_parallel_backends.py

"""
Backends for embarrassingly parallel code.
"""

import gc
import os
import sys
import warnings
import threading
from abc import ABCMeta, abstractmethod

from .format_stack import format_exc
from .my_exceptions import WorkerInterrupt, TransportableException
from ._multiprocessing_helpers import mp
from ._compat import with_metaclass
if mp is not None:
    from .pool import MemmapingPool
    from multiprocessing.pool import ThreadPool


class ParallelBackendBase(with_metaclass(ABCMeta)):
    """Helper abc which defines all methods a ParallelBackend must implement"""

    @abstractmethod
    def effective_n_jobs(self, n_jobs):
        """Determine the number of jobs that can actually run in parallel

        n_jobs is the number of workers requested by the callers. Passing
        n_jobs=-1 means requesting all available workers for instance matching
        the number of CPU cores on the worker host(s).

        This method should return a guesstimate of the number of workers that
        can actually perform work concurrently. The primary use case is to make
        it possible for the caller to know in how many chunks to slice the
        work.

        In general working on larger data chunks is more efficient (less
        scheduling overhead and better use of CPU cache prefetching heuristics)
        as long as all the workers have enough work to do.
        """

    @abstractmethod
    def apply_async(self, func, callback=None):
        """Schedule a func to be run"""

    def configure(self, n_jobs=1, parallel=None, **backend_args):
        """Reconfigure the backend and return the number of workers.

        This makes it possible to reuse an existing backend instance for
        successive independent calls to Parallel with different parameters.
        """
        self.parallel = parallel
        return self.effective_n_jobs(n_jobs)

    def terminate(self):
        """Shutdown the process or thread pool"""

    def compute_batch_size(self):
        """Determine the optimal batch size"""
        return 1

    def batch_completed(self, batch_size, duration):
        """Callback indicate how long it took to run a batch"""

    def get_exceptions(self):
        """List of exception types to be captured."""
        return []

    def abort_everything(self, ensure_ready=True):
        """Abort any running tasks

        This is called when an exception has been raised when executing a tasks
        and all the remaining tasks will be ignored and can therefore be
        aborted to spare computation resources.

        If ensure_ready is True, the backend should be left in an operating
        state as future tasks might be re-submitted via that same backend
        instance.

        If ensure_ready is False, the implementer of this method can decide
        to leave the backend in a closed / terminated state as no new task
        are expected to be submitted to this backend.

        Setting ensure_ready to False is an optimization that can be leveraged
        when aborting tasks via killing processes from a local process pool
        managed by the backend it-self: if we expect no new tasks, there is no
        point in re-creating a new working pool.
        """
        # Does nothing by default: to be overriden in subclasses when canceling
        # tasks is possible.
        pass


class SequentialBackend(ParallelBackendBase):
    """A ParallelBackend which will execute all batches sequentially.

    Does not use/create any threading objects, and hence has minimal
    overhead. Used when n_jobs == 1.
    """

    def effective_n_jobs(self, n_jobs):
        """Determine the number of jobs which are going to run in parallel"""
        if n_jobs == 0:
            raise ValueError('n_jobs == 0 in Parallel has no meaning')
        return 1

    def apply_async(self, func, callback=None):
        """Schedule a func to be run"""
        result = ImmediateResult(func)
        if callback:
            callback(result)
        return result


class PoolManagerMixin(object):
    """A helper class for managing pool of workers."""

    def effective_n_jobs(self, n_jobs):
        """Determine the number of jobs which are going to run in parallel"""
        if n_jobs == 0:
            raise ValueError('n_jobs == 0 in Parallel has no meaning')
        elif mp is None or n_jobs is None:
            # multiprocessing is not available or disabled, fallback
            # to sequential mode
            return 1
        elif n_jobs < 0:
            n_jobs = max(mp.cpu_count() + 1 + n_jobs, 1)
        return n_jobs

    def terminate(self):
        """Shutdown the process or thread pool"""
        if self._pool is not None:
            self._pool.close()
            self._pool.terminate()  # terminate does a join()
            self._pool = None

    def apply_async(self, func, callback=None):
        """Schedule a func to be run"""
        return self._pool.apply_async(SafeFunction(func), callback=callback)

    def abort_everything(self, ensure_ready=True):
        """Shutdown the pool and restart a new one with the same parameters"""
        self.terminate()
        if ensure_ready:
            self.configure(n_jobs=self.parallel.n_jobs, parallel=self.parallel,
                           **self.parallel._backend_args)


class AutoBatchingMixin(object):
    """A helper class for automagically batching jobs."""

    # In seconds, should be big enough to hide multiprocessing dispatching
    # overhead.
    # This settings was found by running benchmarks/bench_auto_batching.py
    # with various parameters on various platforms.
    MIN_IDEAL_BATCH_DURATION = .2

    # Should not be too high to avoid stragglers: long jobs running alone
    # on a single worker while other workers have no work to process any more.
    MAX_IDEAL_BATCH_DURATION = 2

    # Batching counters
    _effective_batch_size = 1
    _smoothed_batch_duration = 0.0

    def compute_batch_size(self):
        """Determine the optimal batch size"""
        old_batch_size = self._effective_batch_size
        batch_duration = self._smoothed_batch_duration
        if (batch_duration > 0 and
                batch_duration < self.MIN_IDEAL_BATCH_DURATION):
            # The current batch size is too small: the duration of the
            # processing of a batch of task is not large enough to hide
            # the scheduling overhead.
            ideal_batch_size = int(old_batch_size *
                                   self.MIN_IDEAL_BATCH_DURATION /
                                   batch_duration)
            # Multiply by two to limit oscilations between min and max.
            batch_size = max(2 * ideal_batch_size, 1)
            self._effective_batch_size = batch_size
            if self.parallel.verbose >= 10:
                self.parallel._print(
                    "Batch computation too fast (%.4fs.) "
                    "Setting batch_size=%d.", (batch_duration, batch_size))
        elif (batch_duration > self.MAX_IDEAL_BATCH_DURATION and
              old_batch_size >= 2):
            # The current batch size is too big. If we schedule overly long
            # running batches some CPUs might wait with nothing left to do
            # while a couple of CPUs a left processing a few long running
            # batches. Better reduce the batch size a bit to limit the
            # likelihood of scheduling such stragglers.
            batch_size = old_batch_size // 2
            self._effective_batch_size = batch_size
            if self.parallel.verbose >= 10:
                self.parallel._print(
                    "Batch computation too slow (%.4fs.) "
                    "Setting batch_size=%d.", (batch_duration, batch_size))
        else:
            # No batch size adjustment
            batch_size = old_batch_size

        if batch_size != old_batch_size:
            # Reset estimation of the smoothed mean batch duration: this
            # estimate is updated in the multiprocessing apply_async
            # CallBack as long as the batch_size is constant. Therefore
            # we need to reset the estimate whenever we re-tune the batch
            # size.
            self._smoothed_batch_duration = 0

        return batch_size

    def batch_completed(self, batch_size, duration):
        """Callback indicate how long it took to run a batch"""
        if batch_size == self._effective_batch_size:
            # Update the smoothed streaming estimate of the duration of a batch
            # from dispatch to completion
            old_duration = self._smoothed_batch_duration
            if old_duration == 0:
                # First record of duration for this batch size after the last
                # reset.
                new_duration = duration
            else:
                # Update the exponentially weighted average of the duration of
                # batch for the current effective size.
                new_duration = 0.8 * old_duration + 0.2 * duration
            self._smoothed_batch_duration = new_duration


class ThreadingBackend(PoolManagerMixin, ParallelBackendBase):
    """A ParallelBackend which will use a thread pool to execute batches in.

    This is a low-overhead backend but it suffers from the Python Global
    Interpreter Lock if the called function relies a lot on Python objects.
    Mostly useful when the execution bottleneck is a compiled extension that
    explicitly releases the GIL (for instance a Cython loop wrapped in a
    "with nogil" block or an expensive call to a library such as NumPy).
    """

    def configure(self, n_jobs=1, parallel=None, **backend_args):
        """Build a process or thread pool and return the number of workers"""
        n_jobs = self.effective_n_jobs(n_jobs)
        if n_jobs == 1:
            # Avoid unnecessary overhead and use sequential backend instead.
            raise FallbackToBackend(SequentialBackend())
        self.parallel = parallel
        self._pool = ThreadPool(n_jobs)
        return n_jobs


class MultiprocessingBackend(PoolManagerMixin, AutoBatchingMixin,
                             ParallelBackendBase):
    """A ParallelBackend which will use a multiprocessing.Pool.

    Will introduce some communication and memory overhead when exchanging
    input and output data with the with the worker Python processes.
    However, does not suffer from the Python Global Interpreter Lock.
    """

    # Environment variables to protect against bad situations when nesting
    JOBLIB_SPAWNED_PROCESS = "__JOBLIB_SPAWNED_PARALLEL__"

    def effective_n_jobs(self, n_jobs):
        """Determine the number of jobs which are going to run in parallel.

        This also checks if we are attempting to create a nested parallel
        loop.
        """
        if mp.current_process().daemon:
            # Daemonic processes cannot have children
            warnings.warn(
                'Multiprocessing-backed parallel loops cannot be nested,'
                ' setting n_jobs=1',
                stacklevel=3)
            return 1

        elif threading.current_thread().name != 'MainThread':
            # Prevent posix fork inside in non-main posix threads
            warnings.warn(
                'Multiprocessing backed parallel loops cannot be nested'
                ' below threads, setting n_jobs=1',
                stacklevel=3)
            return 1

        return super(MultiprocessingBackend, self).effective_n_jobs(n_jobs)

    def configure(self, n_jobs=1, parallel=None, **backend_args):
        """Build a process or thread pool and return the number of workers"""
        n_jobs = self.effective_n_jobs(n_jobs)
        if n_jobs == 1:
            raise FallbackToBackend(SequentialBackend())

        already_forked = int(os.environ.get(self.JOBLIB_SPAWNED_PROCESS, 0))
        if already_forked:
            raise ImportError(
                '[joblib] Attempting to do parallel computing '
                'without protecting your import on a system that does '
                'not support forking. To use parallel-computing in a '
                'script, you must protect your main loop using "if '
                "__name__ == '__main__'"
                '". Please see the joblib documentation on Parallel '
                'for more information')
        # Set an environment variable to avoid infinite loops
        os.environ[self.JOBLIB_SPAWNED_PROCESS] = '1'

        # Make sure to free as much memory as possible before forking
        gc.collect()
        self._pool = MemmapingPool(n_jobs, **backend_args)
        self.parallel = parallel
        return n_jobs

    def terminate(self):
        """Shutdown the process or thread pool"""
        super(MultiprocessingBackend, self).terminate()
        if self.JOBLIB_SPAWNED_PROCESS in os.environ:
            del os.environ[self.JOBLIB_SPAWNED_PROCESS]


class ImmediateResult(object):
    def __init__(self, batch):
        # Don't delay the application, to avoid keeping the input
        # arguments in memory
        self.results = batch()

    def get(self):
        return self.results


class SafeFunction(object):
    """Wrapper that handles the serialization of exception tracebacks.

    If an exception is triggered when calling the inner function, a copy of
    the full traceback is captured to make it possible to serialize
    it so that it can be rendered in a different Python process.
    """
    def __init__(self, func):
        self.func = func

    def __call__(self, *args, **kwargs):
        try:
            return self.func(*args, **kwargs)
        except KeyboardInterrupt:
            # We capture the KeyboardInterrupt and reraise it as
            # something different, as multiprocessing does not
            # interrupt processing for a KeyboardInterrupt
            raise WorkerInterrupt()
        except:
            e_type, e_value, e_tb = sys.exc_info()
            text = format_exc(e_type, e_value, e_tb, context=10, tb_offset=1)
            raise TransportableException(text, e_type)


class FallbackToBackend(Exception):
    """Raised when configuration should fallback to another backend"""

    def __init__(self, backend):
        self.backend = backend