[GPT2] Loss NaN after some time with FP16
Author: ksjae
Pull request: no
Labels: name: wontfix

## Environment info
<!-- You can run the command `transformers-cli env` and copy-and-paste its output below.
     Don't forget to fill out the missing fields in that output! -->
     
- `transformers` version: 3.4.0
- Platform: Linux-4.4.0-176-generic-x86_64-with-glibc2.17
- Python version: 3.8.6
- PyTorch version (GPU?): 1.7.0+cu101 (True)
- Tensorflow version (GPU?): not installed (NA)
- Using GPU in script?: yes
- Using distributed or parallel set-up in script?: yes, HF datasets


### Who can help
 
@LysandreJik 
@sgugger

## Information

Model I am using (Bert, XLNet ...): GPT2

The problem arises when using:
* [ ] the official example scripts: 
* [x] my own modified scripts: examples/language_modeling/run_language_modeling.py

The tasks I am working on is:
* [ ] an official GLUE/SQUaD task: (give the name)
* [x] my own task or dataset

## To reproduce

Steps to reproduce the behavior:

1. Run with ```--fp16 --n_ctx 2048```
2. 
```
  warnings.warn('Was asked to gather along dimension 0, but all '
[W python_anomaly_mode.cpp:104] Warning: Error detected in SoftmaxBackward. Traceback of forward call that caused the error:
  File "/usr/lib/python3.8/threading.py", line 890, in _bootstrap
    self._bootstrap_inner()
  File "/usr/lib/python3.8/threading.py", line 932, in _bootstrap_inner
    self.run()
  File "/usr/lib/python3.8/threading.py", line 870, in run
    self._target(*self._args, **self._kwargs)
  File "/home/ubuntu/.local/lib/python3.8/site-packages/torch/nn/parallel/parallel_apply.py", line 61, in _worker
    output = module(*input, **kwargs)
  File "/home/ubuntu/.local/lib/python3.8/site-packages/torch/nn/modules/module.py", line 727, in _call_impl
    result = self.forward(*input, **kwargs)
  File "/home/ubuntu/.local/lib/python3.8/site-packages/transformers/modeling_gpt2.py", line 765, in forward
    transformer_outputs = self.transformer(
  File "/home/ubuntu/.local/lib/python3.8/site-packages/torch/nn/modules/module.py", line 727, in _call_impl
    result = self.forward(*input, **kwargs)
  File "/home/ubuntu/.local/lib/python3.8/site-packages/transformers/modeling_gpt2.py", line 645, in forward
    outputs = block(
  File "/home/ubuntu/.local/lib/python3.8/site-packages/torch/nn/modules/module.py", line 727, in _call_impl
    result = self.forward(*input, **kwargs)
  File "/home/ubuntu/.local/lib/python3.8/site-packages/transformers/modeling_gpt2.py", line 285, in forward
    attn_outputs = self.attn(
  File "/home/ubuntu/.local/lib/python3.8/site-packages/torch/nn/modules/module.py", line 727, in _call_impl
    result = self.forward(*input, **kwargs)
  File "/home/ubuntu/.local/lib/python3.8/site-packages/transformers/modeling_gpt2.py", line 235, in forward
    attn_outputs = self._attn(query, key, value, attention_mask, head_mask, output_attentions)
  File "/home/ubuntu/.local/lib/python3.8/site-packages/transformers/modeling_gpt2.py", line 176, in _attn
    w = nn.Softmax(dim=-1)(w)
  File "/home/ubuntu/.local/lib/python3.8/site-packages/torch/nn/modules/module.py", line 727, in _call_impl
    result = self.forward(*input, **kwargs)
  File "/home/ubuntu/.local/lib/python3.8/site-packages/torch/nn/modules/activation.py", line 1198, in forward
    return F.softmax(input, self.dim, _stacklevel=5)
  File "/home/ubuntu/.local/lib/python3.8/site-packages/torch/nn/functional.py", line 1512, in softmax
    ret = input.softmax(dim)
 (function _print_stack)
Traceback (most recent call last):
  File "examples/language-modeling/run_language_modeling.py", line 349, in <module>
    main()
  File "examples/language-modeling/run_language_modeling.py", line 313, in main
    trainer.train(model_path=model_path)
  File "/home/ubuntu/.local/lib/python3.8/site-packages/transformers/trainer.py", line 756, in train
    tr_loss += self.training_step(model, inputs)
  File "/home/ubuntu/.local/lib/python3.8/site-packages/transformers/trainer.py", line 1065, in training_step
    self.scaler.scale(loss).backward()
  File "/home/ubuntu/.local/lib/python3.8/site-packages/torch/tensor.py", line 221, in backward
    torch.autograd.backward(self, gradient, retain_graph, create_graph)
  File "/home/ubuntu/.local/lib/python3.8/site-packages/torch/autograd/__init__.py", line 130, in backward
    Variable._execution_engine.run_backward(
RuntimeError: Function 'SoftmaxBackward' returned nan values in its 0th output.
  0%|                                              | 0/19506024 [00:25<?, ?it/s]
```

## Expected behavior

Not print Nan


Comments:
We can't really help without seeing the code you are running. Some of the models do not support FP16 for instance, and we have no idea which model you are using.

Oh, sorry. It's the example script ```examples/language_modeling/run_language_modeling.py``` but with modified data loader.

full code below:
```
import logging
import math
import os
import glob
import datasets
from dataclasses import dataclass, field
from typing import Optional

from datasets import list_datasets, load_dataset

from transformers import (
    CONFIG_MAPPING,
    MODEL_WITH_LM_HEAD_MAPPING,
    AutoConfig,
    AutoModelWithLMHead,
    AutoTokenizer,
    DataCollatorForLanguageModeling,
    DataCollatorForPermutationLanguageModeling,
    HfArgumentParser,
    LineByLineTextDataset,
    PreTrainedTokenizer,
    TextDataset,
    Trainer,
    TrainingArguments,
    set_seed,
)


logger = logging.getLogger(__name__)


MODEL_CONFIG_CLASSES = list(MODEL_WITH_LM_HEAD_MAPPING.keys())
MODEL_TYPES = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)


@dataclass
class ModelArguments:
    """
    Arguments pertaining to which model/config/tokenizer we are going to fine-tune, or train from scratch.
    """

    model_name_or_path: Optional[str] = field(
        default=None,
        metadata={
            "help": "The model checkpoint for weights initialization. Leave None if you want to train a model from scratch."
        },
    )
    model_type: Optional[str] = field(
        default=None,
        metadata={"help": "If training from scratch, pass a model type from the list: " + ", ".join(MODEL_TYPES)},
    )
    config_name: Optional[str] = field(
        default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"}
    )
    tokenizer_name: Optional[str] = field(
        default=None, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}
    )
    cache_dir: Optional[str] = field(
        default=None, metadata={"help": "Where do you want to store the pretrained models downloaded from s3"}
    )


@dataclass
class DataTrainingArguments:
    """
    Arguments pertaining to what data we are going to input our model for training and eval.
    """

    train_data_file: Optional[str] = field(
        default=None, metadata={"help": "The input training data file (a text file)."}
    )
    eval_data_file: Optional[str] = field(
        default=None,
        metadata={"help": "An optional input evaluation data file to evaluate the perplexity on (a text file)."},
    )
    line_by_line: bool = field(
        default=False,
        metadata={"help": "Whether distinct lines of text in the dataset are to be handled as distinct sequences."},
    )

    mlm: bool = field(
        default=False, metadata={"help": "Train with masked-language modeling loss instead of language modeling."}
    )
    mlm_probability: float = field(
        default=0.15, metadata={"help": "Ratio of tokens to mask for masked language modeling loss"}
    )
    plm_probability: float = field(
        default=1 / 6,
        metadata={
            "help": "Ratio of length of a span of masked tokens to surrounding context length for permutation language modeling."
        },
    )
    max_span_length: int = field(
        default=5, metadata={"help": "Maximum length of a span of masked tokens for permutation language modeling."}
    )

    block_size: int = field(
        default=-1,
        metadata={
            "help": "Optional input sequence length after tokenization."
            "The training dataset will be truncated in block of this size for training."
            "Default to the model max input length for single sentence inputs (take into account special tokens)."
        },
    )
    overwrite_cache: bool = field(
        default=False, metadata={"help": "Overwrite the cached training and evaluation sets"}
    )
    arrow: bool = field(
        default=True,
        metadata={
            "help": "Use Arrow-based HF NLP for optimization."
        },
    )


def get_dataset(
    args: DataTrainingArguments,
    tokenizer: PreTrainedTokenizer,
    evaluate: bool = False,
    cache_dir: Optional[str] = "./cache",
):
    tokenizer.pad_token = "
"
    tokenizer._pad_token = "
"
    #tokenizer.pad_token_id = 50256
    file_path = args.eval_data_file if evaluate else args.train_data_file
    if True:
        dataset = datasets.load_from_disk(file_path)
        dataset.set_format(type='torch', columns=['input_ids'])
        return dataset
    
    if False:
        dataset = load_dataset("text", data_files=[file_path], split='train')
        dataset = dataset.map(lambda ex: tokenizer(ex["text"], add_special_tokens=True,
                                                truncation=True, max_length=args.block_size), batched=True)
        dataset.set_format(type='torch', columns=['input_ids'])
        dataset.save_to_disk(file_path+'.arrow')
        return dataset
    
    if args.line_by_line:
        return LineByLineTextDataset(tokenizer=tokenizer, file_path=file_path, block_size=args.block_size)
    else:
        return TextDataset(
            tokenizer=tokenizer,
            file_path=file_path,
            block_size=args.block_size,
            overwrite_cache=args.overwrite_cache,
            cache_dir=cache_dir,
        )
    """
    dataset = load_dataset("text", data_files=file_path, split="train")
    dataset = dataset.map(lambda ex: tokenizer(ex["text"], add_special_tokens=True,
                                            truncation=True, max_length=args.block_size), batched=True)
    dataset.set_format(type='torch', columns=['input_ids'])
    return dataset
    """

def main():
    # See all possible arguments in src/transformers/training_args.py
    # or by passing the --help flag to this script.
    # We now keep distinct sets of args, for a cleaner separation of concerns.

    parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments))
    model_args, data_args, training_args = parser.parse_args_into_dataclasses()

    if data_args.eval_data_file is None and training_args.do_eval:
        raise ValueError(
            "Cannot do evaluation without an evaluation data file. Either supply a file to --eval_data_file "
            "or remove the --do_eval argument."
        )

    if (
        os.path.exists(training_args.output_dir)
        and os.listdir(training_args.output_dir)
        and training_args.do_train
        and not training_args.overwrite_output_dir
    ):
        raise ValueError(
            f"Output directory ({training_args.output_dir}) already exists and is not empty. Use --overwrite_output_dir to overcome."
        )

    # Setup logging
    logging.basicConfig(
        format="%(asctime)s - %(levelname)s - %(name)s -   %(message)s",
        datefmt="%m/%d/%Y %H:%M:%S",
        level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN,
    )
    logger.warning(
        "Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s",
        training_args.local_rank,
        training_args.device,
        training_args.n_gpu,
        bool(training_args.local_rank != -1),
        training_args.fp16,
    )
    logger.info("Training/evaluation parameters %s", training_args)

    # Set seed
    set_seed(training_args.seed)

    # Load pretrained model and tokenizer
    #
    # Distributed training:
    # The .from_pretrained methods guarantee that only one local process can concurrently
    # download model & vocab.

    if model_args.config_name:
        config = AutoConfig.from_pretrained(model_args.config_name, cache_dir=model_args.cache_dir)
    elif model_args.model_name_or_path:
        config = AutoConfig.from_pretrained(model_args.model_name_or_path, cache_dir=model_args.cache_dir)
    else:
        config = CONFIG_MAPPING[model_args.model_type]()
        logger.warning("You are instantiating a new config instance from scratch.")

    if model_args.tokenizer_name:
        tokenizer = AutoTokenizer.from_pretrained(model_args.tokenizer_name, cache_dir=model_args.cache_dir)
    elif model_args.model_name_or_path:
        tokenizer = AutoTokenizer.from_pretrained(model_args.model_name_or_path, cache_dir=model_args.cache_dir)
    else:
        raise ValueError(
            "You are instantiating a new tokenizer from scratch. This is not supported, but you can do it from another script, save it,"
            "and load it from here, using --tokenizer_name"
        )

    tokenizer.pad_token = "
"
    tokenizer._pad_token = "
"

    if model_args.model_name_or_path:
        model = AutoModelWithLMHead.from_pretrained(
            model_args.model_name_or_path,
            from_tf=bool(".ckpt" in model_args.model_name_or_path),
            config=config,
            cache_dir=model_args.cache_dir,
        )
    else:
        logger.info("Training new model from scratch")
        model = AutoModelWithLMHead.from_config(config)

    model.resize_token_embeddings(len(tokenizer))

    if config.model_type in ["bert", "roberta", "distilbert", "camembert"] and not data_args.mlm:
        raise ValueError(
            "BERT and RoBERTa-like models do not have LM heads but masked LM heads. They must be run using the"
            "--mlm flag (masked language modeling)."
        )

    if data_args.block_size <= 0:
        data_args.block_size = tokenizer.max_len
        # Our input block size will be the max possible for the model
    else:
        data_args.block_size = min(data_args.block_size, tokenizer.max_len)

    # Get datasets

    train_dataset = (
        get_dataset(data_args, tokenizer=tokenizer, cache_dir=model_args.cache_dir) if training_args.do_train else None
    )
    eval_dataset = (
        get_dataset(data_args, tokenizer=tokenizer, evaluate=True, cache_dir=model_args.cache_dir)
        if training_args.do_eval
        else None
    )
    if config.model_type == "xlnet":
        data_collator = DataCollatorForPermutationLanguageModeling(
            tokenizer=tokenizer,
            plm_probability=data_args.plm_probability,
            max_span_length=data_args.max_span_length,
        )
    else:
        data_collator = DataCollatorForLanguageModeling(
            tokenizer=tokenizer, mlm=data_args.mlm, mlm_probability=data_args.mlm_probability
        )

    # Initialize our Trainer
    trainer = Trainer(
        model=model,
        args=training_args,
        data_collator=data_collator,
        train_dataset=train_dataset,
        eval_dataset=eval_dataset,
        prediction_loss_only=True,
    )

    # Training
    if training_args.do_train:
        model_path = (
            model_args.model_name_or_path
            if model_args.model_name_or_path is not None and os.path.isdir(model_args.model_name_or_path)
            else None
        )
        trainer.train(model_path=model_path)
        trainer.save_model()
        # For convenience, we also re-save the tokenizer to the same directory,
        # so that you can share your model easily on huggingface.co/models =)
        if trainer.is_world_master():
            tokenizer.save_pretrained(training_args.output_dir)

    # Evaluation
    results = {}
    if training_args.do_eval:
        logger.info("*** Evaluate ***")

        eval_output = trainer.evaluate()

        perplexity = math.exp(eval_output["eval_loss"])
        result = {"perplexity": perplexity}

        output_eval_file = os.path.join(training_args.output_dir, "eval_results_lm.txt")
        if trainer.is_world_master():
            with open(output_eval_file, "w") as writer:
                logger.info("***** Eval results *****")
                for key in sorted(result.keys()):
                    logger.info("  %s = %s", key, str(result[key]))
                    writer.write("%s = %s\n" % (key, str(result[key])))

        results.update(result)

    return results


def _mp_fn(index):
    # For xla_spawn (TPUs)
    main()


if __name__ == "__main__":
    main()
```

Thanks, and which type of model do you use (e.g., what's the full command you launch)?

```
python3.7 examples/language-modeling/run_language_modeling.py --output_dir=kogpt1 --model_type=gpt2 --do_train --train_data_file=/home/ksjae/kogpt-2/data/NEWS_ARROW --overwrite_output_dir --per_device_train_batch_size=12 --per_device_eval_batch_size=8 --save_steps 10000 --num_train_epochs=1 --block_size 2048 --eval_steps 25000 --logging_steps=1000 --tokenizer_name kotok --model_name_or_path gpt2-medium --fp16
```

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