tfp.vi.fit_surrogate_posterior

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Fit a surrogate posterior to a target (unnormalized) log density.

tfp.vi.fit_surrogate_posterior(
    target_log_prob_fn,
    surrogate_posterior,
    optimizer,
    num_steps,
    convergence_criterion=None,
    trace_fn=_trace_loss,
    discrepancy_fn=tfp.vi.kl_reverse,
    sample_size=1,
    importance_sample_size=1,
    trainable_variables=None,
    jit_compile=None,
    seed=None,
    name='fit_surrogate_posterior'
)

Used in the notebooks

The default behavior constructs and minimizes the negative variational evidence lower bound (ELBO), given by

q_samples = surrogate_posterior.sample(num_draws)
elbo_loss = -tf.reduce_mean(
  target_log_prob_fn(q_samples) - surrogate_posterior.log_prob(q_samples))

This corresponds to minimizing the 'reverse' Kullback-Liebler divergence (KL[q||p]) between the variational distribution and the unnormalized target_log_prob_fn, and defines a lower bound on the marginal log likelihood, log p(x) >= -elbo_loss. [1]

More generally, this function supports fitting variational distributions that minimize any Csiszar f-divergence.

Examples

Normal-Normal model. We'll first consider a simple model z ~ N(0, 1), x ~ N(z, 1), where we suppose we are interested in the posterior p(z | x=5):

import tensorflow_probability as tfp
from tensorflow_probability import distributions as tfd

def log_prob(z, x):
  return tfd.Normal(0., 1.).log_prob(z) + tfd.Normal(z, 1.).log_prob(x)
conditioned_log_prob = lambda z: log_prob(z, x=5.)

The posterior is itself normal by conjugacy, and can be computed analytically (it's N(loc=5/2., scale=1/sqrt(2)). But suppose we don't want to bother doing the math: we can use variational inference instead!

q_z = tfp.experimental.util.make_trainable(tfd.Normal, name='q_z')
losses = tfp.vi.fit_surrogate_posterior(
    conditioned_log_prob,
    surrogate_posterior=q_z,
    optimizer=tf.optimizers.Adam(learning_rate=0.1),
    num_steps=100)
print(q_z.mean(), q_z.stddev())  # => approximately [2.5, 1/sqrt(2)]

Custom loss function. Suppose we prefer to fit the same model using the forward KL divergence KL[p||q]. We can pass a custom discrepancy function:

  losses = tfp.vi.fit_surrogate_posterior(
      conditioned_log_prob,
      surrogate_posterior=q_z,
      optimizer=tf.optimizers.Adam(learning_rate=0.1),
      num_steps=100,
      discrepancy_fn=tfp.vi.kl_forward)

Note that in practice this may have substantially higher-variance gradients than the reverse KL.

Importance weighting. A surrogate posterior may be corrected by interpreting it as a proposal for an importance sampler. That is, one can use weighted samples from the surrogate to estimate expectations under the true posterior:

zs, q_log_prob = surrogate_posterior.experimental_sample_and_log_prob(
  num_samples)

# Naive expectation under the surrogate posterior.
expected_x = tf.reduce_mean(f(zs), axis=0)

# Importance-weighted estimate of the expectation under the true posterior.
self_normalized_log_weights = tf.nn.log_softmax(
  target_log_prob_fn(zs) - q_log_prob)
expected_x = tf.reduce_sum(
  tf.exp(self_normalized_log_weights) * f(zs),
  axis=0)

Any distribution may be used as a proposal, but it is often natural to consider surrogates that were themselves fit by optimizing an importance-weighted variational objective [2], which directly optimizes the surrogate's effectiveness as an proposal distribution. This may be specified by passing importance_sample_size > 1. The importance-weighted objective may favor different characteristics than the original objective. For example, effective proposals are generally overdispersed, whereas a surrogate optimizing reverse KL would otherwise tend to be underdispersed.

Although importance sampling is guaranteed to tighten the variational bound, some research has found that this does not necessarily improve the quality of deep generative models, because it also introduces gradient noise that can lead to a weaker training signal [3]. As always, evaluation is important to choose the approach that works best for a particular task.

When using an importance-weighted loss to fit a surrogate, it is also recommended to apply importance sampling when computing expectations under that surrogate.

# Fit `q` with an importance-weighted variational loss.
losses = tfp.vi.fit_surrogate_posterior(
      conditioned_log_prob,
      surrogate_posterior=q_z,
      importance_sample_size=10,
      optimizer=tf.optimizers.Adam(learning_rate=0.1),
      num_steps=200)

# Estimate posterior statistics with importance sampling.
zs, q_log_prob = q_z.experimental_sample_and_log_prob(1000)
self_normalized_log_weights = tf.nn.log_softmax(
  conditioned_log_prob(zs) - q_log_prob)
posterior_mean = tf.reduce_sum(
  tf.exp(self_normalized_log_weights) * zs,
  axis=0)
posterior_variance = tf.reduce_sum(
  tf.exp(self_normalized_log_weights) * (zs - posterior_mean)**2,
  axis=0)

Inhomogeneous Poisson Process. For a more interesting example, let's consider a model with multiple latent variables as well as trainable parameters in the model itself. Given observed counts y from spatial locations X, consider an inhomogeneous Poisson process model log_rates = GaussianProcess(index_points=X); y = Poisson(exp(log_rates)) in which the latent (log) rates are spatially correlated following a Gaussian process. We'll fit a variational model to the latent rates while also optimizing the GP kernel hyperparameters (largely for illustration; in practice we might prefer to 'be Bayesian' about these parameters and include them as latents in our model and variational posterior). First we define the model, including trainable variables:

# Toy 1D data.
index_points = np.array([-10., -7.2, -4., -0.1, 0.1, 4., 6.2, 9.]).reshape(
    [-1, 1]).astype(np.float32)
observed_counts = np.array(
    [100, 90, 60, 13, 18, 37, 55, 42]).astype(np.float32)

# Trainable GP hyperparameters.
kernel_log_amplitude = tf.Variable(0., name='kernel_log_amplitude')
kernel_log_lengthscale = tf.Variable(0., name='kernel_log_lengthscale')
observation_noise_log_scale = tf.Variable(
  0., name='observation_noise_log_scale')

# Generative model.
Root = tfd.JointDistributionCoroutine.Root
def model_fn():
  kernel = tfp.math.psd_kernels.ExponentiatedQuadratic(
      amplitude=tf.exp(kernel_log_amplitude),
      length_scale=tf.exp(kernel_log_lengthscale))
  latent_log_rates = yield Root(tfd.GaussianProcess(
      kernel,
      index_points=index_points,
      observation_noise_variance=tf.exp(observation_noise_log_scale),
      name='latent_log_rates'))
  y = yield tfd.Independent(tfd.Poisson(log_rate=latent_log_rates, name='y'),
                            reinterpreted_batch_ndims=1)
model = tfd.JointDistributionCoroutine(model_fn)

Next we define a variational distribution. We incorporate the observations directly into the variational model using the 'trick' of representing them by a deterministic distribution (observe that the true posterior on an observed value is in fact a point mass at the observed value).

logit_locs = tf.Variable(tf.zeros(observed_counts.shape), name='logit_locs')
logit_softplus_scales = tf.Variable(tf.ones(observed_counts.shape) * -4,
                                    name='logit_softplus_scales')
def variational_model_fn():
  latent_rates = yield Root(tfd.Independent(
    tfd.Normal(loc=logit_locs, scale=tf.nn.softplus(logit_softplus_scales)),
    reinterpreted_batch_ndims=1))
  y = yield tfd.VectorDeterministic(observed_counts)
q = tfd.JointDistributionCoroutine(variational_model_fn)

Note that here we could apply transforms to variables without using DeferredTensor because the JointDistributionCoroutine argument is a function, i.e., executed "on demand." (The same is true when distribution-making functions are supplied to JointDistributionSequential and JointDistributionNamed. That is, as long as variables are transformed within the callable, they will appear on the gradient tape when q.log_prob() or q.sample() are invoked.

Finally, we fit the variational posterior and model variables jointly: by not explicitly specifying trainable_variables, the optimization will automatically include all variables accessed. We'll use a custom trace_fn to see how the kernel amplitudes and a set of sampled latent rates with fixed seed evolve during the course of the optimization:

losses, log_amplitude_path, sample_path = tfp.vi.fit_surrogate_posterior(
  target_log_prob_fn=lambda *args: model.log_prob(args),
  surrogate_posterior=q,
  optimizer=tf.optimizers.Adam(learning_rate=0.1),
  sample_size=1,
  num_steps=500,
  trace_fn=lambda loss, grads, vars: (loss, kernel_log_amplitude,
                                      q.sample(5, seed=42)[0]))

References

[1]: Christopher M. Bishop. Pattern Recognition and Machine Learning. Springer, 2006.

[2] Yuri Burda, Roger Grosse, and Ruslan Salakhutdinov. Importance Weighted Autoencoders. In International Conference on Learning Representations, 2016. https://arxiv.org/abs/1509.00519

[3] Tom Rainforth, Adam R. Kosiorek, Tuan Anh Le, Chris J. Maddison, Maximilian Igl, Frank Wood, and Yee Whye Teh. Tighter Variational Bounds are Not Necessarily Better. In International Conference on Machine Learning (ICML), 2018. https://arxiv.org/abs/1802.04537