Source code for deepmd.fit.dos

# 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
[docs] def compute_input_stats(self, all_stat: dict, protection: float = 1e-2) -> None: """Compute the input statistics. Parameters ---------- all_stat if numb_fparam > 0 must have all_stat['fparam'] if numb_aparam > 0 must have all_stat['aparam'] can be prepared by model.make_stat_input protection Divided-by-zero protection """ # stat fparam if self.numb_fparam > 0: cat_data = np.concatenate(all_stat["fparam"], axis=0) cat_data = np.reshape(cat_data, [-1, self.numb_fparam]) self.fparam_avg = np.average(cat_data, axis=0) self.fparam_std = np.std(cat_data, axis=0) for ii in range(self.fparam_std.size): if self.fparam_std[ii] < protection: self.fparam_std[ii] = protection self.fparam_inv_std = 1.0 / self.fparam_std # stat aparam if self.numb_aparam > 0: sys_sumv = [] sys_sumv2 = [] sys_sumn = [] for ss_ in all_stat["aparam"]: ss = np.reshape(ss_, [-1, self.numb_aparam]) sys_sumv.append(np.sum(ss, axis=0)) sys_sumv2.append(np.sum(np.multiply(ss, ss), axis=0)) sys_sumn.append(ss.shape[0]) sumv = np.sum(sys_sumv, axis=0) sumv2 = np.sum(sys_sumv2, axis=0) sumn = np.sum(sys_sumn) self.aparam_avg = (sumv) / sumn self.aparam_std = self._compute_std(sumv2, sumv, sumn) for ii in range(self.aparam_std.size): if self.aparam_std[ii] < protection: self.aparam_std[ii] = protection self.aparam_inv_std = 1.0 / self.aparam_std
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, "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_dos = get_tensor_by_name_from_graph( graph, "fitting_attr%s/t_bias_dos" % suffix ) 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() )