def __init__(self, hparams=None):
ModuleBase.__init__(self, hparams)
hidden_dim = self._hparams.hidden_dim
ffn_inner_dim = self._hparams.ffn_inner_dim
dropout = self._hparams.dropout
activation = self._hparams.activation
if activation == 'gelu':
activation = layers.gelu
with tf.variable_scope(self.variable_scope):
tf.get_variable_scope().set_initializer(
layers.get_initializer(self._hparams.initializer))
l1_hparams = {
"type": "Dense",
"kwargs": {
"units": ffn_inner_dim,
"activation": activation
}
}
self.linear1 = layers.get_layer(hparams=l1_hparams)
dropout_hparams = {
"type": "Dropout",
"kwargs": {
"rate": dropout
}
}
self.dropout = layers.get_layer(hparams=dropout_hparams)
l2_hparams = {
"type": "Dense",
"kwargs": {
"units": hidden_dim
}
}
self.linear2 = layers.get_layer(hparams=l2_hparams)
def test_get_layer(self):
"""Tests :func:`texar.tf.core.layers.get_layer`.
"""
hparams = {"type": "Conv1D"}
layer = layers.get_layer(hparams)
self.assertTrue(isinstance(layer, tf.layers.Conv1D))
hparams = {
"type": "MergeLayer",
"kwargs": {
"layers": [{
"type": "Conv1D"
}, {
"type": "Conv1D"
}]
}
}
layer = layers.get_layer(hparams)
self.assertTrue(isinstance(layer, tx.core.MergeLayer))
hparams = {"type": tf.layers.Conv1D}
layer = layers.get_layer(hparams)
self.assertTrue(isinstance(layer, tf.layers.Conv1D))
hparams = {"type": tf.layers.Conv1D(filters=10, kernel_size=2)}
layer = layers.get_layer(hparams)
self.assertTrue(isinstance(layer, tf.layers.Conv1D))
def __init__(self,
r_r_bias,
r_w_bias,
r_s_bias=None,
segment_embed=None,
hparams=None):
ModuleBase.__init__(self, hparams=hparams)
self.num_heads = self._hparams.num_heads
self.head_dim = self._hparams.head_dim
hidden_dim = self._hparams.hidden_dim
with tf.variable_scope(self.variable_scope):
if self._hparams.initializer:
tf.get_variable_scope().set_initializer(
layers.get_initializer(self._hparams.initializer))
# Official implementation creates these head variables.
# If we create dense layers instead, there would be dimension
# mismatch while loading the tensors
# TODO(avinash) : Can we reshape tensors while loading the ckpt?
self.q_head = tf.get_variable(
'q/kernel', [hidden_dim, self.num_heads, self.head_dim])
self.k_head = tf.get_variable(
'k/kernel', [hidden_dim, self.num_heads, self.head_dim])
self.v_head = tf.get_variable(
'v/kernel', [hidden_dim, self.num_heads, self.head_dim])
self.k_head_r = tf.get_variable(
'r/kernel', [hidden_dim, self.num_heads, self.head_dim])
self.dropout = layers.get_layer(hparams={
"type": "Dropout",
"kwargs": {
"rate": self._hparams.dropout
}
})
self.dropout_attn = layers.get_layer(
hparams={
"type": "Dropout",
"kwargs": {
"rate": self._hparams.attention_dropout
}
})
self.output_projection = tf.get_variable(
'o/kernel', [hidden_dim, self.num_heads, self.head_dim])
self.r_r_bias = r_r_bias
self.r_w_bias = r_w_bias
if self._hparams.use_segments:
self.segment_embed = segment_embed
self.r_s_bias = r_s_bias
self.scale = 1 / (self.head_dim**0.5)
def __init__(self,
pretrained_model_name=None,
cache_dir=None,
hparams=None):
super(XLNetClassifier, self).__init__(hparams=hparams)
with tf.variable_scope(self.variable_scope):
tf.get_variable_scope().set_initializer(
get_initializer(self._hparams.initializer))
# Creates the underlying encoder
encoder_hparams = dict_fetch(hparams,
XLNetEncoder.default_hparams())
if encoder_hparams is not None:
encoder_hparams['name'] = "encoder"
self._encoder = XLNetEncoder(
pretrained_model_name=pretrained_model_name,
cache_dir=cache_dir,
hparams=encoder_hparams)
if self._hparams.use_projection:
self.projection = get_layer(
hparams={
"type": "Dense",
"kwargs": {
"units": self._encoder.output_size
}
})
# Creates an dropout layer
drop_kwargs = {"rate": self._hparams.dropout}
layer_hparams = {"type": "Dropout", "kwargs": drop_kwargs}
self._dropout_layer = get_layer(hparams=layer_hparams)
# Creates an additional classification layer if needed
self._num_classes = self._hparams.num_classes
if self._num_classes <= 0:
self._logit_layer = None
else:
logit_kwargs = self._hparams.logit_layer_kwargs
if logit_kwargs is None:
logit_kwargs = {}
elif not isinstance(logit_kwargs, HParams):
raise ValueError(
"hparams['logit_layer_kwargs'] must be a dict.")
else:
logit_kwargs = logit_kwargs.todict()
logit_kwargs.update({"units": self._num_classes})
if 'name' not in logit_kwargs:
logit_kwargs['name'] = "logit_layer"
layer_hparams = {"type": "Dense", "kwargs": logit_kwargs}
self._logit_layer = get_layer(hparams=layer_hparams)
def _build_layers(network, layers=None, layer_hparams=None):
"""Builds layers.
Either :attr:`layer_hparams` or :attr:`layers` must be
provided. If both are given, :attr:`layers` will be used.
Args:
network: An instance of a subclass of
:class:`~texar.tf.modules.networks.network_base.FeedForwardNetworkBase`
layers (optional): A list of layer instances.
layer_hparams (optional): A list of layer hparams, each to which
is fed to :func:`~texar.tf.core.layers.get_layer` to create the
layer instance.
"""
with tf.variable_scope(network.variable_scope):
if layers is not None:
network._layers = layers
else:
if layer_hparams is None:
raise ValueError(
'Either `layer` or `layer_hparams` is required.')
network._layers = []
for _, hparams in enumerate(layer_hparams):
network._layers.append(get_layer(hparams=hparams))
for layer in network._layers:
layer_name = uniquify_str(layer.name, network._layer_names)
network._layer_names.append(layer_name)
network._layers_by_name[layer_name] = layer
def __init__(self,
pretrained_model_name=None,
cache_dir=None,
hparams=None):
super(BERTEncoder, self).__init__(hparams=hparams)
self.load_pretrained_config(pretrained_model_name, cache_dir)
with tf.variable_scope(self.variable_scope):
# Word embedding
self.word_embedder = WordEmbedder(
vocab_size=self._hparams.vocab_size,
hparams=self._hparams.embed)
# Segment embedding for each type of tokens
self.segment_embedder = WordEmbedder(
vocab_size=self._hparams.type_vocab_size,
hparams=self._hparams.segment_embed)
# Position embedding
self.position_embedder = PositionEmbedder(
position_size=self._hparams.position_size,
hparams=self._hparams.position_embed)
# The BERT encoder (a TransformerEncoder)
self.encoder = TransformerEncoder(hparams=self._hparams.encoder)
with tf.variable_scope("pooler"):
kwargs_i = {
"units": self._hparams.hidden_size,
"activation": tf.tanh
}
layer_hparams = {"type": "Dense", "kwargs": kwargs_i}
self.pooler = get_layer(hparams=layer_hparams)
def __init__(self,
pretrained_model_name=None,
cache_dir=None,
hparams=None):
super().__init__(hparams=hparams)
with tf.variable_scope(self.variable_scope):
encoder_hparams = dict_fetch(hparams,
GPT2Encoder.default_hparams())
if encoder_hparams is not None:
encoder_hparams['name'] = None
self._encoder = GPT2Encoder(
pretrained_model_name=pretrained_model_name,
cache_dir=cache_dir,
hparams=encoder_hparams)
# Creates an dropout layer
drop_kwargs = {"rate": self._hparams.dropout}
layer_hparams = {"type": "Dropout", "kwargs": drop_kwargs}
self._dropout_layer = get_layer(hparams=layer_hparams)
# Creates an additional classification layer if needed
self._num_classes = self._hparams.num_classes
if self._num_classes <= 0:
self._logit_layer = None
else:
logit_kwargs = self._hparams.logit_layer_kwargs
if logit_kwargs is None:
logit_kwargs = {}
elif not isinstance(logit_kwargs, HParams):
raise ValueError(
"hparams['logit_layer_kwargs'] must be a dict.")
else:
logit_kwargs = logit_kwargs.todict()
logit_kwargs.update({"units": self._num_classes})
if 'name' not in logit_kwargs:
logit_kwargs['name'] = "logit_layer"
layer_hparams = {"type": "Dense", "kwargs": logit_kwargs}
self._logit_layer = get_layer(hparams=layer_hparams)
def append_layer(self, layer):
"""Appends a layer to the end of the network. The method is only
feasible before :attr:`_build` is called.
Args:
layer: A :tf_main:`tf.layers.Layer <layers/Layer>` instance, or
a dict of layer hyperparameters.
"""
if self._built:
raise TexarError("`FeedForwardNetwork.append_layer` can be "
"called only before `_build` is called.")
with tf.variable_scope(self.variable_scope):
layer_ = layer
if not isinstance(layer_, tf.layers.Layer):
layer_ = get_layer(hparams=layer_)
self._layers.append(layer_)
layer_name = uniquify_str(layer_.name, self._layer_names)
self._layer_names.append(layer_name)
self._layers_by_name[layer_name] = layer_
def __init__(self,
pretrained_model_name=None,
cache_dir=None,
hparams=None):
PretrainedBase.__init__(self, pretrained_model_name, cache_dir,
hparams)
if self.pretrained_model_dir:
self._hparams = HParams(self.pretrained_model_hparams,
self._hparams.todict())
with tf.variable_scope(self.variable_scope):
if self._hparams.initializer:
tf.get_variable_scope().set_initializer(
layers.get_initializer(self._hparams.initializer))
# Word embedding
self.word_embedder = WordEmbedder(
vocab_size=self._hparams.vocab_size,
hparams=self._hparams.embed)
# Segment embedding for each type of tokens
self.segment_embedder = WordEmbedder(
vocab_size=self._hparams.type_vocab_size,
hparams=self._hparams.segment_embed)
# Position embedding
self.position_embedder = PositionEmbedder(
position_size=self._hparams.position_size,
hparams=self._hparams.position_embed)
# The BERT encoder (a TransformerEncoder)
self.encoder = TransformerEncoder(hparams=self._hparams.encoder)
with tf.variable_scope("pooler"):
kwargs_i = {
"units": self._hparams.hidden_size,
"activation": tf.tanh
}
layer_hparams = {"type": "Dense", "kwargs": kwargs_i}
self.pooler = layers.get_layer(hparams=layer_hparams)
def __init__(self,
cell=None,
cell_dropout_mode=None,
output_layer=None,
hparams=None):
ClassifierBase.__init__(self, hparams)
with tf.variable_scope(self.variable_scope):
# Creates the underlying encoder
encoder_hparams = utils.dict_fetch(
hparams, UnidirectionalRNNEncoder.default_hparams())
if encoder_hparams is not None:
encoder_hparams['name'] = None
self._encoder = UnidirectionalRNNEncoder(
cell=cell,
cell_dropout_mode=cell_dropout_mode,
output_layer=output_layer,
hparams=encoder_hparams)
# Creates an additional classification layer if needed
self._num_classes = self._hparams.num_classes
if self._num_classes <= 0:
self._logit_layer = None
else:
logit_kwargs = self._hparams.logit_layer_kwargs
if logit_kwargs is None:
logit_kwargs = {}
elif not isinstance(logit_kwargs, HParams):
raise ValueError(
"hparams['logit_layer_kwargs'] must be a dict.")
else:
logit_kwargs = logit_kwargs.todict()
logit_kwargs.update({"units": self._num_classes})
if 'name' not in logit_kwargs:
logit_kwargs['name'] = "logit_layer"
layer_hparams = {"type": "Dense", "kwargs": logit_kwargs}
self._logit_layer = layers.get_layer(hparams=layer_hparams)
def _build_dense_output_layer(hparams):
nlayers = hparams.num_layers
if nlayers <= 0:
return None
layer_size = _to_list(hparams.layer_size, 'output_layer.layer_size',
nlayers)
other_kwargs = hparams.other_dense_kwargs or {}
if isinstance(other_kwargs, HParams):
other_kwargs = other_kwargs.todict()
if not isinstance(other_kwargs, dict):
raise ValueError(
"hparams 'output_layer.other_dense_kwargs' must be a dict.")
dense_layers = []
for i in range(nlayers):
if i == nlayers - 1:
activation = hparams.final_layer_activation
else:
activation = hparams.activation
kwargs_i = {
"units": layer_size[i],
"activation": activation,
"name": "dense_%d" % (i + 1)
}
kwargs_i.update(other_kwargs)
layer_hparams = {"type": "Dense", "kwargs": kwargs_i}
dense_layers.append(layers.get_layer(hparams=layer_hparams))
if len(dense_layers) == 1:
dense_layers = dense_layers[0]
return dense_layers
def __init__(self,
pretrained_model_name=None,
cache_dir=None,
hparams=None):
super(XLNetEncoder, self).__init__(hparams=hparams)
self.load_pretrained_config(pretrained_model_name, cache_dir)
num_layers = self._hparams.num_layers
use_segments = self._hparams.use_segments
untie_r = self._hparams.untie_r
with tf.variable_scope(self.variable_scope):
if untie_r:
self.r_w_bias = tf.get_variable('r_w_bias', [
num_layers, self._hparams.num_heads, self._hparams.head_dim
],
dtype=tf.float32)
self.r_r_bias = tf.get_variable('r_r_bias', [
num_layers, self._hparams.num_heads, self._hparams.head_dim
],
dtype=tf.float32)
else:
self.r_w_bias = tf.get_variable(
'r_w_bias',
[self._hparams.num_heads, self._hparams.head_dim],
dtype=tf.float32)
self.r_r_bias = tf.get_variable(
'r_r_bias',
[self._hparams.num_heads, self._hparams.head_dim],
dtype=tf.float32)
if use_segments:
self.segment_embed = tf.get_variable('seg_embed', [
num_layers, 2, self._hparams.num_heads,
self._hparams.head_dim
],
dtype=tf.float32)
self.r_s_bias = (tf.get_variable(
'r_s_bias', [
num_layers, self._hparams.num_heads,
self._hparams.head_dim
],
dtype=tf.float32) if untie_r else tf.get_variable(
'r_s_bias',
[self._hparams.num_heads, self._hparams.head_dim],
dtype=tf.float32))
else:
self.segment_embed = None
self.r_s_bias = None
# Word embedding
self.word_embedder = WordEmbedder(
vocab_size=self._hparams.vocab_size,
hparams={"dim": self._hparams.hidden_dim})
# Position embedding
self.pos_embed = RelativePositionalEncoding(
hparams={
"dim": self._hparams.hidden_dim,
"max_seq_len": self._hparams.max_seq_len
})
self.attn_layers = []
self.ff_layers = []
rel_attn_hparams = dict_fetch(
self._hparams, RelativeMutiheadAttention.default_hparams())
rel_attn_hparams["name"] = "rel_attn"
ff_hparams = dict_fetch(self._hparams,
PositionWiseFF.default_hparams())
ff_hparams["name"] = "ff"
for i in range(num_layers):
with tf.variable_scope("layer_{}".format(i)):
if self._hparams.untie_r:
if use_segments:
self.attn_layers.append(
RelativeMutiheadAttention(
self.r_r_bias[i],
self.r_w_bias[i],
self.r_s_bias[i],
self.segment_embed[i],
hparams=rel_attn_hparams))
else:
self.attn_layers.append(
RelativeMutiheadAttention(
self.r_r_bias[i],
self.r_w_bias[i],
hparams=rel_attn_hparams))
else:
if use_segments:
self.attn_layers.append(
RelativeMutiheadAttention(
self.r_r_bias,
self.r_w_bias,
self.r_s_bias,
self.segment_embed[i],
hparams=rel_attn_hparams))
else:
self.attn_layers.append(
RelativeMutiheadAttention(
self.r_r_bias,
self.r_w_bias,
hparams=rel_attn_hparams))
self.ff_layers.append(PositionWiseFF(hparams=ff_hparams))
dropout_hparams = {
"type": "Dropout",
"kwargs": {
"rate": self._hparams.dropout
}
}
self.dropout = get_layer(hparams=dropout_hparams)
self.mask_embed = tf.get_variable('mask_emb',
[1, 1, self.hparams.hidden_dim],
dtype=tf.float32)
