Source code for torch.export.dynamic_shapes
# mypy: allow-untyped-defs
import dataclasses
import inspect
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.utils._pytree import (
_get_node_type,
BUILTIN_TYPES,
SUPPORTED_NODES,
tree_flatten,
tree_map,
)
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",
"refine_dynamic_shapes_from_suggested_fixes",
]
class _Dim(type):
"""
Metaclass for :func:`Dim` types.
"""
@staticmethod
def readable(name, min_, max_):
from torch.utils._sympy.numbers import int_oo
if min_ == 2:
min_ = None
if max_ == int_oo:
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 _StaticDim(_Dim):
"""
Meta class for static :func:`Dim` types.
This class is only for setting and checking static dim constraints,
and the user should never interact with it.
"""
@property
def min(self):
return self.value # type: ignore[attr-defined]
@property
def max(self):
return self.value # type: ignore[attr-defined]
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
from torch.utils._sympy.numbers import int_oo
if self.root.min is -int_oo: # type: ignore[attr-defined]
return -int_oo # fn not needed cuz increasing
_min_symint = self.fn(Integer(self.root.min)) # type: ignore[attr-defined]
root = self.root # type: ignore[attr-defined]
assert _min_symint >= 0, (
f"Expected derived min value of {self.__name__} to be >= 0. "
f"Please specify an appropriate min value for {root.__name__} "
f"(currently {root.min})."
)
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
from torch.utils._sympy.numbers import int_oo
if self.root.max is int_oo: # type: ignore[attr-defined]
return int_oo # fn not needed cuz increasing
_max_symint = self.fn(Integer(self.root.max)) # type: ignore[attr-defined]
root = self.root # 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 {root.__name__} "
f"(currently {root.max})."
)
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.
"""
from torch.utils._sympy.numbers import int_oo
_min = 0 if min is None else min
_max = int_oo if max is None else max
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=0, upper=None):
# Import sympy locally
from torch.fx.experimental.symbolic_shapes import StrictMinMaxConstraint
from torch.utils._sympy.numbers import int_oo
from torch.utils._sympy.value_ranges import ValueRanges
if upper is None:
upper = int_oo
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
from torch.fx.experimental.symbolic_shapes import StrictMinMaxConstraint
from torch.utils._sympy.numbers import int_oo
from torch.utils._sympy.value_ranges import ValueRanges
return _create_constraint(
weakref.ref(t),
id(t),
index,
StrictMinMaxConstraint(vr=ValueRanges(lower=0, upper=int_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 _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) -> Dict[str, Any]:
# 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
[docs]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)
[docs] 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.numbers import int_oo
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 != int_oo:
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
[docs]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)
