pt: apply global logger to pt

deepmd/env.py

@@ -13,6 +13,8 @@
     "global_float_prec",
 ]
 
+log = logging.getLogger(__name__)
+
 # FLOAT_PREC
 dp_float_prec = os.environ.get("DP_INTERFACE_PREC", "high").lower()
 if dp_float_prec in ("high", ""):
@@ -47,7 +49,7 @@
     if os.environ.get(key) is None:
         os.environ[key] = value
         if verbose:
-            logging.warning(
+            log.warning(
                 f"Environment variable {key} is empty. Use the default value {value}"
             )
 
@@ -72,7 +74,7 @@
             and "TF_INTER_OP_PARALLELISM_THREADS" not in os.environ
         )
     ):
-        logging.warning(
+        log.warning(
             "To get the best performance, it is recommended to adjust "
             "the number of threads by setting the environment variables "
             "OMP_NUM_THREADS, DP_INTRA_OP_PARALLELISM_THREADS, and "

deepmd/pt/entrypoints/main.py

@@ -30,6 +30,9 @@
 from deepmd.infer.model_devi import (
     make_model_devi,
 )
+from deepmd.loggers.loggers import (
+    set_log_handles,
+)
 from deepmd.main import (
     parse_args,
 )
@@ -42,9 +45,6 @@
 from deepmd.pt.train import (
     training,
 )
-from deepmd.pt.utils import (
-    env,
-)
 from deepmd.pt.utils.dataloader import (
     DpLoaderSet,
 )
@@ -58,6 +58,8 @@
     make_stat_input,
 )
 
+log = logging.getLogger(__name__)
+
 
 def get_trainer(
     config,
@@ -237,7 +239,7 @@
 
 
 def train(FLAGS):
-    logging.info("Configuration path: %s", FLAGS.INPUT)
+    log.info("Configuration path: %s", FLAGS.INPUT)
     with open(FLAGS.INPUT) as fin:
         config = json.load(fin)
     trainer = get_trainer(
@@ -278,28 +280,18 @@
     )
 
 
-# avoid logger conflicts of tf version
-def clean_loggers():
-    logger = logging.getLogger()
-    while logger.hasHandlers():
-        logger.removeHandler(logger.handlers[0])
-
-
 @record
 def main(args: Optional[Union[List[str], argparse.Namespace]] = None):
-    clean_loggers()
-
     if not isinstance(args, argparse.Namespace):
         FLAGS = parse_args(args=args)
     else:
         FLAGS = args
     dict_args = vars(FLAGS)
 
-    logging.basicConfig(
-        level=logging.WARNING if env.LOCAL_RANK else logging.INFO,
-        format=f"%(asctime)-15s {os.environ.get('RANK') or ''} [%(filename)s:%(lineno)d] %(levelname)s %(message)s",
-    )
-    logging.info("DeepMD version: %s", __version__)
+    set_log_handles(FLAGS.log_level, FLAGS.log_path, mpi_log=None)
+    log.debug("Log handles were successfully set")
+
+    log.info("DeepMD version: %s", __version__)
 
     if FLAGS.command == "train":
         train(FLAGS)
@@ -315,17 +307,17 @@
             FLAGS.model = FLAGS.checkpoint_folder
         FLAGS.output = str(Path(FLAGS.output).with_suffix(".pth"))
         freeze(FLAGS)
-    elif args.command == "doc-train-input":
+    elif FLAGS.command == "doc-train-input":
         doc_train_input(**dict_args)
-    elif args.command == "model-devi":
+    elif FLAGS.command == "model-devi":
         dict_args["models"] = [
             str(Path(mm).with_suffix(".pt"))
             if Path(mm).suffix not in (".pb", ".pt")
             else mm
             for mm in dict_args["models"]
         ]
         make_model_devi(**dict_args)
-    elif args.command == "gui":
+    elif FLAGS.command == "gui":
         start_dpgui(**dict_args)
     else:
         raise RuntimeError(f"Invalid command {FLAGS.command}!")

deepmd/pt/infer/inference.py

@@ -40,6 +40,7 @@
 
 if torch.__version__.startswith("2"):
     import torch._dynamo
+log = logging.getLogger(__name__)
 
 
 class Tester:
@@ -95,9 +96,7 @@
                 ), f"Validation systems not found in {input_script}!"
                 self.systems = training_params["validation_data"]["systems"]
                 self.batchsize = training_params["validation_data"]["batch_size"]
-                logging.info(
-                    f"Testing validation systems in input script: {input_script}"
-                )
+                log.info(f"Testing validation systems in input script: {input_script}")
             else:
                 assert (
                     "data_dict" in training_params
@@ -115,18 +114,18 @@
                 self.batchsize = training_params["data_dict"][head]["validation_data"][
                     "batch_size"
                 ]
-                logging.info(
+                log.info(
                     f"Testing validation systems in head {head} of input script: {input_script}"
                 )
         elif system is not None:
             self.systems = expand_sys_str(system)
             self.batchsize = "auto"
-            logging.info("Testing systems in path: %s", system)
+            log.info("Testing systems in path: %s", system)
         elif datafile is not None:
             with open(datafile) as fin:
                 self.systems = fin.read().splitlines()
             self.batchsize = "auto"
-            logging.info("Testing systems in file: %s", datafile)
+            log.info("Testing systems in file: %s", datafile)
         else:
             self.systems = None
             self.batchsize = None
@@ -210,8 +209,8 @@
         system_results = {}
         global_sum_natoms = 0
         for cc, system in enumerate(systems):
-            logging.info("# ---------------output of dp test--------------- ")
-            logging.info(f"# testing system : {system}")
+            log.info("# ---------------output of dp test--------------- ")
+            log.info(f"# testing system : {system}")
             system_pred = []
             system_label = []
             dataset = DpLoaderSet(
@@ -226,7 +225,7 @@
                 dataset, replacement=True, num_samples=dataset.total_batch
             )
             if sampler is None:
-                logging.warning(
+                log.warning(
                     "Sampler not specified!"
                 )  # None sampler will lead to a premature stop iteration. Replacement should be True in attribute of the sampler to produce expected number of items in one iteration.
             dataloader = DataLoader(
@@ -296,8 +295,8 @@
                 for k, v in single_results.items()
             }
             for item in sorted(results.keys()):
-                logging.info(f"{item}: {results[item]:.4f}")
-            logging.info("# ----------------------------------------------- ")
+                log.info(f"{item}: {results[item]:.4f}")
+            log.info("# ----------------------------------------------- ")
             for k, v in single_results.items():
                 system_results[k] = system_results.get(k, 0.0) + v
             global_sum_natoms += sum_natoms
@@ -306,14 +305,14 @@
             k: v / global_sum_natoms if "mae" in k else math.sqrt(v / global_sum_natoms)
             for k, v in system_results.items()
         }
-        logging.info("# ----------weighted average of errors----------- ")
+        log.info("# ----------weighted average of errors----------- ")
         if not self.multi_task:
-            logging.info(f"# number of systems : {len(systems)}")
+            log.info(f"# number of systems : {len(systems)}")
         else:
-            logging.info(f"# number of systems for {self.head}: {len(systems)}")
+            log.info(f"# number of systems for {self.head}: {len(systems)}")
         for item in sorted(global_results.keys()):
-            logging.info(f"{item}: {global_results[item]:.4f}")
-        logging.info("# ----------------------------------------------- ")
+            log.info(f"{item}: {global_results[item]:.4f}")
+        log.info("# ----------------------------------------------- ")
         return global_results
 
 

deepmd/pt/model/model/model.py

@@ -12,6 +12,8 @@
     compute_output_stats,
 )
 
+log = logging.getLogger(__name__)
+
 
 class BaseModel(torch.nn.Module):
     def __init__(self):
@@ -55,7 +57,7 @@
                     if not os.path.exists(stat_file_dir):
                         os.mkdir(stat_file_dir)
                     if not isinstance(stat_file_path, list):
-                        logging.info(f"Saving stat file to {stat_file_path}")
+                        log.info(f"Saving stat file to {stat_file_path}")
                         np.savez_compressed(
                             stat_file_path,
                             sumr=sumr,
@@ -68,7 +70,7 @@
                         )
                     else:
                         for ii, file_path in enumerate(stat_file_path):
-                            logging.info(f"Saving stat file to {file_path}")
+                            log.info(f"Saving stat file to {file_path}")
                             np.savez_compressed(
                                 file_path,
                                 sumr=sumr[ii],
@@ -82,7 +84,7 @@
             else:  # load stat
                 target_type_map = type_map
                 if not isinstance(stat_file_path, list):
-                    logging.info(f"Loading stat file from {stat_file_path}")
+                    log.info(f"Loading stat file from {stat_file_path}")
                     stats = np.load(stat_file_path)
                     stat_type_map = list(stats["type_map"])
                     missing_type = [
@@ -105,7 +107,7 @@
                     sumr, suma, sumn, sumr2, suma2 = [], [], [], [], []
                     id_bias_atom_e = None
                     for ii, file_path in enumerate(stat_file_path):
-                        logging.info(f"Loading stat file from {file_path}")
+                        log.info(f"Loading stat file from {file_path}")
                         stats = np.load(file_path)
                         stat_type_map = list(stats["type_map"])
                         missing_type = [

deepmd/pt/model/task/dipole.py

@@ -10,6 +10,8 @@
     Fitting,
 )
 
+log = logging.getLogger(__name__)
+
 
 class DipoleFittingNetType(Fitting):
     def __init__(
@@ -37,7 +39,7 @@
         self.filter_layers = torch.nn.ModuleList(filter_layers)
 
         if "seed" in kwargs:
-            logging.info("Set seed to %d in fitting net.", kwargs["seed"])
+            log.info("Set seed to %d in fitting net.", kwargs["seed"])
             torch.manual_seed(kwargs["seed"])
 
     def forward(self, inputs, atype, atype_tebd, rot_mat):

deepmd/pt/model/task/ener.py

@@ -40,6 +40,8 @@
 dtype = env.GLOBAL_PT_FLOAT_PRECISION
 device = env.DEVICE
 
+log = logging.getLogger(__name__)
+
 
 @fitting_check_output
 class InvarFitting(Fitting):
@@ -153,7 +155,7 @@ def __init__(
 
         # very bad design...
         if "seed" in kwargs:
-            logging.info("Set seed to %d in fitting net.", kwargs["seed"])
+            log.info("Set seed to %d in fitting net.", kwargs["seed"])
             torch.manual_seed(kwargs["seed"])
 
     def output_def(self) -> FittingOutputDef:
@@ -451,7 +453,7 @@ def __init__(
         self.filter_layers = torch.nn.ModuleList(filter_layers)
 
         if "seed" in kwargs:
-            logging.info("Set seed to %d in fitting net.", kwargs["seed"])
+            log.info("Set seed to %d in fitting net.", kwargs["seed"])
             torch.manual_seed(kwargs["seed"])
 
     def output_def(self):

deepmd/pt/model/task/fitting.py

@@ -23,6 +23,8 @@
     make_stat_input,
 )
 
+log = logging.getLogger(__name__)
+
 
 class Fitting(torch.nn.Module, BaseFitting):
     __plugins = Plugin()
@@ -115,7 +117,7 @@
         ntest : int
             The number of test samples in a system to change the energy bias.
         """
-        logging.info(
+        log.info(
             "Changing energy bias in pretrained model for types {}... "
             "(this step may take long time)".format(str(new_type_map))
         )
@@ -188,7 +190,7 @@
             self.bias_atom_e[idx_type_map] += torch.from_numpy(
                 delta_bias.reshape(-1)
             ).to(DEVICE)
-            logging.info(
+            log.info(
                 f"RMSE of atomic energy after linear regression is: {rmse_ae:10.5e} eV/atom."
             )
         elif bias_shift == "statistic":
@@ -202,7 +204,7 @@
             )
         else:
             raise RuntimeError("Unknown bias_shift mode: " + bias_shift)
-        logging.info(
+        log.info(
             "Change energy bias of {} from {} to {}.".format(
                 str(new_type_map),
                 str(old_bias.detach().cpu().numpy()),

deepmd/pt/optimizer/LKF.py

@@ -7,6 +7,8 @@
     Optimizer,
 )
 
+log = logging.getLogger(__name__)
+
 
 class LKFOptimizer(Optimizer):
     def __init__(
@@ -59,7 +61,7 @@ def __init_P(self):
 
         P = []
         params_packed_index = []
-        logging.info("LKF parameter nums: %s" % param_nums)
+        log.info("LKF parameter nums: %s" % param_nums)
         for param_num in param_nums:
             if param_num >= block_size:
                 block_num = math.ceil(param_num / block_size)