from dargs import Argument, Variant
from dpgen.arginfo import general_mdata_arginfo
from dpgen.generator.arginfo import (
basic_args,
data_args,
fp_style_abacus_args,
fp_style_cp2k_args,
fp_style_custom_args,
fp_style_gaussian_args,
fp_style_pwscf_args,
fp_style_siesta_args,
fp_style_vasp_args,
training_args,
)
[docs]
def general_simplify_arginfo() -> Argument:
"""General simplify arginfo.
Returns
-------
Argument
arginfo
"""
doc_labeled = "If true, the initial data is labeled."
doc_pick_data = "(List of) Path to the directory with the pick data with the deepmd/npy or the HDF5 file with deepmd/hdf5 format. Systems are detected recursively."
doc_init_pick_number = "The number of initial pick data."
doc_iter_pick_number = "The number of pick data in each iteration."
doc_model_devi_f_trust_lo = (
"The lower bound of forces for the selection for the model deviation."
)
doc_model_devi_f_trust_hi = (
"The higher bound of forces for the selection for the model deviation."
)
doc_model_devi_e_trust_lo = "The lower bound of energy per atom for the selection for the model deviation. Requires DeePMD-kit version >=2.2.2."
doc_model_devi_e_trust_hi = "The higher bound of energy per atom for the selection for the model deviation. Requires DeePMD-kit version >=2.2.2."
doc_true_error_f_trust_lo = "The lower bound of forces for the selection for the true error. Requires DeePMD-kit version >=2.2.4."
doc_true_error_f_trust_hi = "The higher bound of forces for the selection for the true error. Requires DeePMD-kit version >=2.2.4."
doc_true_error_e_trust_lo = "The lower bound of energy per atom for the selection for the true error. Requires DeePMD-kit version >=2.2.4."
doc_true_error_e_trust_hi = "The higher bound of energy per atom for the selection for the true error. Requires DeePMD-kit version >=2.2.4."
return [
Argument("labeled", bool, optional=True, default=False, doc=doc_labeled),
Argument("pick_data", [str, list[str]], doc=doc_pick_data),
Argument("init_pick_number", int, doc=doc_init_pick_number),
Argument("iter_pick_number", int, doc=doc_iter_pick_number),
Argument(
"model_devi_f_trust_lo",
float,
optional=False,
doc=doc_model_devi_f_trust_lo,
),
Argument(
"model_devi_f_trust_hi",
float,
optional=False,
doc=doc_model_devi_f_trust_hi,
),
Argument(
"model_devi_e_trust_lo",
float,
optional=True,
default=1e10,
doc=doc_model_devi_e_trust_lo,
),
Argument(
"model_devi_e_trust_hi",
float,
optional=True,
default=1e10,
doc=doc_model_devi_e_trust_hi,
),
Argument(
"true_error_f_trust_lo",
float,
optional=True,
default=1e10,
doc=doc_true_error_f_trust_lo,
),
Argument(
"true_error_f_trust_hi",
float,
optional=True,
default=1e10,
doc=doc_true_error_f_trust_hi,
),
Argument(
"true_error_e_trust_lo",
float,
optional=True,
default=1e10,
doc=doc_true_error_e_trust_lo,
),
Argument(
"true_error_e_trust_hi",
float,
optional=True,
default=1e10,
doc=doc_true_error_e_trust_hi,
),
]
[docs]
def fp_style_variant_type_args() -> Variant:
"""Generate variant for fp style variant type.
Returns
-------
Variant
variant for fp style
"""
doc_fp_style = "Software for First Principles, if `labeled` is false."
doc_fp_style_none = "No fp."
doc_fp_style_vasp = "VASP."
doc_fp_style_pwscf = "pwscf (Quantum Espresso)."
doc_fp_style_gaussian = "Gaussian. The command should be set as `g16 < input`."
doc_custom = (
"Custom FP code. You need to provide the input and output file format and name. "
"The command argument in the machine file should be the script to run custom FP codes. "
"The extra forward and backward files can be defined in the machine file."
)
return Variant(
"fp_style",
[
Argument("none", dict, doc=doc_fp_style_none),
# simplify use the same fp method as run
Argument("vasp", dict, fp_style_vasp_args(), doc=doc_fp_style_vasp),
Argument(
"gaussian", dict, fp_style_gaussian_args(), doc=doc_fp_style_gaussian
),
Argument("siesta", dict, fp_style_siesta_args()),
Argument("cp2k", dict, fp_style_cp2k_args()),
Argument("abacus", dict, fp_style_abacus_args()),
# TODO: not supported yet, as it requires model_devi_engine to be amber
# Argument(
# "amber/diff", dict, fp_style_amber_diff_args(), doc=doc_amber_diff
# ),
Argument("pwmat", dict, [], doc="TODO: add doc"),
Argument("pwscf", dict, fp_style_pwscf_args(), doc=doc_fp_style_pwscf),
Argument("custom", dict, fp_style_custom_args(), doc=doc_custom),
],
optional=True,
default_tag="none",
doc=doc_fp_style,
)
[docs]
def fp_args() -> list[Argument]:
"""Generate arginfo for fp.
Returns
-------
List[Argument]
arginfo
"""
doc_fp_task_max = (
"Maximum of structures to be calculated in 02.fp of each iteration."
)
doc_fp_task_min = (
"Minimum of structures to be calculated in 02.fp of each iteration."
)
doc_fp_accurate_threshold = "If the accurate ratio is larger than this number, no fp calculation will be performed, i.e. fp_task_max = 0."
doc_fp_accurate_soft_threshold = "If the accurate ratio is between this number and fp_accurate_threshold, the fp_task_max linearly decays to zero."
doc_ratio_failed = "Check the ratio of unsuccessfully terminated jobs. If too many FP tasks are not converged, RuntimeError will be raised."
return [
Argument("fp_task_max", int, optional=True, doc=doc_fp_task_max),
Argument("fp_task_min", int, optional=True, doc=doc_fp_task_min),
Argument(
"fp_accurate_threshold", float, optional=True, doc=doc_fp_accurate_threshold
),
Argument(
"fp_accurate_soft_threshold",
float,
optional=True,
doc=doc_fp_accurate_soft_threshold,
),
Argument("ratio_failed", float, optional=True, doc=doc_ratio_failed),
]
[docs]
def simplify_jdata_arginfo() -> Argument:
"""Generate arginfo for dpgen simplify jdata.
Returns
-------
Argument
arginfo
"""
doc_run_jdata = (
"Parameters for simplify.json, the first argument of `dpgen simplify`."
)
return Argument(
"simplify_jdata",
dict,
sub_fields=[
*basic_args(),
# TODO: we may remove sys_configs; it is required in train method
*data_args(),
*general_simplify_arginfo(),
# simplify use the same training method as run
*training_args(),
*fp_args(),
],
sub_variants=[
fp_style_variant_type_args(),
],
doc=doc_run_jdata,
)
[docs]
def simplify_mdata_arginfo() -> Argument:
"""Generate arginfo for dpgen simplify mdata.
Returns
-------
Argument
arginfo
"""
return general_mdata_arginfo("simplify_mdata", ("train", "model_devi", "fp"))