Merge branch 'devel' into tf-call

.devcontainer/READMD.md → .devcontainer/README.md

@@ -26,6 +26,11 @@ When calling and debuging LAMMPS with DeePMD-kit, use the following scripts inst
 - `.devcontainer/lmp`
 - `.devcontainer/gdb_lmp`
 
+Use the following scripts for `pytest` with LAMMPS:
+
+- `.devcontainer/pytest_lmp`
+- `.devcontainer/gdb_pytest_lmp`
+
 ## Rebuild
 
 Usually the Python package does not need to reinstall.

.devcontainer/gdb_pytest_lmp

@@ -0,0 +1,9 @@
+#!/bin/bash
+SCRIPT_PATH=$(dirname $(realpath -s $0))/../..
+
+export CMAKE_PREFIX_PATH=${SCRIPT_PATH}/../libtorch
+TENSORFLOW_ROOT=$(python -c 'import importlib,pathlib;print(pathlib.Path(importlib.util.find_spec("tensorflow").origin).parent)')
+
+env LAMMPS_PLUGIN_PATH=${SCRIPT_PATH}/../dp/lib/deepmd_lmp \
+	LD_LIBRARY_PATH=${SCRIPT_PATH}/../dp/lib:${CMAKE_PREFIX_PATH}/lib:${TENSORFLOW_ROOT} \
+	gdb --args python -m pytest -s "$@"

.devcontainer/pytest_lmp

@@ -0,0 +1,9 @@
+#!/bin/bash
+SCRIPT_PATH=$(dirname $(realpath -s $0))/../..
+
+export CMAKE_PREFIX_PATH=${SCRIPT_PATH}/../libtorch
+TENSORFLOW_ROOT=$(python -c 'import importlib,pathlib;print(pathlib.Path(importlib.util.find_spec("tensorflow").origin).parent)')
+
+env LAMMPS_PLUGIN_PATH=${SCRIPT_PATH}/../dp/lib/deepmd_lmp \
+	LD_LIBRARY_PATH=${SCRIPT_PATH}/../dp/lib:${CMAKE_PREFIX_PATH}/lib:${TENSORFLOW_ROOT} \
+	python -m pytest "$@"

deepmd/calculator.py

@@ -45,6 +45,8 @@ class DP(Calculator):
         will infer this information from model, by default None
     neighbor_list : ase.neighborlist.NeighborList, optional
         The neighbor list object. If None, then build the native neighbor list.
+    head : Union[str, None], optional
+        a specific model branch choosing from pretrained model, by default None
 
     Examples
     --------
@@ -84,10 +86,15 @@ def __init__(
         label: str = "DP",
         type_dict: Optional[dict[str, int]] = None,
         neighbor_list=None,
+        head=None,
         **kwargs,
     ) -> None:
         Calculator.__init__(self, label=label, **kwargs)
-        self.dp = DeepPot(str(Path(model).resolve()), neighbor_list=neighbor_list)
+        self.dp = DeepPot(
+            str(Path(model).resolve()),
+            neighbor_list=neighbor_list,
+            head=head,
+        )
         if type_dict:
             self.type_dict = type_dict
         else:

deepmd/dpmodel/descriptor/hybrid.py

@@ -43,6 +43,8 @@ class DescrptHybrid(BaseDescriptor, NativeOP):
     def __init__(
         self,
         list: list[Union[BaseDescriptor, dict[str, Any]]],
+        type_map: Optional[list[str]] = None,
+        ntypes: Optional[int] = None,  # to be compat with input
     ) -> None:
         super().__init__()
         # warning: list is conflict with built-in list
@@ -56,6 +58,10 @@ def __init__(
             if isinstance(ii, BaseDescriptor):
                 formatted_descript_list.append(ii)
             elif isinstance(ii, dict):
+                ii = ii.copy()
+                # only pass if not already set
+                ii.setdefault("type_map", type_map)
+                ii.setdefault("ntypes", ntypes)
                 formatted_descript_list.append(BaseDescriptor(**ii))
             else:
                 raise NotImplementedError

deepmd/dpmodel/descriptor/repformers.py

@@ -1792,7 +1792,7 @@ def serialize(self) -> dict:
         """
         data = {
             "@class": "RepformerLayer",
-            "@version": 1,
+            "@version": 2,
             "rcut": self.rcut,
             "rcut_smth": self.rcut_smth,
             "sel": self.sel,
@@ -1877,9 +1877,11 @@ def serialize(self) -> dict:
         if self.update_style == "res_residual":
             data.update(
                 {
-                    "g1_residual": [to_numpy_array(aa) for aa in self.g1_residual],
-                    "g2_residual": [to_numpy_array(aa) for aa in self.g2_residual],
-                    "h2_residual": [to_numpy_array(aa) for aa in self.h2_residual],
+                    "@variables": {
+                        "g1_residual": [to_numpy_array(aa) for aa in self.g1_residual],
+                        "g2_residual": [to_numpy_array(aa) for aa in self.g2_residual],
+                        "h2_residual": [to_numpy_array(aa) for aa in self.h2_residual],
+                    }
                 }
             )
         return data
@@ -1894,7 +1896,7 @@ def deserialize(cls, data: dict) -> "RepformerLayer":
             The dict to deserialize from.
         """
         data = data.copy()
-        check_version_compatibility(data.pop("@version"), 1, 1)
+        check_version_compatibility(data.pop("@version"), 2, 1)
         data.pop("@class")
         linear1 = data.pop("linear1")
         update_chnnl_2 = data["update_chnnl_2"]
@@ -1915,9 +1917,10 @@ def deserialize(cls, data: dict) -> "RepformerLayer":
         attn2_ev_apply = data.pop("attn2_ev_apply", None)
         loc_attn = data.pop("loc_attn", None)
         g1_self_mlp = data.pop("g1_self_mlp", None)
-        g1_residual = data.pop("g1_residual", [])
-        g2_residual = data.pop("g2_residual", [])
-        h2_residual = data.pop("h2_residual", [])
+        variables = data.pop("@variables", {})
+        g1_residual = variables.get("g1_residual", data.pop("g1_residual", []))
+        g2_residual = variables.get("g2_residual", data.pop("g2_residual", []))
+        h2_residual = variables.get("h2_residual", data.pop("h2_residual", []))
 
         obj = cls(**data)
         obj.linear1 = NativeLayer.deserialize(linear1)

deepmd/dpmodel/model/model.py

@@ -8,9 +8,6 @@
 from deepmd.dpmodel.descriptor.base_descriptor import (
     BaseDescriptor,
 )
-from deepmd.dpmodel.descriptor.se_e2_a import (
-    DescrptSeA,
-)
 from deepmd.dpmodel.fitting.ener_fitting import (
     EnergyFittingNet,
 )
@@ -39,16 +36,13 @@ def get_standard_model(data: dict) -> EnergyModel:
     data : dict
         The data to construct the model.
     """
-    descriptor_type = data["descriptor"].pop("type")
     data["descriptor"]["type_map"] = data["type_map"]
+    data["descriptor"]["ntypes"] = len(data["type_map"])
     fitting_type = data["fitting_net"].pop("type")
     data["fitting_net"]["type_map"] = data["type_map"]
-    if descriptor_type == "se_e2_a":
-        descriptor = DescrptSeA(
-            **data["descriptor"],
-        )
-    else:
-        raise ValueError(f"Unknown descriptor type {descriptor_type}")
+    descriptor = BaseDescriptor(
+        **data["descriptor"],
+    )
     if fitting_type == "ener":
         fitting = EnergyFittingNet(
             ntypes=descriptor.get_ntypes(),

deepmd/jax/descriptor/__init__.py

@@ -8,6 +8,9 @@
 from deepmd.jax.descriptor.hybrid import (
     DescrptHybrid,
 )
+from deepmd.jax.descriptor.se_atten_v2 import (
+    DescrptSeAttenV2,
+)
 from deepmd.jax.descriptor.se_e2_a import (
     DescrptSeA,
 )
@@ -27,6 +30,7 @@
     "DescrptSeT",
     "DescrptSeTTebd",
     "DescrptDPA1",
+    "DescrptSeAttenV2",
     "DescrptDPA2",
     "DescrptHybrid",
 ]

deepmd/jax/model/model.py

@@ -37,6 +37,7 @@ def get_standard_model(data: dict):
     data = deepcopy(data)
     descriptor_type = data["descriptor"].pop("type")
     data["descriptor"]["type_map"] = data["type_map"]
+    data["descriptor"]["ntypes"] = len(data["type_map"])
     fitting_type = data["fitting_net"].pop("type")
     data["fitting_net"]["type_map"] = data["type_map"]
     descriptor = BaseDescriptor.get_class_by_type(descriptor_type)(

deepmd/pt/model/descriptor/repformer_layer.py

@@ -1295,7 +1295,7 @@ def serialize(self) -> dict:
         """
         data = {
             "@class": "RepformerLayer",
-            "@version": 1,
+            "@version": 2,
             "rcut": self.rcut,
             "rcut_smth": self.rcut_smth,
             "sel": self.sel,
@@ -1380,9 +1380,11 @@ def serialize(self) -> dict:
         if self.update_style == "res_residual":
             data.update(
                 {
-                    "g1_residual": [to_numpy_array(t) for t in self.g1_residual],
-                    "g2_residual": [to_numpy_array(t) for t in self.g2_residual],
-                    "h2_residual": [to_numpy_array(t) for t in self.h2_residual],
+                    "@variables": {
+                        "g1_residual": [to_numpy_array(t) for t in self.g1_residual],
+                        "g2_residual": [to_numpy_array(t) for t in self.g2_residual],
+                        "h2_residual": [to_numpy_array(t) for t in self.h2_residual],
+                    }
                 }
             )
         return data
@@ -1397,7 +1399,7 @@ def deserialize(cls, data: dict) -> "RepformerLayer":
             The dict to deserialize from.
         """
         data = data.copy()
-        check_version_compatibility(data.pop("@version"), 1, 1)
+        check_version_compatibility(data.pop("@version"), 2, 1)
         data.pop("@class")
         linear1 = data.pop("linear1")
         update_chnnl_2 = data["update_chnnl_2"]
@@ -1418,9 +1420,10 @@ def deserialize(cls, data: dict) -> "RepformerLayer":
         attn2_ev_apply = data.pop("attn2_ev_apply", None)
         loc_attn = data.pop("loc_attn", None)
         g1_self_mlp = data.pop("g1_self_mlp", None)
-        g1_residual = data.pop("g1_residual", [])
-        g2_residual = data.pop("g2_residual", [])
-        h2_residual = data.pop("h2_residual", [])
+        variables = data.pop("@variables", {})
+        g1_residual = variables.get("g1_residual", data.pop("g1_residual", []))
+        g2_residual = variables.get("g2_residual", data.pop("g2_residual", []))
+        h2_residual = variables.get("h2_residual", data.pop("h2_residual", []))
 
         obj = cls(**data)
         obj.linear1 = MLPLayer.deserialize(linear1)

deepmd/pt/model/task/fitting.py

@@ -419,8 +419,8 @@ def _forward_common(
 
         if nd != self.dim_descrpt:
             raise ValueError(
-                "get an input descriptor of dim {nd},"
-                "which is not consistent with {self.dim_descrpt}."
+                f"get an input descriptor of dim {nd},"
+                f"which is not consistent with {self.dim_descrpt}."
             )
         # check fparam dim, concate to input descriptor
         if self.numb_fparam > 0:

deepmd/tf/descriptor/hybrid.py

@@ -461,6 +461,8 @@ def update_sel(
         return local_jdata_cpy, min_nbor_dist
 
     def serialize(self, suffix: str = "") -> dict:
+        if hasattr(self, "type_embedding"):
+            raise NotImplementedError("hybrid + type embedding is not supported")
         return {
             "@class": "Descriptor",
             "type": "hybrid",
@@ -485,4 +487,8 @@ def deserialize(cls, data: dict, suffix: str = "") -> "DescrptHybrid":
                 for idx, ii in enumerate(data["list"])
             ],
         )
+        # search for type embedding
+        for ii in obj.descrpt_list:
+            if hasattr(ii, "type_embedding"):
+                raise NotImplementedError("hybrid + type embedding is not supported")
         return obj