Shortcuts

Source code for torch.distributions.constraints

# mypy: allow-untyped-defs
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``
- ``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",
    "nonnegative_integer",
    "one_hot",
    "positive",
    "positive_semidefinite",
    "positive_definite",
    "positive_integer",
    "real",
    "real_vector",
    "simplex",
    "square",
    "stack",
    "symmetric",
    "unit_interval",
]


[docs]class Constraint: """ 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")
[docs]def is_dependent(constraint): """ Checks if ``constraint`` is a ``_Dependent`` object. Args: constraint : A ``Constraint`` object. Returns: ``bool``: True if ``constraint`` can be refined to the type ``_Dependent``, False otherwise. Examples: >>> import torch >>> from torch.distributions import Bernoulli >>> from torch.distributions.constraints import is_dependent >>> dist = Bernoulli(probs = torch.tensor([0.6], requires_grad=True)) >>> constraint1 = dist.arg_constraints["probs"] >>> constraint2 = dist.arg_constraints["logits"] >>> for constraint in [constraint1, constraint2]: >>> if is_dependent(constraint): >>> continue """ 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): # type: ignore[override] """ 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 f"{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 += ( f"(lower_bound={self.lower_bound}, upper_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 += f"(upper_bound={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 += f"(lower_bound={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 += f"(lower_bound={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 += f"(lower_bound={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 += f"(upper_bound={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 += ( f"(lower_bound={self.lower_bound}, upper_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 += ( f"(lower_bound={self.lower_bound}, upper_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 - 1.0).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(0.0) nonnegative = _GreaterThanEq(0.0) greater_than = _GreaterThan greater_than_eq = _GreaterThanEq less_than = _LessThan multinomial = _Multinomial unit_interval = _Interval(0.0, 1.0) 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

Docs

Access comprehensive developer documentation for PyTorch

View Docs

Tutorials

Get in-depth tutorials for beginners and advanced developers

View Tutorials

Resources

Find development resources and get your questions answered

View Resources