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# Copyright 2019 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 einsum layer."""
# pylint: disable=g-classes-have-attributes
from __future__ import absolute_import
from __future__ import division
# from __future__ import google_type_annotations
from __future__ import print_function
import tensorflow as tf
_CHR_IDX = ["a", "b", "c", "d", "e", "f", "g", "h", "i", "j", "k", "l", "m"]
@tf.keras.utils.register_keras_serializable(package="Text")
class DenseEinsum(tf.keras.layers.Layer):
"""A densely connected layer that uses tf.einsum as the backing computation.
This layer can perform einsum calculations of arbitrary dimensionality.
Arguments:
output_shape: Positive integer or tuple, dimensionality of the output space.
num_summed_dimensions: The number of dimensions to sum over. Standard 2D
matmul should use 1, 3D matmul should use 2, and so forth.
activation: Activation function to use. If you don't specify anything, no
activation is applied
(ie. "linear" activation: `a(x) = x`).
use_bias: Boolean, whether the layer uses a bias vector.
kernel_initializer: Initializer for the `kernel` weights matrix.
bias_initializer: Initializer for the bias vector.
kernel_regularizer: Regularizer function applied to the `kernel` weights
matrix.
bias_regularizer: Regularizer function applied to the bias vector.
activity_regularizer: Regularizer function applied to the output of the
layer (its "activation")..
kernel_constraint: Constraint function applied to the `kernel` weights
matrix.
bias_constraint: Constraint function applied to the bias vector.
Input shape:
N-D tensor with shape: `(batch_size, ..., input_dim)`. The most common
situation would be a 2D input with shape `(batch_size, input_dim)`.
Output shape:
N-D tensor with shape: `(batch_size, ..., units)`. For instance, for a 2D
input with shape `(batch_size, input_dim)`, the output would have shape
`(batch_size, units)`.
"""
def __init__(self,
output_shape,
num_summed_dimensions=1,
activation=None,
use_bias=True,
kernel_initializer="glorot_uniform",
bias_initializer="zeros",
kernel_regularizer=None,
bias_regularizer=None,
activity_regularizer=None,
kernel_constraint=None,
bias_constraint=None,
**kwargs):
super(DenseEinsum, self).__init__(**kwargs)
self._output_shape = output_shape if isinstance(
output_shape, (list, tuple)) else (output_shape,)
self._activation = tf.keras.activations.get(activation)
self._use_bias = use_bias
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._kernel_constraint = tf.keras.constraints.get(kernel_constraint)
self._bias_constraint = tf.keras.constraints.get(bias_constraint)
self._num_summed_dimensions = num_summed_dimensions
self._einsum_string = None
def _build_einsum_string(self, free_input_dims, bound_dims, output_dims):
input_str = ""
kernel_str = ""
output_str = ""
letter_offset = 0
for i in range(free_input_dims):
char = _CHR_IDX[i + letter_offset]
input_str += char
output_str += char
letter_offset += free_input_dims
for i in range(bound_dims):
char = _CHR_IDX[i + letter_offset]
input_str += char
kernel_str += char
letter_offset += bound_dims
for i in range(output_dims):
char = _CHR_IDX[i + letter_offset]
kernel_str += char
output_str += char
return input_str + "," + kernel_str + "->" + output_str
def build(self, input_shape):
input_shape = tf.TensorShape(input_shape)
input_rank = input_shape.rank
free_input_dims = input_rank - self._num_summed_dimensions
output_dims = len(self._output_shape)
self._einsum_string = self._build_einsum_string(free_input_dims,
self._num_summed_dimensions,
output_dims)
# This is only saved for testing purposes.
self._kernel_shape = (
input_shape[free_input_dims:].concatenate(self._output_shape))
self._kernel = self.add_weight(
"kernel",
shape=self._kernel_shape,
initializer=self._kernel_initializer,
regularizer=self._kernel_regularizer,
constraint=self._kernel_constraint,
dtype=self.dtype,
trainable=True)
if self._use_bias:
self._bias = self.add_weight(
"bias",
shape=self._output_shape,
initializer=self._bias_initializer,
regularizer=self._bias_regularizer,
constraint=self._bias_constraint,
dtype=self.dtype,
trainable=True)
else:
self._bias = None
super(DenseEinsum, self).build(input_shape)
def get_config(self):
config = {
"output_shape":
self._output_shape,
"num_summed_dimensions":
self._num_summed_dimensions,
"activation":
tf.keras.activations.serialize(self._activation),
"use_bias":
self._use_bias,
"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(DenseEinsum, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
def call(self, inputs):
ret = tf.einsum(self._einsum_string, inputs, self._kernel)
if self._use_bias:
ret += self._bias
if self._activation is not None:
ret = self._activation(ret)
return ret
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