change property.npy to any name

deepmd/dpmodel/output_def.py

@@ -197,6 +197,7 @@ def __init__(
         r_hessian: bool = False,
         magnetic: bool = False,
         intensive: bool = False,
+        sub_var_name: list = None,
     ) -> None:
         self.name = name
         self.shape = list(shape)
@@ -220,6 +221,7 @@ def __init__(
         self.r_hessian = r_hessian
         self.magnetic = magnetic
         self.intensive = intensive
+        self.sub_var_name = sub_var_name
         if self.r_hessian:
             if not self.reducible:
                 raise ValueError("only reducible variable can calculate hessian")

deepmd/pt/loss/property.py

@@ -24,6 +24,8 @@ def __init__(
         loss_func: str = "smooth_mae",
         metric: list = ["mae"],
         beta: float = 1.00,
+        property_name: str|list = "property",
+        property_dim: int|list = 1,
         **kwargs,
     ) -> None:
         r"""Construct a layer to compute loss on property.
@@ -44,6 +46,13 @@ def __init__(
         self.loss_func = loss_func
         self.metric = metric
         self.beta = beta
+        if isinstance(property_name, str):
+            property_name = [property_name]
+        if isinstance(property_dim, int):
+            property_dim = [property_dim]
+        self.property_name = property_name
+        assert self.task_dim == sum(property_dim)
+        self.property_name_dim_mapping = dict(zip(property_name, property_dim))
 
     def forward(self, input_dict, model, label, natoms, learning_rate=0.0, mae=False):
         """Return loss on properties .
@@ -69,8 +78,16 @@ def forward(self, input_dict, model, label, natoms, learning_rate=0.0, mae=False
             Other losses for display.
         """
         model_pred = model(**input_dict)
-        assert label["property"].shape[-1] == self.task_dim
-        assert model_pred["property"].shape[-1] == self.task_dim
+        nbz = model_pred["property"].shape[0]
+        assert model_pred["property"].shape == (nbz, self.task_dim)
+
+        concat_property = []
+        for property_name in self.property_name:
+            assert label[property_name].shape == (nbz, self.property_name_dim_mapping[property_name])
+            concat_property.append(label[property_name])
+        label["property"] = torch.cat([label["dipole_moment"],label["homo"]],dim=1)
+        assert label["property"].shape == (nbz, self.task_dim)
+
         loss = torch.zeros(1, dtype=env.GLOBAL_PT_FLOAT_PRECISION, device=env.DEVICE)[0]
         more_loss = {}
 
@@ -138,13 +155,14 @@ def forward(self, input_dict, model, label, natoms, learning_rate=0.0, mae=False
     def label_requirement(self) -> list[DataRequirementItem]:
         """Return data label requirements needed for this loss calculation."""
         label_requirement = []
-        label_requirement.append(
-            DataRequirementItem(
-                "property",
-                ndof=self.task_dim,
-                atomic=False,
-                must=False,
-                high_prec=True,
+        for property_name in self.property_name:
+            label_requirement.append(
+                DataRequirementItem(
+                    property_name,
+                    ndof=self.property_name_dim_mapping[property_name],
+                    atomic=False,
+                    must=True,
+                    high_prec=True,
+                )
             )
-        )
         return label_requirement

deepmd/pt/model/task/property.py

@@ -82,6 +82,7 @@ def __init__(
         bias_atom_p: Optional[torch.Tensor] = None,
         intensive: bool = False,
         bias_method: str = "normal",
+        property_name: str|list = "property",
         resnet_dt: bool = True,
         numb_fparam: int = 0,
         numb_aparam: int = 0,
@@ -95,6 +96,7 @@ def __init__(
         self.task_dim = task_dim
         self.intensive = intensive
         self.bias_method = bias_method
+        self.property_name = property_name
         super().__init__(
             var_name="property",
             ntypes=ntypes,
@@ -126,6 +128,7 @@ def output_def(self) -> FittingOutputDef:
                     r_differentiable=False,
                     c_differentiable=False,
                     intensive=self.intensive,
+                    sub_var_name=self.property_name,
                 ),
             ]
         )

deepmd/pt/utils/stat.py

@@ -297,6 +297,8 @@ def compute_output_stats(
         ]
         del keys
         keys = new_keys
+        from IPython import embed
+        embed()
         # split system based on label
         atomic_sampled_idx = defaultdict(list)
         global_sampled_idx = defaultdict(list)

deepmd/utils/argcheck.py

@@ -1581,6 +1581,7 @@ def fitting_property():
     doc_task_dim = "The dimension of outputs of fitting net"
     doc_intensive = "Whether the fitting property is intensive"
     doc_bias_method = "The method of applying the bias to each atomic output, user can select 'normal' or 'no_bias'. If 'no_bias' is used, no bias will be added to the atomic output."
+    doc_property_name = "TODO"
     return [
         Argument("numb_fparam", int, optional=True, default=0, doc=doc_numb_fparam),
         Argument("numb_aparam", int, optional=True, default=0, doc=doc_numb_aparam),
@@ -1614,6 +1615,7 @@ def fitting_property():
         Argument(
             "bias_method", str, optional=True, default="normal", doc=doc_bias_method
         ),
+        Argument("property_name", [str, list], optional=True, default="property", doc=doc_property_name),
     ]
 
 
@@ -2481,6 +2483,8 @@ def loss_property():
     doc_loss_func = "The loss function to minimize, such as 'mae','smooth_mae'."
     doc_metric = "The metric for display. This list can include 'smooth_mae', 'mae', 'mse' and 'rmse'."
     doc_beta = "The 'beta' parameter in 'smooth_mae' loss."
+    doc_property_name = "The names of fitting properties, which should be consistent with the property names in the dataset."
+    doc_property_dim = "The dimensions of fitting properties, which should be consistent with the property dimensions in the dataset."
     return [
         Argument(
             "loss_func",
@@ -2503,6 +2507,20 @@ def loss_property():
             default=1.00,
             doc=doc_beta,
         ),
+        Argument(
+            "property_name",
+            [str, list],
+            optional=True,
+            default="property",
+            doc=doc_property_name,
+        ),
+        Argument(
+            "property_dim",
+            [int, list],
+            optional=True,
+            default=1,
+            doc=doc_property_dim,
+        ),
     ]