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We could have SymBool inputs for torch.compile, e.g. in the following situation: ``` def f(x:torch.Tensor): pred = x.size(0) == 3 torch.compile(f)(pred, x) make_fx(f, tracing_mode="symbolic")(x) ``` The idea of this PR (credit to @ezyang) is to support SymBool by re-using the infra we've already had for SymInt so that we don't need to replicate a lot of stuff. Pull Request resolved: https://github.com/pytorch/pytorch/pull/107850 Approved by: https://github.com/ezyang ghstack dependencies: #107662
104 lines
3.0 KiB
Python
104 lines
3.0 KiB
Python
"""
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This is a simple interpreter for Sympy expressions that dispatches to
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classes following the torch._inductor.virtualized calling convention.
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For directness, the interpreter takes the handler directly rather than
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consulting the TLS. It does not use most of the methods on the full
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handler; only those with corresponding Sympy expressions. To see an example
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of a full handler, see torch.utils._sympy.value_ranges.ValueRangeAnalysis.
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"""
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import functools
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from typing import Any, Dict, Union
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import sympy
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from sympy.logic.boolalg import Boolean as SympyBoolean, BooleanAtom
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import torch
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from .functions import CleanDiv, FloorDiv, Mod, ModularIndexing, Where
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# TODO: Dedupe this with SYMPY_INTERP
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@functools.lru_cache(None)
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def handlers():
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from torch.fx.experimental.symbolic_shapes import Pow, TrueDiv
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# TODO add CeilDiv (it doesn't appear in the index_expr)
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# TODO default to some decompositions if the interpreter doesn't have them
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# like decomposing ModularIndexing or implementing Le(a,b) as Ge(b, a)
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HANDLERS = {
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sympy.Or: "or_",
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sympy.And: "and_",
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sympy.Eq: "eq",
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sympy.Ne: "ne",
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sympy.Lt: "lt",
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sympy.Gt: "gt",
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sympy.Le: "le",
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sympy.Ge: "ge",
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sympy.Not: "not_",
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TrueDiv: "truediv",
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FloorDiv: "floordiv",
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CleanDiv: "div",
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Where: "where",
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sympy.Add: "add",
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sympy.Mul: "mul",
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Pow: "pow",
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sympy.Pow: "pow",
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Mod: "mod",
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sympy.Mod: "mod",
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sympy.Abs: "abs",
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sympy.log: "log",
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sympy.exp: "exp",
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sympy.floor: "floor",
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sympy.ceiling: "ceil",
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sympy.Min: "minimum",
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sympy.Max: "maximum",
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ModularIndexing: "modular_indexing",
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sympy.functions.elementary.piecewise.ExprCondPair: "expr_cond_pair",
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sympy.Piecewise: "piecewise",
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}
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return HANDLERS
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ASSOCIATIVE_OPS = {"minimum", "maximum", "mul", "add", "and_", "or_"}
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def sympy_interp(
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analysis, env: Dict[sympy.Symbol, Any], expr: Union[sympy.Expr, SympyBoolean]
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):
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# Handle base cases
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dtype = None
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if isinstance(expr, BooleanAtom):
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dtype = torch.bool
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elif isinstance(expr, sympy.Integer):
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dtype = torch.int64
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elif isinstance(expr, sympy.Number):
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dtype = torch.double
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if dtype is not None:
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return analysis.constant(expr, dtype)
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elif isinstance(expr, sympy.Symbol):
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return env[expr]
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# Special cases
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if isinstance(expr, sympy.Pow) and isinstance(
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expr.args[1], sympy.core.numbers.Half
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):
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return analysis.sqrt(sympy_interp(analysis, env, expr.args[0]))
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# Recursive case
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args = [sympy_interp(analysis, env, arg) for arg in expr.args] # type: ignore[arg-type]
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handler_name = handlers()[expr.func]
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handler = getattr(analysis, handler_name)
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if handler_name in ASSOCIATIVE_OPS:
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assert len(args) > 1
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acc = handler(args[0], args[1])
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for i in range(2, len(args)):
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acc = handler(acc, args[i])
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return acc
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else:
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return handler(*args)
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