if opt == "adam":

optfn = tf.train.AdamOptimizer

elif opt == "sgd":

optfn = tf.train.GradientDescentOptimizer

else:

assert(False)

def __init__(self, num_units, encoder_output, scope=None):

self.hs = encoder_output

with vs.variable_scope(scope or type(self).__name__):

with vs.variable_scope("Attn1"):

hs2d = tf.reshape(self.hs, [-1, num_units])

phi_hs2d = tanh(rnn_cell._linear(hs2d, num_units, True, 1.0))

self.phi_hs = tf.reshape(phi_hs2d, tf.shape(self.hs))

super(GRUCellAttn, self).__init__(num_units)

def __call__(self, inputs, state, scope=None):

gru_out, gru_state = super(GRUCellAttn, self).__call__(inputs, state, scope)

with vs.variable_scope(scope or type(self).__name__):

with vs.variable_scope("Attn2"):

gamma_h = tanh(rnn_cell._linear(gru_out, self._num_units, True, 1.0))

weights = tf.reduce_sum(self.phi_hs * gamma_h, reduction_indices=2, keep_dims=True)

weights = tf.exp(weights - tf.reduce_max(weights, reduction_indices=0, keep_dims=True))

weights = weights / (1e-6 + tf.reduce_sum(weights, reduction_indices=0, keep_dims=True))

context = tf.reduce_sum(self.hs * weights, reduction_indices=0)

with vs.variable_scope("AttnConcat"):

out = tf.nn.relu(rnn_cell._linear([context, gru_out], self._num_units, True, 1.0))

self.attn_map = tf.squeeze(tf.slice(weights, [0, 0, 0], [-1, -1, 1]))

def __init__(self, vocab_size, size, num_layers, max_gradient_norm, batch_size, learning_rate,

learning_rate_decay_factor, dropout, forward_only=False, optimizer="adam"):

self.size = size

self.vocab_size = vocab_size

self.batch_size = batch_size

self.num_layers = num_layers

self.keep_prob_config = 1.0 - dropout

self.learning_rate = tf.Variable(float(learning_rate), trainable=False)

self.learning_rate_decay_op = self.learning_rate.assign(self.learning_rate * learning_rate_decay_factor)

self.global_step = tf.Variable(0, trainable=False)

self.keep_prob = tf.placeholder(tf.float32)

self.source_tokens = tf.placeholder(tf.int32, shape=[None, None])

self.target_tokens = tf.placeholder(tf.int32, shape=[None, None])

self.source_mask = tf.placeholder(tf.int32, shape=[None, None])

self.target_mask = tf.placeholder(tf.int32, shape=[None, None])

self.beam_size = tf.placeholder(tf.int32)

self.target_length = tf.reduce_sum(self.target_mask, reduction_indices=0)

self.decoder_state_input, self.decoder_state_output = [], []

for i in xrange(num_layers):

self.decoder_state_input.append(tf.placeholder(tf.float32, shape=[None, size]))

with tf.variable_scope("NLC", initializer=tf.uniform_unit_scaling_initializer(1.0)):

self.setup_embeddings()

self.setup_encoder()

self.setup_decoder()

self.setup_loss()

self.setup_beam()

params = tf.trainable_variables()

if not forward_only:

opt = get_optimizer(optimizer)(self.learning_rate)

gradients = tf.gradients(self.losses, params)

clipped_gradients, _ = tf.clip_by_global_norm(gradients, max_gradient_norm)

# self.gradient_norm = tf.global_norm(clipped_gradients)

self.gradient_norm = tf.global_norm(gradients)

self.param_norm = tf.global_norm(params)

self.updates = opt.apply_gradients(

zip(clipped_gradients, params), global_step=self.global_step)

self.saver = tf.train.Saver(tf.all_variables(), max_to_keep=0)

def setup_embeddings(self):

with vs.variable_scope("embeddings"):

self.L_enc = tf.get_variable("L_enc", [self.vocab_size, self.size])

self.L_dec = tf.get_variable("L_dec", [self.vocab_size, self.size])

self.encoder_inputs = embedding_ops.embedding_lookup(self.L_enc, self.source_tokens)

self.decoder_inputs = embedding_ops.embedding_lookup(self.L_dec, self.target_tokens)

def setup_encoder(self):

self.encoder_cell = rnn_cell.GRUCell(self.size)

with vs.variable_scope("PryamidEncoder"):

inp = self.encoder_inputs

mask = self.source_mask

out = None

for i in xrange(self.num_layers):

with vs.variable_scope("EncoderCell%d" % i) as scope:

srclen = tf.reduce_sum(mask, reduction_indices=0)

out, _ = self.bidirectional_rnn(self.encoder_cell, inp, srclen, scope=scope)

dropin, mask = self.downscale(out, mask)

inp = self.dropout(dropin)

self.encoder_output = out

def setup_decoder(self):

if self.num_layers > 1:

self.decoder_cell = rnn_cell.GRUCell(self.size)

self.attn_cell = GRUCellAttn(self.size, self.encoder_output, scope="DecoderAttnCell")

with vs.variable_scope("Decoder"):

inp = self.decoder_inputs

for i in xrange(self.num_layers - 1):

with vs.variable_scope("DecoderCell%d" % i) as scope:

out, state_output = rnn.dynamic_rnn(self.decoder_cell, inp, time_major=True,

dtype=dtypes.float32, sequence_length=self.target_length,

scope=scope, initial_state=self.decoder_state_input[i])

inp = self.dropout(out)

self.decoder_state_output.append(state_output)

with vs.variable_scope("DecoderAttnCell") as scope:

out, state_output = rnn.dynamic_rnn(self.attn_cell, inp, time_major=True,

dtype=dtypes.float32, sequence_length=self.target_length,

scope=scope, initial_state=self.decoder_state_input[i+1])

self.decoder_output = self.dropout(out)

self.decoder_state_output.append(state_output)

def decoder_graph(self, decoder_inputs, decoder_state_input):

decoder_output, decoder_state_output = None, []

inp = decoder_inputs

with vs.variable_scope("Decoder", reuse=True):

for i in xrange(self.num_layers - 1):

with vs.variable_scope("DecoderCell%d" % i) as scope:

inp, state_output = self.decoder_cell(inp, decoder_state_input[i])

decoder_state_output.append(state_output)

with vs.variable_scope("DecoderAttnCell") as scope:

decoder_output, state_output = self.attn_cell(inp, decoder_state_input[i+1])

decoder_state_output.append(state_output)

return decoder_output, decoder_state_output

def setup_beam(self):

time_0 = tf.constant(0)

beam_seqs_0 = tf.constant([[nlc_data.SOS_ID]])

beam_probs_0 = tf.constant([0.])

cand_seqs_0 = tf.constant([[nlc_data.EOS_ID]])

cand_probs_0 = tf.constant([-3e38])

state_0 = tf.zeros([1, self.size])

states_0 = [state_0] * self.num_layers

def beam_cond(time, beam_probs, beam_seqs, cand_probs, cand_seqs, *states):

return tf.reduce_max(beam_probs) >= tf.reduce_min(cand_probs)

def beam_step(time, beam_probs, beam_seqs, cand_probs, cand_seqs, *states):

batch_size = tf.shape(beam_probs)[0]

inputs = tf.reshape(tf.slice(beam_seqs, [0, time], [batch_size, 1]), [batch_size])

decoder_input = embedding_ops.embedding_lookup(self.L_dec, inputs)

decoder_output, state_output = self.decoder_graph(decoder_input, states)

with vs.variable_scope("Logistic", reuse=True):

do2d = tf.reshape(decoder_output, [-1, self.size])

logits2d = rnn_cell._linear(do2d, self.vocab_size, True, 1.0)

logprobs2d = tf.nn.log_softmax(logits2d)

total_probs = logprobs2d + tf.reshape(beam_probs, [-1, 1])

total_probs_noEOS = tf.concat(1, [tf.slice(total_probs, [0, 0], [batch_size, nlc_data.EOS_ID]),

tf.tile([[-3e38]], [batch_size, 1]),

tf.slice(total_probs, [0, nlc_data.EOS_ID + 1],

[batch_size, self.vocab_size - nlc_data.EOS_ID - 1])])

flat_total_probs = tf.reshape(total_probs_noEOS, [-1])

beam_k = tf.minimum(tf.size(flat_total_probs), self.beam_size)

next_beam_probs, top_indices = tf.nn.top_k(flat_total_probs, k=beam_k)

next_bases = tf.floordiv(top_indices, self.vocab_size)

next_mods = tf.mod(top_indices, self.vocab_size)

next_states = [tf.gather(state, next_bases) for state in state_output]

next_beam_seqs = tf.concat(1, [tf.gather(beam_seqs, next_bases),

tf.reshape(next_mods, [-1, 1])])

cand_seqs_pad = tf.pad(cand_seqs, [[0, 0], [0, 1]])

beam_seqs_EOS = tf.pad(beam_seqs, [[0, 0], [0, 1]])

new_cand_seqs = tf.concat(0, [cand_seqs_pad, beam_seqs_EOS])

EOS_probs = tf.slice(total_probs, [0, nlc_data.EOS_ID], [batch_size, 1])

new_cand_probs = tf.concat(0, [cand_probs, tf.reshape(EOS_probs, [-1])])

cand_k = tf.minimum(tf.size(new_cand_probs), self.beam_size)

next_cand_probs, next_cand_indices = tf.nn.top_k(new_cand_probs, k=cand_k)

next_cand_seqs = tf.gather(new_cand_seqs, next_cand_indices)

return [time + 1, next_beam_probs, next_beam_seqs, next_cand_probs, next_cand_seqs] + next_states

var_shape = []

var_shape.append((time_0, time_0.get_shape()))

var_shape.append((beam_probs_0, tf.TensorShape([None,])))

var_shape.append((beam_seqs_0, tf.TensorShape([None, None])))

var_shape.append((cand_probs_0, tf.TensorShape([None,])))

var_shape.append((cand_seqs_0, tf.TensorShape([None, None])))

var_shape.extend([(state_0, tf.TensorShape([None, self.size])) for state_0 in states_0])

loop_vars, loop_var_shapes = zip(* var_shape)

ret_vars = tf.while_loop(cond=beam_cond, body=beam_step, loop_vars=loop_vars, shape_invariants=loop_var_shapes, back_prop=False)

# time, beam_probs, beam_seqs, cand_probs, cand_seqs, _ = ret_vars

cand_seqs = ret_vars[4]

cand_probs = ret_vars[3]

self.beam_output = cand_seqs

self.beam_scores = cand_probs

def setup_loss(self):

with vs.variable_scope("Logistic"):

doshape = tf.shape(self.decoder_output)

T, batch_size = doshape[0], doshape[1]

do2d = tf.reshape(self.decoder_output, [-1, self.size])

logits2d = rnn_cell._linear(do2d, self.vocab_size, True, 1.0)

outputs2d = tf.nn.log_softmax(logits2d)

self.outputs = tf.reshape(outputs2d, tf.pack([T, batch_size, self.vocab_size]))

targets_no_GO = tf.slice(self.target_tokens, [1, 0], [-1, -1])

masks_no_GO = tf.slice(self.target_mask, [1, 0], [-1, -1])

# easier to pad target/mask than to split decoder input since tensorflow does not support negative indexing

labels1d = tf.reshape(tf.pad(targets_no_GO, [[0, 1], [0, 0]]), [-1])

mask1d = tf.reshape(tf.pad(masks_no_GO, [[0, 1], [0, 0]]), [-1])

losses1d = tf.nn.sparse_softmax_cross_entropy_with_logits(logits2d, labels1d) * tf.to_float(mask1d)

losses2d = tf.reshape(losses1d, tf.pack([T, batch_size]))

self.losses = tf.reduce_sum(losses2d) / tf.to_float(batch_size)

def dropout(self, inp):

return tf.nn.dropout(inp, self.keep_prob)

def downscale(self, inp, mask):

return inp, mask

with vs.variable_scope("Downscale"):

inshape = tf.shape(inp)

T, batch_size, dim = inshape[0], inshape[1], inshape[2]

inp2d = tf.reshape(tf.transpose(inp, perm=[1, 0, 2]), [-1, 2 * self.size])

out2d = rnn_cell._linear(inp2d, self.size, True, 1.0)

out3d = tf.reshape(out2d, tf.pack((batch_size, tf.to_int32(T/2), dim)))

out3d = tf.transpose(out3d, perm=[1, 0, 2])

out3d.set_shape([None, None, self.size])

out = tanh(out3d)

mask = tf.transpose(mask)

mask = tf.reshape(mask, [-1, 2])

mask = tf.cast(mask, tf.bool)

mask = tf.reduce_any(mask, reduction_indices=1)

mask = tf.to_int32(mask)

mask = tf.reshape(mask, tf.pack([batch_size, -1]))

mask = tf.transpose(mask)

return out, mask

def bidirectional_rnn(self, cell, inputs, lengths, scope=None):

name = scope.name or "BiRNN"

# Forward direction

with vs.variable_scope(name + "_FW") as fw_scope:

output_fw, output_state_fw = rnn.dynamic_rnn(cell, inputs, time_major=True, dtype=dtypes.float32,

sequence_length=lengths, scope=fw_scope)

# Backward direction

inputs_bw = tf.reverse_sequence(inputs, tf.to_int64(lengths), seq_dim=0, batch_dim=1)

with vs.variable_scope(name + "_BW") as bw_scope:

output_bw, output_state_bw = rnn.dynamic_rnn(cell, inputs_bw, time_major=True, dtype=dtypes.float32,

sequence_length=lengths, scope=bw_scope)

output_bw = tf.reverse_sequence(output_bw, tf.to_int64(lengths), seq_dim=0, batch_dim=1)

outputs = output_fw + output_bw

output_state = output_state_fw + output_state_bw

return (outputs, output_state)

def set_default_decoder_state_input(self, input_feed, batch_size):

default_value = np.zeros([batch_size, self.size])

for i in xrange(self.num_layers):

input_feed[self.decoder_state_input[i]] = default_value

def train(self, session, source_tokens, source_mask, target_tokens, target_mask):

input_feed = {}

input_feed[self.source_tokens] = source_tokens

input_feed[self.target_tokens] = target_tokens

input_feed[self.source_mask] = source_mask

input_feed[self.target_mask] = target_mask

input_feed[self.keep_prob] = self.keep_prob_config

self.set_default_decoder_state_input(input_feed, target_tokens.shape[1])

output_feed = [self.updates, self.gradient_norm, self.losses, self.param_norm]

outputs = session.run(output_feed, input_feed)

return outputs[1], outputs[2], outputs[3]

def test(self, session, source_tokens, source_mask, target_tokens, target_mask):

input_feed = {}

input_feed[self.source_tokens] = source_tokens

input_feed[self.target_tokens] = target_tokens

input_feed[self.source_mask] = source_mask

input_feed[self.target_mask] = target_mask

input_feed[self.keep_prob] = 1.

self.set_default_decoder_state_input(input_feed, target_tokens.shape[1])

output_feed = [self.losses]

outputs = session.run(output_feed, input_feed)

return outputs[0]

def encode(self, session, source_tokens, source_mask):

input_feed = {}

input_feed[self.source_tokens] = source_tokens

input_feed[self.source_mask] = source_mask

input_feed[self.keep_prob] = 1.

output_feed = [self.encoder_output]

outputs = session.run(output_feed, input_feed)

return outputs[0]

def decode(self, session, encoder_output, target_tokens, target_mask=None, decoder_states=None):

input_feed = {}

input_feed[self.encoder_output] = encoder_output

input_feed[self.target_tokens] = target_tokens

input_feed[self.target_mask] = target_mask if target_mask else np.ones_like(target_tokens)

input_feed[self.keep_prob] = 1.

if not decoder_states:

self.set_default_decoder_state_input(input_feed, target_tokens.shape[1])

else:

for i in xrange(self.num_layers):

input_feed[self.decoder_state_input[i]] = decoder_states[i]

output_feed = [self.outputs] + self.decoder_state_output

outputs = session.run(output_feed, input_feed)

return outputs[0], None, outputs[1:]

def decode_beam(self, session, encoder_output, beam_size=8):

input_feed = {}

input_feed[self.encoder_output] = encoder_output

input_feed[self.keep_prob] = 1.

input_feed[self.beam_size] = beam_size

output_feed = [self.beam_output, self.beam_scores]

outputs = session.run(output_feed, input_feed)