Repository: pytorch
Issue: Bert model training is not running on cpu (X86/ARM) with torch.compile() mode, generating value error.
Labels: triaged,oncall: pt2,module: dynamo

### 🐛 Describe the bug

I was trying to run Bert model training on ICELAKE CPU with torch.compile mode then it is giving a value error, but when i am running it with eager mode then it is running fine without any error.
this is the error which i am getting when running in compile mode.
![image](https://github.com/user-attachments/assets/3d348e89-a4af-4069-8110-863a625aecf5)
this is script which i am running.
![image](https://github.com/user-attachments/assets/387a12ec-10e1-48e5-b211-2f697839ee54)

the similar behavior is observed in Graviton machine also. 


### Versions
using torch==2.4, the requirement file which i am using.
[requirement.txt](https://github.com/user-attachments/files/16836429/requirement.txt)

Code:-
`
import os, time
from transformers import AutoTokenizer
from transformers import DataCollatorWithPadding
import evaluate
import numpy as np
import torch
from transformers import AutoModelForSequenceClassification, TrainingArguments, Trainer
from torch.profiler import profile, record_function, ProfilerActivity
from datasets import load_dataset

imdb = load_dataset("imdb",split=['train[:5]','test[:5]'])

tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")

def preprocess_function(examples):
    return tokenizer(examples["text"], truncation=True,padding=True)

tokenized_imdb_tr = imdb[0].map(preprocess_function, batched=True)
tokenized_imdb_ts = imdb[1].map(preprocess_function, batched=True)

data_collator = DataCollatorWithPadding(tokenizer=tokenizer)

accuracy = evaluate.load("accuracy")

def compute_metrics(eval_pred):
    predictions, labels = eval_pred
    predictions = np.argmax(predictions, axis=1)
    return accuracy.compute(predictions=predictions, references=labels)

id2label = {0: "NEGATIVE", 1: "POSITIVE"}
label2id = {"NEGATIVE": 0, "POSITIVE": 1}

model = AutoModelForSequenceClassification.from_pretrained(
    "bert-base-uncased", num_labels=2, id2label=id2label, label2id=label2id
)
model = torch.compile(model, backend='inductor')
training_args = TrainingArguments(
    output_dir="my_awesome_model2",
    learning_rate=2e-5,
    per_device_train_batch_size=1,
    per_device_eval_batch_size=1,
    num_train_epochs=1,
    weight_decay=0.01,
    evaluation_strategy="epoch",
    save_strategy="epoch",
    load_best_model_at_end=True,
    # push_to_hub=True,
)
trainer = Trainer(
        model=model,
        args=training_args,
        train_dataset=tokenized_imdb_tr,
        eval_dataset=tokenized_imdb_ts,
        tokenizer=tokenizer,
        data_collator=data_collator,
        compute_metrics=compute_metrics,
    )

with profile(with_stack=True,
    profile_memory=True, record_shapes=True) as prof:
    print("starting the training")
    start_time = time.time()
    trainer.train()
    end_time = time.time()


print(prof.key_averages().table())
total_time = end_time-start_time

print("Training Time Taken with torch.compile:")

print(total_time)
`


cc @ezyang @gchanan @zou3519 @kadeng @msaroufim @chauhang @penguinwu @voznesenskym @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @chenyang78 @amjames @rec