How to solve the logits equality of LLaMA output

[shuaizhao95]

This is my code.

from datasets import load_dataset from transformers import set_seed from openprompt.data_utils import InputExample import os from tqdm import tqdm

device = "cuda" classes = ["negative", "positive"] set_seed(1024) from accelerate import Accelerator

accelerator = Accelerator()

data_path = 'data' test_path = os.path.join(data_path, 'test.json') test_dataset = load_dataset('json', data_files=test_path)['train'] # 1 positive 0 negative y_true = test_dataset['label'] dataset = [] import copy data = [] copy_test_dataset = copy.deepcopy(test_dataset) for example in copy_test_dataset: temp_data = {"guid": example["label"], "text_a": example["sentence"]} data.append(temp_data)

for item in data: dataset.append(InputExample(guid=item["guid"], text_a=item["text_a"]))

from openprompt import plms from openprompt.plms import * from transformers import LlamaConfig, LlamaForCausalLM, LlamaTokenizer plms._MODEL_CLASSES["llama"]= ModelClass(**{"config": LlamaConfig, "tokenizer": LlamaTokenizer, "model": LlamaForCausalLM, "wrapper": LMTokenizerWrapper})

from openprompt.plms import load_plm plm, tokenizer, model_config, WrapperClass = load_plm("llama", "huggyllama/llama-7b") tokenizer.pad_token_id = 0

from openprompt.prompts import ManualTemplate

promptTemplate = ManualTemplate( text=' {"placeholder":"text_a"} This sentence was {"mask"}', tokenizer=tokenizer, )

from openprompt.prompts import ManualVerbalizer promptVerbalizer = ManualVerbalizer(classes=classes, label_words={"negative": ["bad"], "positive": ["good", "wonderful", "great"], }, tokenizer=tokenizer, )

from openprompt import PromptForClassification promptModel = PromptForClassification(template=promptTemplate, plm=plm, verbalizer=promptVerbalizer, ) from openprompt import PromptDataLoader

data_loader = PromptDataLoader(dataset=dataset, tokenizer=tokenizer, template=promptTemplate, tokenizer_wrapper_class=WrapperClass, batch_size=1)

import torch

promptModel.eval() print(promptModel) promptModel, data_loader = accelerator.prepare(promptModel, data_loader) promptModel.to(device) predictions = [] with torch.no_grad(): for batch in tqdm(data_loader, desc="Processing batches"): batch = {k: v.to(device) for k, v in batch.items()} print(batch) logits = promptModel(batch) print(logits) exit() preds = torch.argmax(logits, dim=-1) for i in preds: predictions.append(i.item())

from sklearn.metrics import accuracy_score

accuracy = accuracy_score(y_true, predictions) print('Accuracy: %.2f' % (accuracy * 100))

The output logits is :

tensor([[-1.3863, -1.3863]])

[ManishChandra12]

I am also facing this issue. @shuaizhao95 were u able to solve this issue?

[shuaizhao95]

I am also facing this issue. @shuaizhao95 were u able to solve this issue?

Sorry, I still haven't solved the problem

[Kenneth0901]

normalizer.cc(51) LOG(INFO) precompiled_charsmap is empty. use identity normalization. I am also facing this, were you able to solve the issue?