fix(pt/dp): share params of repinit_three_body

deepmd/dpmodel/descriptor/dpa2.py

@@ -733,16 +733,24 @@ def set_stat_mean_and_stddev(
         stddev: List[np.ndarray],
     ) -> None:
         """Update mean and stddev for descriptor."""
-        for ii, descrpt in enumerate([self.repinit, self.repformers]):
+        descrpt_list = [self.repinit, self.repformers]
+        if self.use_three_body:
+            descrpt_list.append(self.repinit_three_body)
+        for ii, descrpt in enumerate(descrpt_list):
             descrpt.mean = mean[ii]
             descrpt.stddev = stddev[ii]
 
     def get_stat_mean_and_stddev(self) -> Tuple[List[np.ndarray], List[np.ndarray]]:
         """Get mean and stddev for descriptor."""
-        return [self.repinit.mean, self.repformers.mean], [
+        mean_list = [self.repinit.mean, self.repformers.mean]
+        stddev_list = [
             self.repinit.stddev,
             self.repformers.stddev,
         ]
+        if self.use_three_body:
+            mean_list.append(self.repinit_three_body.mean)
+            stddev_list.append(self.repinit_three_body.stddev)
+        return mean_list, stddev_list
 
     def call(
         self,

deepmd/pt/model/descriptor/dpa2.py

@@ -389,6 +389,10 @@ def share_params(self, base_class, shared_level, resume=False):
         if shared_level == 0:
             self._modules["type_embedding"] = base_class._modules["type_embedding"]
             self.repinit.share_params(base_class.repinit, 0, resume=resume)
+            if self.use_three_body:
+                self.repinit_three_body.share_params(
+                    base_class.repinit_three_body, 0, resume=resume
+                )
             self._modules["g1_shape_tranform"] = base_class._modules[
                 "g1_shape_tranform"
             ]
@@ -398,6 +402,10 @@ def share_params(self, base_class, shared_level, resume=False):
         elif shared_level == 1:
             self._modules["type_embedding"] = base_class._modules["type_embedding"]
             self.repinit.share_params(base_class.repinit, 0, resume=resume)
+            if self.use_three_body:
+                self.repinit_three_body.share_params(
+                    base_class.repinit_three_body, 0, resume=resume
+                )
         # shared_level: 2
         # share all parameters in type_embedding and repformers
         elif shared_level == 2:
@@ -499,7 +507,10 @@ def compute_input_stats(
             The path to the stat file.
 
         """
-        for ii, descrpt in enumerate([self.repinit, self.repformers]):
+        descrpt_list = [self.repinit, self.repformers]
+        if self.use_three_body:
+            descrpt_list.append(self.repinit_three_body)
+        for ii, descrpt in enumerate(descrpt_list):
             descrpt.compute_input_stats(merged, path)
 
     def set_stat_mean_and_stddev(
@@ -508,16 +519,24 @@ def set_stat_mean_and_stddev(
         stddev: List[torch.Tensor],
     ) -> None:
         """Update mean and stddev for descriptor."""
-        for ii, descrpt in enumerate([self.repinit, self.repformers]):
+        descrpt_list = [self.repinit, self.repformers]
+        if self.use_three_body:
+            descrpt_list.append(self.repinit_three_body)
+        for ii, descrpt in enumerate(descrpt_list):
             descrpt.mean = mean[ii]
             descrpt.stddev = stddev[ii]
 
     def get_stat_mean_and_stddev(self) -> Tuple[List[torch.Tensor], List[torch.Tensor]]:
         """Get mean and stddev for descriptor."""
-        return [self.repinit.mean, self.repformers.mean], [
+        mean_list = [self.repinit.mean, self.repformers.mean]
+        stddev_list = [
             self.repinit.stddev,
             self.repformers.stddev,
         ]
+        if self.use_three_body:
+            mean_list.append(self.repinit_three_body.mean)
+            stddev_list.append(self.repinit_three_body.stddev)
+        return mean_list, stddev_list
 
     def serialize(self) -> dict:
         repinit = self.repinit

source/tests/pt/model/test_permutation.py

@@ -164,6 +164,58 @@
     },
 }
 
+model_dpa2tebd = {
+    "type_map": ["O", "H", "B"],
+    "descriptor": {
+        "type": "dpa2",
+        "repinit": {
+            "rcut": 6.0,
+            "rcut_smth": 0.5,
+            "nsel": 100,
+            "neuron": [2, 4, 8],
+            "axis_neuron": 4,
+            "activation_function": "tanh",
+            "three_body_sel": 40,
+            "three_body_rcut": 4.0,
+            "three_body_rcut_smth": 3.5,
+            "use_three_body": True,
+        },
+        "repformer": {
+            "rcut": 4.0,
+            "rcut_smth": 0.5,
+            "nsel": 40,
+            "nlayers": 6,
+            "g1_dim": 8,
+            "g2_dim": 5,
+            "attn2_hidden": 3,
+            "attn2_nhead": 1,
+            "attn1_hidden": 5,
+            "attn1_nhead": 1,
+            "axis_neuron": 4,
+            "update_h2": False,
+            "update_g1_has_conv": True,
+            "update_g1_has_grrg": True,
+            "update_g1_has_drrd": True,
+            "update_g1_has_attn": False,
+            "update_g2_has_g1g1": False,
+            "update_g2_has_attn": True,
+            "update_style": "res_residual",
+            "update_residual": 0.01,
+            "update_residual_init": "norm",
+            "attn2_has_gate": True,
+            "use_sqrt_nnei": True,
+            "g1_out_conv": True,
+            "g1_out_mlp": True,
+        },
+        "add_tebd_to_repinit_out": False,
+    },
+    "fitting_net": {
+        "neuron": [24, 24],
+        "resnet_dt": True,
+        "seed": 1,
+    },
+}
+
 model_dpa1 = {
     "type_map": ["O", "H", "B"],
     "descriptor": {

source/tests/pt/test_multitask.py

@@ -31,6 +31,7 @@
 from .model.test_permutation import (
     model_dpa1,
     model_dpa2,
+    model_dpa2tebd,
     model_se_e2_a,
 )
 
@@ -300,5 +301,44 @@ def tearDown(self) -> None:
         MultiTaskTrainTest.tearDown(self)
 
 
+class TestMultiTaskDPA2Tebd(unittest.TestCase, MultiTaskTrainTest):
+    def setUp(self):
+        multitask_DPA2 = deepcopy(multitask_template)
+        multitask_DPA2["model"]["shared_dict"]["my_descriptor"] = model_dpa2tebd[
+            "descriptor"
+        ]
+        data_file = [str(Path(__file__).parent / "water/data/data_0")]
+        self.stat_files = "DPA2Tebd"
+        os.makedirs(self.stat_files, exist_ok=True)
+        self.config = multitask_DPA2
+        self.config["training"]["data_dict"]["model_1"]["training_data"]["systems"] = (
+            data_file
+        )
+        self.config["training"]["data_dict"]["model_1"]["validation_data"][
+            "systems"
+        ] = data_file
+        self.config["training"]["data_dict"]["model_1"]["stat_file"] = (
+            f"{self.stat_files}/model_1"
+        )
+        self.config["training"]["data_dict"]["model_2"]["training_data"]["systems"] = (
+            data_file
+        )
+        self.config["training"]["data_dict"]["model_2"]["validation_data"][
+            "systems"
+        ] = data_file
+        self.config["training"]["data_dict"]["model_2"]["stat_file"] = (
+            f"{self.stat_files}/model_2"
+        )
+        self.config["training"]["numb_steps"] = 1
+        self.config["training"]["save_freq"] = 1
+        self.origin_config = deepcopy(self.config)
+        self.config["model"], self.shared_links = preprocess_shared_params(
+            self.config["model"]
+        )
+
+    def tearDown(self) -> None:
+        MultiTaskTrainTest.tearDown(self)
+
+
 if __name__ == "__main__":
     unittest.main()