chore: add the mlp_engine option (#1576)

I am going to use DP-GEN to develop models trained by other MLP
software. This may or may not be merged into the main branch, but I
think a general `mlp_engine` option can be added anyway.

<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->

## Summary by CodeRabbit

- **New Features**
- Introduced handling for multiple ML potential engines with specialized
training argument functions.

- **Improvements**
- Enhanced training initialization by splitting into common and
engine-specific functions.
	- Improved error handling for unsupported ML potential engines.

- **Bug Fixes**
- Corrected logic to differentiate between `dp` and other engine values
during training and model initialization.

<!-- end of auto-generated comment: release notes by coderabbit.ai -->

---------

Signed-off-by: Jinzhe Zeng <jinzhe.zeng@rutgers.edu>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

Author: njzjz

dpgen/generator/arginfo.py

@@ -79,7 +79,14 @@ def data_args() -> list[Argument]:
 # Training
 
 
-def training_args() -> list[Argument]:
+def training_args_common() -> list[Argument]:
+    doc_numb_models = "Number of models to be trained in 00.train. 4 is recommend."
+    return [
+        Argument("numb_models", int, optional=False, doc=doc_numb_models),
+    ]
+
+
+def training_args_dp() -> list[Argument]:
     """Traning arguments.
 
     Returns
@@ -90,7 +97,6 @@ def training_args() -> list[Argument]:
     doc_train_backend = (
         "The backend of the training. Currently only support tensorflow and pytorch."
     )
-    doc_numb_models = "Number of models to be trained in 00.train. 4 is recommend."
     doc_training_iter0_model_path = "The model used to init the first iter training. Number of element should be equal to numb_models."
     doc_training_init_model = "Iteration > 0, the model parameters will be initilized from the model trained at the previous iteration. Iteration == 0, the model parameters will be initialized from training_iter0_model_path."
     doc_default_training_param = "Training parameters for deepmd-kit in 00.train. You can find instructions from `DeePMD-kit documentation <https://docs.deepmodeling.org/projects/deepmd/>`_."
@@ -133,7 +139,6 @@ def training_args() -> list[Argument]:
             default="tensorflow",
             doc=doc_train_backend,
         ),
-        Argument("numb_models", int, optional=False, doc=doc_numb_models),
         Argument(
             "training_iter0_model_path",
             list[str],
@@ -224,6 +229,19 @@ def training_args() -> list[Argument]:
     ]
 
 
+def training_args() -> Variant:
+    doc_mlp_engine = "Machine learning potential engine. Currently, only DeePMD-kit (defualt) is supported."
+    doc_dp = "DeePMD-kit."
+    return Variant(
+        "mlp_engine",
+        [
+            Argument("dp", dict, training_args_dp(), doc=doc_dp),
+        ],
+        default_tag="dp",
+        doc=doc_mlp_engine,
+    )
+
+
 # Exploration
 def model_devi_jobs_template_args() -> Argument:
     doc_template = (
@@ -987,7 +1005,11 @@ def run_jdata_arginfo() -> Argument:
     return Argument(
         "run_jdata",
         dict,
-        sub_fields=basic_args() + data_args() + training_args() + fp_args(),
-        sub_variants=model_devi_args() + [fp_style_variant_type_args()],
+        sub_fields=basic_args() + data_args() + training_args_common() + fp_args(),
+        sub_variants=[
+            training_args(),
+            *model_devi_args(),
+            fp_style_variant_type_args(),
+        ],
         doc=doc_run_jdata,
     )

dpgen/generator/run.py

@@ -128,15 +128,19 @@
 
 def _get_model_suffix(jdata) -> str:
     """Return the model suffix based on the backend."""
-    suffix_map = {"tensorflow": ".pb", "pytorch": ".pth"}
-    backend = jdata.get("train_backend", "tensorflow")
-    if backend in suffix_map:
-        suffix = suffix_map[backend]
+    mlp_engine = jdata.get("mlp_engine", "dp")
+    if mlp_engine == "dp":
+        suffix_map = {"tensorflow": ".pb", "pytorch": ".pth"}
+        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'."
+            )
+        return suffix
     else:
-        raise ValueError(
-            f"The backend {backend} is not available. Supported backends are: 'tensorflow', 'pytorch'."
-        )
-    return suffix
+        raise ValueError(f"Unsupported engine: {mlp_engine}")
 
 
 def get_job_names(jdata):
@@ -270,6 +274,14 @@ def dump_to_deepmd_raw(dump, deepmd_raw, type_map, fmt="gromacs/gro", charge=Non
 
 
 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}")
+
+
+def make_train_dp(iter_index, jdata, mdata):
     # load json param
     # train_param = jdata['train_param']
     train_input_file = default_train_input_file
@@ -714,6 +726,14 @@ def get_nframes(system):
 
 
 def run_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}")
+
+
+def run_train_dp(iter_index, jdata, mdata):
     # print("debug:run_train:mdata", mdata)
     # load json param
     numb_models = jdata["numb_models"]
@@ -899,6 +919,14 @@ def run_train(iter_index, jdata, mdata):
 
 
 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}")
+
+
+def post_train_dp(iter_index, jdata, mdata):
     # load json param
     numb_models = jdata["numb_models"]
     # paths

dpgen/simplify/arginfo.py

@@ -12,6 +12,7 @@
     fp_style_siesta_args,
     fp_style_vasp_args,
     training_args,
+    training_args_common,
 )
 
 
@@ -201,10 +202,11 @@ def simplify_jdata_arginfo() -> Argument:
             *data_args(),
             *general_simplify_arginfo(),
             # simplify use the same training method as run
-            *training_args(),
+            *training_args_common(),
             *fp_args(),
         ],
         sub_variants=[
+            training_args(),
             fp_style_variant_type_args(),
         ],
         doc=doc_run_jdata,

dpgen/simplify/simplify.py

@@ -103,6 +103,14 @@ def get_multi_system(path: Union[str, list[str]], jdata: dict) -> dpdata.MultiSy
 
 
 def init_model(iter_index, jdata, mdata):
+    mlp_engine = jdata.get("mlp_engine", "dp")
+    if mlp_engine == "dp":
+        init_model_dp(iter_index, jdata, mdata)
+    else:
+        raise TypeError(f"unsupported engine {mlp_engine}")
+
+
+def init_model_dp(iter_index, jdata, mdata):
     training_init_model = jdata.get("training_init_model", False)
     if not training_init_model:
         return