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1"""Numpy pickle compatibility functions."""
3import inspect
4import os
5import pickle
6import zlib
7from io import BytesIO
9from .numpy_pickle_utils import (
10 _ZFILE_PREFIX,
11 Unpickler,
12 _ensure_native_byte_order,
13 _reconstruct,
14)
17def hex_str(an_int):
18 """Convert an int to an hexadecimal string."""
19 return "{:#x}".format(an_int)
22def asbytes(s):
23 if isinstance(s, bytes):
24 return s
25 return s.encode("latin1")
28_MAX_LEN = len(hex_str(2**64))
29_CHUNK_SIZE = 64 * 1024
32def read_zfile(file_handle):
33 """Read the z-file and return the content as a string.
35 Z-files are raw data compressed with zlib used internally by joblib
36 for persistence. Backward compatibility is not guaranteed. Do not
37 use for external purposes.
38 """
39 file_handle.seek(0)
40 header_length = len(_ZFILE_PREFIX) + _MAX_LEN
41 length = file_handle.read(header_length)
42 length = length[len(_ZFILE_PREFIX) :]
43 length = int(length, 16)
45 # With python2 and joblib version <= 0.8.4 compressed pickle header is one
46 # character wider so we need to ignore an additional space if present.
47 # Note: the first byte of the zlib data is guaranteed not to be a
48 # space according to
49 # https://tools.ietf.org/html/rfc6713#section-2.1
50 next_byte = file_handle.read(1)
51 if next_byte != b" ":
52 # The zlib compressed data has started and we need to go back
53 # one byte
54 file_handle.seek(header_length)
56 # We use the known length of the data to tell Zlib the size of the
57 # buffer to allocate.
58 data = zlib.decompress(file_handle.read(), 15, length)
59 assert len(data) == length, (
60 "Incorrect data length while decompressing %s."
61 "The file could be corrupted." % file_handle
62 )
63 return data
66def write_zfile(file_handle, data, compress=1):
67 """Write the data in the given file as a Z-file.
69 Z-files are raw data compressed with zlib used internally by joblib
70 for persistence. Backward compatibility is not guaranteed. Do not
71 use for external purposes.
72 """
73 file_handle.write(_ZFILE_PREFIX)
74 length = hex_str(len(data))
75 # Store the length of the data
76 file_handle.write(asbytes(length.ljust(_MAX_LEN)))
77 file_handle.write(zlib.compress(asbytes(data), compress))
80###############################################################################
81# Utility objects for persistence.
84class NDArrayWrapper(object):
85 """An object to be persisted instead of numpy arrays.
87 The only thing this object does, is to carry the filename in which
88 the array has been persisted, and the array subclass.
89 """
91 def __init__(self, filename, subclass, allow_mmap=True):
92 """Constructor. Store the useful information for later."""
93 self.filename = filename
94 self.subclass = subclass
95 self.allow_mmap = allow_mmap
97 def read(self, unpickler):
98 """Reconstruct the array."""
99 filename = os.path.join(unpickler._dirname, self.filename)
100 # Load the array from the disk
101 # use getattr instead of self.allow_mmap to ensure backward compat
102 # with NDArrayWrapper instances pickled with joblib < 0.9.0
103 allow_mmap = getattr(self, "allow_mmap", True)
104 kwargs = {}
105 if allow_mmap:
106 kwargs["mmap_mode"] = unpickler.mmap_mode
107 if "allow_pickle" in inspect.signature(unpickler.np.load).parameters:
108 # Required in numpy 1.16.3 and later to acknowledge the security
109 # risk.
110 kwargs["allow_pickle"] = True
111 array = unpickler.np.load(filename, **kwargs)
113 # Detect byte order mismatch and swap as needed.
114 array = _ensure_native_byte_order(array)
116 # Reconstruct subclasses. This does not work with old
117 # versions of numpy
118 if hasattr(array, "__array_prepare__") and self.subclass not in (
119 unpickler.np.ndarray,
120 unpickler.np.memmap,
121 ):
122 # We need to reconstruct another subclass
123 new_array = _reconstruct(self.subclass, (0,), "b")
124 return new_array.__array_prepare__(array)
125 else:
126 return array
129class ZNDArrayWrapper(NDArrayWrapper):
130 """An object to be persisted instead of numpy arrays.
132 This object store the Zfile filename in which
133 the data array has been persisted, and the meta information to
134 retrieve it.
135 The reason that we store the raw buffer data of the array and
136 the meta information, rather than array representation routine
137 (tobytes) is that it enables us to use completely the strided
138 model to avoid memory copies (a and a.T store as fast). In
139 addition saving the heavy information separately can avoid
140 creating large temporary buffers when unpickling data with
141 large arrays.
142 """
144 def __init__(self, filename, init_args, state):
145 """Constructor. Store the useful information for later."""
146 self.filename = filename
147 self.state = state
148 self.init_args = init_args
150 def read(self, unpickler):
151 """Reconstruct the array from the meta-information and the z-file."""
152 # Here we a simply reproducing the unpickling mechanism for numpy
153 # arrays
154 filename = os.path.join(unpickler._dirname, self.filename)
155 array = _reconstruct(*self.init_args)
156 with open(filename, "rb") as f:
157 data = read_zfile(f)
158 state = self.state + (data,)
159 array.__setstate__(state)
160 return array
163class ZipNumpyUnpickler(Unpickler):
164 """A subclass of the Unpickler to unpickle our numpy pickles."""
166 dispatch = Unpickler.dispatch.copy()
168 def __init__(self, filename, file_handle, mmap_mode=None):
169 """Constructor."""
170 self._filename = os.path.basename(filename)
171 self._dirname = os.path.dirname(filename)
172 self.mmap_mode = mmap_mode
173 self.file_handle = self._open_pickle(file_handle)
174 Unpickler.__init__(self, self.file_handle)
175 try:
176 import numpy as np
177 except ImportError:
178 np = None
179 self.np = np
181 def _open_pickle(self, file_handle):
182 return BytesIO(read_zfile(file_handle))
184 def load_build(self):
185 """Set the state of a newly created object.
187 We capture it to replace our place-holder objects,
188 NDArrayWrapper, by the array we are interested in. We
189 replace them directly in the stack of pickler.
190 """
191 Unpickler.load_build(self)
192 if isinstance(self.stack[-1], NDArrayWrapper):
193 if self.np is None:
194 raise ImportError(
195 "Trying to unpickle an ndarray, but numpy didn't import correctly"
196 )
197 nd_array_wrapper = self.stack.pop()
198 array = nd_array_wrapper.read(self)
199 self.stack.append(array)
201 dispatch[pickle.BUILD[0]] = load_build
204# More user-friendly error message
205def readPython2Error(exc):
206 new_exc = ValueError(
207 "You may be trying to read with "
208 "python 3 a joblib pickle generated with python 2. "
209 "This feature is not supported by joblib."
210 )
211 new_exc.__cause__ = exc
212 return new_exc
215def load_compatibility(filename):
216 """Reconstruct a Python object from a file persisted with joblib.dump.
218 This function ensures the compatibility with joblib old persistence format
219 (<= 0.9.3).
221 Parameters
222 ----------
223 filename: string
224 The name of the file from which to load the object
226 Returns
227 -------
228 result: any Python object
229 The object stored in the file.
231 See Also
232 --------
233 joblib.dump : function to save an object
235 Notes
236 -----
238 This function can load numpy array files saved separately during the
239 dump.
240 """
241 with open(filename, "rb") as file_handle:
242 # We are careful to open the file handle early and keep it open to
243 # avoid race-conditions on renames. That said, if data is stored in
244 # companion files, moving the directory will create a race when
245 # joblib tries to access the companion files.
246 unpickler = ZipNumpyUnpickler(filename, file_handle=file_handle)
247 try:
248 obj = unpickler.load()
249 except UnicodeDecodeError as exc:
250 raise readPython2Error(exc)
251 finally:
252 if hasattr(unpickler, "file_handle"):
253 unpickler.file_handle.close()
254 return obj