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test_distributions.py
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test_distributions.py
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"""
Note [Randomized statistical tests]
-----------------------------------
This note describes how to maintain tests in this file as random sources
change. This file contains two types of randomized tests:
1. The easier type of randomized test are tests that should always pass but are
initialized with random data. If these fail something is wrong, but it's
fine to use a fixed seed by inheriting from common.TestCase.
2. The trickier tests are statistical tests. These tests explicitly call
set_rng_seed(n) and are marked "see Note [Randomized statistical tests]".
These statistical tests have a known positive failure rate
(we set failure_rate=1e-3 by default). We need to balance strength of these
tests with annoyance of false alarms. One way that works is to specifically
set seeds in each of the randomized tests. When a random generator
occasionally changes (as in #4312 vectorizing the Box-Muller sampler), some
of these statistical tests may (rarely) fail. If one fails in this case,
it's fine to increment the seed of the failing test (but you shouldn't need
to increment it more than once; otherwise something is probably actually
wrong).
"""
import math
import numbers
import unittest
from collections import namedtuple
from itertools import product
from random import shuffle
import torch
# TODO: remove this global setting
# Distributions tests use double as the default dtype
torch.set_default_dtype(torch.double)
from torch._six import inf
from torch.testing._internal.common_utils import TestCase, run_tests, set_rng_seed, TEST_WITH_UBSAN, load_tests
from torch.testing._internal.common_cuda import TEST_CUDA
from torch.autograd import grad, gradcheck
from torch.distributions import (Bernoulli, Beta, Binomial, Categorical,
Cauchy, Chi2, ContinuousBernoulli, Dirichlet,
Distribution, Exponential, ExponentialFamily,
FisherSnedecor, Gamma, Geometric, Gumbel,
HalfCauchy, HalfNormal,
Independent, Kumaraswamy, Laplace, LogisticNormal,
LogNormal, LowRankMultivariateNormal,
MixtureSameFamily, Multinomial, MultivariateNormal,
NegativeBinomial, Normal, OneHotCategorical, Pareto,
Poisson, RelaxedBernoulli, RelaxedOneHotCategorical,
StudentT, TransformedDistribution, Uniform,
VonMises, Weibull, constraints, kl_divergence)
from torch.distributions.constraint_registry import transform_to
from torch.distributions.constraints import Constraint, is_dependent
from torch.distributions.dirichlet import _Dirichlet_backward
from torch.distributions.kl import _kl_expfamily_expfamily
from torch.distributions.transforms import (AbsTransform, AffineTransform,
CatTransform, ComposeTransform, ExpTransform,
LowerCholeskyTransform,
PowerTransform, SigmoidTransform,
TanhTransform, SoftmaxTransform,
StickBreakingTransform,
identity_transform, StackTransform)
from torch.distributions.utils import probs_to_logits, lazy_property
from torch.nn.functional import softmax
# load_tests from torch.testing._internal.common_utils is used to automatically filter tests for
# sharding on sandcastle. This line silences flake warnings
load_tests = load_tests
TEST_NUMPY = True
try:
import numpy as np
import scipy.stats
import scipy.special
except ImportError:
TEST_NUMPY = False
def pairwise(Dist, *params):
"""
Creates a pair of distributions `Dist` initialized to test each element of
param with each other.
"""
params1 = [torch.tensor([p] * len(p)) for p in params]
params2 = [p.transpose(0, 1) for p in params1]
return Dist(*params1), Dist(*params2)
def is_all_nan(tensor):
"""
Checks if all entries of a tensor is nan.
"""
return (tensor != tensor).all()
# Register all distributions for generic tests.
Example = namedtuple('Example', ['Dist', 'params'])
EXAMPLES = [
Example(Bernoulli, [
{'probs': torch.tensor([0.7, 0.2, 0.4], requires_grad=True)},
{'probs': torch.tensor([0.3], requires_grad=True)},
{'probs': 0.3},
{'logits': torch.tensor([0.], requires_grad=True)},
]),
Example(Geometric, [
{'probs': torch.tensor([0.7, 0.2, 0.4], requires_grad=True)},
{'probs': torch.tensor([0.3], requires_grad=True)},
{'probs': 0.3},
]),
Example(Beta, [
{
'concentration1': torch.randn(2, 3).exp().requires_grad_(),
'concentration0': torch.randn(2, 3).exp().requires_grad_(),
},
{
'concentration1': torch.randn(4).exp().requires_grad_(),
'concentration0': torch.randn(4).exp().requires_grad_(),
},
]),
Example(Categorical, [
{'probs': torch.tensor([[0.1, 0.2, 0.3], [0.5, 0.3, 0.2]], requires_grad=True)},
{'probs': torch.tensor([[1.0, 0.0], [0.0, 1.0]], requires_grad=True)},
{'logits': torch.tensor([[0.0, 0.0], [0.0, 0.0]], requires_grad=True)},
]),
Example(Binomial, [
{'probs': torch.tensor([[0.1, 0.2, 0.3], [0.5, 0.3, 0.2]], requires_grad=True), 'total_count': 10},
{'probs': torch.tensor([[1.0, 0.0], [0.0, 1.0]], requires_grad=True), 'total_count': 10},
{'probs': torch.tensor([[1.0, 0.0], [0.0, 1.0]], requires_grad=True), 'total_count': torch.tensor([10])},
{'probs': torch.tensor([[1.0, 0.0], [0.0, 1.0]], requires_grad=True), 'total_count': torch.tensor([10, 8])},
{'probs': torch.tensor([[1.0, 0.0], [0.0, 1.0]], requires_grad=True),
'total_count': torch.tensor([[10., 8.], [5., 3.]])},
{'probs': torch.tensor([[1.0, 0.0], [0.0, 1.0]], requires_grad=True),
'total_count': torch.tensor(0.)},
]),
Example(NegativeBinomial, [
{'probs': torch.tensor([[0.1, 0.2, 0.3], [0.5, 0.3, 0.2]], requires_grad=True), 'total_count': 10},
{'probs': torch.tensor([[0.9, 0.0], [0.0, 0.9]], requires_grad=True), 'total_count': 10},
{'probs': torch.tensor([[0.9, 0.0], [0.0, 0.9]], requires_grad=True), 'total_count': torch.tensor([10])},
{'probs': torch.tensor([[0.9, 0.0], [0.0, 0.9]], requires_grad=True), 'total_count': torch.tensor([10, 8])},
{'probs': torch.tensor([[0.9, 0.0], [0.0, 0.9]], requires_grad=True),
'total_count': torch.tensor([[10., 8.], [5., 3.]])},
{'probs': torch.tensor([[0.9, 0.0], [0.0, 0.9]], requires_grad=True),
'total_count': torch.tensor(0.)},
]),
Example(Multinomial, [
{'probs': torch.tensor([[0.1, 0.2, 0.3], [0.5, 0.3, 0.2]], requires_grad=True), 'total_count': 10},
{'probs': torch.tensor([[1.0, 0.0], [0.0, 1.0]], requires_grad=True), 'total_count': 10},
]),
Example(Cauchy, [
{'loc': 0.0, 'scale': 1.0},
{'loc': torch.tensor([0.0]), 'scale': 1.0},
{'loc': torch.tensor([[0.0], [0.0]]),
'scale': torch.tensor([[1.0], [1.0]])}
]),
Example(Chi2, [
{'df': torch.randn(2, 3).exp().requires_grad_()},
{'df': torch.randn(1).exp().requires_grad_()},
]),
Example(StudentT, [
{'df': torch.randn(2, 3).exp().requires_grad_()},
{'df': torch.randn(1).exp().requires_grad_()},
]),
Example(Dirichlet, [
{'concentration': torch.randn(2, 3).exp().requires_grad_()},
{'concentration': torch.randn(4).exp().requires_grad_()},
]),
Example(Exponential, [
{'rate': torch.randn(5, 5).abs().requires_grad_()},
{'rate': torch.randn(1).abs().requires_grad_()},
]),
Example(FisherSnedecor, [
{
'df1': torch.randn(5, 5).abs().requires_grad_(),
'df2': torch.randn(5, 5).abs().requires_grad_(),
},
{
'df1': torch.randn(1).abs().requires_grad_(),
'df2': torch.randn(1).abs().requires_grad_(),
},
{
'df1': torch.tensor([1.0]),
'df2': 1.0,
}
]),
Example(Gamma, [
{
'concentration': torch.randn(2, 3).exp().requires_grad_(),
'rate': torch.randn(2, 3).exp().requires_grad_(),
},
{
'concentration': torch.randn(1).exp().requires_grad_(),
'rate': torch.randn(1).exp().requires_grad_(),
},
]),
Example(Gumbel, [
{
'loc': torch.randn(5, 5, requires_grad=True),
'scale': torch.randn(5, 5).abs().requires_grad_(),
},
{
'loc': torch.randn(1, requires_grad=True),
'scale': torch.randn(1).abs().requires_grad_(),
},
]),
Example(HalfCauchy, [
{'scale': 1.0},
{'scale': torch.tensor([[1.0], [1.0]])}
]),
Example(HalfNormal, [
{'scale': torch.randn(5, 5).abs().requires_grad_()},
{'scale': torch.randn(1).abs().requires_grad_()},
{'scale': torch.tensor([1e-5, 1e-5], requires_grad=True)}
]),
Example(Independent, [
{
'base_distribution': Normal(torch.randn(2, 3, requires_grad=True),
torch.randn(2, 3).abs().requires_grad_()),
'reinterpreted_batch_ndims': 0,
},
{
'base_distribution': Normal(torch.randn(2, 3, requires_grad=True),
torch.randn(2, 3).abs().requires_grad_()),
'reinterpreted_batch_ndims': 1,
},
{
'base_distribution': Normal(torch.randn(2, 3, requires_grad=True),
torch.randn(2, 3).abs().requires_grad_()),
'reinterpreted_batch_ndims': 2,
},
{
'base_distribution': Normal(torch.randn(2, 3, 5, requires_grad=True),
torch.randn(2, 3, 5).abs().requires_grad_()),
'reinterpreted_batch_ndims': 2,
},
{
'base_distribution': Normal(torch.randn(2, 3, 5, requires_grad=True),
torch.randn(2, 3, 5).abs().requires_grad_()),
'reinterpreted_batch_ndims': 3,
},
]),
Example(Kumaraswamy, [
{
'concentration1': torch.empty(2, 3).uniform_(1, 2).requires_grad_(),
'concentration0': torch.empty(2, 3).uniform_(1, 2).requires_grad_(),
},
{
'concentration1': torch.rand(4).uniform_(1, 2).requires_grad_(),
'concentration0': torch.rand(4).uniform_(1, 2).requires_grad_(),
},
]),
Example(Laplace, [
{
'loc': torch.randn(5, 5, requires_grad=True),
'scale': torch.randn(5, 5).abs().requires_grad_(),
},
{
'loc': torch.randn(1, requires_grad=True),
'scale': torch.randn(1).abs().requires_grad_(),
},
{
'loc': torch.tensor([1.0, 0.0], requires_grad=True),
'scale': torch.tensor([1e-5, 1e-5], requires_grad=True),
},
]),
Example(LogNormal, [
{
'loc': torch.randn(5, 5, requires_grad=True),
'scale': torch.randn(5, 5).abs().requires_grad_(),
},
{
'loc': torch.randn(1, requires_grad=True),
'scale': torch.randn(1).abs().requires_grad_(),
},
{
'loc': torch.tensor([1.0, 0.0], requires_grad=True),
'scale': torch.tensor([1e-5, 1e-5], requires_grad=True),
},
]),
Example(LogisticNormal, [
{
'loc': torch.randn(5, 5).requires_grad_(),
'scale': torch.randn(5, 5).abs().requires_grad_(),
},
{
'loc': torch.randn(1).requires_grad_(),
'scale': torch.randn(1).abs().requires_grad_(),
},
{
'loc': torch.tensor([1.0, 0.0], requires_grad=True),
'scale': torch.tensor([1e-5, 1e-5], requires_grad=True),
},
]),
Example(LowRankMultivariateNormal, [
{
'loc': torch.randn(5, 2, requires_grad=True),
'cov_factor': torch.randn(5, 2, 1, requires_grad=True),
'cov_diag': torch.tensor([2.0, 0.25], requires_grad=True),
},
{
'loc': torch.randn(4, 3, requires_grad=True),
'cov_factor': torch.randn(3, 2, requires_grad=True),
'cov_diag': torch.tensor([5.0, 1.5, 3.], requires_grad=True),
}
]),
Example(MultivariateNormal, [
{
'loc': torch.randn(5, 2, requires_grad=True),
'covariance_matrix': torch.tensor([[2.0, 0.3], [0.3, 0.25]], requires_grad=True),
},
{
'loc': torch.randn(2, 3, requires_grad=True),
'precision_matrix': torch.tensor([[2.0, 0.1, 0.0],
[0.1, 0.25, 0.0],
[0.0, 0.0, 0.3]], requires_grad=True),
},
{
'loc': torch.randn(5, 3, 2, requires_grad=True),
'scale_tril': torch.tensor([[[2.0, 0.0], [-0.5, 0.25]],
[[2.0, 0.0], [0.3, 0.25]],
[[5.0, 0.0], [-0.5, 1.5]]], requires_grad=True),
},
{
'loc': torch.tensor([1.0, -1.0]),
'covariance_matrix': torch.tensor([[5.0, -0.5], [-0.5, 1.5]]),
},
]),
Example(Normal, [
{
'loc': torch.randn(5, 5, requires_grad=True),
'scale': torch.randn(5, 5).abs().requires_grad_(),
},
{
'loc': torch.randn(1, requires_grad=True),
'scale': torch.randn(1).abs().requires_grad_(),
},
{
'loc': torch.tensor([1.0, 0.0], requires_grad=True),
'scale': torch.tensor([1e-5, 1e-5], requires_grad=True),
},
]),
Example(OneHotCategorical, [
{'probs': torch.tensor([[0.1, 0.2, 0.3], [0.5, 0.3, 0.2]], requires_grad=True)},
{'probs': torch.tensor([[1.0, 0.0], [0.0, 1.0]], requires_grad=True)},
{'logits': torch.tensor([[0.0, 0.0], [0.0, 0.0]], requires_grad=True)},
]),
Example(Pareto, [
{
'scale': 1.0,
'alpha': 1.0
},
{
'scale': torch.randn(5, 5).abs().requires_grad_(),
'alpha': torch.randn(5, 5).abs().requires_grad_()
},
{
'scale': torch.tensor([1.0]),
'alpha': 1.0
}
]),
Example(Poisson, [
{
'rate': torch.randn(5, 5).abs().requires_grad_(),
},
{
'rate': torch.randn(3).abs().requires_grad_(),
},
{
'rate': 0.2,
}
]),
Example(RelaxedBernoulli, [
{
'temperature': torch.tensor([0.5], requires_grad=True),
'probs': torch.tensor([0.7, 0.2, 0.4], requires_grad=True),
},
{
'temperature': torch.tensor([2.0]),
'probs': torch.tensor([0.3]),
},
{
'temperature': torch.tensor([7.2]),
'logits': torch.tensor([-2.0, 2.0, 1.0, 5.0])
}
]),
Example(RelaxedOneHotCategorical, [
{
'temperature': torch.tensor([0.5], requires_grad=True),
'probs': torch.tensor([[0.1, 0.2, 0.7], [0.5, 0.3, 0.2]], requires_grad=True)
},
{
'temperature': torch.tensor([2.0]),
'probs': torch.tensor([[1.0, 0.0], [0.0, 1.0]])
},
{
'temperature': torch.tensor([7.2]),
'logits': torch.tensor([[-2.0, 2.0], [1.0, 5.0]])
}
]),
Example(TransformedDistribution, [
{
'base_distribution': Normal(torch.randn(2, 3, requires_grad=True),
torch.randn(2, 3).abs().requires_grad_()),
'transforms': [],
},
{
'base_distribution': Normal(torch.randn(2, 3, requires_grad=True),
torch.randn(2, 3).abs().requires_grad_()),
'transforms': ExpTransform(),
},
{
'base_distribution': Normal(torch.randn(2, 3, 5, requires_grad=True),
torch.randn(2, 3, 5).abs().requires_grad_()),
'transforms': [AffineTransform(torch.randn(3, 5), torch.randn(3, 5)),
ExpTransform()],
},
{
'base_distribution': Normal(torch.randn(2, 3, 5, requires_grad=True),
torch.randn(2, 3, 5).abs().requires_grad_()),
'transforms': AffineTransform(1, 2),
},
]),
Example(Uniform, [
{
'low': torch.zeros(5, 5, requires_grad=True),
'high': torch.ones(5, 5, requires_grad=True),
},
{
'low': torch.zeros(1, requires_grad=True),
'high': torch.ones(1, requires_grad=True),
},
{
'low': torch.tensor([1.0, 1.0], requires_grad=True),
'high': torch.tensor([2.0, 3.0], requires_grad=True),
},
]),
Example(Weibull, [
{
'scale': torch.randn(5, 5).abs().requires_grad_(),
'concentration': torch.randn(1).abs().requires_grad_()
}
]),
Example(MixtureSameFamily, [
{
'mixture_distribution': Categorical(torch.rand(5, requires_grad=True)),
'component_distribution': Normal(torch.randn(5, requires_grad=True),
torch.rand(5, requires_grad=True)),
},
{
'mixture_distribution': Categorical(torch.rand(5, requires_grad=True)),
'component_distribution': MultivariateNormal(
loc=torch.randn(5, 2, requires_grad=True),
covariance_matrix=torch.tensor([[2.0, 0.3], [0.3, 0.25]], requires_grad=True)),
},
]),
Example(VonMises, [
{
'loc': torch.tensor(1.0, requires_grad=True),
'concentration': torch.tensor(10.0, requires_grad=True)
},
{
'loc': torch.tensor([0.0, math.pi / 2], requires_grad=True),
'concentration': torch.tensor([1.0, 10.0], requires_grad=True)
},
]),
Example(ContinuousBernoulli, [
{'probs': torch.tensor([0.7, 0.2, 0.4], requires_grad=True)},
{'probs': torch.tensor([0.3], requires_grad=True)},
{'probs': 0.3},
{'logits': torch.tensor([0.], requires_grad=True)},
])
]
BAD_EXAMPLES = [
Example(Bernoulli, [
{'probs': torch.tensor([1.1, 0.2, 0.4], requires_grad=True)},
{'probs': torch.tensor([-0.5], requires_grad=True)},
{'probs': 1.00001},
]),
Example(Beta, [
{
'concentration1': torch.tensor([0.0], requires_grad=True),
'concentration0': torch.tensor([0.0], requires_grad=True),
},
{
'concentration1': torch.tensor([-1.0], requires_grad=True),
'concentration0': torch.tensor([-2.0], requires_grad=True),
},
]),
Example(Geometric, [
{'probs': torch.tensor([1.1, 0.2, 0.4], requires_grad=True)},
{'probs': torch.tensor([-0.3], requires_grad=True)},
{'probs': 1.00000001},
]),
Example(Categorical, [
{'probs': torch.tensor([[-0.1, 0.2, 0.3], [0.5, 0.3, 0.2]], requires_grad=True)},
{'probs': torch.tensor([[-1.0, 10.0], [0.0, -1.0]], requires_grad=True)},
]),
Example(Binomial, [
{'probs': torch.tensor([[-0.0000001, 0.2, 0.3], [0.5, 0.3, 0.2]], requires_grad=True),
'total_count': 10},
{'probs': torch.tensor([[1.0, 0.0], [0.0, 2.0]], requires_grad=True),
'total_count': 10},
]),
Example(NegativeBinomial, [
{'probs': torch.tensor([[-0.0000001, 0.2, 0.3], [0.5, 0.3, 0.2]], requires_grad=True),
'total_count': 10},
{'probs': torch.tensor([[1.0, 0.0], [0.0, 2.0]], requires_grad=True),
'total_count': 10},
]),
Example(Cauchy, [
{'loc': 0.0, 'scale': -1.0},
{'loc': torch.tensor([0.0]), 'scale': 0.0},
{'loc': torch.tensor([[0.0], [-2.0]]),
'scale': torch.tensor([[-0.000001], [1.0]])}
]),
Example(Chi2, [
{'df': torch.tensor([0.], requires_grad=True)},
{'df': torch.tensor([-2.], requires_grad=True)},
]),
Example(StudentT, [
{'df': torch.tensor([0.], requires_grad=True)},
{'df': torch.tensor([-2.], requires_grad=True)},
]),
Example(Dirichlet, [
{'concentration': torch.tensor([0.], requires_grad=True)},
{'concentration': torch.tensor([-2.], requires_grad=True)}
]),
Example(Exponential, [
{'rate': torch.tensor([0., 0.], requires_grad=True)},
{'rate': torch.tensor([-2.], requires_grad=True)}
]),
Example(FisherSnedecor, [
{
'df1': torch.tensor([0., 0.], requires_grad=True),
'df2': torch.tensor([-1., -100.], requires_grad=True),
},
{
'df1': torch.tensor([1., 1.], requires_grad=True),
'df2': torch.tensor([0., 0.], requires_grad=True),
}
]),
Example(Gamma, [
{
'concentration': torch.tensor([0., 0.], requires_grad=True),
'rate': torch.tensor([-1., -100.], requires_grad=True),
},
{
'concentration': torch.tensor([1., 1.], requires_grad=True),
'rate': torch.tensor([0., 0.], requires_grad=True),
}
]),
Example(Gumbel, [
{
'loc': torch.tensor([1., 1.], requires_grad=True),
'scale': torch.tensor([0., 1.], requires_grad=True),
},
{
'loc': torch.tensor([1., 1.], requires_grad=True),
'scale': torch.tensor([1., -1.], requires_grad=True),
},
]),
Example(HalfCauchy, [
{'scale': -1.0},
{'scale': 0.0},
{'scale': torch.tensor([[-0.000001], [1.0]])}
]),
Example(HalfNormal, [
{'scale': torch.tensor([0., 1.], requires_grad=True)},
{'scale': torch.tensor([1., -1.], requires_grad=True)},
]),
Example(Laplace, [
{
'loc': torch.tensor([1., 1.], requires_grad=True),
'scale': torch.tensor([0., 1.], requires_grad=True),
},
{
'loc': torch.tensor([1., 1.], requires_grad=True),
'scale': torch.tensor([1., -1.], requires_grad=True),
},
]),
Example(LogNormal, [
{
'loc': torch.tensor([1., 1.], requires_grad=True),
'scale': torch.tensor([0., 1.], requires_grad=True),
},
{
'loc': torch.tensor([1., 1.], requires_grad=True),
'scale': torch.tensor([1., -1.], requires_grad=True),
},
]),
Example(MultivariateNormal, [
{
'loc': torch.tensor([1., 1.], requires_grad=True),
'covariance_matrix': torch.tensor([[1.0, 0.0], [0.0, -2.0]], requires_grad=True),
},
]),
Example(Normal, [
{
'loc': torch.tensor([1., 1.], requires_grad=True),
'scale': torch.tensor([0., 1.], requires_grad=True),
},
{
'loc': torch.tensor([1., 1.], requires_grad=True),
'scale': torch.tensor([1., -1.], requires_grad=True),
},
{
'loc': torch.tensor([1.0, 0.0], requires_grad=True),
'scale': torch.tensor([1e-5, -1e-5], requires_grad=True),
},
]),
Example(OneHotCategorical, [
{'probs': torch.tensor([[0.1, 0.2, 0.3], [0.1, -10.0, 0.2]], requires_grad=True)},
{'probs': torch.tensor([[0.0, 0.0], [0.0, 0.0]], requires_grad=True)},
]),
Example(Pareto, [
{
'scale': 0.0,
'alpha': 0.0
},
{
'scale': torch.tensor([0.0, 0.0], requires_grad=True),
'alpha': torch.tensor([-1e-5, 0.0], requires_grad=True)
},
{
'scale': torch.tensor([1.0]),
'alpha': -1.0
}
]),
Example(Poisson, [
{
'rate': torch.tensor([0.0], requires_grad=True),
},
{
'rate': -1.0,
}
]),
Example(RelaxedBernoulli, [
{
'temperature': torch.tensor([1.5], requires_grad=True),
'probs': torch.tensor([1.7, 0.2, 0.4], requires_grad=True),
},
{
'temperature': torch.tensor([2.0]),
'probs': torch.tensor([-1.0]),
}
]),
Example(RelaxedOneHotCategorical, [
{
'temperature': torch.tensor([0.5], requires_grad=True),
'probs': torch.tensor([[-0.1, 0.2, 0.7], [0.5, 0.3, 0.2]], requires_grad=True)
},
{
'temperature': torch.tensor([2.0]),
'probs': torch.tensor([[-1.0, 0.0], [-1.0, 1.1]])
}
]),
Example(TransformedDistribution, [
{
'base_distribution': Normal(0, 1),
'transforms': lambda x: x,
},
{
'base_distribution': Normal(0, 1),
'transforms': [lambda x: x],
},
]),
Example(Uniform, [
{
'low': torch.tensor([2.0], requires_grad=True),
'high': torch.tensor([2.0], requires_grad=True),
},
{
'low': torch.tensor([0.0], requires_grad=True),
'high': torch.tensor([0.0], requires_grad=True),
},
{
'low': torch.tensor([1.0], requires_grad=True),
'high': torch.tensor([0.0], requires_grad=True),
}
]),
Example(Weibull, [
{
'scale': torch.tensor([0.0], requires_grad=True),
'concentration': torch.tensor([0.0], requires_grad=True)
},
{
'scale': torch.tensor([1.0], requires_grad=True),
'concentration': torch.tensor([-1.0], requires_grad=True)
}
]),
Example(ContinuousBernoulli, [
{'probs': torch.tensor([1.1, 0.2, 0.4], requires_grad=True)},
{'probs': torch.tensor([-0.5], requires_grad=True)},
{'probs': 1.00001},
])
]
class TestDistributions(TestCase):
_do_cuda_memory_leak_check = True
_do_cuda_non_default_stream = True
def _gradcheck_log_prob(self, dist_ctor, ctor_params):
# performs gradient checks on log_prob
distribution = dist_ctor(*ctor_params)
s = distribution.sample()
if s.is_floating_point():
s = s.detach().requires_grad_()
expected_shape = distribution.batch_shape + distribution.event_shape
self.assertEqual(s.size(), expected_shape)
def apply_fn(s, *params):
return dist_ctor(*params).log_prob(s)
gradcheck(apply_fn, (s,) + tuple(ctor_params), raise_exception=True)
def _check_log_prob(self, dist, asset_fn):
# checks that the log_prob matches a reference function
s = dist.sample()
log_probs = dist.log_prob(s)
log_probs_data_flat = log_probs.view(-1)
s_data_flat = s.view(len(log_probs_data_flat), -1)
for i, (val, log_prob) in enumerate(zip(s_data_flat, log_probs_data_flat)):
asset_fn(i, val.squeeze(), log_prob)
def _check_sampler_sampler(self, torch_dist, ref_dist, message, multivariate=False,
circular=False, num_samples=10000, failure_rate=1e-3):
# Checks that the .sample() method matches a reference function.
torch_samples = torch_dist.sample((num_samples,)).squeeze()
torch_samples = torch_samples.cpu().numpy()
ref_samples = ref_dist.rvs(num_samples).astype(np.float64)
if multivariate:
# Project onto a random axis.
axis = np.random.normal(size=torch_samples.shape[-1])
axis /= np.linalg.norm(axis)
torch_samples = np.dot(torch_samples, axis)
ref_samples = np.dot(ref_samples, axis)
samples = [(x, +1) for x in torch_samples] + [(x, -1) for x in ref_samples]
if circular:
samples = [(np.cos(x), v) for (x, v) in samples]
shuffle(samples) # necessary to prevent stable sort from making uneven bins for discrete
samples.sort(key=lambda x: x[0])
samples = np.array(samples)[:, 1]
# Aggregate into bins filled with roughly zero-mean unit-variance RVs.
num_bins = 10
samples_per_bin = len(samples) // num_bins
bins = samples.reshape((num_bins, samples_per_bin)).mean(axis=1)
stddev = samples_per_bin ** -0.5
threshold = stddev * scipy.special.erfinv(1 - 2 * failure_rate / num_bins)
message = '{}.sample() is biased:\n{}'.format(message, bins)
for bias in bins:
self.assertLess(-threshold, bias, message)
self.assertLess(bias, threshold, message)
@unittest.skipIf(not TEST_NUMPY, "NumPy not found")
def _check_sampler_discrete(self, torch_dist, ref_dist, message,
num_samples=10000, failure_rate=1e-3):
"""Runs a Chi2-test for the support, but ignores tail instead of combining"""
torch_samples = torch_dist.sample((num_samples,)).squeeze()
torch_samples = torch_samples.cpu().numpy()
unique, counts = np.unique(torch_samples, return_counts=True)
pmf = ref_dist.pmf(unique)
msk = (counts > 5) & ((pmf * num_samples) > 5)
self.assertGreater(pmf[msk].sum(), 0.9, "Distribution is too sparse for test; try increasing num_samples")
chisq, p = scipy.stats.chisquare(counts[msk], pmf[msk] * num_samples)
self.assertGreater(p, failure_rate, message)
def _check_enumerate_support(self, dist, examples):
for params, expected in examples:
params = {k: torch.tensor(v) for k, v in params.items()}
expected = torch.tensor(expected)
d = dist(**params)
actual = d.enumerate_support(expand=False)
# TODO(#38095): Replace assertEqualIgnoreType. See issue #38095
self.assertEqualIgnoreType(actual, expected)
actual = d.enumerate_support(expand=True)
expected_with_expand = expected.expand((-1,) + d.batch_shape + d.event_shape)
# TODO(#38095): Replace assertEqualIgnoreType. See issue #38095
self.assertEqualIgnoreType(actual, expected_with_expand)
def test_repr(self):
for Dist, params in EXAMPLES:
for param in params:
dist = Dist(**param)
self.assertTrue(repr(dist).startswith(dist.__class__.__name__))
def test_sample_detached(self):
for Dist, params in EXAMPLES:
for i, param in enumerate(params):
variable_params = [p for p in param.values() if getattr(p, 'requires_grad', False)]
if not variable_params:
continue
dist = Dist(**param)
sample = dist.sample()
self.assertFalse(sample.requires_grad,
msg='{} example {}/{}, .sample() is not detached'.format(
Dist.__name__, i + 1, len(params)))
def test_rsample_requires_grad(self):
for Dist, params in EXAMPLES:
for i, param in enumerate(params):
if not any(getattr(p, 'requires_grad', False) for p in param.values()):
continue
dist = Dist(**param)
if not dist.has_rsample:
continue
sample = dist.rsample()
self.assertTrue(sample.requires_grad,
msg='{} example {}/{}, .rsample() does not require grad'.format(
Dist.__name__, i + 1, len(params)))
def test_enumerate_support_type(self):
for Dist, params in EXAMPLES:
for i, param in enumerate(params):
dist = Dist(**param)
try:
self.assertTrue(type(dist.sample()) is type(dist.enumerate_support()),
msg=('{} example {}/{}, return type mismatch between ' +
'sample and enumerate_support.').format(Dist.__name__, i + 1, len(params)))
except NotImplementedError:
pass
def test_lazy_property_grad(self):
x = torch.randn(1, requires_grad=True)
class Dummy(object):
@lazy_property
def y(self):
return x + 1
def test():
x.grad = None
Dummy().y.backward()
self.assertEqual(x.grad, torch.ones(1))
test()
with torch.no_grad():
test()
mean = torch.randn(2)
cov = torch.eye(2, requires_grad=True)
distn = MultivariateNormal(mean, cov)
with torch.no_grad():
distn.scale_tril
distn.scale_tril.sum().backward()
self.assertIsNotNone(cov.grad)
def test_has_examples(self):
distributions_with_examples = {e.Dist for e in EXAMPLES}
for Dist in globals().values():
if isinstance(Dist, type) and issubclass(Dist, Distribution) \
and Dist is not Distribution and Dist is not ExponentialFamily:
self.assertIn(Dist, distributions_with_examples,
"Please add {} to the EXAMPLES list in test_distributions.py".format(Dist.__name__))
def test_distribution_expand(self):
shapes = [torch.Size(), torch.Size((2,)), torch.Size((2, 1))]
for Dist, params in EXAMPLES:
for param in params:
for shape in shapes:
d = Dist(**param)
expanded_shape = shape + d.batch_shape
original_shape = d.batch_shape + d.event_shape
expected_shape = shape + original_shape
expanded = d.expand(batch_shape=list(expanded_shape))
sample = expanded.sample()
actual_shape = expanded.sample().shape
self.assertEqual(expanded.__class__, d.__class__)
self.assertEqual(d.sample().shape, original_shape)
self.assertEqual(expanded.log_prob(sample), d.log_prob(sample))
self.assertEqual(actual_shape, expected_shape)
self.assertEqual(expanded.batch_shape, expanded_shape)
try:
self.assertEqual(expanded.mean,
d.mean.expand(expanded_shape + d.event_shape))
self.assertEqual(expanded.variance,
d.variance.expand(expanded_shape + d.event_shape))
except NotImplementedError:
pass
def test_distribution_subclass_expand(self):
expand_by = torch.Size((2,))
for Dist, params in EXAMPLES:
class SubClass(Dist):
pass
for param in params:
d = SubClass(**param)
expanded_shape = expand_by + d.batch_shape
original_shape = d.batch_shape + d.event_shape
expected_shape = expand_by + original_shape
expanded = d.expand(batch_shape=expanded_shape)
sample = expanded.sample()
actual_shape = expanded.sample().shape
self.assertEqual(expanded.__class__, d.__class__)
self.assertEqual(d.sample().shape, original_shape)
self.assertEqual(expanded.log_prob(sample), d.log_prob(sample))
self.assertEqual(actual_shape, expected_shape)
def test_bernoulli(self):
p = torch.tensor([0.7, 0.2, 0.4], requires_grad=True)
r = torch.tensor(0.3, requires_grad=True)
s = 0.3
self.assertEqual(Bernoulli(p).sample((8,)).size(), (8, 3))
self.assertFalse(Bernoulli(p).sample().requires_grad)
self.assertEqual(Bernoulli(r).sample((8,)).size(), (8,))
self.assertEqual(Bernoulli(r).sample().size(), ())
self.assertEqual(Bernoulli(r).sample((3, 2)).size(), (3, 2,))
self.assertEqual(Bernoulli(s).sample().size(), ())
self._gradcheck_log_prob(Bernoulli, (p,))
def ref_log_prob(idx, val, log_prob):
prob = p[idx]
self.assertEqual(log_prob, math.log(prob if val else 1 - prob))
self._check_log_prob(Bernoulli(p), ref_log_prob)
self._check_log_prob(Bernoulli(logits=p.log() - (-p).log1p()), ref_log_prob)
self.assertRaises(NotImplementedError, Bernoulli(r).rsample)
# check entropy computation
self.assertEqual(Bernoulli(p).entropy(), torch.tensor([0.6108, 0.5004, 0.6730]), atol=1e-4, rtol=0)
self.assertEqual(Bernoulli(torch.tensor([0.0])).entropy(), torch.tensor([0.0]))
self.assertEqual(Bernoulli(s).entropy(), torch.tensor(0.6108), atol=1e-4, rtol=0)
def test_bernoulli_enumerate_support(self):
examples = [
({"probs": [0.1]}, [[0], [1]]),
({"probs": [0.1, 0.9]}, [[0], [1]]),
({"probs": [[0.1, 0.2], [0.3, 0.4]]}, [[[0]], [[1]]]),
]
self._check_enumerate_support(Bernoulli, examples)
def test_bernoulli_3d(self):
p = torch.full((2, 3, 5), 0.5).requires_grad_()
self.assertEqual(Bernoulli(p).sample().size(), (2, 3, 5))
self.assertEqual(Bernoulli(p).sample(sample_shape=(2, 5)).size(),
(2, 5, 2, 3, 5))
self.assertEqual(Bernoulli(p).sample((2,)).size(), (2, 2, 3, 5))
def test_geometric(self):
p = torch.tensor([0.7, 0.2, 0.4], requires_grad=True)
r = torch.tensor(0.3, requires_grad=True)
s = 0.3
self.assertEqual(Geometric(p).sample((8,)).size(), (8, 3))
self.assertEqual(Geometric(1).sample(), 0)
self.assertEqual(Geometric(1).log_prob(torch.tensor(1.)), -inf)
self.assertEqual(Geometric(1).log_prob(torch.tensor(0.)), 0)
self.assertFalse(Geometric(p).sample().requires_grad)
self.assertEqual(Geometric(r).sample((8,)).size(), (8,))
self.assertEqual(Geometric(r).sample().size(), ())
self.assertEqual(Geometric(r).sample((3, 2)).size(), (3, 2))
self.assertEqual(Geometric(s).sample().size(), ())
self._gradcheck_log_prob(Geometric, (p,))
self.assertRaises(ValueError, lambda: Geometric(0))
self.assertRaises(NotImplementedError, Geometric(r).rsample)
@unittest.skipIf(not TEST_NUMPY, "NumPy not found")
def test_geometric_log_prob_and_entropy(self):
p = torch.tensor([0.7, 0.2, 0.4], requires_grad=True)
s = 0.3
def ref_log_prob(idx, val, log_prob):
prob = p[idx].detach()
self.assertEqual(log_prob, scipy.stats.geom(prob, loc=-1).logpmf(val))
self._check_log_prob(Geometric(p), ref_log_prob)
self._check_log_prob(Geometric(logits=p.log() - (-p).log1p()), ref_log_prob)
# check entropy computation
self.assertEqual(Geometric(p).entropy(), scipy.stats.geom(p.detach().numpy(), loc=-1).entropy(), atol=1e-3, rtol=0)
self.assertEqual(float(Geometric(s).entropy()), scipy.stats.geom(s, loc=-1).entropy().item(), atol=1e-3, rtol=0)
@unittest.skipIf(not TEST_NUMPY, "NumPy not found")
def test_geometric_sample(self):
set_rng_seed(0) # see Note [Randomized statistical tests]
for prob in [0.01, 0.18, 0.8]:
self._check_sampler_discrete(Geometric(prob),
scipy.stats.geom(p=prob, loc=-1),
'Geometric(prob={})'.format(prob))
def test_binomial(self):
p = torch.arange(0.05, 1, 0.1).requires_grad_()
for total_count in [1, 2, 10]:
self._gradcheck_log_prob(lambda p: Binomial(total_count, p), [p])
self._gradcheck_log_prob(lambda p: Binomial(total_count, None, p.log()), [p])
self.assertRaises(NotImplementedError, Binomial(10, p).rsample)
self.assertRaises(NotImplementedError, Binomial(10, p).entropy)
@unittest.skipIf(not TEST_NUMPY, "NumPy not found")
def test_binomial_sample(self):
set_rng_seed(0) # see Note [Randomized statistical tests]
for prob in [0.01, 0.1, 0.5, 0.8, 0.9]:
for count in [2, 10, 100, 500]:
self._check_sampler_discrete(Binomial(total_count=count, probs=prob),
scipy.stats.binom(count, prob),