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build_data_PersonaChat.py
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build_data_PersonaChat.py
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import random
from tqdm import tqdm
import torch
from collections import defaultdict
from torch.nn.utils.rnn import pad_sequence
import numpy as np
import json
def get_token_id(tokenizer):
bos_id = tokenizer.bos_token_id
eos_id = tokenizer.eos_token_id
pad_id = tokenizer.pad_token_id
sep_id = tokenizer.sep_token_id
query_id, res_id, latent_id, persona_id = tokenizer.convert_tokens_to_ids(
['<query>', '<response>', '<latent>', '<persona>'])
return bos_id, eos_id, pad_id, sep_id, query_id, res_id, latent_id, persona_id
def create_data(data_file):
with open(data_file, "r", encoding="utf8") as f:
persona =[]
query = []
response = []
cand = []
is_persona = False
tmp_persona = []
tmp_query = []
tmp_response = []
tmp_cand = []
first = True
cnt = 0
sum_u = 0
for line in f:
cnt += 1
line = line.strip()
if "your persona: " in line:
if not is_persona and not first:
query.append(tmp_query)
response.append(tmp_response)
cand.append(tmp_cand)
sum_u += len(tmp_query)
tmp_query = []
tmp_response = []
tmp_cand = []
first = False
is_persona = True
line = line.split(": ", maxsplit=1)[1]
tmp_persona.append(line)
else:
if is_persona:
persona.append(tmp_persona)
is_persona = False
tmp_persona = []
line = line[line.find(" ")+1:]
tmp_query.append(line.split("\t")[0])
tmp_response.append(line.split("\t")[1])
tmp_cand.append(line.split("\t")[3].split("|"))
query.append(tmp_query)
response.append(tmp_response)
cand.append(tmp_cand)
sum_u += len(tmp_query)
assert len(query) == len(response) == len(persona) == len(cand)
print("{} has {} dialog and {} query".format(data_file, len(query), sum_u))
return persona, query, response, cand
def create_encoder_input(per, history, query_id, res_id, latent_id, persona_id, sep_id, eos_id):
encoder_input_ids = []
per_input_ids = [latent_id] + [persona_id]
for x in per:
per_input_ids += x + [sep_id]
encoder_input_ids += per_input_ids
for i in range(len(history)):
if i % 2 == 0:
encoder_input_ids += [query_id] + history[i] + [eos_id]
else:
encoder_input_ids += [res_id] + history[i] + [eos_id]
attention_mask = [1] * len(encoder_input_ids)
per_attention_mask = [1] * len(per_input_ids)
return encoder_input_ids, attention_mask, per_input_ids, per_attention_mask
def create_decoder_input(response_ids, res_id, eos_id, golden=None):
assert golden != None
decoder_lmlabel= response_ids + [eos_id]
decoder_input_ids = [res_id] + response_ids
decoder_cls_index = [-100] * (len(decoder_lmlabel) - 1) + [eos_id]
decoder_attention_mask = [1] * len(decoder_input_ids)
if golden == False:
decoder_lmlabel = [-100] * len(decoder_lmlabel)
assert len(decoder_lmlabel) == len(decoder_input_ids)
return decoder_lmlabel, decoder_input_ids, decoder_cls_index, decoder_attention_mask
def build_dataloader(persona, query, response, cand, tokenizer, max_history=4, n_cand=5, use_all=False):
bos_id, eos_id, pad_id, sep_id, query_id, res_id, latent_id, persona_id = get_token_id(tokenizer)
dataset = defaultdict(list)
for i in range(len(persona)):
persona_ = persona[i]
per_list = []
for per in persona_:
persona_ids = tokenizer.convert_tokens_to_ids(tokenizer.tokenize(per, add_prefix_space=True))
per_list.append(persona_ids)
query_ = query[i]
response_ = response[i]
cand_ = cand[i]
history = []
assert len(query_) == len(response_)
for j in range(len(query_)):
if use_all:
noise_candidate = cand_[j][:-1]
else:
noise_candidate = random.sample(cand_[j][:-1], n_cand-1)
query_ids = tokenizer.convert_tokens_to_ids(tokenizer.tokenize(query_[j], add_prefix_space=True))
response_ids = tokenizer.convert_tokens_to_ids(tokenizer.tokenize(response_[j], add_prefix_space=True))
noise_cand_ids_list = [tokenizer.convert_tokens_to_ids(tokenizer.tokenize(text, add_prefix_space=True))
for text in noise_candidate]
history.append(query_ids)
history.append(response_ids)
tmp_history = history[-2 * max_history: -1]
encoder_input_ids, attention_mask, \
per_input_ids, per_attention_mask = create_encoder_input(per_list, tmp_history, query_id, res_id,
latent_id, persona_id, sep_id, eos_id)
decoder_lmlabel, decoder_input_ids, decoder_cls_idx,\
decoder_attention_mask = create_decoder_input(response_ids, res_id, eos_id, golden=True)
dataset["input_ids"].append(encoder_input_ids)
dataset["attention_mask"].append(attention_mask)
dataset["per_input_ids"].append(per_input_ids)
dataset["per_attention_mask"].append(per_attention_mask)
dataset["lmlabels"].append(decoder_lmlabel)
dataset["decoder_input_ids"].append(decoder_input_ids)
dataset["decoder_attention_mask"].append(decoder_attention_mask)
dataset["cls_index"].append(decoder_cls_idx)
dataset["clslabel"].append([0])
for k in range(len(noise_cand_ids_list)):
decoder_lmlabel, decoder_input_ids, decoder_cls_idx,\
decoder_attention_mask = create_decoder_input(noise_cand_ids_list[k], res_id, eos_id, golden=False)
dataset["input_ids"].append(encoder_input_ids)
dataset["attention_mask"].append(attention_mask)
dataset["per_input_ids"].append(per_input_ids)
dataset["per_attention_mask"].append(per_attention_mask)
dataset["lmlabels"].append(decoder_lmlabel)
dataset["decoder_input_ids"].append(decoder_input_ids)
dataset["decoder_attention_mask"].append(decoder_attention_mask)
dataset["cls_index"].append(decoder_cls_idx)
for item_name, item in dataset.items():
if item_name == "input_ids" or item_name == "per_input_ids":
item = pad_sequence([torch.from_numpy(np.array(x)) for x in item],
batch_first=True, padding_value=pad_id)
dataset[item_name] = item
elif item_name == "lmlabels":
item = pad_sequence([torch.from_numpy(np.array(x)) for x in item],
batch_first=True, padding_value=-100)
dataset[item_name] = item
elif item_name == "attention_mask" or item_name == "decoder_attention_mask" or item_name == "per_attention_mask":
item = pad_sequence([torch.from_numpy(np.array(x)) for x in item],
batch_first=True, padding_value=0)
dataset[item_name] = item
elif item_name == "decoder_input_ids":
item = pad_sequence([torch.from_numpy(np.array(x)) for x in item],
batch_first=True, padding_value=pad_id)
dataset[item_name] = item
elif item_name == "clslabel":
dataset[item_name] = torch.tensor(item).view(-1,1)
elif item_name == "cls_index":
item = pad_sequence([torch.from_numpy(np.array(x)) for x in item],
batch_first=True, padding_value=-100)
dataset[item_name] = item
return dataset
def build_infer_dataset(tokenizer, file_path):
bos_id, eos_id, pad_id, sep_id, query_id, res_id, latent_id, persona_id = get_token_id(tokenizer)
positive_set = defaultdict(list)
with open(file_path, "r") as f:
row_data = json.load(f)
for obj in tqdm(row_data, desc='Generate infer data'):
if obj['label'] == "neutral" or obj['label'] == "negative":
continue
pre = obj["sentence1"].lower()
hyp = obj["sentence2"].lower()
pre_ids = tokenizer.convert_tokens_to_ids(tokenizer.tokenize(pre, add_prefix_space=True))
hyp_ids = tokenizer.convert_tokens_to_ids(tokenizer.tokenize(hyp, add_prefix_space=True))
encoder_input_ids = [latent_id] + [persona_id] + pre_ids + [eos_id]
attention_mask = [1] * len(encoder_input_ids)
decoder_input_ids = [res_id] + hyp_ids
decoder_attention_mask = [1] * len(decoder_input_ids)
if obj['label'] == 'positive':
positive_set["encoder_input_ids"].append(encoder_input_ids)
positive_set["decoder_input_ids"].append(decoder_input_ids)
positive_set["attention_mask"].append(attention_mask)
positive_set["decoder_attention_mask"].append(decoder_attention_mask)
decoder_lmlabel = hyp_ids + [eos_id]
positive_set["lmlabels"].append(decoder_lmlabel)
for item_name, item in positive_set.items():
if item_name == "encoder_input_ids":
item = pad_sequence([torch.from_numpy(np.array(x)) for x in item],
batch_first=True, padding_value=pad_id)
positive_set[item_name] = item
elif item_name == "lmlabels":
item = pad_sequence([torch.from_numpy(np.array(x)) for x in item],
batch_first=True, padding_value=-100)
positive_set[item_name] = item
elif item_name == "attention_mask" or item_name == "decoder_attention_mask":
item = pad_sequence([torch.from_numpy(np.array(x)) for x in item],
batch_first=True, padding_value=0)
positive_set[item_name] = item
elif item_name == "decoder_input_ids":
item = pad_sequence([torch.from_numpy(np.array(x)) for x in item],
batch_first=True, padding_value=pad_id)
positive_set[item_name] = item
return positive_set