feat(dpa3): add huber loss

deepmd/pt/loss/ener.py

@@ -20,6 +20,43 @@
 )
 
 
+def custom_huber_loss(predictions, targets, delta=1.0):
+    error = targets - predictions
+    abs_error = torch.abs(error)
+    quadratic_loss = 0.5 * torch.pow(error, 2)
+    linear_loss = delta * (abs_error - 0.5 * delta)
+    loss = torch.where(abs_error <= delta, quadratic_loss, linear_loss)
+    return torch.mean(loss)
+
+
+def custom_step_huber_loss(predictions, targets, delta=1.0):
+    error = targets - predictions
+    abs_error = torch.abs(error)
+    abs_targets = torch.abs(targets)
+
+    # Define the different delta values based on the absolute value of targets
+    delta1 = delta
+    delta2 = 0.7 * delta
+    delta3 = 0.4 * delta
+    delta4 = 0.1 * delta
+
+    # Determine which delta to use based on the absolute value of targets
+    delta_values = torch.where(
+        abs_targets < 100,
+        delta1,
+        torch.where(
+            abs_targets < 200, delta2, torch.where(abs_targets < 300, delta3, delta4)
+        ),
+    )
+
+    # Compute the quadratic and linear loss based on the dynamically selected delta values
+    quadratic_loss = 0.5 * torch.pow(error, 2)
+    linear_loss = delta_values * (abs_error - 0.5 * delta_values)
+    # Select the appropriate loss based on whether abs_error is less than or greater than delta_values
+    loss = torch.where(abs_error <= delta_values, quadratic_loss, linear_loss)
+    return torch.mean(loss)
+
+
 class EnergyStdLoss(TaskLoss):
     def __init__(
         self,
@@ -41,6 +78,9 @@ def __init__(
         numb_generalized_coord: int = 0,
         use_l1_all: bool = False,
         inference=False,
+        use_huber=False,
+        huber_delta=0.01,
+        torch_huber=False,
         **kwargs,
     ) -> None:
         r"""Construct a layer to compute loss on energy, force and virial.
@@ -118,6 +158,10 @@ def __init__(
             )
         self.use_l1_all = use_l1_all
         self.inference = inference
+        self.huber = use_huber
+        self.huber_delta = huber_delta
+        self.torch_huber = torch_huber
+        self.huber_loss = torch.nn.HuberLoss(reduction="mean", delta=huber_delta)
 
     def forward(self, input_dict, model, label, natoms, learning_rate, mae=False):
         """Return loss on energy and force.
@@ -180,7 +224,21 @@ def forward(self, input_dict, model, label, natoms, learning_rate, mae=False):
                     more_loss["l2_ener_loss"] = self.display_if_exist(
                         l2_ener_loss.detach(), find_energy
                     )
-                loss += atom_norm * (pref_e * l2_ener_loss)
+                if not self.huber:
+                    loss += atom_norm * (pref_e * l2_ener_loss)
+                else:
+                    if self.torch_huber:
+                        l_huber_loss = self.huber_loss(
+                            atom_norm * model_pred["energy"],
+                            atom_norm * label["energy"],
+                        )
+                    else:
+                        l_huber_loss = custom_huber_loss(
+                            atom_norm * model_pred["energy"],
+                            atom_norm * label["energy"],
+                            delta=self.huber_delta,
+                        )
+                    loss += pref_e * l_huber_loss
                 rmse_e = l2_ener_loss.sqrt() * atom_norm
                 more_loss["rmse_e"] = self.display_if_exist(
                     rmse_e.detach(), find_energy
@@ -233,7 +291,20 @@ def forward(self, input_dict, model, label, natoms, learning_rate, mae=False):
                         more_loss["l2_force_loss"] = self.display_if_exist(
                             l2_force_loss.detach(), find_force
                         )
-                    loss += (pref_f * l2_force_loss).to(GLOBAL_PT_FLOAT_PRECISION)
+                    if not self.huber:
+                        loss += (pref_f * l2_force_loss).to(GLOBAL_PT_FLOAT_PRECISION)
+                    else:
+                        if self.torch_huber:
+                            l_huber_loss = self.huber_loss(
+                                model_pred["force"], label["force"]
+                            )
+                        else:
+                            l_huber_loss = custom_huber_loss(
+                                force_pred.reshape(-1),
+                                force_label.reshape(-1),
+                                delta=self.huber_delta,
+                            )
+                        loss += pref_f * l_huber_loss
                     rmse_f = l2_force_loss.sqrt()
                     more_loss["rmse_f"] = self.display_if_exist(
                         rmse_f.detach(), find_force
@@ -304,7 +375,21 @@ def forward(self, input_dict, model, label, natoms, learning_rate, mae=False):
                     more_loss["l2_virial_loss"] = self.display_if_exist(
                         l2_virial_loss.detach(), find_virial
                     )
-                loss += atom_norm * (pref_v * l2_virial_loss)
+                if not self.huber:
+                    loss += atom_norm * (pref_v * l2_virial_loss)
+                else:
+                    if self.torch_huber:
+                        l_huber_loss = self.huber_loss(
+                            atom_norm * model_pred["virial"],
+                            atom_norm * label["virial"],
+                        )
+                    else:
+                        l_huber_loss = custom_huber_loss(
+                            atom_norm * model_pred["virial"].reshape(-1),
+                            atom_norm * label["virial"].reshape(-1),
+                            delta=self.huber_delta,
+                        )
+                    loss += pref_v * l_huber_loss
                 rmse_v = l2_virial_loss.sqrt() * atom_norm
                 more_loss["rmse_v"] = self.display_if_exist(
                     rmse_v.detach(), find_virial

deepmd/pt/utils/auto_batch_size.py

@@ -53,6 +53,7 @@ def is_oom_error(self, e: Exception) -> bool:
             "CUDA out of memory." in e.args[0]
             or "CUDA driver error: out of memory" in e.args[0]
             or "cusolver error: CUSOLVER_STATUS_INTERNAL_ERROR" in e.args[0]
+            or "CUDA error: CUBLAS_STATUS_INTERNAL_ERROR" in e.args[0]
         ):
             # Release all unoccupied cached memory
             torch.cuda.empty_cache()

deepmd/utils/argcheck.py

@@ -2506,6 +2506,24 @@ def loss_ener():
             default=0,
             doc=doc_numb_generalized_coord,
         ),
+        Argument(
+            "use_huber",
+            bool,
+            optional=True,
+            default=False,
+        ),
+        Argument(
+            "huber_delta",
+            float,
+            optional=True,
+            default=0.01,
+        ),
+        Argument(
+            "torch_huber",
+            bool,
+            optional=True,
+            default=False,
+        ),
     ]