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Iliyan Angelov
2025-12-01 06:50:10 +02:00
parent 91f51bc6fe
commit 62c1fe5951
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"""Utilities for fast persistence of big data, with optional compression."""
# Author: Gael Varoquaux <gael dot varoquaux at normalesup dot org>
# Copyright (c) 2009 Gael Varoquaux
# License: BSD Style, 3 clauses.
import io
import os
import pickle
import warnings
from pathlib import Path
from .backports import make_memmap
from .compressor import (
_COMPRESSORS,
LZ4_NOT_INSTALLED_ERROR,
BinaryZlibFile,
BZ2CompressorWrapper,
GzipCompressorWrapper,
LZ4CompressorWrapper,
LZMACompressorWrapper,
XZCompressorWrapper,
ZlibCompressorWrapper,
lz4,
register_compressor,
)
# For compatibility with old versions of joblib, we need ZNDArrayWrapper
# to be visible in the current namespace.
from .numpy_pickle_compat import (
NDArrayWrapper,
ZNDArrayWrapper, # noqa: F401
load_compatibility,
)
from .numpy_pickle_utils import (
BUFFER_SIZE,
Pickler,
Unpickler,
_ensure_native_byte_order,
_read_bytes,
_reconstruct,
_validate_fileobject_and_memmap,
_write_fileobject,
)
# Register supported compressors
register_compressor("zlib", ZlibCompressorWrapper())
register_compressor("gzip", GzipCompressorWrapper())
register_compressor("bz2", BZ2CompressorWrapper())
register_compressor("lzma", LZMACompressorWrapper())
register_compressor("xz", XZCompressorWrapper())
register_compressor("lz4", LZ4CompressorWrapper())
###############################################################################
# Utility objects for persistence.
# For convenience, 16 bytes are used to be sure to cover all the possible
# dtypes' alignments. For reference, see:
# https://numpy.org/devdocs/dev/alignment.html
NUMPY_ARRAY_ALIGNMENT_BYTES = 16
class NumpyArrayWrapper(object):
"""An object to be persisted instead of numpy arrays.
This object is used to hack into the pickle machinery and read numpy
array data from our custom persistence format.
More precisely, this object is used for:
* carrying the information of the persisted array: subclass, shape, order,
dtype. Those ndarray metadata are used to correctly reconstruct the array
with low level numpy functions.
* determining if memmap is allowed on the array.
* reading the array bytes from a file.
* reading the array using memorymap from a file.
* writing the array bytes to a file.
Attributes
----------
subclass: numpy.ndarray subclass
Determine the subclass of the wrapped array.
shape: numpy.ndarray shape
Determine the shape of the wrapped array.
order: {'C', 'F'}
Determine the order of wrapped array data. 'C' is for C order, 'F' is
for fortran order.
dtype: numpy.ndarray dtype
Determine the data type of the wrapped array.
allow_mmap: bool
Determine if memory mapping is allowed on the wrapped array.
Default: False.
"""
def __init__(
self,
subclass,
shape,
order,
dtype,
allow_mmap=False,
numpy_array_alignment_bytes=NUMPY_ARRAY_ALIGNMENT_BYTES,
):
"""Constructor. Store the useful information for later."""
self.subclass = subclass
self.shape = shape
self.order = order
self.dtype = dtype
self.allow_mmap = allow_mmap
# We make numpy_array_alignment_bytes an instance attribute to allow us
# to change our mind about the default alignment and still load the old
# pickles (with the previous alignment) correctly
self.numpy_array_alignment_bytes = numpy_array_alignment_bytes
def safe_get_numpy_array_alignment_bytes(self):
# NumpyArrayWrapper instances loaded from joblib <= 1.1 pickles don't
# have an numpy_array_alignment_bytes attribute
return getattr(self, "numpy_array_alignment_bytes", None)
def write_array(self, array, pickler):
"""Write array bytes to pickler file handle.
This function is an adaptation of the numpy write_array function
available in version 1.10.1 in numpy/lib/format.py.
"""
# Set buffer size to 16 MiB to hide the Python loop overhead.
buffersize = max(16 * 1024**2 // array.itemsize, 1)
if array.dtype.hasobject:
# We contain Python objects so we cannot write out the data
# directly. Instead, we will pickle it out with version 5 of the
# pickle protocol.
pickle.dump(array, pickler.file_handle, protocol=5)
else:
numpy_array_alignment_bytes = self.safe_get_numpy_array_alignment_bytes()
if numpy_array_alignment_bytes is not None:
current_pos = pickler.file_handle.tell()
pos_after_padding_byte = current_pos + 1
padding_length = numpy_array_alignment_bytes - (
pos_after_padding_byte % numpy_array_alignment_bytes
)
# A single byte is written that contains the padding length in
# bytes
padding_length_byte = int.to_bytes(
padding_length, length=1, byteorder="little"
)
pickler.file_handle.write(padding_length_byte)
if padding_length != 0:
padding = b"\xff" * padding_length
pickler.file_handle.write(padding)
for chunk in pickler.np.nditer(
array,
flags=["external_loop", "buffered", "zerosize_ok"],
buffersize=buffersize,
order=self.order,
):
pickler.file_handle.write(chunk.tobytes("C"))
def read_array(self, unpickler, ensure_native_byte_order):
"""Read array from unpickler file handle.
This function is an adaptation of the numpy read_array function
available in version 1.10.1 in numpy/lib/format.py.
"""
if len(self.shape) == 0:
count = 1
else:
# joblib issue #859: we cast the elements of self.shape to int64 to
# prevent a potential overflow when computing their product.
shape_int64 = [unpickler.np.int64(x) for x in self.shape]
count = unpickler.np.multiply.reduce(shape_int64)
# Now read the actual data.
if self.dtype.hasobject:
# The array contained Python objects. We need to unpickle the data.
array = pickle.load(unpickler.file_handle)
else:
numpy_array_alignment_bytes = self.safe_get_numpy_array_alignment_bytes()
if numpy_array_alignment_bytes is not None:
padding_byte = unpickler.file_handle.read(1)
padding_length = int.from_bytes(padding_byte, byteorder="little")
if padding_length != 0:
unpickler.file_handle.read(padding_length)
# This is not a real file. We have to read it the
# memory-intensive way.
# crc32 module fails on reads greater than 2 ** 32 bytes,
# breaking large reads from gzip streams. Chunk reads to
# BUFFER_SIZE bytes to avoid issue and reduce memory overhead
# of the read. In non-chunked case count < max_read_count, so
# only one read is performed.
max_read_count = BUFFER_SIZE // min(BUFFER_SIZE, self.dtype.itemsize)
array = unpickler.np.empty(count, dtype=self.dtype)
for i in range(0, count, max_read_count):
read_count = min(max_read_count, count - i)
read_size = int(read_count * self.dtype.itemsize)
data = _read_bytes(unpickler.file_handle, read_size, "array data")
array[i : i + read_count] = unpickler.np.frombuffer(
data, dtype=self.dtype, count=read_count
)
del data
if self.order == "F":
array.shape = self.shape[::-1]
array = array.transpose()
else:
array.shape = self.shape
if ensure_native_byte_order:
# Detect byte order mismatch and swap as needed.
array = _ensure_native_byte_order(array)
return array
def read_mmap(self, unpickler):
"""Read an array using numpy memmap."""
current_pos = unpickler.file_handle.tell()
offset = current_pos
numpy_array_alignment_bytes = self.safe_get_numpy_array_alignment_bytes()
if numpy_array_alignment_bytes is not None:
padding_byte = unpickler.file_handle.read(1)
padding_length = int.from_bytes(padding_byte, byteorder="little")
# + 1 is for the padding byte
offset += padding_length + 1
if unpickler.mmap_mode == "w+":
unpickler.mmap_mode = "r+"
marray = make_memmap(
unpickler.filename,
dtype=self.dtype,
shape=self.shape,
order=self.order,
mode=unpickler.mmap_mode,
offset=offset,
)
# update the offset so that it corresponds to the end of the read array
unpickler.file_handle.seek(offset + marray.nbytes)
if (
numpy_array_alignment_bytes is None
and current_pos % NUMPY_ARRAY_ALIGNMENT_BYTES != 0
):
message = (
f"The memmapped array {marray} loaded from the file "
f"{unpickler.file_handle.name} is not byte aligned. "
"This may cause segmentation faults if this memmapped array "
"is used in some libraries like BLAS or PyTorch. "
"To get rid of this warning, regenerate your pickle file "
"with joblib >= 1.2.0. "
"See https://github.com/joblib/joblib/issues/563 "
"for more details"
)
warnings.warn(message)
return marray
def read(self, unpickler, ensure_native_byte_order):
"""Read the array corresponding to this wrapper.
Use the unpickler to get all information to correctly read the array.
Parameters
----------
unpickler: NumpyUnpickler
ensure_native_byte_order: bool
If true, coerce the array to use the native endianness of the
host system.
Returns
-------
array: numpy.ndarray
"""
# When requested, only use memmap mode if allowed.
if unpickler.mmap_mode is not None and self.allow_mmap:
assert not ensure_native_byte_order, (
"Memmaps cannot be coerced to a given byte order, "
"this code path is impossible."
)
array = self.read_mmap(unpickler)
else:
array = self.read_array(unpickler, ensure_native_byte_order)
# Manage array subclass case
if hasattr(array, "__array_prepare__") and self.subclass not in (
unpickler.np.ndarray,
unpickler.np.memmap,
):
# We need to reconstruct another subclass
new_array = _reconstruct(self.subclass, (0,), "b")
return new_array.__array_prepare__(array)
else:
return array
###############################################################################
# Pickler classes
class NumpyPickler(Pickler):
"""A pickler to persist big data efficiently.
The main features of this object are:
* persistence of numpy arrays in a single file.
* optional compression with a special care on avoiding memory copies.
Attributes
----------
fp: file
File object handle used for serializing the input object.
protocol: int, optional
Pickle protocol used. Default is pickle.DEFAULT_PROTOCOL.
"""
dispatch = Pickler.dispatch.copy()
def __init__(self, fp, protocol=None):
self.file_handle = fp
self.buffered = isinstance(self.file_handle, BinaryZlibFile)
# By default we want a pickle protocol that only changes with
# the major python version and not the minor one
if protocol is None:
protocol = pickle.DEFAULT_PROTOCOL
Pickler.__init__(self, self.file_handle, protocol=protocol)
# delayed import of numpy, to avoid tight coupling
try:
import numpy as np
except ImportError:
np = None
self.np = np
def _create_array_wrapper(self, array):
"""Create and returns a numpy array wrapper from a numpy array."""
order = (
"F" if (array.flags.f_contiguous and not array.flags.c_contiguous) else "C"
)
allow_mmap = not self.buffered and not array.dtype.hasobject
kwargs = {}
try:
self.file_handle.tell()
except io.UnsupportedOperation:
kwargs = {"numpy_array_alignment_bytes": None}
wrapper = NumpyArrayWrapper(
type(array),
array.shape,
order,
array.dtype,
allow_mmap=allow_mmap,
**kwargs,
)
return wrapper
def save(self, obj):
"""Subclass the Pickler `save` method.
This is a total abuse of the Pickler class in order to use the numpy
persistence function `save` instead of the default pickle
implementation. The numpy array is replaced by a custom wrapper in the
pickle persistence stack and the serialized array is written right
after in the file. Warning: the file produced does not follow the
pickle format. As such it can not be read with `pickle.load`.
"""
if self.np is not None and type(obj) in (
self.np.ndarray,
self.np.matrix,
self.np.memmap,
):
if type(obj) is self.np.memmap:
# Pickling doesn't work with memmapped arrays
obj = self.np.asanyarray(obj)
# The array wrapper is pickled instead of the real array.
wrapper = self._create_array_wrapper(obj)
Pickler.save(self, wrapper)
# A framer was introduced with pickle protocol 4 and we want to
# ensure the wrapper object is written before the numpy array
# buffer in the pickle file.
# See https://www.python.org/dev/peps/pep-3154/#framing to get
# more information on the framer behavior.
if self.proto >= 4:
self.framer.commit_frame(force=True)
# And then array bytes are written right after the wrapper.
wrapper.write_array(obj, self)
return
return Pickler.save(self, obj)
class NumpyUnpickler(Unpickler):
"""A subclass of the Unpickler to unpickle our numpy pickles.
Attributes
----------
mmap_mode: str
The memorymap mode to use for reading numpy arrays.
file_handle: file_like
File object to unpickle from.
ensure_native_byte_order: bool
If True, coerce the array to use the native endianness of the
host system.
filename: str
Name of the file to unpickle from. It should correspond to file_handle.
This parameter is required when using mmap_mode.
np: module
Reference to numpy module if numpy is installed else None.
"""
dispatch = Unpickler.dispatch.copy()
def __init__(self, filename, file_handle, ensure_native_byte_order, mmap_mode=None):
# The next line is for backward compatibility with pickle generated
# with joblib versions less than 0.10.
self._dirname = os.path.dirname(filename)
self.mmap_mode = mmap_mode
self.file_handle = file_handle
# filename is required for numpy mmap mode.
self.filename = filename
self.compat_mode = False
self.ensure_native_byte_order = ensure_native_byte_order
Unpickler.__init__(self, self.file_handle)
try:
import numpy as np
except ImportError:
np = None
self.np = np
def load_build(self):
"""Called to set the state of a newly created object.
We capture it to replace our place-holder objects, NDArrayWrapper or
NumpyArrayWrapper, by the array we are interested in. We
replace them directly in the stack of pickler.
NDArrayWrapper is used for backward compatibility with joblib <= 0.9.
"""
Unpickler.load_build(self)
# For backward compatibility, we support NDArrayWrapper objects.
if isinstance(self.stack[-1], (NDArrayWrapper, NumpyArrayWrapper)):
if self.np is None:
raise ImportError(
"Trying to unpickle an ndarray, but numpy didn't import correctly"
)
array_wrapper = self.stack.pop()
# If any NDArrayWrapper is found, we switch to compatibility mode,
# this will be used to raise a DeprecationWarning to the user at
# the end of the unpickling.
if isinstance(array_wrapper, NDArrayWrapper):
self.compat_mode = True
_array_payload = array_wrapper.read(self)
else:
_array_payload = array_wrapper.read(self, self.ensure_native_byte_order)
self.stack.append(_array_payload)
# Be careful to register our new method.
dispatch[pickle.BUILD[0]] = load_build
###############################################################################
# Utility functions
def dump(value, filename, compress=0, protocol=None):
"""Persist an arbitrary Python object into one file.
Read more in the :ref:`User Guide <persistence>`.
Parameters
----------
value: any Python object
The object to store to disk.
filename: str, pathlib.Path, or file object.
The file object or path of the file in which it is to be stored.
The compression method corresponding to one of the supported filename
extensions ('.z', '.gz', '.bz2', '.xz' or '.lzma') will be used
automatically.
compress: int from 0 to 9 or bool or 2-tuple, optional
Optional compression level for the data. 0 or False is no compression.
Higher value means more compression, but also slower read and
write times. Using a value of 3 is often a good compromise.
See the notes for more details.
If compress is True, the compression level used is 3.
If compress is a 2-tuple, the first element must correspond to a string
between supported compressors (e.g 'zlib', 'gzip', 'bz2', 'lzma'
'xz'), the second element must be an integer from 0 to 9, corresponding
to the compression level.
protocol: int, optional
Pickle protocol, see pickle.dump documentation for more details.
Returns
-------
filenames: list of strings
The list of file names in which the data is stored. If
compress is false, each array is stored in a different file.
See Also
--------
joblib.load : corresponding loader
Notes
-----
Memmapping on load cannot be used for compressed files. Thus
using compression can significantly slow down loading. In
addition, compressed files take up extra memory during
dump and load.
"""
if Path is not None and isinstance(filename, Path):
filename = str(filename)
is_filename = isinstance(filename, str)
is_fileobj = hasattr(filename, "write")
compress_method = "zlib" # zlib is the default compression method.
if compress is True:
# By default, if compress is enabled, we want the default compress
# level of the compressor.
compress_level = None
elif isinstance(compress, tuple):
# a 2-tuple was set in compress
if len(compress) != 2:
raise ValueError(
"Compress argument tuple should contain exactly 2 elements: "
"(compress method, compress level), you passed {}".format(compress)
)
compress_method, compress_level = compress
elif isinstance(compress, str):
compress_method = compress
compress_level = None # Use default compress level
compress = (compress_method, compress_level)
else:
compress_level = compress
if compress_method == "lz4" and lz4 is None:
raise ValueError(LZ4_NOT_INSTALLED_ERROR)
if (
compress_level is not None
and compress_level is not False
and compress_level not in range(10)
):
# Raising an error if a non valid compress level is given.
raise ValueError(
'Non valid compress level given: "{}". Possible values are {}.'.format(
compress_level, list(range(10))
)
)
if compress_method not in _COMPRESSORS:
# Raising an error if an unsupported compression method is given.
raise ValueError(
'Non valid compression method given: "{}". Possible values are {}.'.format(
compress_method, _COMPRESSORS
)
)
if not is_filename and not is_fileobj:
# People keep inverting arguments, and the resulting error is
# incomprehensible
raise ValueError(
"Second argument should be a filename or a file-like object, "
"%s (type %s) was given." % (filename, type(filename))
)
if is_filename and not isinstance(compress, tuple):
# In case no explicit compression was requested using both compression
# method and level in a tuple and the filename has an explicit
# extension, we select the corresponding compressor.
# unset the variable to be sure no compression level is set afterwards.
compress_method = None
for name, compressor in _COMPRESSORS.items():
if filename.endswith(compressor.extension):
compress_method = name
if compress_method in _COMPRESSORS and compress_level == 0:
# we choose the default compress_level in case it was not given
# as an argument (using compress).
compress_level = None
if compress_level != 0:
with _write_fileobject(
filename, compress=(compress_method, compress_level)
) as f:
NumpyPickler(f, protocol=protocol).dump(value)
elif is_filename:
with open(filename, "wb") as f:
NumpyPickler(f, protocol=protocol).dump(value)
else:
NumpyPickler(filename, protocol=protocol).dump(value)
# If the target container is a file object, nothing is returned.
if is_fileobj:
return
# For compatibility, the list of created filenames (e.g with one element
# after 0.10.0) is returned by default.
return [filename]
def _unpickle(fobj, ensure_native_byte_order, filename="", mmap_mode=None):
"""Internal unpickling function."""
# We are careful to open the file handle early and keep it open to
# avoid race-conditions on renames.
# That said, if data is stored in companion files, which can be
# the case with the old persistence format, moving the directory
# will create a race when joblib tries to access the companion
# files.
unpickler = NumpyUnpickler(
filename, fobj, ensure_native_byte_order, mmap_mode=mmap_mode
)
obj = None
try:
obj = unpickler.load()
if unpickler.compat_mode:
warnings.warn(
"The file '%s' has been generated with a "
"joblib version less than 0.10. "
"Please regenerate this pickle file." % filename,
DeprecationWarning,
stacklevel=3,
)
except UnicodeDecodeError as exc:
# More user-friendly error message
new_exc = ValueError(
"You may be trying to read with "
"python 3 a joblib pickle generated with python 2. "
"This feature is not supported by joblib."
)
new_exc.__cause__ = exc
raise new_exc
return obj
def load_temporary_memmap(filename, mmap_mode, unlink_on_gc_collect):
from ._memmapping_reducer import JOBLIB_MMAPS, add_maybe_unlink_finalizer
with open(filename, "rb") as f:
with _validate_fileobject_and_memmap(f, filename, mmap_mode) as (
fobj,
validated_mmap_mode,
):
# Memmap are used for interprocess communication, which should
# keep the objects untouched. We pass `ensure_native_byte_order=False`
# to remain consistent with the loading behavior of non-memmaped arrays
# in workers, where the byte order is preserved.
# Note that we do not implement endianness change for memmaps, as this
# would result in inconsistent behavior.
obj = _unpickle(
fobj,
ensure_native_byte_order=False,
filename=filename,
mmap_mode=validated_mmap_mode,
)
JOBLIB_MMAPS.add(obj.filename)
if unlink_on_gc_collect:
add_maybe_unlink_finalizer(obj)
return obj
def load(filename, mmap_mode=None, ensure_native_byte_order="auto"):
"""Reconstruct a Python object from a file persisted with joblib.dump.
Read more in the :ref:`User Guide <persistence>`.
WARNING: joblib.load relies on the pickle module and can therefore
execute arbitrary Python code. It should therefore never be used
to load files from untrusted sources.
Parameters
----------
filename: str, pathlib.Path, or file object.
The file object or path of the file from which to load the object
mmap_mode: {None, 'r+', 'r', 'w+', 'c'}, optional
If not None, the arrays are memory-mapped from the disk. This
mode has no effect for compressed files. Note that in this
case the reconstructed object might no longer match exactly
the originally pickled object.
ensure_native_byte_order: bool, or 'auto', default=='auto'
If True, ensures that the byte order of the loaded arrays matches the
native byte ordering (or _endianness_) of the host system. This is not
compatible with memory-mapped arrays and using non-null `mmap_mode`
parameter at the same time will raise an error. The default 'auto'
parameter is equivalent to True if `mmap_mode` is None, else False.
Returns
-------
result: any Python object
The object stored in the file.
See Also
--------
joblib.dump : function to save an object
Notes
-----
This function can load numpy array files saved separately during the
dump. If the mmap_mode argument is given, it is passed to np.load and
arrays are loaded as memmaps. As a consequence, the reconstructed
object might not match the original pickled object. Note that if the
file was saved with compression, the arrays cannot be memmapped.
"""
if ensure_native_byte_order == "auto":
ensure_native_byte_order = mmap_mode is None
if ensure_native_byte_order and mmap_mode is not None:
raise ValueError(
"Native byte ordering can only be enforced if 'mmap_mode' parameter "
f"is set to None, but got 'mmap_mode={mmap_mode}' instead."
)
if Path is not None and isinstance(filename, Path):
filename = str(filename)
if hasattr(filename, "read"):
fobj = filename
filename = getattr(fobj, "name", "")
with _validate_fileobject_and_memmap(fobj, filename, mmap_mode) as (fobj, _):
obj = _unpickle(fobj, ensure_native_byte_order=ensure_native_byte_order)
else:
with open(filename, "rb") as f:
with _validate_fileobject_and_memmap(f, filename, mmap_mode) as (
fobj,
validated_mmap_mode,
):
if isinstance(fobj, str):
# if the returned file object is a string, this means we
# try to load a pickle file generated with an version of
# Joblib so we load it with joblib compatibility function.
return load_compatibility(fobj)
# A memory-mapped array has to be mapped with the endianness
# it has been written with. Other arrays are coerced to the
# native endianness of the host system.
obj = _unpickle(
fobj,
ensure_native_byte_order=ensure_native_byte_order,
filename=filename,
mmap_mode=validated_mmap_mode,
)
return obj