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Feature/vonmises upstream (#33418)

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Summary:
Third try of https://github.com/pytorch/pytorch/issues/33177 😄
Pull Request resolved: https://github.com/pytorch/pytorch/pull/33418

Differential Revision: D20069683

Pulled By: ezyang

fbshipit-source-id: f58e45e91b672bfde2e41a4480215ba4c613f9de
This commit is contained in:
Ahmad Salim Al-Sibahi
2020-02-26 08:17:09 -08:00
committed by Facebook Github Bot
parent 758ad516f3
commit 24659d28a1
4 changed files with 229 additions and 22 deletions
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9
docs/source/distributions.rst
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@ -311,6 +311,15 @@ Probability distributions - torch.distributions
:undoc-members:
:show-inheritance:
:hidden:`VonMises`
~~~~~~~~~~~~~~~~~~~~~~~
.. currentmodule:: torch.distributions.von_mises
.. autoclass:: VonMises
:members:
:undoc-members:
:show-inheritance:
:hidden:`Weibull`
~~~~~~~~~~~~~~~~~~~~~~~

98
test/test_distributions.py
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@ -50,7 +50,7 @@ from torch.distributions import (Bernoulli, Beta, Binomial, Categorical,
NegativeBinomial, Normal, OneHotCategorical, Pareto,
Poisson, RelaxedBernoulli, RelaxedOneHotCategorical,
StudentT, TransformedDistribution, Uniform,
Weibull, constraints, kl_divergence)
VonMises, Weibull, constraints, kl_divergence)
from torch.distributions.constraint_registry import biject_to, transform_to
from torch.distributions.constraints import Constraint, is_dependent
from torch.distributions.dirichlet import _Dirichlet_backward
@ -443,6 +443,16 @@ EXAMPLES = [
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)
}
])
]
@ -696,7 +706,7 @@ class TestDistributions(TestCase):
asset_fn(i, val.squeeze(), log_prob)
def _check_sampler_sampler(self, torch_dist, ref_dist, message, multivariate=False,
num_samples=10000, failure_rate=1e-3):
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()
@ -708,6 +718,8 @@ class TestDistributions(TestCase):
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]
@ -1361,6 +1373,23 @@ class TestDistributions(TestCase):
low.grad.zero_()
high.grad.zero_()
@unittest.skipIf(not TEST_NUMPY, "NumPy not found")
def test_vonmises_sample(self):
for loc in [0.0, math.pi / 2.0]:
for concentration in [0.03, 0.3, 1.0, 10.0, 100.0]:
self._check_sampler_sampler(VonMises(loc, concentration),
scipy.stats.vonmises(loc=loc, kappa=concentration),
"VonMises(loc={}, concentration={})".format(loc, concentration),
num_samples=int(1e5), circular=True)
def test_vonmises_logprob(self):
concentrations = [0.01, 0.03, 0.1, 0.3, 1.0, 3.0, 10.0, 30.0, 100.0]
for concentration in concentrations:
grid = torch.arange(0., 2 * math.pi, 1e-4)
prob = VonMises(0.0, concentration).log_prob(grid).exp()
norm = prob.mean().item() * 2 * math.pi
self.assertLess(abs(norm - 1), 1e-3)
def test_cauchy(self):
loc = torch.zeros(5, 5, requires_grad=True)
scale = torch.ones(5, 5, requires_grad=True)
@ -3132,6 +3161,27 @@ class TestDistributionShapes(TestCase):
self.assertEqual(gumbel.log_prob(self.tensor_sample_1).size(), torch.Size((3, 2)))
self.assertEqual(gumbel.log_prob(self.tensor_sample_2).size(), torch.Size((3, 2, 3)))
def test_vonmises_shape_tensor_params(self):
von_mises = VonMises(torch.tensor([0., 0.]), torch.tensor([1., 1.]))
self.assertEqual(von_mises._batch_shape, torch.Size((2,)))
self.assertEqual(von_mises._event_shape, torch.Size(()))
self.assertEqual(von_mises.sample().size(), torch.Size((2,)))
self.assertEqual(von_mises.sample(torch.Size((3, 2))).size(), torch.Size((3, 2, 2)))
self.assertEqual(von_mises.log_prob(self.tensor_sample_1).size(), torch.Size((3, 2)))
self.assertEqual(von_mises.log_prob(torch.ones(2, 1)).size(), torch.Size((2, 2)))
def test_vonmises_shape_scalar_params(self):
von_mises = VonMises(0., 1.)
self.assertEqual(von_mises._batch_shape, torch.Size())
self.assertEqual(von_mises._event_shape, torch.Size())
self.assertEqual(von_mises.sample().size(), torch.Size())
self.assertEqual(von_mises.sample(torch.Size((3, 2))).size(),
torch.Size((3, 2)))
self.assertEqual(von_mises.log_prob(self.tensor_sample_1).size(),
torch.Size((3, 2)))
self.assertEqual(von_mises.log_prob(self.tensor_sample_2).size(),
torch.Size((3, 2, 3)))
def test_weibull_scale_scalar_params(self):
weibull = Weibull(1, 1)
self.assertEqual(weibull._batch_shape, torch.Size())
@ -3882,6 +3932,10 @@ class TestAgainstScipy(TestCase):
Uniform(random_var, random_var + positive_var),
scipy.stats.uniform(random_var, positive_var)
),
(
VonMises(random_var, positive_var),
scipy.stats.vonmises(positive_var, loc=random_var)
),
(
Weibull(positive_var[0], positive_var2[0]), # scipy var for Weibull only supports scalars
scipy.stats.weibull_min(c=positive_var2[0], scale=positive_var[0])
@ -3900,8 +3954,9 @@ class TestAgainstScipy(TestCase):
def test_variance_stddev(self):
for pytorch_dist, scipy_dist in self.distribution_pairs:
if isinstance(pytorch_dist, (Cauchy, HalfCauchy)):
if isinstance(pytorch_dist, (Cauchy, HalfCauchy, VonMises)):
# Cauchy, HalfCauchy distributions' standard deviation is nan, skipping check
# VonMises variance is circular and scipy doesn't produce a correct result
continue
elif isinstance(pytorch_dist, (Multinomial, OneHotCategorical)):
self.assertEqual(pytorch_dist.variance, np.diag(scipy_dist.cov()), message=pytorch_dist)
@ -4233,9 +4288,9 @@ class TestTransforms(TestCase):
class TestFunctors(TestCase):
def test_cat_transform(self):
x1 = -1 * torch.range(1, 100).view(-1, 100)
x2 = (torch.range(1, 100).view(-1, 100) - 1) / 100
x3 = torch.range(1, 100).view(-1, 100)
x1 = -1 * torch.arange(1, 101, dtype=torch.float).view(-1, 100)
x2 = (torch.arange(1, 101, dtype=torch.float).view(-1, 100) - 1) / 100
x3 = torch.arange(1, 101, dtype=torch.float).view(-1, 100)
t1, t2, t3 = ExpTransform(), AffineTransform(1, 100), identity_transform
dim = 0
x = torch.cat([x1, x2, x3], dim=dim)
@ -4248,9 +4303,9 @@ class TestFunctors(TestCase):
actual = t(x)
expected = torch.cat([t1(x1), t2(x2), t3(x3)], dim=dim)
self.assertEqual(expected, actual)
y1 = torch.range(1, 100).view(-1, 100)
y2 = torch.range(1, 100).view(-1, 100)
y3 = torch.range(1, 100).view(-1, 100)
y1 = torch.arange(1, 101, dtype=torch.float).view(-1, 100)
y2 = torch.arange(1, 101, dtype=torch.float).view(-1, 100)
y3 = torch.arange(1, 101, dtype=torch.float).view(-1, 100)
y = torch.cat([y1, y2, y3], dim=dim)
actual_cod_check = t.codomain.check(y)
expected_cod_check = torch.cat([t1.codomain.check(y1),
@ -4267,9 +4322,9 @@ class TestFunctors(TestCase):
self.assertEqual(actual_jac, expected_jac)
def test_cat_transform_non_uniform(self):
x1 = -1 * torch.range(1, 100).view(-1, 100)
x2 = torch.cat([(torch.range(1, 100).view(-1, 100) - 1) / 100,
torch.range(1, 100).view(-1, 100)])
x1 = -1 * torch.arange(1, 101, dtype=torch.float).view(-1, 100)
x2 = torch.cat([(torch.arange(1, 101, dtype=torch.float).view(-1, 100) - 1) / 100,
torch.arange(1, 101, dtype=torch.float).view(-1, 100)])
t1 = ExpTransform()
t2 = CatTransform([AffineTransform(1, 100), identity_transform], dim=0)
dim = 0
@ -4282,9 +4337,9 @@ class TestFunctors(TestCase):
actual = t(x)
expected = torch.cat([t1(x1), t2(x2)], dim=dim)
self.assertEqual(expected, actual)
y1 = torch.range(1, 100).view(-1, 100)
y2 = torch.cat([torch.range(1, 100).view(-1, 100),
torch.range(1, 100).view(-1, 100)])
y1 = torch.arange(1, 101, dtype=torch.float).view(-1, 100)
y2 = torch.cat([torch.arange(1, 101, dtype=torch.float).view(-1, 100),
torch.arange(1, 101, dtype=torch.float).view(-1, 100)])
y = torch.cat([y1, y2], dim=dim)
actual_cod_check = t.codomain.check(y)
expected_cod_check = torch.cat([t1.codomain.check(y1),
@ -4299,9 +4354,9 @@ class TestFunctors(TestCase):
self.assertEqual(actual_jac, expected_jac)
def test_stack_transform(self):
x1 = -1 * torch.range(1, 100)
x2 = (torch.range(1, 100) - 1) / 100
x3 = torch.range(1, 100)
x1 = -1 * torch.arange(1, 101, dtype=torch.float)
x2 = (torch.arange(1, 101, dtype=torch.float) - 1) / 100
x3 = torch.arange(1, 101, dtype=torch.float)
t1, t2, t3 = ExpTransform(), AffineTransform(1, 100), identity_transform
dim = 0
x = torch.stack([x1, x2, x3], dim=dim)
@ -4314,9 +4369,9 @@ class TestFunctors(TestCase):
actual = t(x)
expected = torch.stack([t1(x1), t2(x2), t3(x3)], dim=dim)
self.assertEqual(expected, actual)
y1 = torch.range(1, 100)
y2 = torch.range(1, 100)
y3 = torch.range(1, 100)
y1 = torch.arange(1, 101, dtype=torch.float)
y2 = torch.arange(1, 101, dtype=torch.float)
y3 = torch.arange(1, 101, dtype=torch.float)
y = torch.stack([y1, y2, y3], dim=dim)
actual_cod_check = t.codomain.check(y)
expected_cod_check = torch.stack([t1.codomain.check(y1),
@ -4509,6 +4564,7 @@ class TestJit(TestCase):
xfail = [
Cauchy, # aten::cauchy(Double(2,1), float, float, Generator)
HalfCauchy, # aten::cauchy(Double(2, 1), float, float, Generator)
VonMises # Variance is not Euclidean
]
if Dist in xfail:
continue

2
torch/distributions/__init__.py
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@ -108,6 +108,7 @@ from .studentT import StudentT
from .transformed_distribution import TransformedDistribution
from .transforms import *
from .uniform import Uniform
from .von_mises import VonMises
from .weibull import Weibull
__all__ = [
@ -144,6 +145,7 @@ __all__ = [
'StudentT',
'Poisson',
'Uniform',
'VonMises',
'Weibull',
'TransformedDistribution',
'biject_to',

140
torch/distributions/von_mises.py Normal file
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@ -0,0 +1,140 @@
from __future__ import absolute_import, division, print_function
import math
import torch
import torch.jit
from torch.distributions import constraints
from torch.distributions.distribution import Distribution
from torch.distributions.utils import broadcast_all, lazy_property
def _eval_poly(y, coef):
coef = list(coef)
result = coef.pop()
while coef:
result = coef.pop() + y * result
return result
_I0_COEF_SMALL = [1.0, 3.5156229, 3.0899424, 1.2067492, 0.2659732, 0.360768e-1, 0.45813e-2]
_I0_COEF_LARGE = [0.39894228, 0.1328592e-1, 0.225319e-2, -0.157565e-2, 0.916281e-2,
-0.2057706e-1, 0.2635537e-1, -0.1647633e-1, 0.392377e-2]
_I1_COEF_SMALL = [0.5, 0.87890594, 0.51498869, 0.15084934, 0.2658733e-1, 0.301532e-2, 0.32411e-3]
_I1_COEF_LARGE = [0.39894228, -0.3988024e-1, -0.362018e-2, 0.163801e-2, -0.1031555e-1,
0.2282967e-1, -0.2895312e-1, 0.1787654e-1, -0.420059e-2]
_COEF_SMALL = [_I0_COEF_SMALL, _I1_COEF_SMALL]
_COEF_LARGE = [_I0_COEF_LARGE, _I1_COEF_LARGE]
def _log_modified_bessel_fn(x, order=0):
"""
Returns ``log(I_order(x))`` for ``x > 0``,
where `order` is either 0 or 1.
"""
assert order == 0 or order == 1
# compute small solution
y = (x / 3.75)
y = y * y
small = _eval_poly(y, _COEF_SMALL[order])
if order == 1:
small = x.abs() * small
small = small.log()
# compute large solution
y = 3.75 / x
large = x - 0.5 * x.log() + _eval_poly(y, _COEF_LARGE[order]).log()
result = torch.where(x < 3.75, small, large)
return result
@torch.jit.script
def _rejection_sample(loc, concentration, proposal_r, x):
done = torch.zeros(x.shape, dtype=torch.bool, device=loc.device)
while not done.all():
u = torch.rand((3,) + x.shape, dtype=loc.dtype, device=loc.device)
u1, u2, u3 = u.unbind()
z = torch.cos(math.pi * u1)
f = (1 + proposal_r * z) / (proposal_r + z)
c = concentration * (proposal_r - f)
accept = ((c * (2 - c) - u2) > 0) | ((c / u2).log() + 1 - c >= 0)
if accept.any():
x = torch.where(accept, (u3 - 0.5).sign() * f.acos(), x)
done = done | accept
return (x + math.pi + loc) % (2 * math.pi) - math.pi
class VonMises(Distribution):
"""
A circular von Mises distribution.
This implementation uses polar coordinates. The ``loc`` and ``value`` args
can be any real number (to facilitate unconstrained optimization), but are
interpreted as angles modulo 2 pi.
Example::
>>> m = dist.VonMises(torch.tensor([1.0]), torch.tensor([1.0]))
>>> m.sample() # von Mises distributed with loc=1 and concentration=1
tensor([1.9777])
:param torch.Tensor loc: an angle in radians.
:param torch.Tensor concentration: concentration parameter
"""
arg_constraints = {'loc': constraints.real, 'concentration': constraints.positive}
support = constraints.real
has_rsample = False
def __init__(self, loc, concentration, validate_args=None):
self.loc, self.concentration = broadcast_all(loc, concentration)
batch_shape = self.loc.shape
event_shape = torch.Size()
# Parameters for sampling
tau = 1 + (1 + 4 * self.concentration ** 2).sqrt()
rho = (tau - (2 * tau).sqrt()) / (2 * self.concentration)
self._proposal_r = (1 + rho ** 2) / (2 * rho)
super(VonMises, self).__init__(batch_shape, event_shape, validate_args)
def log_prob(self, value):
log_prob = self.concentration * torch.cos(value - self.loc)
log_prob = log_prob - math.log(2 * math.pi) - _log_modified_bessel_fn(self.concentration, order=0)
return log_prob
@torch.no_grad()
def sample(self, sample_shape=torch.Size()):
"""
The sampling algorithm for the von Mises distribution is based on the following paper:
Best, D. J., and Nicholas I. Fisher.
"Efficient simulation of the von Mises distribution." Applied Statistics (1979): 152-157.
"""
shape = self._extended_shape(sample_shape)
x = torch.empty(shape, dtype=self.loc.dtype, device=self.loc.device)
return _rejection_sample(self.loc, self.concentration, self._proposal_r, x)
def expand(self, batch_shape):
try:
return super(VonMises, self).expand(batch_shape)
except NotImplementedError:
validate_args = self.__dict__.get('_validate_args')
loc = self.loc.expand(batch_shape)
concentration = self.concentration.expand(batch_shape)
return type(self)(loc, concentration, validate_args=validate_args)
@property
def mean(self):
"""
The provided mean is the circular one.
"""
return self.loc
@lazy_property
def variance(self):
"""
The provided variance is the circular one.
"""
return 1 - (_log_modified_bessel_fn(self.concentration, order=1) -
_log_modified_bessel_fn(self.concentration, order=0)).exp()