Random

Random sampling functions in MLX use an implicit global PRNG state by default. However, all function take an optional key keyword argument for when more fine-grained control or explicit state management is needed.

For example, you can generate random numbers with:

for _ in range(3):
  print(mx.random.uniform())

which will print a sequence of unique pseudo random numbers. Alternatively you can explicitly set the key:

key = mx.random.key(0)
for _ in range(3):
  print(mx.random.uniform(key=key))

which will yield the same pseudo random number at each iteration.

Following JAX’s PRNG design we use a splittable version of Threefry, which is a counter-based PRNG.

bernoulli([p, shape, key, stream])	Generate Bernoulli random values.
categorical(logits[, axis, shape, ...])	Sample from a categorical distribution.
gumbel([shape, dtype, key, stream])	Sample from the standard Gumbel distribution.
key(seed)	Get a PRNG key from a seed.
normal([shape, dtype, loc, scale, key, stream])	Generate normally distributed random numbers.
multivariate_normal(mean, cov[, shape, ...])	Generate jointly-normal random samples given a mean and covariance.
randint(low, high[, shape, dtype, key, stream])	Generate random integers from the given interval.
seed(seed)	Seed the global PRNG.
split(key[, num, stream])	Split a PRNG key into sub keys.
truncated_normal(lower, upper[, shape, ...])	Generate values from a truncated normal distribution.
uniform([low, high, shape, dtype, key, stream])	Generate uniformly distributed random numbers.
laplace([shape, dtype, loc, scale, key, stream])	Sample numbers from a Laplace distribution.
permutation(x[, axis, key, stream])	Generate a random permutation or permute the entries of an array.