[Python] Loss is “nan” when fine-tuning HuggingFace NLI model (both RoBERTa/BART)

I'm using HuggingFace's Transformer's library and I’m trying to fine-tune a pre-trained NLI model (ynie/roberta-large-snli_mnli_fever_anli_R1_R2_R3-nli) on a dataset of around 276.000 hypothesis-premise pairs. I’m following the instructions from the docs here and here. I have the impression that the fine-tuning works (it does the training and saves the checkpoints), but trainer.train() and trainer.evaluate() return "nan" for the loss.

What I've tried:

• I tried using both ynie/roberta-large-snli_mnli_fever_anli_R1_R2_R3-nli and facebook/bart-large-mnli to make sure that it's not linked to specific model, but I get the issue for both models
• I tried following the advice in this related github issue, but adding num_labels=3 to the config file does not solve the issue. (I think my issue is different because the models are already fine-tuned on NLI in my case)
• I tried many different ways of changing my input data because I suspected that there could be an issue with my input data, but I also couldn't solve it that way.
• The probable source of the issue: I inspected the prediction output from the model during training and the weird thing is the prediction value always seems to be "0" (entailment) in 100% of cases (see printed output in the code below). This is clearly an error. I think the source for this is that the logits the model seems to return during training are torch.tensor([[np.nan, np.nan, np.nan]]) and when you apply .argmax(-1) to this, you get torch.tensor(0). The big mystery for me is why the logits would become "nan", because the model does not do that when I use the same input data only outside of the trainer. => Does anyone know where this issues comes from? See my code below.

Thanks a lot in advance for any suggestion!

Here is my code:


### load model & tokenize
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

max_length = 256
hg_model_hub_name = "ynie/roberta-large-snli_mnli_fever_anli_R1_R2_R3-nli"
# also tried: hg_model_hub_name = "facebook/bart-large-mnli"
tokenizer = AutoTokenizer.from_pretrained(hg_model_hub_name)
model = AutoModelForSequenceClassification.from_pretrained(hg_model_hub_name)
model.config

device = "cuda" if torch.cuda.is_available() else "cpu"
print(f"Device: {device}")
if device == "cuda":
model = model.half()
model.to(device)
model.train();

#... some data preprocessing

encodings_train = tokenizer(premise_train, hypothesis_train, return_tensors="pt", max_length=max_length,
return_token_type_ids=True, truncation=False, padding=True)
encodings_val = tokenizer(premise_val, hypothesis_val, return_tensors="pt", max_length=max_length,
return_token_type_ids=True, truncation=False, padding=True)
encodings_test = tokenizer(premise_test, hypothesis_test, return_tensors="pt", max_length=max_length,
return_token_type_ids=True, truncation=False, padding=True)


### create pytorch dataset object
class XDataset(torch.utils.data.Dataset):
def __init__(self, encodings, labels):
self.encodings = encodings
self.labels = labels
def __getitem__(self, idx):
item = {key: torch.as_tensor(val[idx]) for key, val in self.encodings.items()}
#item = {key: torch.as_tensor(val[idx]).to(device) for key, val in self.encodings.items()}
item['labels'] = torch.as_tensor(self.labels[idx])
#item['labels'] = self.labels[idx]
return item
def __len__(self):
return len(self.labels)

dataset_train = XDataset(encodings_train, label_train)
dataset_val = XDataset(encodings_val, label_val)
dataset_test = XDataset(encodings_test, label_test)

# compute metrics with trainer
from sklearn.metrics import accuracy_score, precision_recall_fscore_support
def compute_metrics(pred):
labels = pred.label_ids
print(labels)
preds = pred.predictions.argmax(-1)
print(preds)
precision, recall, f1, _ = precision_recall_fscore_support(labels, preds, average='binary', pos_label=0)
acc = accuracy_score(labels, preds)
return {
'accuracy': acc,
'f1': f1,
'precision': precision,
'recall': recall
}


## training
from transformers import Trainer, TrainingArguments

# https://huggingface.co/transformers/main_classes/trainer.html#transformers.TrainingArguments
training_args = TrainingArguments(
output_dir='./results', # output directory
num_train_epochs=1, # total number of training epochs
per_device_train_batch_size=8, # batch size per device during training
per_device_eval_batch_size=8, # batch size for evaluation
warmup_steps=500, # number of warmup steps for learning rate scheduler
weight_decay=0.01, # strength of weight decay
logging_dir='./logs', # directory for storing logs
logging_steps=100,
)

trainer = Trainer(
model=model, # the instantiated Transformers model to be trained
args=training_args, # training arguments, defined above
train_dataset=dataset_train, # training dataset
eval_dataset=dataset_val # evaluation dataset
)

trainer.train()
# output: TrainOutput(global_step=181, training_loss=nan)
trainer.evaluate()
# output:
[2 2 2 0 0 2 2 2 0 2 0 0 2 2 2 2 0 2 0 2 2 2 2 0 2 0 2 0 0 2 0 0 2 0 0 0 2
0 2 0 0 0 0 0 2 0 0 2 2 2 0 2 2 2 2 2 0 0 0 0 2 0 0 0 2 2 0 0 0 2 0 0 0 2
2 0 2 0 0 2 2 2 0 2 2 0 0 0 0 0 0 0 2 0 0 0 0 2 0 2 2 0 2 0 0 2 2 2 2 2 2
2 0 0 0 0 2 0 0 2 0 0 0 0 2 2 2 0 0 0 0 0 2 0 0 2 0 2 0 2 0 2 0 0 2 2 0 0
2 2 2 2 2 2 0 0 2 2 2 2 0 2 0 0 2 2 2 0 0 2 0 2 0 2 0 0 0 0 0 0 2 0 0 2 2
0 2 2 2 0 2 2 0 2 2 2 2 2 2 0 0 2 0 0 2 2 0 0 0 2 0 2 2 2 0 0 0 0 0 0 0 0
2 0 2 2 2 0 2 0 0 2 0 2 2 0 0 0 0 2 2 2 0 0 0 2 2 2 2 0 2 0 2 2 2]
[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]

{'epoch': 1.0,
'eval_accuracy': 0.5137254901960784,
'eval_f1': 0.6787564766839378,
'eval_loss': nan,
'eval_precision': 0.5137254901960784,
'eval_recall': 1.0}


Edit: I've also opened a github issue with a but more detailed description of the issue here: https://github.com/huggingface/transformers/issues/9160

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