pytorch/torch/_dynamo/convert_frame.py

import collections
import functools
import itertools
import logging
import os
import random
import types
import typing
import weakref
from typing import Any, Callable, Dict, List, Optional, Set

from torch.fx._lazy_graph_module import (  # type: ignore[attr-defined]
    _use_lazy_graph_module,
)

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

import torch
import torch._logging
from torch._guards import compile_context, CompileContext, CompileId, tracing
from torch._utils_internal import signpost_event
from torch.fx.experimental.symbolic_shapes import (
    ConstraintViolationError,
    GuardOnDataDependentSymNode,
)
from torch.fx.graph_module import _forward_from_src as original_forward_from_src
from torch.utils._traceback import format_traceback_short

from . import config, exc
from .backends.registry import CompilerFn
from .bytecode_analysis import remove_dead_code, remove_pointless_jumps
from .bytecode_transformation import (
    check_inst_exn_tab_entries_valid,
    Instruction,
    is_generator,
    propagate_inst_exn_table_entries,
    transform_code_object,
)
from .cache_size import (
    CacheSizeRelevantForFrame,
    compute_cache_size,
    exceeds_cache_size_limit,
    is_recompilation,
)
from .eval_frame import always_optimize_code_objects, skip_code, TorchPatcher
from .exc import (
    augment_exc_message,
    BackendCompilerFailed,
    format_error_msg,
    InternalTorchDynamoError,
    TorchRuntimeError,
    UncapturedHigherOrderOpError,
    unimplemented,
    Unsupported,
)
from .guards import (
    CheckFunctionManager,
    get_and_maybe_log_recompilation_reason,
    GuardedCode,
)
from .hooks import Hooks
from .output_graph import OutputGraph
from .replay_record import ExecutionRecord
from .symbolic_convert import InstructionTranslator, SpeculationLog
from .trace_rules import is_numpy
from .types import BytecodeHook
from .utils import (
    CleanupManager,
    CompilationMetrics,
    counters,
    cprofile_wrapper,
    dynamo_timed,
    format_bytecode,
    frame_phase_timing,
    gen_record_file_name,
    increment_frame,
    is_namedtuple,
    istype,
    LazyString,
    orig_code_map,
    record_compilation_metrics,
    reset_graph_break_dup_checker,
    setup_compile_debug,
    troubleshooting_url,
    write_record_to_file,
)

log = logging.getLogger(__name__)
bytecode_log = torch._logging.getArtifactLogger(__name__, "bytecode")
GlobalStateGuard = torch._C._dynamo.guards.GlobalStateGuard


class Tracker:
    def __init__(self):
        self.seen = []
        self.seen_ids = set()

    def add(self, strong_obj):
        idx = id(strong_obj)
        if idx not in self.seen_ids:
            obj = weakref.ref(strong_obj, lambda _: self.seen_ids.remove(idx))
            self.seen.append(obj)
            self.seen_ids.add(idx)

    def __contains__(self, item):
        return id(item) in self.seen_ids

    def clear(self):
        self.seen.clear()
        self.seen_ids.clear()


input_codes = Tracker()
output_codes = Tracker()

initial_global_state = None


@functools.wraps(original_forward_from_src)
def fx_forward_from_src_skip_result(*args, **kwargs):
    # we monkey patch FX to prevent infinite loop of trying to convert
    # our generated code
    result: types.FunctionType = original_forward_from_src(*args, **kwargs)
    skip_code(result.__code__)
    return result


def preserve_global_state(fn):
    """
    Context manager to:
        1) Save/restore torch.is_grad_enabled() state
        2) Save/restore python random state
        3) Save/restore torch random state
        4) Monkey patch torch.fx.graph_module._forward_from_src
    """

    @functools.wraps(fn)
    def _fn(*args, **kwargs):
        guards = GlobalStateGuard()
        prior_grad_mode = torch.is_grad_enabled()
        prior_inference_mode = torch.is_inference_mode_enabled()
        prior_deterministic = torch.are_deterministic_algorithms_enabled()
        prior_warn_only = torch.is_deterministic_algorithms_warn_only_enabled()
        py_rng_state = random.getstate()
        torch_rng_state = torch.random.get_rng_state()
        if torch.cuda.is_available():
            cuda_rng_state = torch.cuda.get_rng_state()
        prior_fwd_from_src = torch.fx.graph_module._forward_from_src
        torch.fx.graph_module._forward_from_src = fx_forward_from_src_skip_result
        cleanup = setup_compile_debug()
        try:
            return fn(*args, **kwargs)
        finally:
            cleanup.close()
            torch._C._set_grad_enabled(prior_grad_mode)
            torch.torch.autograd.grad_mode._enter_inference_mode(prior_inference_mode)
            torch.use_deterministic_algorithms(
                prior_deterministic, warn_only=prior_warn_only
            )
            random.setstate(py_rng_state)
            torch.random.set_rng_state(torch_rng_state)
            if torch.cuda.is_available():
                torch.cuda.set_rng_state(cuda_rng_state)
            torch.fx.graph_module._forward_from_src = prior_fwd_from_src
            assert (
                guards.check()
            ), "Global state changed while dynamo tracing, please report a bug"

    _fn._torchdynamo_orig_callable = fn  # type: ignore[attr-defined]
    return _fn


@TorchPatcher.suppress_torch_distributed_warnings
def has_tensor_in_frame(frame):
    """Check if the frame has torch.* related bits"""
    # Check if the function was decorated using torch._dynamo.optimize
    if frame.f_code in always_optimize_code_objects:
        return True

    # Check if there is global import of torch.*
    for co_name in frame.f_code.co_names:
        if co_name in frame.f_globals:
            obj = frame.f_globals[co_name]
            if isinstance(obj, types.ModuleType) and (
                obj.__name__.startswith("torch.") or obj is torch
            ):
                return True
            # ... or a global import of numpy.*
            if np and config.trace_numpy and (obj is np or is_numpy(obj)):
                return True

    seen_ids: Dict[int, bool] = dict()

    def has_tensor(obj):
        """Recursively check if the obj has a tensor"""
        obj_id = id(obj)
        if obj_id in seen_ids:
            return seen_ids[obj_id]
        seen_ids[obj_id] = False

        if isinstance(obj, (torch.Tensor, torch.nn.Module)) or (
            istype(obj, type) and issubclass(obj, torch.nn.Module)
        ):
            seen_ids[obj_id] = True
            return seen_ids[obj_id]
        elif (
            config.trace_numpy
            and np
            and (istype(obj, np.ndarray) or isinstance(obj, np.generic))
        ):
            seen_ids[obj_id] = True
            return seen_ids[obj_id]
        elif istype(obj, (list, tuple)):
            seen_ids[obj_id] = any(has_tensor(v) for v in obj)
            return seen_ids[obj_id]
        elif istype(obj, dict):
            # Some packages like pytest can be updated during runtime. So, make a
            # copy of values to avoid issues like "RuntimeError: dictionary
            # changed size during iteration"
            values = list(obj.values())
            seen_ids[obj_id] = any(has_tensor(v) for v in values)
            return seen_ids[obj_id]
        elif istype(obj, (str, int, float, type(None), bool)):
            seen_ids[obj_id] = False
            return seen_ids[obj_id]
        elif is_namedtuple(obj) and hasattr(obj, "_fields"):
            seen_ids[obj_id] = any(has_tensor(getattr(obj, v)) for v in obj._fields)
            return seen_ids[obj_id]
        else:
            # if config.debug:
            #     print(
            #         f"Assuming that object of type {type(obj)} does not have a tensor"
            #     )
            return False

    # Check if the passed arguments are of type Tensor
    for value in frame.f_locals.values():
        if has_tensor(value):
            return True

    log.debug(
        "skipping because no torch.* %s \
            %s %s",
        frame.f_code.co_name,
        frame.f_code.co_filename,
        frame.f_code.co_firstlineno,
    )

    return False


def exception_handler(e, code, frame=None, export=False):
    record_filename = None
    if hasattr(e, "exec_record"):
        record_filename = gen_record_file_name(e, code)
        write_record_to_file(record_filename, e.exec_record)
        e.record_filename = record_filename

    augment_exc_message(e, export=export)


FRAME_COUNTER = 0
FRAME_COMPILE_COUNTER: typing.Counter[int] = collections.Counter()


def convert_frame_assert(
    compiler_fn: CompilerFn,
    one_graph: bool = True,
    export: bool = False,
    export_constraints=None,
):
    """Fully convert a frame into an FX graph"""
    reset_graph_break_dup_checker()

    def _convert_frame_assert(
        frame: types.FrameType, cache_entry, hooks: Hooks, frame_state
    ):
        increment_frame()

        code = frame.f_code

        cache_size = compute_cache_size(frame, cache_entry)
        recompile_reasons = None
        if is_recompilation(cache_size):
            recompile_reasons = get_and_maybe_log_recompilation_reason(
                cache_entry, frame
            )

        input_codes.add(code)
        if code in output_codes:
            return None
        if (
            os.environ.get("TORCHDYNAMO_DEBUG_FUNCTION")
            and os.environ.get("TORCHDYNAMO_DEBUG_FUNCTION") != code.co_name
        ):
            return None
        if code.co_name == "<genexpr>" and code.co_filename.endswith(
            (
                "transformers/file_utils.py",
                "transformers/utils/generic.py",
                "diffusers/utils/outputs.py",
            )
        ):
            # not needed, but cleans up torchbench error stats
            return None
        if code.co_name == "__setattr__":
            # setattr could be tricky to handle generally,
            # but also not likely useful to compile- skip the whole frame
            return None
        if code.co_name == "__init__" and code.co_filename.startswith(
            os.path.dirname(torch.optim.__file__)
        ):
            # optimizer support is still incomplete see
            # test_state_dict in test/dynamo/test_optimizers.py
            return None

        # Check if the frame is generated by an exec builtin call
        # TODO - Running exec generated frame seems propagates f_globals to the
        # next frames.
        if code.co_name == "<module>" and code.co_filename == "<string>":
            return None

        if (
            code.co_name == "<lambda>"
            and code.co_filename == "<string>"
            and not bool(frame.f_builtins)
        ):
            # namedtuple subclass constructor. Empty builtins cause issue with
            # len keyword in LIST_LEN guard.
            return None

        if is_generator(code):
            unimplemented("generator")
        exceeded, limit_type = exceeds_cache_size_limit(cache_size)
        if exceeded:

            def format_func_info(code):
                return f"'{code.co_name}' ({code.co_filename}:{code.co_firstlineno})"

            def format_guard_failures():
                assert recompile_reasons, "TODO(whc) any other recompile reasons?"
                return recompile_reasons[-1]

            log.warning(
                "torch._dynamo hit config.%s (%s)\n"
                "   function: %s\n"
                "   last reason: %s\n"
                'To log all recompilation reasons, use TORCH_LOGS="recompiles".\n'
                "To diagnose recompilation issues, see %s.",
                limit_type,
                getattr(config, limit_type),
                format_func_info(code),
                format_guard_failures(),
                troubleshooting_url,
            )
            unimplemented(f"{limit_type} reached")

        if not has_tensor_in_frame(frame):
            return None

        global initial_global_state
        initial_global_state = GlobalStateGuard()

        global FRAME_COUNTER
        if "_id" not in frame_state:
            frame_state["_id"] = FRAME_COUNTER
            FRAME_COUNTER += 1
        frame_id = frame_state["_id"]

        frame_compile_id = FRAME_COMPILE_COUNTER[frame_id]
        FRAME_COMPILE_COUNTER[frame_id] += 1

        compile_id = CompileId(frame_id, frame_compile_id)

        signpost_event(
            "dynamo",
            "_convert_frame_assert._compile",
            {
                "co_name": code.co_name,
                "co_filename": code.co_filename,
                "co_firstlineno": code.co_firstlineno,
                "cache_size": cache_size.num_cache_entries_with_same_id_matched_objs,
                "accumulated_cache_size": cache_size.num_cache_entries,
            },
        )

        return _compile(
            frame.f_code,
            frame.f_globals,
            frame.f_locals,
            frame.f_builtins,
            compiler_fn,
            one_graph,
            export,
            export_constraints,
            hooks,
            cache_size,
            frame,
            frame_state=frame_state,
            compile_id=compile_id,
        )

    _convert_frame_assert._torchdynamo_orig_callable = compiler_fn  # type: ignore[attr-defined]

    def _clone_with_backend(backend):
        return convert_frame_assert(backend, one_graph, export, export_constraints)

    _convert_frame_assert._clone_with_backend = _clone_with_backend  # type: ignore[attr-defined]
    return _convert_frame_assert


def maybe_cprofile(func):
    if config.cprofile:
        return cprofile_wrapper(func)
    return func


from collections import OrderedDict

from torch.utils.hooks import RemovableHandle

# we have to use `OrderedDict` to make `RemovableHandle` work.
_bytecode_hooks: Dict[int, BytecodeHook] = OrderedDict()


def register_bytecode_hook(hook: BytecodeHook) -> RemovableHandle:
    """Register hooks for bytecode generated by Dynamo. The hook can do some
    logging, as well as return a new code object to be used. Please refer
    to `BytecodeHook` for the hook signature.
    """
    handle = RemovableHandle(_bytecode_hooks)
    _bytecode_hooks[handle.id] = hook
    return handle


@_use_lazy_graph_module(config.use_lazy_graph_module)
@maybe_cprofile
def _compile(
    code: types.CodeType,
    globals: Dict[str, object],
    locals: Dict[str, object],
    builtins: Dict[str, object],
    compiler_fn: CompilerFn,
    one_graph: bool,
    export: bool,
    export_constraints,
    hooks: Hooks,
    cache_size: CacheSizeRelevantForFrame,
    frame: Optional[types.FrameType] = None,
    frame_state=None,
    compile_id=None,
) -> Optional[GuardedCode]:
    from torch.fx.experimental.validator import (
        bisect,
        BisectValidationException,
        translation_validation_enabled,
        ValidationException,
    )

    output: Optional[OutputGraph] = None
    tracer: Optional[InstructionTranslator] = None
    # This is shared across restarts
    mutated_closure_cell_contents: Set[str] = set()
    fail_type: Optional[str] = None
    fail_reason: Optional[str] = None
    fail_user_frame_filename: Optional[str] = None
    fail_user_frame_lineno: Optional[int] = None
    speculation_log = SpeculationLog()

    @preserve_global_state
    def transform(instructions, code_options):
        nonlocal output
        nonlocal tracer
        speculation_log.restart()
        tracer = InstructionTranslator(
            instructions,
            code,
            locals,
            globals,
            builtins,
            code_options,
            compiler_fn,
            one_graph,
            export,
            export_constraints,
            mutated_closure_cell_contents,
            frame_state=frame_state,
            speculation_log=speculation_log,
        )

        try:
            with tracing(tracer.output.tracing_context), tracer.set_current_tx():
                tracer.run()
        except exc.UnspecializeRestartAnalysis:
            speculation_log.clear()
            raise
        except (exc.SpeculationRestartAnalysis, exc.SkipFrame):
            raise
        except Exception:
            if translation_validation_enabled():
                bisect(tracer.output.shape_env)
            raise
        finally:
            tracer.output.call_cleanup_hooks()

        output = tracer.output
        assert output is not None
        assert output.output_instructions
        instructions[:] = output.output_instructions
        code_options.update(output.code_options)

        if config.dead_code_elimination:
            propagate_inst_exn_table_entries(instructions)
            check_inst_exn_tab_entries_valid(instructions)
            instructions[:] = remove_pointless_jumps(remove_dead_code(instructions))

    @dynamo_timed(phase_name="entire_frame_compile")
    def compile_inner(
        code: types.CodeType,
        one_graph: bool,
        hooks: Hooks,
        transform: Callable[[List[Instruction], Dict[str, Any]], Any],
    ) -> Optional[GuardedCode]:
        nonlocal output
        for attempt in itertools.count():
            CompileContext.get().attempt = attempt
            try:
                out_code = transform_code_object(code, transform)
                break
            except exc.RestartAnalysis as e:
                log.info(
                    "Restarting analysis due to %s",
                    LazyString(format_traceback_short, e.__traceback__),
                )
                if attempt > 100:
                    unimplemented("100+ RestartAnalysis() calls")
            except exc.SkipFrame as e:
                log.debug(
                    "Skipping frame %s %s \
                    %s %s",
                    e,
                    code.co_name,
                    code.co_filename,
                    code.co_firstlineno,
                )
                if one_graph:
                    log.debug("No graph captured with one_graph=True")
                return None

        def log_bytecode(prefix, name, filename, line_no, code):
            if bytecode_log.isEnabledFor(logging.DEBUG):
                bytecode_log.debug(
                    format_bytecode(prefix, name, filename, line_no, code)
                )

        log_bytecode(
            "ORIGINAL BYTECODE",
            code.co_name,
            code.co_filename,
            code.co_firstlineno,
            code,
        )
        log_bytecode(
            "MODIFIED BYTECODE",
            code.co_name,
            code.co_filename,
            code.co_firstlineno,
            out_code,
        )

        for hook in _bytecode_hooks.values():
            hook_output = hook(code, out_code)
            if hook_output is not None:
                out_code = hook_output

        orig_code_map[out_code] = code
        output_codes.add(out_code)

        assert output is not None

        # Tests for new code objects.
        # The rationale for these tests can be found in torch/csrc/dynamo/eval_frame.c
        # Only test once the code object is created.
        # They are not tested during runtime.

        def count_args(code):
            import inspect

            return (
                code.co_argcount
                + code.co_kwonlyargcount
                + bool(code.co_flags & inspect.CO_VARARGS)
                + bool(code.co_flags & inspect.CO_VARKEYWORDS)
            )

        total_argcount_old = count_args(code)
        total_argcount_new = count_args(out_code)
        msg = "arg mismatch: "
        msg += f"old code object has args {code.co_varnames[:total_argcount_old]}, "
        msg += f"new code object has args {out_code.co_varnames[:total_argcount_new]}"
        assert (
            code.co_varnames[:total_argcount_old]
            == out_code.co_varnames[:total_argcount_new]
        ), msg

        msg = "free var mismatch: "
        msg += f"old code object has free var {code.co_freevars}, "
        msg += f"new code object has free var {out_code.co_freevars}"
        assert code.co_freevars == out_code.co_freevars, msg

        msg = "cell var mismatch: "
        msg += f"old code object has cell var {code.co_cellvars}, "
        msg += f"new code object has cell var {out_code.co_cellvars}"
        assert code.co_cellvars == out_code.co_cellvars, msg

        # Skipping Dynamo on a frame without any extracted graph.
        # This does not affect eager functionality. But this is necessary
        # for export for cases where Dynamo-reconstructed bytecode can create
        # new function frames, confusing export in thinking that there
        # are extra graphs now.

        if output.export and output.is_empty_graph():
            return None

        assert output.guards is not None
        CleanupManager.instance[out_code] = output.cleanups
        check_fn = CheckFunctionManager(
            output,
            hooks.guard_fail_fn if hooks else None,
        )

        guarded_code = GuardedCode(out_code, check_fn.check_fn)

        if not output.is_empty_graph() and hooks.guard_export_fn is not None:
            # We should not run the guard_export_fn when Dynamo does not
            # generate any graph. This can happen in export when TorchDynamo
            # generated bytecode has some reconstruction logic for mutated
            # variables which can trigger TorchDynamo on the children frames but
            # they are benign and do not generate any new graphs.
            hooks.guard_export_fn(output.guards)

        output.local_scope.clear()
        return guarded_code

    with compile_context(CompileContext(compile_id)):
        try:
            guarded_code = compile_inner(code, one_graph, hooks, transform)
            return guarded_code
        except (
            Unsupported,
            TorchRuntimeError,
            BackendCompilerFailed,
            AssertionError,
            ConstraintViolationError,
            GuardOnDataDependentSymNode,
            ValidationException,
            UncapturedHigherOrderOpError,
            BisectValidationException,
        ) as e:
            fail_type = str(type(e))
            fail_reason = str(e)
            exception_handler(e, code, frame, export=export)
            if e.innermost_user_frame_summary is not None:  # type: ignore[union-attr]
                fail_user_frame_filename = e.innermost_user_frame_summary.filename  # type: ignore[union-attr]
                fail_user_frame_lineno = e.innermost_user_frame_summary.lineno  # type: ignore[union-attr]
            raise
        except Exception as e:
            fail_type = str(type(e))
            fail_reason = str(e)
            exception_handler(e, code, frame, export=export)
            if e.innermost_user_frame_summary is not None:  # type: ignore[attr-defined]
                fail_user_frame_filename = e.innermost_user_frame_summary.filename  # type: ignore[attr-defined]
                fail_user_frame_lineno = e.innermost_user_frame_summary.lineno  # type: ignore[attr-defined]
            raise InternalTorchDynamoError(str(e)).with_traceback(
                e.__traceback__
            ) from None
        finally:
            if tracer:
                tracer.output.local_scope = {}

            from .utils import curr_frame

            frame_key = str(curr_frame)
            if (
                fail_reason is None
                and output is not None
                and frame_key in frame_phase_timing
            ):
                guard_count = len(output.guards)
                shape_env_guard_count = len(output.shape_env.guards)
                graph_op_count = output.count_calls()
                graph_node_count = len(output.graph.nodes)
                graph_input_count = len(output.placeholders)
                entire_frame_compile_time = frame_phase_timing[frame_key].get(
                    "entire_frame_compile", None
                )
                backend_compile_time = frame_phase_timing[frame_key].get(
                    "backend_compile", None
                )
                non_compliant_ops = {op.__qualname__ for op in output.non_compliant_ops}
                compliant_custom_ops = {
                    op.__qualname__ for op in output.compliant_custom_ops
                }
            else:
                guard_count = None
                shape_env_guard_count = None
                graph_op_count = None
                graph_node_count = None
                graph_input_count = None
                entire_frame_compile_time = None
                backend_compile_time = None
                non_compliant_ops = set({})
                compliant_custom_ops = set({})
            metrics = CompilationMetrics(
                frame_key,
                code.co_name,
                code.co_filename,
                code.co_firstlineno,
                cache_size.num_cache_entries_with_same_id_matched_objs,
                cache_size.num_cache_entries,
                guard_count,
                shape_env_guard_count,
                graph_op_count,
                graph_node_count,
                graph_input_count,
                entire_frame_compile_time,
                backend_compile_time,
                fail_type,
                fail_reason,
                fail_user_frame_filename,
                fail_user_frame_lineno,
                non_compliant_ops,
                compliant_custom_ops,
            )
            record_compilation_metrics(metrics)


def convert_frame(compiler_fn: CompilerFn, hooks: Hooks):
    """Try to convert a frame into an FX graph, if error leave frame unmodified"""
    inner_convert = convert_frame_assert(compiler_fn, one_graph=False)

    def _convert_frame(frame: types.FrameType, cache_entry, hooks: Hooks, frame_state):
        counters["frames"]["total"] += 1
        try:
            result = inner_convert(frame, cache_entry, hooks, frame_state)
            counters["frames"]["ok"] += 1
            return result
        except Exception as e:
            # These two exception types are "soft" failure, in the sense that
            # we know this is due to something we didn't implement all the
            # way, scare the user less about it.  That being said, if you
            # are trying to understand why a graph break happened, it's still
            # important to have this information, so offer it.
            #
            # NB: NotImplementedError used to be on this list, but actually
            # it is impossible for it to reach here, as it is converted into
            # InternalTorchDynamoError.  This behavior seemed reasonable
            # to me (ezyang, Aug 2023) so I kept it, but maybe at some point
            # someone wanted these to also get suppressed.  If so, you'll
            # need to make these exceptions not get wrapped

            # We intentionally don't want to suppress error here.
            if isinstance(e, UncapturedHigherOrderOpError):
                raise

            soft_fail = isinstance(e, Unsupported)
            if not config.suppress_errors and not soft_fail:
                raise

            # Suppress the error.  NB: It's very important to do the
            # suppression logging HERE, where the actual suppression
            # happens. Previously it was somewhere else and so it was
            # possible to accidentally not log at all.
            record_filename = getattr(e, "record_filename", None)
            code = frame.f_code
            error_msg = format_error_msg(e, code, record_filename, frame)

            if soft_fail:
                log.info(error_msg, exc_info=True)
            else:
                log.warning(error_msg, exc_info=True)
        return None

    _convert_frame._torchdynamo_orig_callable = compiler_fn  # type: ignore[attr-defined]
    _convert_frame._clone_with_backend = lambda backend: convert_frame(backend, hooks)  # type: ignore[attr-defined]
    return _convert_frame


# TODO mlazos: add support for same args, or record them
def replay(filename):
    from .backends.debugging import eager

    original_replay_val = config.replay_record_enabled
    config.replay_record_enabled = False
    with open(filename, "rb") as in_file:
        record = ExecutionRecord.load(in_file)
    record.globals = dict(itertools.chain(record.globals.items(), globals().items()))

    try:
        _compile(
            record.code,
            record.globals,
            record.locals,
            record.builtins,
            compiler_fn=eager,
            one_graph=False,
            export=False,
            export_constraints=None,
            hooks=Hooks(),
            cache_size=CacheSizeRelevantForFrame(0, 0),
            frame=None,
            frame_state={},
        )
    finally:
        config.replay_record_enabled = original_replay_val