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[SPARK-32094][PYTHON] Update cloudpickle to v1.5.0

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### What changes were proposed in this pull request?

This PR aims to upgrade PySpark's embedded cloudpickle to the latest cloudpickle v1.5.0 (See https://github.com/cloudpipe/cloudpickle/blob/v1.5.0/cloudpickle/cloudpickle.py)

### Why are the changes needed?

There are many bug fixes. For example, the bug described in the JIRA:

dill unpickling fails because they define `types.ClassType`, which is undefined in dill. This results in the following error:

```
Traceback (most recent call last):
  File "/usr/local/lib/python3.6/site-packages/apache_beam/internal/pickler.py", line 279, in loads
    return dill.loads(s)
  File "/usr/local/lib/python3.6/site-packages/dill/_dill.py", line 317, in loads
    return load(file, ignore)
  File "/usr/local/lib/python3.6/site-packages/dill/_dill.py", line 305, in load
    obj = pik.load()
  File "/usr/local/lib/python3.6/site-packages/dill/_dill.py", line 577, in _load_type
    return _reverse_typemap[name]
KeyError: 'ClassType'
```

See also https://github.com/cloudpipe/cloudpickle/issues/82. This was fixed for cloudpickle 1.3.0+ (https://github.com/cloudpipe/cloudpickle/pull/337), but PySpark's cloudpickle.py doesn't have this change yet.

More notably, now it supports C pickle implementation with Python 3.8 which hugely improve performance. This is already adopted in another project such as Ray.

### Does this PR introduce _any_ user-facing change?

Yes, as described above, the bug fixes. Internally, users also could leverage the fast cloudpickle backed by C pickle.

### How was this patch tested?

Jenkins will test it out.

Closes #29114 from HyukjinKwon/SPARK-32094.

Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
This commit is contained in:
HyukjinKwon 2020-07-17 11:49:18 +09:00
parent 9747e8fc9d
commit ea9e8f365a
9 changed files with 1602 additions and 1366 deletions
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2
LICENSE
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@ -229,7 +229,7 @@ BSD 3-Clause
------------
python/lib/py4j-*-src.zip
python/pyspark/cloudpickle.py
python/pyspark/cloudpickle/*.py
python/pyspark/join.py
core/src/main/resources/org/apache/spark/ui/static/d3.min.js

4
dev/.rat-excludes
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@ -47,8 +47,8 @@ jsonFormatter.min.js
.*json
.*data
.*log
pyspark-coverage-site/
cloudpickle.py
pyspark-coverage-site/*
cloudpickle/*
heapq3.py
join.py
SparkExprTyper.scala

2
dev/tox.ini
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@ -16,4 +16,4 @@
[pycodestyle]
ignore=E226,E241,E305,E402,E722,E731,E741,W503,W504
max-line-length=100
exclude=cloudpickle.py,heapq3.py,shared.py,python/docs/conf.py,work/*/*.py,python/.eggs/*,dist/*,.git/*
exclude=python/pyspark/cloudpickle/*.py,heapq3.py,shared.py,python/docs/conf.py,work/*/*.py,python/.eggs/*,dist/*,.git/*

1362
python/pyspark/cloudpickle.py
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File diff suppressed because it is too large Load diff

7
python/pyspark/cloudpickle/__init__.py Normal file
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@ -0,0 +1,7 @@
from __future__ import absolute_import
from pyspark.cloudpickle.cloudpickle import * # noqa
from pyspark.cloudpickle.cloudpickle_fast import CloudPickler, dumps, dump # noqa
__version__ = '1.5.0'

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python/pyspark/cloudpickle/cloudpickle.py Normal file
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@ -0,0 +1,830 @@
"""
This class is defined to override standard pickle functionality
The goals of it follow:
-Serialize lambdas and nested functions to compiled byte code
-Deal with main module correctly
-Deal with other non-serializable objects
It does not include an unpickler, as standard python unpickling suffices.
This module was extracted from the `cloud` package, developed by `PiCloud, Inc.
<https://web.archive.org/web/20140626004012/http://www.picloud.com/>`_.
Copyright (c) 2012, Regents of the University of California.
Copyright (c) 2009 `PiCloud, Inc. <https://web.archive.org/web/20140626004012/http://www.picloud.com/>`_.
All rights reserved.
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions
are met:
* Redistributions of source code must retain the above copyright
notice, this list of conditions and the following disclaimer.
* Redistributions in binary form must reproduce the above copyright
notice, this list of conditions and the following disclaimer in the
documentation and/or other materials provided with the distribution.
* Neither the name of the University of California, Berkeley nor the
names of its contributors may be used to endorse or promote
products derived from this software without specific prior written
permission.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
"AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED
TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
"""
from __future__ import print_function
import builtins
import dis
import opcode
import platform
import sys
import types
import weakref
import uuid
import threading
import typing
import warnings
from .compat import pickle
from typing import Generic, Union, Tuple, Callable
from pickle import _getattribute
from importlib._bootstrap import _find_spec
try: # pragma: no branch
import typing_extensions as _typing_extensions
from typing_extensions import Literal, Final
except ImportError:
_typing_extensions = Literal = Final = None
if sys.version_info >= (3, 5, 3):
from typing import ClassVar
else: # pragma: no cover
ClassVar = None
if sys.version_info >= (3, 8):
from types import CellType
else:
def f():
a = 1
def g():
return a
return g
CellType = type(f().__closure__[0])
# cloudpickle is meant for inter process communication: we expect all
# communicating processes to run the same Python version hence we favor
# communication speed over compatibility:
DEFAULT_PROTOCOL = pickle.HIGHEST_PROTOCOL
# Track the provenance of reconstructed dynamic classes to make it possible to
# recontruct instances from the matching singleton class definition when
# appropriate and preserve the usual "isinstance" semantics of Python objects.
_DYNAMIC_CLASS_TRACKER_BY_CLASS = weakref.WeakKeyDictionary()
_DYNAMIC_CLASS_TRACKER_BY_ID = weakref.WeakValueDictionary()
_DYNAMIC_CLASS_TRACKER_LOCK = threading.Lock()
PYPY = platform.python_implementation() == "PyPy"
builtin_code_type = None
if PYPY:
# builtin-code objects only exist in pypy
builtin_code_type = type(float.__new__.__code__)
_extract_code_globals_cache = weakref.WeakKeyDictionary()
def _get_or_create_tracker_id(class_def):
with _DYNAMIC_CLASS_TRACKER_LOCK:
class_tracker_id = _DYNAMIC_CLASS_TRACKER_BY_CLASS.get(class_def)
if class_tracker_id is None:
class_tracker_id = uuid.uuid4().hex
_DYNAMIC_CLASS_TRACKER_BY_CLASS[class_def] = class_tracker_id
_DYNAMIC_CLASS_TRACKER_BY_ID[class_tracker_id] = class_def
return class_tracker_id
def _lookup_class_or_track(class_tracker_id, class_def):
if class_tracker_id is not None:
with _DYNAMIC_CLASS_TRACKER_LOCK:
class_def = _DYNAMIC_CLASS_TRACKER_BY_ID.setdefault(
class_tracker_id, class_def)
_DYNAMIC_CLASS_TRACKER_BY_CLASS[class_def] = class_tracker_id
return class_def
def _whichmodule(obj, name):
"""Find the module an object belongs to.
This function differs from ``pickle.whichmodule`` in two ways:
- it does not mangle the cases where obj's module is __main__ and obj was
not found in any module.
- Errors arising during module introspection are ignored, as those errors
are considered unwanted side effects.
"""
if sys.version_info[:2] < (3, 7) and isinstance(obj, typing.TypeVar): # pragma: no branch # noqa
# Workaround bug in old Python versions: prior to Python 3.7,
# T.__module__ would always be set to "typing" even when the TypeVar T
# would be defined in a different module.
#
# For such older Python versions, we ignore the __module__ attribute of
# TypeVar instances and instead exhaustively lookup those instances in
# all currently imported modules.
module_name = None
else:
module_name = getattr(obj, '__module__', None)
if module_name is not None:
return module_name
# Protect the iteration by using a copy of sys.modules against dynamic
# modules that trigger imports of other modules upon calls to getattr or
# other threads importing at the same time.
for module_name, module in sys.modules.copy().items():
# Some modules such as coverage can inject non-module objects inside
# sys.modules
if (
module_name == '__main__' or
module is None or
not isinstance(module, types.ModuleType)
):
continue
try:
if _getattribute(module, name)[0] is obj:
return module_name
except Exception:
pass
return None
def _is_importable(obj, name=None):
"""Dispatcher utility to test the importability of various constructs."""
if isinstance(obj, types.FunctionType):
return _lookup_module_and_qualname(obj, name=name) is not None
elif issubclass(type(obj), type):
return _lookup_module_and_qualname(obj, name=name) is not None
elif isinstance(obj, types.ModuleType):
# We assume that sys.modules is primarily used as a cache mechanism for
# the Python import machinery. Checking if a module has been added in
# is sys.modules therefore a cheap and simple heuristic to tell us whether
# we can assume that a given module could be imported by name in
# another Python process.
return obj.__name__ in sys.modules
else:
raise TypeError(
"cannot check importability of {} instances".format(
type(obj).__name__)
)
def _lookup_module_and_qualname(obj, name=None):
if name is None:
name = getattr(obj, '__qualname__', None)
if name is None: # pragma: no cover
# This used to be needed for Python 2.7 support but is probably not
# needed anymore. However we keep the __name__ introspection in case
# users of cloudpickle rely on this old behavior for unknown reasons.
name = getattr(obj, '__name__', None)
module_name = _whichmodule(obj, name)
if module_name is None:
# In this case, obj.__module__ is None AND obj was not found in any
# imported module. obj is thus treated as dynamic.
return None
if module_name == "__main__":
return None
# Note: if module_name is in sys.modules, the corresponding module is
# assumed importable at unpickling time. See #357
module = sys.modules.get(module_name, None)
if module is None:
# The main reason why obj's module would not be imported is that this
# module has been dynamically created, using for example
# types.ModuleType. The other possibility is that module was removed
# from sys.modules after obj was created/imported. But this case is not
# supported, as the standard pickle does not support it either.
return None
try:
obj2, parent = _getattribute(module, name)
except AttributeError:
# obj was not found inside the module it points to
return None
if obj2 is not obj:
return None
return module, name
def _extract_code_globals(co):
"""
Find all globals names read or written to by codeblock co
"""
out_names = _extract_code_globals_cache.get(co)
if out_names is None:
names = co.co_names
out_names = {names[oparg] for _, oparg in _walk_global_ops(co)}
# Declaring a function inside another one using the "def ..."
# syntax generates a constant code object corresonding to the one
# of the nested function's As the nested function may itself need
# global variables, we need to introspect its code, extract its
# globals, (look for code object in it's co_consts attribute..) and
# add the result to code_globals
if co.co_consts:
for const in co.co_consts:
if isinstance(const, types.CodeType):
out_names |= _extract_code_globals(const)
_extract_code_globals_cache[co] = out_names
return out_names
def _find_imported_submodules(code, top_level_dependencies):
"""
Find currently imported submodules used by a function.
Submodules used by a function need to be detected and referenced for the
function to work correctly at depickling time. Because submodules can be
referenced as attribute of their parent package (``package.submodule``), we
need a special introspection technique that does not rely on GLOBAL-related
opcodes to find references of them in a code object.
Example:
```
import concurrent.futures
import cloudpickle
def func():
x = concurrent.futures.ThreadPoolExecutor
if __name__ == '__main__':
cloudpickle.dumps(func)
```
The globals extracted by cloudpickle in the function's state include the
concurrent package, but not its submodule (here, concurrent.futures), which
is the module used by func. Find_imported_submodules will detect the usage
of concurrent.futures. Saving this module alongside with func will ensure
that calling func once depickled does not fail due to concurrent.futures
not being imported
"""
subimports = []
# check if any known dependency is an imported package
for x in top_level_dependencies:
if (isinstance(x, types.ModuleType) and
hasattr(x, '__package__') and x.__package__):
# check if the package has any currently loaded sub-imports
prefix = x.__name__ + '.'
# A concurrent thread could mutate sys.modules,
# make sure we iterate over a copy to avoid exceptions
for name in list(sys.modules):
# Older versions of pytest will add a "None" module to
# sys.modules.
if name is not None and name.startswith(prefix):
# check whether the function can address the sub-module
tokens = set(name[len(prefix):].split('.'))
if not tokens - set(code.co_names):
subimports.append(sys.modules[name])
return subimports
def cell_set(cell, value):
"""Set the value of a closure cell.
The point of this function is to set the cell_contents attribute of a cell
after its creation. This operation is necessary in case the cell contains a
reference to the function the cell belongs to, as when calling the
function's constructor
``f = types.FunctionType(code, globals, name, argdefs, closure)``,
closure will not be able to contain the yet-to-be-created f.
In Python3.7, cell_contents is writeable, so setting the contents of a cell
can be done simply using
>>> cell.cell_contents = value
In earlier Python3 versions, the cell_contents attribute of a cell is read
only, but this limitation can be worked around by leveraging the Python 3
``nonlocal`` keyword.
In Python2 however, this attribute is read only, and there is no
``nonlocal`` keyword. For this reason, we need to come up with more
complicated hacks to set this attribute.
The chosen approach is to create a function with a STORE_DEREF opcode,
which sets the content of a closure variable. Typically:
>>> def inner(value):
... lambda: cell # the lambda makes cell a closure
... cell = value # cell is a closure, so this triggers a STORE_DEREF
(Note that in Python2, A STORE_DEREF can never be triggered from an inner
function. The function g for example here
>>> def f(var):
... def g():
... var += 1
... return g
will not modify the closure variable ``var```inplace, but instead try to
load a local variable var and increment it. As g does not assign the local
variable ``var`` any initial value, calling f(1)() will fail at runtime.)
Our objective is to set the value of a given cell ``cell``. So we need to
somewhat reference our ``cell`` object into the ``inner`` function so that
this object (and not the smoke cell of the lambda function) gets affected
by the STORE_DEREF operation.
In inner, ``cell`` is referenced as a cell variable (an enclosing variable
that is referenced by the inner function). If we create a new function
cell_set with the exact same code as ``inner``, but with ``cell`` marked as
a free variable instead, the STORE_DEREF will be applied on its closure -
``cell``, which we can specify explicitly during construction! The new
cell_set variable thus actually sets the contents of a specified cell!
Note: we do not make use of the ``nonlocal`` keyword to set the contents of
a cell in early python3 versions to limit possible syntax errors in case
test and checker libraries decide to parse the whole file.
"""
if sys.version_info[:2] >= (3, 7): # pragma: no branch
cell.cell_contents = value
else:
_cell_set = types.FunctionType(
_cell_set_template_code, {}, '_cell_set', (), (cell,),)
_cell_set(value)
def _make_cell_set_template_code():
def _cell_set_factory(value):
lambda: cell
cell = value
co = _cell_set_factory.__code__
_cell_set_template_code = types.CodeType(
co.co_argcount,
co.co_kwonlyargcount, # Python 3 only argument
co.co_nlocals,
co.co_stacksize,
co.co_flags,
co.co_code,
co.co_consts,
co.co_names,
co.co_varnames,
co.co_filename,
co.co_name,
co.co_firstlineno,
co.co_lnotab,
co.co_cellvars, # co_freevars is initialized with co_cellvars
(), # co_cellvars is made empty
)
return _cell_set_template_code
if sys.version_info[:2] < (3, 7):
_cell_set_template_code = _make_cell_set_template_code()
# relevant opcodes
STORE_GLOBAL = opcode.opmap['STORE_GLOBAL']
DELETE_GLOBAL = opcode.opmap['DELETE_GLOBAL']
LOAD_GLOBAL = opcode.opmap['LOAD_GLOBAL']
GLOBAL_OPS = (STORE_GLOBAL, DELETE_GLOBAL, LOAD_GLOBAL)
HAVE_ARGUMENT = dis.HAVE_ARGUMENT
EXTENDED_ARG = dis.EXTENDED_ARG
_BUILTIN_TYPE_NAMES = {}
for k, v in types.__dict__.items():
if type(v) is type:
_BUILTIN_TYPE_NAMES[v] = k
def _builtin_type(name):
if name == "ClassType": # pragma: no cover
# Backward compat to load pickle files generated with cloudpickle
# < 1.3 even if loading pickle files from older versions is not
# officially supported.
return type
return getattr(types, name)
def _walk_global_ops(code):
"""
Yield (opcode, argument number) tuples for all
global-referencing instructions in *code*.
"""
for instr in dis.get_instructions(code):
op = instr.opcode
if op in GLOBAL_OPS:
yield op, instr.arg
def _extract_class_dict(cls):
"""Retrieve a copy of the dict of a class without the inherited methods"""
clsdict = dict(cls.__dict__) # copy dict proxy to a dict
if len(cls.__bases__) == 1:
inherited_dict = cls.__bases__[0].__dict__
else:
inherited_dict = {}
for base in reversed(cls.__bases__):
inherited_dict.update(base.__dict__)
to_remove = []
for name, value in clsdict.items():
try:
base_value = inherited_dict[name]
if value is base_value:
to_remove.append(name)
except KeyError:
pass
for name in to_remove:
clsdict.pop(name)
return clsdict
if sys.version_info[:2] < (3, 7): # pragma: no branch
def _is_parametrized_type_hint(obj):
# This is very cheap but might generate false positives.
# general typing Constructs
is_typing = getattr(obj, '__origin__', None) is not None
# typing_extensions.Literal
is_litteral = getattr(obj, '__values__', None) is not None
# typing_extensions.Final
is_final = getattr(obj, '__type__', None) is not None
# typing.Union/Tuple for old Python 3.5
is_union = getattr(obj, '__union_params__', None) is not None
is_tuple = getattr(obj, '__tuple_params__', None) is not None
is_callable = (
getattr(obj, '__result__', None) is not None and
getattr(obj, '__args__', None) is not None
)
return any((is_typing, is_litteral, is_final, is_union, is_tuple,
is_callable))
def _create_parametrized_type_hint(origin, args):
return origin[args]
else:
_is_parametrized_type_hint = None
_create_parametrized_type_hint = None
def parametrized_type_hint_getinitargs(obj):
# The distorted type check sematic for typing construct becomes:
# ``type(obj) is type(TypeHint)``, which means "obj is a
# parametrized TypeHint"
if type(obj) is type(Literal): # pragma: no branch
initargs = (Literal, obj.__values__)
elif type(obj) is type(Final): # pragma: no branch
initargs = (Final, obj.__type__)
elif type(obj) is type(ClassVar):
initargs = (ClassVar, obj.__type__)
elif type(obj) is type(Generic):
parameters = obj.__parameters__
if len(obj.__parameters__) > 0:
# in early Python 3.5, __parameters__ was sometimes
# preferred to __args__
initargs = (obj.__origin__, parameters)
else:
initargs = (obj.__origin__, obj.__args__)
elif type(obj) is type(Union):
if sys.version_info < (3, 5, 3): # pragma: no cover
initargs = (Union, obj.__union_params__)
else:
initargs = (Union, obj.__args__)
elif type(obj) is type(Tuple):
if sys.version_info < (3, 5, 3): # pragma: no cover
initargs = (Tuple, obj.__tuple_params__)
else:
initargs = (Tuple, obj.__args__)
elif type(obj) is type(Callable):
if sys.version_info < (3, 5, 3): # pragma: no cover
args = obj.__args__
result = obj.__result__
if args != Ellipsis:
if isinstance(args, tuple):
args = list(args)
else:
args = [args]
else:
(*args, result) = obj.__args__
if len(args) == 1 and args[0] is Ellipsis:
args = Ellipsis
else:
args = list(args)
initargs = (Callable, (args, result))
else: # pragma: no cover
raise pickle.PicklingError(
"Cloudpickle Error: Unknown type {}".format(type(obj))
)
return initargs
# Tornado support
def is_tornado_coroutine(func):
"""
Return whether *func* is a Tornado coroutine function.
Running coroutines are not supported.
"""
if 'tornado.gen' not in sys.modules:
return False
gen = sys.modules['tornado.gen']
if not hasattr(gen, "is_coroutine_function"):
# Tornado version is too old
return False
return gen.is_coroutine_function(func)
def _rebuild_tornado_coroutine(func):
from tornado import gen
return gen.coroutine(func)
# including pickles unloading functions in this namespace
load = pickle.load
loads = pickle.loads
# hack for __import__ not working as desired
def subimport(name):
__import__(name)
return sys.modules[name]
def dynamic_subimport(name, vars):
mod = types.ModuleType(name)
mod.__dict__.update(vars)
mod.__dict__['__builtins__'] = builtins.__dict__
return mod
def _gen_ellipsis():
return Ellipsis
def _gen_not_implemented():
return NotImplemented
def _get_cell_contents(cell):
try:
return cell.cell_contents
except ValueError:
# sentinel used by ``_fill_function`` which will leave the cell empty
return _empty_cell_value
def instance(cls):
"""Create a new instance of a class.
Parameters
----------
cls : type
The class to create an instance of.
Returns
-------
instance : cls
A new instance of ``cls``.
"""
return cls()
@instance
class _empty_cell_value(object):
"""sentinel for empty closures
"""
@classmethod
def __reduce__(cls):
return cls.__name__
def _fill_function(*args):
"""Fills in the rest of function data into the skeleton function object
The skeleton itself is create by _make_skel_func().
"""
if len(args) == 2:
func = args[0]
state = args[1]
elif len(args) == 5:
# Backwards compat for cloudpickle v0.4.0, after which the `module`
# argument was introduced
func = args[0]
keys = ['globals', 'defaults', 'dict', 'closure_values']
state = dict(zip(keys, args[1:]))
elif len(args) == 6:
# Backwards compat for cloudpickle v0.4.1, after which the function
# state was passed as a dict to the _fill_function it-self.
func = args[0]
keys = ['globals', 'defaults', 'dict', 'module', 'closure_values']
state = dict(zip(keys, args[1:]))
else:
raise ValueError('Unexpected _fill_value arguments: %r' % (args,))
# - At pickling time, any dynamic global variable used by func is
# serialized by value (in state['globals']).
# - At unpickling time, func's __globals__ attribute is initialized by
# first retrieving an empty isolated namespace that will be shared
# with other functions pickled from the same original module
# by the same CloudPickler instance and then updated with the
# content of state['globals'] to populate the shared isolated
# namespace with all the global variables that are specifically
# referenced for this function.
func.__globals__.update(state['globals'])
func.__defaults__ = state['defaults']
func.__dict__ = state['dict']
if 'annotations' in state:
func.__annotations__ = state['annotations']
if 'doc' in state:
func.__doc__ = state['doc']
if 'name' in state:
func.__name__ = state['name']
if 'module' in state:
func.__module__ = state['module']
if 'qualname' in state:
func.__qualname__ = state['qualname']
if 'kwdefaults' in state:
func.__kwdefaults__ = state['kwdefaults']
# _cloudpickle_subimports is a set of submodules that must be loaded for
# the pickled function to work correctly at unpickling time. Now that these
# submodules are depickled (hence imported), they can be removed from the
# object's state (the object state only served as a reference holder to
# these submodules)
if '_cloudpickle_submodules' in state:
state.pop('_cloudpickle_submodules')
cells = func.__closure__
if cells is not None:
for cell, value in zip(cells, state['closure_values']):
if value is not _empty_cell_value:
cell_set(cell, value)
return func
def _make_empty_cell():
if False:
# trick the compiler into creating an empty cell in our lambda
cell = None
raise AssertionError('this route should not be executed')
return (lambda: cell).__closure__[0]
def _make_cell(value=_empty_cell_value):
cell = _make_empty_cell()
if value is not _empty_cell_value:
cell_set(cell, value)
return cell
def _make_skel_func(code, cell_count, base_globals=None):
""" Creates a skeleton function object that contains just the provided
code and the correct number of cells in func_closure. All other
func attributes (e.g. func_globals) are empty.
"""
# This function is deprecated and should be removed in cloudpickle 1.7
warnings.warn(
"A pickle file created using an old (<=1.4.1) version of cloudpicke "
"is currently being loaded. This is not supported by cloudpickle and "
"will break in cloudpickle 1.7", category=UserWarning
)
# This is backward-compatibility code: for cloudpickle versions between
# 0.5.4 and 0.7, base_globals could be a string or None. base_globals
# should now always be a dictionary.
if base_globals is None or isinstance(base_globals, str):
base_globals = {}
base_globals['__builtins__'] = __builtins__
closure = (
tuple(_make_empty_cell() for _ in range(cell_count))
if cell_count >= 0 else
None
)
return types.FunctionType(code, base_globals, None, None, closure)
def _make_skeleton_class(type_constructor, name, bases, type_kwargs,
class_tracker_id, extra):
"""Build dynamic class with an empty __dict__ to be filled once memoized
If class_tracker_id is not None, try to lookup an existing class definition
matching that id. If none is found, track a newly reconstructed class
definition under that id so that other instances stemming from the same
class id will also reuse this class definition.
The "extra" variable is meant to be a dict (or None) that can be used for
forward compatibility shall the need arise.
"""
skeleton_class = types.new_class(
name, bases, {'metaclass': type_constructor},
lambda ns: ns.update(type_kwargs)
)
return _lookup_class_or_track(class_tracker_id, skeleton_class)
def _rehydrate_skeleton_class(skeleton_class, class_dict):
"""Put attributes from `class_dict` back on `skeleton_class`.
See CloudPickler.save_dynamic_class for more info.
"""
registry = None
for attrname, attr in class_dict.items():
if attrname == "_abc_impl":
registry = attr
else:
setattr(skeleton_class, attrname, attr)
if registry is not None:
for subclass in registry:
skeleton_class.register(subclass)
return skeleton_class
def _make_skeleton_enum(bases, name, qualname, members, module,
class_tracker_id, extra):
"""Build dynamic enum with an empty __dict__ to be filled once memoized
The creation of the enum class is inspired by the code of
EnumMeta._create_.
If class_tracker_id is not None, try to lookup an existing enum definition
matching that id. If none is found, track a newly reconstructed enum
definition under that id so that other instances stemming from the same
class id will also reuse this enum definition.
The "extra" variable is meant to be a dict (or None) that can be used for
forward compatibility shall the need arise.
"""
# enums always inherit from their base Enum class at the last position in
# the list of base classes:
enum_base = bases[-1]
metacls = enum_base.__class__
classdict = metacls.__prepare__(name, bases)
for member_name, member_value in members.items():
classdict[member_name] = member_value
enum_class = metacls.__new__(metacls, name, bases, classdict)
enum_class.__module__ = module
enum_class.__qualname__ = qualname
return _lookup_class_or_track(class_tracker_id, enum_class)
def _make_typevar(name, bound, constraints, covariant, contravariant,
class_tracker_id):
tv = typing.TypeVar(
name, *constraints, bound=bound,
covariant=covariant, contravariant=contravariant
)
if class_tracker_id is not None:
return _lookup_class_or_track(class_tracker_id, tv)
else: # pragma: nocover
# Only for Python 3.5.3 compat.
return tv
def _decompose_typevar(obj):
try:
class_tracker_id = _get_or_create_tracker_id(obj)
except TypeError: # pragma: nocover
# TypeVar instances are not weakref-able in Python 3.5.3
class_tracker_id = None
return (
obj.__name__, obj.__bound__, obj.__constraints__,
obj.__covariant__, obj.__contravariant__,
class_tracker_id,
)
def _typevar_reduce(obj):
# TypeVar instances have no __qualname__ hence we pass the name explicitly.
module_and_name = _lookup_module_and_qualname(obj, name=obj.__name__)
if module_and_name is None:
return (_make_typevar, _decompose_typevar(obj))
return (getattr, module_and_name)
def _get_bases(typ):
if hasattr(typ, '__orig_bases__'):
# For generic types (see PEP 560)
bases_attr = '__orig_bases__'
else:
# For regular class objects
bases_attr = '__bases__'
return getattr(typ, bases_attr)

747
python/pyspark/cloudpickle/cloudpickle_fast.py Normal file
View file

@ -0,0 +1,747 @@
"""
New, fast version of the CloudPickler.
This new CloudPickler class can now extend the fast C Pickler instead of the
previous Python implementation of the Pickler class. Because this functionality
is only available for Python versions 3.8+, a lot of backward-compatibility
code is also removed.
Note that the C Pickler sublassing API is CPython-specific. Therefore, some
guards present in cloudpickle.py that were written to handle PyPy specificities
are not present in cloudpickle_fast.py
"""
import abc
import copyreg
import io
import itertools
import logging
import sys
import struct
import types
import weakref
import typing
from enum import Enum
from collections import ChainMap
from .compat import pickle, Pickler
from .cloudpickle import (
_extract_code_globals, _BUILTIN_TYPE_NAMES, DEFAULT_PROTOCOL,
_find_imported_submodules, _get_cell_contents, _is_importable,
_builtin_type, _get_or_create_tracker_id, _make_skeleton_class,
_make_skeleton_enum, _extract_class_dict, dynamic_subimport, subimport,
_typevar_reduce, _get_bases, _make_cell, _make_empty_cell, CellType,
_is_parametrized_type_hint, PYPY, cell_set,
parametrized_type_hint_getinitargs, _create_parametrized_type_hint,
builtin_code_type
)
if pickle.HIGHEST_PROTOCOL >= 5 and not PYPY:
# Shorthands similar to pickle.dump/pickle.dumps
def dump(obj, file, protocol=None, buffer_callback=None):
"""Serialize obj as bytes streamed into file
protocol defaults to cloudpickle.DEFAULT_PROTOCOL which is an alias to
pickle.HIGHEST_PROTOCOL. This setting favors maximum communication
speed between processes running the same Python version.
Set protocol=pickle.DEFAULT_PROTOCOL instead if you need to ensure
compatibility with older versions of Python.
"""
CloudPickler(
file, protocol=protocol, buffer_callback=buffer_callback
).dump(obj)
def dumps(obj, protocol=None, buffer_callback=None):
"""Serialize obj as a string of bytes allocated in memory
protocol defaults to cloudpickle.DEFAULT_PROTOCOL which is an alias to
pickle.HIGHEST_PROTOCOL. This setting favors maximum communication
speed between processes running the same Python version.
Set protocol=pickle.DEFAULT_PROTOCOL instead if you need to ensure
compatibility with older versions of Python.
"""
with io.BytesIO() as file:
cp = CloudPickler(
file, protocol=protocol, buffer_callback=buffer_callback
)
cp.dump(obj)
return file.getvalue()
else:
# Shorthands similar to pickle.dump/pickle.dumps
def dump(obj, file, protocol=None):
"""Serialize obj as bytes streamed into file
protocol defaults to cloudpickle.DEFAULT_PROTOCOL which is an alias to
pickle.HIGHEST_PROTOCOL. This setting favors maximum communication
speed between processes running the same Python version.
Set protocol=pickle.DEFAULT_PROTOCOL instead if you need to ensure
compatibility with older versions of Python.
"""
CloudPickler(file, protocol=protocol).dump(obj)
def dumps(obj, protocol=None):
"""Serialize obj as a string of bytes allocated in memory
protocol defaults to cloudpickle.DEFAULT_PROTOCOL which is an alias to
pickle.HIGHEST_PROTOCOL. This setting favors maximum communication
speed between processes running the same Python version.
Set protocol=pickle.DEFAULT_PROTOCOL instead if you need to ensure
compatibility with older versions of Python.
"""
with io.BytesIO() as file:
cp = CloudPickler(file, protocol=protocol)
cp.dump(obj)
return file.getvalue()
load, loads = pickle.load, pickle.loads
# COLLECTION OF OBJECTS __getnewargs__-LIKE METHODS
# -------------------------------------------------
def _class_getnewargs(obj):
type_kwargs = {}
if "__slots__" in obj.__dict__:
type_kwargs["__slots__"] = obj.__slots__
__dict__ = obj.__dict__.get('__dict__', None)
if isinstance(__dict__, property):
type_kwargs['__dict__'] = __dict__
return (type(obj), obj.__name__, _get_bases(obj), type_kwargs,
_get_or_create_tracker_id(obj), None)
def _enum_getnewargs(obj):
members = dict((e.name, e.value) for e in obj)
return (obj.__bases__, obj.__name__, obj.__qualname__, members,
obj.__module__, _get_or_create_tracker_id(obj), None)
# COLLECTION OF OBJECTS RECONSTRUCTORS
# ------------------------------------
def _file_reconstructor(retval):
return retval
# COLLECTION OF OBJECTS STATE GETTERS
# -----------------------------------
def _function_getstate(func):
# - Put func's dynamic attributes (stored in func.__dict__) in state. These
# attributes will be restored at unpickling time using
# f.__dict__.update(state)
# - Put func's members into slotstate. Such attributes will be restored at
# unpickling time by iterating over slotstate and calling setattr(func,
# slotname, slotvalue)
slotstate = {
"__name__": func.__name__,
"__qualname__": func.__qualname__,
"__annotations__": func.__annotations__,
"__kwdefaults__": func.__kwdefaults__,
"__defaults__": func.__defaults__,
"__module__": func.__module__,
"__doc__": func.__doc__,
"__closure__": func.__closure__,
}
f_globals_ref = _extract_code_globals(func.__code__)
f_globals = {k: func.__globals__[k] for k in f_globals_ref if k in
func.__globals__}
closure_values = (
list(map(_get_cell_contents, func.__closure__))
if func.__closure__ is not None else ()
)
# Extract currently-imported submodules used by func. Storing these modules
# in a smoke _cloudpickle_subimports attribute of the object's state will
# trigger the side effect of importing these modules at unpickling time
# (which is necessary for func to work correctly once depickled)
slotstate["_cloudpickle_submodules"] = _find_imported_submodules(
func.__code__, itertools.chain(f_globals.values(), closure_values))
slotstate["__globals__"] = f_globals
state = func.__dict__
return state, slotstate
def _class_getstate(obj):
clsdict = _extract_class_dict(obj)
clsdict.pop('__weakref__', None)
if issubclass(type(obj), abc.ABCMeta):
# If obj is an instance of an ABCMeta subclass, dont pickle the
# cache/negative caches populated during isinstance/issubclass
# checks, but pickle the list of registered subclasses of obj.
clsdict.pop('_abc_cache', None)
clsdict.pop('_abc_negative_cache', None)
clsdict.pop('_abc_negative_cache_version', None)
registry = clsdict.pop('_abc_registry', None)
if registry is None:
# in Python3.7+, the abc caches and registered subclasses of a
# class are bundled into the single _abc_impl attribute
clsdict.pop('_abc_impl', None)
(registry, _, _, _) = abc._get_dump(obj)
clsdict["_abc_impl"] = [subclass_weakref()
for subclass_weakref in registry]
else:
# In the above if clause, registry is a set of weakrefs -- in
# this case, registry is a WeakSet
clsdict["_abc_impl"] = [type_ for type_ in registry]
if "__slots__" in clsdict:
# pickle string length optimization: member descriptors of obj are
# created automatically from obj's __slots__ attribute, no need to
# save them in obj's state
if isinstance(obj.__slots__, str):
clsdict.pop(obj.__slots__)
else:
for k in obj.__slots__:
clsdict.pop(k, None)
clsdict.pop('__dict__', None) # unpicklable property object
return (clsdict, {})
def _enum_getstate(obj):
clsdict, slotstate = _class_getstate(obj)
members = dict((e.name, e.value) for e in obj)
# Cleanup the clsdict that will be passed to _rehydrate_skeleton_class:
# Those attributes are already handled by the metaclass.
for attrname in ["_generate_next_value_", "_member_names_",
"_member_map_", "_member_type_",
"_value2member_map_"]:
clsdict.pop(attrname, None)
for member in members:
clsdict.pop(member)
# Special handling of Enum subclasses
return clsdict, slotstate
# COLLECTIONS OF OBJECTS REDUCERS
# -------------------------------
# A reducer is a function taking a single argument (obj), and that returns a
# tuple with all the necessary data to re-construct obj. Apart from a few
# exceptions (list, dict, bytes, int, etc.), a reducer is necessary to
# correctly pickle an object.
# While many built-in objects (Exceptions objects, instances of the "object"
# class, etc), are shipped with their own built-in reducer (invoked using
# obj.__reduce__), some do not. The following methods were created to "fill
# these holes".
def _code_reduce(obj):
"""codeobject reducer"""
if hasattr(obj, "co_posonlyargcount"): # pragma: no branch
args = (
obj.co_argcount, obj.co_posonlyargcount,
obj.co_kwonlyargcount, obj.co_nlocals, obj.co_stacksize,
obj.co_flags, obj.co_code, obj.co_consts, obj.co_names,
obj.co_varnames, obj.co_filename, obj.co_name,
obj.co_firstlineno, obj.co_lnotab, obj.co_freevars,
obj.co_cellvars
)
else:
args = (
obj.co_argcount, obj.co_kwonlyargcount, obj.co_nlocals,
obj.co_stacksize, obj.co_flags, obj.co_code, obj.co_consts,
obj.co_names, obj.co_varnames, obj.co_filename,
obj.co_name, obj.co_firstlineno, obj.co_lnotab,
obj.co_freevars, obj.co_cellvars
)
return types.CodeType, args
def _cell_reduce(obj):
"""Cell (containing values of a function's free variables) reducer"""
try:
obj.cell_contents
except ValueError: # cell is empty
return _make_empty_cell, ()
else:
return _make_cell, (obj.cell_contents, )
def _classmethod_reduce(obj):
orig_func = obj.__func__
return type(obj), (orig_func,)
def _file_reduce(obj):
"""Save a file"""
import io
if not hasattr(obj, "name") or not hasattr(obj, "mode"):
raise pickle.PicklingError(
"Cannot pickle files that do not map to an actual file"
)
if obj is sys.stdout:
return getattr, (sys, "stdout")
if obj is sys.stderr:
return getattr, (sys, "stderr")
if obj is sys.stdin:
raise pickle.PicklingError("Cannot pickle standard input")
if obj.closed:
raise pickle.PicklingError("Cannot pickle closed files")
if hasattr(obj, "isatty") and obj.isatty():
raise pickle.PicklingError(
"Cannot pickle files that map to tty objects"
)
if "r" not in obj.mode and "+" not in obj.mode:
raise pickle.PicklingError(
"Cannot pickle files that are not opened for reading: %s"
% obj.mode
)
name = obj.name
retval = io.StringIO()
try:
# Read the whole file
curloc = obj.tell()
obj.seek(0)
contents = obj.read()
obj.seek(curloc)
except IOError as e:
raise pickle.PicklingError(
"Cannot pickle file %s as it cannot be read" % name
) from e
retval.write(contents)
retval.seek(curloc)
retval.name = name
return _file_reconstructor, (retval,)
def _getset_descriptor_reduce(obj):
return getattr, (obj.__objclass__, obj.__name__)
def _mappingproxy_reduce(obj):
return types.MappingProxyType, (dict(obj),)
def _memoryview_reduce(obj):
return bytes, (obj.tobytes(),)
def _module_reduce(obj):
if _is_importable(obj):
return subimport, (obj.__name__,)
else:
obj.__dict__.pop('__builtins__', None)
return dynamic_subimport, (obj.__name__, vars(obj))
def _method_reduce(obj):
return (types.MethodType, (obj.__func__, obj.__self__))
def _logger_reduce(obj):
return logging.getLogger, (obj.name,)
def _root_logger_reduce(obj):
return logging.getLogger, ()
def _property_reduce(obj):
return property, (obj.fget, obj.fset, obj.fdel, obj.__doc__)
def _weakset_reduce(obj):
return weakref.WeakSet, (list(obj),)
def _dynamic_class_reduce(obj):
"""
Save a class that can't be stored as module global.
This method is used to serialize classes that are defined inside
functions, or that otherwise can't be serialized as attribute lookups
from global modules.
"""
if Enum is not None and issubclass(obj, Enum):
return (
_make_skeleton_enum, _enum_getnewargs(obj), _enum_getstate(obj),
None, None, _class_setstate
)
else:
return (
_make_skeleton_class, _class_getnewargs(obj), _class_getstate(obj),
None, None, _class_setstate
)
def _class_reduce(obj):
"""Select the reducer depending on the dynamic nature of the class obj"""
if obj is type(None): # noqa
return type, (None,)
elif obj is type(Ellipsis):
return type, (Ellipsis,)
elif obj is type(NotImplemented):
return type, (NotImplemented,)
elif obj in _BUILTIN_TYPE_NAMES:
return _builtin_type, (_BUILTIN_TYPE_NAMES[obj],)
elif not _is_importable(obj):
return _dynamic_class_reduce(obj)
return NotImplemented
# COLLECTIONS OF OBJECTS STATE SETTERS
# ------------------------------------
# state setters are called at unpickling time, once the object is created and
# it has to be updated to how it was at unpickling time.
def _function_setstate(obj, state):
"""Update the state of a dynaamic function.
As __closure__ and __globals__ are readonly attributes of a function, we
cannot rely on the native setstate routine of pickle.load_build, that calls
setattr on items of the slotstate. Instead, we have to modify them inplace.
"""
state, slotstate = state
obj.__dict__.update(state)
obj_globals = slotstate.pop("__globals__")
obj_closure = slotstate.pop("__closure__")
# _cloudpickle_subimports is a set of submodules that must be loaded for
# the pickled function to work correctly at unpickling time. Now that these
# submodules are depickled (hence imported), they can be removed from the
# object's state (the object state only served as a reference holder to
# these submodules)
slotstate.pop("_cloudpickle_submodules")
obj.__globals__.update(obj_globals)
obj.__globals__["__builtins__"] = __builtins__
if obj_closure is not None:
for i, cell in enumerate(obj_closure):
try:
value = cell.cell_contents
except ValueError: # cell is empty
continue
cell_set(obj.__closure__[i], value)
for k, v in slotstate.items():
setattr(obj, k, v)
def _class_setstate(obj, state):
state, slotstate = state
registry = None
for attrname, attr in state.items():
if attrname == "_abc_impl":
registry = attr
else:
setattr(obj, attrname, attr)
if registry is not None:
for subclass in registry:
obj.register(subclass)
return obj
class CloudPickler(Pickler):
# set of reducers defined and used by cloudpickle (private)
_dispatch_table = {}
_dispatch_table[classmethod] = _classmethod_reduce
_dispatch_table[io.TextIOWrapper] = _file_reduce
_dispatch_table[logging.Logger] = _logger_reduce
_dispatch_table[logging.RootLogger] = _root_logger_reduce
_dispatch_table[memoryview] = _memoryview_reduce
_dispatch_table[property] = _property_reduce
_dispatch_table[staticmethod] = _classmethod_reduce
_dispatch_table[CellType] = _cell_reduce
_dispatch_table[types.CodeType] = _code_reduce
_dispatch_table[types.GetSetDescriptorType] = _getset_descriptor_reduce
_dispatch_table[types.ModuleType] = _module_reduce
_dispatch_table[types.MethodType] = _method_reduce
_dispatch_table[types.MappingProxyType] = _mappingproxy_reduce
_dispatch_table[weakref.WeakSet] = _weakset_reduce
_dispatch_table[typing.TypeVar] = _typevar_reduce
dispatch_table = ChainMap(_dispatch_table, copyreg.dispatch_table)
# function reducers are defined as instance methods of CloudPickler
# objects, as they rely on a CloudPickler attribute (globals_ref)
def _dynamic_function_reduce(self, func):
"""Reduce a function that is not pickleable via attribute lookup."""
newargs = self._function_getnewargs(func)
state = _function_getstate(func)
return (types.FunctionType, newargs, state, None, None,
_function_setstate)
def _function_reduce(self, obj):
"""Reducer for function objects.
If obj is a top-level attribute of a file-backed module, this
reducer returns NotImplemented, making the CloudPickler fallback to
traditional _pickle.Pickler routines to save obj. Otherwise, it reduces
obj using a custom cloudpickle reducer designed specifically to handle
dynamic functions.
As opposed to cloudpickle.py, There no special handling for builtin
pypy functions because cloudpickle_fast is CPython-specific.
"""
if _is_importable(obj):
return NotImplemented
else:
return self._dynamic_function_reduce(obj)
def _function_getnewargs(self, func):
code = func.__code__
# base_globals represents the future global namespace of func at
# unpickling time. Looking it up and storing it in
# CloudpiPickler.globals_ref allow functions sharing the same globals
# at pickling time to also share them once unpickled, at one condition:
# since globals_ref is an attribute of a CloudPickler instance, and
# that a new CloudPickler is created each time pickle.dump or
# pickle.dumps is called, functions also need to be saved within the
# same invocation of cloudpickle.dump/cloudpickle.dumps (for example:
# cloudpickle.dumps([f1, f2])). There is no such limitation when using
# CloudPickler.dump, as long as the multiple invocations are bound to
# the same CloudPickler.
base_globals = self.globals_ref.setdefault(id(func.__globals__), {})
if base_globals == {}:
# Add module attributes used to resolve relative imports
# instructions inside func.
for k in ["__package__", "__name__", "__path__", "__file__"]:
if k in func.__globals__:
base_globals[k] = func.__globals__[k]
# Do not bind the free variables before the function is created to
# avoid infinite recursion.
if func.__closure__ is None:
closure = None
else:
closure = tuple(
_make_empty_cell() for _ in range(len(code.co_freevars)))
return code, base_globals, None, None, closure
def dump(self, obj):
try:
return Pickler.dump(self, obj)
except RuntimeError as e:
if "recursion" in e.args[0]:
msg = (
"Could not pickle object as excessively deep recursion "
"required."
)
raise pickle.PicklingError(msg) from e
else:
raise
if pickle.HIGHEST_PROTOCOL >= 5:
# `CloudPickler.dispatch` is only left for backward compatibility - note
# that when using protocol 5, `CloudPickler.dispatch` is not an
# extension of `Pickler.dispatch` dictionary, because CloudPickler
# subclasses the C-implemented Pickler, which does not expose a
# `dispatch` attribute. Earlier versions of the protocol 5 CloudPickler
# used `CloudPickler.dispatch` as a class-level attribute storing all
# reducers implemented by cloudpickle, but the attribute name was not a
# great choice given the meaning of `Cloudpickler.dispatch` when
# `CloudPickler` extends the pure-python pickler.
dispatch = dispatch_table
# Implementation of the reducer_override callback, in order to
# efficiently serialize dynamic functions and classes by subclassing
# the C-implemented Pickler.
# TODO: decorrelate reducer_override (which is tied to CPython's
# implementation - would it make sense to backport it to pypy? - and
# pickle's protocol 5 which is implementation agnostic. Currently, the
# availability of both notions coincide on CPython's pickle and the
# pickle5 backport, but it may not be the case anymore when pypy
# implements protocol 5
def __init__(self, file, protocol=None, buffer_callback=None):
if protocol is None:
protocol = DEFAULT_PROTOCOL
Pickler.__init__(
self, file, protocol=protocol, buffer_callback=buffer_callback
)
# map functions __globals__ attribute ids, to ensure that functions
# sharing the same global namespace at pickling time also share
# their global namespace at unpickling time.
self.globals_ref = {}
self.proto = int(protocol)
def reducer_override(self, obj):
"""Type-agnostic reducing callback for function and classes.
For performance reasons, subclasses of the C _pickle.Pickler class
cannot register custom reducers for functions and classes in the
dispatch_table. Reducer for such types must instead implemented in
the special reducer_override method.
Note that method will be called for any object except a few
builtin-types (int, lists, dicts etc.), which differs from reducers
in the Pickler's dispatch_table, each of them being invoked for
objects of a specific type only.
This property comes in handy for classes: although most classes are
instances of the ``type`` metaclass, some of them can be instances
of other custom metaclasses (such as enum.EnumMeta for example). In
particular, the metaclass will likely not be known in advance, and
thus cannot be special-cased using an entry in the dispatch_table.
reducer_override, among other things, allows us to register a
reducer that will be called for any class, independently of its
type.
Notes:
* reducer_override has the priority over dispatch_table-registered
reducers.
* reducer_override can be used to fix other limitations of
cloudpickle for other types that suffered from type-specific
reducers, such as Exceptions. See
https://github.com/cloudpipe/cloudpickle/issues/248
"""
if sys.version_info[:2] < (3, 7) and _is_parametrized_type_hint(obj): # noqa # pragma: no branch
return (
_create_parametrized_type_hint,
parametrized_type_hint_getinitargs(obj)
)
t = type(obj)
try:
is_anyclass = issubclass(t, type)
except TypeError: # t is not a class (old Boost; see SF #502085)
is_anyclass = False
if is_anyclass:
return _class_reduce(obj)
elif isinstance(obj, types.FunctionType):
return self._function_reduce(obj)
else:
# fallback to save_global, including the Pickler's
# distpatch_table
return NotImplemented
else:
# When reducer_override is not available, hack the pure-Python
# Pickler's types.FunctionType and type savers. Note: the type saver
# must override Pickler.save_global, because pickle.py contains a
# hard-coded call to save_global when pickling meta-classes.
dispatch = Pickler.dispatch.copy()
def __init__(self, file, protocol=None):
if protocol is None:
protocol = DEFAULT_PROTOCOL
Pickler.__init__(self, file, protocol=protocol)
# map functions __globals__ attribute ids, to ensure that functions
# sharing the same global namespace at pickling time also share
# their global namespace at unpickling time.
self.globals_ref = {}
assert hasattr(self, 'proto')
def _save_reduce_pickle5(self, func, args, state=None, listitems=None,
dictitems=None, state_setter=None, obj=None):
save = self.save
write = self.write
self.save_reduce(
func, args, state=None, listitems=listitems,
dictitems=dictitems, obj=obj
)
# backport of the Python 3.8 state_setter pickle operations
save(state_setter)
save(obj) # simple BINGET opcode as obj is already memoized.
save(state)
write(pickle.TUPLE2)
# Trigger a state_setter(obj, state) function call.
write(pickle.REDUCE)
# The purpose of state_setter is to carry-out an
# inplace modification of obj. We do not care about what the
# method might return, so its output is eventually removed from
# the stack.
write(pickle.POP)
def save_global(self, obj, name=None, pack=struct.pack):
"""
Save a "global".
The name of this method is somewhat misleading: all types get
dispatched here.
"""
if obj is type(None): # noqa
return self.save_reduce(type, (None,), obj=obj)
elif obj is type(Ellipsis):
return self.save_reduce(type, (Ellipsis,), obj=obj)
elif obj is type(NotImplemented):
return self.save_reduce(type, (NotImplemented,), obj=obj)
elif obj in _BUILTIN_TYPE_NAMES:
return self.save_reduce(
_builtin_type, (_BUILTIN_TYPE_NAMES[obj],), obj=obj)
if sys.version_info[:2] < (3, 7) and _is_parametrized_type_hint(obj): # noqa # pragma: no branch
# Parametrized typing constructs in Python < 3.7 are not
# compatible with type checks and ``isinstance`` semantics. For
# this reason, it is easier to detect them using a
# duck-typing-based check (``_is_parametrized_type_hint``) than
# to populate the Pickler's dispatch with type-specific savers.
self.save_reduce(
_create_parametrized_type_hint,
parametrized_type_hint_getinitargs(obj),
obj=obj
)
elif name is not None:
Pickler.save_global(self, obj, name=name)
elif not _is_importable(obj, name=name):
self._save_reduce_pickle5(*_dynamic_class_reduce(obj), obj=obj)
else:
Pickler.save_global(self, obj, name=name)
dispatch[type] = save_global
def save_function(self, obj, name=None):
""" Registered with the dispatch to handle all function types.
Determines what kind of function obj is (e.g. lambda, defined at
interactive prompt, etc) and handles the pickling appropriately.
"""
if _is_importable(obj, name=name):
return Pickler.save_global(self, obj, name=name)
elif PYPY and isinstance(obj.__code__, builtin_code_type):
return self.save_pypy_builtin_func(obj)
else:
return self._save_reduce_pickle5(
*self._dynamic_function_reduce(obj), obj=obj
)
def save_pypy_builtin_func(self, obj):
"""Save pypy equivalent of builtin functions.
PyPy does not have the concept of builtin-functions. Instead,
builtin-functions are simple function instances, but with a
builtin-code attribute.
Most of the time, builtin functions should be pickled by attribute.
But PyPy has flaky support for __qualname__, so some builtin
functions such as float.__new__ will be classified as dynamic. For
this reason only, we created this special routine. Because
builtin-functions are not expected to have closure or globals,
there is no additional hack (compared the one already implemented
in pickle) to protect ourselves from reference cycles. A simple
(reconstructor, newargs, obj.__dict__) tuple is save_reduced. Note
also that PyPy improved their support for __qualname__ in v3.6, so
this routing should be removed when cloudpickle supports only PyPy
3.6 and later.
"""
rv = (types.FunctionType, (obj.__code__, {}, obj.__name__,
obj.__defaults__, obj.__closure__),
obj.__dict__)
self.save_reduce(*rv, obj=obj)
dispatch[types.FunctionType] = save_function

13
python/pyspark/cloudpickle/compat.py Normal file
View file

@ -0,0 +1,13 @@
import sys
if sys.version_info < (3, 8):
try:
import pickle5 as pickle # noqa: F401
from pickle5 import Pickler # noqa: F401
except ImportError:
import pickle # noqa: F401
from pickle import _Pickler as Pickler # noqa: F401
else:
import pickle # noqa: F401
from _pickle import Pickler # noqa: F401

1
python/setup.py
View file

@ -161,6 +161,7 @@ try:
author_email='dev@spark.apache.org',
url='https://github.com/apache/spark/tree/master/python',
packages=['pyspark',
'pyspark.cloudpickle',
'pyspark.mllib',
'pyspark.mllib.linalg',
'pyspark.mllib.stat',