TheRock/rocm-libraries/projects/hipblaslt/tensilelite/Tensile/Common/Parallel.py

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import concurrent.futures
import itertools
import os
import sys
import time

from joblib import Parallel, delayed

from .Utilities import tqdm


def joblibParallelSupportsGenerator():
    import joblib
    from packaging.version import Version

    joblibVer = joblib.__version__
    return Version(joblibVer) >= Version("1.4.0")


def CPUThreadCount(enable=True):
    from .GlobalParameters import globalParameters

    if not enable:
        return 1
    else:
        if os.name == "nt":
            # Windows supports at most 61 workers because the scheduler uses
            # WaitForMultipleObjects directly, which has the limit (the limit
            # is actually 64, but some handles are needed for accounting).
            cpu_count = min(os.cpu_count(), 61)
        else:
            cpu_count = len(os.sched_getaffinity(0))
        cpuThreads = globalParameters["CpuThreads"]
        if cpuThreads == -1:
            return cpu_count

        return min(cpu_count, cpuThreads)


def pcallWithGlobalParamsMultiArg(f, args, newGlobalParameters):
    OverwriteGlobalParameters(newGlobalParameters)
    return f(*args)


def pcallWithGlobalParamsSingleArg(f, arg, newGlobalParameters):
    OverwriteGlobalParameters(newGlobalParameters)
    return f(arg)


def apply_print_exception(item, *args):
    # print(item, args)
    try:
        if len(args) > 0:
            func = item
            args = args[0]
            return func(*args)
        else:
            func, item = item
            return func(item)
    except Exception:
        import traceback

        traceback.print_exc()
        raise
    finally:
        sys.stdout.flush()
        sys.stderr.flush()


def OverwriteGlobalParameters(newGlobalParameters):
    from . import GlobalParameters

    GlobalParameters.globalParameters.clear()
    GlobalParameters.globalParameters.update(newGlobalParameters)


def ProcessingPool(enable=True, maxTasksPerChild=None):
    import multiprocessing
    import multiprocessing.dummy

    threadCount = CPUThreadCount()

    if (not enable) or threadCount <= 1:
        return multiprocessing.dummy.Pool(1)

    if multiprocessing.get_start_method() == "spawn":
        from . import GlobalParameters

        return multiprocessing.Pool(
            threadCount,
            initializer=OverwriteGlobalParameters,
            maxtasksperchild=maxTasksPerChild,
            initargs=(GlobalParameters.globalParameters,),
        )
    else:
        return multiprocessing.Pool(threadCount, maxtasksperchild=maxTasksPerChild)


def ParallelMap(function, objects, message="", enable=True, method=None, maxTasksPerChild=None):
    """
    Generally equivalent to list(map(function, objects)), possibly executing in parallel.

      message: A message describing the operation to be performed.
      enable: May be set to false to disable parallelism.
      method: A function which can fetch the mapping function from a processing pool object.
          Leave blank to use .map(), other possiblities:
             - `lambda x: x.starmap` - useful if `function` takes multiple parameters.
             - `lambda x: x.imap` - lazy evaluation
             - `lambda x: x.imap_unordered` - lazy evaluation, does not preserve order of return value.
    """
    from .GlobalParameters import globalParameters

    threadCount = CPUThreadCount(enable)
    pool = ProcessingPool(enable, maxTasksPerChild)

    if threadCount <= 1 and globalParameters["ShowProgressBar"]:
        # Provide a progress bar for single-threaded operation.
        # This works for method=None, and for starmap.
        mapFunc = map
        if method is not None:
            # itertools provides starmap which can fill in for pool.starmap.  It provides imap on Python 2.7.
            # If this works, we will use it, otherwise we will fallback to the "dummy" pool for single threaded
            # operation.
            try:
                mapFunc = method(itertools)
            except NameError:
                mapFunc = None

        if mapFunc is not None:
            return list(mapFunc(function, tqdm(objects, message)))

    mapFunc = pool.map
    if method:
        mapFunc = method(pool)

    objects = zip(itertools.repeat(function), objects)
    function = apply_print_exception

    countMessage = ""
    try:
        countMessage = " for {} tasks".format(len(objects))
    except TypeError:
        pass

    if message != "":
        message += ": "

    print("{0}Launching {1} threads{2}...".format(message, threadCount, countMessage))
    sys.stdout.flush()
    currentTime = time.time()
    rv = mapFunc(function, objects)
    totalTime = time.time() - currentTime
    print("{0}Done. ({1:.1f} secs elapsed)".format(message, totalTime))
    sys.stdout.flush()
    pool.close()
    return rv


def ParallelMapReturnAsGenerator(function, objects, message="", enable=True, multiArg=True):
    from .GlobalParameters import globalParameters

    threadCount = CPUThreadCount(enable)
    print("{0}Launching {1} threads...".format(message, threadCount))

    if threadCount <= 1 and globalParameters["ShowProgressBar"]:
        # Provide a progress bar for single-threaded operation.
        callFunc = lambda args: function(*args) if multiArg else lambda args: function(args)
        return [callFunc(args) for args in tqdm(objects, message)]

    with concurrent.futures.ProcessPoolExecutor(max_workers=threadCount) as executor:
        resultFutures = (executor.submit(function, *arg if multiArg else arg) for arg in objects)
        for result in concurrent.futures.as_completed(resultFutures):
            yield result.result()


def ParallelMap2(
    function, objects, message="", enable=True, multiArg=True, return_as="list", procs=None
):
    """
    Generally equivalent to list(map(function, objects)), possibly executing in parallel.

      message: A message describing the operation to be performed.
      enable: May be set to false to disable parallelism.
      multiArg: True if objects represent multiple arguments
                  (differentiates multi args vs single collection arg)
    """
    if return_as in ("generator", "generator_unordered") and not joblibParallelSupportsGenerator():
        return ParallelMapReturnAsGenerator(function, objects, message, enable, multiArg)

    from .GlobalParameters import globalParameters

    threadCount = procs if procs else CPUThreadCount(enable)

    threadCount = CPUThreadCount(enable)

    if threadCount <= 1 and globalParameters["ShowProgressBar"]:
        # Provide a progress bar for single-threaded operation.
        return [function(*args) if multiArg else function(args) for args in tqdm(objects, message)]

    countMessage = ""
    try:
        countMessage = " for {} tasks".format(len(objects))
    except TypeError:
        pass

    if message != "":
        message += ": "
    print("{0}Launching {1} threads{2}...".format(message, threadCount, countMessage))
    sys.stdout.flush()
    currentTime = time.time()

    pcall = pcallWithGlobalParamsMultiArg if multiArg else pcallWithGlobalParamsSingleArg
    pargs = zip(objects, itertools.repeat(globalParameters))

    if joblibParallelSupportsGenerator():
        rv = Parallel(n_jobs=threadCount, timeout=99999, return_as=return_as)(
            delayed(pcall)(function, a, params) for a, params in pargs
        )
    else:
        rv = Parallel(n_jobs=threadCount, timeout=99999)(
            delayed(pcall)(function, a, params) for a, params in pargs
        )

    totalTime = time.time() - currentTime
    print("{0}Done. ({1:.1f} secs elapsed)".format(message, totalTime))
    sys.stdout.flush()
    return rv