model_devi | sort models in default&support user-defined order (#610)

* model_devi | sort models in default&support user-defined order

current behavior:
1）the list of models written in input.lammps for model_devi is created by "glob" without "sort" --> may give different (random) orders in different machine environments  --> would randomly alter the model being used to sample configurations (always use the first model written in imput.lammps)
---> could lead to unexpected performance, such as, when using together with "model_devi_activation_func" that allows four models to be nonequivalent.
2）when preparing input.lammps from iuser-provided template, the line begin with "pair_style deepmd" will be overwritten by dpgen, thus overwrites the user defined order of models ---> could lead to unexpected performance, such as, when using together with "model_devi_activation_func" that allows four models to be nonequivalent and users indeed expected a specific order of models.

changes:
1）sorted the list of models in default, thus always use graph.000.pb to sample configurations;
2）check weather user writes the full line of begin with "pair_style deepmd" (by checking the length),  if yes, leave it be; if not, overwrites with the default settings (use graph.000.pb to sample); besides, the original error trigger is retained if key words "pair_style deepmd" are not provided in the template.

* adjust space/tab

* adjust annotation

Author: Vibsteamer

dpgen/generator/run.py

@@ -804,7 +804,7 @@ def make_model_devi (iter_index,
     iter_name = make_iter_name(iter_index)
     train_path = os.path.join(iter_name, train_name)
     train_path = os.path.abspath(train_path)
-    models = glob.glob(os.path.join(train_path, "graph*pb"))
+    models = sorted(glob.glob(os.path.join(train_path, "graph*pb")))
     work_path = os.path.join(iter_name, model_devi_name)
     create_path(work_path)
     for mm in models :
@@ -890,7 +890,7 @@ def _make_model_devi_revmat(iter_index, jdata, mdata, conf_systems):
     iter_name = make_iter_name(iter_index)
     train_path = os.path.join(iter_name, train_name)
     train_path = os.path.abspath(train_path)
-    models = glob.glob(os.path.join(train_path, "graph*pb"))
+    models = sorted(glob.glob(os.path.join(train_path, "graph*pb")))
     task_model_list = []
     for ii in models:
         task_model_list.append(os.path.join('..', os.path.basename(ii)))
@@ -947,7 +947,23 @@ def _make_model_devi_revmat(iter_index, jdata, mdata, conf_systems):
                 # revise input of lammps
                 with open('input.lammps') as fp:
                     lmp_lines = fp.readlines()
-                lmp_lines = revise_lmp_input_model(lmp_lines, task_model_list, trj_freq, deepmd_version = deepmd_version)
+                # 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 LooseVersion(deepmd_version) < LooseVersion('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)
+                    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 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)
+                
                 lmp_lines = revise_lmp_input_dump(lmp_lines, trj_freq)
                 lmp_lines = revise_by_keys(
                     lmp_lines, total_rev_keys[:total_num_lmp], total_rev_item[:total_num_lmp]