def __repr__(self):
"""Returns a representation of the tensor useful for debugging."""
from crypten.debug import debug_mode
share = self.share
plain_text = self._tensor.get_plain_text() if debug_mode(
) else "HIDDEN"
ptype = self.ptype
return (f"MPCTensor(\n\t_tensor={share}\n"
f"\tplain_text={plain_text}\n\tptype={ptype}\n)")
def div_(self, y):
"""Divide two tensors element-wise"""
# TODO: Add test coverage for this code path (next 4 lines)
if isinstance(y, float) and int(y) == y:
y = int(y)
if is_float_tensor(y) and y.frac().eq(0).all():
y = y.long()
if isinstance(y, int) or is_int_tensor(y):
if debug_mode():
tolerance = 1.0
tensor = self.get_plain_text()
# Truncate protocol for dividing by public integers:
if comm.get().get_world_size() > 2:
wraps = self.wraps()
self.share //= y
# NOTE: The multiplication here must be split into two parts
# to avoid long out-of-bounds when y <= 2 since (2 ** 63) is
# larger than the largest long integer.
self -= wraps * 4 * (int(2 ** 62) // y)
else:
self.share //= y
if debug_mode():
if not torch.lt(
torch.abs(self.get_plain_text() * y - tensor), tolerance
).all():
raise ValueError("Final result of division is incorrect.")
return self
# Otherwise multiply by reciprocal
if isinstance(y, float):
y = torch.tensor([y], dtype=torch.float, device=self.device)
assert is_float_tensor(y), "Unsupported type for div_: %s" % type(y)
return self.mul_(y.reciprocal())
