torch_map.py

# Copyright (c) 2017-2019 Uber Technologies, Inc.
# SPDX-License-Identifier: Apache-2.0

import operator
from functools import reduce

from pyro.ops import packed
from pyro.ops.einsum.adjoint import (
    Backward,
    einsum_backward_sample,
    transpose,
    unflatten,
)
from pyro.ops.einsum.util import Tensordot


class _EinsumBackward(Backward):
    def __init__(self, operands, argmax):
        self.operands = operands
        self.argmax = argmax

    def process(self, message):
        sample1 = self.argmax
        sample2 = message
        return einsum_backward_sample(self.operands, sample1, sample2)


def einsum(equation, *operands):
    """
    Forward-max-sum backward-argmax implementation of einsum.
    This assumes all operands have a ``._pyro_dims`` attribute set.
    """
    equation = packed.rename_equation(equation, *operands)
    inputs, output = equation.split("->")
    any_requires_backward = any(hasattr(x, "_pyro_backward") for x in operands)

    contract_dims = "".join(
        sorted(set().union(*(x._pyro_dims for x in operands)) - set(output))
    )
    dims = output + contract_dims
    result = reduce(operator.add, packed.broadcast_all(*operands, dims=dims))
    argmax = None  # work around lack of pytorch support for zero-sized tensors
    if contract_dims:
        output_shape = result.shape[: len(output)]
        contract_shape = result.shape[len(output) :]
        result, argmax = result.reshape(output_shape + (-1,)).max(-1)
        if any_requires_backward:
            argmax = unflatten(argmax, output, contract_dims, contract_shape)
    elif result is operands[0]:
        result = result[...]  # create a new object
    result._pyro_dims = output
    assert result.dim() == len(result._pyro_dims)

    if any_requires_backward:
        result._pyro_backward = _EinsumBackward(operands, argmax)
    return result


tensordot = Tensordot(einsum)

__all__ = ["transpose", "einsum", "tensordot"]