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# mypy: ignore-errors
import collections
import dataclasses
import functools
import inspect
import itertools
import sys
import types
from typing import Dict, List
import torch._C
import torch._numpy as tnp
import torch.utils._pytree as pytree
from .. import config, variables
from ..bytecode_transformation import create_call_function, create_instruction
from ..exc import unimplemented
from ..guards import GuardBuilder, install_guard
from ..source import AttrSource, GetItemSource, ODictGetItemSource, TypeSource
from ..utils import (
check_constant_args,
check_unspec_python_args,
identity,
is_tensor_base_attr_getter,
proxy_args_kwargs,
)
from .base import VariableTracker
from .functions import NestedUserFunctionVariable, UserFunctionVariable
from .user_defined import UserDefinedObjectVariable
class SuperVariable(VariableTracker):
def __init__(self, typevar, objvar=None, specialized=False, **kwargs):
super().__init__(**kwargs)
# typevar is the fist argument to super(). In the case where no argument
# is provided to super(), it is the __class__ object where
# the super() function is being called
self.typevar = typevar
# objvar here must be an instance or subtype of typevar.
# In the case where super() is called without arguments, it is the first argument
# to the current function where super() is called from (self for regular method,
# cls for a classmethod)
self.objvar = objvar
self.specialized = specialized # directly get attr from self.typevar if true
def reconstruct(self, codegen):
codegen(variables.BuiltinVariable(super))
codegen(self.typevar)
if self.objvar is not None:
codegen(self.objvar)
codegen.extend_output(create_call_function(2, True))
else:
codegen.extend_output(create_call_function(1, True))
def _resolved_getattr_and_source(self, tx, name):
assert self.objvar, "1-arg super not implemented"
if self.specialized:
return getattr(self.typevar.as_python_constant(), name)
search_type = self.typevar.as_python_constant()
# The rest of this function does two things:
# - Walk the mro to find where the attribute comes from to be
# able to provide accurate source
# - Call the getattr to get the object
# Find the class object, where the function lives.
# When objvar is "self", use type(self), when objvar is "cls", use it as-is
type_to_use = self.objvar.python_type()
type_to_use_source = (
TypeSource(self.objvar.source) if self.objvar.source else None
)
if issubclass(type_to_use, type):
type_to_use = self.objvar.value
type_to_use_source = self.objvar.source
source = None
if self.objvar.source is not None:
# Walk the mro tuple to find out the actual class where the
# attribute resides.
search_mro = type_to_use.__mro__
start_index = search_mro.index(search_type) + 1
for index in range(start_index, len(search_mro)):
if hasattr(search_mro[index], name):
# Equivalent of something like type(L['self']).__mro__[1].attr_name
source = AttrSource(
GetItemSource(AttrSource(type_to_use_source, "__mro__"), index),
name,
)
break
# TODO(jansel): there is a small chance this could trigger user code, prevent that
return getattr(super(search_type, type_to_use), name), source
def var_getattr(self, tx, name: str) -> "VariableTracker":
# Check if getattr is a constant. If not, delay the actual work by
# wrapping the result in GetAttrVariable. Mostly super is called with a
# method, so most of the work is delayed to call_function.
#
# We could have just implemented a const_getattr. However, super is
# special when it comes to finding sources. Compared to other VTs, super
# requires the attr name to walk the mro and find the actual source (and
# not just AttrSource).
value, source = self._resolved_getattr_and_source(self, name)
if not variables.ConstantVariable.is_literal(value):
return GetAttrVariable(self, name)
if source:
install_guard(source.make_guard(GuardBuilder.CONSTANT_MATCH))
return variables.ConstantVariable.create(value, source=source)
return variables.ConstantVariable.create(value)
def call_method(
self,
tx,
name,
args: "List[VariableTracker]",
kwargs: "Dict[str, VariableTracker]",
) -> "VariableTracker":
inner_fn, source = self._resolved_getattr_and_source(self, name)
if inner_fn is object.__init__:
return LambdaVariable(identity)
elif inner_fn is torch.nn.Module.__init__:
objvar = self.objvar
from ..side_effects import AttributeMutationNew
if (
isinstance(objvar, variables.UserDefinedObjectVariable)
and isinstance(objvar.mutable_local, AttributeMutationNew)
and not (args or kwargs)
):
tx.output.side_effects.store_attr(
objvar,
"__call_nn_module_init",
variables.ConstantVariable.create(True),
)
return variables.ConstantVariable.create(None)
else:
unimplemented("super() nn.Module.__init__")
elif isinstance(inner_fn, types.FunctionType):
return variables.UserFunctionVariable(
inner_fn, source=source
).call_function(tx, [self.objvar] + args, kwargs)
elif isinstance(inner_fn, types.MethodType):
return variables.UserMethodVariable(
inner_fn.__func__, self.objvar, source=source
).call_function(tx, args, kwargs)
elif (
inner_fn is collections.OrderedDict.__getitem__
and isinstance(self.objvar, variables.UserDefinedObjectVariable)
and self.objvar.source
and len(args) == 1
and len(kwargs) == 0
and args[0].is_python_constant()
):
from .builder import VariableBuilder
key = args[0].as_python_constant()
return VariableBuilder(tx, ODictGetItemSource(self.objvar.source, key))(
collections.OrderedDict.__getitem__(self.objvar.value, key)
)
elif inner_fn in (
collections.OrderedDict.__setitem__,
object.__setattr__,
) and isinstance(self.objvar, variables.CustomizedDictVariable):
assert not kwargs and len(args) == 2
return super(variables.CustomizedDictVariable, self.objvar).call_method(
tx, "__setitem__", args, kwargs
)
else:
unimplemented(f"non-function or method super: {inner_fn}")
class UnknownVariable(VariableTracker):
"""
It could be anything!
"""
class DelayGraphBreakVariable(UnknownVariable):
"""
Used to insert a dummy variable in the stack to do the graph break at CALL_FUNCTION.
"""
class ComptimeVariable(VariableTracker):
"""
This variable is special, it lets you execute arbitrary code at
Dynamo compile time
"""
def reconstruct(self, codegen):
raise NotImplementedError("comptime is special form")
def var_getattr(self, tx, name: str) -> "VariableTracker":
from ..comptime import comptime
# To support the comptime.print_graph convenience accessors
from .functions import UserFunctionVariable
return UserFunctionVariable(
getattr(comptime, name), source=AttrSource(self.source, name)
)
def call_function(
self, tx, args: "List[VariableTracker]", kwargs: "Dict[str, VariableTracker]"
) -> "VariableTracker":
from ..comptime import ComptimeContext
# TODO: support an expression form as well
assert not kwargs
assert len(args) == 1
fn = args[0]
if isinstance(fn, UserFunctionVariable):
fn.get_function()(ComptimeContext(tx))
elif isinstance(fn, NestedUserFunctionVariable):
# We have to manually bind the freevars ourselves
code = fn.get_code()
assert not fn.closure, (
"comptime function must not have free variables, "
f"but these variables were free: {code.co_freevars}"
)
func = types.FunctionType(
code,
fn.f_globals,
fn.fn_name.as_python_constant(),
tuple(fn.defaults.items) if fn.defaults else None,
# We could automatically promote free variables into
# ComptimeVar but this is confusing if you access
# a free variable that we actually DO have the runtime
# value for
# tuple(make_cell(ComptimeVar(i)) for i in fn.closure.items)
tuple(),
)
func(ComptimeContext(tx))
else:
raise RuntimeError(f"unsupported argument to comptime: {type(fn)}")
return variables.ConstantVariable.create(None)
class ClosureVariable(UnknownVariable):
def __init__(self, name, **kwargs):
super().__init__(**kwargs)
self.name = name
def reconstruct(self, codegen):
codegen.append_output(codegen.create_load_closure(self.name))
# closure variable created by an inlined function
class InlinedClosureVariable(UnknownVariable):
def __init__(self, name, **kwargs):
super().__init__(**kwargs)
self.name = name
def reconstruct(self, codegen):
codegen.append_output(codegen.create_load_closure(self.name))
class NewCellVariable(VariableTracker):
def __init__(self, **kwargs):
super().__init__(**kwargs)
class NewGlobalVariable(VariableTracker):
def __init__(self, **kwargs):
super().__init__(**kwargs)
class InspectSignatureVariable(VariableTracker):
"""represents inspect.signature(...)"""
@staticmethod
def create(callable, **kwargs):
if kwargs:
unimplemented(f"inspect.signature with {kwargs}")
return InspectSignatureVariable(callable)
def __init__(self, inspected: VariableTracker, **kwargs):
super().__init__(**kwargs)
self.inspected = inspected
def var_getattr(self, tx, name: str) -> "VariableTracker":
if name == "parameters":
return variables.ConstDictVariable(
{
variables.ConstantVariable.create(name): InspectParameterVariable()
for name in self.inspected.inspect_parameter_names()
},
user_cls=dict,
)
return super().var_getattr(tx, name)
class InspectParameterVariable(VariableTracker):
"""This is not implemented, if used will graph break."""
pass
def produce_trampoline_autograd_apply(fn_cls):
def trampoline_autograd_apply(*args, **kwargs):
return fn_cls.apply(*args, **kwargs)
trampoline_autograd_apply._origin = produce_trampoline_autograd_apply
return trampoline_autograd_apply
class AutogradFunctionVariable(VariableTracker):
"""represents a torch.autograd.Function subclass"""
def __init__(self, fn_cls, **kwargs):
super().__init__(**kwargs)
self.fn_cls = fn_cls
def call_apply(self, tx, args, kwargs):
requires_grad = False
def visit(node):
nonlocal requires_grad
if isinstance(node, variables.TensorVariable):
if node.requires_grad is not False:
requires_grad = True
if isinstance(node, variables.NNModuleVariable):
if node.is_training(tx):
requires_grad = True
return node
VariableTracker.apply(visit, (args, kwargs))
if (
requires_grad
and torch.is_grad_enabled()
and config.capture_autograd_function
):
# Note - this is the same check used in autograd/function.py, except inverted.
# If we want to support functorch transforms here, we will need to enable this.
if (
self.fn_cls.setup_context
!= torch.autograd.function._SingleLevelFunction.setup_context
):
unimplemented(
"NYI - autograd.Function with custom setup_context method"
)
vjp_fn = self.fn_cls.vjp # type: ignore[attr-defined]
if vjp_fn is not torch.autograd.Function.vjp:
unimplemented("NYI - User defind vjp")
jvp_fn = self.fn_cls.jvp # type: ignore[attr-defined]
if jvp_fn is not torch.autograd.Function.jvp:
unimplemented("NYI - User defind jvp")
from .higher_order_ops import AutogradFunctionApplyVariable
source = self.source
if source is None:
source = AttrSource(
tx.import_source(self.fn_cls.__module__), self.fn_cls.__name__
)
return AutogradFunctionApplyVariable(
self.fn_cls.forward,
self.fn_cls.backward,
source,
source=AttrSource(source, member="apply"),
).call_function(tx, args, kwargs)
if self.source:
source = AttrSource(self.source, "forward")
else:
source = None
fn = self.fn_cls.forward
ctx = AutogradFunctionContextVariable.create(tx)
args = [ctx, *args]
if isinstance(fn, types.FunctionType):
return variables.UserFunctionVariable(fn, source=source).call_function(
tx, args, kwargs
)
elif isinstance(fn, types.MethodType):
return variables.UserMethodVariable(
fn.__func__,
variables.UserDefinedClassVariable(self.fn_cls),
source=source,
).call_function(tx, args, kwargs)
else:
unimplemented(
f"non-function or method in subclass of torch.autograd.Function: {fn}"
)
def call_function(self, tx, args, kwargs):
return AutogradFunctionVariable(self.fn_cls)
def call_method(
self,
tx,
name,
args: "List[VariableTracker]",
kwargs: "Dict[str, VariableTracker]",
):
from ..trace_rules import is_callable_allowed
from .builder import wrap_fx_proxy
if name == "apply":
if is_callable_allowed(self.fn_cls):
trampoline_autograd_apply = produce_trampoline_autograd_apply(
self.fn_cls
)
return wrap_fx_proxy(
tx=tx,
proxy=tx.output.create_proxy(
"call_function",
trampoline_autograd_apply,
*proxy_args_kwargs(args, kwargs),
),
)
else:
return self.call_apply(tx, args, kwargs)
else:
unimplemented(f"Unsupported method: {name}")
@dataclasses.dataclass
class SavedTensorBox:
tensors: List[VariableTracker] = dataclasses.field(default_factory=list)
class AutogradFunctionContextVariable(UserDefinedObjectVariable):
"""
Tracks an autograd.Function() context using mutation tracking in side_effects.py
"""
_nonvar_fields = {
"proxy",
"inference",
*UserDefinedObjectVariable._nonvar_fields,
}
def __init__(
self,
value,
value_type=None,
inference=False,
proxy=None,
saved_tensors=None,
**kwargs,
):
super().__init__(value=value, value_type=value_type, **kwargs)
self.inference = inference
self.proxy = proxy
self.saved_tensors = saved_tensors
@staticmethod
def create(tx):
proxy = tx.output.create_proxy(
"call_function", torch.autograd.function.FunctionCtx, tuple(), {}
)
out = tx.output.side_effects.track_object_new(
None,
torch.autograd.function.FunctionCtx,
functools.partial(
AutogradFunctionContextVariable,
inference=True,
proxy=proxy,
saved_tensors=SavedTensorBox(),
),
{},
)
proxy.node.meta["example_value"] = out.value
return out
def as_proxy(self):
if self.proxy is None:
unimplemented("proxy not set")
return self.proxy
def call_method(
self,
tx,
name,
args: "List[VariableTracker]",
kwargs: "Dict[str, VariableTracker]",
) -> "VariableTracker":
if name != "save_for_backward":
unimplemented(f"autograd.Function context method: {name}")
if self.saved_tensors is None:
unimplemented(
"save_for_backward only supported on a newly constructed FunctionCtx"
)
if not self.inference:
assert self.source and not kwargs
tx.output.side_effects.track_save_for_backward(self, args)
# In eager mode, multiple calls to .save_for_backward() will overwrite previous calls.
if len(self.saved_tensors.tensors) > 0:
self.saved_tensors.tensors = []
for arg in args:
self.saved_tensors.tensors.append(arg)
return variables.ConstantVariable.create(None)
def var_getattr(self, tx, name):
if name == "save_for_backward":
return LambdaVariable(
lambda *args, **kwargs: self.call_method(tx, name, args, kwargs)
)
if name == "saved_tensors" and self.saved_tensors is not None:
return variables.TupleVariable(list(self.saved_tensors.tensors))
return super().var_getattr(tx, name)
class LambdaVariable(VariableTracker):
def __init__(self, fn, **kwargs):
super().__init__(**kwargs)
self.fn = fn
def call_function(
self, tx, args: "List[VariableTracker]", kwargs: "Dict[str, VariableTracker]"
) -> "VariableTracker":
return self.fn(*args, **kwargs)
class GetAttrVariable(VariableTracker):
def __init__(self, obj, name, **kwargs):
super().__init__(**kwargs)
assert isinstance(obj, VariableTracker)
assert isinstance(name, str)
self.obj = obj
self.name = name
def __str__(self):
return f"{self.__class__.__name__}({self.obj}, {self.name})"
@staticmethod
def create_getattr_proxy(base_proxy: torch.fx.Proxy, attr):
return getattr(base_proxy, attr)
def as_proxy(self):
return GetAttrVariable.create_getattr_proxy(self.obj.as_proxy(), self.name)
def const_getattr(self, tx, name):
if not isinstance(self.obj, variables.NNModuleVariable):
raise NotImplementedError()
step1 = tx.output.get_submodule(self.obj.module_key)
if self.name not in step1.__dict__:
raise NotImplementedError()
step2 = inspect.getattr_static(step1, self.name)
if name not in step2.__dict__:
raise NotImplementedError()
return inspect.getattr_static(step2, name)
def reconstruct(self, codegen):
codegen(self.obj)
codegen.extend_output(codegen.create_load_attrs(self.name))
def call_function(
self, tx, args: "List[VariableTracker]", kwargs: "Dict[str, VariableTracker]"
) -> "VariableTracker":
return self.obj.call_method(tx, self.name, args, kwargs)
class MethodWrapperVariable(VariableTracker):
def __init__(self, method_wrapper, **kwargs):
super().__init__(**kwargs)
self.method_wrapper = method_wrapper
def call_function(
self, tx, args: "List[VariableTracker]", kwargs: "Dict[str, VariableTracker]"
) -> "VariableTracker":
if is_tensor_base_attr_getter(self.method_wrapper) and isinstance(
args[0], variables.TensorVariable
):
assert len(args) == 1 and len(kwargs) == 0
return args[0].var_getattr(tx, self.method_wrapper.__self__.__name__)
super().call_function(tx, args, kwargs)
def is_python_constant(self):
return True
def as_python_constant(self):
return self.method_wrapper
class GetSetDescriptorVariable(VariableTracker):
def __init__(self, desc, **kwargs):
super().__init__(**kwargs)
self.desc = desc
def var_getattr(self, tx, name):
if name == "__get__" and self.source:
from .builder import VariableBuilder
return VariableBuilder(tx, AttrSource(self.source, "__get__"))(
self.desc.__get__
)
else:
return super().var_getattr(tx, name)
def is_python_constant(self):
return True
def as_python_constant(self):
return self.desc
class PythonModuleVariable(VariableTracker):
def __init__(self, value: types.ModuleType, **kwargs):
super().__init__(**kwargs)
self.value = value
self.is_torch = self.value is torch or self.value.__name__.startswith("torch.")
def python_type(self):
return types.ModuleType
def as_python_constant(self):
return self.value
def __repr__(self):
return f"PythonModuleVariable({self.value})"
def call_hasattr(self, tx, name):
if self.is_torch:
result = hasattr(self.value, name)
return variables.ConstantVariable.create(result)
return super().call_hasattr(tx, name)
class TypingVariable(VariableTracker):
def __init__(self, value, **kwargs):
super().__init__(**kwargs)
self.value = value
def call_method(
self,
tx,
name,
args: "List[VariableTracker]",
kwargs: "Dict[str, VariableTracker]",
) -> "VariableTracker":
if name == "__getitem__" and len(args) == 1:
return variables.ConstantVariable.create(
self.value[args[0].as_python_constant()],
)
unimplemented("typing")
def python_type(self):
return type(self.value)
def as_python_constant(self):
return self.value
@functools.lru_cache(maxsize=1)
def get_np_to_tnp_map():
from ..utils import NP_TO_TNP_MODULE
np_fn_to_tnp_fn = {}
for np_mod, tnp_mod in NP_TO_TNP_MODULE.items():
for fn_name, tnp_fn in tnp_mod.__dict__.items():
if callable(tnp_fn):
# some internal details do leak from tnp
# which are not part of numpy API.
if np_fn := getattr(np_mod, fn_name, None):
np_fn_to_tnp_fn[np_fn] = tnp_fn
return np_fn_to_tnp_fn
class NumpyVariable(VariableTracker):
"""
Wrapper around `numpy.*`. Currently, is able to trace a small subset of numpy functions as well as numpy dtypes.
"""
constant_fold_functions = (tnp.issubdtype,)
def __init__(self, value, **kwargs):
super().__init__(**kwargs)
self.value = value
@classmethod
def can_constant_fold_through(cls, fn):
mod = fn.__module__.split(".")
assert len(mod) >= 2 and mod[:2] == ["torch", "_numpy"]
return fn in cls.constant_fold_functions
def call_function(
self, tx, args: "List[VariableTracker]", kwargs: "Dict[str, VariableTracker]"
) -> "VariableTracker":
if not config.trace_numpy:
unimplemented(f"numpy.{self.value}()")
from ..utils import numpy_to_tensor_wrapper
from .tensor import NumpyNdarrayVariable
# lookup method name in tnp. Things like np.dtype(float) are not supported yet.
if self.value.__name__ == "dtype":
unimplemented(
f"numpy dtype function is not supported yet. Got type {type(self.value)}."
)
else: # We are dealing with a callable.
func = get_np_to_tnp_map().get(self.value)
if func is None:
unimplemented(
f"Can't find numpy function {self.value} in torch._numpy. "
" Please file an issue to request support for this function."
)
if (
func.__module__ == "torch._numpy.random"
and config.use_numpy_random_stream
):
msg = f"delegate '{func.__qualname__}' to NumPy itself via "
msg += f"confg.use_numpy_random_stream={config.use_numpy_random_stream}"
unimplemented(msg)
args, kwargs = NumpyNdarrayVariable.patch_args(func.__name__, args, kwargs)
constant_args = check_constant_args(args, kwargs)
unspec_python_args = check_unspec_python_args(args, kwargs)
if self.can_constant_fold_through(func) and (
constant_args or unspec_python_args
):
# constant fold
return variables.ConstantVariable.create(
self.as_python_constant()(
*[x.as_python_constant() for x in args],
**{k: v.as_python_constant() for k, v in kwargs.items()},
),
)
# TODO Add all the functions that go from constants to constants to can_constant_fold_through
proxy = tx.output.create_proxy(
"call_function",
numpy_to_tensor_wrapper(func),
*proxy_args_kwargs(args, kwargs),
)
return NumpyNdarrayVariable.create(tx, proxy)
def call_method(
self,
tx,
name,
args: "List[VariableTracker]",
kwargs: "Dict[str, VariableTracker]",
) -> "VariableTracker":
unimplemented("numpy")
def python_type(self):
return type(self.value)
def as_python_constant(self):
return self.value
def as_proxy(self):
if config.trace_numpy and isinstance(self.value, type):
# This handles numpy dtype attributes such as np.float32
# We return a string as we don't want to serialize non-PyTorch objects in the output FX graph
# In torch/_numpy we normalize strings to their dtypes when the input is a dtype, as NumPy does
return self.value.__name__
return super().as_proxy()
# Used to keep track of NULLs pushed on the stack for Python 3.11 function calls
class NullVariable(VariableTracker):
def __init__(self, **kwargs):
super().__init__(**kwargs)
def __str__(self):
return "NullVariable"
def reconstruct(self, codegen):
if sys.version_info < (3, 11):
unimplemented("cannot reconstruct NullVariable in < Python 3.11")
codegen.append_output(create_instruction("PUSH_NULL"))
class DeletedVariable(VariableTracker):
"""Marker used to implement delattr()"""
class StringFormatVariable(VariableTracker):
"""
Represents a call to str.format(), we delay calling format until after the graph.
"""
_nonvar_fields = {"format_string", *VariableTracker._nonvar_fields}
@classmethod
def create(cls, format_string, sym_args, sym_kwargs):
if all(
x.is_python_constant()
for x in itertools.chain(sym_args, sym_kwargs.values())
):
return variables.ConstantVariable.create(
format_string.format(
*[v.as_python_constant() for v in sym_args],
**{k: v.as_python_constant() for k, v in sym_kwargs.items()},
)
)
return cls(format_string, list(sym_args), dict(sym_kwargs))
def __init__(self, format_string, sym_args, sym_kwargs, **kwargs):
super().__init__(**kwargs)
assert isinstance(format_string, str)
self.format_string = format_string
self.sym_args = sym_args
self.sym_kwargs = sym_kwargs
def __repr__(self):
return f"{self.__class__.__name__}({self.format_string!r}, {self.sym_args!r}, {self.sym_kwargs!r})"
def reconstruct(self, codegen):
if sys.version_info >= (3, 11):
codegen.append_output(create_instruction("PUSH_NULL"))
codegen.append_output(codegen.create_load_const(self.format_string))
codegen.append_output(codegen.create_load_attr("format"))
codegen(variables.TupleVariable(self.sym_args))
kwargs = {
variables.ConstantVariable.create(k): v for k, v in self.sym_kwargs.items()
}
codegen(variables.ConstDictVariable(kwargs))
codegen.append_output(create_instruction("CALL_FUNCTION_EX", arg=1))
class DebuggingVariable(VariableTracker):
"""
Represents a call to a debugging function like print(), or something
registered to config.reorderable_logging_functions.
"""
def __init__(self, value, **kwargs):
super().__init__(**kwargs)
self.value = value
@staticmethod
def is_reorderable_logging_function(obj):
return (
callable(obj)
and isinstance(obj, (types.FunctionType, types.BuiltinFunctionType))
and obj in torch._dynamo.config.reorderable_logging_functions
)
def call_function(self, tx, args, kwargs):
if tx.export:
# For export cases, we can just make debugging functions no-ops
return
if not self.can_reorder_logs(self.value, args, kwargs):
unimplemented(
f"Reordering debugging function {self.value} "
f"with inputs {args} {kwargs} is not yet implemented."
)
tx.debug_locals.append((self, list(args)))
def reconstruct(self, codegen):
return self.source.reconstruct(codegen)
@staticmethod
def can_reorder_logs(fn, args, kwargs) -> True:
"""
Run some additional checks for what sort of function calls can we
actually reorder.
"""
allowed_input_types = (
variables.TensorVariable,
variables.ConstantVariable,
StringFormatVariable,
)
flat_args = pytree.tree_leaves([args, kwargs])
for arg in flat_args:
if not isinstance(arg, allowed_input_types):
return False
return True
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