def __init__(self, prior_config, rl_config, beam_size=5):
self.prior_config = prior_config
self.rl_config = rl_config
self.rl_config.beam_size = beam_size
print('Loading Vocabulary...')
self.vocab = Vocab()
self.vocab.load(prior_config.word2id_path, prior_config.id2word_path)
self.prior_config.vocab_size = self.vocab.vocab_size
self.rl_config.vocab_size = self.vocab.vocab_size
print(f'Vocabulary size: {self.vocab.vocab_size}')
self.eval_data = self.get_data_loader()
self.build_models()
def __init__(self, vocab: Vocab) -> None:
"""Instantiating MultiChannelEmbedding class
Args:
vocab (model.utils.Vocab): the instance of model.utils.Vocab
"""
super(MultiChannelEmbedding, self).__init__()
self._static = nn.Embedding.from_pretrained(
torch.from_numpy(vocab.embedding),
freeze=True,
padding_idx=vocab.to_indices(vocab.padding_token))
self._non_static = nn.Embedding.from_pretrained(
torch.from_numpy(vocab.embedding),
freeze=False,
padding_idx=vocab.to_indices(vocab.padding_token))
def __init__(self,
vocab: Vocab,
method: str,
encoder_output_dim: int,
decoder_hidden_dim: int,
drop_ratio: int = .2) -> None:
"""Instantiating Encoder class
Args:
vocab (model.utils.Vocab): the instance of model.utils.Vocab
method (str): the method of attention, 'dot', 'general', 'concat'
encoder_hidden_dim (int): the dimension of hidden state and cell state of encoder
decoder_hidden_dim (int): the dimension of hidden state and cell state of decoder
drop_ratio (float): ratio of drop out, default 0.2
"""
super(AttnDecoder, self).__init__()
self._emb = Embedding(vocab=vocab,
padding_idx=vocab.to_indices(
vocab.padding_token),
freeze=False,
permuting=False,
tracking=False)
self._ops = nn.LSTM(self._emb._ops.embedding_dim,
decoder_hidden_dim,
batch_first=True,
num_layers=2,
dropout=drop_ratio)
self._attn = GlobalAttn(method=method,
encoder_output_dim=encoder_output_dim,
decoder_hidden_dim=decoder_hidden_dim)
self._concat = nn.Linear(encoder_output_dim + decoder_hidden_dim,
self._emb._ops.embedding_dim,
bias=False)
# self._classify = nn.Linear(self._emb._ops.embedding_dim, len(vocab))
self._dropout = nn.Dropout(p=drop_ratio)
def __init__(self, num_classes: int, lstm_hidden_dim: int, da: int, r: int,
hidden_dim: int, vocab: Vocab) -> None:
"""Instantiating SAN class
Args:
num_classes (int): the number of classes
lstm_hidden_dim (int): the number of features in the hidden states in bi-directional lstm
da (int): the number of features in hidden layer from self-attention
r (int): the number of aspects of self-attention
hidden_dim (int): the number of features in hidden layer from mlp
vocab (model.utils.Vocab): the instance of model.utils.Vocab
"""
super(SAN, self).__init__()
self._embedding = Embedding(vocab,
padding_idx=vocab.to_indices(
vocab.padding_token),
freeze=False,
permuting=False,
tracking=True)
self._pipe = Linker(permuting=False)
self._bilstm = BiLSTM(self._embedding._ops.embedding_dim,
lstm_hidden_dim,
using_sequence=True)
self._attention = SelfAttention(2 * lstm_hidden_dim, da, r)
self._fc1 = nn.Linear(2 * lstm_hidden_dim * r, hidden_dim)
self._fc2 = nn.Linear(hidden_dim, num_classes)
def __init__(self, num_classes: int, embedding_dim: int,
vocab: Vocab) -> None:
"""Instantiating CharCNN class
Args:
num_classes (int): the number of classes
embedding_dim (int): the dimension of embedding vector for token
vocab (model.utils.Vocab): the instance of model.utils.Vocab
"""
super(CharCNN, self).__init__()
self._extractor = nn.Sequential(
nn.Embedding(len(vocab), embedding_dim,
vocab.to_indices(vocab.padding_token)), Permute(),
nn.Conv1d(in_channels=embedding_dim,
out_channels=256,
kernel_size=7), nn.ReLU(), nn.MaxPool1d(3, 3),
nn.Conv1d(in_channels=256, out_channels=256, kernel_size=7),
nn.ReLU(), nn.MaxPool1d(3, 3),
nn.Conv1d(in_channels=256, out_channels=256, kernel_size=3),
nn.ReLU(),
nn.Conv1d(in_channels=256, out_channels=256, kernel_size=3),
nn.ReLU(),
nn.Conv1d(in_channels=256, out_channels=256, kernel_size=3),
nn.ReLU(),
nn.Conv1d(in_channels=256, out_channels=256, kernel_size=3),
nn.ReLU(), nn.MaxPool1d(3, 3), Flatten())
self._classifier = nn.Sequential(
nn.Linear(in_features=1792, out_features=512), nn.ReLU(),
nn.Dropout(), nn.Linear(in_features=512, out_features=512),
nn.ReLU(), nn.Dropout(),
nn.Linear(in_features=512, out_features=num_classes))
self.apply(self._initailze)
def __init__(self,
vocab: Vocab,
encoder_hidden_dim: int,
drop_ratio: int = .2) -> None:
"""Instantiating Encoder class
Args:
vocab (model.utils.Vocab): the instance of model.utils.Vocab
encoder_hidden_dim (int): the dimension of hidden state and cell state
drop_ratio (float): ratio of drop out, default 0.2
"""
super(BidiEncoder, self).__init__()
self._emb = Embedding(vocab=vocab,
padding_idx=vocab.to_indices(
vocab.padding_token),
freeze=False,
permuting=False,
tracking=True)
self._linker = Linker(permuting=False)
self._ops = nn.LSTM(self._emb._ops.embedding_dim,
encoder_hidden_dim,
batch_first=True,
num_layers=2,
dropout=drop_ratio,
bidirectional=True)
def __init__(self, config, val_config):
self.config = config
self.val_config = val_config
vocab = Vocab()
vocab.load(config.word2id_path, config.id2word_path)
self.vocab = vocab
self.config.vocab_size = vocab.vocab_size
# To initialize simulated conversations
self.start_sentences = self.load_sentences(self.config.dataset_dir)
self.eval_data = self.get_data_loader(train=False)
self.build_models()
if self.config.load_rl_ckpt:
self.load_models()
self.set_up_optimizers()
self.set_up_summary()
self.set_up_logging()
if self.config.rl_batch_size == self.config.beam_size:
raise ValueError('Decoding breaks if batch_size == beam_size')
import json
import pickle
from model.utils import Vocab
from bert.tokenization import BertTokenizer
with open('experiment/config.json') as f:
params = json.loads(f.read())
# loading BertTokenizer
ptr_tokenizer = BertTokenizer.from_pretrained('bert/vocab.korean.rawtext.list',
do_lower_case=False)
idx_to_token = list(ptr_tokenizer.vocab.keys())
# generate vocab
token_vocab = Vocab(idx_to_token,
padding_token='[PAD]',
unknown_token='[UNK]',
bos_token=None,
eos_token=None,
reserved_tokens=['[CLS]', '[SEP]', '[MASK]'],
unknown_token_idx=1)
# save vocab
token_vocab_path = params['filepath'].get('token_vocab')
with open(token_vocab_path, 'wb') as f:
pickle.dump(token_vocab, f)
import pandas as pd
import itertools
import gluonnlp as nlp
from pathlib import Path
from collections import Counter
from model.split import split_morphs
from model.utils import Vocab
from utils import Config
qpair_dir = Path("qpair")
config = Config("conf/dataset/qpair.json")
train = pd.read_csv(config.train, sep="\t")
list_of_tokens_qa = train["question1"].apply(lambda sen: split_morphs(sen)).tolist()
list_of_tokens_qb = train["question2"].apply(lambda sen: split_morphs(sen)).tolist()
list_of_tokens = list_of_tokens_qa + list_of_tokens_qb
count_tokens = Counter(itertools.chain.from_iterable(list_of_tokens))
tmp_vocab = nlp.Vocab(counter=count_tokens, bos_token=None, eos_token=None)
ptr_embedding = nlp.embedding.create("fasttext", source="wiki.ko", load_ngrams=True)
tmp_vocab.set_embedding(ptr_embedding)
vocab = Vocab(tmp_vocab.idx_to_token, bos_token=None, eos_token=None)
vocab.embedding = tmp_vocab.embedding.idx_to_vec.asnumpy()
with open(qpair_dir / "vocab.pkl", mode="wb") as io:
pickle.dump(vocab, io)
config.update({"vocab": str(qpair_dir / "vocab.pkl")})
config.save("conf/dataset/qpair.json")
class DBCQ:
def __init__(self, prior_config, rl_config, beam_size=5):
self.prior_config = prior_config
self.rl_config = rl_config
self.rl_config.beam_size = beam_size
print('Loading Vocabulary...')
self.vocab = Vocab()
self.vocab.load(prior_config.word2id_path, prior_config.id2word_path)
self.prior_config.vocab_size = self.vocab.vocab_size
self.rl_config.vocab_size = self.vocab.vocab_size
print(f'Vocabulary size: {self.vocab.vocab_size}')
self.eval_data = self.get_data_loader()
self.build_models()
def build_models(self):
rl_config = copy.deepcopy(self.rl_config)
rl_config.checkpoint = None
print('Building Q network')
if rl_config.model in VariationalModels:
self.q_net = VariationalSolver(
rl_config, None, self.eval_data, vocab=self.vocab,
is_train=False)
else:
self.q_net = Solver(
rl_config, None, self.eval_data, vocab=self.vocab,
is_train=False)
self.q_net.build()
self.load_q_network()
print('Building prior network')
if self.prior_config.model in VariationalModels:
self.pretrained_prior = VariationalSolver(
self.prior_config, None, self.eval_data, vocab=self.vocab,
is_train=False)
else:
self.pretrained_prior = Solver(
self.prior_config, None, self.eval_data, vocab=self.vocab,
is_train=False)
self.pretrained_prior.build()
# Freeze the weights so they stay constant
self.pretrained_prior.model.eval()
for params in self.pretrained_prior.model.parameters():
params.requires_grad = False
self.q_net.model.eval()
for params in self.q_net.model.parameters():
params.requires_grad = False
def load_q_network(self):
"""Load parameters from RL checkpoint"""
print(f'Loading parameters for Q net from {self.rl_config.checkpoint}')
q_ckpt = torch.load(self.rl_config.checkpoint)
q_ckpt = convert_old_checkpoint_format(q_ckpt)
self.q_net.model.load_state_dict(q_ckpt)
# Ensure weights are initialized to be on the GPU when necessary
if torch.cuda.is_available():
print('Converting checkpointed model to cuda tensors')
self.q_net.model.cuda()
def get_data_loader(self):
# If checkpoint is for an emotion model, load that pickle file
emotion_sentences = None
if self.prior_config.emotion:
emotion_sentences = load_pickle(self.prior_config.emojis_path)
# Load infersent embeddings if necessary
infersent_sentences = None
if self.prior_config.infersent:
print('Loading infersent sentence embeddings...')
infersent_sentences = load_pickle(self.prior_config.infersent_path)
embedding_size = infersent_sentences[0][0].shape[0]
self.prior_config.infersent_output_size = embedding_size
return get_loader(
sentences=load_pickle(self.prior_config.sentences_path),
conversation_length=load_pickle(
self.prior_config.conversation_length_path),
sentence_length=load_pickle(self.prior_config.sentence_length_path),
vocab=self.vocab,
batch_size=self.prior_config.batch_size,
emojis=emotion_sentences,
infersent=infersent_sentences)
def interact(self, max_conversation_length=5, sample_by='priority',
debug=True):
model_name = self.prior_config.model
context_sentences = []
print("Time to start a conversation with the chatbot! It's name is",
model_name)
username = input("What is your name? ")
print("Let's start chatting. You can type 'quit' at any time to quit.")
utterance = input("Input: ")
print("\033[1A\033[K") # Erases last line of output
while (utterance.lower() != 'quit' and utterance.lower() != 'exit'):
# Process utterance
sentences = utterance.split('/')
# Code and decode user input to show how it is transformed for model
coded, lens = self.pretrained_prior.process_user_input(sentences)
decoded = [self.vocab.decode(sent) for i, sent in enumerate(
coded) if i < lens[i]]
print(username + ':', '. '.join(decoded))
# Append to conversation
context_sentences.extend(sentences)
gen_response = self.generate_response_to_input(
context_sentences, max_conversation_length, sample_by=sample_by,
debug=debug)
# Append generated sentences to conversation
context_sentences.append(gen_response)
# Print and get next user input
print("\n" + model_name + ": " + gen_response)
utterance = input("Input: ")
print("\033[1A\033[K")
def process_raw_text_into_input(self, raw_text_sentences,
max_conversation_length=5, debug=False,):
sentences, lens = self.pretrained_prior.process_user_input(
raw_text_sentences, self.rl_config.max_sentence_length)
# Remove any sentences of length 0
sentences = [sent for i, sent in enumerate(sentences) if lens[i] > 0]
good_raw_sentences = [sent for i, sent in enumerate(
raw_text_sentences) if lens[i] > 0]
lens = [l for l in lens if l > 0]
# Trim conversation to max length
sentences = sentences[-max_conversation_length:]
lens = lens[-max_conversation_length:]
good_raw_sentences = good_raw_sentences[-max_conversation_length:]
convo_length = len(sentences)
# Convert to torch variables
input_sentences = to_var(torch.LongTensor(sentences))
input_sentence_length = to_var(torch.LongTensor(lens))
input_conversation_length = to_var(torch.LongTensor([convo_length]))
if debug:
print('\n**Conversation history:**')
for sent in sentences:
print(self.vocab.decode(list(sent)))
return (input_sentences, input_sentence_length,
input_conversation_length)
def duplicate_context_for_beams(self, sentences, sent_lens, conv_lens,
beams):
conv_lens = conv_lens.repeat(len(beams))
# [beam_size * sentences, sentence_len]
if len(sentences) > 1:
targets = torch.cat(
[torch.cat([sentences[1:,:], beams[i,:].unsqueeze(0)], 0)
for i in range(len(beams))], 0)
else:
targets = beams
# HRED
if self.rl_config.model not in VariationalModels:
sent_lens = sent_lens.repeat(len(beams))
return sentences, sent_lens, conv_lens, targets
# VHRED, VHCR
new_sentences = torch.cat(
[torch.cat([sentences, beams[i,:].unsqueeze(0)], 0)
for i in range(len(beams))], 0)
new_len = to_var(torch.LongTensor([self.rl_config.max_sentence_length]))
sent_lens = torch.cat(
[torch.cat([sent_lens, new_len], 0) for i in range(len(beams))])
return new_sentences, sent_lens, conv_lens, targets
def generate_response_to_input(self, raw_text_sentences,
max_conversation_length=5,
sample_by='priority', emojize=True,
debug=True):
with torch.no_grad():
(input_sentences, input_sent_lens,
input_conv_lens) = self.process_raw_text_into_input(
raw_text_sentences, debug=debug,
max_conversation_length=max_conversation_length)
# Initialize a tensor for beams
beams = to_var(torch.LongTensor(
np.ones((self.rl_config.beam_size,
self.rl_config.max_sentence_length))))
# Create a batch with the context duplicated for each beam
(sentences, sent_lens,
conv_lens, targets) = self.duplicate_context_for_beams(
input_sentences, input_sent_lens, input_conv_lens, beams)
# Continuously feed beam sentences into networks to sample the next
# best word, add that to the beam, and continue
for i in range(self.rl_config.max_sentence_length):
# Run both models to obtain logits
prior_output = self.pretrained_prior.model(
sentences, sent_lens, conv_lens, targets, rl_mode=True)
all_prior_logits = prior_output[0]
q_output = self.q_net.model(
sentences, sent_lens, conv_lens, targets, rl_mode=True)
all_q_logits = q_output[0]
# Select only those logits for next word
q_logits = all_q_logits[:, i, :].squeeze()
prior_logits = all_prior_logits[:, i, :].squeeze()
# Get prior distribution for next word in each beam
prior_dists = torch.nn.functional.softmax(prior_logits, 1)
for b in range(self.rl_config.beam_size):
# Sample from the prior bcq_n times for each beam
dist = torch.distributions.Categorical(prior_dists[b,:])
sampled_idxs = dist.sample_n(self.rl_config.bcq_n)
# Select sample with highest q value
q_vals = torch.stack(
[q_logits[b, idx] for idx in sampled_idxs])
_, best_word_i = torch.max(q_vals, 0)
best_word = sampled_idxs[best_word_i]
# Update beams
beams[b, i] = best_word
(sentences, sent_lens,
conv_lens, targets) = self.duplicate_context_for_beams(
input_sentences, input_sent_lens, input_conv_lens, beams)
generated_sentences = beams.cpu().numpy()
if debug:
print('\n**All generated responses:**')
for gen in generated_sentences:
print(detokenize(self.vocab.decode(list(gen))))
gen_response = self.pretrained_prior.select_best_generated_response(
generated_sentences, sample_by, beam_size=self.rl_config.beam_size)
decoded_response = self.vocab.decode(list(gen_response))
decoded_response = detokenize(decoded_response)
if emojize:
inferred_emojis = self.pretrained_prior.botmoji.emojize_text(
raw_text_sentences[-1], 5, 0.07)
decoded_response = inferred_emojis + " " + decoded_response
return decoded_response
import pickle
from model.utils import Vocab
chosung_list = [
'ㄱ', 'ㄲ', 'ㄴ', 'ㄷ', 'ㄸ', 'ㄹ', 'ㅁ', 'ㅂ', 'ㅃ', 'ㅅ', 'ㅆ', 'ㅇ', 'ㅈ', 'ㅉ', 'ㅊ',
'ㅋ', 'ㅌ', 'ㅍ', 'ㅎ'
]
jungsung_list = [
'ㅏ', 'ㅐ', 'ㅑ', 'ㅒ', 'ㅓ', 'ㅔ', 'ㅕ', 'ㅖ', 'ㅗ', 'ㅘ', 'ㅙ', 'ㅚ', 'ㅛ', 'ㅜ', 'ㅝ',
'ㅞ', 'ㅟ', 'ㅠ', 'ㅡ', 'ㅢ', 'ㅣ'
]
jongsung_list = [
' ', 'ㄱ', 'ㄲ', 'ㄳ', 'ㄴ', 'ㄵ', 'ㄶ', 'ㄷ', 'ㄹ', 'ㄺ', 'ㄻ', 'ㄼ', 'ㄽ', 'ㄾ', 'ㄿ',
'ㅀ', 'ㅁ', 'ㅂ', 'ㅄ', 'ㅅ', 'ㅆ', 'ㅇ', 'ㅈ', 'ㅊ', 'ㅋ', 'ㅌ', 'ㅍ', 'ㅎ'
]
list_of_jamos = sorted(set(chosung_list + jungsung_list + jongsung_list))
vocab = Vocab(list_of_jamos, bos_token=None, eos_token=None)
with open('data/vocab.pkl', mode='wb') as io:
pickle.dump(vocab, io)
# extracting morph in sentences
list_of_tokens = tr["document"].apply(split_morphs).tolist()
# generating the vocab
token_counter = Counter(itertools.chain.from_iterable(list_of_tokens))
tmp_vocab = nlp.Vocab(counter=token_counter,
min_freq=10,
bos_token=None,
eos_token=None)
# connecting SISG embedding with vocab
ptr_embedding = nlp.embedding.create("fasttext", source="wiki.ko")
tmp_vocab.set_embedding(ptr_embedding)
array = tmp_vocab.embedding.idx_to_vec.asnumpy()
vocab = Vocab(
tmp_vocab.idx_to_token,
padding_token="<pad>",
unknown_token="<unk>",
bos_token=None,
eos_token=None,
)
vocab.embedding = array
# saving vocab
with open(nsmc_dir / "vocab.pkl", mode="wb") as io:
pickle.dump(vocab, io)
config.update({"vocab": str(nsmc_dir / "vocab.pkl")})
config.save("conf/dataset/nsmc.json")
def __init__(self,
id,
name,
checkpoint_path,
max_conversation_length=5,
max_sentence_length=30,
is_test_bot=False,
rl=False,
safe_mode=True):
"""
All chatbots should extend this class and be registered with the @registerbot decorator
:param id: An id string, must be unique!
:param name: A user-friendly string shown to the end user to identify the chatbot. Should be unique.
:param checkpoint_path: Directory where the trained model checkpoint is saved.
:param max_conversation_length: Maximum number of conversation turns to condition on.
:param max_sentence_length: Maximum number of tokens per sentence.
:param is_test_bot: If True, this bot it can be chosen from the list of
bots you see at /dialogadmins screen, but will never be randomly
assigned to users landing on the home page.
"""
self.id = id
self.name = name
self.checkpoint_path = checkpoint_path
self.max_conversation_length = max_conversation_length
self.max_sentence_length = max_sentence_length
self.is_test_bot = is_test_bot
self.safe_mode = safe_mode
print("\n\nCreating chatbot", name)
self.config = get_config_from_dir(checkpoint_path,
mode='test',
load_rl_ckpt=rl)
self.config.beam_size = 5
print('Loading Vocabulary...')
self.vocab = Vocab()
self.vocab.load(self.config.word2id_path, self.config.id2word_path)
print(f'Vocabulary size: {self.vocab.vocab_size}')
self.config.vocab_size = self.vocab.vocab_size
# If checkpoint is for an emotion model, load that pickle file
emotion_sentences = None
if self.config.emotion:
emotion_sentences = load_pickle(self.config.emojis_path)
# Load infersent embeddings if necessary
infersent_sentences = None
if self.config.infersent:
print('Loading infersent sentence embeddings...')
infersent_sentences = load_pickle(self.config.infersent_path)
embedding_size = infersent_sentences[0][0].shape[0]
self.config.infersent_output_size = embedding_size
self.data_loader = get_loader(
sentences=load_pickle(self.config.sentences_path),
conversation_length=load_pickle(
self.config.conversation_length_path),
sentence_length=load_pickle(self.config.sentence_length_path),
vocab=self.vocab,
batch_size=self.config.batch_size,
emojis=emotion_sentences)
if self.config.model in VariationalModels:
self.solver = VariationalSolver(self.config,
None,
self.data_loader,
vocab=self.vocab,
is_train=False)
elif self.config.model == 'Transformer':
self.solver = ParlAISolver(self.config)
else:
self.solver = Solver(self.config,
None,
self.data_loader,
vocab=self.vocab,
is_train=False)
self.solver.build()
class Chatbot(ABC):
def __init__(self,
id,
name,
checkpoint_path,
max_conversation_length=5,
max_sentence_length=30,
is_test_bot=False,
rl=False,
safe_mode=True):
"""
All chatbots should extend this class and be registered with the @registerbot decorator
:param id: An id string, must be unique!
:param name: A user-friendly string shown to the end user to identify the chatbot. Should be unique.
:param checkpoint_path: Directory where the trained model checkpoint is saved.
:param max_conversation_length: Maximum number of conversation turns to condition on.
:param max_sentence_length: Maximum number of tokens per sentence.
:param is_test_bot: If True, this bot it can be chosen from the list of
bots you see at /dialogadmins screen, but will never be randomly
assigned to users landing on the home page.
"""
self.id = id
self.name = name
self.checkpoint_path = checkpoint_path
self.max_conversation_length = max_conversation_length
self.max_sentence_length = max_sentence_length
self.is_test_bot = is_test_bot
self.safe_mode = safe_mode
print("\n\nCreating chatbot", name)
self.config = get_config_from_dir(checkpoint_path,
mode='test',
load_rl_ckpt=rl)
self.config.beam_size = 5
print('Loading Vocabulary...')
self.vocab = Vocab()
self.vocab.load(self.config.word2id_path, self.config.id2word_path)
print(f'Vocabulary size: {self.vocab.vocab_size}')
self.config.vocab_size = self.vocab.vocab_size
# If checkpoint is for an emotion model, load that pickle file
emotion_sentences = None
if self.config.emotion:
emotion_sentences = load_pickle(self.config.emojis_path)
# Load infersent embeddings if necessary
infersent_sentences = None
if self.config.infersent:
print('Loading infersent sentence embeddings...')
infersent_sentences = load_pickle(self.config.infersent_path)
embedding_size = infersent_sentences[0][0].shape[0]
self.config.infersent_output_size = embedding_size
self.data_loader = get_loader(
sentences=load_pickle(self.config.sentences_path),
conversation_length=load_pickle(
self.config.conversation_length_path),
sentence_length=load_pickle(self.config.sentence_length_path),
vocab=self.vocab,
batch_size=self.config.batch_size,
emojis=emotion_sentences)
if self.config.model in VariationalModels:
self.solver = VariationalSolver(self.config,
None,
self.data_loader,
vocab=self.vocab,
is_train=False)
elif self.config.model == 'Transformer':
self.solver = ParlAISolver(self.config)
else:
self.solver = Solver(self.config,
None,
self.data_loader,
vocab=self.vocab,
is_train=False)
self.solver.build()
def handle_messages(self, messages):
"""
Takes a list of messages, and combines those with magic to return a response string
:param messages: list of strings
:return: string
"""
greetings = [
"hey , how are you ?", "hi , how 's it going ?",
"hey , what 's up ?", "hi . how are you ?",
"hello , how are you doing today ? ",
"hello . how are things with you ?",
"hey ! so, tell me about yourself .", "hi . nice to meet you ."
]
# Check for no response
if len(messages) == 0:
# Respond with canned greeting response
return np.random.choice(greetings)
# Check for overly short intro messages
if len(messages) < 2 and len(messages[0]) <= 6: # 6 for "hello."
first_m = messages[0].lower()
if 'hi' in first_m or 'hey' in first_m or 'hello' in first_m:
# Respond with canned greeting response
return np.random.choice(greetings)
response = self.solver.generate_response_to_input(
messages,
max_conversation_length=self.max_conversation_length,
emojize=True,
debug=False)
# Manually remove inappropriate language from response.
# WARNING: the following code contains inappropriate language
if self.safe_mode:
response = response.replace("f*g", "<unknown>")
response = response.replace("gays", "<unknown>")
response = response.replace("c**t", "%@#$")
response = response.replace("f**k", "%@#$")
response = response.replace("shit", "%@#$")
response = response.replace("dyke", "%@#$")
response = response.replace("hell", "heck")
response = response.replace("dick", "d***")
response = response.replace("bitch", "%@#$")
return response
import itertools
import pickle
from collections import Counter
from pathlib import Path
from model.split import split_morphs, split_space
from model.utils import Vocab
data_dir = Path('data')
tr_filepath = (data_dir / 'train').with_suffix('.txt')
tr_dataset = pd.read_csv(tr_filepath, sep='\t')
# korean vocab
count_ko = Counter(
itertools.chain.from_iterable(
tr_dataset['ko'].apply(split_morphs).tolist()))
list_of_token_ko = [token[0] for token in count_ko.items() if token[1] >= 15]
vocab_ko = Vocab(list_of_token_ko, bos_token=None, eos_token=None)
with open(data_dir / 'vocab_ko.pkl', mode='wb') as io:
pickle.dump(vocab_ko, io)
# english vocab
count_en = Counter(
itertools.chain.from_iterable(
tr_dataset['en'].apply(split_space).tolist()))
list_of_token_en = [token[0] for token in count_en.items() if token[1] >= 15]
vocab_en = Vocab(list_of_token_en)
with open(data_dir / 'vocab_en.pkl', mode='wb') as io:
pickle.dump(vocab_en, io)
list_of_tokens = [
token_count[0] for token_count in token_counter.items()
if token_count[1] >= min_freq
]
list_of_tokens = sorted(list_of_tokens)
list_of_tokens.insert(0, '<pad>')
list_of_tokens.insert(0, '<unk>')
tmp_vocab = nlp.Vocab(counter=Counter(list_of_tokens),
min_freq=1,
bos_token=None,
eos_token=None)
# connecting SISG embedding with vocab
ptr_embedding = nlp.embedding.create('fasttext', source='wiki.ko')
tmp_vocab.set_embedding(ptr_embedding)
array = tmp_vocab.embedding.idx_to_vec.asnumpy()
vocab = Vocab(list_of_tokens,
padding_token='<pad>',
unknown_token='<unk>',
bos_token=None,
eos_token=None)
vocab.embedding = array
# saving vocab
with open('data/vocab.pkl', mode='wb') as io:
pickle.dump(vocab, io)
data_config.vocab = 'data/vocab.pkl'
data_config.save('data/config.json')
line = line.strip()
if line:
data.append(line.split('\t')[1:])
else:
dataset.append([list(elm) for elm in zip(*data)])
data = []
continue
except StopIteration:
print('parsing is done')
label_counter = nlp.data.count_tokens(
itertools.chain.from_iterable(map(lambda elm: elm[1], dataset)))
tmp_label_vocab = nlp.Vocab(label_counter, unknown_token=None)
label_vocab = Vocab(tmp_label_vocab.idx_to_token, unknown_token=None)
with open('./data/label_vocab.pkl', mode='wb') as io:
pickle.dump(label_vocab, io)
tr, val = train_test_split(dataset, test_size=.1, random_state=777)
token_counter = nlp.data.count_tokens(
itertools.chain.from_iterable(map(lambda elm: elm[0], tr)))
tmp_token_vocab = nlp.Vocab(token_counter, min_freq=10)
ptr_embedding = nlp.embedding.create('fasttext', source='wiki.ko')
tmp_token_vocab.set_embedding(ptr_embedding)
token_vocab = Vocab(tmp_token_vocab.idx_to_token)
token_vocab.embedding = tmp_token_vocab.embedding.idx_to_vec.asnumpy()
with open('./data/token_vocab.pkl', mode='wb') as io:
pickle.dump(token_vocab, io)
import pickle
from model.utils import Vocab
from pretrained.tokenization import BertTokenizer
# loading BertTokenizer
ptr_tokenizer = BertTokenizer.from_pretrained(
'pretrained/vocab.korean.rawtext.list', do_lower_case=False)
list_of_tokens = list(ptr_tokenizer.vocab.keys())
# generate vocab
vocab = Vocab(list_of_tokens,
padding_token='[PAD]',
unknown_token='[UNK]',
bos_token=None,
eos_token=None,
reserved_tokens=['[CLS]', '[SEP]', '[MASK]'],
token_to_idx={'[UNK]': 1})
# save vocab
with open('pretrained/vocab.pkl', mode='wb') as io:
pickle.dump(vocab, io)
urlretrieve('https://kobert.blob.core.windows.net/models/kobert/pytorch/pytorch_kobert_2439f391a6.params',
filename=ptr_bert_path)
ptr_bert = torch.load(ptr_bert_path)
ptr_bert = OrderedDict([(('bert.' + k), ptr_bert.get(k)) for k in ptr_bert.keys()])
torch.save(ptr_bert, ptr_bert_path)
else:
print('Already you have pytorch_model_skt.bin!')
if not ptr_vocab_path.exists():
urlretrieve('https://kobert.blob.core.windows.net/models/kobert/vocab/kobertvocab_f38b8a4d6d.json',
filename=ptr_vocab_path)
ptr_bert_vocab = BERTVocab.from_json(ptr_vocab_path.open(mode='rt').read())
vocab = Vocab(ptr_bert_vocab.idx_to_token,
padding_token="[PAD]",
unknown_token="[UNK]",
bos_token=None,
eos_token=None,
reserved_tokens=["[CLS]", "[SEP]", "[MASK]"],
token_to_idx=ptr_bert_vocab.token_to_idx)
# save vocab
with open(ptr_vocab_path.with_suffix('.pkl'), mode="wb") as io:
pickle.dump(vocab, io)
else:
print('Already you have pytorch_model_skt_vocab.json!')
if not ptr_tokenizer_path.exists():
urlretrieve('https://kobert.blob.core.windows.net/models/kobert/tokenizer/tokenizer_78b3253a26.model',
filename=ptr_tokenizer_path)
else:
print('Already you have pytorch_model_skt_tokenizer.model')
# korean vocab
split_ko = Stemmer(language='ko')
count_ko = Counter(
itertools.chain.from_iterable(tr_dataset['ko'].apply(
split_ko.extract_stem).tolist()))
list_of_token_ko = sorted(
[token[0] for token in count_ko.items() if token[1] >= 15])
tmp_vocab = nlp.Vocab(Counter(list_of_token_ko),
bos_token=None,
eos_token=None)
ptr_embedding = nlp.embedding.create('fasttext', source='wiki.ko')
tmp_vocab.set_embedding(ptr_embedding)
array = tmp_vocab.embedding.idx_to_vec.asnumpy()
vocab_ko = Vocab(list_of_token_ko, bos_token=None, eos_token=None)
vocab_ko.embedding = array
with open(data_dir / 'vocab_ko.pkl', mode='wb') as io:
pickle.dump(vocab_ko, io)
# english vocab
split_en = Stemmer(language='en')
count_en = Counter(
itertools.chain.from_iterable(tr_dataset['en'].apply(
split_en.extract_stem).tolist()))
list_of_token_en = [token[0] for token in count_en.items() if token[1] >= 15]
tmp_vocab = nlp.Vocab(Counter(list_of_token_en))
ptr_embedding = nlp.embedding.create('fasttext', source='wiki.simple')
tmp_vocab.set_embedding(ptr_embedding)
array = tmp_vocab.embedding.idx_to_vec.asnumpy()
import json
import pickle
from model.utils import Vocab
from bert.tokenization import BertTokenizer
with open('experiment/config.json') as f:
params = json.loads(f.read())
# loading BertTokenizer
ptr_tokenizer = BertTokenizer.from_pretrained('bert/vocab.korean.rawtext.list', do_lower_case=False)
idx_to_token = list(ptr_tokenizer.vocab.keys())
# generate vocab
token_vocab = Vocab(idx_to_token, padding_token='[PAD]', unknown_token='[UNK]', bos_token=None,
eos_token=None, reserved_tokens=['[CLS]', '[SEP]', '[MASK]'], unknown_token_idx=1)
label_vocab = Vocab(['<split>', '<non_split>'], unknown_token=None, bos_token=None, eos_token=None)
# save vocab
token_vocab_path = params['filepath'].get('token_vocab')
label_vocab_path = params['filepath'].get('label_vocab')
with open(token_vocab_path, 'wb') as f:
pickle.dump(token_vocab, f)
with open(label_vocab_path, 'wb') as f:
pickle.dump(label_vocab, f)
# [n_conversations, conversation_length (various)]
conversation_length = [
min(len(conversation), max_conv_len)
for conversation in conversations
]
sentences, sentence_length = preprocess_utils.pad_sentences(
conversations,
max_sentence_length=max_sent_len,
max_conversation_length=max_conv_len)
print('Saving preprocessed data at', split_data_dir)
to_pickle(conversation_length,
split_data_dir.joinpath('conversation_length.pkl'))
to_pickle(conversations, split_data_dir.joinpath('sentences.pkl'))
to_pickle(sentence_length,
split_data_dir.joinpath('sentence_length.pkl'))
if split_type == 'train':
print('Save Vocabulary...')
vocab = Vocab(tokenizer)
vocab.add_dataframe(conversations)
vocab.update(max_size=max_vocab_size, min_freq=min_freq)
print('Vocabulary size: ', len(vocab))
vocab.pickle(ubuntu_dir.joinpath('word2id.pkl'),
ubuntu_dir.joinpath('id2word.pkl'))
print('Done!')
else:
print("Already you have {}".format(config_filename))
print("Saving the config of {} is done.".format(args.type))
# saving vocab of pretraining model
ptr_tokenizer = BertTokenizer.from_pretrained(
args.type, do_lower_case="uncased" in args.type
)
idx_to_token = list(ptr_tokenizer.vocab.keys())
token_to_idx = {token: idx for idx, token in enumerate(idx_to_token)}
vocab = Vocab(
list_of_tokens=idx_to_token,
unknown_token="[UNK]",
padding_token="[PAD]",
bos_token=None,
eos_token=None,
reserved_tokens=["[CLS]", "[SEP]", "[MASK]"],
token_to_idx=token_to_idx
)
vocab_filename = "{}-vocab.pkl".format(args.type)
vocab_filepath = ptr_dir / vocab_filename
if not vocab_filepath.exists():
with open(vocab_filepath, mode="wb") as io:
pickle.dump(vocab, io)
else:
print("Already you have {}".format(vocab_filename))
print("Saving the vocab of {} is done".format(args.type))
def main():
"""
here is the plan: for each dialogue create a history sequence of sentences
seperated by <s>. The sentences in the history must occur in a short time
span from another so they are relevant. The last sentence becomes the response
where the response must also be in the span
:return:
"""
parser = argparse.ArgumentParser()
parser.add_argument(
"-dataset_dir",
default="./datasets/personachat/raw",
type=str,
required=False,
help="The input data dir. Should contain the xml for the task.")
parser.add_argument("-output_dir",
default="./datasets/personachat/",
type=str,
required=False,
help="The output data dir.")
parser.add_argument("-type",
default="none_original",
type=str,
required=False,
help="The genres you would like to use.")
parser.add_argument("-max_sentence_tokens",
default=30,
type=int,
help="the maximum amout of sentence tokens")
parser.add_argument(
"-a_nice_note",
default="only dialogues 1-10",
type=str,
required=False,
help="leave a nice lil note for yourself in the future")
parser.add_argument(
'-train_split',
default=0.9,
type=float,
help=
'fraction of dataset to use for training, remainder is halved for val & test'
)
parser.add_argument('-vocab_size',
default=20000,
type=int,
help='maximum size of the vocabulary for training')
args = parser.parse_args()
filename = os.path.join(args.dataset_dir, "train_{}.txt".format(args.type))
conversations = create_dialogues(filename, args.max_sentence_tokens)
for conversation in conversations:
for utterance in conversation:
if len(utterance) != args.max_sentence_tokens:
print('Length of utterance not equal max: %s' % len(utterance))
exit()
print(conversations[0])
# shuffle dataset
random.seed('seed')
random.shuffle(conversations)
print('Number of conversations: %s' % len(conversations))
mean_n_convos = sum([len(conv)
for conv in conversations]) / len(conversations)
print('Average utterances per conversations: %s' % mean_n_convos)
# this is format needed to train dialogue models on this domain
def format_for_dialogue(conversations):
conversation_length = [len(conv) for conv in conversations]
sentence_length = [[
sum([1 for token in sent if token != '<pad>']) for sent in conv
] for conv in conversations]
sentences = conversations
return conversation_length, sentence_length, sentences
val_idx = int(len(conversations) * args.train_split)
test_idx = (len(conversations) + val_idx) // 2
print(val_idx)
train_convos = conversations[:val_idx]
val_convos = conversations[val_idx:test_idx]
test_convos = conversations[test_idx:]
# construct vocab
vocab = Vocab()
vocab.add_dataframe(train_convos, tokenized=True)
vocab.update(args.vocab_size)
print('Vocab size: %s' % len(vocab))
word2id_path = os.path.join(args.output_dir, 'word2id.pkl')
id2word_path = os.path.join(args.output_dir, 'id2word.pkl')
vocab.pickle(word2id_path, id2word_path)
print('Split: train %s, val %s, test %s' %
(len(train_convos), len(val_convos), len(test_convos)))
os.makedirs(args.output_dir, exist_ok=True)
train_convo_len, train_sent_len, train_sent = format_for_dialogue(
train_convos)
print('Example data')
print(train_convo_len[0])
print(train_sent_len[0])
print(train_sent[0])
print()
os.makedirs(os.path.join(args.output_dir, 'train'), exist_ok=True)
pickle.dump(
train_convo_len,
open(os.path.join(args.output_dir, 'train', 'conversation_length.pkl'),
'wb'))
pickle.dump(
train_sent_len,
open(os.path.join(args.output_dir, 'train', 'sentence_length.pkl'),
'wb'))
pickle.dump(
train_sent,
open(os.path.join(args.output_dir, 'train', 'sentences.pkl'), 'wb'))
val_convo_len, val_sent_len, val_sent = format_for_dialogue(val_convos)
os.makedirs(os.path.join(args.output_dir, 'valid'), exist_ok=True)
pickle.dump(
val_convo_len,
open(os.path.join(args.output_dir, 'valid', 'conversation_length.pkl'),
'wb'))
pickle.dump(
val_sent_len,
open(os.path.join(args.output_dir, 'valid', 'sentence_length.pkl'),
'wb'))
pickle.dump(
val_sent,
open(os.path.join(args.output_dir, 'valid', 'sentences.pkl'), 'wb'))
test_convo_len, test_sent_len, test_sent = format_for_dialogue(test_convos)
os.makedirs(os.path.join(args.output_dir, 'test'), exist_ok=True)
pickle.dump(
test_convo_len,
open(os.path.join(args.output_dir, 'test', 'conversation_length.pkl'),
'wb'))
pickle.dump(
test_sent_len,
open(os.path.join(args.output_dir, 'test', 'sentence_length.pkl'),
'wb'))
pickle.dump(
test_sent,
open(os.path.join(args.output_dir, 'test', 'sentences.pkl'), 'wb'))
config = Config("conf/dataset/sample.json")
tr = pd.read_csv(config.train, sep='\t')
# korean vocab
split_ko = Stemmer(language='ko')
count_ko = Counter(
itertools.chain.from_iterable(tr['ko'].apply(
split_ko.extract_stem).tolist()))
tmp_vocab = nlp.Vocab(count_ko, bos_token=None, eos_token=None)
ptr_embedding = nlp.embedding.create('fasttext',
source='wiki.ko',
load_ngrams=True)
tmp_vocab.set_embedding(ptr_embedding)
array = tmp_vocab.embedding.idx_to_vec.asnumpy()
vocab_ko = Vocab(tmp_vocab.idx_to_token, bos_token=None, eos_token=None)
vocab_ko.embedding = array
vocab_ko_filepath = sample_dir / "vocab_ko.pkl"
config.update({"source_vocab": str(vocab_ko_filepath)})
with open(vocab_ko_filepath, mode='wb') as io:
pickle.dump(vocab_ko, io)
# english vocab
split_en = Stemmer(language='en')
count_en = Counter(
itertools.chain.from_iterable(tr['en'].apply(
split_en.extract_stem).tolist()))
tmp_vocab = nlp.Vocab(count_en)
ptr_embedding = nlp.embedding.create('fasttext',
source='wiki.simple',
train = pd.read_csv(config.train, sep="\t")
list_of_tokens_qa = train["question1"].apply(
lambda sen: split_morphs(sen)).tolist()
list_of_tokens_qb = train["question2"].apply(
lambda sen: split_morphs(sen)).tolist()
list_of_tokens = list_of_tokens_qa + list_of_tokens_qb
count_tokens = Counter(itertools.chain.from_iterable(list_of_tokens))
tmp_vocab = nlp.Vocab(counter=count_tokens, bos_token=None, eos_token=None)
ptr_embedding = nlp.embedding.create("fasttext",
source="wiki.ko",
load_ngrams=True)
tmp_vocab.set_embedding(ptr_embedding)
morph_vocab = Vocab(tmp_vocab.idx_to_token, bos_token=None, eos_token=None)
morph_vocab.embedding = tmp_vocab.embedding.idx_to_vec.asnumpy()
with open(qpair_dir / "morph_vocab.pkl", mode="wb") as io:
pickle.dump(morph_vocab, io)
config.update({"coarse_vocab": str(qpair_dir / "morph_vocab.pkl")})
# jamo
chosung_list = [
'ㄱ', 'ㄲ', 'ㄴ', 'ㄷ', 'ㄸ', 'ㄹ', 'ㅁ', 'ㅂ', 'ㅃ', 'ㅅ', 'ㅆ', 'ㅇ', 'ㅈ', 'ㅉ', 'ㅊ',
'ㅋ', 'ㅌ', 'ㅍ', 'ㅎ'
]
jungsung_list = [
'ㅏ', 'ㅐ', 'ㅑ', 'ㅒ', 'ㅓ', 'ㅔ', 'ㅕ', 'ㅖ', 'ㅗ', 'ㅘ', 'ㅙ', 'ㅚ', 'ㅛ', 'ㅜ', 'ㅝ',
import pickle
from pathlib import Path
from model.utils import Vocab
from utils import Config
LIST_OF_CHOSUNG = [
"ㄱ", "ㄲ", "ㄴ", "ㄷ", "ㄸ", "ㄹ", "ㅁ", "ㅂ", "ㅃ", "ㅅ", "ㅆ", "ㅇ", "ㅈ", "ㅉ", "ㅊ",
"ㅋ", "ㅌ", "ㅍ", "ㅎ"
]
LIST_OF_JUNGSUNG = [
"ㅏ", "ㅐ", "ㅑ", "ㅒ", "ㅓ", "ㅔ", "ㅕ", "ㅖ", "ㅗ", "ㅘ", "ㅙ", "ㅚ", "ㅛ", "ㅜ", "ㅝ",
"ㅞ", "ㅟ", "ㅠ", "ㅡ", "ㅢ", "ㅣ"
]
LIST_OF_JONGSUNG = [
" ", "ㄱ", "ㄲ", "ㄳ", "ㄴ", "ㄵ", "ㄶ", "ㄷ", "ㄹ", "ㄺ", "ㄻ", "ㄼ", "ㄽ", "ㄾ", "ㄿ",
"ㅀ", "ㅁ", "ㅂ", "ㅄ", "ㅅ", "ㅆ", "ㅇ", "ㅈ", "ㅊ", "ㅋ", "ㅌ", "ㅍ", "ㅎ"
]
LIST_OF_JAMOS = sorted(
set(LIST_OF_CHOSUNG + LIST_OF_JUNGSUNG + LIST_OF_JONGSUNG))
vocab = Vocab(list_of_tokens=LIST_OF_JAMOS, bos_token=None, eos_token=None)
nsmc_dir = Path("nsmc")
with open(nsmc_dir / "vocab.pkl", mode="wb") as io:
pickle.dump(vocab, io)
config = Config("conf/dataset/nsmc.json")
config.update({"vocab": str(nsmc_dir / "vocab.pkl")})
config.save("conf/dataset/nsmc.json")
def load_pickle(path):
with open(path, 'rb') as f:
return pickle.load(f)
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--checkpoint', type=str, default=None)
parser.add_argument('--mode', type=str, default='test') # or valid
kwargs = parser.parse_args()
config = get_config_from_dir(kwargs.checkpoint, mode=kwargs.mode)
print(config)
print('Loading Vocabulary...')
vocab = Vocab()
vocab.load(config.word2id_path, config.id2word_path)
print(f'Vocabulary size: {vocab.vocab_size}')
config.vocab_size = vocab.vocab_size
emotion_sentences = None
if config.emotion:
emotion_sentences = load_pickle(config.emojis_path)
# Load infersent embeddings if necessary
infersent_sentences = None
if config.infersent:
print('Loading infersent sentence embeddings...')
infersent_sentences = load_pickle(config.infersent_path)
embedding_size = infersent_sentences[0][0].shape[0]
default=0.9,
help='momentum for sgd')
parser.add_argument('--clip_grad',
type=float,
default=5.0,
help='clip grad at')
parser.add_argument('--lambda0', type=float, default=1, help='lambda0')
parser.add_argument('--patience',
type=int,
default=15,
help='patience for early stop')
args = parser.parse_args()
# actions the parser can take
acts = ['SHIFT', 'REDUCE_L', 'REDUCE_R']
vocab_acts = Vocab.from_list(acts)
SHIFT = vocab_acts.w2i['SHIFT']
REDUCE_L = vocab_acts.w2i['REDUCE_L']
REDUCE_R = vocab_acts.w2i['REDUCE_R']
NUM_ACTIONS = vocab_acts.size()
vocab_words = Vocab.from_file(args.vocab_file_path)
training_set = list(
read_oracle(args.train_file_path, vocab_words, vocab_acts))
validation_set = list(
read_oracle(args.dev_file_path, vocab_words, vocab_acts))
# # CUDA for PyTorch
# use_cuda = T.cuda.is_available()
# device = T.device("cuda:0" if use_cuda else "cpu")
