# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
r"""
MemProfiler
^^^^^^^^^^^
A powerful memory profiling utility that synthesizes numerous complementary profiling methods.
"""
import os
import pickle
from typing import Any, Dict, Optional, Tuple, DefaultDict, Callable, List, Set
from dataclasses import dataclass, field, fields, asdict
from contextlib import redirect_stdout, contextmanager, ExitStack
from collections import defaultdict
from itertools import chain
from functools import wraps
from psutil import Process
from copy import deepcopy
from pathlib import Path
import yaml
import torch
from torch.distributed._composable.fsdp import FSDPModule
from torch.distributed._tools.fsdp2_mem_tracker import FSDPMemTracker
from lightning.fabric.utilities.rank_zero import _get_rank
from lightning.fabric.utilities.cloud_io import get_filesystem
from lightning.pytorch.utilities.exceptions import MisconfigurationException
from fts_examples.cfg_utils import resolve_funcs
from fts_examples.profiling.config import MemProfilerCfg
[docs]class MemProfiler:
"""MemProfiler is a powerful memory profiling utility that synthesizes numerous complementary profiling
methods.
The following profiling utilities are integrated and simultaneously configured:
- ``FSDP2MemTracker``
- `cuda memory snapshot and allocator history tracking <https://pytorch.org/docs/stable/torch_cuda_memory.html>`_
- host-level memory tracking
- custom memory hooks (e.g. for activation checkpoint memory tracking via ``saved_tensors_hooks`` etc.)
.. warning::
:class:`~fts_examples.profiling.memprofiler.MemProfiler` is in BETA and subject to change.
The interface can bring breaking changes and new features with the next release of Finetuning Scheduler.
"""
def __init__(self, *args, **kwargs) -> None:
"""The MemProfiler is a powerful memory profiling utility that synthesizes numerous complementary profiling
methods.
The following profiling utilities are integrated and simultaneously configured:
- ``FSDP2MemTracker``
- `cuda memory snapshot and allocator history
tracking <https://pytorch.org/docs/stable/torch_cuda_memory.html>`_
- host-level memory tracking
- custom memory hooks (e.g. for activation checkpoint memory tracking via ``saved_tensors_hooks`` etc.)
`See this example for usage guidance.
<https://finetuning-scheduler.readthedocs.io/en/latest/profiling/memprofiler_profiling.html>`_
Args:
*args: Ignored positional arguments.
**kwargs: Ignored keyword arguments.
Attributes:
memory_stats (DefaultDict): A dictionary of dictionaries containing memory statistics.
fsdp_memory_stats (Dict): A dictionary containing FSDP memory statistics.
rank (int): The rank of the current process.
module (Any): The module being profiled.
memprof_log_dir (str): The directory where memory profiles are saved.
fsdp_mem_tracker (FSDPMemTracker): The FSDP memory tracker.
saved_tensors_funcs (List[Callable]): A list of functions used to track saved tensors.
_state (MemProfInternalState): The internal state of the MemProfiler.
"""
super().__init__()
self.memory_stats = defaultdict(dict)
self.fsdp_memory_stats = {}
self.rank = _get_rank() or 0
self.module = None
self.memprof_log_dir = None
self.fsdp_mem_tracker = None
self.saved_tensors_funcs: List[Callable] = []
self._state = _MemProfInternalState()
[docs] def connect(self, obj_ref: Any) -> None:
"""Connects the MemProfiler to the given target module.
Args:
obj_ref: The module to be profiled.
"""
self.module = obj_ref
self._state.curr_pid = Process(os.getpid())
if self.memprofiler_cfg.enable_saved_tensors_hooks:
self.saved_tensors_funcs = resolve_funcs(cfg_obj=self.memprofiler_cfg, func_type='saved_tensors_funcs')
self._state.base_collect_func_set = self._all_base_collect_funcs()
self.memprofiler_cfg.retain_hooks_for_funcs = self._state.base_collect_func_set # conservatively wait for all
def _all_base_collect_funcs(self) -> Set:
funcs = self.memprofiler_cfg.collect_funcs
return set(chain(*[funcs.cuda_allocator_history, funcs.cuda, funcs.cpu]))
@property
def memprofiler_cfg(self) -> MemProfilerCfg:
return self.module.memprofiler_cfg
@property
def schedule(self) -> int:
return self.module.memprofiler_cfg.schedule
@property
def fsdp_mem_tracker_root_module(self) -> Optional[FSDPModule]: # type: ignore
"""If ``track_fsdp_mem`` is enabled, this is the root FSDP module used for FSDP2 memory tracking.
The root module must have ``fully_shard`` applied for FSDP2 memory tracking.
If ``track_fsdp_mem`` is disabled, this is ``None``.
"""
if not self.memprofiler_cfg.track_fsdp_mem:
return None
try:
fsdp_tracker_root = self.module.get_submodule(self.memprofiler_cfg.fsdp_mem_tracker_root_module)
assert issubclass(type(fsdp_tracker_root), FSDPModule), \
"MemProfiler requires the root FSDP module to have ``fully_shard`` applied for FSDP2 memory tracking."
return fsdp_tracker_root
except AttributeError:
raise MisconfigurationException(
"Could not find the specified ``fsdp_mem_tracker_root_module``: "
f"{self.memprofiler_cfg.fsdp_mem_tracker_root_module}. MemProfiler needs to attach to a module that has"
" ``fully_shard`` applied for FSDP2 memory tracking."
)
[docs] def remove_memprofiler_hooks(self) -> None:
"""Removes all hooks added by MemProfiler."""
for handle_list in self._state.hook_handles.values():
for handle in handle_list:
handle.remove()
[docs] def exec_reset_state_hooks(self) -> None:
"""Executes all hooks registered for resetting memory tracking state.
These hooks are responsible for resetting state (e.g. saved tensor sizes, RSS, etc.) tracked by the hooks
registered in :meth:`add_memprofiler_hooks`.
"""
for hook in self._state.configured_hooks["reset_state_hooks"]:
hook(self.module, self.memprofiler_cfg.save_hook_attrs)
[docs] def maybe_init_fsdp_mem_tracker(self) -> None:
"""Initializes FSDP2 memory tracker if ``track_fsdp_mem`` is ``True``."""
if self.memprofiler_cfg.track_fsdp_mem:
self.fsdp_mem_tracker = FSDPMemTracker(self.fsdp_mem_tracker_root_module, self.module.trainer.optimizers[0])
[docs] def add_memprofiler_hooks(self) -> None:
"""Adds all hooks registered for memory tracking.
Currently, hooks are registered for the `pre_forward` and `post_forward` points. Hooks are added to the
`modules` of the provided `module` and are responsible for tracking memory usage. Supported hooks are
registered based on the configuration of `memory_hooks` in `memprofiler_cfg`.
After registering all hooks, this method calls :meth:`exec_reset_state_hooks` to reset the state of all hooks.
"""
# TODO: extend supported hook points (e.g. backward, etc.) and if/once supporting additional hook points,
# use a hook_type to registration function mapping
memory_hooks_cfg = self.memprofiler_cfg.memory_hooks
for supported_hooks in fields(memory_hooks_cfg):
if getattr(memory_hooks_cfg, supported_hooks.name):
self._state.configured_hooks[supported_hooks.name] = resolve_funcs(cfg_obj=memory_hooks_cfg,
func_type=supported_hooks.name)
for module in self.module.modules():
module.mem_info_handle = self._state.curr_pid.memory_info
for hook_func in self._state.configured_hooks["pre_forward_hooks"]:
self._state.hook_handles[hook_func].append(module.register_forward_pre_hook(hook_func))
for hook_func in self._state.configured_hooks["post_forward_hooks"]:
self._state.hook_handles[hook_func].append(module.register_forward_hook(hook_func))
self.exec_reset_state_hooks()
[docs] def init_memprof_log_dir(self) -> None:
"""Initializes the directory where MemProfiler will save profiling artifacts.
The directory is determined by the following logic:
1. If ``memprofiler_cfg.save_dir`` is set, use that directory.
2. Otherwise, use a directory named ``memprofiler`` in the current
Lightning ``log_dir``.
"""
base_log_dir = self.module._trainer.log_dir or self.module._trainer.default_root_dir
self.memprof_log_dir = self.memprofiler_cfg.save_dir or Path(base_log_dir) / "memprofiler"
self.memprof_log_dir = Path(self.memprof_log_dir) # ensure the dir is a Path
self.memprof_log_dir.mkdir(exist_ok=True, parents=True)
[docs] def cuda_allocator_history_snap(self, snap_key: Tuple) -> None:
"""Dumps a snapshot of the CUDA memory allocator history.
The snapshot is saved to a file named ``cuda_alloc_rank_<snap_key>.pickle`` in the directory specified by
``memprof_log_dir``.
Args:
snap_key: A tuple used to identify the snapshot.
"""
cuda_snapshot_file = (self.memprof_log_dir / f"cuda_alloc_rank_{snap_key}.pickle")
torch.cuda.memory._dump_snapshot(cuda_snapshot_file)
[docs] def done(self, iter_idx: int) -> bool:
"""Checks if the profiling is done.
Args:
iter_idx: The current iteration index.
Returns:
bool: Whether the profiling is done.
"""
return self.schedule.max_iter and iter_idx > self.schedule.max_iter
def _process_hooks(self, snap_key) -> None:
if self.memprofiler_cfg.enable_memory_hooks:
if len(self._state.hook_handles) == 0:
self.add_memprofiler_hooks()
collected = {attr: getattr(self.module, attr, None) for attr in self.memprofiler_cfg.save_hook_attrs}
self.memory_stats[snap_key].update(collected)
def _collect_snap(self, snap_key, reset_mem_hooks: bool = False) -> None:
"""Collects a snapshot of the current memory statistics.
Args:
snap_key: A tuple of strings identifying the snapshot.
reset_mem_hooks: Whether to reset the memory hooks after collecting the snapshot.
Notes:
The snapshot is saved to `memory_stats` with the key `snap_key`.
"""
_, fn_name, *_ = snap_key
snap_key = ".".join(map(str, snap_key))
mem_cfg = self.memprofiler_cfg
self._process_hooks(snap_key)
if fn_name in mem_cfg.collect_funcs.cpu:
mem = self._state.curr_pid.memory_info()
self.memory_stats[snap_key].update({"rss": mem.rss, "vms": mem.vms})
if fn_name in mem_cfg.collect_funcs.cuda:
self.memory_stats[snap_key].update(torch.cuda.memory_stats())
if fn_name in mem_cfg.collect_funcs.cuda_allocator_history and mem_cfg.cuda_allocator_history:
self.cuda_allocator_history_snap(snap_key)
if mem_cfg.enable_memory_hooks and reset_mem_hooks:
self.exec_reset_state_hooks()
@property
def _should_remove_hooks(self) -> bool:
return all(func in self._state.done_prof_funcs for func in self.memprofiler_cfg.retain_hooks_for_funcs)
[docs] def teardown_prof(self, fn_name: str, iter_ctx: str) -> None:
"""Tears down profiling state for a function.
This method is responsible for:
1. Disabling profiling for the given function and iteration context.
2. Marking the function as done if it is not being profiled in either the "start" or "end" iteration contexts.
3. Removing all hooks registered by MemProfiler if the function is marked as done and
``retain_hooks_for_funcs`` is ``True``.
Args:
fn_name (str): The name of the function to tear down profiling state for.
iter_ctx (str): The iteration context of the function to tear down profiling state for.
"""
self._state.can_collect[(fn_name, iter_ctx)] = False
if not any(self._state.can_collect[(fn_name, iter_ctx)] for iter_ctx in ["start", "end"]):
self._state.done_prof_funcs.append(fn_name)
if self.memprofiler_cfg.retain_hooks_for_funcs and self._should_remove_hooks:
self.remove_memprofiler_hooks()
self.memprofiler_cfg.enable_memory_hooks = False
[docs] def gen_snap_keys(self, fn_name: str, iter_ctx: str, iter_idx: Optional[int] = None) -> Tuple[int, int, Tuple]:
"""Generates the MemProfiler snapshot key for a given function and iteration context.
Args:
fn_name (str): The name of the function to generate a snapshot key for.
iter_ctx (str): The iteration context of the function to generate a snapshot key for.
iter_idx (Optional[int]): The iteration index to use in the snapshot key. If ``None``, the current iteration
index will be used.
Returns:
A tuple of (iteration index, snapshot key). The snapshot key is a tuple of (rank, function name, iteration
index, iteration context).
Note:
The snapshot key format is rank.fn_name.iter_idx.iter_ctx.
"""
# NOTE [Memprofiler Key Format]
# snap key format is rank.fn_name.iter_idx.iter_ctx
# e.g. 0.training_step.0.end keys hook output for the end of `training_step` 0 (first iteration), for rank 0
# 0.training_step.2.start keys mem stats for the start of `training_step` 2, for rank 0
if iter_idx is None:
iter_idx = self._state.snap_indices[(fn_name, iter_ctx)]
return iter_idx, (self.rank, fn_name, iter_idx, iter_ctx)
[docs] def update_collect_state(self, fn_name: str, iter_ctx: str, iter_idx: Optional[int] = None) -> None:
"""Updates the MemProfiler state for a given function and iteration context.
Args:
fn_name (str): The name of the function to update the state for.
iter_ctx (str): The iteration context of the function to update the state for.
iter_idx (int, optional): The iteration index to use when generating the snapshot key. If ``None`` is
provided, the current iteration index will be used.
"""
self._state.maybe_init_iter_state(fn_name, iter_ctx)
if self._state.can_collect[(fn_name, iter_ctx)] and not self._state._iter_incremented[(fn_name, iter_ctx)]:
self._state.snap_indices[(fn_name, iter_ctx)] += 1
self._state._iter_incremented[(fn_name, iter_ctx)] = True
curr = self._state
curr._iter_idx, curr._snap_key = self.gen_snap_keys(fn_name, iter_ctx, iter_idx)
[docs] def snap(self, fn_name: str, iter_ctx: str, reset_mem_hooks: bool = False) -> None:
"""Collects a memory snapshot for the given function and iteration context.
If the current iteration index is greater than the number of warmup iterations,
then this function will check if the iteration index is within the profiling
range. If it is, then a memory snapshot will be collected. If not, the memory
profiler state for the given function and iteration context will be torn down.
Args:
fn_name (str): The name of the function to collect a memory snapshot for.
iter_ctx (str): The iteration context of the function to collect a memory snapshot for.
reset_mem_hooks (bool, optional): Whether to reset the memory hooks after collecting the snapshot.
Defaults to False.
"""
if self._state._iter_idx > self.schedule.warmup_iters:
if not self.done(self._state._iter_idx):
self._collect_snap(self._state._snap_key, reset_mem_hooks)
else:
self.teardown_prof(fn_name, iter_ctx)
[docs] def dump_memory_stats(self) -> None:
"""Dumps the collected memory statistics to a pickle file and/or a yaml file.
If `dump_memorystats_pickle` is True, the memory statistics will be saved to a pickle file
at `memprof_log_dir / f"rank_{self.rank}_memory_stats.pickle"`.
If `dump_memorystats_yaml` is True, the memory statistics will be saved to a yaml file
at `memprof_log_dir / f"rank_{self.rank}_memory_stats.yaml"`.
If `track_fsdp_mem` is True, the collected FSDP memory statistics will be saved to a yaml file
at `memprof_log_dir / f"rank_{self.rank}_fsdp_memory_stats.yaml"`.
"""
if self.memory_stats:
if self.memprofiler_cfg.dump_memorystats_pickle:
filename = self.memprof_log_dir / f"rank_{self.rank}_memory_stats.pickle"
with open(filename, "wb") as f:
pickle.dump(self.memory_stats, f)
if self.memprofiler_cfg.dump_memorystats_yaml:
filename = self.memprof_log_dir / f"rank_{self.rank}_memory_stats.yaml"
with open(filename, "w", newline="") as f:
yaml.dump(self.memory_stats, f)
if self.fsdp_memory_stats:
self.save_fsdp_mem_reports()
[docs] def save_fsdp_mem_reports(self) -> None:
"""Saves the collected FSDP memory statistics to a yaml file.
For each function name and iteration in the collected FSDP memory statistics, a log file is created
at `memprof_log_dir / f"fsdp_mem_rank_{self.rank}_{fn_name}_{iter}.log"` containing the FSDP memory
statistics in both tabular and modulewise formats.
The modulewise format is displayed up to the specified `fsdp_mem_track_module_depth`, and the units
for the memory statistics are specified by `fsdp_mem_tracker_units`.
If `fsdp_mem_tracker_tabulate` is True, the FSDP memory statistics will be displayed in a tabular format.
Otherwise, a plain text format will be used.
:return: None
"""
for (fn_name, iter) in self.fsdp_memory_stats.keys():
self.fsdp_mem_tracker.memory_tracking = self.fsdp_memory_stats[(fn_name, iter)]
target_fsdp_depth = self.memprofiler_cfg.fsdp_mem_track_module_depth
tabulate_mode = self.memprofiler_cfg.fsdp_mem_tracker_tabulate
report_units = self.memprofiler_cfg.fsdp_mem_tracker_units
memory_report = self.memprof_log_dir / f"fsdp_mem_rank_{self.rank}_{fn_name}_{iter}.log"
fs = get_filesystem(memory_report)
with fs.open(memory_report, "w", newline="") as fp:
with redirect_stdout(fp):
fp.write(f"Iteration {iter} {fn_name} Peak FSDP memory stats: {os.linesep}")
fp.write("-" * 80 + os.linesep)
self.fsdp_mem_tracker.display_snapshot("peak", tabulate=tabulate_mode)
fp.write("-" * 80 + os.linesep)
fp.write(f"Iteration {iter} {fn_name} Modulewise FSDP memory stats: {os.linesep}")
fp.write("-" * 80 + os.linesep)
self.fsdp_mem_tracker.display_modulewise_snapshots(depth=target_fsdp_depth, units=report_units,
tabulate=tabulate_mode)
fp.write("-" * 80 + os.linesep)
def _in_collect_range(self) -> bool:
return self._state._iter_idx > self.schedule.warmup_iters and self._state._iter_idx <= self.schedule.max_iter
def _collecting(self, fn_name: str, iter_ctx: str = 'start') -> bool:
return self._state.can_collect[(fn_name, iter_ctx)] and self._in_collect_range()
def _should_collect_fsdp(self, fn_name: str) -> bool:
return all((self.fsdp_mem_tracker, fn_name in self.memprofiler_cfg.collect_funcs.fsdp,
self._collecting(fn_name)))
def _should_collect_saved_tensors(self, fn_name: str) -> bool:
return self.memprofiler_cfg.enable_saved_tensors_hooks and self._collecting(fn_name)
[docs] @contextmanager
@staticmethod
def memprofile_snap_ctx(memprofiler, fn_name: str):
"""Context manager for taking memory snapshots at the start and end of a method.
This context manager takes care of calling the `snap` method of the `MemProfiler` instance
at the start and end of the context. It also handles the case where we are only collecting
FSDP memory statistics.
Args:
memprofiler (MemProfiler): The MemProfiler instance.
fn_name (str): The name of the function being profiled.
Yields:
None
"""
try:
# no need to execute snap logic if we are only collecting fsdp memory stats
if fn_name not in memprofiler._state.base_collect_func_set:
yield
else:
memprofiler.update_collect_state(fn_name=fn_name, iter_ctx="start")
memprofiler.snap(iter_ctx="start", fn_name=fn_name)
if memprofiler._should_collect_saved_tensors(fn_name):
with torch.autograd.graph.saved_tensors_hooks(*memprofiler.saved_tensors_funcs):
yield
else:
yield
finally:
if fn_name in memprofiler._state.base_collect_func_set:
memprofiler.update_collect_state(fn_name=fn_name, iter_ctx="end")
memprofiler.snap(iter_ctx="end", fn_name=fn_name, reset_mem_hooks=True)
[docs] @contextmanager
@staticmethod
def memprofile_fsdp_ctx(memprofiler, fn_name: str, track_inputs_target: Optional[Tuple] = None):
"""Sets the FSDP memory tracker context manager if ``fsdp_mem_tracker_enabled`` is ``True``.
This context manager takes care of calling the `update_collect_state` and `reset_mod_stats`
methods of the `MemProfiler` instance as well as the `track_inputs` and `reset` methods of
the `FSDPMemTracker` instance.
Args:
memprofiler (MemProfiler): The MemProfiler instance.
fn_name (str): The name of the function being profiled.
track_inputs_target (Optional[Tuple]): The FSDP inputs to track. If ``None``, no inputs will be tracked.
Yields:
None
"""
fsdp_state_collected = False
try:
memprofiler.update_collect_state(fn_name=fn_name, iter_ctx="start")
if track_inputs_target is not None:
memprofiler.fsdp_mem_tracker.track_inputs(track_inputs_target)
if not memprofiler._should_collect_fsdp(fn_name):
yield
else:
fsdp_state_collected = True
with memprofiler.fsdp_mem_tracker:
yield
finally:
memprofiler.update_collect_state(fn_name=fn_name, iter_ctx="end")
if fsdp_state_collected:
memprofiler.fsdp_memory_stats[(fn_name, memprofiler._state._iter_idx)] = \
deepcopy(memprofiler.fsdp_mem_tracker.memory_tracking)
memprofiler.fsdp_mem_tracker.reset_mod_stats()
[docs] @staticmethod
def memprofilable(func):
"""Decorate a function to enable use with MemProfiler.
This decorator is used to enable profiling a method with MemProfiler. If the
method is decorated with this decorator and the object has an enabled MemProfiler
instance, the method will be profiled using the MemProfiler instance.
Args:
func: The function to decorate.
Returns:
The decorated function.
"""
@wraps(func)
def wrapper(self, *args, **kwargs):
if not self.memprofiler:
return func(self, *args, **kwargs)
fn_name = func.__name__
with MemProfiler.memprofile_ctx(self.memprofiler, fn_name=fn_name):
return func(self, *args, **kwargs)
return wrapper
[docs] @contextmanager
@staticmethod
def memprofile_ctx(memprofiler, fn_name: str):
"""Context manager to orchestrate profiling for a given function.
This context manager is responsible for calling the relevant subordinate context managers
that manage profiling for a given function. The context managers invoked are:
1. `memprofile_meta_ctx`: Sets metadata for the profiling invocation.
2. `memprofile_fsdp_ctx`: Collects FSDP memory statistics.
3. `memprofile_snap_ctx`: Collects memory snapshots at the start and end of a method.
Args:
memprofiler (MemProfiler): The MemProfiler instance.
fn_name (str): The name of the function being profiled.
Yields:
None
"""
ctx_managers = []
ctx_managers.append(MemProfiler.memprofile_meta_ctx(memprofiler, fn_name=fn_name))
ctx_managers.append(MemProfiler.memprofile_fsdp_ctx(memprofiler, fn_name=fn_name))
ctx_managers.append(MemProfiler.memprofile_snap_ctx(memprofiler, fn_name=fn_name))
with ExitStack() as stack:
for ctx_manager in ctx_managers:
stack.enter_context(ctx_manager)
try:
yield
finally:
stack.close()
def _memprofiler_cfg_mapping_representer(dumper, data):
return dumper.represent_mapping('tag:yaml.org,2002:map', asdict(data))
yaml.SafeDumper.add_representer(MemProfilerCfg, _memprofiler_cfg_mapping_representer)
@dataclass
class _MemProfInternalState:
can_collect: Dict[str, bool] = field(default_factory=dict)
curr_pid: Optional[Process] = None
snap_indices: Dict[str, int] = field(default_factory=dict)
configured_hooks: Dict[str, Any] = field(default_factory=dict)
hook_handles: DefaultDict[str, List[Any]] = field(default_factory=lambda: defaultdict(list))
done_prof_funcs: List[str] = field(default_factory=list)
base_collect_func_set: Optional[Set] = None
_iter_idx: Optional[int] = None
_snap_key: Optional[Tuple] = None
_iter_incremented: Dict[str, int] = field(default_factory=dict)
def maybe_init_iter_state(self, fn_name: str, iter_ctx: str) -> None:
if not self.snap_indices.get((fn_name, iter_ctx), None):
self._iter_incremented[(fn_name, iter_ctx)] = False
self.snap_indices[(fn_name, iter_ctx)] = 0
self.can_collect[(fn_name, iter_ctx)] = True
def reset_iter_state(self, fn_name: str, iter_ctx: str) -> None:
self._snap_key = None
for (fn_name, iter_ctx) in self._iter_incremented.keys():
self._iter_incremented[(fn_name, iter_ctx)] = False
