def _abstract_mul(self, equation: str, shares: dict, other_shares, **kwargs):
"""Abstractly Multiplies two tensors
Args:
equation: a string reprsentation of the equation to be computed in einstein
summation form
shares: a dictionary <location_id -> PointerTensor) of shares corresponding to
self. Equivalent to calling self.child.
other_shares: a dictionary <location_id -> PointerTensor) of shares corresponding
to the tensor being multiplied by self.
"""
# check to see that operation is either mul or matmul
assert equation == "mul" or equation == "matmul"
cmd = getattr(torch, equation)
# if someone passes in a constant... (i.e., x + 3)
# TODO: Handle public mul more efficiently
if not isinstance(other_shares, dict):
other_shares = torch.Tensor([other_shares]).share(*self.child.keys()).child
assert len(shares) == len(other_shares)
if self.crypto_provider is None:
raise AttributeError("For multiplication a crytoprovider must be passed.")
shares = spdz_mul(cmd, shares, other_shares, self.crypto_provider, self.field)
return AdditiveSharingTensor(
field=self.field, crypto_provider=self.crypto_provider
).set_shares(shares)
def _private_mul(self, other, equation: str):
"""Abstractly Multiplies two tensors
Args:
self: an AdditiveSharingTensor
other: another AdditiveSharingTensor
equation: a string representation of the equation to be computed in einstein
summation form
"""
# check to see that operation is either mul or matmul
assert equation == "mul" or equation == "matmul"
cmd = getattr(torch, equation)
assert isinstance(other, AdditiveSharingTensor)
assert len(self.child) == len(other.child)
if self.crypto_provider is None:
raise AttributeError(
"For multiplication a crypto_provider must be passed.")
shares = spdz.spdz_mul(cmd, self, other, self.crypto_provider,
self.field)
return shares