torch.export.dynamic_shapes — PyTorch 2.7 documentation (original) (raw)
mypy: allow-untyped-defs
import dataclasses import inspect import logging import sys from collections import defaultdict from enum import auto, Enum from typing import Any, Callable, Optional, TYPE_CHECKING, Union
import torch from torch.utils._pytree import ( _get_node_type, BUILTIN_TYPES, keystr, LeafSpec, MappingKey, SequenceKey, SUPPORTED_NODES, tree_flatten, tree_map_with_path, )
from .exported_program import ExportedProgram
if TYPE_CHECKING: from sympy import Symbol
from torch._guards import Source
from torch.fx.experimental.symbolic_shapes import ShapeEnv, StrictMinMaxConstraintall = [ "Constraint", "Dim", "dims", "refine_dynamic_shapes_from_suggested_fixes", ]
log = logging.getLogger(name)
class _DimHint(Enum): """ Enum for dynamic shape hints. - AUTO means automatic inference of shape (static or dynamic). - STATIC means static shape (always specialized). - DYNAMIC means dynamic, will error out if specialized. """
AUTO = auto()
STATIC = auto()
DYNAMIC = auto()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}"
assert name.isidentifier(), f"Dim name must be a valid identifier, got {name}"
dim = _Dim(name, (int,), {"min": _min, "max": _max})
dim.__module__ = getattr(
inspect.getmodule(inspect.stack()[1][0]), "__name__", "__main__"
)
return dimDim.AUTO = _DimHint.AUTO # type: ignore[attr-defined] Dim.STATIC = _DimHint.STATIC # type: ignore[attr-defined] Dim.DYNAMIC = _DimHint.DYNAMIC # type: ignore[attr-defined]
[docs]def dims(
*names: str, min: Optional[int] = None, max: Optional[int] = None
) -> tuple[_Dim, ...]:
"""
Util to create multiple :func:Dim types.
Returns:
A tuple of :func:`Dim` types.
"""
return tuple(Dim(name, min=min, max=max) for name in names)@dataclasses.dataclass class _ConstraintTarget: """ This represents input tensor dimensions. """
t_id: int
dim: int@dataclasses.dataclass class _Constraint(_ConstraintTarget): """ This represents a Dim describing a constraint target.
`name` is the name of the Dim.
`constraint_range` contains the min/max bounds of the Dim.
"""
name: str
constraint_range: "StrictMinMaxConstraint"
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 _Constraint(
self.t_id,
self.dim,
self.name,
constraint_range,
)
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,
}@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).
It can be thought of as a subclass of `_Constraint`, except that it does not
support <, <=, >, >= operations.
"""
name: str
constraint_range: "StrictMinMaxConstraint"
root: Union[_ConstraintTarget, _PhantomRoot]
fn: Callable
@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,
}@dataclasses.dataclass class _RelaxedConstraint(_ConstraintTarget): """ This represents a dim marked with Dim.AUTO/DYNAMIC (i.e. mark_dynamic() or maybe_mark_dynamic()), which leaves relations & min/max ranges for inference, instead of requiring explicit specification. The intention is for constraint violations to not be raised if produce_guards() finds equalities or relations between a _RelaxedConstraint and another type of _Constraint. """
@property
def serializable_spec(self):
return {
"t_id": self.t_id,
"dim": self.dim,
}Constraint = Union[_Constraint, _DerivedConstraint, _RelaxedConstraint]
def _process_equalities(
constraint: Constraint,
get_sources: Callable[[int, int], list["Source"]],
shape_env: "ShapeEnv",
names: dict[str, tuple[int, int]],
source_pairs: list[tuple["Source", "Source"]],
derived_equalities: list[tuple["Source", Union["Source", "Symbol"], Callable]],
phantom_symbols: dict[str, "Symbol"],
relaxed_sources: set["Source"],
):
"""
Updates source_pairs, derived_equalities, and phantom_symbols (which become
fields of EqualityConstraint) based on a given input constraint.
"""
sources = get_sources(constraint.t_id, constraint.dim)
if not sources: # empty sources due to unused shapes
return
source, *other_sources = sources
# 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 isinstance(constraint, _Constraint):
if constraint.name in names:
shared_t_id, shared_dim = names[constraint.name]
other_sources = get_sources(shared_t_id, shared_dim)
source_pairs.extend(
(source, other_source) for other_source in other_sources
)
else:
names[constraint.name] = (constraint.t_id, constraint.dim)
elif isinstance(constraint, _DerivedConstraint):
# 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]
else:
# or root points to a phantom symbol
if constraint.root.name in phantom_symbols:
root = phantom_symbols[constraint.root.name]
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
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))
elif isinstance(constraint, _RelaxedConstraint):
relaxed_sources.add(source)def _tree_map_with_path( func: Callable[..., Any], tree: Any, *dynamic_shapes: Any, tree_name: Optional[str] = None, ) -> 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(path, 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_with_path(
f,
SUPPORTED_NODES[typ].flatten_fn(t)[0],
*dynamic_shapes,
is_leaf=is_leaf,
)
else:
return func(path, t, *dynamic_shapes)
try:
return tree_map_with_path(f, tree, *dynamic_shapes, is_leaf=is_leaf)
except ValueError as e:
if "mismatch" in e.args[0]:
# When PyTree finds a structural mismatch between tree and dynamic_shapes,
# the error message is unfortunately quite horrible. Let's fix that.
assert dynamic_shapes, "Cannot be a mismatch if there is no dynamic_shapes"
assert tree_name, "Must provide a tree_name when there might be a mismatch"
def _key(type_, context, i):
# derive a PyTree key given the type, context, and child # of a TreeSpec
if type_ is dict:
return MappingKey(context[i])
if type_ in (list, tuple):
assert context is None
return SequenceKey(i)
raise AssertionError(f"Did not expect type {type_}")
def raise_mismatch_error(msg):
from torch._dynamo.exc import UserError, UserErrorType
raise UserError(
UserErrorType.INVALID_INPUT,
f"Detected mismatch between the structure of `{tree_name}` and `dynamic_shapes`: {msg}",
case_name="dynamic_shapes_validation",
)
def _compare(tree, dynamic_shapes, path):
# raise an error at the point where tree and dynamic_shapes differ,
# including the path to that point and the reason for the difference
rendered_path = keystr(path)
if isinstance(tree, LeafSpec):
return
if isinstance(dynamic_shapes, LeafSpec):
raise_mismatch_error(
f"`{tree_name}{rendered_path}` is a {tree.type}, "
f"but `dynamic_shapes{rendered_path}` is not"
)
if tree.type != dynamic_shapes.type:
raise_mismatch_error(
f"`{tree_name}{rendered_path}` is a {tree.type}, "
f"but `dynamic_shapes{rendered_path}` is a {dynamic_shapes.type}"
)
if len(tree.children_specs) != len(dynamic_shapes.children_specs):
raise_mismatch_error(
f"`{tree_name}{rendered_path}` has {len(tree.children_specs)} elements, "
f"but `dynamic_shapes{rendered_path}` has {len(dynamic_shapes.children_specs)} elements"
)
if tree.type is dict:
# context, children could be out of order
if sorted(tree.context) != sorted(dynamic_shapes.context):
raise_mismatch_error(
f"`{tree_name}{rendered_path}` has keys {tree.context}, "
f"but `dynamic_shapes{rendered_path}` has keys {dynamic_shapes.context}"
)
_remap = dict(
zip(dynamic_shapes.context, dynamic_shapes.children_specs)
)
dynamic_shapes_children_specs = [_remap[k] for k in tree.context]
else:
dynamic_shapes_children_specs = dynamic_shapes.children_specs
for i, (tree_, dynamic_shapes_) in enumerate(
zip(tree.children_specs, dynamic_shapes_children_specs)
):
_compare(
tree_,
dynamic_shapes_,
path + [_key(tree.type, tree.context, i)],
)
_, tree_spec = tree_flatten(tree, is_leaf=is_leaf)
for other_tree in dynamic_shapes:
_, other_tree_spec = tree_flatten(other_tree, is_leaf)
_compare(tree_spec, other_tree_spec, [])
raisedef _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.
This is useful particularly when :func:`args` is a nested input structure, and it's
easier to index the input tensors, than to replicate the structure of :func:`args` in
the :func:`dynamic_shapes` specification.
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 not in self._shapes:
self._shapes[t_id] = {}
return self._shapes[t_id]
def __len__(self):
return len(self._shapes)[docs] def dynamic_shapes(self, m, args, kwargs=None):
"""
Generates the :func:dynamic_shapes pytree structure according to :func:args and :func:kwargs.
"""
t_ids = set()
def find_shape(path, 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_with_path(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_shapesdef _warn_on_None_dynamic_shape_dimension(): msg = ( "Using None as a dynamic shape dimension is deprecated. " "Please use Dim.STATIC instead" ) # TODO(avik): raise an error in the future log.warning(msg)
def _check_dynamic_shapes( combined_args: dict[str, Any], dynamic_shapes: Union[dict[str, Any], tuple[Any], list[Any], None], ): """ Checks the dynamic_shapes specification for correctness, using combined args + kwargs as reference for inputs structure. """ from torch._dynamo.exc import UserError, UserErrorType
if dynamic_shapes is None or len(dynamic_shapes) == 0:
return
if isinstance(dynamic_shapes, (tuple, list)):
combined_args = type(dynamic_shapes)(combined_args.values()) # type: ignore[assignment, misc]
bounds: dict[str, tuple[int, int]] = {}
def check_same_bounds(dim):
if dim.__name__ in bounds:
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 check_symbols(path, tensor, shape):
if isinstance(shape, dict):
for i, dim in shape.items():
if isinstance(dim, _Dim):
check_same_bounds(dim)
elif dim is None:
_warn_on_None_dynamic_shape_dimension()
elif not (isinstance(dim, (int, _DimHint))):
raise UserError(
UserErrorType.INVALID_INPUT,
f"Unexpected dimension mapped to index {i} in input tensor shape {shape} "
f"specified at `dynamic_shapes{keystr(path)}` "
f"(expected None, an int, a Dim, Dim.AUTO, Dim.STATIC, or Dim.DYNAMIC, "
f" but got {dim} instead)",
case_name="dynamic_shapes_validation",
)
elif isinstance(shape, (tuple, list)):
for i, dim in enumerate(shape):
if isinstance(dim, _Dim):
check_same_bounds(dim)
elif dim is None:
_warn_on_None_dynamic_shape_dimension()
elif not (isinstance(dim, (int, _DimHint))):
raise UserError(
UserErrorType.INVALID_INPUT,
f"Unexpected dimension #{i} in input tensor shape {shape} "
f"specified at `dynamic_shapes{keystr(path)}` "
f"(expected None, an int, a Dim, Dim.AUTO, Dim.STATIC, or Dim.DYNAMIC, "
f"but got {dim} instead)",
case_name="dynamic_shapes_validation",
)
elif shape is not None:
raise UserError(
UserErrorType.INVALID_INPUT,
f"Unexpected input tensor shape {shape} specified at `dynamic_shapes{keystr(path)}` "
f"(expected either a list/tuple of dimensions, or a dict mapping indices to dimensions,"
f" where each dimension is an int, a Dim, Dim.AUTO, Dim.STATIC, or Dim.DYNAMIC)",
case_name="dynamic_shapes_validation",
)
assert isinstance(dynamic_shapes, (dict, tuple, list))
if isinstance(dynamic_shapes, dict):
got_keys = list(dynamic_shapes.keys())
expected_arg_names = list(combined_args.keys())
if sorted(got_keys) != sorted(expected_arg_names):
msg = (
f"When `dynamic_shapes` is specified as a dict, its top-level keys "
f"must be the arg names {expected_arg_names} of `inputs`, but "
f"here they are {got_keys}. "
)
if (
len(combined_args) == 1
and expected_arg_names[0] not in got_keys
and isinstance(combined_args[expected_arg_names[0]], dict)
):
msg += (
"Since here `inputs` is a list/tuple enclosing a single dict, "
"maybe you just forgot to enclose `dynamic_shapes` in a list/tuple?"
)
else:
msg += (
"Alternatively, you could also ignore arg names entirely "
"and specify `dynamic_shapes` as a list/tuple matching `inputs`."
)
raise UserError(
UserErrorType.INVALID_INPUT, msg, case_name="dynamic_shapes_validation"
)
def check_shape(path, t, dynamic_shape):
if isinstance(t, torch.Tensor):
check_symbols(path, t, dynamic_shape)
else:
if dynamic_shape is not None:
rendered_path = keystr(path)
raise UserError(
UserErrorType.INVALID_INPUT,
f"Cannot associate shape {dynamic_shape} specified at `dynamic_shapes{rendered_path}` "
f"to non-tensor type {type(t)} at `inputs{rendered_path}` (expected None)",
case_name="dynamic_shapes_validation",
)
_tree_map_with_path(check_shape, combined_args, dynamic_shapes, tree_name="inputs")def _process_dynamic_shapes( combined_args: dict[str, Any], dynamic_shapes: Union[dict[str, Any], tuple[Any], list[Any], None], ) -> list[Constraint]: """ Reads the dynamic_shapes specification and produces a list of constraints. """ from torch._dynamo.exc import UserError, UserErrorType
if dynamic_shapes is None or len(dynamic_shapes) == 0:
# we run with dynamic by default, so no need to produce constraints
return []
if isinstance(dynamic_shapes, (tuple, list)):
combined_args = type(dynamic_shapes)(combined_args.values()) # type: ignore[assignment, misc]
# 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] = []
# list of constraints to return
constraints: list[Constraint] = []
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])
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.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(
id(tensor),
i,
dim.__name__,
StrictMinMaxConstraint(
vr=ValueRanges(lower=dim.min, upper=dim.max),
warn_only=False,
),
root,
dim.fn, # type: ignore[attr-defined]
)
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 = _Constraint( # type: ignore[assignment]
id(tensor),
i,
dim.__name__,
StrictMinMaxConstraint(
vr=ValueRanges(lower=dim.value, upper=dim.value), warn_only=False # type: ignore[attr-defined]
),
)
else:
assert isinstance(dim, _Dim)
constraint = _Constraint( # type: ignore[assignment]
id(tensor),
i,
dim.__name__,
StrictMinMaxConstraint(
vr=ValueRanges(lower=dim.min, upper=dim.max), warn_only=False # type: ignore[attr-defined]
),
)
return constraint
def update_symbols(path, tensor, shape):
def _create_static_dim(tensor, i, value):
return _StaticDim(str(value), (int,), {"value": value})
# clean out decorators from user side, or previous export call
# we also delete these attributes in non_strict_utils.py/make_constraints()
tensor._dynamo_weak_dynamic_indices = set()
tensor._dynamo_dynamic_indices = set()
tensor._dynamo_dynamic_range = set()
tensor._dynamo_static_indices = set()
tensor._dynamo_unbacked_indices = set()
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)
constraint = to_constraint(dim, tensor, i)
symbols[dim.__name__].append(constraint)
elif isinstance(dim, _DimHint):
if dim == _DimHint.AUTO:
torch._dynamo.maybe_mark_dynamic(tensor, i)
elif dim == _DimHint.STATIC:
torch._dynamo.mark_static(tensor, i)
elif dim == _DimHint.DYNAMIC:
torch._dynamo.mark_dynamic(tensor, i)
constraints.append(_RelaxedConstraint(id(tensor), i))
elif dim is None:
torch._dynamo.mark_static(tensor, i)
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)
constraint = to_constraint(dim, tensor, i)
symbols[dim.__name__].append(constraint)
elif isinstance(dim, _DimHint):
if dim == _DimHint.AUTO:
torch._dynamo.maybe_mark_dynamic(tensor, i)
elif dim == _DimHint.STATIC:
torch._dynamo.mark_static(tensor, i)
elif dim == _DimHint.DYNAMIC:
torch._dynamo.mark_dynamic(tensor, i)
constraints.append(_RelaxedConstraint(id(tensor), i))
elif dim is None:
torch._dynamo.mark_static(tensor, i)
elif shape is None:
for i in range(tensor.dim()):
torch._dynamo.mark_static(tensor, i)
def assoc_shape(path, t, dynamic_shape):
if isinstance(t, torch.Tensor):
update_symbols(path, t, dynamic_shape)
_tree_map_with_path(assoc_shape, combined_args, dynamic_shapes, tree_name="inputs")
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():
constraints.extend(dynamic_dims)
return constraintsdef _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: # NOTE: this must denote a non-Tensor or automatic at this point. continue if isinstance(dim, int): continue elif isinstance(dim, _Dim): name_to_dim[dim.name] = dim if isinstance(dim, _DerivedDim): name_to_dim[dim.root.name] = dim.root # type: ignore[attr-defined] else: assert isinstance(dim, _DimHint) 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]]:
"""
When exporting with :func:dynamic_shapes, export may fail with a ConstraintViolation error if the specification
doesn't match the constraints inferred from tracing the model. The error message may provide suggested fixes -
changes that can be made to :func:dynamic_shapes to export successfully.
Example ConstraintViolation error message::
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
This is a helper function that takes the ConstraintViolation error message and the original :func:`dynamic_shapes` spec,
and returns a new :func:`dynamic_shapes` spec that incorporates the suggested fixes.
Example usage::
try:
ep = export(mod, args, dynamic_shapes=dynamic_shapes)
except torch._dynamo.exc.UserError as exc:
new_shapes = refine_dynamic_shapes_from_suggested_fixes(
exc.msg, dynamic_shapes
)
ep = export(mod, args, dynamic_shapes=new_shapes)
"""
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):
# static, integer
shape_fixes[name] = int(expr)
else:
# relation or derived dim
shape_fixes[name] = expr
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(path, 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:
root = shape_fixes[symbol.name]
else:
assert symbol.name in name_to_dim
root = name_to_dim[symbol.name]
# 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_with_path(apply_fixes, dynamic_shapes, dynamic_shapes)