Fixed logging

examples/lm_finetuning/finetune_on_pregenerated.py

@@ -16,7 +16,9 @@ from pytorch_pretrained_bert.tokenization import BertTokenizer
 from pytorch_pretrained_bert.optimization import BertAdam, warmup_linear
 
 InputFeatures = namedtuple("InputFeatures", "input_ids input_mask segment_ids lm_label_ids is_next")
-logger = logging.getLogger(__name__)
+
+log_format = '%(asctime)-10s: %(message)s'
+logging.basicConfig(level=logging.INFO, format=log_format)
 
 
 def convert_example_to_features(example, tokenizer, max_seq_length):
@@ -68,7 +70,7 @@ class PregeneratedDataset(Dataset):
         segment_ids = np.zeros(shape=(num_samples, seq_len), dtype=np.bool)
         lm_label_ids = np.full(shape=(num_samples, seq_len), dtype=np.int32, fill_value=-1)
         is_nexts = np.zeros(shape=(num_samples,), dtype=np.bool)
-        logger.info(f"Loading training examples for epoch {epoch}")
+        logging.info(f"Loading training examples for epoch {epoch}")
         with data_file.open() as f:
             for i, line in enumerate(tqdm(f, total=num_samples, desc="Training examples")):
                 example = json.loads(line.rstrip())
@@ -79,7 +81,7 @@ class PregeneratedDataset(Dataset):
                 lm_label_ids[i] = features.lm_label_ids
                 is_nexts[i] = features.is_next
         assert i == num_samples - 1  # Assert that the sample count metric was true
-        logger.info("Loading complete!")
+        logging.info("Loading complete!")
         self.num_samples = num_samples
         self.seq_len = seq_len
         self.input_ids = input_ids
@@ -132,8 +134,8 @@ def main():
                         action='store_true',
                         help="Whether to use 16-bit float precision instead of 32-bit")
     parser.add_argument('--loss_scale',
-                        type = float, default = 0,
-                        help = "Loss scaling to improve fp16 numeric stability. Only used when fp16 set to True.\n"
+                        type=float, default=0,
+                        help="Loss scaling to improve fp16 numeric stability. Only used when fp16 set to True.\n"
                         "0 (default value): dynamic loss scaling.\n"
                         "Positive power of 2: static loss scaling value.\n")
     parser.add_argument("--warmup_proportion",
@@ -179,7 +181,7 @@ def main():
         n_gpu = 1
         # Initializes the distributed backend which will take care of sychronizing nodes/GPUs
         torch.distributed.init_process_group(backend='nccl')
-    logger.info("device: {} n_gpu: {}, distributed training: {}, 16-bits training: {}".format(
+    logging.info("device: {} n_gpu: {}, distributed training: {}, 16-bits training: {}".format(
         device, n_gpu, bool(args.local_rank != -1), args.fp16))
 
     if args.gradient_accumulation_steps < 1:
@@ -195,7 +197,7 @@ def main():
         torch.cuda.manual_seed_all(args.seed)
 
     if args.output_dir.is_dir() and list(args.output_dir.iterdir()):
-        logger.warning(f"Output directory ({args.output_dir}) already exists and is not empty!")
+        logging.warning(f"Output directory ({args.output_dir}) already exists and is not empty!")
     args.output_dir.mkdir(parents=True, exist_ok=True)
 
     tokenizer = BertTokenizer.from_pretrained(args.bert_model, do_lower_case=args.do_lower_case)
@@ -258,10 +260,10 @@ def main():
                              t_total=num_train_optimization_steps)
 
         global_step = 0
-        logger.info("***** Running training *****")
-        logger.info(f"  Num examples = {total_train_examples}")
-        logger.info("  Batch size = %d", args.train_batch_size)
-        logger.info("  Num steps = %d", num_train_optimization_steps)
+        logging.info("***** Running training *****")
+        logging.info(f"  Num examples = {total_train_examples}")
+        logging.info("  Batch size = %d", args.train_batch_size)
+        logging.info("  Num steps = %d", num_train_optimization_steps)
         model.train()
         for epoch in range(args.epochs):
             epoch_dataset = PregeneratedDataset(epoch=epoch, training_path=args.pregenerated_data, tokenizer=tokenizer,
@@ -304,7 +306,7 @@ def main():
                         global_step += 1
 
         # Save a trained model
-        logger.info("** ** * Saving fine-tuned model ** ** * ")
+        logging.info("** ** * Saving fine-tuned model ** ** * ")
         model_to_save = model.module if hasattr(model, 'module') else model  # Only save the model it-self
         output_model_file = args.output_dir / "pytorch_model.bin"
         torch.save(model_to_save.state_dict(), str(output_model_file))