python - 微调 HuggingFace NLI 模型（RoBERTa/BART）时，损失为“nan”

问题描述

我正在使用HuggingFaceynie/roberta-large-snli_mnli_fever_anli_R1_R2_R3-nli的 Transformer 库，并尝试在大约 276.000 个假设-前提对的数据集上微调预训练的 NLI 模型 ( )。我正在按照此处和此处的文档中的说明进行操作。我的印象是微调有效（它进行训练并保存检查点），但trainer.train()返回trainer.evaluate()“nan”作为损失。

我试过的：

我尝试使用两者ynie/roberta-large-snli_mnli_fever_anli_R1_R2_R3-nli并facebook/bart-large-mnli确保它没有链接到特定模型，但我得到了两个模型的问题
我尝试按照此相关 github 问题中的建议进行操作，但添加num_labels=3到配置文件并不能解决问题。（我认为我的问题有所不同，因为在我的案例中，模型已经在 NLI 上进行了微调）
我尝试了许多不同的方法来更改我的输入数据，因为我怀疑我的输入数据可能存在问题，但我也无法以这种方式解决它。
问题的可能来源： 我在训练期间检查了模型的预测输出，奇怪的是预测值在 100% 的情况下似乎总是“0”（蕴含）（参见下面代码中的打印输出）。这显然是一个错误。我认为这是因为模型在训练期间似乎返回的 logits 是torch.tensor([[np.nan, np.nan, np.nan]])，当你应用.argmax(-1)到这个时，你会得到 torch.tensor(0)。对我来说最大的谜团是为什么 logits 会变成“nan”，因为当我只在训练器之外使用相同的输入数据时，模型不会这样做。=> 有谁知道这个问题来自哪里？请参阅下面的代码。

非常感谢您的任何建议！

这是我的代码：

### 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}

编辑： 我还打开了一个 github 问题，在此处对问题进行了更详细的描述：https ://github.com/huggingface/transformers/issues/9160

标签： pythonnlppytorchhuggingface-transformershuggingface-tokenizers

python - 微调 HuggingFace NLI 模型（RoBERTa/BART）时，损失为“nan”

问题描述

这是我的代码：

解决方案

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