feat(pt): support use_aparam_as_mask for pt backend

deepmd/dpmodel/fitting/general_fitting.py

@@ -155,13 +155,15 @@ def __init__(
             self.fparam_inv_std = np.ones(self.numb_fparam)  # pylint: disable=no-explicit-dtype
         else:
             self.fparam_avg, self.fparam_inv_std = None, None
-        if self.numb_aparam > 0:
+        if self.numb_aparam > 0 and not self.use_aparam_as_mask:
             self.aparam_avg = np.zeros(self.numb_aparam)  # pylint: disable=no-explicit-dtype
             self.aparam_inv_std = np.ones(self.numb_aparam)  # pylint: disable=no-explicit-dtype
         else:
             self.aparam_avg, self.aparam_inv_std = None, None
         # init networks
-        in_dim = self.dim_descrpt + self.numb_fparam + self.numb_aparam
+        in_dim = self.dim_descrpt + self.numb_fparam
+        if not self.use_aparam_as_mask:
+            in_dim += self.numb_aparam
         self.nets = NetworkCollection(
             1 if not self.mixed_types else 0,
             self.ntypes,
@@ -389,7 +391,7 @@ def _call_common(
                     axis=-1,
                 )
         # check aparam dim, concate to input descriptor
-        if self.numb_aparam > 0:
+        if not self.use_aparam_as_mask and self.numb_aparam > 0:
             assert aparam is not None, "aparam should not be None"
             if aparam.shape[-1] != self.numb_aparam:
                 raise ValueError(

deepmd/dpmodel/fitting/invar_fitting.py

@@ -139,10 +139,6 @@ def __init__(
             raise NotImplementedError("tot_ener_zero is not implemented")
         if spin is not None:
             raise NotImplementedError("spin is not implemented")
-        if use_aparam_as_mask:
-            raise NotImplementedError("use_aparam_as_mask is not implemented")
-        if use_aparam_as_mask:
-            raise NotImplementedError("use_aparam_as_mask is not implemented")
         if layer_name is not None:
             raise NotImplementedError("layer_name is not implemented")
 

deepmd/pt/model/task/fitting.py

@@ -126,6 +126,8 @@ class GeneralFitting(Fitting):
         length as `ntypes` signaling if or not removing the vaccum contribution for the atom types in the list.
     type_map: list[str], Optional
         A list of strings. Give the name to each type of atoms.
+    use_aparam_as_mask: bool
+        If True, the aparam will not be used in fitting net for embedding.
     """
 
     def __init__(
@@ -147,6 +149,7 @@ def __init__(
         trainable: Union[bool, list[bool]] = True,
         remove_vaccum_contribution: Optional[list[bool]] = None,
         type_map: Optional[list[str]] = None,
+        use_aparam_as_mask: bool = False,
         **kwargs,
     ):
         super().__init__()
@@ -164,6 +167,7 @@ def __init__(
         self.rcond = rcond
         self.seed = seed
         self.type_map = type_map
+        self.use_aparam_as_mask = use_aparam_as_mask
         # order matters, should be place after the assignment of ntypes
         self.reinit_exclude(exclude_types)
         self.trainable = trainable
@@ -194,7 +198,7 @@ def __init__(
             )
         else:
             self.fparam_avg, self.fparam_inv_std = None, None
-        if self.numb_aparam > 0:
+        if not self.use_aparam_as_mask and self.numb_aparam > 0:
             self.register_buffer(
                 "aparam_avg",
                 torch.zeros(self.numb_aparam, dtype=self.prec, device=device),
@@ -206,7 +210,9 @@ def __init__(
         else:
             self.aparam_avg, self.aparam_inv_std = None, None
 
-        in_dim = self.dim_descrpt + self.numb_fparam + self.numb_aparam
+        in_dim = self.dim_descrpt + self.numb_fparam
+        if not self.use_aparam_as_mask:
+            in_dim += self.numb_aparam
 
         self.filter_layers = NetworkCollection(
             1 if not self.mixed_types else 0,
@@ -291,13 +297,12 @@ def serialize(self) -> dict:
             # "trainable": self.trainable ,
             # "atom_ener": self.atom_ener ,
             # "layer_name": self.layer_name ,
-            # "use_aparam_as_mask": self.use_aparam_as_mask ,
             # "spin": self.spin ,
             ## NOTICE:  not supported by far
             "tot_ener_zero": False,
             "trainable": [self.trainable] * (len(self.neuron) + 1),
             "layer_name": None,
-            "use_aparam_as_mask": False,
+            "use_aparam_as_mask": self.use_aparam_as_mask,
             "spin": None,
         }
 
@@ -439,7 +444,7 @@ def _forward_common(
                     dim=-1,
                 )
         # check aparam dim, concate to input descriptor
-        if self.numb_aparam > 0:
+        if not self.use_aparam_as_mask and self.numb_aparam > 0:
             assert aparam is not None, "aparam should not be None"
             assert self.aparam_avg is not None
             assert self.aparam_inv_std is not None

deepmd/pt/model/task/invar_fitting.py

@@ -77,7 +77,8 @@ class InvarFitting(GeneralFitting):
         The `set_davg_zero` key in the descrptor should be set.
     type_map: list[str], Optional
         A list of strings. Give the name to each type of atoms.
-
+    use_aparam_as_mask: bool
+        If True, the aparam will not be used in fitting net for embedding.
     """
 
     def __init__(
@@ -99,6 +100,7 @@ def __init__(
         exclude_types: list[int] = [],
         atom_ener: Optional[list[Optional[torch.Tensor]]] = None,
         type_map: Optional[list[str]] = None,
+        use_aparam_as_mask: bool = False,
         **kwargs,
     ):
         self.dim_out = dim_out
@@ -122,6 +124,7 @@ def __init__(
             if atom_ener is None or len([x for x in atom_ener if x is not None]) == 0
             else [x is not None for x in atom_ener],
             type_map=type_map,
+            use_aparam_as_mask=use_aparam_as_mask,
             **kwargs,
         )
 

deepmd/tf/fit/ener.py

@@ -340,7 +340,7 @@
                     self.fparam_std[ii] = protection
             self.fparam_inv_std = 1.0 / self.fparam_std
         # stat aparam
-        if self.numb_aparam > 0:
+        if self.numb_aparam > 0 and not self.use_aparam_as_mask:
             sys_sumv = []
             sys_sumv2 = []
             sys_sumn = []
@@ -384,7 +384,7 @@
             ext_fparam = tf.reshape(ext_fparam, [-1, self.numb_fparam])
             ext_fparam = tf.cast(ext_fparam, self.fitting_precision)
             layer = tf.concat([layer, ext_fparam], axis=1)
-        if aparam is not None:
+        if aparam is not None and not self.use_aparam_as_mask:
             ext_aparam = tf.slice(
                 aparam,
                 [0, start_index * self.numb_aparam],
@@ -505,7 +505,7 @@
                 self.fparam_avg = 0.0
             if self.fparam_inv_std is None:
                 self.fparam_inv_std = 1.0
-        if self.numb_aparam > 0:
+        if self.numb_aparam > 0 and not self.use_aparam_as_mask:
             if self.aparam_avg is None:
                 self.aparam_avg = 0.0
             if self.aparam_inv_std is None:
@@ -561,7 +561,7 @@
                     trainable=False,
                     initializer=tf.constant_initializer(self.fparam_inv_std),
                 )
-            if self.numb_aparam > 0:
+            if self.numb_aparam > 0 and not self.use_aparam_as_mask:
                 t_aparam_avg = tf.get_variable(
                     "t_aparam_avg",
                     self.numb_aparam,
@@ -602,12 +602,11 @@
             fparam = (fparam - t_fparam_avg) * t_fparam_istd
 
         aparam = None
-        if not self.use_aparam_as_mask:
-            if self.numb_aparam > 0:
-                aparam = input_dict["aparam"]
-                aparam = tf.reshape(aparam, [-1, self.numb_aparam])
-                aparam = (aparam - t_aparam_avg) * t_aparam_istd
-                aparam = tf.reshape(aparam, [-1, self.numb_aparam * natoms[0]])
+        if not self.use_aparam_as_mask and self.numb_aparam > 0:
+            aparam = input_dict["aparam"]
+            aparam = tf.reshape(aparam, [-1, self.numb_aparam])
+            aparam = (aparam - t_aparam_avg) * t_aparam_istd
+            aparam = tf.reshape(aparam, [-1, self.numb_aparam * natoms[0]])
 
         atype_nall = tf.reshape(atype, [-1, natoms[1]])
         self.atype_nloc = tf.slice(
@@ -783,7 +782,7 @@
             self.fparam_inv_std = get_tensor_by_name_from_graph(
                 graph, f"fitting_attr{suffix}/t_fparam_istd"
             )
-        if self.numb_aparam > 0:
+        if self.numb_aparam > 0 and not self.use_aparam_as_mask:
             self.aparam_avg = get_tensor_by_name_from_graph(
                 graph, f"fitting_attr{suffix}/t_aparam_avg"
             )
@@ -883,7 +882,7 @@
         if fitting.numb_fparam > 0:
             fitting.fparam_avg = data["@variables"]["fparam_avg"]
             fitting.fparam_inv_std = data["@variables"]["fparam_inv_std"]
-        if fitting.numb_aparam > 0:
+        if fitting.numb_aparam > 0 and not fitting.use_aparam_as_mask:
             fitting.aparam_avg = data["@variables"]["aparam_avg"]
             fitting.aparam_inv_std = data["@variables"]["aparam_inv_std"]
         return fitting
@@ -896,6 +895,9 @@
         dict
             The serialized data
         """
+        in_dim = self.dim_descrpt + self.numb_fparam
+        if not self.use_aparam_as_mask:
+            in_dim += self.numb_aparam
         data = {
             "@class": "Fitting",
             "type": "ener",
@@ -922,7 +924,7 @@
             "nets": self.serialize_network(
                 ntypes=self.ntypes,
                 ndim=0 if self.mixed_types else 1,
-                in_dim=self.dim_descrpt + self.numb_fparam + self.numb_aparam,
+                in_dim=in_dim,
                 neuron=self.n_neuron,
                 activation_function=self.activation_function_name,
                 resnet_dt=self.resnet_dt,

source/tests/consistent/fitting/common.py

@@ -18,7 +18,7 @@
 class FittingTest:
     """Useful utilities for descriptor tests."""
 
-    def build_tf_fitting(self, obj, inputs, natoms, atype, fparam, suffix):
+    def build_tf_fitting(self, obj, inputs, natoms, atype, fparam, aparam, suffix):
         t_inputs = tf.placeholder(GLOBAL_TF_FLOAT_PRECISION, [None], name="i_inputs")
         t_natoms = tf.placeholder(tf.int32, natoms.shape, name="i_natoms")
         t_atype = tf.placeholder(tf.int32, [None], name="i_atype")
@@ -30,6 +30,12 @@ def build_tf_fitting(self, obj, inputs, natoms, atype, fparam, suffix):
             )
             extras["fparam"] = t_fparam
             feed_dict[t_fparam] = fparam
+        if aparam is not None:
+            t_aparam = tf.placeholder(
+                GLOBAL_TF_FLOAT_PRECISION, [None, None], name="i_aparam"
+            )
+            extras["aparam"] = t_aparam
+            feed_dict[t_aparam] = aparam
         t_out = obj.build(
             t_inputs,
             t_natoms,

source/tests/consistent/fitting/test_dos.py

@@ -58,6 +58,7 @@
     ("float64", "float32"),  # precision
     (True, False),  # mixed_types
     (0, 1),  # numb_fparam
+    (0, 1),  # numb_aparam
     (10, 20),  # numb_dos
 )
 class TestDOS(CommonTest, FittingTest, unittest.TestCase):
@@ -68,13 +69,15 @@
             precision,
             mixed_types,
             numb_fparam,
+            numb_aparam,
             numb_dos,
         ) = self.param
         return {
             "neuron": [5, 5, 5],
             "resnet_dt": resnet_dt,
             "precision": precision,
             "numb_fparam": numb_fparam,
+            "numb_aparam": numb_aparam,
             "seed": 20240217,
             "numb_dos": numb_dos,
         }
@@ -86,6 +89,7 @@
             precision,
             mixed_types,
             numb_fparam,
+            numb_aparam,
             numb_dos,
         ) = self.param
         return CommonTest.skip_pt
@@ -115,6 +119,9 @@
         # inconsistent if not sorted
         self.atype.sort()
         self.fparam = -np.ones((1,), dtype=GLOBAL_NP_FLOAT_PRECISION)
+        self.aparam = np.zeros_like(
+            self.atype, dtype=GLOBAL_NP_FLOAT_PRECISION
+        ).reshape(-1, 1)
 
     @property
     def addtional_data(self) -> dict:
@@ -123,6 +130,7 @@
             precision,
             mixed_types,
             numb_fparam,
+            numb_aparam,
             numb_dos,
         ) = self.param
         return {
@@ -137,6 +145,7 @@
             precision,
             mixed_types,
             numb_fparam,
+            numb_aparam,
             numb_dos,
         ) = self.param
         return self.build_tf_fitting(
@@ -145,6 +154,7 @@
             self.natoms,
             self.atype,
             self.fparam if numb_fparam else None,
+            self.aparam if numb_aparam else None,
             suffix,
         )
 
@@ -154,6 +164,7 @@
             precision,
             mixed_types,
             numb_fparam,
+            numb_aparam,
             numb_dos,
         ) = self.param
         return (
@@ -163,6 +174,9 @@
                 fparam=torch.from_numpy(self.fparam).to(device=PT_DEVICE)
                 if numb_fparam
                 else None,
+                aparam=torch.from_numpy(self.aparam).to(device=PT_DEVICE)
+                if numb_aparam
+                else None,
             )["dos"]
             .detach()
             .cpu()
@@ -175,12 +189,14 @@
             precision,
             mixed_types,
             numb_fparam,
+            numb_aparam,
             numb_dos,
         ) = self.param
         return dp_obj(
             self.inputs,
             self.atype.reshape(1, -1),
             fparam=self.fparam if numb_fparam else None,
+            aparam=self.aparam if numb_aparam else None,
         )["dos"]
 
     def eval_jax(self, jax_obj: Any) -> Any:
@@ -189,13 +205,15 @@
             precision,
             mixed_types,
             numb_fparam,
+            numb_aparam,
             numb_dos,
         ) = self.param
         return np.asarray(
             jax_obj(
                 jnp.asarray(self.inputs),
                 jnp.asarray(self.atype.reshape(1, -1)),
                 fparam=jnp.asarray(self.fparam) if numb_fparam else None,
+                aparam=jnp.asarray(self.aparam) if numb_aparam else None,
             )["dos"]
         )
 
@@ -206,13 +224,15 @@
             precision,
             mixed_types,
             numb_fparam,
+            numb_aparam,
             numb_dos,
         ) = self.param
         return np.asarray(
             array_api_strict_obj(
                 array_api_strict.asarray(self.inputs),
                 array_api_strict.asarray(self.atype.reshape(1, -1)),
                 fparam=array_api_strict.asarray(self.fparam) if numb_fparam else None,
+                aparam=array_api_strict.asarray(self.aparam) if numb_aparam else None,
             )["dos"]
         )
 
@@ -230,6 +250,7 @@
             precision,
             mixed_types,
             numb_fparam,
+            numb_aparam,
             numb_dos,
         ) = self.param
         if precision == "float64":
@@ -247,6 +268,7 @@
             precision,
             mixed_types,
             numb_fparam,
+            numb_aparam,
             numb_dos,
         ) = self.param
         if precision == "float64":