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task_LoRA.py
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task_LoRA.py
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import torch
import torch.nn as nn
import bitsandbytes as bnb
from transformers import AutoTokenizer, AutoConfig, AutoModelForCausalLM
# from transformers import pipeline, BitsAndBytesConfig
import argparse
from rank_bm25 import BM25Okapi
# from trl import SFTTrainer, DataCollatorForCompletionOnlyLM
import transformers
from utils import split_batch, get_first_k_tokens, print_trainable_parameters, name2taskid
from utils import extract_citation_title, extract_option, extract_movie, extract_news_cat, extract_news_headline, extract_product_review, extract_scholarly_title, extract_tweet_paraphrasing
import json
from tqdm import tqdm
parser = argparse.ArgumentParser(description="Parser for LoRA")
parser.add_argument('--model_name', type=str, default='meta-llama/Llama-2-7b-hf')
parser.add_argument('--batch_size', type=int, default=16)
parser.add_argument('--k', type=int, default=0)
parser.add_argument('--max_step', type=int, default=5000)
parser.add_argument('--cut_off', type=int, default=2048)
parser.add_argument('--max_epoch', type=int, default=3)
parser.add_argument('--temperature', type=float, default=0.1)
parser.add_argument('--task_name', type=str, default='movie_tagging')
parser.add_argument('--add_profile', action='store_true')
parser.add_argument('--access_token', type=str, default=None)
args = parser.parse_args()
model_name = args.model_name
task_name = args.task_name
batch_size = args.batch_size
k = args.k
# max_step = args.max_step
cutoff_len = args.cut_off
add_eos_token = False
max_epoch = args.max_epoch
# # 4 bit quantization inference
# bnb_config = BitsAndBytesConfig(
# load_in_4bit=True,
# bnb_4bit_quant_type="nf4",
# bnb_4bit_compute_dtype=torch.float16,
# bnb_4bit_use_double_quant=True,
# max_memory=f'{int(torch.cuda.mem_get_info()[0]/1024**3)-2}GB'
# )
# 8-bit quantization inference
# bnb_config = BitsAndBytesConfig(
# load_in_8bit=True,
# bnb_8bit_quant_type="nf8",
# bnb_8bit_compute_dtype=torch.float16,
# bnb_8bit_use_double_quant=True,
# max_memory=f'{int(torch.cuda.mem_get_info()[0]/1024**3)-2}GB'
# )
# 16-bit quantization inference
# bnb_config = BitsAndBytesConfig(
# load_in_16bit=True,
# bnb_16bit_quant_type="bf16",
# bnb_16bit_compute_dtype=torch.bfloat16,
# bnb_16bit_use_double_quant=True,
# max_memory=f'{int(torch.cuda.mem_get_info()[0]/1024**3)-2}GB'
# )
tokenizer = AutoTokenizer.from_pretrained(model_name, padding_side="left", token=args.access_token)
tokenizer.eos_token = "</s>"
tokenizer.pad_token = '[PAD]'
# tokenizer.pad_token = tokenizer.eos_token
tokenizer.pad_token_id = tokenizer.eos_token_id
base_model = AutoModelForCausalLM.from_pretrained(
model_name,
# quantization_config=bnb_config,
local_files_only=False,
device_map='auto',
trust_remote_code=True,
torch_dtype=torch.bfloat16
)
base_model.config.use_cache = False
base_model.config.pad_token_id = tokenizer.pad_token_id
base_model.config.eos_token_id = tokenizer.eos_token_id
base_model.config.bos_token_id = tokenizer.bos_token_id
from peft import prepare_model_for_kbit_training
base_model.gradient_checkpointing_enable()
base_model = prepare_model_for_kbit_training(base_model)
from peft import LoraConfig, get_peft_model
peft_config = LoraConfig(
r=8,
lora_alpha=8,
target_modules=["q_proj", "v_proj", "k_proj", "out_proj"],
lora_dropout=0.05,
bias="none",
task_type="CAUSAL_LM"
)
training_arguments = transformers.TrainingArguments(
output_dir='outputs/',
per_device_train_batch_size=batch_size,
gradient_accumulation_steps=1,
optim='adamw_torch',
num_train_epochs=max_epoch,
save_steps=1e9,
logging_steps=50,
learning_rate=1e-4,
weight_decay=1e-2,
bf16=True,
max_grad_norm=0.3,
# max_steps=max_step,
warmup_ratio=0.1,
group_by_length=True,
lr_scheduler_type='linear',
report_to='none',
)
with open(f"./data/{task_name}/user_others.json", 'r') as f:
train = json.load(f)
with open(f"./data/{task_name}/user_top_100_history.json", 'r') as f:
test_data = json.load(f)
if args.task_name == "movie_tagging":
extract_article = extract_movie
elif args.task_name == "news_categorize":
extract_article = extract_news_cat
elif args.task_name == "news_headline":
extract_article = extract_news_headline
elif args.task_name == "product_rating":
extract_article = extrat_product_review
elif args.task_name == "scholarly_title":
extract_article = extract_scholarly_title
elif args.task_name == "tweet_paraphrase":
extract_article = extrat_tweet_paraphrasing
with open('./prompt/prompt.json', 'r') as f:
prompt_template = json.load(f)
if args.add_profile:
with open(f'./data/{task_name}/profile_user_100.json', 'r') as f:
test_profile = json.load(f)
with open(f'./data/{task_name}/profile_user_others.json', 'r') as f:
train_profile = json.load(f)
def tokenize(prompt, add_eos_token=True):
# there's probably a way to do this with the tokenizer settings
# but again, gotta move fast
result = tokenizer(
prompt,
truncation=True,
max_length=cutoff_len,
padding=False,
return_tensors=None,
)
if (
result["input_ids"][-1] != tokenizer.eos_token_id
and len(result["input_ids"]) < cutoff_len
and add_eos_token
):
result["input_ids"].append(tokenizer.eos_token_id)
result["attention_mask"].append(1)
result["labels"] = result["input_ids"].copy()
return result
def generate_and_tokenize_prompt(data_point):
full_prompt = data_point['full_prompt']
tokenized_full_prompt = tokenize(full_prompt)
# if not train_on_inputs:
user_prompt = data_point['prompt']
tokenized_user_prompt = tokenize(
user_prompt, add_eos_token=add_eos_token
)
user_prompt_len = len(tokenized_user_prompt["input_ids"])
if add_eos_token:
user_prompt_len -= 1
tokenized_full_prompt["labels"] = [
-100
] * user_prompt_len + tokenized_full_prompt["labels"][
user_prompt_len:
] # could be sped up, probably
return tokenized_full_prompt
# training
from datasets import load_dataset, Dataset
model = get_peft_model(base_model, peft_config)
print_trainable_parameters(model)
pred_all = []
actual = []
train_data = []
for i in tqdm(range(len(train))):
if args.add_profile:
profile = train_profile[i]['output']
for idx, q in enumerate(train[i]['query']):
if args.task_name != "citation":
article = get_first_k_tokens(extract_article(q['input']), 768)
prompt = prompt_template[args.task_name]['prompt'].format(article)
full_prompt = prompt_template[args.task_name]['full_prompt'].format(get_first_k_tokens(extract_article(q['input']), 768), q['gold'])
else:
question = q['input']
article = extract_citation_title(question)
option1, option2 = extract_option(question, 1), extract_option(question, 2)
prompt = prompt_template[args.task_name]['prompt'].format(article, option1, option2)
full_prompt = prompt_template[args.task_name]['full_prompt'].format(article, option1, option2, q['gold'])
if k > 0:
visible_history_list = train[i]['profile']
for p in visible_history_list:
for key, value in p.items():
p[key] = get_first_k_tokens(p[key], 368)
history_list = [prompt_template[args.task_name]['retrieval_history'].format(**p) for p in visible_history_list]
tokenized_corpus = [doc.split(" ") for doc in history_list]
bm25 = BM25Okapi(tokenized_corpus)
tokenized_query = prompt_template[args.task_name]["retrieval_query_wokey"].format(article).split(' ')
retrieved_history = bm25.get_top_n(tokenized_query, history_list, n=args.k)
history_string = "".join(retrieved_history)
prompt = history_string + "\n" + prompt
full_prompt = history_string + "\n" + full_prompt
if args.add_profile:
prompt = profile + "\n" + prompt
full_prompt = profile + "\n" + full_prompt
train_data.append(
{
"prompt": prompt,
"full_prompt": full_prompt
}
)
print(train_data)
train_dataset = Dataset.from_list(train_data)
train_dataset = train_dataset.map(generate_and_tokenize_prompt).shuffle()
trainer = transformers.Trainer(
model=model,
train_dataset=train_dataset,
args=training_arguments,
data_collator=transformers.DataCollatorForSeq2Seq(
tokenizer, pad_to_multiple_of=8, return_tensors="pt", padding=True
),
)
for name, module in trainer.model.named_modules():
if "norm" in name:
module = module.to(torch.float32)
model.config.use_cache = False # silence the warnings. Please re-enable for inference!
trainer.train()
if args.add_profile:
output_name = "./ckpt/{}/k{}-{}-{}-profile-task_LoRA_ckpt".format(args.task_name, args.k, args.task_name, model_name.split('/')[-1])
else:
output_name = "./ckpt/{}/k{}-{}-{}-task_LoRA_ckpt".format(args.task_name, args.k, args.task_name, model_name.split('/')[-1])
model.save_pretrained(output_name)
model.eval()
model.config.use_cache = True # silence the warnings. Please re-enable for inference!
for i in tqdm(range(len(test_data))):
if args.add_profile:
profile = test_profile[i]['output']
if k > 0:
visible_history_list = test_data[i]['profile']
for p in visible_history_list:
for key, value in p.items():
p[key] = get_first_k_tokens(p[key], 368)
history_list = [prompt_template[args.task_name]['retrieval_history'].format(**p) for p in visible_history_list]
tokenized_corpus = [doc.split(" ") for doc in history_list]
bm25 = BM25Okapi(tokenized_corpus)
test_question_list = []
question_id_list = []
for q in test_data[i]['query']:
if args.task_name == 'citation':
test_question = q['input']
test_article = extract_citation_title(test_question)
option1, option2 = extract_option(test_question, 1), extract_option(test_question, 2)
test_prompt = prompt_template[args.task_name]['prompt'].format(test_article, option1, option2)
else:
test_question = q['input']
test_article = extract_article(test_question)
test_prompt = prompt_template[args.task_name]['prompt'].format(test_article)
if k > 0:
tokenized_query = prompt_template[args.task_name]['retrieval_query_wokey'].format(test_article).split(" ")
retrieved_history = bm25.get_top_n(tokenized_query, history_list, n=args.k)
history_string = "".join(retrieved_history)
test_prompt = history_string + "\n" + test_prompt
if args.add_profile:
test_prompt = profile + "\n" + test_prompt
test_question_list.append(test_prompt)
question_id_list.append(q['id'])
test_batch_list = split_batch(test_question_list, 1)
out_list = []
with torch.no_grad():
for batch_idx, batch in tqdm(enumerate(test_batch_list), total=len(test_batch_list)):
# try:
sentences = batch
inputs = tokenizer(sentences, return_tensors="pt", padding=True, return_token_type_ids=False)
inputs = inputs.to(model.device)
with torch.autocast(device_type="cuda"):
outputs = model.generate(
**inputs,
do_sample=True,
top_k=10,
temperature=args.temperature,
top_p=0.9,
eos_token_id=tokenizer.eos_token_id,
max_new_tokens=200
)
out_sentence = tokenizer.batch_decode(outputs, skip_special_tokens=True)
out_list += out_sentence
# except:
# out_list += ['']
for i in range(len(out_list)):
output = out_list[i].replace(test_question_list[i], '')
pred_all.append({
"id": question_id_list[i],
"output": output
})
print(output)
output_file = {
'task': name2taskid[args.task_name],
'golds': pred_all,
'model': model_name,
}
if args.add_profile:
with open('./output/{}/output-task-k{}-{}-{}-profile.json'.format(args.k, args.task_name, args.task_name, model_name.split('/')[-1]), 'w') as f:
json.dump(output_file, f, indent=4)
else:
with open('./output/{}/output-task-k{}-{}-{}.json'.format(args.k, args.task_name, args.task_name, model_name.split('/')[-1]), 'w') as f:
json.dump(output_file, f, indent=4)