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268 lines
10 KiB
Python
268 lines
10 KiB
Python
"""
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Fast cryptographic hash of Python objects, with a special case for fast
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hashing of numpy arrays.
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"""
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# Author: Gael Varoquaux <gael dot varoquaux at normalesup dot org>
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# Copyright (c) 2009 Gael Varoquaux
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# License: BSD Style, 3 clauses.
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import pickle
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import hashlib
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import sys
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import types
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import struct
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import io
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import decimal
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from ._compat import _bytes_or_unicode, PY3_OR_LATER
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if PY3_OR_LATER:
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Pickler = pickle._Pickler
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else:
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Pickler = pickle.Pickler
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class _ConsistentSet(object):
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""" Class used to ensure the hash of Sets is preserved
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whatever the order of its items.
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"""
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def __init__(self, set_sequence):
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# Forces order of elements in set to ensure consistent hash.
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try:
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# Trying first to order the set assuming the type of elements is
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# consistent and orderable.
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# This fails on python 3 when elements are unorderable
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# but we keep it in a try as it's faster.
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self._sequence = sorted(set_sequence)
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except (TypeError, decimal.InvalidOperation):
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# If elements are unorderable, sorting them using their hash.
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# This is slower but works in any case.
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self._sequence = sorted((hash(e) for e in set_sequence))
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class _MyHash(object):
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""" Class used to hash objects that won't normally pickle """
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def __init__(self, *args):
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self.args = args
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class Hasher(Pickler):
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""" A subclass of pickler, to do cryptographic hashing, rather than
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pickling.
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"""
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def __init__(self, hash_name='md5'):
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self.stream = io.BytesIO()
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# By default we want a pickle protocol that only changes with
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# the major python version and not the minor one
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protocol = 3 if PY3_OR_LATER else 2
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Pickler.__init__(self, self.stream, protocol=protocol)
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# Initialise the hash obj
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self._hash = hashlib.new(hash_name)
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def hash(self, obj, return_digest=True):
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try:
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self.dump(obj)
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except pickle.PicklingError as e:
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e.args += ('PicklingError while hashing %r: %r' % (obj, e),)
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raise
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dumps = self.stream.getvalue()
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self._hash.update(dumps)
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if return_digest:
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return self._hash.hexdigest()
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def save(self, obj):
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if isinstance(obj, (types.MethodType, type({}.pop))):
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# the Pickler cannot pickle instance methods; here we decompose
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# them into components that make them uniquely identifiable
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if hasattr(obj, '__func__'):
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func_name = obj.__func__.__name__
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else:
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func_name = obj.__name__
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inst = obj.__self__
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if type(inst) == type(pickle):
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obj = _MyHash(func_name, inst.__name__)
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elif inst is None:
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# type(None) or type(module) do not pickle
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obj = _MyHash(func_name, inst)
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else:
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cls = obj.__self__.__class__
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obj = _MyHash(func_name, inst, cls)
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Pickler.save(self, obj)
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def memoize(self, obj):
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# We want hashing to be sensitive to value instead of reference.
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# For example we want ['aa', 'aa'] and ['aa', 'aaZ'[:2]]
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# to hash to the same value and that's why we disable memoization
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# for strings
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if isinstance(obj, _bytes_or_unicode):
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return
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Pickler.memoize(self, obj)
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# The dispatch table of the pickler is not accessible in Python
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# 3, as these lines are only bugware for IPython, we skip them.
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def save_global(self, obj, name=None, pack=struct.pack):
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# We have to override this method in order to deal with objects
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# defined interactively in IPython that are not injected in
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# __main__
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kwargs = dict(name=name, pack=pack)
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if sys.version_info >= (3, 4):
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del kwargs['pack']
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try:
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Pickler.save_global(self, obj, **kwargs)
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except pickle.PicklingError:
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Pickler.save_global(self, obj, **kwargs)
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module = getattr(obj, "__module__", None)
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if module == '__main__':
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my_name = name
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if my_name is None:
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my_name = obj.__name__
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mod = sys.modules[module]
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if not hasattr(mod, my_name):
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# IPython doesn't inject the variables define
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# interactively in __main__
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setattr(mod, my_name, obj)
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dispatch = Pickler.dispatch.copy()
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# builtin
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dispatch[type(len)] = save_global
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# type
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dispatch[type(object)] = save_global
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# classobj
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dispatch[type(Pickler)] = save_global
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# function
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dispatch[type(pickle.dump)] = save_global
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def _batch_setitems(self, items):
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# forces order of keys in dict to ensure consistent hash.
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try:
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# Trying first to compare dict assuming the type of keys is
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# consistent and orderable.
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# This fails on python 3 when keys are unorderable
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# but we keep it in a try as it's faster.
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Pickler._batch_setitems(self, iter(sorted(items)))
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except TypeError:
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# If keys are unorderable, sorting them using their hash. This is
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# slower but works in any case.
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Pickler._batch_setitems(self, iter(sorted((hash(k), v)
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for k, v in items)))
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def save_set(self, set_items):
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# forces order of items in Set to ensure consistent hash
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Pickler.save(self, _ConsistentSet(set_items))
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dispatch[type(set())] = save_set
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class NumpyHasher(Hasher):
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""" Special case the hasher for when numpy is loaded.
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"""
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def __init__(self, hash_name='md5', coerce_mmap=False):
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"""
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Parameters
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----------
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hash_name: string
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The hash algorithm to be used
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coerce_mmap: boolean
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Make no difference between np.memmap and np.ndarray
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objects.
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"""
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self.coerce_mmap = coerce_mmap
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Hasher.__init__(self, hash_name=hash_name)
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# delayed import of numpy, to avoid tight coupling
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import numpy as np
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self.np = np
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if hasattr(np, 'getbuffer'):
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self._getbuffer = np.getbuffer
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else:
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self._getbuffer = memoryview
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def save(self, obj):
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""" Subclass the save method, to hash ndarray subclass, rather
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than pickling them. Off course, this is a total abuse of
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the Pickler class.
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"""
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if isinstance(obj, self.np.ndarray) and not obj.dtype.hasobject:
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# Compute a hash of the object
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# The update function of the hash requires a c_contiguous buffer.
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if obj.shape == ():
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# 0d arrays need to be flattened because viewing them as bytes
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# raises a ValueError exception.
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obj_c_contiguous = obj.flatten()
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elif obj.flags.c_contiguous:
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obj_c_contiguous = obj
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elif obj.flags.f_contiguous:
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obj_c_contiguous = obj.T
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else:
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# Cater for non-single-segment arrays: this creates a
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# copy, and thus aleviates this issue.
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# XXX: There might be a more efficient way of doing this
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obj_c_contiguous = obj.flatten()
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# memoryview is not supported for some dtypes, e.g. datetime64, see
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# https://github.com/numpy/numpy/issues/4983. The
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# workaround is to view the array as bytes before
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# taking the memoryview.
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self._hash.update(
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self._getbuffer(obj_c_contiguous.view(self.np.uint8)))
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# We store the class, to be able to distinguish between
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# Objects with the same binary content, but different
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# classes.
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if self.coerce_mmap and isinstance(obj, self.np.memmap):
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# We don't make the difference between memmap and
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# normal ndarrays, to be able to reload previously
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# computed results with memmap.
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klass = self.np.ndarray
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else:
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klass = obj.__class__
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# We also return the dtype and the shape, to distinguish
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# different views on the same data with different dtypes.
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# The object will be pickled by the pickler hashed at the end.
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obj = (klass, ('HASHED', obj.dtype, obj.shape, obj.strides))
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elif isinstance(obj, self.np.dtype):
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# Atomic dtype objects are interned by their default constructor:
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# np.dtype('f8') is np.dtype('f8')
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# This interning is not maintained by a
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# pickle.loads + pickle.dumps cycle, because __reduce__
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# uses copy=True in the dtype constructor. This
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# non-deterministic behavior causes the internal memoizer
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# of the hasher to generate different hash values
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# depending on the history of the dtype object.
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# To prevent the hash from being sensitive to this, we use
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# .descr which is a full (and never interned) description of
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# the array dtype according to the numpy doc.
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klass = obj.__class__
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obj = (klass, ('HASHED', obj.descr))
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Hasher.save(self, obj)
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def hash(obj, hash_name='md5', coerce_mmap=False):
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""" Quick calculation of a hash to identify uniquely Python objects
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containing numpy arrays.
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Parameters
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-----------
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hash_name: 'md5' or 'sha1'
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Hashing algorithm used. sha1 is supposedly safer, but md5 is
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faster.
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coerce_mmap: boolean
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Make no difference between np.memmap and np.ndarray
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"""
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valid_hash_names = ('md5', 'sha1')
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if hash_name not in valid_hash_names:
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raise ValueError("Valid options for 'hash_name' are {}. "
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"Got hash_name={!r} instead."
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.format(valid_hash_names, hash_name))
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if 'numpy' in sys.modules:
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hasher = NumpyHasher(hash_name=hash_name, coerce_mmap=coerce_mmap)
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else:
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hasher = Hasher(hash_name=hash_name)
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return hasher.hash(obj)
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