File size: 9,602 Bytes
25e57c6 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 | # Copyright 2023 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Transformer-based text encoder network."""
# pylint: disable=g-classes-have-attributes
import tensorflow as tf, tf_keras
from official.modeling import activations
from official.modeling import tf_utils
from official.nlp import modeling
from official.nlp.modeling import layers
from official.projects.bigbird import recompute_grad
from official.projects.bigbird import recomputing_dropout
_MAX_SEQ_LEN = 4096
class RecomputeTransformerLayer(layers.TransformerScaffold):
"""Transformer layer that recomputes the forward pass during backpropagation."""
def call(self, inputs, training=None):
emb, mask = inputs
def f(*args):
# recompute_grad can only handle tensor inputs. so we enumerate the
# nested input [emb, mask] as follows:
# args[0]: emb
# args[1]: mask[0] = band_mask
# args[2]: mask[1] = encoder_from_mask
# args[3]: mask[2] = encoder_to_mask
# args[4]: mask[3] = blocked_encoder_mask
x = super(RecomputeTransformerLayer,
self).call([args[0], [args[1], args[2], args[3], args[4]]],
training=training)
return x
f = recompute_grad.recompute_grad(f)
return f(emb, *mask)
@tf_keras.utils.register_keras_serializable(package='Text')
class BigBirdEncoder(tf_keras.Model):
"""Transformer-based encoder network with BigBird attentions.
*Note* that the network is constructed by
[Keras Functional API](https://keras.io/guides/functional_api/).
Args:
vocab_size: The size of the token vocabulary.
hidden_size: The size of the transformer hidden layers.
num_layers: The number of transformer layers.
num_attention_heads: The number of attention heads for each transformer. The
hidden size must be divisible by the number of attention heads.
max_position_embeddings: The maximum length of position embeddings that this
encoder can consume. If None, max_position_embeddings uses the value from
sequence length. This determines the variable shape for positional
embeddings.
type_vocab_size: The number of types that the 'type_ids' input can take.
intermediate_size: The intermediate size for the transformer layers.
block_size: int. A BigBird Attention parameter: size of block in from/to
sequences.
num_rand_blocks: int. A BigBird Attention parameter: number of random chunks
per row.
activation: The activation to use for the transformer layers.
dropout_rate: The dropout rate to use for the transformer layers.
attention_dropout_rate: The dropout rate to use for the attention layers
within the transformer layers.
initializer: The initialzer to use for all weights in this encoder.
embedding_width: The width of the word embeddings. If the embedding width is
not equal to hidden size, embedding parameters will be factorized into two
matrices in the shape of ['vocab_size', 'embedding_width'] and
['embedding_width', 'hidden_size'] ('embedding_width' is usually much
smaller than 'hidden_size').
use_gradient_checkpointing: Use gradient checkpointing to trade-off compute
for memory.
"""
def __init__(self,
vocab_size,
hidden_size=768,
num_layers=12,
num_attention_heads=12,
max_position_embeddings=_MAX_SEQ_LEN,
type_vocab_size=16,
intermediate_size=3072,
block_size=64,
num_rand_blocks=3,
activation=activations.gelu,
dropout_rate=0.1,
attention_dropout_rate=0.1,
initializer=tf_keras.initializers.TruncatedNormal(stddev=0.02),
embedding_width=None,
use_gradient_checkpointing=False,
**kwargs):
activation = tf_keras.activations.get(activation)
initializer = tf_keras.initializers.get(initializer)
if use_gradient_checkpointing:
tf_keras.layers.Dropout = recomputing_dropout.RecomputingDropout
layer_cls = RecomputeTransformerLayer
else:
layer_cls = layers.TransformerScaffold
self._self_setattr_tracking = False
self._config_dict = {
'vocab_size': vocab_size,
'hidden_size': hidden_size,
'num_layers': num_layers,
'num_attention_heads': num_attention_heads,
'max_position_embeddings': max_position_embeddings,
'type_vocab_size': type_vocab_size,
'intermediate_size': intermediate_size,
'block_size': block_size,
'num_rand_blocks': num_rand_blocks,
'activation': tf_utils.serialize_activation(
activation, use_legacy_format=True
),
'dropout_rate': dropout_rate,
'attention_dropout_rate': attention_dropout_rate,
'initializer': tf_utils.serialize_initializer(
initializer, use_legacy_format=True
),
'embedding_width': embedding_width,
}
word_ids = tf_keras.layers.Input(
shape=(None,), dtype=tf.int32, name='input_word_ids')
mask = tf_keras.layers.Input(
shape=(None,), dtype=tf.int32, name='input_mask')
type_ids = tf_keras.layers.Input(
shape=(None,), dtype=tf.int32, name='input_type_ids')
if embedding_width is None:
embedding_width = hidden_size
self._embedding_layer = modeling.layers.OnDeviceEmbedding(
vocab_size=vocab_size,
embedding_width=embedding_width,
initializer=initializer,
name='word_embeddings')
word_embeddings = self._embedding_layer(word_ids)
# Always uses dynamic slicing for simplicity.
self._position_embedding_layer = modeling.layers.PositionEmbedding(
initializer=initializer,
max_length=max_position_embeddings,
name='position_embedding')
position_embeddings = self._position_embedding_layer(word_embeddings)
self._type_embedding_layer = modeling.layers.OnDeviceEmbedding(
vocab_size=type_vocab_size,
embedding_width=embedding_width,
initializer=initializer,
use_one_hot=True,
name='type_embeddings')
type_embeddings = self._type_embedding_layer(type_ids)
embeddings = tf_keras.layers.Add()(
[word_embeddings, position_embeddings, type_embeddings])
self._embedding_norm_layer = tf_keras.layers.LayerNormalization(
name='embeddings/layer_norm', axis=-1, epsilon=1e-12, dtype=tf.float32)
embeddings = self._embedding_norm_layer(embeddings)
embeddings = tf_keras.layers.Dropout(rate=dropout_rate)(embeddings)
# We project the 'embedding' output to 'hidden_size' if it is not already
# 'hidden_size'.
if embedding_width != hidden_size:
self._embedding_projection = tf_keras.layers.EinsumDense(
'...x,xy->...y',
output_shape=hidden_size,
bias_axes='y',
kernel_initializer=initializer,
name='embedding_projection')
embeddings = self._embedding_projection(embeddings)
self._transformer_layers = []
data = embeddings
masks = layers.BigBirdMasks(block_size=block_size)(
data, mask)
encoder_outputs = []
attn_head_dim = hidden_size // num_attention_heads
for i in range(num_layers):
layer = layer_cls(
num_attention_heads,
intermediate_size,
activation,
attention_cls=layers.BigBirdAttention,
attention_cfg=dict(
num_heads=num_attention_heads,
key_dim=attn_head_dim,
kernel_initializer=initializer,
from_block_size=block_size,
to_block_size=block_size,
num_rand_blocks=num_rand_blocks,
max_rand_mask_length=max_position_embeddings,
seed=i),
dropout_rate=dropout_rate,
attention_dropout_rate=dropout_rate,
kernel_initializer=initializer)
self._transformer_layers.append(layer)
data = layer([data, masks])
encoder_outputs.append(data)
outputs = dict(
sequence_output=encoder_outputs[-1], encoder_outputs=encoder_outputs)
super().__init__(
inputs=[word_ids, mask, type_ids], outputs=outputs, **kwargs)
def get_embedding_table(self):
return self._embedding_layer.embeddings
def get_embedding_layer(self):
return self._embedding_layer
def get_config(self):
return self._config_dict
@property
def transformer_layers(self):
"""List of Transformer layers in the encoder."""
return self._transformer_layers
@property
def pooler_layer(self):
"""The pooler dense layer after the transformer layers."""
return self._pooler_layer
@classmethod
def from_config(cls, config, custom_objects=None):
return cls(**config)
|