Merge branch 'devel' into feat/linear-jit

deepmd/pt/utils/env_mat_stat.py

@@ -146,6 +146,9 @@ def iter(
                 radial_only,
                 protection=self.descriptor.env_protection,
             )
+            # apply excluded_types
+            exclude_mask = self.descriptor.emask(nlist, extended_atype)
+            env_mat *= exclude_mask.unsqueeze(-1)
             # reshape to nframes * nloc at the atom level,
             # so nframes/mixed_type do not matter
             env_mat = env_mat.view(

deepmd/tf/descriptor/descriptor.py

@@ -7,6 +7,7 @@
     Dict,
     List,
     Optional,
+    Set,
     Tuple,
 )
 
@@ -357,7 +358,7 @@ def pass_tensors_from_frz_model(
 
     def build_type_exclude_mask(
         self,
-        exclude_types: List[Tuple[int, int]],
+        exclude_types: Set[Tuple[int, int]],
         ntypes: int,
         sel: List[int],
         ndescrpt: int,

deepmd/tf/descriptor/se_a.py

@@ -288,6 +288,18 @@ def __init__(
                 sel_a=self.sel_a,
                 sel_r=self.sel_r,
             )
+            if len(self.exclude_types):
+                # exclude types applied to data stat
+                mask = self.build_type_exclude_mask(
+                    self.exclude_types,
+                    self.ntypes,
+                    self.sel_a,
+                    self.ndescrpt,
+                    # for data stat, nloc == nall
+                    self.place_holders["type"],
+                    tf.size(self.place_holders["type"]),
+                )
+                self.stat_descrpt *= tf.reshape(mask, tf.shape(self.stat_descrpt))
         self.sub_sess = tf.Session(graph=sub_graph, config=default_tf_session_config)
         self.original_sel = None
         self.multi_task = multi_task

deepmd/tf/descriptor/se_atten.py

@@ -4,6 +4,7 @@
 from typing import (
     List,
     Optional,
+    Set,
     Tuple,
 )
 
@@ -282,6 +283,19 @@ def __init__(
                 sel_a=self.sel_all_a,
                 sel_r=self.sel_all_r,
             )
+            if len(self.exclude_types):
+                # exclude types applied to data stat
+                mask = self.build_type_exclude_mask_mixed(
+                    self.exclude_types,
+                    self.ntypes,
+                    self.sel_a,
+                    self.ndescrpt,
+                    # for data stat, nloc == nall
+                    self.place_holders["type"],
+                    tf.size(self.place_holders["type"]),
+                    self.nei_type_vec_t,  # extra input for atten
+                )
+                self.stat_descrpt *= tf.reshape(mask, tf.shape(self.stat_descrpt))
         self.sub_sess = tf.Session(graph=sub_graph, config=default_tf_session_config)
 
     def compute_input_stats(
@@ -672,7 +686,7 @@ def _pass_filter(
         inputs_i = tf.reshape(inputs_i, [-1, self.ndescrpt])
         type_i = -1
         if len(self.exclude_types):
-            mask = self.build_type_exclude_mask(
+            mask = self.build_type_exclude_mask_mixed(
                 self.exclude_types,
                 self.ntypes,
                 self.sel_a,
@@ -1367,9 +1381,9 @@ def init_variables(
                 )
             )
 
-    def build_type_exclude_mask(
+    def build_type_exclude_mask_mixed(
         self,
-        exclude_types: List[Tuple[int, int]],
+        exclude_types: Set[Tuple[int, int]],
         ntypes: int,
         sel: List[int],
         ndescrpt: int,

deepmd/tf/descriptor/se_r.py

@@ -196,6 +196,18 @@ def __init__(
                 rcut_smth=self.rcut_smth,
                 sel=self.sel_r,
             )
+            if len(self.exclude_types):
+                # exclude types applied to data stat
+                mask = self.build_type_exclude_mask(
+                    self.exclude_types,
+                    self.ntypes,
+                    self.sel_r,
+                    self.ndescrpt,
+                    # for data stat, nloc == nall
+                    self.place_holders["type"],
+                    tf.size(self.place_holders["type"]),
+                )
+                self.stat_descrpt *= tf.reshape(mask, tf.shape(self.stat_descrpt))
             self.sub_sess = tf.Session(
                 graph=sub_graph, config=default_tf_session_config
             )

source/tests/pt/test_stat.py

@@ -337,6 +337,44 @@ def tf_compute_input_stats(self):
         )
 
 
+class TestExcludeTypes(DatasetTest, unittest.TestCase):
+    def setup_data(self):
+        original_data = str(Path(__file__).parent / "water/data/data_0")
+        picked_data = str(Path(__file__).parent / "picked_data_for_test_stat")
+        dpdata.LabeledSystem(original_data, fmt="deepmd/npy")[:2].to_deepmd_npy(
+            picked_data
+        )
+        self.mixed_type = False
+        return picked_data
+
+    def setup_tf(self):
+        return DescrptSeA_tf(
+            rcut=self.rcut,
+            rcut_smth=self.rcut_smth,
+            sel=self.sel,
+            neuron=self.filter_neuron,
+            axis_neuron=self.axis_neuron,
+            exclude_types=[[0, 0], [1, 1]],
+        )
+
+    def setup_pt(self):
+        return DescrptSeA(
+            self.rcut,
+            self.rcut_smth,
+            self.sel,
+            self.filter_neuron,
+            self.axis_neuron,
+            exclude_types=[[0, 0], [1, 1]],
+        ).sea  # get the block who has stat as private vars
+
+    def tf_compute_input_stats(self):
+        coord = self.dp_merged["coord"]
+        atype = self.dp_merged["type"]
+        natoms = self.dp_merged["natoms_vec"]
+        box = self.dp_merged["box"]
+        self.dp_d.compute_input_stats(coord, box, atype, natoms, self.dp_mesh, {})
+
+
 class TestOutputStat(unittest.TestCase):
     def setUp(self):
         self.data_file = [str(Path(__file__).parent / "water/data/data_0")]