class EncoderDecoder(Chain):
def __init__(self, vocab_size, embed_size, hidden_size):
super(EncoderDecoder, self).__init__(
enc=Encoder(vocab_size, embed_size, hidden_size),
dec=Decoder(vocab_size, embed_size, hidden_size),
)
self.vocab_size = vocab_size
self.embed_size = embed_size
self.hidden_size = hidden_size
self.common_function = CommonFunction()
def reset(self, batch_size):
self.zerograds()
self.c = self.common_function.my_zeros((batch_size, self.hidden_size),
np.float32)
self.h = self.common_function.my_zeros((batch_size, self.hidden_size),
np.float32)
def encode(self, x):
self.c, self.h = self.enc(x, self.c, self.h)
def decode(self, y):
y, self.c, self.h = self.dec(y, self.c, self.h)
return y
def save_spec(self, filename):
with open(filename, 'w') as fp:
print(self.vocab_size, file=fp)
print(self.embed_size, file=fp)
print(self.hidden_size, file=fp)
@staticmethod
def load_spec(filename):
with open(filename) as fp:
vocab_size = int(next(fp))
embed_size = int(next(fp))
hidden_size = int(next(fp))
return EncoderDecoder(vocab_size, embed_size, hidden_size)
class EncoderDecoder(Chain):
def __init__(self, vocab_size, embed_size, hidden_size):
super(EncoderDecoder, self).__init__(
enc = Encoder(vocab_size, embed_size, hidden_size),
dec = Decoder(vocab_size, embed_size, hidden_size),
)
self.vocab_size = vocab_size
self.embed_size = embed_size
self.hidden_size = hidden_size
self.common_function = CommonFunction()
def reset(self, batch_size):
self.zerograds()
self.c = self.common_function.my_zeros((batch_size, self.hidden_size), np.float32)
self.h = self.common_function.my_zeros((batch_size, self.hidden_size), np.float32)
def encode(self, x):
self.c, self.h = self.enc(x, self.c, self.h)
def decode(self, y):
y, self.c, self.h = self.dec(y, self.c, self.h)
return y
def save_spec(self, filename):
with open(filename, 'w') as fp:
print(self.vocab_size, file=fp)
print(self.embed_size, file=fp)
print(self.hidden_size, file=fp)
@staticmethod
def load_spec(filename):
with open(filename) as fp:
vocab_size = int(next(fp))
embed_size = int(next(fp))
hidden_size = int(next(fp))
return EncoderDecoder(vocab_size, embed_size, hidden_size)
class EncoderDecoderModelForwardSlack:
def __init__(self, parameter_dict):
self.parameter_dict = parameter_dict
self.source = parameter_dict["source"]
self.target = parameter_dict["target"]
self.test_source = parameter_dict["test_source"]
self.test_target = parameter_dict["test_target"]
self.vocab = parameter_dict["vocab"]
self.embed = parameter_dict["embed"]
self.hidden = parameter_dict["hidden"]
self.epoch = parameter_dict["epoch"]
self.minibatch = parameter_dict["minibatch"]
self.generation_limit = parameter_dict["generation_limit"]
self.word2vec = parameter_dict["word2vec"]
self.word2vecFlag = parameter_dict["word2vecFlag"]
self.common_function = CommonFunction()
self.model = "../ChainerDialogue"
def forward(self, src_batch, trg_batch, src_vocab, trg_vocab, encdec,
is_training, generation_limit):
batch_size = len(src_batch)
src_len = len(src_batch[0])
trg_len = len(trg_batch[0]) if trg_batch else 0
src_stoi = src_vocab.stoi
trg_stoi = trg_vocab.stoi
trg_itos = trg_vocab.itos
encdec.reset(batch_size)
x = self.common_function.my_array(
[src_stoi('</s>') for _ in range(batch_size)], np.int32)
encdec.encode(x)
for l in reversed(range(src_len)):
x = self.common_function.my_array(
[src_stoi(src_batch[k][l]) for k in range(batch_size)],
np.int32)
encdec.encode(x)
t = self.common_function.my_array(
[trg_stoi('<s>') for _ in range(batch_size)], np.int32)
hyp_batch = [[] for _ in range(batch_size)]
if is_training:
loss = self.common_function.my_zeros((), np.float32)
for l in range(trg_len):
y = encdec.decode(t)
t = self.common_function.my_array(
[trg_stoi(trg_batch[k][l]) for k in range(batch_size)],
np.int32)
loss += functions.softmax_cross_entropy(y, t)
output = cuda.to_cpu(y.data.argmax(1))
for k in range(batch_size):
hyp_batch[k].append(trg_itos(output[k]))
return hyp_batch, loss
else:
while len(hyp_batch[0]) < generation_limit:
y = encdec.decode(t)
output = cuda.to_cpu(y.data.argmax(1))
t = self.common_function.my_array(output, np.int32)
for k in range(batch_size):
hyp_batch[k].append(trg_itos(output[k]))
if all(hyp_batch[k][-1] == '</s>' for k in range(batch_size)):
break
return hyp_batch
def train(self):
trace('making vocabularies ...')
src_vocab = Vocabulary.new(gens.word_list(self.source), self.vocab)
trg_vocab = Vocabulary.new(gens.word_list(self.target), self.vocab)
trace('making model ...')
encdec = EncoderDecoder(self.vocab, self.embed, self.hidden)
if self.word2vecFlag:
self.copy_model(self.word2vec, encdec.enc)
self.copy_model(self.word2vec, encdec.dec, dec_flag=True)
else:
encdec = self.encdec
for epoch in range(self.epoch):
trace('epoch %d/%d: ' % (epoch + 1, self.epoch))
trained = 0
gen1 = gens.word_list(self.source)
gen2 = gens.word_list(self.target)
gen3 = gens.batch(
gens.sorted_parallel(gen1, gen2, 100 * self.minibatch),
self.minibatch)
opt = optimizers.AdaGrad(lr=0.01)
opt.setup(encdec)
opt.add_hook(optimizer.GradientClipping(5))
random_number = random.randint(0, self.minibatch - 1)
for src_batch, trg_batch in gen3:
src_batch = fill_batch(src_batch)
trg_batch = fill_batch(trg_batch)
K = len(src_batch)
hyp_batch, loss = self.forward(src_batch, trg_batch, src_vocab,
trg_vocab, encdec, True, 0)
loss.backward()
opt.update()
if trained == 0:
self.print_out(random_number, epoch, trained, src_batch,
trg_batch, hyp_batch)
trained += K
trace('saving model ...')
prefix = self.model
src_vocab.save(prefix + '.srcvocab')
trg_vocab.save(prefix + '.trgvocab')
encdec.save_spec(prefix + '.spec')
serializers.save_hdf5(prefix + '.weights', encdec)
trace('finished.')
def test(self):
trace('loading model ...')
src_vocab = Vocabulary.load(self.model + '.srcvocab')
trg_vocab = Vocabulary.load(self.model + '.trgvocab')
encdec = EncoderDecoder.load_spec(self.model + '.spec')
serializers.load_hdf5(self.model + '.weights', encdec)
trace('generating translation ...')
generated = 0
with open(self.target, 'w') as fp:
for src_batch in gens.batch(gens.word_list(self.source),
self.minibatch):
src_batch = fill_batch(src_batch)
K = len(src_batch)
trace('sample %8d - %8d ...' % (generated + 1, generated + K))
hyp_batch = self.forward(src_batch, None, src_vocab, trg_vocab,
encdec, False, self.generation_limit)
source_cuont = 0
for hyp in hyp_batch:
hyp.append('</s>')
hyp = hyp[:hyp.index('</s>')]
print("src : " +
"".join(src_batch[source_cuont]).replace("</s>", ""))
print('hyp : ' + ''.join(hyp))
print(' '.join(hyp), file=fp)
source_cuont = source_cuont + 1
generated += K
trace('finished.')
def print_out(self, K, i_epoch, trained, src_batch, trg_batch, hyp_batch):
trace('epoch %3d/%3d, sample %8d' %
(i_epoch + 1, self.epoch, trained + K + 1))
trace(' src = ' +
' '.join([x if x != '</s>' else '*' for x in src_batch[K]]))
trace(' trg = ' +
' '.join([x if x != '</s>' else '*' for x in trg_batch[K]]))
trace(' hyp = ' +
' '.join([x if x != '</s>' else '*' for x in hyp_batch[K]]))
def copy_model(self, src, dst, dec_flag=False):
print("start copy")
for child in src.children():
if dec_flag:
if dst["weight_jy"] and child.name == "weight_xi" and self.word2vecFlag:
for a, b in zip(child.namedparams(),
dst["weight_jy"].namedparams()):
b[1].data = a[1].data
print('Copy weight_jy')
if child.name not in dst.__dict__: continue
dst_child = dst[child.name]
if type(child) != type(dst_child): continue
if isinstance(child, link.Chain):
self.copy_model(child, dst_child)
if isinstance(child, link.Link):
match = True
for a, b in zip(child.namedparams(), dst_child.namedparams()):
if a[0] != b[0]:
match = False
break
if a[1].data.shape != b[1].data.shape:
match = False
break
if not match:
print('Ignore %s because of parameter mismatch' %
child.name)
continue
for a, b in zip(child.namedparams(), dst_child.namedparams()):
b[1].data = a[1].data
print('Copy %s' % child.name)
class EncoderDecoderModelForwardSlack:
def __init__(self, parameter_dict):
self.parameter_dict = parameter_dict
self.source = parameter_dict["source"]
self.target = parameter_dict["target"]
self.test_source = parameter_dict["test_source"]
self.test_target = parameter_dict["test_target"]
self.vocab = parameter_dict["vocab"]
self.embed = parameter_dict["embed"]
self.hidden = parameter_dict["hidden"]
self.epoch = parameter_dict["epoch"]
self.minibatch = parameter_dict["minibatch"]
self.generation_limit = parameter_dict["generation_limit"]
self.word2vec = parameter_dict["word2vec"]
self.word2vecFlag = parameter_dict["word2vecFlag"]
self.common_function = CommonFunction()
self.model = "../ChainerDialogue"
def forward(self, src_batch, trg_batch, src_vocab, trg_vocab, encdec, is_training, generation_limit):
batch_size = len(src_batch)
src_len = len(src_batch[0])
trg_len = len(trg_batch[0]) if trg_batch else 0
src_stoi = src_vocab.stoi
trg_stoi = trg_vocab.stoi
trg_itos = trg_vocab.itos
encdec.reset(batch_size)
x = self.common_function.my_array([src_stoi('</s>') for _ in range(batch_size)], np.int32)
encdec.encode(x)
for l in reversed(range(src_len)):
x = self.common_function.my_array([src_stoi(src_batch[k][l]) for k in range(batch_size)], np.int32)
encdec.encode(x)
t = self.common_function.my_array([trg_stoi('<s>') for _ in range(batch_size)], np.int32)
hyp_batch = [[] for _ in range(batch_size)]
if is_training:
loss = self.common_function.my_zeros((), np.float32)
for l in range(trg_len):
y = encdec.decode(t)
t = self.common_function.my_array([trg_stoi(trg_batch[k][l]) for k in range(batch_size)], np.int32)
loss += functions.softmax_cross_entropy(y, t)
output = cuda.to_cpu(y.data.argmax(1))
for k in range(batch_size):
hyp_batch[k].append(trg_itos(output[k]))
return hyp_batch, loss
else:
while len(hyp_batch[0]) < generation_limit:
y = encdec.decode(t)
output = cuda.to_cpu(y.data.argmax(1))
t = self.common_function.my_array(output, np.int32)
for k in range(batch_size):
hyp_batch[k].append(trg_itos(output[k]))
if all(hyp_batch[k][-1] == '</s>' for k in range(batch_size)):
break
return hyp_batch
def train(self):
trace('making vocabularies ...')
src_vocab = Vocabulary.new(gens.word_list(self.source), self.vocab)
trg_vocab = Vocabulary.new(gens.word_list(self.target), self.vocab)
trace('making model ...')
encdec = EncoderDecoder(self.vocab, self.embed, self.hidden)
if self.word2vecFlag:
self.copy_model(self.word2vec, encdec.enc)
self.copy_model(self.word2vec, encdec.dec, dec_flag=True)
else:
encdec = self.encdec
for epoch in range(self.epoch):
trace('epoch %d/%d: ' % (epoch + 1, self.epoch))
trained = 0
gen1 = gens.word_list(self.source)
gen2 = gens.word_list(self.target)
gen3 = gens.batch(gens.sorted_parallel(gen1, gen2, 100 * self.minibatch), self.minibatch)
opt = optimizers.AdaGrad(lr = 0.01)
opt.setup(encdec)
opt.add_hook(optimizer.GradientClipping(5))
random_number = random.randint(0, self.minibatch - 1)
for src_batch, trg_batch in gen3:
src_batch = fill_batch(src_batch)
trg_batch = fill_batch(trg_batch)
K = len(src_batch)
hyp_batch, loss = self.forward(src_batch, trg_batch, src_vocab, trg_vocab, encdec, True, 0)
loss.backward()
opt.update()
if trained == 0:
self.print_out(random_number, epoch, trained, src_batch, trg_batch, hyp_batch)
trained += K
trace('saving model ...')
prefix = self.model
src_vocab.save(prefix + '.srcvocab')
trg_vocab.save(prefix + '.trgvocab')
encdec.save_spec(prefix + '.spec')
serializers.save_hdf5(prefix + '.weights', encdec)
trace('finished.')
def test(self):
trace('loading model ...')
src_vocab = Vocabulary.load(self.model + '.srcvocab')
trg_vocab = Vocabulary.load(self.model + '.trgvocab')
encdec = EncoderDecoder.load_spec(self.model + '.spec')
serializers.load_hdf5(self.model + '.weights', encdec)
trace('generating translation ...')
generated = 0
with open(self.target, 'w') as fp:
for src_batch in gens.batch(gens.word_list(self.source), self.minibatch):
src_batch = fill_batch(src_batch)
K = len(src_batch)
trace('sample %8d - %8d ...' % (generated + 1, generated + K))
hyp_batch = self.forward(src_batch, None, src_vocab, trg_vocab, encdec, False, self.generation_limit)
source_cuont = 0
for hyp in hyp_batch:
hyp.append('</s>')
hyp = hyp[:hyp.index('</s>')]
print("src : " + "".join(src_batch[source_cuont]).replace("</s>", ""))
print('hyp : ' +''.join(hyp))
print(' '.join(hyp), file=fp)
source_cuont = source_cuont + 1
generated += K
trace('finished.')
def print_out(self, K, i_epoch, trained, src_batch, trg_batch, hyp_batch):
trace('epoch %3d/%3d, sample %8d' % (i_epoch + 1, self.epoch, trained + K + 1))
trace(' src = ' + ' '.join([x if x != '</s>' else '*' for x in src_batch[K]]))
trace(' trg = ' + ' '.join([x if x != '</s>' else '*' for x in trg_batch[K]]))
trace(' hyp = ' + ' '.join([x if x != '</s>' else '*' for x in hyp_batch[K]]))
def copy_model(self, src, dst, dec_flag=False):
print("start copy")
for child in src.children():
if dec_flag:
if dst["weight_jy"] and child.name == "weight_xi" and self.word2vecFlag:
for a, b in zip(child.namedparams(), dst["weight_jy"].namedparams()):
b[1].data = a[1].data
print('Copy weight_jy')
if child.name not in dst.__dict__: continue
dst_child = dst[child.name]
if type(child) != type(dst_child): continue
if isinstance(child, link.Chain):
self.copy_model(child, dst_child)
if isinstance(child, link.Link):
match = True
for a, b in zip(child.namedparams(), dst_child.namedparams()):
if a[0] != b[0]:
match = False
break
if a[1].data.shape != b[1].data.shape:
match = False
break
if not match:
print('Ignore %s because of parameter mismatch' % child.name)
continue
for a, b in zip(child.namedparams(), dst_child.namedparams()):
b[1].data = a[1].data
print('Copy %s' % child.name)