transformers: ValueError: You are attempting to pad samples but the tokenizer you are using (GPT2Tokenizer) does not have one.

🐛 Bug

Information

Model I am using (Bert, XLNet …):GPT2

Language I am using the model on (English, Chinese …):English

The problem arises when using:

my own modified scripts: (give details below) python examples/run_language_modeling.py
–train_data_file=temp_gpt2/gpt2.train
–output_dir=checkpoints/gpt2
–model_type=gpt2
–model_name_or_path=gpt2
–eval_data_file=temp_gpt2/test.txt
–line_by_line
–do_train
–do_eval
–evaluate_during_training
–per_gpu_train_batch_size=20
–per_gpu_eval_batch_size=20
–gradient_accumulation_steps=1
–learning_rate=8e-5
–weight_decay=0.075
–adam_epsilon=1e-8
–warmup_steps=500
–max_grad_norm=5.0
–num_train_epochs=20
–logging_steps=500
–save_steps=500

The tasks I am working on is:

  • an official GLUE/SQUaD task: language modeling
  • my own task or dataset: (give details below) Conll2014 GEC

To reproduce

Steps to reproduce the behavior:

  1. run the script I get the following error:
bash train_gpt2.sh 
05/02/2020 10:14:25 - INFO - transformers.training_args -   PyTorch: setting up devices
05/02/2020 10:14:25 - WARNING - __main__ -   Process rank: -1, device: cuda, n_gpu: 2, distributed training: False, 16-bits training: False
05/02/2020 10:14:25 - INFO - __main__ -   Training/evaluation parameters TrainingArguments(output_dir='checkpoints/gpt2', overwrite_output_dir=False, do_train=True, do_eval=True, do_predict=False, evaluate_during_training=True, per_gpu_train_batch_size=20, per_gpu_eval_batch_size=20, gradient_accumulation_steps=1, learning_rate=8e-05, weight_decay=0.075, adam_epsilon=1e-08, max_grad_norm=5.0, num_train_epochs=20.0, max_steps=-1, warmup_steps=500, logging_dir=None, logging_first_step=False, logging_steps=500, save_steps=500, save_total_limit=None, no_cuda=False, seed=42, fp16=False, fp16_opt_level='O1', local_rank=-1)
05/02/2020 10:14:35 - INFO - transformers.configuration_utils -   loading configuration file https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-config.json from cache at /home/zixi/.cache/torch/transformers/4be02c5697d91738003fb1685c9872f284166aa32e061576bbe6aaeb95649fcf.085d5f6a8e7812ea05ff0e6ed0645ab2e75d80387ad55c1ad9806ee70d272f80
05/02/2020 10:14:35 - INFO - transformers.configuration_utils -   Model config GPT2Config {
  "activation_function": "gelu_new",
  "attn_pdrop": 0.1,
  "bos_token_id": 50256,
  "embd_pdrop": 0.1,
  "eos_token_id": 50256,
  "initializer_range": 0.02,
  "layer_norm_epsilon": 1e-05,
  "model_type": "gpt2",
  "n_ctx": 1024,
  "n_embd": 768,
  "n_head": 12,
  "n_layer": 12,
  "n_positions": 1024,
  "resid_pdrop": 0.1,
  "summary_activation": null,
  "summary_first_dropout": 0.1,
  "summary_proj_to_labels": true,
  "summary_type": "cls_index",
  "summary_use_proj": true,
  "vocab_size": 50257
}

05/02/2020 10:14:39 - INFO - transformers.configuration_utils -   loading configuration file https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-config.json from cache at /home/zixi/.cache/torch/transformers/4be02c5697d91738003fb1685c9872f284166aa32e061576bbe6aaeb95649fcf.4c1d7fc2ac6ddabeaf0c8bec2ffc7dc112f668f5871a06efcff113d2797ec7d5
05/02/2020 10:14:39 - INFO - transformers.configuration_utils -   Model config GPT2Config {
  "activation_function": "gelu_new",
  "architectures": [
    "GPT2LMHeadModel"
  ],
  "attn_pdrop": 0.1,
  "bos_token_id": 50256,
  "embd_pdrop": 0.1,
  "eos_token_id": 50256,
  "initializer_range": 0.02,
  "layer_norm_epsilon": 1e-05,
  "model_type": "gpt2",
  "n_ctx": 1024,
  "n_embd": 768,
  "n_head": 12,
  "n_layer": 12,
  "n_positions": 1024,
  "resid_pdrop": 0.1,
  "summary_activation": null,
  "summary_first_dropout": 0.1,
  "summary_proj_to_labels": true,
  "summary_type": "cls_index",
  "summary_use_proj": true,
  "vocab_size": 50257
}

05/02/2020 10:14:42 - INFO - transformers.tokenization_utils -   loading file https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-vocab.json from cache at /home/zixi/.cache/torch/transformers/f2808208f9bec2320371a9f5f891c184ae0b674ef866b79c58177067d15732dd.1512018be4ba4e8726e41b9145129dc30651ea4fec86aa61f4b9f40bf94eac71
05/02/2020 10:14:42 - INFO - transformers.tokenization_utils -   loading file https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-merges.txt from cache at /home/zixi/.cache/torch/transformers/d629f792e430b3c76a1291bb2766b0a047e36fae0588f9dbc1ae51decdff691b.70bec105b4158ed9a1747fea67a43f5dee97855c64d62b6ec3742f4cfdb5feda
05/02/2020 10:14:43 - INFO - transformers.modeling_utils -   loading weights file https://cdn.huggingface.co/gpt2-pytorch_model.bin from cache at /home/zixi/.cache/torch/transformers/d71fd633e58263bd5e91dd3bde9f658bafd81e11ece622be6a3c2e4d42d8fd89.778cf36f5c4e5d94c8cd9cefcf2a580c8643570eb327f0d4a1f007fab2acbdf1
05/02/2020 10:14:47 - INFO - transformers.modeling_utils -   Weights of GPT2LMHeadModel not initialized from pretrained model: ['h.0.attn.masked_bias', 'h.1.attn.masked_bias', 'h.2.attn.masked_bias', 'h.3.attn.masked_bias', 'h.4.attn.masked_bias', 'h.5.attn.masked_bias', 'h.6.attn.masked_bias', 'h.7.attn.masked_bias', 'h.8.attn.masked_bias', 'h.9.attn.masked_bias', 'h.10.attn.masked_bias', 'h.11.attn.masked_bias', 'lm_head.weight']
05/02/2020 10:14:47 - INFO - transformers.data.datasets.language_modeling -   Creating features from dataset file at temp_gpt2/gpt2_train.txt
05/02/2020 10:16:41 - INFO - transformers.data.datasets.language_modeling -   Creating features from dataset file at temp_gpt2/gpt2_test.txt
05/02/2020 10:16:44 - INFO - transformers.trainer -   ***** Running training *****
05/02/2020 10:16:44 - INFO - transformers.trainer -     Num examples = 1130686
05/02/2020 10:16:44 - INFO - transformers.trainer -     Num Epochs = 20
05/02/2020 10:16:44 - INFO - transformers.trainer -     Instantaneous batch size per GPU = 20
05/02/2020 10:16:44 - INFO - transformers.trainer -     Total train batch size (w. parallel, distributed & accumulation) = 40
05/02/2020 10:16:44 - INFO - transformers.trainer -     Gradient Accumulation steps = 1
05/02/2020 10:16:44 - INFO - transformers.trainer -     Total optimization steps = 565360
Epoch:   0%|                                                                                                                                                         | 0/20 [00:00<?, ?it/sTraceback (most recent call last):                                                                                                                                 | 0/28268 [00:00<?, ?it/s]
  File "examples/run_language_modeling.py", line 284, in <module>
    main()
  File "examples/run_language_modeling.py", line 254, in main
    trainer.train(model_path=model_path)
  File "/home/zixi/anaconda3/envs/ROC/lib/python3.7/site-packages/transformers/trainer.py", line 307, in train
    for step, inputs in enumerate(epoch_iterator):
  File "/home/zixi/anaconda3/envs/ROC/lib/python3.7/site-packages/tqdm/std.py", line 1107, in __iter__
    for obj in iterable:
  File "/home/zixi/anaconda3/envs/ROC/lib/python3.7/site-packages/torch/utils/data/dataloader.py", line 345, in __next__
    data = self._next_data()
  File "/home/zixi/anaconda3/envs/ROC/lib/python3.7/site-packages/torch/utils/data/dataloader.py", line 385, in _next_data
    data = self._dataset_fetcher.fetch(index)  # may raise StopIteration
  File "/home/zixi/anaconda3/envs/ROC/lib/python3.7/site-packages/torch/utils/data/_utils/fetch.py", line 47, in fetch
    return self.collate_fn(data)
  File "/home/zixi/anaconda3/envs/ROC/lib/python3.7/site-packages/transformers/data/data_collator.py", line 91, in collate_batch
    batch = self._tensorize_batch(examples)
  File "/home/zixi/anaconda3/envs/ROC/lib/python3.7/site-packages/transformers/data/data_collator.py", line 106, in _tensorize_batch
    "You are attempting to pad samples but the tokenizer you are using"
ValueError: You are attempting to pad samples but the tokenizer you are using (GPT2Tokenizer) does not have one.
Epoch:   0%|                                                                                                                                                         | 0/20 [00:00<?, ?it/s]
Iteration:   0%|

Expected behavior

Environment info

  • transformers version:master
  • Platform:Ubuntu 18.04LTS
  • Python version:3.7
  • PyTorch version (GPU?):1.5
  • Tensorflow version (GPU?):–
  • Using GPU in script?:yes
  • Using distributed or parallel set-up in script?:No

About this issue

  • Original URL
  • State: closed
  • Created 4 years ago
  • Reactions: 3
  • Comments: 20 (8 by maintainers)

Most upvoted comments

@BramVanroy - it’s actually not possible to set the ids equal to each other, doing tokenizer.pad_token = tokenizer.eos_token should work and is my recommend way of doing it 😃

+5
patrickvonplaten on Oct 21, 2020

@borisdayma Thaks for the quick reply!

Where can I find more how these models can be trained with what kind of datasets and what kind of tokenizers and special tokens? Also, Can this be used for the reformer too?

Please help me so that I can create simple and clear collab notebook and share it here so that others can easily use it.

+2
VikasRajashekar on Aug 12, 2020

@julien-c I am stuck with same error. If not line by line how else can I train the GPT2 model from scratch?

Here is my GPT2 config and language Model:

from transformers import GPT2LMHeadModel, GPT2Config

# Initializing a GPT2 configuration
configuration = GPT2Config(vocab_size=52_000)
model = GPT2LMHeadModel(config=configuration)
The logic for Dataset Preparation:

from transformers import LineByLineTextDataset

dataset = LineByLineTextDataset(
    tokenizer=tokenizer,
    file_path="./deu-de_web-public_2019_1M-sentences.txt",
    block_size=128,
)
from transformers import DataCollatorForLanguageModeling

data_collator = DataCollatorForLanguageModeling(
    tokenizer=tokenizer, mlm=False,
)

The training logic:

from transformers import Trainer, TrainingArguments

training_args = TrainingArguments(
    output_dir="./output",
    overwrite_output_dir=True,
    num_train_epochs=1,
    per_gpu_train_batch_size=64,
    save_steps=10_000,
    save_total_limit=2,
)

trainer = Trainer(
    model=model,
    args=training_args,
    data_collator=data_collator,
    train_dataset=dataset,
    prediction_loss_only=True,
)
trainer.train()

Throws me: ValueError: You are attempting to pad samples but the tokenizer you are using (GPT2Tokenizer) does not have one.

+1
VikasRajashekar on Aug 12, 2020
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