refactor: extract shared tabulate math, remove pt_expt→pt dependency

deepmd/pt_expt/descriptor/dpa1.py

@@ -49,9 +49,6 @@ def enable_compression(
         check_frequency
             The overflow check frequency
         """
-        from deepmd.pt.utils.utils import (
-            ActivationFn,
-        )
         from deepmd.pt_expt.utils.tabulate import (
             DPTabulate,
         )
@@ -71,7 +68,7 @@ def enable_compression(
             data["neuron"],
             data["type_one_side"],
             data["exclude_types"],
-            ActivationFn(data["activation_function"]),
+            data["activation_function"],
         )
         self.table_config = [
             table_extrapolate,

deepmd/pt_expt/descriptor/dpa2.py

@@ -57,9 +57,6 @@ def enable_compression(
         check_frequency
             The overflow check frequency
         """
-        from deepmd.pt.utils.utils import (
-            ActivationFn,
-        )
         from deepmd.pt_expt.utils.tabulate import (
             DPTabulate,
         )
@@ -105,7 +102,7 @@ def enable_compression(
                 repinit_data["neuron"],
                 repinit_data.get("type_one_side", False),
                 repinit_data.get("exclude_types", []),
-                ActivationFn(repinit_data["activation_function"]),
+                repinit_data["activation_function"],
             )
             self.table_config = [
                 table_extrapolate,

deepmd/pt_expt/descriptor/se_e2_a.py

@@ -34,9 +34,6 @@ def enable_compression(
         table_stride_2: float = 0.1,
         check_frequency: int = -1,
     ) -> None:
-        from deepmd.pt.utils.utils import (
-            ActivationFn,
-        )
         from deepmd.pt_expt.utils.tabulate import (
             DPTabulate,
         )
@@ -49,7 +46,7 @@ def enable_compression(
             data["neuron"],
             data["type_one_side"],
             data["exclude_types"],
-            ActivationFn(data["activation_function"]),
+            data["activation_function"],
         )
         self.table_config = [
             table_extrapolate,

deepmd/pt_expt/descriptor/se_r.py

@@ -34,9 +34,6 @@ def enable_compression(
         table_stride_2: float = 0.1,
         check_frequency: int = -1,
     ) -> None:
-        from deepmd.pt.utils.utils import (
-            ActivationFn,
-        )
         from deepmd.pt_expt.utils.tabulate import (
             DPTabulate,
         )
@@ -49,7 +46,7 @@ def enable_compression(
             data["neuron"],
             data["type_one_side"],
             data["exclude_types"],
-            ActivationFn(data["activation_function"]),
+            data["activation_function"],
         )
         self.table_config = [
             table_extrapolate,

deepmd/pt_expt/descriptor/se_t.py

@@ -35,9 +35,6 @@ def enable_compression(
         table_stride_2: float = 0.1,
         check_frequency: int = -1,
     ) -> None:
-        from deepmd.pt.utils.utils import (
-            ActivationFn,
-        )
         from deepmd.pt_expt.utils.tabulate import (
             DPTabulate,
         )
@@ -49,7 +46,7 @@ def enable_compression(
             self,
             data["neuron"],
             exclude_types=data["exclude_types"],
-            activation_fn=ActivationFn(data["activation_function"]),
+            activation_fn_name=data["activation_function"],
         )
         # SE_T scales strides by 10
         stride_1_scaled = table_stride_1 * 10

deepmd/pt_expt/descriptor/se_t_tebd.py

@@ -48,9 +48,6 @@ def enable_compression(
         check_frequency
             The overflow check frequency
         """
-        from deepmd.pt.utils.utils import (
-            ActivationFn,
-        )
         from deepmd.pt_expt.utils.tabulate import (
             DPTabulate,
         )
@@ -65,7 +62,7 @@ def enable_compression(
             self,
             data["neuron"],
             exclude_types=data["exclude_types"],
-            activation_fn=ActivationFn(data["activation_function"]),
+            activation_fn_name=data["activation_function"],
         )
         # SE_T scales strides by 10
         stride_1_scaled = table_stride_1 * 10

deepmd/pt_expt/utils/tabulate.py

@@ -1,22 +1,20 @@
 # SPDX-License-Identifier: LGPL-3.0-or-later
 """DPTabulate for the pt_expt backend.
 
-Subclasses the pt backend's DPTabulate, overriding _get_descrpt_type() to
-detect descriptor types via serialized data rather than isinstance checks
-against pt-specific classes.
+Inherits the numpy math from ``deepmd.utils.tabulate_math.DPTabulate``
+and overrides ``_convert_numpy_to_tensor`` for torch tensor conversion
+and ``_get_descrpt_type`` for serialization-based type detection.
+No dependency on the pt backend.
 """
 
 from typing import (
     Any,
 )
 
-from deepmd.pt.utils.tabulate import DPTabulate as DPTabulatePT
-from deepmd.pt.utils.utils import (
-    ActivationFn,
-)
+from deepmd.utils.tabulate_math import DPTabulate as DPTabulateBase
 
 
-class DPTabulate(DPTabulatePT):
+class DPTabulate(DPTabulateBase):
     """Tabulation helper for pt_expt descriptors.
 
     The descriptor passed to this class must serialize to a dict with
@@ -34,8 +32,8 @@ class DPTabulate(DPTabulatePT):
         Whether to use one-side type embedding.
     exclude_types
         Excluded type pairs.
-    activation_fn
-        The activation function used in the embedding net.
+    activation_fn_name
+        Name of the activation function (e.g. "tanh", "gelu").
     """
 
     def __init__(
@@ -44,23 +42,20 @@ def __init__(
         neuron: list[int],
         type_one_side: bool = False,
         exclude_types: list[list[int]] = [],
-        activation_fn: ActivationFn = ActivationFn("tanh"),
+        activation_fn_name: str = "tanh",
     ) -> None:
-        # DPTabulatePT.__init__ works here because:
-        # 1. _get_descrpt_type is overridden to use serialized data (not isinstance)
-        # 2. The isinstance(descrpt, DescrptDPA2) check in parent just returns False
-        #    for pt_expt descriptors — callers pass the repinit block directly.
-        super().__init__(descrpt, neuron, type_one_side, exclude_types, activation_fn)
+        super().__init__(
+            descrpt,
+            neuron,
+            type_one_side,
+            exclude_types,
+            activation_fn_name=activation_fn_name,
+        )
 
     def _get_descrpt_type(self) -> str:
-        """Determine descriptor type from serialized data.
-
-        Instead of isinstance checks against pt classes, use the "type" key
-        from the serialized descriptor dict.
-        """
+        """Determine descriptor type from serialized data."""
         data = self.descrpt.serialize()
         type_str = data.get("type", "")
-
         type_map = {
             "se_e2_a": "A",
             "se_r": "R",
@@ -69,8 +64,19 @@ def _get_descrpt_type(self) -> str:
             "dpa1": "Atten",
             "se_atten_v2": "Atten",
         }
-
         descrpt_type = type_map.get(type_str)
         if descrpt_type is None:
             raise RuntimeError(f"Unsupported descriptor type: {type_str}")
         return descrpt_type
+
+    def _convert_numpy_to_tensor(self) -> None:
+        """Convert self.data from np.ndarray to torch.Tensor."""
+        import torch
+
+        from deepmd.pt_expt.utils.env import (
+            DEVICE,
+        )
+
+        self._convert_numpy_float_to_int()
+        for ii in self.data:
+            self.data[ii] = torch.tensor(self.data[ii], device=DEVICE)  # pylint: disable=no-explicit-dtype

deepmd/tf/utils/tabulate.py

@@ -78,7 +78,6 @@ def __init__(
             neuron,
             type_one_side,
             exclude_types,
-            False,
         )
 
         self.descrpt_type = self._get_descrpt_type()

deepmd/utils/tabulate.py

@@ -28,7 +28,6 @@ def __init__(
         neuron: list[int],
         type_one_side: bool,
         exclude_types: list[list[int]],
-        is_pt: bool,
     ) -> None:
         """Constructor."""
         super().__init__()
@@ -38,7 +37,6 @@ def __init__(
         self.neuron = neuron
         self.type_one_side = type_one_side
         self.exclude_types = exclude_types
-        self.is_pt = is_pt
 
         """Need to be initialized in the subclass."""
         self.descrpt_type = "Base"
@@ -91,6 +89,11 @@ def build(
         """
         # tabulate range [lower, upper] with stride0 'stride0'
         lower, upper = self._get_env_mat_range(min_nbor_dist)
+        # Normalize to per-type scalars: PT serialized data produces
+        # multi-dimensional arrays (ntypes, nnei) while TF produces 1D.
+        if lower.ndim > 1:
+            lower = np.min(lower, axis=tuple(range(1, lower.ndim)))
+            upper = np.max(upper, axis=tuple(range(1, upper.ndim)))
         if self.descrpt_type in ("Atten", "AEbdV2"):
             uu = np.max(upper)
             ll = np.min(lower)
@@ -127,12 +130,8 @@ def build(
                         net = (
                             "filter_" + str(ielement) + "_net_" + str(ii % self.ntypes)
                         )
-                        if self.is_pt:
-                            uu = np.max(upper[ielement])
-                            ll = np.min(lower[ielement])
-                        else:
-                            uu = upper[ielement]
-                            ll = lower[ielement]
+                        uu = upper[ielement]
+                        ll = lower[ielement]
                     xx = np.arange(ll, uu, stride0, dtype=self.data_type)
                     xx = np.append(
                         xx,
@@ -150,13 +149,8 @@ def build(
         elif self.descrpt_type == "T":
             xx_all = []
             for ii in range(self.ntypes):
-                """Pt and tf is different here. Pt version is a two-dimensional array."""
-                if self.is_pt:
-                    uu = np.max(upper[ii])
-                    ll = np.min(lower[ii])
-                else:
-                    ll = lower[ii]
-                    uu = upper[ii]
+                ll = lower[ii]
+                uu = upper[ii]
                 xx = np.arange(extrapolate * ll, ll, stride1, dtype=self.data_type)
                 xx = np.append(xx, np.arange(ll, uu, stride0, dtype=self.data_type))
                 xx = np.append(
@@ -176,12 +170,8 @@ def build(
             ).astype(int)
             idx = 0
             for ii in range(self.ntypes):
-                if self.is_pt:
-                    uu = np.max(upper[ii])
-                    ll = np.min(lower[ii])
-                else:
-                    ll = lower[ii]
-                    uu = upper[ii]
+                ll = lower[ii]
+                uu = upper[ii]
                 for jj in range(ii, self.ntypes):
                     net = "filter_" + str(ii) + "_net_" + str(jj)
                     self._build_lower(
@@ -193,7 +183,7 @@ def build(
                         stride0,
                         stride1,
                         extrapolate,
-                        nspline[ii][0] if self.is_pt else nspline[ii],
+                        nspline[ii],
                     )
                     idx += 1
         elif self.descrpt_type == "T_TEBD":
@@ -279,8 +269,6 @@ def build(
             raise RuntimeError("Unsupported descriptor")
 
         self._convert_numpy_to_tensor()
-        if self.is_pt:
-            self._convert_numpy_float_to_int()
         return self.lower, self.upper
 
     # generate_spline_table