ops.py
import copy
from typing import List, Union, Optional

import tensorflow as tf

def eye(num: int, value: tf.Tensor, dtype: Optional[tf.DType] = None) -> tf.Tensor:
if dtype is not None:
value = tf.cast(value, dtype)
return tf.linalg.diag(tf.fill([num], value))

def leading_transpose(tensor: tf.Tensor, perm: List[Union[int, type(...)]], leading_dim: int = 0) -> tf.Tensor:
"""
Transposes tensors with leading dimensions. Leading dimensions in
permutation list represented via ellipsis `...`.
When leading dimensions are found, `transpose` method
considers them as a single grouped element indexed by 0 in `perm` list. So, passing
`perm=[-2, ..., -1]`, you assume that your input tensor has [..., A, B] shape,
and you want to move leading dims between A and B dimensions.
Dimension indices in permutation list can be negative or positive. Valid positive
indices start from 1 up to the tensor rank, viewing leading dimensions `...` as zero
index.
Example:
a = tf.random.normal((1, 2, 3, 4, 5, 6))
# [..., A, B, C],
# where A is 1st element,
# B is 2nd element and
# C is 3rd element in
# permutation list,
# leading dimentions are [1, 2, 3]
# which are 0th element in permutation
# list
b = leading_transpose(a, [3, -3, ..., -2])  # [C, A, ..., B]
sess.run(b).shape
output> (6, 4, 1, 2, 3, 5)
:param tensor: TensorFlow tensor.
:param perm: List of permutation indices.
:returns: TensorFlow tensor.
:raises: ValueError when `...` cannot be found.
"""
perm = copy.copy(perm)
idx = perm.index(...)
perm[idx] = leading_dim

rank = tf.rank(tensor)
perm_tf = perm % rank

leading_dims = tf.range(rank - len(perm) + 1)
perm = tf.concat([perm_tf[:idx], leading_dims, perm_tf[idx + 1:]], 0)
return tf.transpose(tensor, perm)

def broadcasting_elementwise(op, a, b):
"""
Apply binary operation `op` to every pair in tensors `a` and `b`.

:param op: binary operator on tensors, e.g. tf.add, tf.substract
:param a: tf.Tensor, shape [n_1, ..., n_a]
:param b: tf.Tensor, shape [m_1, ..., m_b]
:return: tf.Tensor, shape [n_1, ..., n_a, m_1, ..., m_b]
"""
flatres = op(tf.reshape(a, [-1, 1]), tf.reshape(b, [1, -1]))
return tf.reshape(flatres, tf.concat([tf.shape(a), tf.shape(b)], 0))

def square_distance(X, X2):
"""
Returns (X - X2ᵀ)²
Due to the implementation and floating-point imprecision, the
result may actually be very slightly negative for entries very
close to each other.
"""
if X2 is None:
Xs = tf.reduce_sum(tf.square(X), axis=-1, keepdims=True)
dist = -2 * tf.matmul(X, X, transpose_b=True)
dist += Xs + tf.linalg.adjoint(Xs)
return dist
Xs = tf.reduce_sum(tf.square(X), axis=-1)
X2s = tf.reduce_sum(tf.square(X2), axis=-1)
dist = -2 * tf.tensordot(X, X2, [[-1], [-1]])
dist += broadcasting_elementwise(tf.add, Xs, X2s)
return dist