MLPY/Lib/site-packages/torch/_dynamo/guards.py

from __future__ import annotations

import ast
import builtins
import collections
import dataclasses
import enum
import functools
import importlib
import inspect
import itertools
import logging
import math
import os
import re
import sys
import textwrap
import types
import weakref
from inspect import currentframe, getframeinfo
from typing import Any, Callable, Dict, List, Optional, Tuple, Type, Union
from weakref import ReferenceType


try:
    import numpy as np
except ModuleNotFoundError:
    np = None  # type: ignore[assignment]

import torch
import torch.utils._device
from torch._dynamo.source import (
    is_from_local_source,
    TensorProperty,
    TensorPropertySource,
)

from torch._guards import (
    DuplicateInputs,
    Guard,
    GuardBuilderBase,
    GuardEnvExpr,
    GuardSource,
    Source,
)

from torch._logging import structured
from torch.fx.experimental.symbolic_shapes import (
    EqualityConstraint,
    is_symbolic,
    SYMPY_INTERP,
)
from torch.utils._traceback import format_frame, report_compile_source_on_error
from torch.utils.weak import TensorWeakRef

from . import config, convert_frame, exc, mutation_guard
from .eval_frame import set_guard_error_hook
from .source import AttrSource, DefaultsSource, LocalSource, TypeSource
from .types import CacheEntry, ExtraState, GuardedCode, GuardFail, GuardFn  # noqa: F401
from .utils import (
    common_constant_types,
    dict_keys_repr,
    guard_failures,
    istype,
    key_is_id,
    key_to_id,
    orig_code_map,
    tensor_always_has_static_shape,
    tuple_iterator_getitem,
    tuple_iterator_len,
)

log = logging.getLogger(__name__)
guards_log = torch._logging.getArtifactLogger(__name__, "guards")
recompiles_log = torch._logging.getArtifactLogger(__name__, "recompiles")
recompiles_verbose_log = torch._logging.getArtifactLogger(
    __name__, "recompiles_verbose"
)
verbose_guards_log = torch._logging.getArtifactLogger(__name__, "verbose_guards")

TensorGuards = torch._C._dynamo.guards.TensorGuards
check_obj_id = torch._C._dynamo.guards.check_obj_id
check_type_id = torch._C._dynamo.guards.check_type_id
dict_version = torch._C._dynamo.guards.dict_version


# For user stack printing
@functools.lru_cache(None)
def uninteresting_files():
    import torch._dynamo.external_utils

    mods = [
        torch._dynamo.external_utils,
    ]
    return {inspect.getfile(m) for m in mods}


CLOSURE_VARS = {
    "___check_type_id": check_type_id,
    "___check_obj_id": check_obj_id,
    "___odict_getitem": collections.OrderedDict.__getitem__,
    "___key_to_id": key_to_id,
    "___dict_version": dict_version,
    "___dict_contains": lambda a, b: a in b,
    "___tuple_iterator_len": tuple_iterator_len,
    "___tuple_iterator_getitem": tuple_iterator_getitem,
    "__math_isnan": math.isnan,
    "__numpy_isnan": None if np is None else np.isnan,
    "inf": float("inf"),
    "__load_module": importlib.import_module,
    "utils_device": torch.utils._device,
    "device": torch.device,
    "___from_numpy":
    # If not numpy array, piggy back on e.g. tensor guards to check type
    (lambda a: torch.as_tensor(a) if isinstance(a, (np.generic, np.ndarray)) else a),
    "torch": torch,
    "inspect": inspect,
}

if sys.version_info[:2] <= (3, 8):
    # [Note: Python Version <= 3.8]
    # This branch should be dropped when we drop support for Python 3.8.
    # Reason: 'ast.unparse' function was introduced in Python 3.9.

    try:
        import astunparse  # type: ignore[import]

        def _ast_unparse(node: ast.AST) -> str:
            return astunparse.unparse(node).replace("\n", "")

        HAS_UNPARSE_FUNCTIONS = True
    except ImportError:
        HAS_UNPARSE_FUNCTIONS = False
        pass
else:
    HAS_UNPARSE_FUNCTIONS = True

    def _ast_unparse(node: ast.AST) -> str:
        return ast.unparse(node).replace("\n", "")


def strip_function_call(name):
    """

    "___odict_getitem(a, 1)" => "a"

    "a.layers[slice(2)][0]._xyz" ==> "a"

    "getattr(a.layers[slice(2)][0]._abc, '0')" ==> "a"

    "getattr(getattr(a.x[3], '0'), '3')" ==> "a"

    "a.layers[slice(None, -1, None)][0]._xyz" ==> "a"

    """
    # recursively find valid object name in function
    valid_name = re.compile("[A-Za-z_].*")
    curr = ""
    for char in name:
        if char in " (":
            curr = ""
        elif char in "),[]":
            if curr and curr != "None" and valid_name.match(curr):
                return strip_function_call(curr)
        else:
            curr += char

    return strip_getattr_getitem(name)


def strip_getattr_getitem(name):
    """

    "a[1]" => "a"

    "a.foo" => "a"

    """
    return re.split(r"[.\[]", name)[0]


def get_verbose_code_part(code_part, guard):
    extra = ""
    if guard.user_stack:
        for fs in reversed(guard.user_stack):
            if fs.filename not in uninteresting_files():
                extra = f"  # {format_frame(fs, line=True)}"
                break
    elif guard.stack:
        extra = f"  # {format_frame(guard.stack.summary()[-1])}"

    return f"{code_part:<60}{extra}"


def convert_to_concrete_values(size_or_stride):
    converted: List[Optional[int]] = []
    for dim in size_or_stride:
        if not is_symbolic(dim):
            converted.append(dim)
        else:
            assert isinstance(dim, torch.SymInt)
            converted.append(dim.node.maybe_as_int())
    return converted


def get_tensor_guard_code_part(value, name, sizes, strides):
    pytype = type(value)
    dispatch_key = (
        torch._C._dispatch_keys(value) | torch._C._dispatch_tls_local_include_set()
    ) - torch._C._dispatch_tls_local_exclude_set()
    dtype = value.dtype
    device_index = value.device.index
    requires_grad = value.requires_grad
    guard_str = (
        f"check_tensor({name}, {pytype.__qualname__}, {dispatch_key}, {dtype}, "
        f"device={device_index}, requires_grad={requires_grad}, size={sizes}, stride={strides})"
    )
    return guard_str


# The ready to eval generated code (possibly multiple parts) for a guard, plus
# the original guard object that created it for provenance
@dataclasses.dataclass
class GuardCodeList:
    code_list: List[str]
    guard: Guard


class GuardBuilder(GuardBuilderBase):
    def __init__(

        self,

        id_ref: Callable[[Any], str],

        source_ref: Callable[[Source], str],

        lookup_weakrefs: Callable[[object], ReferenceType[object]],

        local_scope: Dict[str, object],

        global_scope: Dict[str, object],

        check_fn_manager: CheckFunctionManager,

    ):
        self.id_ref = id_ref
        self.source_ref = source_ref
        self.lookup_weakrefs = lookup_weakrefs
        self.scope: Dict[str, Dict[str, object]] = {"L": local_scope, "G": global_scope}
        self.scope["__builtins__"] = builtins.__dict__.copy()
        for (
            name,
            package_module,
        ) in torch.package.package_importer._package_imported_modules.items():
            name = name.replace(">", "_").replace("<", "_").replace(".", "_dot_")
            # Write the package module into the scope so that we can import it
            self.scope["__builtins__"][name] = package_module
            # Write the demangled name to the scope so that we can use it
            self.scope[name] = package_module

        self.argnames: List[str] = []
        # Code is python expression strings generated for each guard
        self.code: List[GuardCodeList] = []
        # shape_env_code is only used by builder and is used for
        # shape env code.  This exists only because we need to make sure
        # shape env guards get run after tensor match guards (since the
        # tensor match guards make sure we actually have tensors)
        self.shape_env_code: List[GuardCodeList] = []

        # [Note - On Eager Tensor Guards]
        # Most of the time, we generate Python code in a guard to directly
        # check various properties.  However, tensors are a bit special;
        # it is too slow to check their properties one-by-one in Python.
        # Instead, there is a C++ function TensorGuards.check which takes
        # all of the tensor arguments and checks them all against compile-time
        # examples entirely in C++.  Thus, every time we process a
        # TENSOR_MATCH guard, we just add another entry to
        # tensor_check_names/tensor_check_examples, saying "for this local,
        # check it against this example", and it all ends up getting
        # swept up into a single call to ___check_tensors.  Invariant:
        # len(tensor_check_names) == len(tensor_check_examples).
        # TODO: something here
        self.tensor_check_names: List[str] = []
        self.tensor_check_examples: List[torch.Tensor] = []
        self.tensor_check_guards: List[Guard] = []

        self.check_fn_manager: CheckFunctionManager = check_fn_manager
        # Keep track of weak references of objects with ID_MATCH guard. This
        # info is stored alongside optimized_code and check_fn and is used to
        # limit the number of cache entries with same ID_MATCH'd object.
        self.id_matched_objs: Dict[str, ReferenceType[object]] = {}

    # Warning: use this with care!  This lets you access what the current
    # value of the value you are guarding on is.  You probably don't want
    # to actually durably save this value though (because it's specific
    # to this frame!)  Instead, you should be reading out some property
    # (like its type) which is what you permanently install into the
    # guard code.
    def get(self, name: str) -> Any:
        return eval(name, self.scope, CLOSURE_VARS)

    # Registers the usage of the source name referenced by the
    # string (or stored in the Guard) as being guarded upon.  It's important
    # to call this before generating some code that makes use of 'guard',
    # because without this call, we won't actually bind the variable
    # you reference in the actual guard closure (oops!)
    def arg_ref(self, guard: Union[str, Guard]) -> str:
        name: str
        if isinstance(guard, str):
            name = guard
        else:
            name = guard.name
        base = strip_getattr_getitem(strip_function_call(name))
        if base not in self.argnames:
            if re.match(r"[a-zA-Z0-9_]+", base):
                if re.match(r"^\d+$", base):
                    log.warning("invalid var name: %s", guard)
                self.argnames.append(base)

        return name

    def _guard_on_attribute(self, guard: Guard, attr_name: str, guard_fn):
        attr_source = AttrSource(guard.originating_source, attr_name)
        # Copy the stack info
        new_guard = Guard(
            attr_source, guard_fn, stack=guard.stack, user_stack=guard.user_stack
        )
        new_guard.create(self)

    def TYPE_MATCH(self, guard: Guard) -> None:
        # ___check_type_id is same as `id(type(x)) == y`
        t = type(self.get(guard.name))
        obj_id = self.id_ref(t)
        code = f"___check_type_id({self.arg_ref(guard)}, {obj_id})"
        self._produce_guard_code(guard, [code])

    def DICT_VERSION(self, guard: Guard):
        # ___check_dict_version is same as `dict_version(x) == y`
        ref = self.arg_ref(guard)
        version = dict_version(self.get(guard.name))
        code = f"___dict_version({ref}) == {version}"
        self._produce_guard_code(guard, [code])

    def DICT_CONTAINS(self, guard: Guard, key: str, invert: bool):
        dict_ref = self.arg_ref(guard)

        maybe_not = "not " if invert else ""
        code = f"{maybe_not}___dict_contains({key!r}, {dict_ref})"
        return self._produce_guard_code(guard, [code])

    def BOOL_FALSE(self, guard: Guard):
        # Guard on the runtime value being 'False',
        # can be faster than seemingly equivalent checks like DICT_KEYS for empty dict
        #
        # WARNING: this guard is not safe to use generally.  It only works if the runtime
        # value is of a type that supports bool(), and some types e.g. Tensor do not.
        # Only use this guard in cases you can guarantee the runtime type will be friendly.
        # (e.g. Specialized NNModule with mutation protection via setattr)
        #
        # Why not simply check the runtime type inside this guard?  It's slow enough to defeat
        # the purpose of using this guard, which itself is supposed to be a faster alternative
        # to DICT_KEYS.
        ref = self.arg_ref(guard)
        code = f"not {ref}"
        self._produce_guard_code(guard, [code])

    def ID_MATCH(self, guard: Guard):
        # ___check_obj_id is same as `id(x) == y`
        if isinstance(guard.originating_source, TypeSource):
            # optional optimization to produce cleaner/faster guard code
            return self.TYPE_MATCH(
                Guard(guard.originating_source.base, GuardBuilder.TYPE_MATCH)  # type: ignore[arg-type]
            )

        ref = self.arg_ref(guard)
        val = self.get(guard.name)
        code = f"___check_obj_id({ref}, {self.id_ref(val)})"
        self._produce_guard_code(guard, [code])

        # Keep track of ID_MATCH'd objects. This will be used to modify the
        # cache size logic
        if isinstance(guard.originating_source, LocalSource):
            # TODO(janimesh) - This is currently restricted to nn.Module objects
            # because many other ID_MATCH'd objects fail - like DeviceMesh.
            # Increase the scope of ID_MATCH'd objects.
            if isinstance(val, torch.nn.Module):
                local_name = guard.originating_source.local_name
                weak_id = self.lookup_weakrefs(val)
                if weak_id is not None:
                    self.id_matched_objs[local_name] = weak_id

    def NAME_MATCH(self, guard: Guard):
        obj = self.get(guard.name)
        self._guard_on_attribute(guard, "__name__", GuardBuilder.EQUALS_MATCH)

    def DATA_PTR_MATCH(self, guard: Guard):
        obj = self.get(guard.name)
        code = f"{self.arg_ref(guard)}.data_ptr() == {obj.data_ptr()}"
        self._produce_guard_code(guard, [code])

    def HASATTR(self, guard: Guard):
        assert isinstance(
            guard.originating_source, AttrSource
        ), f"invalid source {guard.name}"
        base_source = guard.originating_source.base
        base = base_source.name()
        attr = guard.originating_source.member

        ref = self.arg_ref(base)
        val = hasattr(self.get(base), attr)
        code = None
        if val:
            code = f"hasattr({ref}, {attr!r})"
        else:
            code = f"not hasattr({ref}, {attr!r})"

        self._produce_guard_code(guard, [code], provided_guarded_object=self.get(base))

    def FUNCTORCH_STACK_MATCH(self, guard: Guard):
        # Invalidate functorch code if current level is different than
        # the one when FX graph was generated
        # if torch._C._functorch.peek_interpreter_stack() is not None:
        cis = torch._functorch.pyfunctorch.retrieve_all_functorch_interpreters()
        states = [ci.get_state() for ci in cis]
        code = [f"torch._functorch.pyfunctorch.compare_functorch_state({states})"]
        self._produce_guard_code(guard, code)

    def EQUALS_MATCH(self, guard: Guard):
        ref = self.arg_ref(guard)
        val = self.get(guard.name)
        t = type(val)
        if np:
            np_types: Tuple[Type[Any], ...] = (
                np.int8,
                np.int16,
                np.int32,
                np.int64,
                np.uint8,
                np.uint16,
                np.uint32,
                np.uint64,
                np.float16,
                np.float32,
                np.float64,
            )
        else:
            np_types = ()
        ok_types = tuple(
            common_constant_types
            | {
                type,
                list,
                tuple,
                set,
                frozenset,
                slice,
                range,
                torch.Size,
                *np_types,
            }
        )
        if istype(val, dict):
            assert all(
                istype(x, ok_types) for x in itertools.chain(val.keys(), val.values())
            )
        else:
            assert istype(
                val,
                ok_types,
            ), f"Unexpected type {type(val)}, not in {ok_types}"

        # Special case for nan because float("nan") == float("nan") evaluates to False
        if istype(val, float) and math.isnan(val):
            self.TYPE_MATCH(guard)
            code = list()
            code.append(f"__math_isnan({ref})")
            self._produce_guard_code(guard, code)
            return
        # Python math library doesn't support complex nan, so we need to use numpy
        elif istype(val, complex) and np.isnan(val):
            self.TYPE_MATCH(guard)
            code = list()
            code.append(f"__numpy_isnan({ref})")
            self._produce_guard_code(guard, code)
            return

        code = list()

        # If matching equality against list/tuple, we must also check that
        # the internal types match.  (TODO: what about nested lists?)
        if istype(val, (list, tuple)):
            # NB: SEQUENCE_LENGTH takes care of the outer __check_type_id test
            self.SEQUENCE_LENGTH(guard)

            for idx, elem in enumerate(val):
                code.append(
                    f"___check_type_id({ref}[{idx}], {self.id_ref(type(elem))})"
                )
        else:
            # Add type check to prevent equality check between tensor and non-tensor.
            self.TYPE_MATCH(guard)

        if istype(val, torch.Size):
            val = tuple(val)

        # Code object can not be compared against their string representation
        # I.e `eval(f"{compile('2+2','','exec')!r}")` raises SyntaxError
        assert not istype(val, types.CodeType)

        # TODO: It feels like it would be better to just implement our own
        # equality test in C that handles all of the necessary type checking
        # and NaN tests
        code.append(f"{ref} == {val!r}")
        self._produce_guard_code(guard, code)

    def CONSTANT_MATCH(self, guard: Guard):
        val = self.get(guard.name)
        if istype(val, (bool, type(None), types.CodeType)):
            self.ID_MATCH(guard)
        else:
            self.EQUALS_MATCH(guard)

    def NN_MODULE(self, guard: Guard):
        self.ID_MATCH(guard)
        ref = self.arg_ref(guard)
        val = self.get(guard.name)

        def setup_guard():
            assert istype(val.training, bool)
            self._guard_on_attribute(guard, "training", GuardBuilder.CONSTANT_MATCH)

        if hasattr(val, "training"):
            # There are cases where a monkeypatched object has a guard made between __new__ and __init__
            setup_guard()
        else:
            exc.unimplemented(f"Guard setup for uninitialized class {type(val)}")

    def FUNCTION_MATCH(self, guard: Guard):
        """things like torch.add and user defined functions"""
        if guard.is_local():
            return self.ID_MATCH(guard)

    def CLOSURE_MATCH(self, guard: Guard):
        """matches a closure by __code__ id."""
        if guard.is_local():
            val = self.get(guard.name)
            # Strictly only want user-defined functions
            if type(val) == types.FunctionType and hasattr(val, "__code__"):
                self._guard_on_attribute(guard, "__code__", GuardBuilder.HASATTR)
                self._guard_on_attribute(guard, "__code__", GuardBuilder.FUNCTION_MATCH)
            else:
                self.FUNCTION_MATCH(guard)

    def BUILTIN_MATCH(self, guard: Guard):
        return self.FUNCTION_MATCH(guard)

    def PYMODULE_MATCH(self, guard: Guard):
        return self.FUNCTION_MATCH(guard)

    def SEQUENCE_LENGTH(self, guard):
        # This guard is used to check lenght of PySequence objects like list,
        # tuple, collections.deque etc
        ref = self.arg_ref(guard)
        value = self.get(guard.name)
        t = type(value)

        self.TYPE_MATCH(guard)
        code = list()
        if len(value) == 0:
            code.append(f"not {ref}")
        else:
            code.append(f"len({ref}) == {len(value)}")

        self._produce_guard_code(guard, code)

    def DICT_LENGTH(self, guard):
        self.SEQUENCE_LENGTH(guard)

    def TUPLE_ITERATOR_LEN(self, guard):
        ref = self.arg_ref(guard)
        value = self.get(guard.name)
        t = type(value)

        self.TYPE_MATCH(guard)
        code = list()
        code.append(f"___tuple_iterator_len({ref}) == {tuple_iterator_len(value)}")

        self._produce_guard_code(guard, code)

    # TODO(voz): Deduplicate w/ AOTAutograd dupe input guards
    def DUPLICATE_INPUT(self, guard, source_b):
        ref_a = self.arg_ref(guard)
        ref_b = self.arg_ref(source_b.name())

        code = [f"{ref_b} is {ref_a}"]
        self._produce_guard_code(guard, code)

    def DICT_KEYS(self, guard):
        # Guard on the keys and their order
        ref = self.arg_ref(guard)
        value = self.get(guard.name)
        t = type(value)

        self.TYPE_MATCH(guard)
        code = list()
        any_key_is_id = any(key_is_id(k) for k in value.keys())
        const_keys_repr = dict_keys_repr(
            key_to_id(value),
            local=is_from_local_source(guard.originating_source),
        )
        if any_key_is_id:
            code.append(f"___key_to_id({ref}) == {const_keys_repr}")
        else:
            code.append(f"list({ref}.keys()) == {const_keys_repr}")

        self._produce_guard_code(guard, code)

    def WEAKREF_ALIVE(self, guard):
        self._produce_guard_code(guard, [f"{self.arg_ref(guard)} is not None"])

    def NN_MODULE_PARAM_NAMES(self, guard):
        ref = self.arg_ref(guard)
        value = self.get(guard.name)
        t = type(value)
        keys = {k for k, v in value.named_parameters()}

        self.TYPE_MATCH(guard)
        code = list()
        code.append(f"{{k for k, v in {ref}.named_parameters()}} == {keys!r}")

        self._produce_guard_code(guard, code)

    def DICT_CONST_KEYS(self, guard):
        """Constant keys match"""
        ref = self.arg_ref(guard)
        value = self.get(guard.name)
        t = type(value)

        self.TYPE_MATCH(guard)
        code = list()
        code.append(f"list({ref}.keys()) == {list(value.keys())!r}")

        self._produce_guard_code(guard, code)

    def OBJECT_MUTATION(self, guard: Guard):
        mutation_guard.watch(self.get(guard.name), self.check_fn_manager)

    def GRAD_MODE(self, guard: Guard):
        pass  # we always guard on this via GlobalStateGuard()

    def DETERMINISTIC_ALGORITHMS(self, guard: Guard):
        pass  # we always guard on this via GlobalStateGuard()

    def TORCH_FUNCTION_STATE(self, guard: Guard):
        pass  # we always guard on this via GlobalStateGuard()

    def DEFAULT_DEVICE(self, guard: Guard):
        """Guard on CURRENT_DEVICE per torch.utils._device"""
        assert guard.source is GuardSource.GLOBAL
        import torch.utils._device as m

        self._produce_guard_code(
            guard, [f"utils_device.CURRENT_DEVICE == {m.CURRENT_DEVICE!r}"]
        )

    def BACKEND_MATCH(self, guard: Guard):
        """Guard on backend matching based on id of current_backend"""
        assert guard.source is GuardSource.GLOBAL
        backend_id = (
            f"{id(torch._dynamo.eval_frame.guarded_backend_cache.current_backend)}"
        )
        code = [f"___check_current_backend({backend_id})"]
        self._produce_guard_code(guard, code)

    def SHAPE_ENV(self, guard: Guard):
        # Let's handle ShapeEnv guards.  To do this, we will resolve
        # shape variables to sources from tracked_fakes.  This must happen after
        # tensor checks.
        assert guard.name == ""
        output_graph = self.check_fn_manager.output_graph
        # NB: self.output_graph can be None in the debug_nops tests
        fs = output_graph.tracked_fakes
        input_contexts = [a.symbolic_context for a in fs]

        def get_sources(t_id, dim):
            # Looks up base sources mapped to a tensor id and uses them to create
            # sources for the corresponding tensor dimension.
            return [
                TensorPropertySource(source, TensorProperty.SIZE, dim)
                for source in output_graph.tracked_fakes_id_to_source[t_id]
            ]

        if output_graph.export_constraints:
            from sympy import Symbol

            source_pairs: List[Tuple[Source, Source]] = []
            derived_equalities: List[  # type: ignore[type-arg]
                Tuple[Source, Union[Source, Symbol], Callable]
            ] = []
            phantom_symbols: Dict[str, Symbol] = {}
            for constraint in output_graph.export_constraints:
                if constraint.t_id in output_graph.tracked_fakes_id_to_source:
                    torch.export.dynamic_shapes._process_equalities(
                        constraint,
                        get_sources,
                        output_graph.shape_env,
                        source_pairs,
                        derived_equalities,
                        phantom_symbols,
                    )
                else:
                    log.warning("Untracked tensor used in export constraints")
            equalities_inputs = EqualityConstraint(
                source_pairs=source_pairs,
                derived_equalities=derived_equalities,
                phantom_symbols=list(phantom_symbols.values()),
                warn_only=False,
            )
        else:
            equalities_inputs = None
        guards = output_graph.shape_env.produce_guards(
            [a.fake for a in fs],
            [a.source for a in fs],
            input_contexts=input_contexts,
            equalities_inputs=equalities_inputs,
            source_ref=self.source_ref,
            # Export keeps static.
            ignore_static=(not self.check_fn_manager.output_graph.export),
        )
        # When exporting, we may work with the shape constraints some more in
        # postprocessing, so don't freeze yet
        if not self.check_fn_manager.output_graph.export:
            output_graph.shape_env.freeze()
        for shape_guard in guards:
            self._produce_guard_code(guard, [shape_guard], shape_env=True)

    def TENSOR_MATCH(self, guard: Guard, value=None):
        if guard.is_nn_module() or guard.originating_source.is_dict_key():
            self.ID_MATCH(guard)
        else:
            if isinstance(value, TensorWeakRef):
                value = value()

            value = value if value is not None else self.get(guard.name)
            assert isinstance(value, torch.Tensor)

            tensor_name = self.arg_ref(guard)
            # [Note - On Export Tensor Guards]
            #
            # In eager mode, tensor guards are evaluated through C++, in guards.cpp
            # see [Note - On Eager Tensor Guards] for more info.
            #
            # In export mode, we instead maintain parallel logic between C++ and python
            # here, with an exception of checking the dispatch key - with the idea that a dispatch key
            # is an entirely runtime notion that would make no sense to keep in an exported graph.
            #
            # Now, this idea is okay, but to paraphrase @ezyang, this mental model is sufficient for now, although
            # not entirely true.
            # For example, suppose one of the input tensors had the negative dispatch key.
            # You should end up with a graph that is specialized for tensors that have a negative dispatch key.
            # If you allow a Tensor that does NOT have this bit set, you will accidentally run it "as if" it were negated.
            # Now, negative key only shows up for complex numbers, and most likely, the exported to target doesn't
            # support this feature at all, but the point stands that :some: tensor state only shows up on dispatch key.
            # TODO(voz): Either populate a dispatch_key check into the guards, or error on users passing in an unsupported
            # subset of keys during export.
            #
            # The list of tensor fields and calls we care about can be found in `terms` below.
            # TODO(voz): We are missing storage offset in all our tensor guards?
            code: List[str] = list()
            if self.check_fn_manager.output_graph.export:
                self.TYPE_MATCH(guard)
                terms = [
                    "dtype",
                    "device",
                    "requires_grad",
                    "ndimension()",
                ]

                for term in terms:
                    real_value = self.get(tensor_name + "." + term)
                    if istype(real_value, (torch.device, torch.dtype)):
                        # copy pasted from EQUALS_MATCH
                        code.append(f"str({tensor_name}.{term}) == {str(real_value)!r}")
                    else:
                        code.append(f"{tensor_name}.{term} == {real_value}")
            else:
                self.tensor_check_names.append(tensor_name)
                self.tensor_check_examples.append(value)
                self.tensor_check_guards.append(guard)

            # A frame is valid for reuse with dynamic dimensions if the new
            # (user-requested) dynamic dimensions are a subset of the old
            # (already compiled) dynamic dimensions.
            #
            # It's a little non-obvious why you'd want this: in particular,
            # if an already compiled frame matches all of the guards, why
            # not just use it, why force a recompile?
            #
            # We force it for two reasons:
            #
            #   - The user *required* us to compile with a new dynamic dimension,
            #     we should not ignore that and serve up the old, specialized
            #     frame.  Listen to the user!
            #
            #   - In fact, we are obligated to *raise an error* if we fail to
            #     make the requested dimension dynamic.  If we don't
            #     recompile, we can't tell if that dimension can actually be
            #     made dynamic.
            #
            # If the new dynamic dims are a subset of the old, we already know
            # we can make them dynamic (since we made them dynamic in old).
            # This is slightly unsound, because maybe your input size is
            # [s0, s0, s1] and so you can do it dynamic if you say dynamic
            # dims {0, 1, 2} but you can't if you only do {0, 2} (because now
            # the second s0 is specialized).  But we're not entirely sure if
            # this is a good idea anyway lol... (if you want to try removing
            # this logic, be my guest!  -- ezyang 2024)
            #
            assert guard.source is not None
            static, reason = tensor_always_has_static_shape(
                value, is_tensor=True, guard_source=guard.source
            )
            if not static:
                if hasattr(value, "_dynamo_dynamic_indices"):
                    code.append(
                        f"(({tensor_name}._dynamo_dynamic_indices.issubset({value._dynamo_dynamic_indices})) if hasattr({tensor_name}, '_dynamo_dynamic_indices') else True)"  # noqa: B950
                    )
                # In the case of us not having any dynamic dimension indices, we compiled the frame with no chance of
                # raising for this specific tensor - and any inputs with more dynamic user directives specified must be recompiled.
                else:
                    code.append(
                        f"hasattr({tensor_name}, '_dynamo_dynamic_indices') == False"
                    )
            if len(code) > 0:
                self._produce_guard_code(guard, code)

    # A util that appends guarded code, or, in the case of export, adds data onto guards
    def _produce_guard_code(

        self, guard, code_list, provided_guarded_object=None, shape_env=False

    ):
        # WARNING: It is important that cur_frame/caller do NOT stay in
        # the current frame, because they will keep things live longer
        # than they should.  See TestMisc.test_release_module_memory
        cur_frame = currentframe()
        assert cur_frame is not None
        caller = cur_frame.f_back
        del cur_frame
        assert caller is not None
        func_name = getframeinfo(caller)[2]
        del caller
        # We use func_name for export, so might as well get a nice defensive check out of it
        assert func_name in dir(
            self.__class__
        ), f"_produce_guard_code must be called from inside GuardedCode. Called from {func_name}"

        if shape_env:
            self.shape_env_code.append(GuardCodeList(code_list, guard))
        else:
            self.code.append(GuardCodeList(code_list, guard))

        # Not all guards have names, some can be installed globally (see asserts on HAS_GRAD)
        if provided_guarded_object is None:
            name_valid = guard.name is not None and guard.name != ""

            guarded_object = self.get(guard.name) if name_valid else None
        else:
            guarded_object = provided_guarded_object

        guarded_object_type = (
            weakref.ref(type(guarded_object)) if guarded_object is not None else None
        )
        obj_ref = None
        # Not necessary to have weakref for Enum type, but there is a bug that
        # makes hasattr(guarded_object.__class__, "__weakref__") return True.
        if hasattr(guarded_object.__class__, "__weakref__") and not isinstance(
            guarded_object, enum.Enum
        ):
            obj_ref = weakref.ref(guarded_object)

        guard.set_export_info(
            func_name,
            guarded_object_type,
            code_list,
            obj_ref,
        )


# Common Sub-Expression Elimination for Python expressions.
#
# There are 2 steps to this pass:
#     1. Count the frequency of each sub-expression (i.e. inner
#        node in the AST tree)
#
#     2. Replace those that occur more than once by a fresh variable 'v'.
#        'v' will be defined in the 'preface' list (output argument to
#        'NodeTransformer')
#
# NB: the use of 'ast.unparse' while visiting the nodes makes this pass
# quadratic on the depth of the tree.
#
# NB: this pass creates a new variable for each AST node that is repeated
# more than 'USE_THRESHOLD'. e.g. if 'a.b.c.d' is used 10 times, 'a.b.c'
# and 'a.b' are also used 10 times. So, there will be a new variable for
# each of them.
class PyExprCSEPass:
    # Maximum number of times a given expression can be used without being
    # replaced by a fresh variable.
    USE_THRESHOLD = 1

    # Ad-Hoc: AST nodes this pass focuses on.
    ALLOWED_NODE_TYPES = (ast.Attribute, ast.Call, ast.Subscript)

    @dataclasses.dataclass
    class Config:
        expr_count: Dict[str, int]
        expr_to_name: Dict[str, str]

    class ExprCounter(ast.NodeVisitor):
        def __init__(self, config: PyExprCSEPass.Config) -> None:
            self._config = config

        def visit(self, node: ast.AST) -> Any:
            if isinstance(node, PyExprCSEPass.ALLOWED_NODE_TYPES):
                self._config.expr_count[_ast_unparse(node)] += 1
            super().visit(node)

    class Replacer(ast.NodeTransformer):
        def __init__(

            self,

            config: PyExprCSEPass.Config,

            gen_name: Callable[[], str],

        ) -> None:
            super().__init__()
            self._config = config
            self._gen_name = gen_name
            self.preface: List[str] = []

        def visit(self, node: ast.AST) -> Any:
            if isinstance(node, PyExprCSEPass.ALLOWED_NODE_TYPES):
                expr = _ast_unparse(node)

                # Replacement only occurs if a given expression is used more
                # than once.
                if self._config.expr_count[expr] > PyExprCSEPass.USE_THRESHOLD:
                    if expr not in self._config.expr_to_name:
                        # Parent 'visit' is called so that we CSE the inner expressions first.
                        #
                        # The resulting expression is used as right-hand-side of the variable
                        # assignment. i.e. we are CSE-ing the children before the parents.
                        #
                        # Indexing still uses the old 'node', since that's what was counted
                        # by the 'NodeVisitor'.
                        node_ = super().visit(node)
                        expr_ = _ast_unparse(node_)
                        var_name = self._gen_name()
                        self.preface.append(f"{var_name} = {expr_}")
                        self._config.expr_to_name[expr] = var_name
                    else:
                        var_name = self._config.expr_to_name[expr]
                    return ast.Name(var_name, ast.Load())

            return super().visit(node)

    def __init__(self) -> None:
        self._counter = 0
        self._config = self.Config(
            expr_count=collections.defaultdict(lambda: 0), expr_to_name={}
        )

    def _new_var(self, prefix: str = "_var") -> str:
        name = f"{prefix}{self._counter}"
        self._counter += 1
        return name

    def count(self, exprs: List[str]) -> None:
        counter = self.ExprCounter(self._config)
        for e in exprs:
            try:
                counter.visit(ast.parse(e))
            except SyntaxError as ex:
                log.exception("Failed to visit expr at line %s.\n%s", ex.lineno, e)
                raise

    def replace(self, expr: str) -> Tuple[List[str], str]:
        replacer = self.Replacer(self._config, self._new_var)
        new_node = replacer.visit(ast.parse(expr))
        return replacer.preface, _ast_unparse(new_node)


def must_add_nn_module_guards(guard):
    # For config.guard_nn_modules=False, we can skip all the guards that
    # originate from inside of nn module except for a few categories.
    return (
        # Guard for defaults
        isinstance(guard.originating_source, DefaultsSource)
        # Guard using dict tags if the config flag is set
        or (
            config.guard_nn_modules_using_dict_tags
            and guard.create_fn is GuardBuilder.NN_MODULE
        )
    )


class DeletedGuardFn:
    pass


# NB: Naively, you'd expect this to only be a function that produces
# the callable that constitutes the guard.  However, there is some
# delicate handling for invalidating this check function when the
# locals/globals get invalidated, so there's some extra state
# we have to hold in this manager class.
class CheckFunctionManager:
    def __init__(

        self,

        output_graph=None,

        guard_fail_fn: Optional[Callable[[GuardFail], None]] = None,

    ):
        guards = output_graph.guards if output_graph else None
        self._weakrefs: Dict[int, ReferenceType[object]] = {}
        self.output_graph = output_graph
        w_builder = None

        def source_ref(source):
            guard_source = source.guard_source()
            if guard_source is GuardSource.CONSTANT:
                # No need to track constants
                return source.name()
            assert w_builder
            r_builder = w_builder()
            assert r_builder is not None
            return r_builder.arg_ref(source.name())

        builder = GuardBuilder(
            self.id_ref,
            source_ref,
            self.lookup_weakrefs,
            output_graph.local_scope,
            output_graph.global_scope,
            self,
        )

        # Break retain cycle. See test_release_scope_memory
        def cleanup_builder(weak_b):
            b = weak_b()
            if b:
                b.scope = None

        # Break retain cycle. See test_release_input_memory
        w_builder = weakref.ref(builder, cleanup_builder)

        for guard in sorted(guards or [], key=Guard.sort_key):
            if (
                not config.guard_nn_modules
                and guard.is_nn_module()
                # Default func args must be guarded on.
                # TODO: we could make use of 'DefaultsSource' and offer a .guard.is_defaults() API
                and "__defaults__" not in guard.name
                and "__kwdefaults__" not in guard.name
                and (config.skip_nnmodule_hook_guards or "hooks" not in guard.name)
            ):
                continue

            guard.create(builder)
        self.check_fn = self.compile_check_fn(builder, guards, guard_fail_fn)
        # Keep track of weak references of objects with ID_MATCH guard. This
        # info is stored alongside optimized_code and check_fn and is used to
        # limit the number of cache entries with same ID_MATCH'd object.
        # TODO(janimesh) - Currently this information is stored as an attr on
        # the check_fn itself to avoid changing CacehEntry datastructure in
        # eval_frame.c. In future, we should probably replace check_fn with a
        # queryable data structure such that this information is already present
        # in some form.
        self.check_fn.id_matched_objs = builder.id_matched_objs

        # NB - We have to very careful of cleaning up here. Because of the
        # invalidate function, we can create a weakref finalizer that keeps
        # `self` alive for very long. Sometimes by mistake, we can run
        # invalidate for a type/object (check id_ref method) that Python can
        # leak by design, preventing us from calling the finalizer. In that
        # case, the `self` will be alive even though the cache entry will be
        # deleted (check invalidate method), which can cause a memory leak,
        # e.g., not setting output_graph = None can keep hold of nn_modules.
        self._weakrefs.clear()
        self.output_graph = None

    def compile_check_fn(self, builder, guards_out, guard_fail_fn):
        # see parallel handling of ".0" / "___implicit0" in _eval_frame.c
        largs = builder.argnames
        largs += ["**___kwargs_ignored"]

        guards_log.debug("GUARDS:")

        # Don't report this guard, it's always the same, useless!
        code_parts = ["___check_global_state()"]
        verbose_code_parts = code_parts[:]
        structured_guard_fns = []

        def add_code_part(code_part, guard, log_only=False):
            verbose_code_part = get_verbose_code_part(code_part, guard)
            guards_log.debug("%s", verbose_code_part)

            structured_guard_fns.append(
                lambda: {
                    "code": code_part,
                    "stack": structured.from_traceback(guard.stack.summary())
                    if guard.stack
                    else None,
                    "user_stack": structured.from_traceback(guard.user_stack)
                    if guard.user_stack
                    else None,
                }
            )

            if verbose_guards_log.isEnabledFor(logging.DEBUG):
                maybe_stack = ""
                maybe_user_stack = ""
                if guard is not None:
                    if guard.stack:
                        maybe_stack = f"\nStack:\n{''.join(guard.stack.format())}"
                    if guard.user_stack:
                        maybe_user_stack = (
                            f"\nUser stack:\n{''.join(guard.user_stack.format())}"
                        )
                verbose_guards_log.debug(
                    "Guard: %s%s%s",
                    code_part,
                    maybe_stack,
                    maybe_user_stack,
                )

            if not log_only:
                code_parts.append(code_part)
                verbose_code_parts.append(verbose_code_part)

        seen = set()
        for gcl in builder.code:
            for code in gcl.code_list:
                if code not in seen:
                    add_code_part(code, gcl.guard)
                    seen.add(code)

        tensor_check_names = builder.tensor_check_names
        check_tensors_fn = None
        check_tensors_verbose_fn = None
        if tensor_check_names:
            assert (
                not self.output_graph.export
            ), "Illegal to set tensor_check_names in export."
            tensor_check_examples = builder.tensor_check_examples

            dynamic_dims_sizes = [
                convert_to_concrete_values(
                    self.output_graph.tensor_weakref_to_sizes_strides[t]["size"]
                )
                for t in tensor_check_examples
            ]

            dynamic_dims_strides = [
                convert_to_concrete_values(
                    self.output_graph.tensor_weakref_to_sizes_strides[t]["stride"]
                )
                for t in tensor_check_examples
            ]

            tensor_guards = TensorGuards(
                *tensor_check_examples,
                dynamic_dims_sizes=dynamic_dims_sizes,
                dynamic_dims_strides=dynamic_dims_strides,
            )
            check_tensors_fn = tensor_guards.check
            check_tensors_verbose_fn = tensor_guards.check_verbose
            tensor_check_args = ", ".join(
                tensor_check_names + ["tensor_check_names=tensor_check_names"]
            )
            # Do this manually, to un-stagger the guards in log message
            code_parts.append(f"___check_tensors({tensor_check_args})")
            verbose_code_parts.append(f"___check_tensors({tensor_check_args})")
            tensor_check_guards = builder.tensor_check_guards

            for i, name in enumerate(tensor_check_names):
                # This is a copy of what guards.cpp checks against
                # Keep this in sync with TensorCheck constructor
                t = tensor_check_examples[i]
                sizes = dynamic_dims_sizes[i]
                strides = dynamic_dims_strides[i]
                code_part = get_tensor_guard_code_part(t, name, sizes, strides)
                add_code_part(code_part, tensor_check_guards[i], log_only=True)

        aotautograd_guards: List[GuardEnvExpr] = (
            self.output_graph.tracing_context.guards_context.aotautograd_guards
            if self.output_graph
            else []
        )
        for guard in aotautograd_guards:
            if isinstance(guard, DuplicateInputs):
                source_a = guard.input_source_a
                source_b = guard.input_source_b
                add_code_part(f"{source_a.name()} is {source_b.name()}", None)
            else:
                raise RuntimeError(f"Unknown GuardEnvExpr: {guard}")

        # TODO: the "guard" here is actually just the top level SHAPE_ENV
        # which is useless.  Get ShapeEnv to pass in more provenance.
        for gcl in builder.shape_env_code:
            for code in gcl.code_list:
                add_code_part(code, gcl.guard)

        # OK, all done generating guards
        torch._logging.trace_structured(
            "dynamo_guards", payload_fn=lambda: [f() for f in structured_guard_fns]
        )

        global_state = convert_frame.initial_global_state
        if global_state is None:
            # we should only hit this case in NopTests()
            global_state = convert_frame.GlobalStateGuard()
        closure_vars = {
            "___check_tensors": check_tensors_fn,
            "___check_tensors_verbose": check_tensors_verbose_fn,
            "___check_global_state": global_state.check,
            "___check_current_backend": torch._dynamo.eval_frame.check_current_backend,
            "tensor_check_names": tensor_check_names,
            **SYMPY_INTERP,
            **CLOSURE_VARS,
        }

        unique_code_parts = list(unique(code_parts))
        make_guard_fn_args = ", ".join(closure_vars.keys())
        guard_body, pycode = build_guard_function(unique_code_parts, make_guard_fn_args)

        if os.environ.get("TORCHDYNAMO_PRINT_GUARDS", None) == "1":
            print("GUARDS\n", guard_body)

        out: Dict[str, Any] = dict()

        # We don't put builder.scope as the globals in exec call because
        # guard_fn.__globals__ becomes equal to builder.scope. This causes
        # guard_fn to hold a referece to f_locals sitting in builder.scope["L"]
        globals_for_guard_fn = {"G": builder.scope["G"]}
        try:
            exec(pycode, globals_for_guard_fn, out)
        except SyntaxError as ex:
            log.exception("Failed to exec guard at line %s.\n%s", ex.lineno, pycode)
            raise
        guard_fn = out["___make_guard_fn"](*closure_vars.values())
        guard_fn.closure_vars = closure_vars
        # TODO(whc) maybe '.code_parts' was only kept around for the guard callback? so we don't need both
        guard_fn.args = largs
        guard_fn.code_parts = code_parts
        guard_fn.verbose_code_parts = verbose_code_parts
        # Grab only G, but preserve "G" because guards access it as "G"
        guard_fn.global_scope = globals_for_guard_fn
        guard_fn.guard_fail_fn = guard_fail_fn
        # will be populated by a non-owning reference to CacheEntry/ExtraState
        # when the CacheEntry is constructed
        guard_fn.cache_entry = None
        guard_fn.extra_state = None
        return guard_fn

    def invalidate(self):
        # Some tests reveal that CheckFunctionManager has no attribute
        # check_fn, but this case should not be of any concern.
        # This case doesn't seem easy to repro.
        if (
            hasattr(self, "check_fn")
            and self.check_fn is not DeletedGuardFn
            and (cache_entry := self.check_fn.cache_entry) is not None
            and (extra_state := self.check_fn.extra_state) is not None
        ):
            assert isinstance(cache_entry, CacheEntry)
            assert isinstance(extra_state, ExtraState)
            extra_state.invalidate(cache_entry)
            self.check_fn.cache_entry = None
            self.check_fn.extra_state = None
            self.check_fn = DeletedGuardFn

    def id_ref(self, obj):
        """add a weakref, return the id"""
        try:
            if id(obj) not in self._weakrefs:
                # We will clear the _weakrefs dict at the end of __init__
                # function, which will delete the callbacks as well. Therefore,
                # we are using a finalizer which is kept alive.
                self._weakrefs[id(obj)] = weakref.ref(obj)
                weakref.finalize(obj, self.invalidate)
        except TypeError:
            pass  # cannot weakref bool object
        return id(obj)

    def lookup_weakrefs(self, obj):
        """Lookup the _weakrefs created in id_ref function for ID_MATCH'd objects"""
        if id(obj) in self._weakrefs:
            return self._weakrefs[id(obj)]
        return None


def build_guard_function(code_parts, closure_args) -> Tuple[str, str]:
    from torch._inductor.utils import IndentedBuffer

    if HAS_UNPARSE_FUNCTIONS:
        csepass = PyExprCSEPass()
        csepass.count(code_parts)

        def replace(expr: str) -> Tuple[List[str], str]:
            return csepass.replace(expr)

    else:

        def replace(expr: str) -> Tuple[List[str], str]:
            return [], expr

    # Generate the inner body of the guard function.
    # i.e. if-chain of the guard expressions.
    guard_body = IndentedBuffer()
    for expr in code_parts:
        preface, expr = replace(expr)
        guard_body.writelines(preface)
        guard_body.writeline(f"if not ({expr}):")
        with guard_body.indent():
            guard_body.writeline("return False")

    # Wrap the inner body into the actual guard function.
    guard = IndentedBuffer()
    guard.writeline("def guard(L):")
    with guard.indent():
        guard.splice(guard_body)
        guard.writeline("return True")

    # Wrap the whole guard function into another function
    # with the closure variables.
    make_guard_fn = IndentedBuffer()
    make_guard_fn.writeline(f"def ___make_guard_fn({closure_args}):")
    with make_guard_fn.indent():
        make_guard_fn.splice(guard)
        make_guard_fn.writeline("return guard")

    return guard_body.getvalue(), make_guard_fn.getvalue()


def is_recompiles_enabled():
    return torch._logging._internal.log_state.is_artifact_enabled("recompiles")


def is_recompiles_verbose_enabled():
    return torch._logging._internal.log_state.is_artifact_enabled("recompiles_verbose")


def get_guard_fail_reason(

    guard_fn: GuardFn,

    code: types.CodeType,

    f_locals: Dict[str, object],

) -> str:
    """

    Return the reason why `guard_fn` failed.

    Updates `guard_failures` with the generated reason.

    Only the first failed check of guard_fn is reported.

    """
    scope = {"L": f_locals, "G": guard_fn.global_scope["G"]}
    scope.update(guard_fn.closure_vars)
    scope["___check_tensors"] = scope["___check_tensors_verbose"]
    reasons: List[str] = []
    for part in guard_fn.verbose_code_parts:
        global_scope = dict(guard_fn.global_scope)
        global_scope["__compile_source__"] = part
        with report_compile_source_on_error():
            try:
                fail_reason = eval(part, global_scope, scope)
            except Exception as e:
                if is_recompiles_verbose_enabled():
                    continue
                else:
                    raise
        # Only ___check_tensors knows how to return a fancy fail reason;
        # for everything else we just report the code that failed

        if isinstance(fail_reason, bool) and not fail_reason:
            fail_reason = part
        if isinstance(fail_reason, str):
            reasons.append(fail_reason)
            if not is_recompiles_verbose_enabled():
                break

    reason_str = "\n".join(reasons)
    guard_failures[orig_code_map[code]].append(reason_str)

    try:
        if guard_fn.guard_fail_fn is not None:
            guard_fn.guard_fail_fn(
                GuardFail(reason_str or "unknown reason", orig_code_map[code])
            )
    except Exception as e:
        log.exception(
            "Failure in guard_fail_fn callback - raising here will cause a NULL Error on guard eval",
        )

    return reason_str


def get_and_maybe_log_recompilation_reason(

    cache_entry, frame: types.FrameType

) -> List[str]:
    """

    Return the list of guard failure reasons using cache_entry.

    Logs the recompilation reason if `recompiles` logging is enabled.

    Raises a RecompileError if `config.error_on_recompile` is enabled.

    """
    reasons = []
    while cache_entry is not None:
        reason = get_guard_fail_reason(
            cache_entry.check_fn, cache_entry.code, frame.f_locals
        )
        if reason:
            reasons.append(reason)
        cache_entry = cache_entry.next

    code = frame.f_code

    # at least one of "recompiles" or "recompiles_verbose" is enabled
    do_recompiles_log = is_recompiles_enabled() or is_recompiles_verbose_enabled()

    if do_recompiles_log or config.error_on_recompile:
        if is_recompiles_verbose_enabled():
            failures = "\n\n".join(
                f"guard {i} failures:\n" + textwrap.indent(reason, "- ")
                for i, reason in enumerate(reasons)
            )
        else:
            failures = textwrap.indent("\n".join(reasons), "- ")
        guard_failure_details = (
            f"triggered by the following guard failure(s):\n{failures}"
        )
        message = (
            f"Recompiling function {code.co_name} in {code.co_filename}:{code.co_firstlineno}\n"
            f"{textwrap.indent(guard_failure_details, '    ')}"
        )
        if do_recompiles_log:
            if is_recompiles_verbose_enabled():
                recompiles_verbose_log.debug(message)
            else:
                recompiles_log.debug(message)
        if config.error_on_recompile:
            raise exc.RecompileError(message)

    return reasons


def guard_error_hook(

    guard_fn: GuardFn,

    code: types.CodeType,

    f_locals: Dict[str, object],

    index: int,

    last: bool,

):
    print(
        f"ERROR RUNNING GUARDS {code.co_name} {code.co_filename}:{code.co_firstlineno}"
    )
    print("lambda " + ", ".join(guard_fn.args) + ":")
    print(" ", " and\n  ".join(guard_fn.code_parts))
    local_scope = {"L": f_locals, **guard_fn.closure_vars}
    for guard in guard_fn.code_parts:
        try:
            eval(guard, guard_fn.global_scope, local_scope)
        except:  # noqa: B001,E722
            print(f"Malformed guard:\n{guard}")


set_guard_error_hook(guard_error_hook)


def unique(seq):
    seen = set()
    for x in seq:
        if x not in seen:
            yield x
            seen.add(x)


def make_dupe_guard(obj_source, dupe_source):
    # Note - we may end up in a situation where we invoke something like
    # def fn(x, y)
    # with fn(x, x)
    # Prior to the addition of tracking to all relevant objects, we would handle this just fine by
    # eagerly re-entering VB and rewrapping inputs, correctly creating graphargs and placeholders. However,
    # with tracking on inputs, duplicate inputs or aliased relationships may end up getting erased here -
    # In the fn(x, x) example call above look like a graph with a single input.
    # In order to ensure that we do not reuse fn(x, x) for fn(x, y), we create a duplicate input guard.

    # Note - we may not have a source, that is fine, it just means we had an object that is safe to have
    # leave unsourced - like a local list created and discharged entirely within a local scope.
    if dupe_source and dupe_source != obj_source:
        ser_source_is_local = is_from_local_source(dupe_source)
        source_is_local = is_from_local_source(obj_source)
        # Note - both must be local, or global, or we will run afoul of a lack of merging in how we currently
        # reconcile guards builder scopes in compile_check_fn. This technically means we miss a guard here,
        # so maybe we should do this refactor before we land this...
        # TODO(voz): Combine local and global guard builders.
        if ser_source_is_local == source_is_local:
            # Note - this is a little aggressive - these being duplicate input does not always matter.
            # However, this should always be a sound guard to add here.
            return functools.partial(GuardBuilder.DUPLICATE_INPUT, source_b=dupe_source)
    return None


def install_guard(*guards, skip=0):
    """

    Add dynamo guards to the current tracing context.



    Args:

        guards: guard(s) to add

        skip: number of stack frames to ignore for debug stack trace

    """
    from torch._guards import TracingContext

    collect_debug_stack = guards_log.isEnabledFor(
        logging.DEBUG
    ) or verbose_guards_log.isEnabledFor(logging.DEBUG)
    add = TracingContext.get().guards_context.dynamo_guards.add
    for guard in guards:
        assert isinstance(guard, Guard)
        add(guard, collect_debug_stack=collect_debug_stack, skip=skip + 1)