#!/usr/bin/env python3
"""init: data
iter:
00.train
01.model_devi
02.vasp
03.data.
"""
import argparse
import copy
import glob
import itertools
import json
import logging
import logging.handlers
import os
import queue
import random
import re
import shlex
import shutil
import sys
import warnings
from collections import Counter
from collections.abc import Iterable
from pathlib import Path
from typing import Optional
import dpdata
import numpy as np
import scipy.constants as pc
from numpy.linalg import norm
from packaging.version import Version
from dpgen import ROOT_PATH, SHORT_CMD, dlog
from dpgen.auto_test.lib.vasp import make_kspacing_kpoints
from dpgen.dispatcher.Dispatcher import make_submission
from dpgen.generator.lib.abacus_scf import (
get_abacus_input_parameters,
get_abacus_STRU,
make_abacus_scf_input,
make_abacus_scf_kpt,
make_abacus_scf_stru,
)
from dpgen.generator.lib.cp2k import (
make_cp2k_input,
make_cp2k_input_from_external,
make_cp2k_xyz,
)
from dpgen.generator.lib.ele_temp import NBandsEsti
from dpgen.generator.lib.gaussian import make_gaussian_input, take_cluster
from dpgen.generator.lib.lammps import (
get_all_dumped_forces,
get_dumped_forces,
make_lammps_input,
)
from dpgen.generator.lib.make_calypso import (
_make_model_devi_buffet,
_make_model_devi_native_calypso,
)
from dpgen.generator.lib.parse_calypso import (
_parse_calypso_dis_mtx,
_parse_calypso_input,
)
# from dpgen.generator.lib.pwscf import cvt_1frame
from dpgen.generator.lib.pwmat import (
make_pwmat_input_dict,
make_pwmat_input_user_dict,
write_input_dict,
)
from dpgen.generator.lib.pwscf import make_pwscf_input
from dpgen.generator.lib.run_calypso import (
run_calypso_model_devi,
)
from dpgen.generator.lib.siesta import make_siesta_input
from dpgen.generator.lib.utils import (
check_api_version,
create_path,
log_iter,
log_task,
make_iter_name,
record_iter,
symlink_user_forward_files,
)
from dpgen.generator.lib.vasp import (
incar_upper,
make_vasp_incar_user_dict,
write_incar_dict,
)
from dpgen.remote.decide_machine import convert_mdata
from dpgen.util import (
convert_training_data_to_hdf5,
expand_sys_str,
load_file,
normalize,
sepline,
set_directory,
setup_ele_temp,
)
from .arginfo import run_jdata_arginfo
template_name = "template"
train_name = "00.train"
train_task_fmt = "%03d"
train_tmpl_path = os.path.join(template_name, train_name)
default_train_input_file = "input.json"
data_system_fmt = "%03d"
model_devi_name = "01.model_devi"
model_devi_task_fmt = data_system_fmt + ".%06d"
model_devi_conf_fmt = data_system_fmt + ".%04d"
fp_name = "02.fp"
fp_task_fmt = data_system_fmt + ".%06d"
cvasp_file = os.path.join(ROOT_PATH, "generator/lib/cvasp.py")
# for calypso
calypso_run_opt_name = "gen_stru_analy"
calypso_model_devi_name = "model_devi_results"
calypso_run_model_devi_file = os.path.join(
ROOT_PATH, "generator/lib/calypso_run_model_devi.py"
)
check_outcar_file = os.path.join(ROOT_PATH, "generator/lib/calypso_check_outcar.py")
run_opt_file = os.path.join(ROOT_PATH, "generator/lib/calypso_run_opt.py")
def _get_model_suffix(jdata) -> str:
"""Return the model suffix based on the backend."""
mlp_engine = jdata.get("mlp_engine", "dp")
if mlp_engine == "dp":
suffix_map = {"tensorflow": ".pb", "pytorch": ".pth", "jax": ".savedmodel"}
backend = jdata.get("train_backend", "tensorflow")
if backend in suffix_map:
suffix = suffix_map[backend]
else:
raise ValueError(
f"The backend {backend} is not available. Supported backends are: 'tensorflow', 'pytorch', 'jax'."
)
return suffix
else:
raise ValueError(f"Unsupported engine: {mlp_engine}")
[docs]
def get_job_names(jdata):
jobkeys = []
for ii in jdata.keys():
if ii.split("_")[0] == "job":
jobkeys.append(ii)
jobkeys.sort()
return jobkeys
[docs]
def make_model_devi_task_name(sys_idx, task_idx):
return "task." + model_devi_task_fmt % (sys_idx, task_idx)
[docs]
def make_model_devi_conf_name(sys_idx, conf_idx):
return model_devi_conf_fmt % (sys_idx, conf_idx)
[docs]
def make_fp_task_name(sys_idx, counter):
return "task." + fp_task_fmt % (sys_idx, counter)
[docs]
def get_sys_index(task):
task.sort()
system_index = []
for ii in task:
system_index.append(os.path.basename(ii).split(".")[1])
set_tmp = set(system_index)
system_index = list(set_tmp)
system_index.sort()
return system_index
def _check_empty_iter(iter_index, max_v=0):
fp_path = os.path.join(make_iter_name(iter_index), fp_name)
# check the number of collected data
sys_data = glob.glob(os.path.join(fp_path, "data.*"))
empty_sys = []
for ii in sys_data:
nframe = 0
sys_paths = expand_sys_str(ii)
for single_sys in sys_paths:
sys = dpdata.LabeledSystem(os.path.join(single_sys), fmt="deepmd/npy")
nframe += len(sys)
empty_sys.append(nframe < max_v)
return all(empty_sys)
[docs]
def copy_model(numb_model, prv_iter_index, cur_iter_index, suffix=".pb"):
cwd = os.getcwd()
prv_train_path = os.path.join(make_iter_name(prv_iter_index), train_name)
cur_train_path = os.path.join(make_iter_name(cur_iter_index), train_name)
prv_train_path = os.path.abspath(prv_train_path)
cur_train_path = os.path.abspath(cur_train_path)
create_path(cur_train_path)
for ii in range(numb_model):
prv_train_task = os.path.join(prv_train_path, train_task_fmt % ii)
os.chdir(cur_train_path)
os.symlink(os.path.relpath(prv_train_task), train_task_fmt % ii)
os.symlink(
os.path.join(train_task_fmt % ii, f"frozen_model{suffix}"),
"graph.%03d%s" % (ii, suffix),
)
os.chdir(cwd)
with open(os.path.join(cur_train_path, "copied"), "w") as fp:
None
[docs]
def poscar_natoms(lines):
numb_atoms = 0
for ii in lines[6].split():
numb_atoms += int(ii)
return numb_atoms
[docs]
def expand_idx(in_list):
ret = []
for ii in in_list:
if isinstance(ii, int):
ret.append(ii)
elif isinstance(ii, str):
step_str = ii.split(":")
if len(step_str) > 1:
step = int(step_str[1])
else:
step = 1
range_str = step_str[0].split("-")
assert (len(range_str)) == 2
ret += range(int(range_str[0]), int(range_str[1]), step)
return ret
def _check_skip_train(job):
try:
skip = _get_param_alias(job, ["s_t", "sk_tr", "skip_train", "skip_training"])
except ValueError:
skip = False
return skip
[docs]
def poscar_to_conf(poscar, conf):
sys = dpdata.System(poscar, fmt="vasp/poscar")
sys.to_lammps_lmp(conf)
# def dump_to_poscar(dump, poscar, type_map, fmt = "lammps/dump") :
# sys = dpdata.System(dump, fmt = fmt, type_map = type_map)
# sys.to_vasp_poscar(poscar)
[docs]
def dump_to_deepmd_raw(dump, deepmd_raw, type_map, fmt="gromacs/gro", charge=None):
system = dpdata.System(dump, fmt=fmt, type_map=type_map)
system.to_deepmd_raw(deepmd_raw)
if charge is not None:
with open(os.path.join(deepmd_raw, "charge"), "w") as f:
f.write(str(charge))
[docs]
def make_train(iter_index, jdata, mdata):
mlp_engine = jdata.get("mlp_engine", "dp")
if mlp_engine == "dp":
return make_train_dp(iter_index, jdata, mdata)
else:
raise ValueError(f"Unsupported engine: {mlp_engine}")
[docs]
def make_train_dp(iter_index, jdata, mdata):
# load json param
# train_param = jdata['train_param']
train_input_file = default_train_input_file
numb_models = jdata["numb_models"]
init_data_prefix = os.path.abspath(jdata["init_data_prefix"])
init_data_sys_ = jdata["init_data_sys"]
fp_task_min = jdata["fp_task_min"]
model_devi_jobs = jdata["model_devi_jobs"]
use_ele_temp = jdata.get("use_ele_temp", 0)
training_iter0_model = jdata.get("training_iter0_model_path", [])
training_init_model = jdata.get("training_init_model", False)
training_reuse_iter = jdata.get("training_reuse_iter")
training_reuse_old_ratio = jdata.get("training_reuse_old_ratio", "auto")
# if you want to use DP-ZBL potential , you have to give the path of your energy potential file
if "srtab_file_path" in jdata.keys():
srtab_file_path = os.path.abspath(jdata.get("srtab_file_path", None))
if "training_reuse_stop_batch" in jdata.keys():
training_reuse_stop_batch = jdata["training_reuse_stop_batch"]
elif "training_reuse_numb_steps" in jdata.keys():
training_reuse_stop_batch = jdata["training_reuse_numb_steps"]
else:
training_reuse_stop_batch = None
training_reuse_start_lr = jdata.get("training_reuse_start_lr")
training_reuse_start_pref_e = jdata.get("training_reuse_start_pref_e")
training_reuse_start_pref_f = jdata.get("training_reuse_start_pref_f")
model_devi_activation_func = jdata.get("model_devi_activation_func", None)
training_init_frozen_model = (
jdata.get("training_init_frozen_model") if iter_index == 0 else None
)
training_finetune_model = (
jdata.get("training_finetune_model") if iter_index == 0 else None
)
auto_ratio = False
if (
training_reuse_iter is not None
and isinstance(training_reuse_old_ratio, str)
and training_reuse_old_ratio.startswith("auto")
):
s = training_reuse_old_ratio.split(":")
if len(s) == 1:
new_to_old_ratio = 10.0
elif len(s) == 2:
new_to_old_ratio = float(s[1])
else:
raise ValueError(
f"training_reuse_old_ratio is not correct, got {training_reuse_old_ratio}"
)
dlog.info(
"Use automatic training_reuse_old_ratio to make new-to-old ratio close to %d times of the default value.",
new_to_old_ratio,
)
auto_ratio = True
number_old_frames = 0
number_new_frames = 0
suffix = _get_model_suffix(jdata)
model_devi_engine = jdata.get("model_devi_engine", "lammps")
if iter_index > 0 and _check_empty_iter(iter_index - 1, fp_task_min):
log_task("prev data is empty, copy prev model")
copy_model(numb_models, iter_index - 1, iter_index, suffix)
return
elif (
model_devi_engine != "calypso"
and iter_index > 0
and _check_skip_train(model_devi_jobs[iter_index - 1])
):
log_task("skip training at step %d " % (iter_index - 1))
copy_model(numb_models, iter_index - 1, iter_index, suffix)
return
else:
iter_name = make_iter_name(iter_index)
work_path = os.path.join(iter_name, train_name)
copy_flag = os.path.join(work_path, "copied")
if os.path.isfile(copy_flag):
os.remove(copy_flag)
# establish work path
iter_name = make_iter_name(iter_index)
work_path = os.path.join(iter_name, train_name)
create_path(work_path)
# link init data
cwd = os.getcwd()
os.chdir(work_path)
os.symlink(os.path.abspath(init_data_prefix), "data.init")
# link iter data
os.mkdir("data.iters")
os.chdir("data.iters")
for ii in range(iter_index):
os.symlink(
os.path.relpath(os.path.join(cwd, make_iter_name(ii))), make_iter_name(ii)
)
os.chdir(cwd)
init_data_sys = []
init_batch_size = []
if "init_batch_size" in jdata:
init_batch_size_ = list(jdata["init_batch_size"])
if len(init_data_sys_) > len(init_batch_size_):
warnings.warn(
"The batch sizes are not enough. Assume auto for those not spefified."
)
init_batch_size.extend(
["auto" for aa in range(len(init_data_sys_) - len(init_batch_size))]
)
else:
init_batch_size_ = ["auto" for aa in range(len(jdata["init_data_sys"]))]
if "sys_batch_size" in jdata:
sys_batch_size = jdata["sys_batch_size"]
else:
sys_batch_size = ["auto" for aa in range(len(jdata["sys_configs"]))]
# make sure all init_data_sys has the batch size -- for the following `zip`
assert len(init_data_sys_) <= len(init_batch_size_)
for ii, ss in zip(init_data_sys_, init_batch_size_):
sys_paths = expand_sys_str(os.path.join(init_data_prefix, ii))
for single_sys in sys_paths:
init_data_sys.append(
Path(
os.path.normpath(
os.path.join(
"../data.init",
ii,
os.path.relpath(
single_sys, os.path.join(init_data_prefix, ii)
),
)
)
).as_posix()
)
init_batch_size.append(detect_batch_size(ss, single_sys))
if auto_ratio:
number_old_frames += get_nframes(single_sys)
old_range = None
if iter_index > 0:
for ii in range(iter_index):
if ii == iter_index - 1:
old_range = len(init_data_sys)
fp_path = os.path.join(make_iter_name(ii), fp_name)
fp_data_sys = glob.glob(os.path.join(fp_path, "data.*"))
if model_devi_engine == "calypso":
_modd_path = os.path.join(
make_iter_name(ii), model_devi_name, calypso_model_devi_name
)
sys_list = glob.glob(os.path.join(_modd_path, "*.structures"))
sys_batch_size = ["auto" for aa in range(len(sys_list))]
for jj in fp_data_sys:
sys_idx = int(jj.split(".")[-1])
sys_paths = expand_sys_str(jj)
nframes = 0
for sys_single in sys_paths:
nframes += dpdata.LabeledSystem(
sys_single, fmt="deepmd/npy"
).get_nframes()
if auto_ratio:
if ii == iter_index - 1:
number_new_frames += nframes
else:
number_old_frames += nframes
if nframes < fp_task_min:
log_task(
"nframes (%d) in data sys %s is too small, skip" % (nframes, jj)
)
continue
for sys_single in sys_paths:
init_data_sys.append(
Path(
os.path.normpath(os.path.join("../data.iters", sys_single))
).as_posix()
)
batch_size = (
sys_batch_size[sys_idx]
if sys_idx < len(sys_batch_size)
else "auto"
)
init_batch_size.append(detect_batch_size(batch_size, sys_single))
# establish tasks
jinput = jdata["default_training_param"]
try:
mdata["deepmd_version"]
except KeyError:
mdata = set_version(mdata)
# setup data systems
if Version(mdata["deepmd_version"]) >= Version("1") and Version(
mdata["deepmd_version"]
) < Version("2"):
# 1.x
jinput["training"]["systems"] = init_data_sys
jinput["training"]["batch_size"] = init_batch_size
jinput["model"]["type_map"] = jdata["type_map"]
# electron temperature
if use_ele_temp == 0:
pass
elif use_ele_temp == 1:
jinput["model"]["fitting_net"]["numb_fparam"] = 1
jinput["model"]["fitting_net"].pop("numb_aparam", None)
elif use_ele_temp == 2:
jinput["model"]["fitting_net"]["numb_aparam"] = 1
jinput["model"]["fitting_net"].pop("numb_fparam", None)
else:
raise RuntimeError("invalid setting for use_ele_temp " + str(use_ele_temp))
elif Version(mdata["deepmd_version"]) >= Version("2") and Version(
mdata["deepmd_version"]
) < Version("3"):
# 2.x
jinput["training"].setdefault("training_data", {})
jinput["training"]["training_data"]["systems"] = init_data_sys
old_batch_size = jinput["training"]["training_data"].get("batch_size", "")
if not (
isinstance(old_batch_size, str) and old_batch_size.startswith("mixed:")
):
jinput["training"]["training_data"]["batch_size"] = init_batch_size
jinput["model"]["type_map"] = jdata["type_map"]
# electron temperature
if use_ele_temp == 0:
pass
elif use_ele_temp == 1:
jinput["model"]["fitting_net"]["numb_fparam"] = 1
jinput["model"]["fitting_net"].pop("numb_aparam", None)
elif use_ele_temp == 2:
jinput["model"]["fitting_net"]["numb_aparam"] = 1
jinput["model"]["fitting_net"].pop("numb_fparam", None)
else:
raise RuntimeError("invalid setting for use_ele_temp " + str(use_ele_temp))
else:
raise RuntimeError(
"DP-GEN currently only supports for DeePMD-kit 1.x or 2.x version!"
)
# set training reuse model
if auto_ratio:
training_reuse_old_ratio = number_old_frames / (
number_old_frames + number_new_frames * new_to_old_ratio
)
if training_reuse_iter is not None and iter_index >= training_reuse_iter:
if "numb_steps" in jinput["training"] and training_reuse_stop_batch is not None:
jinput["training"]["numb_steps"] = training_reuse_stop_batch
elif (
"stop_batch" in jinput["training"] and training_reuse_stop_batch is not None
):
jinput["training"]["stop_batch"] = training_reuse_stop_batch
if Version("1") <= Version(mdata["deepmd_version"]) < Version("2"):
jinput["training"]["auto_prob_style"] = (
"prob_sys_size; 0:%d:%f; %d:%d:%f"
% (
old_range,
training_reuse_old_ratio,
old_range,
len(init_data_sys),
1.0 - training_reuse_old_ratio,
)
)
elif Version("2") <= Version(mdata["deepmd_version"]) < Version("3"):
jinput["training"]["training_data"]["auto_prob"] = (
"prob_sys_size; 0:%d:%f; %d:%d:%f"
% (
old_range,
training_reuse_old_ratio,
old_range,
len(init_data_sys),
1.0 - training_reuse_old_ratio,
)
)
else:
raise RuntimeError(
"Unsupported DeePMD-kit version: {}".format(mdata["deepmd_version"])
)
if (
jinput["loss"].get("start_pref_e") is not None
and training_reuse_start_pref_e is not None
):
jinput["loss"]["start_pref_e"] = training_reuse_start_pref_e
if (
jinput["loss"].get("start_pref_f") is not None
and training_reuse_start_pref_f is not None
):
jinput["loss"]["start_pref_f"] = training_reuse_start_pref_f
if training_reuse_start_lr is not None:
jinput["learning_rate"]["start_lr"] = training_reuse_start_lr
input_files = []
for ii in range(numb_models):
task_path = os.path.join(work_path, train_task_fmt % ii)
create_path(task_path)
os.chdir(task_path)
if "srtab_file_path" in jdata.keys():
shutil.copyfile(srtab_file_path, os.path.basename(srtab_file_path))
for jj in init_data_sys:
# HDF5 path contains #
if not (
os.path.isdir(jj) if "#" not in jj else os.path.isfile(jj.split("#")[0])
):
raise RuntimeError(
f"data sys {jj} does not exists, cwd is {os.getcwd()}"
)
os.chdir(cwd)
# set random seed for each model
if Version(mdata["deepmd_version"]) >= Version("1") and Version(
mdata["deepmd_version"]
) < Version("3"):
# 1.x
if "descriptor" not in jinput["model"]:
pass
elif jinput["model"]["descriptor"]["type"] == "hybrid":
for desc in jinput["model"]["descriptor"]["list"]:
desc["seed"] = random.randrange(sys.maxsize) % (2**32)
elif jinput["model"]["descriptor"]["type"] == "loc_frame":
pass
else:
jinput["model"]["descriptor"]["seed"] = random.randrange(
sys.maxsize
) % (2**32)
if "fitting_net" in jinput["model"]:
jinput["model"]["fitting_net"]["seed"] = random.randrange(
sys.maxsize
) % (2**32)
if "type_embedding" in jinput["model"]:
jinput["model"]["type_embedding"]["seed"] = random.randrange(
sys.maxsize
) % (2**32)
jinput["training"]["seed"] = random.randrange(sys.maxsize) % (2**32)
else:
raise RuntimeError(
"DP-GEN currently only supports for DeePMD-kit 1.x or 2.x version!"
)
# set model activation function
if model_devi_activation_func is not None:
if Version(mdata["deepmd_version"]) < Version("1"):
raise RuntimeError(
"model_devi_activation_func does not suppport deepmd version",
mdata["deepmd_version"],
)
assert (
isinstance(model_devi_activation_func, list)
and len(model_devi_activation_func) == numb_models
)
if (
len(np.array(model_devi_activation_func).shape) == 2
): # 2-dim list for emd/fitting net-resolved assignment of actF
jinput["model"]["descriptor"]["activation_function"] = (
model_devi_activation_func[ii][0]
)
jinput["model"]["fitting_net"]["activation_function"] = (
model_devi_activation_func[ii][1]
)
if (
len(np.array(model_devi_activation_func).shape) == 1
): # for backward compatibility, 1-dim list, not net-resolved
jinput["model"]["descriptor"]["activation_function"] = (
model_devi_activation_func[ii]
)
jinput["model"]["fitting_net"]["activation_function"] = (
model_devi_activation_func[ii]
)
# dump the input.json
with open(os.path.join(task_path, train_input_file), "w") as outfile:
json.dump(jinput, outfile, indent=4)
input_files.append(os.path.join(task_path, train_input_file))
# link old models
if iter_index > 0:
prev_iter_name = make_iter_name(iter_index - 1)
prev_work_path = os.path.join(prev_iter_name, train_name)
for ii in range(numb_models):
prev_task_path = os.path.join(prev_work_path, train_task_fmt % ii)
old_model_files = glob.glob(os.path.join(prev_task_path, "model.ckpt*"))
_link_old_models(work_path, old_model_files, ii)
else:
if isinstance(training_iter0_model, str):
training_iter0_model = [training_iter0_model]
iter0_models = []
for ii in training_iter0_model:
model_is = glob.glob(ii)
model_is.sort()
iter0_models += [os.path.abspath(ii) for ii in model_is]
if training_init_model:
assert numb_models == len(iter0_models), (
"training_iter0_model should be provided, and the number of models should be equal to %d"
% numb_models
)
for ii in range(len(iter0_models)):
old_model_path = os.path.join(iter0_models[ii], "model.ckpt*")
old_model_files = glob.glob(old_model_path)
if not len(old_model_files):
raise FileNotFoundError(f"{old_model_path} not found!")
_link_old_models(work_path, old_model_files, ii)
copied_models = next(
(
item
for item in (training_init_frozen_model, training_finetune_model)
if item is not None
),
None,
)
if copied_models is not None:
for ii in range(len(copied_models)):
_link_old_models(
work_path, [copied_models[ii]], ii, basename=f"init{suffix}"
)
# Copy user defined forward files
symlink_user_forward_files(mdata=mdata, task_type="train", work_path=work_path)
# HDF5 format for training data
if jdata.get("one_h5", False):
convert_training_data_to_hdf5(input_files, os.path.join(work_path, "data.hdf5"))
def _link_old_models(work_path, old_model_files, ii, basename: Optional[str] = None):
"""Link the `ii`th old model given by `old_model_files` to
the `ii`th training task in `work_path`.
"""
task_path = os.path.join(work_path, train_task_fmt % ii)
task_old_path = os.path.join(task_path, "old")
create_path(task_old_path)
cwd = os.getcwd()
for jj in old_model_files:
absjj = os.path.abspath(jj)
if basename is None:
basejj = os.path.basename(jj)
else:
basejj = basename
os.chdir(task_old_path)
os.symlink(os.path.relpath(absjj), basejj)
os.chdir(cwd)
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def detect_batch_size(batch_size, system=None):
if isinstance(batch_size, int):
return batch_size
elif batch_size == "auto":
# automaticcaly set batch size, batch_size = 32 // atom_numb (>=1, <=fram_numb)
# check if h5 file
format = "deepmd/npy" if "#" not in system else "deepmd/hdf5"
s = dpdata.LabeledSystem(system, fmt=format)
return int(
min(np.ceil(32.0 / float(s["coords"].shape[1])), s["coords"].shape[0])
)
else:
raise RuntimeError("Unsupported batch size")
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def get_nframes(system):
format = "deepmd/npy" if "#" not in system else "deepmd/hdf5"
s = dpdata.LabeledSystem(system, fmt=format)
return s.get_nframes()
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def run_train(iter_index, jdata, mdata):
mlp_engine = jdata.get("mlp_engine", "dp")
if mlp_engine == "dp":
return run_train_dp(iter_index, jdata, mdata)
else:
raise ValueError(f"Unsupported engine: {mlp_engine}")
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def run_train_dp(iter_index, jdata, mdata):
# print("debug:run_train:mdata", mdata)
# load json param
numb_models = jdata["numb_models"]
suffix = _get_model_suffix(jdata)
# train_param = jdata['train_param']
train_input_file = default_train_input_file
training_reuse_iter = jdata.get("training_reuse_iter")
training_init_model = jdata.get("training_init_model", False)
training_init_frozen_model = (
jdata.get("training_init_frozen_model") if iter_index == 0 else None
)
training_finetune_model = (
jdata.get("training_finetune_model") if iter_index == 0 else None
)
if "srtab_file_path" in jdata.keys():
zbl_file = os.path.basename(jdata.get("srtab_file_path", None))
if training_reuse_iter is not None and iter_index >= training_reuse_iter:
training_init_model = True
try:
mdata["deepmd_version"]
except KeyError:
mdata = set_version(mdata)
if (
training_init_model
+ (training_init_frozen_model is not None)
+ (training_finetune_model is not None)
> 1
):
raise RuntimeError(
"training_init_model, training_init_frozen_model, and training_finetune_model are mutually exclusive."
)
train_command = mdata.get("train_command", "dp").strip()
# assert train_command == "dp", "The 'train_command' should be 'dp'" # the tests should be updated to run this command
if suffix == ".pth":
train_command += " --pt"
elif suffix == ".savedmodel":
train_command += " --jax"
# paths
iter_name = make_iter_name(iter_index)
work_path = os.path.join(iter_name, train_name)
# check if is copied
copy_flag = os.path.join(work_path, "copied")
if os.path.isfile(copy_flag):
log_task("copied model, do not train")
return
# make tasks
all_task = []
for ii in range(numb_models):
task_path = os.path.join(work_path, train_task_fmt % ii)
all_task.append(task_path)
commands = []
if Version(mdata["deepmd_version"]) >= Version("1") and Version(
mdata["deepmd_version"]
) < Version("3"):
# 1.x
## Commands are like `dp train` and `dp freeze`
## train_command should not be None
assert train_command
extra_flags = ""
init_flag = ""
if jdata.get("dp_train_skip_neighbor_stat", False):
extra_flags += " --skip-neighbor-stat"
if training_init_model:
init_flag = " --init-model old/model.ckpt"
elif training_init_frozen_model is not None:
init_flag = f" --init-frz-model old/init{suffix}"
elif training_finetune_model is not None:
init_flag = f" --finetune old/init{suffix}"
command = f"{train_command} train {train_input_file}{extra_flags}"
if suffix == ".pb":
ckpt_suffix = ".index"
elif suffix == ".pth":
ckpt_suffix = ".pt"
elif suffix == ".savedmodel":
ckpt_suffix = ".jax"
else:
raise RuntimeError(f"Unknown suffix {suffix}")
command = f"{{ if [ ! -f model.ckpt{ckpt_suffix} ]; then {command}{init_flag}; else {command} --restart model.ckpt; fi }}"
command = f"/bin/sh -c {shlex.quote(command)}"
commands.append(command)
command = f"{train_command} freeze"
commands.append(command)
if jdata.get("dp_compress", False):
commands.append(f"{train_command} compress")
else:
raise RuntimeError(
"DP-GEN currently only supports for DeePMD-kit 1.x or 2.x version!"
)
# _tasks = [os.path.basename(ii) for ii in all_task]
# run_tasks = []
# for ii in all_task:
# check_pb = os.path.join(ii, "frozen_model.pb")
# check_lcurve = os.path.join(ii, "lcurve.out")
# if os.path.isfile(check_pb) and os.path.isfile(check_lcurve):
# pass
# else:
# run_tasks.append(ii)
run_tasks = [os.path.basename(ii) for ii in all_task]
forward_files = [train_input_file]
if "srtab_file_path" in jdata.keys():
forward_files.append(zbl_file)
if training_init_model:
if suffix == ".pb":
forward_files += [
os.path.join("old", "model.ckpt.meta"),
os.path.join("old", "model.ckpt.index"),
os.path.join("old", "model.ckpt.data-00000-of-00001"),
]
elif suffix == ".pth":
forward_files += [os.path.join("old", "model.ckpt.pt")]
elif suffix == ".savedmodel":
forward_files += [os.path.join("old", "model.ckpt.jax")]
else:
raise RuntimeError(f"Unknown suffix {suffix}")
elif training_init_frozen_model is not None or training_finetune_model is not None:
forward_files.append(os.path.join("old", f"init{suffix}"))
backward_files = [
f"frozen_model{suffix}",
"lcurve.out",
"train.log",
"checkpoint",
]
if jdata.get("dp_compress", False):
backward_files.append(f"frozen_model_compressed{suffix}")
if suffix == ".pb":
backward_files += [
"model.ckpt.meta",
"model.ckpt.index",
"model.ckpt.data-00000-of-00001",
]
elif suffix == ".pth":
backward_files += ["model.ckpt.pt"]
elif suffix == ".savedmodel":
backward_files += ["model.ckpt.jax"]
else:
raise RuntimeError(f"Unknown suffix {suffix}")
if not jdata.get("one_h5", False):
init_data_sys_ = jdata["init_data_sys"]
init_data_sys = []
for ii in init_data_sys_:
init_data_sys.append(os.path.join("data.init", ii))
trans_comm_data = []
cwd = os.getcwd()
os.chdir(work_path)
fp_data = glob.glob(os.path.join("data.iters", "iter.*", "02.fp", "data.*"))
for ii in itertools.chain(init_data_sys, fp_data):
sys_paths = expand_sys_str(ii)
for single_sys in sys_paths:
if "#" not in single_sys:
trans_comm_data += glob.glob(os.path.join(single_sys, "set.*"))
trans_comm_data += glob.glob(os.path.join(single_sys, "type*.raw"))
trans_comm_data += glob.glob(os.path.join(single_sys, "nopbc"))
else:
# H5 file
trans_comm_data.append(single_sys.split("#")[0])
else:
cwd = os.getcwd()
trans_comm_data = ["data.hdf5"]
# remove duplicated files
trans_comm_data = list(set(trans_comm_data))
os.chdir(cwd)
try:
train_group_size = mdata["train_group_size"]
except Exception:
train_group_size = 1
user_forward_files = mdata.get("train" + "_user_forward_files", [])
forward_files += [os.path.basename(file) for file in user_forward_files]
backward_files += mdata.get("train" + "_user_backward_files", [])
### Submit jobs
check_api_version(mdata)
submission = make_submission(
mdata["train_machine"],
mdata["train_resources"],
commands=commands,
work_path=work_path,
run_tasks=run_tasks,
group_size=train_group_size,
forward_common_files=trans_comm_data,
forward_files=forward_files,
backward_files=backward_files,
outlog="train.log",
errlog="train.log",
)
submission.run_submission()
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def post_train(iter_index, jdata, mdata):
mlp_engine = jdata.get("mlp_engine", "dp")
if mlp_engine == "dp":
return post_train_dp(iter_index, jdata, mdata)
else:
raise ValueError(f"Unsupported engine: {mlp_engine}")
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def post_train_dp(iter_index, jdata, mdata):
# load json param
numb_models = jdata["numb_models"]
# paths
iter_name = make_iter_name(iter_index)
work_path = os.path.join(iter_name, train_name)
# check if is copied
copy_flag = os.path.join(work_path, "copied")
if os.path.isfile(copy_flag):
log_task("copied model, do not post train")
return
# symlink models
suffix = _get_model_suffix(jdata)
for ii in range(numb_models):
model_name = f"frozen_model{suffix}"
if jdata.get("dp_compress", False):
model_name = f"frozen_model_compressed{suffix}"
ofile = os.path.join(work_path, "graph.%03d%s" % (ii, suffix))
task_file = os.path.join(train_task_fmt % ii, model_name)
if os.path.isfile(ofile):
os.remove(ofile)
os.symlink(task_file, ofile)
def _get_param_alias(jdata, names):
for ii in names:
if ii in jdata:
return jdata[ii]
raise ValueError(
f"one of the keys {str(names)} should be in jdata {json.dumps(jdata, indent=4)}"
)
[docs]
def parse_cur_job(cur_job):
ensemble = _get_param_alias(cur_job, ["ens", "ensemble"])
temps = [-1]
press = [-1]
if "npt" in ensemble:
temps = _get_param_alias(cur_job, ["Ts", "temps"])
press = _get_param_alias(cur_job, ["Ps", "press"])
elif "nvt" == ensemble or "nve" == ensemble:
temps = _get_param_alias(cur_job, ["Ts", "temps"])
nsteps = _get_param_alias(cur_job, ["nsteps"])
trj_freq = _get_param_alias(cur_job, ["t_freq", "trj_freq", "traj_freq"])
if "pka_e" in cur_job:
pka_e = _get_param_alias(cur_job, ["pka_e"])
else:
pka_e = None
if "dt" in cur_job:
dt = _get_param_alias(cur_job, ["dt"])
else:
dt = None
if "nbeads" in cur_job:
nbeads = _get_param_alias(cur_job, ["nbeads"])
else:
nbeads = None
return ensemble, nsteps, trj_freq, temps, press, pka_e, dt, nbeads
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def expand_matrix_values(target_list, cur_idx=0):
nvar = len(target_list)
if cur_idx == nvar:
return [[]]
else:
res = []
prev = expand_matrix_values(target_list, cur_idx + 1)
for ii in target_list[cur_idx]:
tmp = copy.deepcopy(prev)
for jj in tmp:
jj.insert(0, ii)
res.append(jj)
return res
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def parse_cur_job_revmat(cur_job, use_plm=False):
templates = [cur_job["template"]["lmp"]]
if use_plm:
templates.append(cur_job["template"]["plm"])
revise_keys = []
revise_values = []
if "rev_mat" not in cur_job.keys():
cur_job["rev_mat"] = {}
if "lmp" not in cur_job["rev_mat"].keys():
cur_job["rev_mat"]["lmp"] = {}
for ii in cur_job["rev_mat"]["lmp"].keys():
revise_keys.append(ii)
revise_values.append(cur_job["rev_mat"]["lmp"][ii])
n_lmp_keys = len(revise_keys)
if use_plm:
if "plm" not in cur_job["rev_mat"].keys():
cur_job["rev_mat"]["plm"] = {}
for ii in cur_job["rev_mat"]["plm"].keys():
revise_keys.append(ii)
revise_values.append(cur_job["rev_mat"]["plm"][ii])
revise_matrix = expand_matrix_values(revise_values)
return revise_keys, revise_matrix, n_lmp_keys
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def parse_cur_job_sys_revmat(cur_job, sys_idx, use_plm=False):
templates = [cur_job["template"]["lmp"]]
if use_plm:
templates.append(cur_job["template"]["plm"])
sys_revise_keys = []
sys_revise_values = []
if "sys_rev_mat" not in cur_job.keys():
cur_job["sys_rev_mat"] = {}
local_rev = cur_job["sys_rev_mat"].get(str(sys_idx), {})
if "lmp" not in local_rev.keys():
local_rev["lmp"] = {}
for ii in local_rev["lmp"].keys():
sys_revise_keys.append(ii)
sys_revise_values.append(local_rev["lmp"][ii])
n_sys_lmp_keys = len(sys_revise_keys)
if use_plm:
if "plm" not in local_rev.keys():
local_rev["plm"] = {}
for ii in local_rev["plm"].keys():
sys_revise_keys.append(ii)
sys_revise_values.append(local_rev["plm"][ii])
sys_revise_matrix = expand_matrix_values(sys_revise_values)
return sys_revise_keys, sys_revise_matrix, n_sys_lmp_keys
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def find_only_one_key(lmp_lines, key):
found = []
for idx in range(len(lmp_lines)):
words = lmp_lines[idx].split()
nkey = len(key)
if len(words) >= nkey and words[:nkey] == key:
found.append(idx)
if len(found) > 1:
raise RuntimeError("found %d keywords %s" % (len(found), key))
if len(found) == 0:
raise RuntimeError(f"failed to find keyword {key}")
return found[0]
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def revise_by_keys(lmp_lines, keys, values):
for kk, vv in zip(keys, values):
for ii in range(len(lmp_lines)):
lmp_lines[ii] = lmp_lines[ii].replace(kk, str(vv))
return lmp_lines
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def make_model_devi(iter_index, jdata, mdata):
# The MD engine to perform model deviation
# Default is lammps
model_devi_engine = jdata.get("model_devi_engine", "lammps")
model_devi_jobs = jdata["model_devi_jobs"]
if model_devi_engine != "calypso":
if iter_index >= len(model_devi_jobs):
return False
else:
# mode 1: generate structures according to the user-provided input.dat file, so calypso_input_path and model_devi_max_iter are needed
run_mode = 1
if "calypso_input_path" in jdata:
try:
maxiter = jdata.get("model_devi_max_iter")
except KeyError:
raise KeyError(
"calypso_input_path key exists so you should provide model_devi_max_iter key to control the max iter number"
)
# mode 2: control each iteration to generate structures in specific way by providing model_devi_jobs key
else:
try:
maxiter = max(model_devi_jobs[-1].get("times"))
run_mode = 2
except KeyError:
raise KeyError('did not find model_devi_jobs["times"] key')
if iter_index > maxiter:
dlog.info(f"iter_index is {iter_index} and maxiter is {maxiter}")
return False
if "sys_configs_prefix" in jdata:
sys_configs = []
for sys_list in jdata["sys_configs"]:
# assert (isinstance(sys_list, list) ), "Currently only support type list for sys in 'sys_conifgs' "
temp_sys_list = [
os.path.join(jdata["sys_configs_prefix"], sys) for sys in sys_list
]
sys_configs.append(temp_sys_list)
else:
sys_configs = jdata["sys_configs"]
shuffle_poscar = jdata.get("shuffle_poscar", False)
if model_devi_engine != "calypso":
cur_job = model_devi_jobs[iter_index]
sys_idx = expand_idx(cur_job["sys_idx"])
else:
cur_job = {"model_devi_engine": "calypso", "input.dat": "user_provided"}
sys_idx = []
if len(sys_idx) != len(list(set(sys_idx))):
raise RuntimeError("system index should be uniq")
conf_systems = []
for idx in sys_idx:
cur_systems = []
ss = sys_configs[idx]
for ii in ss:
ii_systems = sorted(glob.glob(ii))
if ii_systems == []:
warnings.warn(
f"There is no system in the path {ii}. Please check if the path is correct."
)
cur_systems += ii_systems
# cur_systems should not be sorted, as we may add specific constrict to the similutions
# cur_systems.sort()
cur_systems = [os.path.abspath(ii) for ii in cur_systems]
conf_systems.append(cur_systems)
iter_name = make_iter_name(iter_index)
train_path = os.path.join(iter_name, train_name)
train_path = os.path.abspath(train_path)
suffix = _get_model_suffix(jdata)
models = sorted(glob.glob(os.path.join(train_path, f"graph*{suffix}")))
work_path = os.path.join(iter_name, model_devi_name)
create_path(work_path)
if model_devi_engine == "calypso":
_calypso_run_opt_path = os.path.join(work_path, calypso_run_opt_name)
calypso_model_devi_path = os.path.join(work_path, calypso_model_devi_name)
create_path(calypso_model_devi_path)
# run model devi script
calypso_run_model_devi_script = os.path.join(
calypso_model_devi_path, "calypso_run_model_devi.py"
)
shutil.copyfile(calypso_run_model_devi_file, calypso_run_model_devi_script)
# Create work path list
calypso_run_opt_path = []
# mode 1: generate structures according to the user-provided input.dat file,
# so calypso_input_path and model_devi_max_iter are needed
if run_mode == 1:
if jdata.get("vsc", False) and len(jdata.get("type_map")) > 1:
# [input.dat.Li.250, input.dat.Li.300]
one_ele_inputdat_list = glob.glob(
f"{jdata.get('calypso_input_path')}/input.dat.{jdata.get('type_map')[0]}.*"
)
if len(one_ele_inputdat_list) == 0:
number_of_pressure = 1
else:
number_of_pressure = len(list(set(one_ele_inputdat_list)))
# calypso_run_opt_path = ['gen_struc_analy.000','gen_struc_analy.001']
for temp_idx in range(number_of_pressure):
calypso_run_opt_path.append(
"%s.%03d" % (_calypso_run_opt_path, temp_idx)
)
elif not jdata.get("vsc", False):
calypso_run_opt_path.append("%s.%03d" % (_calypso_run_opt_path, 0))
# mode 2: control each iteration to generate structures in specific way
# by providing model_devi_jobs key
elif run_mode == 2:
for iiidx, jobbs in enumerate(model_devi_jobs):
if iter_index in jobbs.get("times"):
cur_job = model_devi_jobs[iiidx]
pressures_list = cur_job.get("PSTRESS", [0.0001])
for temp_idx in range(len(pressures_list)):
calypso_run_opt_path.append(
"%s.%03d" % (_calypso_run_opt_path, temp_idx)
)
# to different directory
# calypso_run_opt_path = ['gen_struc_analy.000','gen_struc_analy.001','gen_struc_analy.002',]
for temp_calypso_run_opt_path in calypso_run_opt_path:
create_path(temp_calypso_run_opt_path)
# run confs opt script
run_opt_script = os.path.join(
temp_calypso_run_opt_path, "calypso_run_opt.py"
)
shutil.copyfile(run_opt_file, run_opt_script)
# check outcar script
check_outcar_script = os.path.join(
temp_calypso_run_opt_path, "check_outcar.py"
)
shutil.copyfile(check_outcar_file, check_outcar_script)
for mm in models:
model_name = os.path.basename(mm)
if model_devi_engine != "calypso":
os.symlink(mm, os.path.join(work_path, model_name))
else:
for temp_calypso_run_opt_path in calypso_run_opt_path:
models_path = os.path.join(temp_calypso_run_opt_path, model_name)
if not os.path.exists(models_path):
os.symlink(mm, models_path)
with open(os.path.join(work_path, "cur_job.json"), "w") as outfile:
json.dump(cur_job, outfile, indent=4)
conf_path = os.path.join(work_path, "confs")
create_path(conf_path)
sys_counter = 0
rng = np.random.default_rng()
for ss in conf_systems:
conf_counter = 0
for cc in ss:
if model_devi_engine == "lammps":
conf_name = make_model_devi_conf_name(
sys_idx[sys_counter], conf_counter
)
poscar_name = conf_name + ".poscar"
lmp_name = conf_name + ".lmp"
os.symlink(cc, os.path.join(conf_path, poscar_name))
if "sys_format" in jdata:
fmt = jdata["sys_format"]
else:
fmt = "vasp/poscar"
system = dpdata.System(
os.path.join(conf_path, poscar_name),
fmt=fmt,
type_map=jdata["type_map"],
)
if shuffle_poscar:
system.data["coords"] = rng.permuted(system.data["coords"], axis=1)
if jdata.get("model_devi_nopbc", False):
system.remove_pbc()
system.to_lammps_lmp(os.path.join(conf_path, lmp_name))
elif model_devi_engine == "gromacs":
pass
elif model_devi_engine == "amber":
# Jinzhe's specific Amber version
conf_name = make_model_devi_conf_name(
sys_idx[sys_counter], conf_counter
)
rst7_name = conf_name + ".rst7"
# link restart file
os.symlink(cc, os.path.join(conf_path, rst7_name))
conf_counter += 1
sys_counter += 1
input_mode = "native"
if "calypso_input_path" in jdata:
input_mode = "buffet"
if "template" in cur_job:
input_mode = "revise_template"
use_plm = jdata.get("model_devi_plumed", False)
use_plm_path = jdata.get("model_devi_plumed_path", False)
if input_mode == "native":
if model_devi_engine == "lammps":
_make_model_devi_native(iter_index, jdata, mdata, conf_systems)
elif model_devi_engine == "gromacs":
_make_model_devi_native_gromacs(iter_index, jdata, mdata, conf_systems)
elif model_devi_engine == "amber":
_make_model_devi_amber(iter_index, jdata, mdata, conf_systems)
elif model_devi_engine == "calypso":
_make_model_devi_native_calypso(
iter_index, model_devi_jobs, calypso_run_opt_path
) # generate input.dat automatic in each iter
else:
raise RuntimeError("unknown model_devi engine", model_devi_engine)
elif input_mode == "revise_template":
_make_model_devi_revmat(iter_index, jdata, mdata, conf_systems)
elif input_mode == "buffet":
_make_model_devi_buffet(
jdata, calypso_run_opt_path
) # generate confs according to the input.dat provided
else:
raise RuntimeError("unknown model_devi input mode", input_mode)
# Copy user defined forward_files
symlink_user_forward_files(mdata=mdata, task_type="model_devi", work_path=work_path)
return True
def _make_model_devi_revmat(iter_index, jdata, mdata, conf_systems):
model_devi_jobs = jdata["model_devi_jobs"]
if iter_index >= len(model_devi_jobs):
return False
cur_job = model_devi_jobs[iter_index]
sys_idx = expand_idx(cur_job["sys_idx"])
if len(sys_idx) != len(list(set(sys_idx))):
raise RuntimeError("system index should be uniq")
use_ele_temp = jdata.get("use_ele_temp", 0)
mass_map = jdata["mass_map"]
use_plm = jdata.get("model_devi_plumed", False)
use_plm_path = jdata.get("model_devi_plumed_path", False)
trj_freq = _get_param_alias(cur_job, ["t_freq", "trj_freq", "traj_freq"])
rev_keys, rev_mat, num_lmp = parse_cur_job_revmat(cur_job, use_plm=use_plm)
lmp_templ = cur_job["template"]["lmp"]
lmp_templ = os.path.abspath(lmp_templ)
if use_plm:
plm_templ = cur_job["template"]["plm"]
plm_templ = os.path.abspath(plm_templ)
if use_plm_path:
plm_path_templ = cur_job["template"]["plm_path"]
plm_path_templ = os.path.abspath(plm_path_templ)
iter_name = make_iter_name(iter_index)
train_path = os.path.join(iter_name, train_name)
train_path = os.path.abspath(train_path)
suffix = _get_model_suffix(jdata)
models = sorted(glob.glob(os.path.join(train_path, f"graph*{suffix}")))
task_model_list = []
for ii in models:
task_model_list.append(os.path.join("..", os.path.basename(ii)))
work_path = os.path.join(iter_name, model_devi_name)
try:
mdata["deepmd_version"]
except KeyError:
mdata = set_version(mdata)
deepmd_version = mdata["deepmd_version"]
sys_counter = 0
for ss in conf_systems:
conf_counter = 0
task_counter = 0
for cc in ss:
sys_rev = cur_job.get("sys_rev_mat", None)
total_rev_keys = rev_keys
total_rev_mat = rev_mat
total_num_lmp = num_lmp
if sys_rev is not None:
total_rev_mat = []
sys_rev_keys, sys_rev_mat, sys_num_lmp = parse_cur_job_sys_revmat(
cur_job, sys_idx=sys_idx[sys_counter], use_plm=use_plm
)
_lmp_keys = rev_keys[:num_lmp] + sys_rev_keys[:sys_num_lmp]
if use_plm:
_plm_keys = rev_keys[num_lmp:] + sys_rev_keys[sys_num_lmp:]
_lmp_keys += _plm_keys
total_rev_keys = _lmp_keys
total_num_lmp = num_lmp + sys_num_lmp
for pub in rev_mat:
for pri in sys_rev_mat:
_lmp_mat = pub[:num_lmp] + pri[:sys_num_lmp]
if use_plm:
_plm_mat = pub[num_lmp:] + pri[sys_num_lmp:]
_lmp_mat += _plm_mat
total_rev_mat.append(_lmp_mat)
for ii in range(len(total_rev_mat)):
total_rev_item = total_rev_mat[ii]
task_name = make_model_devi_task_name(
sys_idx[sys_counter], task_counter
)
conf_name = (
make_model_devi_conf_name(sys_idx[sys_counter], conf_counter)
+ ".lmp"
)
task_path = os.path.join(work_path, task_name)
# create task path
create_path(task_path)
model_devi_merge_traj = jdata.get("model_devi_merge_traj", False)
if not model_devi_merge_traj:
create_path(os.path.join(task_path, "traj"))
# link conf
loc_conf_name = "conf.lmp"
os.symlink(
os.path.join(os.path.join("..", "confs"), conf_name),
os.path.join(task_path, loc_conf_name),
)
cwd_ = os.getcwd()
# chdir to task path
os.chdir(task_path)
shutil.copyfile(lmp_templ, "input.lammps")
# revise input of lammps
with open("input.lammps") as fp:
lmp_lines = fp.readlines()
# only revise the line "pair_style deepmd" if the user has not written the full line (checked by then length of the line)
template_has_pair_deepmd = 1
for line_idx, line_context in enumerate(lmp_lines):
if (
(line_context[0] != "#")
and ("pair_style" in line_context)
and ("deepmd" in line_context)
):
template_has_pair_deepmd = 0
template_pair_deepmd_idx = line_idx
if template_has_pair_deepmd == 0:
if Version(deepmd_version) < Version("1"):
if len(lmp_lines[template_pair_deepmd_idx].split()) != (
len(models)
+ len(["pair_style", "deepmd", "10", "model_devi.out"])
):
lmp_lines = revise_lmp_input_model(
lmp_lines,
task_model_list,
trj_freq,
deepmd_version=deepmd_version,
use_ele_temp=use_ele_temp,
)
else:
if len(lmp_lines[template_pair_deepmd_idx].split()) != (
len(models)
+ len(
[
"pair_style",
"deepmd",
"out_freq",
"10",
"out_file",
"model_devi.out",
]
)
):
lmp_lines = revise_lmp_input_model(
lmp_lines,
task_model_list,
trj_freq,
deepmd_version=deepmd_version,
use_ele_temp=use_ele_temp,
)
# use revise_lmp_input_model to raise error message if "part_style" or "deepmd" not found
else:
lmp_lines = revise_lmp_input_model(
lmp_lines,
task_model_list,
trj_freq,
deepmd_version=deepmd_version,
use_ele_temp=use_ele_temp,
)
lmp_lines = revise_lmp_input_dump(
lmp_lines, trj_freq, model_devi_merge_traj
)
lmp_lines = revise_by_keys(
lmp_lines,
total_rev_keys[:total_num_lmp],
total_rev_item[:total_num_lmp],
)
# revise input of plumed
if use_plm:
lmp_lines = revise_lmp_input_plm(lmp_lines, "input.plumed")
shutil.copyfile(plm_templ, "input.plumed")
with open("input.plumed") as fp:
plm_lines = fp.readlines()
# allow using the same list as lmp
# user should not use the same key name for plm
plm_lines = revise_by_keys(
plm_lines, total_rev_keys, total_rev_item
)
with open("input.plumed", "w") as fp:
fp.write("".join(plm_lines))
if use_plm_path:
shutil.copyfile(plm_path_templ, "plmpath.pdb")
# dump input of lammps
with open("input.lammps", "w") as fp:
fp.write("".join(lmp_lines))
with open("job.json", "w") as fp:
job = {}
for ii, jj in zip(total_rev_keys, total_rev_item):
job[ii] = jj
json.dump(job, fp, indent=4)
os.chdir(cwd_)
task_counter += 1
conf_counter += 1
sys_counter += 1
def _make_model_devi_native(iter_index, jdata, mdata, conf_systems):
model_devi_jobs = jdata["model_devi_jobs"]
if iter_index >= len(model_devi_jobs):
return False
cur_job = model_devi_jobs[iter_index]
ensemble, nsteps, trj_freq, temps, press, pka_e, dt, nbeads = parse_cur_job(cur_job)
model_devi_f_avg_relative = jdata.get("model_devi_f_avg_relative", False)
model_devi_merge_traj = jdata.get("model_devi_merge_traj", False)
if (nbeads is not None) and model_devi_f_avg_relative:
raise RuntimeError(
"model_devi_f_avg_relative has not been supported for pimd. Set model_devi_f_avg_relative to False."
)
if (nbeads is not None) and (model_devi_merge_traj):
raise RuntimeError(
"model_devi_merge_traj has not been supported for pimd. Set model_devi_merge_traj to False."
)
if (nbeads is not None) and (not (nsteps % trj_freq == 0)):
raise RuntimeError(
"trj_freq should be a factor of nsteps for pimd. Please check your input."
)
if dt is not None:
model_devi_dt = dt
sys_idx = expand_idx(cur_job["sys_idx"])
if len(sys_idx) != len(list(set(sys_idx))):
raise RuntimeError("system index should be uniq")
use_ele_temp = jdata.get("use_ele_temp", 0)
model_devi_dt = jdata["model_devi_dt"]
model_devi_neidelay = None
if "model_devi_neidelay" in jdata:
model_devi_neidelay = jdata["model_devi_neidelay"]
model_devi_taut = 0.1
if "model_devi_taut" in jdata:
model_devi_taut = jdata["model_devi_taut"]
model_devi_taup = 0.5
if "model_devi_taup" in jdata:
model_devi_taup = jdata["model_devi_taup"]
mass_map = jdata["mass_map"]
nopbc = jdata.get("model_devi_nopbc", False)
iter_name = make_iter_name(iter_index)
train_path = os.path.join(iter_name, train_name)
train_path = os.path.abspath(train_path)
suffix = _get_model_suffix(jdata)
models = sorted(glob.glob(os.path.join(train_path, f"graph*{suffix}")))
task_model_list = []
for ii in models:
task_model_list.append(Path(f"../{Path(ii).name}").as_posix())
work_path = os.path.join(iter_name, model_devi_name)
sys_counter = 0
for ss in conf_systems:
conf_counter = 0
task_counter = 0
for cc in ss:
for tt_ in temps:
if use_ele_temp:
if isinstance(tt_, list):
tt = tt_[0]
if use_ele_temp == 1:
te_f = tt_[1]
te_a = None
else:
te_f = None
te_a = tt_[1]
else:
assert isinstance(tt_, (float, int))
tt = float(tt_)
if use_ele_temp == 1:
te_f = tt
te_a = None
else:
te_f = None
te_a = tt
else:
tt = tt_
te_f = None
te_a = None
for pp in press:
task_name = make_model_devi_task_name(
sys_idx[sys_counter], task_counter
)
conf_name = (
make_model_devi_conf_name(sys_idx[sys_counter], conf_counter)
+ ".lmp"
)
task_path = os.path.join(work_path, task_name)
# dlog.info(task_path)
create_path(task_path)
model_devi_merge_traj = jdata.get("model_devi_merge_traj", False)
if not model_devi_merge_traj:
create_path(os.path.join(task_path, "traj"))
loc_conf_name = "conf.lmp"
os.symlink(
os.path.join(os.path.join("..", "confs"), conf_name),
os.path.join(task_path, loc_conf_name),
)
cwd_ = os.getcwd()
os.chdir(task_path)
try:
mdata["deepmd_version"]
except KeyError:
mdata = set_version(mdata)
deepmd_version = mdata["deepmd_version"]
file_c = make_lammps_input(
ensemble,
loc_conf_name,
task_model_list,
nsteps,
model_devi_dt,
model_devi_neidelay,
trj_freq,
mass_map,
tt,
jdata=jdata,
tau_t=model_devi_taut,
pres=pp,
tau_p=model_devi_taup,
pka_e=pka_e,
ele_temp_f=te_f,
ele_temp_a=te_a,
nopbc=nopbc,
deepmd_version=deepmd_version,
nbeads=nbeads,
)
job = {}
job["ensemble"] = ensemble
job["press"] = pp
job["temps"] = tt
if te_f is not None:
job["ele_temp"] = te_f
if te_a is not None:
job["ele_temp"] = te_a
job["model_devi_dt"] = model_devi_dt
with open("job.json", "w") as _outfile:
json.dump(job, _outfile, indent=4)
os.chdir(cwd_)
with open(os.path.join(task_path, "input.lammps"), "w") as fp:
fp.write(file_c)
task_counter += 1
conf_counter += 1
sys_counter += 1
def _make_model_devi_native_gromacs(iter_index, jdata, mdata, conf_systems):
try:
from gromacs.fileformats.mdp import MDP
except ImportError as e:
raise RuntimeError(
"GromacsWrapper>=0.8.0 is needed for DP-GEN + Gromacs."
) from e
# only support for deepmd v2.0
if Version(mdata["deepmd_version"]) < Version("2.0"):
raise RuntimeError(
"Only support deepmd-kit 2.x for model_devi_engine='gromacs'"
)
model_devi_jobs = jdata["model_devi_jobs"]
if iter_index >= len(model_devi_jobs):
return False
cur_job = model_devi_jobs[iter_index]
dt = cur_job.get("dt", None)
if dt is not None:
model_devi_dt = dt
else:
model_devi_dt = jdata["model_devi_dt"]
nsteps = cur_job.get("nsteps", None)
lambdas = cur_job.get("lambdas", [1.0])
temps = cur_job.get("temps", [298.0])
for ll in lambdas:
assert ll >= 0.0 and ll <= 1.0, "Lambda should be in [0,1]"
if nsteps is None:
raise RuntimeError("nsteps is None, you should set nsteps in model_devi_jobs!")
# Currently Gromacs engine is not supported for different temperatures!
# If you want to change temperatures, you should change it in mdp files.
sys_idx = expand_idx(cur_job["sys_idx"])
if len(sys_idx) != len(list(set(sys_idx))):
raise RuntimeError("system index should be uniq")
mass_map = jdata["mass_map"]
iter_name = make_iter_name(iter_index)
train_path = os.path.join(iter_name, train_name)
train_path = os.path.abspath(train_path)
suffix = _get_model_suffix(jdata)
models = sorted(glob.glob(os.path.join(train_path, f"graph*{suffix}")))
task_model_list = []
for ii in models:
task_model_list.append(os.path.join("..", os.path.basename(ii)))
work_path = os.path.join(iter_name, model_devi_name)
sys_counter = 0
for ss in conf_systems:
conf_counter = 0
task_counter = 0
for cc in ss:
for ll in lambdas:
for tt in temps:
task_name = make_model_devi_task_name(
sys_idx[sys_counter], task_counter
)
task_path = os.path.join(work_path, task_name)
create_path(task_path)
gromacs_settings = jdata.get("gromacs_settings", "")
for key, file in gromacs_settings.items():
if (
key != "traj_filename"
and key != "mdp_filename"
and key != "group_name"
and key != "maxwarn"
):
os.symlink(
os.path.join(cc, file), os.path.join(task_path, file)
)
# input.json for DP-Gromacs
with open(os.path.join(cc, "input.json")) as f:
input_json = json.load(f)
input_json["graph_file"] = models[0]
input_json["lambda"] = ll
with open(os.path.join(task_path, "input.json"), "w") as _outfile:
json.dump(input_json, _outfile, indent=4)
# trj_freq
trj_freq = cur_job.get("trj_freq", 10)
mdp = MDP()
mdp.read(os.path.join(cc, gromacs_settings["mdp_filename"]))
mdp["nstcomm"] = trj_freq
mdp["nstxout"] = trj_freq
mdp["nstlog"] = trj_freq
mdp["nstenergy"] = trj_freq
# dt
mdp["dt"] = model_devi_dt
# nsteps
mdp["nsteps"] = nsteps
# temps
if "ref_t" in list(mdp.keys()):
mdp["ref_t"] = tt
else:
mdp["ref-t"] = tt
mdp.write(os.path.join(task_path, gromacs_settings["mdp_filename"]))
cwd_ = os.getcwd()
os.chdir(task_path)
job = {}
job["trj_freq"] = cur_job["trj_freq"]
job["model_devi_dt"] = model_devi_dt
job["nsteps"] = nsteps
with open("job.json", "w") as _outfile:
json.dump(job, _outfile, indent=4)
os.chdir(cwd_)
task_counter += 1
conf_counter += 1
sys_counter += 1
def _make_model_devi_amber(
iter_index: int, jdata: dict, mdata: dict, conf_systems: list
):
"""Make amber's MD inputs.
Parameters
----------
iter_index : int
iter index
jdata : dict
run parameters. The following parameters will be used in this method:
model_devi_jobs : list[dict]
The list including the dict for information of each cycle:
sys_idx : list[int]
list of systems to run
trj_freq : int
freq to dump trajectory
low_level : str
low level method
cutoff : float
cutoff radius of the DPRc model
parm7_prefix : str
The path prefix to AMBER PARM7 files
parm7 : list[str]
List of paths to AMBER PARM7 files. Each file maps to a system.
mdin_prefix : str
The path prefix to AMBER mdin files
mdin : list[str]
List of paths to AMBER mdin files. Each files maps to a system.
The following keywords will be replaced by the actual value:
@freq@ : freq to dump trajectory
@nstlim@ : total time step to run
@qm_region@ : AMBER mask of the QM region
@qm_theory@ : The QM theory, such as DFTB2
@qm_charge@ : The total charge of the QM theory, such as -2
@rcut@ : cutoff radius of the DPRc model
@GRAPH_FILE0@, @GRAPH_FILE1@, ... : graph files
qm_region : list[str]
AMBER mask of the QM region. Each mask maps to a system.
qm_charge : list[int]
Charge of the QM region. Each charge maps to a system.
nsteps : list[int]
The number of steps to run. Each number maps to a system.
r : list[list[float]] or list[list[list[float]]]
Constrict values for the enhanced sampling. The first dimension maps to systems.
The second dimension maps to confs in each system. The third dimension is the
constrict value. It can be a single float for 1D or list of floats for nD.
disang_prefix : str
The path prefix to disang prefix.
disang : list[str]
List of paths to AMBER disang files. Each file maps to a sytem.
The keyword RVAL will be replaced by the constrict values, or RVAL1, RVAL2, ...
for an nD system.
mdata : dict
machine parameters. Nothing will be used in this method.
conf_systems : list
conf systems
References
----------
.. [1] Development of Range-Corrected Deep Learning Potentials for Fast, Accurate Quantum
Mechanical/Molecular Mechanical Simulations of Chemical Reactions in Solution,
Jinzhe Zeng, Timothy J. Giese, Şölen Ekesan, and Darrin M. York, Journal of Chemical
Theory and Computation 2021 17 (11), 6993-7009
inputs: restart (coords), param, mdin, graph, disang (optional)
"""
model_devi_jobs = jdata["model_devi_jobs"]
if iter_index >= len(model_devi_jobs):
return False
cur_job = model_devi_jobs[iter_index]
sys_idx = expand_idx(cur_job["sys_idx"])
if len(sys_idx) != len(list(set(sys_idx))):
raise RuntimeError("system index should be uniq")
iter_name = make_iter_name(iter_index)
train_path = os.path.join(iter_name, train_name)
train_path = os.path.abspath(train_path)
work_path = os.path.join(iter_name, model_devi_name)
# parm7 - list
parm7 = jdata["parm7"]
parm7_prefix = jdata.get("parm7_prefix", "")
parm7 = [os.path.join(parm7_prefix, pp) for pp in parm7]
# link parm file
for ii, pp in enumerate(parm7):
os.symlink(pp, os.path.join(work_path, "qmmm%d.parm7" % ii))
# TODO: consider writing input in json instead of a given file
# mdin
mdin = jdata["mdin"]
mdin_prefix = jdata.get("mdin_prefix", "")
mdin = [os.path.join(mdin_prefix, pp) for pp in mdin]
qm_region = jdata["qm_region"]
qm_charge = jdata["qm_charge"]
nsteps = jdata["nsteps"]
for ii, pp in enumerate(mdin):
with (
open(pp) as f,
open(os.path.join(work_path, "init%d.mdin" % ii), "w") as fw,
):
mdin_str = f.read()
# freq, nstlim, qm_region, qm_theory, qm_charge, rcut, graph
mdin_str = (
mdin_str.replace("@freq@", str(cur_job.get("trj_freq", 50)))
.replace("@nstlim@", str(nsteps[ii]))
.replace("@qm_region@", qm_region[ii])
.replace("@qm_charge@", str(qm_charge[ii]))
.replace("@qm_theory@", jdata["low_level"])
.replace("@rcut@", str(jdata["cutoff"]))
)
suffix = _get_model_suffix(jdata)
models = sorted(glob.glob(os.path.join(train_path, f"graph.*{suffix}")))
task_model_list = []
for ii in models:
task_model_list.append(os.path.join("..", os.path.basename(ii)))
# graph
for jj, mm in enumerate(task_model_list):
# replace graph
mdin_str = mdin_str.replace("@GRAPH_FILE%d@" % jj, mm)
fw.write(mdin_str)
# disang - list
disang = jdata["disang"]
disang_prefix = jdata.get("disang_prefix", "")
disang = [os.path.join(disang_prefix, pp) for pp in disang]
for sys_counter, ss in enumerate(conf_systems):
for idx_cc, cc in enumerate(ss):
task_counter = idx_cc
conf_counter = idx_cc
task_name = make_model_devi_task_name(sys_idx[sys_counter], task_counter)
conf_name = make_model_devi_conf_name(sys_idx[sys_counter], conf_counter)
task_path = os.path.join(work_path, task_name)
# create task path
create_path(task_path)
# link restart file
loc_conf_name = "init.rst7"
if cur_job.get("restart_from_iter") is None:
os.symlink(
os.path.join(os.path.join("..", "confs"), conf_name + ".rst7"),
os.path.join(task_path, loc_conf_name),
)
else:
restart_from_iter = cur_job["restart_from_iter"]
restart_iter_name = make_iter_name(restart_from_iter)
os.symlink(
os.path.relpath(
os.path.join(
restart_iter_name, model_devi_name, task_name, "rc.rst7"
),
task_path,
),
os.path.join(task_path, loc_conf_name),
)
cwd_ = os.getcwd()
# chdir to task path
os.chdir(task_path)
# reaction coordinates of umbrella sampling
# TODO: maybe consider a better name instead of `r`?
if "r" in jdata:
r = jdata["r"][sys_idx[sys_counter]][conf_counter]
# r can either be a float or a list of float (for 2D coordinates)
if not isinstance(r, Iterable) or isinstance(r, str):
r = [r]
# disang file should include RVAL, RVAL2, ...
with (
open(disang[sys_idx[sys_counter]]) as f,
open("TEMPLATE.disang", "w") as fw,
):
tl = f.read()
for ii, rr in enumerate(r):
if isinstance(rr, Iterable) and not isinstance(rr, str):
raise RuntimeError(
"rr should not be iterable! sys: %d rr: %s r: %s"
% (sys_idx[sys_counter], str(rr), str(r))
)
tl = tl.replace("RVAL" + str(ii + 1), str(rr))
if len(r) == 1:
tl = tl.replace("RVAL", str(r[0]))
fw.write(tl)
with open("job.json", "w") as fp:
json.dump(cur_job, fp, indent=4)
os.chdir(cwd_)
[docs]
def run_md_model_devi(iter_index, jdata, mdata):
# rmdlog.info("This module has been run !")
model_devi_exec = mdata["model_devi_command"]
model_devi_group_size = mdata["model_devi_group_size"]
model_devi_resources = mdata["model_devi_resources"]
use_plm = jdata.get("model_devi_plumed", False)
use_plm_path = jdata.get("model_devi_plumed_path", False)
model_devi_merge_traj = jdata.get("model_devi_merge_traj", False)
iter_name = make_iter_name(iter_index)
work_path = os.path.join(iter_name, model_devi_name)
assert os.path.isdir(work_path)
all_task = glob.glob(os.path.join(work_path, "task.*"))
all_task.sort()
fp = open(os.path.join(work_path, "cur_job.json"))
cur_job = json.load(fp)
run_tasks_ = all_task
# for ii in all_task:
# fres = os.path.join(ii, 'model_devi.out')
# if os.path.isfile(fres) :
# nlines = np.loadtxt(fres).shape[0]
# if nframes != nlines :
# run_tasks_.append(ii)
# else :
# run_tasks_.append(ii)
run_tasks = [os.path.basename(ii) for ii in run_tasks_]
# dlog.info("all_task is ", all_task)
# dlog.info("run_tasks in run_model_deviation",run_tasks_)
suffix = _get_model_suffix(jdata)
all_models = glob.glob(os.path.join(work_path, f"graph*{suffix}"))
model_names = [os.path.basename(ii) for ii in all_models]
model_devi_engine = jdata.get("model_devi_engine", "lammps")
if model_devi_engine == "lammps":
nbeads = jdata["model_devi_jobs"][iter_index].get("nbeads")
if nbeads is None:
command = f"{{ if [ ! -f dpgen.restart.10000 ]; then {model_devi_exec} -i input.lammps -v restart 0; else {model_devi_exec} -i input.lammps -v restart 1; fi }}"
else:
command = f"{{ all_exist=true; for i in $(seq -w 1 {nbeads}); do [[ ! -f dpgen.restart${{i}}.10000 ]] && {{ all_exist=false; break; }}; done; $all_exist && {{ {model_devi_exec} -p {nbeads}x1 -i input.lammps -v restart 1; }} || {{ {model_devi_exec} -p {nbeads}x1 -i input.lammps -v restart 0; }} }}"
command = f"/bin/bash -c {shlex.quote(command)}"
commands = [command]
forward_files = ["conf.lmp", "input.lammps"]
backward_files = ["model_devi.log"]
if nbeads is None:
backward_files += ["model_devi.out"]
else:
num_digits = np.ceil(np.log10(nbeads + 1)).astype(int)
backward_files += [
f"model_devi{i+1:0{num_digits}d}.out" for i in range(nbeads)
]
backward_files += [f"log.lammps.{i:d}" for i in range(nbeads)]
if model_devi_merge_traj:
backward_files += ["all.lammpstrj"]
else:
forward_files += ["traj"]
backward_files += ["traj"]
if use_plm:
forward_files += ["input.plumed"]
# backward_files += ['output.plumed']
backward_files += ["output.plumed", "COLVAR"]
if use_plm_path:
forward_files += ["plmpath.pdb"]
elif model_devi_engine == "gromacs":
gromacs_settings = jdata.get("gromacs_settings", {})
mdp_filename = gromacs_settings.get("mdp_filename", "md.mdp")
topol_filename = gromacs_settings.get("topol_filename", "processed.top")
conf_filename = gromacs_settings.get("conf_filename", "conf.gro")
index_filename = gromacs_settings.get("index_filename", "index.raw")
type_filename = gromacs_settings.get("type_filename", "type.raw")
ndx_filename = gromacs_settings.get("ndx_filename", "")
# Initial reference to process pbc condition.
# Default is em.tpr
ref_filename = gromacs_settings.get("ref_filename", "em.tpr")
deffnm = gromacs_settings.get("deffnm", "deepmd")
maxwarn = gromacs_settings.get("maxwarn", 1)
traj_filename = gromacs_settings.get("traj_filename", "deepmd_traj.gro")
grp_name = gromacs_settings.get("group_name", "Other")
trj_freq = cur_job.get("trj_freq", 10)
command = "%s grompp -f %s -p %s -c %s -o %s -maxwarn %d" % (
model_devi_exec,
mdp_filename,
topol_filename,
conf_filename,
deffnm,
maxwarn,
)
command += f"&& {model_devi_exec} mdrun -deffnm {deffnm} -cpi"
if ndx_filename:
command += f'&& echo -e "{grp_name}\\n{grp_name}\\n" | {model_devi_exec} trjconv -s {ref_filename} -f {deffnm}.trr -n {ndx_filename} -o {traj_filename} -pbc mol -ur compact -center'
else:
command += f'&& echo -e "{grp_name}\\n{grp_name}\\n" | {model_devi_exec} trjconv -s {ref_filename} -f {deffnm}.trr -o {traj_filename} -pbc mol -ur compact -center'
command += "&& if [ ! -d traj ]; then \n mkdir traj; fi\n"
command += f"python -c \"import dpdata;system = dpdata.System('{traj_filename}', fmt='gromacs/gro'); [system.to_gromacs_gro('traj/%d.gromacstrj' % (i * {trj_freq}), frame_idx=i) for i in range(system.get_nframes())]; system.to_deepmd_npy('traj_deepmd')\""
_rel_model_names = " ".join([str(os.path.join("..", ii)) for ii in model_names])
command += f"&& dp model-devi -m {_rel_model_names} -s traj_deepmd -o model_devi.out -f {trj_freq}"
del _rel_model_names
commands = [command]
forward_files = [
mdp_filename,
topol_filename,
conf_filename,
index_filename,
ref_filename,
type_filename,
"input.json",
"job.json",
]
if ndx_filename:
forward_files.append(ndx_filename)
backward_files = [
f"{deffnm}.tpr",
f"{deffnm}.log",
traj_filename,
"model_devi.out",
"traj",
"traj_deepmd",
]
elif model_devi_engine == "amber":
commands = [
(
"TASK=$(basename $(pwd)) && "
"SYS1=${TASK:5:3} && "
"SYS=$((10#$SYS1)) && "
)
+ model_devi_exec
+ (
" -O -p ../qmmm$SYS.parm7 -c init.rst7 -i ../init$SYS.mdin -o rc.mdout -r rc.rst7 -x rc.nc -inf rc.mdinfo -ref init.rst7"
)
]
forward_files = ["init.rst7", "TEMPLATE.disang"]
backward_files = ["rc.mdout", "rc.nc", "rc.rst7", "TEMPLATE.dumpave"]
model_names.extend(["qmmm*.parm7", "init*.mdin"])
cwd = os.getcwd()
user_forward_files = mdata.get("model_devi" + "_user_forward_files", [])
forward_files += [os.path.basename(file) for file in user_forward_files]
backward_files += mdata.get("model_devi" + "_user_backward_files", [])
if len(run_tasks) == 0:
raise RuntimeError(
"run_tasks for model_devi should not be empty! Please check your files."
)
### Submit jobs
check_api_version(mdata)
submission = make_submission(
mdata["model_devi_machine"],
mdata["model_devi_resources"],
commands=commands,
work_path=work_path,
run_tasks=run_tasks,
group_size=model_devi_group_size,
forward_common_files=model_names,
forward_files=forward_files,
backward_files=backward_files,
outlog="model_devi.log",
errlog="model_devi.log",
)
submission.run_submission()
[docs]
def run_model_devi(iter_index, jdata, mdata):
model_devi_engine = jdata.get("model_devi_engine", "lammps")
if model_devi_engine != "calypso":
run_md_model_devi(iter_index, jdata, mdata)
else:
run_calypso_model_devi(iter_index, jdata, mdata)
[docs]
def post_model_devi(iter_index, jdata, mdata):
pass
def _to_face_dist(box_):
box = np.reshape(box_, [3, 3])
vol = np.abs(np.linalg.det(box))
dists = []
for [ii, jj] in [[0, 1], [1, 2], [2, 0]]:
vv = np.cross(box[ii], box[jj])
dists.append(vol / np.linalg.norm(vv))
return np.array(dists)
[docs]
def check_cluster(conf_name, fp_cluster_vacuum, fmt="lammps/dump"):
sys = dpdata.System(conf_name, fmt)
assert sys.get_nframes() == 1
cell = sys.data["cells"][0]
coord = sys.data["coords"][0]
xlim = max(coord[:, 0]) - min(coord[:, 0])
ylim = max(coord[:, 1]) - min(coord[:, 1])
zlim = max(coord[:, 2]) - min(coord[:, 2])
a, b, c = map(norm, [cell[0, :], cell[1, :], cell[2, :]])
min_vac = min([a - xlim, b - ylim, c - zlim])
# print([a-xlim,b-ylim,c-zlim])
# _,r3d=miniball.get_bounding_ball(coord)
if min_vac < fp_cluster_vacuum:
is_bad = True
else:
is_bad = False
return is_bad
[docs]
def check_bad_box(conf_name, criteria, fmt="lammps/dump"):
all_c = criteria.split(";")
sys = dpdata.System(conf_name, fmt)
assert sys.get_nframes() == 1
is_bad = False
for ii in all_c:
[key, value] = ii.split(":")
if key == "length_ratio":
lengths = np.linalg.norm(sys["cells"][0], axis=1)
ratio = np.max(lengths) / np.min(lengths)
if ratio > float(value):
is_bad = True
elif key == "height_ratio":
lengths = np.linalg.norm(sys["cells"][0], axis=1)
dists = _to_face_dist(sys["cells"][0])
ratio = np.max(lengths) / np.min(dists)
if ratio > float(value):
is_bad = True
#
elif key == "wrap_ratio":
ratio = [
sys["cells"][0][1][0] / sys["cells"][0][0][0],
sys["cells"][0][2][1] / sys["cells"][0][1][1],
sys["cells"][0][2][0] / sys["cells"][0][0][0],
]
if np.max(np.abs(ratio)) > float(value):
is_bad = True
elif key == "tilt_ratio":
ratio = [
sys["cells"][0][1][0] / sys["cells"][0][1][1],
sys["cells"][0][2][1] / sys["cells"][0][2][2],
sys["cells"][0][2][0] / sys["cells"][0][2][2],
]
if np.max(np.abs(ratio)) > float(value):
is_bad = True
else:
raise RuntimeError("unknow key", key)
return is_bad
def _read_model_devi_file(
task_path: str,
model_devi_f_avg_relative: bool = False,
model_devi_merge_traj: bool = False,
):
model_devi_files = glob.glob(os.path.join(task_path, "model_devi*.out"))
if len(model_devi_files) > 1:
model_devi_files_sorted = sorted(
model_devi_files,
key=lambda x: int(re.search(r"model_devi(\d+)\.out", x).group(1)),
)
with open(model_devi_files_sorted[0]) as f:
first_line = f.readline()
if not (first_line.startswith("#")):
first_line = "#"
num_beads = len(model_devi_files_sorted)
model_devi_contents = []
for file in model_devi_files_sorted:
model_devi_contents.append(np.loadtxt(file))
assert all(
model_devi_content.shape[0] == model_devi_contents[0].shape[0]
for model_devi_content in model_devi_contents
), r"Not all beads generated the same number of lines in the model_devi${ibead}.out file. Check your pimd task carefully."
last_step = model_devi_contents[0][-1, 0]
for ibead in range(1, num_beads):
model_devi_contents[ibead][:, 0] = model_devi_contents[ibead][
:, 0
] + ibead * (last_step + 1)
model_devi = np.concatenate(model_devi_contents, axis=0)
num_columns = model_devi.shape[1]
formats = ["%12d"] + ["%22.6e"] * (num_columns - 1)
np.savetxt(
os.path.join(task_path, "model_devi.out"),
model_devi,
fmt=formats,
header=first_line.rstrip(),
comments="",
)
if not model_devi_merge_traj:
num_digits = np.ceil(np.log10(num_beads + 1)).astype(int)
traj_files_sorted = []
for ibead in range(num_beads):
traj_files = glob.glob(
os.path.join(
task_path, "traj", f"*lammpstrj{ibead+1:0{num_digits}d}"
)
)
traj_files_sorted.append(
sorted(
traj_files,
key=lambda x: int(
re.search(r"^(\d+)\.lammpstrj", os.path.basename(x)).group(
1
)
),
)
)
assert all(
len(traj_list) == len(traj_files_sorted[0])
for traj_list in traj_files_sorted
), "Not all beads generated the same number of frames. Check your pimd task carefully."
for ibead in range(num_beads):
for itraj in range(len(traj_files_sorted[0])):
base_path, original_filename = os.path.split(
traj_files_sorted[ibead][itraj]
)
frame_number = int(original_filename.split(".")[0])
new_filename = os.path.join(
base_path,
f"{frame_number + ibead * (int(last_step)+1):d}.lammpstrj",
)
os.rename(traj_files_sorted[ibead][itraj], new_filename)
model_devi = np.loadtxt(os.path.join(task_path, "model_devi.out"))
if model_devi_f_avg_relative:
if model_devi_merge_traj is True:
all_traj = os.path.join(task_path, "all.lammpstrj")
all_f = get_all_dumped_forces(all_traj)
else:
trajs = glob.glob(os.path.join(task_path, "traj", "*.lammpstrj"))
all_f = []
for ii in trajs:
all_f.append(get_dumped_forces(ii))
all_f = np.array(all_f)
all_f = all_f.reshape([-1, 3])
avg_f = np.sqrt(np.average(np.sum(np.square(all_f), axis=1)))
model_devi[:, 4:7] = model_devi[:, 4:7] / avg_f
np.savetxt(
os.path.join(task_path, "model_devi_avgf.out"), model_devi, fmt="%16.6e"
)
return model_devi
def _select_by_model_devi_standard(
modd_system_task: list[str],
f_trust_lo: float,
f_trust_hi: float,
v_trust_lo: float,
v_trust_hi: float,
cluster_cutoff: float,
model_devi_engine: str,
model_devi_skip: int = 0,
model_devi_f_avg_relative: bool = False,
model_devi_merge_traj: bool = False,
detailed_report_make_fp: bool = True,
):
if model_devi_engine == "calypso":
iter_name = modd_system_task[0].split("/")[0]
_work_path = os.path.join(iter_name, model_devi_name)
# calypso_run_opt_path = os.path.join(_work_path,calypso_run_opt_name)
calypso_run_opt_path = glob.glob(f"{_work_path}/{calypso_run_opt_name}.*")[0]
numofspecies = _parse_calypso_input("NumberOfSpecies", calypso_run_opt_path)
min_dis = _parse_calypso_dis_mtx(numofspecies, calypso_run_opt_path)
fp_candidate = []
fp_rest_accurate = []
fp_rest_failed = []
cc = 0
counter = Counter()
counter["candidate"] = 0
counter["failed"] = 0
counter["accurate"] = 0
for tt in modd_system_task:
with warnings.catch_warnings():
warnings.simplefilter("ignore")
all_conf = _read_model_devi_file(
tt, model_devi_f_avg_relative, model_devi_merge_traj
)
if all_conf.shape == (7,):
all_conf = all_conf.reshape(1, all_conf.shape[0])
elif model_devi_engine == "calypso" and all_conf.shape == (8,):
all_conf = all_conf.reshape(1, all_conf.shape[0])
for ii in range(all_conf.shape[0]):
if all_conf[ii][0] < model_devi_skip:
continue
cc = int(all_conf[ii][0])
if cluster_cutoff is None:
if model_devi_engine == "calypso":
if float(all_conf[ii][-1]) <= float(min_dis):
if detailed_report_make_fp:
fp_rest_failed.append([tt, cc])
counter["failed"] += 1
continue
if (
all_conf[ii][1] < v_trust_hi and all_conf[ii][1] >= v_trust_lo
) or (
all_conf[ii][4] < f_trust_hi and all_conf[ii][4] >= f_trust_lo
):
fp_candidate.append([tt, cc])
counter["candidate"] += 1
elif (all_conf[ii][1] >= v_trust_hi) or (
all_conf[ii][4] >= f_trust_hi
):
if detailed_report_make_fp:
fp_rest_failed.append([tt, cc])
counter["failed"] += 1
elif all_conf[ii][1] < v_trust_lo and all_conf[ii][4] < f_trust_lo:
if detailed_report_make_fp:
fp_rest_accurate.append([tt, cc])
counter["accurate"] += 1
else:
if model_devi_engine == "calypso":
dlog.info(
"ase opt traj %s frame %d with f devi %f does not belong to either accurate, candidiate and failed "
% (tt, ii, all_conf[ii][4])
)
else:
raise RuntimeError(
"md traj %s frame %d with f devi %f does not belong to either accurate, candidiate and failed, it should not happen"
% (tt, ii, all_conf[ii][4])
)
else:
idx_candidate = np.where(
np.logical_and(
all_conf[ii][7:] < f_trust_hi,
all_conf[ii][7:] >= f_trust_lo,
)
)[0]
for jj in idx_candidate:
fp_candidate.append([tt, cc, jj])
counter["candidate"] += len(idx_candidate)
idx_rest_accurate = np.where(all_conf[ii][7:] < f_trust_lo)[0]
if detailed_report_make_fp:
for jj in idx_rest_accurate:
fp_rest_accurate.append([tt, cc, jj])
counter["accurate"] += len(idx_rest_accurate)
idx_rest_failed = np.where(all_conf[ii][7:] >= f_trust_hi)[0]
if detailed_report_make_fp:
for jj in idx_rest_failed:
fp_rest_failed.append([tt, cc, jj])
counter["failed"] += len(idx_rest_failed)
return fp_rest_accurate, fp_candidate, fp_rest_failed, counter
def _select_by_model_devi_adaptive_trust_low(
modd_system_task: list[str],
f_trust_hi: float,
numb_candi_f: int,
perc_candi_f: float,
v_trust_hi: float,
numb_candi_v: int,
perc_candi_v: float,
model_devi_skip: int = 0,
model_devi_f_avg_relative: bool = False,
model_devi_merge_traj: bool = False,
):
"""modd_system_task model deviation tasks belonging to one system
f_trust_hi
numb_candi_f number of candidate due to the f model deviation
perc_candi_f percentage of candidate due to the f model deviation
v_trust_hi
numb_candi_v number of candidate due to the v model deviation
perc_candi_v percentage of candidate due to the v model deviation
model_devi_skip.
Returns
-------
accur the accurate set
candi the candidate set
failed the failed set
counter counters, number of elements in the sets
f_trust_lo adapted trust level of f
v_trust_lo adapted trust level of v
"""
idx_v = 1
idx_f = 4
accur = set()
candi = set()
failed = []
coll_v = []
coll_f = []
for tt in modd_system_task:
with warnings.catch_warnings():
warnings.simplefilter("ignore")
model_devi = np.loadtxt(os.path.join(tt, "model_devi.out"))
model_devi = _read_model_devi_file(
tt, model_devi_f_avg_relative, model_devi_merge_traj
)
for ii in range(model_devi.shape[0]):
if model_devi[ii][0] < model_devi_skip:
continue
cc = int(model_devi[ii][0])
# tt: name of task folder
# cc: time step of the frame
md_v = model_devi[ii][idx_v]
md_f = model_devi[ii][idx_f]
if md_f > f_trust_hi or md_v > v_trust_hi:
failed.append([tt, cc])
else:
coll_v.append([model_devi[ii][idx_v], tt, cc])
coll_f.append([model_devi[ii][idx_f], tt, cc])
# now accur takes all non-failed frames,
# will be substracted by candidate lat er
accur.add((tt, cc))
# sort
coll_v.sort()
coll_f.sort()
assert len(coll_v) == len(coll_f)
# calcuate numbers
numb_candi_v = max(numb_candi_v, int(perc_candi_v * 0.01 * len(coll_v)))
numb_candi_f = max(numb_candi_f, int(perc_candi_f * 0.01 * len(coll_f)))
# adjust number of candidate
if len(coll_v) < numb_candi_v:
numb_candi_v = len(coll_v)
if len(coll_f) < numb_candi_f:
numb_candi_f = len(coll_f)
# compute trust lo
if numb_candi_v == 0:
v_trust_lo = v_trust_hi
else:
v_trust_lo = coll_v[-numb_candi_v][0]
if numb_candi_f == 0:
f_trust_lo = f_trust_hi
else:
f_trust_lo = coll_f[-numb_candi_f][0]
# add to candidate set
for ii in range(len(coll_v) - numb_candi_v, len(coll_v)):
candi.add(tuple(coll_v[ii][1:]))
for ii in range(len(coll_f) - numb_candi_f, len(coll_f)):
candi.add(tuple(coll_f[ii][1:]))
# accurate set is substracted by the candidate set
accur = accur - candi
# convert to list
candi = [list(ii) for ii in candi]
accur = [list(ii) for ii in accur]
# counters
counter = Counter()
counter["candidate"] = len(candi)
counter["failed"] = len(failed)
counter["accurate"] = len(accur)
return accur, candi, failed, counter, f_trust_lo, v_trust_lo
def _make_fp_vasp_inner(
iter_index,
modd_path,
work_path,
model_devi_skip,
v_trust_lo,
v_trust_hi,
f_trust_lo,
f_trust_hi,
fp_task_min,
fp_task_max,
fp_link_files,
type_map,
jdata,
):
"""iter_index int iter index
modd_path string path of model devi
work_path string path of fp
fp_task_max int max number of tasks
fp_link_files [string] linked files for fp, POTCAR for example
fp_params map parameters for fp.
"""
# --------------------------------------------------------------------------------------------------------------------------------------
model_devi_engine = jdata.get("model_devi_engine", "lammps")
if model_devi_engine == "calypso":
iter_name = work_path.split("/")[0]
_work_path = os.path.join(iter_name, model_devi_name)
# calypso_run_opt_path = os.path.join(_work_path,calypso_run_opt_name)
calypso_run_opt_path = glob.glob(f"{_work_path}/{calypso_run_opt_name}.*")[0]
numofspecies = _parse_calypso_input("NumberOfSpecies", calypso_run_opt_path)
min_dis = _parse_calypso_dis_mtx(numofspecies, calypso_run_opt_path)
calypso_total_fp_num = 300
modd_path = os.path.join(modd_path, calypso_model_devi_name)
model_devi_skip = -1
with open(os.path.join(modd_path, "Model_Devi.out")) as summfile:
summary = np.loadtxt(summfile)
summaryfmax = summary[:, -4]
dis = summary[:, -1]
acc = np.where((summaryfmax <= f_trust_lo) & (dis > float(min_dis)))
fail = np.where((summaryfmax > f_trust_hi) | (dis <= float(min_dis)))
nnan = np.where(np.isnan(summaryfmax))
acc_num = len(acc[0])
fail_num = len(fail[0])
nan_num = len(nnan[0])
tot = len(summaryfmax) - nan_num
candi_num = tot - acc_num - fail_num
dlog.info(
f"summary accurate_ratio: {acc_num * 100 / tot:8.4f}% candidata_ratio: {candi_num * 100 / tot:8.4f}% failed_ratio: {fail_num * 100 / tot:8.4f}% in {tot:d} structures"
)
# --------------------------------------------------------------------------------------------------------------------------------------
modd_task = glob.glob(os.path.join(modd_path, "task.*"))
modd_task.sort()
system_index = []
for ii in modd_task:
system_index.append(os.path.basename(ii).split(".")[1])
set_tmp = set(system_index)
system_index = list(set_tmp)
system_index.sort()
fp_tasks = []
charges_recorder = [] # record charges for each fp_task
charges_map = jdata.get("sys_charges", [])
cluster_cutoff = jdata.get("cluster_cutoff", None)
model_devi_adapt_trust_lo = jdata.get("model_devi_adapt_trust_lo", False)
model_devi_f_avg_relative = jdata.get("model_devi_f_avg_relative", False)
model_devi_merge_traj = jdata.get("model_devi_merge_traj", False)
# skip save *.out if detailed_report_make_fp is False, default is True
detailed_report_make_fp = jdata.get("detailed_report_make_fp", True)
# skip bad box criteria
skip_bad_box = jdata.get("fp_skip_bad_box")
# skip discrete structure in cluster
fp_cluster_vacuum = jdata.get("fp_cluster_vacuum", None)
def _trust_limitation_check(sys_idx, lim):
if isinstance(lim, list):
sys_lim = lim[sys_idx]
elif isinstance(lim, dict):
sys_lim = lim[str(sys_idx)]
else:
sys_lim = lim
return sys_lim
for ss in system_index:
modd_system_glob = os.path.join(modd_path, "task." + ss + ".*")
modd_system_task = glob.glob(modd_system_glob)
modd_system_task.sort()
if model_devi_engine in ("lammps", "gromacs", "calypso"):
# convert global trust limitations to local ones
f_trust_lo_sys = _trust_limitation_check(int(ss), f_trust_lo)
f_trust_hi_sys = _trust_limitation_check(int(ss), f_trust_hi)
v_trust_lo_sys = _trust_limitation_check(int(ss), v_trust_lo)
v_trust_hi_sys = _trust_limitation_check(int(ss), v_trust_hi)
# assumed e -> v
if not model_devi_adapt_trust_lo:
(
fp_rest_accurate,
fp_candidate,
fp_rest_failed,
counter,
) = _select_by_model_devi_standard(
modd_system_task,
f_trust_lo_sys,
f_trust_hi_sys,
v_trust_lo_sys,
v_trust_hi_sys,
cluster_cutoff,
model_devi_engine,
model_devi_skip,
model_devi_f_avg_relative=model_devi_f_avg_relative,
model_devi_merge_traj=model_devi_merge_traj,
detailed_report_make_fp=detailed_report_make_fp,
)
else:
numb_candi_f = jdata.get("model_devi_numb_candi_f", 10)
numb_candi_v = jdata.get("model_devi_numb_candi_v", 0)
perc_candi_f = jdata.get("model_devi_perc_candi_f", 0.0)
perc_candi_v = jdata.get("model_devi_perc_candi_v", 0.0)
(
fp_rest_accurate,
fp_candidate,
fp_rest_failed,
counter,
f_trust_lo_ad,
v_trust_lo_ad,
) = _select_by_model_devi_adaptive_trust_low(
modd_system_task,
f_trust_hi_sys,
numb_candi_f,
perc_candi_f,
v_trust_hi_sys,
numb_candi_v,
perc_candi_v,
model_devi_skip=model_devi_skip,
model_devi_f_avg_relative=model_devi_f_avg_relative,
model_devi_merge_traj=model_devi_merge_traj,
)
dlog.info(
"system {:s} {:9s} : f_trust_lo {:6.3f} v_trust_lo {:6.3f}".format(
ss, "adapted", f_trust_lo_ad, v_trust_lo_ad
)
)
elif model_devi_engine == "amber":
counter = Counter()
counter["candidate"] = 0
counter["failed"] = 0
counter["accurate"] = 0
fp_rest_accurate = []
fp_candidate = []
fp_rest_failed = []
for tt in modd_system_task:
cc = 0
with open(os.path.join(tt, "rc.mdout")) as f:
skip_first = False
first_active = True
for line in f:
if line.startswith(" ntx = 1"):
skip_first = True
if line.startswith(
"Active learning frame written with max. frc. std.:"
):
if skip_first and first_active:
first_active = False
continue
model_devi = (
float(line.split()[-2])
* dpdata.unit.EnergyConversion("kcal_mol", "eV").value()
)
if model_devi < f_trust_lo:
# accurate
if detailed_report_make_fp:
fp_rest_accurate.append([tt, cc])
counter["accurate"] += 1
elif model_devi > f_trust_hi:
# failed
if detailed_report_make_fp:
fp_rest_failed.append([tt, cc])
counter["failed"] += 1
else:
# candidate
fp_candidate.append([tt, cc])
counter["candidate"] += 1
cc += 1
else:
raise RuntimeError("unknown model_devi_engine", model_devi_engine)
# print a report
fp_sum = sum(counter.values())
if fp_sum == 0:
dlog.info(f"system {ss:s} has no fp task, maybe the model devi is nan %")
continue
for cc_key, cc_value in counter.items():
dlog.info(
f"system {ss:s} {cc_key:9s} : {cc_value:6d} in {fp_sum:6d} {cc_value / fp_sum * 100:6.2f} %"
)
random.shuffle(fp_candidate)
if detailed_report_make_fp:
random.shuffle(fp_rest_failed)
random.shuffle(fp_rest_accurate)
with open(
os.path.join(work_path, f"candidate.shuffled.{ss}.out"), "w"
) as fp:
for ii in fp_candidate:
fp.write(" ".join([str(nn) for nn in ii]) + "\n")
with open(
os.path.join(work_path, f"rest_accurate.shuffled.{ss}.out"), "w"
) as fp:
for ii in fp_rest_accurate:
fp.write(" ".join([str(nn) for nn in ii]) + "\n")
with open(
os.path.join(work_path, f"rest_failed.shuffled.{ss}.out"), "w"
) as fp:
for ii in fp_rest_failed:
fp.write(" ".join([str(nn) for nn in ii]) + "\n")
# set number of tasks
accurate_ratio = float(counter["accurate"]) / float(fp_sum)
fp_accurate_threshold = jdata.get("fp_accurate_threshold", 1)
fp_accurate_soft_threshold = jdata.get(
"fp_accurate_soft_threshold", fp_accurate_threshold
)
if accurate_ratio < fp_accurate_soft_threshold:
this_fp_task_max = fp_task_max
elif (
accurate_ratio >= fp_accurate_soft_threshold
and accurate_ratio < fp_accurate_threshold
):
this_fp_task_max = int(
fp_task_max
* (accurate_ratio - fp_accurate_threshold)
/ (fp_accurate_soft_threshold - fp_accurate_threshold)
)
else:
this_fp_task_max = 0
# ----------------------------------------------------------------------------
if model_devi_engine == "calypso":
calypso_intend_fp_num_temp = (
len(fp_candidate) / candi_num
) * calypso_total_fp_num
if calypso_intend_fp_num_temp < 1:
calypso_intend_fp_num = 1
else:
calypso_intend_fp_num = int(calypso_intend_fp_num_temp)
# ----------------------------------------------------------------------------
numb_task = min(this_fp_task_max, len(fp_candidate))
if numb_task < fp_task_min:
numb_task = 0
# ----------------------------------------------------------------------------
if (model_devi_engine == "calypso" and len(jdata.get("type_map")) == 1) or (
model_devi_engine == "calypso"
and len(jdata.get("type_map")) > 1
and candi_num <= calypso_total_fp_num
):
numb_task = min(this_fp_task_max, len(fp_candidate))
if numb_task < fp_task_min:
numb_task = 0
elif (
model_devi_engine == "calypso"
and len(jdata.get("type_map")) > 1
and candi_num > calypso_total_fp_num
):
numb_task = calypso_intend_fp_num
if len(fp_candidate) < numb_task:
numb_task = 0
# ----------------------------------------------------------------------------
dlog.info(
f"system {ss:s} accurate_ratio: {accurate_ratio:8.4f} thresholds: {fp_accurate_soft_threshold:6.4f} and {fp_accurate_threshold:6.4f} eff. task min and max {fp_task_min:4d} {this_fp_task_max:4d} number of fp tasks: {numb_task:6d}"
)
# make fp tasks
# read all.lammpstrj, save in all_sys for each system_index
all_sys = []
trj_freq = None
netcdftraj = None
if model_devi_merge_traj:
for ii in modd_system_task:
all_traj = os.path.join(ii, "all.lammpstrj")
all_sys_per_task = dpdata.System(
all_traj, fmt="lammps/dump", type_map=type_map
)
all_sys.append(all_sys_per_task)
model_devi_jobs = jdata["model_devi_jobs"]
cur_job = model_devi_jobs[iter_index]
trj_freq = int(
_get_param_alias(cur_job, ["t_freq", "trj_freq", "traj_freq"])
)
count_bad_box = 0
count_bad_cluster = 0
fp_candidate = sorted(fp_candidate[:numb_task])
for cc in range(numb_task):
tt = fp_candidate[cc][0]
ii = fp_candidate[cc][1]
ss = os.path.basename(tt).split(".")[1]
conf_name = os.path.join(tt, "traj")
conf_sys = None
if model_devi_engine == "lammps":
if model_devi_merge_traj:
conf_sys = all_sys[int(os.path.basename(tt).split(".")[-1])][
int(int(ii) / trj_freq)
]
else:
conf_name = os.path.join(conf_name, str(ii) + ".lammpstrj")
ffmt = "lammps/dump"
elif model_devi_engine == "gromacs":
conf_name = os.path.join(conf_name, str(ii) + ".gromacstrj")
ffmt = "lammps/dump"
elif model_devi_engine == "amber":
conf_name = os.path.join(tt, "rc.nc")
rst_name = os.path.abspath(os.path.join(tt, "init.rst7"))
elif model_devi_engine == "calypso":
conf_name = os.path.join(conf_name, str(ii) + ".poscar")
ffmt = "vasp/poscar"
else:
raise RuntimeError("unknown model_devi engine", model_devi_engine)
conf_name = os.path.abspath(conf_name)
if skip_bad_box is not None:
skip = check_bad_box(conf_name, skip_bad_box, fmt=ffmt)
if skip:
count_bad_box += 1
continue
if fp_cluster_vacuum is not None:
assert fp_cluster_vacuum > 0
skip_cluster = check_cluster(conf_name, fp_cluster_vacuum)
if skip_cluster:
count_bad_cluster += 1
continue
if model_devi_engine != "calypso":
# link job.json
job_name = os.path.join(tt, "job.json")
job_name = os.path.abspath(job_name)
if cluster_cutoff is not None:
# take clusters
jj = fp_candidate[cc][2]
poscar_name = f"{conf_name}.cluster.{jj}.POSCAR"
new_system = take_cluster(conf_name, type_map, jj, jdata)
new_system.to_vasp_poscar(poscar_name)
fp_task_name = make_fp_task_name(int(ss), cc)
fp_task_path = os.path.join(work_path, fp_task_name)
create_path(fp_task_path)
fp_tasks.append(fp_task_path)
if charges_map:
charges_recorder.append(charges_map[int(ss)])
cwd = os.getcwd()
os.chdir(fp_task_path)
if cluster_cutoff is None:
if model_devi_engine == "lammps":
if model_devi_merge_traj:
conf_sys.to("lammps/lmp", "conf.dump")
else:
os.symlink(os.path.relpath(conf_name), "conf.dump")
os.symlink(os.path.relpath(job_name), "job.json")
elif model_devi_engine == "gromacs":
os.symlink(os.path.relpath(conf_name), "conf.dump")
os.symlink(os.path.relpath(job_name), "job.json")
elif model_devi_engine == "amber":
# read and write with ase
from ase.io.netcdftrajectory import (
NetCDFTrajectory,
write_netcdftrajectory,
)
if cc > 0 and tt == fp_candidate[cc - 1][0]:
# same MD task, use the same file
pass
else:
# not the same file
if cc > 0:
# close the old file
netcdftraj.close()
netcdftraj = NetCDFTrajectory(conf_name)
# write nc file
write_netcdftrajectory("rc.nc", netcdftraj[ii])
if cc >= numb_task - 1:
netcdftraj.close()
# link restart since it's necessary to start Amber
os.symlink(os.path.relpath(rst_name), "init.rst7")
os.symlink(os.path.relpath(job_name), "job.json")
elif model_devi_engine == "calypso":
os.symlink(os.path.relpath(conf_name), "POSCAR")
fjob = open("job.json", "w+")
fjob.write('{"model_devi_engine":"calypso"}')
fjob.close()
# os.system('touch job.json')
else:
raise RuntimeError("unknown model_devi_engine", model_devi_engine)
else:
os.symlink(os.path.relpath(poscar_name), "POSCAR")
np.save("atom_pref", new_system.data["atom_pref"])
for pair in fp_link_files:
os.symlink(pair[0], pair[1])
os.chdir(cwd)
if count_bad_box > 0:
dlog.info(
f"system {ss:s} skipped {count_bad_box:6d} confs with bad box, {numb_task - count_bad_box:6d} remains"
)
if count_bad_cluster > 0:
dlog.info(
f"system {ss:s} skipped {count_bad_cluster:6d} confs with bad cluster, {numb_task - count_bad_cluster:6d} remains"
)
if model_devi_engine == "calypso":
dlog.info(
f"summary accurate_ratio: {acc_num * 100 / tot:8.4f}% candidata_ratio: {candi_num * 100 / tot:8.4f}% failed_ratio: {fail_num * 100 / tot:8.4f}% in {tot:d} structures"
)
if cluster_cutoff is None:
cwd = os.getcwd()
for idx, task in enumerate(fp_tasks):
os.chdir(task)
if model_devi_engine == "lammps":
sys = None
if model_devi_merge_traj:
sys = dpdata.System(
"conf.dump", fmt="lammps/lmp", type_map=type_map
)
else:
sys = dpdata.System(
"conf.dump", fmt="lammps/dump", type_map=type_map
)
sys.to_vasp_poscar("POSCAR")
# dump to poscar
if charges_map:
warnings.warn(
'"sys_charges" keyword only support for gromacs engine now.'
)
elif model_devi_engine == "gromacs":
# dump_to_poscar('conf.dump', 'POSCAR', type_map, fmt = "gromacs/gro")
if charges_map:
dump_to_deepmd_raw(
"conf.dump",
"deepmd.raw",
type_map,
fmt="gromacs/gro",
charge=charges_recorder[idx],
)
else:
dump_to_deepmd_raw(
"conf.dump",
"deepmd.raw",
type_map,
fmt="gromacs/gro",
charge=None,
)
elif model_devi_engine in ("amber", "calypso"):
pass
else:
raise RuntimeError("unknown model_devi engine", model_devi_engine)
os.chdir(cwd)
return fp_tasks
[docs]
def make_vasp_incar(jdata, filename):
if "fp_incar" in jdata.keys():
fp_incar_path = jdata["fp_incar"]
assert os.path.exists(fp_incar_path)
fp_incar_path = os.path.abspath(fp_incar_path)
fr = open(fp_incar_path)
incar = fr.read()
fr.close()
elif "user_fp_params" in jdata.keys():
incar = write_incar_dict(jdata["user_fp_params"])
else:
incar = make_vasp_incar_user_dict(jdata["fp_params"])
with open(filename, "w") as fp:
fp.write(incar)
return incar
[docs]
def make_vasp_incar_ele_temp(jdata, filename, ele_temp, nbands_esti=None):
from pymatgen.io.vasp import Incar
with open(filename) as fp:
incar = fp.read()
try:
incar = incar_upper(Incar.from_string(incar))
except AttributeError:
incar = incar_upper(Incar.from_str(incar))
incar["ISMEAR"] = -1
incar["SIGMA"] = ele_temp * pc.Boltzmann / pc.electron_volt
incar.write_file("INCAR")
if nbands_esti is not None:
nbands = nbands_esti.predict(".")
with open(filename) as fp:
try:
incar = Incar.from_string(fp.read())
except AttributeError:
incar = Incar.from_str(fp.read())
incar["NBANDS"] = nbands
incar.write_file("INCAR")
[docs]
def make_fp_vasp_incar(iter_index, jdata, nbands_esti=None):
iter_name = make_iter_name(iter_index)
work_path = os.path.join(iter_name, fp_name)
fp_tasks = glob.glob(os.path.join(work_path, "task.*"))
fp_tasks.sort()
if len(fp_tasks) == 0:
return
cwd = os.getcwd()
for ii in fp_tasks:
os.chdir(ii)
make_vasp_incar(jdata, "INCAR")
if os.path.exists("job.json"):
with open("job.json") as fp:
job_data = json.load(fp)
if "ele_temp" in job_data:
make_vasp_incar_ele_temp(
jdata, "INCAR", job_data["ele_temp"], nbands_esti=nbands_esti
)
os.chdir(cwd)
def _make_fp_pwmat_input(iter_index, jdata):
iter_name = make_iter_name(iter_index)
work_path = os.path.join(iter_name, fp_name)
fp_tasks = glob.glob(os.path.join(work_path, "task.*"))
fp_tasks.sort()
if len(fp_tasks) == 0:
return
cwd = os.getcwd()
for ii in fp_tasks:
os.chdir(ii)
make_pwmat_input(jdata, "etot.input")
os.system("sed -i '1,2c 4 1' etot.input")
os.chdir(cwd)
[docs]
def make_fp_vasp_cp_cvasp(iter_index, jdata):
# Move cvasp interface to jdata
if ("cvasp" in jdata) and (jdata["cvasp"] is True):
pass
else:
return
iter_name = make_iter_name(iter_index)
work_path = os.path.join(iter_name, fp_name)
fp_tasks = glob.glob(os.path.join(work_path, "task.*"))
fp_tasks.sort()
if len(fp_tasks) == 0:
return
cwd = os.getcwd()
for ii in fp_tasks:
os.chdir(ii)
# copy cvasp.py
shutil.copyfile(cvasp_file, "cvasp.py")
os.chdir(cwd)
[docs]
def make_fp_vasp_kp(iter_index, jdata):
from pymatgen.io.vasp import Incar, Kpoints
iter_name = make_iter_name(iter_index)
work_path = os.path.join(iter_name, fp_name)
fp_aniso_kspacing = jdata.get("fp_aniso_kspacing")
fp_tasks = glob.glob(os.path.join(work_path, "task.*"))
fp_tasks.sort()
if len(fp_tasks) == 0:
return
cwd = os.getcwd()
for ii in fp_tasks:
os.chdir(ii)
# get kspacing and kgamma from incar
assert os.path.exists("INCAR")
with open("INCAR") as fp:
incar = fp.read()
try:
standard_incar = incar_upper(Incar.from_string(incar))
except AttributeError:
standard_incar = incar_upper(Incar.from_str(incar))
if fp_aniso_kspacing is None:
try:
kspacing = standard_incar["KSPACING"]
except KeyError:
raise RuntimeError("KSPACING must be given in INCAR")
else:
kspacing = fp_aniso_kspacing
try:
gamma = standard_incar["KGAMMA"]
if isinstance(gamma, bool):
pass
else:
if gamma[0].upper() == "T":
gamma = True
else:
gamma = False
except KeyError:
raise RuntimeError("KGAMMA must be given in INCAR")
# check poscar
assert os.path.exists("POSCAR")
# make kpoints
ret = make_kspacing_kpoints("POSCAR", kspacing, gamma)
try:
kp = Kpoints.from_string(ret)
except AttributeError:
kp = Kpoints.from_str(ret)
kp.write_file("KPOINTS")
os.chdir(cwd)
def _link_fp_vasp_pp(iter_index, jdata):
fp_pp_path = jdata["fp_pp_path"]
fp_pp_files = jdata["fp_pp_files"]
assert os.path.exists(fp_pp_path)
fp_pp_path = os.path.abspath(fp_pp_path)
iter_name = make_iter_name(iter_index)
work_path = os.path.join(iter_name, fp_name)
fp_tasks = glob.glob(os.path.join(work_path, "task.*"))
fp_tasks.sort()
if len(fp_tasks) == 0:
return
cwd = os.getcwd()
for ii in fp_tasks:
os.chdir(ii)
for jj in fp_pp_files:
pp_file = os.path.join(fp_pp_path, jj)
os.symlink(pp_file, jj)
os.chdir(cwd)
[docs]
def sys_link_fp_vasp_pp(iter_index, jdata):
fp_pp_path = jdata["fp_pp_path"]
fp_pp_files = jdata["fp_pp_files"]
fp_pp_path = os.path.abspath(fp_pp_path)
type_map = jdata["type_map"]
assert os.path.exists(fp_pp_path)
assert len(fp_pp_files) == len(
type_map
), "size of fp_pp_files should be the same as the size of type_map"
iter_name = make_iter_name(iter_index)
work_path = os.path.join(iter_name, fp_name)
fp_tasks = glob.glob(os.path.join(work_path, "task.*"))
fp_tasks.sort()
if len(fp_tasks) == 0:
return
system_idx_str = [os.path.basename(ii).split(".")[1] for ii in fp_tasks]
system_idx_str = list(set(system_idx_str))
system_idx_str.sort()
for ii in system_idx_str:
potcars = []
sys_tasks = glob.glob(os.path.join(work_path, f"task.{ii}.*"))
assert len(sys_tasks) != 0
sys_poscar = os.path.join(sys_tasks[0], "POSCAR")
sys = dpdata.System(sys_poscar, fmt="vasp/poscar")
for ele_name in sys["atom_names"]:
ele_idx = jdata["type_map"].index(ele_name)
potcars.append(fp_pp_files[ele_idx])
with open(os.path.join(work_path, f"POTCAR.{ii}"), "w") as fp_pot:
for jj in potcars:
with open(os.path.join(fp_pp_path, jj)) as fp:
fp_pot.write(fp.read())
sys_tasks = glob.glob(os.path.join(work_path, f"task.{ii}.*"))
cwd = os.getcwd()
for jj in sys_tasks:
os.chdir(jj)
os.symlink(os.path.join("..", f"POTCAR.{ii}"), "POTCAR")
os.chdir(cwd)
def _link_fp_abacus_pporb_descript(iter_index, jdata):
# assume pp orbital files, numerical descrptors and model for dpks are all in fp_pp_path.
fp_pp_path = os.path.abspath(jdata["fp_pp_path"])
type_map = jdata["type_map"]
iter_name = make_iter_name(iter_index)
work_path = os.path.join(iter_name, fp_name)
fp_tasks = glob.glob(os.path.join(work_path, "task.*"))
fp_tasks.sort()
if len(fp_tasks) == 0:
return
cwd = os.getcwd()
for ii in fp_tasks:
os.chdir(ii)
# get value of 'deepks_model' from INPUT
input_param = get_abacus_input_parameters("INPUT")
fp_dpks_model = input_param.get("deepks_model", None)
if fp_dpks_model is not None:
model_file = os.path.join(
fp_pp_path, os.path.split(fp_dpks_model)[1]
) # only the filename
assert os.path.isfile(
model_file
), f"Can not find the deepks model file {model_file}, which is defined in {ii}/INPUT"
os.symlink(model_file, fp_dpks_model) # link to the model file
# get pp, orb, descriptor filenames from STRU
stru_param = get_abacus_STRU("STRU")
atom_names = stru_param["atom_names"]
pp_files_stru = stru_param.get("pp_files", None)
orb_files_stru = stru_param.get("orb_files", None)
descriptor_file_stru = stru_param.get("dpks_descriptor", None)
if pp_files_stru:
assert "fp_pp_files" in jdata, "need to define fp_pp_files in jdata"
if orb_files_stru:
assert "fp_orb_files" in jdata, "need to define fp_orb_files in jdata"
if descriptor_file_stru:
assert (
"fp_dpks_descriptor" in jdata
), "need to define fp_dpks_descriptor in jdata"
for idx, iatom in enumerate(atom_names):
type_map_idx = type_map.index(iatom)
if iatom not in type_map:
raise RuntimeError(
f"atom name {iatom} in STRU is not defined in type_map"
)
if pp_files_stru:
src_file = os.path.join(fp_pp_path, jdata["fp_pp_files"][type_map_idx])
assert os.path.isfile(
src_file
), f"Can not find the pseudopotential file {src_file}"
os.symlink(src_file, pp_files_stru[idx])
if orb_files_stru:
src_file = os.path.join(fp_pp_path, jdata["fp_orb_files"][type_map_idx])
assert os.path.isfile(
src_file
), f"Can not find the orbital file {src_file}"
os.symlink(src_file, orb_files_stru[idx])
if descriptor_file_stru:
src_file = os.path.join(fp_pp_path, jdata["fp_dpks_descriptor"])
assert os.path.isfile(
src_file
), f"Can not find the descriptor file {src_file}"
os.symlink(src_file, descriptor_file_stru)
os.chdir(cwd)
def _make_fp_vasp_configs(iter_index: int, jdata: dict):
"""Read the model deviation from model_devi step, and then generate the candidated structures
in 02.fp directory.
Currently, the formats of generated structures are decided by model_devi_eigne.
Parameters
----------
iter_index : int
The index of iteration.
jdata : dict
The json data.
Returns
-------
int
The number of the candidated structures.
"""
# TODO: we need to unify different data formats
fp_task_max = jdata["fp_task_max"]
model_devi_skip = jdata["model_devi_skip"]
type_map = jdata["type_map"]
iter_name = make_iter_name(iter_index)
work_path = os.path.join(iter_name, fp_name)
create_path(work_path)
modd_path = os.path.join(iter_name, model_devi_name)
task_min = -1
if os.path.isfile(os.path.join(modd_path, "cur_job.json")):
cur_job = json.load(open(os.path.join(modd_path, "cur_job.json")))
if "task_min" in cur_job:
task_min = cur_job["task_min"]
else:
cur_job = {}
# support iteration dependent trust levels
v_trust_lo = cur_job.get(
"model_devi_v_trust_lo", jdata.get("model_devi_v_trust_lo", 1e10)
)
v_trust_hi = cur_job.get(
"model_devi_v_trust_hi", jdata.get("model_devi_v_trust_hi", 1e10)
)
if cur_job.get("model_devi_f_trust_lo") is not None:
f_trust_lo = cur_job.get("model_devi_f_trust_lo")
else:
f_trust_lo = jdata["model_devi_f_trust_lo"]
if cur_job.get("model_devi_f_trust_hi") is not None:
f_trust_hi = cur_job.get("model_devi_f_trust_hi")
else:
f_trust_hi = jdata["model_devi_f_trust_hi"]
# make configs
fp_tasks = _make_fp_vasp_inner(
iter_index,
modd_path,
work_path,
model_devi_skip,
v_trust_lo,
v_trust_hi,
f_trust_lo,
f_trust_hi,
task_min,
fp_task_max,
[],
type_map,
jdata,
)
return fp_tasks
[docs]
def make_fp_vasp(iter_index, jdata):
# abs path for fp_incar if it exists
if "fp_incar" in jdata:
jdata["fp_incar"] = os.path.abspath(jdata["fp_incar"])
# get nbands esti if it exists
if "fp_nbands_esti_data" in jdata:
nbe = NBandsEsti(jdata["fp_nbands_esti_data"])
else:
nbe = None
# order is critical!
# 1, create potcar
sys_link_fp_vasp_pp(iter_index, jdata)
# 2, create incar
make_fp_vasp_incar(iter_index, jdata, nbands_esti=nbe)
# 3, create kpoints
make_fp_vasp_kp(iter_index, jdata)
# 4, copy cvasp
make_fp_vasp_cp_cvasp(iter_index, jdata)
[docs]
def make_fp_pwscf(iter_index, jdata):
work_path = os.path.join(make_iter_name(iter_index), fp_name)
fp_tasks = glob.glob(os.path.join(work_path, "task.*"))
# make pwscf input
iter_name = make_iter_name(iter_index)
work_path = os.path.join(iter_name, fp_name)
fp_pp_files = jdata["fp_pp_files"]
if "user_fp_params" in jdata.keys():
fp_params = jdata["user_fp_params"]
user_input = True
else:
fp_params = jdata["fp_params"]
user_input = False
cwd = os.getcwd()
for ii in fp_tasks:
os.chdir(ii)
sys_data = dpdata.System("POSCAR").data
sys_data["atom_masses"] = []
pps = []
for iii in sys_data["atom_names"]:
sys_data["atom_masses"].append(
jdata["mass_map"][jdata["type_map"].index(iii)]
)
pps.append(fp_pp_files[jdata["type_map"].index(iii)])
ret = make_pwscf_input(sys_data, pps, fp_params, user_input=user_input)
with open("input", "w") as fp:
fp.write(ret)
os.chdir(cwd)
# link pp files
_link_fp_vasp_pp(iter_index, jdata)
[docs]
def make_fp_abacus_scf(iter_index, jdata):
work_path = os.path.join(make_iter_name(iter_index), fp_name)
fp_tasks = glob.glob(os.path.join(work_path, "task.*"))
pporb_path = "pporb"
# make abacus/pw/scf input
iter_name = make_iter_name(iter_index)
work_path = os.path.join(iter_name, fp_name)
fp_pp_files = jdata["fp_pp_files"]
fp_orb_files = None
fp_dpks_descriptor = None
# get paramters for writting INPUT file
fp_params = {}
if "user_fp_params" in jdata.keys():
fp_params = jdata["user_fp_params"]
elif "fp_incar" in jdata.keys():
fp_input_path = jdata["fp_incar"]
assert os.path.exists(fp_input_path)
fp_input_path = os.path.abspath(fp_input_path)
fp_params = get_abacus_input_parameters(fp_input_path)
else:
raise RuntimeError(
"Set 'user_fp_params' or 'fp_incar' in json file to make INPUT of ABACUS"
)
ret_input = make_abacus_scf_input(fp_params, extra_file_path=pporb_path)
# Get orbital and deepks setting
if "basis_type" in fp_params:
if fp_params["basis_type"] == "lcao":
assert (
"fp_orb_files" in jdata
and isinstance(jdata["fp_orb_files"], list)
and len(jdata["fp_orb_files"]) == len(fp_pp_files)
)
fp_orb_files = jdata["fp_orb_files"]
dpks_out_labels = fp_params.get("deepks_out_labels", 0)
dpks_scf = fp_params.get("deepks_scf", 0)
if dpks_out_labels or dpks_scf:
assert "fp_dpks_descriptor" in jdata and isinstance(
jdata["fp_dpks_descriptor"], str
)
fp_dpks_descriptor = jdata["fp_dpks_descriptor"]
# get paramters for writting KPT file
if "kspacing" not in fp_params.keys():
if "gamma_only" in fp_params.keys():
if fp_params["gamma_only"] == 1:
gamma_param = {"k_points": [1, 1, 1, 0, 0, 0]}
ret_kpt = make_abacus_scf_kpt(gamma_param)
else:
if "k_points" in jdata.keys():
ret_kpt = make_abacus_scf_kpt(jdata)
elif "fp_kpt_file" in jdata.keys():
fp_kpt_path = jdata["fp_kpt_file"]
assert os.path.exists(fp_kpt_path)
fp_kpt_path = os.path.abspath(fp_kpt_path)
fk = open(fp_kpt_path)
ret_kpt = fk.read()
fk.close()
else:
raise RuntimeError("Cannot find any k-points information")
else:
if "k_points" in jdata.keys():
ret_kpt = make_abacus_scf_kpt(jdata)
elif "fp_kpt_file" in jdata.keys():
fp_kpt_path = jdata["fp_kpt_file"]
assert os.path.exists(fp_kpt_path)
fp_kpt_path = os.path.abspath(fp_kpt_path)
fk = open(fp_kpt_path)
ret_kpt = fk.read()
fk.close()
else:
gamma_param = {"k_points": [1, 1, 1, 0, 0, 0]}
ret_kpt = make_abacus_scf_kpt(gamma_param)
warnings.warn(
"Cannot find k-points information, gamma_only will be generated."
)
cwd = os.getcwd()
for ii in fp_tasks:
os.chdir(ii)
sys_data = dpdata.System("POSCAR").data
if "mass_map" in jdata:
sys_data["atom_masses"] = jdata["mass_map"]
with open("INPUT", "w") as fp:
fp.write(ret_input)
if "kspacing" not in fp_params.keys():
with open("KPT", "w") as fp:
fp.write(ret_kpt)
ret_stru = make_abacus_scf_stru(
sys_data,
fp_pp_files,
fp_orb_files,
fp_dpks_descriptor,
fp_params,
type_map=jdata["type_map"],
pporb=pporb_path,
)
with open("STRU", "w") as fp:
fp.write(ret_stru)
if not os.path.isdir(pporb_path):
os.makedirs(pporb_path)
os.chdir(cwd)
# link pp and orbital files
_link_fp_abacus_pporb_descript(iter_index, jdata)
[docs]
def make_fp_siesta(iter_index, jdata):
work_path = os.path.join(make_iter_name(iter_index), fp_name)
fp_tasks = glob.glob(os.path.join(work_path, "task.*"))
# make siesta input
iter_name = make_iter_name(iter_index)
work_path = os.path.join(iter_name, fp_name)
fp_pp_files = jdata["fp_pp_files"]
if "user_fp_params" in jdata.keys():
fp_params = jdata["user_fp_params"]
user_input = True
else:
fp_params = jdata["fp_params"]
user_input = False
cwd = os.getcwd()
for ii in fp_tasks:
os.chdir(ii)
sys_data = dpdata.System("POSCAR").data
ret = make_siesta_input(sys_data, fp_pp_files, fp_params)
with open("input", "w") as fp:
fp.write(ret)
os.chdir(cwd)
# link pp files
_link_fp_vasp_pp(iter_index, jdata)
[docs]
def make_fp_gaussian(iter_index, jdata):
work_path = os.path.join(make_iter_name(iter_index), fp_name)
fp_tasks = glob.glob(os.path.join(work_path, "task.*"))
# make gaussian gjf file
iter_name = make_iter_name(iter_index)
work_path = os.path.join(iter_name, fp_name)
if "user_fp_params" in jdata.keys():
fp_params = jdata["user_fp_params"]
else:
fp_params = jdata["fp_params"]
cwd = os.getcwd()
model_devi_engine = jdata.get("model_devi_engine", "lammps")
for ii in fp_tasks:
os.chdir(ii)
if model_devi_engine == "lammps":
sys_data = dpdata.System("POSCAR").data
elif model_devi_engine == "gromacs":
sys_data = dpdata.System("deepmd.raw", fmt="deepmd/raw").data
if os.path.isfile("deepmd.raw/charge"):
sys_data["charge"] = int(np.loadtxt("deepmd.raw/charge", dtype=int))
ret = make_gaussian_input(sys_data, fp_params)
with open("input", "w") as fp:
fp.write(ret)
os.chdir(cwd)
[docs]
def make_fp_cp2k(iter_index, jdata):
work_path = os.path.join(make_iter_name(iter_index), fp_name)
fp_tasks = glob.glob(os.path.join(work_path, "task.*"))
# make cp2k input
iter_name = make_iter_name(iter_index)
work_path = os.path.join(iter_name, fp_name)
if "user_fp_params" in jdata.keys():
fp_params = jdata["user_fp_params"]
# some users might use own inputs
# specify the input path string
elif "external_input_path" in jdata.keys():
fp_params = None
exinput_path = os.path.abspath(jdata["external_input_path"])
else:
fp_params = jdata["fp_params"]
cwd = os.getcwd()
for ii in fp_tasks:
os.chdir(ii)
sys_data = dpdata.System("POSCAR").data
# make input for every task
# if fp_params exits, make keys
if fp_params:
cp2k_input = make_cp2k_input(sys_data, fp_params)
else:
# else read from user input
cp2k_input = make_cp2k_input_from_external(sys_data, exinput_path)
with open("input.inp", "w") as fp:
fp.write(cp2k_input)
fp.close()
# make coord.xyz used by cp2k for every task
cp2k_coord = make_cp2k_xyz(sys_data)
with open("coord.xyz", "w") as fp:
fp.write(cp2k_coord)
fp.close()
os.chdir(cwd)
# link pp files
_link_fp_vasp_pp(iter_index, jdata)
[docs]
def make_fp_pwmat(iter_index, jdata):
# abs path for fp_incar if it exists
if "fp_incar" in jdata:
jdata["fp_incar"] = os.path.abspath(jdata["fp_incar"])
# order is critical!
# 1, link pp files
_link_fp_vasp_pp(iter_index, jdata)
# 2, create pwmat input
_make_fp_pwmat_input(iter_index, jdata)
[docs]
def make_fp_amber_diff(iter_index: int, jdata: dict):
"""Run amber twice to calculate high-level and low-level potential,
and then generate difference between them.
Besides AMBER, one needs to install `dpamber` package, which is avaiable at
https://github.com/njzjz/dpamber
Currently, it should be used with the AMBER model_devi driver.
Parameters
----------
iter_index : int
iter index
jdata : dict
Run parameters. The following parameters are used in this method:
mdin_prefix : str
The path prefix to AMBER mdin files
qm_region : list[str]
AMBER mask of the QM region. Each mask maps to a system.
qm_charge : list[int]
Charge of the QM region. Each charge maps to a system.
high_level : str
high level method
low_level : str
low level method
fp_params : dict
This parameters includes:
high_level_mdin : str
High-level AMBER mdin file. %qm_theory%, %qm_region%,
and %qm_charge% will be replace.
low_level_mdin : str
Low-level AMBER mdin file. %qm_theory%, %qm_region%,
and %qm_charge% will be replace.
parm7_prefix : str
The path prefix to AMBER PARM7 files
parm7 : list[str]
List of paths to AMBER PARM7 files. Each file maps to a system.
References
----------
.. [1] Development of Range-Corrected Deep Learning Potentials for Fast, Accurate Quantum
Mechanical/Molecular Mechanical Simulations of Chemical Reactions in Solution,
Jinzhe Zeng, Timothy J. Giese, Şölen Ekesan, and Darrin M. York, Journal of Chemical
Theory and Computation 2021 17 (11), 6993-7009
"""
assert jdata["model_devi_engine"] == "amber"
work_path = os.path.join(make_iter_name(iter_index), fp_name)
fp_tasks = glob.glob(os.path.join(work_path, "task.*"))
# make amber input
cwd = os.getcwd()
# link two mdin files and param7
os.chdir(os.path.join(fp_tasks[0], ".."))
mdin_prefix = jdata.get("mdin_prefix", "")
low_level_mdin = jdata["fp_params"]["low_level_mdin"]
low_level_mdin = os.path.join(mdin_prefix, low_level_mdin)
high_level_mdin = jdata["fp_params"]["high_level_mdin"]
high_level_mdin = os.path.join(mdin_prefix, high_level_mdin)
with open(low_level_mdin) as f:
low_level_mdin_str = f.read()
with open(high_level_mdin) as f:
high_level_mdin_str = f.read()
qm_region = jdata["qm_region"]
high_level = jdata["high_level"]
low_level = jdata["low_level"]
qm_charge = jdata["qm_charge"]
# qm_theory qm_region qm_charge
for ii, _ in enumerate(qm_region):
mdin_new_str = (
low_level_mdin_str.replace("%qm_theory%", low_level)
.replace("%qm_region%", qm_region[ii])
.replace("%qm_charge%", str(qm_charge[ii]))
)
with open("low_level%d.mdin" % ii, "w") as f:
f.write(mdin_new_str)
mdin_new_str = (
high_level_mdin_str.replace("%qm_theory%", high_level)
.replace("%qm_region%", qm_region[ii])
.replace("%qm_charge%", str(qm_charge[ii]))
)
with open("high_level%d.mdin" % ii, "w") as f:
f.write(mdin_new_str)
parm7 = jdata["parm7"]
parm7_prefix = jdata.get("parm7_prefix", "")
parm7 = [os.path.join(parm7_prefix, pp) for pp in parm7]
for ii, pp in enumerate(parm7):
os.symlink(pp, "qmmm%d.parm7" % ii)
rst7_prefix = jdata.get("sys_configs_prefix", "")
for ii, ss in enumerate(jdata["sys_configs"]):
os.symlink(os.path.join(rst7_prefix, ss[0]), "init%d.rst7" % ii)
with open("qm_region", "w") as f:
f.write("\n".join(qm_region))
os.chdir(cwd)
[docs]
def make_fp_custom(iter_index, jdata):
"""Make input file for customized FP style.
Convert the POSCAR file to custom format.
Parameters
----------
iter_index : int
iter index
jdata : dict
Run parameters.
"""
work_path = os.path.join(make_iter_name(iter_index), fp_name)
fp_tasks = glob.glob(os.path.join(work_path, "task.*"))
fp_params = jdata["fp_params"]
input_fn = fp_params["input_fn"]
input_fmt = fp_params["input_fmt"]
for ii in fp_tasks:
with set_directory(Path(ii)):
system = dpdata.System("POSCAR", fmt="vasp/poscar")
system.to(input_fmt, input_fn)
[docs]
def make_fp(iter_index, jdata, mdata):
"""Select the candidate strutures and make the input file of FP calculation.
Parameters
----------
iter_index : int
iter index
jdata : dict
Run parameters.
mdata : dict
Machine parameters.
"""
fp_tasks = _make_fp_vasp_configs(iter_index, jdata)
if len(fp_tasks) == 0:
return
make_fp_calculation(iter_index, jdata, mdata)
[docs]
def make_fp_calculation(iter_index, jdata, mdata):
"""Make the input file of FP calculation.
Parameters
----------
iter_index : int
iter index
jdata : dict
Run parameters.
mdata : dict
Machine parameters.
"""
fp_style = jdata["fp_style"]
if fp_style == "vasp":
make_fp_vasp(iter_index, jdata)
elif fp_style == "pwscf":
make_fp_pwscf(iter_index, jdata)
elif fp_style == "abacus":
make_fp_abacus_scf(iter_index, jdata)
elif fp_style == "siesta":
make_fp_siesta(iter_index, jdata)
elif fp_style == "gaussian":
make_fp_gaussian(iter_index, jdata)
elif fp_style == "cp2k":
make_fp_cp2k(iter_index, jdata)
elif fp_style == "pwmat":
make_fp_pwmat(iter_index, jdata)
elif fp_style == "amber/diff":
make_fp_amber_diff(iter_index, jdata)
elif fp_style == "custom":
make_fp_custom(iter_index, jdata)
else:
raise RuntimeError("unsupported fp style")
# Copy user defined forward_files
iter_name = make_iter_name(iter_index)
work_path = os.path.join(iter_name, fp_name)
symlink_user_forward_files(mdata=mdata, task_type="fp", work_path=work_path)
def _vasp_check_fin(ii):
if os.path.isfile(os.path.join(ii, "OUTCAR")):
with open(os.path.join(ii, "OUTCAR")) as fp:
content = fp.read()
count = content.count("Elapse")
if count != 1:
return False
else:
return False
return True
def _qe_check_fin(ii):
if os.path.isfile(os.path.join(ii, "output")):
with open(os.path.join(ii, "output")) as fp:
content = fp.read()
count = content.count("JOB DONE")
if count != 1:
return False
else:
return False
return True
def _abacus_scf_check_fin(ii):
if os.path.isfile(os.path.join(ii, "OUT.ABACUS/running_scf.log")):
with open(os.path.join(ii, "OUT.ABACUS/running_scf.log")) as fp:
content = fp.read()
count = content.count("!FINAL_ETOT_IS")
if count != 1:
return False
else:
return False
return True
def _siesta_check_fin(ii):
if os.path.isfile(os.path.join(ii, "output")):
with open(os.path.join(ii, "output")) as fp:
content = fp.read()
count = content.count("End of run")
if count != 1:
return False
else:
return False
return True
def _gaussian_check_fin(ii):
if os.path.isfile(os.path.join(ii, "output")):
with open(os.path.join(ii, "output")) as fp:
content = fp.read()
count = content.count("termination")
if count == 0:
return False
else:
return False
return True
def _cp2k_check_fin(ii):
if os.path.isfile(os.path.join(ii, "output")):
with open(os.path.join(ii, "output")) as fp:
content = fp.read()
count = content.count("SCF run converged")
if count == 0:
return False
else:
return False
return True
def _pwmat_check_fin(ii):
if os.path.isfile(os.path.join(ii, "REPORT")):
with open(os.path.join(ii, "REPORT")) as fp:
content = fp.read()
count = content.count("time")
if count != 1:
return False
else:
return False
return True
[docs]
def run_fp_inner(
iter_index,
jdata,
mdata,
forward_files,
backward_files,
check_fin,
log_file="fp.log",
forward_common_files=[],
):
fp_command = mdata["fp_command"]
fp_group_size = mdata["fp_group_size"]
fp_resources = mdata["fp_resources"]
mark_failure = fp_resources.get("mark_failure", False)
iter_name = make_iter_name(iter_index)
work_path = os.path.join(iter_name, fp_name)
fp_tasks = glob.glob(os.path.join(work_path, "task.*"))
fp_tasks.sort()
if len(fp_tasks) == 0:
return
fp_style = jdata["fp_style"]
if fp_style == "amber/diff":
# firstly get sys_idx
fp_command = (
(
"TASK=$(basename $(pwd)) && "
"SYS1=${TASK:5:3} && "
"SYS=$((10#$SYS1)) && "
'QM_REGION=$(awk "NR==$SYS+1" ../qm_region) &&'
)
+ fp_command
+ (
" -O -p ../qmmm$SYS.parm7 -c ../init$SYS.rst7 -i ../low_level$SYS.mdin -o low_level.mdout -r low_level.rst7 "
"-x low_level.nc -y rc.nc -frc low_level.mdfrc -inf low_level.mdinfo && "
)
+ fp_command
+ (
" -O -p ../qmmm$SYS.parm7 -c ../init$SYS.rst7 -i ../high_level$SYS.mdin -o high_level.mdout -r high_level.rst7 "
"-x high_level.nc -y rc.nc -frc high_level.mdfrc -inf high_level.mdinfo && "
)
+ (
'dpamber corr --cutoff {:f} --parm7_file ../qmmm$SYS.parm7 --nc rc.nc --hl high_level --ll low_level --qm_region "$QM_REGION"'
).format(jdata["cutoff"])
)
fp_run_tasks = fp_tasks
# for ii in fp_tasks :
# if not check_fin(ii) :
# fp_run_tasks.append(ii)
run_tasks = [os.path.basename(ii) for ii in fp_run_tasks]
user_forward_files = mdata.get("fp" + "_user_forward_files", [])
forward_files += [os.path.basename(file) for file in user_forward_files]
backward_files += mdata.get("fp" + "_user_backward_files", [])
### Submit jobs
check_api_version(mdata)
submission = make_submission(
mdata["fp_machine"],
mdata["fp_resources"],
commands=[fp_command],
work_path=work_path,
run_tasks=run_tasks,
group_size=fp_group_size,
forward_common_files=forward_common_files,
forward_files=forward_files,
backward_files=backward_files,
outlog=log_file,
errlog=log_file,
)
submission.run_submission()
[docs]
def run_fp(iter_index, jdata, mdata):
fp_style = jdata["fp_style"]
fp_pp_files = jdata.get("fp_pp_files", [])
if fp_style == "vasp":
forward_files = ["POSCAR", "INCAR", "POTCAR", "KPOINTS"]
backward_files = ["fp.log", "OUTCAR", "vasprun.xml"]
# Move cvasp interface to jdata
if ("cvasp" in jdata) and (jdata["cvasp"] is True):
mdata["fp_resources"]["cvasp"] = True
if ("cvasp" in mdata["fp_resources"]) and (
mdata["fp_resources"]["cvasp"] is True
):
dlog.info("cvasp is on !")
forward_files.append("cvasp.py")
forward_common_files = []
else:
forward_common_files = []
run_fp_inner(
iter_index,
jdata,
mdata,
forward_files,
backward_files,
_vasp_check_fin,
forward_common_files=forward_common_files,
)
elif fp_style == "pwscf":
forward_files = ["input"] + fp_pp_files
backward_files = ["output"]
run_fp_inner(
iter_index,
jdata,
mdata,
forward_files,
backward_files,
_qe_check_fin,
log_file="output",
)
elif fp_style == "abacus":
fp_params = {}
if "user_fp_params" in jdata.keys():
fp_params = jdata["user_fp_params"]
elif "fp_incar" in jdata.keys():
fp_input_path = jdata["fp_incar"]
assert os.path.exists(fp_input_path)
fp_input_path = os.path.abspath(fp_input_path)
fp_params = get_abacus_input_parameters(fp_input_path)
forward_files = ["INPUT", "STRU", "pporb"]
if "kspacing" not in fp_params.keys():
forward_files.append("KPT")
backward_files = ["output", "OUT.ABACUS"]
run_fp_inner(
iter_index,
jdata,
mdata,
forward_files,
backward_files,
_abacus_scf_check_fin,
log_file="output",
)
elif fp_style == "siesta":
forward_files = ["input"] + fp_pp_files
backward_files = ["output"]
run_fp_inner(
iter_index,
jdata,
mdata,
forward_files,
backward_files,
_siesta_check_fin,
log_file="output",
)
elif fp_style == "gaussian":
forward_files = ["input"]
backward_files = ["output"]
run_fp_inner(
iter_index,
jdata,
mdata,
forward_files,
backward_files,
_gaussian_check_fin,
log_file="output",
)
elif fp_style == "cp2k":
forward_files = ["input.inp", "coord.xyz"]
backward_files = ["output"]
run_fp_inner(
iter_index,
jdata,
mdata,
forward_files,
backward_files,
_cp2k_check_fin,
log_file="output",
)
elif fp_style == "pwmat":
forward_files = ["atom.config", "etot.input"] + fp_pp_files
backward_files = ["REPORT", "OUT.MLMD", "output"]
run_fp_inner(
iter_index,
jdata,
mdata,
forward_files,
backward_files,
_pwmat_check_fin,
log_file="output",
)
elif fp_style == "amber/diff":
forward_files = ["rc.nc"]
backward_files = [
"low_level.mdfrc",
"low_level.mdout",
"high_level.mdfrc",
"high_level.mdout",
"output",
"dataset",
]
forward_common_files = [
"low_level*.mdin",
"high_level*.mdin",
"qmmm*.parm7",
"qm_region",
"init*.rst7",
]
run_fp_inner(
iter_index,
jdata,
mdata,
forward_files,
backward_files,
None,
log_file="output",
forward_common_files=forward_common_files,
)
elif fp_style == "custom":
fp_params = jdata["fp_params"]
forward_files = [fp_params["input_fn"]]
backward_files = [fp_params["output_fn"]]
run_fp_inner(
iter_index,
jdata,
mdata,
forward_files,
backward_files,
None,
log_file="output",
)
else:
raise RuntimeError("unsupported fp style")
[docs]
def post_fp_check_fail(iter_index, jdata, rfailed=None):
ratio_failed = rfailed if rfailed else jdata.get("ratio_failed", 0.05)
iter_name = make_iter_name(iter_index)
work_path = os.path.join(iter_name, fp_name)
fp_tasks = glob.glob(os.path.join(work_path, "task.*"))
fp_tasks.sort()
if len(fp_tasks) == 0:
return
ntask = len(fp_tasks)
nfail = 0
# check fail according to the number of collected data
sys_data = glob.glob(os.path.join(work_path, "data.*"))
sys_data.sort()
nframe = 0
for ii in sys_data:
sys_paths = expand_sys_str(ii)
for single_sys in sys_paths:
sys = dpdata.LabeledSystem(os.path.join(single_sys), fmt="deepmd/npy")
nframe += len(sys)
nfail = ntask - nframe
rfail = float(nfail) / float(ntask)
dlog.info("failed tasks: %6d in %6d %6.2f %% " % (nfail, ntask, rfail * 100.0))
if rfail > ratio_failed:
raise RuntimeError("find too many unsuccessfully terminated jobs")
[docs]
def post_fp_vasp(iter_index, jdata, rfailed=None):
ratio_failed = rfailed if rfailed else jdata.get("ratio_failed", 0.05)
model_devi_engine = jdata.get("model_devi_engine", "lammps")
if model_devi_engine != "calypso":
model_devi_jobs = jdata["model_devi_jobs"]
assert iter_index < len(model_devi_jobs)
use_ele_temp = jdata.get("use_ele_temp", 0)
iter_name = make_iter_name(iter_index)
work_path = os.path.join(iter_name, fp_name)
fp_tasks = glob.glob(os.path.join(work_path, "task.*"))
fp_tasks.sort()
if len(fp_tasks) == 0:
return
system_index = []
for ii in fp_tasks:
system_index.append(os.path.basename(ii).split(".")[1])
system_index.sort()
set_tmp = set(system_index)
system_index = list(set_tmp)
system_index.sort()
cwd = os.getcwd()
tcount = 0
icount = 0
for ss in system_index:
sys_outcars = glob.glob(os.path.join(work_path, f"task.{ss}.*/OUTCAR"))
sys_outcars.sort()
tcount += len(sys_outcars)
all_sys = None
all_te = []
for oo in sys_outcars:
try:
_sys = dpdata.LabeledSystem(oo, type_map=jdata["type_map"])
except Exception:
dlog.info("Try to parse from vasprun.xml")
try:
_sys = dpdata.LabeledSystem(
oo.replace("OUTCAR", "vasprun.xml"), type_map=jdata["type_map"]
)
except Exception:
_sys = dpdata.LabeledSystem()
dlog.info("Failed fp path: {}".format(oo.replace("OUTCAR", "")))
if len(_sys) == 1:
# save ele_temp, if any
if os.path.exists(oo.replace("OUTCAR", "job.json")):
with open(oo.replace("OUTCAR", "job.json")) as fp:
job_data = json.load(fp)
if "ele_temp" in job_data:
assert use_ele_temp
ele_temp = job_data["ele_temp"]
all_te.append(ele_temp)
if use_ele_temp == 0:
raise RuntimeError(
"should not get ele temp at setting: use_ele_temp == 0"
)
elif use_ele_temp == 1:
_sys.data["fparam"] = np.array(ele_temp).reshape(1, 1)
elif use_ele_temp == 2:
tile_te = np.tile(ele_temp, [_sys.get_natoms()])
_sys.data["aparam"] = tile_te.reshape(
1, _sys.get_natoms(), 1
)
else:
raise RuntimeError(
"invalid setting of use_ele_temp " + str(use_ele_temp)
)
# check if ele_temp shape is correct
_sys.check_data()
if all_sys is None:
all_sys = _sys
else:
all_sys.append(_sys)
elif len(_sys) >= 2:
raise RuntimeError("The vasp parameter NSW should be set as 1")
else:
icount += 1
all_te = np.array(all_te)
if all_sys is not None:
sys_data_path = os.path.join(work_path, f"data.{ss}")
all_sys.to_deepmd_raw(sys_data_path)
all_sys.to_deepmd_npy(sys_data_path, set_size=len(sys_outcars))
if tcount == 0:
rfail = 0.0
dlog.info("failed frame: %6d in %6d " % (icount, tcount))
else:
rfail = float(icount) / float(tcount)
dlog.info(
"failed frame: %6d in %6d %6.2f %% " % (icount, tcount, rfail * 100.0)
)
if rfail > ratio_failed:
raise RuntimeError(
"find too many unsuccessfully terminated jobs. Too many FP tasks are not converged. Please check your input parameters (e.g. INCAR) or configuration (e.g. POSCAR) in directories 'iter.*.*/02.fp/task.*.*/.'"
)
[docs]
def post_fp_pwscf(iter_index, jdata):
model_devi_jobs = jdata["model_devi_jobs"]
assert iter_index < len(model_devi_jobs)
iter_name = make_iter_name(iter_index)
work_path = os.path.join(iter_name, fp_name)
fp_tasks = glob.glob(os.path.join(work_path, "task.*"))
fp_tasks.sort()
if len(fp_tasks) == 0:
return
system_index = []
for ii in fp_tasks:
system_index.append(os.path.basename(ii).split(".")[1])
system_index.sort()
set_tmp = set(system_index)
system_index = list(set_tmp)
system_index.sort()
cwd = os.getcwd()
for ss in system_index:
sys_output = glob.glob(os.path.join(work_path, f"task.{ss}.*/output"))
sys_input = glob.glob(os.path.join(work_path, f"task.{ss}.*/input"))
sys_output.sort()
sys_input.sort()
flag = True
for ii, oo in zip(sys_input, sys_output):
if flag:
_sys = dpdata.LabeledSystem(
oo, fmt="qe/pw/scf", type_map=jdata["type_map"]
)
if len(_sys) > 0:
all_sys = _sys
flag = False
else:
pass
else:
_sys = dpdata.LabeledSystem(
oo, fmt="qe/pw/scf", type_map=jdata["type_map"]
)
if len(_sys) > 0:
all_sys.append(_sys)
sys_data_path = os.path.join(work_path, f"data.{ss}")
all_sys.to_deepmd_raw(sys_data_path)
all_sys.to_deepmd_npy(sys_data_path, set_size=len(sys_output))
[docs]
def post_fp_abacus_scf(iter_index, jdata):
model_devi_jobs = jdata["model_devi_jobs"]
assert iter_index < len(model_devi_jobs)
iter_name = make_iter_name(iter_index)
work_path = os.path.join(iter_name, fp_name)
fp_tasks = glob.glob(os.path.join(work_path, "task.*"))
fp_tasks.sort()
if len(fp_tasks) == 0:
return
system_index = []
for ii in fp_tasks:
system_index.append(os.path.basename(ii).split(".")[1])
system_index.sort()
set_tmp = set(system_index)
system_index = list(set_tmp)
system_index.sort()
cwd = os.getcwd()
for ss in system_index:
sys_output = glob.glob(os.path.join(work_path, f"task.{ss}.*"))
sys_input = glob.glob(os.path.join(work_path, f"task.{ss}.*/INPUT"))
sys_output.sort()
sys_input.sort()
all_sys = None
for ii, oo in zip(sys_input, sys_output):
_sys = dpdata.LabeledSystem(
oo, fmt="abacus/scf", type_map=jdata["type_map"]
)
if len(_sys) > 0:
if all_sys is None:
all_sys = _sys
else:
all_sys.append(_sys)
if all_sys is not None:
sys_data_path = os.path.join(work_path, f"data.{ss}")
all_sys.to_deepmd_raw(sys_data_path)
all_sys.to_deepmd_npy(sys_data_path, set_size=len(sys_output))
[docs]
def post_fp_siesta(iter_index, jdata):
model_devi_jobs = jdata["model_devi_jobs"]
assert iter_index < len(model_devi_jobs)
iter_name = make_iter_name(iter_index)
work_path = os.path.join(iter_name, fp_name)
fp_tasks = glob.glob(os.path.join(work_path, "task.*"))
fp_tasks.sort()
if len(fp_tasks) == 0:
return
system_index = []
for ii in fp_tasks:
system_index.append(os.path.basename(ii).split(".")[1])
system_index.sort()
set_tmp = set(system_index)
system_index = list(set_tmp)
system_index.sort()
cwd = os.getcwd()
for ss in system_index:
sys_output = glob.glob(os.path.join(work_path, f"task.{ss}.*/output"))
sys_input = glob.glob(os.path.join(work_path, f"task.{ss}.*/input"))
sys_output.sort()
sys_input.sort()
for idx, oo in enumerate(sys_output):
_sys = dpdata.LabeledSystem()
(
_sys.data["atom_names"],
_sys.data["atom_numbs"],
_sys.data["atom_types"],
_sys.data["cells"],
_sys.data["coords"],
_sys.data["energies"],
_sys.data["forces"],
_sys.data["virials"],
) = dpdata.siesta.output.obtain_frame(oo)
if idx == 0:
all_sys = _sys
else:
all_sys.append(_sys)
sys_data_path = os.path.join(work_path, f"data.{ss}")
all_sys.to_deepmd_raw(sys_data_path)
all_sys.to_deepmd_npy(sys_data_path, set_size=len(sys_output))
[docs]
def post_fp_gaussian(iter_index, jdata):
model_devi_jobs = jdata["model_devi_jobs"]
assert iter_index < len(model_devi_jobs)
iter_name = make_iter_name(iter_index)
work_path = os.path.join(iter_name, fp_name)
fp_tasks = glob.glob(os.path.join(work_path, "task.*"))
fp_tasks.sort()
if len(fp_tasks) == 0:
return
system_index = []
for ii in fp_tasks:
system_index.append(os.path.basename(ii).split(".")[1])
system_index.sort()
set_tmp = set(system_index)
system_index = list(set_tmp)
system_index.sort()
cwd = os.getcwd()
for ss in system_index:
sys_output = glob.glob(os.path.join(work_path, f"task.{ss}.*/output"))
sys_output.sort()
for idx, oo in enumerate(sys_output):
sys = dpdata.LabeledSystem(oo, fmt="gaussian/log")
if len(sys) > 0:
sys.check_type_map(type_map=jdata["type_map"])
if jdata.get("use_atom_pref", False):
sys.data["atom_pref"] = np.load(
os.path.join(os.path.dirname(oo), "atom_pref.npy")
)
if idx == 0:
if jdata.get("use_clusters", False):
all_sys = dpdata.MultiSystems(sys, type_map=jdata["type_map"])
else:
all_sys = sys
else:
all_sys.append(sys)
sys_data_path = os.path.join(work_path, f"data.{ss}")
all_sys.to_deepmd_raw(sys_data_path)
all_sys.to_deepmd_npy(sys_data_path, set_size=len(sys_output))
[docs]
def post_fp_cp2k(iter_index, jdata, rfailed=None):
ratio_failed = rfailed if rfailed else jdata.get("ratio_failed", 0.10)
model_devi_jobs = jdata["model_devi_jobs"]
assert iter_index < len(model_devi_jobs)
iter_name = make_iter_name(iter_index)
work_path = os.path.join(iter_name, fp_name)
fp_tasks = glob.glob(os.path.join(work_path, "task.*"))
fp_tasks.sort()
if len(fp_tasks) == 0:
return
system_index = []
for ii in fp_tasks:
system_index.append(os.path.basename(ii).split(".")[1])
system_index.sort()
set_tmp = set(system_index)
system_index = list(set_tmp)
system_index.sort()
cwd = os.getcwd()
# tcount: num of all fp tasks
tcount = 0
# icount: num of converged fp tasks
icount = 0
for ss in system_index:
sys_output = glob.glob(os.path.join(work_path, f"task.{ss}.*/output"))
sys_output.sort()
tcount += len(sys_output)
all_sys = dpdata.MultiSystems(type_map=jdata["type_map"])
for oo in sys_output:
_sys = dpdata.LabeledSystem(
oo, fmt="cp2kdata/e_f", type_map=jdata["type_map"]
)
all_sys.append(_sys)
icount += 1
if (all_sys is not None) and (len(all_sys) > 0):
sys_data_path = os.path.join(work_path, f"data.{ss}")
all_sys.to_deepmd_raw(sys_data_path)
all_sys.to_deepmd_npy(sys_data_path)
if tcount == 0:
rfail = 0.0
dlog.info("failed frame: %6d in %6d " % (tcount - icount, tcount))
else:
rfail = float(tcount - icount) / float(tcount)
dlog.info(
"failed frame: %6d in %6d %6.2f %% "
% (tcount - icount, tcount, rfail * 100.0)
)
if rfail > ratio_failed:
raise RuntimeError(
"find too many unsuccessfully terminated jobs. Too many FP tasks are not converged. Please check your files in directories 'iter.*.*/02.fp/task.*.*/.'"
)
[docs]
def post_fp_pwmat(iter_index, jdata, rfailed=None):
ratio_failed = rfailed if rfailed else jdata.get("ratio_failed", 0.05)
model_devi_jobs = jdata["model_devi_jobs"]
assert iter_index < len(model_devi_jobs)
iter_name = make_iter_name(iter_index)
work_path = os.path.join(iter_name, fp_name)
fp_tasks = glob.glob(os.path.join(work_path, "task.*"))
fp_tasks.sort()
if len(fp_tasks) == 0:
return
system_index = []
for ii in fp_tasks:
system_index.append(os.path.basename(ii).split(".")[1])
system_index.sort()
set_tmp = set(system_index)
system_index = list(set_tmp)
system_index.sort()
cwd = os.getcwd()
tcount = 0
icount = 0
for ss in system_index:
sys_output = glob.glob(os.path.join(work_path, f"task.{ss}.*/OUT.MLMD"))
sys_output.sort()
tcount += len(sys_output)
all_sys = None
for oo in sys_output:
_sys = dpdata.LabeledSystem(oo, type_map=jdata["type_map"])
if len(_sys) == 1:
if all_sys is None:
all_sys = _sys
else:
all_sys.append(_sys)
else:
icount += 1
if all_sys is not None:
sys_data_path = os.path.join(work_path, f"data.{ss}")
all_sys.to_deepmd_raw(sys_data_path)
all_sys.to_deepmd_npy(sys_data_path, set_size=len(sys_output))
dlog.info(f"failed frame number: {icount} ")
dlog.info(f"total frame number: {tcount} ")
reff = icount / tcount
dlog.info(f"ratio of failed frame: {reff:.2%}")
if reff > ratio_failed:
raise RuntimeError("find too many unsuccessfully terminated jobs")
[docs]
def post_fp_amber_diff(iter_index, jdata):
model_devi_jobs = jdata["model_devi_jobs"]
assert iter_index < len(model_devi_jobs)
iter_name = make_iter_name(iter_index)
work_path = os.path.join(iter_name, fp_name)
fp_tasks = glob.glob(os.path.join(work_path, "task.*"))
fp_tasks.sort()
if len(fp_tasks) == 0:
return
system_index = []
for ii in fp_tasks:
system_index.append(os.path.basename(ii).split(".")[1])
system_index.sort()
set_tmp = set(system_index)
system_index = list(set_tmp)
system_index.sort()
for ss in system_index:
sys_output = glob.glob(os.path.join(work_path, f"task.{ss}.*"))
sys_output.sort()
all_sys = dpdata.MultiSystems(type_map=jdata["type_map"])
for oo in sys_output:
sys = dpdata.MultiSystems(type_map=jdata["type_map"]).from_deepmd_npy(
os.path.join(oo, "dataset")
)
all_sys.append(sys)
sys_data_path = os.path.join(work_path, f"data.{ss}")
all_sys.to_deepmd_raw(sys_data_path)
all_sys.to_deepmd_npy(sys_data_path, set_size=len(sys_output), prec=np.float64)
[docs]
def post_fp_custom(iter_index, jdata):
"""Post fp for custom fp. Collect data from user-defined `output_fn`.
Parameters
----------
iter_index : int
The index of the current iteration.
jdata : dict
The parameter data.
"""
model_devi_jobs = jdata["model_devi_jobs"]
assert iter_index < len(model_devi_jobs)
iter_name = make_iter_name(iter_index)
work_path = os.path.join(iter_name, fp_name)
fp_tasks = glob.glob(os.path.join(work_path, "task.*"))
fp_tasks.sort()
if len(fp_tasks) == 0:
return
system_index = []
for ii in fp_tasks:
system_index.append(os.path.basename(ii).split(".")[1])
system_index.sort()
set_tmp = set(system_index)
system_index = list(set_tmp)
system_index.sort()
fp_params = jdata["fp_params"]
output_fn = fp_params["output_fn"]
output_fmt = fp_params["output_fmt"]
for ss in system_index:
sys_output = glob.glob(os.path.join(work_path, f"task.{ss}.*"))
sys_output.sort()
all_sys = dpdata.MultiSystems(type_map=jdata["type_map"])
for oo in sys_output:
if os.path.exists(os.path.join(oo, output_fn)):
sys = dpdata.LabeledSystem(os.path.join(oo, output_fn), fmt=output_fmt)
all_sys.append(sys)
sys_data_path = os.path.join(work_path, f"data.{ss}")
all_sys.to_deepmd_raw(sys_data_path)
all_sys.to_deepmd_npy(sys_data_path, set_size=len(sys_output), prec=np.float64)
[docs]
def post_fp(iter_index, jdata):
fp_style = jdata["fp_style"]
if fp_style == "vasp":
post_fp_vasp(iter_index, jdata)
elif fp_style == "pwscf":
post_fp_pwscf(iter_index, jdata)
elif fp_style == "abacus":
post_fp_abacus_scf(iter_index, jdata)
elif fp_style == "siesta":
post_fp_siesta(iter_index, jdata)
elif fp_style == "gaussian":
post_fp_gaussian(iter_index, jdata)
elif fp_style == "cp2k":
post_fp_cp2k(iter_index, jdata)
elif fp_style == "pwmat":
post_fp_pwmat(iter_index, jdata)
elif fp_style == "amber/diff":
post_fp_amber_diff(iter_index, jdata)
elif fp_style == "custom":
post_fp_custom(iter_index, jdata)
else:
raise RuntimeError("unsupported fp style")
post_fp_check_fail(iter_index, jdata)
# clean traj
clean_traj = True
if "model_devi_clean_traj" in jdata:
clean_traj = jdata["model_devi_clean_traj"]
modd_path = None
if isinstance(clean_traj, bool):
iter_name = make_iter_name(iter_index)
if clean_traj:
modd_path = os.path.join(iter_name, model_devi_name)
elif isinstance(clean_traj, int):
clean_index = iter_index - clean_traj
if clean_index >= 0:
modd_path = os.path.join(make_iter_name(clean_index), model_devi_name)
if modd_path is not None:
md_trajs = glob.glob(os.path.join(modd_path, "task*/traj"))
for ii in md_trajs:
shutil.rmtree(ii)
[docs]
def set_version(mdata):
deepmd_version = "2"
mdata["deepmd_version"] = deepmd_version
return mdata
[docs]
def run_iter(param_file, machine_file):
jdata = load_file(param_file)
mdata = load_file(machine_file)
jdata_arginfo = run_jdata_arginfo()
jdata = normalize(jdata_arginfo, jdata, strict_check=False)
update_mass_map(jdata)
# set up electron temperature
use_ele_temp = jdata.get("use_ele_temp", 0)
if use_ele_temp == 1:
setup_ele_temp(False)
elif use_ele_temp == 2:
setup_ele_temp(True)
if jdata.get("pretty_print", False):
from monty.serialization import dumpfn
# assert(jdata["pretty_format"] in ['json','yaml'])
fparam = (
SHORT_CMD
+ "_"
+ param_file.split(".")[0]
+ "."
+ jdata.get("pretty_format", "json")
)
dumpfn(jdata, fparam, indent=4)
fmachine = (
SHORT_CMD
+ "_"
+ machine_file.split(".")[0]
+ "."
+ jdata.get("pretty_format", "json")
)
dumpfn(mdata, fmachine, indent=4)
if mdata.get("handlers", None):
if mdata["handlers"].get("smtp", None):
que = queue.Queue(-1)
queue_handler = logging.handlers.QueueHandler(que)
smtp_handler = logging.handlers.SMTPHandler(**mdata["handlers"]["smtp"])
listener = logging.handlers.QueueListener(que, smtp_handler)
dlog.addHandler(queue_handler)
listener.start()
# Convert mdata
mdata = convert_mdata(mdata)
max_tasks = 10000
numb_task = 9
record = "record.dpgen"
iter_rec = [0, -1]
if os.path.isfile(record):
with open(record) as frec:
for line in frec:
iter_rec = [int(x) for x in line.split()]
if len(iter_rec) == 0:
raise ValueError("There should not be blank lines in record.dpgen.")
dlog.info("continue from iter %03d task %02d" % (iter_rec[0], iter_rec[1]))
cont = True
ii = -1
while cont:
ii += 1
if ii < iter_rec[0]:
continue
iter_name = make_iter_name(ii)
sepline(iter_name, "=")
for jj in range(numb_task):
if ii * max_tasks + jj <= iter_rec[0] * max_tasks + iter_rec[1]:
continue
task_name = "task %02d" % jj
sepline(f"{iter_name} {task_name}", "-")
if jj == 0:
log_iter("make_train", ii, jj)
make_train(ii, jdata, mdata)
elif jj == 1:
log_iter("run_train", ii, jj)
run_train(ii, jdata, mdata)
elif jj == 2:
log_iter("post_train", ii, jj)
post_train(ii, jdata, mdata)
elif jj == 3:
log_iter("make_model_devi", ii, jj)
cont = make_model_devi(ii, jdata, mdata)
if not cont:
break
elif jj == 4:
log_iter("run_model_devi", ii, jj)
run_model_devi(ii, jdata, mdata)
elif jj == 5:
log_iter("post_model_devi", ii, jj)
post_model_devi(ii, jdata, mdata)
elif jj == 6:
log_iter("make_fp", ii, jj)
make_fp(ii, jdata, mdata)
elif jj == 7:
log_iter("run_fp", ii, jj)
run_fp(ii, jdata, mdata)
elif jj == 8:
log_iter("post_fp", ii, jj)
post_fp(ii, jdata)
else:
raise RuntimeError("unknown task %d, something wrong" % jj)
record_iter(record, ii, jj)
[docs]
def get_atomic_masses(atom):
element_names = [
"Hydrogen",
"Helium",
"Lithium",
"Beryllium",
"Boron",
"Carbon",
"Nitrogen",
"Oxygen",
"Fluorine",
"Neon",
"Sodium",
"Magnesium",
"Aluminium",
"Silicon",
"Phosphorus",
"Sulfur",
"Chlorine",
"Argon",
"Potassium",
"Calcium",
"Scandium",
"Titanium",
"Vanadium",
"Chromium",
"Manganese",
"Iron",
"Cobalt",
"Nickel",
"Copper",
"Zinc",
"Gallium",
"Germanium",
"Arsenic",
"Selenium",
"Bromine",
"Krypton",
"Rubidium",
"Strontium",
"Yttrium",
"Zirconium",
"Niobium",
"Molybdenum",
"Technetium",
"Ruthenium",
"Rhodium",
"Palladium",
"Silver",
"Cadmium",
"Indium",
"Tin",
"Antimony",
"Tellurium",
"Iodine",
"Xenon",
"Caesium",
"Barium",
"Lanthanum",
"Cerium",
"Praseodymium",
"Neodymium",
"Promethium",
"Samarium",
"Europium",
"Gadolinium",
"Terbium",
"Dysprosium",
"Holmium",
"Erbium",
"Thulium",
"Ytterbium",
"Lutetium",
"Hafnium",
"Tantalum",
"Tungsten",
"Rhenium",
"Osmium",
"Iridium",
"Platinum",
"Gold",
"Mercury",
"Thallium",
"Lead",
"Bismuth",
"Polonium",
"Astatine",
"Radon",
"Francium",
"Radium",
"Actinium",
"Thorium",
"Protactinium",
"Uranium",
"Neptunium",
"Plutonium",
"Americium",
"Curium",
"Berkelium",
"Californium",
"Einsteinium",
"Fermium",
"Mendelevium",
"Nobelium",
"Lawrencium",
"Rutherfordium",
"Dubnium",
"Seaborgium",
"Bohrium",
"Hassium",
"Meitnerium",
"Darmastadtium",
"Roentgenium",
"Copernicium",
"Nihonium",
"Flerovium",
"Moscovium",
"Livermorium",
"Tennessine",
"Oganesson",
]
chemical_symbols = [
"H",
"He",
"Li",
"Be",
"B",
"C",
"N",
"O",
"F",
"Ne",
"Na",
"Mg",
"Al",
"Si",
"P",
"S",
"Cl",
"Ar",
"K",
"Ca",
"Sc",
"Ti",
"V",
"Cr",
"Mn",
"Fe",
"Co",
"Ni",
"Cu",
"Zn",
"Ga",
"Ge",
"As",
"Se",
"Br",
"Kr",
"Rb",
"Sr",
"Y",
"Zr",
"Nb",
"Mo",
"Tc",
"Ru",
"Rh",
"Pd",
"Ag",
"Cd",
"In",
"Sn",
"Sb",
"Te",
"I",
"Xe",
"Cs",
"Ba",
"La",
"Ce",
"Pr",
"Nd",
"Pm",
"Sm",
"Eu",
"Gd",
"Tb",
"Dy",
"Ho",
"Er",
"Tm",
"Yb",
"Lu",
"Hf",
"Ta",
"W",
"Re",
"Os",
"Ir",
"Pt",
"Au",
"Hg",
"Tl",
"Pb",
"Bi",
"Po",
"At",
"Rn",
"Fr",
"Ra",
"Ac",
"Th",
"Pa",
"U",
"Np",
"Pu",
"Am",
"Cm",
"Bk",
"Cf",
"Es",
"Fm",
"Md",
"No",
"Lr",
"Rf",
"Db",
"Sg",
"Bh",
"Hs",
"Mt",
"Ds",
"Rg",
"Cn",
"Nh",
"Fl",
"Mc",
"Lv",
"Ts",
"Og",
]
atomic_number = [i + 1 for i in range(len(chemical_symbols))]
# NIST Standard Reference Database 144
# URL: https://physics.nist.gov/cgi-bin/Compositions/stand_alone.pl?ele=&ascii=ascii&isotype=all
atomic_masses_common = [
1.00782503223,
4.00260325413,
7.0160034366,
9.012183065,
11.00930536,
12.0,
14.00307400443,
15.99491461957,
18.99840316273,
19.9924401762,
22.989769282,
23.985041697,
26.98153853,
27.97692653465,
30.97376199842,
31.9720711744,
34.968852682,
39.9623831237,
38.9637064864,
39.962590863,
44.95590828,
47.94794198,
50.94395704,
51.94050623,
54.93804391,
55.93493633,
58.93319429,
57.93534241,
62.92959772,
63.92914201,
68.9255735,
73.921177761,
74.92159457,
79.9165218,
78.9183376,
83.9114977282,
84.9117897379,
87.9056125,
88.9058403,
89.9046977,
92.906373,
97.90540482,
96.9063667,
101.9043441,
102.905498,
105.9034804,
106.9050916,
113.90336509,
114.903878776,
119.90220163,
120.903812,
129.906222748,
126.9044719,
131.9041550856,
132.905451961,
137.905247,
138.9063563,
139.9054431,
140.9076576,
141.907729,
144.9127559,
151.9197397,
152.921238,
157.9241123,
158.9253547,
163.9291819,
164.9303288,
165.9302995,
168.9342179,
173.9388664,
174.9407752,
179.946557,
180.9479958,
183.95093092,
186.9557501,
191.961477,
192.9629216,
194.9647917,
196.96656879,
201.9706434,
204.9744278,
207.9766525,
208.9803991,
208.9824308,
209.9871479,
222.0175782,
223.019736,
226.0254103,
227.0277523,
232.0380558,
231.0358842,
238.0507884,
237.0481736,
244.0642053,
243.0613813,
247.0703541,
247.0703073,
251.0795886,
252.08298,
257.0951061,
258.0984315,
259.10103,
262.10961,
267.12179,
268.12567,
271.13393,
272.13826,
270.13429,
276.15159,
281.16451,
280.16514,
285.17712,
284.17873,
289.19042,
288.19274,
293.20449,
292.20746,
294.21392,
]
# IUPAC Technical Report
# doi:10.1515/pac-2015-0305
atomic_masses_2013 = [
1.00784,
4.002602,
6.938,
9.0121831,
10.806,
12.0096,
14.00643,
15.99903,
18.99840316,
20.1797,
22.98976928,
24.304,
26.9815385,
28.084,
30.973762,
32.059,
35.446,
39.948,
39.0983,
40.078,
44.955908,
47.867,
50.9415,
51.9961,
54.938044,
55.845,
58.933194,
58.6934,
63.546,
65.38,
69.723,
72.63,
74.921595,
78.971,
79.901,
83.798,
85.4678,
87.62,
88.90584,
91.224,
92.90637,
95.95,
None,
101.07,
102.9055,
106.42,
107.8682,
112.414,
114.818,
118.71,
121.76,
127.6,
126.90447,
131.293,
132.905452,
137.327,
138.90547,
140.116,
140.90766,
144.242,
None,
150.36,
151.964,
157.25,
158.92535,
162.5,
164.93033,
167.259,
168.93422,
173.054,
174.9668,
178.49,
180.94788,
183.84,
186.207,
190.23,
192.217,
195.084,
196.966569,
200.592,
204.382,
207.2,
208.9804,
None,
None,
None,
None,
None,
None,
232.0377,
231.03588,
238.02891,
None,
None,
None,
None,
None,
None,
None,
None,
None,
None,
None,
None,
None,
None,
None,
None,
None,
None,
None,
None,
None,
None,
None,
None,
None,
None,
]
# IUPAC Technical Report
# doi:10.1515/pac-2019-0603
atomic_masses_2021 = [
1.00784,
4.002602,
6.938,
9.0121831,
10.806,
12.0096,
14.00643,
15.99903,
18.99840316,
20.1797,
22.98976928,
24.304,
26.9815384,
28.084,
30.973762,
32.059,
35.446,
39.792,
39.0983,
40.078,
44.955907,
47.867,
50.9415,
51.9961,
54.938043,
55.845,
58.933194,
58.6934,
63.546,
65.38,
69.723,
72.63,
74.921595,
78.971,
79.901,
83.798,
85.4678,
87.62,
88.905838,
91.224,
92.90637,
95.95,
None,
101.07,
102.90549,
106.42,
107.8682,
112.414,
114.818,
118.71,
121.76,
127.6,
126.90447,
131.293,
132.905452,
137.327,
138.90547,
140.116,
140.90766,
144.242,
None,
150.36,
151.964,
157.25,
158.925354,
162.5,
164.930329,
167.259,
168.934219,
173.045,
174.9668,
178.486,
180.94788,
183.84,
186.207,
190.23,
192.217,
195.084,
196.96657,
200.592,
204.382,
206.14,
208.9804,
None,
None,
None,
None,
None,
None,
232.0377,
231.03588,
238.02891,
None,
None,
None,
None,
None,
None,
None,
None,
None,
None,
None,
None,
None,
None,
None,
None,
None,
None,
None,
None,
None,
None,
None,
None,
None,
None,
]
atomic_masses = [
atomic_masses_common[n] if i is None else i
for n, i in enumerate(atomic_masses_2021)
]
if atom in element_names:
return atomic_masses[element_names.index(atom)]
elif atom in chemical_symbols:
return atomic_masses[chemical_symbols.index(atom)]
elif atom in atomic_number:
return atomic_masses[atomic_number.index(atom)]
else:
raise RuntimeError(
"unknown atomic identifier",
atom,
'if one want to use isotopes, or non-standard element names, chemical symbols, or atomic number in the type_map list, please customize the mass_map list instead of using "auto".',
)
[docs]
def update_mass_map(jdata):
if jdata["mass_map"] == "auto":
jdata["mass_map"] = [get_atomic_masses(i) for i in jdata["type_map"]]
[docs]
def gen_run(args):
if args.PARAM and args.MACHINE:
if args.debug:
dlog.setLevel(logging.DEBUG)
dlog.info("start running")
run_iter(args.PARAM, args.MACHINE)
dlog.info("finished")
def _main():
parser = argparse.ArgumentParser()
parser.add_argument("PARAM", type=str, help="The parameters of the generator")
parser.add_argument(
"MACHINE", type=str, help="The settings of the machine running the generator"
)
args = parser.parse_args()
logging.basicConfig(level=logging.INFO, format="%(asctime)s %(message)s")
# logging.basicConfig (filename="compute_string.log", filemode="a", level=logging.INFO, format='%(asctime)s %(message)s')
logging.getLogger("paramiko").setLevel(logging.WARNING)
logging.info("start running")
run_iter(args.PARAM, args.MACHINE)
logging.info("finished!")
if __name__ == "__main__":
_main()
