# tf.contrib.distributions.SeedStream

## Class SeedStream

Local PRNG for amplifying seed entropy into seeds for base operations.

Writing sampling code which correctly sets the pseudo-random number generator (PRNG) seed is surprisingly difficult. This class serves as a helper for the TensorFlow Probability coding pattern designed to avoid common mistakes.

# Motivating Example

A common first-cut implementation of a sampler for the beta distribution is to compute the ratio of a gamma with itself plus another gamma. This code snippet tries to do that, but contains a surprisingly common error:

def broken_beta(shape, alpha, beta, seed):
x = tf.random_gamma(shape, alpha, seed=seed)
y = tf.random_gamma(shape, beta, seed=seed)
return x / (x + y)


The mistake is that the two gamma draws are seeded with the same seed. This causes them to always produce the same results, which, in turn, leads this code snippet to always return 0.5. Because it can happen across abstraction boundaries, this kind of error is surprisingly easy to make when handling immutable seeds.

# Goals

TensorFlow Probability adopts a code style designed to eliminate the above class of error, without exacerbating others. The goals of this code style are:

• Support reproducibility of results (by encouraging seeding of all pseudo-random operations).

• Avoid shared-write global state (by not relying on a global PRNG).

• Prevent accidental seed reuse by TF Probability implementers. This goal is served with the local pseudo-random seed generator provided in this module.

• Mitigate potential accidental seed reuse by TF Probability clients (with a salting scheme).

• Prevent accidental resonances with downstream PRNGs (by hashing the output).

## Non-goals

• Implementing a high-performance PRNG for generating large amounts of entropy. That's the job of the underlying TensorFlow PRNG we are seeding.

• Avoiding random seed collisions, aka "birthday attacks".

# Code pattern

def random_beta(shape, alpha, beta, seed):        # (a)
seed = SeedStream(seed, salt="random_beta")     # (b)
x = tf.random_gamma(shape, alpha, seed=seed())  # (c)
y = tf.random_gamma(shape, beta, seed=seed())   # (c)
return x / (x + y)


The elements of this pattern are:

• Accept an explicit seed (line a) as an argument in all public functions, and write the function to be deterministic (up to any numerical issues) for fixed seed.

• Rationale: This provides the client with the ability to reproduce results. Accepting an immutable seed rather than a mutable PRNG object reduces code coupling, permitting different sections to be reproducible independently.
• Use that seed only to initialize a local SeedStream instance (line b).

• Rationale: Avoids accidental seed reuse.
• Supply the name of the function being implemented as a salt to the SeedStream instance (line b). This serves to keep the salts unique; unique salts ensure that clients of TF Probability will see different functions always produce independent results even if called with the same seeds.

• Seed each callee operation with the output of a unique call to the SeedStream instance (lines c). This ensures reproducibility of results while preventing seed reuse across callee invocations.

# Why salt?

Salting the SeedStream instances (with unique salts) is defensive programming against a client accidentally committing a mistake similar to our motivating example. Consider the following situation that might arise without salting:

def tfp_foo(seed):
seed = SeedStream(seed, salt="")
foo_stuff = tf.random_normal(seed=seed())
...

def tfp_bar(seed):
seed = SeedStream(seed, salt="")
bar_stuff = tf.random_normal(seed=seed())
...

def client_baz(seed):
foo = tfp_foo(seed=seed)
bar = tfp_bar(seed=seed)
...


The client should have used different seeds as inputs to foo and bar. However, because they didn't, and because foo and bar both sample a Gaussian internally as their first action, the internal foo_stuff and bar_stuff will be the same, and the returned foo and bar will not be independent, leading to subtly incorrect answers from the client's simulation. This kind of bug is particularly insidious for the client, because it depends on a Distributions implementation detail, namely the order in which foo and bar invoke the samplers they depend on. In particular, a Bayesflow team member can introduce such a bug in previously (accidentally) correct client code by performing an internal refactoring that causes this operation order alignment.

A salting discipline eliminates this problem by making sure that the seeds seen by foo's callees will differ from those seen by bar's callees, even if foo and bar are invoked with the same input seed.

## Methods

### __init__

__init__(
seed,
salt
)


Initializes a SeedStream.

#### Args:

• seed: Any Python object convertible to string, supplying the initial entropy. If None, operations seeded with seeds drawn from this SeedStream will follow TensorFlow semantics for not being seeded.
• salt: Any Python object convertible to string, supplying auxiliary entropy. Must be unique across the Distributions and TensorFlow Probability code base. See class docstring for rationale.

### __call__

__call__()


Returns a fresh integer usable as a seed in downstream operations.

If this SeedStream was initialized with seed=None, returns None. This has the effect that downstream operations (both SeedStreams and primitive TensorFlow ops) will behave as though they were unseeded.

The returned integer is non-negative, and uniformly distributed in the half-open interval [0, 2**512). This is consistent with TensorFlow, as TensorFlow operations internally use the residue of the given seed modulo 2**31 - 1 (see tensorflow/python/framework/random_seed.py).

#### Returns:

• seed: A fresh integer usable as a seed in downstream operations, or None.