Revision e24fd815cdfb8c654249da4576aeff6c2ce5a8ea authored by vdutor on 10 September 2020, 15:35 UTC, committed by vdutor on 10 September 2020, 15:35 UTC
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# Copyright 2016 James Hensman, alexggmatthews
# 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
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# See the License for the specific language governing permissions and
# limitations under the License.

# -*- coding: utf-8 -*-

import tensorflow as tf
from .config import default_float, default_jitter
from .covariances.kuus import Kuu
from .inducing_variables import InducingVariables
from .kernels import Kernel
from .utilities import Dispatcher, to_default_float

prior_kl = Dispatcher("prior_kl")

@prior_kl.register(InducingVariables, Kernel, object, object)
def _(inducing_variable, kernel, q_mu, q_sqrt, whiten=False):
    if whiten:
        return gauss_kl(q_mu, q_sqrt, None)
        K = Kuu(inducing_variable, kernel, jitter=default_jitter())  # [P, M, M] or [M, M]
        return gauss_kl(q_mu, q_sqrt, K)

def gauss_kl(q_mu, q_sqrt, K=None, *, K_cholesky=None):
    Compute the KL divergence KL[q || p] between

          q(x) = N(q_mu, q_sqrt^2)
          p(x) = N(0, K)    if K is not None
          p(x) = N(0, I)    if K is None

    We assume L multiple independent distributions, given by the columns of
    q_mu and the first or last dimension of q_sqrt. Returns the *sum* of the

    q_mu is a matrix ([M, L]), each column contains a mean.

    q_sqrt can be a 3D tensor ([L, M, M]), each matrix within is a lower
        triangular square-root matrix of the covariance of q.
    q_sqrt can be a matrix ([M, L]), each column represents the diagonal of a
        square-root matrix of the covariance of q.

    K is the covariance of p (positive-definite matrix).  The K matrix can be
    passed either directly as `K`, or as its Cholesky factor, `K_cholesky`.  In
    either case, it can be a single matrix [M, M], in which case the sum of the
    L KL divergences is computed by broadcasting, or L different covariances
    [L, M, M].

    Note: if no K matrix is given (both `K` and `K_cholesky` are None),
    `gauss_kl` computes the KL divergence from p(x) = N(0, I) instead.

    if (K is not None) and (K_cholesky is not None):
        raise ValueError(
            "Ambiguous arguments: gauss_kl() must only be passed one of `K` or `K_cholesky`."

    is_white = (K is None) and (K_cholesky is None)
    is_diag = len(q_sqrt.shape) == 2

    shape_constraints = [
        (q_mu, ["M", "L"]),
        (q_sqrt, (["M", "L"] if is_diag else ["L", "M", "M"])),
    if not is_white:
        if K is not None:
            shape_constraints.append((K, (["L", "M", "M"] if len(K.shape) == 3 else ["M", "M"])))
                (K_cholesky, (["L", "M", "M"] if len(K_cholesky.shape) == 3 else ["M", "M"]))
    tf.debugging.assert_shapes(shape_constraints, message="gauss_kl() arguments")

    M, L = tf.shape(q_mu)[0], tf.shape(q_mu)[1]

    if is_white:
        alpha = q_mu  # [M, L]
        if K is not None:
            Lp = tf.linalg.cholesky(K)  # [L, M, M] or [M, M]
        elif K_cholesky is not None:
            Lp = K_cholesky  # [L, M, M] or [M, M]

        is_batched = len(Lp.shape) == 3

        q_mu = tf.transpose(q_mu)[:, :, None] if is_batched else q_mu  # [L, M, 1] or [M, L]
        alpha = tf.linalg.triangular_solve(Lp, q_mu, lower=True)  # [L, M, 1] or [M, L]

    if is_diag:
        Lq = Lq_diag = q_sqrt
        Lq_full = tf.linalg.diag(tf.transpose(q_sqrt))  # [L, M, M]
        Lq = Lq_full = tf.linalg.band_part(q_sqrt, -1, 0)  # force lower triangle # [L, M, M]
        Lq_diag = tf.linalg.diag_part(Lq)  # [M, L]

    # Mahalanobis term: μqᵀ Σp⁻¹ μq
    mahalanobis = tf.reduce_sum(tf.square(alpha))

    # Constant term: - L * M
    constant = -to_default_float(tf.size(q_mu, out_type=tf.int64))

    # Log-determinant of the covariance of q(x):
    logdet_qcov = tf.reduce_sum(tf.math.log(tf.square(Lq_diag)))

    # Trace term: tr(Σp⁻¹ Σq)
    if is_white:
        trace = tf.reduce_sum(tf.square(Lq))
        if is_diag and not is_batched:
            # K is [M, M] and q_sqrt is [M, L]: fast specialisation
            LpT = tf.transpose(Lp)  # [M, M]
            Lp_inv = tf.linalg.triangular_solve(
                Lp, tf.eye(M, dtype=default_float()), lower=True
            )  # [M, M]
            K_inv = tf.linalg.diag_part(tf.linalg.triangular_solve(LpT, Lp_inv, lower=False))[
                :, None
            ]  # [M, M] -> [M, 1]
            trace = tf.reduce_sum(K_inv * tf.square(q_sqrt))
            # TODO: broadcast instead of tile when tf allows -- tf2.1 segfaults
            # (
            # See #
            Lp_full = Lp if is_batched else tf.tile(tf.expand_dims(Lp, 0), [L, 1, 1])
            LpiLq = tf.linalg.triangular_solve(Lp_full, Lq_full, lower=True)
            trace = tf.reduce_sum(tf.square(LpiLq))

    twoKL = mahalanobis + constant - logdet_qcov + trace

    # Log-determinant of the covariance of p(x):
    if not is_white:
        log_sqdiag_Lp = tf.math.log(tf.square(tf.linalg.diag_part(Lp)))
        sum_log_sqdiag_Lp = tf.reduce_sum(log_sqdiag_Lp)
        # If K is [L, M, M], num_latent_gps is no longer implicit, no need to multiply the single kernel logdet
        scale = 1.0 if is_batched else to_default_float(L)
        twoKL += scale * sum_log_sqdiag_Lp

    tf.debugging.assert_shapes([(twoKL, ())], message="gauss_kl() return value")  # returns scalar
    return 0.5 * twoKL
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