Source code for torch.distributions.constraints
r"""
The following constraints are implemented:
- ``constraints.boolean``
- ``constraints.cat``
- ``constraints.corr_cholesky``
- ``constraints.dependent``
- ``constraints.greater_than(lower_bound)``
- ``constraints.greater_than_eq(lower_bound)``
- ``constraints.independent(constraint, reinterpreted_batch_ndims)``
- ``constraints.integer_interval(lower_bound, upper_bound)``
- ``constraints.interval(lower_bound, upper_bound)``
- ``constraints.less_than(upper_bound)``
- ``constraints.lower_cholesky``
- ``constraints.lower_triangular``
- ``constraints.multinomial``
- ``constraints.nonnegative_integer``
- ``constraints.one_hot``
- ``constraints.positive_integer``
- ``constraints.positive``
- ``constraints.positive_semidefinite``
- ``constraints.positive_definite``
- ``constraints.real_vector``
- ``constraints.real``
- ``constraints.simplex``
- ``constraints.symmetric``
- ``constraints.stack``
- ``constraints.square``
- ``constraints.symmetric``
- ``constraints.unit_interval``
"""
import torch
__all__ = [
'Constraint',
'boolean',
'cat',
'corr_cholesky',
'dependent',
'dependent_property',
'greater_than',
'greater_than_eq',
'independent',
'integer_interval',
'interval',
'half_open_interval',
'is_dependent',
'less_than',
'lower_cholesky',
'lower_triangular',
'multinomial',
'nonnegative_integer',
'positive',
'positive_semidefinite',
'positive_definite',
'positive_integer',
'real',
'real_vector',
'simplex',
'square',
'stack',
'symmetric',
'unit_interval',
]
[docs]class Constraint(object):
"""
Abstract base class for constraints.
A constraint object represents a region over which a variable is valid,
e.g. within which a variable can be optimized.
Attributes:
is_discrete (bool): Whether constrained space is discrete.
Defaults to False.
event_dim (int): Number of rightmost dimensions that together define
an event. The :meth:`check` method will remove this many dimensions
when computing validity.
"""
is_discrete = False # Default to continuous.
event_dim = 0 # Default to univariate.
[docs] def check(self, value):
"""
Returns a byte tensor of ``sample_shape + batch_shape`` indicating
whether each event in value satisfies this constraint.
"""
raise NotImplementedError
def __repr__(self):
return self.__class__.__name__[1:] + '()'
class _Dependent(Constraint):
"""
Placeholder for variables whose support depends on other variables.
These variables obey no simple coordinate-wise constraints.
Args:
is_discrete (bool): Optional value of ``.is_discrete`` in case this
can be computed statically. If not provided, access to the
``.is_discrete`` attribute will raise a NotImplementedError.
event_dim (int): Optional value of ``.event_dim`` in case this
can be computed statically. If not provided, access to the
``.event_dim`` attribute will raise a NotImplementedError.
"""
def __init__(self, *, is_discrete=NotImplemented, event_dim=NotImplemented):
self._is_discrete = is_discrete
self._event_dim = event_dim
super().__init__()
@property
def is_discrete(self):
if self._is_discrete is NotImplemented:
raise NotImplementedError(".is_discrete cannot be determined statically")
return self._is_discrete
@property
def event_dim(self):
if self._event_dim is NotImplemented:
raise NotImplementedError(".event_dim cannot be determined statically")
return self._event_dim
def __call__(self, *, is_discrete=NotImplemented, event_dim=NotImplemented):
"""
Support for syntax to customize static attributes::
constraints.dependent(is_discrete=True, event_dim=1)
"""
if is_discrete is NotImplemented:
is_discrete = self._is_discrete
if event_dim is NotImplemented:
event_dim = self._event_dim
return _Dependent(is_discrete=is_discrete, event_dim=event_dim)
def check(self, x):
raise ValueError('Cannot determine validity of dependent constraint')
def is_dependent(constraint):
return isinstance(constraint, _Dependent)
class _DependentProperty(property, _Dependent):
"""
Decorator that extends @property to act like a `Dependent` constraint when
called on a class and act like a property when called on an object.
Example::
class Uniform(Distribution):
def __init__(self, low, high):
self.low = low
self.high = high
@constraints.dependent_property(is_discrete=False, event_dim=0)
def support(self):
return constraints.interval(self.low, self.high)
Args:
fn (Callable): The function to be decorated.
is_discrete (bool): Optional value of ``.is_discrete`` in case this
can be computed statically. If not provided, access to the
``.is_discrete`` attribute will raise a NotImplementedError.
event_dim (int): Optional value of ``.event_dim`` in case this
can be computed statically. If not provided, access to the
``.event_dim`` attribute will raise a NotImplementedError.
"""
def __init__(self, fn=None, *, is_discrete=NotImplemented, event_dim=NotImplemented):
super().__init__(fn)
self._is_discrete = is_discrete
self._event_dim = event_dim
def __call__(self, fn):
"""
Support for syntax to customize static attributes::
@constraints.dependent_property(is_discrete=True, event_dim=1)
def support(self):
...
"""
return _DependentProperty(fn, is_discrete=self._is_discrete, event_dim=self._event_dim)
class _IndependentConstraint(Constraint):
"""
Wraps a constraint by aggregating over ``reinterpreted_batch_ndims``-many
dims in :meth:`check`, so that an event is valid only if all its
independent entries are valid.
"""
def __init__(self, base_constraint, reinterpreted_batch_ndims):
assert isinstance(base_constraint, Constraint)
assert isinstance(reinterpreted_batch_ndims, int)
assert reinterpreted_batch_ndims >= 0
self.base_constraint = base_constraint
self.reinterpreted_batch_ndims = reinterpreted_batch_ndims
super().__init__()
@property
def is_discrete(self):
return self.base_constraint.is_discrete
@property
def event_dim(self):
return self.base_constraint.event_dim + self.reinterpreted_batch_ndims
def check(self, value):
result = self.base_constraint.check(value)
if result.dim() < self.reinterpreted_batch_ndims:
expected = self.base_constraint.event_dim + self.reinterpreted_batch_ndims
raise ValueError(f"Expected value.dim() >= {expected} but got {value.dim()}")
result = result.reshape(result.shape[:result.dim() - self.reinterpreted_batch_ndims] + (-1,))
result = result.all(-1)
return result
def __repr__(self):
return "{}({}, {})".format(self.__class__.__name__[1:], repr(self.base_constraint),
self.reinterpreted_batch_ndims)
class _Boolean(Constraint):
"""
Constrain to the two values `{0, 1}`.
"""
is_discrete = True
def check(self, value):
return (value == 0) | (value == 1)
class _OneHot(Constraint):
"""
Constrain to one-hot vectors.
"""
is_discrete = True
event_dim = 1
def check(self, value):
is_boolean = (value == 0) | (value == 1)
is_normalized = value.sum(-1).eq(1)
return is_boolean.all(-1) & is_normalized
class _IntegerInterval(Constraint):
"""
Constrain to an integer interval `[lower_bound, upper_bound]`.
"""
is_discrete = True
def __init__(self, lower_bound, upper_bound):
self.lower_bound = lower_bound
self.upper_bound = upper_bound
super().__init__()
def check(self, value):
return (value % 1 == 0) & (self.lower_bound <= value) & (value <= self.upper_bound)
def __repr__(self):
fmt_string = self.__class__.__name__[1:]
fmt_string += '(lower_bound={}, upper_bound={})'.format(self.lower_bound, self.upper_bound)
return fmt_string
class _IntegerLessThan(Constraint):
"""
Constrain to an integer interval `(-inf, upper_bound]`.
"""
is_discrete = True
def __init__(self, upper_bound):
self.upper_bound = upper_bound
super().__init__()
def check(self, value):
return (value % 1 == 0) & (value <= self.upper_bound)
def __repr__(self):
fmt_string = self.__class__.__name__[1:]
fmt_string += '(upper_bound={})'.format(self.upper_bound)
return fmt_string
class _IntegerGreaterThan(Constraint):
"""
Constrain to an integer interval `[lower_bound, inf)`.
"""
is_discrete = True
def __init__(self, lower_bound):
self.lower_bound = lower_bound
super().__init__()
def check(self, value):
return (value % 1 == 0) & (value >= self.lower_bound)
def __repr__(self):
fmt_string = self.__class__.__name__[1:]
fmt_string += '(lower_bound={})'.format(self.lower_bound)
return fmt_string
class _Real(Constraint):
"""
Trivially constrain to the extended real line `[-inf, inf]`.
"""
def check(self, value):
return value == value # False for NANs.
class _GreaterThan(Constraint):
"""
Constrain to a real half line `(lower_bound, inf]`.
"""
def __init__(self, lower_bound):
self.lower_bound = lower_bound
super().__init__()
def check(self, value):
return self.lower_bound < value
def __repr__(self):
fmt_string = self.__class__.__name__[1:]
fmt_string += '(lower_bound={})'.format(self.lower_bound)
return fmt_string
class _GreaterThanEq(Constraint):
"""
Constrain to a real half line `[lower_bound, inf)`.
"""
def __init__(self, lower_bound):
self.lower_bound = lower_bound
super().__init__()
def check(self, value):
return self.lower_bound <= value
def __repr__(self):
fmt_string = self.__class__.__name__[1:]
fmt_string += '(lower_bound={})'.format(self.lower_bound)
return fmt_string
class _LessThan(Constraint):
"""
Constrain to a real half line `[-inf, upper_bound)`.
"""
def __init__(self, upper_bound):
self.upper_bound = upper_bound
super().__init__()
def check(self, value):
return value < self.upper_bound
def __repr__(self):
fmt_string = self.__class__.__name__[1:]
fmt_string += '(upper_bound={})'.format(self.upper_bound)
return fmt_string
class _Interval(Constraint):
"""
Constrain to a real interval `[lower_bound, upper_bound]`.
"""
def __init__(self, lower_bound, upper_bound):
self.lower_bound = lower_bound
self.upper_bound = upper_bound
super().__init__()
def check(self, value):
return (self.lower_bound <= value) & (value <= self.upper_bound)
def __repr__(self):
fmt_string = self.__class__.__name__[1:]
fmt_string += '(lower_bound={}, upper_bound={})'.format(self.lower_bound, self.upper_bound)
return fmt_string
class _HalfOpenInterval(Constraint):
"""
Constrain to a real interval `[lower_bound, upper_bound)`.
"""
def __init__(self, lower_bound, upper_bound):
self.lower_bound = lower_bound
self.upper_bound = upper_bound
super().__init__()
def check(self, value):
return (self.lower_bound <= value) & (value < self.upper_bound)
def __repr__(self):
fmt_string = self.__class__.__name__[1:]
fmt_string += '(lower_bound={}, upper_bound={})'.format(self.lower_bound, self.upper_bound)
return fmt_string
class _Simplex(Constraint):
"""
Constrain to the unit simplex in the innermost (rightmost) dimension.
Specifically: `x >= 0` and `x.sum(-1) == 1`.
"""
event_dim = 1
def check(self, value):
return torch.all(value >= 0, dim=-1) & ((value.sum(-1) - 1).abs() < 1e-6)
class _Multinomial(Constraint):
"""
Constrain to nonnegative integer values summing to at most an upper bound.
Note due to limitations of the Multinomial distribution, this currently
checks the weaker condition ``value.sum(-1) <= upper_bound``. In the future
this may be strengthened to ``value.sum(-1) == upper_bound``.
"""
is_discrete = True
event_dim = 1
def __init__(self, upper_bound):
self.upper_bound = upper_bound
def check(self, x):
return (x >= 0).all(dim=-1) & (x.sum(dim=-1) <= self.upper_bound)
class _LowerTriangular(Constraint):
"""
Constrain to lower-triangular square matrices.
"""
event_dim = 2
def check(self, value):
value_tril = value.tril()
return (value_tril == value).view(value.shape[:-2] + (-1,)).min(-1)[0]
class _LowerCholesky(Constraint):
"""
Constrain to lower-triangular square matrices with positive diagonals.
"""
event_dim = 2
def check(self, value):
value_tril = value.tril()
lower_triangular = (value_tril == value).view(value.shape[:-2] + (-1,)).min(-1)[0]
positive_diagonal = (value.diagonal(dim1=-2, dim2=-1) > 0).min(-1)[0]
return lower_triangular & positive_diagonal
class _CorrCholesky(Constraint):
"""
Constrain to lower-triangular square matrices with positive diagonals and each
row vector being of unit length.
"""
event_dim = 2
def check(self, value):
tol = torch.finfo(value.dtype).eps * value.size(-1) * 10 # 10 is an adjustable fudge factor
row_norm = torch.linalg.norm(value.detach(), dim=-1)
unit_row_norm = (row_norm - 1.).abs().le(tol).all(dim=-1)
return _LowerCholesky().check(value) & unit_row_norm
class _Square(Constraint):
"""
Constrain to square matrices.
"""
event_dim = 2
def check(self, value):
return torch.full(
size=value.shape[:-2],
fill_value=(value.shape[-2] == value.shape[-1]),
dtype=torch.bool,
device=value.device
)
class _Symmetric(_Square):
"""
Constrain to Symmetric square matrices.
"""
def check(self, value):
square_check = super().check(value)
if not square_check.all():
return square_check
return torch.isclose(value, value.mT, atol=1e-6).all(-2).all(-1)
class _PositiveSemidefinite(_Symmetric):
"""
Constrain to positive-semidefinite matrices.
"""
def check(self, value):
sym_check = super().check(value)
if not sym_check.all():
return sym_check
return torch.linalg.eigvalsh(value).ge(0).all(-1)
class _PositiveDefinite(_Symmetric):
"""
Constrain to positive-definite matrices.
"""
def check(self, value):
sym_check = super().check(value)
if not sym_check.all():
return sym_check
return torch.linalg.cholesky_ex(value).info.eq(0)
class _Cat(Constraint):
"""
Constraint functor that applies a sequence of constraints
`cseq` at the submatrices at dimension `dim`,
each of size `lengths[dim]`, in a way compatible with :func:`torch.cat`.
"""
def __init__(self, cseq, dim=0, lengths=None):
assert all(isinstance(c, Constraint) for c in cseq)
self.cseq = list(cseq)
if lengths is None:
lengths = [1] * len(self.cseq)
self.lengths = list(lengths)
assert len(self.lengths) == len(self.cseq)
self.dim = dim
super().__init__()
@property
def is_discrete(self):
return any(c.is_discrete for c in self.cseq)
@property
def event_dim(self):
return max(c.event_dim for c in self.cseq)
def check(self, value):
assert -value.dim() <= self.dim < value.dim()
checks = []
start = 0
for constr, length in zip(self.cseq, self.lengths):
v = value.narrow(self.dim, start, length)
checks.append(constr.check(v))
start = start + length # avoid += for jit compat
return torch.cat(checks, self.dim)
class _Stack(Constraint):
"""
Constraint functor that applies a sequence of constraints
`cseq` at the submatrices at dimension `dim`,
in a way compatible with :func:`torch.stack`.
"""
def __init__(self, cseq, dim=0):
assert all(isinstance(c, Constraint) for c in cseq)
self.cseq = list(cseq)
self.dim = dim
super().__init__()
@property
def is_discrete(self):
return any(c.is_discrete for c in self.cseq)
@property
def event_dim(self):
dim = max(c.event_dim for c in self.cseq)
if self.dim + dim < 0:
dim += 1
return dim
def check(self, value):
assert -value.dim() <= self.dim < value.dim()
vs = [value.select(self.dim, i) for i in range(value.size(self.dim))]
return torch.stack([constr.check(v)
for v, constr in zip(vs, self.cseq)], self.dim)
# Public interface.
dependent = _Dependent()
dependent_property = _DependentProperty
independent = _IndependentConstraint
boolean = _Boolean()
one_hot = _OneHot()
nonnegative_integer = _IntegerGreaterThan(0)
positive_integer = _IntegerGreaterThan(1)
integer_interval = _IntegerInterval
real = _Real()
real_vector = independent(real, 1)
positive = _GreaterThan(0.)
nonnegative = _GreaterThanEq(0.)
greater_than = _GreaterThan
greater_than_eq = _GreaterThanEq
less_than = _LessThan
multinomial = _Multinomial
unit_interval = _Interval(0., 1.)
interval = _Interval
half_open_interval = _HalfOpenInterval
simplex = _Simplex()
lower_triangular = _LowerTriangular()
lower_cholesky = _LowerCholesky()
corr_cholesky = _CorrCholesky()
square = _Square()
symmetric = _Symmetric()
positive_semidefinite = _PositiveSemidefinite()
positive_definite = _PositiveDefinite()
cat = _Cat
stack = _Stack
