feat(pt): DPLR in PyTorch backend

backend/dynamic_metadata.py

@@ -48,8 +48,11 @@ def dynamic_metadata(
         ]
         optional_dependencies["lmp"].extend(find_libpython_requires)
         optional_dependencies["ipi"].extend(find_libpython_requires)
+        torch_static_requirement = optional_dependencies.pop("torch", ())
         return {
             **optional_dependencies,
             **get_tf_requirement(tf_version),
-            **get_pt_requirement(pt_version),
+            **get_pt_requirement(
+                pt_version, static_requirement=tuple(torch_static_requirement)
+            ),
         }

backend/find_pytorch.py

@@ -90,7 +90,10 @@ def find_pytorch() -> tuple[str | None, list[str]]:
 
 
 @lru_cache
-def get_pt_requirement(pt_version: str = "") -> dict:
+def get_pt_requirement(
+    pt_version: str = "",
+    static_requirement: tuple[str] | None = None,
+) -> dict:
     """Get PyTorch requirement when PT is not installed.
 
     If pt_version is not given and the environment variable `PYTORCH_VERSION` is set, use it as the requirement.
@@ -99,6 +102,8 @@ def get_pt_requirement(pt_version: str = "") -> dict:
     ----------
     pt_version : str, optional
         PT version
+    static_requirement : tuple[str] or None, optional
+        Static requirements
 
     Returns
     -------
@@ -125,6 +130,8 @@ def get_pt_requirement(pt_version: str = "") -> dict:
         mpi_requirement = ["mpich"]
     else:
         mpi_requirement = []
+    if static_requirement is None:
+        static_requirement = ()
 
     return {
         "torch": [
@@ -138,6 +145,7 @@ def get_pt_requirement(pt_version: str = "") -> dict:
             else "torch>=2.1.0",
             *mpi_requirement,
             *cibw_requirement,
+            *static_requirement,
         ],
     }
 

deepmd/dpmodel/modifier/base_modifier.py

@@ -36,7 +36,6 @@ def serialize(self) -> dict:
             dict
                 The serialized data
             """
-            pass
 
         @classmethod
         def deserialize(cls, data: dict) -> "BaseModifier":

deepmd/pt/entrypoints/main.py

@@ -1,10 +1,10 @@
 # SPDX-License-Identifier: LGPL-3.0-or-later
 import argparse
 import copy
-import io
 import json
 import logging
 import os
+import pickle
 from pathlib import (
     Path,
 )
@@ -401,17 +401,13 @@ def freeze(
     model.eval()
     model = torch.jit.script(model)
 
-    dm_output = "data_modifier.pth"
-    extra_files = {dm_output: ""}
-    if tester.modifier is not None:
-        dm = tester.modifier
-        dm.eval()
-        buffer = io.BytesIO()
-        torch.jit.save(
-            torch.jit.script(dm),
-            buffer,
-        )
-        extra_files = {dm_output: buffer.getvalue()}
+    extra_files = {"modifier_data": ""}
+    dm = tester.modifier
+    if dm is not None:
+        # dict from dm.serialize() includes np.ndarray
+        # use pickle rather than json
+        bytes_data = pickle.dumps(dm.serialize())
+        extra_files = {"modifier_data": bytes_data}
     torch.jit.save(
         model,
         output,

deepmd/pt/infer/deep_eval.py

@@ -1,7 +1,7 @@
 # SPDX-License-Identifier: LGPL-3.0-or-later
-import io
 import json
 import logging
+import pickle
 from collections.abc import (
     Callable,
 )
@@ -49,6 +49,9 @@
 from deepmd.pt.model.network.network import (
     TypeEmbedNetConsistent,
 )
+from deepmd.pt.modifier import (
+    BaseModifier,
+)
 from deepmd.pt.train.wrapper import (
     ModelWrapper,
 )
@@ -172,19 +175,20 @@ def __init__(
             self.dp = ModelWrapper(model)
             self.dp.load_state_dict(state_dict)
         elif str(self.model_path).endswith(".pth"):
-            extra_files = {"data_modifier.pth": ""}
+            extra_files = {"modifier_data": ""}
             model = torch.jit.load(
                 model_file, map_location=env.DEVICE, _extra_files=extra_files
             )
             modifier = None
             # Load modifier if it exists in extra_files
-            if len(extra_files["data_modifier.pth"]) > 0:
-                # Create a file-like object from the in-memory data
-                modifier_data = extra_files["data_modifier.pth"]
+            if len(extra_files["modifier_data"]) > 0:
+                modifier_data = extra_files["modifier_data"]
                 if isinstance(modifier_data, bytes):
-                    modifier_data = io.BytesIO(modifier_data)
+                    modifier_data = pickle.loads(modifier_data)
                 # Load the modifier directly from the file-like object
-                modifier = torch.jit.load(modifier_data, map_location=env.DEVICE)
+                modifier = BaseModifier.get_class_by_type(
+                    modifier_data["type"]
+                ).deserialize(modifier_data)
             self.dp = ModelWrapper(model, modifier=modifier)
             self.modifier = modifier
             model_def_script = self.dp.model["Default"].get_model_def_script()

deepmd/pt/modifier/__init__.py

@@ -7,9 +7,13 @@
 from .base_modifier import (
     BaseModifier,
 )
+from .dipole_charge import (
+    DipoleChargeModifier,
+)
 
 __all__ = [
     "BaseModifier",
+    "DipoleChargeModifier",
     "get_data_modifier",
 ]
 

deepmd/pt/modifier/base_modifier.py

@@ -47,6 +47,7 @@ def serialize(self) -> dict:
         data = {
             "@class": "Modifier",
             "type": self.modifier_type,
+            "use_cache": self.use_cache,
             "@version": 3,
         }
         return data