def fluid_sequence_pad(input, pad_value, maxlen=None):
"""
args:
input: (batch*seq_len, dim)
returns:
(batch, max_seq_len, dim)
"""
pad_value = layers.cast(
fluid.layers.assign(input=np.array([pad_value], 'float32')),
input.dtype)
input_padded, _ = layers.sequence_pad(
input, pad_value, maxlen=maxlen) # (batch, max_seq_len, 1), (batch, 1)
# TODO, maxlen=300, used to solve issues: https://github.com/PaddlePaddle/Paddle/issues/14164
return input_padded
def recv_func(msg):
pad_value = L.assign(input=np.array([0.0], dtype=np.float32))
output, length = L.sequence_pad(msg, pad_value, maxlen=max_neigh)
mask = L.sequence_mask(length, dtype="float32", maxlen=max_neigh)
mask = L.unsqueeze(mask, [2])
input_mask = (L.matmul(mask, mask, transpose_y=True) - 1) * -10000
for layer in range(num_layers):
output = self_attention_and_residual(output,
hidden_size,
input_mask,
name="cross_feat_%s" % layer,
maxlen=max_neigh)
return L.reduce_sum(output * mask, 1) / L.reduce_sum(mask, 1)
def knowledge_seq2seq(config):
""" knowledge seq2seq """
emb_size = config.embed_size
hidden_size = config.hidden_size
input_size = emb_size
num_layers = config.num_layers
bi_direc = config.bidirectional
batch_size = config.batch_size
vocab_size = config.vocab_size
run_type = config.run_type
enc_input = layers.data(name="enc_input",
shape=[1],
dtype='int64',
lod_level=1) #enc_input --> goal
enc_mask = layers.data(name="enc_mask", shape=[-1, 1], dtype='float32')
goal_input = layers.data(name="goal_input",
shape=[1],
dtype='int64',
lod_level=1) #goal_input --> x
cue_input = layers.data(name="cue_input",
shape=[1],
dtype='int64',
lod_level=1) #cue_input --> kg
#cue_mask = layers.data(name='cue_mask', shape=[-1, 1], dtype='float32')
memory_mask = layers.data(name='memory_mask',
shape=[-1, 1],
dtype='float32')
tar_input = layers.data(name='tar_input',
shape=[1],
dtype='int64',
lod_level=1) #tar_input --> y
# tar_mask = layers.data(name="tar_mask", shape=[-1, 1], dtype='float32')
rnn_hidden_size = hidden_size
if bi_direc:
rnn_hidden_size //= 2
enc_out, enc_last_hidden = \
rnn_encoder(enc_input, vocab_size, input_size, rnn_hidden_size, batch_size, num_layers, bi_direc,
dropout=0.0, batch_first=True, name="rnn_enc")
goal_out, goal_last_hidden = \
rnn_encoder(goal_input, vocab_size, input_size, rnn_hidden_size, batch_size, num_layers, bi_direc,
dropout=0.0, batch_first=True, name="rnn_enc1")
context_goal_out = fluid.layers.concat(
input=[enc_last_hidden, goal_last_hidden], axis=2)
context_goal_out = layers.reshape(context_goal_out,
shape=[-1, 1, rnn_hidden_size * 4])
# context_goal_out = layers.squeeze(context_goal_out, axes=[1])
context_goal_out = fluid.layers.fc(context_goal_out,
size=rnn_hidden_size * 2,
bias_attr=False)
context_goal_out = layers.unsqueeze(context_goal_out, axes=[0])
bridge_out = fc(context_goal_out, hidden_size, hidden_size, name="bridge")
bridge_out = layers.tanh(bridge_out)
cue_last_mask = layers.data(name='cue_last_mask',
shape=[-1],
dtype='float32')
knowledge_out, knowledge_last_hidden = \
rnn_encoder(cue_input, vocab_size, input_size, rnn_hidden_size, batch_size, num_layers, bi_direc,
dropout=0.0, batch_first=True, last_mask=cue_last_mask, name="knowledge_enc")
query = layers.slice(bridge_out, axes=[0], starts=[0], ends=[1])
query = layers.squeeze(query, axes=[0])
query = layers.unsqueeze(query, axes=[1])
query = layers.reshape(query, shape=[batch_size, -1, hidden_size])
cue_memory = layers.slice(knowledge_last_hidden,
axes=[0],
starts=[0],
ends=[1])
cue_memory = layers.reshape(cue_memory,
shape=[batch_size, -1, hidden_size])
memory_mask = layers.reshape(memory_mask, shape=[batch_size, 1, -1])
weighted_cue, cue_att = dot_attention(query, cue_memory, mask=memory_mask)
cue_att = layers.reshape(cue_att, shape=[batch_size, -1])
knowledge = weighted_cue
if config.use_posterior:
print("config.use_posterior", config.use_posterior)
target_out, target_last_hidden = \
rnn_encoder(tar_input, vocab_size, input_size, rnn_hidden_size, batch_size, num_layers, bi_direc,
dropout=0.0, batch_first=True, name="knowledge_enc1")
target_goal_out = fluid.layers.concat(
input=[target_last_hidden, goal_last_hidden], axis=2)
target_goal_out = layers.reshape(target_goal_out,
shape=[-1, 1, rnn_hidden_size * 4])
# target_goal_out = layers.squeeze(target_goal_out, axes=[1])
target_goal_out = fluid.layers.fc(target_goal_out,
size=rnn_hidden_size * 2,
bias_attr=False)
target_goal_out = layers.unsqueeze(target_goal_out, axes=[0])
# get attenion
# target_query = layers.slice(target_last_hidden, axes=[0], starts=[0], ends=[1])
target_query = layers.slice(target_goal_out,
axes=[0],
starts=[0],
ends=[1])
target_query = layers.squeeze(target_query, axes=[0])
target_query = layers.unsqueeze(target_query, axes=[1])
target_query = layers.reshape(target_query,
shape=[batch_size, -1, hidden_size])
weight_target, target_att = dot_attention(target_query,
cue_memory,
mask=memory_mask)
target_att = layers.reshape(target_att, shape=[batch_size, -1])
# add to output
knowledge = weight_target
enc_memory_mask = layers.data(name="enc_memory_mask",
shape=[-1, 1],
dtype='float32')
enc_memory_mask = layers.unsqueeze(enc_memory_mask, axes=[1])
# decoder init_hidden, enc_memory, enc_mask
dec_init_hidden = bridge_out
pad_value = fluid.layers.assign(input=np.array([0.0], dtype='float32'))
enc_memory, origl_len_1 = layers.sequence_pad(x=enc_out,
pad_value=pad_value)
enc_memory.persistable = True
gru_unit = GRU_unit(input_size + hidden_size,
hidden_size,
num_layers=num_layers,
dropout=0.0,
name="decoder_gru_unit")
cue_gru_unit = GRU_unit(hidden_size + hidden_size,
hidden_size,
num_layers=num_layers,
dropout=0.0,
name="decoder_cue_gru_unit")
tgt_vocab_size = config.vocab_size
if run_type == "train":
if config.use_bow:
bow_logits = fc(knowledge,
hidden_size,
hidden_size,
name='bow_fc_1')
bow_logits = layers.tanh(bow_logits)
bow_logits = fc(bow_logits,
hidden_size,
tgt_vocab_size,
name='bow_fc_2')
bow_logits = layers.softmax(bow_logits)
bow_label = layers.data(name='bow_label',
shape=[-1, config.max_len],
dtype='int64')
bow_mask = layers.data(name="bow_mask",
shape=[-1, config.max_len],
dtype='float32')
bow_logits = layers.expand(bow_logits, [1, config.max_len, 1])
bow_logits = layers.reshape(bow_logits, shape=[-1, tgt_vocab_size])
bow_label = layers.reshape(bow_label, shape=[-1, 1])
bow_loss = layers.cross_entropy(bow_logits,
bow_label,
soft_label=False)
bow_loss = layers.reshape(bow_loss, shape=[-1, config.max_len])
bow_loss *= bow_mask
bow_loss = layers.reduce_sum(bow_loss, dim=[1])
bow_loss = layers.reduce_mean(bow_loss)
dec_input = layers.data(name="dec_input",
shape=[-1, 1, 1],
dtype='int64')
dec_mask = layers.data(name="dec_mask", shape=[-1, 1], dtype='float32')
dec_knowledge = weight_target
knowledge_goal_out = fluid.layers.concat(
input=[dec_knowledge, target_query], axis=2)
knowledge_goal_out = layers.reshape(knowledge_goal_out,
shape=[-1, 1, rnn_hidden_size * 4])
# knowledge_goal_out = layers.squeeze(knowledge_goal_out, axes=[1])
knowledge_goal_out = fluid.layers.fc(knowledge_goal_out,
size=rnn_hidden_size * 2,
bias_attr=False)
knowledge_goal_out = layers.unsqueeze(knowledge_goal_out, axes=[0])
decoder_logits = \
rnn_decoder(gru_unit, cue_gru_unit, dec_input, input_size, hidden_size, num_layers,
enc_memory, enc_memory_mask, dec_knowledge, vocab_size,
init_hidden=dec_init_hidden, mask=dec_mask, dropout=config.dropout)
target_label = layers.data(name='target_label',
shape=[-1, 1],
dtype='int64')
target_mask = layers.data(name='target_mask',
shape=[-1, 1],
dtype='float32')
decoder_logits = layers.reshape(decoder_logits,
shape=[-1, tgt_vocab_size])
target_label = layers.reshape(target_label, shape=[-1, 1])
nll_loss = layers.cross_entropy(decoder_logits,
target_label,
soft_label=False)
nll_loss = layers.reshape(nll_loss, shape=[batch_size, -1])
nll_loss *= target_mask
nll_loss = layers.reduce_sum(nll_loss, dim=[1])
nll_loss = layers.reduce_mean(nll_loss)
prior_attn = cue_att + 1e-10
posterior_att = target_att
posterior_att.stop_gradient = True
prior_attn = layers.log(prior_attn)
kl_loss = posterior_att * (layers.log(posterior_att + 1e-10) -
prior_attn)
kl_loss = layers.reduce_mean(kl_loss)
kl_and_nll_factor = layers.data(name='kl_and_nll_factor',
shape=[1],
dtype='float32')
kl_and_nll_factor = layers.reshape(kl_and_nll_factor, shape=[-1])
final_loss = bow_loss + kl_loss * kl_and_nll_factor + nll_loss * kl_and_nll_factor
return [bow_loss, kl_loss, nll_loss, final_loss]
elif run_type == "test":
beam_size = config.beam_size
batch_size = config.batch_size
token = layers.fill_constant(shape=[batch_size * beam_size, 1],
value=config.bos_id,
dtype='int64')
token = layers.reshape(token, shape=[-1, 1])
max_decode_len = config.max_dec_len
dec_knowledge = knowledge
INF = 100000000.0
init_score_np = np.ones([beam_size * batch_size],
dtype='float32') * -INF
for i in range(batch_size):
init_score_np[i * beam_size] = 0.0
pre_score = layers.assign(init_score_np)
pos_index_np = np.arange(batch_size).reshape(-1, 1)
pos_index_np = \
np.tile(pos_index_np, (1, beam_size)).reshape(-1).astype('int32') * beam_size
pos_index = layers.assign(pos_index_np)
id_array = []
score_array = []
index_array = []
init_enc_memory = layers.expand(enc_memory, [1, beam_size, 1])
init_enc_memory = layers.reshape(
init_enc_memory, shape=[batch_size * beam_size, -1, hidden_size])
init_enc_mask = layers.expand(enc_memory_mask, [1, beam_size, 1])
init_enc_mask = layers.reshape(init_enc_mask,
shape=[batch_size * beam_size, 1, -1])
dec_knowledge = layers.reshape(dec_knowledge,
shape=[-1, 1, hidden_size])
init_dec_knowledge = layers.expand(dec_knowledge, [1, beam_size, 1])
init_dec_knowledge = layers.reshape(
init_dec_knowledge,
shape=[batch_size * beam_size, -1, hidden_size])
dec_init_hidden = layers.expand(dec_init_hidden, [1, 1, beam_size])
dec_init_hidden = layers.reshape(dec_init_hidden,
shape=[1, -1, hidden_size])
length_average = config.length_average
UNK = config.unk_id
EOS = config.eos_id
for i in range(1, max_decode_len + 1):
dec_emb = get_embedding(token, input_size, vocab_size)
dec_out, dec_last_hidden = \
decoder_step(gru_unit, cue_gru_unit,
dec_emb, dec_init_hidden, input_size, hidden_size,
init_enc_memory, init_enc_mask, init_dec_knowledge, mask=None)
output_in_size = hidden_size + hidden_size
rnnout = layers.dropout(dec_out,
dropout_prob=config.dropout,
is_test=True)
rnnout = fc(rnnout,
output_in_size,
hidden_size,
name='dec_out_fc1')
rnnout = fc(rnnout, hidden_size, vocab_size, name='dec_out_fc2')
log_softmax_output = log_softmax(rnnout)
log_softmax_output = layers.squeeze(log_softmax_output, axes=[1])
if i > 1:
if length_average:
log_softmax_output = layers.elementwise_add(
(log_softmax_output / i),
(pre_score * (1.0 - 1.0 / i)),
axis=0)
else:
log_softmax_output = layers.elementwise_add(
log_softmax_output, pre_score, axis=0)
else:
log_softmax_output = layers.elementwise_add(log_softmax_output,
pre_score,
axis=0)
log_softmax_output = layers.reshape(log_softmax_output,
shape=[batch_size, -1])
topk_score, topk_index = layers.topk(log_softmax_output,
k=beam_size)
topk_score = layers.reshape(topk_score, shape=[-1])
topk_index = layers.reshape(topk_index, shape=[-1])
vocab_var = layers.fill_constant([1],
dtype='int64',
value=vocab_size)
new_token = topk_index % vocab_var
index = topk_index // vocab_var
id_array.append(new_token)
index_array.append(index)
index = index + pos_index
score_array.append(topk_score)
eos_ids = layers.fill_constant([beam_size * batch_size],
dtype='int64',
value=EOS)
unk_ids = layers.fill_constant([beam_size * batch_size],
dtype='int64',
value=UNK)
eos_eq = layers.cast(layers.equal(new_token, eos_ids),
dtype='float32')
topk_score += eos_eq * -100000000.0
unk_eq = layers.cast(layers.equal(new_token, unk_ids),
dtype='float32')
topk_score += unk_eq * -100000000.0
# update
token = new_token
pre_score = topk_score
token = layers.reshape(token, shape=[-1, 1])
index = layers.cast(index, dtype='int32')
dec_last_hidden = layers.squeeze(dec_last_hidden, axes=[0])
dec_init_hidden = layers.gather(dec_last_hidden, index=index)
dec_init_hidden = layers.unsqueeze(dec_init_hidden, axes=[0])
init_enc_memory = layers.gather(init_enc_memory, index)
init_enc_mask = layers.gather(init_enc_mask, index)
init_dec_knowledge = layers.gather(init_dec_knowledge, index)
final_score = layers.concat(score_array, axis=0)
final_ids = layers.concat(id_array, axis=0)
final_index = layers.concat(index_array, axis=0)
final_score = layers.reshape(
final_score, shape=[max_decode_len, beam_size * batch_size])
final_ids = layers.reshape(
final_ids, shape=[max_decode_len, beam_size * batch_size])
final_index = layers.reshape(
final_index, shape=[max_decode_len, beam_size * batch_size])
return final_score, final_ids, final_index
def __init__(self, embedding_dim, encoder_size, decoder_size,
source_dict_dim, target_dict_dim, tag_dict_dim, is_generating,
beam_size, max_length, source_entity_dim, source_pos_dim,
embedding_entity_dim, embedding_pos_dim, end_id):
# The encoding process. Encodes the input words into tensors.
self.encoder_size = encoder_size
self.decoder_size = decoder_size
self.embedding_dim = embedding_dim
self.source_dict_dim = target_dict_dim
self.is_generating = is_generating
self.source_dict_dim = source_dict_dim
self.target_dict_dim = target_dict_dim
self.tag_dict_dim = tag_dict_dim
self.max_length = max_length
self.end_id = end_id
self.beam_size = beam_size
self.no_grad_set = []
self.dropout_prob = 0.5
src_word_idx = fluid.layers.data(name='source_sequence',
shape=[1],
dtype='int64',
lod_level=1)
# print(src_word_idx.shape)
self.src_word_idx = src_word_idx
src_embedding = fluid.layers.embedding(
input=src_word_idx,
size=[source_dict_dim, embedding_dim],
dtype='float32',
param_attr=fluid.ParamAttr(name='emb'))
src_entity_idx = fluid.layers.data(name='source_entities',
shape=[1],
dtype='int64',
lod_level=1)
entity_embedding = fluid.layers.embedding(
input=src_entity_idx,
size=[source_entity_dim, embedding_entity_dim],
dtype='float32')
src_pos_idx = fluid.layers.data(name='source_pos',
shape=[1],
dtype='int64',
lod_level=1)
pos_embedding = fluid.layers.embedding(
input=src_pos_idx,
size=[source_pos_dim, embedding_pos_dim],
dtype='float32')
# print(src_embedding)
# print(entity_embedding)
# print(pos_embedding)
embeddings = fluid.layers.concat(
input=[src_embedding, entity_embedding, pos_embedding], axis=1)
# print(embeddings)
# if not is_generating:
# embeddings = fluid.layers.dropout(
# embeddings, dropout_prob=self.dropout_prob)
src_forward, src_reversed = self.bi_lstm_encoder(
input_seq=embeddings, gate_size=encoder_size)
encoded_vector = fluid.layers.concat(input=[src_forward, src_reversed],
axis=1)
pad_zero = pd.fill_constant(shape=[self.encoder_size * 2],
dtype='float32',
value=0)
encoded_vector_full, encoded_vector_length = pd.sequence_pad(
encoded_vector,
pad_zero,
maxlen=self.max_length,
name="copy_score_padding")
print(encoded_vector_full)
# if not is_generating:
# encoded_vector = fluid.layers.dropout(
# encoded_vector, dropout_prob=self.dropout_prob)
self.encoder_vec = encoded_vector
self.encoder_vec_full = encoded_vector_full
encoded_proj = fluid.layers.fc(input=encoded_vector,
size=decoder_size,
bias_attr=False)
self.encoder_proj = encoded_proj
backward_first = fluid.layers.sequence_pool(input=src_reversed,
pool_type='first')
decoder_boot = fluid.layers.fc(input=backward_first,
size=decoder_size,
bias_attr=False,
act='tanh')
cell_init = fluid.layers.fill_constant_batch_size_like(
input=decoder_boot,
value=1.0,
shape=[-1, decoder_size],
dtype='float32')
# cell_init.stop_gradient = False
cell_init.stop_gradient = True
# Create a RNN state cell by providing the input and hidden states, and
# specifies the hidden state as output.
# h = InitState(init=decoder_boot, need_reorder=True)
self.h = decoder_boot
self.c = cell_init
event_cla_id = fluid.layers.data(name='event_class',
shape=[1],
dtype='int64')
self.event_embedding = fluid.layers.embedding(
input=event_cla_id,
size=[self.tag_dict_dim, embedding_entity_dim],
dtype='float32')
# self.decoder_lstm = fluid.contrib.layers.BasicLSTMUnit(
# "decoder_lstm",
# self.decoder_size,
# fluid.ParamAttr(initializer=fluid.initializer.UniformInitializer(
# low=-0.1, high=0.1)),
# fluid.ParamAttr(initializer=fluid.initializer.Constant(0.0)), )
#####
# DECODER
#####
label = fluid.layers.data(name='label_sequence',
shape=[1],
dtype='int64',
lod_level=1)
if not is_generating:
rnn_out = self.train_decoder(decoder_boot)
predict_label = fluid.layers.argmax(x=rnn_out, axis=1)
# print(label.shape)
# print(rnn_out.shape)
# print(predict_label.shape)
cost = fluid.layers.cross_entropy(input=rnn_out, label=label)
avg_cost = fluid.layers.mean(x=cost)
self.predict = rnn_out
self.label = predict_label
self.avg_cost = avg_cost
feeding_list = [
"source_sequence", "source_entities", "source_pos",
"event_class", "source_index", "target_sequence",
"label_sequence"
]
# return avg_cost, feeding_list
self.feeding_list = feeding_list
else:
# rnn_out = self.train_decoder(decoder_boot)
# translation_ids = fluid.layers.argmax(x=rnn_out, axis=-1)
beam_search_out = self.decoder(decoder_boot)
translation_ids, translation_scores = beam_search_out
feeding_list = [
"source_sequence", "source_entities", "source_pos",
"event_class", "source_index", "label_sequence"
]
# feeding_list = ["source_sequence", "source_entities",
# "source_pos", "target_sequence"]
# feeding_list = ["source_sequence", "source_entities",
# "source_pos", "target_sequence", "label_sequence"]
# return translation_ids, translation_scores, feeding_list
self.translation_ids = translation_ids
self.translation_scores = translation_scores
self.feeding_list = feeding_list
self.no_grad_set = set(self.no_grad_set)
