Source code for torch.export.dynamic_shapes
import builtins
import dataclasses
import inspect
import math
import sys
import weakref
from collections import defaultdict
from typing import Any, Callable, Dict, List, Optional, Set, Tuple, TYPE_CHECKING, Union
import torch
from torch._subclasses.fake_tensor import FakeTensor
from torch.utils._pytree import SUPPORTED_NODES
from .exported_program import ExportedProgram
if TYPE_CHECKING:
from sympy import Symbol
from torch._guards import Source
from ..fx.experimental.symbolic_shapes import ShapeEnv, StrictMinMaxConstraint
__all__ = ["Constraint", "Dim", "dims", "dynamic_dim"]
class _Dim(type):
"""
Metaclass for :func:`Dim` types.
"""
@staticmethod
def readable(name, min_, max_):
if min_ == 2:
min_ = None
if max_ == sys.maxsize - 1:
max_ = None
if min_ is None and max_ is None:
return f"Dim('{name}')"
if min_ is None:
return f"Dim('{name}', max={max_})"
if max_ is None:
return f"Dim('{name}', min={min_})"
return f"Dim('{name}', min={min_}, max={max_})"
def __add__(cls, other):
# e.g., dim + 1
if type(other) is not int:
raise NotImplementedError(
f"Attempted to add {other} to {cls.__name__}, where an integer was expected. "
"(Only increasing linear operations with integer coefficients are supported.)"
)
return cls._derive(lambda x: x + other)
def __radd__(cls, other):
return cls + other
def __sub__(cls, other):
# e.g., dim - 1
if type(other) is not int:
raise NotImplementedError(
f"Attempted to subtract {other} from {cls.__name__}, where an integer was expected. "
"(Only increasing linear operations with integer coefficients are supported.)"
)
return cls._derive(lambda x: x - other)
def __rsub__(cls, other):
raise NotImplementedError(
f"Attempted to negate {cls.__name__}. "
"(Only increasing linear operations with integer coefficients are supported.)"
)
def __mul__(cls, other):
# e.g., dim * 2
if type(other) is not int or other <= 0:
raise NotImplementedError(
f"Attempted to multiply {other} with {cls.__name__}, where a positive integer was expected. "
"(Only increasing linear operations with integer coefficients are supported.)"
)
return cls._derive(lambda x: x * other)
def __rmul__(cls, other):
return cls * other
def _derived_name(cls, fn):
from sympy import sympify
return str(fn(sympify(cls.__name__)))
def _derive(cls, fn):
return _DerivedDim(cls._derived_name(fn), (int,), {"root": cls, "fn": fn})
class _DerivedDim(_Dim):
"""
Metaclass for derived :func:`Dim` types.
Currently we only support increasing linear expressions with integer coefficients.
In other words, a derived Dim can always be written in the form Ax + B, where
x is a regular Dim (i.e., non-derived Dim), A and B are integers, and A is positive.
(In particular, the latter ensures that x < y => Ax + B < Ay + B.)
These restrictions on the form of derived Dims makes the metatheory simpler: e.g.,
it simplifies computing ranges for derived Dims, solving for underlying regular Dims,
deciding equalities between derived Dims, and so on.
The function lambda x: Ax + B is expressed by `fn`, where x is a normal Dim, `root`.
The range of a derived Dim is computed by mapping `fn` over the range of its `root`.
"""
@property
def min(self):
# assume that self.fn is an increasing function
# TODO(avik): use sympy value range analysis instead?
from sympy import Integer
_min_symint = self.fn(Integer(self.root.min)) # type: ignore[attr-defined]
assert _min_symint >= 2, (
f"Expected derived min value of {self.__name__} to be >= 2. "
f"Please specify an appropriate min value for {self.root.__name__} " # type: ignore[attr-defined]
f"(currently {self.root.min})." # type: ignore[attr-defined]
)
return int(_min_symint)
@property
def max(self):
# assume that self.fn is an increasing function
# TODO(avik): use sympy value range analysis instead?
from sympy import Integer
_max_symint = self.fn(Integer(self.root.max)) # type: ignore[attr-defined]
assert _max_symint <= sys.maxsize - 1, (
f"Expected derived max value of {self.__name__} to be <= {sys.maxsize - 1}. "
f"Please specify an appropriate max value for {self.root.__name__} " # type: ignore[attr-defined]
f"(currently {self.root.max})." # type: ignore[attr-defined]
)
return int(_max_symint)
def _derive(self, fn):
# We support nesting, e.g., 2*dim + 1.
# This is implemented by composing operations on the same root.
# As a consequence, roots are always regular Dims (i.e., not derived Dims).
return _DerivedDim(
self._derived_name(fn),
(int,),
{"root": self.root, "fn": lambda x: fn(self.fn(x))}, # type: ignore[attr-defined]
)
[docs]def Dim(name: str, *, min: Optional[int] = None, max: Optional[int] = None):
"""
:func:`Dim` constructs a type analogous to a named symbolic integer with a range.
It can be used to describe multiple possible values of a dynamic tensor dimension.
Note that different dynamic dimensions of the same tensor, or of different tensors,
can be described by the same type.
Args:
name (str): Human-readable name for debugging.
min (Optional[int]): Minimum possible value of given symbol (inclusive)
max (Optional[int]): Maximum possible value of given symbol (inclusive)
Returns:
A type that can be used in dynamic shape specifications for tensors.
"""
_min = 2 if min is None else builtins.max(min, 2)
_max = sys.maxsize - 1 if max is None else builtins.min(max, sys.maxsize - 1)
assert _max > _min, f"Cannot create Dim with inconsistent min={min}, max={max}"
dim = _Dim(name, (int,), {"min": _min, "max": _max})
dim.__module__ = getattr(
inspect.getmodule(inspect.stack()[1][0]), "__name__", "__main__"
)
return dim
[docs]def dims(*names: str, min: Optional[int] = None, max: Optional[int] = None):
"""
Util to create multiple :func:`Dim` types.
"""
return tuple(Dim(name, min=min, max=max) for name in names)
@dataclasses.dataclass
class _ConstraintTarget:
"""
This represents input tensor dimensions. Don't create this
class directly; instead, use :func:`dynamic_dim`.
"""
w_tensor: Any # weakref to torch.Tensor
# TODO: We don't need t_id; we can get it off of w_tensor
t_id: int
dim: int
class _ConstraintFactory(type):
"""
Metaclass that ensures a private constructor for :class:`_Constraint`
"""
def __call__(cls, *args, **kwargs):
raise TypeError(
f"{cls.__module__}.{cls.__qualname__} has no public constructor. "
f"Please use torch.export.dynamic_dim() to create one"
)
def _create(
cls, w_tensor, t_id, dim, constraint_range, shared=None, debug_name=None
):
return super().__call__(
w_tensor, t_id, dim, constraint_range, shared, debug_name
)
def _create_constraint(
w_tensor, t_id, dim, constraint_range, shared=None, debug_name=None
):
return _Constraint._create(
w_tensor, t_id, dim, constraint_range, shared, debug_name
)
@dataclasses.dataclass
class _Constraint(_ConstraintTarget, metaclass=_ConstraintFactory):
"""
.. warning::
Do not construct :class:`_Constraint` directly, use :func:`dynamic_dim` instead.
This represents constraints on input tensor dimensions, e.g., requiring
them to be fully polymorphic or within some range.
"""
# NOTE(avik): In the future, this could be Union[StrictMinMaxConstraint, <other kinds>]
constraint_range: "StrictMinMaxConstraint"
# Represent that `constraint_range` is shared with another _ConstraintTarget, which
# typically arises because of a specified equality with another dynamic dimension.
shared: Optional[_ConstraintTarget] = None
debug_name: Optional[str] = None
def _clone_with_range(self, lower=2, upper=math.inf):
# Import sympy locally
from torch.fx.experimental.symbolic_shapes import StrictMinMaxConstraint
from torch.utils._sympy.value_ranges import ValueRanges
constraint_range = StrictMinMaxConstraint(
vr=self.constraint_range.vr & ValueRanges(lower=lower, upper=upper),
warn_only=False,
)
return _create_constraint(
self.w_tensor,
self.t_id,
self.dim,
constraint_range,
self.shared,
self.debug_name,
)
def __ge__(self, lower):
return self._clone_with_range(lower=lower)
def __gt__(self, lower):
return self._clone_with_range(lower=lower + 1)
def __le__(self, upper):
return self._clone_with_range(upper=upper)
def __lt__(self, upper):
return self._clone_with_range(upper=upper - 1)
def __bool__(self):
# NOTE(avik): We do not support compound expressions like a <= x <= b.
# This is because Python implicitly desugars them into bool(a <= x) and bool(x <= b),
# and moreover, enforces that any overload of __bool__ must return True or False.
# FWIW, sympy also raises TypeError in this case.
raise TypeError(
"Cannot determine truth value of _Constraint. "
"If you are trying to combine _Constraint's with logical connectives, "
"you can specify them separately instead."
)
@property
def serializable_spec(self):
# We need a serialization compatible format of the constraint so that it
# can be savedin the graph module w/o breaking the module serialization.
# The saved constraints will be used directly for the post-exporting pass
# that converts constraints to runtime assertion. The saved constraints
# will not be saved in the serialized module.
# TODO: A better way is needed. Currently we use 't_id' to map the constraint,
# which is not reliable
return {
"t_id": self.t_id,
"dim": self.dim,
"min": self.constraint_range.vr.lower,
"max": self.constraint_range.vr.upper,
}
def __eq__(self, other):
if not isinstance(other, _Constraint):
raise TypeError(
"A dynamic dim can be specified equal only to another dynamic dim. "
f"Equality with {type(other)} is not supported."
)
# import sympy locally
from torch.fx.experimental.symbolic_shapes import StrictMinMaxConstraint
constraint_range = StrictMinMaxConstraint(
vr=self.constraint_range.vr & other.constraint_range.vr,
warn_only=False,
)
if self.debug_name is None:
debug_name = other.debug_name
else:
assert other.debug_name is None or self.debug_name == other.debug_name
debug_name = self.debug_name
return _create_constraint(
self.w_tensor,
self.t_id,
self.dim,
constraint_range,
shared=_ConstraintTarget(other.w_tensor, other.t_id, other.dim),
debug_name=debug_name,
)
@dataclasses.dataclass
class _PhantomRoot:
"""
This represents the root of a derived Dim where the root does not directly
specify the shape of any input dimension, but the derived Dim does.
e.g., the input shapes 2*dim and dim + 1 are related via a "phantom" dim.
The fields `name`, `constraint_range`, and `val` carried by a phantom root
help create a symbol for it. Any derived dims with this phantom root are
backed by expressions over this symbol.
"""
name: str
constraint_range: "StrictMinMaxConstraint"
val: int
@dataclasses.dataclass
class _DerivedConstraint(_ConstraintTarget):
"""
This represents a derived Dim, whose root is either a regular constraint target
(which directly specifies the shape of some input dimension) or a phantom root
(which does so indirectly).
"""
# NOTE: This is not currently a subclass of _Constraint because we do not support
# `shared` for derived `Dim`s. Indeed, sharing is a necessary concept only for
# legacy constraints based on `dynamic_dim`: equality can be expressed simply by
# reusing the same (derived or normal) `Dim`.
root: Union[_ConstraintTarget, _PhantomRoot]
fn: Callable
constraint_range: "StrictMinMaxConstraint"
debug_name: Optional[str] = None
@property
def shared(self):
# Some code paths expect a union of _Constraint and _DerivedConstraint.
# Thus we expose a `shared` field that is always None.
# TODO(avik): clean this up
return None
@property
def serializable_spec(self):
# same as _Constraint.serializable_spec
return {
"t_id": self.t_id,
"dim": self.dim,
"min": self.constraint_range.vr.lower,
"max": self.constraint_range.vr.upper,
}
Constraint = Union[_Constraint, _DerivedConstraint]
[docs]def dynamic_dim(t: torch.Tensor, index: int, debug_name: Optional[str] = None):
"""
.. warning::
(This feature is DEPRECATED. See :func:`Dim` instead.)
:func:`dynamic_dim` constructs a :class:`_Constraint` object that describes the dynamism of
a dimension ``index`` of tensor ``t``. :class:`_Constraint` objects should be passed to
``constraints`` argument of :func:`export`.
Args:
t (torch.Tensor): Example input tensor that have dynamic dimension size(s)
index (int): Index of dynamic dimension
Returns:
A :class:`_Constraint` object that describes shape dynamism. It can be passed to :func:`export` so
that :func:`export` does not assume static size of specified tensor, i.e. keeping it dynamic
as a symbolic size rather than specializing according to size of example tracing input.
Specifically :func:`dynamic_dim` can be used to express following types of dynamism.
- Size of a dimension is dynamic and unbounded::
t0 = torch.rand(2, 3)
t1 = torch.rand(3, 4)
# First dimension of t0 can be dynamic size rather than always being static size 2
constraints = [dynamic_dim(t0, 0)]
ep = export(fn, (t0, t1), constraints=constraints)
- Size of a dimension is dynamic with a lower bound::
t0 = torch.rand(10, 3)
t1 = torch.rand(3, 4)
# First dimension of t0 can be dynamic size with a lower bound of 5 (inclusive)
# Second dimension of t1 can be dynamic size with a lower bound of 2 (exclusive)
constraints = [
dynamic_dim(t0, 0) >= 5,
dynamic_dim(t1, 1) > 2,
]
ep = export(fn, (t0, t1), constraints=constraints)
- Size of a dimension is dynamic with an upper bound::
t0 = torch.rand(10, 3)
t1 = torch.rand(3, 4)
# First dimension of t0 can be dynamic size with a upper bound of 16 (inclusive)
# Second dimension of t1 can be dynamic size with a upper bound of 8 (exclusive)
constraints = [
dynamic_dim(t0, 0) <= 16,
dynamic_dim(t1, 1) < 8,
]
ep = export(fn, (t0, t1), constraints=constraints)
- Size of a dimension is dynamic and it is always equal to size of another dynamic dimension::
t0 = torch.rand(10, 3)
t1 = torch.rand(3, 4)
# Sizes of second dimension of t0 and first dimension are always equal
constraints = [
dynamic_dim(t0, 1) == dynamic_dim(t1, 0),
]
ep = export(fn, (t0, t1), constraints=constraints)
- Mix and match all types above as long as they do not express conflicting requirements
"""
from torch._dynamo.exc import UserError, UserErrorType
if not isinstance(t, torch.Tensor):
raise UserError(
UserErrorType.DYNAMIC_DIM,
f"Expected tensor as input to dynamic_dim but got {type(t)}",
)
if t.dim() < 1:
raise UserError(
UserErrorType.DYNAMIC_DIM, "Cannot mark 0-dimension tensors to be dynamic"
)
if index >= t.dim():
raise UserError(
UserErrorType.DYNAMIC_DIM,
f"Expected the dimension passed to dynamic_dim to be in the range [0:{t.dim()-1}]"
f" but got {index}, which is out of bounds for the given tensor.",
)
# Import sympy locally
import sympy
from torch.fx.experimental.symbolic_shapes import StrictMinMaxConstraint
from torch.utils._sympy.value_ranges import ValueRanges
return _create_constraint(
weakref.ref(t),
id(t),
index,
StrictMinMaxConstraint(
vr=ValueRanges(lower=2, upper=sympy.oo), warn_only=False
),
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 _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,
) -> Optional[List[Constraint]]:
from collections import defaultdict
from collections.abc import Mapping, Sequence
from torch._dynamo.exc import UserError, UserErrorType
if dynamic_shapes is None or len(dynamic_shapes) == 0:
return None
kwargs = kwargs if kwargs is not None else {}
def tree_zip(combined_args, dynamic_shapes):
if isinstance(combined_args, (tuple, list)):
if not isinstance(dynamic_shapes, Sequence):
raise UserError(
UserErrorType.INVALID_INPUT,
f"Expected dynamic_shapes of a {type(combined_args)} to be a Sequence, "
f"got {dynamic_shapes} instead",
)
if len(combined_args) != len(dynamic_shapes):
raise UserError(
UserErrorType.INVALID_INPUT,
f"Expected {dynamic_shapes} to have {len(combined_args)} items",
)
for i, shape in enumerate(dynamic_shapes):
yield from tree_zip(combined_args[i], shape)
elif isinstance(combined_args, dict):
if not isinstance(dynamic_shapes, Mapping):
raise UserError(
UserErrorType.INVALID_INPUT,
f"Expected dynamic_shapes of a {type(combined_args)} to be a Mapping, "
f"got {dynamic_shapes} instead",
)
if len(combined_args) != len(dynamic_shapes):
raise UserError(
UserErrorType.INVALID_INPUT,
f"Expected {dynamic_shapes} to have {len(combined_args)} items",
)
for k, shape in dynamic_shapes.items():
yield from tree_zip(combined_args[k], shape)
elif type(combined_args) in SUPPORTED_NODES:
if not isinstance(dynamic_shapes, Sequence):
raise UserError(
UserErrorType.INVALID_INPUT,
f"Expected dynamic_shapes of a user-registered class (e.g., "
f"{type(combined_args)}) to be a Sequence that matches the "
f"flattened structure, but got {dynamic_shapes} instead",
)
yield from tree_zip(
SUPPORTED_NODES[type(combined_args)].flatten_fn(combined_args)[0],
dynamic_shapes,
)
elif isinstance(combined_args, torch.Tensor):
yield (combined_args, dynamic_shapes)
else:
if dynamic_shapes is not None:
raise UserError(
UserErrorType.INVALID_INPUT,
f"Expected dynamic_shapes of a {type(combined_args)} to be None, "
f"got {dynamic_shapes} instead",
)
# 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: TRY200
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)
else:
constraint = dynamic_dim(tensor, i, debug_name=dim.__name__)
if dim.min != 2:
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):
if isinstance(shape, dict):
for i, dim in shape.items():
if isinstance(dim, _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, _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",
)
import inspect
if isinstance(f, ExportedProgram):
f = f.module()
signature = (
inspect.signature(f.forward)
if isinstance(f, torch.nn.Module)
else inspect.signature(f)
)
combined_args = signature.bind(*args, **kwargs).arguments
# This means user didn't specify dynamic shapes with argument names.
combined_args = combined_args if isinstance(dynamic_shapes, Mapping) else list(combined_args.values()) # type: ignore[assignment]
for tensor, shape in tree_zip(combined_args, dynamic_shapes):
update_symbols(tensor, shape)
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 _process_constraints(
fake_mode,
graph_module: torch.fx.GraphModule,
num_lifted_params_buffers: int,
example_inputs: List[torch.Tensor],
) -> Dict:
"""
Process the constraints stored in the graph module to return something more readable.
Args:
graph_module (torch.fx.GraphModule): GraphModule returned from
dynamo.export, which contains the "input_shape_constraints" and
"inline_constraints" metadata
example_inputs: Flattened list of example inputs used to export the graph module
Returns:
range_constraints (Dict[sympy.Symbol, ValueRanges]): Mapping of
symbols (from SymInts) appearing in the fake tensors in
node.meta["val"] to their range constraints, which are a tuple
containing (lower, upper) constraints.
"""
from torch._export.passes.add_runtime_assertions_for_constraints_pass import (
InputDim,
)
# Import sympy locally
from torch.fx.experimental.symbolic_shapes import SymInt
from torch.utils._sympy.value_ranges import ValueRanges
input_shape_constraints = graph_module.meta.get("input_shape_constraints", [])
inline_constraints = graph_module.meta.get("inline_constraints", [])
# Create dict mapping tensor_id to node names
tensor_id_to_nodes: Dict[int, List[str]] = defaultdict(list)
# Create dict mapping placeholder node names to their nodes
placeholder_nodes: Dict[str, torch.fx.Node] = {}
for i, node in enumerate(graph_module.graph.nodes):
if node.op != "placeholder":
# All placeholder nodes should be together in the beginning of the
# graph
break
if i >= num_lifted_params_buffers:
example_input = example_inputs[i - num_lifted_params_buffers]
tensor_id_to_nodes[id(example_input)].append(node.name)
placeholder_nodes[node.name] = node
# Create dict mapping (node name, dim) a list of range (lower, upper)
# constraints
multi_range_constraints: Dict[InputDim, List[ValueRanges]] = defaultdict(list)
for constraint in input_shape_constraints:
for node in tensor_id_to_nodes[constraint["t_id"]]:
node_dim = InputDim(node, constraint["dim"])
# Accumulate range constraints
multi_range_constraints[node_dim].append(
ValueRanges(constraint["min"], constraint["max"])
)
# Create dict mapping symbol to a singular range (lower, upper)
range_constraints: Dict[Any, ValueRanges] = {}
# Add inline constraints to range_constraints
range_constraints = {
symbol: inline_constraints[symbol] for symbol in inline_constraints
}
free_symbols: Set["Symbol"] = set()
# Add input range constraints to range_constraints
for input_dim, multi_range_constraint in multi_range_constraints.items(): # type: ignore[assignment]
# Simplify the range constraints into a single range constraint
# Ex. ranges [2, 10] and [3, 11] would get merged to [3, 10]
min_vals = [rc.lower for rc in multi_range_constraint]
max_vals = [rc.upper for rc in multi_range_constraint]
min_val = max(min_vals) # type: ignore[type-var]
max_val = min(max_vals) # type: ignore[type-var]
assert min_val <= max_val # type: ignore[operator]
# Add input node range constraints
val = placeholder_nodes[input_dim.input_name].meta["val"]
assert isinstance(val, FakeTensor)
symint = val.shape[input_dim.dim]
assert isinstance(
symint, SymInt
), f"Expected SymInt but got {symint}: {type(symint)}"
symbol = symint.node.expr
range_constraints[symbol] = ValueRanges(min_val, max_val)
free_symbols.update(symbol.free_symbols)
for symbol in free_symbols:
if symbol not in range_constraints:
# Placeholders can have symbolic shapes that are derived expressions.
# The above code will record direct range constraints for them
# so that we can do runtime assertions. In addition, for serde checks
# we want to record range constraints for their root symbols.
range_constraints[symbol] = fake_mode.shape_env.var_to_range[symbol]
return range_constraints
