""" Backends for embarrassingly parallel code. """ import gc import os import warnings import threading import contextlib from abc import ABCMeta, abstractmethod from ._utils import ( _TracebackCapturingWrapper, _retrieve_traceback_capturing_wrapped_call ) from ._multiprocessing_helpers import mp if mp is not None: from .pool import MemmappingPool from multiprocessing.pool import ThreadPool from .executor import get_memmapping_executor # Import loky only if multiprocessing is present from .externals.loky import process_executor, cpu_count from .externals.loky.process_executor import ShutdownExecutorError class ParallelBackendBase(metaclass=ABCMeta): """Helper abc which defines all methods a ParallelBackend must implement""" supports_inner_max_num_threads = False supports_retrieve_callback = False default_n_jobs = 1 @property def supports_return_generator(self): return self.supports_retrieve_callback @property def supports_timeout(self): return self.supports_retrieve_callback nesting_level = None def __init__(self, nesting_level=None, inner_max_num_threads=None, **kwargs): super().__init__(**kwargs) self.nesting_level = nesting_level self.inner_max_num_threads = inner_max_num_threads MAX_NUM_THREADS_VARS = [ 'OMP_NUM_THREADS', 'OPENBLAS_NUM_THREADS', 'MKL_NUM_THREADS', 'BLIS_NUM_THREADS', 'VECLIB_MAXIMUM_THREADS', 'NUMBA_NUM_THREADS', 'NUMEXPR_NUM_THREADS', ] TBB_ENABLE_IPC_VAR = "ENABLE_IPC" @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 retrieve_result_callback(self, out): """Called within the callback function passed in apply_async. The argument of this function is the argument given to a callback in the considered backend. It is supposed to return the outcome of a task if it succeeded or raise the exception if it failed. """ def configure(self, n_jobs=1, parallel=None, prefer=None, require=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 start_call(self): """Call-back method called at the beginning of a Parallel call""" def stop_call(self): """Call-back method called at the end of a Parallel call""" def terminate(self): """Shutdown the workers and free the shared memory.""" 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 task 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 new workers. """ # Does nothing by default: to be overridden in subclasses when # canceling tasks is possible. pass def get_nested_backend(self): """Backend instance to be used by nested Parallel calls. By default a thread-based backend is used for the first level of nesting. Beyond, switch to sequential backend to avoid spawning too many threads on the host. """ nesting_level = getattr(self, 'nesting_level', 0) + 1 if nesting_level > 1: return SequentialBackend(nesting_level=nesting_level), None else: return ThreadingBackend(nesting_level=nesting_level), None @contextlib.contextmanager def retrieval_context(self): """Context manager to manage an execution context. Calls to Parallel.retrieve will be made inside this context. By default, this does nothing. It may be useful for subclasses to handle nested parallelism. In particular, it may be required to avoid deadlocks if a backend manages a fixed number of workers, when those workers may be asked to do nested Parallel calls. Without 'retrieval_context' this could lead to deadlock, as all the workers managed by the backend may be "busy" waiting for the nested parallel calls to finish, but the backend has no free workers to execute those tasks. """ yield def _prepare_worker_env(self, n_jobs): """Return environment variables limiting threadpools in external libs. This function return a dict containing environment variables to pass when creating a pool of process. These environment variables limit the number of threads to `n_threads` for OpenMP, MKL, Accelerated and OpenBLAS libraries in the child processes. """ explicit_n_threads = self.inner_max_num_threads default_n_threads = max(cpu_count() // n_jobs, 1) # Set the inner environment variables to self.inner_max_num_threads if # it is given. Else, default to cpu_count // n_jobs unless the variable # is already present in the parent process environment. env = {} for var in self.MAX_NUM_THREADS_VARS: if explicit_n_threads is None: var_value = os.environ.get(var, default_n_threads) else: var_value = explicit_n_threads env[var] = str(var_value) if self.TBB_ENABLE_IPC_VAR not in os.environ: # To avoid over-subscription when using TBB, let the TBB schedulers # use Inter Process Communication to coordinate: env[self.TBB_ENABLE_IPC_VAR] = "1" return env @staticmethod def in_main_thread(): return isinstance(threading.current_thread(), threading._MainThread) 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. """ uses_threads = True supports_timeout = False supports_retrieve_callback = False supports_sharedmem = True 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""" raise RuntimeError("Should never be called for SequentialBackend.") def retrieve_result_callback(self, out): raise RuntimeError("Should never be called for SequentialBackend.") def get_nested_backend(self): # import is not top level to avoid cyclic import errors. from .parallel import get_active_backend # SequentialBackend should neither change the nesting level, the # default backend or the number of jobs. Just return the current one. return get_active_backend() class PoolManagerMixin(object): """A helper class for managing pool of workers.""" _pool = None 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(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 _get_pool(self): """Used by apply_async to make it possible to implement lazy init""" return self._pool def apply_async(self, func, callback=None): """Schedule a func to be run""" # Here, we need a wrapper to avoid crashes on KeyboardInterruptErrors. # We also call the callback on error, to make sure the pool does not # wait on crashed jobs. return self._get_pool().apply_async( _TracebackCapturingWrapper(func), (), callback=callback, error_callback=callback ) def retrieve_result_callback(self, out): """Mimic concurrent.futures results, raising an error if needed.""" return _retrieve_traceback_capturing_wrapped_call(out) 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 default values _DEFAULT_EFFECTIVE_BATCH_SIZE = 1 _DEFAULT_SMOOTHED_BATCH_DURATION = 0.0 def __init__(self, **kwargs): super().__init__(**kwargs) self._effective_batch_size = self._DEFAULT_EFFECTIVE_BATCH_SIZE self._smoothed_batch_duration = self._DEFAULT_SMOOTHED_BATCH_DURATION 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. ideal_batch_size *= 2 # dont increase the batch size too fast to limit huge batch sizes # potentially leading to starving worker batch_size = min(2 * old_batch_size, ideal_batch_size) batch_size = max(batch_size, 1) self._effective_batch_size = batch_size if self.parallel.verbose >= 10: self.parallel._print( f"Batch computation too fast ({batch_duration}s.) " f"Setting batch_size={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. # decrease the batch size quickly to limit potential starving 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( f"Batch computation too slow ({batch_duration}s.) " f"Setting batch_size={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 = \ self._DEFAULT_SMOOTHED_BATCH_DURATION 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 == self._DEFAULT_SMOOTHED_BATCH_DURATION: # 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 def reset_batch_stats(self): """Reset batch statistics to default values. This avoids interferences with future jobs. """ self._effective_batch_size = self._DEFAULT_EFFECTIVE_BATCH_SIZE self._smoothed_batch_duration = self._DEFAULT_SMOOTHED_BATCH_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). The actual thread pool is lazily initialized: the actual thread pool construction is delayed to the first call to apply_async. ThreadingBackend is used as the default backend for nested calls. """ supports_retrieve_callback = True uses_threads = True supports_sharedmem = True 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(nesting_level=self.nesting_level)) self.parallel = parallel self._n_jobs = n_jobs return n_jobs def _get_pool(self): """Lazily initialize the thread pool The actual pool of worker threads is only initialized at the first call to apply_async. """ if self._pool is None: self._pool = ThreadPool(self._n_jobs) return self._pool 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. """ supports_retrieve_callback = True supports_return_generator = False 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 is None: return 1 if mp.current_process().daemon: # Daemonic processes cannot have children if n_jobs != 1: if inside_dask_worker(): msg = ( "Inside a Dask worker with daemon=True, " "setting n_jobs=1.\nPossible work-arounds:\n" "- dask.config.set(" "{'distributed.worker.daemon': False})" "- set the environment variable " "DASK_DISTRIBUTED__WORKER__DAEMON=False\n" "before creating your Dask cluster." ) else: msg = ( 'Multiprocessing-backed parallel loops ' 'cannot be nested, setting n_jobs=1' ) warnings.warn(msg, stacklevel=3) return 1 if process_executor._CURRENT_DEPTH > 0: # Mixing loky and multiprocessing in nested loop is not supported if n_jobs != 1: warnings.warn( 'Multiprocessing-backed parallel loops cannot be nested,' ' below loky, setting n_jobs=1', stacklevel=3) return 1 elif not (self.in_main_thread() or self.nesting_level == 0): # Prevent posix fork inside in non-main posix threads if n_jobs != 1: 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, prefer=None, require=None, **memmappingpool_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(nesting_level=self.nesting_level)) # Make sure to free as much memory as possible before forking gc.collect() self._pool = MemmappingPool(n_jobs, **memmappingpool_args) self.parallel = parallel return n_jobs def terminate(self): """Shutdown the process or thread pool""" super(MultiprocessingBackend, self).terminate() self.reset_batch_stats() class LokyBackend(AutoBatchingMixin, ParallelBackendBase): """Managing pool of workers with loky instead of multiprocessing.""" supports_retrieve_callback = True supports_inner_max_num_threads = True def configure(self, n_jobs=1, parallel=None, prefer=None, require=None, idle_worker_timeout=300, **memmappingexecutor_args): """Build a process executor and return the number of workers""" n_jobs = self.effective_n_jobs(n_jobs) if n_jobs == 1: raise FallbackToBackend( SequentialBackend(nesting_level=self.nesting_level)) self._workers = get_memmapping_executor( n_jobs, timeout=idle_worker_timeout, env=self._prepare_worker_env(n_jobs=n_jobs), context_id=parallel._id, **memmappingexecutor_args) self.parallel = parallel return n_jobs 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 mp.current_process().daemon: # Daemonic processes cannot have children if n_jobs != 1: if inside_dask_worker(): msg = ( "Inside a Dask worker with daemon=True, " "setting n_jobs=1.\nPossible work-arounds:\n" "- dask.config.set(" "{'distributed.worker.daemon': False})\n" "- set the environment variable " "DASK_DISTRIBUTED__WORKER__DAEMON=False\n" "before creating your Dask cluster." ) else: msg = ( 'Loky-backed parallel loops cannot be called in a' ' multiprocessing, setting n_jobs=1' ) warnings.warn(msg, stacklevel=3) return 1 elif not (self.in_main_thread() or self.nesting_level == 0): # Prevent posix fork inside in non-main posix threads if n_jobs != 1: warnings.warn( 'Loky-backed parallel loops cannot be nested below ' 'threads, setting n_jobs=1', stacklevel=3) return 1 elif n_jobs < 0: n_jobs = max(cpu_count() + 1 + n_jobs, 1) return n_jobs def apply_async(self, func, callback=None): """Schedule a func to be run""" future = self._workers.submit(func) if callback is not None: future.add_done_callback(callback) return future def retrieve_result_callback(self, out): try: return out.result() except ShutdownExecutorError: raise RuntimeError( "The executor underlying Parallel has been shutdown. " "This is likely due to the garbage collection of a previous " "generator from a call to Parallel with return_as='generator'." " Make sure the generator is not garbage collected when " "submitting a new job or that it is first properly exhausted." ) def terminate(self): if self._workers is not None: # Don't terminate the workers as we want to reuse them in later # calls, but cleanup the temporary resources that the Parallel call # created. This 'hack' requires a private, low-level operation. self._workers._temp_folder_manager._clean_temporary_resources( context_id=self.parallel._id, force=False ) self._workers = None self.reset_batch_stats() def abort_everything(self, ensure_ready=True): """Shutdown the workers and restart a new one with the same parameters """ self._workers.terminate(kill_workers=True) self._workers = None if ensure_ready: self.configure(n_jobs=self.parallel.n_jobs, parallel=self.parallel) class FallbackToBackend(Exception): """Raised when configuration should fallback to another backend""" def __init__(self, backend): self.backend = backend def inside_dask_worker(): """Check whether the current function is executed inside a Dask worker. """ # This function can not be in joblib._dask because there would be a # circular import: # _dask imports _parallel_backend that imports _dask ... try: from distributed import get_worker except ImportError: return False try: get_worker() return True except ValueError: return False