Spaces:
Runtime error
Runtime error
File size: 7,243 Bytes
5672777 |
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 |
# 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.
"""Keras-based gated feedforward layer."""
# pylint: disable=g-classes-have-attributes
from typing import Optional
import tensorflow as tf, tf_keras
from official.modeling import tf_utils
class BlockDiagFeedforward(tf_keras.layers.Layer):
"""Block diagonal feedforward layer.
This layer replaces the weight matrix of the output_dense layer with a block
diagonal matrix to save layer parameters and FLOPs. A linear mixing layer can
be added optionally to improve layer expressibility.
Args:
intermediate_size: Size of the intermediate layer.
intermediate_activation: Activation for the intermediate layer.
dropout: Dropout probability for the output dropout.
num_blocks: The number of blocks for the block diagonal matrix of the
output_dense layer.
apply_mixing: Apply linear mixing if True.
kernel_initializer: Initializer for dense layer kernels.
bias_initializer: Initializer for dense layer biases.
kernel_regularizer: Regularizer for dense layer kernels.
bias_regularizer: Regularizer for dense layer biases.
activity_regularizer: Regularizer for dense layer activity.
kernel_constraint: Constraint for dense layer kernels.
bias_constraint: Constraint for dense layer kernels.
"""
def __init__(
self,
intermediate_size: int,
intermediate_activation: str,
dropout: float,
num_blocks: int = 1,
apply_mixing: bool = True,
kernel_initializer: str = "glorot_uniform",
bias_initializer: str = "zeros",
kernel_regularizer: Optional[tf_keras.regularizers.Regularizer] = None,
bias_regularizer: Optional[tf_keras.regularizers.Regularizer] = None,
activity_regularizer: Optional[tf_keras.regularizers.Regularizer] = None,
kernel_constraint: Optional[tf_keras.constraints.Constraint] = None,
bias_constraint: Optional[tf_keras.constraints.Constraint] = None,
**kwargs): # pylint: disable=g-doc-args
super().__init__(**kwargs)
self._intermediate_size = intermediate_size
self._intermediate_activation = intermediate_activation
self._dropout = dropout
self._num_blocks = num_blocks
self._apply_mixing = apply_mixing
if intermediate_size % num_blocks != 0:
raise ValueError("Intermediate_size (%d) isn't a multiple of num_blocks "
"(%d)." % (intermediate_size, num_blocks))
self._kernel_initializer = tf_keras.initializers.get(kernel_initializer)
self._bias_initializer = tf_keras.initializers.get(bias_initializer)
self._kernel_regularizer = tf_keras.regularizers.get(kernel_regularizer)
self._bias_regularizer = tf_keras.regularizers.get(bias_regularizer)
self._activity_regularizer = tf_keras.regularizers.get(activity_regularizer)
self._kernel_constraint = tf_keras.constraints.get(kernel_constraint)
self._bias_constraint = tf_keras.constraints.get(bias_constraint)
def build(self, input_shape):
hidden_size = input_shape.as_list()[-1]
common_kwargs = dict(
kernel_regularizer=self._kernel_regularizer,
bias_regularizer=self._bias_regularizer,
activity_regularizer=self._activity_regularizer,
kernel_constraint=self._kernel_constraint,
bias_constraint=self._bias_constraint)
self._intermediate_dense = tf_keras.layers.EinsumDense(
"abc,cde->abde",
output_shape=(None, self._num_blocks,
self._intermediate_size // self._num_blocks),
bias_axes="de",
name="intermediate",
kernel_initializer=tf_utils.clone_initializer(self._kernel_initializer),
bias_initializer=tf_utils.clone_initializer(self._bias_initializer),
**common_kwargs)
policy = tf_keras.mixed_precision.global_policy()
if policy.name == "mixed_bfloat16":
# bfloat16 causes BERT with the LAMB optimizer to not converge
# as well, so we use float32.
policy = tf.float32
self._intermediate_activation_layer = tf_keras.layers.Activation(
self._intermediate_activation, dtype=policy)
self._output_dense = tf_keras.layers.EinsumDense(
"abde,deo->abdo",
output_shape=(None, self._num_blocks, hidden_size // self._num_blocks),
bias_axes="do",
name="output",
kernel_initializer=tf_utils.clone_initializer(self._kernel_initializer),
bias_initializer=tf_utils.clone_initializer(self._bias_initializer),
**common_kwargs)
if self._apply_mixing:
self._output_mixing = tf_keras.layers.EinsumDense(
"abdo,de->abeo",
output_shape=(None, self._num_blocks,
hidden_size // self._num_blocks),
name="output_mixing",
kernel_initializer=tf_utils.clone_initializer(
self._kernel_initializer),
bias_initializer=tf_utils.clone_initializer(self._bias_initializer),
**common_kwargs)
self._output_reshape = tf_keras.layers.Reshape((-1, hidden_size))
self._output_dropout = tf_keras.layers.Dropout(rate=self._dropout)
def get_config(self):
config = {
"intermediate_size":
self._intermediate_size,
"intermediate_activation":
self._intermediate_activation,
"dropout":
self._dropout,
"num_blocks":
self._num_blocks,
"apply_mixing":
self._apply_mixing,
"kernel_initializer":
tf_keras.initializers.serialize(self._kernel_initializer),
"bias_initializer":
tf_keras.initializers.serialize(self._bias_initializer),
"kernel_regularizer":
tf_keras.regularizers.serialize(self._kernel_regularizer),
"bias_regularizer":
tf_keras.regularizers.serialize(self._bias_regularizer),
"activity_regularizer":
tf_keras.regularizers.serialize(self._activity_regularizer),
"kernel_constraint":
tf_keras.constraints.serialize(self._kernel_constraint),
"bias_constraint":
tf_keras.constraints.serialize(self._bias_constraint)
}
base_config = super().get_config()
return dict(list(base_config.items()) + list(config.items()))
def call(self, inputs):
intermediate_output = self._intermediate_dense(inputs)
intermediate_output = self._intermediate_activation_layer(
intermediate_output)
layer_output = self._output_dense(intermediate_output)
if self._apply_mixing:
layer_output = self._output_mixing(layer_output)
layer_output = self._output_reshape(layer_output)
layer_output = self._output_dropout(layer_output)
return layer_output
|