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Model.py
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#!/usr/bin/env python3
import os
import sys
import time
import shutil
import warnings
import numpy as np
import tensorflow as tf
from deepmd.RunOptions import global_tf_float_precision
from deepmd.RunOptions import global_np_float_precision
from deepmd.RunOptions import global_ener_float_precision
from deepmd.RunOptions import global_cvt_2_tf_float
from deepmd.RunOptions import global_cvt_2_ener_float
from tensorflow.python.framework import ops
from tensorflow.python.client import timeline
# load force module
module_path = os.path.dirname(os.path.realpath(__file__)) + "/"
assert (os.path.isfile (module_path + "libop_abi.so" )), "op module does not exist"
op_module = tf.load_op_library(module_path + "libop_abi.so")
# load grad of force module
sys.path.append (module_path )
import deepmd._prod_force_grad
import deepmd._prod_virial_grad
import deepmd._prod_force_norot_grad
import deepmd._prod_virial_norot_grad
from deepmd.RunOptions import RunOptions
def j_must_have (jdata, key) :
if not key in jdata.keys() :
raise RuntimeError ("json database must provide key " + key )
else :
return jdata[key]
def j_must_have_d (jdata, key, deprecated_key) :
if not key in jdata.keys() :
# raise RuntimeError ("json database must provide key " + key )
for ii in deprecated_key :
if ii in jdata.keys() :
warnings.warn("the key \"%s\" is deprecated, please use \"%s\" instead" % (ii,key))
return jdata[ii]
raise RuntimeError ("json database must provide key " + key )
else :
return jdata[key]
def j_have (jdata, key) :
return key in jdata.keys()
def _is_subdir(path, directory):
path = os.path.realpath(path)
directory = os.path.realpath(directory)
if path == directory:
return False
relative = os.path.relpath(path, directory) + os.sep
return not relative.startswith(os.pardir + os.sep)
class LearingRate (object) :
def __init__ (self,
jdata,
tot_numb_batches) :
self.decay_steps_ = j_must_have(jdata, 'decay_steps')
self.decay_rate_ = j_must_have(jdata, 'decay_rate')
self.start_lr_ = j_must_have(jdata, 'start_lr')
self.tot_numb_batches = tot_numb_batches
def value (self,
batch) :
return self.start_lr_ * np.power (self.decay_rate_, (batch // self.decay_steps()))
def decay_steps (self) :
# return self.decay_steps_ * self.tot_numb_batches
return self.decay_steps_
def decay_rate (self) :
return self.decay_rate_
def start_lr (self) :
return self.start_lr_
class NNPModel (object):
def __init__(self,
jdata,
run_opt):
self.run_opt = run_opt
self._init_param(jdata)
self.null_mesh = tf.constant ([-1])
def _init_param(self, jdata):
# descrpt config
self.use_smooth = False
if j_have (jdata, "use_smooth") :
self.use_smooth = jdata["use_smooth"]
self.sel_a = j_must_have (jdata, 'sel_a')
self.sel_r = [ 0 for ii in range(len(self.sel_a)) ]
if not self.use_smooth :
self.sel_r = j_must_have (jdata, 'sel_r')
else :
if j_have (jdata, 'sel_r') :
warnings.warn ('ignoring key sel_r in the json database and set sel_r to %s' % str(self.sel_r))
self.rcut_a = -1
self.rcut_r = j_must_have (jdata, 'rcut')
if j_have(jdata, 'rcut_smth') :
self.rcut_r_smth = jdata['rcut_smth']
else :
self.rcut_r_smth = self.rcut_r
# axis
self.axis_rule = []
if j_have (jdata, 'axis_rule') :
self.axis_rule = jdata['axis_rule']
# filter of smooth version
if self.use_smooth :
if j_have(jdata, 'coord_norm') :
self.coord_norm = jdata['coord_norm']
else :
self.coord_norm = True
self.filter_neuron = j_must_have (jdata, 'filter_neuron')
self.n_axis_neuron = j_must_have_d (jdata, 'axis_neuron', ['n_axis_neuron'])
self.filter_resnet_dt = False
if j_have(jdata, 'filter_resnet_dt') :
self.filter_resnet_dt = jdata['filter_resnet_dt']
# numb of neighbors and numb of descrptors
self.nnei_a = np.cumsum(self.sel_a)[-1]
self.nnei_r = np.cumsum(self.sel_r)[-1]
self.nnei = self.nnei_a + self.nnei_r
self.ndescrpt_a = self.nnei_a * 4
self.ndescrpt_r = self.nnei_r * 1
self.ndescrpt = self.ndescrpt_a + self.ndescrpt_r
# network size
self.n_neuron = j_must_have_d (jdata, 'fitting_neuron', ['n_neuron'])
self.resnet_dt = True
if j_have(jdata, 'resnet_dt') :
warnings.warn("the key \"%s\" is deprecated, please use \"%s\" instead" % ('resnet_dt','fitting_resnet_dt'))
self.resnet_dt = jdata['resnet_dt']
if j_have(jdata, 'fitting_resnet_dt') :
self.resnet_dt = jdata['fitting_resnet_dt']
if self.use_smooth :
if j_have(jdata, 'type_fitting_net') :
self.type_fitting_net = jdata['type_fitting_net']
else :
self.type_fitting_net = False
self.numb_test = j_must_have (jdata, 'numb_test')
self.useBN = False
self.start_pref_e = j_must_have (jdata, 'start_pref_e')
self.limit_pref_e = j_must_have (jdata, 'limit_pref_e')
self.start_pref_f = j_must_have (jdata, 'start_pref_f')
self.limit_pref_f = j_must_have (jdata, 'limit_pref_f')
self.start_pref_v = j_must_have (jdata, 'start_pref_v')
self.limit_pref_v = j_must_have (jdata, 'limit_pref_v')
self.start_pref_ae = 0
if j_have(jdata, 'start_pref_ae') :
self.start_pref_ae = jdata['start_pref_ae']
self.limit_pref_ae = 0
if j_have(jdata, 'limit_pref_ae') :
self.limit_pref_ae = jdata['limit_pref_ae']
self.has_e = (self.start_pref_e != 0 or self.limit_pref_e != 0)
self.has_f = (self.start_pref_f != 0 or self.limit_pref_f != 0)
self.has_v = (self.start_pref_v != 0 or self.limit_pref_v != 0)
self.has_ae = (self.start_pref_ae != 0 or self.limit_pref_ae != 0)
self.disp_file = "lcurve.out"
if j_have (jdata, "disp_file") : self.disp_file = jdata["disp_file"]
self.disp_freq = j_must_have (jdata, 'disp_freq')
self.save_freq = j_must_have (jdata, 'save_freq')
self.save_ckpt = j_must_have (jdata, 'save_ckpt')
self.seed = None
if j_have (jdata, 'seed') :
self.seed = jdata['seed']
self.display_in_training = j_must_have (jdata, 'disp_training')
self.timing_in_training = j_must_have (jdata, 'time_training')
self.profiling = False
if j_have (jdata, 'profiling') :
self.profiling = jdata['profiling']
if self.profiling :
self.profiling_file = j_must_have (jdata, 'profiling_file')
self.sys_weights = None
if j_have(jdata, 'sys_weights') :
self.sys_weights = jdata['sys_weights']
def _message (self, msg) :
self.run_opt.message(msg)
def build (self,
data,
lr) :
self.lr = lr
self.ntypes = len(self.sel_a)
assert (self.ntypes == len(self.sel_r)), "size sel r array should match ntypes"
assert (self.ntypes == data.get_ntypes()), "ntypes should match that found in data"
self.batch_size = data.get_batch_size()
davg, dstd, bias_e = self._data_stat(data)
worker_device = "/job:%s/task:%d/%s" % (self.run_opt.my_job_name,
self.run_opt.my_task_index,
self.run_opt.my_device)
with tf.device(tf.train.replica_device_setter(worker_device = worker_device,
cluster = self.run_opt.cluster_spec)):
self._build_lr(lr)
self._build_network(davg, dstd, bias_e)
self._build_training()
def _data_stat(self, data):
all_stat_coord = []
all_stat_box = []
all_stat_type = []
all_natoms_vec = []
all_default_mesh = []
for ii in range(data.get_nsystems()) :
stat_prop_c, \
stat_energy, stat_force, stat_virial, start_atom_ener, \
stat_coord, stat_box, stat_type, natoms_vec, default_mesh \
= data.get_batch (sys_idx = ii)
natoms_vec = natoms_vec.astype(np.int32)
all_stat_coord.append(stat_coord)
all_stat_box.append(stat_box)
all_stat_type.append(stat_type)
all_natoms_vec.append(natoms_vec)
all_default_mesh.append(default_mesh)
if self.use_smooth and not self.coord_norm :
davg, dstd = self.no_norm_dstats ()
self._message("skipped coord/descrpt stats")
else :
davg, dstd = self.compute_dstats (all_stat_coord, all_stat_box, all_stat_type, all_natoms_vec, all_default_mesh)
self._message("computed coord/descrpt stats")
if self.run_opt.is_chief:
np.savetxt ("stat.avg.out", davg.T)
np.savetxt ("stat.std.out", dstd.T)
bias_atom_e = data.compute_energy_shift()
self._message("computed energy bias")
return davg, dstd, bias_atom_e
def _build_lr(self, lr):
self._extra_train_ops = []
self.global_step = tf.train.get_or_create_global_step()
self.starter_learning_rate = lr.start_lr()
self.learning_rate = tf.train.exponential_decay(lr.start_lr(),
self.global_step,
lr.decay_steps(),
lr.decay_rate(),
staircase=True)
self._message("built lr")
def _build_network(self, davg, dstd, bias_atom_e):
self.t_avg = tf.get_variable('t_avg',
davg.shape,
dtype = global_tf_float_precision,
trainable = False,
initializer = tf.constant_initializer(davg, dtype = global_tf_float_precision))
self.t_std = tf.get_variable('t_std',
dstd.shape,
dtype = global_tf_float_precision,
trainable = False,
initializer = tf.constant_initializer(dstd, dtype = global_tf_float_precision))
t_rcut = tf.constant(np.max([self.rcut_r, self.rcut_a]), name = 't_rcut', dtype = global_tf_float_precision)
t_ntypes = tf.constant(self.ntypes, name = 't_ntypes', dtype = tf.int32)
self.t_prop_c = tf.placeholder(tf.float32, [4], name='t_prop_c')
self.t_energy = tf.placeholder(global_ener_float_precision, [None], name='t_energy')
self.t_force = tf.placeholder(global_tf_float_precision, [None], name='t_force')
self.t_virial = tf.placeholder(global_tf_float_precision, [None], name='t_virial')
self.t_atom_ener = tf.placeholder(global_tf_float_precision, [None], name='t_atom_ener')
self.t_coord = tf.placeholder(global_tf_float_precision, [None], name='t_coord')
self.t_type = tf.placeholder(tf.int32, [None], name='t_type')
self.t_natoms = tf.placeholder(tf.int32, [self.ntypes+2], name='t_natoms')
self.t_box = tf.placeholder(global_tf_float_precision, [None, 9], name='t_box')
self.t_mesh = tf.placeholder(tf.int32, [None], name='t_mesh')
self.is_training = tf.placeholder(tf.bool)
self.batch_size_value = list(set(self.batch_size))
self.batch_size_value.sort()
self.numb_batch_size_value = len(self.batch_size_value)
self.energy_frz, self.force_frz, self.virial_frz, self.atom_ener_frz \
= self.build_interaction (1,
self.t_coord,
self.t_type,
self.t_natoms,
self.t_box,
self.t_mesh,
bias_atom_e = bias_atom_e,
suffix = "test",
reuse = False)
self.energy_tst, self.force_tst, self.virial_tst, self.atom_ener_tst \
= self.build_interaction (self.numb_test,
self.t_coord,
self.t_type,
self.t_natoms,
self.t_box,
self.t_mesh,
bias_atom_e = bias_atom_e,
suffix = "train_test",
reuse = True)
self.energy_bch = []
self.force_bch = []
self.virial_bch = []
self.atom_ener_bch = []
for ii in range(self.numb_batch_size_value) :
tmp_energy_bch, tmp_force_bch, tmp_virial_bch, tmp_atom_ener_bch \
= self.build_interaction (self.batch_size_value[ii],
self.t_coord,
self.t_type,
self.t_natoms,
self.t_box,
self.t_mesh,
bias_atom_e = bias_atom_e,
suffix = "train_batch_" + str(self.batch_size_value[ii]),
reuse = True)
self.energy_bch.append(tmp_energy_bch)
self.force_bch.append(tmp_force_bch)
self.virial_bch.append(tmp_virial_bch)
self.atom_ener_bch.append(tmp_atom_ener_bch)
self.l2_l_tst, self.l2_el_tst, self.l2_fl_tst, self.l2_vl_tst, self.l2_ael_tst \
= self.loss (self.t_natoms, \
self.t_prop_c, \
self.t_energy, self.energy_tst, \
self.t_force, self.force_tst, \
self.t_virial, self.virial_tst, \
self.t_atom_ener, self.atom_ener_tst, \
suffix = "train_test")
self.l2_l_bch = []
self.l2_el_bch = []
self.l2_fl_bch = []
self.l2_vl_bch = []
self.l2_ael_bch = []
for ii in range(self.numb_batch_size_value) :
tmp_l2_l_bch, tmp_l2_el_bch, tmp_l2_fl_bch, tmp_l2_vl_bch, tmp_l2_ael_bch \
= self.loss (self.t_natoms, \
self.t_prop_c, \
self.t_energy, self.energy_bch[ii], \
self.t_force, self.force_bch[ii], \
self.t_virial, self.virial_bch[ii], \
self.t_atom_ener, self.atom_ener_bch[ii], \
suffix = "train_batch_" + str(self.batch_size_value[ii]))
self.l2_l_bch.append(tmp_l2_l_bch)
self.l2_el_bch.append(tmp_l2_el_bch)
self.l2_fl_bch.append(tmp_l2_fl_bch)
self.l2_vl_bch.append(tmp_l2_vl_bch)
self.l2_ael_bch.append(tmp_l2_ael_bch)
self._message("built network")
def _build_training(self):
self.train_op = []
trainable_variables = tf.trainable_variables()
optimizer = tf.train.AdamOptimizer(learning_rate = self.learning_rate)
if self.run_opt.is_distrib :
optimizer = tf.train.SyncReplicasOptimizer(
optimizer,
replicas_to_aggregate = self.run_opt.cluster_spec.num_tasks("worker"),
total_num_replicas = self.run_opt.cluster_spec.num_tasks("worker"),
name = "sync_replicas")
self.sync_replicas_hook = optimizer.make_session_run_hook(self.run_opt.is_chief)
for ii in range(self.numb_batch_size_value) :
grads = tf.gradients(self.l2_l_bch[ii], trainable_variables)
apply_op = optimizer.apply_gradients (zip (grads, trainable_variables),
global_step=self.global_step,
name='train_step')
train_ops = [apply_op] + self._extra_train_ops
self.train_op.append(tf.group(*train_ops))
self._message("built training")
def _init_sess_serial(self) :
self.sess = tf.Session(
config=tf.ConfigProto(intra_op_parallelism_threads=self.run_opt.num_intra_threads,
inter_op_parallelism_threads=self.run_opt.num_inter_threads
))
self.saver = tf.train.Saver()
saver = self.saver
if self.run_opt.init_mode == 'init_from_scratch' :
self._message("initialize model from scratch")
init_op = tf.global_variables_initializer()
self.sess.run(init_op)
fp = open(self.disp_file, "w")
fp.close ()
elif self.run_opt.init_mode == 'init_from_model' :
self._message("initialize from model %s" % self.run_opt.init_model)
init_op = tf.global_variables_initializer()
self.sess.run(init_op)
saver.restore (self.sess, self.run_opt.init_model)
self.sess.run(self.global_step.assign(0))
fp = open(self.disp_file, "w")
fp.close ()
elif self.run_opt.init_mode == 'restart' :
self._message("restart from model %s" % self.run_opt.restart)
init_op = tf.global_variables_initializer()
self.sess.run(init_op)
saver.restore (self.sess, self.run_opt.restart)
else :
raise RuntimeError ("unkown init mode")
def _init_sess_distrib(self):
ckpt_dir = os.path.join(os.getcwd(), self.save_ckpt)
assert(_is_subdir(ckpt_dir, os.getcwd())), "the checkpoint dir must be a subdir of the current dir"
if self.run_opt.init_mode == 'init_from_scratch' :
self._message("initialize model from scratch")
if self.run_opt.is_chief :
if os.path.exists(ckpt_dir):
shutil.rmtree(ckpt_dir)
if not os.path.exists(ckpt_dir) :
os.makedirs(ckpt_dir)
fp = open(self.disp_file, "w")
fp.close ()
elif self.run_opt.init_mode == 'init_from_model' :
raise RuntimeError("distributed training does not support %s" % self.run_opt.init_mode)
elif self.run_opt.init_mode == 'restart' :
self._message("restart from model %s" % ckpt_dir)
if self.run_opt.is_chief :
assert(os.path.isdir(ckpt_dir)), "the checkpoint dir %s should exists" % ckpt_dir
else :
raise RuntimeError ("unkown init mode")
saver = tf.train.Saver(max_to_keep = 1)
self.saver = None
# gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.5)
# config = tf.ConfigProto(allow_soft_placement=True,
# gpu_options = gpu_options,
# intra_op_parallelism_threads=self.run_opt.num_intra_threads,
# inter_op_parallelism_threads=self.run_opt.num_inter_threads)
config = tf.ConfigProto(intra_op_parallelism_threads=self.run_opt.num_intra_threads,
inter_op_parallelism_threads=self.run_opt.num_inter_threads)
# The stop_hook handles stopping after running given steps
# stop_hook = tf.train.StopAtStepHook(last_step = stop_batch)
# hooks = [self.sync_replicas_hook, stop_hook]
hooks = [self.sync_replicas_hook]
scaffold = tf.train.Scaffold(saver=saver)
# Use monitor session for distributed computation
self.sess = tf.train.MonitoredTrainingSession(master = self.run_opt.server.target,
is_chief = self.run_opt.is_chief,
config = config,
hooks = hooks,
scaffold = scaffold,
checkpoint_dir = ckpt_dir)
# ,
# save_checkpoint_steps = self.save_freq)
def train (self,
data,
stop_batch) :
if self.run_opt.is_distrib :
self._init_sess_distrib()
else :
self._init_sess_serial()
self.print_head()
fp = None
if self.run_opt.is_chief :
fp = open(self.disp_file, "a")
cur_batch = self.sess.run(self.global_step)
self.cur_batch = cur_batch
self.run_opt.message("start training at lr %.2e (== %.2e), final lr will be %.2e" %
(self.sess.run(self.learning_rate),
self.lr.value(cur_batch),
self.lr.value(stop_batch))
)
prf_options = None
prf_run_metadata = None
if self.profiling :
prf_options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE)
prf_run_metadata = tf.RunMetadata()
train_time = 0
while cur_batch < stop_batch :
batch_prop_c, \
batch_energy, batch_force, batch_virial, batch_atom_ener, \
batch_coord, batch_box, batch_type, \
natoms_vec, \
default_mesh \
= data.get_batch (sys_weights = self.sys_weights)
cur_batch_size = batch_energy.shape[0]
cur_bs_idx = self.batch_size_value.index(cur_batch_size)
feed_dict_batch = {self.t_prop_c: batch_prop_c,
self.t_energy: batch_energy,
self.t_force: np.reshape(batch_force, [-1]),
self.t_virial: np.reshape(batch_virial, [-1]),
self.t_atom_ener: np.reshape(batch_atom_ener, [-1]),
self.t_coord: np.reshape(batch_coord, [-1]),
self.t_box: batch_box,
self.t_type: np.reshape(batch_type, [-1]),
self.t_natoms: natoms_vec,
self.t_mesh: default_mesh,
self.is_training: True}
if self.display_in_training and cur_batch == 0 :
self.test_on_the_fly(fp, data, feed_dict_batch, cur_bs_idx)
if self.timing_in_training : tic = time.time()
self.sess.run([self.train_op[cur_bs_idx]], feed_dict = feed_dict_batch, options=prf_options, run_metadata=prf_run_metadata)
if self.timing_in_training : toc = time.time()
if self.timing_in_training : train_time += toc - tic
cur_batch = self.sess.run(self.global_step)
self.cur_batch = cur_batch
if self.display_in_training and (cur_batch % self.disp_freq == 0) :
tic = time.time()
self.test_on_the_fly(fp, data, feed_dict_batch, cur_bs_idx)
toc = time.time()
test_time = toc - tic
if self.timing_in_training :
self._message("batch %7d training time %.2f s, testing time %.2f s"
% (cur_batch, train_time, test_time))
train_time = 0
if self.save_freq > 0 and cur_batch % self.save_freq == 0 and self.run_opt.is_chief :
if self.saver is not None :
self.saver.save (self.sess, os.getcwd() + "/" + self.save_ckpt)
self._message("saved checkpoint %s" % self.save_ckpt)
if self.run_opt.is_chief:
fp.close ()
if self.profiling and self.run_opt.is_chief :
fetched_timeline = timeline.Timeline(prf_run_metadata.step_stats)
chrome_trace = fetched_timeline.generate_chrome_trace_format()
with open(self.profiling_file, 'w') as f:
f.write(chrome_trace)
def get_global_step (self) :
return self.sess.run(self.global_step)
def print_head (self) :
if self.run_opt.is_chief:
fp = open(self.disp_file, "a")
print_str = "# %5s" % 'batch'
prop_fmt = ' %9s %9s'
print_str += prop_fmt % ('l2_tst', 'l2_trn')
if self.has_e :
print_str += prop_fmt % ('l2_e_tst', 'l2_e_trn')
if self.has_ae :
print_str += prop_fmt % ('l2_ae_tst', 'l2_ae_trn')
if self.has_f :
print_str += prop_fmt % ('l2_f_tst', 'l2_f_trn')
if self.has_v :
print_str += prop_fmt % ('l2_v_tst', 'l2_v_trn')
print_str += ' %8s\n' % 'lr'
fp.write(print_str)
fp.close ()
def test_on_the_fly (self,
fp,
data,
feed_dict_batch,
ii) :
test_prop_c, \
test_energy, test_force, test_virial, test_atom_ener, \
test_coord, test_box, test_type, \
natoms_vec, \
default_mesh \
= data.get_test ()
feed_dict_test = {self.t_prop_c: test_prop_c,
self.t_energy: test_energy [:self.numb_test],
self.t_force: np.reshape(test_force [:self.numb_test, :], [-1]),
self.t_virial: np.reshape(test_virial [:self.numb_test, :], [-1]),
self.t_atom_ener: np.reshape(test_atom_ener[:self.numb_test, :], [-1]),
self.t_coord: np.reshape(test_coord [:self.numb_test, :], [-1]),
self.t_box: test_box [:self.numb_test, :],
self.t_type: np.reshape(test_type [:self.numb_test, :], [-1]),
self.t_natoms: natoms_vec,
self.t_mesh: default_mesh,
self.is_training: False}
error_test, error_e_test, error_f_test, error_v_test, error_ae_test \
= self.sess.run([self.l2_l_tst, \
self.l2_el_tst, \
self.l2_fl_tst, \
self.l2_vl_tst, \
self.l2_ael_tst],
feed_dict=feed_dict_test)
error_train, error_e_train, error_f_train, error_v_train, error_ae_train \
= self.sess.run([self.l2_l_bch[ii], \
self.l2_el_bch[ii], \
self.l2_fl_bch[ii], \
self.l2_vl_bch[ii], \
self.l2_ael_bch[ii]],
feed_dict=feed_dict_batch)
cur_batch = self.cur_batch
current_lr = self.sess.run(self.learning_rate)
if self.run_opt.is_chief:
print_str = "%7d" % cur_batch
prop_fmt = " %9.2e %9.2e"
print_str += prop_fmt % (np.sqrt(error_test), np.sqrt(error_train))
if self.has_e :
print_str += prop_fmt % (np.sqrt(error_e_test) / natoms_vec[0], np.sqrt(error_e_train) / natoms_vec[0])
if self.has_ae :
print_str += prop_fmt % (np.sqrt(error_ae_test), np.sqrt(error_ae_train))
if self.has_f :
print_str += prop_fmt % (np.sqrt(error_f_test), np.sqrt(error_f_train))
if self.has_v :
print_str += prop_fmt % (np.sqrt(error_v_test) / natoms_vec[0], np.sqrt(error_v_train) / natoms_vec[0])
print_str += " %8.1e\n" % current_lr
fp.write(print_str)
fp.flush ()
def compute_dstats_sys_smth (self,
data_coord,
data_box,
data_atype,
natoms_vec,
mesh,
reuse = None) :
avg_zero = np.zeros([self.ntypes,self.ndescrpt]).astype(global_np_float_precision)
std_ones = np.ones ([self.ntypes,self.ndescrpt]).astype(global_np_float_precision)
sub_graph = tf.Graph()
with sub_graph.as_default():
descrpt, descrpt_deriv, rij, nlist \
= op_module.descrpt_norot (tf.constant(data_coord),
tf.constant(data_atype),
tf.constant(natoms_vec, dtype = tf.int32),
tf.constant(data_box),
tf.constant(mesh),
tf.constant(avg_zero),
tf.constant(std_ones),
rcut_a = self.rcut_a,
rcut_r = self.rcut_r,
rcut_r_smth = self.rcut_r_smth,
sel_a = self.sel_a,
sel_r = self.sel_r)
# self.sess.run(tf.global_variables_initializer())
sub_sess = tf.Session(graph = sub_graph,
config=tf.ConfigProto(intra_op_parallelism_threads=self.run_opt.num_intra_threads,
inter_op_parallelism_threads=self.run_opt.num_inter_threads
))
dd_all = sub_sess.run(descrpt)
sub_sess.close()
natoms = natoms_vec
dd_all = np.reshape(dd_all, [-1, self.ndescrpt * natoms[0]])
start_index = 0
sysr = []
sysa = []
sysn = []
sysr2 = []
sysa2 = []
for type_i in range(self.ntypes):
end_index = start_index + self.ndescrpt * natoms[2+type_i]
dd = dd_all[:, start_index:end_index]
dd = np.reshape(dd, [-1, self.ndescrpt])
start_index = end_index
# compute
dd = np.reshape (dd, [-1, 4])
ddr = dd[:,:1]
dda = dd[:,1:]
sumr = np.sum(ddr)
suma = np.sum(dda) / 3.
sumn = dd.shape[0]
sumr2 = np.sum(np.multiply(ddr, ddr))
suma2 = np.sum(np.multiply(dda, dda)) / 3.
sysr.append(sumr)
sysa.append(suma)
sysn.append(sumn)
sysr2.append(sumr2)
sysa2.append(suma2)
return sysr, sysr2, sysa, sysa2, sysn
def compute_dstats_sys_nonsmth (self,
data_coord,
data_box,
data_atype,
natoms_vec,
mesh,
reuse = None) :
avg_zero = np.zeros([self.ntypes,self.ndescrpt]).astype(global_np_float_precision)
std_ones = np.ones ([self.ntypes,self.ndescrpt]).astype(global_np_float_precision)
sub_graph = tf.Graph()
with sub_graph.as_default():
descrpt, descrpt_deriv, rij, nlist, axis \
= op_module.descrpt (tf.constant(data_coord),
tf.constant(data_atype),
tf.constant(natoms_vec, dtype = tf.int32),
tf.constant(data_box),
tf.constant(mesh),
tf.constant(avg_zero),
tf.constant(std_ones),
rcut_a = self.rcut_a,
rcut_r = self.rcut_r,
sel_a = self.sel_a,
sel_r = self.sel_r,
axis_rule = self.axis_rule)
# self.sess.run(tf.global_variables_initializer())
sub_sess = tf.Session(graph = sub_graph,
config=tf.ConfigProto(intra_op_parallelism_threads=self.run_opt.num_intra_threads,
inter_op_parallelism_threads=self.run_opt.num_inter_threads
))
dd_all = sub_sess.run(descrpt)
sub_sess.close()
natoms = natoms_vec
dd_all = np.reshape(dd_all, [-1, self.ndescrpt * natoms[0]])
start_index = 0
sysv = []
sysn = []
sysv2 = []
for type_i in range(self.ntypes):
end_index = start_index + self.ndescrpt * natoms[2+type_i]
dd = dd_all[:, start_index:end_index]
dd = np.reshape(dd, [-1, self.ndescrpt])
start_index = end_index
# compute
sumv = np.sum(dd, axis = 0)
sumn = dd.shape[0]
sumv2 = np.sum(np.multiply(dd,dd), axis = 0)
sysv.append(sumv)
sysn.append(sumn)
sysv2.append(sumv2)
return sysv, sysv2, sysn
def compute_std (self,sumv2, sumv, sumn) :
return np.sqrt(sumv2/sumn - np.multiply(sumv/sumn, sumv/sumn))
def compute_dstats (self,
data_coord,
data_box,
data_atype,
natoms_vec,
mesh,
reuse = None) :
env_bk = None
if 'TF_CPP_MIN_LOG_LEVEL' in os.environ:
env_bk = os.environ['TF_CPP_MIN_LOG_LEVEL']
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
all_davg = []
all_dstd = []
if self.use_smooth:
sumr = []
suma = []
sumn = []
sumr2 = []
suma2 = []
for cc,bb,tt,nn,mm in zip(data_coord,data_box,data_atype,natoms_vec,mesh) :
sysr,sysr2,sysa,sysa2,sysn \
= self.compute_dstats_sys_smth(cc,bb,tt,nn,mm,reuse)
sumr.append(sysr)
suma.append(sysa)
sumn.append(sysn)
sumr2.append(sysr2)
suma2.append(sysa2)
sumr = np.sum(sumr, axis = 0)
suma = np.sum(suma, axis = 0)
sumn = np.sum(sumn, axis = 0)
sumr2 = np.sum(sumr2, axis = 0)
suma2 = np.sum(suma2, axis = 0)
for type_i in range(self.ntypes) :
davgunit = [sumr[type_i]/sumn[type_i], 0, 0, 0]
dstdunit = [self.compute_std(sumr2[type_i], sumr[type_i], sumn[type_i]),
self.compute_std(suma2[type_i], suma[type_i], sumn[type_i]),
self.compute_std(suma2[type_i], suma[type_i], sumn[type_i]),
self.compute_std(suma2[type_i], suma[type_i], sumn[type_i])
]
davg = np.tile(davgunit, self.ndescrpt // 4)
dstd = np.tile(dstdunit, self.ndescrpt // 4)
all_davg.append(davg)
all_dstd.append(dstd)
else :
sumv = []
sumn = []
sumv2 = []
for cc,bb,tt,nn,mm in zip(data_coord,data_box,data_atype,natoms_vec,mesh) :
sysv,sysv2,sysn \
= self.compute_dstats_sys_nonsmth(cc,bb,tt,nn,mm,reuse)
sumv.append(sysv)
sumn.append(sysn)
sumv2.append(sysv2)
sumv = np.sum(sumv, axis = 0)
sumn = np.sum(sumn, axis = 0)
sumv2 = np.sum(sumv2, axis = 0)
for type_i in range(self.ntypes) :
davg = sumv[type_i] / sumn[type_i]
dstd = self.compute_std(sumv2[type_i], sumv[type_i], sumn[type_i])
for ii in range (len(dstd)) :
if (np.abs(dstd[ii]) < 1e-2) :
dstd[ii] = 1e-2
all_davg.append(davg)
all_dstd.append(dstd)
davg = np.array(all_davg)
dstd = np.array(all_dstd)
if env_bk is not None :
os.environ['TF_CPP_MIN_LOG_LEVEL'] = env_bk
else :
os.environ.pop('TF_CPP_MIN_LOG_LEVEL', None)
return davg, dstd
def no_norm_dstats (self, avgv = 0, stdv = 1) :
davg = np.zeros([self.ntypes, self.ndescrpt]) + avgv
dstd = np.ones ([self.ntypes, self.ndescrpt]) * stdv
return davg, dstd
def loss (self,
natoms,
prop_c,
energy,
energy_hat,
force,
force_hat,
virial,
virial_hat,
atom_ener,
atom_ener_hat,
suffix):
l2_ener_loss = tf.reduce_mean( tf.square(energy - energy_hat), name='l2_'+suffix)
force_reshape = tf.reshape (force, [-1])
force_hat_reshape = tf.reshape (force_hat, [-1])
l2_force_loss = tf.reduce_mean (tf.square(force_hat_reshape - force_reshape), name = "l2_force_" + suffix)
virial_reshape = tf.reshape (virial, [-1])
virial_hat_reshape = tf.reshape (virial_hat, [-1])
l2_virial_loss = tf.reduce_mean (tf.square(virial_hat_reshape - virial_reshape), name = "l2_virial_" + suffix)
atom_ener_reshape = tf.reshape (atom_ener, [-1])
atom_ener_hat_reshape = tf.reshape (atom_ener_hat, [-1])
l2_atom_ener_loss = tf.reduce_mean (tf.square(atom_ener_hat_reshape - atom_ener_reshape), name = "l2_atom_ener_" + suffix)
atom_norm = 1./ global_cvt_2_tf_float(natoms[0])
atom_norm_ener = 1./ global_cvt_2_ener_float(natoms[0])
pref_e = global_cvt_2_ener_float(prop_c[0] * (self.limit_pref_e + (self.start_pref_e - self.limit_pref_e) * self.learning_rate / self.starter_learning_rate) )
pref_f = global_cvt_2_tf_float(prop_c[1] * (self.limit_pref_f + (self.start_pref_f - self.limit_pref_f) * self.learning_rate / self.starter_learning_rate) )
pref_v = global_cvt_2_tf_float(prop_c[2] * (self.limit_pref_v + (self.start_pref_v - self.limit_pref_v) * self.learning_rate / self.starter_learning_rate) )
pref_ae= global_cvt_2_tf_float(prop_c[3] * (self.limit_pref_ae+ (self.start_pref_ae-self.limit_pref_ae) * self.learning_rate / self.starter_learning_rate) )
l2_loss = 0
if self.has_e :
l2_loss += atom_norm_ener * (pref_e * l2_ener_loss)
if self.has_f :
l2_loss += global_cvt_2_ener_float(pref_f * l2_force_loss)
if self.has_v :
l2_loss += global_cvt_2_ener_float(atom_norm * (pref_v * l2_virial_loss))
if self.has_ae :
l2_loss += global_cvt_2_ener_float(pref_ae * l2_atom_ener_loss)
return l2_loss, l2_ener_loss, l2_force_loss, l2_virial_loss, l2_atom_ener_loss
def build_interaction (self,
nframes,
coord_,
atype_,
natoms,
box,
mesh,
suffix,
bias_atom_e = None,
reuse = None):
coord = tf.reshape (coord_, [-1, natoms[1] * 3])
atype = tf.reshape (atype_, [-1, natoms[1]])
if self.use_smooth :
descrpt, descrpt_deriv, rij, nlist \
= op_module.descrpt_norot (coord,
atype,
natoms,
box,
mesh,
self.t_avg,
self.t_std,
rcut_a = self.rcut_a,
rcut_r = self.rcut_r,
rcut_r_smth = self.rcut_r_smth,
sel_a = self.sel_a,
sel_r = self.sel_r)
else :
descrpt, descrpt_deriv, rij, nlist, axis \
= op_module.descrpt (coord,
atype,
natoms,
box,
mesh,
self.t_avg,
self.t_std,
rcut_a = self.rcut_a,
rcut_r = self.rcut_r,
sel_a = self.sel_a,
sel_r = self.sel_r,
axis_rule = self.axis_rule)
descrpt_reshape = tf.reshape(descrpt, [-1, self.ndescrpt])
atom_ener = self.build_atom_net (nframes, descrpt_reshape, natoms, bias_atom_e = bias_atom_e, reuse = reuse)
energy_raw = tf.reshape(atom_ener, [-1, natoms[0]], name = 'atom_energy_'+suffix)
energy = tf.reduce_sum(global_cvt_2_ener_float(energy_raw), axis=1, name='energy_'+suffix)
net_deriv_tmp = tf.gradients (atom_ener, descrpt_reshape)
net_deriv = net_deriv_tmp[0]
net_deriv_reshape = tf.reshape (net_deriv, [-1, natoms[0] * self.ndescrpt])
if self.use_smooth :
force = op_module.prod_force_norot (net_deriv_reshape,
descrpt_deriv,
nlist,
natoms,
n_a_sel = self.nnei_a,
n_r_sel = self.nnei_r)
else :
force = op_module.prod_force (net_deriv_reshape,
descrpt_deriv,
nlist,
axis,
natoms,
n_a_sel = self.nnei_a,
n_r_sel = self.nnei_r)
force = tf.reshape (force, [-1, 3 * natoms[1]], name = "force_"+suffix)
if self.use_smooth :
virial, atom_virial \
= op_module.prod_virial_norot (net_deriv_reshape,
descrpt_deriv,
rij,
nlist,
natoms,
n_a_sel = self.nnei_a,
n_r_sel = self.nnei_r)
else :
virial, atom_virial \
= op_module.prod_virial (net_deriv_reshape,
descrpt_deriv,
rij,
nlist,
axis,
natoms,
n_a_sel = self.nnei_a,
n_r_sel = self.nnei_r)
virial = tf.reshape (virial, [-1, 9], name = "virial_"+suffix)
atom_virial = tf.reshape (atom_virial, [-1, 9 * natoms[1]], name = "atom_virial_"+suffix)
return energy, force, virial, energy_raw
def build_atom_net (self,
nframes,
inputs,
natoms,
bias_atom_e = None,
reuse = None) :
start_index = 0
inputs = tf.reshape(inputs, [-1, self.ndescrpt * natoms[0]])
shape = inputs.get_shape().as_list()
if bias_atom_e is not None :
assert(len(bias_atom_e) == self.ntypes)
for type_i in range(self.ntypes):
# cut-out inputs
inputs_i = tf.slice (inputs,
[ 0, start_index* self.ndescrpt],
[-1, natoms[2+type_i]* self.ndescrpt] )
inputs_i = tf.reshape(inputs_i, [-1, self.ndescrpt])
start_index += natoms[2+type_i]
if bias_atom_e is None :
type_bias_ae = 0.0
else :
type_bias_ae = bias_atom_e[type_i]
# compute atom energy
if self.use_smooth :
if self.type_fitting_net :
layer = self._DS_layer_type_ext(inputs_i, name='DS_layer_type_'+str(type_i), natoms=natoms, reuse=reuse, seed = self.seed)
else :
layer = self._DS_layer(inputs_i, name='DS_layer_type_'+str(type_i), natoms=natoms, reuse=reuse, seed = self.seed)
for ii in range(0,len(self.n_neuron)) :
if ii >= 1 and self.n_neuron[ii] == self.n_neuron[ii-1] :
layer+= self._one_layer(layer, self.n_neuron[ii], name='layer_'+str(ii)+'_type_'+str(type_i), reuse=reuse, seed = self.seed, use_timestep = self.resnet_dt)
else :
layer = self._one_layer(layer, self.n_neuron[ii], name='layer_'+str(ii)+'_type_'+str(type_i), reuse=reuse, seed = self.seed)
else :
layer = self._one_layer(inputs_i, self.n_neuron[0], name='layer_0_type_'+str(type_i), reuse=reuse, seed = self.seed)
for ii in range(1,len(self.n_neuron)) :
layer = self._one_layer(layer, self.n_neuron[ii], name='layer_'+str(ii)+'_type_'+str(type_i), reuse=reuse, seed = self.seed)
final_layer = self._one_layer(layer, 1, activation_fn = None, bavg = type_bias_ae, name='final_layer_type_'+str(type_i), reuse=reuse, seed = self.seed)
final_layer = tf.reshape(final_layer, [nframes, natoms[2+type_i]])
# final_layer = tf.cond (tf.equal(natoms[2+type_i], 0), lambda: tf.zeros((0, 0), dtype=global_tf_float_precision), lambda : tf.reshape(final_layer, [-1, natoms[2+type_i]]))
# concat the results
if type_i == 0:
outs = final_layer
else:
outs = tf.concat([outs, final_layer], axis = 1)
return tf.reshape(outs, [-1])
def _one_layer(self,
inputs,
outputs_size,
activation_fn=tf.nn.tanh,
stddev=1.0,
bavg=0.0,
name='linear',
reuse=None,
seed=None,
use_timestep = False):
with tf.variable_scope(name, reuse=reuse):
shape = inputs.get_shape().as_list()