pytorch/torch/fx/experimental/_constant_symnode.py
soulitzer 161a2340ee Switch to using Python nested int (#141166)
Doesn't seem to noticeably slow down eager - TestNestedTensorSubclass tests with and without the PR finished in similar amounts of time (around 57s, 58s)

Pull Request resolved: https://github.com/pytorch/pytorch/pull/141166
Approved by: https://github.com/ezyang
2024-12-02 19:17:30 +00:00

70 lines
1.5 KiB
Python

from typing import * # noqa: F403
# Python version of c10/core/ConstantSymNodeImpl.cpp
# This needs to exist because the Python version of nested int is not compatible
# with the C++ version of constant symnode.
class ConstantIntNode:
def __init__(self, val: int):
self.val = val
def is_constant(self) -> bool:
return True
def maybe_as_int(self) -> int:
return self.val
def is_int(self) -> bool:
return True
def is_float(self) -> bool:
return False
def is_bool(self) -> bool:
return False
def is_nested_int(self) -> bool:
return False
def clone(self) -> "ConstantIntNode":
return self
def _str(self) -> str:
return str(self.val)
def __str__(self) -> str:
return self._str()
def __repr__(self) -> str:
return self._str()
def _graph_repr(self) -> str:
return self._str()
def mul(self, other: Any) -> Any:
return other.mul(self)
def eq(self, other: Any) -> Any:
return other.eq(self)
def ne(self, other: Any) -> Any:
return other.ne(self)
def gt(self, other: Any) -> Any:
return other.lt(self)
def lt(self, other: Any) -> Any:
return other.gt(self)
def le(self, other: Any) -> Any:
return other.ge(self)
def ge(self, other: Any) -> Any:
return other.le(self)
def is_symbolic(self) -> bool:
return False
def constant_int(self) -> int:
return self.val