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import random
import numpy as np
from openrec.preprocess.ctc_label_encode import BaseRecLabelEncode
class MDiffLabelEncode(BaseRecLabelEncode):
"""Convert between text-label and text-index."""
MASK = '<mask>'
EOS = '</s>'
PAD = '<pad>'
def __init__(self,
max_text_length,
character_dict_path=None,
use_space_char=False,
semi_ar=False,
mask_tpye=[0, 1, 2, 3, 4, 5],
train_all_layer=False,
sample_num=1,
**kwargs):
super(MDiffLabelEncode,
self).__init__(max_text_length, character_dict_path,
use_space_char)
self.semi_ar = semi_ar
self.mask_tpye = mask_tpye #[0, 1, 2, 3, 4, 5]
# 5种mask：
# 全部mask，纯并行
# 正向自回归
# 反向自回归
# block mask（1, 2，3，4，5，6步）
# 随机mask
self.train_all_layer = train_all_layer
if not self.train_all_layer:
self.sample_num = 1
else:
self.sample_num = sample_num
def random_mask(self, text):
l = len(text)
p_mask = random.random()
noisy_batch = text[:]
masked_indices = [False] * l
none_pad_indices = []
for i in range(l):
if random.random() < p_mask and text[i] != self.dict[self.PAD]:
noisy_batch[i] = self.dict[self.MASK]
masked_indices[i] = True
if text[i] != self.dict[self.PAD]:
none_pad_indices.append(i)
if noisy_batch[i] == self.dict[self.PAD]:
noisy_batch[i] = self.dict[self.MASK]
if not any(masked_indices) and len(none_pad_indices) > 0:
idx = random.choice(none_pad_indices)
noisy_batch[idx] = self.dict[self.MASK]
masked_indices[idx] = True
return noisy_batch, masked_indices
def full_mask(self, text):
noisy_batch = [self.dict[self.MASK]] * (self.max_text_len + 1)
masked_indices = [True] * len(text) + [False] * (self.max_text_len +
1 - len(text))
return noisy_batch, masked_indices
def left_to_right_mask(self, text):
rand_split = random.randint(1, len(text) - 1)
noisy_batch = text[:rand_split] + [self.dict[self.MASK]] * (
self.max_text_len + 1 - rand_split)
masked_indices = [False] * rand_split + [True] * (len(
text) - rand_split) + [False] * (self.max_text_len + 1 - len(text))
return noisy_batch, masked_indices
def semi_left_to_right_mask(self, text, block_size=5, step=3, i=0):
text = text + [self.dict[self.PAD]
] * (self.max_text_len + 1 - len(text))
noisy_batch_step_i = []
masked_indices_step_i = []
if i == 0:
for t in text[:block_size]:
if t == self.dict[self.PAD]:
masked_indices_step_i.append(False)
else:
masked_indices_step_i.append(True)
noisy_batch_step_i = [self.dict[self.MASK]] * block_size + [
self.dict[self.PAD]
] * (self.max_text_len + 1)
masked_indices_step_i = masked_indices_step_i + [False] * (
self.max_text_len + 1 - block_size)
elif i == 1:
noisy_batch_r, masked_indices_r = self.random_mask(
text[:block_size])
noisy_batch_step_i = noisy_batch_r
masked_indices_step_i = masked_indices_r
if i == step - 1:
for t in text[i * block_size:]:
if t == self.dict[self.PAD]:
masked_indices_step_i.append(False)
else:
masked_indices_step_i.append(True)
noisy_batch_step_i.append(self.dict[self.MASK])
else:
for t in text[i * block_size:(i + 1) * block_size]:
if t == self.dict[self.PAD]:
masked_indices_step_i.append(False)
else:
masked_indices_step_i.append(True)
noisy_batch_step_i.append(self.dict[self.MASK])
noisy_batch_step_i = noisy_batch_step_i + [self.dict[self.PAD]] * (
self.max_text_len + 1 - len(noisy_batch_step_i))
masked_indices_step_i = masked_indices_step_i + [False] * (
self.max_text_len + 1 - len(noisy_batch_step_i))
elif i >= 2:
for t in text[:(i - 1) * block_size]:
if t == self.dict[self.PAD]:
noisy_batch_step_i.append(self.dict[self.MASK])
else:
noisy_batch_step_i.append(text[t])
masked_indices_step_i = [False] * ((i - 1) * block_size)
noisy_batch_r, masked_indices_r = self.random_mask(
text[(i - 1) * block_size:i * block_size])
noisy_batch_step_i = noisy_batch_step_i + noisy_batch_r
masked_indices_step_i = masked_indices_step_i + masked_indices_r
if i == step - 1:
for t in text[i * block_size:]:
if t == self.dict[self.PAD]:
masked_indices_step_i.append(False)
else:
masked_indices_step_i.append(True)
noisy_batch_step_i.append(self.dict[self.MASK])
else:
for t in text[i * block_size:(i + 1) * block_size]:
if t == self.dict[self.PAD]:
masked_indices_step_i.append(False)
else:
masked_indices_step_i.append(True)
noisy_batch_step_i.append(self.dict[self.MASK])
noisy_batch_step_i = noisy_batch_step_i + [self.dict[self.PAD]] * (
self.max_text_len + 1 - len(noisy_batch_step_i))
masked_indices_step_i = masked_indices_step_i + [False] * (
self.max_text_len + 1 - len(masked_indices_step_i))
return noisy_batch_step_i, masked_indices_step_i
def forward_process_semi_ar(self, text):
step = 5
block_size = (self.max_text_len + 1) // step
noisy_batch_semi_ar = []
masked_indices_semi_ar = []
for i in range(step):
noisy_batch_step_i, masked_indices_step_i = self.semi_left_to_right_mask(
text, block_size=block_size, step=step, i=i)
noisy_batch_semi_ar.append(noisy_batch_step_i)
masked_indices_semi_ar.append(masked_indices_step_i)
rd_step = random.choice([2, 3, 4, 6, 7, 8])
block_size = (self.max_text_len + 1) // rd_step
rd_step_i = random.randint(0, rd_step - 1)
noisy_batch, masked_indices = self.semi_left_to_right_mask(
text, block_size=block_size, step=rd_step, i=rd_step_i)
noisy_batch_semi_ar.append(noisy_batch)
masked_indices_semi_ar.append(masked_indices)
# 随机将text中的部分token mask掉
noisy_batch, masked_indices = self.random_mask(text)
noisy_batch = noisy_batch + [self.dict[self.MASK]
] * (self.max_text_len + 1 - len(text))
masked_indices = masked_indices + [False] * (self.max_text_len + 1 -
len(text))
noisy_batch_semi_ar.append(noisy_batch)
masked_indices_semi_ar.append(masked_indices)
return noisy_batch_semi_ar, noisy_batch_semi_ar
def right_to_left_mask(self, text):
rand_split = random.randint(1, len(text) - 1)
noisy_batch = [self.dict[self.MASK]
] * rand_split + text[rand_split:] + [
self.dict[self.MASK]
] * (self.max_text_len + 1 - len(text))
masked_indices = [True] * rand_split + [False] * (len(
text) - rand_split) + [False] * (self.max_text_len + 1 - len(text))
return noisy_batch, masked_indices
def forward_process(self, text):
rand_choice = random.choice(self.mask_tpye)
if rand_choice == 0: # 并行mask full mask
return self.full_mask(text)
elif rand_choice == 1 and len(text) > 2: # 正向自回归 right mask
return self.left_to_right_mask(text)
elif rand_choice == 2 and len(text) > 2: # 反向自回归 left mask
return self.right_to_left_mask(text)
elif rand_choice == 3 and len(text) > 2: # block mask
rand_step = min(random.randint(2, 6), len(text))
if rand_step <= 1: # len(text) <= 1
return self.full_mask(text)
block_size = len(text) // rand_step
if block_size == 1:
return self.left_to_right_mask(text) if random.random(
) < 0.5 else self.right_to_left_mask(text)
# 余数处理
if len(text) % rand_step != 0:
rand_step += 1
# 选择一个随机的block_size
rand_step_from_mask = random.randint(2, rand_step)
if rand_step == 2:
rand_step_from_mask = 1
else:
rand_step_from_mask = random.randint(2, rand_step)
noisy_batch = text[:block_size * (rand_step_from_mask - 1)]
masked_indices = [False] * (block_size * (rand_step_from_mask - 1))
noisy_batch = noisy_batch + [self.dict[self.MASK]] * (
self.max_text_len + 1 - len(noisy_batch))
masked_indices = masked_indices + [True] * (
len(text) - block_size *
(rand_step_from_mask - 1)) + [False] * (self.max_text_len + 1 -
len(text))
return noisy_batch, masked_indices
elif rand_choice == 4 and len(text) > 2: # cloze mask
noisy_batch = text[:]
masked_indices = [False] * len(text)
rand_index = random.randint(0, len(text) - 1)
noisy_batch[rand_index] = self.dict[self.MASK]
masked_indices[rand_index] = True
noisy_batch = noisy_batch + [self.dict[self.MASK]] * (
self.max_text_len + 1 - len(text))
masked_indices = masked_indices + [False] * (self.max_text_len +
1 - len(text))
return noisy_batch, masked_indices
else: # random mask
# 随机将text中的部分token mask掉
noisy_batch, masked_indices = self.random_mask(text)
noisy_batch = noisy_batch + [self.dict[self.MASK]] * (
self.max_text_len + 1 - len(text))
masked_indices = masked_indices + [False] * (self.max_text_len +
1 - len(text))
return noisy_batch, masked_indices
def reflect_random_idices(self, text, eps=1e-3):
l = len(text)
t = random.random()
p_mask = (1 - eps) * t + eps
reflect_ids = text[:]
for i in range(l):
if random.random() < p_mask:
reflect_ids[i] = random.randint(0, len(self.dict) - 1)
return reflect_ids
def __call__(self, data):
text = data['label']
text = self.encode(text)
if text is None:
return None
data['length'] = np.array(len(text) + 1)
text = text + [self.dict[self.EOS]]
p_mask_list = []
noisy_batch_list = []
masked_indices_list = []
reflect_ids_list = []
for i in range(self.sample_num):
reflect_ids = self.reflect_random_idices(text)
reflect_ids = reflect_ids + [self.dict[self.MASK]] * (
self.max_text_len + 1 - len(reflect_ids))
if self.semi_ar:
noisy_batch, masked_indices = self.forward_process_semi_ar(
text)
else:
noisy_batch, masked_indices = self.forward_process(text)
p_mask = float(sum(masked_indices)) / float(len(text))
p_mask_list.append(np.array(p_mask))
noisy_batch_list.append(np.array(noisy_batch))
masked_indices_list.append(np.array(masked_indices))
reflect_ids_list.append(np.array(reflect_ids))
if not self.semi_ar:
data['p_mask'] = np.array(
p_mask_list) if self.train_all_layer else np.array(
p_mask_list[0])
data['noisy_batch'] = np.array(
noisy_batch_list) if self.train_all_layer else np.array(
noisy_batch_list[0])
data['masked_indices'] = np.array(
masked_indices_list) if self.train_all_layer else np.array(
masked_indices_list[0])
data['reflect_ids'] = np.array(
reflect_ids_list) if self.train_all_layer else np.array(
reflect_ids_list[0])
text = text + [self.dict[self.PAD]
] * (self.max_text_len + 1 - len(text))
data['label'] = np.array(text)
return data
def add_special_char(self, dict_character):
dict_character = [self.EOS] + dict_character + [self.MASK, self.PAD]
return dict_character
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