[pre-commit.ci] pre-commit autoupdate

.pre-commit-config.yaml

@@ -30,7 +30,7 @@ repos:
       exclude: ^source/3rdparty
 -   repo: https://github.com/astral-sh/ruff-pre-commit
     # Ruff version.
-    rev: v0.2.2
+    rev: v0.3.2
     hooks:
     - id: ruff
       args: ["--fix"]
@@ -53,7 +53,7 @@ repos:
     -   id: blacken-docs
 # C++
 -   repo: https://github.com/pre-commit/mirrors-clang-format
-    rev: v17.0.6
+    rev: v18.1.1
     hooks:
     -   id: clang-format
         exclude: ^source/3rdparty|source/lib/src/gpu/cudart/.+\.inc

backend/dp_backend.py

@@ -1,5 +1,6 @@
 # SPDX-License-Identifier: LGPL-3.0-or-later
 """A PEP-517 backend to find TensorFlow."""
+
 from typing import (
     List,
 )

deepmd/__init__.py

@@ -7,6 +7,7 @@
 The top module (deepmd.__init__) should not import any third-party
 modules for performance.
 """
+
 try:
     from deepmd._version import version as __version__
 except ImportError:

deepmd/backend/__init__.py

@@ -3,6 +3,7 @@
 
 Avoid directly importing third-party libraries in this module for performance.
 """
+
 # copy from dpdata
 from importlib import (
     import_module,

deepmd/dpmodel/atomic_model/__init__.py

@@ -14,7 +14,6 @@
 
 """
 
-
 from .base_atomic_model import (
     BaseAtomicModel,
 )

deepmd/dpmodel/utils/network.py

@@ -3,6 +3,7 @@
 
 See issue #2982 for more information.
 """
+
 import copy
 import itertools
 import json

deepmd/dpmodel/utils/nlist.py

@@ -182,7 +182,7 @@ def build_multiple_neighbor_list(
     ret = {}
     for rc, ns in zip(rcuts[::-1], nsels[::-1]):
         tnlist_1 = np.copy(nlist0[:, :, :ns])
-        tnlist_1[rr[:, :, :ns] > rc] = int(-1)
+        tnlist_1[rr[:, :, :ns] > rc] = -1
         ret[get_multiple_nlist_key(rc, ns)] = tnlist_1
     return ret
 

deepmd/driver.py

@@ -1,5 +1,6 @@
 # SPDX-License-Identifier: LGPL-3.0-or-later
 """dpdata driver."""
+
 # Derived from https://github.com/deepmodeling/dpdata/blob/18a0ed5ebced8b1f6887038883d46f31ae9990a4/dpdata/plugins/deepmd.py#L361-L443
 # under LGPL-3.0-or-later license.
 # The original deepmd driver maintained in the dpdata package will be overriden.

deepmd/entrypoints/test.py

@@ -1,5 +1,6 @@
 # SPDX-License-Identifier: LGPL-3.0-or-later
 """Test trained DeePMD model."""
+
 import logging
 from pathlib import (
     Path,

deepmd/infer/model_devi.py

@@ -29,8 +29,7 @@ def calc_model_devi_f(
     real_f: Optional[np.ndarray] = None,
     relative: Optional[float] = None,
     atomic: Literal[False] = False,
-) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
-    ...
+) -> Tuple[np.ndarray, np.ndarray, np.ndarray]: ...
 
 
 @overload
@@ -40,8 +39,7 @@ def calc_model_devi_f(
     relative: Optional[float] = None,
     *,
     atomic: Literal[True],
-) -> Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
-    ...
+) -> Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]: ...
 
 
 def calc_model_devi_f(

deepmd/main.py

@@ -4,6 +4,7 @@
 If only printing the help message, this module does not call
 the main DeePMD-kit module to avoid the slow import of TensorFlow.
 """
+
 import argparse
 import logging
 import os

deepmd/pt/loss/tensor.py

@@ -127,9 +127,9 @@ def forward(self, model_pred, label, natoms, learning_rate=0.0, mae=False):
                 atom_num = natoms
                 l2_global_loss = torch.mean(torch.square(diff))
             if not self.inference:
-                more_loss[
-                    f"l2_global_{self.tensor_name}_loss"
-                ] = l2_global_loss.detach()
+                more_loss[f"l2_global_{self.tensor_name}_loss"] = (
+                    l2_global_loss.detach()
+                )
             loss += self.global_weight * l2_global_loss
             rmse_global = l2_global_loss.sqrt() / atom_num
             more_loss[f"rmse_global_{self.tensor_name}"] = rmse_global.detach()

deepmd/pt/model/task/fitting.py

@@ -118,8 +118,8 @@ def change_energy_bias(
             The number of test samples in a system to change the energy bias.
         """
         log.info(
-            "Changing energy bias in pretrained model for types {}... "
-            "(this step may take long time)".format(str(new_type_map))
+            f"Changing energy bias in pretrained model for types {new_type_map!s}... "
+            "(this step may take long time)"
         )
         # data
         systems = config["training"]["training_data"]["systems"]

deepmd/pt/utils/nlist.py

@@ -256,7 +256,7 @@ def build_multiple_neighbor_list(
     nlist0 = nlist
     ret = {}
     for rc, ns in zip(rcuts[::-1], nsels[::-1]):
-        nlist0 = nlist0[:, :, :ns].masked_fill(rr[:, :, :ns] > rc, int(-1))
+        nlist0 = nlist0[:, :, :ns].masked_fill(rr[:, :, :ns] > rc, -1)
         ret[get_multiple_nlist_key(rc, ns)] = nlist0
     return ret
 

deepmd/pt/utils/plugin.py

@@ -1,5 +1,6 @@
 # SPDX-License-Identifier: LGPL-3.0-or-later
 """Base of plugin systems."""
+
 from deepmd.utils.plugin import (
     Plugin,
     PluginVariant,

deepmd/pt/utils/utils.py

@@ -65,13 +65,11 @@ def forward(self, x: torch.Tensor) -> torch.Tensor:
 
 
 @overload
-def to_numpy_array(xx: torch.Tensor) -> np.ndarray:
-    ...
+def to_numpy_array(xx: torch.Tensor) -> np.ndarray: ...
 
 
 @overload
-def to_numpy_array(xx: None) -> None:
-    ...
+def to_numpy_array(xx: None) -> None: ...
 
 
 def to_numpy_array(
@@ -91,13 +89,11 @@ def to_numpy_array(
 
 
 @overload
-def to_torch_tensor(xx: np.ndarray) -> torch.Tensor:
-    ...
+def to_torch_tensor(xx: np.ndarray) -> torch.Tensor: ...
 
 
 @overload
-def to_torch_tensor(xx: None) -> None:
-    ...
+def to_torch_tensor(xx: None) -> None: ...
 
 
 def to_torch_tensor(

deepmd/tf/entrypoints/freeze.py

@@ -152,10 +152,8 @@ def _modify_model_suffix(output_graph_def, out_suffix, freeze_type):
             else:
                 jdata["training"]["training_data"] = {}
                 log.warning(
-                    "The fitting net {} has no training data in input script, resulting in "
-                    "untrained frozen model, and cannot be compressed directly! ".format(
-                        out_suffix
-                    )
+                    f"The fitting net {out_suffix} has no training data in input script, resulting in "
+                    "untrained frozen model, and cannot be compressed directly! "
                 )
             # loss
             if "loss_dict" in jdata:

deepmd/tf/entrypoints/ipi.py

@@ -1,5 +1,6 @@
 # SPDX-License-Identifier: LGPL-3.0-or-later
 """Use dp_ipi inside the Python package."""
+
 import os
 import subprocess
 import sys

deepmd/tf/env.py

@@ -376,20 +376,14 @@ def get_module(module_name: str) -> "ModuleType":
             if TF_VERSION != tf_py_version:
                 raise RuntimeError(
                     "The version of TensorFlow used to compile this "
-                    "deepmd-kit package is {}, but the version of TensorFlow "
-                    "runtime you are using is {}. These two versions are "
-                    "incompatible and thus an error is raised when loading {}. "
-                    "You need to install TensorFlow {}, or rebuild deepmd-kit "
-                    "against TensorFlow {}.\nIf you are using a wheel from "
+                    f"deepmd-kit package is {TF_VERSION}, but the version of TensorFlow "
+                    f"runtime you are using is {tf_py_version}. These two versions are "
+                    f"incompatible and thus an error is raised when loading {module_name}. "
+                    f"You need to install TensorFlow {TF_VERSION}, or rebuild deepmd-kit "
+                    f"against TensorFlow {tf_py_version}.\nIf you are using a wheel from "
                     "pypi, you may consider to install deepmd-kit execuating "
                     "`pip install deepmd-kit --no-binary deepmd-kit` "
-                    "instead.".format(
-                        TF_VERSION,
-                        tf_py_version,
-                        module_name,
-                        TF_VERSION,
-                        tf_py_version,
-                    )
+                    "instead."
                 ) from e
             error_message = (
                 "This deepmd-kit package is inconsitent with TensorFlow "

deepmd/tf/fit/fitting.py

@@ -246,9 +246,9 @@ def deserialize_network(cls, data: dict, suffix: str = "") -> dict:
                 fitting_net_variables[f"{layer_name}{key}{suffix}/matrix"] = layer.w
                 fitting_net_variables[f"{layer_name}{key}{suffix}/bias"] = layer.b
                 if layer.idt is not None:
-                    fitting_net_variables[
-                        f"{layer_name}{key}{suffix}/idt"
-                    ] = layer.idt.reshape(1, -1)
+                    fitting_net_variables[f"{layer_name}{key}{suffix}/idt"] = (
+                        layer.idt.reshape(1, -1)
+                    )
                 else:
                     # prevent keyError
                     fitting_net_variables[f"{layer_name}{key}{suffix}/idt"] = 0.0

deepmd/tf/lmp.py

@@ -1,5 +1,6 @@
 # SPDX-License-Identifier: LGPL-3.0-or-later
 """Register entry points for lammps-wheel."""
+
 import os
 import platform
 from importlib import (

deepmd/tf/loggers/loggers.py

@@ -1,5 +1,6 @@
 # SPDX-License-Identifier: LGPL-3.0-or-later
 """Alias of deepmd.loggers.loggers for backward compatibility."""
+
 from deepmd.loggers.loggers import (
     set_log_handles,
 )

deepmd/tf/model/model_stat.py

@@ -1,5 +1,6 @@
 # SPDX-License-Identifier: LGPL-3.0-or-later
 """Alias for backward compatibility."""
+
 from deepmd.utils.model_stat import (
     _make_all_stat_ref,
     make_stat_input,

deepmd/tf/model/multi.py

@@ -135,9 +135,9 @@ def __init__(
                 fitting_dict[item] = item_fitting_param
             else:
                 if item_fitting_param["type"] in ["dipole", "polar"]:
-                    item_fitting_param[
-                        "embedding_width"
-                    ] = self.descrpt.get_dim_rot_mat_1()
+                    item_fitting_param["embedding_width"] = (
+                        self.descrpt.get_dim_rot_mat_1()
+                    )
                 fitting_dict[item] = Fitting(
                     **item_fitting_param,
                     descrpt=self.descrpt,

deepmd/tf/nvnmd/utils/argcheck.py

@@ -1,5 +1,6 @@
 # SPDX-License-Identifier: LGPL-3.0-or-later
 """Alias for backward compatibility."""
+
 from deepmd.utils.argcheck_nvnmd import (
     nvnmd_args,
 )

deepmd/tf/op/_gelu.py

@@ -1,6 +1,7 @@
 #!/usr/bin/env python3
 # SPDX-License-Identifier: LGPL-3.0-or-later
 """First-order derivatives and second-order derivatives for gelu function."""
+
 import tensorflow
 from tensorflow.python.framework import (
     ops,

deepmd/tf/train/trainer.py

@@ -236,9 +236,7 @@ def build(self, data=None, stop_batch=0, origin_type_map=None, suffix=""):
                 if data[fitting_key].mixed_type:
                     assert isinstance(
                         self.fitting[fitting_key], EnerFitting
-                    ), "Data for fitting net {} in mixed_type format must use ener fitting!".format(
-                        fitting_key
-                    )
+                    ), f"Data for fitting net {fitting_key} in mixed_type format must use ener fitting!"
                 if self.numb_fparam_dict[fitting_key] > 0:
                     log.info(
                         "fitting net %s training with %d frame parameter(s)"
@@ -1086,10 +1084,7 @@ def _init_from_frz_model(self):
         except FileNotFoundError as e:
             # throw runtime error if there's no frozen model
             raise RuntimeError(
-                "The input frozen model {} ({}) does not exist! Please check the path of the frozen model. ".format(
-                    self.run_opt.init_frz_model,
-                    os.path.abspath(self.run_opt.init_frz_model),
-                )
+                f"The input frozen model {self.run_opt.init_frz_model} ({os.path.abspath(self.run_opt.init_frz_model)}) does not exist! Please check the path of the frozen model. "
             ) from e
         # get the model type from the frozen model(self.run_opt.init_frz_model)
         try:
@@ -1142,10 +1137,8 @@ def _init_from_pretrained_model(
         except FileNotFoundError as e:
             # throw runtime error if there's no frozen model
             raise RuntimeError(
-                "The input frozen pretrained model {} ({}) does not exist! "
-                "Please check the path of the frozen pretrained model. ".format(
-                    self.run_opt.finetune, os.path.abspath(self.run_opt.finetune)
-                )
+                f"The input frozen pretrained model {self.run_opt.finetune} ({os.path.abspath(self.run_opt.finetune)}) does not exist! "
+                "Please check the path of the frozen pretrained model. "
             ) from e
         # get the model type from the frozen model(self.run_opt.finetune)
         try:
@@ -1164,8 +1157,8 @@ def _init_from_pretrained_model(
         ), "Compressed models are not supported for finetuning!"
         self.model.init_variables(graph, graph_def, model_type=self.model_type)
         log.info(
-            "Changing energy bias in pretrained model for types {}... "
-            "(this step may take long time)".format(str(origin_type_map))
+            f"Changing energy bias in pretrained model for types {origin_type_map!s}... "
+            "(this step may take long time)"
         )
         self._change_energy_bias(
             data, self.run_opt.finetune, origin_type_map, bias_shift

deepmd/tf/utils/argcheck.py

@@ -1,5 +1,6 @@
 # SPDX-License-Identifier: LGPL-3.0-or-later
 """Alias for backward compatibility."""
+
 from deepmd.utils.argcheck import (
     gen_args,
     gen_doc,

deepmd/tf/utils/compat.py

@@ -1,5 +1,6 @@
 # SPDX-License-Identifier: LGPL-3.0-or-later
 """Alias for backward compatibility."""
+
 from deepmd.utils.compat import (
     convert_input_v0_v1,
     convert_input_v1_v2,

deepmd/tf/utils/data.py

@@ -1,5 +1,6 @@
 # SPDX-License-Identifier: LGPL-3.0-or-later
 """Alias for backward compatibility."""
+
 from deepmd.utils.data import (
     DeepmdData,
 )

deepmd/tf/utils/data_system.py

@@ -1,5 +1,6 @@
 # SPDX-License-Identifier: LGPL-3.0-or-later
 """Alias for backward compatibility."""
+
 from deepmd.utils.data_system import (
     DeepmdDataSystem,
     prob_sys_size_ext,

deepmd/tf/utils/finetune.py

@@ -63,9 +63,7 @@ def replace_model_params_with_pretrained_model(
     )
     if cur_type_map != pretrained_type_map:
         log.info(
-            "Change the type_map from {} to {}.".format(
-                str(cur_type_map), str(pretrained_type_map)
-            )
+            f"Change the type_map from {cur_type_map!s} to {pretrained_type_map!s}."
         )
         jdata["model"]["type_map"] = pretrained_type_map
 

deepmd/tf/utils/graph.py

@@ -308,13 +308,13 @@ def get_extra_embedding_net_variables_from_graph_def(
     extra_embedding_net_variables = {}
     for i in range(1, layer_size + 1):
         matrix_pattern = f"filter_type_all{suffix}/matrix_{i}{extra_suffix}"
-        extra_embedding_net_variables[
-            matrix_pattern
-        ] = get_variables_from_graph_def_as_numpy_array(graph_def, matrix_pattern)
+        extra_embedding_net_variables[matrix_pattern] = (
+            get_variables_from_graph_def_as_numpy_array(graph_def, matrix_pattern)
+        )
         bias_pattern = f"filter_type_all{suffix}/bias_{i}{extra_suffix}"
-        extra_embedding_net_variables[
-            bias_pattern
-        ] = get_variables_from_graph_def_as_numpy_array(graph_def, bias_pattern)
+        extra_embedding_net_variables[bias_pattern] = (
+            get_variables_from_graph_def_as_numpy_array(graph_def, bias_pattern)
+        )
     return extra_embedding_net_variables