[pre-commit.ci] pre-commit autoupdate (#3720)

<!--pre-commit.ci start-->
updates:
- [github.com/astral-sh/ruff-pre-commit: v0.4.1 →
v0.4.2](astral-sh/ruff-pre-commit@v0.4.1...v0.4.2)
<!--pre-commit.ci end-->

---------

Signed-off-by: Jinzhe Zeng <jinzhe.zeng@rutgers.edu>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Jinzhe Zeng <jinzhe.zeng@rutgers.edu>

.pre-commit-config.yaml

@@ -29,7 +29,7 @@ repos:
         exclude: ^source/3rdparty
   - repo: https://github.com/astral-sh/ruff-pre-commit
     # Ruff version.
-    rev: v0.4.1
+    rev: v0.4.2
     hooks:
       - id: ruff
         args: ["--fix"]

backend/read_env.py

@@ -63,7 +63,7 @@ def get_argument_from_env() -> Tuple[str, list, list, dict, str]:
         if hipcc_flags is not None:
             os.environ["HIPFLAGS"] = os.environ.get("HIPFLAGS", "") + " " + hipcc_flags
     else:
-        raise RuntimeError("Unsupported DP_VARIANT option: %s" % dp_variant)
+        raise RuntimeError(f"Unsupported DP_VARIANT option: {dp_variant}")
 
     if os.environ.get("DP_BUILD_TESTING", "0") == "1":
         cmake_args.append("-DBUILD_TESTING:BOOL=TRUE")

data/raw/copy_raw.py

@@ -85,7 +85,7 @@ def _main():
     )
     args = parser.parse_args()
 
-    print("# copy the system by %s copies" % args.ncopies)  # noqa: T201
+    print(f"# copy the system by {args.ncopies} copies")  # noqa: T201
     assert np.all(
         np.array(args.ncopies, dtype=int) >= np.array([1, 1, 1], dtype=int)
     ), "number of copies should be larger than or equal to 1"

deepmd/dpmodel/descriptor/make_base_descriptor.py

@@ -135,7 +135,7 @@ def deserialize(cls, data: dict) -> "BD":
             """
             if cls is BD:
                 return BD.get_class_by_type(data["type"]).deserialize(data)
-            raise NotImplementedError("Not implemented in class %s" % cls.__name__)
+            raise NotImplementedError(f"Not implemented in class {cls.__name__}")
 
         @classmethod
         @abstractmethod

deepmd/dpmodel/fitting/make_base_fitting.py

@@ -88,7 +88,7 @@ def deserialize(cls, data: dict) -> "BF":
             """
             if cls is BF:
                 return BF.get_class_by_type(data["type"]).deserialize(data)
-            raise NotImplementedError("Not implemented in class %s" % cls.__name__)
+            raise NotImplementedError(f"Not implemented in class {cls.__name__}")
 
     setattr(BF, fwd_method_name, BF.fwd)
     delattr(BF, "fwd")

deepmd/dpmodel/model/base_model.py

@@ -126,7 +126,7 @@ def deserialize(cls, data: dict) -> "BaseBaseModel":
                 if model_type == "standard":
                     model_type = data.get("fitting", {}).get("type", "ener")
                 return cls.get_class_by_type(model_type).deserialize(data)
-            raise NotImplementedError("Not implemented in class %s" % cls.__name__)
+            raise NotImplementedError(f"Not implemented in class {cls.__name__}")
 
         model_def_script: str
 

deepmd/entrypoints/doc.py

@@ -16,5 +16,5 @@ def doc_train_input(*, out_type: str = "rst", **kwargs):
     elif out_type == "json":
         doc_str = gen_json()
     else:
-        raise RuntimeError("Unsupported out type %s" % out_type)
+        raise RuntimeError(f"Unsupported out type {out_type}")
     print(doc_str)  # noqa: T201

deepmd/entrypoints/neighbor_stat.py

@@ -91,6 +91,6 @@ def neighbor_stat(
     data.get_batch()
     nei = NeighborStat(data.get_ntypes(), rcut, mixed_type=mixed_type)
     min_nbor_dist, max_nbor_size = nei.get_stat(data)
-    log.info("min_nbor_dist: %f" % min_nbor_dist)
-    log.info("max_nbor_size: %s" % str(max_nbor_size))
+    log.info(f"min_nbor_dist: {min_nbor_dist:f}")
+    log.info(f"max_nbor_size: {max_nbor_size!s}")
     return min_nbor_dist, max_nbor_size

deepmd/entrypoints/test.py

@@ -460,14 +460,14 @@ def test_ener(
         save_txt_file(
             detail_path.with_suffix(".e.out"),
             pe,
-            header="%s: data_e pred_e" % system,
+            header=f"{system}: data_e pred_e",
             append=append_detail,
         )
         pe_atom = pe / natoms
         save_txt_file(
             detail_path.with_suffix(".e_peratom.out"),
             pe_atom,
-            header="%s: data_e pred_e" % system,
+            header=f"{system}: data_e pred_e",
             append=append_detail,
         )
         if not out_put_spin:
@@ -481,7 +481,7 @@ def test_ener(
             save_txt_file(
                 detail_path.with_suffix(".f.out"),
                 pf,
-                header="%s: data_fx data_fy data_fz pred_fx pred_fy pred_fz" % system,
+                header=f"{system}: data_fx data_fy data_fz pred_fx pred_fy pred_fz",
                 append=append_detail,
             )
         else:
@@ -496,14 +496,13 @@ def test_ener(
             save_txt_file(
                 detail_path.with_suffix(".fr.out"),
                 pf_real,
-                header="%s: data_fx data_fy data_fz pred_fx pred_fy pred_fz" % system,
+                header=f"{system}: data_fx data_fy data_fz pred_fx pred_fy pred_fz",
                 append=append_detail,
             )
             save_txt_file(
                 detail_path.with_suffix(".fm.out"),
                 pf_mag,
-                header="%s: data_fmx data_fmy data_fmz pred_fmx pred_fmy pred_fmz"
-                % system,
+                header=f"{system}: data_fmx data_fmy data_fmz pred_fmx pred_fmy pred_fmz",
                 append=append_detail,
             )
         pv = np.concatenate(

deepmd/env.py

@@ -46,9 +46,9 @@
     global_float_prec = "float"
 else:
     raise RuntimeError(
-        "Unsupported float precision option: %s. Supported: high,"
+        f"Unsupported float precision option: {dp_float_prec}. Supported: high,"
         "low. Please set precision with environmental variable "
-        "DP_INTERFACE_PREC." % dp_float_prec
+        "DP_INTERFACE_PREC."
     )
 
 

deepmd/main.py

@@ -650,7 +650,7 @@ def main_parser() -> argparse.ArgumentParser:
 
     # --version
     parser.add_argument(
-        "--version", action="version", version="DeePMD-kit v%s" % __version__
+        "--version", action="version", version=f"DeePMD-kit v{__version__}"
     )
 
     # * train nvnmd script ******************************************************************

deepmd/pt/optimizer/LKF.py

@@ -75,7 +75,7 @@ def __init_P(self):
 
         P = []
         params_packed_index = []
-        logging.info("LKF parameter nums: %s" % param_nums)
+        logging.info(f"LKF parameter nums: {param_nums}")
         if self.dist_init:
             block_num = 0
             for param_num in param_nums:

deepmd/pt/train/training.py

@@ -651,7 +651,7 @@ def warm_up_linear(step, warmup_steps):
                 self.wrapper.parameters(), 0.98, 0.99870, self.opt_param["kf_blocksize"]
             )
         else:
-            raise ValueError("Not supported optimizer type '%s'" % self.opt_type)
+            raise ValueError(f"Not supported optimizer type '{self.opt_type}'")
 
         # Get model prob for multi-task
         if self.multi_task:
@@ -808,7 +808,7 @@ def fake_model():
                         learning_rate=pref_lr,
                     )
             else:
-                raise ValueError("Not supported optimizer type '%s'" % self.opt_type)
+                raise ValueError(f"Not supported optimizer type '{self.opt_type}'")
 
             # Log and persist
             if _step_id % self.disp_freq == 0:
@@ -1170,7 +1170,7 @@ def print_on_training(self, fout, step_id, cur_lr, train_results, valid_results)
                     prop_fmt = "   %11.2e"
                     for k in sorted(train_results[model_key].keys()):
                         print_str += prop_fmt % (train_results[model_key][k])
-        print_str += "   %8.1e\n" % cur_lr
+        print_str += f"   {cur_lr:8.1e}\n"
         fout.write(print_str)
         fout.flush()
 

deepmd/tf/cluster/local.py

@@ -45,7 +45,7 @@ def get_gpus():
         stdout, stderr = p.communicate()
         if p.returncode != 0:
             decoded = stderr.decode("UTF-8")
-            raise RuntimeError("Failed to detect availbe GPUs due to:\n%s" % decoded)
+            raise RuntimeError(f"Failed to detect availbe GPUs due to:\n{decoded}")
         decoded = stdout.decode("UTF-8").strip()
         num_gpus = int(decoded)
         return list(range(num_gpus)) if num_gpus > 0 else None

deepmd/tf/descriptor/descriptor.py

@@ -247,7 +247,7 @@ def enable_compression(
         This method is called by others when the descriptor supported compression.
         """
         raise NotImplementedError(
-            "Descriptor %s doesn't support compression!" % type(self).__name__
+            f"Descriptor {type(self).__name__} doesn't support compression!"
         )
 
     def enable_mixed_precision(self, mixed_prec: Optional[dict] = None) -> None:
@@ -263,8 +263,7 @@ def enable_mixed_precision(self, mixed_prec: Optional[dict] = None) -> None:
         This method is called by others when the descriptor supported compression.
         """
         raise NotImplementedError(
-            "Descriptor %s doesn't support mixed precision training!"
-            % type(self).__name__
+            f"Descriptor {type(self).__name__} doesn't support mixed precision training!"
         )
 
     @abstractmethod
@@ -315,8 +314,7 @@ def init_variables(
         This method is called by others when the descriptor supported initialization from the given variables.
         """
         raise NotImplementedError(
-            "Descriptor %s doesn't support initialization from the given variables!"
-            % type(self).__name__
+            f"Descriptor {type(self).__name__} doesn't support initialization from the given variables!"
         )
 
     def get_tensor_names(self, suffix: str = "") -> Tuple[str]:
@@ -333,7 +331,7 @@ def get_tensor_names(self, suffix: str = "") -> Tuple[str]:
             Names of tensors
         """
         raise NotImplementedError(
-            "Descriptor %s doesn't support this property!" % type(self).__name__
+            f"Descriptor {type(self).__name__} doesn't support this property!"
         )
 
     def pass_tensors_from_frz_model(
@@ -353,7 +351,7 @@ def pass_tensors_from_frz_model(
         :meth:`get_tensor_names`.
         """
         raise NotImplementedError(
-            "Descriptor %s doesn't support this method!" % type(self).__name__
+            f"Descriptor {type(self).__name__} doesn't support this method!"
         )
 
     def build_type_exclude_mask(
@@ -498,7 +496,7 @@ def deserialize(cls, data: dict, suffix: str = "") -> "Descriptor":
             return Descriptor.get_class_by_type(
                 j_get_type(data, cls.__name__)
             ).deserialize(data, suffix=suffix)
-        raise NotImplementedError("Not implemented in class %s" % cls.__name__)
+        raise NotImplementedError(f"Not implemented in class {cls.__name__}")
 
     def serialize(self, suffix: str = "") -> dict:
         """Serialize the model.
@@ -513,4 +511,4 @@ def serialize(self, suffix: str = "") -> dict:
         suffix : str, optional
             Name suffix to identify this descriptor
         """
-        raise NotImplementedError("Not implemented in class %s" % self.__name__)
+        raise NotImplementedError(f"Not implemented in class {self.__name__}")

deepmd/tf/descriptor/loc_frame.py

@@ -427,12 +427,8 @@ def init_variables(
         suffix : str, optional
             The suffix of the scope
         """
-        self.davg = get_tensor_by_name_from_graph(
-            graph, "descrpt_attr%s/t_avg" % suffix
-        )
-        self.dstd = get_tensor_by_name_from_graph(
-            graph, "descrpt_attr%s/t_std" % suffix
-        )
+        self.davg = get_tensor_by_name_from_graph(graph, f"descrpt_attr{suffix}/t_avg")
+        self.dstd = get_tensor_by_name_from_graph(graph, f"descrpt_attr{suffix}/t_std")
 
     @classmethod
     def update_sel(cls, global_jdata: dict, local_jdata: dict):

deepmd/tf/descriptor/se.py

@@ -141,12 +141,8 @@ def init_variables(
         self.embedding_net_variables = get_embedding_net_variables_from_graph_def(
             graph_def, suffix=suffix
         )
-        self.davg = get_tensor_by_name_from_graph(
-            graph, "descrpt_attr%s/t_avg" % suffix
-        )
-        self.dstd = get_tensor_by_name_from_graph(
-            graph, "descrpt_attr%s/t_std" % suffix
-        )
+        self.davg = get_tensor_by_name_from_graph(graph, f"descrpt_attr{suffix}/t_avg")
+        self.dstd = get_tensor_by_name_from_graph(graph, f"descrpt_attr{suffix}/t_std")
 
     @property
     def precision(self) -> tf.DType:

deepmd/tf/descriptor/se_a.py

@@ -554,12 +554,8 @@ def enable_compression(
             min_nbor_dist, table_extrapolate, table_stride_1, table_stride_2
         )
 
-        self.davg = get_tensor_by_name_from_graph(
-            graph, "descrpt_attr%s/t_avg" % suffix
-        )
-        self.dstd = get_tensor_by_name_from_graph(
-            graph, "descrpt_attr%s/t_std" % suffix
-        )
+        self.davg = get_tensor_by_name_from_graph(graph, f"descrpt_attr{suffix}/t_avg")
+        self.dstd = get_tensor_by_name_from_graph(graph, f"descrpt_attr{suffix}/t_std")
 
     def enable_mixed_precision(self, mixed_prec: Optional[dict] = None) -> None:
         """Reveive the mixed precision setting.
@@ -1305,14 +1301,14 @@ def init_variables(
         super().init_variables(graph=graph, graph_def=graph_def, suffix=suffix)
         try:
             self.original_sel = get_tensor_by_name_from_graph(
-                graph, "descrpt_attr%s/original_sel" % suffix
+                graph, f"descrpt_attr{suffix}/original_sel"
             )
         except GraphWithoutTensorError:
             # original_sel is not restored in old graphs, assume sel never changed before
             pass
         # check sel == original sel?
         try:
-            sel = get_tensor_by_name_from_graph(graph, "descrpt_attr%s/sel" % suffix)
+            sel = get_tensor_by_name_from_graph(graph, f"descrpt_attr{suffix}/sel")
         except GraphWithoutTensorError:
             # sel is not restored in old graphs
             pass
@@ -1387,7 +1383,7 @@ def deserialize(cls, data: dict, suffix: str = ""):
             The deserialized model
         """
         if cls is not DescrptSeA:
-            raise NotImplementedError("Not implemented in class %s" % cls.__name__)
+            raise NotImplementedError(f"Not implemented in class {cls.__name__}")
         data = data.copy()
         check_version_compatibility(data.pop("@version", 1), 1, 1)
         data.pop("@class", None)
@@ -1422,7 +1418,7 @@ def serialize(self, suffix: str = "") -> dict:
         """
         if type(self) is not DescrptSeA:
             raise NotImplementedError(
-                "Not implemented in class %s" % self.__class__.__name__
+                f"Not implemented in class {self.__class__.__name__}"
             )
         if self.stripped_type_embedding:
             raise NotImplementedError(

deepmd/tf/descriptor/se_atten.py

@@ -492,12 +492,8 @@ def enable_compression(
         )
         self.two_embd = make_data(self, self.final_type_embedding)
 
-        self.davg = get_tensor_by_name_from_graph(
-            graph, "descrpt_attr%s/t_avg" % suffix
-        )
-        self.dstd = get_tensor_by_name_from_graph(
-            graph, "descrpt_attr%s/t_std" % suffix
-        )
+        self.davg = get_tensor_by_name_from_graph(graph, f"descrpt_attr{suffix}/t_avg")
+        self.dstd = get_tensor_by_name_from_graph(graph, f"descrpt_attr{suffix}/t_std")
 
     def build(
         self,
@@ -1672,7 +1668,7 @@ def deserialize(cls, data: dict, suffix: str = ""):
             The deserialized model
         """
         if cls is not DescrptSeAtten:
-            raise NotImplementedError("Not implemented in class %s" % cls.__name__)
+            raise NotImplementedError(f"Not implemented in class {cls.__name__}")
         data = data.copy()
         check_version_compatibility(data.pop("@version"), 1, 1)
         data.pop("@class")
@@ -1711,7 +1707,7 @@ def serialize(self, suffix: str = "") -> dict:
         """
         if type(self) not in [DescrptSeAtten, DescrptDPA1Compat]:
             raise NotImplementedError(
-                "Not implemented in class %s" % self.__class__.__name__
+                f"Not implemented in class {self.__class__.__name__}"
             )
         if self.stripped_type_embedding:
             raise NotImplementedError(
@@ -2075,7 +2071,7 @@ def deserialize(cls, data: dict, suffix: str = ""):
             The deserialized model
         """
         if cls is not DescrptDPA1Compat:
-            raise NotImplementedError("Not implemented in class %s" % cls.__name__)
+            raise NotImplementedError(f"Not implemented in class {cls.__name__}")
         data = data.copy()
         check_version_compatibility(data.pop("@version"), 1, 1)
         data.pop("@class")

deepmd/tf/descriptor/se_r.py

@@ -376,12 +376,8 @@ def enable_compression(
             min_nbor_dist, table_extrapolate, table_stride_1, table_stride_2
         )
 
-        self.davg = get_tensor_by_name_from_graph(
-            graph, "descrpt_attr%s/t_avg" % suffix
-        )
-        self.dstd = get_tensor_by_name_from_graph(
-            graph, "descrpt_attr%s/t_std" % suffix
-        )
+        self.davg = get_tensor_by_name_from_graph(graph, f"descrpt_attr{suffix}/t_avg")
+        self.dstd = get_tensor_by_name_from_graph(graph, f"descrpt_attr{suffix}/t_std")
 
     def build(
         self,
@@ -737,7 +733,7 @@ def deserialize(cls, data: dict, suffix: str = ""):
             The deserialized model
         """
         if cls is not DescrptSeR:
-            raise NotImplementedError("Not implemented in class %s" % cls.__name__)
+            raise NotImplementedError(f"Not implemented in class {cls.__name__}")
         data = data.copy()
         check_version_compatibility(data.pop("@version", 1), 1, 1)
         embedding_net_variables = cls.deserialize_network(
@@ -770,7 +766,7 @@ def serialize(self, suffix: str = "") -> dict:
         """
         if type(self) is not DescrptSeR:
             raise NotImplementedError(
-                "Not implemented in class %s" % self.__class__.__name__
+                f"Not implemented in class {self.__class__.__name__}"
             )
         if self.embedding_net_variables is None:
             raise RuntimeError("init_variables must be called before serialize")