Python 使用pytorch使用xsum微调t5时的堆栈大小错误

我正试图微调T5-Small与Xsum数据集在pytorch Windows 10(CUDA 12.1)上.

遗憾的是，bitandbytes中的Trainer(或Seq2SeqTrainer)类在Windows上不可用，因此需要创建一个纪元循环:

from datasets import load_dataset
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, get_scheduler
from torch.utils.data import DataLoader
from torch.optim import AdamW
import torch
from tqdm.auto import tqdm

dataset = load_dataset("xsum")
MODEL_NAME = "t5-small"
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)

prefix = "summarize: "
max_input_length = 1024
max_target_length = 128

def tokenize_function(examples):
    
    inputs = [prefix + doc for doc in examples["document"]]
    model_inputs = tokenizer(inputs, max_length=max_input_length, truncation=True)

    # Setup the tokenizer for targets
    labels = tokenizer(text_target=examples["summary"], max_length=max_target_length, truncation=True)

    model_inputs["labels"] = labels["input_ids"]
    return model_inputs


tokenized_datasets = dataset.map(tokenize_function, batched=True)
tokenized_datasets = tokenized_datasets.remove_columns(['document', 'summary', 'id'])
tokenized_datasets.set_format("torch")

small_train_dataset = tokenized_datasets["train"].shuffle(seed=42).select(range(1000))
small_eval_dataset = tokenized_datasets["test"].shuffle(seed=42).select(range(1000))

train_dataloader = DataLoader(small_train_dataset, shuffle=True, batch_size=8)
eval_dataloader = DataLoader(small_eval_dataset, batch_size=8)

model = AutoModelForSeq2SeqLM.from_pretrained(MODEL_NAME)
optimizer = AdamW(model.parameters(), lr=5e-5)

num_epochs = 3
num_training_steps = num_epochs * len(train_dataloader)
lr_scheduler = get_scheduler(
    name="linear", optimizer=optimizer, num_warmup_steps=0, num_training_steps=num_training_steps
)

device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
model.to(device)

progress_bar = tqdm(range(num_training_steps))

model.train()

for epoch in range(num_epochs):
    for batch in train_dataloader:
        batch = {k: v.to(device) for k, v in batch.items()}
        outputs = model(**batch)
        loss = outputs.loss
        loss.backward()

        optimizer.step()
        lr_scheduler.step()
        optimizer.zero_grad()
        progress_bar.update(1)

model.save_pretrained("outputs/trained")

我收到了这样的错误:

RuntimeError: stack expects each tensor to be equal size, but got [352] at entry 0 and [930] at entry 1

我怎么才能解决这个问题呢？