""" Reducer using memory mapping for numpy arrays """ # Author: Thomas Moreau # Copyright: 2017, Thomas Moreau # License: BSD 3 clause from mmap import mmap import errno import os import stat import threading import atexit import tempfile import warnings import weakref from uuid import uuid4 try: WindowsError except NameError: WindowsError = type(None) from pickle import whichmodule try: # Python 2 compat from cPickle import loads from cPickle import dumps except ImportError: from pickle import loads from pickle import dumps from pickle import HIGHEST_PROTOCOL, PicklingError try: import numpy as np from numpy.lib.stride_tricks import as_strided except ImportError: np = None from .numpy_pickle import load from .numpy_pickle import dump from .backports import make_memmap from .disk import delete_folder # Some system have a ramdisk mounted by default, we can use it instead of /tmp # as the default folder to dump big arrays to share with subprocesses. SYSTEM_SHARED_MEM_FS = '/dev/shm' # Minimal number of bytes available on SYSTEM_SHARED_MEM_FS to consider using # it as the default folder to dump big arrays to share with subprocesses. SYSTEM_SHARED_MEM_FS_MIN_SIZE = int(2e9) # Folder and file permissions to chmod temporary files generated by the # memmapping pool. Only the owner of the Python process can access the # temporary files and folder. FOLDER_PERMISSIONS = stat.S_IRUSR | stat.S_IWUSR | stat.S_IXUSR FILE_PERMISSIONS = stat.S_IRUSR | stat.S_IWUSR class _WeakArrayKeyMap: """A variant of weakref.WeakKeyDictionary for unhashable numpy arrays. This datastructure will be used with numpy arrays as obj keys, therefore we do not use the __get__ / __set__ methods to avoid any conflict with the numpy fancy indexing syntax. """ def __init__(self): self._data = {} def get(self, obj): ref, val = self._data[id(obj)] if ref() is not obj: # In case of race condition with on_destroy: could never be # triggered by the joblib tests with CPython. raise KeyError(obj) return val def set(self, obj, value): key = id(obj) try: ref, _ = self._data[key] if ref() is not obj: # In case of race condition with on_destroy: could never be # triggered by the joblib tests with CPython. raise KeyError(obj) except KeyError: # Insert the new entry in the mapping along with a weakref # callback to automatically delete the entry from the mapping # as soon as the object used as key is garbage collected. def on_destroy(_): del self._data[key] ref = weakref.ref(obj, on_destroy) self._data[key] = ref, value def __getstate__(self): raise PicklingError("_WeakArrayKeyMap is not pickleable") ############################################################################### # Support for efficient transient pickling of numpy data structures def _get_backing_memmap(a): """Recursively look up the original np.memmap instance base if any.""" b = getattr(a, 'base', None) if b is None: # TODO: check scipy sparse datastructure if scipy is installed # a nor its descendants do not have a memmap base return None elif isinstance(b, mmap): # a is already a real memmap instance. return a else: # Recursive exploration of the base ancestry return _get_backing_memmap(b) def _get_temp_dir(pool_folder_name, temp_folder=None): """Get the full path to a subfolder inside the temporary folder. Parameters ---------- pool_folder_name : str Sub-folder name used for the serialization of a pool instance. temp_folder: str, optional Folder to be used by the pool for memmapping large arrays for sharing memory with worker processes. If None, this will try in order: - a folder pointed by the JOBLIB_TEMP_FOLDER environment variable, - /dev/shm if the folder exists and is writable: this is a RAMdisk filesystem available by default on modern Linux distributions, - the default system temporary folder that can be overridden with TMP, TMPDIR or TEMP environment variables, typically /tmp under Unix operating systems. Returns ------- pool_folder : str full path to the temporary folder use_shared_mem : bool whether the temporary folder is written to the system shared memory folder or some other temporary folder. """ use_shared_mem = False if temp_folder is None: temp_folder = os.environ.get('JOBLIB_TEMP_FOLDER', None) if temp_folder is None: if os.path.exists(SYSTEM_SHARED_MEM_FS): try: shm_stats = os.statvfs(SYSTEM_SHARED_MEM_FS) available_nbytes = shm_stats.f_bsize * shm_stats.f_bavail if available_nbytes > SYSTEM_SHARED_MEM_FS_MIN_SIZE: # Try to see if we have write access to the shared mem # folder only if it is reasonably large (that is 2GB or # more). temp_folder = SYSTEM_SHARED_MEM_FS pool_folder = os.path.join(temp_folder, pool_folder_name) if not os.path.exists(pool_folder): os.makedirs(pool_folder) use_shared_mem = True except (IOError, OSError): # Missing rights in the /dev/shm partition, fallback to regular # temp folder. temp_folder = None if temp_folder is None: # Fallback to the default tmp folder, typically /tmp temp_folder = tempfile.gettempdir() temp_folder = os.path.abspath(os.path.expanduser(temp_folder)) pool_folder = os.path.join(temp_folder, pool_folder_name) return pool_folder, use_shared_mem def has_shareable_memory(a): """Return True if a is backed by some mmap buffer directly or not.""" return _get_backing_memmap(a) is not None def _strided_from_memmap(filename, dtype, mode, offset, order, shape, strides, total_buffer_len): """Reconstruct an array view on a memory mapped file.""" if mode == 'w+': # Do not zero the original data when unpickling mode = 'r+' if strides is None: # Simple, contiguous memmap return make_memmap(filename, dtype=dtype, shape=shape, mode=mode, offset=offset, order=order) else: # For non-contiguous data, memmap the total enclosing buffer and then # extract the non-contiguous view with the stride-tricks API base = make_memmap(filename, dtype=dtype, shape=total_buffer_len, mode=mode, offset=offset, order=order) return as_strided(base, shape=shape, strides=strides) def _reduce_memmap_backed(a, m): """Pickling reduction for memmap backed arrays. a is expected to be an instance of np.ndarray (or np.memmap) m is expected to be an instance of np.memmap on the top of the ``base`` attribute ancestry of a. ``m.base`` should be the real python mmap object. """ # offset that comes from the striding differences between a and m a_start, a_end = np.byte_bounds(a) m_start = np.byte_bounds(m)[0] offset = a_start - m_start # offset from the backing memmap offset += m.offset if m.flags['F_CONTIGUOUS']: order = 'F' else: # The backing memmap buffer is necessarily contiguous hence C if not # Fortran order = 'C' if a.flags['F_CONTIGUOUS'] or a.flags['C_CONTIGUOUS']: # If the array is a contiguous view, no need to pass the strides strides = None total_buffer_len = None else: # Compute the total number of items to map from which the strided # view will be extracted. strides = a.strides total_buffer_len = (a_end - a_start) // a.itemsize return (_strided_from_memmap, (m.filename, a.dtype, m.mode, offset, order, a.shape, strides, total_buffer_len)) def reduce_memmap(a): """Pickle the descriptors of a memmap instance to reopen on same file.""" m = _get_backing_memmap(a) if m is not None: # m is a real mmap backed memmap instance, reduce a preserving striding # information return _reduce_memmap_backed(a, m) else: # This memmap instance is actually backed by a regular in-memory # buffer: this can happen when using binary operators on numpy.memmap # instances return (loads, (dumps(np.asarray(a), protocol=HIGHEST_PROTOCOL),)) class ArrayMemmapReducer(object): """Reducer callable to dump large arrays to memmap files. Parameters ---------- max_nbytes: int Threshold to trigger memmapping of large arrays to files created a folder. temp_folder: str Path of a folder where files for backing memmapped arrays are created. mmap_mode: 'r', 'r+' or 'c' Mode for the created memmap datastructure. See the documentation of numpy.memmap for more details. Note: 'w+' is coerced to 'r+' automatically to avoid zeroing the data on unpickling. verbose: int, optional, 0 by default If verbose > 0, memmap creations are logged. If verbose > 1, both memmap creations, reuse and array pickling are logged. prewarm: bool, optional, False by default. Force a read on newly memmapped array to make sure that OS pre-cache it memory. This can be useful to avoid concurrent disk access when the same data array is passed to different worker processes. """ def __init__(self, max_nbytes, temp_folder, mmap_mode, verbose=0, prewarm=True): self._max_nbytes = max_nbytes self._temp_folder = temp_folder self._mmap_mode = mmap_mode self.verbose = int(verbose) self._prewarm = prewarm self._memmaped_arrays = _WeakArrayKeyMap() def __reduce__(self): # The ArrayMemmapReducer is passed to the children processes: it needs # to be pickled but the _WeakArrayKeyMap need to be skipped as it's # only guaranteed to be consistent with the parent process memory # garbage collection. args = (self._max_nbytes, self._temp_folder, self._mmap_mode) kwargs = { 'verbose': self.verbose, 'prewarm': self._prewarm, } return ArrayMemmapReducer, args, kwargs def __call__(self, a): m = _get_backing_memmap(a) if m is not None and isinstance(m, np.memmap): # a is already backed by a memmap file, let's reuse it directly return _reduce_memmap_backed(a, m) if (not a.dtype.hasobject and self._max_nbytes is not None and a.nbytes > self._max_nbytes): # check that the folder exists (lazily create the pool temp folder # if required) try: os.makedirs(self._temp_folder) os.chmod(self._temp_folder, FOLDER_PERMISSIONS) except OSError as e: if e.errno != errno.EEXIST: raise e try: basename = self._memmaped_arrays.get(a) except KeyError: # Generate a new unique random filename. The process and thread # ids are only useful for debugging purpose and to make it # easier to cleanup orphaned files in case of hard process # kill (e.g. by "kill -9" or segfault). basename = "{}-{}-{}.pkl".format( os.getpid(), id(threading.current_thread()), uuid4().hex) self._memmaped_arrays.set(a, basename) filename = os.path.join(self._temp_folder, basename) # In case the same array with the same content is passed several # times to the pool subprocess children, serialize it only once # XXX: implement an explicit reference counting scheme to make it # possible to delete temporary files as soon as the workers are # done processing this data. if not os.path.exists(filename): if self.verbose > 0: print("Memmapping (shape={}, dtype={}) to new file {}" .format(a.shape, a.dtype, filename)) for dumped_filename in dump(a, filename): os.chmod(dumped_filename, FILE_PERMISSIONS) if self._prewarm: # Warm up the data by accessing it. This operation ensures # that the disk access required to create the memmapping # file are performed in the reducing process and avoids # concurrent memmap creation in multiple children # processes. load(filename, mmap_mode=self._mmap_mode).max() elif self.verbose > 1: print("Memmapping (shape={}, dtype={}) to old file {}" .format(a.shape, a.dtype, filename)) # The worker process will use joblib.load to memmap the data return (load, (filename, self._mmap_mode)) else: # do not convert a into memmap, let pickler do its usual copy with # the default system pickler if self.verbose > 1: print("Pickling array (shape={}, dtype={})." .format(a.shape, a.dtype)) return (loads, (dumps(a, protocol=HIGHEST_PROTOCOL),)) def get_memmapping_reducers( pool_id, forward_reducers=None, backward_reducers=None, temp_folder=None, max_nbytes=1e6, mmap_mode='r', verbose=0, prewarm=False, **kwargs): """Construct a pair of memmapping reducer linked to a tmpdir. This function manage the creation and the clean up of the temporary folders underlying the memory maps and should be use to get the reducers necessary to construct joblib pool or executor. """ if forward_reducers is None: forward_reducers = dict() if backward_reducers is None: backward_reducers = dict() # Prepare a sub-folder name for the serialization of this particular # pool instance (do not create in advance to spare FS write access if # no array is to be dumped): pool_folder_name = "joblib_memmapping_folder_{}_{}".format( os.getpid(), pool_id) pool_folder, use_shared_mem = _get_temp_dir(pool_folder_name, temp_folder) # Register the garbage collector at program exit in case caller forgets # to call terminate explicitly: note we do not pass any reference to # self to ensure that this callback won't prevent garbage collection of # the pool instance and related file handler resources such as POSIX # semaphores and pipes pool_module_name = whichmodule(delete_folder, 'delete_folder') def _cleanup(): # In some cases the Python runtime seems to set delete_folder to # None just before exiting when accessing the delete_folder # function from the closure namespace. So instead we reimport # the delete_folder function explicitly. # https://github.com/joblib/joblib/issues/328 # We cannot just use from 'joblib.pool import delete_folder' # because joblib should only use relative imports to allow # easy vendoring. delete_folder = __import__( pool_module_name, fromlist=['delete_folder']).delete_folder try: delete_folder(pool_folder) except WindowsError: warnings.warn("Failed to clean temporary folder: {}" .format(pool_folder)) atexit.register(_cleanup) if np is not None: # Register smart numpy.ndarray reducers that detects memmap backed # arrays and that is also able to dump to memmap large in-memory # arrays over the max_nbytes threshold if prewarm == "auto": prewarm = not use_shared_mem forward_reduce_ndarray = ArrayMemmapReducer( max_nbytes, pool_folder, mmap_mode, verbose, prewarm=prewarm) forward_reducers[np.ndarray] = forward_reduce_ndarray forward_reducers[np.memmap] = reduce_memmap # Communication from child process to the parent process always # pickles in-memory numpy.ndarray without dumping them as memmap # to avoid confusing the caller and make it tricky to collect the # temporary folder backward_reduce_ndarray = ArrayMemmapReducer( None, pool_folder, mmap_mode, verbose) backward_reducers[np.ndarray] = backward_reduce_ndarray backward_reducers[np.memmap] = reduce_memmap return forward_reducers, backward_reducers, pool_folder