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Editing File: /home/ubuntu/.local/lib/python3.10/site-packages/joblib/_memmapping_reducer.py

"""
Reducer using memory mapping for numpy arrays
"""
# Author: Thomas Moreau <thomas.moreau.2010@gmail.com>
# 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 time
import warnings
import weakref
from uuid import uuid4
from multiprocessing import util

from pickle import whichmodule, loads, dumps, HIGHEST_PROTOCOL, PicklingError

try:
    WindowsError
except NameError:
    WindowsError = type(None)

try:
    import numpy as np
    from numpy.lib.stride_tricks import as_strided
except ImportError:
    np = None

from .numpy_pickle import dump, load, load_temporary_memmap
from .backports import make_memmap
from .disk import delete_folder
from .externals.loky.backend import resource_tracker

# 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

# Set used in joblib workers, referencing the filenames of temporary memmaps
# created by joblib to speed up data communication. In child processes, we add
# a finalizer to these memmaps that sends a maybe_unlink call to the
# resource_tracker, in order to free main memory as fast as possible.
JOBLIB_MMAPS = set()

def _log_and_unlink(filename):
    from .externals.loky.backend.resource_tracker import _resource_tracker
    util.debug(
        "[FINALIZER CALL] object mapping to {} about to be deleted,"
        " decrementing the refcount of the file (pid: {})".format(
            os.path.basename(filename), os.getpid()))
    _resource_tracker.maybe_unlink(filename, "file")

def add_maybe_unlink_finalizer(memmap):
    util.debug(
        "[FINALIZER ADD] adding finalizer to {} (id {}, filename {}, pid  {})"
        "".format(type(memmap), id(memmap), os.path.basename(memmap.filename),
                  os.getpid()))
    weakref.finalize(memmap, _log_and_unlink, memmap.filename)

def unlink_file(filename):
    """Wrapper around os.unlink with a retry mechanism.

The retry mechanism has been implemented primarily to overcome a race
    condition happening during the finalizer of a np.memmap: when a process
    holding the last reference to a mmap-backed np.memmap/np.array is about to
    delete this array (and close the reference), it sends a maybe_unlink
    request to the resource_tracker. This request can be processed faster than
    it takes for the last reference of the memmap to be closed, yielding (on
    Windows) a PermissionError in the resource_tracker loop.
    """
    NUM_RETRIES = 10
    for retry_no in range(1, NUM_RETRIES + 1):
        try:
            os.unlink(filename)
            break
        except PermissionError:
            util.debug(
                '[ResourceTracker] tried to unlink {}, got '
                'PermissionError'.format(filename)
            )
            if retry_no == NUM_RETRIES:
                raise
            else:
                time.sleep(.2)
        except FileNotFoundError:
            # In case of a race condition when deleting the temporary folder,
            # avoid noisy FileNotFoundError exception in the resource tracker.
            pass

resource_tracker._CLEANUP_FUNCS['file'] = unlink_file

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) and hasattr(os, 'statvfs'):
            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, unlink_on_gc_collect):
    """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, unlink_on_gc_collect=unlink_on_gc_collect
        )
    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, offset=offset,
            mode=mode, order=order, unlink_on_gc_collect=unlink_on_gc_collect
        )
        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
    util.debug('[MEMMAP REDUCE] reducing a memmap-backed array '
               '(shape, {}, pid: {})'.format(a.shape, os.getpid()))
    try:
        from numpy.lib.array_utils import byte_bounds
    except (ModuleNotFoundError, ImportError):
        # Backward-compat for numpy < 2.0
        from numpy import byte_bounds
    a_start, a_end = byte_bounds(a)
    m_start = 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, False))

def reduce_array_memmap_backward(a):
    """reduce a np.array or a np.memmap from a child process"""
    m = _get_backing_memmap(a)
    if isinstance(m, np.memmap) and m.filename not in JOBLIB_MMAPS:
        # if a is backed by a memmaped file, reconstruct a using the
        # memmaped file.
        return _reduce_memmap_backed(a, m)
    else:
        # a is either a regular (not memmap-backed) numpy array, or an array
        # backed by a shared temporary file created by joblib. In the latter
        # case, in order to limit the lifespan of these temporary files, we
        # serialize the memmap as a regular numpy array, and decref the
        # file backing the memmap (done implicitly in a previously registered
        # finalizer, see ``unlink_on_gc_collect`` for more details)
        return (
            loads, (dumps(np.asarray(a), protocol=HIGHEST_PROTOCOL), )
        )

class ArrayMemmapForwardReducer(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_resolver: callable
        An callable in charge of resolving a temporary folder name 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_resolver, mmap_mode,
                 unlink_on_gc_collect, verbose=0, prewarm=True):
        self._max_nbytes = max_nbytes
        self._temp_folder_resolver = temp_folder_resolver
        self._mmap_mode = mmap_mode
        self.verbose = int(verbose)
        if prewarm == "auto":
            self._prewarm = not self._temp_folder.startswith(
                SYSTEM_SHARED_MEM_FS
            )
        else:
            self._prewarm = prewarm
        self._prewarm = prewarm
        self._memmaped_arrays = _WeakArrayKeyMap()
        self._temporary_memmaped_filenames = set()
        self._unlink_on_gc_collect = unlink_on_gc_collect

@property
    def _temp_folder(self):
        return self._temp_folder_resolver()

def __reduce__(self):
        # The ArrayMemmapForwardReducer 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.
        # Although this reducer is pickled, it is not needed in its destination
        # process (child processes), as we only use this reducer to send
        # memmaps from the parent process to the children processes. For this
        # reason, we can afford skipping the resolver, (which would otherwise
        # be unpicklable), and pass it as None instead.
        args = (self._max_nbytes, None, self._mmap_mode,
                self._unlink_on_gc_collect)
        kwargs = {
            'verbose': self.verbose,
            'prewarm': self._prewarm,
        }
        return ArrayMemmapForwardReducer, 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

is_new_memmap = filename not in self._temporary_memmaped_filenames

# add the memmap to the list of temporary memmaps created by joblib
            self._temporary_memmaped_filenames.add(filename)

if self._unlink_on_gc_collect:
                # Bump reference count of the memmap by 1 to account for
                # shared usage of the memmap by a child process. The
                # corresponding decref call will be executed upon calling
                # resource_tracker.maybe_unlink, registered as a finalizer in
                # the child.
                # the incref/decref calls here are only possible when the child
                # and the parent share the same resource_tracker. It is not the
                # case for the multiprocessing backend, but it does not matter
                # because unlinking a memmap from a child process is only
                # useful to control the memory usage of long-lasting child
                # processes, while the multiprocessing-based pools terminate
                # their workers at the end of a map() call.
                resource_tracker.register(filename, "file")

if is_new_memmap:
                # Incref each temporary memmap created by joblib one extra
                # time.  This means that these memmaps will only be deleted
                # once an extra maybe_unlink() is called, which is done once
                # all the jobs have completed (or been canceled) in the
                # Parallel._terminate_backend() method.
                resource_tracker.register(filename, "file")

if not os.path.exists(filename):
                util.debug(
                    "[ARRAY DUMP] Pickling new array (shape={}, dtype={}) "
                    "creating a new memmap at {}".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()

else:
                util.debug(
                    "[ARRAY DUMP] Pickling known array (shape={}, dtype={}) "
                    "reusing memmap file: {}".format(
                        a.shape, a.dtype, os.path.basename(filename)))

# The worker process will use joblib.load to memmap the data
            return (
                (load_temporary_memmap, (filename, self._mmap_mode,
                                         self._unlink_on_gc_collect))
            )
        else:
            # do not convert a into memmap, let pickler do its usual copy with
            # the default system pickler
            util.debug(
                '[ARRAY DUMP] Pickling array (NO MEMMAPPING) (shape={}, '
                ' dtype={}).'.format(a.shape, a.dtype))
            return (loads, (dumps(a, protocol=HIGHEST_PROTOCOL),))

def get_memmapping_reducers(
        forward_reducers=None, backward_reducers=None,
        temp_folder_resolver=None, max_nbytes=1e6, mmap_mode='r', verbose=0,
        prewarm=False, unlink_on_gc_collect=True, **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()

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
        forward_reduce_ndarray = ArrayMemmapForwardReducer(
            max_nbytes, temp_folder_resolver, mmap_mode, unlink_on_gc_collect,
            verbose, prewarm=prewarm)
        forward_reducers[np.ndarray] = forward_reduce_ndarray
        forward_reducers[np.memmap] = forward_reduce_ndarray

# 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_reducers[np.ndarray] = reduce_array_memmap_backward
        backward_reducers[np.memmap] = reduce_array_memmap_backward

return forward_reducers, backward_reducers

class TemporaryResourcesManager(object):
    """Stateful object able to manage temporary folder and pickles

It exposes:
    - a per-context folder name resolving API that memmap-based reducers will
      rely on to know where to pickle the temporary memmaps
    - a temporary file/folder management API that internally uses the
      resource_tracker.
    """

def __init__(self, temp_folder_root=None, context_id=None):
        self._current_temp_folder = None
        self._temp_folder_root = temp_folder_root
        self._use_shared_mem = None
        self._cached_temp_folders = dict()
        self._id = uuid4().hex
        self._finalizers = {}
        if context_id is None:
            # It would be safer to not assign a default context id (less silent
            # bugs), but doing this while maintaining backward compatibility
            # with the previous, context-unaware version get_memmaping_executor
            # exposes too many low-level details.
            context_id = uuid4().hex
        self.set_current_context(context_id)

def set_current_context(self, context_id):
        self._current_context_id = context_id
        self.register_new_context(context_id)

def register_new_context(self, context_id):
        # Prepare a sub-folder name specific to a context (usually a unique id
        # generated by each instance of the Parallel class). Do not create in
        # advance to spare FS write access if no array is to be dumped).
        if context_id in self._cached_temp_folders:
            return
        else:
            # During its lifecycle, one Parallel object can have several
            # executors associated to it (for instance, if a loky worker raises
            # an exception, joblib shutdowns the executor and instantly
            # recreates a new one before raising the error - see
            # ``ensure_ready``.  Because we don't want two executors tied to
            # the same Parallel object (and thus the same context id) to
            # register/use/delete the same folder, we also add an id specific
            # to the current Manager (and thus specific to its associated
            # executor) to the folder name.
            new_folder_name = (
                "joblib_memmapping_folder_{}_{}_{}".format(
                    os.getpid(), self._id, context_id)
            )
            new_folder_path, _ = _get_temp_dir(
                new_folder_name, self._temp_folder_root
            )
            self.register_folder_finalizer(new_folder_path, context_id)
            self._cached_temp_folders[context_id] = new_folder_path

def resolve_temp_folder_name(self):
        """Return a folder name specific to the currently activated context"""
        return self._cached_temp_folders[self._current_context_id]

# resource management API

def register_folder_finalizer(self, pool_subfolder, context_id):
        # Register the garbage collector at program exit in case caller forgets
        # to call terminate explicitly: note we do not pass any reference to
        # ensure that this callback won't prevent garbage collection of
        # parallel instance and related file handler resources such as POSIX
        # semaphores and pipes
        pool_module_name = whichmodule(delete_folder, 'delete_folder')
        resource_tracker.register(pool_subfolder, "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_subfolder, allow_non_empty=True)
                resource_tracker.unregister(pool_subfolder, "folder")
            except OSError:
                warnings.warn("Failed to delete temporary folder: {}"
                              .format(pool_subfolder))

self._finalizers[context_id] = atexit.register(_cleanup)

def _clean_temporary_resources(self, context_id=None, force=False,
                                   allow_non_empty=False):
        """Clean temporary resources created by a process-based pool"""
        if context_id is None:
            # Iterates over a copy of the cache keys to avoid Error due to
            # iterating over a changing size dictionary.
            for context_id in list(self._cached_temp_folders):
                self._clean_temporary_resources(
                    context_id, force=force, allow_non_empty=allow_non_empty
                )
        else:
            temp_folder = self._cached_temp_folders.get(context_id)
            if temp_folder and os.path.exists(temp_folder):
                for filename in os.listdir(temp_folder):
                    if force:
                        # Some workers have failed and the ref counted might
                        # be off. The workers should have shut down by this
                        # time so forcefully clean up the files.
                        resource_tracker.unregister(
                            os.path.join(temp_folder, filename), "file"
                        )
                    else:
                        resource_tracker.maybe_unlink(
                            os.path.join(temp_folder, filename), "file"
                        )

# When forcing clean-up, try to delete the folder even if some
                # files are still in it. Otherwise, try to delete the folder
                allow_non_empty |= force

# Clean up the folder if possible, either if it is empty or
                # if none of the files in it are in used and allow_non_empty.
                try:
                    delete_folder(
                        temp_folder, allow_non_empty=allow_non_empty
                    )
                    # Forget the folder once it has been deleted
                    self._cached_temp_folders.pop(context_id, None)
                    resource_tracker.unregister(temp_folder, "folder")

# Also cancel the finalizers  that gets triggered at gc.
                    finalizer = self._finalizers.pop(context_id, None)
                    if finalizer is not None:
                        atexit.unregister(finalizer)

except OSError:
                    # Temporary folder cannot be deleted right now.
                    # This folder will be cleaned up by an atexit
                    # finalizer registered by the memmapping_reducer.
                    pass

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