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model.py
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model.py
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# %%
import math
import tensorflow as tf
import tensorflow.contrib.seq2seq as seq2seq
from tensorflow.contrib.rnn import LSTMCell, LSTMStateTuple
from tensorflow.python.layers.core import Dense
from decoder_helpers import VAEDecoder, VAEDecoderHelper
from helpers import lazy, pr, get_loss_op # noqa
class ModelComponent(object):
@classmethod
def define(cls, *args, **kwargs):
return cls.get_model(False, *args, **kwargs)
@classmethod
def reuse_params(cls, *args, **kwargs):
return cls.get_model(True, *args, **kwargs)
@classmethod
def get_model(cls, reuse, *args, **kwargs):
with tf.variable_scope(cls.__name__, reuse=reuse) as scope:
component = cls(*args, **kwargs)
component.scope = scope
return component
class Encoder(ModelComponent):
def __init__(self, cell, model, input_embs=None, use_z=True):
self.hparams = model.hparams
self.src_seq_len = model.seq_lens
self.tasks = model.tasks
self.global_step = model.global_step
if input_embs is None:
self.src_seq_emb = tf.nn.embedding_lookup(
model.embedding_matrix, model.sequences)
else:
self.src_seq_emb = input_embs
self._init_bidirectional(cell)
if use_z:
self._init_z()
def _init_bidirectional(self, cell):
with tf.variable_scope("bidirectional"):
inputs = tf.Print(self.src_seq_emb, [self.tasks, self.global_step], "-- src_seq_emb passing thru enc bi --")
((fw_outputs, bw_outputs), (fw_state, bw_state)) = (
tf.nn.bidirectional_dynamic_rnn(
cell_fw=cell,
cell_bw=cell,
inputs=inputs,
sequence_length=self.src_seq_len,
dtype=tf.float32)
)
# outputs = tf.concat((fw_outputs, bw_outputs), 2)
state_c = tf.concat((fw_state.c, bw_state.c), axis=1)
state_h = tf.concat((fw_state.h, bw_state.h), axis=1)
self.state = LSTMStateTuple(c=state_c, h=state_h)
def _init_z(self):
with tf.variable_scope("z"):
if not self.hparams.variational:
self.z_sample = self.state
return
h = self.state.h
self.z_mu = Dense(self.hparams.z_units, name="z_mu")(h)
self.z_log_var = Dense(self.hparams.z_units, name="z_log_var")(h)
self.z_var = tf.exp(self.z_log_var, name="z_var")
z = tf.distributions.Normal(
loc=self.z_mu, # mean
scale=tf.sqrt(self.z_var), # std dev
allow_nan_stats=False,
name="z_distribution")
self.z_sample = z.sample(name="z_sample")
class Decoder(ModelComponent):
PAD = 0
EOS = 1
def __init__(self, cell, model, z_sample, encoder=None,
soft=False, sampling_prob=None):
self.cell = cell
self.encoder = encoder
self.z_sample = z_sample
self.trg_seq = model.sequences
self.trg_seq_len = model.seq_lens
self.embedding_matrix = model.embedding_matrix
self.vocab_size = model.vocab_size
self.global_step = model.global_step
self.hparams = model.hparams
self.soft = soft
self.fixed_sampling_prob = sampling_prob
if model.mode in ["train", "eval"]:
self._init_train_inputs_and_targets()
self._init_train()
else:
self._init_train_inputs_and_targets() # TODO can we avoid this?
self._init_inference()
def _init_train_inputs_and_targets(self):
with tf.variable_scope('Inputs'):
batch_size, sequence_size = tf.unstack(tf.shape(self.trg_seq))
# TODO separate symbols for EOS and SOS
self.EOS_SLICE = self.EOS * tf.ones([batch_size, 1], dtype=tf.int32, name="EOS_slice")
self.PAD_SLICE = self.PAD * tf.ones([batch_size, 1], dtype=tf.int32, name="PAD_slice")
train_inputs = tf.concat([self.EOS_SLICE, self.trg_seq], axis=1)
self.train_len = self.trg_seq_len + 1
self.train_inputs_emb = tf.nn.embedding_lookup(
self.embedding_matrix, train_inputs, name="train_inputs_emb")
# put EOS symbol at the end of target sequence
train_targets = tf.concat([self.trg_seq, self.PAD_SLICE], axis=1)
train_targets_eos_mask = tf.one_hot( # (batch, t) = (len(trg_seq_len), sequence_size + 1)
self.trg_seq_len,
sequence_size + 1,
on_value=self.EOS,
off_value=self.PAD,
dtype=tf.int32)
self.train_targets = tf.add(train_targets, train_targets_eos_mask, name="train_targets")
self.loss_weights = tf.ones([
batch_size,
tf.reduce_max(self.train_len),
], dtype=tf.float32, name="loss_weights")
def _init_train(self):
helper = VAEDecoderHelper(
self.train_inputs_emb,
self.train_len,
self.embedding_matrix,
z_sample=self.z_sample,
sampling_probability=self.sampling_prob,
name="VAEDecoderHelper",
)
outputs = self._decode(helper)
self.logits = outputs.rnn_output
self.predictions = outputs.sample_id
self.avg_emb = outputs.avg_emb
def _init_inference(self):
helper = tf.contrib.seq2seq.GreedyEmbeddingHelper( # TODO z_sample
self.embedding_matrix,
tf.squeeze(self.EOS_SLICE, axis=[1]),
self.EOS)
outputs = self._decode(helper)
self.predictions = outputs.sample_id
def _decode(self, helper):
decoder = VAEDecoder(
self.cell,
helper,
self.initial_state(),
output_layer=self.output_network,
temperature=self.sampling_temperature,
soft=self.soft,
embedding_size=self.hparams.embedding_size,
)
outputs, state, seq_len = tf.contrib.seq2seq.dynamic_decode(
decoder,
swap_memory=True,
maximum_iterations=self.hparams.max_seq_len
)
return outputs
@lazy
def output_network(self):
# TODO why not use bias?
return Dense(self.vocab_size, use_bias=False, name="decoder_out")
@lazy
def sampling_prob(self):
if self.fixed_sampling_prob is not None:
return self.fixed_sampling_prob
# inverse sigmoid decay for scheduled sampling https://arxiv.org/abs/1506.03099
k = tf.constant(self.hparams.scheduled_sampling_k, dtype=tf.float32)
i = tf.cast(self.global_step, tf.float32)
p = k / (k + tf.exp(i / k))
return p
@lazy
def sampling_temperature(self):
# formula 2 of "Toward Controlled Generation of Text"
# TODO anneal from 1 to 0
tau = tf.Variable(1.0, name="sampling_temperature", trainable=False)
return tau
@property
def recon_loss(self):
return seq2seq.sequence_loss(
logits=self.logits,
targets=self.train_targets, # * max sequence length?
weights=self.loss_weights,
name="reconstruction_loss",
)
def initial_state(self):
if self.encoder is None:
batch_shape = [self.hparams.batch_size, self.hparams.dec_units]
return LSTMStateTuple(
c=tf.zeros(batch_shape, dtype=tf.float32),
h=tf.zeros(batch_shape, dtype=tf.float32),
)
else:
return LSTMStateTuple(
c=tf.zeros_like(self.encoder.state.c),
h=tf.zeros_like(self.encoder.state.h),
)
class Classifier(ModelComponent):
def __init__(self, cell, model, input_embs=None, encoder=None):
self.global_step = model.global_step
self.hparams = model.hparams
self.labels = model.labels
if encoder: # used by soft (and synth if has_stage(vae_cond_lab))
self.encoder = encoder
else:
with tf.variable_scope("Encoder"):
self.encoder = Encoder(
cell=cell,
model=model,
input_embs=input_embs,
use_z=False,
)
self.loss = self.get_loss()
@lazy
def logits(self):
out_net = Dense(self.hparams.num_classes, use_bias=False, name="logits")
return out_net(self.encoder.state.h)
@lazy
def predictions(self):
return tf.cast(
tf.argmax(self.logits, axis=1),
tf.int32,
name="predictions"
)
def get_loss(self):
labels = tf.one_hot(self.labels, self.hparams.num_classes)
loss = tf.losses.softmax_cross_entropy(
logits=self.logits,
onehot_labels=labels,
)
loss = tf.reduce_sum(loss, name="loss_sum")
return loss
class Model(object):
all_comps = ["Encoder", "Decoder", "Classifier"]
def __init__(self, mode, features, labels, vocab_size, hparams):
print("[%s] Initializing model" % mode)
self.mode = mode
self.vocab_size = vocab_size
self.hparams = hparams
self.sequences = features["sequences"]
self.seq_lens = tf.squeeze(features["seq_lens"], axis=1) # undo the expand_dims in input_fn,
self.labels = tf.squeeze(labels, axis=1) if labels is not None else None
self.embedding_matrix = self._get_embeddings()
self.tasks = features["tasks"]
if "tasks" in hparams.log:
self.tasks = tf.Print(self.tasks, [self.tasks], "-- (%s) TASK " % mode)
self.init_model()
def _get_embeddings(self):
with tf.variable_scope("embedding"):
sqrt3 = math.sqrt(3) # Uniform(-sqrt(3), sqrt(3)) has variance=1.
initializer = tf.random_uniform_initializer(-sqrt3, sqrt3)
return tf.get_variable(
name="embedding_matrix",
shape=[self.vocab_size, self.hparams.embedding_size],
initializer=initializer,
dtype=tf.float32)
@lazy
def train_op(self):
if self.mode in ["train", "eval"]:
with tf.variable_scope("train_op", reuse=False):
self.optimizer = tf.train.AdamOptimizer(
learning_rate=self.hparams.learning_rate,
)
train_ops = [
self.minimize_component_loss(comp)
for comp in self.comps
]
self.setup_unused_optimizer_params()
inc_step = tf.assign_add(self.global_step, 1)
with tf.control_dependencies([inc_step]):
train_op = tf.group(*train_ops, name="train_op_group")
return train_op
def minimize_component_loss(self, comp):
return self.optimizer.minimize(
loss=self.loss, # TODO ensure it's not calculated many times
var_list=self.vars_in_scopes([comp, "embedding"]),
name="Minimize" + comp,
)
def setup_unused_optimizer_params(self):
# need to declare optimizer ops that are used by non-chief workers,
# otherwise the chief worker won't initialize those optimizer vars
if self.is_chief:
for comp in Model.all_comps:
if comp not in self.comps:
print("successful setup_unused_optimizer_params", comp)
self.minimize_component_loss(comp)
else:
print(comp, "in", self.comps)
else:
print("-- not chief --")
@lazy
def is_chief(self):
return type(self).__name__ == self.hparams.worker_processes[0]
@lazy
def global_step(self):
return (tf.contrib.framework.get_global_step() or
tf.Variable(0, trainable=False, name='global_step'))
@property
def eval_metrics(self):
return {}
def to_metrics(self, loss_components):
return {
k: tf.contrib.metrics.streaming_mean(v)
for k, v in loss_components.items()
}
def vars_in_scopes(self, scopes):
vars = []
for scope in scopes:
v = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope=scope)
vars.extend(v)
return vars
class VAEModel(Model):
@property
def comps(self):
return ["Encoder", "Decoder"]
def init_model(self):
self.encoder = Encoder.define( # tb Encoder
cell=LSTMCell(self.hparams.enc_units),
model=self,
)
# identity needed so that z is not re-sampled?
self.z_sample = tf.identity(self.encoder.z_sample, name="z_sample_copy")
self.decoder = Decoder.define( # tb Decoder
cell=LSTMCell(self.hparams.dec_units),
model=self,
encoder=self.encoder,
z_sample=self.z_sample,
)
@get_loss_op
def vae_loss(self):
return self.recon_loss + self.kl_loss
@get_loss_op
def recon_loss(self):
return self.decoder.recon_loss
@get_loss_op
def kl_loss(self):
mu = self.encoder.z_mu
var = self.encoder.z_var
log_var = self.encoder.z_log_var
kl = 1 + log_var - tf.square(mu) - var
kl = -0.5 * tf.reduce_sum(kl, axis=-1)
kl = tf.reduce_mean(kl, axis=-1) # average the KL loss of all the training samples
return self.kl_weight * kl
@get_loss_op
def kl_weight(self):
# see ad_hoc/sigmoid.py for example schedules
multiplier = tf.constant(self.hparams.kl_weight_multiplier, dtype=tf.float32)
offset = tf.constant(self.hparams.kl_weight_offset, dtype=tf.float32)
x = tf.cast(self.global_step, tf.float32)
return 1 / (1 + tf.exp((multiplier * -x) + offset))
@property
def perplexity(self):
return tf.exp(self.recon_loss)
@property
def eval_metrics(self):
return self.to_metrics({
'perplexity': self.perplexity,
'KL': self.kl_loss,
'recon': self.recon_loss,
})
class VAEUncondModel(VAEModel):
@lazy
def loss(self):
return self.vae_loss
@lazy
def predictions(self):
return self.decoder.predictions
class VAECondModel(VAEModel):
def init_model(self):
super(VAECondModel, self).init_model()
self.decoder_soft = Decoder.reuse_params( # tb Decoder_1
cell=LSTMCell(self.hparams.dec_units),
model=self,
encoder=self.encoder,
z_sample=self.z_sample,
soft=True,
)
# TODO test that encoder_soft only encodes once,
# not once for disent_loss and once for attr_preserve_loss
self.encoder_soft = Encoder.reuse_params( # tb Encoder_1
cell=LSTMCell(self.hparams.enc_units),
model=self,
input_embs=self.decoder_soft.avg_emb,
)
self.classifier_soft = Classifier.define( # tb Classifier_1
cell=LSTMCell(self.hparams.enc_units),
model=self,
input_embs=self.decoder_soft.avg_emb,
# encoder=self.encoder_soft, # can't use because VAECondModel is chief and needs to define classifier ops
)
@lazy
def loss(self):
return self.vae_loss + self.generator_loss
@lazy
def predictions(self):
return self.decoder.predictions
@get_loss_op
def generator_loss(self):
return (
self.hparams.lam_c * self.attr_preserve_loss +
self.hparams.lam_z * self.disentanglement_loss
)
@get_loss_op
def attr_preserve_loss(self):
return self.classifier_soft.loss
@get_loss_op
def disentanglement_loss(self):
return tf.losses.mean_squared_error(
labels=self.z_sample,
predictions=self.encoder_soft.z_sample,
)
@property
def eval_metrics(self):
return self.to_metrics({
'perplexity': self.perplexity,
'KL': self.kl_loss,
'recon': self.recon_loss,
'attr_preserve': self.attr_preserve_loss,
'disent': self.disentanglement_loss,
})
class ClassifierModel(Model):
@property
def comps(self):
return ["Classifier"]
def init_model(self):
self.classifier = Classifier.define( # tb Classifier
cell=LSTMCell(self.hparams.enc_units),
model=self,
)
@lazy
def loss(self):
return self.classifier_loss
@lazy
def predictions(self):
return self.classifier.predictions
@get_loss_op
def classifier_loss(self):
return self.classifier.loss
class ClassifierSynthModel(Model):
@property
def comps(self):
return ["Classifier"]
def init_model(self):
self.generator = Decoder.define( # tb Decoder_2
cell=LSTMCell(self.hparams.dec_units),
model=self,
sampling_prob=1.0,
z_sample=self.random_z_sample(),
)
synth_input_embs = tf.nn.embedding_lookup(
self.embedding_matrix,
self.generator.predictions,
name="synth_input_embs"
)
self.classifier_synth = Classifier.define( # tb Classifier_2
cell=LSTMCell(self.hparams.enc_units),
model=self,
input_embs=synth_input_embs,
)
def random_z_sample(self):
with tf.variable_scope("random_z"):
batch_shape = [self.hparams.batch_size, self.hparams.z_units]
normal = tf.distributions.Normal(
loc=tf.zeros(batch_shape, dtype=tf.float32),
scale=tf.ones(batch_shape, dtype=tf.float32),
# loc=[[0.] * hparams.z_units],
# scale=[[1.] * hparams.z_units],
allow_nan_stats=False,
)
return normal.sample()
@lazy
def loss(self):
return self.classify_synth_loss
@lazy
def predictions(self):
return self.classifier_synth.predictions
@get_loss_op
def classify_synth_loss(self):
return self.classifier_synth.loss - self.synth_class_entropy
@get_loss_op
def synth_class_entropy(self):
# Empirical entropy of the classifier for synthetic data.
# High entropy means uniformp+ class distribution; penalizing high entropy
# encourages the model to have high confidence in predicting labels.
log_p = self.classifier_synth.logits
p = tf.nn.softmax(log_p)
empirical_entropy = p * log_p
return self.hparams.beta * tf.reduce_sum(empirical_entropy)
@property
def eval_metrics(self):
return self.to_metrics({
'synth_class_entropy': self.synth_class_entropy,
})