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https://github.com/pytorch/pytorch.git
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7c12cc7ce4
See #127836 for details. Pull Request resolved: https://github.com/pytorch/pytorch/pull/127843 Approved by: https://github.com/oulgen ghstack dependencies: #127842
1065 lines
40 KiB
Python
1065 lines
40 KiB
Python
# mypy: allow-untyped-defs
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import builtins
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import dataclasses
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import inspect
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import math
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import sys
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import weakref
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from collections import defaultdict
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from typing import Any, Callable, Dict, List, Optional, Set, Tuple, TYPE_CHECKING, Union
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import torch
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from torch.utils._pytree import (
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_get_node_type,
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BUILTIN_TYPES,
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SUPPORTED_NODES,
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tree_flatten,
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tree_map,
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)
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from .exported_program import ExportedProgram
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if TYPE_CHECKING:
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from sympy import Symbol
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from torch._guards import Source
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from ..fx.experimental.symbolic_shapes import ShapeEnv, StrictMinMaxConstraint
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__all__ = [
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"Constraint",
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"Dim",
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"dims",
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"dynamic_dim",
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"refine_dynamic_shapes_from_suggested_fixes",
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]
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class _Dim(type):
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"""
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Metaclass for :func:`Dim` types.
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"""
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@staticmethod
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def readable(name, min_, max_):
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if min_ == 2:
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min_ = None
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if max_ == sys.maxsize - 1:
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max_ = None
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if min_ is None and max_ is None:
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return f"Dim('{name}')"
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if min_ is None:
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return f"Dim('{name}', max={max_})"
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if max_ is None:
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return f"Dim('{name}', min={min_})"
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return f"Dim('{name}', min={min_}, max={max_})"
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def __add__(cls, other):
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# e.g., dim + 1
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if type(other) is not int:
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raise NotImplementedError(
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f"Attempted to add {other} to {cls.__name__}, where an integer was expected. "
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"(Only increasing linear operations with integer coefficients are supported.)"
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)
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return cls._derive(lambda x: x + other)
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def __radd__(cls, other):
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return cls + other
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def __sub__(cls, other):
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# e.g., dim - 1
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if type(other) is not int:
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raise NotImplementedError(
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f"Attempted to subtract {other} from {cls.__name__}, where an integer was expected. "
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"(Only increasing linear operations with integer coefficients are supported.)"
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)
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return cls._derive(lambda x: x - other)
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def __rsub__(cls, other):
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raise NotImplementedError(
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f"Attempted to negate {cls.__name__}. "
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"(Only increasing linear operations with integer coefficients are supported.)"
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)
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def __mul__(cls, other):
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# e.g., dim * 2
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if type(other) is not int or other <= 0:
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raise NotImplementedError(
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f"Attempted to multiply {other} with {cls.__name__}, where a positive integer was expected. "
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"(Only increasing linear operations with integer coefficients are supported.)"
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)
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return cls._derive(lambda x: x * other)
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def __rmul__(cls, other):
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return cls * other
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def _derived_name(cls, fn):
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from sympy import sympify
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return str(fn(sympify(cls.__name__)))
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def _derive(cls, fn):
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return _DerivedDim(cls._derived_name(fn), (int,), {"root": cls, "fn": fn})
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class _StaticDim(_Dim):
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"""
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Meta class for static :func:`Dim` types.
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This class is only for setting and checking static dim constraints,
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and the user should never interact with it.
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"""
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@property
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def min(self):
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return self.value # type: ignore[attr-defined]
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@property
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def max(self):
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return self.value # type: ignore[attr-defined]
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class _DerivedDim(_Dim):
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"""
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Metaclass for derived :func:`Dim` types.
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Currently we only support increasing linear expressions with integer coefficients.
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In other words, a derived Dim can always be written in the form Ax + B, where
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x is a regular Dim (i.e., non-derived Dim), A and B are integers, and A is positive.
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(In particular, the latter ensures that x < y => Ax + B < Ay + B.)
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These restrictions on the form of derived Dims makes the metatheory simpler: e.g.,
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it simplifies computing ranges for derived Dims, solving for underlying regular Dims,
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deciding equalities between derived Dims, and so on.
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The function lambda x: Ax + B is expressed by `fn`, where x is a normal Dim, `root`.
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The range of a derived Dim is computed by mapping `fn` over the range of its `root`.
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"""
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@property
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def min(self):
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# assume that self.fn is an increasing function
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# TODO(avik): use sympy value range analysis instead?
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from sympy import Integer
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_min_symint = self.fn(Integer(self.root.min)) # type: ignore[attr-defined]
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root = self.root # type: ignore[attr-defined]
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assert _min_symint >= 0, (
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f"Expected derived min value of {self.__name__} to be >= 0. "
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f"Please specify an appropriate min value for {root.__name__} "
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f"(currently {root.min})."
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)
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return int(_min_symint)
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@property
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def max(self):
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# assume that self.fn is an increasing function
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# TODO(avik): use sympy value range analysis instead?
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from sympy import Integer
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_max_symint = self.fn(Integer(self.root.max)) # type: ignore[attr-defined]
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root = self.root # type: ignore[attr-defined]
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assert _max_symint <= sys.maxsize - 1, (
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f"Expected derived max value of {self.__name__} to be <= {sys.maxsize - 1}. "
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f"Please specify an appropriate max value for {root.__name__} "
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f"(currently {root.max})."
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)
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return int(_max_symint)
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def _derive(self, fn):
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# We support nesting, e.g., 2*dim + 1.
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# This is implemented by composing operations on the same root.
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# As a consequence, roots are always regular Dims (i.e., not derived Dims).
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return _DerivedDim(
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self._derived_name(fn),
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(int,),
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{"root": self.root, "fn": lambda x: fn(self.fn(x))}, # type: ignore[attr-defined]
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)
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def Dim(name: str, *, min: Optional[int] = None, max: Optional[int] = None):
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"""
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:func:`Dim` constructs a type analogous to a named symbolic integer with a range.
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It can be used to describe multiple possible values of a dynamic tensor dimension.
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Note that different dynamic dimensions of the same tensor, or of different tensors,
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can be described by the same type.
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Args:
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name (str): Human-readable name for debugging.
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min (Optional[int]): Minimum possible value of given symbol (inclusive)
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max (Optional[int]): Maximum possible value of given symbol (inclusive)
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Returns:
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A type that can be used in dynamic shape specifications for tensors.
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"""
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_min = 0 if min is None else min
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_max = sys.maxsize - 1 if max is None else builtins.min(max, sys.maxsize - 1)
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assert _max > _min, f"Cannot create Dim with inconsistent min={min}, max={max}"
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dim = _Dim(name, (int,), {"min": _min, "max": _max})
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dim.__module__ = getattr(
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inspect.getmodule(inspect.stack()[1][0]), "__name__", "__main__"
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)
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return dim
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def dims(*names: str, min: Optional[int] = None, max: Optional[int] = None):
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"""
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Util to create multiple :func:`Dim` types.
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"""
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return tuple(Dim(name, min=min, max=max) for name in names)
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@dataclasses.dataclass
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class _ConstraintTarget:
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"""
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This represents input tensor dimensions. Don't create this
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class directly; instead, use :func:`dynamic_dim`.
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"""
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w_tensor: Any # weakref to torch.Tensor
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# TODO: We don't need t_id; we can get it off of w_tensor
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t_id: int
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dim: int
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class _ConstraintFactory(type):
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"""
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Metaclass that ensures a private constructor for :class:`_Constraint`
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"""
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def __call__(cls, *args, **kwargs):
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raise TypeError(
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f"{cls.__module__}.{cls.__qualname__} has no public constructor. "
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f"Please use torch.export.dynamic_dim() to create one"
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)
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def _create(
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cls, w_tensor, t_id, dim, constraint_range, shared=None, debug_name=None
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):
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return super().__call__(
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w_tensor, t_id, dim, constraint_range, shared, debug_name
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)
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def _create_constraint(
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w_tensor, t_id, dim, constraint_range, shared=None, debug_name=None
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):
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return _Constraint._create(
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w_tensor, t_id, dim, constraint_range, shared, debug_name
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)
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@dataclasses.dataclass
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class _Constraint(_ConstraintTarget, metaclass=_ConstraintFactory):
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"""
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.. warning::
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Do not construct :class:`_Constraint` directly, use :func:`dynamic_dim` instead.
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This represents constraints on input tensor dimensions, e.g., requiring
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them to be fully polymorphic or within some range.
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"""
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# NOTE(avik): In the future, this could be Union[StrictMinMaxConstraint, <other kinds>]
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constraint_range: "StrictMinMaxConstraint"
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# Represent that `constraint_range` is shared with another _ConstraintTarget, which
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# typically arises because of a specified equality with another dynamic dimension.
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shared: Optional[_ConstraintTarget] = None
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debug_name: Optional[str] = None
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def _clone_with_range(self, lower=0, upper=math.inf):
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# Import sympy locally
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from torch.fx.experimental.symbolic_shapes import StrictMinMaxConstraint
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from torch.utils._sympy.value_ranges import ValueRanges
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constraint_range = StrictMinMaxConstraint(
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vr=self.constraint_range.vr & ValueRanges(lower=lower, upper=upper),
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warn_only=False,
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)
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return _create_constraint(
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self.w_tensor,
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self.t_id,
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self.dim,
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constraint_range,
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self.shared,
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self.debug_name,
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)
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def __ge__(self, lower):
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return self._clone_with_range(lower=lower)
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def __gt__(self, lower):
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return self._clone_with_range(lower=lower + 1)
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def __le__(self, upper):
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return self._clone_with_range(upper=upper)
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def __lt__(self, upper):
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return self._clone_with_range(upper=upper - 1)
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def __bool__(self):
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# NOTE(avik): We do not support compound expressions like a <= x <= b.
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# This is because Python implicitly desugars them into bool(a <= x) and bool(x <= b),
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# and moreover, enforces that any overload of __bool__ must return True or False.
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# FWIW, sympy also raises TypeError in this case.
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raise TypeError(
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"Cannot determine truth value of _Constraint. "
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"If you are trying to combine _Constraint's with logical connectives, "
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"you can specify them separately instead."
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)
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@property
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def serializable_spec(self):
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# We need a serialization compatible format of the constraint so that it
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# can be savedin the graph module w/o breaking the module serialization.
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# The saved constraints will be used directly for the post-exporting pass
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# that converts constraints to runtime assertion. The saved constraints
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# will not be saved in the serialized module.
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# TODO: A better way is needed. Currently we use 't_id' to map the constraint,
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# which is not reliable
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return {
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"t_id": self.t_id,
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"dim": self.dim,
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"min": self.constraint_range.vr.lower,
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"max": self.constraint_range.vr.upper,
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}
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def __eq__(self, other):
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if not isinstance(other, _Constraint):
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raise TypeError(
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"A dynamic dim can be specified equal only to another dynamic dim. "
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f"Equality with {type(other)} is not supported."
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)
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# import sympy locally
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from torch.fx.experimental.symbolic_shapes import StrictMinMaxConstraint
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constraint_range = StrictMinMaxConstraint(
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vr=self.constraint_range.vr & other.constraint_range.vr,
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warn_only=False,
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)
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if self.debug_name is None:
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debug_name = other.debug_name
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else:
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assert other.debug_name is None or self.debug_name == other.debug_name
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debug_name = self.debug_name
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return _create_constraint(
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self.w_tensor,
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self.t_id,
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self.dim,
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constraint_range,
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shared=_ConstraintTarget(other.w_tensor, other.t_id, other.dim),
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debug_name=debug_name,
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)
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@dataclasses.dataclass
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class _PhantomRoot:
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"""
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This represents the root of a derived Dim where the root does not directly
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specify the shape of any input dimension, but the derived Dim does.
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e.g., the input shapes 2*dim and dim + 1 are related via a "phantom" dim.
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The fields `name`, `constraint_range`, and `val` carried by a phantom root
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help create a symbol for it. Any derived dims with this phantom root are
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backed by expressions over this symbol.
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"""
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name: str
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constraint_range: "StrictMinMaxConstraint"
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val: int
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@dataclasses.dataclass
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class _DerivedConstraint(_ConstraintTarget):
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"""
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This represents a derived Dim, whose root is either a regular constraint target
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(which directly specifies the shape of some input dimension) or a phantom root
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(which does so indirectly).
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"""
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# NOTE: This is not currently a subclass of _Constraint because we do not support
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# `shared` for derived `Dim`s. Indeed, sharing is a necessary concept only for
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# legacy constraints based on `dynamic_dim`: equality can be expressed simply by
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# reusing the same (derived or normal) `Dim`.
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root: Union[_ConstraintTarget, _PhantomRoot]
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fn: Callable
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constraint_range: "StrictMinMaxConstraint"
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debug_name: Optional[str] = None
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@property
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def shared(self):
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# Some code paths expect a union of _Constraint and _DerivedConstraint.
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# Thus we expose a `shared` field that is always None.
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# TODO(avik): clean this up
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return None
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@property
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def serializable_spec(self):
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# same as _Constraint.serializable_spec
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return {
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"t_id": self.t_id,
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"dim": self.dim,
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"min": self.constraint_range.vr.lower,
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"max": self.constraint_range.vr.upper,
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}
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Constraint = Union[_Constraint, _DerivedConstraint]
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def dynamic_dim(t: torch.Tensor, index: int, debug_name: Optional[str] = None):
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"""
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.. warning::
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(This feature is DEPRECATED. See :func:`Dim` instead.)
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:func:`dynamic_dim` constructs a :class:`_Constraint` object that describes the dynamism of
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a dimension ``index`` of tensor ``t``. :class:`_Constraint` objects should be passed to
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``constraints`` argument of :func:`export`.
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Args:
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t (torch.Tensor): Example input tensor that have dynamic dimension size(s)
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index (int): Index of dynamic dimension
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Returns:
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A :class:`_Constraint` object that describes shape dynamism. It can be passed to :func:`export` so
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that :func:`export` does not assume static size of specified tensor, i.e. keeping it dynamic
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as a symbolic size rather than specializing according to size of example tracing input.
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Specifically :func:`dynamic_dim` can be used to express following types of dynamism.
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- Size of a dimension is dynamic and unbounded::
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t0 = torch.rand(2, 3)
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t1 = torch.rand(3, 4)
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# First dimension of t0 can be dynamic size rather than always being static size 2
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constraints = [dynamic_dim(t0, 0)]
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ep = export(fn, (t0, t1), constraints=constraints)
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- Size of a dimension is dynamic with a lower bound::
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t0 = torch.rand(10, 3)
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t1 = torch.rand(3, 4)
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# First dimension of t0 can be dynamic size with a lower bound of 5 (inclusive)
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# Second dimension of t1 can be dynamic size with a lower bound of 2 (exclusive)
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constraints = [
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dynamic_dim(t0, 0) >= 5,
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dynamic_dim(t1, 1) > 2,
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]
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ep = export(fn, (t0, t1), constraints=constraints)
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- Size of a dimension is dynamic with an upper bound::
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t0 = torch.rand(10, 3)
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t1 = torch.rand(3, 4)
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# First dimension of t0 can be dynamic size with a upper bound of 16 (inclusive)
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# Second dimension of t1 can be dynamic size with a upper bound of 8 (exclusive)
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constraints = [
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dynamic_dim(t0, 0) <= 16,
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dynamic_dim(t1, 1) < 8,
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]
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ep = export(fn, (t0, t1), constraints=constraints)
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- Size of a dimension is dynamic and it is always equal to size of another dynamic dimension::
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t0 = torch.rand(10, 3)
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t1 = torch.rand(3, 4)
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# Sizes of second dimension of t0 and first dimension are always equal
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constraints = [
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dynamic_dim(t0, 1) == dynamic_dim(t1, 0),
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]
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ep = export(fn, (t0, t1), constraints=constraints)
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- Mix and match all types above as long as they do not express conflicting requirements
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"""
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from torch._dynamo.exc import UserError, UserErrorType
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if not isinstance(t, torch.Tensor):
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raise UserError(
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UserErrorType.DYNAMIC_DIM,
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f"Expected tensor as input to dynamic_dim but got {type(t)}",
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)
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if t.dim() < 1:
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raise UserError(
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UserErrorType.DYNAMIC_DIM, "Cannot mark 0-dimension tensors to be dynamic"
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)
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if index >= t.dim():
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raise UserError(
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UserErrorType.DYNAMIC_DIM,
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f"Expected the dimension passed to dynamic_dim to be in the range [0:{t.dim()-1}]"
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f" but got {index}, which is out of bounds for the given tensor.",
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)
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# Import sympy locally
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import sympy
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from torch.fx.experimental.symbolic_shapes import StrictMinMaxConstraint
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from torch.utils._sympy.value_ranges import ValueRanges
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return _create_constraint(
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weakref.ref(t),
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id(t),
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index,
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StrictMinMaxConstraint(
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vr=ValueRanges(lower=0, upper=sympy.oo), warn_only=False
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),
|
|
debug_name=debug_name,
|
|
)
|
|
|
|
|
|
def _process_equalities(
|
|
constraint: Constraint,
|
|
get_sources: Callable[[int, int], List["Source"]],
|
|
shape_env: "ShapeEnv",
|
|
source_pairs: List[Tuple["Source", "Source"]],
|
|
derived_equalities: List[Tuple["Source", Union["Source", "Symbol"], Callable]],
|
|
phantom_symbols: Dict[str, "Symbol"],
|
|
):
|
|
"""
|
|
Updates `source_pairs`, `derived_equalities`, and `phantom_symbols` (which become
|
|
fields of `EqualityConstraint`) based on a given input `constraint`.
|
|
"""
|
|
|
|
source, *other_sources = get_sources(constraint.t_id, constraint.dim)
|
|
# When t.size()[dim] maps to src0, src1, ..., srcN, we add
|
|
# constraints that make src0 "equal" to src1, ..., srcN.
|
|
source_pairs.extend((source, other_source) for other_source in other_sources)
|
|
if not isinstance(constraint, _DerivedConstraint):
|
|
if constraint.shared is not None:
|
|
# Moreover, when t.size()[dim] is specified equal to t'.size()[dim']
|
|
# and t'.size()[dim'] maps to src1', ..., srcN', we add
|
|
# constraints that also make src0 "equal" to src1', ..., srcN'.
|
|
other_sources = get_sources(constraint.shared.t_id, constraint.shared.dim)
|
|
source_pairs.extend(
|
|
(source, other_source) for other_source in other_sources
|
|
)
|
|
else:
|
|
# branch based on the root of the _DerivedConstraint
|
|
if not isinstance(constraint.root, _PhantomRoot):
|
|
# either root points to an input source
|
|
root = get_sources(constraint.root.t_id, constraint.root.dim)[0] # type: ignore[assignment]
|
|
else:
|
|
# or root points to a phantom symbol
|
|
if constraint.root.name in phantom_symbols:
|
|
root = phantom_symbols[constraint.root.name] # type: ignore[assignment]
|
|
else:
|
|
# create a phantom symbol in the shape env based on the _PhantomRoot
|
|
root = shape_env.create_symbol(
|
|
val=constraint.root.val,
|
|
source=torch._dynamo.source.ConstantSource(constraint.root.name),
|
|
dynamic_dim=torch.fx.experimental.symbolic_shapes.DimDynamic.DYNAMIC,
|
|
constraint_dim=constraint.root.constraint_range,
|
|
)
|
|
phantom_symbols[constraint.root.name] = root # type: ignore[assignment]
|
|
|
|
fn = constraint.fn
|
|
# A derived equality (source, root, fn) informally corresponds to source = fn(root).
|
|
# Here source describes an input and root might describe another input or a phantom symbol.
|
|
derived_equalities.append((source, root, fn))
|
|
|
|
|
|
def _tree_map(
|
|
func: Callable[..., Any],
|
|
tree: Any,
|
|
*dynamic_shapes: Any,
|
|
) -> Any:
|
|
"""
|
|
Customized tree_map for mapping pytrees to dynamic_shapes.
|
|
|
|
For built-in types (e.g., standard collections) this behaves exactly like tree_map.
|
|
|
|
OTOH for a user-defined class C registered with pytree, we cannot assume that a C
|
|
containing tensors can be mapped to a C containing dynamic shapes (i.e., C may not
|
|
be a polymorphic container). In that case we use the flattened form of C instead.
|
|
Thus a C(**tensors) that flattens to (**tensors) will map to (**dynamic_shapes).
|
|
|
|
Args:
|
|
func: function to apply to each (int, float, str, bool, None, torch.Tensor)
|
|
tree: input pytree
|
|
dynamic_shapes: zero or more (typically one) dynamic_shapes to match
|
|
|
|
Returns:
|
|
output pytree mapping func to each (int, float, str, bool, None, torch.Tensor)
|
|
"""
|
|
|
|
def is_leaf(t):
|
|
# BUILTIN_TYPES is a subset of SUPPORTED_NODES, the latter being all types
|
|
# registered with pytree. Types *not* in BUILTIN_TYPES include primitive types
|
|
# (int, float, str, bool, None, torch.Tensor), which are not in SUPPORTED_NODES,
|
|
# as well as user-defined classes registered with pytree, which are.
|
|
return _get_node_type(t) not in BUILTIN_TYPES
|
|
|
|
def f(t, *dynamic_shapes):
|
|
typ = _get_node_type(t)
|
|
# typ is not in BUILTIN_TYPES
|
|
if typ in SUPPORTED_NODES:
|
|
# thus typ is a user-defined class registered with pytree,
|
|
# in which case flatten and recurse
|
|
return tree_map(
|
|
f,
|
|
SUPPORTED_NODES[typ].flatten_fn(t)[0],
|
|
*dynamic_shapes,
|
|
is_leaf=is_leaf,
|
|
)
|
|
else:
|
|
return func(t, *dynamic_shapes)
|
|
|
|
return tree_map(f, tree, *dynamic_shapes, is_leaf=is_leaf)
|
|
|
|
|
|
def _combine_args(f, args, kwargs, _is_torch_jit_trace=False):
|
|
# combine args and kwargs following the signature of f, as it happens
|
|
# in the body of f when called with *args, **kwargs
|
|
if isinstance(f, ExportedProgram):
|
|
f = f.module()
|
|
if not _is_torch_jit_trace:
|
|
signature = (
|
|
inspect.signature(f.forward)
|
|
if isinstance(f, torch.nn.Module)
|
|
else inspect.signature(f)
|
|
)
|
|
kwargs = kwargs if kwargs is not None else {}
|
|
return signature.bind(*args, **kwargs).arguments
|
|
return args
|
|
|
|
|
|
class ShapesCollection:
|
|
"""
|
|
Builder for dynamic_shapes.
|
|
Used to assign dynamic shape specifications to tensors that appear in inputs.
|
|
|
|
Example::
|
|
args = ({"x": tensor_x, "others": [tensor_y, tensor_z]})
|
|
|
|
dim = torch.export.Dim(...)
|
|
dynamic_shapes = torch.export.ShapesCollection()
|
|
dynamic_shapes[tensor_x] = (dim, dim + 1, 8)
|
|
dynamic_shapes[tensor_y] = {0: dim * 2}
|
|
# This is equivalent to the following (now auto-generated):
|
|
# dynamic_shapes = {"x": (dim, dim + 1, 8), "others": [{0: dim * 2}, None]}
|
|
|
|
torch.export(..., args, dynamic_shapes=dynamic_shapes)
|
|
"""
|
|
|
|
def __init__(self):
|
|
self._shapes = {}
|
|
|
|
def __setitem__(self, t, shape):
|
|
assert isinstance(
|
|
t, torch.Tensor
|
|
), f"Cannot assign shape to non-tensor type {type(t)}"
|
|
# TODO(avik): check that shape is indeed a Shape
|
|
t_id = id(t)
|
|
if t_id in self._shapes:
|
|
_shape = self._shapes[t_id]
|
|
assert (
|
|
shape == _shape
|
|
), f"Shapes assigned to tensor do not match: expected {_shape}, got {shape}"
|
|
else:
|
|
self._shapes[id(t)] = shape
|
|
|
|
def __getitem__(self, t):
|
|
t_id = id(t)
|
|
if t_id in self._shapes:
|
|
return self._shapes[t_id]
|
|
else:
|
|
return None
|
|
|
|
def __len__(self):
|
|
return len(self._shapes)
|
|
|
|
def dynamic_shapes(self, m, args, kwargs=None):
|
|
"""
|
|
Generate dynamic_shapes.
|
|
"""
|
|
|
|
t_ids = set()
|
|
|
|
def find_shape(t):
|
|
t_id = id(t)
|
|
if t_id in self._shapes:
|
|
t_ids.add(t_id)
|
|
return self._shapes[t_id]
|
|
else:
|
|
return None
|
|
|
|
combined_args = _combine_args(m, args, kwargs)
|
|
dynamic_shapes = _tree_map(find_shape, combined_args)
|
|
if any(t_id not in t_ids for t_id in self._shapes):
|
|
raise ValueError(
|
|
"Some tensors that were assigned shapes were not found in args. "
|
|
"Maybe such tensors were copied when passing them as args? "
|
|
"Maybe such tensors are contained in classes that were not registered with pytree?"
|
|
)
|
|
return dynamic_shapes
|
|
|
|
|
|
def _process_dynamic_shapes(
|
|
f: Callable,
|
|
args: Tuple[Any, ...],
|
|
kwargs: Optional[Dict[str, Any]] = None,
|
|
dynamic_shapes: Optional[Union[Dict[str, Any], Tuple[Any], List[Any]]] = None,
|
|
_is_torch_jit_trace=False,
|
|
) -> Optional[List[Constraint]]:
|
|
from torch._dynamo.exc import UserError, UserErrorType
|
|
|
|
if dynamic_shapes is None or len(dynamic_shapes) == 0:
|
|
return None
|
|
|
|
# map of Dim names representing input shape dimensions to constraints on them
|
|
symbols: Dict[str, List[Constraint]] = defaultdict(list)
|
|
# track roots that do not directly represent input shape dimensions
|
|
phantom_roots: Dict[str, _PhantomRoot] = {}
|
|
derived_constraints_with_phantom_root: List[_DerivedConstraint] = []
|
|
|
|
def to_constraint(dim, tensor, i):
|
|
import sympy
|
|
|
|
from torch.fx.experimental.symbolic_shapes import StrictMinMaxConstraint
|
|
from torch.utils._sympy.solve import try_solve
|
|
from torch.utils._sympy.value_ranges import ValueRanges
|
|
|
|
def root_value():
|
|
# given tensor.shape[i] is the value of dim = fn(root),
|
|
# find the value of root
|
|
symbol = sympy.Symbol(dim.root.__name__, integer=True)
|
|
expr = dim.fn(symbol)
|
|
solution = try_solve(sympy.Eq(expr, tensor.shape[i]), symbol)
|
|
if solution is not None:
|
|
return int(solution[1]) # type: ignore[call-overload]
|
|
else:
|
|
raise UserError( # noqa: B904
|
|
UserErrorType.CONSTRAINT_VIOLATION,
|
|
f"Expected shape[{i}] = {tensor.shape[i]} of input Tensor to be "
|
|
f"of the form {expr}, where {symbol} is an integer",
|
|
)
|
|
|
|
if isinstance(dim, _DerivedDim):
|
|
# generate a _DerivedConstraint where the root is:
|
|
# - either a _ConstraintTarget (if dim.root directly describes an input shape)
|
|
# - or a _PhantomRoot (otherwise)
|
|
dim_root = dim.root # type: ignore[attr-defined]
|
|
if dim_root.__name__ in symbols:
|
|
# root represents an input shape dimension
|
|
root_constraint = symbols[dim_root.__name__][0]
|
|
root = _ConstraintTarget(
|
|
root_constraint.w_tensor,
|
|
root_constraint.t_id,
|
|
root_constraint.dim,
|
|
)
|
|
elif dim_root.__name__ not in phantom_roots:
|
|
# create a phantom root
|
|
root = _PhantomRoot( # type: ignore[assignment]
|
|
name=dim_root.__name__,
|
|
constraint_range=StrictMinMaxConstraint(
|
|
vr=ValueRanges(lower=dim_root.min, upper=dim_root.max),
|
|
warn_only=False,
|
|
),
|
|
val=root_value(),
|
|
)
|
|
phantom_roots[dim_root.__name__] = root # type: ignore[assignment]
|
|
else:
|
|
root = phantom_roots[dim_root.__name__] # type: ignore[assignment]
|
|
constraint = _DerivedConstraint(
|
|
weakref.ref(tensor),
|
|
id(tensor),
|
|
i,
|
|
root,
|
|
dim.fn, # type: ignore[attr-defined]
|
|
StrictMinMaxConstraint(
|
|
vr=ValueRanges(lower=dim.min, upper=dim.max),
|
|
warn_only=False,
|
|
),
|
|
debug_name=dim.__name__,
|
|
)
|
|
if isinstance(root, _PhantomRoot):
|
|
# NOTE(avik): since we have not processed all inputs yet, we may replace this
|
|
# with a root that does represent an input shape dimension later (see below)
|
|
derived_constraints_with_phantom_root.append(constraint)
|
|
elif isinstance(dim, _StaticDim):
|
|
constraint = _create_constraint(
|
|
weakref.ref(tensor),
|
|
id(tensor),
|
|
i,
|
|
StrictMinMaxConstraint(
|
|
vr=ValueRanges(lower=dim.value, upper=dim.value), warn_only=False # type: ignore[attr-defined]
|
|
),
|
|
debug_name=dim.__name__,
|
|
)
|
|
else:
|
|
constraint = dynamic_dim(tensor, i, debug_name=dim.__name__)
|
|
if dim.min != 0:
|
|
constraint = constraint >= dim.min
|
|
if dim.max != sys.maxsize - 1:
|
|
constraint = constraint <= dim.max
|
|
return constraint
|
|
|
|
bounds: Dict[str, Tuple[int, int]] = {}
|
|
|
|
def check_same_bounds(dim):
|
|
if dim.__name__ in symbols:
|
|
min_, max_ = bounds[dim.__name__]
|
|
if dim.min != min_ or dim.max != max_:
|
|
this_ = _Dim.readable(dim.__name__, min_, max_)
|
|
that_ = _Dim.readable(dim.__name__, dim.min, dim.max)
|
|
raise UserError(
|
|
UserErrorType.INVALID_INPUT,
|
|
f"Found different definitions {this_} and {that_} "
|
|
f"for the same symbolic dimension {dim}!",
|
|
)
|
|
|
|
else:
|
|
bounds[dim.__name__] = (dim.min, dim.max)
|
|
|
|
def update_symbols(tensor, shape):
|
|
def _create_static_dim(tensor, i, value):
|
|
return _StaticDim(str(value), (int,), {"value": value})
|
|
|
|
if isinstance(shape, dict):
|
|
for i, dim in shape.items():
|
|
if isinstance(dim, (int, _Dim)):
|
|
if isinstance(dim, int):
|
|
dim = _create_static_dim(tensor, i, dim)
|
|
check_same_bounds(dim)
|
|
constraint = to_constraint(dim, tensor, i)
|
|
symbols[dim.__name__].append(constraint)
|
|
else:
|
|
if dim is not None:
|
|
raise UserError(
|
|
UserErrorType.INVALID_INPUT,
|
|
f"Unexpected item #{i} ({dim}) in dynamic_shape {shape} of Tensor, "
|
|
"try None instead",
|
|
)
|
|
elif isinstance(shape, (tuple, list)):
|
|
for i, dim in enumerate(shape):
|
|
if isinstance(dim, (int, _Dim)):
|
|
if isinstance(dim, int):
|
|
dim = _create_static_dim(tensor, i, dim)
|
|
check_same_bounds(dim)
|
|
constraint = to_constraint(dim, tensor, i)
|
|
symbols[dim.__name__].append(constraint)
|
|
else:
|
|
if dim is not None:
|
|
raise UserError(
|
|
UserErrorType.INVALID_INPUT,
|
|
f"Unexpected item #{i} ({dim}) in dynamic_shape {shape} of Tensor, "
|
|
"try None instead",
|
|
)
|
|
else:
|
|
if shape is not None:
|
|
raise UserError(
|
|
UserErrorType.INVALID_INPUT,
|
|
f"Unexpected dynamic_shape {shape} of Tensor, " "try None instead",
|
|
)
|
|
|
|
def assoc_shapes(combined_args, dynamic_shapes):
|
|
def assoc_shape(t, dynamic_shape):
|
|
if isinstance(t, torch.Tensor):
|
|
update_symbols(t, dynamic_shape)
|
|
else:
|
|
if dynamic_shape is not None:
|
|
raise UserError(
|
|
UserErrorType.INVALID_INPUT,
|
|
f"Cannot associate shape {dynamic_shape} to non-tensor type {type(t)}, "
|
|
f"expected None",
|
|
)
|
|
|
|
_tree_map(assoc_shape, combined_args, dynamic_shapes)
|
|
|
|
combined_args = _combine_args(
|
|
f, args, kwargs, _is_torch_jit_trace=_is_torch_jit_trace
|
|
)
|
|
if not isinstance(dynamic_shapes, dict):
|
|
assert isinstance(dynamic_shapes, (tuple, list))
|
|
combined_args = type(dynamic_shapes)(combined_args.values()) # type: ignore[assignment, misc]
|
|
assoc_shapes(combined_args, dynamic_shapes)
|
|
|
|
constraints = []
|
|
for derived_constraint_with_phantom_root in derived_constraints_with_phantom_root:
|
|
phantom_root_name = derived_constraint_with_phantom_root.root.name # type: ignore[union-attr]
|
|
if phantom_root_name in symbols:
|
|
# We found an input shape dimension corresponding to this name, so we
|
|
# do not need a phantom symbol for it after all.
|
|
# NOTE(avik): Overall we want to maintain the invariant that roots that
|
|
# are phantom symbols are really "phantom," i.e., they cannot be represented
|
|
# by any input source. This is important when we are deciding derived equalities,
|
|
# since we can focus our attention exclusively on input sources: deciding
|
|
# derived equalities involving phantom symbols are, in comparison, trivial.
|
|
derived_constraint_with_phantom_root.root = symbols[phantom_root_name][0]
|
|
|
|
for dynamic_dims in symbols.values():
|
|
if all(
|
|
isinstance(dynamic_dim, _DerivedConstraint) for dynamic_dim in dynamic_dims
|
|
):
|
|
constraints.extend(dynamic_dims)
|
|
else:
|
|
primary, *others = dynamic_dims
|
|
if others:
|
|
for other in others:
|
|
constraints.append(primary == other) # type: ignore[arg-type]
|
|
else:
|
|
constraints.append(primary)
|
|
|
|
return constraints # type: ignore[return-value]
|
|
|
|
|
|
def _get_dim_name_mapping(
|
|
dynamic_shapes: Union[Dict[str, Any], Tuple[Any], List[Any], None]
|
|
):
|
|
name_to_dim = {}
|
|
for dim in tree_flatten(
|
|
dynamic_shapes,
|
|
is_leaf=lambda x: isinstance(x, _Dim),
|
|
)[0]:
|
|
if dim is None or isinstance(dim, int):
|
|
continue
|
|
name_to_dim[dim.__name__] = dim
|
|
if isinstance(dim, _DerivedDim):
|
|
name_to_dim[dim.root.__name__] = dim.root # type: ignore[attr-defined]
|
|
return name_to_dim
|
|
|
|
|
|
def refine_dynamic_shapes_from_suggested_fixes(
|
|
msg: str,
|
|
dynamic_shapes: Union[Dict[str, Any], Tuple[Any], List[Any]],
|
|
) -> Union[Dict[str, Any], Tuple[Any], List[Any]]:
|
|
"""
|
|
For working with export's dynamic shapes suggested fixes, and/or automatic dynamic shapes.
|
|
Refines the given dynamic shapes spec, given a ConstraintViolation error message and the original dynamic shapes.
|
|
|
|
For most cases behavior is straightforward - i.e. for suggested fixes that specialize or refine a Dim's range,
|
|
or fixes that suggest a derived relation, the new dynamic shapes spec will be updated as such.
|
|
|
|
e.g.
|
|
Suggested fixes:
|
|
|
|
dim = Dim('dim', min=3, max=6) -> this just refines the dim's range
|
|
dim = 4 -> this specializes to a constant
|
|
dy = dx + 1 -> dy was specified as an independent dim, but is actually tied to dx with this relation
|
|
|
|
However, suggested fixes associated with derived dims can be more complicated.
|
|
For example, if a suggested fix is provided for a root dim, the new derived dim value is evaluated based on the root.
|
|
|
|
e.g.
|
|
dx = Dim('dx')
|
|
dy = dx + 2
|
|
dynamic_shapes = {"x": (dx,), "y": (dy,)}
|
|
|
|
Suggested fixes:
|
|
|
|
dx = 4 # specialization will lead to dy also specializing = 6
|
|
dx = Dim('dx', max=6) # dy now has max = 8
|
|
|
|
Derived dims suggested fixes can also be used to express divisibility constraints.
|
|
This involves creating new root dims that aren't tied to a particular input shape.
|
|
In this case the root dims won't appear directly in the new spec, but as a root of
|
|
one of the dims.
|
|
|
|
e.g.
|
|
Suggested fixes:
|
|
|
|
_dx = Dim('_dx', max=1024) # this won't appear in the return result, but dx will
|
|
dx = 4*_dx # dx is now divisible by 4, with a max value of 4096
|
|
"""
|
|
|
|
import re
|
|
|
|
import sympy
|
|
|
|
from torch._dynamo.exc import UserError, UserErrorType
|
|
from torch.fx.experimental.symbolic_shapes import _is_supported_equivalence
|
|
|
|
try:
|
|
shape_fixes_msg = msg.split("Suggested fixes:")[1].strip()
|
|
except Exception as exc:
|
|
raise UserError(
|
|
UserErrorType.INVALID_INPUT,
|
|
"Suggested fixes not found in error message given to refine_dynamic_shapes_from_suggested_fixes()",
|
|
) from exc
|
|
|
|
# build shape_fixes dictionary
|
|
shape_fixes = {}
|
|
for fix in shape_fixes_msg.split("\n"):
|
|
fix = fix.strip()
|
|
if match := re.match(r"(.*) = Dim\('(.*)'.*\)", fix):
|
|
name = match.group(1)
|
|
_min, _max = None, None
|
|
if match_min := re.match(r".* = Dim\('.*', min\=([0-9]+).*\)", fix):
|
|
_min = int(match_min.group(1))
|
|
if match_max := re.match(r".* = Dim\('.*'.*max\=([0-9]+)\)", fix):
|
|
_max = int(match_max.group(1))
|
|
shape_fixes[name] = Dim(name, min=_min, max=_max)
|
|
else:
|
|
name, expr = fix.split(" = ")
|
|
expr = sympy.sympify(expr)
|
|
if isinstance(expr, sympy.Number):
|
|
shape_fixes[name] = int(expr) # static, integer
|
|
else:
|
|
shape_fixes[name] = expr # relation or derived dim
|
|
|
|
name_to_dim = _get_dim_name_mapping(dynamic_shapes)
|
|
|
|
# track derived dim roots
|
|
roots: Set[str] = set()
|
|
for k, c in shape_fixes.items():
|
|
assert isinstance(c, (int, _Dim, _DerivedDim, sympy.Expr))
|
|
if isinstance(c, sympy.Expr): # check dim/derived dim expression
|
|
assert _is_supported_equivalence(c)
|
|
shape_fixes[k] = c
|
|
roots.add(str(next(iter(c.free_symbols))))
|
|
if isinstance(c, _DerivedDim):
|
|
roots.add(c.root.__name__) # type: ignore[attr-defined]
|
|
|
|
# check keys are existing dims or new roots
|
|
for k, c in shape_fixes.items():
|
|
assert k in name_to_dim or k in roots
|
|
|
|
# cache so we don't produce multiple derived dim objects
|
|
derived_dim_cache: Dict[str, _DerivedDim] = {}
|
|
|
|
def apply_fixes(dim, dummy):
|
|
if dim is None or isinstance(dim, int): # not dynamic
|
|
return dim
|
|
elif dim.__name__ in shape_fixes: # directly fix
|
|
fix = shape_fixes[dim.__name__]
|
|
if isinstance(fix, sympy.Expr): # now derived or related
|
|
if str(fix) in derived_dim_cache:
|
|
return derived_dim_cache[str(fix)]
|
|
else:
|
|
symbol = next(iter(fix.free_symbols))
|
|
# try to locate symbol
|
|
if symbol.name in shape_fixes: # type: ignore[attr-defined]
|
|
root = shape_fixes[symbol.name] # type: ignore[attr-defined]
|
|
else:
|
|
assert symbol.name in name_to_dim # type: ignore[attr-defined]
|
|
root = name_to_dim[symbol.name] # type: ignore[attr-defined]
|
|
# figure out value of fix
|
|
modulus, remainder = sympy.polys.polytools.div(fix, symbol)
|
|
dim = root
|
|
if modulus != 1:
|
|
dim = int(modulus) * dim
|
|
if remainder != 0:
|
|
dim = dim + int(remainder)
|
|
derived_dim_cache[str(fix)] = dim
|
|
return dim
|
|
else:
|
|
return fix
|
|
elif isinstance(dim, _DerivedDim) and dim.root.__name__ in shape_fixes: # type: ignore[attr-defined]
|
|
if dim.__name__ in derived_dim_cache:
|
|
return derived_dim_cache[dim.__name__]
|
|
else: # evaluate new derived value based on root
|
|
_dim = dim.fn(shape_fixes[dim.root.__name__]) # type: ignore[attr-defined]
|
|
derived_dim_cache[dim.__name__] = _dim
|
|
return _dim
|
|
return dim # unchanged dim
|
|
|
|
return _tree_map(apply_fixes, dynamic_shapes, dynamic_shapes)
|