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485127c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 | from transformers import AutoTokenizer, AutoModelForCausalLM, AutoModel
import torch
import torch.nn.functional as F
import os
import json
from tqdm import tqdm
import pickle
import huggingface_hub
huggingface_hub.login("hf_xxxxx") # replace hf_xxxxx with your actual token
def last_token_pool(last_hidden_states,
attention_mask):
left_padding = (attention_mask[:, -1].sum() == attention_mask.shape[0])
if left_padding:
return last_hidden_states[:, -1]
else:
sequence_lengths = attention_mask.sum(dim=1) - 1
batch_size = last_hidden_states.shape[0]
return last_hidden_states[torch.arange(batch_size, device='cpu'), sequence_lengths]
def load_model(model_name, cache_dir):
tokenizer = AutoTokenizer.from_pretrained(
model_name, # <-- Hub ID, *not* your cache path
cache_dir=cache_dir, # where to store/download files
trust_remote_code=True, # allow loading the repo’s custom code
use_auth_token=True,
)
tokenizer.pad_token = tokenizer.eos_token
model = AutoModelForCausalLM.from_pretrained(
model_name,
trust_remote_code=True,
device_map='auto',
use_auth_token=True,
cache_dir=cache_dir
)
return tokenizer, model
def get_kl(model, tokenizer, input_texts, max_length, device):
batch_dict = tokenizer(input_texts, max_length=max_length,
padding=True, truncation=True, return_tensors='pt').to(device)
with torch.no_grad():
outputs = model(**batch_dict, output_hidden_states=True)
last_logits = model.lm_head(outputs.hidden_states[-1]).squeeze()
first_logits = model.lm_head(outputs.hidden_states[0]).squeeze()
kls = []
for i in range(1, len(outputs.hidden_states) - 1):
with torch.no_grad():
middle_logits = model.lm_head(outputs.hidden_states[i]).squeeze()
kls.append(F.kl_div(F.log_softmax(middle_logits, dim=-1), F.softmax(first_logits, dim=-1), reduction='batchmean').item() +
F.kl_div(F.log_softmax(middle_logits, dim=-1), F.softmax(last_logits, dim=-1), reduction='batchmean').item())
return kls
def compute_kl_feat(model, tokenizer, file_name, save_dir, max_length, device):
test_datasets = {}
if not os.path.exists(save_dir):
os.makedirs(save_dir)
data_name = file_name.split('/')[-1]
if not os.path.exists(save_dir + data_name + '.pkl'):
print(file_name)
test_datasets[file_name] = {'data': [], 'label': []}
with open(file_name + '.raw_data.json', 'r') as f:
data = json.load(f)
kls = []
n_samples = len(data['original'])
# n_samples = 30
for idx in tqdm(range(n_samples)):
human_text = data['original'][idx]
kl = get_kl(model, tokenizer, [human_text], max_length, device)
kls.append(kl)
llm_text = data['sampled'][idx]
kl = get_kl(model, tokenizer, [llm_text], max_length, device)
kls.append(kl)
# if len(kls) >= 300:
# break
print(save_dir + data_name + '.pkl')
pickle.dump(kls, open(save_dir + data_name + '.pkl', 'wb'))
def load_model2(model_name, cache_dir):
tokenizer = AutoTokenizer.from_pretrained(
model_name, # <-- Hub ID, *not* your cache path
cache_dir=cache_dir, # where to store/download files
trust_remote_code=True, # allow loading the repo’s custom code
use_auth_token=True,
)
tokenizer.pad_token = tokenizer.eos_token
model = AutoModel.from_pretrained(
model_name,
trust_remote_code=True,
device_map='auto',
use_auth_token=True,
cache_dir=cache_dir
)
return tokenizer, model
def get_all_embedding(model, tokenizer, input_texts, max_length, device):
batch_dict = tokenizer(input_texts, max_length=max_length, padding=True, truncation=True, return_tensors='pt').to(device)
with torch.no_grad():
outputs = model(**batch_dict, output_hidden_states=True)
all_embed = [last_token_pool(outputs.hidden_states[i].cpu(), batch_dict['attention_mask']) for i in range(len(outputs.hidden_states))]
all_embed = torch.concat(all_embed, 1).cpu()
return all_embed
def compute_embedding(model, tokenizer, file_name, save_dir, max_length, device):
test_datasets = {}
if not os.path.exists(save_dir):
os.makedirs(save_dir)
data_name = file_name.split('/')[-1]
if not os.path.exists(save_dir + data_name + '.pt'):
print(file_name)
test_datasets[file_name] = {'data':[], 'label':[]}
with open(file_name + '.raw_data.json', 'r') as f:
data = json.load(f)
embeddings = []
n_samples = len(data['original'])
# n_samples = 30
for idx in tqdm(range(n_samples)):
human_text = data['original'][idx]
embedding = get_all_embedding(model, tokenizer, [human_text], max_length, device)
embeddings.append(embedding)
llm_text = data['sampled'][idx]
embedding = get_all_embedding(model, tokenizer, [llm_text], max_length, device)
embeddings.append(embedding)
# if len(embeddings) >=300:
# break
embeddings = torch.cat(embeddings, dim=0)
print('embedding shape: ', embeddings.shape)
print(save_dir + data_name +'.pt')
torch.save(embeddings, save_dir + data_name + '.pt')
if __name__ == '__main__':
device = 'cuda'
max_length = 512
model_name = "Alibaba-NLP/gte-Qwen1.5-7B-instruct"
# cache_dir = '/root/autodl-tmp/cache'
cache_dir = '../../../cache'
which_embedding='gte-qwen_KL_with_first_and_last_layer'
save_dir = f'save/{which_embedding}/'
data_dir = 'dataset/processed_data/'
tokenizer, model = load_model(model_name, cache_dir)
compute_kl_feat(model, tokenizer, data_dir, save_dir, max_length, device)
which_embedding='gte-qwen_all_embedding'
save_dir = f'save/{which_embedding}/save_embedding/'
data_dir = 'dataset/processed_data/'
tokenizer, model = load_model2(model_name, cache_dir)
compute_embedding(model, tokenizer, data_dir, save_dir, max_length, device) |