import torch
from torch.utils.data import Dataset,DataLoader
from torch.autograd import Variable
import json
import random
import sys
import pickle
from tqdm import tqdm
from collections import deque
import copy
sys.path.append("..")
from utils import text_standardize
USE_CUDA = torch.cuda.is_available()
FloatTensor = torch.cuda.FloatTensor if USE_CUDA else torch.FloatTensor
LongTensor = torch.cuda.LongTensor if USE_CUDA else torch.LongTensor
ByteTensor = torch.cuda.ByteTensor if USE_CUDA else torch.ByteTensor
# ==== Code for data loading =====
class GptDataset(Dataset):
"""Take a list of samples with form [[x,...],y,meta]
"""
# need 3 special tokens
# # as <ref start> 2
# $ as <speaker1> 3
# % as <speaker2> 4
# '<|endoftext|>' as <eos> 50256
def _split(self,x_y_meta):
x_all = []
y_all = []
meta_all = []
for x,y,meta in x_y_meta:
meta_all.append(meta)
x_all.append([self.tokenizer.encode(text_standardize(x_i)) for x_i in x])
y_all.append(self.tokenizer.encode(text_standardize(y)))
return x_all,y_all,meta_all
def _filter(self,x_all,y_all,meta_all,filter_mode=None):
allowed_pattern = ['SR_only','CR_only','Smoking_only','Diet_only']
data = zip(x_all,y_all,meta_all)
if filter_mode not in allowed_pattern:
data_filt = data
if filter_mode=='SR_only':
data_filt = [x for x in data if x[2][2]=='SR']
if filter_mode=='CR_only':
data_filt = [x for x in data if x[2][2]=='CR']
if filter_mode=='Smoking_only':
data_filt = [x for x in data if x[2][1]=='Smoking cessation']
if filter_mode=='Diet_only':
data_filt = [x for x in data if x[2][1]=='Weight management']
x_filt,y_filt,meta_filt = zip(*data_filt)
return x_filt, y_filt, meta_filt
def __init__(self,x_y_meta,tokenizer,filter_mode=None,num_turns=5):
self.x_y_meta = x_y_meta
self.num_turns = num_turns
self.tokenizer = tokenizer
self.x_encoded,self.y_encoded,self.meta = self._split(x_y_meta)
self.x_encoded,self.y_encoded,self.meta = self._filter(self.x_encoded,self.y_encoded,self.meta,filter_mode)
self.ref_start, self.speaker1,self.speaker2,self.eos = 2,3,4,50256
def __getitem__(self,index):
x = []
type_x = []
lm_x = []
is_speaker1 = bool(self.num_turns % 2) # which speaker start the conversation
for utt in self.x_encoded[index][-self.num_turns:]:
if is_speaker1: # add the prefix special token for each utterance
x+=[self.speaker1]
type_x += [self.speaker1]*(len(utt)+1)
else:
x+=[self.speaker2]
type_x += [self.speaker2]*(len(utt)+1)
x += utt
is_speaker1 = not is_speaker1
lm_x += [-1]*len(x) # all position for the input is masked for loss calculation
total_input_length = len(x)
x += [self.ref_start] + self.y_encoded[index] + [self.eos]
type_x += [self.ref_start]*(len(self.y_encoded[index])+2)
lm_x += [-1] + self.y_encoded[index] + [self.eos]
position_x = list(range(len(x)))
x = torch.Tensor(x)
type_x = torch.Tensor(type_x)
position_x = torch.Tensor(position_x)
lm_x = torch.Tensor(lm_x)
x_len = x.shape[0]
return x,type_x,position_x,lm_x,total_input_length,self.meta[index]
def __len__(self):
return len(self.x_encoded)
class GptDataset_aug(Dataset):
def _split(self,x_y_meta):
x_all = []
y_all = []
meta_all = []
aug_all = []
for x,y,meta,aug in x_y_meta:
meta_all.append(meta)
x_all.append([self.tokenizer.encode(text_standardize(x_i)) for x_i in x])
y_all.append(self.tokenizer.encode(text_standardize(y)))
aug_all.append(self.tokenizer.encode(text_standardize(aug)))
return x_all,y_all,meta_all,aug_all
def __init__(self,x_y_meta,tokenizer,num_turns=5):
self.x_y_meta = x_y_meta
self.num_turns = num_turns
self.tokenizer = tokenizer
self.x_encoded,self.y_encoded,self.meta,self.aug_encoded = self._split(x_y_meta)
self.ref_start, self.speaker1,self.speaker2,self.eos = 2,3,4,50256
self.augment = 5
def __getitem__(self,index):
x = []
type_x = []
lm_x = []
x += [self.augment] + self.aug_encoded[index]
type_x += [self.augment] * len(x)
is_speaker1 = bool(self.num_turns % 2) # which speaker start the conversation
for utt in self.x_encoded[index][-self.num_turns:]:
if is_speaker1: # add the prefix special token for each utterance
x+=[self.speaker1]
type_x += [self.speaker1]*(len(utt)+1)
else:
x+=[self.speaker2]
type_x += [self.speaker2]*(len(utt)+1)
x += utt
is_speaker1 = not is_speaker1
lm_x += [-1]*len(x) # all position for the input is masked for loss calculation
total_input_length = len(x)
x += [self.ref_start] + self.y_encoded[index] + [self.eos]
type_x += [self.ref_start]*(len(self.y_encoded[index])+2)
lm_x += [-1] + self.y_encoded[index] + [self.eos]
position_x = list(range(len(x)))
x = torch.Tensor(x)
type_x = torch.Tensor(type_x)
position_x = torch.Tensor(position_x)
lm_x = torch.Tensor(lm_x)
x_len = x.shape[0]
return x,type_x,position_x,lm_x,total_input_length,self.meta[index]
def __len__(self):
return len(self.x_encoded)
def collate_fn(data):
"""Creates mini-batch tensors from the list of tuples (src_seq, trg_seq).
We should build a custom collate_fn rather than using default collate_fn,
because merging sequences (including padding) is not supported in default.
Seqeuences are padded to the maximum length of mini-batch sequences (dynamic padding).
Args:
data: list of tuple (src_seq, trg_seq).
- src_seq: torch tensor of shape (?); variable length.
- trg_seq: torch tensor of shape (?); variable length.
Returns:
src_seqs: torch tensor of shape (batch_size, padded_length).
src_lengths: list of length (batch_size); valid length for each padded source sequence.
trg_seqs: torch tensor of shape (batch_size, padded_length).
trg_lengths: list of length (batch_size); valid length for each padded target sequence.
"""
def merge(sequences):
lengths = [len(seq) for seq in sequences]
padded_seqs = torch.zeros(len(sequences), max(lengths)).long()
for i, seq in enumerate(sequences):
end = lengths[i]
padded_seqs[i, :end] = seq[:end]
return padded_seqs, lengths
def merge_matrix(matrices):
max_size = max([m.shape[-1] for m in attention_mask])
padded_matrices = torch.zeros(len(matrices), 1, max_size, max_size)
for i,m in enumerate(matrices):
m_size = m.shape[-1]
padded_matrices[i,:,:m_size,:m_size] = m
return padded_matrices
# sort a list by sequence length (descending order) to use pack_padded_sequence
data.sort(key=lambda x: len(x[0]), reverse=True)
# seperate source and target sequences
src_seqs, trg_seqs, pos_seqs,lm_seqs,total_input_length,attention_mask = zip(*data)
# merge sequences (from tuple of 1D tensor to 2D tensor)
src_seqs, src_lengths = merge(src_seqs)
trg_seqs, trg_lengths = merge(trg_seqs)
pos_seqs, pos_lengths = merge(pos_seqs)
lm_seqs, lm_lengths = merge(lm_seqs)
if type(attention_mask[0]) is not list:
attention_mask = merge_matrix(attention_mask)
if USE_CUDA:
src_seqs = src_seqs.cuda()
trg_seqs = trg_seqs.cuda()
pos_seqs = pos_seqs.cuda()
lm_seqs = lm_seqs.cuda()
attention_mask = attention_mask.cuda()
return Variable(LongTensor(src_seqs)), Variable(LongTensor(trg_seqs)), Variable(LongTensor(pos_seqs)),Variable(LongTensor(lm_seqs)), total_input_length, attention_mask
def collate_fn_conditional(data):
def merge(sequences):
lengths = [len(seq) for seq in sequences]
padded_seqs = torch.zeros(len(sequences), max(lengths)).long()
for i, seq in enumerate(sequences):
end = lengths[i]
padded_seqs[i, :end] = seq[:end]
return padded_seqs, lengths
# sort a list by sequence length (descending order) to use pack_padded_sequence
data.sort(key=lambda x: len(x[0]), reverse=True)
# seperate source and target sequences
src_seqs, trg_seqs, pos_seqs,lm_seqs,total_input_length, meta = zip(*data)
# merge sequences (from tuple of 1D tensor to 2D tensor)
src_seqs, src_lengths = merge(src_seqs)
trg_seqs, trg_lengths = merge(trg_seqs)
pos_seqs, pos_lengths = merge(pos_seqs)
lm_seqs, lm_lengths = merge(lm_seqs)
if USE_CUDA:
src_seqs = src_seqs.cuda()
trg_seqs = trg_seqs.cuda()
pos_seqs = pos_seqs.cuda()
lm_seqs = lm_seqs.cuda()
return Variable(LongTensor(src_seqs)), Variable(LongTensor(trg_seqs)), Variable(LongTensor(pos_seqs)),Variable(LongTensor(lm_seqs)), total_input_length, meta
def collate_fn_nli(data):
"""Creates mini-batch tensors from the list of tuples (src_seq, trg_seq).
We should build a custom collate_fn rather than using default collate_fn,
because merging sequences (including padding) is not supported in default.
Seqeuences are padded to the maximum length of mini-batch sequences (dynamic padding).
Args:
data: list of tuple (src_seq, trg_seq).
- src_seq: torch tensor of shape (?); variable length.
- trg_seq: torch tensor of shape (?); variable length.
Returns:
src_seqs: torch tensor of shape (batch_size, padded_length).
src_lengths: list of length (batch_size); valid length for each padded source sequence.
trg_seqs: torch tensor of shape (batch_size, padded_length).
trg_lengths: list of length (batch_size); valid length for each padded target sequence.
"""
def merge(sequences):
lengths = [len(seq) for seq in sequences]
padded_seqs = torch.zeros(len(sequences), max(lengths)).long()
for i, seq in enumerate(sequences):
end = lengths[i]
padded_seqs[i, :end] = seq[:end]
return padded_seqs, lengths
# sort a list by sequence length (descending order) to use pack_padded_sequence
data.sort(key=lambda x: len(x[0]), reverse=True)
# seperate source and target sequences
src_seqs, trg_seqs, pos_seqs,lm_seqs,label = zip(*data)
# merge sequences (from tuple of 1D tensor to 2D tensor)
src_seqs, src_lengths = merge(src_seqs)
trg_seqs, trg_lengths = merge(trg_seqs)
pos_seqs, pos_lengths = merge(pos_seqs)
# lm_seqs, lm_lengths = merge(lm_seqs)
label = torch.tensor(label)
if USE_CUDA:
src_seqs = src_seqs.cuda()
trg_seqs = trg_seqs.cuda()
pos_seqs = pos_seqs.cuda()
# lm_seqs = lm_seqs.cuda()
label = label.cuda()
return Variable(LongTensor(src_seqs)), Variable(LongTensor(trg_seqs)), Variable(LongTensor(pos_seqs)),lm_seqs, label
def collate_fn_keyword(data):
def merge(sequences):
lengths = [len(seq) for seq in sequences]
padded_seqs = torch.zeros(len(sequences), max(lengths)).long()
for i, seq in enumerate(sequences):
end = lengths[i]
padded_seqs[i, :end] = seq[:end]
return padded_seqs, lengths
# sort a list by sequence length (descending order) to use pack_padded_sequence
data.sort(key=lambda x: len(x[0]), reverse=True)
# seperate source and target sequences
src_seqs, trg_seqs, pos_seqs, lm_seqs, total_input_length, meta, keyword_x = zip(*data)
# merge sequences (from tuple of 1D tensor to 2D tensor)
src_seqs, src_lengths = merge(src_seqs)
trg_seqs, trg_lengths = merge(trg_seqs)
pos_seqs, pos_lengths = merge(pos_seqs)
lm_seqs, lm_lengths = merge(lm_seqs)
keyword_x, _ = merge(keyword_x)
if USE_CUDA:
src_seqs = src_seqs.cuda()
trg_seqs = trg_seqs.cuda()
pos_seqs = pos_seqs.cuda()
lm_seqs = lm_seqs.cuda()
keyword_x = keyword_x.cuda()
return Variable(LongTensor(src_seqs)), Variable(LongTensor(trg_seqs)), Variable(LongTensor(pos_seqs)),Variable(LongTensor(lm_seqs)), total_input_length, meta,Variable(LongTensor(keyword_x))
class SnliDataset(Dataset):
"""Take a list of samples with form [[x,...],y,meta]
"""
# need 3 special tokens
# # as <ref start> 2
# $ as <speaker1> 3
# % as <speaker2> 4
# '<|endoftext|>' as <eos> 50256
def _split(self,data):
positive_label = set(['entailment'])
premise = []
hypothesis = []
label = []
for p,h,l in tqdm(data):
premise.append(self.tokenizer.encode(text_standardize(p)))
hypothesis.append(self.tokenizer.encode(text_standardize(h)))
if l in positive_label:
label.append(torch.tensor(1))
else:
label.append(torch.tensor(0))
return premise,hypothesis,label
def _filter(self,premise,hypothesis,label,filter_mode=None):
data = zip(premise,hypothesis,label)
if filter_mode == None:
data_filt = data
else:
data_filt = [x for x in data if x[2]!='-']
premise_filt,hypothesis_filt,label_filt = zip(*data_filt)
return premise_filt,hypothesis_filt,label_filt
def parse_snli(self,path=None):
with open(path) as f:
data = [json.loads(line) for line in f]
data_processed = [(line['sentence1'],line['sentence2'],line['gold_label']) for line in data]
return data_processed
def __init__(self,tokenizer,path='../data/snli_1.0/snli_1.0_train.jsonl',filter_mode=None,num_turns=5):
self.data = self.parse_snli(path)
self.tokenizer = tokenizer
self.premise_encoded,self.hypothesis_encoded,self.label = self._split(self.data)
self.premise_encoded,self.hypothesis_encoded,self.label = self._filter(self.premise_encoded,self.hypothesis_encoded,self.label,filter_mode)
self.ref_start, self.speaker1,self.speaker2,self.eos = 2,3,4,50256
def __getitem__(self,index):
x = []
type_x = []
lm_x = []
x += [self.speaker1]
x += self.premise_encoded[index]
type_x += [self.speaker1]*(len(self.premise_encoded[index])+1) # the premise part
x += [self.ref_start]
x += self.hypothesis_encoded[index]
x += [self.eos]
type_x += [self.ref_start]*(len(self.hypothesis_encoded[index])+2) # the hypothesis part
label = self.label[index]
position_x = list(range(len(x)))
x = torch.Tensor(x)
type_x = torch.Tensor(type_x)
position_x = torch.Tensor(position_x)
return x,type_x,position_x,lm_x,label
def __len__(self):
return len(self.premise_encoded)
class GptDataset_full(Dataset):
def _split(self,x_y_meta):
x_all = []
y_all = []
meta_all = []
aug_all = []
keyword_all = []
for x, y, meta, aug, keyword in x_y_meta:
meta_all.append(meta)
# update for the new data format
aug = ''.join([a[1] for a in aug])
x_all.append([self.tokenizer.encode(text_standardize(x_i)) for x_i in x])
y_all.append(self.tokenizer.encode(text_standardize(y)))
aug_all.append(self.tokenizer.encode(text_standardize(aug)))
keyword_all.append(self.tokenizer.encode(text_standardize(keyword)))
return x_all,y_all,meta_all,aug_all, keyword_all
def _filt(self, length=1024):
data = zip(self.x_encoded,self.y_encoded,self.meta,self.aug_encoded, self.keyword_encoded)
data = [sample for sample in data if sum([len(sen) for sen in sample[0]][-self.args.num_turns:])+len(sample[1])+len(sample[3])+len(sample[4]) < 850]
self.x_encoded,self.y_encoded,self.meta,self.aug_encoded, self.keyword_encoded = zip(*data)
self.x_encoded = list(self.x_encoded)
self.y_encoded = list(self.y_encoded)
self.meta = list(self.meta)
self.aug_encoded = list(self.aug_encoded)
self.keyword_encoded = list(self.keyword_encoded)
def __init__(self,x_y_meta,tokenizer,args):
self.x_y_meta = x_y_meta
self.num_turns = args.num_turns
self.tokenizer = tokenizer
self.args = args
self.x_encoded,self.y_encoded,self.meta,self.aug_encoded, self.keyword_encoded = self._split(x_y_meta)
self._filt() # TODO: add back filt for mix-review
self.ref_start, self.speaker1,self.speaker2,self.eos = 2,3,4,50256
self.augment = 5
if self.args.augment:
print("Using augment sentences.")
if self.args.keyword:
print("Using keywords.")
def __getitem__(self,index):
x = []
type_x = []
lm_x = []
if self.args.augment:
x += [self.augment] + self.aug_encoded[index]
if self.args.keyword:
x += [self.augment] + self.keyword_encoded[index]
type_x += [self.augment] * len(x)
is_speaker1 = bool(self.num_turns % 2) # which speaker start the conversation
for utt in self.x_encoded[index][-self.num_turns:]:
if is_speaker1: # add the prefix special token for each utterance
x+=[self.speaker1]
type_x += [self.speaker1]*(len(utt)+1)
else:
x+=[self.speaker2]
type_x += [self.speaker2]*(len(utt)+1)
x += utt
is_speaker1 = not is_speaker1
lm_x += [-100]*len(x) # all position for the input is masked for loss calculation
total_input_length = len(x)
x += [self.ref_start] + self.y_encoded[index] + [self.eos]
type_x += [self.ref_start]*(len(self.y_encoded[index])+2)
lm_x += [-100] + self.y_encoded[index] + [self.eos]
position_x = list(range(len(x)))
x = torch.Tensor(x)
type_x = torch.Tensor(type_x)
position_x = torch.Tensor(position_x)
lm_x = torch.Tensor(lm_x)
x_len = x.shape[0]
return x,type_x,position_x,lm_x,total_input_length,self.meta[index]
def __len__(self):
return len(self.x_encoded)
class GptDataset_full_condition(Dataset):
def _split(self, x_y_meta):
x_all = []
y_all = []
meta_all = []
aug_all = []
keyword_all = []
for x, y, meta, aug, keyword in x_y_meta:
meta_all.append(meta)
# update for the new data format
aug = ''.join([a[1] for a in aug])
x_all.append([self.tokenizer.encode(text_standardize(x_i)) for x_i in x])
y_all.append(self.tokenizer.encode(text_standardize(y)))
aug_all.append(self.tokenizer.encode(text_standardize(aug)))
keyword_all.append(self.tokenizer.encode(text_standardize(keyword)))
return x_all, y_all, meta_all, aug_all, keyword_all
def _filt(self, length=1024):
data = zip(self.x_encoded, self.y_encoded, self.meta, self.aug_encoded, self.keyword_encoded)
data = [sample for sample in data if
sum([len(sen) for sen in sample[0]][-self.args.num_turns:]) + len(sample[1]) + len(sample[3]) + len(
sample[4]) < 850]
self.x_encoded, self.y_encoded, self.meta, self.aug_encoded, self.keyword_encoded = zip(*data)
self.x_encoded = list(self.x_encoded)
self.y_encoded = list(self.y_encoded)
self.meta = list(self.meta)
self.aug_encoded = list(self.aug_encoded)
self.keyword_encoded = list(self.keyword_encoded)
def __init__(self, x_y_meta, tokenizer, args):
self.x_y_meta = x_y_meta
self.num_turns = args.num_turns
self.tokenizer = tokenizer
self.args = args
self.x_encoded, self.y_encoded, self.meta, self.aug_encoded, self.keyword_encoded = self._split(x_y_meta)
self._filt() # TODO: add back filt for mix-review
self.ref, self.speaker1, self.speaker2 = tokenizer.ref, tokenizer.speaker1, tokenizer.speaker2
self.eos = tokenizer.eos
self.augment = tokenizer.augment
self.is_ref, self.is_non_ref = tokenizer.is_ref, tokenizer.is_non_ref
self.code_set = set(['GIV', 'QUEST', 'SEEK', 'AF', 'EMPH', 'PWOP', 'PWP', 'CON'])
def __getitem__(self, index):
x = []
type_x = []
lm_x = []
is_speaker1 = bool(self.num_turns % 2) # which speaker start the conversation
if self.meta[index][2] in self.code_set:
x += [self.is_non_ref]
type_x += [self.is_non_ref]
else:
x += [self.is_ref]
type_x += [self.is_ref]
for utt in self.x_encoded[index][-self.num_turns:]:
if is_speaker1: # add the prefix special token for each utterance
x += [self.speaker1]
type_x += [self.speaker1] * (len(utt) + 1)
else:
x += [self.speaker2]
type_x += [self.speaker2] * (len(utt) + 1)
x += utt
is_speaker1 = not is_speaker1
lm_x += [-100] * len(x) # all position for the input is masked for loss calculation
total_input_length = len(x)
x += [self.ref] + self.y_encoded[index] + [self.eos]
type_x += [self.ref] * (len(self.y_encoded[index]) + 2)
lm_x += [-100] + self.y_encoded[index] + [self.eos]
position_x = list(range(len(x)))
x = torch.tensor(x)
type_x = torch.tensor(type_x)
position_x = torch.tensor(position_x)
lm_x = torch.tensor(lm_x)
x_len = x.shape[0]
return x, type_x, position_x, lm_x, total_input_length, self.meta[index]
def __len__(self):
return len(self.x_encoded)
class GptDataset_KBERT_old(Dataset):
def get_comet_aug_deque(self, comet_data, num_turns=5):
clause_dq = deque()
for comet_in, comet_out in comet_data:
if comet_out == "":
continue
loc = int(comet_in.split()[0])
if loc >= (10 - num_turns):
clause_dq.append((loc, comet_out))
return clause_dq
def __init__(self, x_y_meta, tokenizer, args):
self.data = x_y_meta
self.num_turns = args.num_turns
self.tokenizer = tokenizer
self.args = args
self.ref_start, self.speaker1, self.speaker2, self.eos = 2, 3, 4, 50256
self.augment = 5
if self.args.augment:
print("Using augment sentences.")
if self.args.keyword:
print("Using keywords.")
def __getitem__(self, index):
x = []
type_x = []
lm_x = []
soft_position_x = []
dq = self.get_comet_aug_deque(self.data[index][3]) # the comet info
mask_info = []
context = self.data[index][0]
response = self.data[index][1]
is_speaker1 = bool(self.args.num_turns % 2)
soft_loc = 0 # keep tract of the location of main sentences, point to the next token to be added
utterance_start_loc = 0
for i in range(10 - self.args.num_turns, 10):
utternace_encoded = self.tokenizer.encode(text_standardize(context[i]))
# add the prefix special token for each utterance
if is_speaker1:
x += [self.speaker1]
type_x += [self.speaker1] * (len(utternace_encoded) + 1)
else:
x += [self.speaker2]
type_x += [self.speaker2] * (len(utternace_encoded) + 1)
x += utternace_encoded
utterance_end_loc = len(x)
soft_position_x += list(range(soft_loc, soft_loc + len(utternace_encoded) + 1))
# add the aug, if it is the right place
while len(dq) != 0 and dq[0][0] == i:
comet_output = dq.popleft()[1]
comet_encoded = self.tokenizer.encode(text_standardize(comet_output))
x += [self.augment] + comet_encoded
type_x += [self.augment] * (len(comet_encoded) + 1)
soft_position_x += list(range(soft_loc, soft_loc + len(comet_encoded) + 1))
mask_info.append([utterance_start_loc, utterance_end_loc, len(comet_encoded)+1])
# update the pointer to the new seq end, add one for the delimiter token
soft_loc += len(utternace_encoded) + 1
is_speaker1 = not is_speaker1
utterance_start_loc = len(x)
lm_x += [-100] * len(x) # all position for the input is masked for loss calculation
total_input_length = len(x)
response_encoded = self.tokenizer.encode(text_standardize(response))
x += [self.ref_start] + response_encoded + [self.eos]
type_x += [self.ref_start] * (len(response_encoded) + 2)
lm_x += [-100] + response_encoded + [self.eos]
soft_position_x += list(range(soft_loc, soft_loc + len(response_encoded) + 2))
x = torch.Tensor(x)
type_x = torch.Tensor(type_x)
soft_position_x = torch.Tensor(soft_position_x)
lm_x = torch.Tensor(lm_x)
x_len = x.shape[0]
# process the mask
attention_mask = torch.tril(torch.ones(x_len, x_len))
for u_start, u_end, branch_len in mask_info:
attention_mask[u_end+branch_len+1: u_end+1:u_end+branch_len+1] = 0 # [1st token after branch: , 1st token in branch: last token in branch+1]
attention_mask = attention_mask.view(1, x_len, x_len)
return x, type_x, soft_position_x, lm_x, total_input_length, attention_mask
def __len__(self):
return len(self.data)
class GptDataset_KBERT(Dataset):
def __init__(self, tokenizer, args, file_path="../data_processed/data_comet_dict"):
pickle_handler = open(file_path, 'rb')
self.data = pickle.load(pickle_handler)
self.max_length = 510
self.tokenizer = tokenizer
self.args = args
self.num_turns = args.num_turns
self.ref, self.speaker1, self.speaker2 = tokenizer.ref, tokenizer.speaker1, tokenizer.speaker2
self.eos = tokenizer.eos
self.augment = tokenizer.augment
if not self.args.kbert:
self.args.kbert_mask = False
self.args.kbert_position = False
print("Not using kbert scheme.")
if self.args.kbert_mask:
print("using kbert-style attention mask")
if self.args.kbert_position:
print("using kbert-style soft-postional encoding")
def __getitem__(self, index):
# preprare variables
x = []
type_x = []
lm_x = []
soft_position_x = []
attention_mask = []
# 0. unpack needed input info
context = self.data[index]['context']
srl_mask = self.data[index]['srl_mask']
comet_output = self.data[index]['comet'] # a list of dict or None
response = self.data[index]['response']
# 1. encode the response.
response_encoded = self.tokenizer.encode(text_standardize(response))
# 2. encode each utterance.
context_encoded = []
for i in range(10 - self.args.num_turns, 10):
context_encoded.append(self.tokenizer.encode(text_standardize(context[i])))
# 3. encode the comet output for each utterance.
comet_encoded = []
for i in range(len(comet_output)):
comet_text_i = ""
if comet_output[i] is None:
comet_encoded.append(None)
continue
for rel in comet_output[i]:
for candidate in comet_output[i][rel]['beams']:
if candidate != 'none':
comet_text_i += rel + " " + candidate + " "
break
comet_encoded.append(self.tokenizer.encode(text_standardize(comet_text_i)))
# 4. use the encoded seq to build the input and attention mask
is_speaker1 = bool(self.args.num_turns % 2)
soft_loc = 0
for i in range(self.args.num_turns):
# add an utterance. update x & type_x
if is_speaker1:
x += [self.speaker1]
type_x += [self.speaker1] * (len(context_encoded[i]) + 1)
else:
x += [self.speaker2]
type_x += [self.speaker2] * (len(context_encoded[i]) + 1)
x += context_encoded[i]
# update pos_x
# concate aug part after x. but the index is from the last related token
soft_position_x += list(range(soft_loc, soft_loc + (len(context_encoded[i]) + 1)))
last_related_token_index = len(srl_mask[i]) - 1 - srl_mask[i][::-1].index(1)
# add comet output
if self.args.kbert:
if comet_encoded[i] is not None:
x += [self.augment] + comet_encoded[i]
type_x += [self.augment] * (len(comet_encoded[i]) + 1)
# +2 for the special token and the requirement of one-number larger than the utterance
soft_position_x += list(range(soft_loc + 2 + last_related_token_index,
soft_loc + 2 + last_related_token_index + (len(comet_encoded[i]) + 1)))
soft_loc += (len(context_encoded[i]) + 1)
is_speaker1 = not is_speaker1
lm_x += [-100] * len(x) # all position for the input is masked for loss calculation
total_input_length = len(x)
response_encoded = self.tokenizer.encode(text_standardize(response))
x += [self.ref] + response_encoded + [self.eos]
type_x += [self.ref] * (len(response_encoded) + 2)
lm_x += [-100] + response_encoded + [self.eos]
soft_position_x += list(range(soft_loc, soft_loc + len(response_encoded) + 2))
x = x[:self.max_length]
type_x = type_x[:self.max_length]
lm_x = lm_x[:self.max_length]
soft_position_x = soft_position_x[:self.max_length]
# build attention mask
attention_mask = torch.tril(torch.ones(len(x), len(x)))
if self.args.kbert_mask:
aug_start = 0 # where the aug begin
utt_start = 0 # where the utt begin
for turn in range(self.args.num_turns):
aug_start += len(context_encoded[turn]) + 1
# iter through every token in the comet output
if comet_encoded[turn] is not None:
for aug_token_pos in range(aug_start, aug_start + len(comet_encoded[turn]) + 1):
# set the attention related to the aug part to be all zero
attention_mask[aug_token_pos, :] = torch.zeros_like(attention_mask[aug_token_pos, :])
attention_mask[:, aug_token_pos] = torch.zeros_like(attention_mask[:, aug_token_pos])
# set attention on related token to be one
for normal_token_pos in range(len(context_encoded[turn])):
attention_mask[aug_token_pos, utt_start + normal_token_pos + 1] += srl_mask[turn][
normal_token_pos]
# set attention on previous aug tokens to be one
for previous_aug_token_poc in range(aug_start, aug_token_pos + 1):
attention_mask[aug_token_pos, previous_aug_token_poc] += 1
aug_start += len(comet_encoded[turn]) + 1
utt_start += len(comet_encoded[turn]) + 1
utt_start += (len(context_encoded[turn]) + 1)
x = torch.tensor(x)
type_x = torch.tensor(type_x)
if not self.args.kbert_position:
soft_position_x = list(range(len(x)))
soft_position_x = torch.tensor(soft_position_x)
lm_x = torch.tensor(lm_x)
return x, type_x, soft_position_x, lm_x, total_input_length, attention_mask
def __len__(self):
return len(self.data)
def get_data(args, tokenizer, split_size):
"""
Return the data loaders needed for training and evaluation.
:param args: command line arguments.
:param tokenizer: the tokenizer used in preparing the data.
:param split_size: the portion of train, test, validation set.
:return data_loader: The data loader for the training set.
:return val_loader: The data loader for the validation set.
"""
# random.seed(args.seed)
# torch.random.manual_seed(args.seed)
# torch.cuda.manual_seed(args.seed)
# torch.manual_seed(args.seed)
if args.special_input:
print("Using mutated data.")
pickle_handler = open('../data_processed/' + args.special_input, 'rb')
x_y_meta = pickle.load(pickle_handler)
gpt_data = GptDataset(x_y_meta, tokenizer, args.output_dir, num_turns=args.num_turns)
# TODO: not finished
# #======================origin without kbert======
# elif not args.kbert:
# print("Using full data.")
# pickle_handler = open('../data_processed/x_y_with_comet', 'rb') # TODO: change back to the old data.
# x_y_meta = pickle.load(pickle_handler)
# gpt_data = GptDataset_full(x_y_meta, tokenizer, args=args)
elif args.conditional:
print("using conditional generation data")
file_path = "../data_processed/"
data_train = pickle.load(open(file_path+'train_ref', 'rb')) + pickle.load(open(file_path+'train_non_ref','rb'))
gpt_train= GptDataset_full_condition(data_train, tokenizer, args=args)
data_test = pickle.load(open(file_path+'test_ref', 'rb'))
gpt_test = GptDataset_full_condition(data_test, tokenizer, args=args)
data_val = pickle.load(open(file_path+'test_ref', 'rb'))
gpt_val = GptDataset_full_condition(data_val, tokenizer, args=args)
elif args.kbert:
print("Using KBERT data")
gpt_data = GptDataset_KBERT(tokenizer, args=args)
print("Dataset initialized. There are {} samples.".format(len(gpt_data)))
test_size = int(len(gpt_data) * split_size['test'])
val_size = int(len(gpt_data) * split_size['val'])
gpt_train, gpt_test, gpt_val = torch.utils.data.random_split(gpt_data,
[len(gpt_data) - test_size - val_size, test_size,
val_size])
else:
print("Using full data.")
file_path = "../data_processed/"
data_train = pickle.load(open(file_path+'train_ref', 'rb'))
gpt_train= GptDataset_full_condition(data_train, tokenizer, args=args)
data_test = pickle.load(open(file_path+'test_ref', 'rb'))
gpt_test = GptDataset_full_condition(data_test, tokenizer, args=args)
data_val = pickle.load(open(file_path+'test_ref', 'rb'))
gpt_val = GptDataset_full_condition(data_val, tokenizer, args=args)
# # ======= one-time plug in========
# x_y_meta_pre = pickle.load(open("../data_processed/data_comet_dict",'rb'))
# x_y_meta = []
# for x in x_y_meta_pre:
# context_i = x['context']
# response_i = x['response']
# meta_i = x['meta']
# x_y_meta.append([context_i, response_i, meta_i, "", ""])
#
# with open('../data_processed/train_ref', 'wb') as f:
# idx = gpt_train.indices
# gpt_train =[x_y_meta[i] for i in idx]
# pickle.dump(gpt_train, f)
# with open('../data_processed/test_ref', 'wb') as f:
# idx = gpt_test.indices
# gpt_test = [x_y_meta[i] for i in idx]
# pickle.dump(gpt_test, f)
# with open('../data_processed/val_ref', 'wb') as f:
# idx = gpt_val.indices
# gpt_val = [x_y_meta[i] for i in idx]
# pickle.dump(gpt_val, f)
if 'train_batch_size' not in args:
args.train_batch_size = 1
if args.conditional:
data_loader = DataLoader(dataset=gpt_train, batch_size=args.train_batch_size, shuffle=True, drop_last=True,
collate_fn=collate_fn_conditional)
test_loader = DataLoader(dataset=gpt_test, batch_size=1, shuffle=False, drop_last=False, collate_fn=collate_fn_conditional)
val_loader = DataLoader(dataset=gpt_val, batch_size=1, shuffle=False, drop_last=False, collate_fn=collate_fn_conditional)
else:
data_loader = DataLoader(dataset=gpt_train, batch_size=args.train_batch_size, shuffle=True, drop_last=True,
collate_fn=collate_fn)
test_loader = DataLoader(dataset=gpt_test, batch_size=1, shuffle=False, drop_last=False, collate_fn=collate_fn)
val_loader = DataLoader(dataset=gpt_val, batch_size=1, shuffle=False, drop_last=False, collate_fn=collate_fn)
return data_loader, test_loader, val_loader
def prepare_mix_review(args, tokenizer):
print("Preparing Alexander dataset")
pickle_handler = open('../data_processed/data_alex', 'rb')
data = pickle.load(pickle_handler)
gpt_alex = GptDataset_full(data, tokenizer, args=args)
print("Alexander dataset prepared. Has {} samples".format(len(gpt_alex)))
return gpt_alex
def update_mix_review(gpt_train, gpt_alex, epoch, args, mix_ratio=4, mix_decay=0.7, collate_fn=collate_fn):
mix_amount = int(mix_ratio*(0.7**epoch)*len(gpt_train))
gpt_alex_active,_ = torch.utils.data.random_split(gpt_alex, [mix_amount, len(gpt_alex)-mix_amount])
data_loader = DataLoader(dataset=gpt_train+gpt_alex_active, batch_size=args.train_batch_size, shuffle=True, drop_last=True,
collate_fn=collate_fn)
return data_loader