feat (tf/pt): add atomic weights to tensor loss

deepmd/pt/loss/tensor.py

@@ -22,6 +22,7 @@
         pref_atomic: float = 0.0,
         pref: float = 0.0,
         inference=False,
+        enable_atomic_weight: bool = False,
         **kwargs,
     ) -> None:
         r"""Construct a loss for local and global tensors.
@@ -40,6 +41,8 @@
             The prefactor of the weight of global loss. It should be larger than or equal to 0.
         inference : bool
             If true, it will output all losses found in output, ignoring the pre-factors.
+        enable_atomic_weight : bool
+            If true, atomic weight will be used in the loss calculation.
         **kwargs
             Other keyword arguments.
         """
@@ -50,6 +53,7 @@
         self.local_weight = pref_atomic
         self.global_weight = pref
         self.inference = inference
+        self.enable_atomic_weight = enable_atomic_weight
 
         assert (
             self.local_weight >= 0.0 and self.global_weight >= 0.0
@@ -85,6 +89,12 @@
         """
         model_pred = model(**input_dict)
         del learning_rate, mae
+
+        if self.enable_atomic_weight:
+            atomic_weight = label["atom_weight"].reshape([-1, 1])
+        else:
+            atomic_weight = 1.0
+
         loss = torch.zeros(1, dtype=env.GLOBAL_PT_FLOAT_PRECISION, device=env.DEVICE)[0]
         more_loss = {}
         if (
@@ -103,6 +113,7 @@
             diff = (local_tensor_pred - local_tensor_label).reshape(
                 [-1, self.tensor_size]
             )
+            diff = diff * atomic_weight
             if "mask" in model_pred:
                 diff = diff[model_pred["mask"].reshape([-1]).bool()]
             l2_local_loss = torch.mean(torch.square(diff))
@@ -171,4 +182,15 @@
                     high_prec=False,
                 )
             )
+        if self.enable_atomic_weight:
+            label_requirement.append(
+                DataRequirementItem(
+                    "atomic_weight",
+                    ndof=1,
+                    atomic=True,
+                    must=False,
+                    high_prec=False,
+                    default=1.0,
+                )
+            )
         return label_requirement

deepmd/tf/loss/tensor.py

@@ -40,6 +40,7 @@
         # YWolfeee: modify, use pref / pref_atomic, instead of pref_weight / pref_atomic_weight
         self.local_weight = jdata.get("pref_atomic", None)
         self.global_weight = jdata.get("pref", None)
+        self.enable_atomic_weight = jdata.get("enable_atomic_weight", False)
 
         assert (
             self.local_weight is not None and self.global_weight is not None
@@ -66,9 +67,16 @@
             "global_loss": global_cvt_2_tf_float(0.0),
         }
 
+        if self.enable_atomic_weight:
+            atomic_weight = tf.reshape(label_dict["atom_weight"], [-1, 1])
+        else:
+            atomic_weight = global_cvt_2_tf_float(1.0)
+
         if self.local_weight > 0.0:
+            diff = polar - atomic_polar_hat
+            diff = tf.reshape(diff, [-1, self.tensor_size]) * atomic_weight
             local_loss = global_cvt_2_tf_float(find_atomic) * tf.reduce_mean(
-                tf.square(self.scale * (polar - atomic_polar_hat)), name="l2_" + suffix
+                tf.square(self.scale * diff), name="l2_" + suffix
             )
             more_loss["local_loss"] = self.display_if_exist(local_loss, find_atomic)
             l2_loss += self.local_weight * local_loss
@@ -163,4 +171,16 @@
                 type_sel=self.type_sel,
             )
         )
+        if self.enable_atomic_weight:
+            data_requirements.append(
+                DataRequirementItem(
+                    "atom_weight",
+                    1,
+                    atomic=True,
+                    must=False,
+                    high_prec=False,
+                    default=1.0,
+                    type_sel=self.type_sel,
+                )
+            )
         return data_requirements

deepmd/utils/argcheck.py

@@ -2511,8 +2511,9 @@ def loss_property():
 def loss_tensor():
     # doc_global_weight = "The prefactor of the weight of global loss. It should be larger than or equal to 0. If only `pref` is provided or both are not provided, training will be global mode, i.e. the shape of 'polarizability.npy` or `dipole.npy` should be #frams x [9 or 3]."
     # doc_local_weight =  "The prefactor of the weight of atomic loss. It should be larger than or equal to 0. If only `pref_atomic` is provided, training will be atomic mode, i.e. the shape of `polarizability.npy` or `dipole.npy` should be #frames x ([9 or 3] x #selected atoms). If both `pref` and `pref_atomic` are provided, training will be combined mode, and atomic label should be provided as well."
-    doc_global_weight = "The prefactor of the weight of global loss. It should be larger than or equal to 0. If controls the weight of loss corresponding to global label, i.e. 'polarizability.npy` or `dipole.npy`, whose shape should be #frames x [9 or 3]. If it's larger than 0.0, this npy should be included."
-    doc_local_weight = "The prefactor of the weight of atomic loss. It should be larger than or equal to 0. If controls the weight of loss corresponding to atomic label, i.e. `atomic_polarizability.npy` or `atomic_dipole.npy`, whose shape should be #frames x ([9 or 3] x #selected atoms). If it's larger than 0.0, this npy should be included. Both `pref` and `pref_atomic` should be provided, and either can be set to 0.0."
+    doc_global_weight = "The prefactor of the weight of global loss. It should be larger than or equal to 0. It controls the weight of loss corresponding to global label, i.e. 'polarizability.npy` or `dipole.npy`, whose shape should be #frames x [9 or 3]. If it's larger than 0.0, this npy should be included."
+    doc_local_weight = "The prefactor of the weight of atomic loss. It should be larger than or equal to 0. It controls the weight of loss corresponding to atomic label, i.e. `atomic_polarizability.npy` or `atomic_dipole.npy`, whose shape should be #frames x ([9 or 3] x #atoms). If it's larger than 0.0, this npy should be included. Both `pref` and `pref_atomic` should be provided, and either can be set to 0.0."
+    doc_enable_atomic_weight = "If true, the atomic loss will be reweighted."
     return [
         Argument(
             "pref", [float, int], optional=False, default=None, doc=doc_global_weight
@@ -2524,6 +2525,13 @@ def loss_tensor():
             default=None,
             doc=doc_local_weight,
         ),
+        Argument(
+            "enable_atomic_weight",
+            bool,
+            optional=True,
+            default=False,
+            doc=doc_enable_atomic_weight,
+        ),
     ]