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pytorch/torch/_dynamo/variables/builtin.py
Yuanyuan Chen 3255e7872b Enable all flake8-logging-format rules (#164655) 
These rules are enabled by removing existing suppressions.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/164655
Approved by: https://github.com/janeyx99, https://github.com/mlazos
2025-10-19 00:59:28 +00:00

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# mypy: allow-untyped-defs
"""
Built-in function and type variable tracking for TorchDynamo's symbolic execution.
This module contains variable tracker classes for Python built-in functions, types,
and operations during graph compilation. It handles symbolic execution of:
- Built-in functions (len, getattr, isinstance, etc.)
- Type constructors (int, float, str, list, dict, etc.)
- Built-in operators and methods
- Special Python constructs (super, hasattr, etc.)
Key classes:
- BuiltinVariable: Tracks built-in functions and handles their execution
- TypeVariable: Manages type constructor calls and type checking
- SuperVariable: Handles super() calls in class hierarchies
These variable trackers ensure that built-in Python operations are correctly
handled during symbolic execution, either by executing them directly when safe
or by creating appropriate graph nodes when needed.
"""
import contextlib
import functools
import inspect
import itertools
import logging
import math
import operator
import types
import typing
import unittest
from collections import defaultdict, OrderedDict
from collections.abc import Iterable, KeysView, Sequence
from typing import Any, Callable, TYPE_CHECKING, Union
import torch
from torch import sym_float, sym_int
from torch._subclasses.meta_utils import is_sparse_any
from torch.overrides import BaseTorchFunctionMode
from torch.utils._python_dispatch import is_traceable_wrapper_subclass
from .. import config, graph_break_hints, polyfills, variables
from ..exc import (
AttributeMutationError,
ObservedAttributeError,
ObservedUserStopIteration,
raise_observed_exception,
unimplemented_v2,
Unsupported,
UserError,
UserErrorType,
)
from ..guards import GuardBuilder, install_guard
from ..replay_record import DummyModule
from ..source import (
AttrSource,
GetItemSource,
GlobalSource,
is_constant_source,
TypeSource,
)
from ..utils import (
check_constant_args,
check_numpy_ndarray_args,
check_unspec_or_constant_args,
check_unspec_python_args,
cmp_name_to_op_mapping,
dict_methods,
extract_fake_example_value,
frozenset_methods,
get_fake_value,
guard_if_dyn,
is_tensor_getset_descriptor,
is_wrapper_or_member_descriptor,
istype,
numpy_operator_wrapper,
proxy_args_kwargs,
set_methods,
str_methods,
tensortype_to_dtype,
)
from .base import AsPythonConstantNotImplementedError, ValueMutationNew, VariableTracker
from .constant import ConstantVariable
from .dicts import (
ConstDictVariable,
DefaultDictVariable,
DictKeysVariable,
DictViewVariable,
FrozensetVariable,
is_hashable,
SetVariable,
)
from .lists import (
BaseListVariable,
ListIteratorVariable,
ListVariable,
SizeVariable,
TupleIteratorVariable,
TupleVariable,
)
from .streams import EventVariable, StreamVariable
from .tensor import (
FakeItemVariable,
supported_comparison_ops,
SymNodeVariable,
TensorVariable,
UnspecializedPythonVariable,
)
from .user_defined import (
MutableMappingVariable,
UserDefinedDictVariable,
UserDefinedObjectVariable,
UserDefinedVariable,
)
if TYPE_CHECKING:
# Cyclic dependency...
from torch._dynamo.codegen import PyCodegen
from torch._dynamo.symbolic_convert import InstructionTranslator
log = logging.getLogger(__name__)
IN_PLACE_DESUGARING_MAP = {
operator.iadd: operator.add,
operator.isub: operator.sub,
operator.imul: operator.mul,
operator.ifloordiv: operator.floordiv,
operator.itruediv: operator.truediv,
operator.imod: operator.mod,
operator.imatmul: operator.imatmul,
operator.ilshift: operator.lshift,
operator.irshift: operator.rshift,
operator.ipow: operator.pow,
operator.iand: operator.and_,
operator.ior: operator.or_,
operator.ixor: operator.xor,
}
_HandlerCallback = Callable[
["InstructionTranslator", typing.Any, typing.Any], VariableTracker
]
_TrackersType = Union[type[VariableTracker], tuple[type[VariableTracker], ...]]
polyfill_fn_mapping = {
operator.eq: polyfills.cmp_eq,
operator.ne: polyfills.cmp_ne,
operator.lt: polyfills.cmp_lt,
operator.le: polyfills.cmp_le,
operator.gt: polyfills.cmp_gt,
operator.ge: polyfills.cmp_ge,
}
bin_ops = (
operator.pow,
operator.mul,
operator.matmul,
operator.floordiv,
operator.truediv,
operator.mod,
operator.add,
operator.lt,
operator.gt,
operator.ge,
operator.le,
operator.ne,
operator.eq,
operator.sub,
operator.ipow,
operator.imul,
operator.imatmul,
operator.ifloordiv,
operator.itruediv,
operator.imod,
operator.iadd,
operator.isub,
)
bin_int_ops = (
operator.and_,
operator.or_,
operator.xor,
operator.iand,
operator.ixor,
operator.ior,
)
un_int_ops = (operator.invert,)
tensor_and_int_ops = (
operator.lshift,
operator.rshift,
operator.ilshift,
operator.irshift,
operator.getitem,
)
un_ops = (
operator.abs,
operator.pos,
operator.neg,
operator.not_, # Note: this has a local scalar dense call
operator.length_hint,
)
BUILTIN_TO_TENSOR_FN_MAP: dict[Callable[..., Any], Callable[..., Any]] = {}
# These functions represent the r* versions of the above ops
# Basically, if __add__(1, Tensor) is called, it is translated
# to __radd__(Tensor, 1).
# In the builtin var, we check if there is a tensor in the first args position,
# if not, we swap the args and use the r* version of the op.
BUILTIN_TO_TENSOR_RFN_MAP: dict[Callable[..., Any], Callable[..., Any]] = {}
def populate_builtin_to_tensor_fn_map():
global BUILTIN_TO_TENSOR_FN_MAP
if len(BUILTIN_TO_TENSOR_FN_MAP) > 0:
# Only populate once; after there are elements present no need to
# repopulate
return
most_recent_func = None
class GetMethodMode(BaseTorchFunctionMode):
"""
Mode to extract the correct methods from torch function invocations
(Used to get the correct torch.Tensor methods from builtins)
"""
def __torch_function__(self, func, types, args=(), kwargs=None):
kwargs = kwargs or {}
nonlocal most_recent_func
most_recent_func = func
return func(*args, **kwargs)
inp0 = torch.ones(1)
inp1 = torch.ones(1)
inp0_int = torch.ones(1, dtype=torch.int32)
inp1_int = torch.ones(1, dtype=torch.int32)
with GetMethodMode():
setups_and_oplists: list[tuple[Callable[..., Any], Iterable[Any]]] = [
(lambda o: o(inp0), un_ops),
(lambda o: o(inp0_int), un_int_ops),
(lambda o: o(inp0, inp1), bin_ops),
(lambda o: o(inp0_int, inp1_int), bin_int_ops),
(lambda o: o(inp0_int, 0), tensor_and_int_ops),
]
for setup_fn, op_list in setups_and_oplists:
for op in op_list:
setup_fn(op)
assert most_recent_func is not None
BUILTIN_TO_TENSOR_FN_MAP[op] = most_recent_func
# gather the reverse functions
rsetups_and_oplists: list[tuple[Callable[..., Any], Iterable[Any]]] = [
(
lambda o: o(1, inp1),
bin_ops,
), # Get r* ops, (ex. __sub__(int, Tensor) -> __rsub__(Tensor, int))
(lambda o: o(1, inp1_int), bin_int_ops),
(lambda o: o(0, inp0_int), tensor_and_int_ops),
]
rskips = {operator.matmul, operator.imatmul, operator.getitem}
for setup_fn, op_list in rsetups_and_oplists:
for op in op_list:
if op in rskips:
continue
setup_fn(op)
assert most_recent_func is not None
if most_recent_func != BUILTIN_TO_TENSOR_FN_MAP[op]:
BUILTIN_TO_TENSOR_RFN_MAP[op] = most_recent_func
class BuiltinVariable(VariableTracker):
"""
A VariableTracker that represents a built-in value (functions and operators).
A lot of the code here assumes it will be a function object.
The BuiltinVariable class wraps Python built-in functions (like len, isinstance, etc.)
and operators (like +, -, *, etc.) to enable symbolic execution during tracing. This allows
Dynamo to properly handle these operations when converting Python code to FX graphs while
maintaining correct semantics and enabling optimizations.
"""
_SENTINEL = object()
_nonvar_fields = {
"fn",
*VariableTracker._nonvar_fields,
}
@classmethod
def create_with_source(cls, value, source):
install_guard(source.make_guard(GuardBuilder.BUILTIN_MATCH))
return cls(value, source=source)
@staticmethod
@functools.cache
def _constant_fold_functions():
fns = {
abs,
all,
any,
bool,
callable,
chr,
complex,
divmod,
float,
getattr,
int,
len,
max,
min,
ord,
pow,
repr,
round,
str,
str.format,
sum,
type,
operator.abs,
operator.pos,
operator.neg,
operator.not_,
operator.truth,
operator.invert,
operator.pow,
operator.mul,
operator.matmul,
operator.floordiv,
operator.truediv,
operator.mod,
operator.add,
operator.sub,
operator.getitem,
operator.length_hint,
operator.lshift,
operator.rshift,
operator.and_,
operator.or_,
operator.xor,
operator.ipow,
operator.imul,
operator.imatmul,
operator.ifloordiv,
operator.itruediv,
operator.imod,
operator.iadd,
operator.isub,
operator.ilshift,
operator.irshift,
operator.iand,
operator.ixor,
operator.ior,
operator.index,
}
from .tensor import supported_comparison_ops
fns.update(supported_comparison_ops.values())
fns.update(x for x in math.__dict__.values() if isinstance(x, type(math.sqrt)))
return fns
def can_constant_fold_through(self):
return self.fn in self._constant_fold_functions()
@staticmethod
@functools.cache
def _fx_graph_functions():
fns = {
operator.abs,
operator.pos,
operator.neg,
operator.not_,
operator.invert,
operator.pow,
operator.mul,
operator.matmul,
operator.floordiv,
operator.truediv,
operator.mod,
operator.add,
operator.lt,
operator.gt,
operator.ge,
operator.le,
operator.ne,
operator.eq,
operator.sub,
operator.length_hint,
operator.lshift,
operator.rshift,
operator.and_,
operator.or_,
operator.xor,
operator.ipow,
operator.imul,
operator.imatmul,
operator.ifloordiv,
operator.itruediv,
operator.getitem,
operator.imod,
operator.iadd,
operator.isub,
operator.ilshift,
operator.irshift,
operator.iand,
operator.ixor,
operator.ior,
}
return fns
@staticmethod
@functools.cache
def _binops() -> dict[
Callable[..., object], tuple[list[str], Callable[..., object]]
]:
# function -> ([forward name, reverse name, in-place name], in-place op)
fns: dict[Callable[..., object], tuple[list[str], Callable[..., object]]] = {
operator.add: (["__add__", "__radd__", "__iadd__"], operator.iadd),
operator.sub: (["__sub__", "__rsub__", "__isub__"], operator.isub),
operator.mul: (["__mul__", "__rmul__", "__imul__"], operator.imul),
operator.truediv: (
["__truediv__", "__rtruediv__", "__itruediv__"],
operator.itruediv,
),
operator.floordiv: (
["__floordiv__", "__rfloordiv__", "__ifloordiv__"],
operator.ifloordiv,
),
operator.mod: (["__mod__", "__rmod__", "__imod__"], operator.imod),
pow: (["__pow__", "__rpow__", "__ipow__"], operator.ipow),
operator.pow: (["__pow__", "__rpow__", "__ipow__"], operator.ipow),
operator.lshift: (
["__lshift__", "__rlshift__", "__ilshift__"],
operator.ilshift,
),
operator.rshift: (
["__rshift__", "__rrshift__", "__irshift__"],
operator.irshift,
),
# NB: The follow binary operators are not supported for now, since the
# corresponding magic methods aren't defined on SymInt / SymFloat:
# operator.matmul
# divmod
# operator.and_
# operator.or_
# operator.xor
}
return fns
@staticmethod
@functools.cache
def _binop_handlers():
# Multiple dispatch mechanism defining custom binop behavior for certain type
# combinations. Handlers are attempted in order, and will be used if the type checks
# match. They are expected to have the signature:
# fn(tx, arg0: VariableTracker, arg1: VariableTracker) -> VariableTracker
from .functions import BaseUserFunctionVariable, UserFunctionVariable
from .nn_module import NNModuleVariable
from .tensor import supported_const_comparison_ops
from .torch import BaseTorchVariable
from .user_defined import (
UserDefinedClassVariable,
UserDefinedObjectVariable,
UserDefinedVariable,
)
# Override table contains: op_fn -> [list of handlers]
op_handlers: dict[
Callable[..., object],
list[
tuple[
tuple[
type[VariableTracker],
_TrackersType,
],
_HandlerCallback,
]
],
] = {}
for (
op,
(magic_method_names, in_place_op),
) in BuiltinVariable._binops().items():
op_handlers[op] = []
op_handlers[in_place_op] = []
forward_name, reverse_name, inplace_name = magic_method_names
# User-defined args (highest precedence)
def user_defined_handler(
tx,
a,
b,
*,
forward_name=forward_name,
reverse_name=reverse_name,
):
# Manually handle reversing logic if needed (e.g. call __radd__)
# TODO: If we expand this to handle tensor args, we need to manually
# handle cases like this:
#
# class A(int):
# def __radd__(self, other):
# print("woof")
# torch.randn(3) + A(3)
#
# In this example, A.__radd__() is not called -> nothing is printed, because
# Tensor.__add__ only does a subtype test against int, ignoring the subclass.
# To be fully correct, we should not call A.__radd__() here, and there may be
# other cases to reason about and add exceptions for.
if isinstance(a, UserDefinedVariable):
return a.call_method(tx, forward_name, [b], {})
else:
return b.call_method(tx, reverse_name, [a], {})
op_handlers[op].append(
((UserDefinedVariable, VariableTracker), user_defined_handler)
)
op_handlers[op].append(
((VariableTracker, UserDefinedVariable), user_defined_handler)
)
def user_defined_inplace_handler(
tx: "InstructionTranslator", a, b, *, forward_name=inplace_name
):
return a.call_method(tx, forward_name, [b], {})
op_handlers[in_place_op].append(
((UserDefinedVariable, VariableTracker), user_defined_inplace_handler)
)
op_handlers[in_place_op].append(
((VariableTracker, UserDefinedVariable), user_defined_inplace_handler)
)
# Dynamic shape args
def dynamic_handler(tx: "InstructionTranslator", a, b, *, fn=op):
from .builder import wrap_fx_proxy
return wrap_fx_proxy(
tx,
tx.output.create_proxy(
"call_function", fn, *proxy_args_kwargs([a, b], {})
),
)
op_handlers[op].append(
((SymNodeVariable, VariableTracker), dynamic_handler)
)
op_handlers[op].append(
((VariableTracker, SymNodeVariable), dynamic_handler)
)
# NB: Prefer out-of-place op when calling in-place op to generate valid graph
op_handlers[in_place_op].append(
((SymNodeVariable, VariableTracker), dynamic_handler)
)
op_handlers[in_place_op].append(
((VariableTracker, SymNodeVariable), dynamic_handler)
)
# Special cases - lower precedence but still prefer these over constant folding
# List-like addition (e.g. [1, 2] + [3, 4])
def tuple_add_handler(tx: "InstructionTranslator", a, b):
return TupleVariable([*a.items, *b.unpack_var_sequence(tx)])
def size_add_handler(tx: "InstructionTranslator", a, b):
return SizeVariable([*a.items, *b.unpack_var_sequence(tx)])
list_like_addition_handlers: list[
tuple[
tuple[
type[VariableTracker],
_TrackersType,
],
_HandlerCallback,
]
] = [
# NB: Prefer the tuple-specific logic over base logic because of
# some SizeVariable weirdness. Specifically, the tuple-specific logic
# drops the subclass type (e.g. SizeVariable) and returns TupleVariables.
(
(SizeVariable, SizeVariable),
size_add_handler,
),
(
(SizeVariable, TupleVariable),
size_add_handler,
),
(
(TupleVariable, SizeVariable),
size_add_handler,
),
(
(TupleVariable, TupleVariable),
tuple_add_handler,
),
(
(TupleVariable, ConstantVariable),
tuple_add_handler,
),
(
(ConstantVariable, TupleVariable),
lambda tx, a, b: TupleVariable(
[
*a.unpack_var_sequence(tx),
*b.items,
],
),
),
(
(
ListVariable,
(BaseListVariable, ConstantVariable, ListIteratorVariable),
),
lambda tx, a, b: ListVariable(
[*a.items, *b.unpack_var_sequence(tx)],
mutation_type=ValueMutationNew(),
),
),
(
(BaseListVariable, BaseListVariable),
lambda tx, a, b: type(a)(
[
*a.items,
*b.items,
]
),
),
]
op_handlers[operator.add].extend(list_like_addition_handlers)
def list_iadd_handler(tx: "InstructionTranslator", a, b):
if a.is_immutable() or not b.has_unpack_var_sequence(tx):
# Handler doesn't apply
return None
seq = b.unpack_var_sequence(tx)
tx.output.side_effects.mutation(a)
a.items.extend(seq)
return a
list_like_iadd_handlers: list[
tuple[
tuple[type[VariableTracker], type[VariableTracker]],
_HandlerCallback,
]
] = [
(
(ListVariable, VariableTracker),
list_iadd_handler,
),
(
(TupleVariable, TupleVariable),
tuple_add_handler,
),
(
(TupleVariable, ConstantVariable),
tuple_add_handler,
),
]
op_handlers[operator.iadd].extend(list_like_iadd_handlers)
# List-like expansion (e.g. [1, 2, 3] * 3)
def expand_list_like(tx: "InstructionTranslator", lst, const):
if isinstance(lst, ConstantVariable):
lst, const = const, lst
try:
return lst.__class__(
items=lst.items * const.as_python_constant(),
mutation_type=ValueMutationNew(),
)
except MemoryError as exc:
raise_observed_exception(
type(exc),
tx,
args=list(map(ConstantVariable.create, exc.args)),
)
list_like_expansion_handlers: list[
tuple[
tuple[type[VariableTracker], type[VariableTracker]],
_HandlerCallback,
]
] = [
((ListVariable, ConstantVariable), expand_list_like),
((TupleVariable, ConstantVariable), expand_list_like),
((ConstantVariable, ListVariable), expand_list_like),
((ConstantVariable, TupleVariable), expand_list_like),
]
op_handlers[operator.mul].extend(list_like_expansion_handlers)
def create_cmp_op_handlers(op):
def compare_by_value(tx: "InstructionTranslator", a, b):
try:
return ConstantVariable(op(a.value, b.value))
except TypeError as exc:
raise_observed_exception(
type(exc),
tx,
args=list(map(ConstantVariable.create, exc.args)),
)
result: list[
tuple[
tuple[
_TrackersType,
_TrackersType,
],
_HandlerCallback,
]
] = [((ConstantVariable, ConstantVariable), compare_by_value)]
if op in polyfill_fn_mapping:
# For constants, speedup the comparison instead of using
# polyfill. Removing this line causes major regression for pr
# time benchmark - add_loop_eager.
result = [((ConstantVariable, ConstantVariable), compare_by_value)]
op_var = BuiltinVariable(op)
# Special handling of SymNode variable
result.extend(
[
(
(SymNodeVariable, VariableTracker),
op_var._comparison_with_symnode,
),
(
(VariableTracker, SymNodeVariable),
op_var._comparison_with_symnode,
),
]
)
def handler(tx, a, b):
return tx.inline_user_function_return(
VariableTracker.build(tx, polyfill_fn_mapping[op]), [a, b], {}
)
result.append(((VariableTracker, VariableTracker), handler))
return result
result = [((ConstantVariable, ConstantVariable), compare_by_value)]
if op in supported_const_comparison_ops.values() and op.__name__.startswith(
"is_"
):
# Tensor is None, List is not None, etc
none_result = op(object(), None)
def never(tx: "InstructionTranslator", a, b):
return ConstantVariable(none_result)
obj_op_none = never
none_op_obj = never
types_that_are_never_none = (
TensorVariable,
SymNodeVariable,
NNModuleVariable,
BaseListVariable,
UserDefinedVariable,
BaseUserFunctionVariable,
ConstDictVariable,
BaseTorchVariable,
)
result.extend(
[
(
(types_that_are_never_none, ConstantVariable),
obj_op_none,
),
(
(ConstantVariable, types_that_are_never_none),
none_op_obj,
),
]
)
op_var = BuiltinVariable(op)
result.extend(
[
(
(
(UserFunctionVariable, BuiltinVariable),
(UserFunctionVariable, BuiltinVariable),
),
lambda tx, a, b: ConstantVariable(op(a.fn, b.fn)),
),
(
(
NNModuleVariable,
NNModuleVariable,
),
lambda tx, a, b: ConstantVariable(
op(
tx.output.get_submodule(a.module_key),
tx.output.get_submodule(b.module_key),
)
),
),
(
(UserDefinedObjectVariable, UserDefinedObjectVariable),
compare_by_value,
),
(
(UserDefinedClassVariable, UserDefinedClassVariable),
compare_by_value,
),
(
(
(StreamVariable, EventVariable, ConstantVariable),
(StreamVariable, EventVariable, ConstantVariable),
),
compare_by_value,
),
(
(TensorVariable, VariableTracker),
op_var._comparison_with_tensor,
),
(
(VariableTracker, TensorVariable),
op_var._comparison_with_tensor,
),
(
(SymNodeVariable, VariableTracker),
op_var._comparison_with_symnode,
),
(
(VariableTracker, SymNodeVariable),
op_var._comparison_with_symnode,
),
]
)
def handle_is(tx: "InstructionTranslator", left, right):
# If the two objects are of different type, we can safely return False
# and True for `is` and `is not`, respectively
if type(left) is not type(right):
return ConstantVariable.create(op.__name__ != "is_")
if left is right:
return ConstantVariable.create(op(left, right))
if (
istype(left, variables.ExceptionVariable)
and istype(right, variables.ExceptionVariable)
and left.exc_type is not right.exc_type
):
return ConstantVariable.create(op(left, right))
result.append(((VariableTracker, VariableTracker), handle_is))
return result
for op in supported_comparison_ops.values():
assert callable(op)
assert op not in op_handlers
op_handlers[op] = create_cmp_op_handlers(op)
return op_handlers
@staticmethod
def _find_binop_handler(op, a_type, b_type):
handlers = BuiltinVariable._binop_handlers().get(op)
if handlers is None:
return None
matches = []
for (type1, type2), handler in handlers:
if issubclass(a_type, type1) and issubclass(b_type, type2):
matches.append(handler)
return matches
def can_insert_in_graph(self):
return self.fn in self._fx_graph_functions()
def __init__(self, fn, **kwargs) -> None:
super().__init__(**kwargs)
self.fn = fn
def __repr__(self) -> str:
if self.fn is None:
name = "None"
else:
name = self.fn.__name__
return f"{self.__class__.__name__}({name})"
def as_python_constant(self):
return self.fn
def as_proxy(self):
DTYPE = {
bool: torch.bool,
int: torch.int64,
float: torch.float64,
}
if self.fn in DTYPE:
return DTYPE[self.fn]
return super().as_proxy()
def reconstruct(self, codegen: "PyCodegen"):
name = self.fn.__name__
assert self.fn.__module__ == "builtins"
assert name not in codegen.tx.f_globals, "shadowed global"
codegen.append_output(codegen.create_load_global(name, add=True))
def constant_args(self, *args, **kwargs):
return check_constant_args(args, kwargs)
def tensor_args(self, *args):
any_tensor = False
for arg in args:
if isinstance(arg, variables.GetAttrVariable):
return False
any_tensor = any_tensor or isinstance(arg, variables.TensorVariable)
return any_tensor
def tensor_args_type(self, arg_types):
any_tensor = False
for arg_type in arg_types:
if issubclass(arg_type, variables.GetAttrVariable):
return False
any_tensor = any_tensor or issubclass(arg_type, variables.TensorVariable)
return any_tensor
def python_and_tensor_constant_only(self, *args, **kwargs):
tensor_args = []
non_tensor_args = []
for i in itertools.chain(args, kwargs.values()):
if isinstance(i, variables.TensorVariable):
tensor_args.append(i)
else:
non_tensor_args.append(i)
return all(
is_constant_source(t.source) if t.source is not None else False
for t in tensor_args
) and self.constant_args(*non_tensor_args)
@staticmethod
def unwrap_unspec_args_kwargs(args, kwargs):
return [x.as_python_constant() for x in args], {
k: v.as_python_constant() for k, v in kwargs.items()
}
def has_constant_handler(self, args, kwargs):
return self.can_constant_fold_through() and check_unspec_or_constant_args(
args, kwargs
)
@staticmethod
def _make_handler(fn, arg_types: list[type], has_kwargs: bool):
from .lazy import LazyVariableTracker
obj = BuiltinVariable(fn)
handlers: list[_HandlerCallback] = []
if any(issubclass(t, LazyVariableTracker) for t in arg_types):
return lambda tx, args, kwargs: obj.call_function(
tx, [v.realize() for v in args], kwargs
)
if inspect.isclass(fn) and (
issubclass(fn, Exception)
# GeneratorExit doesn't inherit from Exception
# >>> issubclass(GeneratorExit, Exception)
# False
or fn is GeneratorExit
):
def create_exception_class_object(
tx: "InstructionTranslator", args, kwargs
):
if fn is AssertionError and not all(
isinstance(x, variables.ConstantVariable)
and isinstance(x.value, str)
for x in args
):
unimplemented_v2(
gb_type="assert with non-string message",
context=str(args),
explanation="Dynamo only supports asserts with string messages",
hints=[*graph_break_hints.SUPPORTABLE],
)
return variables.ExceptionVariable(fn, args, kwargs)
return create_exception_class_object
if obj.can_insert_in_graph() and not (
fn is operator.getitem
and not issubclass(arg_types[0], variables.TensorVariable)
):
if obj.tensor_args_type(arg_types):
return obj._handle_insert_op_in_graph
elif has_kwargs:
# need runtime check for kwargs
handlers.append(obj._handle_insert_op_in_graph)
# Handle binary ops (e.g. __add__ / __radd__, __iadd__, etc.)
# NB: Tensor args are handled above and not here
if len(arg_types) == 2 and not has_kwargs:
# Try to find a handler for the arg types; otherwise, fall through to constant handler
binop_handlers = BuiltinVariable._find_binop_handler(fn, *arg_types)
if not binop_handlers:
pass
elif len(binop_handlers) == 1:
(binop_handler,) = binop_handlers
handlers.append(lambda tx, args, _: binop_handler(tx, *args))
else:
def call_binop_handlers(tx: "InstructionTranslator", args, _):
for fn in binop_handlers:
rv = fn(tx, *args)
if rv:
return rv
handlers.append(call_binop_handlers)
self_handler = getattr(obj, f"call_{fn.__name__}", None)
if self_handler:
def call_self_handler(tx: "InstructionTranslator", args, kwargs):
try:
# pyrefly: ignore # not-callable
result = self_handler(tx, *args, **kwargs)
if result is not None:
return result
except TypeError:
# Check if binding is bad. inspect signature bind is expensive.
# So check only when handler call fails.
try:
# pyrefly: ignore # bad-argument-type
inspect.signature(self_handler).bind(tx, *args, **kwargs)
except TypeError as e:
has_constant_handler = obj.has_constant_handler(args, kwargs)
if not has_constant_handler:
log.warning( # noqa: G200
"incorrect arg count %s %s and no constant handler",
self_handler,
e,
)
unimplemented_v2(
gb_type="invalid call to builtin op handler",
context=f"invalid args to {self_handler}: {args} {kwargs}",
explanation=f"Encountered TypeError when trying to handle op {fn.__name__}",
hints=[*graph_break_hints.DIFFICULT],
)
else:
raise
except Unsupported as exc:
has_constant_handler = obj.has_constant_handler(args, kwargs)
if not has_constant_handler:
raise
# Actually, we will handle this just fine
exc.remove_from_stats()
handlers.append(call_self_handler)
if obj.can_constant_fold_through():
if (
all(issubclass(x, ConstantVariable) for x in arg_types)
and not has_kwargs
):
def constant_fold_handler(tx: "InstructionTranslator", args, kwargs):
# fast path
try:
res = fn(
*[x.as_python_constant() for x in args],
)
except Exception as exc:
raise_observed_exception(
type(exc),
tx,
args=list(map(ConstantVariable.create, exc.args)),
)
except AsPythonConstantNotImplementedError as exc:
unimplemented_v2(
gb_type="constant fold exception",
context=f"attempted to run function {fn} with arguments {args}",
explanation="Encountered exception when attempting to constant fold.",
hints=[*graph_break_hints.DYNAMO_BUG],
from_exc=exc,
)
# pyrefly: ignore # unbound-name
return VariableTracker.build(tx, res)
else:
def constant_fold_handler(tx: "InstructionTranslator", args, kwargs):
# path with a runtime check
if check_unspec_or_constant_args(args, kwargs):
try:
res = fn(
*[x.as_python_constant() for x in args],
**{
k: v.as_python_constant() for k, v in kwargs.items()
},
)
except AsPythonConstantNotImplementedError as exc:
unimplemented_v2(
gb_type="constant fold exception",
context=f"attempted to run function {fn} with arguments {args}",
explanation="Encountered exception when attempting to constant fold.",
hints=[*graph_break_hints.DYNAMO_BUG],
from_exc=exc,
)
except Exception as exc:
raise_observed_exception(
type(exc),
tx,
args=list(map(ConstantVariable.create, exc.args)),
)
# pyrefly: ignore # unbound-name
return VariableTracker.build(tx, res)
handlers.append(constant_fold_handler)
def call_unimplemented_v2(args):
real_arg_types = [arg.python_type_name() for arg in args]
unimplemented_v2(
gb_type="Failed to trace builtin operator",
context=f"builtin {fn.__name__} {arg_types} {has_kwargs}",
explanation=f"Dynamo does not know how to trace builtin operator `{fn.__name__}` "
f"with argument types {real_arg_types} (has_kwargs {has_kwargs})",
hints=[
f"Avoid calling builtin `{fn.__name__}` with argument types {real_arg_types}. "
f"Consider using an equivalent alternative function/method to `{fn.__name__}`.",
"If you are attempting to call a logging function (e.g. `print`), "
"you can try adding it to `torch._dynamo.config.reorderable_logging_functions`.",
"Please report an issue to PyTorch.",
],
)
if len(handlers) == 0:
return lambda tx, args, kwargs: call_unimplemented_v2(args)
elif len(handlers) == 1:
(handler,) = handlers
def builtin_dispatch(tx: "InstructionTranslator", args, kwargs):
rv = handler(tx, args, kwargs)
if rv:
return rv
call_unimplemented_v2(args)
else:
def builtin_dispatch(tx: "InstructionTranslator", args, kwargs):
for fn in handlers:
rv = fn(tx, args, kwargs)
if rv:
return rv
call_unimplemented_v2(args)
return builtin_dispatch
def call_vars(self, tx: "InstructionTranslator", *args):
if len(args) == 0:
unimplemented_v2(
gb_type="unimplemented builtin op vars() with no arguments",
context=f"vars: {self} {args}",
explanation=f"Dynamo does not know how to trace builtin operator {self.fn} with no arguments",
hints=[*graph_break_hints.SUPPORTABLE],
)
assert len(args) == 1
# vars(obj) is obj.__dict__ if __dict__ is present else TypeError
try:
return args[0].var_getattr(tx, "__dict__")
except ObservedAttributeError:
raise_observed_exception(TypeError, tx)
def _handle_insert_op_in_graph(self, tx: "InstructionTranslator", args, kwargs):
from .builder import wrap_fx_proxy, wrap_fx_proxy_cls
if kwargs and not self.tensor_args(*args, *kwargs.values()):
return
# insert handling for torch function here
from .builder import SourcelessBuilder
from .torch_function import can_dispatch_torch_function, dispatch_torch_function
global BUILTIN_TO_TENSOR_RFN_MAP, BUILTIN_TO_TENSOR_FN_MAP
if can_dispatch_torch_function(tx, args, kwargs):
# Only remap the fn to tensor methods if we aren't exporting
# export serde does not handle method descriptors today
if not tx.export:
# Ensure the builtin maps are populated before accessing them
populate_builtin_to_tensor_fn_map()
# Use sourceless builder, we built the map ourselves
if not isinstance(args[0], TensorVariable):
if self.fn in BUILTIN_TO_TENSOR_RFN_MAP:
func = BUILTIN_TO_TENSOR_RFN_MAP[self.fn]
else:
func = BUILTIN_TO_TENSOR_FN_MAP[self.fn]
tmp = args[0]
# swap args and call reverse version of func
args[0] = args[1]
args[1] = tmp
else:
func = BUILTIN_TO_TENSOR_FN_MAP[self.fn]
else:
func = self.fn
fn_var = SourcelessBuilder.create(tx, func)
return dispatch_torch_function(tx, fn_var, args, kwargs)
fn = self.fn
try:
# Constant fold for constant tensor and python constants
if self.python_and_tensor_constant_only(*args, **kwargs):
from ..bytecode_transformation import unique_id
from .functions import invoke_and_store_as_constant
return invoke_and_store_as_constant(
tx, fn, unique_id(fn.__name__), args, kwargs
)
if fn in IN_PLACE_DESUGARING_MAP and isinstance(
args[0], variables.ConstantVariable
):
# In-place operators like += usually mustate tensor
# values, but in the edge case of immutable values they
# re-bind the variable.
#
# The easiest way to keep the graph consistent in this
# scenario is to de-sugar eagerly.
fn, args = IN_PLACE_DESUGARING_MAP[fn], [args[0], args[1]]
if fn is operator.getitem and isinstance(args[1], SymNodeVariable):
# Standard indexing will force specialization due to
# __index__. Rewrite as a regular torch op which will
# trace fine
fn, args = (
torch.select,
[
args[0],
variables.ConstantVariable.create(0),
args[1],
],
)
# Interaction between ndarray and tensors:
# We prefer the tensor op whenever there are tensors involved
if check_numpy_ndarray_args(args, kwargs) and not any(
type(arg) is variables.TensorVariable for arg in args
):
proxy = tx.output.create_proxy(
"call_function",
numpy_operator_wrapper(fn),
*proxy_args_kwargs(args, kwargs),
)
return wrap_fx_proxy_cls(variables.NumpyNdarrayVariable, tx, proxy)
if (
fn is operator.eq
and len(args) == 2
and isinstance(args[0], variables.TensorVariable)
):
# Dynamo expects `__eq__` str while operator.eq gives just `eq`
# TODO - supporting all comparison operators could also work but
# it fails lots of tests because graph str changes.
return args[0].call_method(tx, "__eq__", args[1:], kwargs)
proxy = tx.output.create_proxy(
"call_function",
fn,
*proxy_args_kwargs(args, kwargs),
)
if any(isinstance(arg, FakeItemVariable) for arg in args):
return wrap_fx_proxy_cls(
FakeItemVariable,
tx,
proxy,
)
elif check_unspec_python_args(args, kwargs):
_args, _kwargs = self.unwrap_unspec_args_kwargs(args, kwargs)
raw_value = fn(*_args, **_kwargs)
need_unwrap = any(
x.need_unwrap
for x in itertools.chain(args, kwargs.values())
if isinstance(x, variables.UnspecializedPythonVariable)
)
return wrap_fx_proxy_cls(
UnspecializedPythonVariable,
tx,
proxy,
raw_value=raw_value,
need_unwrap=need_unwrap,
)
elif all(isinstance(x, SymNodeVariable) for x in args):
return SymNodeVariable.create(tx, proxy, None)
else:
# Work around for vision_maskrcnn due to precision difference
# specialize the dividend when float divide by tensor
if fn is operator.truediv and isinstance(
args[0], variables.UnspecializedPythonVariable
):
args[0] = args[0].as_python_constant()
return wrap_fx_proxy(tx, proxy)
except NotImplementedError:
unimplemented_v2(
gb_type="unimplemented builtin op on tensor arguments",
context=f"partial tensor op: {self} {args} {kwargs}",
explanation=f"Dynamo does not know how to trace builtin operator {self.fn} with tensor arguments",
hints=[*graph_break_hints.SUPPORTABLE],
)
call_function_handler_cache: dict[
tuple[object, ...],
Callable[
[
"InstructionTranslator",
Sequence[VariableTracker],
dict[str, VariableTracker],
],
VariableTracker,
],
] = {}
def call_function(
self,
tx: "InstructionTranslator",
args: Sequence["VariableTracker"],
kwargs: "dict[str, VariableTracker]",
) -> "VariableTracker":
key: tuple[object, ...]
if kwargs:
kwargs = {k: v.realize() for k, v in kwargs.items()}
key = (self.fn, *(type(x) for x in args), True)
else:
key = (self.fn, *(type(x) for x in args))
handler = self.call_function_handler_cache.get(key)
if not handler:
self.call_function_handler_cache[key] = handler = self._make_handler(
self.fn, [type(x) for x in args], bool(kwargs)
)
return handler(tx, args, kwargs)
def call_method(
self,
tx,
name,
args: "list[VariableTracker]",
kwargs: "dict[str, VariableTracker]",
) -> "VariableTracker":
if self.fn is object and name == "__setattr__":
assert len(args) == 3
assert len(kwargs) == 0
obj, name_var, val = args
obj = obj.realize()
if (
isinstance(obj, UserDefinedObjectVariable)
and tx.output.side_effects.is_attribute_mutation(obj)
and name_var.is_python_constant()
):
return obj.method_setattr_standard(tx, name_var, val)
if name == "__new__":
# Supported __new__ methods
if self.fn is object and len(args) == 1:
assert len(kwargs) == 0
return tx.output.side_effects.track_new_user_defined_object(
self, args[0], args[1:]
)
if self.fn is dict and len(args) == 1 and not kwargs:
dict_vt = ConstDictVariable({}, dict, mutation_type=ValueMutationNew())
if isinstance(args[0], BuiltinVariable) and args[0].fn is dict:
return dict_vt
# We don't have to set the underlying dict_vt in
# UserDefinedDictVariable because it will be set to empty
# ConstDictVariableTracker in the constructor.
return tx.output.side_effects.track_new_user_defined_object(
self,
args[0],
args[1:],
)
if (
self.fn is tuple
and len(args) == 2
and args[1].has_force_unpack_var_sequence(tx)
and not kwargs
):
if isinstance(args[0], BuiltinVariable) and args[0].fn is tuple:
init_args = args[1].force_unpack_var_sequence(tx)
return variables.TupleVariable(
init_args, mutation_type=ValueMutationNew()
)
return tx.output.side_effects.track_new_user_defined_object(
self,
args[0],
args[1:],
)
if self.fn is list:
list_vt = ListVariable([], mutation_type=ValueMutationNew())
if isinstance(args[0], BuiltinVariable) and args[0].fn is list:
return list_vt
return tx.output.side_effects.track_new_user_defined_object(
self,
args[0],
args[1:],
)
if self.fn is float and len(args) == 1 and name in ("fromhex", "hex"):
if isinstance(args[0], ConstantVariable):
try:
fn = getattr(float, name)
res = fn(args[0].as_python_constant())
return variables.ConstantVariable.create(res)
except (OverflowError, ValueError) as e:
raise_observed_exception(
type(e),
tx,
args=list(map(ConstantVariable.create, e.args)),
)
if self.fn is object and name == "__init__":
# object.__init__ is a no-op
return variables.ConstantVariable(None)
if self.fn is dict and name == "fromkeys":
return BuiltinVariable.call_custom_dict_fromkeys(tx, dict, *args, **kwargs)
if self.fn is dict:
resolved_fn = getattr(self.fn, name)
if resolved_fn in dict_methods:
if isinstance(args[0], variables.UserDefinedDictVariable):
# pyrefly: ignore # missing-attribute
return args[0]._dict_vt.call_method(tx, name, args[1:], kwargs)
elif isinstance(args[0], variables.ConstDictVariable):
return args[0].call_method(tx, name, args[1:], kwargs)
if self.fn is set:
resolved_fn = getattr(self.fn, name)
if resolved_fn in set_methods:
if isinstance(args[0], variables.UserDefinedSetVariable):
# pyrefly: ignore # missing-attribute
return args[0]._set_vt.call_method(tx, name, args[1:], kwargs)
elif isinstance(args[0], variables.SetVariable):
return args[0].call_method(tx, name, args[1:], kwargs)
if self.fn is frozenset:
resolved_fn = getattr(self.fn, name)
if resolved_fn in frozenset_methods:
if isinstance(args[0], variables.FrozensetVariable):
return args[0].call_method(tx, name, args[1:], kwargs)
if self.fn is str and len(args) >= 1:
resolved_fn = getattr(self.fn, name)
if resolved_fn in str_methods:
if isinstance(args[0], ConstantVariable):
return args[0].call_method(tx, name, args[1:], kwargs)
if self.fn is float and len(args) >= 1:
if isinstance(args[0], ConstantVariable):
return ConstantVariable.create(
getattr(float, name)(args[0].as_python_constant())
)
return super().call_method(tx, name, args, kwargs)
def _call_int_float(self, tx: "InstructionTranslator", arg):
# Handle cases like int(torch.seed())
# Also handle sym_float to sym_int cases
if isinstance(arg, (SymNodeVariable, variables.TensorVariable)):
if isinstance(arg, variables.TensorVariable):
item = arg.call_method(tx, "item", [], {})
else:
item = arg
fn_ = sym_int if self.fn is int else sym_float
from torch._dynamo.variables.builder import wrap_fx_proxy
return wrap_fx_proxy(
tx=tx,
proxy=tx.output.create_proxy(
"call_function",
fn_,
(item.as_proxy(),),
{},
),
)
call_int = _call_int_float
call_float = _call_int_float
def call_bool(self, tx: "InstructionTranslator", arg):
# Emulate `PyBool_Type.tp_vectorcall` which boils down to `PyObject_IsTrue`.
# https://github.com/python/cpython/blob/3.12/Objects/object.c#L1674-L1697
if isinstance(arg, SymNodeVariable):
# Note that we delay specializing on symbolic values to avoid
# unnecessary guards. Specialization will happen later if, e.g., the
# resulting boolean is used for branching.
if isinstance(arg.sym_num, torch.SymBool):
return arg
# Emulate `nb_bool` of int/float objects
# - https://github.com/python/cpython/blob/3.12/Objects/longobject.c#L4940-L4944
# - https://github.com/python/cpython/blob/3.12/Objects/floatobject.c#L878-L882
assert istype(arg.sym_num, (torch.SymInt, torch.SymFloat))
return SymNodeVariable.create(tx, arg.as_proxy() != 0)
# TODO handle more cases and merge this with this with `generic_jump`.
def call_str(self, tx: "InstructionTranslator", arg):
# Handle `str` on a user defined function or object
if isinstance(arg, (variables.UserFunctionVariable)):
return variables.ConstantVariable.create(value=str(arg.fn))
elif isinstance(arg, (variables.UserDefinedObjectVariable)):
# Check if object has __str__ method
if hasattr(arg.value, "__str__"):
str_method = arg.value.__str__
elif hasattr(arg.value, "__repr__"):
# account for __repr__ functions when __str__ is absent
str_method = arg.value.__repr__
else:
unimplemented_v2(
gb_type="failed to call str() on user defined object",
context=str(arg),
explanation="User defined object has no __str__ or __repr__ method",
hints=[*graph_break_hints.USER_ERROR],
)
if type(arg.value).__str__ is object.__str__:
# Rely on the object str method
try:
# pyrefly: ignore # unbound-name
return variables.ConstantVariable.create(value=str_method())
except AttributeError:
# Graph break
return
# pyrefly: ignore # unbound-name
elif is_wrapper_or_member_descriptor(str_method):
unimplemented_v2(
gb_type="Attempted to a str() method implemented in C/C++",
context="",
explanation=f"{type(arg.value)} has a C/C++ based str method. This is not supported.",
hints=["Write the str method in Python"],
)
else:
# Overrides for custom str method
# Pass method as function to call tx.inline_user_function_return
bound_method = str_method.__func__ # type: ignore[attr-defined]
try:
# Only supports certain function types
user_func_variable = variables.UserFunctionVariable(bound_method)
except AssertionError:
# Won't be able to do inline the str method, return to avoid graph break
log.warning("Failed to create UserFunctionVariable", exc_info=True)
return
# Inline the user function
return tx.inline_user_function_return(user_func_variable, [arg], {})
elif isinstance(arg, (variables.ExceptionVariable,)):
if len(arg.args) == 0:
value = f"{arg.exc_type}"
else:
value = ", ".join(a.as_python_constant() for a in arg.args)
return variables.ConstantVariable.create(value=value)
def _call_min_max(self, tx: "InstructionTranslator", *args):
if len(args) == 1 and args[0].has_force_unpack_var_sequence(tx):
items = args[0].force_unpack_var_sequence(tx)
return self._call_min_max_seq(tx, items)
elif len(args) == 2:
return self._call_min_max_binary(tx, args[0], args[1])
elif len(args) > 2:
return self._call_min_max_seq(tx, args)
def _call_min_max_seq(self, tx: "InstructionTranslator", items):
assert len(items) > 0
if len(items) == 1:
return items[0]
return functools.reduce(functools.partial(self._call_min_max_binary, tx), items)
def _call_min_max_binary(self, tx: "InstructionTranslator", a, b):
if a is None or b is None:
# a or b could be none if we reduce and _call_min_max_binary failed
# to return something
return
if self.tensor_args(a, b):
if not isinstance(a, variables.TensorVariable):
a, b = b, a
assert isinstance(a, variables.TensorVariable)
# result of an item call is a scalar convert to a tensor
if isinstance(a, FakeItemVariable):
a = variables.TorchInGraphFunctionVariable(torch.tensor).call_function(
tx, [a], {}
)
# Dynamic input does not get resolved, rather, gets stored as call_function
if isinstance(a, SymNodeVariable) or isinstance(b, SymNodeVariable):
from .builder import wrap_fx_proxy_cls
return wrap_fx_proxy_cls(
type(a),
tx=tx,
proxy=tx.output.create_proxy(
"call_function",
self.fn,
*proxy_args_kwargs([a, b], {}),
),
)
# convert min/max to torch ops
if b.is_python_constant():
fn: VariableTracker
if isinstance(a, variables.NumpyNdarrayVariable):
import numpy as np
fn = variables.NumpyVariable(np.clip)
else:
fn = variables.TorchInGraphFunctionVariable(torch.clamp)
kwargs = {"min": b} if (self.fn is max) else {"max": b}
result = fn.call_function(tx, [a], kwargs)
else:
if isinstance(a, variables.NumpyNdarrayVariable):
import numpy as np
np_fn = {max: np.maximum, min: np.minimum}[self.fn]
fn = variables.NumpyVariable(np_fn)
else:
torch_fn = {max: torch.maximum, min: torch.minimum}[self.fn]
fn = variables.TorchInGraphFunctionVariable(torch_fn)
result = fn.call_function(tx, [a, b], {})
# return unspec if both a, b are unspec or const
if all(
isinstance(
i,
(
variables.UnspecializedPythonVariable,
variables.ConstantVariable,
),
)
for i in [a, b]
):
if any(isinstance(val, FakeItemVariable) for val in [a, b]):
return variables.FakeItemVariable.from_tensor_variable(result)
if b.is_python_constant():
raw_b = b.as_python_constant()
else:
raw_b = b.raw_value
if self.fn is max:
# pyrefly: ignore # missing-attribute
raw_res = max(a.raw_value, raw_b)
else:
# pyrefly: ignore # missing-attribute
raw_res = min(a.raw_value, raw_b)
need_unwrap = any(
x.need_unwrap
for x in [a, b]
if isinstance(x, variables.UnspecializedPythonVariable)
)
return variables.UnspecializedPythonVariable.from_tensor_variable(
result, raw_res, need_unwrap
)
# otherwise return tensor
else:
return result
elif isinstance(a, SymNodeVariable) or isinstance(b, SymNodeVariable):
py_fn = torch.sym_max if self.fn is max else torch.sym_min
proxy = tx.output.create_proxy(
"call_function", py_fn, *proxy_args_kwargs([a, b], {})
)
return SymNodeVariable.create(tx, proxy, None)
elif isinstance(a, ConstantVariable) and isinstance(b, ConstantVariable):
value = self.fn(
a.as_python_constant(),
b.as_python_constant(),
)
return ConstantVariable(value)
call_min = _call_min_max
call_max = _call_min_max
def call_abs(self, tx: "InstructionTranslator", arg: "VariableTracker"):
# Call arg.__abs__()
abs_method = BuiltinVariable(getattr).call_function(
tx, [arg, ConstantVariable.create("__abs__")], {}
)
return abs_method.call_function(tx, [], {})
def call_pos(self, tx: "InstructionTranslator", arg: "VariableTracker"):
# Call arg.__pos__()
pos_method = BuiltinVariable(getattr).call_function(
tx, [arg, ConstantVariable.create("__pos__")], {}
)
return pos_method.call_function(tx, [], {})
def call_index(self, tx: "InstructionTranslator", arg: "VariableTracker"):
if isinstance(arg, variables.TensorVariable):
unimplemented_v2(
gb_type="unsupported index(Tensor)",
context="",
explanation="Dynamo does not support tracing builtin index() on a Tensor",
hints=[],
)
arg = guard_if_dyn(arg)
constant_value = operator.index(arg)
return variables.ConstantVariable.create(constant_value)
def call_round(self, tx: "InstructionTranslator", arg, *args, **kwargs):
# Call arg.__round__()
round_method = BuiltinVariable(getattr).call_function(
tx, [arg, ConstantVariable.create("__round__")], {}
)
return round_method.call_function(tx, args, kwargs)
def call_range(self, tx: "InstructionTranslator", *args):
if check_unspec_or_constant_args(args, {}):
return variables.RangeVariable(args)
elif self._dynamic_args(*args):
args = tuple(
variables.ConstantVariable.create(guard_if_dyn(arg)) for arg in args
)
return variables.RangeVariable(args)
# None no-ops this handler and lets the driving function proceed
return None
def _dynamic_args(self, *args, **kwargs):
return any(isinstance(x, SymNodeVariable) for x in args) or any(
isinstance(x, SymNodeVariable) for x in kwargs.values()
)
def call_slice(self, tx: "InstructionTranslator", *args):
return variables.SliceVariable(args)
def _dyn_proxy(self, tx: "InstructionTranslator", *args, **kwargs):
from .builder import wrap_fx_proxy
return wrap_fx_proxy(
tx,
tx.output.create_proxy(
"call_function", self.fn, *proxy_args_kwargs(args, kwargs)
),
)
# NOTE must handle IteratorVariable separately!
def _call_iter_tuple_list(
self, tx: "InstructionTranslator", obj=None, *args, **kwargs
):
assert not isinstance(obj, variables.IteratorVariable)
if self._dynamic_args(*args, **kwargs):
return self._dyn_proxy(tx, *args, **kwargs)
cls = variables.BaseListVariable.cls_for(self.fn)
if obj is None:
return cls(
[],
mutation_type=ValueMutationNew(),
)
elif obj.has_unpack_var_sequence(tx):
if obj.source and not is_constant_source(obj.source):
if isinstance(obj, TupleIteratorVariable):
install_guard(
obj.source.make_guard(GuardBuilder.TUPLE_ITERATOR_LEN)
)
else:
if (
getattr(obj, "source", False)
and isinstance(obj, ConstDictVariable)
and not istype(obj, (SetVariable, FrozensetVariable))
):
tx.output.guard_on_key_order.add(obj.source)
if isinstance(obj, variables.MappingProxyVariable):
# This could be an overguarding, but its rare to iterate
# through a mapping proxy and not use the keys.
install_guard(
obj.source.make_guard(GuardBuilder.MAPPING_KEYS_CHECK)
)
elif not isinstance(obj, variables.UnspecializedNNModuleVariable):
# Prevent calling __len__ method for guards, the tracing
# of __iter__ will insert the right guards later.
install_guard(
obj.source.make_guard(GuardBuilder.SEQUENCE_LENGTH)
)
return cls(
list(obj.unpack_var_sequence(tx)),
mutation_type=ValueMutationNew(),
)
def _call_iter_tuple_generator(self, tx, obj, *args, **kwargs):
cls = variables.BaseListVariable.cls_for(self.fn)
return cls(
list(obj.force_unpack_var_sequence(tx)), # exhaust generator
mutation_type=ValueMutationNew(),
)
def _call_tuple_list(self, tx, obj=None, *args, **kwargs):
if isinstance(obj, variables.IteratorVariable):
cls = variables.BaseListVariable.cls_for(self.fn)
return cls(
list(obj.force_unpack_var_sequence(tx)),
mutation_type=ValueMutationNew(),
)
elif isinstance(obj, variables.LocalGeneratorObjectVariable) or (
isinstance(obj, UserDefinedObjectVariable)
and obj.has_force_unpack_var_sequence(tx)
):
return self._call_iter_tuple_generator(tx, obj, *args, **kwargs)
else:
return self._call_iter_tuple_list(tx, obj, *args, **kwargs)
def call_iter(self, tx: "InstructionTranslator", obj, *args, **kwargs):
if isinstance(obj, variables.IteratorVariable):
ret = obj
elif isinstance(obj, variables.RangeVariable):
ret = obj.call_method(tx, "__iter__", [], {})
elif isinstance(obj, variables.LocalGeneratorObjectVariable):
ret = obj # type: ignore[assignment]
else:
# Handle the case where we are iterating over a tuple, list or iterator
ret = self._call_iter_tuple_list(tx, obj, *args, **kwargs)
if ret is None:
# If the object doesn't implement a __iter__ method, it will be an error in eager mode when calling iter on it anyway.
# If the object implements a __iter__ method, inlining effectively forwards the call to another iter call
# (e.g. when __iter__ just returns iter(self.list)) or return a user-defined iterator.
# If the object implements a __getitem__ method, iter(...) will call obj.__getitem__()
# with an integer argument starting at 0, until __getitem__ raises IndexError
ret = variables.UserFunctionVariable(
polyfills.builtins.iter_
).call_function(tx, [obj, *args], {})
if args:
# iter(obj, sentinel) returns an object that implements
# __iter__ and __next__ methods (UserDefinedObjectVariable)
# Wrap the return value in a IteratorVariable subclass (LazyObjectIteratorVariable)
# that forwards the next_variable call to the object.
ret = variables.ObjectIteratorVariable(ret)
return ret
call_tuple = _call_tuple_list
call_list = _call_tuple_list
def call_callable(self, tx: "InstructionTranslator", arg):
from .functions import BaseUserFunctionVariable, FunctoolsPartialVariable
from .nn_module import NNModuleVariable
if isinstance(
arg,
(
variables.UserDefinedClassVariable,
BaseUserFunctionVariable,
FunctoolsPartialVariable,
NNModuleVariable,
),
):
return variables.ConstantVariable.create(True)
elif isinstance(arg, UserDefinedVariable):
return variables.ConstantVariable.create(callable(arg.value))
elif isinstance(
arg,
(
ConstantVariable,
SymNodeVariable,
TensorVariable,
ListVariable,
TupleVariable,
ListIteratorVariable,
),
):
return variables.ConstantVariable.create(False)
def call_cast(self, _, *args, **kwargs):
if len(args) == 2:
return args[1]
unimplemented_v2(
gb_type="bad args to builtin cast()",
context=f"got args {args} {kwargs}",
explanation="Dynamo expects exactly 2 args to builtin cast().",
hints=["Ensure your call to cast() has exactly 2 arguments."],
)
def call_dir(self, tx: "InstructionTranslator", arg):
if isinstance(arg, variables.UserDefinedClassVariable):
return VariableTracker.build(tx, dir(arg.value))
if isinstance(arg, BuiltinVariable):
return VariableTracker.build(tx, dir(arg.fn))
def call_dict(self, tx: "InstructionTranslator", *args, **kwargs):
return BuiltinVariable.call_custom_dict(tx, dict, *args, **kwargs)
@staticmethod
def call_custom_dict(tx: "InstructionTranslator", user_cls, *args, **kwargs):
args = list(args)
if (
len(args) == 1
and isinstance(args[0], variables.GetAttrVariable)
and isinstance(args[0].obj, variables.UserDefinedClassVariable)
and not tx.output.side_effects.has_pending_mutation(args[0].obj)
):
# Forward the GetAttrVariable(foo, "__dict__") to a realized vt of
# VT(foo.__dict__). This simplifies the construction of the new
# dict.
args[0] = args[0].get_forwarded_dict(tx)
return tx.inline_user_function_return(
VariableTracker.build(tx, polyfills.construct_dict),
[VariableTracker.build(tx, user_cls), *args],
kwargs,
)
@staticmethod
def call_custom_dict_fromkeys(
tx: "InstructionTranslator", user_cls, *args, **kwargs
):
assert user_cls in {dict, OrderedDict, defaultdict}
if kwargs:
# Only `OrderedDict.fromkeys` accepts `value` passed by keyword
assert user_cls is OrderedDict
assert len(args) == 1 and len(kwargs) == 1 and "value" in kwargs
args = (*args, kwargs.pop("value"))
if len(args) == 0:
msg = ConstantVariable.create(
"fromkeys expected at least 1 arguments, got 0"
)
raise_observed_exception(TypeError, tx, args=[msg])
if len(args) == 1:
args = (*args, ConstantVariable.create(None))
assert len(args) == 2
arg, value = args
DictVariableType = (
ConstDictVariable if user_cls is not defaultdict else DefaultDictVariable
)
if isinstance(arg, dict):
arg = [ConstantVariable.create(k) for k in arg.keys()]
return DictVariableType(
dict.fromkeys(arg, value), user_cls, mutation_type=ValueMutationNew()
)
elif arg.has_force_unpack_var_sequence(tx):
keys = arg.force_unpack_var_sequence(tx)
if all(is_hashable(v) for v in keys):
return DictVariableType(
dict.fromkeys(keys, value),
user_cls,
mutation_type=ValueMutationNew(),
)
unimplemented_v2(
gb_type="failed to call dict.fromkeys()",
context=f"{user_cls.__name__}.fromkeys(): {args} {kwargs}",
explanation=f"Failed to call {user_cls.__name__}.fromkeys() because "
"arguments could not be automatically converted to a list, "
"or some dict key is not hashable.",
hints=[
"Manually convert the argument to a list.",
"Ensure all keys are hashable.",
],
)
def call_set(self, tx: "InstructionTranslator", *args, **kwargs):
# Can we merge this implementation and call_dict's one?
assert not kwargs
if not args:
return SetVariable([], mutation_type=ValueMutationNew())
if len(args) != 1:
raise_observed_exception(
TypeError,
tx,
args=[
ConstantVariable.create(
f"set() takes 1 positional argument but {len(args)} were given"
)
],
)
arg = args[0]
if istype(arg, variables.SetVariable):
return arg.clone(mutation_type=ValueMutationNew())
elif arg.has_force_unpack_var_sequence(tx):
items = arg.force_unpack_var_sequence(tx)
return SetVariable(items, mutation_type=ValueMutationNew())
elif isinstance(arg, variables.UserDefinedObjectVariable) and isinstance(
arg.value, KeysView
):
iter_fn = arg.var_getattr(tx, "__iter__")
if isinstance(iter_fn, variables.UserMethodVariable):
out = tx.inline_user_function_return(iter_fn, args, kwargs)
if isinstance(out, SetVariable):
return out
return BuiltinVariable(set).call_set(tx, out)
raise_observed_exception(
TypeError,
tx,
args=[ConstantVariable.create("failed to construct builtin set()")],
)
def call_frozenset(self, tx: "InstructionTranslator", *args, **kwargs):
assert not kwargs
if not args:
return FrozensetVariable([])
if len(args) != 1:
raise_observed_exception(
TypeError,
tx,
args=[
ConstantVariable.create(
f"frozenset() takes 1 positional argument but {len(args)} were given"
)
],
)
arg = args[0]
if istype(arg, variables.FrozensetVariable):
return FrozensetVariable([x.vt for x in arg.set_items])
elif arg.has_force_unpack_var_sequence(tx):
items = arg.force_unpack_var_sequence(tx)
return FrozensetVariable(items)
raise_observed_exception(
TypeError,
tx,
args=[ConstantVariable.create("failed to construct builtin frozenset()")],
)
def call_zip(self, tx: "InstructionTranslator", *args, **kwargs):
if kwargs:
assert len(kwargs) == 1 and "strict" in kwargs
strict = kwargs.pop("strict", False)
args = [BuiltinVariable(iter).call_function(tx, [arg], {}) for arg in args]
return variables.ZipVariable(
args, strict=strict, mutation_type=ValueMutationNew()
)
def call_len(self, tx: "InstructionTranslator", *args, **kwargs):
try:
return args[0].call_method(tx, "__len__", args[1:], kwargs)
except AttributeError as e:
raise_observed_exception(type(e), tx, args=list(e.args))
def call_getitem(self, tx: "InstructionTranslator", *args, **kwargs):
return args[0].call_method(tx, "__getitem__", args[1:], kwargs)
def call_isinstance(self, tx: "InstructionTranslator", arg, isinstance_type):
try:
arg_type = arg.python_type()
except NotImplementedError:
unimplemented_v2(
gb_type="builtin isinstance() cannot determine type of argument",
context=f"isinstance({arg}, {isinstance_type})",
explanation=f"Dynamo doesn't have a rule to determine the type of argument {arg}",
hints=[*graph_break_hints.DYNAMO_BUG],
)
isinstance_type = isinstance_type.as_python_constant()
if isinstance(arg, variables.TensorVariable) and arg.dtype is not None:
def _tensor_isinstance(tensor_var, tensor_type):
def check_type(ty):
if ty not in tensortype_to_dtype:
example_val = arg.as_proxy().node.meta["example_value"]
if (
is_traceable_wrapper_subclass(example_val)
and ty is torch.nn.parameter.Parameter
):
# N.B: we are calling isinstance directly on the example value.
# torch.nn.Parameter has a meta-class that overrides __isinstance__,
# the isinstance check here allows us to invoke that logic.
return isinstance(example_val, ty)
else:
return issubclass(arg.python_type(), ty)
dtypes = tensortype_to_dtype[ty]
return arg.dtype in dtypes
if type(tensor_type) is tuple:
return any(check_type(ty) for ty in tensor_type)
else:
return check_type(tensor_type)
return variables.ConstantVariable.create(
_tensor_isinstance(arg, isinstance_type)
)
# UserDefinedObject with C extensions can have torch.Tensor attributes,
# so break graph.
if isinstance(arg, variables.UserDefinedObjectVariable) and isinstance(
arg.value, types.MemberDescriptorType
):
unimplemented_v2(
gb_type="isinstance() called on user defined object with C extensions",
context=f"isinstance({arg}, {isinstance_type})",
explanation="User-defined object with C extensions can have torch.Tensor "
"attributes; intentionally graph breaking.",
hints=[*graph_break_hints.SUPPORTABLE],
)
# handle __instancecheck__ defined in user class
if (
isinstance(arg, variables.UserDefinedObjectVariable)
and "__instancecheck__" in isinstance_type.__class__.__dict__
):
return variables.ConstantVariable.create(
isinstance_type.__class__.__instancecheck__(isinstance_type, arg.value)
)
if isinstance(arg, variables.UserDefinedExceptionClassVariable):
# pyrefly: ignore # unbound-name
return ConstantVariable.create(isinstance(arg_type, isinstance_type))
isinstance_type_tuple: tuple[type, ...]
if isinstance(isinstance_type, type) or callable(
# E.g. isinstance(obj, typing.Sequence)
getattr(isinstance_type, "__instancecheck__", None)
):
isinstance_type_tuple = (isinstance_type,)
elif isinstance(isinstance_type, types.UnionType):
isinstance_type_tuple = isinstance_type.__args__
elif isinstance(isinstance_type, tuple) and all(
isinstance(tp, type) or callable(getattr(tp, "__instancecheck__", None))
for tp in isinstance_type
):
isinstance_type_tuple = isinstance_type
else:
raise_observed_exception(
TypeError,
tx,
args=[
"isinstance() arg 2 must be a type, a tuple of types, or a union"
],
)
try:
# NB: `isinstance()` does not call `__subclasscheck__` but use `__instancecheck__`.
# But usually `isinstance(obj, type_info)` and `issubclass(type(obj), type_info)` gives
# the same result.
# WARNING: This might run arbitrary user code `__subclasscheck__` and we did not trace
# through it. This is a limitation of the current implementation.
# Usually `__subclasscheck__` and `__instancecheck__` can be constant fold through, it
# might not be a big issue and we trade off it for performance.
# pyrefly: ignore # unbound-name
val = issubclass(arg_type, isinstance_type_tuple)
except TypeError:
# pyrefly: ignore # unbound-name
val = arg_type in isinstance_type_tuple
return variables.ConstantVariable.create(val)
def call_issubclass(self, tx: "InstructionTranslator", left_ty, right_ty):
"""Checks if first arg is subclass of right arg"""
try:
left_ty_py = left_ty.as_python_constant()
right_ty_py = right_ty.as_python_constant()
except NotImplementedError:
unimplemented_v2(
gb_type="issubclass() with non-constant arguments",
context=f"issubclass({left_ty}, {right_ty})",
explanation="issubclass() with non-constant arguments not supported.",
hints=[
"Make sure your arguments are types.",
*graph_break_hints.USER_ERROR,
],
)
# WARNING: This might run arbitrary user code `__subclasscheck__`.
# See the comment in call_isinstance above.
# pyrefly: ignore # unbound-name
return variables.ConstantVariable(issubclass(left_ty_py, right_ty_py))
def call_super(self, tx: "InstructionTranslator", a, b):
return variables.SuperVariable(a, b)
def call_next(self, tx: "InstructionTranslator", *args):
arg = args[0]
try:
return arg.next_variable(tx)
except ObservedUserStopIteration:
if len(args) == 2:
return args[1]
raise
except Unsupported as ex:
if isinstance(arg, variables.BaseListVariable):
ex.remove_from_stats()
return arg.items[0]
raise
def call_hasattr(self, tx: "InstructionTranslator", obj, attr):
if attr.is_python_constant():
name = attr.as_python_constant()
if isinstance(obj, variables.BuiltinVariable):
return variables.ConstantVariable(hasattr(obj.fn, name))
return obj.call_obj_hasattr(tx, name)
def call_map(self, tx: "InstructionTranslator", fn, *seqs):
seqs = [
seq.unpack_var_sequence(tx) if seq.has_unpack_var_sequence(tx) else seq
for seq in seqs
]
return variables.MapVariable(fn, seqs, mutation_type=ValueMutationNew())
def call_filter(self, tx: "InstructionTranslator", fn, seq):
seq = seq.unpack_var_sequence(tx) if seq.has_unpack_var_sequence(tx) else seq
return variables.FilterVariable(fn, seq, mutation_type=ValueMutationNew())
def var_getattr(self, tx: "InstructionTranslator", name):
source = self.source and AttrSource(self.source, name)
if self.fn is object:
# for object, we can just directly read the attribute
try:
value = getattr(self.fn, name)
except AttributeError:
raise_observed_exception(AttributeError, tx)
# pyrefly: ignore # unbound-name
if not callable(value):
# pyrefly: ignore # unbound-name
return VariableTracker.build(tx, value, source)
return variables.GetAttrVariable(self, name, source=source)
def call_getattr(
self,
tx: "InstructionTranslator",
obj: VariableTracker,
name_var: VariableTracker,
default=None,
):
if not name_var.is_python_constant():
unimplemented_v2(
gb_type="getattr() with non-constant name argument",
context=f"getattr({obj}, {name_var}, {default})",
explanation="getattr() with non-constant name argument is not supported",
hints=["Ensure the name argument of getattr() is a string"],
)
name = name_var.as_python_constant()
# See NOTE [Tensor "grad" and "_grad" attr]
if isinstance(obj, TensorVariable) and name == "_grad":
name = "grad"
if tx.output.side_effects.is_attribute_mutation(obj):
if isinstance(obj, variables.UnspecializedNNModuleVariable):
if (
name
in (
"named_parameters",
"parameters",
"named_buffers",
"buffers",
"named_modules",
"modules",
)
and obj.is_state_mutated
and tx.output.side_effects.has_pending_mutation(obj)
):
unimplemented_v2(
gb_type="getattr() on nn.Module with pending mutation",
context=f"getattr({obj}, {name}, {default})",
explanation="Intentionally graph breaking on getattr() on a nn.Module "
"with a pending mutation",
hints=[],
)
if tx.output.side_effects.has_pending_mutation_of_attr(obj, name):
return tx.output.side_effects.load_attr(obj, name)
if default is not None:
hasattr_var = self.call_hasattr(tx, obj, name_var)
assert hasattr_var.as_python_constant() in (True, False)
if not hasattr_var.as_python_constant():
return default
source = obj.source and AttrSource(obj.source, name)
if name in {"__bases__", "__base__", "__flags__"}:
try:
value = obj.as_python_constant()
if isinstance(value, type):
if name == "__bases__":
tuple_args = [
VariableTracker.build(
tx, b, source and GetItemSource(source, i)
)
for i, b in enumerate(value.__bases__)
]
return variables.TupleVariable(tuple_args, source=source)
if name == "__base__":
return VariableTracker.build(tx, value.__base__, source)
if name == "__flags__":
return ConstantVariable.create(value.__flags__)
except NotImplementedError:
pass
if isinstance(obj, variables.NNModuleVariable):
return obj.var_getattr(tx, name)
elif isinstance(
obj,
(
variables.TensorVariable,
variables.NamedTupleVariable,
variables.ConstantVariable,
variables.DistributedVariable,
variables.UserDefinedClassVariable,
variables.UserDefinedObjectVariable,
),
):
if (
isinstance(obj, variables.UserDefinedObjectVariable)
and issubclass(obj.value.__class__, unittest.TestCase)
and config.enable_trace_unittest
and name
in (
"assertRaisesRegex",
"assertNotWarns",
"assertWarnsRegex",
"assertWarns",
)
):
unimplemented_v2(
gb_type="Failed to trace unittest method",
context=f"function: unittest.TestCase.{name}",
explanation=f"Dynamo does not know how to trace unittest method `{name}` ",
hints=[
f"Avoid calling `TestCase.{name}`. "
"Please report an issue to PyTorch.",
],
)
if isinstance(obj, TensorVariable):
fake_val = obj.proxy.node.meta["example_value"]
if (
isinstance(fake_val, torch.Tensor)
and is_sparse_any(fake_val)
and (not tx.export or not config.capture_sparse_compute)
):
unimplemented_v2(
gb_type="Attempted to wrap sparse Tensor",
context="",
explanation="torch.compile does not support sparse Tensors",
hints=[*graph_break_hints.SUPPORTABLE],
)
try:
return obj.var_getattr(tx, name)
except NotImplementedError:
return variables.GetAttrVariable(obj, name, source=source)
elif isinstance(obj, variables.TorchInGraphFunctionVariable):
# Get OpOverload from an OpOverloadPacket, e.g., torch.ops.aten.add.default.
member = getattr(obj.value, name)
if isinstance(
member, (torch._ops.OpOverloadPacket, torch._ops.OpOverload)
) and torch._dynamo.trace_rules.is_aten_op_or_tensor_method(member):
return variables.TorchInGraphFunctionVariable(member, source=source)
elif name in cmp_name_to_op_mapping:
return variables.GetAttrVariable(obj, name, source=source)
elif isinstance(obj, DummyModule):
# TODO(mlazos) - Do we need this?
if obj.is_torch or name not in obj.value.__dict__:
member = getattr(obj.value, name)
else:
member = obj.value.__dict__[name]
if config.replay_record_enabled:
tx.exec_recorder.record_module_access(obj.value, name, member) # type: ignore[arg-type, union-attr]
return VariableTracker.build(tx, member, source)
elif istype(obj, variables.UserFunctionVariable) and name in (
"__name__",
"__module__",
):
return ConstantVariable.create(getattr(obj.fn, name))
else:
try:
return obj.var_getattr(tx, name)
except NotImplementedError:
return variables.GetAttrVariable(obj, name, source=source)
def call_setattr(
self,
tx: "InstructionTranslator",
obj: VariableTracker,
name_var: VariableTracker,
val: VariableTracker,
):
if isinstance(
obj,
(
variables.PlacementVariable,
variables.NamedTupleVariable,
variables.UserDefinedObjectVariable,
variables.NestedUserFunctionVariable,
variables.ExceptionVariable,
),
):
return obj.call_method(tx, "__setattr__", [name_var, val], {})
elif (
tx.output.side_effects.is_attribute_mutation(obj)
and name_var.is_python_constant()
):
name = name_var.as_python_constant()
if isinstance(obj, variables.TensorVariable):
from .builder import wrap_fx_proxy
# Some special handling for tensor attributes.
if name == "requires_grad":
# TODO(voz): Make it work properly
unimplemented_v2(
gb_type="setattr() on Tensor.requires_grad",
context=f"setattr({obj}, {name}, {val})",
explanation="setattr() on Tensor.requires_grad not supported. "
"Mutating requires_grad can introduce a new leaf from non-leaf or vice versa in "
"the middle of the graph, which AOTAutograd does not currently know how to handle.",
hints=[*graph_break_hints.SUPPORTABLE],
)
elif name == "data":
# See comments on `test_set_data_on_scoped_tensor` for plans
# to support this.
if obj.source is None:
unimplemented_v2(
gb_type="Failed to mutate tensor data attribute",
context=f"setattr({obj}, {name}, {val})",
explanation="Dyanmo only supports mutating `.data`"
" of tensor created outside `torch.compile` region",
hints=[
"Don't mutate `.data` on this tensor, or move "
"the mutation out of `torch.compile` region",
],
)
elif obj.dtype != val.dtype: # type: ignore[attr-defined]
unimplemented_v2(
gb_type="Failed to mutate tensor data attribute to different dtype",
context=f"setattr({obj}, {name}, {val})",
explanation="Dyanmo only supports mutating `.data`"
" of tensor to a new one with the same dtype",
hints=[
"Don't mutate `.data` on this tensor, or move "
"the mutation out of `torch.compile` region",
],
)
# Remove the old reference in tracked fakes - if we don't do this
# new .data value size and shape differences will cause
# tracked fakes to produce incorrect guards. This is sound because the TensorVariable
# coming out of set_() below will be a new one, and get
# installed in tracked fakes.
to_remove = [
tf for tf in tx.output.tracked_fakes if tf.source == obj.source
]
for tf in to_remove:
tx.output.tracked_fakes.remove(tf)
# Step 1 - disable grads
with dynamo_disable_grad(tx), torch.no_grad():
# Step 2 - call `set_`
out = wrap_fx_proxy(
tx,
tx.output.create_proxy(
"call_function",
torch.Tensor.set_,
*proxy_args_kwargs([obj, val], {}),
),
)
# Step 3 - drop the version counter - this is a step required to get
# .data setting to play correctly with the autograd engine.
# Essentially, dynamo is trying to faithfully preserve the (absurd)
# behavior of .data= from eager mode
def _lower_version_count_by_1(x):
version = x._version
if version > 0:
version = version - 1
torch._C._autograd._unsafe_set_version_counter((x,), (version,))
return x
tx.output.create_proxy(
"call_function",
_lower_version_count_by_1,
(out.as_proxy(),),
{},
)
_lower_version_count_by_1(obj.as_proxy().node.meta["example_value"])
# This handles options prop, guards and ends with a clone
# Step 4 - replace all reference to the current object with the new one
return out
elif name in ("_grad", "grad"):
# NOTE: [Tensor "grad" and "_grad" attr]
# _grad and grad share the same setter/getter, see
# THPVariable_properties, and here we make sure setting one
# enables reading `val` from the other, by routing all
# read/write to `grad`.
name = "grad"
elif is_tensor_getset_descriptor(name):
# Attribute like `torch.Tensor.real` has special setters we
# don't yet support; it's not as simple adding an entry to
# the side effect mapping.
unimplemented_v2(
gb_type="Failed to set tensor attribute",
context=f"setattr({obj}, {name}, {val})",
explanation="Dyanmo doesn't support setting these tensor attributes",
hints=[
f"Don't mutate attribute '{name}' on tensors, or "
"move the mutation out of `torch.compile` region",
],
)
tx.output.side_effects.store_attr(obj, name, val)
return val
elif isinstance(obj, variables.NNModuleVariable):
if not tx.output.is_root_tracer():
raise AttributeMutationError(
"Can't inplace modify module params/buffers inside HigherOrderOp"
)
if name_var.is_python_constant() and isinstance(
val, variables.TensorVariable
):
assigning_fake_val = get_fake_value(val.as_proxy().node, tx)
try:
getattr_var = obj.var_getattr(tx, name_var.as_python_constant())
except (AttributeError, ObservedAttributeError):
getattr_var = None
if isinstance(getattr_var, variables.TensorVariable):
# get_fake_val will get the same fake tensor
existing_fake_attr = get_fake_value(getattr_var.as_proxy().node, tx)
# same tensor identity, setattr is a no-op
mod_setattr = inspect.getattr_static(obj.module_type, "__setattr__")
if (
existing_fake_attr is assigning_fake_val
and mod_setattr is torch.nn.Module.__setattr__
):
return getattr_var
obj.convert_to_unspecialized(tx)
def call_delattr(
self,
tx: "InstructionTranslator",
obj: VariableTracker,
name_var: VariableTracker,
):
return obj.call_method(tx, "__delattr__", [name_var], {})
def call_type(self, tx: "InstructionTranslator", obj: VariableTracker):
try:
py_type = obj.python_type()
except NotImplementedError as error:
raise UserError(
UserErrorType.INVALID_INPUT,
str(error),
case_name="unknown_python_type",
) from None
source = obj.source and TypeSource(obj.source)
if (
source is None
and isinstance(obj, variables.UserDefinedObjectVariable)
and obj.cls_source
):
source = obj.cls_source
if py_type is torch.Tensor:
# In some cases torch isn't available in globals
name = tx.output.install_global_by_id("", torch)
source = AttrSource(GlobalSource(name), "Tensor")
return VariableTracker.build(tx, py_type, source)
def call_reversed(self, tx: "InstructionTranslator", obj: VariableTracker):
if obj.has_unpack_var_sequence(tx):
items = list(reversed(obj.unpack_var_sequence(tx)))
return variables.TupleVariable(items)
def call_sorted(
self,
tx: "InstructionTranslator",
obj: VariableTracker,
**kwargs: VariableTracker,
):
if obj.has_force_unpack_var_sequence(tx) and not isinstance(
obj, variables.TensorVariable
):
list_var = variables.ListVariable(
obj.force_unpack_var_sequence(tx),
mutation_type=ValueMutationNew(),
)
list_var.call_method(tx, "sort", [], kwargs)
return list_var
# neg is a constant fold function, so we only get here if constant fold is not valid
def call_neg(self, tx: "InstructionTranslator", a):
if isinstance(a, SymNodeVariable):
return SymNodeVariable.create(
tx,
(operator.neg)(a.as_proxy()),
sym_num=None,
)
if (
isinstance(a, UserDefinedObjectVariable)
and a.call_obj_hasattr(tx, "__neg__").value # type: ignore[attr-defined]
):
return a.call_method(tx, "__neg__", [], {})
# None no-ops this handler and lets the driving function proceed
return None
def call_format(self, tx: "InstructionTranslator", _format_string, *args, **kwargs):
format_string = _format_string.as_python_constant()
format_string = str(format_string)
return variables.StringFormatVariable.create(format_string, args, kwargs)
def call_id(self, tx: "InstructionTranslator", *args):
if len(args) > 0 and isinstance(args[0], variables.NNModuleVariable):
nn_mod_variable = args[0]
mod = tx.output.get_submodule(nn_mod_variable.module_key)
return variables.ConstantVariable.create(id(mod))
elif len(args) == 1 and isinstance(
args[0],
(variables.UserDefinedClassVariable, variables.UserDefinedObjectVariable),
):
if args[0].source:
install_guard(args[0].source.make_guard(GuardBuilder.ID_MATCH))
constant_result = id(args[0].value)
return variables.ConstantVariable.create(constant_result)
elif len(args) == 1 and isinstance(args[0], TensorVariable):
tensor_variable = args[0]
return tensor_variable.call_id(tx)
elif istype(args[0], variables.UserFunctionVariable):
return variables.ConstantVariable.create(id(args[0].fn))
elif istype(args[0], variables.SkipFunctionVariable):
return variables.ConstantVariable.create(id(args[0].value))
elif istype(args[0], variables.FunctoolsPartialVariable):
return variables.ConstantVariable.create(id(args[0].fake_value))
else:
unimplemented_v2(
gb_type="id() with unsupported args",
context=str(args),
explanation=f"Dynamo doesn't know how to trace id() call with args {args}",
hints=[
"Supported args are Tensors, and functions/nn.Modules/user-defined objects "
"from outside the compiled region.",
*graph_break_hints.SUPPORTABLE,
],
)
def call_deepcopy(self, tx: "InstructionTranslator", x):
unimplemented_v2(
gb_type="copy.deepcopy()",
context=f"copy.deepcopy({x})",
explanation="Dynamo does not support copy.deepcopy()",
hints=[
"Avoid calling copy.deepcopy()",
*graph_break_hints.SUPPORTABLE,
],
)
def _comparison_with_tensor(self, tx: "InstructionTranslator", left, right):
from .builder import wrap_fx_proxy_cls
from .tensor import supported_tensor_comparison_op_values
op = self.fn
if op in [operator.is_, operator.is_not]:
is_result = (
isinstance(left, TensorVariable)
and isinstance(right, TensorVariable)
and id(extract_fake_example_value(left.as_proxy().node))
== id(extract_fake_example_value(right.as_proxy().node))
)
if op is operator.is_:
return ConstantVariable.create(is_result)
else:
return ConstantVariable.create(not is_result)
if op not in supported_tensor_comparison_op_values:
unimplemented_v2(
gb_type="unsupported Tensor comparison op",
context=f"{op.__name__}({left}, {right})",
explanation=f"Dynamo does not support the comparison op {op.__name__} "
f"with Tensor arguments {left}, {right}",
hints=[*graph_break_hints.SUPPORTABLE],
)
if (
isinstance(left, TensorVariable)
and isinstance(right, TensorVariable)
and (left.size and right.size) is not None
and left.size != right.size
):
try:
torch.broadcast_shapes(left.size, right.size)
except RuntimeError:
# not broadcastable, can't be compared
unimplemented_v2(
gb_type="failed to broadcast when attempting Tensor comparison op",
context=f"{op.__name__}({left}, {right})",
explanation=f"Dynamo was unable to broad cast the arguments {left}, {right} "
f"when attempting to trace the comparison op {op.__name__}.",
hints=[*graph_break_hints.USER_ERROR],
)
tensor_cls = left if isinstance(left, TensorVariable) else right
proxy = tx.output.create_proxy(
"call_function", op, (left.as_proxy(), right.as_proxy()), {}
)
return wrap_fx_proxy_cls(
type(tensor_cls), # handle Ndarrays and Tensors
tx,
proxy,
)
def _comparison_with_symnode(self, tx: "InstructionTranslator", left, right):
from .tensor import supported_tensor_comparison_op_values
op = self.fn
if op not in supported_tensor_comparison_op_values:
unimplemented_v2(
gb_type="unsupported SymNode comparison op",
context=f"{op.__name__}({left}, {right})",
explanation=f"Dynamo does not support the comparison op {op.__name__} "
f"with SymNode arguments {left}, {right}",
hints=[*graph_break_hints.SUPPORTABLE],
)
# This is seen in inspect signature where we check if the value is a default value
if isinstance(right, variables.UserDefinedClassVariable):
return variables.ConstantVariable(op(object(), None))
proxy = tx.output.create_proxy(
"call_function", op, (left.as_proxy(), right.as_proxy()), {}
)
return SymNodeVariable.create(
tx,
proxy,
sym_num=None,
)
def call_xor(self, tx: "InstructionTranslator", a, b):
if isinstance(a, (DictKeysVariable, SetVariable, UserDefinedObjectVariable)):
return a.call_method(tx, "__xor__", [b], {})
def call_ixor(self, tx: "InstructionTranslator", a, b):
if isinstance(a, (DictKeysVariable, SetVariable, UserDefinedObjectVariable)):
return a.call_method(tx, "__ixor__", [b], {})
def call_sub(self, tx: "InstructionTranslator", a, b):
if isinstance(a, (DictKeysVariable, SetVariable, UserDefinedObjectVariable)):
return a.call_method(tx, "__sub__", [b], {})
def call_isub(self, tx: "InstructionTranslator", a, b):
if isinstance(a, (DictKeysVariable, SetVariable, UserDefinedObjectVariable)):
return a.call_method(tx, "__isub__", [b], {})
def call_and_(self, tx: "InstructionTranslator", a, b):
# Rely on constant_handler
if isinstance(a, ConstantVariable) and isinstance(b, ConstantVariable):
return None
if isinstance(a, (SymNodeVariable, ConstantVariable)) and isinstance(
b, (SymNodeVariable, ConstantVariable)
):
return SymNodeVariable.create(
tx,
tx.output.create_proxy(
"call_function", operator.and_, *proxy_args_kwargs([a, b], {})
),
sym_num=None,
)
if isinstance(a, (DictKeysVariable, SetVariable, UserDefinedObjectVariable)):
return a.call_method(tx, "__and__", [b], {})
# None no-ops this handler and lets the driving function proceed
def call_iand(self, tx: "InstructionTranslator", a, b):
# Rely on constant_handler
if isinstance(a, ConstantVariable) and isinstance(b, ConstantVariable):
return None
if isinstance(a, (SymNodeVariable, ConstantVariable)) and isinstance(
b, (SymNodeVariable, ConstantVariable)
):
return SymNodeVariable.create(
tx,
tx.output.create_proxy(
"call_function", operator.iand, *proxy_args_kwargs([a, b], {})
),
sym_num=None,
)
if isinstance(a, (DictKeysVariable, SetVariable, UserDefinedObjectVariable)):
return a.call_method(tx, "__iand__", [b], {})
def call_or_(self, tx: "InstructionTranslator", a, b):
# Rely on constant_handler
if isinstance(a, ConstantVariable) and isinstance(b, ConstantVariable):
return None
if isinstance(a, (SymNodeVariable, ConstantVariable)) and isinstance(
b, (SymNodeVariable, ConstantVariable)
):
return SymNodeVariable.create(
tx,
tx.output.create_proxy(
"call_function", operator.or_, *proxy_args_kwargs([a, b], {})
),
sym_num=None,
)
# This call looks like `{"one": torch.ones(1)} | {"two": torch.ones(2)}`.
if isinstance(
a,
(
ConstDictVariable,
DictKeysVariable,
MutableMappingVariable,
SetVariable,
UserDefinedDictVariable,
UserDefinedObjectVariable,
),
):
# TODO(guilhermeleobas): forward the call to b.__ror__(a) if
# a.__ror__(b) returns NotImplemented
return a.call_method(tx, "__or__", [b], {})
# None no-ops this handler and lets the driving function proceed
return None
def call_ior(self, tx: "InstructionTranslator", a, b):
# Rely on constant_handler
if isinstance(a, ConstantVariable) and isinstance(b, ConstantVariable):
return None
if isinstance(a, (SymNodeVariable, ConstantVariable)) and isinstance(
b, (SymNodeVariable, ConstantVariable)
):
return SymNodeVariable.create(
tx,
tx.output.create_proxy(
"call_function", operator.ior, *proxy_args_kwargs([a, b], {})
),
sym_num=None,
)
# This call looks like `{"one": torch.ones(1)} |= {"two": torch.ones(2)}`.
if isinstance(
a,
(
ConstDictVariable,
DictKeysVariable,
MutableMappingVariable,
SetVariable,
UserDefinedObjectVariable,
),
):
return a.call_method(tx, "__ior__", [b], {})
# None no-ops this handler and lets the driving function proceed
return None
def call_not_(self, tx: "InstructionTranslator", a):
if isinstance(a, SymNodeVariable):
return SymNodeVariable.create(
tx,
tx.output.create_proxy(
"call_function", operator.not_, *proxy_args_kwargs([a], {})
),
sym_num=None,
)
# Unwrap the underlying ConstDictVariable
if isinstance(a, DictViewVariable):
a = a.dv_dict
if isinstance(a, (ListVariable, ConstDictVariable)):
return ConstantVariable.create(len(a.items) == 0)
return None
def call_contains(
self, tx: "InstructionTranslator", a: VariableTracker, b: VariableTracker
):
return a.call_method(tx, "__contains__", [b], {})
@contextlib.contextmanager
def dynamo_disable_grad(tx):
from . import GradModeVariable
gmv = GradModeVariable.create(tx, False)
try:
gmv.enter(tx)
yield
finally:
gmv.exit(tx)
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