Source code for gpflow.optimizers.natgrad

# Copyright 2018-2020 The GPflow Contributors. All Rights Reserved.
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# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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# http://www.apache.org/licenses/LICENSE-2.0
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# Unless required by applicable law or agreed to in writing, software
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import abc
import functools
from typing import Any, Callable, Dict, Optional, Sequence, Tuple, Union

import tensorflow as tf
from check_shapes import check_shapes

from ..base import AnyNDArray, Parameter, _to_constrained

Scalar = Union[float, tf.Tensor, AnyNDArray]
LossClosure = Callable[[], tf.Tensor]
NatGradParameters = Union[Tuple[Parameter, Parameter], Tuple[Parameter, Parameter, "XiTransform"]]

__all__ = [
    "NaturalGradient",
    "XiNat",
    "XiSqrtMeanVar",
    "XiTransform",
]


#
# Xi transformations necessary for natural gradient optimizer.
# Abstract class and two implementations: XiNat and XiSqrtMeanVar.
#


[docs] class XiTransform(metaclass=abc.ABCMeta): """ XiTransform is the base class that implements three transformations necessary for the natural gradient calculation wrt any parameterization. """
[docs] @staticmethod @abc.abstractmethod @check_shapes( "mean: [N, D]", "varsqrt: [D, N, N]", "return[0]: [N, D]", "return[1]: [D, N, N]", ) def meanvarsqrt_to_xi(mean: tf.Tensor, varsqrt: tf.Tensor) -> Tuple[tf.Tensor, tf.Tensor]: """ Transforms the parameter `mean` and `varsqrt` to `xi1`, `xi2` :param mean: the mean parameter :param varsqrt: the varsqrt parameter :return: tuple (xi1, xi2), the xi parameters """
[docs] @staticmethod @abc.abstractmethod @check_shapes( "xi1: [N, D]", "xi2: [D, N, N]", "return[0]: [N, D]", "return[1]: [D, N, N]", ) def xi_to_meanvarsqrt(xi1: tf.Tensor, xi2: tf.Tensor) -> Tuple[tf.Tensor, tf.Tensor]: """ Transforms the parameter `xi1`, `xi2` to `mean`, `varsqrt` :param xi1: the ξ₁ parameter :param xi2: the ξ₂ parameter :return: tuple (mean, varsqrt), the meanvarsqrt parameters """
[docs] @staticmethod @abc.abstractmethod @check_shapes( "nat1: [N, D]", "nat2: [D, N, N]", "return[0]: [N, D]", "return[1]: [D, N, N]", ) def naturals_to_xi(nat1: tf.Tensor, nat2: tf.Tensor) -> Tuple[tf.Tensor, tf.Tensor]: """ Applies the transform so that `nat1`, `nat2` is mapped to `xi1`, `xi2` :param nat1: the θ₁ parameter :param nat2: the θ₂ parameter :return: tuple `xi1`, `xi2` """
[docs] class XiNat(XiTransform): """ This is the default transform. Using the natural directly saves the forward mode gradient, and also gives the analytic optimal solution for gamma=1 in the case of Gaussian likelihood. """
[docs] @staticmethod @check_shapes( "mean: [N, D]", "varsqrt: [D, N, N]", "return[0]: [N, D]", "return[1]: [D, N, N]", ) def meanvarsqrt_to_xi(mean: tf.Tensor, varsqrt: tf.Tensor) -> Tuple[tf.Tensor, tf.Tensor]: return meanvarsqrt_to_natural(mean, varsqrt)
[docs] @staticmethod @check_shapes( "xi1: [N, D]", "xi2: [D, N, N]", "return[0]: [N, D]", "return[1]: [D, N, N]", ) def xi_to_meanvarsqrt(xi1: tf.Tensor, xi2: tf.Tensor) -> Tuple[tf.Tensor, tf.Tensor]: return natural_to_meanvarsqrt(xi1, xi2)
[docs] @staticmethod @check_shapes( "nat1: [N, D]", "nat2: [D, N, N]", "return[0]: [N, D]", "return[1]: [D, N, N]", ) def naturals_to_xi(nat1: tf.Tensor, nat2: tf.Tensor) -> Tuple[tf.Tensor, tf.Tensor]: return nat1, nat2
[docs] class XiSqrtMeanVar(XiTransform): """ This transformation will perform natural gradient descent on the model parameters, so saves the conversion to and from Xi. """
[docs] @staticmethod @check_shapes( "mean: [N, D]", "varsqrt: [D, N, N]", "return[0]: [N, D]", "return[1]: [D, N, N]", ) def meanvarsqrt_to_xi(mean: tf.Tensor, varsqrt: tf.Tensor) -> Tuple[tf.Tensor, tf.Tensor]: return mean, varsqrt
[docs] @staticmethod @check_shapes( "xi1: [N, D]", "xi2: [D, N, N]", "return[0]: [N, D]", "return[1]: [D, N, N]", ) def xi_to_meanvarsqrt(xi1: tf.Tensor, xi2: tf.Tensor) -> Tuple[tf.Tensor, tf.Tensor]: return xi1, xi2
[docs] @staticmethod @check_shapes( "nat1: [N, D]", "nat2: [D, N, N]", "return[0]: [N, D]", "return[1]: [D, N, N]", ) def naturals_to_xi(nat1: tf.Tensor, nat2: tf.Tensor) -> Tuple[tf.Tensor, tf.Tensor]: return natural_to_meanvarsqrt(nat1, nat2)
[docs] class NaturalGradient(tf.optimizers.Optimizer): """ Implements a natural gradient descent optimizer for variational models that are based on a distribution q(u) = N(q_mu, q_sqrt q_sqrtᵀ) that is parameterized by mean q_mu and lower-triangular Cholesky factor q_sqrt of the covariance. Note that this optimizer does not implement the standard API of tf.optimizers.Optimizer. Its only public method is minimize(), which has a custom signature (var_list needs to be a list of (q_mu, q_sqrt) tuples, where q_mu and q_sqrt are gpflow.Parameter instances, not tf.Variable). Note furthermore that the natural gradients are implemented only for the full covariance case (i.e., q_diag=True is NOT supported). When using in your work, please cite :cite:t:`salimbeni18`. """ def __init__( self, gamma: Scalar, xi_transform: XiTransform = XiNat(), name: Optional[str] = None ) -> None: """ :param gamma: natgrad step length :param xi_transform: default ξ transform (can be overridden in the call to minimize()) The XiNat default choice works well in general. """ name = self.__class__.__name__ if name is None else name super().__init__(name) # explicitly store name (as TF <2.12 stores it differently from TF 2.12+) self.natgrad_name = name self.gamma = gamma self.xi_transform = xi_transform
[docs] @check_shapes( "var_list[all][0]: [N, D]", "var_list[all][1]: [D, N, N]", ) def minimize( self, loss_fn: LossClosure, var_list: Sequence[NatGradParameters], ) -> None: """ Minimizes objective function of the model. Natural Gradient optimizer works with variational parameters only. GPflow implements the `XiNat` (default) and `XiSqrtMeanVar` transformations for parameters. Custom transformations that implement the `XiTransform` interface are also possible. :param loss_fn: Loss function. :param var_list: List of pair tuples of variational parameters or triplet tuple with variational parameters and ξ transformation. If ξ is not specified, will use self.xi_transform. For example, `var_list` could be:: var_list = [ (q_mu1, q_sqrt1), (q_mu2, q_sqrt2, XiSqrtMeanVar()) ] """ parameters = [(v[0], v[1], (v[2] if len(v) > 2 else None)) for v in var_list] # type: ignore[misc] self._natgrad_steps(loss_fn, parameters)
@check_shapes( "parameters[all][0]: [N, D]", "parameters[all][1]: [D, N, N]", ) def _natgrad_steps( self, loss_fn: LossClosure, parameters: Sequence[Tuple[Parameter, Parameter, Optional[XiTransform]]], ) -> None: """ Computes gradients of loss_fn() w.r.t. q_mu and q_sqrt, and updates these parameters using the natgrad backwards step, for all sets of variational parameters passed in. :param loss_fn: Loss function. :param parameters: List of tuples (q_mu, q_sqrt, xi_transform) """ q_mus, q_sqrts, xis = zip(*parameters) q_mu_vars = [p.unconstrained_variable for p in q_mus] q_sqrt_vars = [p.unconstrained_variable for p in q_sqrts] with tf.GradientTape(watch_accessed_variables=False) as tape: tape.watch(q_mu_vars + q_sqrt_vars) loss = loss_fn() q_mu_grads, q_sqrt_grads = tape.gradient(loss, [q_mu_vars, q_sqrt_vars]) # NOTE that these are the gradients in *unconstrained* space with tf.name_scope(f"{self.natgrad_name}/natural_gradient_steps"): for q_mu_grad, q_sqrt_grad, q_mu, q_sqrt, xi_transform in zip( q_mu_grads, q_sqrt_grads, q_mus, q_sqrts, xis ): self._natgrad_apply_gradients(q_mu_grad, q_sqrt_grad, q_mu, q_sqrt, xi_transform) @check_shapes( "q_mu_grad: [N, D]", "q_sqrt_grad: [D, N_N_transformed...]", "q_mu: [N, D]", "q_sqrt: [D, N, N]", ) def _natgrad_apply_gradients( self, q_mu_grad: tf.Tensor, q_sqrt_grad: tf.Tensor, q_mu: Parameter, q_sqrt: Parameter, xi_transform: Optional[XiTransform] = None, ) -> None: """ This function does the backward step on the q_mu and q_sqrt parameters, given the gradients of the loss function with respect to their unconstrained variables. I.e., it expects the arguments to come from with tf.GradientTape() as tape: loss = loss_function() q_mu_grad, q_mu_sqrt = tape.gradient(loss, [q_mu, q_sqrt]) (Note that tape.gradient() returns the gradients in *unconstrained* space!) Implements equation [10] from :cite:t:`salimbeni18`. In addition, for convenience with the rest of GPflow, this code computes ∂L/∂η using the chain rule (the following assumes a numerator layout where the gradient is a row vector; note that TensorFlow actually returns a column vector), where L is the loss: ∂L/∂η = (∂L / ∂[q_mu, q_sqrt])(∂[q_mu, q_sqrt] / ∂η) In total there are three derivative calculations: natgrad of L w.r.t ξ = (∂ξ / ∂θ) [(∂L / ∂[q_mu, q_sqrt]) (∂[q_mu, q_sqrt] / ∂η)]ᵀ Note that if ξ = θ (i.e. [q_mu, q_sqrt]) some of these calculations are the identity. In the code η = eta, ξ = xi, θ = nat. :param q_mu_grad: gradient of loss w.r.t. q_mu (in unconstrained space) :param q_sqrt_grad: gradient of loss w.r.t. q_sqrt (in unconstrained space) :param q_mu: parameter for the mean of q(u) with shape [M, L] :param q_sqrt: parameter for the square root of the covariance of q(u) with shape [L, M, M] (the diagonal parametrization, q_diag=True, is NOT supported) :param xi_transform: the ξ transform to use (self.xi_transform if not specified) """ if xi_transform is None: xi_transform = self.xi_transform # 1) the ordinary gpflow gradient dL_dmean = _to_constrained(q_mu_grad, q_mu.transform) dL_dvarsqrt = _to_constrained(q_sqrt_grad, q_sqrt.transform) with tf.GradientTape(persistent=True, watch_accessed_variables=False) as tape: tape.watch([q_mu.unconstrained_variable, q_sqrt.unconstrained_variable]) # the three parameterizations as functions of [q_mu, q_sqrt] eta1, eta2 = meanvarsqrt_to_expectation(q_mu, q_sqrt) # we need these to calculate the relevant gradients meanvarsqrt = expectation_to_meanvarsqrt(eta1, eta2) if not isinstance(xi_transform, XiNat): nat1, nat2 = meanvarsqrt_to_natural(q_mu, q_sqrt) xi1_nat, xi2_nat = xi_transform.naturals_to_xi(nat1, nat2) dummy_tensors = tf.ones_like(xi1_nat), tf.ones_like(xi2_nat) with tf.GradientTape(watch_accessed_variables=False) as forward_tape: forward_tape.watch(dummy_tensors) dummy_gradients = tape.gradient( [xi1_nat, xi2_nat], [nat1, nat2], output_gradients=dummy_tensors ) # 2) the chain rule to get ∂L/∂η, where η (eta) are the expectation parameters dL_deta1, dL_deta2 = tape.gradient( meanvarsqrt, [eta1, eta2], output_gradients=[dL_dmean, dL_dvarsqrt] ) if not isinstance(xi_transform, XiNat): nat_dL_xi1, nat_dL_xi2 = forward_tape.gradient( dummy_gradients, dummy_tensors, output_gradients=[dL_deta1, dL_deta2] ) else: nat_dL_xi1, nat_dL_xi2 = dL_deta1, dL_deta2 del tape # Remove "persistent" tape xi1, xi2 = xi_transform.meanvarsqrt_to_xi(q_mu, q_sqrt) xi1_new = xi1 - self.gamma * nat_dL_xi1 xi2_new = xi2 - self.gamma * nat_dL_xi2 # Transform back to the model parameters [q_mu, q_sqrt] mean_new, varsqrt_new = xi_transform.xi_to_meanvarsqrt(xi1_new, xi2_new) q_mu.assign(mean_new) q_sqrt.assign(varsqrt_new)
[docs] def get_config(self) -> Dict[str, Any]: config: Dict[str, Any] = super().get_config() config.update({"gamma": self._serialize_hyperparameter("gamma")}) return config
# # Auxiliary gaussian parameter conversion functions. # # The following functions expect their first and second inputs to have shape # [D, N, 1] and [D, N, N], respectively. Return values are also of shapes [D, N, 1] and [D, N, N].
[docs] def swap_dimensions( method: Callable[[tf.Tensor, tf.Tensor], Tuple[tf.Tensor, tf.Tensor]] ) -> Callable[..., Tuple[tf.Tensor, tf.Tensor]]: """ Converts between GPflow indexing and tensorflow indexing `method` is a function that broadcasts over the first dimension (i.e. like all tensorflow matrix ops): * `method` inputs [D, N, 1], [D, N, N] * `method` outputs [D, N, 1], [D, N, N] :return: Function that broadcasts over the final dimension (i.e. compatible with GPflow): * inputs: [N, D], [D, N, N] * outputs: [N, D], [D, N, N] """ @functools.wraps(method) @check_shapes( "a_nd: [N, D] if swap", "a_nd: [D, N, 1] if not swap", "b_dnn: [D, N, N]", "return[0]: [N, D] if swap", "return[0]: [D, N, 1] if not swap", "return[1]: [D, N, N]", ) def wrapper( a_nd: tf.Tensor, b_dnn: tf.Tensor, swap: bool = True ) -> Tuple[tf.Tensor, tf.Tensor]: if swap: a_dn1 = tf.linalg.adjoint(a_nd)[:, :, None] A_dn1, B_dnn = method(a_dn1, b_dnn) A_nd = tf.linalg.adjoint(A_dn1[:, :, 0]) return A_nd, B_dnn else: return method(a_nd, b_dnn) return wrapper
[docs] @swap_dimensions @check_shapes( "nat1: [D, N, 1]", "nat2: [D, N, N]", "return[0]: [D, N, 1]", "return[1]: [D, N, N]", ) def natural_to_meanvarsqrt(nat1: tf.Tensor, nat2: tf.Tensor) -> Tuple[tf.Tensor, tf.Tensor]: var_sqrt_inv = tf.linalg.cholesky(-2 * nat2) var_sqrt = _inverse_lower_triangular(var_sqrt_inv) S = tf.linalg.matmul(var_sqrt, var_sqrt, transpose_a=True) mu = tf.linalg.matmul(S, nat1) # We need the decomposition of S as L L^T, not as L^T L, # hence we need another cholesky. return mu, tf.linalg.cholesky(S)
[docs] @swap_dimensions @check_shapes( "mu: [D, N, 1]", "s_sqrt: [D, N, N]", "return[0]: [D, N, 1]", "return[1]: [D, N, N]", ) def meanvarsqrt_to_natural(mu: tf.Tensor, s_sqrt: tf.Tensor) -> Tuple[tf.Tensor, tf.Tensor]: s_sqrt_inv = _inverse_lower_triangular(s_sqrt) s_inv = tf.linalg.matmul(s_sqrt_inv, s_sqrt_inv, transpose_a=True) return tf.linalg.matmul(s_inv, mu), -0.5 * s_inv
[docs] @swap_dimensions @check_shapes( "nat1: [D, N, 1]", "nat2: [D, N, N]", "return[0]: [D, N, 1]", "return[1]: [D, N, N]", ) def natural_to_expectation(nat1: tf.Tensor, nat2: tf.Tensor) -> Tuple[tf.Tensor, tf.Tensor]: args = natural_to_meanvarsqrt(nat1, nat2, swap=False) return meanvarsqrt_to_expectation(*args, swap=False)
[docs] @swap_dimensions @check_shapes( "eta1: [D, N, 1]", "eta2: [D, N, N]", "return[0]: [D, N, 1]", "return[1]: [D, N, N]", ) def expectation_to_natural(eta1: tf.Tensor, eta2: tf.Tensor) -> Tuple[tf.Tensor, tf.Tensor]: args = expectation_to_meanvarsqrt(eta1, eta2, swap=False) return meanvarsqrt_to_natural(*args, swap=False)
[docs] @swap_dimensions @check_shapes( "eta1: [D, N, 1]", "eta2: [D, N, N]", "return[0]: [D, N, 1]", "return[1]: [D, N, N]", ) def expectation_to_meanvarsqrt(eta1: tf.Tensor, eta2: tf.Tensor) -> Tuple[tf.Tensor, tf.Tensor]: var = eta2 - tf.linalg.matmul(eta1, eta1, transpose_b=True) return eta1, tf.linalg.cholesky(var)
[docs] @swap_dimensions @check_shapes( "m: [D, N, 1]", "v_sqrt: [D, N, N]", "return[0]: [D, N, 1]", "return[1]: [D, N, N]", ) def meanvarsqrt_to_expectation(m: tf.Tensor, v_sqrt: tf.Tensor) -> Tuple[tf.Tensor, tf.Tensor]: v = tf.linalg.matmul(v_sqrt, v_sqrt, transpose_b=True) return m, v + tf.linalg.matmul(m, m, transpose_b=True)
@check_shapes( "M: [D, N, N]", "return: [D, N, N]", ) def _inverse_lower_triangular(M: tf.Tensor) -> tf.Tensor: """ Take inverse of lower triangular (e.g. Cholesky) matrix. This function broadcasts over the first index. :param M: Tensor with lower triangular structure of shape [D, N, N] :return: The inverse of the Cholesky decomposition. Same shape as input. """ if M.shape.ndims != 3: # pragma: no cover raise ValueError("Number of dimensions for input is required to be 3.") D, N = tf.shape(M)[0], tf.shape(M)[1] I_dnn = tf.eye(N, dtype=M.dtype)[None, :, :] * tf.ones((D, 1, 1), dtype=M.dtype) return tf.linalg.triangular_solve(M, I_dnn)