# 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,
tf,
)
from deepmd.fit.fitting import (
Fitting,
)
from deepmd.loss.dos import (
DOSLoss,
)
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,
)
log = logging.getLogger(__name__)
[docs]@Fitting.register("dos")
class DOSFitting(Fitting):
r"""Fitting the density of states (DOS) of the system.
The energy should be shifted by the fermi level.
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
! numb_dos (added)
Number of gridpoints on which the DOS is evaluated (NEDOS in VASP)
rcond
The condition number for the regression of atomic energy.
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.
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,
numb_dos: int = 300,
rcond: Optional[float] = None,
trainable: Optional[List[bool]] = None,
seed: Optional[int] = None,
activation_function: str = "tanh",
precision: str = "default",
uniform_seed: bool = False,
layer_name: Optional[List[Optional[str]]] = None,
use_aparam_as_mask: bool = False,
**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
self.numb_fparam = numb_fparam
self.numb_aparam = numb_aparam
self.numb_dos = numb_dos
self.n_neuron = neuron
self.resnet_dt = resnet_dt
self.rcond = rcond
self.seed = seed
self.uniform_seed = uniform_seed
self.seed_shift = one_layer_rand_seed_shift()
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.useBN = False
self.bias_dos = np.zeros((self.ntypes, self.numb_dos), 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 get_numb_dos(self) -> int:
"""Get the number of gridpoints in energy space."""
return self.numb_dos
# not used
[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['dos'] of shape n_sys x n_batch x n_frame x numb_dos
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_dos = 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["dos"]
# data[sys_idx][batch_idx][frame_idx]
sys_dos = []
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).reshape(-1, self.numb_dos)
sys_dos.append(np.average(sys_data, axis=0))
sys_dos = np.array(sys_dos).reshape(-1, self.numb_dos)
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:]
dos_shift, resd, rank, s_value = np.linalg.lstsq(
sys_tynatom, sys_dos, rcond=rcond
)
return dos_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_dos=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,
self.numb_dos, # TODO: output a vector
activation_fn=None,
bavg=bias_dos,
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_dos = self.bias_dos
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
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)
t_numb_dos = tf.constant(self.numb_dos, name="numb_dos", dtype=tf.int32)
self.t_bias_dos = tf.get_variable(
"t_bias_dos",
self.bias_dos.shape,
dtype=GLOBAL_TF_FLOAT_PRECISION,
trainable=False,
initializer=tf.constant_initializer(self.bias_dos),
)
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 bias_dos is not None:
assert len(bias_dos) == 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.reshape(
tf.slice(atype_nall, [0, 0], [-1, natoms[0]]), [-1]
) ## lammps will make error
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(self.ntypes):
final_layer = self._build_lower(
start_index,
natoms[2 + type_i],
inputs,
fparam,
aparam,
bias_dos=0.0,
type_suffix="_type_" + str(type_i),
suffix=suffix,
reuse=reuse,
)
final_layer = tf.reshape(
final_layer,
[tf.shape(inputs)[0] * self.numb_dos, 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_dos=0.0,
suffix=suffix,
reuse=reuse,
)
outs = tf.reshape(
final_layer, [tf.shape(inputs)[0] * self.numb_dos, natoms[0]]
)
# add bias
# self.atom_ener_before = outs
# self.add_type = tf.reshape(
# tf.nn.embedding_lookup(self.t_bias_dos, self.atype_nloc),
# [tf.shape(inputs)[0], natoms[0]],
# )
# outs = outs + self.add_type
# self.atom_ener_after = outs
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, f"fitting_attr{suffix}/t_fparam_avg"
)
self.fparam_inv_std = get_tensor_by_name_from_graph(
graph, f"fitting_attr{suffix}/t_fparam_istd"
)
if self.numb_aparam > 0:
self.aparam_avg = get_tensor_by_name_from_graph(
graph, f"fitting_attr{suffix}/t_aparam_avg"
)
self.aparam_inv_std = get_tensor_by_name_from_graph(
graph, f"fitting_attr{suffix}/t_aparam_istd"
)
try:
self.bias_dos = get_tensor_by_name_from_graph(
graph, f"fitting_attr{suffix}/t_bias_dos"
)
except GraphWithoutTensorError:
# for compatibility, old models has no t_bias_dos
pass
[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 dict
lr : LearningRateExp
the learning rate
Returns
-------
Loss
the loss function
"""
return DOSLoss(
**loss, starter_learning_rate=lr.start_lr(), numb_dos=self.get_numb_dos()
)