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247 lines
7.6 KiB
Python
247 lines
7.6 KiB
Python
#
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# This file is licensed under the Affero General Public License (AGPL) version 3.
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#
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# Copyright (C) 2023 New Vector, Ltd
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#
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# This program is free software: you can redistribute it and/or modify
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# it under the terms of the GNU Affero General Public License as
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# published by the Free Software Foundation, either version 3 of the
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# License, or (at your option) any later version.
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#
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# See the GNU Affero General Public License for more details:
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# <https://www.gnu.org/licenses/agpl-3.0.html>.
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#
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# Originally licensed under the Apache License, Version 2.0:
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# <http://www.apache.org/licenses/LICENSE-2.0>.
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#
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# [This file includes modifications made by New Vector Limited]
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#
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#
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import logging
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from functools import wraps
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from types import TracebackType
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from typing import Awaitable, Callable, Dict, Generator, Optional, Type, TypeVar
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from prometheus_client import CollectorRegistry, Counter, Metric
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from typing_extensions import Concatenate, ParamSpec, Protocol
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from synapse.logging.context import (
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ContextResourceUsage,
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LoggingContext,
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current_context,
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)
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from synapse.metrics import InFlightGauge
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from synapse.util import Clock
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logger = logging.getLogger(__name__)
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block_counter = Counter("synapse_util_metrics_block_count", "", ["block_name"])
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block_timer = Counter("synapse_util_metrics_block_time_seconds", "", ["block_name"])
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block_ru_utime = Counter(
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"synapse_util_metrics_block_ru_utime_seconds", "", ["block_name"]
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)
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block_ru_stime = Counter(
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"synapse_util_metrics_block_ru_stime_seconds", "", ["block_name"]
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)
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block_db_txn_count = Counter(
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"synapse_util_metrics_block_db_txn_count", "", ["block_name"]
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)
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# seconds spent waiting for db txns, excluding scheduling time, in this block
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block_db_txn_duration = Counter(
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"synapse_util_metrics_block_db_txn_duration_seconds", "", ["block_name"]
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)
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# seconds spent waiting for a db connection, in this block
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block_db_sched_duration = Counter(
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"synapse_util_metrics_block_db_sched_duration_seconds", "", ["block_name"]
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)
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# This is dynamically created in InFlightGauge.__init__.
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class _InFlightMetric(Protocol):
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real_time_max: float
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real_time_sum: float
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# Tracks the number of blocks currently active
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in_flight: InFlightGauge[_InFlightMetric] = InFlightGauge(
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"synapse_util_metrics_block_in_flight",
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"",
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labels=["block_name"],
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sub_metrics=["real_time_max", "real_time_sum"],
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)
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P = ParamSpec("P")
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R = TypeVar("R")
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class HasClock(Protocol):
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clock: Clock
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def measure_func(
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name: Optional[str] = None,
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) -> Callable[[Callable[P, Awaitable[R]]], Callable[P, Awaitable[R]]]:
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"""Decorate an async method with a `Measure` context manager.
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The Measure is created using `self.clock`; it should only be used to decorate
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methods in classes defining an instance-level `clock` attribute.
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Usage:
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@measure_func()
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async def foo(...):
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...
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Which is analogous to:
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async def foo(...):
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with Measure(...):
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...
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"""
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def wrapper(
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func: Callable[Concatenate[HasClock, P], Awaitable[R]]
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) -> Callable[P, Awaitable[R]]:
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block_name = func.__name__ if name is None else name
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@wraps(func)
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async def measured_func(self: HasClock, *args: P.args, **kwargs: P.kwargs) -> R:
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with Measure(self.clock, block_name):
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r = await func(self, *args, **kwargs)
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return r
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# There are some shenanigans here, because we're decorating a method but
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# explicitly making use of the `self` parameter. The key thing here is that the
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# return type within the return type for `measure_func` itself describes how the
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# decorated function will be called.
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return measured_func # type: ignore[return-value]
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return wrapper # type: ignore[return-value]
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class Measure:
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__slots__ = [
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"clock",
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"name",
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"_logging_context",
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"start",
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]
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def __init__(self, clock: Clock, name: str) -> None:
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"""
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Args:
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clock: An object with a "time()" method, which returns the current
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time in seconds.
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name: The name of the metric to report.
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"""
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self.clock = clock
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self.name = name
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curr_context = current_context()
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if not curr_context:
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logger.warning(
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"Starting metrics collection %r from sentinel context: metrics will be lost",
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name,
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)
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parent_context = None
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else:
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assert isinstance(curr_context, LoggingContext)
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parent_context = curr_context
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self._logging_context = LoggingContext(str(curr_context), parent_context)
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self.start: Optional[float] = None
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def __enter__(self) -> "Measure":
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if self.start is not None:
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raise RuntimeError("Measure() objects cannot be re-used")
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self.start = self.clock.time()
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self._logging_context.__enter__()
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in_flight.register((self.name,), self._update_in_flight)
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logger.debug("Entering block %s", self.name)
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return self
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def __exit__(
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self,
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exc_type: Optional[Type[BaseException]],
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exc_val: Optional[BaseException],
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exc_tb: Optional[TracebackType],
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) -> None:
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if self.start is None:
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raise RuntimeError("Measure() block exited without being entered")
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logger.debug("Exiting block %s", self.name)
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duration = self.clock.time() - self.start
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usage = self.get_resource_usage()
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in_flight.unregister((self.name,), self._update_in_flight)
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self._logging_context.__exit__(exc_type, exc_val, exc_tb)
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try:
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block_counter.labels(self.name).inc()
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block_timer.labels(self.name).inc(duration)
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block_ru_utime.labels(self.name).inc(usage.ru_utime)
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block_ru_stime.labels(self.name).inc(usage.ru_stime)
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block_db_txn_count.labels(self.name).inc(usage.db_txn_count)
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block_db_txn_duration.labels(self.name).inc(usage.db_txn_duration_sec)
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block_db_sched_duration.labels(self.name).inc(usage.db_sched_duration_sec)
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except ValueError:
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logger.warning("Failed to save metrics! Usage: %s", usage)
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def get_resource_usage(self) -> ContextResourceUsage:
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"""Get the resources used within this Measure block
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If the Measure block is still active, returns the resource usage so far.
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"""
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return self._logging_context.get_resource_usage()
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def _update_in_flight(self, metrics: _InFlightMetric) -> None:
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"""Gets called when processing in flight metrics"""
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assert self.start is not None
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duration = self.clock.time() - self.start
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metrics.real_time_max = max(metrics.real_time_max, duration)
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metrics.real_time_sum += duration
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# TODO: Add other in flight metrics.
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class DynamicCollectorRegistry(CollectorRegistry):
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"""
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Custom Prometheus Collector registry that calls a hook first, allowing you
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to update metrics on-demand.
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Don't forget to register this registry with the main registry!
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"""
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def __init__(self) -> None:
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super().__init__()
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self._pre_update_hooks: Dict[str, Callable[[], None]] = {}
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def collect(self) -> Generator[Metric, None, None]:
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"""
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Collects metrics, calling pre-update hooks first.
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"""
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for pre_update_hook in self._pre_update_hooks.values():
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pre_update_hook()
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yield from super().collect()
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def register_hook(self, metric_name: str, hook: Callable[[], None]) -> None:
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"""
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Registers a hook that is called before metric collection.
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"""
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self._pre_update_hooks[metric_name] = hook
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