speechbrain/speechbrain/utils/parallel.py at develop · speechbrain/speechbrain · GitHub

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"""Parallel processing tools to help speed up certain tasks like data

preprocessing.

Authors

* Sylvain de Langen 2023

"""

import itertools

import os

import sys

from collections import deque

from concurrent.futures import Executor, ProcessPoolExecutor

from threading import Condition

from typing import Any, Callable, Iterable, Optional

from tqdm.auto import tqdm

def get_available_cpu_count() -> int:

"""Return the number of CPUs available to the current process.

This function provides a reliable way to determine CPU count that respects:

1. User override via SB_NUM_PROC environment variable

2. CPU affinity limits (e.g., SLURM allocations)

3. System CPU count as fallback

The fallback hierarchy is:

1. SB_NUM_PROC environment variable (if set and valid)

2. os.process_cpu_count() (Python 3.13+, respects affinity)

3. len(os.sched_getaffinity(0)) (Unix, respects SLURM/cgroups)

4. os.cpu_count() (fallback for Windows or when above fail)

Returns

-------

int

The number of CPUs available. Falls back to 1 if detection fails.

Examples

--------

>>> # With environment variable override:

>>> import os

>>> os.environ["SB_NUM_PROC"] = "2"

>>> get_available_cpu_count()

"""

# Priority 1: Environment variable override

env_override = os.environ.get("SB_NUM_PROC")

if env_override is not None:

try:

count = int(env_override)

if count > 0:

return count

except ValueError:

pass # Invalid value, fall through to auto-detection

# Priority 2: os.process_cpu_count() (Python 3.13+)

if sys.version_info >= (3, 13):

try:

count = os.process_cpu_count()

if count is not None and count > 0:

return count

except AttributeError:

# os.process_cpu_count may be unavailable in some Python builds

# Fall through to the next detection method

pass

# Priority 3: os.sched_getaffinity() (Unix systems)

try:

count = len(os.sched_getaffinity(0))

if count > 0:

return count

except (AttributeError, OSError):

# AttributeError: sched_getaffinity not available (Windows)

# OSError: might occur in some containerized environments

pass

# Priority 4: os.cpu_count() (universal fallback)

count = os.cpu_count()

if count is not None and count > 0:

return count

# Ultimate fallback

return 1

def _chunk_process_wrapper(fn, chunk):

return list(map(fn, chunk))

class CancelFuturesOnExit:

"""Context manager that .cancel()s all elements of a list upon exit.

This is used to abort futures faster when raising an exception."""

def __init__(self, future_list):

self.future_list = future_list

def __enter__(self):

pass

def __exit__(self, _type, _value, _traceback):

for future in self.future_list:

future.cancel()

class _ParallelMapper:

"""Internal class for `parallel_map`, arguments match the constructor's."""

def __init__(

self,

fn: Callable[[Any], Any],

source: Iterable[Any],

process_count: int,

chunk_size: int,

queue_size: int,

executor: Optional[Executor],

progress_bar: bool,

progress_bar_kwargs: dict,

self.future_chunks = deque()

self.cv = Condition()

self.just_finished_count = 0

"""Number of jobs that were just done processing, guarded by

`self.cv`."""

self.remote_exception = None

"""Set by a worker when it encounters an exception, guarded by

`self.cv`."""

self.fn = fn

self.source = source

self.process_count = process_count

self.chunk_size = chunk_size

self.queue_size = queue_size

self.executor = executor

self.known_len = len(source) if hasattr(source, "__len__") else None

self.source_it = iter(source)

self.depleted_source = False

if progress_bar:

tqdm_final_kwargs = {"total": self.known_len}

tqdm_final_kwargs.update(progress_bar_kwargs)

self.pbar = tqdm(**tqdm_final_kwargs)

else:

self.pbar = None

def run(self):

"""Spins up an executor (if none were provided), then yields all

processed chunks in order."""

with CancelFuturesOnExit(self.future_chunks):

if self.executor is not None:

# just use the executor we were provided

yield from self._map_all()

else:

# start and shut down a process pool executor -- ok for

# long-running tasks

with ProcessPoolExecutor(

max_workers=self.process_count

) as pool:

self.executor = pool

yield from self._map_all()

def _bump_processed_count(self, future):

"""Notifies the main thread of the finished job, bumping the number of

jobs it should requeue. Updates the progress bar based on the returned

chunk length.

Arguments

---------

future: concurrent.futures.Future

A future holding a processed chunk (of type `list`).

Returns

-------

None

"""

if future.cancelled():

# the scheduler wants us to stop or something else happened, give up

return

future_exception = future.exception()

# wake up dispatcher thread to refill the queue

with self.cv:

if future_exception is not None:

# signal to the main thread that it should raise

self.remote_exception = future_exception

self.just_finished_count += 1

self.cv.notify()

if future_exception is None:

# update progress bar with the length of the output as the progress

# bar is over element count, not chunk count.

if self.pbar is not None:

self.pbar.update(len(future.result()))

def _enqueue_job(self):

"""Pulls a chunk from the source iterable and submits it to the

pool; must be run from the main thread.

Returns

-------

`True` if any job was submitted (that is, if there was any chunk

left to process), `False` otherwise.

"""

# immediately deplete the input stream of chunk_size elems (or less)

chunk = list(itertools.islice(self.source_it, self.chunk_size))

# empty chunk? then we finished iterating over the input stream

if len(chunk) == 0:

self.depleted_source = True

return False

future = self.executor.submit(_chunk_process_wrapper, self.fn, chunk)

future.add_done_callback(self._bump_processed_count)

self.future_chunks.append(future)

return True

def _map_all(self):

"""Performs all the parallel mapping logic.

Yields

------

The items from source processed by fn

"""

# initial queue fill

for _ in range(self.queue_size):

if not self._enqueue_job():

break

# consume & requeue logic

while (not self.depleted_source) or (len(self.future_chunks) != 0):

with self.cv:

# if `cv.notify` was called by a worker _after_ the `with cv`

# block last iteration, then `just_finished_count` would be

# incremented, but this `cv.wait` would not wake up -- skip it.

while self.just_finished_count == 0:

# wait to be woken up by a worker thread, which could mean:

# - that a chunk was processed: try to yield any

# - that a call failed with an exception: raise it

# - nothing; it could be a spurious CV wakeup: keep looping

self.cv.wait()

if self.remote_exception is not None:

raise self.remote_exception

# store the amount to requeue, avoiding data races

to_queue_count = self.just_finished_count

self.just_finished_count = 0

# try to enqueue as many jobs as there were just finished.

# when the input is finished, the queue will not be refilled.

for _ in range(to_queue_count):

if not self._enqueue_job():

break

# yield from left to right as long as there is enough ready

# would yield from the first two. we might deplete the entire queue

# at that point, the `depleted_source` loop check is needed as such.

while len(self.future_chunks) != 0 and self.future_chunks[0].done():

yield from self.future_chunks.popleft().result()

if self.pbar is not None:

self.pbar.close()

def parallel_map(

fn: Callable[[Any], Any],

source: Iterable[Any],

process_count: Optional[int] = None,

chunk_size: int = 8,

queue_size: int = 128,

executor: Optional[Executor] = None,

progress_bar: bool = True,

progress_bar_kwargs: dict = {"smoothing": 0.02},

"""Maps iterable items with a function, processing chunks of items in

parallel with multiple processes and displaying progress with tqdm.

Processed elements will always be returned in the original, correct order.

Unlike `ProcessPoolExecutor.map`, elements are produced AND consumed lazily.

Arguments

---------

fn: Callable

The function that is called for every element in the source list.

The output is an iterator over the source list after fn(elem) is called.

source: Iterable

Iterator whose elements are passed through the mapping function.

process_count: int, optional

The number of processes to spawn. Ignored if a custom executor is

provided. If None (the default), uses `get_available_cpu_count()` which

respects SLURM allocations, CPU affinity, and SB_NUM_PROC env var.

For CPU-bound tasks, it is generally not useful to exceed logical core

count.

For IO-bound tasks, it may make sense to as to limit the amount of time

spent in iowait.

chunk_size: int

How many elements are fed to the worker processes at once. A value of 8

is generally fine. Low values may increase overhead and reduce CPU

occupancy.

queue_size: int

Number of chunks to be waited for on the main process at a time.

Low values increase the chance of the queue being starved, forcing

workers to idle.

Very high values may cause high memory usage, especially if the source

iterable yields large objects.

executor: Optional[Executor]

Allows providing an existing executor (preferably a

ProcessPoolExecutor). If None (the default), a process pool will be

spawned for this mapping task and will be shut down after.

progress_bar: bool

Whether to show a tqdm progress bar.

progress_bar_kwargs: dict

A dict of keyword arguments that is forwarded to tqdm when

`progress_bar == True`. Allows overriding the defaults or e.g.

specifying `total` when it cannot be inferred from the source iterable.

Yields

------

The items from source processed by fn

"""

if process_count is None:

process_count = get_available_cpu_count()

mapper = _ParallelMapper(

fn,

source,

process_count,

chunk_size,

queue_size,

executor,

progress_bar,

progress_bar_kwargs,

)

yield from mapper.run()

speechbrain/speechbrain/utils/parallel.py at develop · speechbrain/speechbrain · GitHub

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