def _build_decoder_cell(self):
# no beam
encoder_outputs = self.encoder_outputs
encoder_last_state = self.encoder_last_state
encoder_inputs_length = self.encoder_inputs_length
def attn_decoder_input_fn(inputs, attention):
if not self.attn_input_feeding:
return inputs
_input_layer = Dense(self.hidden_units, dtype=self.dtype, name="attn_input_feeding")
return _input_layer(array_ops.concat([inputs, attention], -1))
# attention mechanism 'luong'
with tf.variable_scope('shared_attention_mechanism'):
self.attention_mechanism = attention_wrapper.LuongAttention(num_units=self.hidden_units, \
memory=encoder_outputs, memory_sequence_length=encoder_inputs_length)
# build decoder cell
self.init_decoder_cell_list = [self._build_single_cell() for i in range(self.depth)]
decoder_initial_state = encoder_last_state
self.decoder_cell_list = self.init_decoder_cell_list[:-1] + [attention_wrapper.AttentionWrapper(\
cell = self.init_decoder_cell_list[-1], \
attention_mechanism=self.attention_mechanism,\
attention_layer_size=self.hidden_units,\
cell_input_fn=attn_decoder_input_fn,\
initial_cell_state=encoder_last_state[-1],\
alignment_history=False)]
batch_size = self.batch_size
initial_state = [state for state in encoder_last_state]
initial_state[-1] = self.decoder_cell_list[-1].zero_state(batch_size=batch_size, dtype=self.dtype)
decoder_initial_state = tuple(initial_state)
# beam
beam_encoder_outputs = seq2seq.tile_batch(self.encoder_outputs, multiplier=self.beam_width)
beam_encoder_last_state = nest.map_structure(lambda s: seq2seq.tile_batch(s, self.beam_width), self.encoder_last_state)
beam_encoder_inputs_length = seq2seq.tile_batch(self.encoder_inputs_length, multiplier=self.beam_width)
with tf.variable_scope('shared_attention_mechanism', reuse=True):
self.beam_attention_mechanism = attention_wrapper.LuongAttention(num_units=self.hidden_units, \
memory=beam_encoder_outputs, \
memory_sequence_length=beam_encoder_inputs_length)
beam_decoder_initial_state = beam_encoder_last_state
self.beam_decoder_cell_list = self.init_decoder_cell_list[:-1] + [attention_wrapper.AttentionWrapper(\
cell = self.init_decoder_cell_list[-1], \
attention_mechanism=self.beam_attention_mechanism,\
attention_layer_size=self.hidden_units,\
cell_input_fn=attn_decoder_input_fn,\
initial_cell_state=beam_encoder_last_state[-1],\
alignment_history=False)]
beam_batch_size = self.batch_size * self.beam_width
beam_initial_state = [state for state in beam_encoder_last_state]
beam_initial_state[-1] = self.beam_decoder_cell_list[-1].zero_state(batch_size=beam_batch_size, dtype=self.dtype)
beam_decoder_initial_state = tuple(beam_initial_state)
return MultiRNNCell(self.decoder_cell_list), decoder_initial_state, \
MultiRNNCell(self.beam_decoder_cell_list), beam_decoder_initial_state
def testLuongScaledDType(self):
# Test case for GitHub issue 18099
for dtype in [np.float16, np.float32, np.float64]:
num_units = 128
encoder_outputs = array_ops.placeholder(dtype, shape=[64, None, 256])
encoder_sequence_length = array_ops.placeholder(dtypes.int32, shape=[64])
decoder_inputs = array_ops.placeholder(dtype, shape=[64, None, 128])
decoder_sequence_length = array_ops.placeholder(dtypes.int32, shape=[64])
batch_size = 64
attention_mechanism = wrapper.LuongAttention(
num_units=num_units,
memory=encoder_outputs,
memory_sequence_length=encoder_sequence_length,
scale=True,
dtype=dtype,
)
cell = rnn_cell.LSTMCell(num_units)
cell = wrapper.AttentionWrapper(cell, attention_mechanism)
helper = helper_py.TrainingHelper(decoder_inputs,
decoder_sequence_length)
my_decoder = basic_decoder.BasicDecoder(
cell=cell,
helper=helper,
initial_state=cell.zero_state(
dtype=dtype, batch_size=batch_size))
final_outputs, final_state, _ = decoder.dynamic_decode(my_decoder)
self.assertTrue(
isinstance(final_outputs, basic_decoder.BasicDecoderOutput))
self.assertEqual(final_outputs.rnn_output.dtype, dtype)
self.assertTrue(
isinstance(final_state, wrapper.AttentionWrapperState))
self.assertTrue(
isinstance(final_state.cell_state, rnn_cell.LSTMStateTuple))
def testCustomizedAttention(self):
batch_size = 2
max_time = 3
num_units = 2
memory = constant_op.constant([[[1., 1.], [2., 2.], [3., 3.]],
[[4., 4.], [5., 5.], [6., 6.]]])
memory_sequence_length = constant_op.constant([3, 2])
attention_mechanism = wrapper.BahdanauAttention(num_units, memory,
memory_sequence_length)
# Sets all returned values to be all ones.
def _customized_attention(unused_attention_mechanism, unused_cell_output,
unused_attention_state, unused_attention_layer):
"""Customized attention.
Returns:
attention: `Tensor` of shape [batch_size, num_units], attention output.
alignments: `Tensor` of shape [batch_size, max_time], sigma value for
each input memory (prob. function of input keys).
next_attention_state: A `Tensor` representing the next state for the
attention.
"""
attention = array_ops.ones([batch_size, num_units])
alignments = array_ops.ones([batch_size, max_time])
next_attention_state = alignments
return attention, alignments, next_attention_state
attention_cell = wrapper.AttentionWrapper(
rnn_cell.LSTMCell(2),
attention_mechanism,
attention_layer_size=None, # don't use attention layer.
output_attention=False,
alignment_history=(),
attention_fn=_customized_attention,
name='attention')
self.assertEqual(num_units, attention_cell.output_size)
initial_state = attention_cell.zero_state(
batch_size=2, dtype=dtypes.float32)
source_input_emb = array_ops.ones([2, 3, 2])
source_input_length = constant_op.constant([3, 2])
# 'state' is a tuple of
# (cell_state, h, attention, alignments, alignment_history, attention_state)
output, state = rnn.dynamic_rnn(
attention_cell,
inputs=source_input_emb,
sequence_length=source_input_length,
initial_state=initial_state,
dtype=dtypes.float32)
with self.session() as sess:
sess.run(variables.global_variables_initializer())
output_value, state_value = sess.run([output, state], feed_dict={})
self.assertAllEqual(np.array([2, 3, 2]), output_value.shape)
self.assertAllClose(np.array([[1., 1.], [1., 1.]]), state_value.attention)
self.assertAllClose(
np.array([[1., 1., 1.], [1., 1., 1.]]), state_value.alignments)
self.assertAllClose(
np.array([[1., 1., 1.], [1., 1., 1.]]), state_value.attention_state)
def build_decoder_cell(self):
encoder_outputs = self.encoder_outputs
encoder_last_state = self.encoder_last_state
encoder_inputs_length = self.encoder_inputs_length
if self.use_beamsearch_decode:
print ("use beamsearch decoding..")
encoder_outputs = seq2seq.tile_batch(
self.encoder_outputs, multiplier=self.beam_width)
encoder_last_state = nest.map_structure(
lambda s: seq2seq.tile_batch(s, self.beam_width), self.encoder_last_state)
encoder_inputs_length = seq2seq.tile_batch(
self.encoder_inputs_length, multiplier=self.beam_width)
# Building attention mechanism: Default Bahdanau
# 'Bahdanau' style attention: https://arxiv.org/abs/1409.0473
self.attention_mechanism = attention_wrapper.BahdanauAttention(
num_units=self.hidden_units, memory=encoder_outputs,
memory_sequence_length=encoder_inputs_length,)
# 'Luong' style attention: https://arxiv.org/abs/1508.04025
if self.attention_type.lower() == 'luong':
self.attention_mechanism = attention_wrapper.LuongAttention(
num_units=self.hidden_units, memory=encoder_outputs,
memory_sequence_length=encoder_inputs_length,)
# Building decoder_cell
self.decoder_cell_list = [
self.build_single_cell() for i in range(self.depth)]
decoder_initial_state = encoder_last_state
def attn_decoder_input_fn(inputs, attention):
if not self.attn_input_feeding:
return inputs
# Essential when use_residual=True
_input_layer = Dense(self.hidden_units, dtype=self.dtype,
name='attn_input_feeding')
return _input_layer(array_ops.concat([inputs, attention], -1))
# AttentionWrapper wraps RNNCell with the attention_mechanism
# Note: We implement Attention mechanism only on the top decoder layer
self.decoder_cell_list[-1] = attention_wrapper.AttentionWrapper(
cell=self.decoder_cell_list[-1],
attention_mechanism=self.attention_mechanism,
attention_layer_size=self.hidden_units,
cell_input_fn=attn_decoder_input_fn,
initial_cell_state=encoder_last_state[-1],
alignment_history=False,
name='Attention_Wrapper')
batch_size = self.batch_size if not self.use_beamsearch_decode \
else self.batch_size * self.beam_width
initial_state = [state for state in encoder_last_state]
initial_state[-1] = self.decoder_cell_list[-1].zero_state(
batch_size=batch_size, dtype=self.dtype)
decoder_initial_state = tuple(initial_state)
return MultiRNNCell(self.decoder_cell_list), decoder_initial_state
def testLuongScaledDType(self, dtype):
# Test case for GitHub issue 18099
encoder_outputs = self.encoder_outputs.astype(dtype)
decoder_inputs = self.decoder_inputs.astype(dtype)
attention_mechanism = wrapper.LuongAttentionV2(
units=self.units,
memory=encoder_outputs,
memory_sequence_length=self.encoder_sequence_length,
scale=True,
dtype=dtype,
)
cell = keras.layers.LSTMCell(self.units,
recurrent_activation="sigmoid")
cell = wrapper.AttentionWrapper(cell, attention_mechanism)
sampler = sampler_py.TrainingSampler()
my_decoder = basic_decoder.BasicDecoderV2(cell=cell, sampler=sampler)
final_outputs, final_state, _ = my_decoder(
decoder_inputs,
initial_state=cell.zero_state(dtype=dtype, batch_size=self.batch),
sequence_length=self.decoder_sequence_length)
self.assertIsInstance(final_outputs, basic_decoder.BasicDecoderOutput)
self.assertEqual(final_outputs.rnn_output.dtype, dtype)
self.assertIsInstance(final_state, wrapper.AttentionWrapperState)
def build_dec_cell(self, hidden_size):
enc_outputs = self.enc_outputs
enc_last_state = self.enc_last_state
enc_inputs_length = self.enc_inp_len
if self.use_beam_search:
self.logger.info("using beam search decoding")
enc_outputs = seq2seq.tile_batch(self.enc_outputs,
multiplier=self.p.beam_width)
enc_last_state = nest.map_structure(
lambda s: seq2seq.tile_batch(s, self.p.beam_width),
self.enc_last_state)
enc_inputs_length = seq2seq.tile_batch(self.enc_inp_len,
self.p.beam_width)
if self.p.attention_type.lower() == 'luong':
self.attention_mechanism = attention_wrapper.LuongAttention(
num_units=hidden_size,
memory=enc_outputs,
memory_sequence_length=enc_inputs_length)
else:
self.attention_mechanism = attention_wrapper.BahdanauAttention(
num_units=hidden_size,
memory=enc_outputs,
memory_sequence_length=enc_inputs_length)
def attn_dec_input_fn(inputs, attention):
if not self.p.attn_input_feeding:
return inputs
else:
_input_layer = Dense(hidden_size,
dtype=self.p.dtype,
name='attn_input_feeding')
return _input_layer(tf.concat([inputs, attention], -1))
self.dec_cell_list = [
self.build_single_cell(hidden_size) for _ in range(self.p.depth)
]
if self.p.use_attn:
self.dec_cell_list[-1] = attention_wrapper.AttentionWrapper(
cell=self.dec_cell_list[-1],
attention_mechanism=self.attention_mechanism,
attention_layer_size=hidden_size,
cell_input_fn=attn_dec_input_fn,
initial_cell_state=enc_last_state[-1],
alignment_history=False,
name='attention_wrapper')
batch_size = self.p.batch_size if not self.use_beam_search else self.p.batch_size * self.p.beam_width
initial_state = [state for state in enc_last_state]
if self.p.use_attn:
initial_state[-1] = self.dec_cell_list[-1].zero_state(
batch_size=batch_size, dtype=self.p.dtype)
dec_initial_state = tuple(initial_state)
return MultiRNNCell(self.dec_cell_list), dec_initial_state
def create_decoder(self, encoded, inputs, speaker_embed, train=True):
config = self.config
attention_mech = wrapper.BahdanauAttention(
config.attention_units,
encoded,
memory_sequence_length=inputs['text_length'])
inner_cell = [GRUCell(config.decoder_units) for _ in range(3)]
decoder_cell = OutputProjectionWrapper(
InputProjectionWrapper(ResidualWrapper(MultiRNNCell(inner_cell)),
config.decoder_units),
config.mel_features * config.r)
# feed in rth frame at each time step
decoder_frame_input = \
lambda inputs, attention: tf.concat(
[self.pre_net(tf.slice(inputs,
[0, (config.r - 1)*config.mel_features], [-1, -1]),
dropout=config.audio_dropout_prob,
train=train),
attention]
, -1)
cell = wrapper.AttentionWrapper(
decoder_cell,
attention_mech,
attention_layer_size=config.attention_units,
cell_input_fn=decoder_frame_input,
alignment_history=True,
output_attention=False)
if train:
if config.scheduled_sample:
print("if train if config.scheduled_sample: %s" % str(
(inputs['mel'], inputs['speech_length'],
config.scheduled_sample)))
decoder_helper = helper.ScheduledOutputTrainingHelper(
inputs['mel'], inputs['speech_length'],
config.scheduled_sample)
else:
decoder_helper = helper.TrainingHelper(inputs['mel'],
inputs['speech_length'])
else:
decoder_helper = ops.InferenceHelper(
tf.shape(inputs['text'])[0], config.mel_features * config.r)
initial_state = cell.zero_state(dtype=tf.float32,
batch_size=tf.shape(inputs['text'])[0])
#if speaker_embed is not None:
#initial_state.attention = tf.layers.dense(speaker_embed, config.attention_units)
dec = basic_decoder.BasicDecoder(cell, decoder_helper, initial_state)
return dec
def _attentive_bidirectional_cudnn_LSTM(self,
inputs,
input_size,
lengths,
attention_mechanism=False,
num_units=256,
num_layers=1,
dp_input_keep_prob=1.0,
dp_output_keep_prob=1.0):
with tf.variable_scope('fw'):
cell_fw = create_cudnn_LSTM_cell(
num_units=num_units,
input_size=input_size,
num_layers=num_layers,
dp_input_keep_prob=dp_input_keep_prob,
dp_output_keep_prob=dp_output_keep_prob)
with tf.variable_scope('bw'):
cell_bw = create_cudnn_LSTM_cell(
num_units=num_units,
input_size=input_size,
num_layers=num_layers,
dp_input_keep_prob=dp_input_keep_prob,
dp_output_keep_prob=dp_output_keep_prob)
if attention_mechanism:
cell_fw = attention_wrapper.AttentionWrapper(
cell=cell_fw,
attention_mechanism=attention_mechanism,
output_attention=False)
cell_bw = attention_wrapper.AttentionWrapper(
cell=cell_bw,
attention_mechanism=attention_mechanism,
output_attention=False)
return bidirectional_dynamic_rnn(cell_fw=cell_fw,
cell_bw=cell_bw,
inputs=inputs,
sequence_length=lengths,
dtype=getdtype())
def build_decoder(self):
"""
decoder
:return:
"""
print('build decoder with attention...')
with tf.variable_scope('decoder'):
self.decoder_embeddings = tf.Variable(
tf.random_uniform([self.vocab_size, self.embedding_size]))
# 2.1 add attention
def build_decoder_cell():
decoder_cell = tf.contrib.rnn.LSTMCell(
self.hidden_size,
initializer=tf.random_uniform_initializer(-0.1,
0.1,
seed=2))
return decoder_cell
attention_states = self.encoder_outputs
attention_mechanism = tf.contrib.seq2seq.LuongAttention(
num_units=self.hidden_size,
memory=attention_states,
memory_sequence_length=self.source_sequence_length)
decoder_cells_list = [
build_decoder_cell() for _ in range(self.num_layers)
]
decoder_cells_list[-1] = attention_wrapper.AttentionWrapper(
cell=decoder_cells_list[-1],
attention_mechanism=attention_mechanism,
attention_layer_size=self.hidden_size)
self.decoder_cells = tf.contrib.rnn.MultiRNNCell(
decoder_cells_list)
initial_state = [state for state in self.encoder_states]
initial_state[-1] = decoder_cells_list[-1].zero_state(
batch_size=self.batch_size, dtype=tf.float32)
self.decoder_initial_state = tuple(initial_state)
# 全连接
self.output_layer = Dense(
self.vocab_size,
kernel_initializer=tf.truncated_normal_initializer(mean=0.0,
stddev=0.1))
if self.mode == 'train':
self.interfer()
elif self.mode == 'decode':
self.decode()
def _attentive_bidirectional_rnn(self,
inputs,
lengths,
num_units,
attention_mechanism=False,
cell_type='gru',
num_layers=1,
dp_input_keep_prob=1.0,
dp_output_keep_prob=1.0):
cell_fw = create_rnn_cell(cell_type=cell_type,
num_units=num_units,
num_layers=num_layers,
dp_input_keep_prob=dp_input_keep_prob,
dp_output_keep_prob=dp_output_keep_prob)
cell_bw = create_rnn_cell(cell_type=cell_type,
num_units=num_units,
num_layers=num_layers,
dp_input_keep_prob=dp_input_keep_prob,
dp_output_keep_prob=dp_output_keep_prob)
if attention_mechanism:
cell_fw = attention_wrapper.AttentionWrapper(
cell=cell_fw,
attention_mechanism=attention_mechanism,
output_attention=False)
cell_bw = attention_wrapper.AttentionWrapper(
cell=cell_bw,
attention_mechanism=attention_mechanism,
output_attention=False)
return bidirectional_dynamic_rnn(cell_fw=cell_fw,
cell_bw=cell_bw,
inputs=inputs,
sequence_length=lengths,
dtype=getdtype())
def build_decoder_cell(self):
encoder_outputs = self.encoder_outputs
encoder_last_state = self.encoder_last_state
encoder_inputs_length = self.encoder_inputs_length
# building attention mechanism: default Bahdanau
# 'Bahdanau': https://arxiv.org/abs/1409.0473
self.attention_mechanism = attention_wrapper.BahdanauAttention(
num_units=self.hidden_size,
memory=encoder_outputs,
memory_sequence_length=encoder_inputs_length)
# 'Luong': https://arxiv.org/abs/1508.04025
if self.attention_type.lower() == 'luong':
self.attention_mechanism = attention_wrapper.LuongAttention(
num_units=self.hidden_size,
memory=self.encoder_outputs,
memory_sequence_length=self.encoder_inputs_length)
# building decoder_cell
self.decoder_cell_list = [
self.build_single_cell() for _ in range(self.layer_num)
]
def att_decoder_input_fn(inputs, attention):
if not self.use_att_decoding:
return inputs
_input_layer = Dense(self.hidden_size,
dtype=self.dtype,
name='att_input_feeding')
return _input_layer(array_ops.concat([inputs, attention], axis=-1))
# AttentionWrapper wraps RNNCell with the attention_mechanism
# implement attention mechanism only on the top of decoder layer
self.decoder_cell_list[-1] = attention_wrapper.AttentionWrapper(
cell=self.decoder_cell_list[-1],
attention_mechanism=self.attention_mechanism,
attention_layer_size=self.hidden_size,
cell_input_fn=att_decoder_input_fn,
initial_cell_state=encoder_last_state[
-1], # last hidden state of last encode layer
alignment_history=False,
name='Attention_Wrapper')
initial_state = [state for state in encoder_last_state]
initial_state[-1] = self.decoder_cell_list[-1].zero_state(
batch_size=self.batch_size, dtype=self.dtype)
decoder_initial_state = tuple(initial_state)
return MultiRNNCell(self.decoder_cell_list), decoder_initial_state
def build_decoder_cell(self, num_units, num_layers, keep_prob):
encoder_outputs = tf.concat(self.encoder_outputs, axis=-1)
encoder_final_state = []
encoder_fw_fs, encoder_bw_fs = self.encoder_fs
for i in range(num_layers):
final_state_c = tf.concat((encoder_fw_fs[i].c, encoder_bw_fs[i].c),
axis=1)
final_state_h = tf.concat((encoder_fw_fs[i].h, encoder_bw_fs[i].h),
axis=1)
encoder_final_state.append(
LSTMStateTuple(c=final_state_c, h=final_state_h))
encoder_fs = tuple(encoder_final_state)
# build decoder cell
decoder_cells = [
self.make_rnn_cell(num_units, keep_prob) for _ in range(num_layers)
]
attention_cell = decoder_cells.pop()
# use Bahdanua attention to all cell layers.
self.attention_machenism = attention_wrapper.BahdanauAttention(
num_units=num_units,
memory=encoder_outputs,
normalize=False,
memory_sequence_length=self.encoder_length)
attention_cell = attention_wrapper.AttentionWrapper(
attention_cell,
self.attention_machenism,
attention_layer_size=None,
initial_cell_state=None,
output_attention=False,
alignment_history=False,
)
decoder_cells.append(attention_cell)
decoder_cells = tf.nn.rnn_cell.MultiRNNCell(decoder_cells)
batch = self.batch
decoder_init_state = tuple(
zs.clone(cell_state=es) if isinstance(
zs, tf.contrib.seq2seq.AttentionWrapperState) else es
for zs, es in zip(
decoder_cells.zero_state(batch, dtype=tf.float32), encoder_fs))
# why the last layers' zero state different with
# init_state = [state for state in encoder_fs]
return decoder_cells, decoder_init_state
def build_decoder_cell(self):
encoder_outputs = self.encoder_outputs
encoder_last_state = self.encoder_last_state
encoder_inputs_length = self.encoder_inputs_length
# Building Attention Mechanism: Default Bahdanau
self.attention_mechanism = attention_wrapper.BahdanauAttention(
num_units=self.decoder_hidden_units,
memory=encoder_outputs,
memory_sequence_length=encoder_inputs_length)
self.decoder_cell_list = [
self.build_single_cell() for i in range(self.depth)
]
decoder_initial_state = encoder_last_state
def attn_decoder_input_fn(inputs, attention):
if not self.attn_input_feeding:
return inputs
_input_layer = Dense(self.decoder_hidden_units,
dtype=tf.float32,
name='attn_input_feeding')
return _input_layer(array_ops.concat([inputs, attention], -1))
# AttentionWrapper wraps RNNCell with the attention_mechanism
self.decoder_cell_list[-1] = attention_wrapper.AttentionWrapper(
cell=self.decoder_cell_list[-1],
attention_mechanism=self.attention_mechanism,
attention_layer_size=self.decoder_hidden_units,
cell_input_fn=attn_decoder_input_fn,
initial_cell_state=encoder_last_state[-1],
alignment_history=False,
name='Attention_Wrapper')
batch_size = self.batch_size
initial_state = [state for state in encoder_last_state]
initial_state[-1] = self.decoder_cell_list[-1].zero_state(
batch_size=batch_size, dtype=tf.float32)
decoder_initial_state = tuple(initial_state)
return tf.contrib.rnn.MultiRNNCell(
self.decoder_cell_list), decoder_initial_state
def build_decoder_cell(self):
self.decoder_cell_list = \
[self.build_single_cell() for i in range(self.para.num_layers)]
if self.para.mode == 'train':
encoder_outputs = self.encoder_outputs
encoder_inputs_len = self.encoder_inputs_len
encoder_states = self.encoder_states
batch_size = self.para.batch_size
else:
encoder_outputs = seq2seq.tile_batch(
self.encoder_outputs, multiplier=self.para.beam_width)
encoder_inputs_len = seq2seq.tile_batch(
self.encoder_inputs_len, multiplier=self.para.beam_width)
encoder_states = seq2seq.tile_batch(
self.encoder_states, multiplier=self.para.beam_width)
batch_size = self.para.batch_size * self.para.beam_width
if self.para.attention_mode == 'luong':
# scaled luong: recommended by authors of NMT
self.attention_mechanism = attention_wrapper.LuongAttention(
num_units=self.para.num_units,
memory=encoder_outputs,
memory_sequence_length=encoder_inputs_len,
scale=True)
output_attention = True
else:
self.attention_mechanism = attention_wrapper.BahdanauAttention(
num_units=self.para.num_units,
memory=encoder_outputs,
memory_sequence_length=encoder_inputs_len)
output_attention = False
cell = tf.contrib.rnn.MultiRNNCell(self.decoder_cell_list)
cell = attention_wrapper.AttentionWrapper(
cell=cell,
attention_mechanism=self.attention_mechanism,
attention_layer_size=self.para.num_units,
name='attention')
decoder_initial_state = cell.zero_state(
batch_size, self.dtype).clone(cell_state=encoder_states)
return cell, decoder_initial_state
def build_attention_decoder_cell(self):
encoder_outputs = self.encoder_outputs
encoder_last_state = self.encoder_last_state
encoder_inputs_length = self.encoder_inputs_length
self.attention_mechanism = attention_wrapper.BahdanauAttention(
num_units=self.hidden_units, memory=encoder_outputs,
memory_sequence_length=encoder_inputs_length, )
if self.attention_type.lower()=='luong':
self.attention_mechanism = attention_wrapper.LuongAttention(
num_units=self.hidden_units, memory=encoder_outputs,
memory_sequence_length=encoder_inputs_length, )
# # Building decoder_cell
self.decoder_cell_list = [self.build_single_cell() for i in range(self.num_layers)]
def attn_decoder_input_fn(inputs, attention):
if not self.attn_input_feeding:
return inputs
# Essential when use_residual=True
_input_layer = tf.layers.dense(tf.concat([inputs, attention], axis=-1), self.hidden_units,
name='attn_input_feeding')
return _input_layer
self.decoder_cell_list[-1] = attention_wrapper.AttentionWrapper(
cell=self.decoder_cell_list[-1],
attention_mechanism=self.attention_mechanism,
attention_layer_size=self.hidden_units,
cell_input_fn=attn_decoder_input_fn,
initial_cell_state=encoder_last_state[-1],
alignment_history=False,
name='Attention_Wrapper'
)
batch_size = self.config['batch_size']
initial_state = [state for state in encoder_last_state]
initial_state[-1] = self.decoder_cell_list[-1].zero_state(
batch_size=batch_size, dtype=self.dtype)
decoder_initial_state = tuple(initial_state)
return MultiRNNCell(self.decoder_cell_list), decoder_initial_state
def testAttentionWrapperStateShapePropgation(self):
batch_size = 5
max_time = 5
num_units = 5
memory = random_ops.random_uniform(
[batch_size, max_time, num_units], seed=1)
mechanism = wrapper.LuongAttention(num_units, memory)
cell = wrapper.AttentionWrapper(rnn_cell.LSTMCell(num_units), mechanism)
# Create zero state with static batch size.
static_state = cell.zero_state(batch_size, dtypes.float32)
# Create zero state without static batch size.
state = cell.zero_state(array_ops.shape(memory)[0], dtypes.float32)
state = static_state.clone(
cell_state=state.cell_state, attention=state.attention)
self.assertEqual(state.cell_state.c.shape, static_state.cell_state.c.shape)
self.assertEqual(state.cell_state.h.shape, static_state.cell_state.h.shape)
self.assertEqual(state.attention.shape, static_state.attention.shape)
def _testBahdanauNormalizedDType(self, dtype):
encoder_outputs = self.encoder_outputs.astype(dtype)
decoder_inputs = self.decoder_inputs.astype(dtype)
attention_mechanism = wrapper.BahdanauAttentionV2(
units=self.units,
memory=encoder_outputs,
memory_sequence_length=self.encoder_sequence_length,
normalize=True,
dtype=dtype)
cell = rnn_cell.LSTMCell(self.units)
cell = wrapper.AttentionWrapper(cell, attention_mechanism)
sampler = sampler_py.TrainingSampler()
my_decoder = basic_decoder.BasicDecoderV2(cell=cell, sampler=sampler)
final_outputs, final_state, _ = my_decoder(
decoder_inputs,
initial_state=cell.zero_state(dtype=dtype, batch_size=self.batch),
sequence_length=self.decoder_sequence_length)
self.assertIsInstance(final_outputs, basic_decoder.BasicDecoderOutput)
self.assertEqual(final_outputs.rnn_output.dtype, dtype)
self.assertIsInstance(final_state, wrapper.AttentionWrapperState)
self.assertIsInstance(final_state.cell_state, rnn_cell.LSTMStateTuple)
def _testWithMaybeMultiAttention(self,
is_multi,
create_attention_mechanisms,
expected_final_output,
expected_final_state,
attention_mechanism_depths,
alignment_history=False,
expected_final_alignment_history=None,
attention_layer_sizes=None,
attention_layers=None,
create_query_layer=False,
create_memory_layer=True,
create_attention_kwargs=None):
# Allow is_multi to be True with a single mechanism to enable test for
# passing in a single mechanism in a list.
assert len(create_attention_mechanisms) == 1 or is_multi
encoder_sequence_length = [3, 2, 3, 1, 1]
decoder_sequence_length = [2, 0, 1, 2, 3]
batch_size = 5
encoder_max_time = 8
decoder_max_time = 4
input_depth = 7
encoder_output_depth = 10
cell_depth = 9
create_attention_kwargs = create_attention_kwargs or {}
if attention_layer_sizes is not None:
# Compute sum of attention_layer_sizes. Use encoder_output_depth if None.
attention_depth = sum(
attention_layer_size or encoder_output_depth
for attention_layer_size in attention_layer_sizes)
elif attention_layers is not None:
# Compute sum of attention_layers output depth.
attention_depth = sum(
attention_layer.compute_output_shape(
[batch_size, cell_depth +
encoder_output_depth]).dims[-1].value
for attention_layer in attention_layers)
else:
attention_depth = encoder_output_depth * len(
create_attention_mechanisms)
decoder_inputs = np.random.randn(batch_size, decoder_max_time,
input_depth).astype(np.float32)
encoder_outputs = np.random.randn(batch_size, encoder_max_time,
encoder_output_depth).astype(
np.float32)
attention_mechanisms = []
for creator, depth in zip(create_attention_mechanisms,
attention_mechanism_depths):
# Create a memory layer with deterministic initializer to avoid randomness
# in the test between graph and eager.
if create_query_layer:
create_attention_kwargs["query_layer"] = keras.layers.Dense(
depth, kernel_initializer="ones", use_bias=False)
if create_memory_layer:
create_attention_kwargs["memory_layer"] = keras.layers.Dense(
depth, kernel_initializer="ones", use_bias=False)
attention_mechanisms.append(
creator(units=depth,
memory=encoder_outputs,
memory_sequence_length=encoder_sequence_length,
**create_attention_kwargs))
with self.cached_session(use_gpu=True):
attention_layer_size = attention_layer_sizes
attention_layer = attention_layers
if not is_multi:
if attention_layer_size is not None:
attention_layer_size = attention_layer_size[0]
if attention_layer is not None:
attention_layer = attention_layer[0]
cell = keras.layers.LSTMCell(cell_depth,
recurrent_activation="sigmoid",
kernel_initializer="ones",
recurrent_initializer="ones")
cell = wrapper.AttentionWrapper(
cell,
attention_mechanisms if is_multi else attention_mechanisms[0],
attention_layer_size=attention_layer_size,
alignment_history=alignment_history,
attention_layer=attention_layer)
if cell._attention_layers is not None:
for layer in cell._attention_layers:
if getattr(layer, "kernel_initializer") is None:
layer.kernel_initializer = initializers.glorot_uniform(
seed=1337)
sampler = sampler_py.TrainingSampler()
my_decoder = basic_decoder.BasicDecoderV2(cell=cell,
sampler=sampler)
initial_state = cell.get_initial_state(dtype=dtypes.float32,
batch_size=batch_size)
final_outputs, final_state, _ = my_decoder(
decoder_inputs,
initial_state=initial_state,
sequence_length=decoder_sequence_length)
self.assertIsInstance(final_outputs,
basic_decoder.BasicDecoderOutput)
self.assertIsInstance(final_state, wrapper.AttentionWrapperState)
expected_time = (expected_final_state.time
if context.executing_eagerly() else None)
self.assertEqual(
(batch_size, expected_time, attention_depth),
tuple(final_outputs.rnn_output.get_shape().as_list()))
self.assertEqual(
(batch_size, expected_time),
tuple(final_outputs.sample_id.get_shape().as_list()))
self.assertEqual(
(batch_size, attention_depth),
tuple(final_state.attention.get_shape().as_list()))
self.assertEqual(
(batch_size, cell_depth),
tuple(final_state.cell_state[0].get_shape().as_list()))
self.assertEqual(
(batch_size, cell_depth),
tuple(final_state.cell_state[1].get_shape().as_list()))
if alignment_history:
if is_multi:
state_alignment_history = []
for history_array in final_state.alignment_history:
history = history_array.stack()
self.assertEqual(
(expected_time, batch_size, encoder_max_time),
tuple(history.get_shape().as_list()))
state_alignment_history.append(history)
state_alignment_history = tuple(state_alignment_history)
else:
state_alignment_history = final_state.alignment_history.stack(
)
self.assertEqual(
(expected_time, batch_size, encoder_max_time),
tuple(state_alignment_history.get_shape().as_list()))
nest.assert_same_structure(
cell.state_size, cell.zero_state(batch_size,
dtypes.float32))
# Remove the history from final_state for purposes of the
# remainder of the tests.
final_state = final_state._replace(alignment_history=()) # pylint: disable=protected-access
else:
state_alignment_history = ()
self.evaluate(variables.global_variables_initializer())
eval_result = self.evaluate({
"final_outputs":
final_outputs,
"final_state":
final_state,
"state_alignment_history":
state_alignment_history,
})
final_output_info = nest.map_structure(
get_result_summary, eval_result["final_outputs"])
final_state_info = nest.map_structure(get_result_summary,
eval_result["final_state"])
print("final_output_info: ", final_output_info)
print("final_state_info: ", final_state_info)
nest.map_structure(self.assertAllCloseOrEqual,
expected_final_output, final_output_info)
nest.map_structure(self.assertAllCloseOrEqual,
expected_final_state, final_state_info)
if alignment_history: # by default, the wrapper emits attention as output
final_alignment_history_info = nest.map_structure(
get_result_summary, eval_result["state_alignment_history"])
print("final_alignment_history_info: ",
final_alignment_history_info)
nest.map_structure(
self.assertAllCloseOrEqual,
# outputs are batch major but the stacked TensorArray is time major
expected_final_alignment_history,
final_alignment_history_info)
def _testWithAttention(self,
create_attention_mechanism,
expected_final_output,
expected_final_state,
attention_mechanism_depth=3,
alignment_history=False,
expected_final_alignment_history=None,
attention_layer_size=6,
name=''):
encoder_sequence_length = [3, 2, 3, 1, 1]
decoder_sequence_length = [2, 0, 1, 2, 3]
batch_size = 5
encoder_max_time = 8
decoder_max_time = 4
input_depth = 7
encoder_output_depth = 10
cell_depth = 9
if attention_layer_size is not None:
attention_depth = attention_layer_size
else:
attention_depth = encoder_output_depth
decoder_inputs = array_ops.placeholder_with_default(
np.random.randn(batch_size, decoder_max_time,
input_depth).astype(np.float32),
shape=(None, None, input_depth))
encoder_outputs = array_ops.placeholder_with_default(
np.random.randn(batch_size, encoder_max_time,
encoder_output_depth).astype(np.float32),
shape=(None, None, encoder_output_depth))
attention_mechanism = create_attention_mechanism(
num_units=attention_mechanism_depth,
memory=encoder_outputs,
memory_sequence_length=encoder_sequence_length)
with self.test_session(use_gpu=True) as sess:
with vs.variable_scope(
'root',
initializer=init_ops.random_normal_initializer(stddev=0.01,
seed=3)):
cell = rnn_cell.LSTMCell(cell_depth)
cell = wrapper.AttentionWrapper(
cell,
attention_mechanism,
attention_layer_size=attention_layer_size,
alignment_history=alignment_history)
helper = helper_py.TrainingHelper(decoder_inputs,
decoder_sequence_length)
my_decoder = basic_decoder.BasicDecoder(
cell=cell,
helper=helper,
initial_state=cell.zero_state(dtype=dtypes.float32,
batch_size=batch_size))
final_outputs, final_state, _ = decoder.dynamic_decode(
my_decoder)
self.assertTrue(
isinstance(final_outputs, basic_decoder.BasicDecoderOutput))
self.assertTrue(
isinstance(final_state, wrapper.AttentionWrapperState))
self.assertTrue(
isinstance(final_state.cell_state, rnn_cell.LSTMStateTuple))
self.assertEqual(
(batch_size, None, attention_depth),
tuple(final_outputs.rnn_output.get_shape().as_list()))
self.assertEqual(
(batch_size, None),
tuple(final_outputs.sample_id.get_shape().as_list()))
self.assertEqual(
(batch_size, attention_depth),
tuple(final_state.attention.get_shape().as_list()))
self.assertEqual(
(batch_size, cell_depth),
tuple(final_state.cell_state.c.get_shape().as_list()))
self.assertEqual(
(batch_size, cell_depth),
tuple(final_state.cell_state.h.get_shape().as_list()))
if alignment_history:
state_alignment_history = final_state.alignment_history.stack()
# Remove the history from final_state for purposes of the
# remainder of the tests.
final_state = final_state._replace(alignment_history=()) # pylint: disable=protected-access
self.assertEqual(
(None, batch_size, None),
tuple(state_alignment_history.get_shape().as_list()))
else:
state_alignment_history = ()
sess.run(variables.global_variables_initializer())
sess_results = sess.run({
'final_outputs':
final_outputs,
'final_state':
final_state,
'state_alignment_history':
state_alignment_history,
})
final_output_info = nest.map_structure(
get_result_summary, sess_results['final_outputs'])
final_state_info = nest.map_structure(get_result_summary,
sess_results['final_state'])
print(name)
print('Copy/paste:\nexpected_final_output = %s' %
str(final_output_info))
print('expected_final_state = %s' % str(final_state_info))
nest.map_structure(self.assertAllCloseOrEqual,
expected_final_output, final_output_info)
nest.map_structure(self.assertAllCloseOrEqual,
expected_final_state, final_state_info)
if alignment_history: # by default, the wrapper emits attention as output
final_alignment_history_info = nest.map_structure(
get_result_summary,
sess_results['state_alignment_history'])
print('expected_final_alignment_history = %s' %
str(final_alignment_history_info))
nest.map_structure(
self.assertAllCloseOrEqual,
# outputs are batch major but the stacked TensorArray is time major
expected_final_alignment_history,
final_alignment_history_info)
def build_decoder_cell(self):
encoder_outputs = self.encoder_outputs
encoder_last_state = self.encoder_last_state
encoder_inputs_length = self.encoder_inputs_length
# To use BeamSearchDecoder, encoder_outputs, encoder_last_state, encoder_inputs_length
# needs to be tiled so that: [batch_size, .., ..] -> [batch_size x beam_width, .., ..]
if self.use_beamsearch_decode:
print("use beamsearch decoding..")
encoder_outputs = seq2seq.tile_batch(self.encoder_outputs,
multiplier=self.beam_width)
encoder_last_state = nest.map_structure(
lambda s: seq2seq.tile_batch(s, self.beam_width),
self.encoder_last_state)
encoder_inputs_length = seq2seq.tile_batch(
self.encoder_inputs_length, multiplier=self.beam_width)
# Building attention mechanism: Default Bahdanau
# 'Bahdanau' style attention: https://arxiv.org/abs/1409.0473
self.attention_mechanism = attention_wrapper.BahdanauAttention(
num_units=self.hidden_units,
memory=encoder_outputs,
memory_sequence_length=encoder_inputs_length,
)
# 'Luong' style attention: https://arxiv.org/abs/1508.04025
if self.attention_type.lower() == 'luong':
self.attention_mechanism = attention_wrapper.LuongAttention(
num_units=self.hidden_units,
memory=encoder_outputs,
memory_sequence_length=encoder_inputs_length,
)
# Building decoder_cell
self.decoder_cell_list = [
self.build_single_cell() for i in range(self.depth)
]
decoder_initial_state = encoder_last_state
def attn_decoder_input_fn(inputs, attention):
if not self.attn_input_feeding:
return inputs
# Essential when use_residual=True
_input_layer = Dense(self.hidden_units,
dtype=self.dtype,
name='attn_input_feeding')
return _input_layer(array_ops.concat([inputs, attention], -1))
# AttentionWrapper wraps RNNCell with the attention_mechanism
# Note: We implement Attention mechanism only on the top decoder layer
self.decoder_cell_list[-1] = attention_wrapper.AttentionWrapper(
cell=self.decoder_cell_list[-1],
attention_mechanism=self.attention_mechanism,
attention_layer_size=self.hidden_units,
cell_input_fn=attn_decoder_input_fn,
initial_cell_state=encoder_last_state[-1],
alignment_history=True,
name='Attention_Wrapper')
# To be compatible with AttentionWrapper, the encoder last state
# of the top layer should be converted into the AttentionWrapperState form
# We can easily do this by calling AttentionWrapper.zero_state
# Also if beamsearch decoding is used, the batch_size argument in .zero_state
# should be ${decoder_beam_width} times to the origianl batch_size
batch_size = self.batch_size if not self.use_beamsearch_decode \
else self.batch_size * self.beam_width
initial_state = [state for state in encoder_last_state]
initial_state[-1] = self.decoder_cell_list[-1].zero_state(
batch_size=batch_size, dtype=self.dtype)
decoder_initial_state = tuple(initial_state)
return MultiRNNCell(self.decoder_cell_list), decoder_initial_state
def _build_output_layer_context(self, ques_outputs, ques_sequence_length,
ctx_outputs, ctx_sequence_length):
with tf.variable_scope('Output'):
attention_depth_ques = self._model_params[
'output_attention_layer_size_ques']
if self._attention_type_output_layer == 'Luong':
attention_depth_ques = self._model_params['ques_param_size']
attention_mechanism_ques = self._build_attention(
num_units=attention_depth_ques,
memory=ques_outputs,
memory_sequence_length=ques_sequence_length,
attention_type=self._attention_type_output_layer)
self.V_ques = tf.get_variable(
name='V_ques',
shape=[1, self._model_params['ques_param_size']],
dtype=getdtype())
alignments_ques = attention_mechanism_ques(
tf.tile(self.V_ques, [self._model_params['batch_size'], 1]),
previous_alignments=None)
expanded_alignments_ques = tf.expand_dims(alignments_ques, 1)
context = tf.matmul(expanded_alignments_ques, ques_outputs)
context = tf.squeeze(context, [1])
attention_depth_ans = self._model_params[
'output_attention_layer_size_ans']
if self._attention_type_output_layer == 'Luong':
attention_depth_ans = self.embedded_dim
attention_mechanism_ans = self._build_attention(
num_units=attention_depth_ans,
memory=ctx_outputs,
memory_sequence_length=ctx_sequence_length,
attention_type=self._attention_type_output_layer)
output_rnn_cell = create_rnn_cell(
cell_type=self._model_params['output_cell_type'],
num_units=self.embedded_dim,
num_layers=self._model_params['output_layers'],
dp_input_keep_prob=self.
_model_params['output_dp_input_keep_prob'],
dp_output_keep_prob=self.
_model_params['output_dp_output_keep_prob'])
# context as initial state
if self._model_params['output_cell_type'] == 'gru':
if self._model_params['output_layers'] == 1:
initial_state = context
else:
initial_state = tuple(
context
for _ in range(self._model_params['output_layers']))
elif self._model_params['output_cell_type'] == 'lstm':
if self._model_params['output_layers'] == 1:
initial_state = rnn_cell_impl.LSTMStateTuple(
tf.zeros_like(context, dtype=getdtype()), context)
else:
initial_state = tuple(
rnn_cell_impl.LSTMStateTuple(
tf.zeros_like(context, dtype=getdtype()), context)
for _ in range(self._model_params['output_layers']))
attentive_output_cell = attention_wrapper.AttentionWrapper(
cell=output_rnn_cell,
attention_mechanism=attention_mechanism_ans,
alignment_history=True,
cell_input_fn=lambda _, attention: attention,
initial_cell_state=initial_state,
output_attention=False)
final_outputs, final_state = tf.nn.static_rnn(
cell=attentive_output_cell,
inputs=[
tf.zeros([self._model_params['batch_size'], 1]),
tf.zeros([self._model_params['batch_size'], 1])
],
dtype=getdtype())
alignment_history = final_state.alignment_history
return alignment_history
def _attention_decoder_wrapper(batch_size, num_units, memory, mutli_layer, dtype=dtypes.float32 ,\
attention_layer_size=None, cell_input_fn=None, attention_type='B',\
probability_fn=None, alignment_history=False, output_attention=True, \
initial_cell_state=None, normalization=False, sigmoid_noise=0.,
sigmoid_noise_seed=None, score_bias_init=0.):
"""
A wrapper for rnn-decoder with attention mechanism
the detail about params explanation can be found at :
blog.csdn.net/qsczse943062710/article/details/79539005
:param mutli_layer: a object returned by function _mutli_layer_rnn()
:param attention_type, string
'B' is for BahdanauAttention as described in:
Dzmitry Bahdanau, Kyunghyun Cho, Yoshua Bengio.
"Neural Machine Translation by Jointly Learning to Align and Translate."
ICLR 2015. https://arxiv.org/abs/1409.0473
'L' is for LuongAttention as described in:
Minh-Thang Luong, Hieu Pham, Christopher D. Manning.
"Effective Approaches to Attention-based Neural Machine Translation."
EMNLP 2015. https://arxiv.org/abs/1508.04025
MonotonicAttention is described in :
Colin Raffel, Minh-Thang Luong, Peter J. Liu, Ron J. Weiss, Douglas Eck,
"Online and Linear-Time Attention by Enforcing Monotonic Alignments."
ICML 2017. https://arxiv.org/abs/1704.00784
'BM' : Monotonic attention mechanism with Bahadanau-style energy function
'LM' : Monotonic attention mechanism with Luong-style energy function
or maybe something user defined in the future
**warning** :
if normalization is set True,
then normalization will be applied to all types of attentions as described in:
Tim Salimans, Diederik P. Kingma.
"Weight Normalization: A Simple Reparameterization to Accelerate
Training of Deep Neural Networks."
https://arxiv.org/abs/1602.07868
A example usage:
att_wrapper, states = _attention_decoder_wrapper(*args)
while decoding:
output, states = att_wrapper(input, states)
...
some processing on output
...
input = processed_output
"""
if attention_type == 'B':
attention_mechanism = att_w.BahdanauAttention(
num_units=num_units,
memory=memory,
probability_fn=probability_fn,
normalize=normalization)
elif attention_type == 'BM':
attention_mechanism = att_w.BahdanauMonotonicAttention(
num_units=num_units,
memory=memory,
normalize=normalization,
sigmoid_noise=sigmoid_noise,
sigmoid_noise_seed=sigmoid_noise_seed,
score_bias_init=score_bias_init)
elif attention_type == 'L':
attention_mechanism = att_w.LuongAttention(
num_units=num_units,
memory=memory,
probability_fn=probability_fn,
scale=normalization)
elif attention_type == 'LM':
attention_mechanism = att_w.LuongMonotonicAttention(
num_units=num_units,
memory=memory,
scale=normalization,
sigmoid_noise=sigmoid_noise,
sigmoid_noise_seed=sigmoid_noise_seed,
score_bias_init=score_bias_init)
else:
raise 'Invalid attention type'
att_wrapper = att_w.AttentionWrapper(
cell=mutli_layer,
attention_mechanism=attention_mechanism,
attention_layer_size=attention_layer_size,
cell_input_fn=cell_input_fn,
alignment_history=alignment_history,
output_attention=output_attention,
initial_cell_state=initial_cell_state)
init_states = att_wrapper.zero_state(batch_size=batch_size, dtype=dtype)
return att_wrapper, init_states
def _testWithAttention(self,
create_attention_mechanism,
expected_final_output,
expected_final_state,
attention_mechanism_depth=3,
alignment_history=False,
expected_final_alignment_history=None,
name=""):
encoder_sequence_length = [3, 2, 3, 1, 0]
decoder_sequence_length = [2, 0, 1, 2, 3]
batch_size = 5
encoder_max_time = 8
decoder_max_time = 4
input_depth = 7
encoder_output_depth = 10
cell_depth = 9
attention_depth = 6
decoder_inputs = np.random.randn(batch_size, decoder_max_time,
input_depth).astype(np.float32)
encoder_outputs = np.random.randn(batch_size, encoder_max_time,
encoder_output_depth).astype(
np.float32)
attention_mechanism = create_attention_mechanism(
num_units=attention_mechanism_depth,
memory=encoder_outputs,
memory_sequence_length=encoder_sequence_length)
with self.test_session(use_gpu=True) as sess:
with vs.variable_scope(
"root",
initializer=init_ops.random_normal_initializer(stddev=0.01,
seed=3)):
cell = core_rnn_cell.LSTMCell(cell_depth)
cell = wrapper.AttentionWrapper(
cell,
attention_mechanism,
attention_size=attention_depth,
alignment_history=alignment_history)
helper = helper_py.TrainingHelper(decoder_inputs,
decoder_sequence_length)
my_decoder = basic_decoder.BasicDecoder(
cell=cell,
helper=helper,
initial_state=cell.zero_state(dtype=dtypes.float32,
batch_size=batch_size))
final_outputs, final_state = decoder.dynamic_decode(my_decoder)
self.assertTrue(
isinstance(final_outputs, basic_decoder.BasicDecoderOutput))
self.assertTrue(
isinstance(final_state, wrapper.AttentionWrapperState))
self.assertTrue(
isinstance(final_state.cell_state,
core_rnn_cell.LSTMStateTuple))
self.assertEqual(
(batch_size, None, attention_depth),
tuple(final_outputs.rnn_output.get_shape().as_list()))
self.assertEqual(
(batch_size, None),
tuple(final_outputs.sample_id.get_shape().as_list()))
self.assertEqual(
(batch_size, attention_depth),
tuple(final_state.attention.get_shape().as_list()))
self.assertEqual(
(batch_size, cell_depth),
tuple(final_state.cell_state.c.get_shape().as_list()))
self.assertEqual(
(batch_size, cell_depth),
tuple(final_state.cell_state.h.get_shape().as_list()))
if alignment_history:
state_alignment_history = final_state.alignment_history.stack()
# Remove the history from final_state for purposes of the
# remainder of the tests.
final_state = final_state._replace(alignment_history=()) # pylint: disable=protected-access
self.assertEqual(
(None, batch_size, encoder_max_time),
tuple(state_alignment_history.get_shape().as_list()))
else:
state_alignment_history = ()
sess.run(variables.global_variables_initializer())
sess_results = sess.run({
"final_outputs":
final_outputs,
"final_state":
final_state,
"state_alignment_history":
state_alignment_history,
})
print("Copy/paste (%s)\nexpected_final_output = " % name,
sess_results["final_outputs"])
sys.stdout.flush()
print("Copy/paste (%s)\nexpected_final_state = " % name,
sess_results["final_state"])
sys.stdout.flush()
print(
"Copy/paste (%s)\nexpected_final_alignment_history = " % name,
sess_results["state_alignment_history"])
sys.stdout.flush()
nest.map_structure(self.assertAllClose, expected_final_output,
sess_results["final_outputs"])
nest.map_structure(self.assertAllClose, expected_final_state,
sess_results["final_state"])
if alignment_history: # by default, the wrapper emits attention as output
self.assertAllClose(
# outputs are batch major but the stacked TensorArray is time major
sess_results["state_alignment_history"],
expected_final_alignment_history)
def _build_decoder(self,
encoder_outputs,
enc_src_lengths,
tgt_inputs = None,
tgt_lengths = None,
GO_SYMBOL = 1,
END_SYMBOL = 2,
out_layer_activation = None):
"""
Builds decoder part of the graph, for training and inference
TODO: add param tensor shapes
:param encoder_outputs:
:param enc_src_lengths:
:param tgt_inputs:
:param tgt_lengths:
:param GO_SYMBOL:
:param END_SYMBOL:
:param out_layer_activation:
:return:
"""
with tf.variable_scope("Decoder"):
tgt_vocab_size = self.model_params['tgt_vocab_size']
tgt_emb_size = self.model_params['tgt_emb_size']
self._tgt_w = tf.get_variable(name='W_tgt_embedding',
shape=[tgt_vocab_size, tgt_emb_size], dtype=getdtype())
batch_size = self.model_params['batch_size']
decoder_cell = create_rnn_cell(cell_type=self.model_params['decoder_cell_type'],
cell_params={"num_units": self.model_params['decoder_cell_units']},
num_layers=self.model_params['decoder_layers'],
dp_input_keep_prob=self.model_params['decoder_dp_input_keep_prob'] if self._mode == "train" else 1.0,
dp_output_keep_prob=self.model_params['decoder_dp_output_keep_prob'] if self._mode == "train" else 1.0,
residual_connections=self.model_params['decoder_use_skip_connections'])
output_layer = layers_core.Dense(tgt_vocab_size, use_bias=False,
activation = out_layer_activation)
def attn_decoder_custom_fn(inputs, attention):
# to make shapes equal for skip connections
if self.model_params['decoder_use_skip_connections']:
input_layer = layers_core.Dense(self.model_params['decoder_cell_units'], dtype=getdtype())
return input_layer(tf.concat([inputs, attention], -1))
else:
return tf.concat([inputs, attention], -1)
if self.mode == "infer":
if self._decoder_type == "beam_search":
self._length_penalty_weight = 1.0 if "length_penalty" not in self.model_params else self.model_params[
"length_penalty"]
# beam_width of 1 should be same as argmax decoder
self._beam_width = 1 if "beam_width" not in self.model_params else self.model_params["beam_width"]
tiled_enc_outputs = tf.contrib.seq2seq.tile_batch(encoder_outputs, multiplier=self._beam_width)
tiled_enc_src_lengths = tf.contrib.seq2seq.tile_batch(enc_src_lengths, multiplier=self._beam_width)
attention_mechanism = self._build_attention(tiled_enc_outputs, tiled_enc_src_lengths)
attentive_decoder_cell = attention_wrapper.AttentionWrapper(cell=decoder_cell,
attention_mechanism=attention_mechanism,
cell_input_fn=attn_decoder_custom_fn)
batch_size_tensor = tf.constant(batch_size)
decoder = tf.contrib.seq2seq.BeamSearchDecoder(
cell=attentive_decoder_cell,
embedding=self._tgt_w,
start_tokens=tf.tile([GO_SYMBOL], [batch_size]),
end_token=END_SYMBOL,
initial_state=attentive_decoder_cell.zero_state(dtype=getdtype(),
batch_size=batch_size_tensor * self._beam_width),
beam_width=self._beam_width,
output_layer=output_layer,
length_penalty_weight=self._length_penalty_weight)
else:
attention_mechanism = self._build_attention(encoder_outputs, enc_src_lengths)
attentive_decoder_cell = attention_wrapper.AttentionWrapper(cell=decoder_cell,
attention_mechanism=attention_mechanism,
cell_input_fn=attn_decoder_custom_fn)
helper = tf.contrib.seq2seq.GreedyEmbeddingHelper(
embedding=self._tgt_w,
start_tokens=tf.fill([batch_size], GO_SYMBOL),
end_token=END_SYMBOL)
decoder = tf.contrib.seq2seq.BasicDecoder(
cell=attentive_decoder_cell,
helper=helper,
initial_state=attentive_decoder_cell.zero_state(batch_size=batch_size, dtype=getdtype()),
output_layer=output_layer)
elif self.mode == "train" or self.mode == "eval":
attention_mechanism = self._build_attention(encoder_outputs, enc_src_lengths)
attentive_decoder_cell = attention_wrapper.AttentionWrapper(cell=decoder_cell,
attention_mechanism=attention_mechanism,
cell_input_fn=attn_decoder_custom_fn)
input_vectors = tf.nn.embedding_lookup(self._tgt_w, tgt_inputs)
helper = tf.contrib.seq2seq.TrainingHelper(
inputs = input_vectors,
sequence_length = tgt_lengths)
decoder = tf.contrib.seq2seq.BasicDecoder(
cell=attentive_decoder_cell,
helper=helper,
output_layer=output_layer,
initial_state=attentive_decoder_cell.zero_state(batch_size, dtype=getdtype()))
else:
raise NotImplementedError("Unknown mode")
final_outputs, final_state, final_sequence_lengths = tf.contrib.seq2seq.dynamic_decode(
decoder = decoder,
impute_finished=False if self._decoder_type == "beam_search" else True,
maximum_iterations=tf.reduce_max(tgt_lengths) if self._mode == 'train' else tf.reduce_max(enc_src_lengths)*2,
swap_memory = False if 'use_swap_memory' not in self.model_params else self.model_params['use_swap_memory'])
return final_outputs, final_state, final_sequence_lengths
def _testWithMaybeMultiAttention(self,
is_multi,
create_attention_mechanisms,
expected_final_output,
expected_final_state,
attention_mechanism_depths,
alignment_history=False,
expected_final_alignment_history=None,
attention_layer_sizes=None,
name=''):
# Allow is_multi to be True with a single mechanism to enable test for
# passing in a single mechanism in a list.
assert len(create_attention_mechanisms) == 1 or is_multi
encoder_sequence_length = [3, 2, 3, 1, 1]
decoder_sequence_length = [2, 0, 1, 2, 3]
batch_size = 5
encoder_max_time = 8
decoder_max_time = 4
input_depth = 7
encoder_output_depth = 10
cell_depth = 9
if attention_layer_sizes is None:
attention_depth = encoder_output_depth * len(create_attention_mechanisms)
else:
# Compute sum of attention_layer_sizes. Use encoder_output_depth if None.
attention_depth = sum([attention_layer_size or encoder_output_depth
for attention_layer_size in attention_layer_sizes])
decoder_inputs = array_ops.placeholder_with_default(
np.random.randn(batch_size, decoder_max_time,
input_depth).astype(np.float32),
shape=(None, None, input_depth))
encoder_outputs = array_ops.placeholder_with_default(
np.random.randn(batch_size, encoder_max_time,
encoder_output_depth).astype(np.float32),
shape=(None, None, encoder_output_depth))
attention_mechanisms = [
creator(num_units=depth,
memory=encoder_outputs,
memory_sequence_length=encoder_sequence_length)
for creator, depth in zip(create_attention_mechanisms,
attention_mechanism_depths)]
with self.test_session(use_gpu=True) as sess:
with vs.variable_scope(
'root',
initializer=init_ops.random_normal_initializer(stddev=0.01, seed=3)):
cell = rnn_cell.LSTMCell(cell_depth)
cell = wrapper.AttentionWrapper(
cell,
attention_mechanisms if is_multi else attention_mechanisms[0],
attention_layer_size=(attention_layer_sizes if is_multi
else attention_layer_sizes[0]),
alignment_history=alignment_history)
helper = helper_py.TrainingHelper(decoder_inputs,
decoder_sequence_length)
my_decoder = basic_decoder.BasicDecoder(
cell=cell,
helper=helper,
initial_state=cell.zero_state(
dtype=dtypes.float32, batch_size=batch_size))
final_outputs, final_state, _ = decoder.dynamic_decode(my_decoder)
self.assertTrue(
isinstance(final_outputs, basic_decoder.BasicDecoderOutput))
self.assertTrue(
isinstance(final_state, wrapper.AttentionWrapperState))
self.assertTrue(
isinstance(final_state.cell_state, rnn_cell.LSTMStateTuple))
self.assertEqual((batch_size, None, attention_depth),
tuple(final_outputs.rnn_output.get_shape().as_list()))
self.assertEqual((batch_size, None),
tuple(final_outputs.sample_id.get_shape().as_list()))
self.assertEqual((batch_size, attention_depth),
tuple(final_state.attention.get_shape().as_list()))
self.assertEqual((batch_size, cell_depth),
tuple(final_state.cell_state.c.get_shape().as_list()))
self.assertEqual((batch_size, cell_depth),
tuple(final_state.cell_state.h.get_shape().as_list()))
if alignment_history:
if is_multi:
state_alignment_history = []
for history_array in final_state.alignment_history:
history = history_array.stack()
self.assertEqual(
(None, batch_size, None),
tuple(history.get_shape().as_list()))
state_alignment_history.append(history)
state_alignment_history = tuple(state_alignment_history)
else:
state_alignment_history = final_state.alignment_history.stack()
self.assertEqual(
(None, batch_size, None),
tuple(state_alignment_history.get_shape().as_list()))
nest.assert_same_structure(
cell.state_size,
cell.zero_state(batch_size, dtypes.float32))
# Remove the history from final_state for purposes of the
# remainder of the tests.
final_state = final_state._replace(alignment_history=()) # pylint: disable=protected-access
else:
state_alignment_history = ()
sess.run(variables.global_variables_initializer())
sess_results = sess.run({
'final_outputs': final_outputs,
'final_state': final_state,
'state_alignment_history': state_alignment_history,
})
final_output_info = nest.map_structure(get_result_summary,
sess_results['final_outputs'])
final_state_info = nest.map_structure(get_result_summary,
sess_results['final_state'])
print(name)
print('Copy/paste:\nexpected_final_output = %s' % str(final_output_info))
print('expected_final_state = %s' % str(final_state_info))
nest.map_structure(self.assertAllCloseOrEqual, expected_final_output,
final_output_info)
nest.map_structure(self.assertAllCloseOrEqual, expected_final_state,
final_state_info)
if alignment_history: # by default, the wrapper emits attention as output
final_alignment_history_info = nest.map_structure(
get_result_summary, sess_results['state_alignment_history'])
print('expected_final_alignment_history = %s' %
str(final_alignment_history_info))
nest.map_structure(
self.assertAllCloseOrEqual,
# outputs are batch major but the stacked TensorArray is time major
expected_final_alignment_history,
final_alignment_history_info)
def _build_decoder(self, encoder_outputs, encoder_state):
with tf.name_scope("seq_decoder"):
batch_size = self.batch_size
# sequence_length = tf.fill([self.batch_size], self.num_steps)
if self.mode == tf.contrib.learn.ModeKeys.TRAIN:
sequence_length = self.iterator.target_length
else:
sequence_length = self.iterator.source_length
if (self.mode !=
tf.contrib.learn.ModeKeys.TRAIN) and self.beam_width > 1:
batch_size = batch_size * self.beam_width
encoder_outputs = beam_search_decoder.tile_batch(
encoder_outputs, multiplier=self.beam_width)
encoder_state = nest.map_structure(
lambda s: beam_search_decoder.tile_batch(
s, self.beam_width), encoder_state)
sequence_length = beam_search_decoder.tile_batch(
sequence_length, multiplier=self.beam_width)
single_cell = single_rnn_cell(self.hparams.unit_type,
self.num_units, self.dropout)
decoder_cell = MultiRNNCell(
[single_cell for _ in range(self.num_layers_decoder)])
decoder_cell = InputProjectionWrapper(decoder_cell,
num_proj=self.num_units)
attention_mechanism = create_attention_mechanism(
self.hparams.attention_mechanism,
self.num_units,
memory=encoder_outputs,
source_sequence_length=sequence_length)
decoder_cell = wrapper.AttentionWrapper(
decoder_cell,
attention_mechanism,
attention_layer_size=self.num_units,
output_attention=True,
alignment_history=False)
# AttentionWrapperState의 cell_state를 encoder의 state으로 설정한다.
initial_state = decoder_cell.zero_state(batch_size=batch_size,
dtype=tf.float32)
embeddings_decoder = tf.get_variable(
"embedding_decoder",
[self.num_decoder_symbols, self.num_units],
initializer=self.initializer,
dtype=tf.float32)
output_layer = Dense(units=self.num_decoder_symbols,
use_bias=True,
name="output_layer")
if self.mode == tf.contrib.learn.ModeKeys.TRAIN:
decoder_inputs = tf.nn.embedding_lookup(
embeddings_decoder, self.iterator.target_in)
decoder_helper = helper.TrainingHelper(
decoder_inputs, sequence_length=sequence_length)
dec = basic_decoder.BasicDecoder(decoder_cell,
decoder_helper,
initial_state,
output_layer=output_layer)
final_outputs, final_state, _ = decoder.dynamic_decode(dec)
output_ids = final_outputs.rnn_output
outputs = final_outputs.sample_id
else:
def embedding_fn(inputs):
return tf.nn.embedding_lookup(embeddings_decoder, inputs)
decoding_length_factor = 2.0
max_encoder_length = tf.reduce_max(self.iterator.source_length)
maximum_iterations = tf.to_int32(
tf.round(
tf.to_float(max_encoder_length) *
decoding_length_factor))
tgt_sos_id = tf.cast(
self.tgt_vocab_table.lookup(tf.constant(self.hparams.sos)),
tf.int32)
tgt_eos_id = tf.cast(
self.tgt_vocab_table.lookup(tf.constant(self.hparams.eos)),
tf.int32)
start_tokens = tf.fill([self.batch_size], tgt_sos_id)
end_token = tgt_eos_id
if self.beam_width == 1:
decoder_helper = helper.GreedyEmbeddingHelper(
embedding=embedding_fn,
start_tokens=start_tokens,
end_token=end_token)
dec = basic_decoder.BasicDecoder(decoder_cell,
decoder_helper,
initial_state,
output_layer=output_layer)
else:
dec = beam_search_decoder.BeamSearchDecoder(
cell=decoder_cell,
embedding=embedding_fn,
start_tokens=start_tokens,
end_token=end_token,
initial_state=initial_state,
output_layer=output_layer,
beam_width=self.beam_width)
final_outputs, final_state, _ = decoder.dynamic_decode(
dec,
# swap_memory=True,
maximum_iterations=maximum_iterations)
if self.mode == tf.contrib.learn.ModeKeys.TRAIN or self.beam_width == 1:
output_ids = final_outputs.sample_id
outputs = final_outputs.rnn_output
else:
output_ids = final_outputs.predicted_ids
outputs = final_outputs.beam_search_decoder_output.scores
return output_ids, outputs
def _testDynamicDecodeRNN(self, time_major, has_attention):
encoder_sequence_length = np.array([3, 2, 3, 1, 1])
decoder_sequence_length = np.array([2, 0, 1, 2, 3])
batch_size = 5
decoder_max_time = 4
input_depth = 7
cell_depth = 9
attention_depth = 6
vocab_size = 20
end_token = vocab_size - 1
start_token = 0
embedding_dim = 50
max_out = max(decoder_sequence_length)
output_layer = layers_core.Dense(vocab_size,
use_bias=True,
activation=None)
beam_width = 3
with self.test_session() as sess:
batch_size_tensor = constant_op.constant(batch_size)
embedding = np.random.randn(vocab_size,
embedding_dim).astype(np.float32)
cell = rnn_cell.LSTMCell(cell_depth)
initial_state = cell.zero_state(batch_size, dtypes.float32)
if has_attention:
inputs = array_ops.placeholder_with_default(
np.random.randn(batch_size, decoder_max_time,
input_depth).astype(np.float32),
shape=(None, None, input_depth))
tiled_inputs = beam_search_decoder.tile_batch(
inputs, multiplier=beam_width)
tiled_sequence_length = beam_search_decoder.tile_batch(
encoder_sequence_length, multiplier=beam_width)
attention_mechanism = attention_wrapper.BahdanauAttention(
num_units=attention_depth,
memory=tiled_inputs,
memory_sequence_length=tiled_sequence_length)
initial_state = beam_search_decoder.tile_batch(
initial_state, multiplier=beam_width)
cell = attention_wrapper.AttentionWrapper(
cell=cell,
attention_mechanism=attention_mechanism,
attention_layer_size=attention_depth,
alignment_history=False)
cell_state = cell.zero_state(dtype=dtypes.float32,
batch_size=batch_size_tensor *
beam_width)
if has_attention:
cell_state = cell_state.clone(cell_state=initial_state)
bsd = beam_search_decoder.BeamSearchDecoder(
cell=cell,
embedding=embedding,
start_tokens=array_ops.fill([batch_size_tensor], start_token),
end_token=end_token,
initial_state=cell_state,
beam_width=beam_width,
output_layer=output_layer,
length_penalty_weight=0.0)
final_outputs, final_state, final_sequence_lengths = (
decoder.dynamic_decode(bsd,
output_time_major=time_major,
maximum_iterations=max_out))
def _t(shape):
if time_major:
return (shape[1], shape[0]) + shape[2:]
return shape
self.assertTrue(
isinstance(final_outputs,
beam_search_decoder.FinalBeamSearchDecoderOutput))
self.assertTrue(
isinstance(final_state,
beam_search_decoder.BeamSearchDecoderState))
beam_search_decoder_output = final_outputs.beam_search_decoder_output
self.assertEqual(
_t((batch_size, None, beam_width)),
tuple(beam_search_decoder_output.scores.get_shape().as_list()))
self.assertEqual(
_t((batch_size, None, beam_width)),
tuple(final_outputs.predicted_ids.get_shape().as_list()))
sess.run(variables.global_variables_initializer())
sess_results = sess.run({
'final_outputs':
final_outputs,
'final_state':
final_state,
'final_sequence_lengths':
final_sequence_lengths
})
max_sequence_length = np.max(
sess_results['final_sequence_lengths'])
# A smoke test
self.assertEqual(
_t((batch_size, max_sequence_length, beam_width)),
sess_results['final_outputs'].beam_search_decoder_output.
scores.shape)
self.assertEqual(
_t((batch_size, max_sequence_length, beam_width)),
sess_results['final_outputs'].beam_search_decoder_output.
predicted_ids.shape)
from tensorflow.python.ops import rnn_cell
#
# tf.enable_eager_execution()
batch_size = 5
src_len = [4, 5, 3, 5, 6]
max_times = 6
num_units = 16
enc_output = tf.random.normal((batch_size, max_times, num_units),
dtype=tf.float32)
#
# attenRNNCell
rnncell = rnn_cell.LSTMCell(num_units=16)
attention_mechanism = attention_wrapper.BahdanauAttention(
num_units=num_units, memory=enc_output, memory_sequence_length=src_len)
attnRNNCell = attention_wrapper.AttentionWrapper(
cell=rnncell,
attention_mechanism=attention_mechanism,
alignment_history=True)
# training
tgt_len = [5, 6, 2, 7, 4]
tgt_max_times = 7
tgt_inputs = tf.random.normal((batch_size, tgt_max_times, num_units),
dtype=tf.float32)
training_helper = helper_py.TrainingHelper(tgt_inputs, tgt_len)
# train helper
train_decoder = basic_decoder.BasicDecoder(
cell=attnRNNCell,
helper=training_helper,
initial_state=attnRNNCell.zero_state(batch_size, tf.float32))
