breaking: deprecate set_prefix

deepmd/entrypoints/test.py

@@ -67,7 +67,6 @@ def test(
     model: str,
     system: str,
     datafile: str,
-    set_prefix: str,
     numb_test: int,
     rand_seed: Optional[int],
     shuffle_test: bool,
@@ -86,8 +85,6 @@ def test(
         system directory
     datafile : str
         the path to the list of systems to test
-    set_prefix : str
-        string prefix of set
     numb_test : int
         munber of tests to do. 0 means all data.
     rand_seed : Optional[int]
@@ -137,7 +134,7 @@ def test(
         tmap = dp.get_type_map() if isinstance(dp, DeepPot) else None
         data = DeepmdData(
             system,
-            set_prefix,
+            set_prefix="set",
             shuffle_test=shuffle_test,
             type_map=tmap,
             sort_atoms=False,

deepmd/infer/model_devi.py

@@ -350,7 +350,6 @@ def make_model_devi(
     *,
     models: list,
     system: str,
-    set_prefix: str,
     output: str,
     frequency: int,
     real_error: bool = False,
@@ -367,8 +366,6 @@ def make_model_devi(
         A list of paths of models to use for making model deviation
     system : str
         The path of system to make model deviation calculation
-    set_prefix : str
-        The set prefix of the system
     output : str
         The output file for model deviation results
     frequency : int
@@ -410,7 +407,7 @@ def make_model_devi(
     for system in all_sys:
         # create data-system
         dp_data = DeepmdData(
-            system, set_prefix, shuffle_test=False, type_map=tmap, sort_atoms=False
+            system, "set", shuffle_test=False, type_map=tmap, sort_atoms=False
         )
         if first_dp.get_dim_fparam() > 0:
             dp_data.add(

deepmd/main.py

@@ -9,6 +9,7 @@
 import logging
 import os
 import textwrap
+import warnings
 from collections import (
     defaultdict,
 )
@@ -69,6 +70,24 @@ def __call__(self, parser, namespace, values, option_string=None):
         setattr(namespace, self.dest, BACKEND_TABLE[values])
 
 
+class DeprecateAction(argparse.Action):
+    # See https://stackoverflow.com/a/69052677/9567349 by Ibolit under CC BY-SA 4.0
+    def __init__(self, *args, **kwargs):
+        self.call_count = 0
+        if "help" in kwargs:
+            kwargs["help"] = f'[DEPRECATED] {kwargs["help"]}'
+        super().__init__(*args, **kwargs)
+
+    def __call__(self, parser, namespace, values, option_string=None):
+        if self.call_count == 0:
+            warnings.warn(
+                f"The option `{option_string}` is deprecated. It will be ignored.",
+                FutureWarning,
+            )
+            delattr(namespace, self.dest)
+        self.call_count += 1
+
+
 def main_parser() -> argparse.ArgumentParser:
     """DeePMD-Kit commandline options argument parser.
 
@@ -355,9 +374,8 @@ def main_parser() -> argparse.ArgumentParser:
     parser_tst.add_argument(
         "-S",
         "--set-prefix",
-        default="set",
-        type=str,
-        help="(Supported backend: TensorFlow) The set prefix",
+        action=DeprecateAction,
+        help="Deprecated argument.",
     )
     parser_tst.add_argument(
         "-n",
@@ -528,7 +546,7 @@ def main_parser() -> argparse.ArgumentParser:
         help="The system directory. Recursively detect systems in this directory.",
     )
     parser_model_devi.add_argument(
-        "-S", "--set-prefix", default="set", type=str, help="The set prefix"
+        "-S", "--set-prefix", action=DeprecateAction, help="Deprecated argument."
     )
     parser_model_devi.add_argument(
         "-o",

deepmd/tf/nvnmd/data/data.py

@@ -319,7 +319,7 @@
         "disp_training": True,
         "time_training": True,
         "profiling": False,
-        "training_data": {"systems": "dataset", "set_prefix": "set", "batch_size": 1},
+        "training_data": {"systems": "dataset", "batch_size": 1},
     },
 }
 
@@ -385,7 +385,7 @@
         "disp_training": True,
         "time_training": True,
         "profiling": False,
-        "training_data": {"systems": "dataset", "set_prefix": "set", "batch_size": 1},
+        "training_data": {"systems": "dataset", "batch_size": 1},
     },
 }
 

deepmd/utils/argcheck.py

@@ -1,6 +1,7 @@
 # SPDX-License-Identifier: LGPL-3.0-or-later
 import json
 import logging
+import warnings
 from typing import (
     Callable,
     List,
@@ -54,6 +55,24 @@ def make_link(content, ref_key):
     )
 
 
+def deprecate_argument_extra_check(key: str) -> Callable[[dict], bool]:
+    """Generate an extra check to deprecate an argument in sub fields.
+
+    Parameters
+    ----------
+    key : str
+        The name of the deprecated argument.
+    """
+
+    def deprecate_something(data: Optional[dict]):
+        if data is not None and key in data:
+            warnings.warn(f"{key} has been removed and takes no effect.", FutureWarning)
+            data.pop(key)
+        return True
+
+    return deprecate_something
+
+
 def type_embedding_args():
     doc_neuron = "Number of neurons in each hidden layers of the embedding net. When two layers are of the same size or one layer is twice as large as the previous layer, a skip connection is built."
     doc_resnet_dt = 'Whether to use a "Timestep" in the skip connection'
@@ -1951,7 +1970,6 @@ def training_data_args():  # ! added by Ziyao: new specification style for data
         "This key can be provided with a list that specifies the systems, or be provided with a string "
         "by which the prefix of all systems are given and the list of the systems is automatically generated."
     )
-    doc_set_prefix = f"The prefix of the sets in the {link_sys}."
     doc_batch_size = f'This key can be \n\n\
 - list: the length of which is the same as the {link_sys}. The batch size of each system is given by the elements of the list.\n\n\
 - int: all {link_sys} use the same batch size.\n\n\
@@ -1973,7 +1991,6 @@ def training_data_args():  # ! added by Ziyao: new specification style for data
         Argument(
             "systems", [List[str], str], optional=False, default=".", doc=doc_systems
         ),
-        Argument("set_prefix", str, optional=True, default="set", doc=doc_set_prefix),
         Argument(
             "batch_size",
             [List[int], int, str],
@@ -2009,6 +2026,7 @@ def training_data_args():  # ! added by Ziyao: new specification style for data
         sub_fields=args,
         sub_variants=[],
         doc=doc_training_data,
+        extra_check=deprecate_argument_extra_check("set_prefix"),
     )
 
 
@@ -2019,7 +2037,6 @@ def validation_data_args():  # ! added by Ziyao: new specification style for dat
         "This key can be provided with a list that specifies the systems, or be provided with a string "
         "by which the prefix of all systems are given and the list of the systems is automatically generated."
     )
-    doc_set_prefix = f"The prefix of the sets in the {link_sys}."
     doc_batch_size = f'This key can be \n\n\
 - list: the length of which is the same as the {link_sys}. The batch size of each system is given by the elements of the list.\n\n\
 - int: all {link_sys} use the same batch size.\n\n\
@@ -2040,7 +2057,6 @@ def validation_data_args():  # ! added by Ziyao: new specification style for dat
         Argument(
             "systems", [List[str], str], optional=False, default=".", doc=doc_systems
         ),
-        Argument("set_prefix", str, optional=True, default="set", doc=doc_set_prefix),
         Argument(
             "batch_size",
             [List[int], int, str],
@@ -2090,6 +2106,7 @@ def validation_data_args():  # ! added by Ziyao: new specification style for dat
         sub_fields=args,
         sub_variants=[],
         doc=doc_validation_data,
+        extra_check=deprecate_argument_extra_check("set_prefix"),
     )
 
 

deepmd/utils/compat.py

@@ -263,7 +263,8 @@ def _jcopy(src: Dict[str, Any], dst: Dict[str, Any], keys: Sequence[str]):
         list of keys to copy
     """
     for k in keys:
-        dst[k] = src[k]
+        if k in src:
+            dst[k] = src[k]
 
 
 def remove_decay_rate(jdata: Dict[str, Any]):

doc/nvnmd/nvnmd.md

@@ -153,7 +153,6 @@ The "training" section is defined as
   "save_freq": 10000,
   "training_data": {
     "systems": ["system1_path", "system2_path", "..."],
-    "set_prefix": "set",
     "batch_size": ["batch_size_of_system1", "batch_size_of_system2", "..."]
   }
 }
@@ -171,7 +170,6 @@ where items are defined as:
 | save_ckpt  | path prefix of check point files                    | a string           |
 | save_freq  | save frequency                                      | a positive integer |
 | systems    | a list of data directory which contains the dataset | string list        |
-| set_prefix | the prefix of dataset                               | a string           |
 | batch_size | a list of batch size of corresponding dataset       | a integer list     |
 
 ## Training

doc/train/tensorboard.md

@@ -28,7 +28,6 @@ directory by modifying the input script, setting {ref}`tensorboard <training/ten
 ```json
     "training" : {
 	"systems":	["../data/"],
-	"set_prefix":	"set",
 	"stop_batch":	1000000,
 	"batch_size":	1,
 

examples/dos/train/input.json

@@ -66,7 +66,6 @@
       "systems": [
         "../data/heat-221/"
       ],
-      "set_prefix": "set",
       "batch_size": 1
     }
   },

examples/dos/train/input_torch.json

@@ -67,7 +67,6 @@
         "../data/heat-221-reformat/atomic_system/",
         "../data/heat-221-reformat/global_system/"
       ],
-      "set_prefix": "set",
       "batch_size": 1
     }
   },

examples/fparam/train/input.json

@@ -58,7 +58,6 @@
         "../data/e3000_i2000/",
         "../data/e8000_i2000/"
       ],
-      "set_prefix": "set",
       "batch_size": 1
     },
     "stop_batch": 1000000,

examples/fparam/train/input_aparam.json

@@ -55,7 +55,6 @@
         "../data/e3000_i2000/",
         "../data/e8000_i2000/"
       ],
-      "set_prefix": "set",
       "batch_size": 1
     },
     "stop_batch": 1000000,

examples/nvnmd/train/train_cnn.json

@@ -40,7 +40,6 @@
       "systems": [
         "../data"
       ],
-      "set_prefix": "set",
       "batch_size": [
         1
       ]

examples/nvnmd/train/train_qnn.json

@@ -40,7 +40,6 @@
       "systems": [
         "../data"
       ],
-      "set_prefix": "set",
       "batch_size": [
         1
       ]

examples/water/dplr/train/dw.json

@@ -65,7 +65,6 @@
         "data"
       ],
       "batch_size": "auto",
-      "set_prefix": "set",
       "_comment8": "that's all"
     },
 

source/lmp/tests/lrmodel.pbtxt

@@ -35,7 +35,7 @@ node {
         dtype: DT_STRING
         tensor_shape {
         }
-        string_val: "{\"model\":{\"type_map\":[\"O\",\"H\"],\"descriptor\":{\"type\":\"hybrid\",\"list\":[{\"type\":\"se_e2_a\",\"sel\":[20,40],\"rcut_smth\":0.5,\"rcut\":4.0,\"neuron\":[2,4,8],\"resnet_dt\":false,\"axis_neuron\":8,\"seed\":1,\"activation_function\":\"tanh\",\"type_one_side\":false,\"precision\":\"default\",\"trainable\":true,\"exclude_types\":[],\"set_davg_zero\":false}]},\"fitting_net\":{\"neuron\":[2,2,2],\"resnet_dt\":true,\"seed\":1,\"type\":\"ener\",\"numb_fparam\":0,\"numb_aparam\":0,\"activation_function\":\"tanh\",\"precision\":\"default\",\"trainable\":true,\"rcond\":0.001,\"atom_ener\":[],\"use_aparam_as_mask\":false},\"modifier\":{\"type\":\"dipole_charge\",\"model_name\":\"lrdipole.pb\",\"model_charge_map\":[-8],\"sys_charge_map\":[6,1],\"ewald_h\":1.0,\"ewald_beta\":0.4},\"data_stat_nbatch\":10,\"data_stat_protect\":0.01,\"data_bias_nsample\":10},\"learning_rate\":{\"type\":\"exp\",\"decay_steps\":5000,\"start_lr\":0.001,\"stop_lr\":3.51e-08,\"scale_by_worker\":\"linear\"},\"loss\":{\"type\":\"ener\",\"start_pref_e\":0.02,\"limit_pref_e\":1,\"start_pref_f\":1000,\"limit_pref_f\":1,\"start_pref_v\":0,\"limit_pref_v\":0,\"start_pref_ae\":0.0,\"limit_pref_ae\":0.0,\"start_pref_pf\":0.0,\"limit_pref_pf\":0.0,\"enable_atom_ener_coeff\":false},\"training\":{\"training_data\":{\"systems\":[\"../../4-deep-wannier/data/dpgen.lowd.merged/iter.000016/02.fp/data.012\",\"../../4-deep-wannier/data/dpgen.lowd.merged/iter.000016/02.fp/data.016\",\"../../4-deep-wannier/data/dpgen.lowd.merged/iter.000016/02.fp/data.020\",\"../../4-deep-wannier/data/dpgen.merged/init/00.liq/state.0300.1.00e00.00/vasp/data\",\"../../4-deep-wannier/data/dpgen.merged/init/00.liq/state.0400.1.00e04.00/vasp/data\",\"../../4-deep-wannier/data/dpgen.merged/init/01.liq/state.0300.1.00e00.00/vasp/data\",\"../../4-deep-wannier/data/dpgen.merged/init/01.liq/state.0400.1.00e04.00/vasp/data\",\"../../4-deep-wannier/data/dpgen.merged/iter.000000/02.fp/data.000\",\"../../4-deep-wannier/data/dpgen.merged/iter.000001/02.fp/data.001\",\"../../4-deep-wannier/data/dpgen.merged/iter.000002/02.fp/data.002\",\"../../4-deep-wannier/data/dpgen.merged/iter.000005/02.fp/data.001\",\"../../4-deep-wannier/data/dpgen.merged/iter.000008/02.fp/data.004\",\"../../4-deep-wannier/data/dpgen.merged/iter.000009/02.fp/data.005\",\"../../4-deep-wannier/data/dpgen.merged/iter.000010/02.fp/data.006\"],\"set_prefix\":\"set\",\"batch_size\":\"auto\",\"auto_prob\":\"prob_sys_size\",\"sys_probs\":null},\"numb_steps\":1,\"seed\":1,\"disp_file\":\"lcurve.out\",\"disp_freq\":1,\"save_freq\":1,\"validation_data\":null,\"save_ckpt\":\"model.ckpt\",\"disp_training\":true,\"time_training\":true,\"profiling\":false,\"profiling_file\":\"timeline.json\",\"enable_profiler\":false,\"tensorboard\":false,\"tensorboard_log_dir\":\"log\",\"tensorboard_freq\":1}}"
+        string_val: "{\"model\":{\"type_map\":[\"O\",\"H\"],\"descriptor\":{\"type\":\"hybrid\",\"list\":[{\"type\":\"se_e2_a\",\"sel\":[20,40],\"rcut_smth\":0.5,\"rcut\":4.0,\"neuron\":[2,4,8],\"resnet_dt\":false,\"axis_neuron\":8,\"seed\":1,\"activation_function\":\"tanh\",\"type_one_side\":false,\"precision\":\"default\",\"trainable\":true,\"exclude_types\":[],\"set_davg_zero\":false}]},\"fitting_net\":{\"neuron\":[2,2,2],\"resnet_dt\":true,\"seed\":1,\"type\":\"ener\",\"numb_fparam\":0,\"numb_aparam\":0,\"activation_function\":\"tanh\",\"precision\":\"default\",\"trainable\":true,\"rcond\":0.001,\"atom_ener\":[],\"use_aparam_as_mask\":false},\"modifier\":{\"type\":\"dipole_charge\",\"model_name\":\"lrdipole.pb\",\"model_charge_map\":[-8],\"sys_charge_map\":[6,1],\"ewald_h\":1.0,\"ewald_beta\":0.4},\"data_stat_nbatch\":10,\"data_stat_protect\":0.01,\"data_bias_nsample\":10},\"learning_rate\":{\"type\":\"exp\",\"decay_steps\":5000,\"start_lr\":0.001,\"stop_lr\":3.51e-08,\"scale_by_worker\":\"linear\"},\"loss\":{\"type\":\"ener\",\"start_pref_e\":0.02,\"limit_pref_e\":1,\"start_pref_f\":1000,\"limit_pref_f\":1,\"start_pref_v\":0,\"limit_pref_v\":0,\"start_pref_ae\":0.0,\"limit_pref_ae\":0.0,\"start_pref_pf\":0.0,\"limit_pref_pf\":0.0,\"enable_atom_ener_coeff\":false},\"training\":{\"training_data\":{\"systems\":[\"../../4-deep-wannier/data/dpgen.lowd.merged/iter.000016/02.fp/data.012\",\"../../4-deep-wannier/data/dpgen.lowd.merged/iter.000016/02.fp/data.016\",\"../../4-deep-wannier/data/dpgen.lowd.merged/iter.000016/02.fp/data.020\",\"../../4-deep-wannier/data/dpgen.merged/init/00.liq/state.0300.1.00e00.00/vasp/data\",\"../../4-deep-wannier/data/dpgen.merged/init/00.liq/state.0400.1.00e04.00/vasp/data\",\"../../4-deep-wannier/data/dpgen.merged/init/01.liq/state.0300.1.00e00.00/vasp/data\",\"../../4-deep-wannier/data/dpgen.merged/init/01.liq/state.0400.1.00e04.00/vasp/data\",\"../../4-deep-wannier/data/dpgen.merged/iter.000000/02.fp/data.000\",\"../../4-deep-wannier/data/dpgen.merged/iter.000001/02.fp/data.001\",\"../../4-deep-wannier/data/dpgen.merged/iter.000002/02.fp/data.002\",\"../../4-deep-wannier/data/dpgen.merged/iter.000005/02.fp/data.001\",\"../../4-deep-wannier/data/dpgen.merged/iter.000008/02.fp/data.004\",\"../../4-deep-wannier/data/dpgen.merged/iter.000009/02.fp/data.005\",\"../../4-deep-wannier/data/dpgen.merged/iter.000010/02.fp/data.006\"],\"batch_size\":\"auto\",\"auto_prob\":\"prob_sys_size\",\"sys_probs\":null},\"numb_steps\":1,\"seed\":1,\"disp_file\":\"lcurve.out\",\"disp_freq\":1,\"save_freq\":1,\"validation_data\":null,\"save_ckpt\":\"model.ckpt\",\"disp_training\":true,\"time_training\":true,\"profiling\":false,\"profiling_file\":\"timeline.json\",\"enable_profiler\":false,\"tensorboard\":false,\"tensorboard_log_dir\":\"log\",\"tensorboard_freq\":1}}"
       }
     }
   }

source/tests/common/test_argument_parser.py

@@ -317,7 +317,6 @@ def test_parser_test(self):
         ARGS = {
             "--model": {"type": str, "value": "MODEL.PB"},
             "--system": {"type": str, "value": "SYSTEM_DIR"},
-            "--set-prefix": {"type": str, "value": "SET_PREFIX"},
             "--numb-test": {"type": int, "value": 1},
             "--rand-seed": {"type": (int, type(None)), "value": 12321},
             "--detail-file": {"type": (str, type(None)), "value": "TARGET.FILE"},
@@ -355,7 +354,6 @@ def test_parser_model_devi(self):
                 "expected": ["GRAPH.000.pb", "GRAPH.001.pb"],
             },
             "--system": {"type": str, "value": "SYSTEM_DIR"},
-            "--set-prefix": {"type": str, "value": "SET_PREFIX"},
             "--output": {"type": str, "value": "OUTFILE"},
             "--frequency": {"type": int, "value": 1},
         }