import config
from math import log2
import os
import numpy as np
import ujson as json
import re
from collections import Counter
import string
from tqdm import tqdm
import random
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import torch.cuda
from torch.utils.data import Dataset
import argparse
'''
Some functions are from the official evaluation script.
'''
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
class SQuADDataset(Dataset):
def __init__(self, npz_file, num_steps, batch_size):
super().__init__()
data = np.load(npz_file)
self.context_idxs = torch.from_numpy(data["context_idxs"]).long()
self.context_char_idxs = torch.from_numpy(data["context_char_idxs"]).long()
self.ques_idxs = torch.from_numpy(data["ques_idxs"]).long()
self.ques_char_idxs = torch.from_numpy(data["ques_char_idxs"]).long()
self.y1s = torch.from_numpy(data["y1s"]).long()
self.y2s = torch.from_numpy(data["y2s"]).long()
self.ids = torch.from_numpy(data["ids"]).long()
num = len(self.ids)
self.batch_size = batch_size
self.num_steps = num_steps if num_steps >= 0 else num // batch_size
num_items = num_steps * batch_size
idxs = list(range(num))
self.idx_map = []
i, j = 0, num
while j <= num_items:
random.shuffle(idxs)
self.idx_map += idxs.copy()
i = j
j += num
random.shuffle(idxs)
self.idx_map += idxs[:num_items - i]
def __len__(self):
return self.num_steps
def __getitem__(self, item):
idxs = torch.LongTensor(self.idx_map[item:item + self.batch_size])
res = (self.context_idxs[idxs],
self.context_char_idxs[idxs],
self.ques_idxs[idxs],
self.ques_char_idxs[idxs],
self.y1s[idxs],
self.y2s[idxs], self.ids[idxs])
return res
class EMA(object):
def __init__(self, decay):
self.decay = decay
self.shadows = {}
self.devices = {}
def __len__(self):
return len(self.shadows)
def get(self, name: str):
return self.shadows[name].to(self.devices[name])
def set(self, name: str, param: nn.Parameter):
self.shadows[name] = param.data.to('cpu').clone()
self.devices[name] = param.data.device
def update_parameter(self, name: str, param: nn.Parameter):
if name in self.shadows:
data = param.data
new_shadow = self.decay * data + (1.0 - self.decay) * self.get(name)
param.data.copy_(new_shadow)
self.shadows[name] = new_shadow.to('cpu').clone()
def convert_tokens(eval_file, qa_id, pp1, pp2):
answer_dict = {}
remapped_dict = {}
for qid, p1, p2 in zip(qa_id, pp1, pp2):
context = eval_file[str(qid)]["context"]
spans = eval_file[str(qid)]["spans"]
uuid = eval_file[str(qid)]["uuid"]
l = len(spans)
if p1 >= l or p2 >= l:
ans = ""
else:
start_idx = spans[p1][0]
end_idx = spans[p2][1]
ans = context[start_idx: end_idx]
answer_dict[str(qid)] = ans
remapped_dict[uuid] = ans
return answer_dict, remapped_dict
def evaluate(eval_file, answer_dict):
f1 = exact_match = total = 0
for key, value in answer_dict.items():
total += 1
ground_truths = eval_file[key]["answers"]
prediction = value
exact_match += metric_max_over_ground_truths(exact_match_score, prediction, ground_truths)
f1 += metric_max_over_ground_truths(f1_score, prediction, ground_truths)
exact_match = 100.0 * exact_match / total
f1 = 100.0 * f1 / total
return {'exact_match': exact_match, 'f1': f1}
def normalize_answer(s):
def remove_articles(text):
return re.sub(r'\b(a|an|the)\b', ' ', text)
def white_space_fix(text):
return ' '.join(text.split())
def remove_punc(text):
exclude = set(string.punctuation)
return ''.join(ch for ch in text if ch not in exclude)
def lower(text):
return text.lower()
return white_space_fix(remove_articles(remove_punc(lower(s))))
def f1_score(prediction, ground_truth):
prediction_tokens = normalize_answer(prediction).split()
ground_truth_tokens = normalize_answer(ground_truth).split()
common = Counter(prediction_tokens) & Counter(ground_truth_tokens)
num_same = sum(common.values())
if num_same == 0:
return 0
precision = 1.0 * num_same / len(prediction_tokens)
recall = 1.0 * num_same / len(ground_truth_tokens)
f1 = (2 * precision * recall) / (precision + recall)
return f1
def exact_match_score(prediction, ground_truth):
return (normalize_answer(prediction) == normalize_answer(ground_truth))
def metric_max_over_ground_truths(metric_fn, prediction, ground_truths):
scores_for_ground_truths = []
for ground_truth in ground_truths:
score = metric_fn(prediction, ground_truth)
scores_for_ground_truths.append(score)
return max(scores_for_ground_truths)
def train(model, optimizer, scheduler, ema, dataset, start, length):
model.train()
losses = []
for i in tqdm(range(start, length + start), total=length):
optimizer.zero_grad()
Cwid, Ccid, Qwid, Qcid, y1, y2, ids = dataset[i]
Cwid, Ccid, Qwid, Qcid = Cwid.to(device), Ccid.to(device), Qwid.to(device), Qcid.to(device)
p1, p2 = model(Cwid, Ccid, Qwid, Qcid)
y1, y2 = y1.to(device), y2.to(device)
loss1 = F.nll_loss(p1, y1, reduction='mean')
loss2 = F.nll_loss(p2, y2, reduction='mean')
loss = (loss1 + loss2) / 2
losses.append(loss.item())
loss.backward()
optimizer.step()
scheduler.step()
for name, p in model.named_parameters():
if p.requires_grad: ema.update_parameter(name, p)
torch.nn.utils.clip_grad_norm_(model.parameters(), config.grad_clip)
loss_avg = np.mean(losses)
print("STEP {:8d} loss {:8f}\n".format(i + 1, loss_avg))
def valid(model, dataset, eval_file):
model.eval()
answer_dict = {}
losses = []
num_batches = config.val_num_batches
with torch.no_grad():
for i in tqdm(random.sample(range(0, len(dataset)), num_batches), total=num_batches):
Cwid, Ccid, Qwid, Qcid, y1, y2, ids = dataset[i]
Cwid, Ccid, Qwid, Qcid = Cwid.to(device), Ccid.to(device), Qwid.to(device), Qcid.to(device)
p1, p2 = model(Cwid, Ccid, Qwid, Qcid)
y1, y2 = y1.to(device), y2.to(device)
loss1 = F.nll_loss(p1, y1, reduction='mean')
loss2 = F.nll_loss(p2, y2, reduction='mean')
loss = (loss1 + loss2) / 2
losses.append(loss.item())
yp1 = torch.argmax(p1, 1)
yp2 = torch.argmax(p2, 1)
yps = torch.stack([yp1, yp2], dim=1)
ymin, _ = torch.min(yps, 1)
ymax, _ = torch.max(yps, 1)
answer_dict_, _ = convert_tokens(eval_file, ids.tolist(), ymin.tolist(), ymax.tolist())
answer_dict.update(answer_dict_)
loss = np.mean(losses)
metrics = evaluate(eval_file, answer_dict)
metrics["loss"] = loss
print("VALID loss {:8f} F1 {:8f} EM {:8f}\n".format(loss, metrics["f1"], metrics["exact_match"]))
def test(model, dataset, eval_file):
model.eval()
answer_dict = {}
losses = []
num_batches = config.test_num_batches
with torch.no_grad():
for i in tqdm(range(num_batches), total=num_batches):
Cwid, Ccid, Qwid, Qcid, y1, y2, ids = dataset[i]
Cwid, Ccid, Qwid, Qcid = Cwid.to(device), Ccid.to(device), Qwid.to(device), Qcid.to(device)
p1, p2 = model(Cwid, Ccid, Qwid, Qcid)
y1, y2 = y1.to(device), y2.to(device)
loss1 = F.nll_loss(p1, y1, reduction='mean')
loss2 = F.nll_loss(p2, y2, reduction='mean')
loss = (loss1 + loss2) / 2
losses.append(loss.item())
yp1 = torch.argmax(p1, 1)
yp2 = torch.argmax(p2, 1)
yps = torch.stack([yp1, yp2], dim=1)
ymin, _ = torch.min(yps, 1)
ymax, _ = torch.max(yps, 1)
answer_dict_, _ = convert_tokens(eval_file, ids.tolist(), ymin.tolist(), ymax.tolist())
answer_dict.update(answer_dict_)
loss = np.mean(losses)
metrics = evaluate(eval_file, answer_dict)
f = open("log/answers.json", "w")
json.dump(answer_dict, f)
f.close()
metrics["loss"] = loss
print("TEST loss {:8f} F1 {:8f} EM {:8f}\n".format(loss, metrics["f1"], metrics["exact_match"]))
return metrics
def train_entry():
from models import QANet
with open(config.word_emb_file, "r") as fh:
word_mat = np.array(json.load(fh), dtype=np.float32)
with open(config.char_emb_file, "r") as fh:
char_mat = np.array(json.load(fh), dtype=np.float32)
with open(config.train_eval_file, "r") as fh:
train_eval_file = json.load(fh)
with open(config.dev_eval_file, "r") as fh:
dev_eval_file = json.load(fh)
print("Building model...")
train_dataset = SQuADDataset(config.train_record_file, config.num_steps, config.batch_size)
dev_dataset = SQuADDataset(config.dev_record_file, config.test_num_batches, config.batch_size)
lr = config.learning_rate
base_lr = 1.0
warm_up = config.lr_warm_up_num
model = QANet(word_mat, char_mat).to(device)
ema = EMA(config.ema_decay)
for name, p in model.named_parameters():
if p.requires_grad: ema.set(name, p)
params = filter(lambda param: param.requires_grad, model.parameters())
optimizer = optim.Adam(lr=base_lr, betas=(config.beta1, config.beta2), eps=1e-7, weight_decay=3e-7, params=params)
cr = lr / log2(warm_up)
scheduler = optim.lr_scheduler.LambdaLR(optimizer, lr_lambda=lambda ee: cr * log2(ee + 1) if ee < warm_up else lr)
L = config.checkpoint
N = config.num_steps
best_f1 = best_em = patience = 0
for iter in range(0, N, L):
train(model, optimizer, scheduler, ema, train_dataset, iter, L)
valid(model, train_dataset, train_eval_file)
metrics = test(model, dev_dataset, dev_eval_file)
print("Learning rate: {}".format(scheduler.get_lr()))
dev_f1 = metrics["f1"]
dev_em = metrics["exact_match"]
if dev_f1 < best_f1 and dev_em < best_em:
patience += 1
if patience > config.early_stop: break
else:
patience = 0
best_f1 = max(best_f1, dev_f1)
best_em = max(best_em, dev_em)
fn = os.path.join(config.save_dir, "model.pt")
torch.save(model, fn)
def test_entry():
with open(config.dev_eval_file, "r") as fh:
dev_eval_file = json.load(fh)
dev_dataset = SQuADDataset(config.dev_record_file, -1, config.batch_size)
fn = os.path.join(config.save_dir, "model.pt")
model = torch.load(fn)
test(model, dev_dataset, dev_eval_file)
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument("--mode", action="store", dest="mode", default="train", help="train/test/debug")
pargs = parser.parse_args()
print("Current device is {}".format(device))
if pargs.mode == "train":
train_entry()
elif pargs.mode == "debug":
config.batch_size = 2
config.num_steps = 32
config.test_num_batches = 2
config.val_num_batches = 2
config.checkpoint = 2
config.period = 1
train_entry()
elif pargs.mode == "test":
test_entry()
else:
print("Unknown mode")
exit(0)
