def _compute_logits(self, rnn_out):
if self._num_layers == 1 and self._weights is not None:
assert tensor_utils.shape(rnn_out, -1) == self._hidden_dim
if self._num_layers == 1:
with tf.variable_scope("mlp1", reuse=self._reuse):
if self._weights is None:
scale = (3.0 / self._hidden_dim) ** 0.5
weight_initializer = tf.random_uniform_initializer(
minval=-scale, maxval=scale)
self._linear1 = Linear(
rnn_out,
self._output_size,
True, weights=None,
weight_initializer=weight_initializer)
else:
self._linear1 = Linear(
rnn_out, self._output_size, True, weights=self._weights)
logits = self._linear1(rnn_out)
else:
assert False
assert self._num_layers == 2
with tf.variable_scope("mlp1", reuse=self._reuse):
if self._linear1 is None:
self._linear1 = Linear(
rnn_out, self._hidden_dim, True,
weights=None,
weight_initializer=tf.contrib.layers.xavier_initializer())
hidden = self._linear1(rnn_out)
if self._activation:
hidden = self._activation(hidden)
if self._mode == tf.estimator.ModeKeys.TRAIN and self._dropout > 0.:
hidden = tf.nn.dropout(hidden, keep_prob=1.-self._dropout)
with tf.variable_scope("mlp2", reuse=self._reuse):
if self._linear2 is None:
if self._weights is None:
scale = (3.0 / self._hidden_dim) ** 0.5
weight_initializer = tf.random_uniform_initializer(
minval=-scale, maxval=scale)
self._linear2 = Linear(
hidden,
self._output_size,
True, weights=None,
weight_initializer=weight_initializer)
else:
self._linear2 = Linear(
hidden, self._output_size, True, weights=self._weights)
logits = self._linear2(hidden)
return logits
def beam_decoder(features, mode, vocab, encoder_outputs, hps):
"""Beam search decoder.
Args:
features: Dictionary of input Tensors.
mode: train or eval. Keys from tf.estimator.ModeKeys.
vocab: A list of strings of words in the vocabulary.
encoder_outputs: output tensors from the encoder
hps: Hyperparams.
Returns:
Decoder outputs
"""
assert mode is not tf.estimator.ModeKeys.TRAIN, "Not using beam in training."
embeddings = encoder_outputs.embeddings
mem_input = encoder_outputs.mem_input
batch_size = tensor_utils.shape(features["src_len"], 0)
src_len, src_inputs = features["src_len"], features["src_inputs"]
src_mask = tf.sequence_mask(src_len, tf.shape(src_inputs)[1])
if hps.att_neighbor:
neighbor_len, neighbor_inputs \
= features["neighbor_len"], features["neighbor_inputs"]
neighbor_mask = tf.sequence_mask(neighbor_len,
tf.shape(neighbor_inputs)[1])
inputs = tf.concat([src_inputs, neighbor_inputs], 1)
# lens = src_len + neighbor_len
mask = tf.concat([src_mask, neighbor_mask], axis=1)
lens = tf.shape(mask)[1] * tf.ones([batch_size], tf.int32)
else:
inputs = features["src_inputs"]
lens = features["src_len"]
mask = src_mask
sparse_inputs = None
float_mask = tf.cast(mask, dtype=tf.float32)
if hps.use_copy:
sparse_inputs = sparse_map(tf.expand_dims(inputs, axis=2),
tf.expand_dims(float_mask, axis=2),
vocab.size())
sparse_inputs = sparse_tile_batch(sparse_inputs,
multiplier=hps.beam_width)
tiled_mask = tf.contrib.seq2seq.tile_batch(mask, multiplier=hps.beam_width)
inputs = tf.contrib.seq2seq.tile_batch(inputs, multiplier=hps.beam_width)
lens = tf.contrib.seq2seq.tile_batch(lens, multiplier=hps.beam_width)
def _beam_decode(cell):
"""Beam decode."""
with tf.variable_scope("beam_decoder"):
initial_state = cell.zero_state(batch_size=batch_size *
hps.beam_width,
dtype=tf.float32)
if hps.use_bridge:
h_state, c_state = encoder_outputs.states
h_state = tf.contrib.seq2seq.tile_batch(
h_state, multiplier=hps.beam_width)
c_state = tf.contrib.seq2seq.tile_batch(
c_state, multiplier=hps.beam_width)
initial_cell_state = tf.contrib.rnn.LSTMStateTuple(
h_state, c_state)
initial_state = initial_state.clone(
cell_state=(initial_cell_state, ))
decoder = tf.contrib.seq2seq.BeamSearchDecoder(
cell=cell,
embedding=embeddings,
start_tokens=tf.fill([batch_size],
vocab.word2id(data.START_DECODING)),
end_token=vocab.word2id(data.STOP_DECODING),
initial_state=initial_state,
beam_width=hps.beam_width,
length_penalty_weight=hps.length_norm,
coverage_penalty_weight=hps.cp)
with tf.variable_scope("dynamic_decode", reuse=tf.AUTO_REUSE):
decoder_outputs, _, decoder_len = tf.contrib.seq2seq.dynamic_decode(
decoder=decoder, maximum_iterations=hps.max_dec_steps)
return decoder_outputs, decoder_len
# [batch_size*beam_width, src_len, encoder_dim]
att_context = tf.contrib.seq2seq.tile_batch(encoder_outputs.att_context,
multiplier=hps.beam_width)
copy_context = None
if hps.use_copy:
copy_context = tf.contrib.seq2seq.tile_batch(
encoder_outputs.copy_context, multiplier=hps.beam_width)
with tf.variable_scope("attention", reuse=tf.AUTO_REUSE):
if hps.att_type == "luong":
attention = tf.contrib.seq2seq.LuongAttention(
num_units=hps.decoder_dim,
memory=att_context,
memory_sequence_length=lens)
elif hps.att_type == "bahdanau":
attention = tf.contrib.seq2seq.BahdanauAttention(
num_units=hps.decoder_dim,
memory=att_context,
memory_sequence_length=lens)
elif hps.att_type == "hyper":
attention = HyperAttention(num_units=hps.decoder_dim,
mem_input=mem_input,
hps=hps,
memory=att_context,
use_beam=True,
memory_sequence_length=lens)
elif hps.att_type == "my":
attention = MyAttention(num_units=hps.decoder_dim,
memory=att_context,
memory_sequence_length=lens,
mask=tiled_mask)
with tf.variable_scope("rnn_decoder", reuse=tf.AUTO_REUSE):
decoder_cell = get_rnn_cell(mode=mode,
hps=hps,
input_dim=hps.decoder_dim + hps.emb_dim,
num_units=hps.decoder_dim,
num_layers=hps.num_decoder_layers,
dropout=hps.decoder_drop,
mem_input=mem_input,
use_beam=True,
cell_type=hps.rnn_cell)
with tf.variable_scope("attention_wrapper", reuse=tf.AUTO_REUSE):
decoder_cell = tf.contrib.seq2seq.AttentionWrapper(
decoder_cell,
attention,
attention_layer_size=hps.decoder_dim,
alignment_history=hps.use_copy)
with tf.variable_scope("output_projection", reuse=tf.AUTO_REUSE):
weights = tf.transpose(embeddings) if hps.tie_embedding else None
hidden_dim = hps.emb_dim if hps.tie_embedding else hps.decoder_dim
decoder_cell = OutputWrapper(
decoder_cell,
num_layers=hps.num_mlp_layers,
hidden_dim=hidden_dim,
output_size=vocab.size() if hps.tie_embedding else hps.output_size,
weights=weights,
dropout=hps.out_drop,
use_copy=hps.use_copy,
encoder_emb=copy_context,
sparse_inputs=sparse_inputs,
mask=tf.cast(tiled_mask, dtype=tf.float32),
hps=hps,
mode=mode,
reuse=tf.AUTO_REUSE)
decoder_outputs, decoder_len = _beam_decode(decoder_cell)
return DecoderOutputs(decoder_outputs=decoder_outputs,
decoder_len=decoder_len)
def basic_decoder(features, mode, vocab, encoder_outputs, hps):
"""Decoder.
Args:
features: Dictionary of input Tensors.
mode: train or eval. Keys from tf.estimator.ModeKeys.
vocab: A list of strings of words in the vocabulary.
encoder_outputs: output tensors from the encoder
hps: Hyperparams.
Returns:
Decoder outputs
"""
embeddings = encoder_outputs.embeddings
mem_input = encoder_outputs.mem_input
batch_size = tensor_utils.shape(mem_input, 0)
src_len, src_inputs = features["src_len"], features["src_inputs"]
src_mask = tf.sequence_mask(src_len, tf.shape(src_inputs)[1])
if hps.att_neighbor:
neighbor_len, neighbor_inputs \
= features["neighbor_len"], features["neighbor_inputs"]
neighbor_mask = tf.sequence_mask(neighbor_len,
tf.shape(neighbor_inputs)[1])
inputs = tf.concat([src_inputs, neighbor_inputs], 1)
lens = src_len + neighbor_len
mask = tf.concat([src_mask, neighbor_mask], axis=1)
else:
inputs = features["src_inputs"]
lens = features["src_len"]
mask = src_mask
sparse_inputs = None
float_mask = tf.cast(mask, dtype=tf.float32)
if hps.use_copy:
sparse_inputs = sparse_map(tf.expand_dims(inputs, axis=2),
tf.expand_dims(float_mask, axis=2),
vocab.size())
# [batch_size, dec_len]
decoder_inputs = features["decoder_inputs"]
# [batch_size, dec_len, emb_dim]
decoder_input_emb = tf.nn.embedding_lookup(embeddings, decoder_inputs)
if mode == tf.estimator.ModeKeys.TRAIN and hps.emb_drop > 0.:
decoder_input_emb = tf.nn.dropout(decoder_input_emb,
keep_prob=1.0 - hps.emb_drop)
def _decode(cell, helper):
"""Decode function.
Args:
cell: rnn cell
helper: a helper instance from tf.contrib.seq2seq.
Returns:
decoded outputs and lengths.
"""
with tf.variable_scope("decoder"):
initial_state = cell.zero_state(batch_size, tf.float32)
if hps.use_bridge:
h_state, c_state = encoder_outputs.states
initial_cell_state = tf.contrib.rnn.LSTMStateTuple(
h_state, c_state)
initial_state = initial_state.clone(
cell_state=(initial_cell_state, ))
decoder = tf.contrib.seq2seq.BasicDecoder(
cell=cell, helper=helper, initial_state=initial_state)
with tf.variable_scope("dynamic_decode", reuse=tf.AUTO_REUSE):
decoder_outputs, _, decoder_len = tf.contrib.seq2seq.dynamic_decode(
decoder=decoder, maximum_iterations=hps.max_dec_steps)
return decoder_outputs, decoder_len
att_context = encoder_outputs.att_context
with tf.variable_scope("attention"):
if hps.att_type == "luong":
attention = tf.contrib.seq2seq.LuongAttention(
num_units=hps.decoder_dim,
memory=att_context,
memory_sequence_length=lens)
elif hps.att_type == "bahdanau":
attention = tf.contrib.seq2seq.BahdanauAttention(
num_units=hps.decoder_dim,
memory=att_context,
memory_sequence_length=lens)
elif hps.att_type == "hyper":
attention = HyperAttention(num_units=hps.decoder_dim,
mem_input=mem_input,
hps=hps,
memory=att_context,
use_beam=False,
memory_sequence_length=lens)
elif hps.att_type == "my":
attention = MyAttention(num_units=hps.decoder_dim,
memory=att_context,
memory_sequence_length=lens,
mask=mask)
with tf.variable_scope("rnn_decoder"):
decoder_cell = get_rnn_cell(mode=mode,
hps=hps,
input_dim=hps.decoder_dim + hps.emb_dim,
num_units=hps.decoder_dim,
num_layers=hps.num_decoder_layers,
dropout=hps.decoder_drop,
mem_input=mem_input,
use_beam=False,
cell_type=hps.rnn_cell)
with tf.variable_scope("attention_wrapper"):
decoder_cell = tf.contrib.seq2seq.AttentionWrapper(
decoder_cell,
attention,
attention_layer_size=hps.decoder_dim,
alignment_history=hps.use_copy)
with tf.variable_scope("output_projection"):
weights = tf.transpose(embeddings) if hps.tie_embedding else None
hidden_dim = hps.emb_dim if hps.tie_embedding else hps.decoder_dim
decoder_cell = OutputWrapper(
decoder_cell,
num_layers=hps.num_mlp_layers,
hidden_dim=hidden_dim,
output_size=hps.output_size,
# output_size=vocab.size(),
weights=weights,
dropout=hps.out_drop,
use_copy=hps.use_copy,
encoder_emb=encoder_outputs.copy_context,
sparse_inputs=sparse_inputs,
mask=float_mask,
mode=mode,
hps=hps)
if mode == tf.estimator.ModeKeys.TRAIN:
if hps.sampling_probability > 0.:
helper = tf.contrib.seq2seq.ScheduledEmbeddingTrainingHelper(
inputs=decoder_input_emb,
sequence_length=features["decoder_len"],
embedding=embeddings,
sampling_probability=hps.sampling_probability)
else:
helper = tf.contrib.seq2seq.TrainingHelper(decoder_input_emb,
features["decoder_len"])
decoder_outputs, _ = _decode(decoder_cell, helper=helper)
return DecoderOutputs(decoder_outputs=decoder_outputs,
decoder_len=features["decoder_len"]), None
# used to compute loss
teacher_helper = tf.contrib.seq2seq.TrainingHelper(decoder_input_emb,
features["decoder_len"])
teacher_decoder_outputs, _ = _decode(decoder_cell, helper=teacher_helper)
helper = tf.contrib.seq2seq.GreedyEmbeddingHelper(
embedding=embeddings,
start_tokens=tf.fill([batch_size], vocab.word2id(data.START_DECODING)),
end_token=vocab.word2id(data.STOP_DECODING))
decoder_outputs, decoder_len = _decode(decoder_cell, helper=helper)
return DecoderOutputs(
decoder_outputs=decoder_outputs,
decoder_len=decoder_len), \
DecoderOutputs(
decoder_outputs=teacher_decoder_outputs,
decoder_len=features["decoder_len"]
)
def __init__(self,
cell,
num_layers,
hidden_dim,
output_size,
weights=None,
activation=tf.tanh,
dropout=0.,
use_copy=False,
encoder_emb=None,
sparse_inputs=None,
mask=None,
hps=None,
mode=tf_estimator.ModeKeys.EVAL,
reuse=None):
"""Create a cell with output projection.
Args:
cell: an RNNCell, a projection to output_size is added to it.
num_layers: number of MLP layers.
hidden_dim: hidden size of the MLP.
output_size: integer, the size of the output after projection.
weights: (optional) a specified tensor.
activation: (optional) an optional activation function.
dropout: dropout rate for dropout at the output layer.
use_copy: Use copy mechanism or not.
encoder_emb: Outputs of the encoder.
sparse_inputs: Sparse inputs.
mask: mask.
hps: Hyperparameters.
mode: train/eval.
reuse: (optional) Python boolean describing whether to reuse variables
in an existing scope. If not `True`, and the existing scope already has
the given variables, an error is raised.
Raises:
TypeError: if cell is not an RNNCell.
ValueError: if output_size is not positive.
"""
super(OutputWrapper, self).__init__(_reuse=reuse)
if output_size < 1:
raise ValueError("Parameter output_size must be > 0: %d." %
output_size)
self._cell = cell
self._num_layers = num_layers
self._activation = activation
self._weights = weights
if self._weights is None:
self._output_size = output_size
else:
self._output_size = tensor_utils.shape(self._weights, 1)
self._hidden_dim = hidden_dim
self._dropout = dropout
self._reuse = reuse
self._mode = None
self._sigmoid = tf.sigmoid
self._linear1, self._linear2, self._linear_copy = None, None, None
assert self._num_layers <= 2
self._use_copy = use_copy
self._encoder_emb = encoder_emb
self._sparse_inputs, self._mask = sparse_inputs, mask
self._mode = mode
self._reuse_attention = hps.reuse_attention
if self._use_copy:
assert self._sparse_inputs is not None
assert self._mask is not None
if not self._reuse_attention:
assert self._encoder_emb is not None
encoder_dim = tf.shape(self._encoder_emb)[-1]
if encoder_dim != self._hidden_dim:
assert False
self._eps = 1e-8
self._vocab_offset = hps.output_size
