Crash at the end of training
Hi, I tried running the Squad model this morning (on a single GPU with gradient accumulation over 3 steps) but after 3 hours of training, my job failed with the following output:

I was running the code, unmodified, from commit 3bfbc21376af691b912f3b6256bbeaf8e0046ba8

Is this an issue you know about?
```
11/08/2018 17:50:03 - INFO - __main__ -   device cuda n_gpu 1 distributed training False
11/08/2018 17:50:18 - INFO - __main__ -   *** Example ***
11/08/2018 17:50:18 - INFO - __main__ -   unique_id: 1000000000
11/08/2018 17:50:18 - INFO - __main__ -   example_index: 0
11/08/2018 17:50:18 - INFO - __main__ -   doc_span_index: 0
11/08/2018 17:50:18 - INFO - __main__ -   tokens: [CLS] to whom did the virgin mary allegedly appear in 1858 in lou ##rdes france ? [SEP] architectural ##ly , the school has a catholic character . atop the main building ' s gold dome is a golden statue of the virgin mary . immediately in front of the main building and facing it , is a copper statue of christ with arms up ##rai ##sed with the legend " ve ##ni ##te ad me om ##nes " . next to the main building is the basilica of the sacred heart . immediately behind the basilica is the gr ##otto , a marian place of prayer and reflection . it is a replica of the gr ##otto at lou ##rdes , france where the virgin mary reputed ##ly appeared to saint bern ##ade ##tte so ##ub ##iro ##us in 1858 . at the end of the main drive ( and in a direct line that connects through 3 statues and the gold dome ) , is a simple , modern stone statue of mary . [SEP]
11/08/2018 17:50:18 - INFO - __main__ -   token_to_orig_map: 17:0 18:0 19:0 20:1 21:2 22:3 23:4 24:5 25:6 26:6 27:7 28:8 29:9 30:10 31:10 32:10 33:11 34:12 35:13 36:14 37:15 38:16 39:17 40:18 41:19 42:20 43:20 44:21 45:22 46:23 47:24 48:25 49:26 50:27 51:28 52:29 53:30 54:30 55:31 56:32 57:33 58:34 59:35 60:36 61:37 62:38 63:39 64:39 65:39 66:40 67:41 68:42 69:43 70:43 71:43 72:43 73:44 74:45 75:46 76:46 77:46 78:46 79:47 80:48 81:49 82:50 83:51 84:52 85:53 86:54 87:55 88:56 89:57 90:58 91:58 92:59 93:60 94:61 95:62 96:63 97:64 98:65 99:65 100:65 101:66 102:67 103:68 104:69 105:70 106:71 107:72 108:72 109:73 110:74 111:75 112:76 113:77 114:78 115:79 116:79 117:80 118:81 119:81 120:81 121:82 122:83 123:84 124:85 125:86 126:87 127:87 128:88 129:89 130:90 131:91 132:91 133:91 134:92 135:92 136:92 137:92 138:93 139:94 140:94 141:95 142:96 143:97 144:98 145:99 146:100 147:101 148:102 149:102 150:103 151:104 152:105 153:106 154:107 155:108 156:109 157:110 158:111 159:112 160:113 161:114 162:115 163:115 164:115 165:116 166:117 167:118 168:118 169:119 170:120 171:121 172:122 173:123 174:123
11/08/2018 17:50:18 - INFO - __main__ -   token_is_max_context: 17:True 18:True 19:True 20:True 21:True 22:True 23:True 24:True 25:True 26:True 27:True 28:True 29:True 30:True 31:True 32:True 33:True 34:True 35:True 36:True 37:True 38:True 39:True 40:True 41:True 42:True 43:True 44:True 45:True 46:True 47:True 48:True 49:True 50:True 51:True 52:True 53:True 54:True 55:True 56:True 57:True 58:True 59:True 60:True 61:True 62:True 63:True 64:True 65:True 66:True 67:True 68:True 69:True 70:True 71:True 72:True 73:True 74:True 75:True 76:True 77:True 78:True 79:True 80:True 81:True 82:True 83:True 84:True 85:True 86:True 87:True 88:True 89:True 90:True 91:True 92:True 93:True 94:True 95:True 96:True 97:True 98:True 99:True 100:True 101:True 102:True 103:True 104:True 105:True 106:True 107:True 108:True 109:True 110:True 111:True 112:True 113:True 114:True 115:True 116:True 117:True 118:True 119:True 120:True 121:True 122:True 123:True 124:True 125:True 126:True 127:True 128:True 129:True 130:True 131:True 132:True 133:True 134:True 135:True 136:True 137:True 138:True 139:True 140:True 141:True 142:True 143:True 144:True 145:True 146:True 147:True 148:True 149:True 150:True 151:True 152:True 153:True 154:True 155:True 156:True 157:True 158:True 159:True 160:True 161:True 162:True 163:True 164:True 165:True 166:True 167:True 168:True 169:True 170:True 171:True 172:True 173:True 174:True
11/08/2018 17:50:18 - INFO - __main__ -   input_ids: 101 2000 3183 2106 1996 6261 2984 9382 3711 1999 8517 1999 10223 26371 2605 1029 102 6549 2135 1010 1996 2082 2038 1037 3234 2839 1012 10234 1996 2364 2311 1005 1055 2751 8514 2003 1037 3585 6231 1997 1996 6261 2984 1012 3202 1999 2392 1997 1996 2364 2311 1998 5307 2009 1010 2003 1037 6967 6231 1997 4828 2007 2608 2039 14995 6924 2007 1996 5722 1000 2310 3490 2618 4748 2033 18168 5267 1000 1012 2279 2000 1996 2364 2311 2003 1996 13546 1997 1996 6730 2540 1012 3202 2369 1996 13546 2003 1996 24665 23052 1010 1037 14042 2173 1997 7083 1998 9185 1012 2009 2003 1037 15059 1997 1996 24665 23052 2012 10223 26371 1010 2605 2073 1996 6261 2984 22353 2135 2596 2000 3002 16595 9648 4674 2061 12083 9711 2271 1999 8517 1012 2012 1996 2203 1997 1996 2364 3298 1006 1998 1999 1037 3622 2240 2008 8539 2083 1017 11342 1998 1996 2751 8514 1007 1010 2003 1037 3722 1010 2715 2962 6231 1997 2984 1012 102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
11/08/2018 17:50:18 - INFO - __main__ -   input_mask: 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

... [truncated] ...

Iteration: 100%|█████████▉| 29314/29324 [3:27:55<00:04,  2.36it/s][A

Iteration: 100%|█████████▉| 29315/29324 [3:27:55<00:03,  2.44it/s][A

Iteration: 100%|█████████▉| 29316/29324 [3:27:56<00:03,  2.26it/s][A

Iteration: 100%|█████████▉| 29317/29324 [3:27:56<00:02,  2.35it/s][A

Iteration: 100%|█████████▉| 29318/29324 [3:27:56<00:02,  2.44it/s][A

Iteration: 100%|█████████▉| 29319/29324 [3:27:57<00:02,  2.25it/s][A

Iteration: 100%|█████████▉| 29320/29324 [3:27:57<00:01,  2.35it/s][A

Iteration: 100%|█████████▉| 29321/29324 [3:27:58<00:01,  2.41it/s][A

Iteration: 100%|█████████▉| 29322/29324 [3:27:58<00:00,  2.25it/s][A

Iteration: 100%|█████████▉| 29323/29324 [3:27:59<00:00,  2.36it/s][ATraceback (most recent call last):
  File "code/run_squad.py", line 929, in <module>
    main()
  File "code/run_squad.py", line 862, in main
    loss = model(input_ids, segment_ids, input_mask, start_positions, end_positions)
  File "/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py", line 477, in __call__
    result = self.forward(*input, **kwargs)
  File "/0x0d4ff90d01fa4168983197b17d73bb0c_dependencies/code/modeling.py", line 467, in forward
    start_loss = loss_fct(start_logits, start_positions)
  File "/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py", line 477, in __call__
    result = self.forward(*input, **kwargs)
  File "/usr/local/lib/python3.6/dist-packages/torch/nn/modules/loss.py", line 862, in forward
    ignore_index=self.ignore_index, reduction=self.reduction)
  File "/usr/local/lib/python3.6/dist-packages/torch/nn/functional.py", line 1550, in cross_entropy
    return nll_loss(log_softmax(input, 1), target, weight, None, ignore_index, None, reduction)
  File "/usr/local/lib/python3.6/dist-packages/torch/nn/functional.py", line 1403, in nll_loss
    if input.size(0) != target.size(0):
RuntimeError: dimension specified as 0 but tensor has no dimensions

Exception ignored in: <bound method tqdm.__del__ of Iteration: 100%|█████████▉| 29323/29324 [3:27:59<00:00,  2.36it/s]>
Traceback (most recent call last):
  File "/usr/local/lib/python3.6/dist-packages/tqdm/_tqdm.py", line 931, in __del__
    self.close()
  File "/usr/local/lib/python3.6/dist-packages/tqdm/_tqdm.py", line 1133, in close
    self._decr_instances(self)
  File "/usr/local/lib/python3.6/dist-packages/tqdm/_tqdm.py", line 496, in _decr_instances
    cls.monitor.exit()
  File "/usr/local/lib/python3.6/dist-packages/tqdm/_monitor.py", line 52, in exit
    self.join()
  File "/usr/lib/python3.6/threading.py", line 1053, in join
    raise RuntimeError("cannot join current thread")
RuntimeError: cannot join current thread
```
Here's the specific command I ran for more context: 
```
python3.6 code/run_squad.py \
  --bert_config_file bert/bert_config.json \
  --vocab_file bert/vocab.txt \
  --output_dir output \
  --train_file data/original/train.json \
  --predict_file data/original/dev.json \
  --init_checkpoint bert-pytorch/pytorch_model.bin \
  --do_lower_case \
  --do_train \
  --do_predict \
  --train_batch_size 10 \
  --gradient_accumulation_steps 3 \
  --accumulate_gradients 3
```<|||||>Hi Kerem, yes I fixed this bug yesterday in commit 2c5d993 (a bug with batches of dimension 1)
You can try again with the current version and it should be fine.

I got good results with these hyperparameters last night:
```bash
python run_squad.py \
  --vocab_file $BERT_BASE_DIR/vocab.txt \
  --bert_config_file $BERT_BASE_DIR/bert_config.json \
  --init_checkpoint $BERT_PYTORCH_DIR/pytorch_model.bin \
  --do_train \
  --do_predict \
  --do_lower_case
  --train_file $SQUAD_DIR/train-v1.1.json \
  --predict_file $SQUAD_DIR/dev-v1.1.json \
  --train_batch_size 12 \
  --learning_rate 3e-5 \
  --num_train_epochs 2.0 \
  --max_seq_length 384 \
  --doc_stride 128 \
  --output_dir ../debug_squad/
```

I found:
```bash
{"f1": 88.52381567990474, "exact_match": 81.22043519394512}
```

Feel free to reopen the issue if needed.