feat(pt/tf): add bias changing param/interface

deepmd/main.py

@@ -659,6 +659,72 @@ def main_parser() -> argparse.ArgumentParser:
         help="treat all types as a single type. Used with se_atten descriptor.",
     )
 
+    # change_bias
+    parser_change_bias = subparsers.add_parser(
+        "change-bias",
+        parents=[parser_log],
+        help="(Supported backend: PyTorch) Change model out bias according to the input data.",
+        formatter_class=RawTextArgumentDefaultsHelpFormatter,
+        epilog=textwrap.dedent(
+            """\
+        examples:
+            dp change-bias model.pt -s data -n 10 -m change
+        """
+        ),
+    )
+    parser_change_bias.add_argument(
+        "INPUT", help="The input checkpoint file or frozen model file"
+    )
+    parser_change_bias_source = parser_change_bias.add_mutually_exclusive_group()
+    parser_change_bias_source.add_argument(
+        "-s",
+        "--system",
+        default=".",
+        type=str,
+        help="The system dir. Recursively detect systems in this directory",
+    )
+    parser_change_bias_source.add_argument(
+        "-b",
+        "--bias-value",
+        default=None,
+        type=float,
+        nargs="+",
+        help="The user defined value for each type in the type_map of the model, split with spaces.\n"
+        "For example, '-93.57 -187.1' for energy bias of two elements. "
+        "Only supports energy bias changing.",
+    )
+    parser_change_bias.add_argument(
+        "-n",
+        "--numb-batch",
+        default=0,
+        type=int,
+        help="The number of frames for bias changing in one data system. 0 means all data.",
+    )
+    parser_change_bias.add_argument(
+        "-m",
+        "--mode",
+        type=str,
+        default="change",
+        choices=["change", "set"],
+        help="The mode for changing energy bias: \n"
+        "change (default) : perform predictions using input model on target dataset, "
+        "and do least square on the errors to obtain the target shift as bias.\n"
+        "set : directly use the statistic bias in the target dataset.",
+    )
+    parser_change_bias.add_argument(
+        "-o",
+        "--output",
+        default=None,
+        type=str,
+        help="The model after changing bias.",
+    )
+    parser_change_bias.add_argument(
+        "--model-branch",
+        type=str,
+        default=None,
+        help="Model branch chosen for changing bias if multi-task model.",
+    )
+
     # --version
     parser.add_argument(
         "--version", action="version", version=f"DeePMD-kit v{__version__}"
@@ -831,6 +897,7 @@ def main():
         "convert-from",
         "train-nvnmd",
         "show",
+        "change-bias",
     ):
         deepmd_main = BACKENDS[args.backend]().entry_point_hook
     elif args.command is None:

deepmd/pt/entrypoints/main.py

@@ -1,5 +1,6 @@
 # SPDX-License-Identifier: LGPL-3.0-or-later
 import argparse
+import copy
 import json
 import logging
 import os
@@ -23,6 +24,9 @@
 from deepmd import (
     __version__,
 )
+from deepmd.common import (
+    expand_sys_str,
+)
 from deepmd.env import (
     GLOBAL_CONFIG,
 )
@@ -44,6 +48,9 @@
 from deepmd.pt.train import (
     training,
 )
+from deepmd.pt.train.wrapper import (
+    ModelWrapper,
+)
 from deepmd.pt.utils import (
     env,
 )
@@ -59,6 +66,12 @@
 from deepmd.pt.utils.multi_task import (
     preprocess_shared_params,
 )
+from deepmd.pt.utils.stat import (
+    make_stat_input,
+)
+from deepmd.pt.utils.utils import (
+    to_numpy_array,
+)
 from deepmd.utils.argcheck import (
     normalize,
 )
@@ -377,6 +390,128 @@
             log.info(f"The fitting_net parameter is {fitting_net}")
 
 
+def change_bias(FLAGS):
+    if FLAGS.INPUT.endswith(".pt"):
+        old_state_dict = torch.load(FLAGS.INPUT, map_location=env.DEVICE)
+        model_state_dict = copy.deepcopy(old_state_dict.get("model", old_state_dict))
+        model_params = model_state_dict["_extra_state"]["model_params"]
+    elif FLAGS.INPUT.endswith(".pth"):
+        old_model = torch.jit.load(FLAGS.INPUT, map_location=env.DEVICE)
+        model_params_string = old_model.get_model_def_script()
+        model_params = json.loads(model_params_string)
+        old_state_dict = old_model.state_dict()
+        model_state_dict = old_state_dict
+    else:
+        raise RuntimeError(
+            "The model provided must be a checkpoint file with a .pt extension "
+            "or a frozen model with a .pth extension"
+        )
+    multi_task = "model_dict" in model_params
+    model_branch = FLAGS.model_branch
+    bias_adjust_mode = (
+        "change-by-statistic" if FLAGS.mode == "change" else "set-by-statistic"
+    )
+    if multi_task:
+        assert (
+            model_branch is not None
+        ), "For multitask model, the model branch must be set!"
+        assert model_branch in model_params["model_dict"], (
+            f"For multitask model, the model branch must be in the 'model_dict'! "
+            f"Available options are : {list(model_params['model_dict'].keys())}."
+        )
+        log.info(f"Changing out bias for model {model_branch}.")
+    model = training.get_model_for_wrapper(model_params)
+    type_map = (
+        model_params["type_map"]
+        if not multi_task
+        else model_params["model_dict"][model_branch]["type_map"]
+    )
+    model_to_change = model if not multi_task else model[model_branch]
+    if FLAGS.INPUT.endswith(".pt"):
+        wrapper = ModelWrapper(model)
+        wrapper.load_state_dict(old_state_dict["model"])
+    else:
+        # for .pth
+        model.load_state_dict(old_state_dict)
+
+    if FLAGS.bias_value is not None:
+        # use user-defined bias
+        assert model_to_change.model_type in [
+            "ener"
+        ], "User-defined bias is only available for energy model!"
+        assert (
+            len(FLAGS.bias_value) == len(type_map)
+        ), f"The number of elements in the bias should be the same as that in the type_map: {type_map}."
+        old_bias = model_to_change.get_out_bias()
+        bias_to_set = torch.tensor(
+            FLAGS.bias_value, dtype=old_bias.dtype, device=old_bias.device
+        ).view(old_bias.shape)
+        model_to_change.set_out_bias(bias_to_set)
+        log.info(
+            f"Change output bias of {type_map!s} "
+            f"from {to_numpy_array(old_bias).reshape(-1)!s} "
+            f"to {to_numpy_array(bias_to_set).reshape(-1)!s}."
+        )
+        updated_model = model_to_change
+    else:
+        # calculate bias on given systems
+        data_systems = process_systems(expand_sys_str(FLAGS.system))
+        data_single = DpLoaderSet(
+            data_systems,
+            1,
+            type_map,
+        )
+        mock_loss = training.get_loss(
+            {"inference": True}, 1.0, len(type_map), model_to_change
+        )
+        data_requirement = mock_loss.label_requirement
+        data_requirement += training.get_additional_data_requirement(model_to_change)
+        data_single.add_data_requirement(data_requirement)
+        nbatches = FLAGS.numb_batch if FLAGS.numb_batch != 0 else float("inf")
+        sampled_data = make_stat_input(
+            data_single.systems,
+            data_single.dataloaders,
+            nbatches,
+        )
+        updated_model = training.model_change_out_bias(
+            model_to_change, sampled_data, _bias_adjust_mode=bias_adjust_mode
+        )
+
+    if not multi_task:
+        model = updated_model
+    else:
+        model[model_branch] = updated_model
+
+    if FLAGS.INPUT.endswith(".pt"):
+        output_path = (
+            FLAGS.output
+            if FLAGS.output is not None
+            else FLAGS.INPUT.replace(".pt", "_updated.pt")
+        )
+        wrapper = ModelWrapper(model)
+        if "model" in old_state_dict:
+            old_state_dict["model"] = wrapper.state_dict()
+            old_state_dict["model"]["_extra_state"] = model_state_dict["_extra_state"]
+        else:
+            old_state_dict = wrapper.state_dict()
+            old_state_dict["_extra_state"] = model_state_dict["_extra_state"]
+        torch.save(old_state_dict, output_path)
+    else:
+        # for .pth
+        output_path = (
+            FLAGS.output
+            if FLAGS.output is not None
+            else FLAGS.INPUT.replace(".pth", "_updated.pth")
+        )
+        model = torch.jit.script(model)
+        torch.jit.save(
+            model,
+            output_path,
+            {},
+        )
+    log.info(f"Saved model to {output_path}")
+
+
 @record
 def main(args: Optional[Union[List[str], argparse.Namespace]] = None):
     if not isinstance(args, argparse.Namespace):
@@ -401,6 +536,8 @@
         freeze(FLAGS)
     elif FLAGS.command == "show":
         show(FLAGS)
+    elif FLAGS.command == "change-bias":
+        change_bias(FLAGS)
     else:
         raise RuntimeError(f"Invalid command {FLAGS.command}!")
 

deepmd/pt/loss/ener.py

@@ -96,7 +96,7 @@ def __init__(
         self.has_v = (start_pref_v != 0.0 and limit_pref_v != 0.0) or inference
         self.has_ae = (start_pref_ae != 0.0 and limit_pref_ae != 0.0) or inference
         self.has_pf = (start_pref_pf != 0.0 and limit_pref_pf != 0.0) or inference
-        self.has_gf = (start_pref_gf != 0.0 and limit_pref_gf != 0.0) or inference
+        self.has_gf = start_pref_gf != 0.0 and limit_pref_gf != 0.0
 
         self.start_pref_e = start_pref_e
         self.limit_pref_e = limit_pref_e

deepmd/pt/model/atomic_model/base_atomic_model.py

@@ -103,6 +103,9 @@
         self.register_buffer("out_bias", out_bias_data)
         self.register_buffer("out_std", out_std_data)
 
+    def set_out_bias(self, out_bias: torch.Tensor) -> None:
+        self.out_bias = out_bias
+
     def __setitem__(self, key, value):
         if key in ["out_bias"]:
             self.out_bias = value

deepmd/pt/model/model/make_model.py

@@ -175,6 +175,9 @@
         def get_out_bias(self) -> torch.Tensor:
             return self.atomic_model.get_out_bias()
 
+        def set_out_bias(self, out_bias: torch.Tensor) -> None:
+            self.atomic_model.set_out_bias(out_bias)
+
         def change_out_bias(
             self,
             merged,