std = get_tensor_by_name_from_graph(
graph, "fitting_attr%s/t_fparam_istd" % suffix
)
if self.numb_aparam > 0:
self.aparam_avg = get_tensor_by_name_from_graph(
graph, "fitting_attr%s/t_aparam_avg" % suffix
)
self.aparam_inv_std = get_tensor_by_name_from_graph(
graph, "fitting_attr%s/t_aparam_istd" % suffix
)
try:
self.bias_atom_e = get_tensor_by_name_from_graph(
graph, "fitting_attr%s/t_bias_atom_e" % suffix
)
except GraphWithoutTensorError:
# for compatibility, old models has no t_bias_atom_e
pass
[docs] def change_energy_bias(
self,
data,
frozen_model,
origin_type_map,
full_type_map,
bias_shift="delta",
ntest=10,
) -> None:
"""Change the energy bias according to the input data and the pretrained model.
Parameters
----------
data : DeepmdDataSystem
The training data.
frozen_model : str
The path file of frozen model.
origin_type_map : list
The original type_map in dataset, they are targets to change the energy bias.
full_type_map : str
The full type_map in pretrained model
bias_shift : str
The mode for changing energy bias : ['delta', 'statistic']
'delta' : perform predictions on energies of target dataset,
and do least sqaure on the errors to obtain the target shift as bias.
'statistic' : directly use the statistic energy bias in the target dataset.
ntest : int
The number of test samples in a system to change the energy bias.
"""
type_numbs = []
energy_ground_truth = []
energy_predict = []
sorter = np.argsort(full_type_map)
idx_type_map = sorter[
np.searchsorted(full_type_map, origin_type_map, sorter=sorter)
]
mixed_type = data.mixed_type
numb_type = len(full_type_map)
dp = None
if bias_shift == "delta":
# init model
dp = DeepPotential(frozen_model)
for sys in data.data_systems:
test_data = sys.get_test()
nframes = test_data["box"].shape[0]
numb_test = min(nframes, ntest)
if mixed_type:
atype = test_data["type"][:numb_test].reshape([numb_test, -1])
else:
atype = test_data["type"][0]
assert np.array(
[i in idx_type_map for i in list(set(atype.reshape(-1)))]
).all(), "Some types are not in 'type_map'!"
energy_ground_truth.append(
test_data["energy"][:numb_test].reshape([numb_test, 1])
)
if mixed_type:
type_numbs.append(
np.array(
[(atype == i).sum(axis=-1) for i in idx_type_map],
dtype=np.int32,
).T
)
else:
type_numbs.append(
np.tile(
np.bincount(atype, minlength=numb_type)[idx_type_map],
(numb_test, 1),
)
)
if bias_shift == "delta":
coord = test_data["coord"][:numb_test].reshape([numb_test, -1])
if sys.pbc:
box = test_data["box"][:numb_test]
else:
box = None
ret = dp.eval(coord, box, atype, mixed_type=mixed_type)
energy_predict.append(ret[0].reshape([numb_test, 1]))
type_numbs = np.concatenate(type_numbs)
energy_ground_truth = np.concatenate(energy_ground_truth)
old_bias = self.bias_atom_e[idx_type_map]
if bias_shift == "delta":
energy_predict = np.concatenate(energy_predict)
bias_diff = energy_ground_truth - energy_predict
delta_bias = np.linalg.lstsq(type_numbs, bias_diff, rcond=None)[0]
unbias_e = energy_predict + type_numbs @ delta_bias
atom_numbs = type_numbs.sum(-1)
rmse_ae = np.sqrt(
np.mean(
np.square(
(unbias_e.ravel() - energy_ground_truth.ravel()) / atom_numbs
)
)
)
self.bias_atom_e[idx_type_map] += delta_bias.reshape(-1)
log.info(
f"RMSE of atomic energy after linear regression is: {rmse_ae} eV/atom."
)
elif bias_shift == "statistic":
statistic_bias = np.linalg.lstsq(
type_numbs, energy_ground_truth, rcond=None
)[0]
self.bias_atom_e[idx_type_map] = statistic_bias.reshape(-1)
else:
raise RuntimeError("Unknown bias_shift mode: " + bias_shift)
log.info(
"Change energy bias of {} from {} to {}.".format(
str(origin_type_map), str(old_bias), str(self.bias_atom_e[idx_type_map])
)
)
[docs] def enable_mixed_precision(self, mixed_prec: Optional[dict] = None) -> None:
"""Reveive the mixed precision setting.
Parameters
----------
mixed_prec
The mixed precision setting used in the embedding net
"""
self.mixed_prec = mixed_prec
self.fitting_precision = get_precision(mixed_prec["output_prec"])
[docs] def get_loss(self, loss: dict, lr) -> Loss:
"""Get the loss function.
Parameters
----------
loss : dict
The loss function parameters.
lr : LearningRateExp
The learning rate.
Returns
-------
Loss
The loss function.
"""
_loss_type = loss.pop("type", "ener")
loss["starter_learning_rate"] = lr.start_lr()
if _loss_type == "ener":
return EnerStdLoss(**loss)
elif _loss_type == "ener_dipole":
return EnerDipoleLoss(**loss)
elif _loss_type == "ener_spin":
return EnerSpinLoss(**loss, use_spin=self.spin.use_spin)
else:
raise RuntimeError("unknown loss type")