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engine_prompt_tuning.py
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engine_prompt_tuning.py
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import json, os, sys
import torch
from datetime import datetime
from textwrap import wrap
from transformers.optimization import Adafactor
from transformers import (
T5Tokenizer,
GPT2Tokenizer,
Trainer,
TrainingArguments,
)
from model_prompt_tuning import T5PromptTuningLM, GPT2PromptTuningLM
from utils_prompt_tuning import evaluate
from prepare_data import get_dataset
from collator import T2TDataCollator
os.environ["WANDB_DISABLED"] = "true"
class Unbuffered(object):
def __init__(self, stream):
self.stream = stream
def write(self, data):
self.stream.write(data)
self.stream.flush()
def writelines(self, datas):
self.stream.writelines(datas)
self.stream.flush()
def __getattr__(self, attr):
return getattr(self.stream, attr)
sys.stdout = Unbuffered(sys.stdout)
def setup_logs(args):
if args['logging']:
print('File logging is enabled')
timestamp = datetime.now().strftime("%Y-%m-%d-%H%M%S")
path = "{}/{}/{}/{}/{}".format(args["method"], args["train_set"], args["model"], args["n_tokens"], timestamp)
isExist = os.path.exists(path)
if not isExist:
os.makedirs(path)
sys.stderr = open(os.path.join(path, "err.txt"), "w")
sys.stdout = open(os.path.join(path, "log.txt"), "w")
args['path'] = path
return path
def get_data_model(args):
if "t5" in args["model"]:
tokenizer = T5Tokenizer.from_pretrained(args["model"])
model = T5PromptTuningLM.from_pretrained(
args["model"],
n_tokens=args["n_tokens"],
soft_prompt_path=args["soft_prompt_path"],
initialize_from_vocab=args["initialize_from_vocab"],
random_range=args["random_range"],
)
if "gpt" in args["model"]:
tokenizer = GPT2Tokenizer.from_pretrained(args["model"])
model = GPT2PromptTuningLM.from_pretrained(
args["model"],
n_tokens=args["n_tokens"],
soft_prompt_path=args["soft_prompt_path"],
initialize_from_vocab=args["initialize_from_vocab"],
random_range=args["random_range"],
)
if args['task'] == 'qa':
train_set, val_set, test_set = get_dataset(tokenizer, args)
return {
'model': model,
'tokenizer': tokenizer,
'train_set': train_set,
'val_set': val_set,
'test_set': test_set,
}
def get_optim(model, args):
if args['optimizer'] == 'adafactor':
optimizer = Adafactor(model.parameters(),
scale_parameter=args['scale_parameter'],
relative_step=args['relative_step'],
warmup_init=args['warmup_init'],
lr=args['lr'],
clip_threshold=args['clip_threshold'])
# TODO: we might need s scheduler
lr_scheduler = None
else:
pass
return {
'optim': optimizer,
'scheduler': lr_scheduler,
}
def get_training_args(args):
if args['logging']:
args['artifact_dir'] = args['path'] + '/artifact'
else:
args['artifact_dir'] = './tmp'
training_args = TrainingArguments(
remove_unused_columns=False,
per_device_train_batch_size=args['bz'], # batch size per device during training
per_device_eval_batch_size=args['bz'], # batch size for evaluation
num_train_epochs=args['epoch'],
disable_tqdm=args['logging'],
report_to=None,
output_dir=args['artifact_dir'], # output directory
save_strategy='epoch',
save_total_limit=1,
logging_dir=args['path'], # directory for storing logs
logging_steps=20,
)
return training_args
def generate_predictions(model, tokenizer, dataset, debug):
predictions = {}
length = 10 if debug else len(dataset)
for i in range(length):
if debug:
print(f'evaluating example {i} of {length}')
qid, question, context = dataset['qid'][i], dataset['question'][i], dataset['context'][i]
input_ids = tokenizer.encode('question: %s context: %s' % (question, context),
return_tensors='pt').to(model.device)
decoder_input_ids = torch.tensor([[tokenizer.encode(tokenizer.pad_token)[0]]]).to(input_ids.device)
for i in range(10):
idx = model(input_ids, decoder_input_ids=decoder_input_ids, return_dict=True).logits.argmax(-1)[0][-1]
decoder_input_ids=torch.cat((decoder_input_ids,torch.tensor([[idx]]).to(decoder_input_ids.device)), dim=1)
pred = ' '.join([tokenizer.decode(decoder_input_ids[0], skip_special_tokens=False)])
pred = pred.replace('</s>','').replace('<pad>','').lower().strip()
predictions[qid] = pred
return predictions
def compute_metrics(wrapper, debug=False):
model, tokenizer, val_set, test_set = wrapper['model'], wrapper['tokenizer'], wrapper['val_set'], wrapper['test_set']
val_set_gts = dict(zip(val_set['qid'], val_set['answers']))
val_set_pred = generate_predictions(model, tokenizer, val_set, debug)
val_set_metric = evaluate(val_set_gts, val_set_pred, True)
print(f' val_set: {val_set_metric}')
test_set_gts = dict(zip(test_set['qid'], test_set['answers']))
test_set_pred = generate_predictions(model, tokenizer, test_set, debug)
test_set_metric = evaluate(test_set_gts, test_set_pred, True)
print(f' test_set: {test_set_metric}')
return val_set_metric, test_set_metric
def save_logs(model, args):
if args['logging']:
model.save_soft_prompt(args['path'], filename='soft_prompt.model')
metainfo_file = os.path.join(args['path'], 'info.json')
with open(metainfo_file, 'w') as fp:
json.dump(args, fp)
def run(args):
args['path'] = None
path = setup_logs(args)
model_data_wrapper = get_data_model(args)
optim_wrapper = get_optim(model_data_wrapper['model'], args)
training_args = get_training_args(args)
trainer = Trainer(
model=model_data_wrapper['model'],
args=training_args,
train_dataset=model_data_wrapper['train_set'],
eval_dataset=model_data_wrapper['val_set'],
data_collator=T2TDataCollator(),
optimizers=(optim_wrapper['optim'], optim_wrapper['scheduler']),
)
trainer.train()
save_logs(model_data_wrapper['model'], args)
# TODO: add evaluation on validation set and the test set
compute_metrics(model_data_wrapper)
def test_model(args):
# TODO: remove hardcoding directory
model = T5PromptTuningLM.from_pretrained(args["model"],
return_dict=False,
soft_prompt_path='prompt_tuning/SQuAD/t5-small/1/2022-02-04-221142/soft_prompt.model')
tokenizer = T5Tokenizer.from_pretrained(args["model"])
train_set, val_set, test_set = get_dataset(tokenizer, args)
wrapper = {
'model': model,
'tokenizer': tokenizer,
'train_set': train_set,
'val_set': val_set,
'test_set': test_set,
}
compute_metrics(wrapper, True)