Use  `run_language_modeling.py` to finetune gpt2, but it core unexpectedly.
# ❓ Questions & Help
Use  `run_language_modeling.py` to finetune gpt2, but it core unexpectedly. How can i find the cause of the core please?

## Details

I'm using transformers's `run_language_modeling.py` to finetune gpt2  as follows.

### （1）with gpu
```shell
python3 run_language_modeling.py     --output_dir=/home/xxx/transformers/examples/language-modeling/output_dir     --model_type=gpt2     --model_name_or_path=gpt2     --per_gpu_train_batch_size=1     --do_train     --train_data_file=/home/xxx/data_info/transformer.data     --block_size=512 --save_steps=500 --overwrite_output_dir
```
But it core unexpectedly.
```shell
terminate called after throwing an instance of 'std::runtime_error'                                                                                  | 0/1348 [00:00<?, ?it/s]
  what():  NCCL Error 1: unhandled cuda error
Aborted
```
And the returncode is `134`。
![image](https://user-images.githubusercontent.com/12140508/92548004-863f7080-f288-11ea-8e26-50532f59c516.png)

### (2) with cpu
```shell
python3 run_language_modeling.py     --output_dir=/home/xxx/transformers/examples/language-modeling/output_dir     --model_type=gpt2     --model_name_or_path=gpt2        --do_train     --train_data_file=/home/xxx/data_info/transformer.data     --block_size=512 --save_steps=500 --overwrite_output_dir --no_cuda 
```
At first the training is normal , but will quit after a while with return code `137`.
![image](https://user-images.githubusercontent.com/12140508/92548295-39a86500-f289-11ea-86c1-d91d13121162.png)

### (3) question
The dataset i use is just a `data.txt` which is a file combined with  multiple articles ,  and a  `<|endoftext|>` is added at the end of each article. How can i find the cause of the core please? Hope for your help. Thanks a lot.

Hi, do you mind copy-pasting the text in your terminal instead of putting images? It'll be easier for us to understand, to debug, and for other users to search for similar issues. Thanks!<|||||>> Hi, do you mind copy-pasting the text in your terminal instead of putting images? It'll be easier for us to understand, to debug, and for other users to search for similar issues. Thanks!

I've figure out the problem of gpu core.  According to https://github.com/pytorch/pytorch/issues/31285, my gpu card is not supported by pytorch. I'm trying to build pytorch from source.
But it's weird when i using `run_language_modeling.py ` with `--no_cuda `. There is no error message.
The command i used is 
```shell
python3 run_language_modeling.py     --output_dir=/home/xxx/gpt_model/transformers/examples/language-modeling/output_dir     --model_type=gpt2     --model_name_or_path=gpt2     --per_device_train_batch_size=1     --do_train     --train_data_file=/home/xxx/gpt_model/data_info/transformer.data     --block_size=512 --save_steps=500 --overwrite_output_dir --no_cuda

```
The output message is 
```shell
09/09/2020 16:23:23 - WARNING - __main__ -   Process rank: -1, device: cpu, n_gpu: 0, distributed training: False, 16-bits training: False
09/09/2020 16:23:23 - INFO - __main__ -   Training/evaluation parameters TrainingArguments(output_dir='/home/xxx/gpt_model/transformers/examples/language-modeling/output_dir', overwrite_output_dir=True, do_train=True, do_eval=False, do_predict=False, evaluate_during_training=False, prediction_loss_only=False, per_device_train_batch_size=1, per_device_eval_batch_size=8, per_gpu_train_batch_size=None, per_gpu_eval_batch_size=None, gradient_accumulation_steps=1, learning_rate=5e-05, weight_decay=0.0, adam_beta1=0.9, adam_beta2=0.999, adam_epsilon=1e-08, max_grad_norm=1.0, num_train_epochs=3.0, max_steps=-1, warmup_steps=0, logging_dir='runs/Sep09_16-23-23_TENCENT64.site', logging_first_step=False, logging_steps=500, save_steps=500, save_total_limit=None, no_cuda=True, seed=42, fp16=False, fp16_opt_level='O1', local_rank=-1, tpu_num_cores=None, tpu_metrics_debug=False, debug=False, dataloader_drop_last=False, eval_steps=1000, past_index=-1, run_name=None, disable_tqdm=False, remove_unused_columns=True)
/home/xxx/anaconda3/envs/transformers/lib/python3.6/site-packages/transformers/modeling_auto.py:821: FutureWarning: The class `AutoModelWithLMHead` is deprecated and will be removed in a future version. Please use `AutoModelForCausalLM` for causal language models, `AutoModelForMaskedLM` for masked language models and `AutoModelForSeq2SeqLM` for encoder-decoder models.
  FutureWarning,
/home/xxx/anaconda3/envs/transformers/lib/python3.6/site-packages/transformers/tokenization_utils_base.py:1321: FutureWarning: The `max_len` attribute has been deprecated and will be removed in a future version, use `model_max_length` instead.
  FutureWarning,
09/09/2020 16:23:31 - INFO - filelock -   Lock 140250526322472 acquired on /home/xxx/gpt_model/data_info/cached_lm_GPT2Tokenizer_512_transformer.data.lock
09/09/2020 16:23:32 - INFO - filelock -   Lock 140250526322472 released on /home/xxx/gpt_model/data_info/cached_lm_GPT2Tokenizer_512_transformer.data.lock


/home/xxx/anaconda3/envs/transformers/lib/python3.6/site-packages/transformers/trainer.py:247: FutureWarning: Passing `prediction_loss_only` as a keyword argument is deprecated and won't be possible in a future version. Use `args.prediction_loss_only` instead.
  FutureWarning,
<library>In get_train_dataloader ,tarin_batch_size =  1
Epoch:   0%|                                                                                                                                            | 0/3 

Killedion:   4%|████▎                                                                                                                    | 194/5390 [04:30<2:58:02,  2.06s/it]  
```
And `echo $?`, the return code is `137`. Thanks a lot.<|||||>The return code 137 means that you have an out of memory error. Do you get the same error if you use `distilgpt2` with a `--block_size=64`? (Just for testing purposes).

We've also recently patched a memory error on the `Trainer`, could you install from source to benefit from the fix? You can do so as such:

`pip install git+https://github.com/huggingface/transformers`<|||||>> pip install git+https://github.com/huggingface/transformers

Yes, I found `Out of memory` log in `/var/log/messages` and it turns out the process use a lot of memory.
```shell
Sep  9 17:10:44 centos kernel: Out of memory: Kill process 126138 (python3) score 939 or sacrifice child
Sep  9 17:10:44 centos kernel: Killed process 126170 (python3) total-vm:325690732kB, anon-rss:125105968kB, file-rss:0kB
```
And this is my machine info (caused in shell with `top` command)
```shell
top - 17:47:55 up 303 days,  2:35,  4 users,  load average: 1.14, 2.98, 3.44
Tasks: 468 total,   1 running, 467 sleeping,   0 stopped,   0 zombie
%Cpu(s):  0.0 us,  0.0 sy,  0.0 ni,100.0 id,  0.0 wa,  0.0 hi,  0.0 si,  0.0 st
KiB Mem : 13132300+total, 12383201+free,  3294300 used,  4196688 buff/cache
KiB Swap:  2088956 total,       12 free,  2088944 used. 12565272+avail Mem 

   PID USER      PR  NI    VIRT    RES    SHR S  %CPU %MEM     TIME+ COMMAND                                                                                                 
  2868 root      20   0 3006004  19072      0 S   0.3  0.0 225:15.55 dockerd                                                                                                 
```
Then i use `pip uninstall transformers;  pip install git+https://github.com/huggingface/transformers` to reinstall the `transformers` library, and run the `run_language_modeling.py` again.
```shell
python3 run_language_modeling.py     --output_dir=/home/xxx/gpt_model/transformers/examples/language-modeling/output_dir --model_type=gpt2  --model_name_or_path=distilgpt2 --per_device_train_batch_size=1     --do_train     --train_data_file=/home/xxx/gpt_model/data_info/data.txt --block_size=64 --save_steps=500 --overwrite_output_dir --no_cuda
```
This is the memory info showed by `top` after i run `run_language_modeling.py` with cpu.
```shell
top - 17:51:40 up 303 days,  2:38,  4 users,  load average: 23.42, 9.30, 5.50
Tasks: 463 total,   2 running, 461 sleeping,   0 stopped,   0 zombie
%Cpu(s): 34.4 us, 49.5 sy,  0.0 ni, 16.1 id,  0.0 wa,  0.0 hi,  0.0 si,  0.0 st
KiB Mem : 13132300+total, 12142824+free,  5283664 used,  4611096 buff/cache
KiB Swap:  2088956 total,       12 free,  2088944 used. 12365893+avail Mem 

   PID USER      PR  NI    VIRT    RES    SHR S  %CPU %MEM     TIME+ COMMAND                                                                                                 
 25414 xxx   20   0  0.188t 1.945g  83876 R  3356  1.6  47:31.03 python3   
```
It already runs about two hours and works well. Thanks a lot.<|||||>By the way, how can i use the fine-tuned model please? There are 6 files generated under `output_dir/checkpoint`, which are 
`config.json  log_history.json  optimizer.pt  pytorch_model.bin  scheduler.pt  training_args.bin`. Then how can i use them? Should i just use them as follows? Does the fine-tuned model need to be renamed? Which level of the checkpoint directory should I specify？ Thanks a lot.
```shell
tokenizer = GPT2Tokenizer.from_pretrained('/home/xxx/gpt_model/transformers/examples/language-modeling/output_dir/checkpoint-15000')
``` 
<|||||>Cool! Yes, the way you load the model is correct. I'm guessing you want to use the resulting model, so given that you passed the following as `--output_dir`: `--output_dir=/home/xxx/gpt_model/transformers/examples/language-modeling/output_dir`, you should be able to load it like

```py
tokenizer = GPT2Tokenizer.from_pretrained('/home/xxx/gpt_model/transformers/examples/language-modeling/output_dir')
model = GPT2LMHeadModel.from_pretrained('/home/xxx/gpt_model/transformers/examples/language-modeling/output_dir')

```<|||||>> Cool! Yes, the way you load the model is correct. I'm guessing you want to use the resulting model, so given that you passed the following as `--output_dir`: `--output_dir=/home/xxx/gpt_model/transformers/examples/language-modeling/output_dir`, you should be able to load it like
> 
> ```python
> tokenizer = GPT2Tokenizer.from_pretrained('/home/xxx/gpt_model/transformers/examples/language-modeling/output_dir')
> model = GPT2LMHeadModel.from_pretrained('/home/xxx/gpt_model/transformers/examples/language-modeling/output_dir')
> ```

Got it! 
I use `--output_dir`: `--output_dir=/home/xxx/gpt_model/transformers/examples/language-modeling/output_dir` and there are many checkpoint generate under it.  Just choose one of them, for example, `checkpoint-14000` and copy `'vocab.json', 'merges.txt'` into `/home/xxx/gpt_model/transformers/examples/language-modeling/output_dir/checkpoint-14000`.
```shell
/home/xxx/gpt_model/transformers/examples/language-modeling/output_dir/checkpoint-14000 >>> wget https://cdn.huggingface.co/distilgpt2-vocab.json -O vocab.json
/home/xxx/gpt_model/transformers/examples/language-modeling/output_dir/checkpoint-14000 >>> wget https://cdn.huggingface.co/distilgpt2-merges.txt -O merges.txt
```
Then i use the [run_generation.py](https://github.com/huggingface/transformers/blob/master/examples/text-generation/run_generation.py) like follows and it generate text as expect.
```shell
python run_generation.py --model_type=gpt2 --model_name_or_path=/home/xxx/gpt_model/transformers/examples/language-modeling/output_dir/checkpoint-14000  --no_cuda
```
Thank you for your help! <|||||>Very cool, glad you got it to work! Let us know if you face any other issues.<|||||>It seems like I still have the same issue. I try to use run_language_modeling.py to train a small-bert model (6 layer) from scratch. The process is killed after about 3 hours of training. The error message is simply "Killed" and I observes there's a constant increasing usage of memory. So I think the issue is also OOM

```
top - 16:36:33 up  5:29,  1 user,  load average: 1.07, 1.19, 1.16
Tasks: 353 total,   3 running, 349 sleeping,   0 stopped,   1 zombie
%Cpu(s): 10.4 us,  1.2 sy,  0.0 ni, 88.1 id,  0.1 wa,  0.0 hi,  0.2 si,  0.0 st
MiB Mem :  16010.9 total,    151.5 free,   3386.3 used,  12473.2 buff/cache
MiB Swap:   2048.0 total,   1550.2 free,    497.8 used.  12181.5 avail Mem 

```
Is this normal? I can't think of a reason why it would use this much memory.