def apply(self, is_train, inputs, mask=None):
inputs = tf.transpose(inputs, [1, 0, 2]) # to time first
with tf.variable_scope("forward"):
cell = LSTMBlockFusedCell(self.n_units, use_peephole=self.use_peepholes)
fw = cell(inputs, dtype=tf.float32, sequence_length=mask)[0]
with tf.variable_scope("backward"):
cell = LSTMBlockFusedCell(self.n_units, use_peephole=self.use_peepholes)
inputs = tf.reverse_sequence(inputs, mask, seq_axis=0, batch_axis=1)
bw = cell(inputs, dtype=tf.float32, sequence_length=mask)[0]
bw = tf.reverse_sequence(bw, mask, seq_axis=0, batch_axis=1)
out = tf.concat([fw, bw], axis=2)
out = tf.transpose(out, [1, 0, 2]) # back to batch first
return out
def build_fused_bidirectional_rnn(inputs,
num_units,
num_layers,
inputs_length,
input_keep_prob=1.0,
scope=None,
dtype=tf.float32):
''' The input of sequences should be time major
And the Dropout is independent per time. '''
assert num_layers > 0
with tf.variable_scope(scope or "bidirectional_rnn"):
inputs = flatten(inputs, 2) # [N * M, JX, d]
current_inputs = tf.transpose(
inputs, [1, 0, 2]) #[time_len, batch_size, input_size]
for layer_id in range(num_layers):
reverse_inputs = tf.reverse_sequence(current_inputs,
inputs_length,
batch_dim=1,
seq_dim=0)
fw_inputs = tf.nn.dropout(current_inputs, input_keep_prob)
bw_inputs = tf.nn.dropout(reverse_inputs, input_keep_prob)
fw_cell = LSTMBlockFusedCell(num_units, cell_clip=0)
bw_cell = LSTMBlockFusedCell(num_units, cell_clip=0)
fw_outputs, fw_final = fw_cell(fw_inputs,
dtype=dtype,
sequence_length=inputs_length,
scope="fw_" + str(layer_id))
bw_outputs, bw_final = bw_cell(bw_inputs,
dtype=dtype,
sequence_length=inputs_length,
scope="bw_" + str(layer_id))
bw_outputs = tf.reverse_sequence(bw_outputs,
inputs_length,
batch_dim=1,
seq_dim=0)
current_inputs = tf.concat((fw_outputs, bw_outputs), 2)
output = tf.transpose(current_inputs, [1, 0, 2])
output = tf.expand_dims(output, 1) # [N, M, JX, 2d]
final_state_c = tf.concat((fw_final[0], bw_final[0]),
1,
name=scope + '_final_c')
final_state_h = tf.concat((fw_final[1], bw_final[1]),
1,
name=scope + '_final_h')
final_state = LSTMStateTuple(
c=final_state_c, h=final_state_h) # ([N, 2 * d], [N, 2 * d])
return output, final_state
def __init__(self,
GPU,
num_layers,
num_units,
dropout=0.,
dtype=tf.dtypes.float32,
name=None):
'''
create a lstm adapter. equal to `LSTMBlockFusedCell` if GPU, else `CudnnLSTM`.
'''
base_layer.Layer.__init__(self, dtype=dtype, name=name)
self.GPU = GPU
self.dropout = dropout
if GPU:
self.model = CudnnLSTM(num_layers,
num_units,
dtype=self.dtype,
name=name)
else:
self.model = MultiFusedRNNCell([
LSTMBlockFusedCell(num_units,
dtype=self.dtype,
name='%s_%d' % (name, i))
for i in range(num_layers)
])
def fused_lstm_module(input, name, train, units, recomp=False):
"""
tensorflow LSTM implementation - for inference only
:param input: input tensor
:param name: name for variable scope etc.
:param train: is_train placeholder
:param units: number of lstm units
:param recomp: recompute_gradient environment
:return: output tensor after LSTM
"""
# only for inference due to forget_bias=0.0
# CUDNN forget bias is trained without offset, but initialized to 1
# fused forget bias trained with offset 1, but initialized to 0
# fix: initialize fused forget bias to 1 and use forget_bias=0 to have compatibility with cudnn lstm
should_learn = tf.logical_and(train, tf.logical_not(recomp))
# manually set name to reflect cudnn weight names
# forget bias = 0 needed as otherwise 1 added to already learned bias
lstm = LSTMBlockFusedCell(
num_units=units,
forget_bias=0.0,
name=name + "/rnn/multi_rnn_cell/cell_0/cudnn_compatible_lstm_cell")
# lstm swaps batch and time dimension
input = tf.transpose(input, perm=[1, 0, 2])
input, c = lstm(input, dtype=tf.float32)
# swap back lstm batch and time dimension
input = tf.transpose(input, perm=[1, 0, 2])
return input
def lstm(self, inputs, batch_size, num_units, swap_axes=True):
if swap_axes:
inputs = tf.transpose(inputs, [1, 0, 2])
cell = LSTMBlockFusedCell(num_units)
self.rnn_state = self.zero_state(batch_size, num_units)
outputs, new_rnn_state = cell(inputs=inputs,
initial_state=self.rnn_state,
dtype=tf.float32)
if swap_axes:
outputs = tf.transpose(outputs, [1, 0, 2])
return outputs, new_rnn_state
def apply(self, is_train, x, mask=None):
x = tf.transpose(x, [1, 0, 2]) # to time first
state = LSTMBlockFusedCell(self.n_units)(x, dtype=tf.float32, sequence_length=mask)[1]
if self.state and self.hidden:
state = tf.concat(state, 1)
elif self.hidden:
state = state.h
elif self.state:
state = state.c
else:
raise ValueError()
return state
def get_lstm_outputs(self, chars, last_state=None, reuse=False):
with tf.variable_scope('char_embedding', reuse=reuse):
self.char_embedding = tf.get_variable('char_embedding', initializer=tf.orthogonal_initializer()(
(self.NUM_CHARS, self.CHAR_EMBEDDING_SIZE)), dtype=tf.float32)
out = tf.nn.embedding_lookup(self.char_embedding, chars)
with tf.variable_scope('spam_gen_rnn', reuse=reuse):
next_state = []
for layer in xrange(self.LAYERS):
with tf.variable_scope('lstm_layer_%d' % layer, initializer=tf.orthogonal_initializer()) as scope:
out, next_state_part = LSTMBlockFusedCell(self.HIDDEN_LAYER_SIZE)(out,
last_state[layer] if last_state is not None else None,
dtype=tf.float32,
scope=scope)
out = tf.nn.relu(out) # ???? already applied by LSTMBlockFusedCell?
next_state.append(next_state_part)
return out, next_state
def _init(self):
ExtractionQAModel._init(self)
if self._composition == "GRU":
if self._layer_norm:
rnn_constructor = lambda size: FusedRNNCellAdaptor(LayerNormGRUCell(size), use_dynamic_rnn=True)
else:
rnn_constructor = lambda size: FusedRNNCellAdaptor(GRUBlockCell(size), use_dynamic_rnn=True)
elif self._composition == "RNN":
rnn_constructor = lambda size: FusedRNNCellAdaptor(BasicRNNCell(size), use_dynamic_rnn=True)
else:
if self._layer_norm:
rnn_constructor = lambda size: FusedRNNCellAdaptor(LayerNormLSTMCell(size), use_dynamic_rnn=True)
else:
rnn_constructor = lambda size: LSTMBlockFusedCell(size)
with tf.device(self._device0):
self._eval = tf.get_variable("is_eval", initializer=False, trainable=False)
self._set_train = self._eval.initializer
self._set_eval = self._eval.assign(True)
self.context_mask = tfutil.mask_for_lengths(self.context_length, self._batch_size, self.embedder.max_length)
question_binary_mask = tfutil.mask_for_lengths(self.question_length,
self.question_embedder.batch_size,
self.question_embedder.max_length,
value=1.0,
mask_right=False)
with tf.variable_scope("preprocessing_layer"):
question_binary_mask = tf.gather(question_binary_mask, self.context_partition)
self._embedded_question_not_dropped = tf.gather(self._embedded_question_not_dropped, self.context_partition)
# context
if self._with_features:
mask = tf.get_variable("attention_mask", [1, 1, self._embedded_question_not_dropped.get_shape()[-1].value],
initializer=tf.constant_initializer(1.0))
# compute word wise features
#masked_question = self.question_embedder.output * mask
# [B, Q, L]
q2c_scores = tf.matmul(self._embedded_question_not_dropped * mask,
self._embedded_context_not_dropped, adjoint_b=True)
q2c_scores = q2c_scores + tf.expand_dims(self.context_mask, 1)
#c2q_weights = tf.reduce_max(q2c_scores / (tf.reduce_max(q2c_scores, [2], keep_dims=True) + 1e-5), [1])
q2c_weights = tf.reduce_sum(tf.nn.softmax(q2c_scores) * \
tf.expand_dims(question_binary_mask, 2), [1])
# [B, L , 1]
self.context_features = tf.concat(axis=2, values=[tf.expand_dims(self._word_in_question, 2),
#tf.expand_dims(c2q_weights, 2),
tf.expand_dims(q2c_weights, 2)])
embedded_ctxt = tf.concat(axis=2, values=[self.embedded_context, self.context_features])
in_question_feature = tf.ones(tf.stack([self.question_embedder.batch_size,
self.question_embedder.max_length, 2]))
embedded_question = tf.concat(axis=2, values=[self.embedded_question, in_question_feature])
else:
embedded_ctxt = self.embedded_context
embedded_question = self.embedded_question
if self._with_question_type_features:
# Need to add another zero vector so that the total number
# of features is even, for LSTM performance reasons.
question_type_features = tf.stack([self._is_factoid,
self._is_list,
self._is_yesno,
tf.zeros(tf.shape(self._is_list),
dtype=tf.bool)],
axis=1)
question_type_features = tf.cast(question_type_features, tf.float32)
question_type_features = tf.expand_dims(question_type_features, 1)
embedded_question = tf.concat(axis=2, values=[embedded_question,
tf.tile(question_type_features,
tf.stack([1, tf.shape(embedded_question)[1], 1]))])
question_type_features = tf.gather(question_type_features, self.context_partition)
embedded_ctxt = tf.concat(axis=2, values=[embedded_ctxt,
tf.tile(question_type_features,
tf.stack([1, tf.shape(embedded_ctxt)[1], 1]))])
if self._with_entity_tag_features:
embedded_question = tf.concat(axis=2, values=[embedded_question,
tf.cast(self._question_tags, tf.float32)])
embedded_ctxt = tf.concat(axis=2, values=[embedded_ctxt,
tf.cast(self._context_tags, tf.float32)])
self.encoded_question = self._preprocessing_layer(rnn_constructor, embedded_question,
self.question_length, projection_scope="question_proj")
self.encoded_ctxt = self._preprocessing_layer(rnn_constructor, embedded_ctxt, self.context_length,
share_rnn=True, projection_scope="context_proj",
num_fusion_layers=self._num_intrafusion_layers)
# single time attention over question
attention_scores = tf.contrib.layers.fully_connected(self.encoded_question, 1,
activation_fn=None,
weights_initializer=None,
biases_initializer=None,
scope="attention")
attention_scores = attention_scores + tf.expand_dims(
tfutil.mask_for_lengths(self.question_length, self.question_embedder.batch_size,
self.question_embedder.max_length), 2)
attention_weights = tf.nn.softmax(attention_scores, 1)
self.question_attention_weights = attention_weights
self.question_representation = tf.reduce_sum(attention_weights * self.encoded_question, [1])
# Multiply question features for each paragraph
self.encoded_question = tf.gather(self.encoded_question, self.context_partition)
self.question_representation_per_context = tf.gather(self.question_representation, self.context_partition)
self.question_length = tf.gather(self.question_length, self.context_partition)
if self._with_inter_fusion:
with tf.variable_scope("inter_fusion"):
with tf.variable_scope("associative") as vs:
mask = tf.get_variable("attention_mask", [1, 1, self.size], initializer=tf.constant_initializer(1.0))
mask = tf.nn.relu(mask)
for i in range(1):
# [B, Q, L]
inter_scores = tf.matmul(self.encoded_question * mask, self.encoded_ctxt, adjoint_b=True)
inter_scores = inter_scores + tf.expand_dims(self.context_mask, 1)
inter_weights = tf.nn.softmax(inter_scores)
inter_weights = inter_weights * tf.expand_dims(question_binary_mask, 2)
# [B, L, Q] x [B, Q, S] -> [B, L, S]
co_states = tf.matmul(inter_weights, self.encoded_question, adj_x=True)
u = tf.contrib.layers.fully_connected(tf.concat(axis=2, values=[self.encoded_ctxt, co_states]), self.size,
activation_fn=tf.sigmoid,
biases_initializer=tf.constant_initializer(1.0),
scope="update_gate")
self.encoded_ctxt = u * self.encoded_ctxt + (1.0 - u) * co_states
vs.reuse_variables()
with tf.variable_scope("recurrent") as vs:
self.encoded_ctxt.set_shape([None, None, self.size])
self.encoded_ctxt = dynamic_rnn(GatedAggregationRNNCell(self.size),
tf.reverse_sequence(self.encoded_ctxt, self.context_length, 1),
self.context_length,
dtype=tf.float32, time_major=False, scope="backward")[0]
self.encoded_ctxt = dynamic_rnn(GatedAggregationRNNCell(self.size),
tf.reverse_sequence(self.encoded_ctxt, self.context_length, 1),
self.context_length,
dtype=tf.float32, time_major=False, scope="forward")[0]
# No matching layer, so set matched_output to encoded_ctxt (for compatibility)
self.matched_output = self.encoded_ctxt
with tf.variable_scope("pointer_layer"):
self.predicted_context_indices, \
self._start_scores, self._start_pointer, self.start_probs, \
self._end_scores, self._end_pointer, self.end_probs = \
self._spn_answer_layer(self.question_representation_per_context, self.encoded_ctxt)
self.yesno_added = False
if self._with_yesno:
self.add_yesno(add_model_scope=False)
self._train_variables = [p for p in tf.trainable_variables() if self.name in p.name]
def _build_forward(self):
config = self.config
N, M, JX, JQ, VW, VC, d, W = \
config.batch_size, config.max_num_sents, config.max_sent_size, \
config.max_ques_size, config.word_vocab_size, config.char_vocab_size, config.hidden_size, \
config.max_word_size
print("VW:", VW, "N:", N, "M:", M, "JX:", JX, "JQ:", JQ)
JA = config.max_answer_length
JX = tf.shape(self.x)[2]
JQ = tf.shape(self.q)[1]
M = tf.shape(self.x)[1]
print("VW:", VW, "N:", N, "M:", M, "JX:", JX, "JQ:", JQ)
dc, dw, dco = config.char_emb_size, config.word_emb_size, config.char_out_size
with tf.variable_scope("emb"):
# Char-CNN Embedding
if config.use_char_emb:
with tf.variable_scope("emb_var"), tf.device("/cpu:0"):
char_emb_mat = tf.get_variable("char_emb_mat",
shape=[VC, dc],
dtype='float')
with tf.variable_scope("char"):
Acx = tf.nn.embedding_lookup(char_emb_mat,
self.cx) # [N, M, JX, W, dc]
Acq = tf.nn.embedding_lookup(char_emb_mat,
self.cq) # [N, JQ, W, dc]
Acx = tf.reshape(Acx, [-1, JX, W, dc])
Acq = tf.reshape(Acq, [-1, JQ, W, dc])
filter_sizes = list(
map(int, config.out_channel_dims.split(',')))
heights = list(map(int, config.filter_heights.split(',')))
assert sum(filter_sizes) == dco, (filter_sizes, dco)
with tf.variable_scope("conv"):
xx = multi_conv1d(Acx,
filter_sizes,
heights,
"VALID",
self.is_train,
config.keep_prob,
scope="xx")
if config.share_cnn_weights:
tf.get_variable_scope().reuse_variables()
qq = multi_conv1d(Acq,
filter_sizes,
heights,
"VALID",
self.is_train,
config.keep_prob,
scope="xx")
else:
qq = multi_conv1d(Acq,
filter_sizes,
heights,
"VALID",
self.is_train,
config.keep_prob,
scope="qq")
xx = tf.reshape(xx, [-1, M, JX, dco])
qq = tf.reshape(qq, [-1, JQ, dco])
# Word Embedding
if config.use_word_emb:
with tf.variable_scope("emb_var") as scope, tf.device(
"/cpu:0"):
if config.mode == 'train':
word_emb_mat = tf.get_variable(
"word_emb_mat",
dtype='float',
shape=[VW, dw],
initializer=get_initializer(config.emb_mat))
else:
word_emb_mat = tf.get_variable("word_emb_mat",
shape=[VW, dw],
dtype='float')
tf.get_variable_scope().reuse_variables()
self.word_emb_scope = scope
if config.use_glove_for_unk:
word_emb_mat = tf.concat(
[word_emb_mat, self.new_emb_mat], 0)
with tf.name_scope("word"):
Ax = tf.nn.embedding_lookup(word_emb_mat,
self.x) # [N, M, JX, d]
Aq = tf.nn.embedding_lookup(word_emb_mat,
self.q) # [N, JQ, d]
self.tensor_dict['x'] = Ax
self.tensor_dict['q'] = Aq
# Concat Char-CNN Embedding and Word Embedding
if config.use_char_emb:
xx = tf.concat([xx, Ax], 3) # [N, M, JX, di]
qq = tf.concat([qq, Aq], 2) # [N, JQ, di]
else:
xx = Ax
qq = Aq
# exact match
if config.use_exact_match:
emx = tf.expand_dims(tf.cast(self.emx, tf.float32), -1)
xx = tf.concat([xx, emx], 3) # [N, M, JX, di+1]
emq = tf.expand_dims(tf.cast(self.emq, tf.float32), -1)
qq = tf.concat([qq, emq], 2) # [N, JQ, di+1]
# 2 layer highway network on Concat Embedding
if config.highway:
with tf.variable_scope("highway"):
xx = highway_network(xx,
config.highway_num_layers,
True,
wd=config.wd,
is_train=self.is_train)
tf.get_variable_scope().reuse_variables()
qq = highway_network(qq,
config.highway_num_layers,
True,
wd=config.wd,
is_train=self.is_train)
self.tensor_dict['xx'] = xx
self.tensor_dict['qq'] = qq
# Bidirection-LSTM (3rd layer on paper)
cell = GRUCell(d) if config.GRU else BasicLSTMCell(d,
state_is_tuple=True)
d_cell = SwitchableDropoutWrapper(
cell, self.is_train, input_keep_prob=config.input_keep_prob)
x_len = tf.reduce_sum(tf.cast(self.x_mask, 'int32'), 2) # [N, M]
q_len = tf.reduce_sum(tf.cast(self.q_mask, 'int32'), 1) # [N]
flat_x_len = flatten(x_len, 0) # [N * M]
with tf.variable_scope("prepro"):
if config.use_fused_lstm:
with tf.variable_scope("u1"):
fw_inputs = tf.transpose(
qq, [1, 0, 2]) #[time_len, batch_size, input_size]
bw_inputs = tf.reverse_sequence(fw_inputs,
q_len,
batch_dim=1,
seq_dim=0)
fw_inputs = tf.nn.dropout(fw_inputs,
config.input_keep_prob)
bw_inputs = tf.nn.dropout(bw_inputs,
config.input_keep_prob)
prep_fw_cell = LSTMBlockFusedCell(d, cell_clip=0)
prep_bw_cell = LSTMBlockFusedCell(d, cell_clip=0)
fw_outputs, fw_final = prep_fw_cell(fw_inputs,
dtype=tf.float32,
sequence_length=q_len,
scope="fw")
bw_outputs, bw_final = prep_bw_cell(bw_inputs,
dtype=tf.float32,
sequence_length=q_len,
scope="bw")
bw_outputs = tf.reverse_sequence(bw_outputs,
q_len,
batch_dim=1,
seq_dim=0)
current_inputs = tf.concat((fw_outputs, bw_outputs), 2)
output = tf.transpose(current_inputs, [1, 0, 2])
u = output
flat_xx = flatten(xx, 2) # [N * M, JX, d]
if config.share_lstm_weights:
tf.get_variable_scope().reuse_variables()
with tf.variable_scope("u1"):
fw_inputs = tf.transpose(
flat_xx,
[1, 0, 2]) #[time_len, batch_size, input_size]
bw_inputs = tf.reverse_sequence(fw_inputs,
flat_x_len,
batch_dim=1,
seq_dim=0)
# fw_inputs = tf.nn.dropout(fw_inputs, config.input_keep_prob)
# bw_inputs = tf.nn.dropout(bw_inputs, config.input_keep_prob)
fw_outputs, fw_final = prep_fw_cell(
fw_inputs,
dtype=tf.float32,
sequence_length=flat_x_len,
scope="fw")
bw_outputs, bw_final = prep_bw_cell(
bw_inputs,
dtype=tf.float32,
sequence_length=flat_x_len,
scope="bw")
bw_outputs = tf.reverse_sequence(bw_outputs,
flat_x_len,
batch_dim=1,
seq_dim=0)
current_inputs = tf.concat((fw_outputs, bw_outputs), 2)
output = tf.transpose(current_inputs, [1, 0, 2])
else:
with tf.variable_scope("h1"):
fw_inputs = tf.transpose(
flat_xx,
[1, 0, 2]) #[time_len, batch_size, input_size]
bw_inputs = tf.reverse_sequence(fw_inputs,
flat_x_len,
batch_dim=1,
seq_dim=0)
# fw_inputs = tf.nn.dropout(fw_inputs, config.input_keep_prob)
# bw_inputs = tf.nn.dropout(bw_inputs, config.input_keep_prob)
prep_fw_cell = LSTMBlockFusedCell(d, cell_clip=0)
prep_bw_cell = LSTMBlockFusedCell(d, cell_clip=0)
fw_outputs, fw_final = prep_fw_cell(
fw_inputs,
dtype=tf.float32,
sequence_length=flat_x_len,
scope="fw")
bw_outputs, bw_final = prep_bw_cell(
bw_inputs,
dtype=tf.float32,
sequence_length=flat_x_len,
scope="bw")
bw_outputs = tf.reverse_sequence(bw_outputs,
flat_x_len,
batch_dim=1,
seq_dim=0)
current_inputs = tf.concat((fw_outputs, bw_outputs), 2)
output = tf.transpose(current_inputs, [1, 0, 2])
h = tf.expand_dims(output, 1) # [N, M, JX, 2d]
else:
(fw_u, bw_u), _ = bidirectional_dynamic_rnn(
d_cell, d_cell, qq, q_len, dtype='float',
scope='u1') # [N, J, d], [N, d]
u = tf.concat([fw_u, bw_u], 2)
if config.share_lstm_weights:
tf.get_variable_scope().reuse_variables()
(fw_h, bw_h), _ = bidirectional_dynamic_rnn(
cell, cell, xx, x_len, dtype='float',
scope='u1') # [N, M, JX, 2d]
h = tf.concat([fw_h, bw_h], 3) # [N, M, JX, 2d]
else:
(fw_h, bw_h), _ = bidirectional_dynamic_rnn(
cell, cell, xx, x_len, dtype='float',
scope='h1') # [N, M, JX, 2d]
h = tf.concat([fw_h, bw_h], 3) # [N, M, JX, 2d]
self.tensor_dict['u'] = u
self.tensor_dict['h'] = h
# Attention Flow Layer (4th layer on paper)
with tf.variable_scope("main"):
if config.dynamic_att:
p0 = h
u = tf.reshape(tf.tile(tf.expand_dims(u, 1), [1, M, 1, 1]),
[N * M, JQ, 2 * d])
q_mask = tf.reshape(
tf.tile(tf.expand_dims(self.q_mask, 1), [1, M, 1]),
[N * M, JQ])
first_cell = AttentionCell(
cell,
u,
size=d,
mask=q_mask,
mapper='sim',
input_keep_prob=self.config.input_keep_prob,
is_train=self.is_train)
else:
p0 = attention_layer(config,
self.is_train,
h,
u,
h_mask=self.x_mask,
u_mask=self.q_mask,
scope="p0",
tensor_dict=self.tensor_dict)
first_cell = d_cell
tp0 = p0
# Modeling layer (5th layer on paper)
with tf.variable_scope('modeling_layer'):
if config.use_fused_lstm:
g1, encoder_state_final = build_fused_bidirectional_rnn(
inputs=p0,
num_units=config.hidden_size,
num_layers=config.num_modeling_layers,
inputs_length=flat_x_len,
input_keep_prob=config.input_keep_prob,
scope='modeling_layer_g')
else:
for layer_idx in range(config.num_modeling_layers - 1):
(fw_g0, bw_g0), _ = bidirectional_dynamic_rnn(
first_cell,
first_cell,
p0,
x_len,
dtype='float',
scope="g_{}".format(layer_idx)) # [N, M, JX, 2d]
p0 = tf.concat([fw_g0, bw_g0], 3)
(fw_g1, bw_g1), (fw_s_f, bw_s_f) = bidirectional_dynamic_rnn(
first_cell,
first_cell,
p0,
x_len,
dtype='float',
scope='g1') # [N, M, JX, 2d]
g1 = tf.concat([fw_g1, bw_g1], 3) # [N, M, JX, 2d]
# Self match layer
if config.use_self_match:
s0 = tf.reshape(g1, [N * M, JX, 2 * d]) # [N * M, JX, 2d]
x_mask = tf.reshape(self.x_mask, [N * M, JX]) # [N * M, JX]
if config.use_static_self_match:
with tf.variable_scope(
"StaticSelfMatch"
): # implemented follow r-net section 3.3
W_x_Vj = tf.contrib.layers.fully_connected( # [N * M, JX, d]
s0,
int(d / 2),
scope='row_first',
activation_fn=None,
biases_initializer=None)
W_x_Vt = tf.contrib.layers.fully_connected( # [N * M, JX, d]
s0,
int(d / 2),
scope='col_first',
activation_fn=None,
biases_initializer=None)
sum_rc = tf.add( # [N * M, JX, JX, d]
tf.expand_dims(W_x_Vj, 1), tf.expand_dims(W_x_Vt, 2))
v = tf.get_variable('second',
shape=[1, 1, 1, int(d / 2)],
dtype=tf.float32)
Sj = tf.reduce_sum(tf.multiply(v, tf.tanh(sum_rc)),
-1) # [N * M, JX, JX]
Ai = softmax(Sj, mask=tf.expand_dims(x_mask,
1)) # [N * M, JX, JX]
Ai = tf.expand_dims(Ai, -1) # [N * M, JX, JX, 1]
Vi = tf.expand_dims(s0, 1) # [N * M, 1, JX, 2d]
Ct = tf.reduce_sum( # [N * M, JX, 2d]
tf.multiply(Ai, Vi), axis=2)
inputs_Vt_Ct = tf.concat([s0, Ct], 2) # [N * M, JX, 4d]
if config.use_fused_lstm:
fw_inputs = tf.transpose(
inputs_Vt_Ct,
[1, 0, 2]) # [time_len, batch_size, input_size]
bw_inputs = tf.reverse_sequence(fw_inputs,
flat_x_len,
batch_dim=1,
seq_dim=0)
fw_inputs = tf.nn.dropout(fw_inputs,
config.input_keep_prob)
bw_inputs = tf.nn.dropout(bw_inputs,
config.input_keep_prob)
prep_fw_cell = LSTMBlockFusedCell(d, cell_clip=0)
prep_bw_cell = LSTMBlockFusedCell(d, cell_clip=0)
fw_outputs, fw_s_f = prep_fw_cell(
fw_inputs,
dtype=tf.float32,
sequence_length=flat_x_len,
scope="fw")
bw_outputs, bw_s_f = prep_bw_cell(
bw_inputs,
dtype=tf.float32,
sequence_length=flat_x_len,
scope="bw")
fw_s_f = LSTMStateTuple(c=fw_s_f[0], h=fw_s_f[1])
bw_s_f = LSTMStateTuple(c=bw_s_f[0], h=bw_s_f[1])
bw_outputs = tf.reverse_sequence(bw_outputs,
flat_x_len,
batch_dim=1,
seq_dim=0)
current_inputs = tf.concat((fw_outputs, bw_outputs), 2)
s1 = tf.transpose(current_inputs, [1, 0, 2])
else:
(fw_s, bw_s), (fw_s_f,
bw_s_f) = bidirectional_dynamic_rnn(
first_cell,
first_cell,
inputs_Vt_Ct,
flat_x_len,
dtype='float',
scope='s') # [N, M, JX, 2d]
s1 = tf.concat([fw_s, bw_s],
2) # [N * M, JX, 2d], M == 1
else:
with tf.variable_scope("DynamicSelfMatch"):
first_cell = AttentionCell(cell,
s0,
size=d,
mask=x_mask,
is_train=self.is_train)
(fw_s, bw_s), (fw_s_f, bw_s_f) = bidirectional_dynamic_rnn(
first_cell,
first_cell,
s0,
x_len,
dtype='float',
scope='s') # [N, M, JX, 2d]
s1 = tf.concat([fw_s, bw_s], 2) # [N * M, JX, 2d], M == 1
g1 = tf.expand_dims(s1, 1) # [N, M, JX, 2d]
# prepare for PtrNet
encoder_output = g1 # [N, M, JX, 2d]
encoder_output = tf.expand_dims(tf.cast(self.x_mask, tf.float32),
-1) * encoder_output # [N, M, JX, 2d]
if config.use_self_match or not config.use_fused_lstm:
if config.GRU:
encoder_state_final = tf.concat((fw_s_f, bw_s_f),
1,
name='encoder_concat')
else:
if isinstance(fw_s_f, LSTMStateTuple):
encoder_state_c = tf.concat((fw_s_f.c, bw_s_f.c),
1,
name='encoder_concat_c')
encoder_state_h = tf.concat((fw_s_f.h, bw_s_f.h),
1,
name='encoder_concat_h')
encoder_state_final = LSTMStateTuple(c=encoder_state_c,
h=encoder_state_h)
elif isinstance(fw_s_f, tf.Tensor):
encoder_state_final = tf.concat((fw_s_f, bw_s_f),
1,
name='encoder_concat')
else:
encoder_state_final = None
tf.logging.error("encoder_state_final not set")
print("encoder_state_final:", encoder_state_final)
with tf.variable_scope("output"):
# eos_symbol = config.eos_symbol
# next_symbol = config.next_symbol
tf.assert_equal(
M,
1) # currently dynamic M is not supported, thus we assume M==1
answer_string = tf.placeholder(
shape=(N, 1, JA + 1), dtype=tf.int32,
name='answer_string') # [N, M, JA + 1]
answer_string_mask = tf.placeholder(
shape=(N, 1, JA + 1), dtype=tf.bool,
name='answer_string_mask') # [N, M, JA + 1]
answer_string_length = tf.placeholder(
shape=(N, 1),
dtype=tf.int32,
name='answer_string_length',
) # [N, M]
self.tensor_dict['answer_string'] = answer_string
self.tensor_dict['answer_string_mask'] = answer_string_mask
self.tensor_dict['answer_string_length'] = answer_string_length
self.answer_string = answer_string
self.answer_string_mask = answer_string_mask
self.answer_string_length = answer_string_length
answer_string_flattened = tf.reshape(answer_string,
[N * M, JA + 1])
self.answer_string_flattened = answer_string_flattened # [N * M, JA+1]
print("answer_string_flattened:", answer_string_flattened)
answer_string_length_flattened = tf.reshape(
answer_string_length, [N * M])
self.answer_string_length_flattened = answer_string_length_flattened # [N * M]
print("answer_string_length_flattened:",
answer_string_length_flattened)
decoder_cell = GRUCell(2 * d) if config.GRU else BasicLSTMCell(
2 * d, state_is_tuple=True)
with tf.variable_scope("Decoder"):
decoder_train_logits = ptr_decoder(
decoder_cell,
tf.reshape(tp0, [N * M, JX, 2 * d]), # [N * M, JX, 2d]
tf.reshape(encoder_output,
[N * M, JX, 2 * d]), # [N * M, JX, 2d]
flat_x_len,
encoder_final_state=encoder_state_final,
max_encoder_length=config.sent_size_th,
decoder_output_length=
answer_string_length_flattened, # [N * M]
batch_size=N, # N * M (M=1)
attention_proj_dim=self.config.decoder_proj_dim,
scope='ptr_decoder'
) # [batch_size, dec_len*, enc_seq_len + 1]
self.decoder_train_logits = decoder_train_logits
print("decoder_train_logits:", decoder_train_logits)
self.decoder_train_softmax = tf.nn.softmax(
self.decoder_train_logits)
self.decoder_inference = tf.argmax(
decoder_train_logits, axis=2,
name='decoder_inference') # [N, JA + 1]
self.yp = tf.ones([N, M, JX], dtype=tf.int32) * -1
self.yp2 = tf.ones([N, M, JX], dtype=tf.int32) * -1