def __init__(self, xlnet_config, run_config, start_n_top, end_n_top,
**kwargs):
super(QAXLNetModel, self).__init__(**kwargs)
self.run_config = run_config
self.initializer = _get_initializer(run_config)
self.xlnet_config = copy.deepcopy(xlnet_config)
self.xlnet_model = networks.XLNetBase(
vocab_size=self.xlnet_config.n_token,
initializer=self.initializer,
attention_type="bi",
num_layers=self.xlnet_config.n_layer,
hidden_size=self.xlnet_config.d_model,
num_attention_heads=self.xlnet_config.n_head,
head_size=self.xlnet_config.d_head,
inner_size=self.xlnet_config.d_inner,
tie_attention_biases=not self.xlnet_config.untie_r,
inner_activation=self.xlnet_config.ff_activation,
dropout_rate=self.run_config.dropout,
attention_dropout_rate=self.run_config.dropout_att,
two_stream=False,
memory_length=self.run_config.mem_len,
reuse_length=self.run_config.reuse_len,
bi_data=self.run_config.bi_data,
clamp_length=self.run_config.clamp_len,
use_cls_mask=False,
name="xlnet_model")
self.qa_loss_layer = QALossLayer(
hidden_size=self.xlnet_config.d_model,
start_n_top=start_n_top,
end_n_top=end_n_top,
initializer=self.initializer,
dropout_rate=self.run_config.dropout,
name="qa_loss_layer")
def __init__(self, xlnet_config, run_config, n_class, summary_type,
use_legacy_mask=True, **kwargs):
super(ClassificationXLNetModel, self).__init__(**kwargs)
warnings.warn(
"`ClassificationXLNetModel` is deprecated, please use `XLNetClassifier`"
"instead.", DeprecationWarning, stacklevel=2)
self.run_config = run_config
self.initializer = _get_initializer(run_config)
self.xlnet_config = copy.deepcopy(xlnet_config)
self._use_legacy_mask = use_legacy_mask
self.xlnet_model = networks.XLNetBase(
vocab_size=self.xlnet_config.n_token,
initializer=self.initializer,
attention_type="bi",
num_layers=self.xlnet_config.n_layer,
hidden_size=self.xlnet_config.d_model,
num_attention_heads=self.xlnet_config.n_head,
head_size=self.xlnet_config.d_head,
inner_size=self.xlnet_config.d_inner,
two_stream=False,
tie_attention_biases=not self.xlnet_config.untie_r,
inner_activation=self.xlnet_config.ff_activation,
dropout_rate=self.run_config.dropout,
attention_dropout_rate=self.run_config.dropout_att,
memory_length=self.run_config.mem_len,
reuse_length=self.run_config.reuse_len,
bi_data=self.run_config.bi_data,
clamp_length=self.run_config.clamp_len,
use_cls_mask=False,
name="xlnet_model")
self.summarization_layer = Summarization(
hidden_size=self.xlnet_config.d_model,
num_attention_heads=self.xlnet_config.n_head,
head_size=self.xlnet_config.d_head,
dropout_rate=self.run_config.dropout,
attention_dropout_rate=self.run_config.dropout_att,
initializer=self.initializer,
use_proj=True,
summary_type=summary_type,
name="sequence_summary")
self.cl_loss_layer = ClassificationLossLayer(
n_class=n_class, initializer=self.initializer, name="classification")
def _get_xlnet_base() -> tf.keras.layers.Layer:
"""Returns a trivial base XLNet model."""
return networks.XLNetBase(
vocab_size=100,
num_layers=2,
hidden_size=4,
num_attention_heads=2,
head_size=2,
inner_size=2,
dropout_rate=0.,
attention_dropout_rate=0.,
attention_type='bi',
bi_data=True,
initializer=tf.keras.initializers.RandomNormal(stddev=0.1),
two_stream=False,
tie_attention_biases=True,
reuse_length=0,
inner_activation='relu')
def get_xlnet_base(model_config: xlnet_config.XLNetConfig,
run_config: xlnet_config.RunConfig, attention_type: str,
two_stream: bool, use_cls_mask: bool) -> tf.keras.Model:
"""Gets an 'XLNetBase' object.
Args:
model_config: the config that defines the core XLNet model.
run_config: separate runtime configuration with extra parameters.
attention_type: the attention type for the base XLNet model, "uni" or "bi".
two_stream: whether or not to use two strema attention.
use_cls_mask: whether or not cls mask is included in the input sequences.
Returns:
An XLNetBase object.
"""
initializer = _get_initializer(
initialization_method=run_config.init_method,
initialization_range=run_config.init_range,
initialization_std=run_config.init_std)
kwargs = dict(vocab_size=model_config.n_token,
num_layers=model_config.n_layer,
hidden_size=model_config.d_model,
num_attention_heads=model_config.n_head,
head_size=model_config.d_head,
inner_size=model_config.d_inner,
dropout_rate=run_config.dropout,
attention_dropout_rate=run_config.dropout_att,
attention_type=attention_type,
bi_data=run_config.bi_data,
initializer=initializer,
two_stream=two_stream,
tie_attention_biases=not model_config.untie_r,
memory_length=run_config.mem_len,
clamp_length=run_config.clamp_len,
reuse_length=run_config.reuse_len,
inner_activation=model_config.ff_activation,
use_cls_mask=use_cls_mask)
return networks.XLNetBase(**kwargs)
def __init__(self, use_proj, xlnet_config, run_config, use_legacy_mask=True,
**kwargs):
super(PretrainingXLNetModel, self).__init__(**kwargs)
self.run_config = run_config
self.initializer = _get_initializer(run_config)
self.xlnet_config = copy.deepcopy(xlnet_config)
self._use_legacy_mask = use_legacy_mask
self.xlnet_model = networks.XLNetBase(
vocab_size=self.xlnet_config.n_token,
initializer=self.initializer,
attention_type="bi",
num_layers=self.xlnet_config.n_layer,
hidden_size=self.xlnet_config.d_model,
num_attention_heads=self.xlnet_config.n_head,
head_size=self.xlnet_config.d_head,
inner_size=self.xlnet_config.d_inner,
two_stream=True,
tie_attention_biases=not self.xlnet_config.untie_r,
inner_activation=self.xlnet_config.ff_activation,
dropout_rate=self.run_config.dropout,
attention_dropout_rate=self.run_config.dropout_att,
memory_length=self.run_config.mem_len,
reuse_length=self.run_config.reuse_len,
bi_data=self.run_config.bi_data,
clamp_length=self.run_config.clamp_len,
use_cls_mask=self.run_config.use_cls_mask,
name="xlnet_model")
self.lmloss_layer = LMLossLayer(
vocab_size=self.xlnet_config.n_token,
hidden_size=self.xlnet_config.d_model,
initializer=self.initializer,
tie_weight=True,
bi_data=self.run_config.bi_data,
use_one_hot=self.run_config.use_tpu,
use_proj=use_proj,
name="lm_loss")
def build_encoder(config: EncoderConfig,
embedding_layer: Optional[tf.keras.layers.Layer] = None,
encoder_cls=None,
bypass_config: bool = False):
"""Instantiate a Transformer encoder network from EncoderConfig.
Args:
config: the one-of encoder config, which provides encoder parameters of a
chosen encoder.
embedding_layer: an external embedding layer passed to the encoder.
encoder_cls: an external encoder cls not included in the supported encoders,
usually used by gin.configurable.
bypass_config: whether to ignore config instance to create the object with
`encoder_cls`.
Returns:
An encoder instance.
"""
if bypass_config:
return encoder_cls()
encoder_type = config.type
encoder_cfg = config.get()
if encoder_cls and encoder_cls.__name__ == "EncoderScaffold":
embedding_cfg = dict(
vocab_size=encoder_cfg.vocab_size,
type_vocab_size=encoder_cfg.type_vocab_size,
hidden_size=encoder_cfg.hidden_size,
max_seq_length=encoder_cfg.max_position_embeddings,
initializer=tf.keras.initializers.TruncatedNormal(
stddev=encoder_cfg.initializer_range),
dropout_rate=encoder_cfg.dropout_rate,
)
hidden_cfg = dict(
num_attention_heads=encoder_cfg.num_attention_heads,
intermediate_size=encoder_cfg.intermediate_size,
intermediate_activation=tf_utils.get_activation(
encoder_cfg.hidden_activation),
dropout_rate=encoder_cfg.dropout_rate,
attention_dropout_rate=encoder_cfg.attention_dropout_rate,
kernel_initializer=tf.keras.initializers.TruncatedNormal(
stddev=encoder_cfg.initializer_range),
)
kwargs = dict(
embedding_cfg=embedding_cfg,
hidden_cfg=hidden_cfg,
num_hidden_instances=encoder_cfg.num_layers,
pooled_output_dim=encoder_cfg.hidden_size,
pooler_layer_initializer=tf.keras.initializers.TruncatedNormal(
stddev=encoder_cfg.initializer_range),
return_all_layer_outputs=encoder_cfg.return_all_encoder_outputs,
dict_outputs=True)
return encoder_cls(**kwargs)
if encoder_type == "mobilebert":
return networks.MobileBERTEncoder(
word_vocab_size=encoder_cfg.word_vocab_size,
word_embed_size=encoder_cfg.word_embed_size,
type_vocab_size=encoder_cfg.type_vocab_size,
max_sequence_length=encoder_cfg.max_sequence_length,
num_blocks=encoder_cfg.num_blocks,
hidden_size=encoder_cfg.hidden_size,
num_attention_heads=encoder_cfg.num_attention_heads,
intermediate_size=encoder_cfg.intermediate_size,
intermediate_act_fn=encoder_cfg.hidden_activation,
hidden_dropout_prob=encoder_cfg.hidden_dropout_prob,
attention_probs_dropout_prob=encoder_cfg.
attention_probs_dropout_prob,
intra_bottleneck_size=encoder_cfg.intra_bottleneck_size,
initializer_range=encoder_cfg.initializer_range,
use_bottleneck_attention=encoder_cfg.use_bottleneck_attention,
key_query_shared_bottleneck=encoder_cfg.
key_query_shared_bottleneck,
num_feedforward_networks=encoder_cfg.num_feedforward_networks,
normalization_type=encoder_cfg.normalization_type,
classifier_activation=encoder_cfg.classifier_activation,
input_mask_dtype=encoder_cfg.input_mask_dtype)
if encoder_type == "albert":
return networks.AlbertEncoder(
vocab_size=encoder_cfg.vocab_size,
embedding_width=encoder_cfg.embedding_width,
hidden_size=encoder_cfg.hidden_size,
num_layers=encoder_cfg.num_layers,
num_attention_heads=encoder_cfg.num_attention_heads,
max_sequence_length=encoder_cfg.max_position_embeddings,
type_vocab_size=encoder_cfg.type_vocab_size,
intermediate_size=encoder_cfg.intermediate_size,
activation=tf_utils.get_activation(encoder_cfg.hidden_activation),
dropout_rate=encoder_cfg.dropout_rate,
attention_dropout_rate=encoder_cfg.attention_dropout_rate,
initializer=tf.keras.initializers.TruncatedNormal(
stddev=encoder_cfg.initializer_range),
dict_outputs=True)
if encoder_type == "bigbird":
# TODO(frederickliu): Support use_gradient_checkpointing and update
# experiments to use the EncoderScaffold only.
if encoder_cfg.use_gradient_checkpointing:
return bigbird_encoder.BigBirdEncoder(
vocab_size=encoder_cfg.vocab_size,
hidden_size=encoder_cfg.hidden_size,
num_layers=encoder_cfg.num_layers,
num_attention_heads=encoder_cfg.num_attention_heads,
intermediate_size=encoder_cfg.intermediate_size,
activation=tf_utils.get_activation(
encoder_cfg.hidden_activation),
dropout_rate=encoder_cfg.dropout_rate,
attention_dropout_rate=encoder_cfg.attention_dropout_rate,
num_rand_blocks=encoder_cfg.num_rand_blocks,
block_size=encoder_cfg.block_size,
max_position_embeddings=encoder_cfg.max_position_embeddings,
type_vocab_size=encoder_cfg.type_vocab_size,
initializer=tf.keras.initializers.TruncatedNormal(
stddev=encoder_cfg.initializer_range),
embedding_width=encoder_cfg.embedding_width,
use_gradient_checkpointing=encoder_cfg.
use_gradient_checkpointing)
embedding_cfg = dict(
vocab_size=encoder_cfg.vocab_size,
type_vocab_size=encoder_cfg.type_vocab_size,
hidden_size=encoder_cfg.hidden_size,
max_seq_length=encoder_cfg.max_position_embeddings,
initializer=tf.keras.initializers.TruncatedNormal(
stddev=encoder_cfg.initializer_range),
dropout_rate=encoder_cfg.dropout_rate)
attention_cfg = dict(
num_heads=encoder_cfg.num_attention_heads,
key_dim=int(encoder_cfg.hidden_size //
encoder_cfg.num_attention_heads),
kernel_initializer=tf.keras.initializers.TruncatedNormal(
stddev=encoder_cfg.initializer_range),
max_rand_mask_length=encoder_cfg.max_position_embeddings,
num_rand_blocks=encoder_cfg.num_rand_blocks,
from_block_size=encoder_cfg.block_size,
to_block_size=encoder_cfg.block_size,
)
hidden_cfg = dict(
num_attention_heads=encoder_cfg.num_attention_heads,
intermediate_size=encoder_cfg.intermediate_size,
intermediate_activation=tf_utils.get_activation(
encoder_cfg.hidden_activation),
dropout_rate=encoder_cfg.dropout_rate,
attention_dropout_rate=encoder_cfg.attention_dropout_rate,
norm_first=encoder_cfg.norm_first,
kernel_initializer=tf.keras.initializers.TruncatedNormal(
stddev=encoder_cfg.initializer_range),
attention_cls=layers.BigBirdAttention,
attention_cfg=attention_cfg)
kwargs = dict(
embedding_cfg=embedding_cfg,
hidden_cls=layers.TransformerScaffold,
hidden_cfg=hidden_cfg,
num_hidden_instances=encoder_cfg.num_layers,
mask_cls=layers.BigBirdMasks,
mask_cfg=dict(block_size=encoder_cfg.block_size),
pooled_output_dim=encoder_cfg.hidden_size,
pooler_layer_initializer=tf.keras.initializers.TruncatedNormal(
stddev=encoder_cfg.initializer_range),
return_all_layer_outputs=False,
dict_outputs=True,
layer_idx_as_attention_seed=True)
return networks.EncoderScaffold(**kwargs)
if encoder_type == "kernel":
embedding_cfg = dict(
vocab_size=encoder_cfg.vocab_size,
type_vocab_size=encoder_cfg.type_vocab_size,
hidden_size=encoder_cfg.hidden_size,
max_seq_length=encoder_cfg.max_position_embeddings,
initializer=tf.keras.initializers.TruncatedNormal(
stddev=encoder_cfg.initializer_range),
dropout_rate=encoder_cfg.dropout_rate)
attention_cfg = dict(
num_heads=encoder_cfg.num_attention_heads,
key_dim=int(encoder_cfg.hidden_size //
encoder_cfg.num_attention_heads),
kernel_initializer=tf.keras.initializers.TruncatedNormal(
stddev=encoder_cfg.initializer_range),
feature_transform=encoder_cfg.feature_transform,
num_random_features=encoder_cfg.num_random_features,
redraw=encoder_cfg.redraw,
is_short_seq=encoder_cfg.is_short_seq,
begin_kernel=encoder_cfg.begin_kernel,
scale=encoder_cfg.scale,
)
hidden_cfg = dict(
num_attention_heads=encoder_cfg.num_attention_heads,
intermediate_size=encoder_cfg.intermediate_size,
intermediate_activation=tf_utils.get_activation(
encoder_cfg.hidden_activation),
dropout_rate=encoder_cfg.dropout_rate,
attention_dropout_rate=encoder_cfg.attention_dropout_rate,
norm_first=encoder_cfg.norm_first,
kernel_initializer=tf.keras.initializers.TruncatedNormal(
stddev=encoder_cfg.initializer_range),
attention_cls=layers.KernelAttention,
attention_cfg=attention_cfg)
kwargs = dict(
embedding_cfg=embedding_cfg,
hidden_cls=layers.TransformerScaffold,
hidden_cfg=hidden_cfg,
num_hidden_instances=encoder_cfg.num_layers,
mask_cls=layers.KernelMask,
pooled_output_dim=encoder_cfg.hidden_size,
pooler_layer_initializer=tf.keras.initializers.TruncatedNormal(
stddev=encoder_cfg.initializer_range),
return_all_layer_outputs=False,
dict_outputs=True,
layer_idx_as_attention_seed=True)
return networks.EncoderScaffold(**kwargs)
if encoder_type == "xlnet":
return networks.XLNetBase(
vocab_size=encoder_cfg.vocab_size,
num_layers=encoder_cfg.num_layers,
hidden_size=encoder_cfg.hidden_size,
num_attention_heads=encoder_cfg.num_attention_heads,
head_size=encoder_cfg.head_size,
inner_size=encoder_cfg.inner_size,
dropout_rate=encoder_cfg.dropout_rate,
attention_dropout_rate=encoder_cfg.attention_dropout_rate,
attention_type=encoder_cfg.attention_type,
bi_data=encoder_cfg.bi_data,
two_stream=encoder_cfg.two_stream,
tie_attention_biases=encoder_cfg.tie_attention_biases,
memory_length=encoder_cfg.memory_length,
clamp_length=encoder_cfg.clamp_length,
reuse_length=encoder_cfg.reuse_length,
inner_activation=encoder_cfg.inner_activation,
use_cls_mask=encoder_cfg.use_cls_mask,
embedding_width=encoder_cfg.embedding_width,
initializer=tf.keras.initializers.RandomNormal(
stddev=encoder_cfg.initializer_range))
if encoder_type == "teams":
embedding_cfg = dict(
vocab_size=encoder_cfg.vocab_size,
type_vocab_size=encoder_cfg.type_vocab_size,
hidden_size=encoder_cfg.hidden_size,
embedding_width=encoder_cfg.embedding_size,
max_seq_length=encoder_cfg.max_position_embeddings,
initializer=tf.keras.initializers.TruncatedNormal(
stddev=encoder_cfg.initializer_range),
dropout_rate=encoder_cfg.dropout_rate,
)
embedding_network = networks.PackedSequenceEmbedding(**embedding_cfg)
hidden_cfg = dict(
num_attention_heads=encoder_cfg.num_attention_heads,
intermediate_size=encoder_cfg.intermediate_size,
intermediate_activation=tf_utils.get_activation(
encoder_cfg.hidden_activation),
dropout_rate=encoder_cfg.dropout_rate,
attention_dropout_rate=encoder_cfg.attention_dropout_rate,
kernel_initializer=tf.keras.initializers.TruncatedNormal(
stddev=encoder_cfg.initializer_range),
)
kwargs = dict(
embedding_cfg=embedding_cfg,
embedding_cls=embedding_network,
hidden_cfg=hidden_cfg,
num_hidden_instances=encoder_cfg.num_layers,
pooled_output_dim=encoder_cfg.hidden_size,
pooler_layer_initializer=tf.keras.initializers.TruncatedNormal(
stddev=encoder_cfg.initializer_range),
return_all_layer_outputs=encoder_cfg.return_all_encoder_outputs,
dict_outputs=True)
return networks.EncoderScaffold(**kwargs)
# Uses the default BERTEncoder configuration schema to create the encoder.
# If it does not match, please add a switch branch by the encoder type.
return networks.BertEncoder(
vocab_size=encoder_cfg.vocab_size,
hidden_size=encoder_cfg.hidden_size,
num_layers=encoder_cfg.num_layers,
num_attention_heads=encoder_cfg.num_attention_heads,
intermediate_size=encoder_cfg.intermediate_size,
activation=tf_utils.get_activation(encoder_cfg.hidden_activation),
dropout_rate=encoder_cfg.dropout_rate,
attention_dropout_rate=encoder_cfg.attention_dropout_rate,
max_sequence_length=encoder_cfg.max_position_embeddings,
type_vocab_size=encoder_cfg.type_vocab_size,
initializer=tf.keras.initializers.TruncatedNormal(
stddev=encoder_cfg.initializer_range),
output_range=encoder_cfg.output_range,
embedding_width=encoder_cfg.embedding_size,
embedding_layer=embedding_layer,
return_all_encoder_outputs=encoder_cfg.return_all_encoder_outputs,
dict_outputs=True,
norm_first=encoder_cfg.norm_first)
