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# mypy: allow-untyped-defs
import math
import operator
import sympy
import torch
from torch.utils._sympy.functions import (
_keep_float,
FloatPow,
FloatTrueDiv,
FloorDiv,
IntTrueDiv,
Mod,
OpaqueUnaryFn_exp,
OpaqueUnaryFn_log,
OpaqueUnaryFn_sqrt,
PowByNatural,
RoundDecimal,
RoundToInt,
ToFloat,
TruncToInt,
)
# The sympy interpretation of operators. It will also sometimes work with
# plain int/float, but if you do certain operations you will get out a
# sympy.Basic in the end. If you want the Python/FX traceable interpretation,
# check PythonReferenceAnalysis.
# NB: For magic methods this needs to use normal magic methods
# so that test_magic_methods works
class ReferenceAnalysis:
@staticmethod
def constant(c, dtype):
return sympy.sympify(c)
@staticmethod
def or_(a, b):
return a | b
@staticmethod
def and_(a, b):
return a & b
@staticmethod
def eq(a, b):
if isinstance(a, sympy.Expr) or isinstance(b, sympy.Expr):
return sympy.Eq(a, b)
return a == b
@classmethod
def ne(cls, a, b):
return cls.not_(cls.eq(a, b))
@staticmethod
def lt(a, b):
return a < b
@staticmethod
def gt(a, b):
return a > b
@staticmethod
def le(a, b):
return a <= b
@staticmethod
def ge(a, b):
return a >= b
@staticmethod
def not_(a):
assert not isinstance(a, bool)
return ~a
@staticmethod
def reciprocal(x):
return FloatTrueDiv(1.0, x)
@staticmethod
def square(x):
return PowByNatural(x, 2)
@staticmethod
def trunc_to_int(x, dtype):
return TruncToInt(x)
@staticmethod
def ceil_to_int(x, dtype):
return sympy.ceiling(x)
@staticmethod
def floor_to_int(x, dtype):
return sympy.floor(x)
@staticmethod
def floor(x):
return _keep_float(sympy.floor)(x)
@staticmethod
def ceil(x):
return _keep_float(sympy.ceiling)(x)
@staticmethod
def to_dtype(x, dtype):
if dtype == torch.float64:
return ToFloat(x)
raise NotImplementedError(f"to_dtype {dtype} NYI")
@staticmethod
def mod(x, y):
return Mod(x, y)
@staticmethod
def abs(x):
return abs(x)
@staticmethod
def neg(x):
return -x
@staticmethod
def truediv(a, b):
return FloatTrueDiv(a, b)
@staticmethod
def int_truediv(a, b):
return IntTrueDiv(a, b)
@staticmethod
def floordiv(a, b):
return FloorDiv(a, b)
@staticmethod
def truncdiv(a, b):
raise NotImplementedError("TODO: truncdiv")
@staticmethod
def add(a, b):
return _keep_float(operator.add)(a, b)
@staticmethod
def mul(a, b):
return _keep_float(operator.mul)(a, b)
@staticmethod
def sub(a, b):
return _keep_float(operator.sub)(a, b)
@staticmethod
def exp(x):
return OpaqueUnaryFn_exp(x)
@staticmethod
def log(x):
return OpaqueUnaryFn_log(x)
@staticmethod
def sqrt(x):
return OpaqueUnaryFn_sqrt(x)
@staticmethod
def pow(a, b):
return _keep_float(FloatPow)(a, b)
@staticmethod
def pow_by_natural(a, b):
return PowByNatural(a, b)
@staticmethod
def minimum(a, b):
return sympy.Min(a, b)
@staticmethod
def maximum(a, b):
return sympy.Max(a, b)
@staticmethod
def round_to_int(a, dtype):
return RoundToInt(a)
@staticmethod
def round_decimal(a, b):
return RoundDecimal(a, b)
# Unlike ReferenceAnalysis, does NOT sympyify, instead, works with plain
# Python types and is FX traceable. Inheritance here is purely for code
# sharing (TODO: considering splitting out a BaseReferenceAnalysis).
class PythonReferenceAnalysis(ReferenceAnalysis):
@staticmethod
def constant(c, dtype):
if dtype is torch.int64:
return int(c)
elif dtype is torch.double:
return float(c)
elif dtype is torch.bool:
return bool(c)
else:
raise AssertionError(f"unrecognized dtype {dtype}")
@staticmethod
def not_(a):
return torch.sym_not(a)
@staticmethod
def floordiv(a, b):
return a // b
@staticmethod
def mod(x, y):
return x % y
@staticmethod
def truncdiv(a, b):
return a / b
@staticmethod
def to_dtype(x, dtype):
if dtype == torch.float64:
return float(x)
raise NotImplementedError(f"to_dtype {dtype} NYI")
@staticmethod
def exp(x):
raise AssertionError("exp is not valid shape sympy expr")
@staticmethod
def log(x):
raise AssertionError("log is not valid shape sympy expr")
@staticmethod
def sqrt(x):
return torch._sym_sqrt(x) # type: ignore[attr-defined]
@staticmethod
def minimum(a, b):
return torch.sym_min(a, b)
@staticmethod
def maximum(a, b):
return torch.sym_max(a, b)
@staticmethod
def floor_to_int(x, dtype):
return math.floor(x)
@staticmethod
def ceil_to_int(x, dtype):
return math.ceil(x)
@staticmethod
def floor(x):
return float(math.floor(x))
@staticmethod
def ceil(x):
return float(math.ceil(x))
@staticmethod
def truediv(a, b):
return a / b
@staticmethod
def pow(a, b):
return a**b
@staticmethod
def pow_by_natural(a, b):
# Pray that safe_pow is not needed here lol. In particular, this
# never participates in VR low/high ranges, so overflow should be
# unlikely
return a**b
@staticmethod
def round_to_int(a, dtype):
return round(a)
@staticmethod
def round_decimal(a, b):
return round(a, ndigits=b)
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