Print for debug run_squad

run_squad_pytorch.py

@@ -818,6 +818,7 @@ def main():
         logger.info("  Batch size = %d", args.train_batch_size)
         logger.info("  Num steps = %d", num_train_steps)
 
+        logger.info("HHHHH Loading data")
         all_input_ids = torch.tensor([f.input_ids for f in train_features], dtype=torch.long)
         all_input_mask = torch.tensor([f.input_mask for f in train_features], dtype=torch.long)
         all_segment_ids = torch.tensor([f.segment_ids for f in train_features], dtype=torch.long)
@@ -825,14 +826,17 @@ def main():
         all_start_positions = torch.tensor([f.start_position for f in train_features], dtype=torch.long)
         all_end_positions = torch.tensor([f.end_position for f in train_features], dtype=torch.long)
 
+        logger.info("HHHHH Creating dataset")
         #train_data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_label_ids)
         train_data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_start_positions, all_end_positions)
         if args.local_rank == -1:
             train_sampler = RandomSampler(train_data)
         else:
             train_sampler = DistributedSampler(train_data)
+        logger.info("HHHHH Dataloader")
         train_dataloader = DataLoader(train_data, sampler=train_sampler, batch_size=args.train_batch_size)
 
+        logger.info("HHHHH Starting Traing")
         model.train()
         for epoch in range(int(args.num_train_epochs)):
             #for input_ids, input_mask, segment_ids, label_ids in train_dataloader:
@@ -846,11 +850,15 @@ def main():
                 
                 start_positions = start_positions.view(-1, 1)
                 end_positions = end_positions.view(-1, 1)
-
+                
+                logger.info("HHHHH Forward")
                 loss, _ = model(input_ids, segment_ids, input_mask, start_positions, end_positions)
+                logger.info("HHHHH Backward")
                 loss.backward()
+                logger.info("HHHHH Loading data")
                 optimizer.step()
                 global_step += 1
+                logger.info("Done %s steps", global_step)
 
     if args.do_predict:
         eval_examples = read_squad_examples(
@@ -884,6 +892,7 @@ def main():
 
         model.eval()
         all_results = []
+        logger.info("Start evaulating")
         #for input_ids, input_mask, segment_ids, label_ids, example_index in eval_dataloader:
         for input_ids, input_mask, segment_ids, example_index in eval_dataloader:
             if len(all_results) % 1000 == 0: