class AbstractRecurrentEstimator(AbstractIcecapsEstimator):
@classmethod
def construct_expected_params(cls):
expected_params = super().construct_expected_params()
expected_params["max_length"] = cls.make_param(50)
expected_params["cell_type"] = cls.make_param('gru')
expected_params["hidden_units"] = cls.make_param(32)
expected_params["depth"] = cls.make_param(1)
expected_params["token_embed_dim"] = cls.make_param(16)
expected_params["tie_token_embeddings"] = cls.make_param(True)
expected_params["beam_width"] = cls.make_param(8)
expected_params["vocab_file"] = cls.make_param(
"./dummy_data/vocab.dic")
expected_params["vocab_size"] = cls.make_param(0)
expected_params["skip_tokens"] = cls.make_param('')
expected_params["skip_tokens_start"] = cls.make_param('')
return expected_params
def extract_args(self, features, mode, params):
super().extract_args(features, mode, params)
if self.hparams.vocab_size > 0:
self.vocab = Vocabulary(size=self.hparams.vocab_size)
else:
self.vocab = Vocabulary(
fname=self.hparams.vocab_file,
skip_tokens=self.hparams.skip_tokens,
skip_tokens_start=self.hparams.skip_tokens_start)
def build_cell(self, name=None):
if self.hparams.cell_type == 'linear':
cell = BasicRNNCell(self.hparams.hidden_units,
activation=tf.identity,
name=name)
elif self.hparams.cell_type == 'tanh':
cell = BasicRNNCell(self.hparams.hidden_units,
activation=tf.tanh,
name=name)
elif self.hparams.cell_type == 'relu':
cell = BasicRNNCell(self.hparams.hidden_units,
activation=tf.nn.relu,
name=name)
elif self.hparams.cell_type == 'gru':
cell = GRUCell(self.hparams.hidden_units, name=name)
elif self.hparams.cell_type == 'lstm':
cell = LSTMCell(self.hparams.hidden_units, name=name)
else:
raise ValueError('Provided cell type not supported.')
return cell
def build_deep_cell(self,
cell_list=None,
name=None,
return_raw_list=False):
if name is None:
name = "cell"
if cell_list is None:
cell_list = []
for i in range(self.hparams.depth):
cell = self.build_cell(name=name + "_" + str(i))
cell = DropoutWrapper(cell, output_keep_prob=self.keep_prob)
cell_list.append(cell)
if return_raw_list:
return cell_list
if len(cell_list) == 1:
return cell_list[0]
return MultiRNNCell(cell_list)
def build_rnn(self, input_key="inputs"):
with tf.variable_scope('rnn'):
self.cell = self.build_deep_cell()
self.build_inputs(input_key)
self.outputs, self.last_state = tf.nn.dynamic_rnn(
cell=self.cell,
inputs=self.inputs_dense,
sequence_length=self.inputs_length,
time_major=False,
dtype=tf.float32
) # [batch_size, max_time_step, cell_output_size], [batch_size, cell_output_size]
def build_embeddings(self):
if "token_embeddings" in self.features and self.hparams.tie_token_embeddings:
self.token_embeddings = self.features["token_embeddings"]
else:
self.token_embeddings = tf.get_variable(
name='token_embeddings',
shape=[self.vocab.size(), self.hparams.token_embed_dim])
if self.hparams.token_embed_dim != self.hparams.hidden_units:
projection = tf.get_variable(name='token_embed_proj',
shape=[
self.hparams.token_embed_dim,
self.hparams.hidden_units
])
self.token_embeddings = self.token_embeddings @ projection
def embed_sparse_to_dense(self, sparse):
with tf.variable_scope('embed_sparse_to_dense', reuse=tf.AUTO_REUSE):
dense = tf.nn.embedding_lookup(self.token_embeddings, sparse)
return dense
def build_inputs(self, input_key):
self.build_embeddings()
self.inputs_sparse_untrimmed = tf.cast(self.features[input_key],
tf.int32)
self.inputs_length = tf.cast(
tf.count_nonzero(
self.inputs_sparse_untrimmed - self.vocab.end_token_id, -1),
tf.int32)
self.inputs_max_length = tf.reduce_max(self.inputs_length)
self.inputs_sparse = tf.slice(self.inputs_sparse_untrimmed, [0, 0],
[-1, self.inputs_max_length])
self.inputs_dense = self.embed_sparse_to_dense(self.inputs_sparse)
self.batch_size = tf.shape(self.inputs_sparse)[0]
def build_loss(self):
with tf.name_scope('build_loss'):
self.loss = seq2seq.sequence_loss(
logits=self.logits,
targets=self.targets_sparse,
weights=self.target_mask,
average_across_timesteps=True,
average_across_batch=True,
)
self.reported_loss = tf.identity(self.loss, 'reported_loss')
class AbstractTransformerEstimator(AbstractIcecapsEstimator):
@classmethod
def construct_expected_params(cls):
expected_params = super().construct_expected_params()
expected_params["vocab_file"] = cls.make_param(
"icecaps/examples/dummy_data/vocab.dic")
expected_params["vocab_size"] = cls.make_param(0)
expected_params["depth"] = cls.make_param(1)
expected_params["num_heads"] = cls.make_param(8)
expected_params["d_model"] = cls.make_param(32)
expected_params["d_pos"] = cls.make_param(32)
expected_params["d_ff"] = cls.make_param(64)
expected_params["max_length"] = cls.make_param(10)
expected_params["min_wavelength"] = cls.make_param(1.0)
expected_params["max_wavelength"] = cls.make_param(1000.0)
expected_params["warmup_steps"] = cls.make_param(4000.0)
expected_params["fixed_learning_rate"] = cls.make_param(False)
expected_params["learn_wavelengths"] = cls.make_param(False)
expected_params["modality"] = cls.make_param("seq")
expected_params["tree_depth"] = cls.make_param(256)
expected_params["tree_width"] = cls.make_param(2)
expected_params["learn_positional_embeddings"] = cls.make_param(False)
return expected_params
def extract_args(self, features, mode, params):
super().extract_args(features, mode, params)
self.d_k = self.hparams.d_model // self.hparams.num_heads
self.d_pos = self.hparams.d_pos if self.hparams.d_pos == 0 else self.hparams.d_pos
self.d_ff = self.hparams.d_ff if self.hparams.d_ff == 0 else self.hparams.d_ff
if self.hparams.vocab_size > 0:
self.vocab = Vocabulary(size=self.hparams.vocab_size)
else:
self.vocab = Vocabulary(fname=self.hparams.vocab_file)
if not self.hparams.fixed_learning_rate:
self.train_step = tf.get_variable(
'train_step',
shape=[],
dtype=tf.float32,
initializer=tf.zeros_initializer(dtype=tf.int32),
trainable=False)
self.learning_rate = ( # magic formula provided in transformer paper
tf.sqrt(1.0 / self.hparams.d_model) * tf.minimum(
self.train_step * tf.pow(self.hparams.warmup_steps, -1.5),
tf.pow(self.train_step, -0.5)))
def build_embeddings(self):
# self.hparams.max_length), dtype=tf.float32), 1)
position = tf.expand_dims(tf.cast(tf.range(0, 2048), dtype=tf.float32),
1)
if self.hparams.learn_wavelengths:
wavelength_logs = tf.get_variable("wavelength_logs",
[self.d_pos // 2], tf.float32)
else:
wavelength_logs = tf.linspace(
math.log(self.hparams.min_wavelength),
math.log(self.hparams.max_wavelength), self.d_pos // 2)
div_term = tf.expand_dims(tf.exp(-wavelength_logs), 0)
outer_product = tf.matmul(position, div_term)
cosines = tf.cos(outer_product)
sines = tf.sin(outer_product)
self.positional_embeddings = tf.concat([cosines, sines], -1)
if self.hparams.learn_positional_embeddings:
self.positional_embeddings = tf.get_variable(
name='positional_embeddings',
shape=[self.hparams.max_length, self.hparams.d_model
]) * np.sqrt(float(self.hparams.d_model))
self.token_embeddings = tf.get_variable(
name='token_embeddings',
shape=[self.vocab.size(), self.hparams.d_model]) * np.sqrt(
float(self.hparams.d_model))
if self.hparams.modality == "tree":
self.d_tree_param = self.d_pos // (self.hparams.tree_depth *
self.hparams.tree_width)
self.tree_params = tf.tanh(
tf.get_variable("tree_params", [self.d_tree_param]))
self.tiled_tree_params = tf.tile(
tf.reshape(self.tree_params, [1, 1, -1]),
[self.hparams.tree_depth, self.hparams.tree_width, 1])
self.tiled_depths = tf.tile(
tf.reshape(tf.range(self.hparams.tree_depth, dtype=tf.float32),
[-1, 1, 1]),
[1, self.hparams.tree_width, self.d_tree_param])
self.tree_norm = tf.sqrt(
(1 - tf.square(self.tree_params)) * self.hparams.d_model / 2)
self.tree_weights = tf.reshape(
tf.pow(self.tiled_tree_params, self.tiled_depths) *
self.tree_norm, [
self.hparams.tree_depth * self.hparams.tree_width,
self.d_tree_param
])
def treeify_positions(self, positions):
treeified = tf.expand_dims(positions, -1) * self.tree_weights
shape = tf.shape(treeified)
shape = tf.concat([shape[:-2], [self.d_pos]], -1)
treeified = tf.reshape(treeified, shape)
return treeified
def init_inputs(self):
self.inputs_sparse = tf.cast(self.features["inputs"], tf.int32)
self.mask = tf.cast(
tf.not_equal(self.inputs_sparse, self.vocab.end_token_id),
tf.float32)
self.inputs_length = tf.cast(tf.count_nonzero(self.mask, -1), tf.int32)
self.inputs_max_length = tf.reduce_max(self.inputs_length)
self.batch_size = tf.shape(self.inputs_sparse)[0]
self.inputs_sparse = tf.slice(
self.inputs_sparse, [0, 0],
[self.batch_size, self.inputs_max_length])
self.mask = tf.slice(self.mask, [0, 0],
[self.batch_size, self.inputs_max_length])
self.inputs_dense = tf.nn.embedding_lookup(
params=self.token_embeddings, ids=self.inputs_sparse)
if self.hparams.modality == "seq":
self.positions = tf.slice(self.positional_embeddings, [0, 0],
[self.inputs_max_length, self.d_pos])
elif self.hparams.modality == "tree":
self.positions = tf.reshape(self.features["inputs_positions"], [
self.batch_size, self.inputs_max_length,
self.hparams.tree_depth * self.hparams.tree_width
])
self.positions = self.treeify_positions(self.positions)
else:
raise ValueError("This input modality is not supported.")
if self.d_pos != self.hparams.d_model:
self.positions = tf.layers.dense(self.positions,
self.hparams.d_model)
self.inputs_dense = self.inputs_dense + self.positions
self.inputs_dense = tf.nn.dropout(self.inputs_dense, self.keep_prob)
self.inputs_dense = tf.transpose(
tf.transpose(self.inputs_dense) * tf.transpose(self.mask))
def build_layer_norm(self, x):
return tf.contrib.layers.layer_norm(x, begin_norm_axis=-1)
def build_sublayer_fn(self, x, f):
x = self.build_layer_norm(x)
x = x + tf.nn.dropout(f(x), self.keep_prob)
return x
def attention(self, query, key, value, d_k, enc_mask=None, dec_mask=None):
scores = tf.matmul(query, tf.transpose(key,
[0, 1, 3, 2])) / math.sqrt(d_k)
if enc_mask is not None:
scores = tf.transpose(
scores, [1, 2, 0, 3]) * enc_mask - 1e24 * (1.0 - enc_mask)
scores = tf.transpose(scores, [2, 0, 1, 3])
if dec_mask is not None:
scores = scores * dec_mask - 1e24 * (1.0 - dec_mask)
p_attn = tf.nn.softmax(scores)
p_attn = tf.nn.dropout(p_attn, keep_prob=self.keep_prob)
attended_values = tf.matmul(p_attn, value)
return attended_values, p_attn
def mha_fn(self, query, key, value, batch_size, enc_mask_, dec_mask_):
with tf.variable_scope("mha", reuse=tf.AUTO_REUSE) as scope:
query = tf.transpose(
tf.reshape(
tf.layers.dense(query, self.hparams.d_model,
use_bias=True),
[batch_size, -1, self.hparams.num_heads, self.d_k]),
[0, 2, 1, 3])
key = tf.transpose(
tf.reshape(
tf.layers.dense(key, self.hparams.d_model, use_bias=True),
[batch_size, -1, self.hparams.num_heads, self.d_k]),
[0, 2, 1, 3])
value = tf.transpose(
tf.reshape(
tf.layers.dense(value, self.hparams.d_model,
use_bias=True),
[batch_size, -1, self.hparams.num_heads, self.d_k]),
[0, 2, 1, 3])
attended, _ = self.attention(query, key, value, self.d_k,
enc_mask_, dec_mask_)
attended = tf.reshape(tf.transpose(attended, [0, 2, 1, 3]),
[batch_size, -1, self.hparams.d_model])
return attended
def build_mha_sublayer(self,
x,
m,
batch_size,
enc_mask=None,
dec_mask=None):
with tf.variable_scope("attn", reuse=tf.AUTO_REUSE) as scope:
return self.build_sublayer_fn(
x, lambda q: tf.layers.dense(
self.mha_fn(q, m, m, batch_size, enc_mask, dec_mask), self.
hparams.d_model))
def build_ffn_sublayer(self, x, d_ff):
with tf.variable_scope("ffn", reuse=tf.AUTO_REUSE) as scope:
def ffn_fn(q):
return tf.layers.dense(tf.layers.dense(q, d_ff, tf.nn.relu),
self.hparams.d_model)
return self.build_sublayer_fn(x, ffn_fn)
def build_optimizer(self, trainable_params=None):
super().build_optimizer(trainable_params)
self.step_update_op = tf.assign_add(self.train_step, 1.0)
with tf.control_dependencies([self.step_update_op]):
self.train_op = tf.group([self.step_update_op, self.train_op])
class RNNEstimator(AbstractRecurrentEstimator):
def _model_fn(self, features, mode, params):
with tf.variable_scope(self.scope, reuse=tf.AUTO_REUSE):
self.extract_args(features, mode, params)
self.init_inputs()
self.build_cell()
self.build_obj()
if mode == tf.estimator.ModeKeys.PREDICT:
self.build_rt_decoder()
self.predictions = {
"inputs": self.features["inputs"],
"outputs": self.hypotheses,
"scores": self.scores
}
if "metadata" in self.features:
self.predictions["metadata"] = self.features["metadata"]
return tf.estimator.EstimatorSpec(mode,
predictions=self.predictions)
self.init_targets()
self.build_loss()
if mode == tf.estimator.ModeKeys.TRAIN:
self.build_optimizer()
for var in tf.trainable_variables():
# Add histograms for trainable variables
tf.summary.histogram(var.op.name, var)
return tf.estimator.EstimatorSpec(mode,
loss=self.reported_loss,
train_op=self.train_op)
if mode == tf.estimator.ModeKeys.EVAL:
print("Number of parameters: " +
str(self.get_num_model_params()))
self.eval_metric_ops = dict()
return tf.estimator.EstimatorSpec(
mode,
loss=self.reported_loss,
eval_metric_ops=self.eval_metric_ops)
@classmethod
def construct_expected_params(cls):
expected_params = super().construct_expected_params()
expected_params["src_vocab_file"] = cls.make_param("")
expected_params["tgt_vocab_file"] = cls.make_param("")
expected_params["src_vocab_size"] = cls.make_param(0)
expected_params["tgt_vocab_size"] = cls.make_param(0)
return expected_params
def extract_args(self, features, mode, params):
super().extract_args(features, mode, params)
if (self.hparams.src_vocab_size == 0
and self.hparams.tgt_vocab_size == 0
and self.hparams.src_vocab_file == ""
and self.hparams.tgt_vocab_file == ""):
self.src_vocab = self.vocab
self.tgt_vocab = self.vocab
else:
if self.hparams.src_vocab_size > 0:
self.src_vocab = Vocabulary(size=self.hparams.src_vocab_size)
else:
self.src_vocab = Vocabulary(fname=self.hparams.src_vocab_file)
if self.hparams.tgt_vocab_size > 0:
self.tgt_vocab = Vocabulary(size=self.hparams.tgt_vocab_size)
else:
self.tgt_vocab = Vocabulary(fname=self.hparams.tgt_vocab_file)
def init_inputs(self):
with tf.name_scope('init_encoder'):
inputs = tf.cast(self.features["inputs"], tf.int32)
self.batch_size = tf.shape(inputs)[0]
inputs_length = tf.cast(
tf.count_nonzero(inputs - self.vocab.end_token_id, -1),
tf.int32)
inputs_max_length = tf.reduce_max(inputs_length)
end_token = tf.ones(shape=[
self.batch_size, self.hparams.max_length - inputs_max_length
],
dtype=tf.int32) * self.vocab.end_token_id
# [batch_size, max_time_steps + 1]
self.inputs_sparse = tf.concat([inputs, end_token], axis=1)
def init_targets(self):
with tf.name_scope('init_decoder'):
targets = tf.cast(self.features["targets"], tf.int32)
targets_length = tf.cast(
tf.count_nonzero(targets - self.vocab.end_token_id, -1),
tf.int32)
targets_max_length = tf.reduce_max(targets_length)
end_token = tf.ones(shape=[
self.batch_size, self.hparams.max_length - targets_max_length
],
dtype=tf.int32) * self.vocab.end_token_id
# [batch_size, max_time_steps + 1]
self.targets_sparse = tf.concat([targets, end_token], axis=1)
self.targets_length = targets_length + 1
self.target_mask = tf.sequence_mask(lengths=self.targets_length,
maxlen=self.hparams.max_length,
dtype=tf.float32)
def build_cell(self):
sequence_length = tf.ones([self.batch_size],
dtype=tf.int32) * self.hparams.max_length
super().build_cell(sequence_length, self.src_vocab.size())
def build_obj(self):
output_layer = Dense(self.tgt_vocab.size(), name='output_projection')
self.logits = output_layer(self.outputs)
def build_rt_decoder(self):
with tf.name_scope('predict_decoder'):
self.hypotheses = tf.argmax(self.logits, -1)
self.scores = tf.reduce_sum(
tf.reduce_max(tf.nn.log_softmax(self.logits), -1), -1)
