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Functional analysis + tensors + symbolic algebra
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eb8680 Add additional einsum backend support (#198)
* Add torch_map backend to einsum tests

* add xfailing torch_map adjoint tests

* make einsum test generation deterministic and use pytest-xdist in Makefile

* fix makefile

* try to fix travis

* try again

* try again, pin xdist version
Latest commit 0309698 Aug 19, 2019

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Functional analysis + tensors + symbolic algebra.

This library is an experimental work in progress. Beware building on top of this unstable prototype.


See design doc.

The goal of this library is to generalize Pyro's delayed inference algorithms from discrete to continuous variables, and to create machinery to enable partially delayed sampling compatible with universality. To achieve this goal this library makes three orthogonal design choices:

  1. Functions are first class objects. Funsors generalize the tensor interface to also cover arbitrary functions of multiple variables ("inputs"), where variables may be integers, real numbers or themselves tensors. Function evaluation / substitution is the basic operation, generalizing tensor indexing. This allows probability distributions to be first-class Funsors and make use of existing tensor machinery, for example we can generalize tensor contraction to computing analytic integrals in conjugate probabilistic models.

  2. Support nonstandard interpretation. Funsors support user-defined interpretations, including, eager, lazy, mixed eager+lazy, memoized (like opt_einsum's sharing), and approximate interpretations like Monte Carlo approximations of integration operations (e.g. .sum() over a funsor dimension).

  3. Named dimensions. Substitution is the most basic operation of Funsors. To avoid the difficulties of broadcasting and advanced indexing in positionally-indexed tensor libraries, all Funsor dimensions are named. Indexing uses the .__call__() method and can be interpreted as substitution (with well-understood semantics). Funsors are viewed as algebraic expressions with one algebraic free variable per dimension. Each dimension is either covariant (an output) or contravariant (an input).

Using funsor we can easily implement Pyro-style delayed sampling, roughly:

trace_log_prob = 0.

def pyro_sample(name, dist, obs=None):
    assert isinstance(dist, Funsor)
    if obs is not None:
        value = obs
    elif lazy:
        # delayed sampling (like Pyro's parallel enumeration)
        value = funsor.Variable(name,
        value = dist.sample('value')[0]['value']

    # save log_prob in trace
    trace_log_prob += dist(value)

    return value

# ...later during inference...
log_prob = trace_log_prob.reduce(logaddexp)  # collapses delayed variables
loss = -funsor.eval(log_prob)                 # performs variable elimination

See examples/ for a more complete example.

Code organization

  • funsor.ops is a collection of basic ops: unary, binary, and reductions.
  • funsor.terms contains AST classes for symbolic algebra.
  • funsor.torch contains wrappers around PyTorch Tensors and functions.
  • funsor.distributions contains standard probability distributions.
  • funsor.interpreter implements different evaluation strategies.
  • funsor.minipyro a small Funsor-compatible implementation of Pyro.

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