Source code for pyro.distributions.transforms.affine_coupling

import torch
from torch.distributions import constraints

from pyro.distributions.torch_transform import TransformModule
from pyro.distributions.util import copy_docs_from
from pyro.distributions.transforms.utils import clamp_preserve_gradients


[docs]@copy_docs_from(TransformModule) class AffineCoupling(TransformModule): """ An implementation of the affine coupling layer of RealNVP (Dinh et al., 2017) that uses the transformation, :math:`\\mathbf{y}_{1:d} = \\mathbf{x}_{1:d}` :math:`\\mathbf{y}_{(d+1):D} = \\mu + \\sigma\\odot\\mathbf{x}_{(d+1):D}` where :math:`\\mathbf{x}` are the inputs, :math:`\\mathbf{y}` are the outputs, e.g. :math:`\\mathbf{x}_{1:d} represents the first :math:`d` elements of the inputs, and :math:`\\mu,\\sigma` are shift and translation parameters calculated as the output of a function inputting only :math:`\\mathbf{x}_{1:d}`. That is, the first :math:`d` components remain unchanged, and the subsequent :math:`D-d` are shifted and translated by a function of the previous components. Together with `TransformedDistribution` this provides a way to create richer variational approximations. Example usage: >>> from pyro.nn import DenseNN >>> input_dim = 10 >>> split_dim = 6 >>> base_dist = dist.Normal(torch.zeros(input_dim), torch.ones(input_dim)) >>> hypernet = DenseNN(split_dim, [10*input_dim], [input_dim-split_dim, input_dim-split_dim]) >>> flow = AffineCoupling(split_dim, hypernet) >>> pyro.module("my_flow", flow) # doctest: +SKIP >>> flow_dist = dist.TransformedDistribution(base_dist, [flow]) >>> flow_dist.sample() # doctest: +SKIP tensor([-0.4071, -0.5030, 0.7924, -0.2366, -0.2387, -0.1417, 0.0868, 0.1389, -0.4629, 0.0986]) The inverse of the Bijector is required when, e.g., scoring the log density of a sample with `TransformedDistribution`. This implementation caches the inverse of the Bijector when its forward operation is called, e.g., when sampling from `TransformedDistribution`. However, if the cached value isn't available, either because it was overwritten during sampling a new value or an arbitary value is being scored, it will calculate it manually. This is an operation that scales as O(1), i.e. constant in the input dimension. So in general, it is cheap to sample *and* score (an arbitrary value) from AffineCoupling. :param split_dim: Zero-indexed dimension :math:`d` upon which to perform input/output split for transformation. :type split_dim: int :param hypernet: an autoregressive neural network whose forward call returns a real-valued mean and logit-scale as a tuple. The input should have final dimension split_dim and the output final dimension input_dim-split_dim for each member of the tuple. :type hypernet: callable :param log_scale_min_clip: The minimum value for clipping the log(scale) from the autoregressive NN :type log_scale_min_clip: float :param log_scale_max_clip: The maximum value for clipping the log(scale) from the autoregressive NN :type log_scale_max_clip: float References: Laurent Dinh, Jascha Sohl-Dickstein, and Samy Bengio. Density estimation using Real NVP. ICLR 2017. """ domain = constraints.real codomain = constraints.real bijective = True event_dim = 1 def __init__(self, split_dim, hypernet, log_scale_min_clip=-5., log_scale_max_clip=3.): super(AffineCoupling, self).__init__(cache_size=1) self.split_dim = split_dim self.hypernet = hypernet self._cached_log_scale = None self.log_scale_min_clip = log_scale_min_clip self.log_scale_max_clip = log_scale_max_clip 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 TransformedDistribution `x` is a sample from the base distribution (or the output of a previous flow) """ x1, x2 = x[..., :self.split_dim], x[..., self.split_dim:] mean, log_scale = self.hypernet(x1) log_scale = clamp_preserve_gradients(log_scale, self.log_scale_min_clip, self.log_scale_max_clip) self._cached_log_scale = log_scale y1 = x1 y2 = torch.exp(log_scale) * x2 + mean return torch.cat([y1, y2], dim=-1) def _inverse(self, y): """ :param y: the output of the bijection :type y: torch.Tensor Inverts y => x. Uses a previously cached inverse if available, otherwise performs the inversion afresh. """ y1, y2 = y[..., :self.split_dim], y[..., self.split_dim:] x1 = y1 mean, log_scale = self.arn(x1) log_scale = clamp_preserve_gradients(log_scale, self.log_scale_min_clip, self.log_scale_max_clip) self._cached_log_scale = log_scale x2 = (y2 - mean) * torch.exp(-log_scale) return torch.cat([x1, x2], dim=-1)
[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 self._cached_log_scale is not None and x is x_old and y is y_old: log_scale = self._cached_log_scale else: x1 = x[..., :self.split_dim] _, log_scale = self.hypernet(x1) log_scale = clamp_preserve_gradients(log_scale, self.log_scale_min_clip, self.log_scale_max_clip) return log_scale.sum(-1)