From d80a60c1296bc4147f210fe64b2c9fc97178020b Mon Sep 17 00:00:00 2001
From: Maja Jablonska <majajjablonska@gmail.com>
Date: Sun, 12 Nov 2023 18:08:49 +1100
Subject: [PATCH] Add a custom logger
---
combo/main.py | 64 ++++++++++++++++++++++++++++++-----------
combo/utils/__init__.py | 3 +-
combo/utils/logging.py | 43 +++++++++++++++++++++++++++
3 files changed, 93 insertions(+), 17 deletions(-)
create mode 100644 combo/utils/logging.py
diff --git a/combo/main.py b/combo/main.py
index 2faaf18..5dee611 100755
--- a/combo/main.py
+++ b/combo/main.py
@@ -9,13 +9,14 @@ from absl import app, flags
import pytorch_lightning as pl
from combo.training.trainable_combo import TrainableCombo
-from combo.utils import checks
+from combo.utils import checks, ComboLogger
-from config import resolve
-from default_model import default_ud_dataset_reader, default_data_loader
-from modules.archival import load_archive, archive
-from predict import COMBO
+from combo.config import resolve
+from combo.default_model import default_ud_dataset_reader, default_data_loader
+from combo.modules.archival import load_archive, archive
+from combo.predict import COMBO
+logging.setLoggerClass(ComboLogger)
logger = logging.getLogger(__name__)
_FEATURES = ["token", "char", "upostag", "xpostag", "lemma", "feats"]
_TARGETS = ["deprel", "feats", "head", "lemma", "upostag", "xpostag", "semrel", "sent", "deps"]
@@ -54,14 +55,14 @@ flags.DEFINE_string(name="serialization_dir", default=None,
help="Model serialization directory (default - system temp dir).")
flags.DEFINE_boolean(name="tensorboard", default=False,
help="When provided model will log tensorboard metrics.")
+flags.DEFINE_string(name="config_path", default=str(pathlib.Path(__file__).parent / "config.json"),
+ help="Config file path.")
# Finetune after training flags
flags.DEFINE_string(name="finetuning_training_data_path", default="",
- help="Training data path(s)")
+ help="Training data path(s)")
flags.DEFINE_string(name="finetuning_validation_data_path", default="",
- help="Validation data path(s)")
-flags.DEFINE_string(name="config_path", default=str(pathlib.Path(__file__).parent / "params.json"),
- help="Config file path.")
+ help="Validation data path(s)")
# Test after training flags
flags.DEFINE_string(name="test_path", default=None,
@@ -99,51 +100,81 @@ def get_predictor() -> COMBO:
def run(_):
if FLAGS.mode == 'train':
if not FLAGS.finetuning:
+ prefix = 'Training'
+ logger.info('Setting up the model for training', prefix=prefix)
checks.file_exists(FLAGS.config_path)
+
+ logger.info(f'Reading parameters from configuration path {FLAGS.config_path}', prefix=prefix)
with open(FLAGS.config_path, 'r') as f:
params = json.load(f)
params = {**params, **_get_ext_vars()}
serialization_dir = tempfile.mkdtemp(prefix='combo', dir=FLAGS.serialization_dir)
- model = resolve(params['model'])
+ try:
+ vocabulary = resolve(params['vocabulary'])
+ except KeyError:
+ logger.error('No vocabulary in config.json!')
+ return
+
+ model = resolve(params['model'], pass_down_parameters={'vocabulary': vocabulary})
+ dataset_reader = None
if 'data_loader' in params:
+ logger.info(f'Resolving the training data loader from parameters', prefix=prefix)
train_data_loader = resolve(params['data_loader'])
else:
checks.file_exists(FLAGS.training_data_path)
+ logger.info(f'Using a default UD data loader with training data path {FLAGS.training_data_path}',
+ prefix=prefix)
train_data_loader = default_data_loader(default_ud_dataset_reader(),
FLAGS.training_data_path)
+
+ logger.info('Indexing training data loader')
train_data_loader.index_with(model.vocab)
validation_data_loader = None
+
if 'validation_data_loader' in params:
+ logger.info(f'Resolving the validation data loader from parameters', prefix=prefix)
validation_data_loader = resolve(params['validation_data_loader'])
+ logger.info('Indexing validation data loader', prefix=prefix)
validation_data_loader.index_with(model.vocab)
elif FLAGS.validation_data_path:
checks.file_exists(FLAGS.validation_data_path)
+ logger.info(f'Using a default UD data loader with validation data path {FLAGS.training_data_path}',
+ prefix=prefix)
validation_data_loader = default_data_loader(default_ud_dataset_reader(),
FLAGS.validation_data_path)
+ logger.info('Indexing validation data loader', prefix=prefix)
validation_data_loader.index_with(model.vocab)
else:
- model, train_data_loader, validation_data_loader = load_archive(FLAGS.model_path)
+ prefix = 'Finetuning'
+ logger.info('Loading the model for finetuning', prefix=prefix)
+ model, _, train_data_loader, validation_data_loader, dataset_reader = load_archive(FLAGS.model_path)
serialization_dir = tempfile.mkdtemp(prefix='combo', suffix='-finetuning', dir=FLAGS.serialization_dir)
if not train_data_loader:
checks.file_exists(FLAGS.finetuning_training_data_path)
+ logger.info(
+ f'Using a default UD data loader with training data path {FLAGS.finetuning_training_data_path}',
+ prefix=prefix)
train_data_loader = default_data_loader(default_ud_dataset_reader(),
FLAGS.finetuning_training_data_path)
if not validation_data_loader and FLAGS.finetuning_validation_data_path:
checks.file_exists(FLAGS.finetuning_validation_data_path)
+ logger.info(
+ f'Using a default UD data loader with validation data path {FLAGS.finetuning_validation_data_path}',
+ prefix=prefix)
validation_data_loader = default_data_loader(default_ud_dataset_reader(),
FLAGS.finetuning_validation_data_path)
- print("Indexing train loader")
+ logger.info("Indexing train loader", prefix=prefix)
train_data_loader.index_with(model.vocab)
- print("Indexing validation loader")
+ logger.info("Indexing validation loader", prefix=prefix)
validation_data_loader.index_with(model.vocab)
- print("Indexed")
+ logger.info("Indexed", prefix=prefix)
nlp = TrainableCombo(model, torch.optim.Adam,
optimizer_kwargs={'betas': [0.9, 0.9], 'lr': 0.002},
@@ -153,8 +184,9 @@ def run(_):
gradient_clip_val=5)
trainer.fit(model=nlp, train_dataloaders=train_data_loader, val_dataloaders=validation_data_loader)
- archive(model, serialization_dir)
- logger.info(f"Training model stored in: {serialization_dir}")
+ logger.info(f'Archiving the fine-tuned model in {serialization_dir}', prefix=prefix)
+ archive(model, serialization_dir, train_data_loader, validation_data_loader, dataset_reader)
+ logger.info(f"Training model stored in: {serialization_dir}", prefix=prefix)
elif FLAGS.mode == 'predict':
predictor = get_predictor()
diff --git a/combo/utils/__init__.py b/combo/utils/__init__.py
index b36a4d9..4503226 100644
--- a/combo/utils/__init__.py
+++ b/combo/utils/__init__.py
@@ -1,4 +1,5 @@
from .checks import *
from .sequence import *
from .exceptions import *
-from .typing import *
\ No newline at end of file
+from .typing import *
+from .logging import ComboLogger
diff --git a/combo/utils/logging.py b/combo/utils/logging.py
new file mode 100644
index 0000000..cd04b6a
--- /dev/null
+++ b/combo/utils/logging.py
@@ -0,0 +1,43 @@
+import logging
+from overrides import overrides
+from datetime import datetime
+
+
+class ComboLogger(logging.Logger):
+ def __init__(self, name: str, prefix: str = None, display_date: bool = True):
+ super().__init__(name)
+ self.__prefix = prefix or ''
+ self.__display_date = display_date
+
+ @overrides(check_signature=False)
+ def log(self, level: int, msg: str, prefix: str = None):
+ prefix = prefix or self.__prefix
+ super().log(level, '[{date} UTC {prefix}] {msg}'.format(
+ date=datetime.utcnow().strftime('%Y-%m-%d %H:%M:%S'),
+ prefix=prefix,
+ msg=msg
+ ))
+
+ @overrides(check_signature=False)
+ def debug(self, msg: str, prefix: str = None):
+ self.log(level=logging.DEBUG, msg=msg, prefix=prefix)
+
+ @overrides(check_signature=False)
+ def info(self, msg: str, prefix: str = None):
+ self.log(level=logging.INFO, msg=msg, prefix=prefix)
+
+ @overrides(check_signature=False)
+ def warn(self, msg: str, prefix: str = None):
+ self.log(level=logging.WARN, msg=msg, prefix=prefix)
+
+ @overrides(check_signature=False)
+ def error(self, msg: str, prefix: str = None):
+ self.log(level=logging.ERROR, msg=msg, prefix=prefix)
+
+ @overrides(check_signature=False)
+ def fatal(self, msg: str, prefix: str = None):
+ self.log(level=logging.FATAL, msg=msg, prefix=prefix)
+
+ @overrides(check_signature=False)
+ def critical(self, msg: str, prefix: str = None):
+ self.log(level=logging.CRITICAL, msg=msg, prefix=prefix)
--
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