Funsor is a tensor-like library for functions and distributions

Domains

class Domain

Bases: funsor.domains.Domain

An object representing the type and shape of a Funsor input or output.

num_elements

size

reals(*shape)

Construct a real domain of given shape.

bint(size)

Construct a bounded integer domain of scalar shape.

find_domain(op, *domains)

Finds the Domain resulting when applying op to domains. :param callable op: An operation. :param Domain *domains: One or more input domains.

Operations

Built-in operations

abs = ops.abs

add = ops.add

and_ = ops.and_

eq = ops.eq

exp = ops.exp

ge = ops.ge

getitem = ops.GetitemOp(0)

Op encoding an index into one dimension, e.g. x[:,:,y] for offset of 2.

gt = ops.gt

invert = ops.invert

le = ops.le

log = ops.log

log1p = ops.log1p

lt = ops.lt

matmul = ops.matmul

max = ops.max

min = ops.min

mul = ops.mul

ne = ops.ne

neg = ops.neg

or_ = ops.or_

pow = ops.pow

safediv = ops.safediv

safesub = ops.safesub

sigmoid = ops.sigmoid

sqrt = ops.sqrt

sub = ops.sub

truediv = ops.truediv

xor = ops.xor

Operation classes

class Op(fn)

Bases: multipledispatch.dispatcher.Dispatcher

class AssociativeOp(fn)

Bases: funsor.ops.Op

class AddOp(fn)

Bases: funsor.ops.AssociativeOp

class LogAddExpOp(fn)

Bases: funsor.ops.AssociativeOp

class SubOp(fn)

Bases: funsor.ops.Op

class NegOp(fn)

Bases: funsor.ops.Op

class ReshapeOp(shape)

Bases: funsor.ops.Op

class GetitemOp(offset)

Bases: funsor.ops.Op

Op encoding an index into one dimension, e.g. x[:,:,y] for offset of 2.

class ExpOp(fn)

Bases: funsor.ops.TransformOp

class LogOp(fn)

Bases: funsor.ops.TransformOp

class ReciprocalOp(fn)

Bases: funsor.ops.Op

Interpretations

Interpreter

set_interpretation(new)

interpretation(new)

reinterpret(x)

Overloaded reinterpretation of a deferred expression.

This handles a limited class of expressions, raising ValueError in unhandled cases.

Parameters:x (A funsor or data structure holding funsors.) – An input, typically involving deferred Funsor s. Returns:A reinterpreted version of the input. Raises:ValueError

dispatched_interpretation(fn)

Decorator to create a dispatched interpretation function.

exception PatternMissingError

Bases: NotImplementedError

Monte Carlo

monte_carlo(cls, *args)

A Monte Carlo interpretation of Integrate expressions. This falls back to eager in other cases.

monte_carlo_interpretation(**sample_inputs)

Context manager to set monte_carlo.sample_inputs and install the monte_carlo() interpretation.

Memoize

memoize(cache=None)

Exploit cons-hashing to do implicit common subexpression elimination

Funsors

Basic Funsors

reflect(cls, *args, **kwargs)

Construct a funsor, populate ._ast_values, and cons hash. This is the only interpretation allowed to construct funsors.

lazy(cls, *args)

Substitute eagerly but perform ops lazily.

eager(cls, *args)

Eagerly execute ops with known implementations.

eager_or_die(cls, *args)

Eagerly execute ops with known implementations. Disallows lazy Subs , Unary , Binary , and Reduce .

Raises:NotImplementedError no pattern is found.

sequential(cls, *args)

Eagerly execute ops with known implementations; additonally execute vectorized ops sequentially if no known vectorized implementation exists.

moment_matching(cls, *args)

A moment matching interpretation of Reduce expressions. This falls back to eager in other cases.

class Funsor(inputs, output, fresh=None, bound=None)

Bases: object

Abstract base class for immutable functional tensors.

Concrete derived classes must implement __init__() methods taking hashable *args and no optional **kwargs so as to support cons hashing.

Derived classes with .fresh variables must implement an eager_subs() method. Derived classes with .bound variables must implement an _alpha_convert() method.

Parameters:

• inputs (OrderedDict) – A mapping from input name to domain. This can be viewed as a typed context or a mapping from free variables to domains.
• output (Domain) – An output domain.

dtype

shape

quote()

pretty(maxlen=40)

item()

requires_grad

reduce(op, reduced_vars=None)

Reduce along all or a subset of inputs.

Parameters:

• op (callable) – A reduction operation.
• reduced_vars (str, Variable, or set or frozenset thereof.) – An optional input name or set of names to reduce. If unspecified, all inputs will be reduced.

sample(sampled_vars, sample_inputs=None)

Create a Monte Carlo approximation to this funsor by replacing functions of sampled_vars with Delta s.

The result is a Funsor with the same .inputs and .output as the original funsor (plus sample_inputs if provided), so that self can be replaced by the sample in expectation computations:

y = x.sample(sampled_vars)
assert y.inputs == x.inputs
assert y.output == x.output
exact = (x.exp() * integrand).reduce(ops.add)
approx = (y.exp() * integrand).reduce(ops.add)

If sample_inputs is provided, this creates a batch of samples scaled samples.

Parameters:

• sampled_vars (str, Variable, or set or frozenset thereof.) – A set of input variables to sample.
• sample_inputs (OrderedDict) – An optional mapping from variable name to Domain over which samples will be batched.

unscaled_sample(sampled_vars, sample_inputs)

Internal method to draw an unscaled sample. This should be overridden by subclasses.

align(names)

Align this funsor to match given names. This is mainly useful in preparation for extracting .data of a funsor.torch.Tensor.

Parameters:names (tuple) – A tuple of strings representing all names but in a new order. Returns:A permuted funsor equivalent to self. Return type:Funsor

eager_subs(subs)

Internal substitution function. This relies on the user-facing __call__() method to coerce non-Funsors to Funsors. Once all inputs are Funsors, eager_subs() implementations can recurse to call Subs.

eager_unary(op)

eager_reduce(op, reduced_vars)

sequential_reduce(op, reduced_vars)

moment_matching_reduce(op, reduced_vars)

abs()

sqrt()

exp()

log()

log1p()

sigmoid()

reshape(shape)

sum()

prod()

logsumexp()

all()

any()

min()

max()

to_data(x)

Extract a python object from a Funsor.

Raises a ValueError if free variables remain or if the funsor is lazy.

Parameters:x – An object, possibly a Funsor. Returns:A non-funsor equivalent to x. Raises:ValueError if any free variables remain. Raises:PatternMissingError if funsor is not fully evaluated.

class Variable(name, output)

Bases: funsor.terms.Funsor

Funsor representing a single free variable.

Parameters:

• name (str) – A variable name.
• output (funsor.domains.Domain) – A domain.

eager_subs(subs)

class Subs(arg, subs)

Bases: funsor.terms.Funsor

Lazy substitution of the form x(u=y, v=z).

Parameters:

• arg (Funsor) – A funsor being substituted into.
• subs (tuple) – A tuple of (name, value) pairs, where name is a string and value can be coerced to a Funsor via to_funsor().

unscaled_sample(sampled_vars, sample_inputs)

class Unary(op, arg)

Bases: funsor.terms.Funsor

Lazy unary operation.

Parameters:

• op (Op) – A unary operator.
• arg (Funsor) – An argument.

class Binary(op, lhs, rhs)

Bases: funsor.terms.Funsor

Lazy binary operation.

Parameters:

• op (Op) – A binary operator.
• lhs (Funsor) – A left hand side argument.
• rhs (Funsor) – A right hand side argument.

class Reduce(op, arg, reduced_vars)

Bases: funsor.terms.Funsor

Lazy reduction over multiple variables.

Parameters:

• op (Op) – A binary operator.
• arg (funsor) – An argument to be reduced.
• reduced_vars (frozenset) – A set of variable names over which to reduce.

class Number(data, dtype=None)

Bases: funsor.terms.Funsor

Funsor backed by a Python number.

Parameters:

• data (numbers.Number) – A python number.
• dtype – A nonnegative integer or the string “real”.

item()

eager_unary(op)

class Slice(name, start, stop, step, dtype)

Bases: funsor.terms.Funsor

Symbolic representation of a Python slice object.

Parameters:

• name (str) – A name for the new slice dimension.
• start (int) –
• stop (int) –
• step (int) – Three args following slice semantics.
• dtype (int) – An optional bounded integer type of this slice.

eager_subs(subs)

class Stack(name, parts)

Bases: funsor.terms.Funsor

Stack of funsors along a new input dimension.

Parameters:

• name (str) – The name of the new input variable along which to stack.
• parts (tuple) – A tuple of Funsors of homogenous output domain.

eager_subs(subs)

eager_reduce(op, reduced_vars)

class Cat(name, parts, part_name=None)

Bases: funsor.terms.Funsor

Concatenate funsors along an existing input dimension.

Parameters:

• name (str) – The name of the input variable along which to concatenate.
• parts (tuple) – A tuple of Funsors of homogenous output domain.

eager_subs(subs)

class Lambda(var, expr)

Bases: funsor.terms.Funsor

Lazy inverse to ops.getitem.

This is useful to simulate higher-order functions of integers by representing those functions as arrays.

Parameters:

• var (Variable) – A variable to bind.
• expr (funsor) – A funsor.

class Independent(fn, reals_var, bint_var, diag_var)

Bases: funsor.terms.Funsor

Creates an independent diagonal distribution.

This is equivalent to substitution followed by reduction:

f = ...  # a batched distribution
assert f.inputs['x_i'] == reals(4, 5)
assert f.inputs['i'] == bint(3)

g = Independent(f, 'x', 'i', 'x_i')
assert g.inputs['x'] == reals(3, 4, 5)
assert 'x_i' not in g.inputs
assert 'i' not in g.inputs

x = Variable('x', reals(3, 4, 5))
g == f(x_i=x['i']).reduce(ops.logaddexp, 'i')

Parameters:

• fn (Funsor) – A funsor.
• reals_var (str) – The name of a real-tensor input.
• bint_var (str) – The name of a new batch input of fn.
• diag_var – The name of a smaller-shape real input of fn.

unscaled_sample(sampled_vars, sample_inputs)

eager_subs(subs)

of_shape(*shape)

Decorator to construct a Funsor with one free Variable per function arg.

to_funsor(*args, **kwargs)

Multiply dispatched method: to_funsor

Convert to a Funsor .

Only Funsor s and scalars are accepted.

param x:An object. param funsor.domains.Domain output:  An optional output hint. return:A Funsor equivalent to x. rtype:Funsor raises:ValueError

Other signatures:

Funsor object, object object, Domain Funsor, Domain str, Domain Number Number, Domain Tensor Tensor, Domain ndarray ndarray, Domain

Delta

solve(expr, value)

Tries to solve for free inputs of an expr such that expr == value, and computes the log-abs-det-Jacobian of the resulting substitution.

Parameters:

• expr (Funsor) – An expression with a free variable.
• value (Funsor) – A target value.

Returns:

A tuple (name, point, log_abs_det_jacobian)

Return type:

tuple

Raises:

ValueError

class Delta(terms)

Bases: funsor.terms.Funsor

Normalized delta distribution binding multiple variables.

align(names)

eager_subs(subs)

eager_reduce(op, reduced_vars)

unscaled_sample(sampled_vars, sample_inputs)

PyTorch

ignore_jit_warnings()

align_tensor(new_inputs, x, expand=False)

Permute and add dims to a tensor to match desired new_inputs.

Parameters:

• new_inputs (OrderedDict) – A target set of inputs.
• x (funsor.terms.Funsor) – A Tensor or Number .
• expand (bool) – If False (default), set result size to 1 for any input of x not in new_inputs; if True expand to new_inputs size.

Returns:

a number or torch.Tensor that can be broadcast to other tensors with inputs new_inputs.

Return type:

int or float or torch.Tensor

align_tensors(*args, **kwargs)

Permute multiple tensors before applying a broadcasted op.

This is mainly useful for implementing eager funsor operations.

Parameters:

• *args (funsor.terms.Funsor) – Multiple Tensor s and Number s.
• expand (bool) – Whether to expand input tensors. Defaults to False.

Returns:

a pair (inputs, tensors) where tensors are all torch.Tensor s that can be broadcast together to a single data with given inputs.

Return type:

tuple

class Tensor(data, inputs=None, dtype='real')

Bases: funsor.terms.Funsor

Funsor backed by a PyTorch Tensor.

This follows the torch.distributions convention of arranging named “batch” dimensions on the left and remaining “event” dimensions on the right. The output shape is determined by all remaining dims. For example:

data = torch.zeros(5,4,3,2)
x = Tensor(data, OrderedDict([("i", bint(5)), ("j", bint(4))]))
assert x.output == reals(3, 2)

Operators like matmul and .sum() operate only on the output shape, and will not change the named inputs.

Parameters:

• data (torch.Tensor) – A PyTorch tensor.
• inputs (OrderedDict) – An optional mapping from input name (str) to datatype (Domain ). Defaults to empty.
• dtype (int or the string "real".) – optional output datatype. Defaults to “real”.

item()

clamp_finite()

requires_grad

align(names)

eager_subs(subs)

eager_unary(op)

eager_reduce(op, reduced_vars)

unscaled_sample(sampled_vars, sample_inputs)

arange(name, *args, **kwargs)

Helper to create a named torch.arange() funsor. In some cases this can be replaced by a symbolic Slice .

Parameters:

• name (str) – A variable name.
• start (int) –
• stop (int) –
• step (int) – Three args following slice semantics.
• dtype (int) – An optional bounded integer type of this slice.

Return type:

Tensor

materialize(x)

Attempt to convert a Funsor to a Number or Tensor by substituting arange() s into its free variables.

Parameters:x (Funsor) – A funsor. Return type:Funsor

class Function(fn, output, args)

Bases: funsor.terms.Funsor

Funsor wrapped by a PyTorch function.

Functions are assumed to support broadcasting and can be eagerly evaluated on funsors with free variables of int type (i.e. batch dimensions).

Function s are usually created via the function() decorator.

Parameters:

• fn (callable) – A PyTorch function to wrap.
• output (funsor.domains.Domain) – An output domain.
• args (Funsor) – Funsor arguments.

function(*signature)

Decorator to wrap a PyTorch function.

Example:

@funsor.torch.function(reals(3,4), reals(4,5), reals(3,5))
def matmul(x, y):
    return torch.matmul(x, y)

@funsor.torch.function(reals(10), reals(10, 10), reals())
def mvn_log_prob(loc, scale_tril, x):
    d = torch.distributions.MultivariateNormal(loc, scale_tril)
    return d.log_prob(x)

To support functions that output nested tuples of tensors, specify a nested tuple of output types, for example:

@funsor.torch.function(reals(8), (reals(), bint(8)))
def max_and_argmax(x):
    return torch.max(x, dim=-1)

Parameters:*signature – A sequence if input domains followed by a final output domain or nested tuple of output domains.

class Einsum(equation, operands)

Bases: funsor.terms.Funsor

Wrapper around torch.einsum() to operate on real-valued Funsors.

Note this operates only on the output tensor. To perform sum-product contractions on named dimensions, instead use + and Reduce.

Parameters:

• equation (str) – An torch.einsum() equation.
• operands (tuple) – A tuple of input funsors.

torch_tensordot(x, y, dims)

Wrapper around torch.tensordot() to operate on real-valued Funsors.

Note this operates only on the output tensor. To perform sum-product contractions on named dimensions, instead use + and Reduce.

Arguments should satisfy:

len(x.shape) >= dims
len(y.shape) >= dims
dims == 0 or x.shape[-dims:] == y.shape[:dims]

Parameters:

• x (Funsor) – A left hand argument.
• y (Funsor) – A y hand argument.
• dims (int) – The number of dimension of overlap of output shape.

Return type:

Funsor

NumPy

align_array(new_inputs, x)

Permute and expand an array to match desired new_inputs.

Parameters:

• new_inputs (OrderedDict) – A target set of inputs.
• x (funsor.terms.Funsor) – A Array s or or Number .

Returns:

a number or numpy.ndarray that can be broadcast to other array with inputs new_inputs.

Return type:

tuple

align_arrays(*args)

Permute multiple arrays before applying a broadcasted op.

This is mainly useful for implementing eager funsor operations.

Parameters:*args (funsor.terms.Funsor) – Multiple Array s and Number s. Returns:a pair (inputs, arrays) where arrayss are all numpy.ndarray s that can be broadcast together to a single data with given inputs. Return type:tuple

class ArrayMeta(name, bases, dct)

Bases: funsor.terms.FunsorMeta

Wrapper to fill in default args and convert between OrderedDict and tuple.

class Array(data, inputs=None, dtype='real')

Bases: funsor.terms.Funsor

Funsor backed by a NumPy Array.

This follows the torch.distributions convention of arranging named “batch” dimensions on the left and remaining “event” dimensions on the right. The output shape is determined by all remaining dims. For example:

data = np.zeros((5,4,3,2))
x = Array(data, OrderedDict([("i", bint(5)), ("j", bint(4))]))
assert x.output == reals(3, 2)

Operators like matmul and .sum() operate only on the output shape, and will not change the named inputs.

Parameters:

• data (np.ndarray) – A NumPy array.
• inputs (OrderedDict) – An optional mapping from input name (str) to datatype (Domain ). Defaults to empty.
• dtype (int or the string "real".) – optional output datatype. Defaults to “real”.

item()

align(names)

eager_subs(subs)

eager_binary_array_number(op, lhs, rhs)

eager_binary_number_array(op, lhs, rhs)

eager_binary_array_array(op, lhs, rhs)

arange(name, size)

Helper to create a named numpy.arange() funsor.

Parameters:

• name (str) – A variable name.
• size (int) – A size.

Return type:

Array

materialize(x)

Attempt to convert a Funsor to a Number or numpy.ndarray by substituting arange() s into its free variables.

Gaussian

class BlockVector(shape)

Bases: object

Jit-compatible helper to build blockwise vectors. Syntax is similar to torch.zeros()

x = BlockVector((100, 20))
x[..., 0:4] = x1
x[..., 6:10] = x2
x = x.as_tensor()
assert x.shape == (100, 20)

as_tensor()

class BlockMatrix(shape)

Bases: object

Jit-compatible helper to build blockwise matrices. Syntax is similar to torch.zeros()

x = BlockMatrix((100, 20, 20))
x[..., 0:4, 0:4] = x11
x[..., 0:4, 6:10] = x12
x[..., 6:10, 0:4] = x12.transpose(-1, -2)
x[..., 6:10, 6:10] = x22
x = x.as_tensor()
assert x.shape == (100, 20, 20)

as_tensor()

align_gaussian(new_inputs, old)

Align data of a Gaussian distribution to a new inputs shape.

class Gaussian(info_vec, precision, inputs)

Bases: funsor.terms.Funsor

Funsor representing a batched joint Gaussian distribution as a log-density function.

Mathematically, a Gaussian represents the density function:

f(x) = < x | info_vec > - 0.5 * < x | precision | x >
     = < x | info_vec - 0.5 * precision @ x >

Note that Gaussian s are not normalized, rather they are canonicalized to evaluate to zero log density at the origin: f(0) = 0. This canonical form is useful in combination with the information filter representation because it allows Gaussian s with incomplete information, i.e. zero eigenvalues in the precision matrix. These incomplete distributions arise when making low-dimensional observations on higher dimensional hidden state.

Parameters:

• info_vec (torch.Tensor) – An optional batched information vector, where info_vec = precision @ mean.
• precision (torch.Tensor) – A batched positive semidefinite precision matrix.
• inputs (OrderedDict) – Mapping from name to Domain .

log_normalizer

align(names)

eager_subs(subs)

eager_reduce(op, reduced_vars)

unscaled_sample(sampled_vars, sample_inputs)

Joint

moment_matching_contract_default(*args)

moment_matching_contract_joint(red_op, bin_op, reduced_vars, discrete, gaussian)

eager_reduce_exp(op, arg, reduced_vars)

eager_independent_joint(joint, reals_var, bint_var, diag_var)

Contraction

class Contraction(red_op, bin_op, reduced_vars, terms)

Bases: funsor.terms.Funsor

Declarative representation of a finitary sum-product operation.

After normalization via the normalize() interpretation contractions will canonically order their terms by type:

Delta, Number, Tensor, Gaussian

unscaled_sample(sampled_vars, sample_inputs)

align(names)

GaussianMixture

alias of funsor.cnf.Contraction

recursion_reinterpret_contraction(x)

eager_contraction_generic_to_tuple(red_op, bin_op, reduced_vars, *terms)

eager_contraction_generic_recursive(red_op, bin_op, reduced_vars, terms)

eager_contraction_to_reduce(red_op, bin_op, reduced_vars, term)

eager_contraction_to_binary(red_op, bin_op, reduced_vars, lhs, rhs)

eager_contraction_tensor(red_op, bin_op, reduced_vars, *terms)

eager_contraction_gaussian(red_op, bin_op, reduced_vars, x, y)

normalize_contraction_commutative_canonical_order(red_op, bin_op, reduced_vars, *terms)

normalize_contraction_commute_joint(red_op, bin_op, reduced_vars, other, mixture)

normalize_contraction_generic_args(red_op, bin_op, reduced_vars, *terms)

normalize_trivial(red_op, bin_op, reduced_vars, term)

normalize_contraction_generic_tuple(red_op, bin_op, reduced_vars, terms)

binary_to_contract(op, lhs, rhs)

reduce_funsor(op, arg, reduced_vars)

unary_neg_variable(op, arg)

do_fresh_subs(arg, subs)

distribute_subs_contraction(arg, subs)

normalize_fuse_subs(arg, subs)

binary_subtract(op, lhs, rhs)

binary_divide(op, lhs, rhs)

unary_log_exp(op, arg)

unary_contract(op, arg)

Integrate

class Integrate(log_measure, integrand, reduced_vars)

Bases: funsor.terms.Funsor

Funsor representing an integral wrt a log density funsor.

Parameters:

• log_measure (Funsor) – A log density funsor treated as a measure.
• integrand (Funsor) – An integrand funsor.
• reduced_vars (str, Variable, or set or frozenset thereof.) – An input name or set of names to reduce.

Optimizer

unfold(cls, *args)

unfold_contraction_generic_tuple(red_op, bin_op, reduced_vars, terms)

optimize(cls, *args)

eager_contract_base(red_op, bin_op, reduced_vars, *terms)

optimize_contract_finitary_funsor(red_op, bin_op, reduced_vars, terms)

apply_optimizer(x)

Adjoint Algorithms

class AdjointTape

Bases: object

adjoint(red_op, bin_op, root, targets)

adjoint_tensor(adj_redop, adj_binop, out_adj, data, inputs, dtype)

adjoint_binary(adj_redop, adj_binop, out_adj, op, lhs, rhs)

adjoint_reduce(adj_redop, adj_binop, out_adj, op, arg, reduced_vars)

adjoint_contract_unary(adj_redop, adj_binop, out_adj, sum_op, prod_op, reduced_vars, arg)

adjoint_contract_generic(adj_redop, adj_binop, out_adj, sum_op, prod_op, reduced_vars, terms)

adjoint_contract(adj_redop, adj_binop, out_adj, sum_op, prod_op, reduced_vars, lhs, rhs)

adjoint_cat(adj_redop, adj_binop, out_adj, name, parts, part_name)

adjoint_subs_tensor(adj_redop, adj_binop, out_adj, arg, subs)

adjoint_subs_gaussianmixture_gaussianmixture(adj_redop, adj_binop, out_adj, arg, subs)

adjoint_subs_gaussian_gaussian(adj_redop, adj_binop, out_adj, arg, subs)

adjoint_subs_gaussianmixture_discrete(adj_redop, adj_binop, out_adj, arg, subs)

Sum-Product Algorithms

partial_sum_product(sum_op, prod_op, factors, eliminate=frozenset(), plates=frozenset())

Performs partial sum-product contraction of a collection of factors.

Returns:a list of partially contracted Funsors. Return type:list

sum_product(sum_op, prod_op, factors, eliminate=frozenset(), plates=frozenset())

Performs sum-product contraction of a collection of factors.

Returns:a single contracted Funsor. Return type:Funsor

naive_sequential_sum_product(sum_op, prod_op, trans, time, step)

sequential_sum_product(sum_op, prod_op, trans, time, step)

For a funsor trans with dimensions time, prev and curr, computes a recursion equivalent to:

tail_time = 1 + arange("time", trans.inputs["time"].size - 1)
tail = sequential_sum_product(sum_op, prod_op,
                              trans(time=tail_time),
                              time, {"prev": "curr"})
return prod_op(trans(time=0)(curr="drop"), tail(prev="drop"))            .reduce(sum_op, "drop")

but does so efficiently in parallel in O(log(time)).

Parameters:

• sum_op (AssociativeOp) – A semiring sum operation.
• prod_op (AssociativeOp) – A semiring product operation.
• trans (Funsor) – A transition funsor.
• time (Variable) – The time input dimension.
• step (dict) – A dict mapping previous variables to current variables. This can contain multiple pairs of prev->curr variable names.

class MarkovProductMeta(name, bases, dct)

Bases: funsor.terms.FunsorMeta

Wrapper to convert step to a tuple and fill in default step_names.

class MarkovProduct(sum_op, prod_op, trans, time, step, step_names)

Bases: funsor.terms.Funsor

Lazy representation of sequential_sum_product() .

Parameters:

• sum_op (AssociativeOp) – A marginalization op.
• prod_op (AssociativeOp) – A Bayesian fusion op.
• trans (Funsor) – A sequence of transition factors, usually varying along the time input.
• time (str or Variable) – A time dimension.
• step (dict) – A str-to-str mapping of “previous” inputs of trans to “current” inputs of trans.
• step_names (dict) – Optional, for internal use by alpha conversion.

eager_subs(subs)

eager_markov_product(sum_op, prod_op, trans, time, step, step_names)

Affine Pattern Matching

is_affine(fn)

A sound but incomplete test to determine whether a funsor is affine with respect to all of its real inputs.

Parameters:fn (Funsor) – A funsor. Return type:bool

affine_inputs(fn)

Returns a [sound sub]set of real inputs of fn wrt which fn is known to be affine.

Parameters:fn (Funsor) – A funsor. Returns:A set of input names wrt which fn is affine. Return type:frozenset

extract_affine(fn)

Extracts an affine representation of a funsor, satisfying:

x = ...
const, coeffs = extract_affine(x)
y = sum(Einsum(eqn, (coeff, Variable(var, coeff.output)))
        for var, (coeff, eqn) in coeffs.items())
assert_close(y, x)
assert frozenset(coeffs) == affine_inputs(x)

The coeffs will have one key per input wrt which fn is known to be affine (via affine_inputs() ), and const and coeffs.values will all be constant wrt these inputs.

The affine approximation is computed by ev evaluating fn at zero and each basis vector. To improve performance, users may want to run under the memoize() interpretation.

Parameters:fn (Funsor) – A funsor that is affine wrt the (add,mul) semiring in some subset of its inputs. Returns:A pair (const, coeffs) where const is a funsor with no real inputs and coeffs is an OrderedDict mapping input name to a (coefficient, eqn) pair in einsum form. Return type:tuple

Testing Utiltites

xfail_if_not_implemented(msg='Not implemented')

class ActualExpected

Bases: funsor.testing.LazyComparison

Lazy string formatter for test assertions.

id_from_inputs(inputs)

assert_close(actual, expected, atol=1e-06, rtol=1e-06)

check_funsor(x, inputs, output, data=None)

Check dims and shape modulo reordering.

xfail_param(*args, **kwargs)

make_einsum_example(equation, fill=None, sizes=(2, 3))

assert_equiv(x, y)

Check that two funsors are equivalent up to permutation of inputs.

random_tensor(inputs, output=reals())

Creates a random funsor.torch.Tensor with given inputs and output.

random_array(inputs, output)

Creates a random funsor.numpy.Array with given inputs and output.

random_gaussian(inputs)

Creates a random funsor.gaussian.Gaussian with given inputs.

random_mvn(batch_shape, dim, diag=False)

Generate a random torch.distributions.MultivariateNormal with given shape.

make_plated_hmm_einsum(num_steps, num_obs_plates=1, num_hidden_plates=0)

make_chain_einsum(num_steps)

make_hmm_einsum(num_steps)

Pyro-Compatible Distributions

This interface provides a number of PyTorch-style distributions that use funsors internally to perform inference. These high-level objects are based on a wrapping class: FunsorDistribution which wraps a funsor in a PyTorch-distributions-compatible interface. FunsorDistribution objects can be used directly in Pyro models (using the standard Pyro backend).

FunsorDistribution Base Class

class FunsorDistribution(funsor_dist, batch_shape=torch.Size([]), event_shape=torch.Size([]), dtype='real', validate_args=None)

Bases: pyro.distributions.torch_distribution.TorchDistribution

Distribution wrapper around a Funsor for use in Pyro code. This is typically used as a base class for specific funsor inference algorithms wrapped in a distribution interface.

Parameters:

• funsor_dist (funsor.terms.Funsor) – A funsor with an input named “value” that is treated as a random variable. The distribution should be normalized over “value”.
• batch_shape (torch.Size) – The distribution’s batch shape. This must be in the same order as the input of the funsor_dist, but may contain extra dims of size 1.
• event_shape – The distribution’s event shape.

arg_constraints = {}

support

log_prob(value)

sample(sample_shape=torch.Size([]))

rsample(sample_shape=torch.Size([]))

expand(batch_shape, _instance=None)

Hidden Markov Models

class DiscreteHMM(initial_logits, transition_logits, observation_dist, validate_args=None)

Bases: funsor.pyro.distribution.FunsorDistribution

Hidden Markov Model with discrete latent state and arbitrary observation distribution. This uses [1] to parallelize over time, achieving O(log(time)) parallel complexity.

The event_shape of this distribution includes time on the left:

event_shape = (num_steps,) + observation_dist.event_shape

This distribution supports any combination of homogeneous/heterogeneous time dependency of transition_logits and observation_dist. However, because time is included in this distribution’s event_shape, the homogeneous+homogeneous case will have a broadcastable event_shape with num_steps = 1, allowing log_prob() to work with arbitrary length data:

# homogeneous + homogeneous case:
event_shape = (1,) + observation_dist.event_shape

This class should be interchangeable with pyro.distributions.hmm.DiscreteHMM .

References:

[1] Simo Sarkka, Angel F. Garcia-Fernandez (2019)

“Temporal Parallelization of Bayesian Filters and Smoothers” https://arxiv.org/pdf/1905.13002.pdf

Parameters:

• initial_logits (Tensor) – A logits tensor for an initial categorical distribution over latent states. Should have rightmost size state_dim and be broadcastable to batch_shape + (state_dim,).
• transition_logits (Tensor) – A logits tensor for transition conditional distributions between latent states. Should have rightmost shape (state_dim, state_dim) (old, new), and be broadcastable to batch_shape + (num_steps, state_dim, state_dim).
• observation_dist (Distribution) – A conditional distribution of observed data conditioned on latent state. The .batch_shape should have rightmost size state_dim and be broadcastable to batch_shape + (num_steps, state_dim). The .event_shape may be arbitrary.

has_rsample

log_prob(value)

expand(batch_shape, _instance=None)

class GaussianHMM(initial_dist, transition_matrix, transition_dist, observation_matrix, observation_dist, validate_args=None)

Bases: funsor.pyro.distribution.FunsorDistribution

Hidden Markov Model with Gaussians for initial, transition, and observation distributions. This adapts [1] to parallelize over time to achieve O(log(time)) parallel complexity, however it differs in that it tracks the log normalizer to ensure log_prob() is differentiable.

This corresponds to the generative model:

z = initial_distribution.sample()
x = []
for t in range(num_steps):
    z = z @ transition_matrix + transition_dist.sample()
    x.append(z @ observation_matrix + observation_dist.sample())

The event_shape of this distribution includes time on the left:

event_shape = (num_steps,) + observation_dist.event_shape

This distribution supports any combination of homogeneous/heterogeneous time dependency of transition_dist and observation_dist. However, because time is included in this distribution’s event_shape, the homogeneous+homogeneous case will have a broadcastable event_shape with num_steps = 1, allowing log_prob() to work with arbitrary length data:

event_shape = (1, obs_dim)  # homogeneous + homogeneous case

This class should be compatible with pyro.distributions.hmm.GaussianHMM , but additionally supports funsor adjoint algorithms.

References:

[1] Simo Sarkka, Angel F. Garcia-Fernandez (2019)

“Temporal Parallelization of Bayesian Filters and Smoothers” https://arxiv.org/pdf/1905.13002.pdf

Variables:

• hidden_dim (int) – The dimension of the hidden state.
• obs_dim (int) – The dimension of the observed state.

Parameters:

• initial_dist (MultivariateNormal) – A distribution over initial states. This should have batch_shape broadcastable to self.batch_shape. This should have event_shape (hidden_dim,).
• transition_matrix (Tensor) – A linear transformation of hidden state. This should have shape broadcastable to self.batch_shape + (num_steps, hidden_dim, hidden_dim) where the rightmost dims are ordered (old, new).
• transition_dist (MultivariateNormal) – A process noise distribution. This should have batch_shape broadcastable to self.batch_shape + (num_steps,). This should have event_shape (hidden_dim,).
• transition_matrix – A linear transformation from hidden to observed state. This should have shape broadcastable to self.batch_shape + (num_steps, hidden_dim, obs_dim).
• observation_dist (MultivariateNormal or Normal) – An observation noise distribution. This should have batch_shape broadcastable to self.batch_shape + (num_steps,). This should have event_shape (obs_dim,).

has_rsample = True

arg_constraints = {}

class GaussianMRF(initial_dist, transition_dist, observation_dist, validate_args=None)

Bases: funsor.pyro.distribution.FunsorDistribution

Temporal Markov Random Field with Gaussian factors for initial, transition, and observation distributions. This adapts [1] to parallelize over time to achieve O(log(time)) parallel complexity, however it differs in that it tracks the log normalizer to ensure log_prob() is differentiable.

The event_shape of this distribution includes time on the left:

event_shape = (num_steps,) + observation_dist.event_shape

This distribution supports any combination of homogeneous/heterogeneous time dependency of transition_dist and observation_dist. However, because time is included in this distribution’s event_shape, the homogeneous+homogeneous case will have a broadcastable event_shape with num_steps = 1, allowing log_prob() to work with arbitrary length data:

event_shape = (1, obs_dim)  # homogeneous + homogeneous case

This class should be compatible with pyro.distributions.hmm.GaussianMRF , but additionally supports funsor adjoint algorithms.

References:

[1] Simo Sarkka, Angel F. Garcia-Fernandez (2019)

“Temporal Parallelization of Bayesian Filters and Smoothers” https://arxiv.org/pdf/1905.13002.pdf

Variables:

• hidden_dim (int) – The dimension of the hidden state.
• obs_dim (int) – The dimension of the observed state.

Parameters:

• initial_dist (MultivariateNormal) – A distribution over initial states. This should have batch_shape broadcastable to self.batch_shape. This should have event_shape (hidden_dim,).
• transition_dist (MultivariateNormal) – A joint distribution factor over a pair of successive time steps. This should have batch_shape broadcastable to self.batch_shape + (num_steps,). This should have event_shape (hidden_dim + hidden_dim,) (old+new).
• observation_dist (MultivariateNormal) – A joint distribution factor over a hidden and an observed state. This should have batch_shape broadcastable to self.batch_shape + (num_steps,). This should have event_shape (hidden_dim + obs_dim,).

has_rsample = True

class SwitchingLinearHMM(initial_logits, initial_mvn, transition_logits, transition_matrix, transition_mvn, observation_matrix, observation_mvn, exact=False, validate_args=None)

Bases: funsor.pyro.distribution.FunsorDistribution

Switching Linear Dynamical System represented as a Hidden Markov Model.

This corresponds to the generative model:

z = Categorical(logits=initial_logits).sample()
y = initial_mvn[z].sample()
x = []
for t in range(num_steps):
    z = Categorical(logits=transition_logits[t, z]).sample()
    y = y @ transition_matrix[t, z] + transition_mvn[t, z].sample()
    x.append(y @ observation_matrix[t, z] + observation_mvn[t, z].sample())

Viewed as a dynamic Bayesian network:

z[t-1] ----> z[t] ---> z[t+1]         Discrete latent class
   |  \       |  \       |   \
   | y[t-1] ----> y[t] ----> y[t+1]   Gaussian latent state
   |   /      |   /      |   /
   V  /       V  /       V  /
x[t-1]       x[t]      x[t+1]         Gaussian observation

Let class be the latent class, state be the latent multivariate normal state, and value be the observed multivariate normal value.

Parameters:

• initial_logits (Tensor) – Represents p(class[0]).
• initial_mvn (MultivariateNormal) – Represents p(state[0] | class[0]).
• transition_logits (Tensor) – Represents p(class[t+1] | class[t]).
• transition_matrix (Tensor) –
• transition_mvn (MultivariateNormal) – Together with transition_matrix, this represents p(state[t], state[t+1] | class[t]).
• observation_matrix (Tensor) –
• observation_mvn (MultivariateNormal) – Together with observation_matrix, this represents p(value[t+1], state[t+1] | class[t+1]).
• exact (bool) – If True, perform exact inference at cost exponential in num_steps. If False, use a moment_matching() approximation and use parallel scan algorithm to reduce parallel complexity to logarithmic in num_steps. Defaults to False.

has_rsample = True

arg_constraints = {}

log_prob(value)

expand(batch_shape, _instance=None)

filter(value)

Compute posterior over final state given a sequence of observations.

Parameters:value (Tensor) – A sequence of observations. Returns:A posterior distribution over latent states at the final time step, represented as a pair (cat, mvn), where Categorical distribution over mixture components and mvn is a MultivariateNormal with rightmost batch dimension ranging over mixture components. This can then be used to initialize a sequential Pyro model for prediction. Return type:tuple

Conversion Utilities

This module follows a convention for converting between funsors and PyTorch distribution objects. This convention is compatible with NumPy/PyTorch-style broadcasting. Following PyTorch distributions (and Tensorflow distributions), we consider “event shapes” to be on the right and broadcast-compatible “batch shapes” to be on the left.

This module also aims to be forgiving in inputs and pedantic in outputs: methods accept either the superclass torch.distributions.Distribution objects or the subclass pyro.distributions.TorchDistribution objects. Methods return only the narrower subclass pyro.distributions.TorchDistribution objects.

tensor_to_funsor(tensor, event_inputs=(), event_output=0, dtype='real')

Convert a torch.Tensor to a funsor.torch.Tensor .

Note this should not touch data, but may trigger a torch.Tensor.reshape() op.

Parameters:

• tensor (torch.Tensor) – A PyTorch tensor.
• event_inputs (tuple) – A tuple of names for rightmost tensor dimensions. If tensor has these names, they will be converted to result.inputs.
• event_output (int) – The number of tensor dimensions assigned to result.output. These must be on the right of any event_input dimensions.

Returns:

A funsor.

Return type:

funsor.torch.Tensor

funsor_to_tensor(funsor_, ndims, event_inputs=())

Convert a funsor.torch.Tensor to a torch.Tensor .

Note this should not touch data, but may trigger a torch.Tensor.reshape() op.

Parameters:

• funsor (funsor.torch.Tensor) – A funsor.
• ndims (int) – The number of result dims, == result.dim().
• event_inputs (tuple) – Names assigned to rightmost dimensions.

Returns:

A PyTorch tensor.

Return type:

torch.Tensor

mvn_to_funsor(pyro_dist, event_dims=(), real_inputs={})

Convert a joint torch.distributions.MultivariateNormal distribution into a Funsor with multiple real inputs.

This should satisfy:

sum(d.num_elements for d in real_inputs.values())
  == pyro_dist.event_shape[0]

Parameters:

• pyro_dist (torch.distributions.MultivariateNormal) – A multivariate normal distribution over one or more variables of real or vector or tensor type.
• event_dims (tuple) – A tuple of names for rightmost dimensions. These will be assigned to result.inputs of type bint.
• real_inputs (OrderedDict) – A dict mapping real variable name to appropriately sized reals(). The sum of all .numel() of all real inputs should be equal to the pyro_dist dimension.

Returns:

A funsor with given real_inputs and possibly additional bint inputs.

Return type:

funsor.terms.Funsor

funsor_to_mvn(gaussian, ndims, event_inputs=())

Convert a Funsor to a pyro.distributions.MultivariateNormal , dropping the normalization constant.

Parameters:

• gaussian (funsor.gaussian.Gaussian or funsor.joint.Joint) – A Gaussian funsor.
• ndims (int) – The number of batch dimensions in the result.
• event_inputs (tuple) – A tuple of names to assign to rightmost dimensions.

Returns:

a multivariate normal distribution.

Return type:

pyro.distributions.MultivariateNormal

funsor_to_cat_and_mvn(funsor_, ndims, event_inputs)

Converts a labeled gaussian mixture model to a pair of distributions.

Parameters:

• funsor (funsor.joint.Joint) – A Gaussian mixture funsor.
• ndims (int) – The number of batch dimensions in the result.

Returns:

A pair (cat, mvn), where cat is a Categorical distribution over mixture components and mvn is a MultivariateNormal with rightmost batch dimension ranging over mixture components.

class AffineNormal(matrix, loc, scale, value_x, value_y)

Bases: funsor.terms.Funsor

Represents a conditional diagonal normal distribution over a random variable Y whose mean is an affine function of a random variable X. The likelihood of X is thus:

AffineNormal(matrix, loc, scale).condition(y).log_density(x)

which is equivalent to:

Normal(x @ matrix + loc, scale).to_event(1).log_prob(y)

Parameters:

• matrix (Funsor) – A transformation from X to Y. Should have rightmost shape (x_dim, y_dim).
• loc (Funsor) – A constant offset for Y’s mean. Should have output shape (y_dim,).
• scale (Funsor) – Standard deviation for Y. Should have output shape (y_dim,).
• value_x (Funsor) – A value X.
• value_y (Funsor) – A value Y.

matrix_and_mvn_to_funsor(matrix, mvn, event_dims=(), x_name='value_x', y_name='value_y')

Convert a noisy affine function to a Gaussian. The noisy affine function is defined as:

y = x @ matrix + mvn.sample()

The result is a non-normalized Gaussian funsor with two real inputs, x_name and y_name, corresponding to a conditional distribution of real vector y` given real vector ``x.

Parameters:

• matrix (torch.Tensor) – A matrix with rightmost shape (x_size, y_size).
• mvn (torch.distributions.MultivariateNormal or torch.distributions.Independent of torch.distributions.Normal) – A multivariate normal distribution with event_shape == (y_size,).
• event_dims (tuple) – A tuple of names for rightmost dimensions. These will be assigned to result.inputs of type bint.
• x_name (str) – The name of the x random variable.
• y_name (str) – The name of the y random variable.

Returns:

A funsor with given real_inputs and possibly additional bint inputs.

Return type:

funsor.terms.Funsor

dist_to_funsor(pyro_dist, event_inputs=())

Convert a PyTorch distribution to a Funsor.

This is currently implemented for only a subset of distribution types.

Parameters:torch.distribution.Distribution – A PyTorch distribution. Returns:A funsor. Return type:funsor.terms.Funsor

Distribution Funsors

This interface provides a number of standard normalized probability distributions implemented as funsors.

class Distribution(*args)

Bases: funsor.terms.Funsor

Funsor backed by a PyTorch distribution object.

Parameters:*args – Distribution-dependent parameters. These can be either funsors or objects that can be coerced to funsors via to_funsor() . See derived classes for details.

dist_class = 'defined by derived classes'

eager_reduce(op, reduced_vars)

classmethod eager_log_prob(**params)

class BernoulliLogits(logits, value=None)

Bases: funsor.distributions.Distribution

Wraps pyro.distributions.Bernoulli .

Parameters:

• logits (Funsor) – Log likelihood ratio of 1. This should equal log(p1 / p0).
• value (Funsor) – Optional observation in {0,1}.

dist_class

alias of pyro.distributions.torch.Bernoulli

Bernoulli(probs=None, logits=None, value='value')

Wraps pyro.distributions.Bernoulli .

This dispatches to either BernoulliProbs or BernoulliLogits to accept either probs or logits args.

Parameters:

• probs (Funsor) – Probability of 1.
• value (Funsor) – Optional observation in {0,1}.

class Beta(concentration1, concentration0, value=None)

Bases: funsor.distributions.Distribution

Wraps pyro.distributions.Beta .

Parameters:

• concentration1 (Funsor) – Positive concentration parameter.
• concentration0 (Funsor) – Positive concentration parameter.
• value (Funsor) – Optional observation in (0,1).

dist_class

alias of pyro.distributions.torch.Beta

class Binomial(total_count, probs, value=None)

Bases: funsor.distributions.Distribution

Wraps pyro.distributions.Binomial .

Parameters:

• total_count (Funsor) – Total number of trials.
• probs (Funsor) – Probability of each positive trial.
• value (Funsor) – Optional integer observation (encoded as “real”).

dist_class

alias of pyro.distributions.torch.Binomial

class Categorical(probs, value='value')

Bases: funsor.distributions.Distribution

Wraps pyro.distributions.Categorical .

Parameters:

• probs (Funsor) – Probability vector over outcomes.
• value (Funsor) – Optional bouded integer observation.

dist_class

alias of pyro.distributions.torch.Categorical

class Delta(v, log_density=0, value='value')

Bases: funsor.distributions.Distribution

Wraps pyro.distributions.Delta .

Parameters:

• v (Funsor) – The unique point of concentration.
• log_density (Funsor) – Optional density (used by transformed distributions).
• value (Funsor) – Optional observation of similar domain as v.

dist_class

alias of pyro.distributions.delta.Delta

class Dirichlet(concentration, value='value')

Bases: funsor.distributions.Distribution

Wraps pyro.distributions.Dirichlet .

Parameters:

• concentration (Funsor) – Positive concentration vector.
• value (Funsor) – Optional observation in the unit simplex.

dist_class

alias of pyro.distributions.torch.Dirichlet

class DirichletMultinomial(concentration, total_count, value='value')

Bases: funsor.distributions.Distribution

Wraps pyro.distributions.DirichletMultinomial .

Parameters:

• concentration (Funsor) – Positive concentration vector.
• total_count (Funsor) – Total number of trials.
• value (Funsor) – Optional observation in the unit simplex.

dist_class

alias of pyro.distributions.conjugate.DirichletMultinomial

LogNormal(loc, scale, value='value')

Wraps pyro.distributions.LogNormal .

Parameters:

• loc (Funsor) – Mean of the untransformed Normal distribution.
• scale (Funsor) – Standard deviation of the untransformed Normal distribution.
• value (Funsor) – Optional real observation.

class Multinomial(total_count, probs, value=None)

Bases: funsor.distributions.Distribution

Wraps pyro.distributions.Multinomial .

Parameters:

• probs (Funsor) – Probability vector over outcomes.
• total_count (Funsor) – Total number of trials.
• value (Funsor) – Optional value in the unit simplex.

dist_class

alias of pyro.distributions.torch.Multinomial

class Normal(loc, scale, value='value')

Bases: funsor.distributions.Distribution

Wraps pyro.distributions.Normal .

Parameters:

• loc (Funsor) – Mean.
• scale (Funsor) – Standard deviation.
• value (Funsor) – Optional real observation.

dist_class

alias of pyro.distributions.torch.Normal

class MultivariateNormal(loc, scale_tril, value='value')

Bases: funsor.distributions.Distribution

Wraps pyro.distributions.MultivariateNormal .

Parameters:

• loc (Funsor) – Mean vector.
• scale_tril (Funsor) – Lower Cholesky factor of the covariance matrix.
• value (Funsor) – Optional real vector observation.

dist_class

alias of pyro.distributions.torch.MultivariateNormal

class Poisson(rate, value=None)

Bases: funsor.distributions.Distribution

Wraps pyro.distributions.Poisson .

Parameters:

• rate (Funsor) – Mean parameter.
• value (Funsor) – Optional integer observation (coded as “real”).

dist_class

alias of pyro.distributions.torch.Poisson

class Gamma(concentration, rate, value=None)

Bases: funsor.distributions.Distribution

Wraps pyro.distributions.Gamma .

Parameters:

• concentration (Funsor) – Positive concentration parameter.
• rate (Funsor) – Positive rate parameter.
• value (Funsor) – Optional positive observation.

dist_class

alias of pyro.distributions.torch.Gamma

class VonMises(loc, concentration, value=None)

Bases: funsor.distributions.Distribution

Wraps pyro.distributions.VonMises .

Parameters:

• loc (Funsor) – A location angle.
• concentration (Funsor) – Positive concentration parameter.
• value (Funsor) – Optional angular observation.

dist_class

alias of pyro.distributions.torch.VonMises

Mini-Pyro Interface

This interface provides a backend for the Pyro probabilistic programming language. This interface is intended to be used indirectly by writing standard Pyro code and setting pyro_backend("funsor"). See examples/minipyro.py for example usage.

Mini Pyro

This file contains a minimal implementation of the Pyro Probabilistic Programming Language. The API (method signatures, etc.) match that of the full implementation as closely as possible. This file is independent of the rest of Pyro, with the exception of the pyro.distributions module.

An accompanying example that makes use of this implementation can be found at examples/minipyro.py.

class Distribution(funsor_dist, sample_inputs=None)

Bases: object

log_prob(value)

expand_inputs(name, size)

get_param_store()

class Messenger(fn=None)

Bases: object

process_message(msg)

postprocess_message(msg)

class trace(fn=None)

Bases: funsor.minipyro.Messenger

postprocess_message(msg)

get_trace(*args, **kwargs)

class replay(fn, guide_trace)

Bases: funsor.minipyro.Messenger

process_message(msg)

class block(fn=None, hide_fn=<function block.<lambda>>)

Bases: funsor.minipyro.Messenger

process_message(msg)

class seed(fn=None, rng_seed=None)

Bases: funsor.minipyro.Messenger

class CondIndepStackFrame(name, size, dim)

Bases: tuple

dim

Alias for field number 2

name

Alias for field number 0

size

Alias for field number 1

class PlateMessenger(fn, name, size, dim)

Bases: funsor.minipyro.Messenger

process_message(msg)

tensor_to_funsor(value, cond_indep_stack, output)

class log_joint(fn=None)

Bases: funsor.minipyro.Messenger

process_message(msg)

postprocess_message(msg)

apply_stack(msg)

sample(name, fn, obs=None, infer=None)

param(name, init_value=None, constraint=Real(), event_dim=None)

plate(name, size, dim)

class PyroOptim(optim_args)

Bases: object

class Adam(optim_args)

Bases: funsor.minipyro.PyroOptim

TorchOptimizer

alias of torch.optim.adam.Adam

class ClippedAdam(optim_args)

Bases: funsor.minipyro.PyroOptim

TorchOptimizer

alias of pyro.optim.clipped_adam.ClippedAdam

class SVI(model, guide, optim, loss)

Bases: object

step(*args, **kwargs)

Expectation(log_probs, costs, sum_vars, prod_vars)

elbo(model, guide, *args, **kwargs)

class ELBO(**kwargs)

Bases: object

class Trace_ELBO(**kwargs)

Bases: funsor.minipyro.ELBO

class TraceMeanField_ELBO(**kwargs)

Bases: funsor.minipyro.ELBO

class TraceEnum_ELBO(**kwargs)

Bases: funsor.minipyro.ELBO

class Jit(fn, **kwargs)

Bases: object

class Jit_ELBO(elbo, **kwargs)

Bases: funsor.minipyro.ELBO

JitTrace_ELBO(**kwargs)

JitTraceMeanField_ELBO(**kwargs)

JitTraceEnum_ELBO(**kwargs)

Einsum Interface

This interface implements tensor variable elimination among tensors. In particular it does not implement continuous variable elimination.

naive_contract_einsum(eqn, *terms, **kwargs)

Use for testing Contract against einsum

naive_einsum(eqn, *terms, **kwargs)

Implements standard variable elimination.

naive_plated_einsum(eqn, *terms, **kwargs)

Implements Tensor Variable Elimination (Algorithm 1 in [Obermeyer et al 2019])

[Obermeyer et al 2019] Obermeyer, F., Bingham, E., Jankowiak, M., Chiu, J.,

Pradhan, N., Rush, A., and Goodman, N. Tensor Variable Elimination for Plated Factor Graphs, 2019

einsum(eqn, *terms, **kwargs)

Top-level interface for optimized tensor variable elimination.

Parameters:

• equation (str) – An einsum equation.
• *terms (funsor.terms.Funsor) – One or more operands.
• plates (set) – Optional keyword argument denoting which funsor dimensions are plate dimensions. Among all input dimensions (from terms): dimensions in plates but not in outputs are product-reduced; dimensions in neither plates nor outputs are sum-reduced.

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