breaking: remove multi-task support in tf (#3763)

<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->
## Summary by CodeRabbit

- **New Features**
- Removed the `multi_task` parameter across various descriptor
initialization methods, streamlining the setup process.
- Introduced a new option `--head` for specifying a model branch to
freeze in multi-task mode.
- **Bug Fixes**
- Corrected initialization and training processes by removing outdated
multi-task functionalities.
- **Documentation**
- Updated guides on model freezing and training to reflect the removal
of multi-task functionalities and the shift towards using the PyTorch
backend.
- **Refactor**
- Eliminated redundant code and simplified parameter assignments in
training scripts.
- **Chores**
- Removed unused dictionaries and outdated code across several modules
to clean up the codebase.
<!-- end of auto-generated comment: release notes by coderabbit.ai -->

---------

Signed-off-by: Duo <50307526+iProzd@users.noreply.github.com>
Co-authored-by: coderabbitai[bot] <136622811+coderabbitai[bot]@users.noreply.github.com>

deepmd/dpmodel/descriptor/dpa1.py

@@ -245,7 +245,7 @@ def __init__(
         # consistent with argcheck, not used though
         seed: Optional[int] = None,
     ) -> None:
-        ## seed, uniform_seed, multi_task, not included.
+        ## seed, uniform_seed, not included.
         # Ensure compatibility with the deprecated stripped_type_embedding option.
         if stripped_type_embedding is not None:
             # Use the user-set stripped_type_embedding parameter first

deepmd/dpmodel/descriptor/se_e2_a.py

@@ -119,8 +119,6 @@ class DescrptSeA(NativeOP, BaseDescriptor):
             The activation function in the embedding net. Supported options are |ACTIVATION_FN|
     precision
             The precision of the embedding net parameters. Supported options are |PRECISION|
-    multi_task
-            If the model has multi fitting nets to train.
     spin
             The deepspin object.
 
@@ -159,7 +157,7 @@ def __init__(
         # consistent with argcheck, not used though
         seed: Optional[int] = None,
     ) -> None:
-        ## seed, uniform_seed, multi_task, not included.
+        ## seed, uniform_seed, not included.
         if spin is not None:
             raise NotImplementedError("spin is not implemented")
 

deepmd/dpmodel/descriptor/se_r.py

@@ -75,8 +75,6 @@ class DescrptSeR(NativeOP, BaseDescriptor):
             The activation function in the embedding net. Supported options are |ACTIVATION_FN|
     precision
             The precision of the embedding net parameters. Supported options are |PRECISION|
-    multi_task
-            If the model has multi fitting nets to train.
     spin
             The deepspin object.
 
@@ -114,7 +112,7 @@ def __init__(
         # consistent with argcheck, not used though
         seed: Optional[int] = None,
     ) -> None:
-        ## seed, uniform_seed, multi_task, not included.
+        ## seed, uniform_seed, not included.
         if not type_one_side:
             raise NotImplementedError("type_one_side == False not implemented")
         if spin is not None:

deepmd/main.py

@@ -323,12 +323,6 @@ def main_parser() -> argparse.ArgumentParser:
         default=None,
         help="(Supported backend: TensorFlow) the name of weight file (.npy), if set, save the model's weight into the file",
     )
-    parser_frz.add_argument(
-        "--united-model",
-        action="store_true",
-        default=False,
-        help="(Supported backend: TensorFlow) When in multi-task mode, freeze all nodes into one united model",
-    )
     parser_frz.add_argument(
         "--head",
         default=None,

deepmd/tf/descriptor/hybrid.py

@@ -46,7 +46,6 @@ class DescrptHybrid(Descriptor):
     def __init__(
         self,
         list: List[Union[Descriptor, Dict[str, Any]]],
-        multi_task: bool = False,
         ntypes: Optional[int] = None,
         spin: Optional[Spin] = None,
         **kwargs,
@@ -59,13 +58,12 @@ def __init__(
                 "cannot build descriptor from an empty list of descriptors."
             )
         formatted_descript_list = []
-        self.multi_task = multi_task
         for ii in descrpt_list:
             if isinstance(ii, Descriptor):
                 formatted_descript_list.append(ii)
             elif isinstance(ii, dict):
                 formatted_descript_list.append(
-                    Descriptor(**ii, ntypes=ntypes, spin=spin, multi_task=multi_task)
+                    Descriptor(**ii, ntypes=ntypes, spin=spin)
                 )
             else:
                 raise NotImplementedError

deepmd/tf/descriptor/se_a.py

@@ -152,8 +152,6 @@ class DescrptSeA(DescrptSe):
             The precision of the embedding net parameters. Supported options are |PRECISION|
     uniform_seed
             Only for the purpose of backward compatibility, retrieves the old behavior of using the random seed
-    multi_task
-            If the model has multi fitting nets to train.
     env_protection: float
             Protection parameter to prevent division by zero errors during environment matrix calculations.
 
@@ -181,7 +179,6 @@ def __init__(
         activation_function: str = "tanh",
         precision: str = "default",
         uniform_seed: bool = False,
-        multi_task: bool = False,
         spin: Optional[Spin] = None,
         tebd_input_mode: str = "concat",
         env_protection: float = 0.0,  # not implement!!
@@ -304,15 +301,6 @@ def __init__(
                 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
-        if multi_task:
-            self.stat_dict = {
-                "sumr": [],
-                "suma": [],
-                "sumn": [],
-                "sumr2": [],
-                "suma2": [],
-            }
 
     def get_rcut(self) -> float:
         """Returns the cut-off radius."""
@@ -392,21 +380,14 @@ def compute_input_stats(
                 sumn.append(sysn)
                 sumr2.append(sysr2)
                 suma2.append(sysa2)
-            if not self.multi_task:
-                stat_dict = {
-                    "sumr": sumr,
-                    "suma": suma,
-                    "sumn": sumn,
-                    "sumr2": sumr2,
-                    "suma2": suma2,
-                }
-                self.merge_input_stats(stat_dict)
-            else:
-                self.stat_dict["sumr"] += sumr
-                self.stat_dict["suma"] += suma
-                self.stat_dict["sumn"] += sumn
-                self.stat_dict["sumr2"] += sumr2
-                self.stat_dict["suma2"] += suma2
+            stat_dict = {
+                "sumr": sumr,
+                "suma": suma,
+                "sumn": sumn,
+                "sumr2": sumr2,
+                "suma2": suma2,
+            }
+            self.merge_input_stats(stat_dict)
 
     def merge_input_stats(self, stat_dict):
         """Merge the statisitcs computed from compute_input_stats to obtain the self.davg and self.dstd.

deepmd/tf/descriptor/se_a_ebd_v2.py

@@ -43,7 +43,6 @@ def __init__(
         activation_function: str = "tanh",
         precision: str = "default",
         uniform_seed: bool = False,
-        multi_task: bool = False,
         spin: Optional[Spin] = None,
         **kwargs,
     ) -> None:
@@ -63,7 +62,6 @@ def __init__(
             activation_function=activation_function,
             precision=precision,
             uniform_seed=uniform_seed,
-            multi_task=multi_task,
             spin=spin,
             tebd_input_mode="strip",
             **kwargs,

deepmd/tf/descriptor/se_atten.py

@@ -142,8 +142,6 @@ class DescrptSeAtten(DescrptSeA):
             Whether to mask the diagonal in the attention weights.
     ln_eps: float, Optional
             The epsilon value for layer normalization.
-    multi_task: bool
-            If the model has multi fitting nets to train.
     tebd_input_mode: str
             The input mode of the type embedding. Supported modes are ["concat", "strip"].
             - "concat": Concatenate the type embedding with the smoothed radial information as the union input for the embedding network.
@@ -188,7 +186,6 @@ def __init__(
         attn_layer: int = 2,
         attn_dotr: bool = True,
         attn_mask: bool = False,
-        multi_task: bool = False,
         smooth_type_embedding: bool = False,
         tebd_input_mode: str = "concat",
         # not implemented
@@ -246,7 +243,6 @@ def __init__(
             activation_function=activation_function,
             precision=precision,
             uniform_seed=uniform_seed,
-            multi_task=multi_task,
         )
         """
         Constructor
@@ -403,21 +399,14 @@ def compute_input_stats(
                     sumn.append(sysn)
                     sumr2.append(sysr2)
                     suma2.append(sysa2)
-            if not self.multi_task:
-                stat_dict = {
-                    "sumr": sumr,
-                    "suma": suma,
-                    "sumn": sumn,
-                    "sumr2": sumr2,
-                    "suma2": suma2,
-                }
-                self.merge_input_stats(stat_dict)
-            else:
-                self.stat_dict["sumr"] += sumr
-                self.stat_dict["suma"] += suma
-                self.stat_dict["sumn"] += sumn
-                self.stat_dict["sumr2"] += sumr2
-                self.stat_dict["suma2"] += suma2
+            stat_dict = {
+                "sumr": sumr,
+                "suma": suma,
+                "sumn": sumn,
+                "sumr2": sumr2,
+                "suma2": suma2,
+            }
+            self.merge_input_stats(stat_dict)
 
     def enable_compression(
         self,
@@ -2117,7 +2106,6 @@ def __init__(
             attn_layer=attn_layer,
             attn_dotr=attn_dotr,
             attn_mask=attn_mask,
-            multi_task=True,
             trainable_ln=trainable_ln,
             ln_eps=ln_eps,
             smooth_type_embedding=smooth_type_embedding,

deepmd/tf/descriptor/se_atten_v2.py

@@ -59,8 +59,6 @@ class DescrptSeAttenV2(DescrptSeAtten):
             Whether to dot the relative coordinates on the attention weights as a gated scheme.
     attn_mask
             Whether to mask the diagonal in the attention weights.
-    multi_task
-            If the model has multi fitting nets to train.
     """
 
     def __init__(
@@ -84,7 +82,6 @@ def __init__(
         attn_layer: int = 2,
         attn_dotr: bool = True,
         attn_mask: bool = False,
-        multi_task: bool = False,
         **kwargs,
     ) -> None:
         DescrptSeAtten.__init__(
@@ -108,7 +105,6 @@ def __init__(
             attn_layer=attn_layer,
             attn_dotr=attn_dotr,
             attn_mask=attn_mask,
-            multi_task=multi_task,
             tebd_input_mode="strip",
             smooth_type_embedding=True,
             **kwargs,

deepmd/tf/descriptor/se_r.py

@@ -102,7 +102,6 @@ def __init__(
         activation_function: str = "tanh",
         precision: str = "default",
         uniform_seed: bool = False,
-        multi_task: bool = False,
         spin: Optional[Spin] = None,
         env_protection: float = 0.0,  # not implement!!
         **kwargs,
@@ -211,9 +210,6 @@ def __init__(
             self.sub_sess = tf.Session(
                 graph=sub_graph, config=default_tf_session_config
             )
-        self.multi_task = multi_task
-        if multi_task:
-            self.stat_dict = {"sumr": [], "sumn": [], "sumr2": []}
 
     def get_rcut(self):
         """Returns the cut-off radius."""
@@ -282,13 +278,8 @@ def compute_input_stats(
             sumr.append(sysr)
             sumn.append(sysn)
             sumr2.append(sysr2)
-        if not self.multi_task:
-            stat_dict = {"sumr": sumr, "sumn": sumn, "sumr2": sumr2}
-            self.merge_input_stats(stat_dict)
-        else:
-            self.stat_dict["sumr"] += sumr
-            self.stat_dict["sumn"] += sumn
-            self.stat_dict["sumr2"] += sumr2
+        stat_dict = {"sumr": sumr, "sumn": sumn, "sumr2": sumr2}
+        self.merge_input_stats(stat_dict)
 
     def merge_input_stats(self, stat_dict):
         """Merge the statisitcs computed from compute_input_stats to obtain the self.davg and self.dstd.

deepmd/tf/descriptor/se_t.py

@@ -90,7 +90,6 @@ def __init__(
         activation_function: str = "tanh",
         precision: str = "default",
         uniform_seed: bool = False,
-        multi_task: bool = False,
         **kwargs,
     ) -> None:
         """Constructor."""
@@ -172,15 +171,6 @@ def __init__(
                 sel_r=self.sel_r,
             )
         self.sub_sess = tf.Session(graph=sub_graph, config=default_tf_session_config)
-        self.multi_task = multi_task
-        if multi_task:
-            self.stat_dict = {
-                "sumr": [],
-                "suma": [],
-                "sumn": [],
-                "sumr2": [],
-                "suma2": [],
-            }
 
     def get_rcut(self) -> float:
         """Returns the cut-off radius."""
@@ -256,21 +246,14 @@ def compute_input_stats(
                 sumn.append(sysn)
                 sumr2.append(sysr2)
                 suma2.append(sysa2)
-            if not self.multi_task:
-                stat_dict = {
-                    "sumr": sumr,
-                    "suma": suma,
-                    "sumn": sumn,
-                    "sumr2": sumr2,
-                    "suma2": suma2,
-                }
-                self.merge_input_stats(stat_dict)
-            else:
-                self.stat_dict["sumr"] += sumr
-                self.stat_dict["suma"] += suma
-                self.stat_dict["sumn"] += sumn
-                self.stat_dict["sumr2"] += sumr2
-                self.stat_dict["suma2"] += suma2
+            stat_dict = {
+                "sumr": sumr,
+                "suma": suma,
+                "sumn": sumn,
+                "sumr2": sumr2,
+                "suma2": suma2,
+            }
+            self.merge_input_stats(stat_dict)
 
     def merge_input_stats(self, stat_dict):
         """Merge the statisitcs computed from compute_input_stats to obtain the self.davg and self.dstd.