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GPT2-coarse-to-fine.py
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import argparse
import logging
from tqdm import trange
import torch
import torch.nn.functional as F
import numpy as np
from torch import nn
from torch.autograd import Variable
from transformers import GPT2Config
from transformers import GPT2LMHeadModel, GPT2Tokenizer, BertTokenizer
from DataLoader import *
from Model import BERTGen
from utils import sample_sequence
import torch.optim as optim
import math
import sys
import pandas
import os
import numpy
import nltk
import re
import time
from torch.utils.tensorboard import SummaryWriter
from datetime import datetime
device = torch.device('cuda')
def set_seed(args):
np.random.seed(args.seed)
torch.manual_seed(args.seed)
if args.n_gpu > 0:
torch.cuda.manual_seed_all(args.seed)
def clean_str(strings):
new_strings = []
for string in strings:
string = re.sub(r' +', ' ', string)
if len(string.split(' ')) < 6 and len(new_strings) > 0:
string = new_strings[-1]
new_strings.append(string)
return new_strings
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument("--model", default='gpt2', type=str)
parser.add_argument("--temperature", type=float, default=1.0,
help="temperature of 0 implies greedy sampling")
parser.add_argument("--top_k", type=int, default=0)
parser.add_argument("--top_p", type=float, default=0.9)
parser.add_argument('--seed', type=int, default=42,
help="random seed for initialization")
parser.add_argument('--do_train', default=False, action="store_true",
help="whether to train or test the model")
parser.add_argument('--do_test', default=False, action="store_true",
help="whether to train or test the model")
parser.add_argument('--do_verify', default=False, action="store_true",
help="whether to train or test the model")
parser.add_argument('--epoch', default=10, type=int,
help="whether to train or test the model")
parser.add_argument('--batch_size', default=6, type=int,
help="whether to train or test the model")
parser.add_argument('--do_test_challenge', default=False, action="store_true",
help="whether to train or test the model")
parser.add_argument('--learning_rate', default=2e-6, type=float,
help="whether to train or test the model")
parser.add_argument('--do_verify_challenge', default=False, action="store_true",
help="whether compute the adv-acc score on challenge split")
parser.add_argument('--every', default=50, type=int, help="evaluate how many steps")
parser.add_argument('--start_epoch', default=0, type=int, help="resuming from which epoch")
parser.add_argument('--load_from', default='', type=str, help="load model path")
parser.add_argument('--id', default='models', type=str, help="specify the id of the experiment")
parser.add_argument('--max_len', default=800, type=int, help="whether to train or test the model")
parser.add_argument('--stage', default=1, type=int, help="whether to train or test the model")
parser.add_argument("--modelpath", type=str, default="bert-base-uncased",
help="For distributed training: local_rank")
parser.add_argument('--gradient_accumulation_steps', type=int, default=5, help="accumulation steps for gradient")
parser.add_argument('--decode_first_K', type=int, default=10000, help="For debugging purpose")
args = parser.parse_args()
args.device = torch.device("cuda")
args.n_gpu = torch.cuda.device_count()
if args.model == 'gpt2-medium':
args.batch_size = 2
print(args)
tokenizer = GPT2Tokenizer.from_pretrained(args.model)
tokenizer.add_tokens(['[ENT]', '[SEP]'])
model = GPT2LMHeadModel.from_pretrained(args.model)
model.resize_token_embeddings(len(tokenizer))
model = nn.DataParallel(model)
model.to(args.device)
if not os.path.exists(args.id):
os.mkdir(args.id)
criterion = nn.CrossEntropyLoss(reduction='none', ignore_index=-1)
if args.do_train:
recording_time = datetime.now().strftime('%m_%d_%H_%M')
tb_writer = SummaryWriter(log_dir='tensorboard/GPT_stage{}_C2F/{}'.format(args.stage, recording_time))
dataset = GPTTableCoarseFineDatabase2('data/train_lm_preprocessed.json', None, None,
tokenizer, args.batch_size, args.max_len, args.stage)
if args.stage == 2:
model.load_state_dict(torch.load(args.load_from))
model.train()
optimizer = optim.Adam(model.parameters(), args.learning_rate)
avg_loss = 0
global_step = 0
for epoch_idx in range(args.start_epoch, args.start_epoch + args.epoch):
print("start training {}th epoch".format(epoch_idx))
for idx in range(0, dataset.train_len()):
batch = dataset.get_train_data(idx)
batch = tuple(Variable(t).to(device) for t in batch)
trg_inp, trg_out, mask, caption = batch
inputs = torch.cat([caption, trg_inp], 1)
model.zero_grad()
optimizer.zero_grad()
logits = model(inputs)[0]
logits = logits[:, -trg_out.shape[1]:, :].contiguous()
loss = criterion(logits.view(-1, logits.shape[-1]), trg_out.view(-1))
loss = loss * mask.view(-1)
loss = loss.sum() / mask.sum()
avg_loss += loss.item()
loss.backward()
optimizer.step()
global_step += 1
if idx % args.every == 0 and idx > 0:
tb_writer.add_scalar("perplexity", math.exp(avg_loss / args.every), global_step)
fake_inputs = caption
gt_inputs = trg_out.cpu().data.numpy()
samples = sample_sequence(model, 50, fake_inputs, [])
samples = samples[:, caption.shape[1]:]
samples = samples.cpu().data.numpy()
for s, gt in zip(samples, gt_inputs):
print("EPOCH {}; FINISHED {}/{}".format(epoch_idx, idx, dataset.train_len()))
text = tokenizer.decode(s, clean_up_tokenization_spaces=True)
text = text[: text.find(tokenizer.eos_token)]
print("PREDICTION |||||| ", text)
text = tokenizer.decode(gt, clean_up_tokenization_spaces=True)
text = text[: text.find(tokenizer.eos_token)]
print("GROUNDTRUH |||||| ",text)
break
avg_loss = 0
if args.model == 'gpt2':
torch.save(model.state_dict(), '{}/GPT_stage{}_C2F_ep{}.pt'.format(args.id, args.stage, epoch_idx))
else:
torch.save(model.state_dict(), '{}/GPT_stage{}_C2F_medium_ep{}.pt'.format(args.id, args.stage, epoch_idx))
if args.do_test:
assert 'stage2' in args.load_from, "The testing can only be done with stage2 model"
dataset = GPTTableCoarseFineDatabase2(None, None, 'data/test_lm.json', tokenizer, args.batch_size, args.max_len, args.stage)
model.load_state_dict(torch.load(args.load_from))
model.eval()
sent_bleus_1 = []
sent_bleus_2 = []
sent_bleus_3 = []
results = {}
start_time = time.time()
with torch.no_grad():
for idx in range(0, min(args.decode_first_K, dataset.test_len())):
batch = dataset.get_data(idx, 'test')
references = dataset.get_reference(idx, 'test')
table_id = dataset.get_table_id(idx, 'test')
results[table_id] = []
batch = tuple(Variable(t).to(device) for t in batch)
trg_inp, trg_out, mask, caption = batch
fake_inputs = caption
samples = sample_sequence(model, 50, fake_inputs, [], stop_token=tokenizer.eos_token_id,
top_k=1, trigger=tokenizer.convert_tokens_to_ids('[SEP]'),
supress=[tokenizer.convert_tokens_to_ids('[SEP]'),
tokenizer.convert_tokens_to_ids('[ENT]')],
repetition=tokenizer.convert_tokens_to_ids('[ENT]'))
samples = samples[:, caption.shape[1]:]
samples = samples.cpu().data.numpy()
intermediate = []
for s in samples:
text = tokenizer.decode(s, clean_up_tokenization_spaces=True)
text = text[text.find('[SEP]') + 6: text.find(tokenizer.eos_token)].strip()
#text = text[: text.find(tokenizer.eos_token)]
intermediate.append(text)
results[table_id] = clean_str(intermediate)
for text in results[table_id]:
hypothesis = text.lower().split()
sent_bleus_1.append(nltk.translate.bleu_score.sentence_bleu(
references, hypothesis, weights=(1, 0, 0)))
sent_bleus_2.append(nltk.translate.bleu_score.sentence_bleu(
references, hypothesis, weights=(0.5, 0.5, 0)))
sent_bleus_3.append(nltk.translate.bleu_score.sentence_bleu(
references, hypothesis, weights=(0.33, 0.33, 0.33)))
bleu_1 = format((sum(sent_bleus_1) / len(sent_bleus_1) * 100), '.2f')
bleu_2 = format((sum(sent_bleus_2) / len(sent_bleus_2) * 100), '.2f')
bleu_3 = format((sum(sent_bleus_3) / len(sent_bleus_3) * 100), '.2f')
sys.stdout.write("finished {}/{}; BLEU score {}/{}/{}; speed={}s/sent \r".format(idx,
dataset.test_len(), bleu_1, bleu_2, bleu_3, (time.time() - start_time) / len(sent_bleus_1)))
print("total corpus BLEU score = {}/{}/{}".format(bleu_1, bleu_2, bleu_3))
with open('outputs/GPT_{}_C2F_{}.json'.format(args.model, bleu_3), 'w') as f:
json.dump(results, f, indent=2)
if args.do_test_challenge:
assert 'stage2' in args.load_from, "The testing can only be done with stage2 model"
dataset = GPTTableCoarseFineDatabase2(None, None, 'challenge/blind_test_lm_inputs.json', tokenizer, args.batch_size, args.max_len, args.stage)
model.load_state_dict(torch.load(args.load_from))
model.eval()
sent_bleus_1 = []
sent_bleus_2 = []
sent_bleus_3 = []
results = {}
start_time = time.time()
with torch.no_grad():
for idx in range(0, min(args.decode_first_K, dataset.test_len())):
batch = dataset.get_data(idx, 'test')
references = dataset.get_reference(idx, 'test')
table_id = dataset.get_table_id(idx, 'test')
results[table_id] = []
batch = tuple(Variable(t).to(device) for t in batch)
trg_inp, trg_out, mask, caption = batch
fake_inputs = caption
samples = sample_sequence(model, 50, fake_inputs, [], stop_token=tokenizer.eos_token_id,
top_k=1, trigger=tokenizer.convert_tokens_to_ids('[SEP]'),
supress=[tokenizer.convert_tokens_to_ids('[SEP]'),
tokenizer.convert_tokens_to_ids('[ENT]')],
repetition=tokenizer.convert_tokens_to_ids('[ENT]'))
samples = samples[:, caption.shape[1]:]
samples = samples.cpu().data.numpy()
intermediate = []
for s in samples:
text = tokenizer.decode(s, clean_up_tokenization_spaces=True)
text = text[text.find('[SEP]') + 6: text.find(tokenizer.eos_token)].strip()
#text = text[: text.find(tokenizer.eos_token)]
intermediate.append(text)
results[table_id] = clean_str(intermediate)
sys.stdout.write("finished {}/{}; speed={}s/sent \r".format(idx,
dataset.test_len(), (time.time() - start_time) / len(results)))
with open('challenge/test_results.json', 'w') as f:
json.dump(results, f, indent=2)
if args.do_verify:
assert 'stage2' in args.load_from, "The testing can only be done with stage2 model"
assert args.stage == 2, "The verification can only be done with stage 2 model"
dataset = GPTTableCoarseFineDatabase2(None, None, 'data/test_lm_pos_neg.json',
tokenizer, args.batch_size, args.max_len, args.stage)
model.load_state_dict(torch.load(args.load_from))
model.eval()
correct, total = 0, 0
with torch.no_grad():
for idx in range(0, dataset.test_len()):
batch_pos, batch_neg = dataset.get_pair_data(idx, 'test', mask_type='both')
batch = tuple(Variable(t).to(device) for t in batch_pos)
trg_inp, trg_out, mask, caption = batch
inputs = torch.cat([caption, trg_inp], 1)
logits = model(inputs)[0][:, -trg_out.shape[1]:, :].contiguous()
loss = criterion(logits.view(-1, logits.shape[-1]), trg_out.view(-1))
pos_loss = loss.reshape(logits.shape[0], -1) * mask
pos_loss_per_instance = pos_loss.sum(1) / mask.sum(1)
batch = tuple(Variable(t).to(device) for t in batch_neg)
trg_inp, trg_out, mask, caption = batch
inputs = torch.cat([caption, trg_inp], 1)
logits = model(inputs)[0][:, -trg_out.shape[1]:, :].contiguous()
loss = criterion(logits.view(-1, logits.shape[-1]), trg_out.view(-1))
neg_loss = loss.reshape(logits.shape[0], -1) * mask
neg_loss_per_instance = neg_loss.sum(1) / mask.sum(1)
comparison = (pos_loss_per_instance < neg_loss_per_instance).float()
correct += comparison.sum(-1).item()
total += comparison.shape[0]
sys.stdout.write('finished {}/{} accuracy {} \r'.format(idx, dataset.test_len(), correct / total))
print('total accuracy = {}'.format(correct / total))
if args.do_verify_challenge:
assert 'stage2' in args.load_from, "The testing can only be done with stage2 model"
assert args.stage == 2, "The verification can only be done with stage 2 model"
dataset = GPTTableCoarseFineDatabase2(None, None, 'challenge/blind_test_lm_pos_neg.json',
tokenizer, args.batch_size, args.max_len, args.stage)
model.load_state_dict(torch.load(args.load_from))
model.eval()
correct, total = 0, 0
results = {}
with torch.no_grad():
for idx in range(0, dataset.test_len()):
batch_pos, batch_neg = dataset.get_pair_data(idx, 'test', mask_type='both')
table_name = dataset.get_item(idx, 'test')
results[table_name] = []
batch = tuple(Variable(t).to(device) for t in batch_pos)
trg_inp, trg_out, mask, caption = batch
inputs = torch.cat([caption, trg_inp], 1)
logits = model(inputs)[0][:, -trg_out.shape[1]:, :].contiguous()
loss = criterion(logits.view(-1, logits.shape[-1]), trg_out.view(-1))
pos_loss = loss.reshape(logits.shape[0], -1) * mask
pos_loss_per_instance = pos_loss.sum(1) / mask.sum(1)
pos_perpelexity_per_instance = torch.exp(pos_loss_per_instance.cpu().data).tolist()
batch = tuple(Variable(t).to(device) for t in batch_neg)
trg_inp, trg_out, mask, caption = batch
inputs = torch.cat([caption, trg_inp], 1)
logits = model(inputs)[0][:, -trg_out.shape[1]:, :].contiguous()
loss = criterion(logits.view(-1, logits.shape[-1]), trg_out.view(-1))
neg_loss = loss.reshape(logits.shape[0], -1) * mask
neg_loss_per_instance = neg_loss.sum(1) / mask.sum(1)
neg_perpelexity_per_instance = torch.exp(neg_loss_per_instance.cpu().data).tolist()
for p1, p2 in zip(pos_perpelexity_per_instance, neg_perpelexity_per_instance):
if p1 < p2:
results[table_name].append('unknown1')
else:
results[table_name].append('unknown2')
sys.stdout.write('finished {}/{}\r'.format(idx, dataset.test_len()))
with open('challenge/verify_results.json', 'w') as f:
json.dump(results, f, indent=2)