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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
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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()
return Variable(LongTensor(src_seqs)), Variable(LongTensor(trg_seqs)), Variable(LongTensor(pos_seqs)),Variable(LongTensor(lm_seqs)), total_input_length, attention_mask
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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])
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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)
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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)
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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_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)
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):
soft_position_x = []
dq = self.get_comet_aug_deque(self.data[index][3]) # the comet 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
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
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
lm_x += [-100] * len(x) # all position for the input is masked for loss calculation
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)
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 = ""
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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)))
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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)
# #======================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)
else:
print("Using KBERT data")
gpt_data = GptDataset_KBERT(tokenizer, args=args)
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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])
# # ======= 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
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)