Source code for pyro.distributions.transforms.polynomial

# Copyright (c) 2017-2019 Uber Technologies, Inc.
# SPDX-License-Identifier: Apache-2.0

import math

import torch
import torch.nn as nn

from pyro.nn import AutoRegressiveNN

from .. import constraints
from ..torch_transform import TransformModule
from ..util import copy_docs_from

[docs]@copy_docs_from(TransformModule) class Polynomial(TransformModule): r""" An autoregressive bijective transform as described in Jaini et al. (2019) applying following equation element-wise, :math:`y_n = c_n + \int^{x_n}_0\sum^K_{k=1}\left(\sum^R_{r=0}a^{(n)}_{r,k}u^r\right)du` where :math:`x_n` is the :math:`n`th input, :math:`y_n` is the :math:`n`th output, and :math:`c_n\in\mathbb{R}`, :math:`\left\{a^{(n)}_{r,k}\in\mathbb{R}\right\}` are learnable parameters that are the output of an autoregressive NN inputting :math:`x_{\prec n}={x_1,x_2,\ldots,x_{n-1}}`. Together with :class:`~pyro.distributions.TransformedDistribution` this provides a way to create richer variational approximations. Example usage: >>> from pyro.nn import AutoRegressiveNN >>> input_dim = 10 >>> count_degree = 4 >>> count_sum = 3 >>> base_dist = dist.Normal(torch.zeros(input_dim), torch.ones(input_dim)) >>> param_dims = [(count_degree + 1)*count_sum] >>> arn = AutoRegressiveNN(input_dim, [input_dim*10], param_dims) >>> transform = Polynomial(arn, input_dim=input_dim, count_degree=count_degree, ... count_sum=count_sum) >>> pyro.module("my_transform", transform) # doctest: +SKIP >>> flow_dist = dist.TransformedDistribution(base_dist, [transform]) >>> flow_dist.sample() # doctest: +SKIP The inverse of this transform does not possess an analytical solution and is left unimplemented. However, the inverse is cached when the forward operation is called during sampling, and so samples drawn using a polynomial transform can be scored. :param autoregressive_nn: an autoregressive neural network whose forward call returns a tensor of real-valued numbers of size (batch_size, (count_degree+1)*count_sum, input_dim) :type autoregressive_nn: nn.Module :param count_degree: The degree of the polynomial to use for each element-wise transformation. :type count_degree: int :param count_sum: The number of polynomials to sum in each element-wise transformation. :type count_sum: int References: [1] Priyank Jaini, Kira A. Shelby, Yaoliang Yu. Sum-of-squares polynomial flow. [arXiv:1905.02325] """ domain = constraints.real_vector codomain = constraints.real_vector bijective = True autoregressive = True def __init__(self, autoregressive_nn, input_dim, count_degree, count_sum): super().__init__(cache_size=1) self.arn = autoregressive_nn self.input_dim = input_dim self.count_degree = count_degree self.count_sum = count_sum self._cached_logDetJ = None self.c = nn.Parameter(torch.Tensor(input_dim)) self.reset_parameters() # Vector of powers of input dimension powers = torch.arange(1, count_degree + 2, dtype=torch.get_default_dtype()) self.register_buffer("powers", powers) # Build mask of constants mask = self.powers + torch.arange(count_degree + 1).unsqueeze(-1).type_as( powers ) power_mask = mask mask = mask.reciprocal() self.register_buffer("power_mask", power_mask) self.register_buffer("mask", mask)
[docs] def reset_parameters(self): stdv = 1.0 / math.sqrt(self.c.size(0)), stdv)
def _call(self, x): """ :param x: the input into the bijection :type x: torch.Tensor Invokes the bijection x=>y; in the prototypical context of a :class:`~pyro.distributions.TransformedDistribution` `x` is a sample from the base distribution (or the output of a previous transform) """ # Calculate the polynomial coefficients # ~ (batch_size, count_sum, count_degree+1, input_dim) A = self.arn(x).view(-1, self.count_sum, self.count_degree + 1, self.input_dim) # Take cross product of coefficients across degree dim # ~ (batch_size, count_sum, count_degree+1, count_degree+1, input_dim) coefs = A.unsqueeze(-2) * A.unsqueeze(-3) # Calculate output as sum-of-squares polynomial x_view = x.view(-1, 1, 1, self.input_dim) x_pow_matrix = x_view.pow(self.power_mask.unsqueeze(-1)).unsqueeze(-4) # Eq (8) from the paper, expanding the squared term and integrating # NOTE: The view_as is necessary because the batch dimensions were collapsed previously y = self.c + (coefs * x_pow_matrix * self.mask.unsqueeze(-1)).sum( (1, 2, 3) ).view_as(x) # log(|det(J)|) is calculated by the fundamental theorem of calculus, i.e. remove the constant # term and the integral from eq (8) (the equation for this isn't given in the paper) x_pow_matrix = x_view.pow(self.power_mask.unsqueeze(-1) - 1).unsqueeze(-4) self._cached_logDetJ = torch.log( (coefs * x_pow_matrix).sum((1, 2, 3)).view_as(x) + 1e-8 ).sum(-1) return y def _inverse(self, y): """ :param y: the output of the bijection :type y: torch.Tensor Inverts y => x. As noted above, this implementation is incapable of inverting arbitrary values `y`; rather it assumes `y` is the result of a previously computed application of the bijector to some `x` (which was cached on the forward call) """ raise KeyError( "Polynomial object expected to find key in intermediates cache but didn't" )
[docs] def log_abs_det_jacobian(self, x, y): """ Calculates the elementwise determinant of the log Jacobian """ x_old, y_old = self._cached_x_y if x is not x_old or y is not y_old: # This call to the parent class Transform will update the cache # as well as calling self._call and recalculating y and log_detJ self(x) return self._cached_logDetJ
[docs]def polynomial(input_dim, hidden_dims=None): """ A helper function to create a :class:`~pyro.distributions.transforms.Polynomial` object that takes care of constructing an autoregressive network with the correct input/output dimensions. :param input_dim: Dimension of input variable :type input_dim: int :param hidden_dims: The desired hidden dimensions of of the autoregressive network. Defaults to using [input_dim * 10] """ count_degree = 4 count_sum = 3 if hidden_dims is None: hidden_dims = [input_dim * 10] arn = AutoRegressiveNN( input_dim, hidden_dims, param_dims=[(count_degree + 1) * count_sum] ) return Polynomial( arn, input_dim=input_dim, count_degree=count_degree, count_sum=count_sum )