[pre-commit.ci] pre-commit autoupdate (deepmodeling#3454)

<!--pre-commit.ci start-->
updates:
- [github.com/astral-sh/ruff-pre-commit: v0.2.2 →
v0.3.2](astral-sh/[email protected])
- [github.com/pre-commit/mirrors-clang-format: v17.0.6 →
v18.1.1](pre-commit/[email protected])
<!--pre-commit.ci end-->

---------

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
(cherry picked from commit da68686)
Signed-off-by: Jinzhe Zeng <[email protected]>

Author: njzjz

.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/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/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/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/env.py

@@ -423,20 +423,14 @@ 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/fit/ener.py

@@ -895,9 +895,7 @@ def change_energy_bias(
         else:
             raise RuntimeError("Unknown bias_shift mode: " + bias_shift)
         log.info(
-            "Change energy bias of {} from {} to {}.".format(
-                str(origin_type_map), str(old_bias), str(self.bias_atom_e[idx_type_map])
-            )
+            f"Change energy bias of {origin_type_map!s} from {old_bias!s} to {self.bias_atom_e[idx_type_map]!s}."
         )
 
     def enable_mixed_precision(self, mixed_prec: Optional[dict] = None) -> None:

deepmd/infer/model_devi.py

@@ -33,8 +33,7 @@ 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
@@ -44,8 +43,7 @@ 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/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/loggers/loggers.py

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

deepmd/model/model_stat.py

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

deepmd/nvnmd/utils/argcheck.py

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

deepmd/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/train/trainer.py

@@ -231,9 +231,7 @@ 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)"
@@ -1057,10 +1055,7 @@ 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:
@@ -1113,10 +1108,8 @@ 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:
@@ -1135,8 +1128,8 @@ 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/utils/argcheck.py

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

deepmd/utils/compat.py

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

deepmd/utils/data.py

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

deepmd/utils/data_system.py

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

deepmd/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/utils/graph.py

@@ -312,13 +312,13 @@ 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
 
 

deepmd/utils/multi_init.py

@@ -59,9 +59,7 @@ def replace_model_params_with_frz_multi_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/utils/pair_tab.py

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

deepmd/utils/path.py

@@ -1,5 +1,6 @@
 # SPDX-License-Identifier: LGPL-3.0-or-later
 """Alias for backward compatibility."""
+
 from deepmd_utils.utils.path import (
     DPH5Path,
     DPOSPath,

deepmd/utils/plugin.py

@@ -1,5 +1,6 @@
 # SPDX-License-Identifier: LGPL-3.0-or-later
 """Alias for backward compatibility."""
+
 from deepmd_utils.utils.plugin import (
     Plugin,
     PluginVariant,

deepmd/utils/random.py

@@ -1,5 +1,6 @@
 # SPDX-License-Identifier: LGPL-3.0-or-later
 """Alias for backward compatibility."""
+
 from deepmd_utils.utils.random import (
     choice,
     random,

deepmd/utils/weight_avg.py

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

deepmd_utils/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 textwrap

deepmd_utils/model_format/network.py

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

deepmd_utils/utils/argcheck.py

@@ -1870,10 +1870,10 @@ def normalize_multi_task(data):
                 data["model"]["fitting_net_dict"].keys(), data["learning_rate_dict"]
             )
         elif single_learning_rate:
-            data[
-                "learning_rate_dict"
-            ] = normalize_learning_rate_dict_with_single_learning_rate(
-                data["model"]["fitting_net_dict"].keys(), data["learning_rate"]
+            data["learning_rate_dict"] = (
+                normalize_learning_rate_dict_with_single_learning_rate(
+                    data["model"]["fitting_net_dict"].keys(), data["learning_rate"]
+                )
             )
         fitting_weight = (
             data["training"]["fitting_weight"] if multi_fitting_weight else None
@@ -1916,11 +1916,7 @@ def normalize_data_dict(data_dict):
 def normalize_loss_dict(fitting_keys, loss_dict):
     # check the loss dict
     failed_loss_keys = [item for item in loss_dict if item not in fitting_keys]
-    assert (
-        not failed_loss_keys
-    ), "Loss dict key(s) {} not have corresponding fitting keys in {}! ".format(
-        str(failed_loss_keys), str(list(fitting_keys))
-    )
+    assert not failed_loss_keys, f"Loss dict key(s) {failed_loss_keys!s} not have corresponding fitting keys in {list(fitting_keys)!s}! "
     new_dict = {}
     base = Argument("base", dict, [], [loss_variant_type_args()], doc="")
     for item in loss_dict:
@@ -1935,9 +1931,7 @@ def normalize_learning_rate_dict(fitting_keys, learning_rate_dict):
     failed_learning_rate_keys = [
         item for item in learning_rate_dict if item not in fitting_keys
     ]
-    assert not failed_learning_rate_keys, "Learning rate dict key(s) {} not have corresponding fitting keys in {}! ".format(
-        str(failed_learning_rate_keys), str(list(fitting_keys))
-    )
+    assert not failed_learning_rate_keys, f"Learning rate dict key(s) {failed_learning_rate_keys!s} not have corresponding fitting keys in {list(fitting_keys)!s}! "
     new_dict = {}
     base = Argument("base", dict, [], [learning_rate_variant_type_args()], doc="")
     for item in learning_rate_dict:
@@ -1960,11 +1954,7 @@ def normalize_learning_rate_dict_with_single_learning_rate(fitting_keys, learnin
 def normalize_fitting_weight(fitting_keys, data_keys, fitting_weight=None):
     # check the mapping
     failed_data_keys = [item for item in data_keys if item not in fitting_keys]
-    assert (
-        not failed_data_keys
-    ), "Data dict key(s) {} not have corresponding fitting keys in {}! ".format(
-        str(failed_data_keys), str(list(fitting_keys))
-    )
+    assert not failed_data_keys, f"Data dict key(s) {failed_data_keys!s} not have corresponding fitting keys in {list(fitting_keys)!s}! "
     empty_fitting_keys = []
     valid_fitting_keys = []
     for item in fitting_keys:
@@ -1974,9 +1964,7 @@ def normalize_fitting_weight(fitting_keys, data_keys, fitting_weight=None):
             valid_fitting_keys.append(item)
     if empty_fitting_keys:
         log.warning(
-            "Fitting net(s) {} have no data and will not be used in training.".format(
-                str(empty_fitting_keys)
-            )
+            f"Fitting net(s) {empty_fitting_keys!s} have no data and will not be used in training."
         )
     num_pair = len(valid_fitting_keys)
     assert num_pair > 0, "No valid training data systems for fitting nets!"
@@ -1991,9 +1979,7 @@ def normalize_fitting_weight(fitting_keys, data_keys, fitting_weight=None):
         failed_weight_keys = [
             item for item in fitting_weight if item not in fitting_keys
         ]
-        assert not failed_weight_keys, "Fitting weight key(s) {} not have corresponding fitting keys in {}! ".format(
-            str(failed_weight_keys), str(list(fitting_keys))
-        )
+        assert not failed_weight_keys, f"Fitting weight key(s) {failed_weight_keys!s} not have corresponding fitting keys in {list(fitting_keys)!s}! "
         sum_prob = 0.0
         for item in fitting_keys:
             if item in valid_fitting_keys: