chore(dpmodel): move save_dp_model and load_dp_model to a seperated module

deepmd/backend/dpmodel.py

@@ -100,7 +100,7 @@ def serialize_hook(self) -> Callable[[str], dict]:
         Callable[[str], dict]
             The serialize hook of the backend.
         """
-        from deepmd.dpmodel.utils.network import (
+        from deepmd.dpmodel.utils.serialization import (
             load_dp_model,
         )
 
@@ -115,7 +115,7 @@ def deserialize_hook(self) -> Callable[[str, dict], None]:
         Callable[[str, dict], None]
             The deserialize hook of the backend.
         """
-        from deepmd.dpmodel.utils.network import (
+        from deepmd.dpmodel.utils.serialization import (
             save_dp_model,
         )
 

deepmd/dpmodel/infer/deep_eval.py

@@ -24,7 +24,7 @@
 from deepmd.dpmodel.utils.batch_size import (
     AutoBatchSize,
 )
-from deepmd.dpmodel.utils.network import (
+from deepmd.dpmodel.utils.serialization import (
     load_dp_model,
 )
 from deepmd.infer.deep_dipole import (

deepmd/dpmodel/utils/__init__.py

@@ -12,12 +12,9 @@
     NativeLayer,
     NativeNet,
     NetworkCollection,
-    load_dp_model,
     make_embedding_network,
     make_fitting_network,
     make_multilayer_network,
-    save_dp_model,
-    traverse_model_dict,
 )
 from .nlist import (
     build_multiple_neighbor_list,
@@ -32,6 +29,11 @@
     phys2inter,
     to_face_distance,
 )
+from .serialization import (
+    load_dp_model,
+    save_dp_model,
+    traverse_model_dict,
+)
 
 __all__ = [
     "EnvMat",
@@ -46,8 +48,6 @@
     "load_dp_model",
     "save_dp_model",
     "traverse_model_dict",
-    "PRECISION_DICT",
-    "DEFAULT_PRECISION",
     "build_neighbor_list",
     "nlist_distinguish_types",
     "get_multiple_nlist_key",

deepmd/dpmodel/utils/network.py

@@ -6,10 +6,6 @@
 
 import copy
 import itertools
-import json
-from datetime import (
-    datetime,
-)
 from typing import (
     Callable,
     ClassVar,
@@ -19,7 +15,6 @@
     Union,
 )
 
-import h5py
 import numpy as np
 
 from deepmd.utils.version import (
@@ -38,108 +33,6 @@
 )
 
 
-def traverse_model_dict(model_obj, callback: callable, is_variable: bool = False):
-    """Traverse a model dict and call callback on each variable.
-
-    Parameters
-    ----------
-    model_obj : object
-        The model object to traverse.
-    callback : callable
-        The callback function to call on each variable.
-    is_variable : bool, optional
-        Whether the current node is a variable.
-
-    Returns
-    -------
-    object
-        The model object after traversing.
-    """
-    if isinstance(model_obj, dict):
-        for kk, vv in model_obj.items():
-            model_obj[kk] = traverse_model_dict(
-                vv, callback, is_variable=is_variable or kk == "@variables"
-            )
-    elif isinstance(model_obj, list):
-        for ii, vv in enumerate(model_obj):
-            model_obj[ii] = traverse_model_dict(vv, callback, is_variable=is_variable)
-    elif model_obj is None:
-        return model_obj
-    elif is_variable:
-        model_obj = callback(model_obj)
-    return model_obj
-
-
-class Counter:
-    """A callable counter.
-
-    Examples
-    --------
-    >>> counter = Counter()
-    >>> counter()
-    0
-    >>> counter()
-    1
-    """
-
-    def __init__(self):
-        self.count = -1
-
-    def __call__(self):
-        self.count += 1
-        return self.count
-
-
-# TODO: move save_dp_model and load_dp_model to a seperated module
-# should be moved to otherwhere...
-def save_dp_model(filename: str, model_dict: dict) -> None:
-    """Save a DP model to a file in the native format.
-
-    Parameters
-    ----------
-    filename : str
-        The filename to save to.
-    model_dict : dict
-        The model dict to save.
-    """
-    model_dict = model_dict.copy()
-    variable_counter = Counter()
-    with h5py.File(filename, "w") as f:
-        model_dict = traverse_model_dict(
-            model_dict,
-            lambda x: f.create_dataset(
-                f"variable_{variable_counter():04d}", data=x
-            ).name,
-        )
-        save_dict = {
-            "software": "deepmd-kit",
-            "version": __version__,
-            # use UTC+0 time
-            "time": str(datetime.utcnow()),
-            **model_dict,
-        }
-        f.attrs["json"] = json.dumps(save_dict, separators=(",", ":"))
-
-
-def load_dp_model(filename: str) -> dict:
-    """Load a DP model from a file in the native format.
-
-    Parameters
-    ----------
-    filename : str
-        The filename to load from.
-
-    Returns
-    -------
-    dict
-        The loaded model dict, including meta information.
-    """
-    with h5py.File(filename, "r") as f:
-        model_dict = json.loads(f.attrs["json"])
-        model_dict = traverse_model_dict(model_dict, lambda x: f[x][()].copy())
-    return model_dict
-
-
 class NativeLayer(NativeOP):
     """Native representation of a layer.
 

deepmd/dpmodel/utils/serialization.py

@@ -0,0 +1,115 @@
+# SPDX-License-Identifier: LGPL-3.0-or-later
+import json
+from datetime import (
+    datetime,
+)
+from typing import (
+    Callable,
+)
+
+import h5py
+
+try:
+    from deepmd._version import version as __version__
+except ImportError:
+    __version__ = "unknown"
+
+
+def traverse_model_dict(model_obj, callback: Callable, is_variable: bool = False):
+    """Traverse a model dict and call callback on each variable.
+
+    Parameters
+    ----------
+    model_obj : object
+        The model object to traverse.
+    callback : callable
+        The callback function to call on each variable.
+    is_variable : bool, optional
+        Whether the current node is a variable.
+
+    Returns
+    -------
+    object
+        The model object after traversing.
+    """
+    if isinstance(model_obj, dict):
+        for kk, vv in model_obj.items():
+            model_obj[kk] = traverse_model_dict(
+                vv, callback, is_variable=is_variable or kk == "@variables"
+            )
+    elif isinstance(model_obj, list):
+        for ii, vv in enumerate(model_obj):
+            model_obj[ii] = traverse_model_dict(vv, callback, is_variable=is_variable)
+    elif model_obj is None:
+        return model_obj
+    elif is_variable:
+        model_obj = callback(model_obj)
+    return model_obj
+
+
+class Counter:
+    """A callable counter.
+
+    Examples
+    --------
+    >>> counter = Counter()
+    >>> counter()
+    0
+    >>> counter()
+    1
+    """
+
+    def __init__(self):
+        self.count = -1
+
+    def __call__(self):
+        self.count += 1
+        return self.count
+
+
+def save_dp_model(filename: str, model_dict: dict) -> None:
+    """Save a DP model to a file in the native format.
+
+    Parameters
+    ----------
+    filename : str
+        The filename to save to.
+    model_dict : dict
+        The model dict to save.
+    """
+    model_dict = model_dict.copy()
+    variable_counter = Counter()
+    with h5py.File(filename, "w") as f:
+        model_dict = traverse_model_dict(
+            model_dict,
+            lambda x: f.create_dataset(
+                f"variable_{variable_counter():04d}", data=x
+            ).name,
+        )
+        save_dict = {
+            "software": "deepmd-kit",
+            "version": __version__,
+            # use UTC+0 time
+            "time": str(datetime.utcnow()),
+            **model_dict,
+        }
+        f.attrs["json"] = json.dumps(save_dict, separators=(",", ":"))
+
+
+def load_dp_model(filename: str) -> dict:
+    """Load a DP model from a file in the native format.
+
+    Parameters
+    ----------
+    filename : str
+        The filename to load from.
+
+    Returns
+    -------
+    dict
+        The loaded model dict, including meta information.
+    """
+    with h5py.File(filename, "r") as f:
+        model_dict = json.loads(f.attrs["json"])
+        model_dict = traverse_model_dict(model_dict, lambda x: f[x][()].copy())
+    return model_dict