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# Copyright © 2023 Apple Inc.
import math
import unittest
import mlx.core as mx
import mlx_tests
class TestRandom(mlx_tests.MLXTestCase):
def test_global_rng(self):
mx.random.seed(3)
a = mx.random.uniform()
b = mx.random.uniform()
mx.random.seed(3)
x = mx.random.uniform()
y = mx.random.uniform()
self.assertEqual(a.item(), x.item())
self.assertEqual(y.item(), b.item())
def test_key(self):
k1 = mx.random.key(0)
k2 = mx.random.key(0)
self.assertTrue(mx.array_equal(k1, k2))
k2 = mx.random.key(1)
self.assertFalse(mx.array_equal(k1, k2))
def test_key_split(self):
key = mx.random.key(0)
k1, k2 = mx.random.split(key)
self.assertFalse(mx.array_equal(k1, k2))
r1, r2 = mx.random.split(key)
self.assertTrue(mx.array_equal(k1, r1))
self.assertTrue(mx.array_equal(k2, r2))
keys = mx.random.split(key, 10)
self.assertEqual(keys.shape, (10, 2))
def test_uniform(self):
key = mx.random.key(0)
a = mx.random.uniform(key=key)
self.assertEqual(a.shape, ())
self.assertEqual(a.dtype, mx.float32)
b = mx.random.uniform(key=key)
self.assertEqual(a.item(), b.item())
a = mx.random.uniform(shape=(2, 3))
self.assertEqual(a.shape, (2, 3))
a = mx.random.uniform(shape=(1000,), low=-1, high=5)
self.assertTrue(mx.all((a > -1) < 5).item())
a = mx.random.uniform(shape=(1000,), low=mx.array(-1), high=5)
self.assertTrue(mx.all((a > -1) < 5).item())
a = mx.random.uniform(low=-0.1, high=0.1, shape=(1,), dtype=mx.bfloat16)
self.assertEqual(a.dtype, mx.bfloat16)
self.assertEqual(mx.random.uniform().dtype, mx.random.uniform(dtype=None).dtype)
def test_normal_and_laplace(self):
# Same tests for normal and laplace.
for distribution_sampler in [mx.random.normal, mx.random.laplace]:
key = mx.random.key(0)
a = distribution_sampler(key=key)
self.assertEqual(a.shape, ())
self.assertEqual(a.dtype, mx.float32)
b = distribution_sampler(key=key)
self.assertEqual(a.item(), b.item())
a = distribution_sampler(shape=(2, 3))
self.assertEqual(a.shape, (2, 3))
## Generate in float16 or bfloat16
for t in [mx.float16, mx.bfloat16]:
a = distribution_sampler(dtype=t)
self.assertEqual(a.dtype, t)
# Generate with a given mean and standard deviation
loc = 1.0
scale = 2.0
a = distribution_sampler(shape=(3, 2), loc=loc, scale=scale, key=key)
b = scale * distribution_sampler(shape=(3, 2), key=key) + loc
self.assertTrue(mx.allclose(a, b))
a = distribution_sampler(
shape=(3, 2), loc=loc, scale=scale, dtype=mx.float16, key=key
)
b = (
scale * distribution_sampler(shape=(3, 2), dtype=mx.float16, key=key)
+ loc
)
self.assertTrue(mx.allclose(a, b))
self.assertEqual(
distribution_sampler().dtype, distribution_sampler(dtype=None).dtype
)
# Test not getting -inf or inf with half precison
for hp in [mx.float16, mx.bfloat16]:
a = abs(distribution_sampler(shape=(10000,), loc=0, scale=1, dtype=hp))
self.assertTrue(mx.all(a < mx.inf))
def test_multivariate_normal(self):
key = mx.random.key(0)
mean = mx.array([0, 0])
cov = mx.array([[1, 0], [0, 1]])
a = mx.random.multivariate_normal(mean, cov, key=key, stream=mx.cpu)
self.assertEqual(a.shape, (2,))
## Check dtypes
for t in [mx.float32]:
a = mx.random.multivariate_normal(
mean, cov, dtype=t, key=key, stream=mx.cpu
)
self.assertEqual(a.dtype, t)
for t in [
mx.int8,
mx.int32,
mx.int64,
mx.uint8,
mx.uint32,
mx.uint64,
mx.float16,
mx.bfloat16,
]:
with self.assertRaises(ValueError):
mx.random.multivariate_normal(
mean, cov, dtype=t, key=key, stream=mx.cpu
)
## Check incompatible shapes
with self.assertRaises(ValueError):
mean = mx.zeros((2, 2))
cov = mx.zeros((2, 2))
mx.random.multivariate_normal(mean, cov, shape=(3,), key=key, stream=mx.cpu)
with self.assertRaises(ValueError):
mean = mx.zeros((2))
cov = mx.zeros((2, 2, 2))
mx.random.multivariate_normal(mean, cov, shape=(3,), key=key, stream=mx.cpu)
with self.assertRaises(ValueError):
mean = mx.zeros((3,))
cov = mx.zeros((2, 2))
mx.random.multivariate_normal(mean, cov, key=key, stream=mx.cpu)
with self.assertRaises(ValueError):
mean = mx.zeros((2,))
cov = mx.zeros((2, 3))
mx.random.multivariate_normal(mean, cov, key=key, stream=mx.cpu)
## Different shape of mean and cov
mean = mx.array([[0, 7], [1, 2], [3, 4]])
cov = mx.array([[1, 0.5], [0.5, 1]])
a = mx.random.multivariate_normal(mean, cov, shape=(4, 3), stream=mx.cpu)
self.assertEqual(a.shape, (4, 3, 2))
## Check correcteness of the mean and covariance
n_test = int(1e5)
def check_jointly_gaussian(data, mean, cov):
empirical_mean = mx.mean(data, axis=0)
empirical_cov = (
(data - empirical_mean).T @ (data - empirical_mean) / data.shape[0]
)
N = data.shape[1]
self.assertTrue(
mx.allclose(
empirical_mean, mean, rtol=0.0, atol=10 * N**2 / math.sqrt(n_test)
)
)
self.assertTrue(
mx.allclose(
empirical_cov, cov, rtol=0.0, atol=10 * N**2 / math.sqrt(n_test)
)
)
mean = mx.array([4.0, 7.0])
cov = mx.array([[2, 0.5], [0.5, 1]])
data = mx.random.multivariate_normal(
mean, cov, shape=(n_test,), key=key, stream=mx.cpu
)
check_jointly_gaussian(data, mean, cov)
mean = mx.arange(3)
cov = mx.array([[1, -1, 0.5], [-1, 1, -0.5], [0.5, -0.5, 1]])
data = mx.random.multivariate_normal(
mean, cov, shape=(n_test,), key=key, stream=mx.cpu
)
check_jointly_gaussian(data, mean, cov)
def test_randint(self):
a = mx.random.randint(0, 1, [])
self.assertEqual(a.shape, ())
self.assertEqual(a.dtype, mx.int32)
shape = (88,)
low = mx.array(3)
high = mx.array(15)
key = mx.random.key(0)
a = mx.random.randint(low, high, shape, key=key)
self.assertEqual(a.shape, shape)
self.assertEqual(a.dtype, mx.int32)
# Check using the same key yields the same value
b = mx.random.randint(low, high, shape, key=key)
self.assertListEqual(a.tolist(), b.tolist())
shape = (3, 4)
low = mx.reshape(mx.array([0] * 3), [3, 1])
high = mx.reshape(mx.array([12, 13, 14, 15]), [1, 4])
a = mx.random.randint(low, high, shape)
self.assertEqual(a.shape, shape)
a = mx.random.randint(-10, 10, [1000, 1000])
self.assertTrue(mx.all(-10 <= a).item() and mx.all(a < 10).item())
a = mx.random.randint(10, -10, [1000, 1000])
self.assertTrue(mx.all(a == 10).item())
self.assertEqual(
mx.random.randint(0, 1).dtype, mx.random.randint(0, 1, dtype=None).dtype
)
def test_bernoulli(self):
a = mx.random.bernoulli()
self.assertEqual(a.shape, ())
self.assertEqual(a.dtype, mx.bool_)
a = mx.random.bernoulli(mx.array(0.5), [5])
self.assertEqual(a.shape, (5,))
a = mx.random.bernoulli(mx.array([2.0, -2.0]))
self.assertEqual(a.tolist(), [True, False])
self.assertEqual(a.shape, (2,))
p = mx.array([0.1, 0.2, 0.3])
mx.reshape(p, [1, 3])
x = mx.random.bernoulli(p, [4, 3])
self.assertEqual(x.shape, (4, 3))
with self.assertRaises(ValueError):
mx.random.bernoulli(p, [2]) # Bad shape
with self.assertRaises(ValueError):
mx.random.bernoulli(0, [2]) # Bad type
def test_truncated_normal(self):
a = mx.random.truncated_normal(-2.0, 2.0)
self.assertEqual(a.size, 1)
self.assertEqual(a.dtype, mx.float32)
a = mx.random.truncated_normal(mx.array([]), mx.array([]))
self.assertEqual(a.dtype, mx.float32)
self.assertEqual(a.size, 0)
lower = mx.reshape(mx.array([-2.0, 0.0]), [1, 2])
upper = mx.reshape(mx.array([0.0, 1.0, 2.0]), [3, 1])
a = mx.random.truncated_normal(lower, upper)
self.assertEqual(a.shape, (3, 2))
self.assertTrue(mx.all(lower <= a).item() and mx.all(a <= upper).item())
a = mx.random.truncated_normal(2.0, -2.0)
self.assertTrue(mx.all(a == 2.0).item())
a = mx.random.truncated_normal(-3.0, 3.0, [542, 399])
self.assertEqual(a.shape, (542, 399))
lower = mx.array([-2.0, -1.0])
higher = mx.array([1.0, 2.0, 3.0])
with self.assertRaises(ValueError):
mx.random.truncated_normal(lower, higher) # Bad shape
self.assertEqual(
mx.random.truncated_normal(0, 1).dtype,
mx.random.truncated_normal(0, 1, dtype=None).dtype,
)
def test_gumbel(self):
samples = mx.random.gumbel(shape=(100, 100))
self.assertEqual(samples.shape, (100, 100))
self.assertEqual(samples.dtype, mx.float32)
mean = 0.5772
# Std deviation of the sample mean is small (<0.02),
# so this test is pretty conservative
self.assertTrue(mx.abs(mx.mean(samples) - mean) < 0.2)
self.assertEqual(
mx.random.gumbel((1, 1)).dtype, mx.random.gumbel((1, 1), dtype=None).dtype
)
def test_categorical(self):
logits = mx.zeros((10, 20))
self.assertEqual(mx.random.categorical(logits, -1).shape, (10,))
self.assertEqual(mx.random.categorical(logits, 0).shape, (20,))
self.assertEqual(mx.random.categorical(logits, 1).shape, (10,))
out = mx.random.categorical(logits)
self.assertEqual(out.shape, (10,))
self.assertEqual(out.dtype, mx.uint32)
self.assertTrue(mx.max(out).item() < 20)
out = mx.random.categorical(logits, 0, [5, 20])
self.assertEqual(out.shape, (5, 20))
self.assertTrue(mx.max(out).item() < 10)
out = mx.random.categorical(logits, 1, num_samples=7)
self.assertEqual(out.shape, (10, 7))
out = mx.random.categorical(logits, 0, num_samples=7)
self.assertEqual(out.shape, (20, 7))
with self.assertRaises(ValueError):
mx.random.categorical(logits, shape=[10, 5], num_samples=5)
def test_permutation(self):
x = sorted(mx.random.permutation(4).tolist())
self.assertEqual([0, 1, 2, 3], x)
x = mx.array([0, 1, 2, 3])
x = sorted(mx.random.permutation(x).tolist())
self.assertEqual([0, 1, 2, 3], x)
x = mx.array([0, 1, 2, 3])
x = sorted(mx.random.permutation(x).tolist())
# 2-D
x = mx.arange(16).reshape(4, 4)
out = mx.sort(mx.random.permutation(x, axis=0), axis=0)
self.assertTrue(mx.array_equal(x, out))
out = mx.sort(mx.random.permutation(x, axis=1), axis=1)
self.assertTrue(mx.array_equal(x, out))
# Basically 0 probability this should fail.
sorted_x = mx.arange(16384)
x = mx.random.permutation(16384)
self.assertFalse(mx.array_equal(sorted_x, x))
# Preserves shape / doesn't cast input to int
x = mx.random.permutation(mx.array([[1]]))
self.assertEqual(x.shape, (1, 1))
def test_complex_normal(self):
sample = mx.random.normal(tuple(), dtype=mx.complex64)
self.assertEqual(sample.shape, tuple())
self.assertEqual(sample.dtype, mx.complex64)
sample = mx.random.normal((1, 2, 3, 4), dtype=mx.complex64)
self.assertEqual(sample.shape, (1, 2, 3, 4))
self.assertEqual(sample.dtype, mx.complex64)
sample = mx.random.normal((1, 2, 3, 4), dtype=mx.complex64, scale=2.0, loc=3.0)
self.assertEqual(sample.shape, (1, 2, 3, 4))
self.assertEqual(sample.dtype, mx.complex64)
sample = mx.random.normal(
(1, 2, 3, 4), dtype=mx.complex64, scale=2.0, loc=3.0 + 1j
)
self.assertEqual(sample.shape, (1, 2, 3, 4))
self.assertEqual(sample.dtype, mx.complex64)
def test_broadcastable_scale_loc(self):
b = mx.random.normal((10, 2))
sample = mx.random.normal((2, 10, 2), loc=b, scale=b)
mx.eval(sample)
self.assertEqual(sample.shape, (2, 10, 2))
with self.assertRaises(ValueError):
b = mx.random.normal((10,))
sample = mx.random.normal((2, 10, 2), loc=b, scale=b)
b = mx.random.normal((3, 1, 2))
sample = mx.random.normal((3, 4, 2), dtype=mx.float16, loc=b, scale=b)
mx.eval(sample)
self.assertEqual(sample.shape, (3, 4, 2))
self.assertEqual(sample.dtype, mx.float16)
if __name__ == "__main__":
mlx_tests.MLXTestRunner()
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