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changes coming to JAX's RNG – auto-parallelizable by default · jax-ml/jax · Discussion #18480 · GitHub

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Nov 10, 2023 · 3 comments · 2 replies

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Important

You can stop reading now if:

• you do not generate random numbers with JAX; or
• you already set the JAX config option jax_threefry_partitionable to True; or
• you only use the non-default unsafe_rbg JAX RNG algorithm.

• JAX's default RNG algorithm ("Threefry") is changing under the hood, to make random number generation efficiently auto-parallelizable ("partitionable"). This makes random numbers faster with multiple devices.

• The current behavior corresponds to setting the config field jax_threefry_partitionable to False, its current default value. The new behavior corresponds to setting jax_threefry_partitionable to True, its future default value.

• Some time soon, we will set out to change the default value of the configuration flag jax_threefry_partitionable from False to True. This will cause a one-time change in the random values generated from a given RNG key. See code below.

• Want to ensure this goes smoothly? Try flipping jax_threefry_partitionable to True today to detect any issues in your code ahead of the upgrade. See code below.

What do we mean by a one-time change to random values? Here is today's default behavior:

$ JAX_THREEFRY_PARTITIONABLE=False python -c '
> import jax
> print(jax.random.randint(jax.random.key(72), (), 0, 10))'
3

And here is what will happen when the default setting changes soon:

$ JAX_THREEFRY_PARTITIONABLE=True python -c '
> import jax
> print(jax.random.randint(jax.random.key(72), (), 0, 10))'
7

Same key, different generated value when JAX_THREEFRY_PARTITIONABLE=False versus JAX_THREEFRY_PARTITIONABLE=True.

JAX's RNG will remain deterministic. We try to rarely change the output of JAX's pseudorandom functions. That said, our API policy promises stability in distribution, not in value. This particular change is broad-based, and is the first of its kind in a long time, so we're drawing extra attention to it.

This change will break code that depends on specific RNG keys generating specific RNG values. Common examples include reference tests, high-variance randomized tests, or a machine learning experiment that depends on random values (e.g. model initialization) that you want to reproduce exactly.

Non-partitionable Threefry will later be deprecated. That is, at some point after we've upgraded the default value of the jax_threefry_partitionable setting, we will deprecate the flag entirely.

JAX supports several RNG schemes. Its current three built-in modes are called threefry2x32, rbg and unsafe_rbg. These modes can be set using the impl argument to jax.random.PRNGKey and jax.random.key, or with the configuration flag jax_default_prng_impl.

You are affected if you use either threefry2x32 or rbg. Specifically:

• The random values generated from a key will change for you if you use threefry2x32.
• Key derivation will change in rbg mode. That is, the keys generated from a specific key, using jax.random.split or jax.random.fold_in, will be different than before. In turn, random values generated from such derived keys will change.

To try the upgrade now, you can set the configuration flag jax_threefry_partitionable to True in your code explicitly. This can be done with the environment variable JAX_THREEFRY_PARTITIONABLE=True, the command-line flag --jax_threefry_partitionable=True, or programmatically, using jax.config.update or the jax.threefry_partitionable context manager. For example:

jax.config.update("jax_threefry_partitionable", True)

# OR

with jax.threefry_partitionable(True):
  ...

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There is a typo in what is happening:
Change jax_threefry_patitionable to jax_threefry_partitionable if you want.

Thanks for the heads up!

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This is unrelated to the topic here - please open a new discussion for this question.

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As of jax v.0.5.0, partitionable Threefry is enabled by default, i.e. the default value of the jax_threefry_partitionable setting is True. If this breaks your code or project, what can you do?

One option, for now, is to ignore the issue and revert the upgrade by setting jax_threefry_partitionable to False.

To investigate the issue further, consider where your code might possibly be sensitive to random values, rather than only to their (pseudorandom) distribution.

For example, consider this unit test:

def test_normal_negative():
  key = jax.random.key(42)
  value = jax.random.normal(key)
  assert value < 0.

Because pseudorandom values are not guaranteed stable, value-sensitive tests are fragile. For example, they can fail for different values of the random seed, or when JAX's PRNG changes.

The unit test above might pass at v0.5.0, but it will fail if we change the seed argument to jax.random.key from 42 to 727, because then value becomes positive. Or it may change if we change JAX's PRNG algorithm again, even if the distribution stays the same.

One way to identify value-sensitive tests heuristically is to run them under several different random seeds, which in turn changes the values they generate. If several such trials fail, the test may be too value-sensitive. To run a test under several seeds, you can use the environment variable JAX_RANDOM_SEED_OFFSET. Its value is an integer (zero by default) that JAX adds to every seed in the program (i.e. any argument to jax.random.key or jax.random.PRNGKey). For example:

$ JAX_RANDOM_SEED_OFFSET=0 python -c 'import jax.random as jr; print(jr.normal(jr.key(7)))'
0.45123515
$ JAX_RANDOM_SEED_OFFSET=3 python -c 'import jax.random as jr; print(jr.normal(jr.key(4)))'
0.45123515

Two common flavors of value-sensitive tests are "reference tests," that compare a randomized outcome against a fixed reference value, and tests with overly tight numerical tolerances. Well-behaved randomized tests may also be affected if they have some chance of false failure. For instance, if a test suite runs a thousand independent tests, each with a false failure probability of 5%, then we might expect roughly 50 tests to fail when the random bits that they consume change.

To address issues like this, consider (a) rewriting code to somehow be less sensitive to random values, or (b) changing its random seed (provided that the code is behaving as intended to begin with).

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