hpython-0.1: test/files/joblib.py
"""
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 dis
from functools import partial
import imp
import io
import itertools
import logging
import opcode
import operator
import pickle
import struct
import sys
import traceback
import types
import weakref
# 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
if sys.version < '3':
from pickle import Pickler
try:
from cStringIO import StringIO
except ImportError:
from StringIO import StringIO
PY3 = False
else:
types.ClassType = type
from pickle import _Pickler as Pickler
from io import BytesIO as StringIO
PY3 = True
def _make_cell_set_template_code():
"""Get the Python compiler to emit LOAD_FAST(arg); STORE_DEREF
Notes
-----
In Python 3, we could use an easier function:
.. code-block:: python
def f():
cell = None
def _stub(value):
nonlocal cell
cell = value
return _stub
_cell_set_template_code = f()
This function is _only_ a LOAD_FAST(arg); STORE_DEREF, but that is
invalid syntax on Python 2. If we use this function we also don't need
to do the weird freevars/cellvars swap below
"""
def inner(value):
lambda: cell # make ``cell`` a closure so that we get a STORE_DEREF
cell = value
co = inner.__code__
# NOTE: we are marking the cell variable as a free variable intentionally
# so that we simulate an inner function instead of the outer function. This
# is what gives us the ``nonlocal`` behavior in a Python 2 compatible way.
if not PY3:
return types.CodeType(
co.co_argcount,
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, # this is the trickery
(),
)
else:
return types.CodeType(
co.co_argcount,
co.co_kwonlyargcount,
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, # this is the trickery
(),
)
_cell_set_template_code = _make_cell_set_template_code()
def cell_set(cell, value):
"""Set the value of a closure cell.
"""
return types.FunctionType(
_cell_set_template_code,
{},
'_cell_set_inner',
(),
(cell,),
)(value)
#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
def islambda(func):
return getattr(func,'__name__') == '<lambda>'
_BUILTIN_TYPE_NAMES = {}
for k, v in types.__dict__.items():
if type(v) is type:
_BUILTIN_TYPE_NAMES[v] = k
def _builtin_type(name):
return getattr(types, name)
def _make__new__factory(type_):
def _factory():
return type_.__new__
return _factory
# NOTE: These need to be module globals so that they're pickleable as globals.
_get_dict_new = _make__new__factory(dict)
_get_frozenset_new = _make__new__factory(frozenset)
_get_list_new = _make__new__factory(list)
_get_set_new = _make__new__factory(set)
_get_tuple_new = _make__new__factory(tuple)
_get_object_new = _make__new__factory(object)
# Pre-defined set of builtin_function_or_method instances that can be
# serialized.
_BUILTIN_TYPE_CONSTRUCTORS = {
dict.__new__: _get_dict_new,
frozenset.__new__: _get_frozenset_new,
set.__new__: _get_set_new,
list.__new__: _get_list_new,
tuple.__new__: _get_tuple_new,
object.__new__: _get_object_new,
}
if sys.version_info < (3, 4):
def _walk_global_ops(code):
"""
Yield (opcode, argument number) tuples for all
global-referencing instructions in *code*.
"""
code = getattr(code, 'co_code', b'')
if not PY3:
code = map(ord, code)
n = len(code)
i = 0
extended_arg = 0
while i < n:
op = code[i]
i += 1
if op >= HAVE_ARGUMENT:
oparg = code[i] + code[i + 1] * 256 + extended_arg
extended_arg = 0
i += 2
if op == EXTENDED_ARG:
extended_arg = oparg * 65536
if op in GLOBAL_OPS:
yield op, oparg
else:
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
class CloudPickler(Pickler):
dispatch = Pickler.dispatch.copy()
def __init__(self, file, protocol=None):
if protocol is None:
protocol = DEFAULT_PROTOCOL
Pickler.__init__(self, file, protocol=protocol)
# set of modules to unpickle
self.modules = set()
# map ids to dictionary. used to ensure that functions can share global env
self.globals_ref = {}
def dump(self, obj):
self.inject_addons()
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)
def save_memoryview(self, obj):
self.save(obj.tobytes())
dispatch[memoryview] = save_memoryview
if not PY3:
def save_buffer(self, obj):
self.save(str(obj))
dispatch[buffer] = save_buffer
def save_unsupported(self, obj):
raise pickle.PicklingError("Cannot pickle objects of type %s" % type(obj))
dispatch[types.GeneratorType] = save_unsupported
# itertools objects do not pickle!
for v in itertools.__dict__.values():
if type(v) is type:
dispatch[v] = save_unsupported
def save_module(self, obj):
"""
Save a module as an import
"""
mod_name = obj.__name__
# If module is successfully found then it is not a dynamically created module
if hasattr(obj, '__file__'):
is_dynamic = False
else:
try:
_find_module(mod_name)
is_dynamic = False
except ImportError:
is_dynamic = True
self.modules.add(obj)
if is_dynamic:
self.save_reduce(dynamic_subimport, (obj.__name__, vars(obj)), obj=obj)
else:
self.save_reduce(subimport, (obj.__name__,), obj=obj)
dispatch[types.ModuleType] = save_module
def save_codeobject(self, obj):
"""
Save a code object
"""
if PY3:
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
)
else:
args = (
obj.co_argcount, 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
)
self.save_reduce(types.CodeType, args, obj=obj)
dispatch[types.CodeType] = save_codeobject
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 obj in _BUILTIN_TYPE_CONSTRUCTORS:
# We keep a special-cased cache of built-in type constructors at
# global scope, because these functions are structured very
# differently in different python versions and implementations (for
# example, they're instances of types.BuiltinFunctionType in
# CPython, but they're ordinary types.FunctionType instances in
# PyPy).
#
# If the function we've received is in that cache, we just
# serialize it as a lookup into the cache.
return self.save_reduce(_BUILTIN_TYPE_CONSTRUCTORS[obj], (), obj=obj)
write = self.write
if name is None:
name = obj.__name__
try:
# whichmodule() could fail, see
# https://bitbucket.org/gutworth/six/issues/63/importing-six-breaks-pickling
modname = pickle.whichmodule(obj, name)
except Exception:
modname = None
# print('which gives %s %s %s' % (modname, obj, name))
try:
themodule = sys.modules[modname]
except KeyError:
# eval'd items such as namedtuple give invalid items for their function __module__
modname = '__main__'
if modname == '__main__':
themodule = None
try:
lookedup_by_name = getattr(themodule, name, None)
except Exception:
lookedup_by_name = None
if themodule:
self.modules.add(themodule)
if lookedup_by_name is obj:
return self.save_global(obj, name)
# a builtin_function_or_method which comes in as an attribute of some
# object (e.g., itertools.chain.from_iterable) will end
# up with modname "__main__" and so end up here. But these functions
# have no __code__ attribute in CPython, so the handling for
# user-defined functions below will fail.
# So we pickle them here using save_reduce; have to do it differently
# for different python versions.
if not hasattr(obj, '__code__'):
if PY3:
rv = obj.__reduce_ex__(self.proto)
else:
if hasattr(obj, '__self__'):
rv = (getattr, (obj.__self__, name))
else:
raise pickle.PicklingError("Can't pickle %r" % obj)
return self.save_reduce(obj=obj, *rv)
# if func is lambda, def'ed at prompt, is in main, or is nested, then
# we'll pickle the actual function object rather than simply saving a
# reference (as is done in default pickler), via save_function_tuple.
if (islambda(obj)
or getattr(obj.__code__, 'co_filename', None) == '<stdin>'
or themodule is None):
self.save_function_tuple(obj)
return
else:
# func is nested
if lookedup_by_name is None or lookedup_by_name is not obj:
self.save_function_tuple(obj)
return
if obj.__dict__:
# essentially save_reduce, but workaround needed to avoid recursion
self.save(_restore_attr)
write(pickle.MARK + pickle.GLOBAL + modname + '\n' + name + '\n')
self.memoize(obj)
self.save(obj.__dict__)
write(pickle.TUPLE + pickle.REDUCE)
else:
write(pickle.GLOBAL + modname + '\n' + name + '\n')
self.memoize(obj)
dispatch[types.FunctionType] = save_function
def _save_subimports(self, code, top_level_dependencies):
"""
Ensure de-pickler imports any package child-modules that
are needed by the function
"""
# 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__ + '.'
for name, module in sys.modules.items():
# 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):
# ensure unpickler executes this import
self.save(module)
# then discards the reference to it
self.write(pickle.POP)
def save_dynamic_class(self, 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.
"""
clsdict = dict(obj.__dict__) # copy dict proxy to a dict
clsdict.pop('__weakref__', None)
# On PyPy, __doc__ is a readonly attribute, so we need to include it in
# the initial skeleton class. This is safe because we know that the
# doc can't participate in a cycle with the original class.
type_kwargs = {'__doc__': clsdict.pop('__doc__', None)}
# If type overrides __dict__ as a property, include it in the type kwargs.
# In Python 2, we can't set this attribute after construction.
__dict__ = clsdict.pop('__dict__', None)
if isinstance(__dict__, property):
type_kwargs['__dict__'] = __dict__
save = self.save
write = self.write
# We write pickle instructions explicitly here to handle the
# possibility that the type object participates in a cycle with its own
# __dict__. We first write an empty "skeleton" version of the class and
# memoize it before writing the class' __dict__ itself. We then write
# instructions to "rehydrate" the skeleton class by restoring the
# attributes from the __dict__.
#
# A type can appear in a cycle with its __dict__ if an instance of the
# type appears in the type's __dict__ (which happens for the stdlib
# Enum class), or if the type defines methods that close over the name
# of the type, (which is common for Python 2-style super() calls).
# Push the rehydration function.
save(_rehydrate_skeleton_class)
# Mark the start of the args tuple for the rehydration function.
write(pickle.MARK)
# Create and memoize an skeleton class with obj's name and bases.
tp = type(obj)
self.save_reduce(tp, (obj.__name__, obj.__bases__, type_kwargs), obj=obj)
# Now save the rest of obj's __dict__. Any references to obj
# encountered while saving will point to the skeleton class.
save(clsdict)
# Write a tuple of (skeleton_class, clsdict).
write(pickle.TUPLE)
# Call _rehydrate_skeleton_class(skeleton_class, clsdict)
write(pickle.REDUCE)
def save_function_tuple(self, func):
""" Pickles an actual func object.
A func comprises: code, globals, defaults, closure, and dict. We
extract and save these, injecting reducing functions at certain points
to recreate the func object. Keep in mind that some of these pieces
can contain a ref to the func itself. Thus, a naive save on these
pieces could trigger an infinite loop of save's. To get around that,
we first create a skeleton func object using just the code (this is
safe, since this won't contain a ref to the func), and memoize it as
soon as it's created. The other stuff can then be filled in later.
"""
if is_tornado_coroutine(func):
self.save_reduce(_rebuild_tornado_coroutine, (func.__wrapped__,),
obj=func)
return
save = self.save
write = self.write
code, f_globals, defaults, closure_values, dct, base_globals = self.extract_func_data(func)
save(_fill_function) # skeleton function updater
write(pickle.MARK) # beginning of tuple that _fill_function expects
self._save_subimports(
code,
itertools.chain(f_globals.values(), closure_values or ()),
)
# create a skeleton function object and memoize it
save(_make_skel_func)
save((
code,
len(closure_values) if closure_values is not None else -1,
base_globals,
))
write(pickle.REDUCE)
self.memoize(func)
# save the rest of the func data needed by _fill_function
state = {
'globals': f_globals,
'defaults': defaults,
'dict': dct,
'module': func.__module__,
'closure_values': closure_values,
}
if hasattr(func, '__qualname__'):
state['qualname'] = func.__qualname__
save(state)
write(pickle.TUPLE)
write(pickle.REDUCE) # applies _fill_function on the tuple
_extract_code_globals_cache = (
weakref.WeakKeyDictionary()
if not hasattr(sys, "pypy_version_info")
else {})
@classmethod
def extract_code_globals(cls, co):
"""
Find all globals names read or written to by codeblock co
"""
out_names = cls._extract_code_globals_cache.get(co)
if out_names is None:
try:
names = co.co_names
except AttributeError:
# PyPy "builtin-code" object
out_names = set()
else:
out_names = set(names[oparg]
for op, oparg in _walk_global_ops(co))
# see if nested function have any global refs
if co.co_consts:
for const in co.co_consts:
if type(const) is types.CodeType:
out_names |= cls.extract_code_globals(const)
cls._extract_code_globals_cache[co] = out_names
return out_names
def extract_func_data(self, func):
"""
Turn the function into a tuple of data necessary to recreate it:
code, globals, defaults, closure_values, dict
"""
code = func.__code__
# extract all global ref's
func_global_refs = self.extract_code_globals(code)
# process all variables referenced by global environment
f_globals = {}
for var in func_global_refs:
if var in func.__globals__:
f_globals[var] = func.__globals__[var]
# defaults requires no processing
defaults = func.__defaults__
# process closure
closure = (
list(map(_get_cell_contents, func.__closure__))
if func.__closure__ is not None
else None
)
# save the dict
dct = func.__dict__
base_globals = self.globals_ref.get(id(func.__globals__), {})
self.globals_ref[id(func.__globals__)] = base_globals
return (code, f_globals, defaults, closure, dct, base_globals)
def save_builtin_function(self, obj):
if obj.__module__ == "__builtin__":
return self.save_global(obj)
return self.save_function(obj)
dispatch[types.BuiltinFunctionType] = save_builtin_function
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.__module__ == "__main__":
return self.save_dynamic_class(obj)
try:
return Pickler.save_global(self, obj, name=name)
except Exception:
if obj.__module__ == "__builtin__" or obj.__module__ == "builtins":
if obj in _BUILTIN_TYPE_NAMES:
return self.save_reduce(
_builtin_type, (_BUILTIN_TYPE_NAMES[obj],), obj=obj)
typ = type(obj)
if typ is not obj and isinstance(obj, (type, types.ClassType)):
return self.save_dynamic_class(obj)
raise
dispatch[type] = save_global
dispatch[types.ClassType] = save_global
def save_instancemethod(self, obj):
# Memoization rarely is ever useful due to python bounding
if obj.__self__ is None:
self.save_reduce(getattr, (obj.im_class, obj.__name__))
else:
if PY3:
self.save_reduce(types.MethodType, (obj.__func__, obj.__self__), obj=obj)
else:
self.save_reduce(types.MethodType, (obj.__func__, obj.__self__, obj.__self__.__class__),
obj=obj)
dispatch[types.MethodType] = save_instancemethod
def save_inst(self, obj):
"""Inner logic to save instance. Based off pickle.save_inst"""
cls = obj.__class__
# Try the dispatch table (pickle module doesn't do it)
f = self.dispatch.get(cls)
if f:
f(self, obj) # Call unbound method with explicit self
return
memo = self.memo
write = self.write
save = self.save
if hasattr(obj, '__getinitargs__'):
args = obj.__getinitargs__()
len(args) # XXX Assert it's a sequence
pickle._keep_alive(args, memo)
else:
args = ()
write(pickle.MARK)
if self.bin:
save(cls)
for arg in args:
save(arg)
write(pickle.OBJ)
else:
for arg in args:
save(arg)
write(pickle.INST + cls.__module__ + '\n' + cls.__name__ + '\n')
self.memoize(obj)
try:
getstate = obj.__getstate__
except AttributeError:
stuff = obj.__dict__
else:
stuff = getstate()
pickle._keep_alive(stuff, memo)
save(stuff)
write(pickle.BUILD)
if not PY3:
dispatch[types.InstanceType] = save_inst
def save_property(self, obj):
# properties not correctly saved in python
self.save_reduce(property, (obj.fget, obj.fset, obj.fdel, obj.__doc__), obj=obj)
dispatch[property] = save_property
def save_classmethod(self, obj):
orig_func = obj.__func__
self.save_reduce(type(obj), (orig_func,), obj=obj)
dispatch[classmethod] = save_classmethod
dispatch[staticmethod] = save_classmethod
def save_itemgetter(self, obj):
"""itemgetter serializer (needed for namedtuple support)"""
class Dummy:
def __getitem__(self, item):
return item
items = obj(Dummy())
if not isinstance(items, tuple):
items = (items, )
return self.save_reduce(operator.itemgetter, items)
if type(operator.itemgetter) is type:
dispatch[operator.itemgetter] = save_itemgetter
def save_attrgetter(self, obj):
"""attrgetter serializer"""
class Dummy(object):
def __init__(self, attrs, index=None):
self.attrs = attrs
self.index = index
def __getattribute__(self, item):
attrs = object.__getattribute__(self, "attrs")
index = object.__getattribute__(self, "index")
if index is None:
index = len(attrs)
attrs.append(item)
else:
attrs[index] = ".".join([attrs[index], item])
return type(self)(attrs, index)
attrs = []
obj(Dummy(attrs))
return self.save_reduce(operator.attrgetter, tuple(attrs))
if type(operator.attrgetter) is type:
dispatch[operator.attrgetter] = save_attrgetter
def save_file(self, obj):
"""Save a file"""
try:
import StringIO as pystringIO #we can't use cStringIO as it lacks the name attribute
except ImportError:
import io as pystringIO
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 self.save_reduce(getattr, (sys,'stdout'), obj=obj)
if obj is sys.stderr:
return self.save_reduce(getattr, (sys,'stderr'), obj=obj)
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 = pystringIO.StringIO()
try:
# Read the whole file
curloc = obj.tell()
obj.seek(0)
contents = obj.read()
obj.seek(curloc)
except IOError:
raise pickle.PicklingError("Cannot pickle file %s as it cannot be read" % name)
retval.write(contents)
retval.seek(curloc)
retval.name = name
self.save(retval)
self.memoize(obj)
def save_ellipsis(self, obj):
self.save_reduce(_gen_ellipsis, ())
def save_not_implemented(self, obj):
self.save_reduce(_gen_not_implemented, ())
if PY3:
dispatch[io.TextIOWrapper] = save_file
else:
dispatch[file] = save_file
dispatch[type(Ellipsis)] = save_ellipsis
dispatch[type(NotImplemented)] = save_not_implemented
def save_weakset(self, obj):
self.save_reduce(weakref.WeakSet, (list(obj),))
dispatch[weakref.WeakSet] = save_weakset
def save_logger(self, obj):
self.save_reduce(logging.getLogger, (obj.name,), obj=obj)
dispatch[logging.Logger] = save_logger
"""Special functions for Add-on libraries"""
def inject_addons(self):
"""Plug in system. Register additional pickling functions if modules already loaded"""
pass
# 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)
# Shorthands for legacy support
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.
"""
file = StringIO()
try:
cp = CloudPickler(file, protocol=protocol)
cp.dump(obj)
return file.getvalue()
finally:
file.close()
# 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 = imp.new_module(name)
mod.__dict__.update(vars)
sys.modules[name] = mod
return mod
# restores function attributes
def _restore_attr(obj, attr):
for key, val in attr.items():
setattr(obj, key, val)
return obj
def _get_module_builtins():
return pickle.__builtins__
def print_exec(stream):
ei = sys.exc_info()
traceback.print_exception(ei[0], ei[1], ei[2], None, stream)
def _modules_to_main(modList):
"""Force every module in modList to be placed into main"""
if not modList:
return
main = sys.modules['__main__']
for modname in modList:
if type(modname) is str:
try:
mod = __import__(modname)
except Exception as e:
sys.stderr.write('warning: could not import %s\n. '
'Your function may unexpectedly error due to this import failing;'
'A version mismatch is likely. Specific error was:\n' % modname)
print_exec(sys.stderr)
else:
setattr(main, mod.__name__, mod)
#object generators:
def _genpartial(func, args, kwds):
if not args:
args = ()
if not kwds:
kwds = {}
return partial(func, *args, **kwds)
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,))
func.__globals__.update(state['globals'])
func.__defaults__ = state['defaults']
func.__dict__ = state['dict']
if 'module' in state:
func.__module__ = state['module']
if 'qualname' in state:
func.__qualname__ = state['qualname']
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_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.
"""
if base_globals is None:
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 _rehydrate_skeleton_class(skeleton_class, class_dict):
"""Put attributes from `class_dict` back on `skeleton_class`.
See CloudPickler.save_dynamic_class for more info.
"""
for attrname, attr in class_dict.items():
setattr(skeleton_class, attrname, attr)
return skeleton_class
def _find_module(mod_name):
"""
Iterate over each part instead of calling imp.find_module directly.
This function is able to find submodules (e.g. sickit.tree)
"""
path = None
for part in mod_name.split('.'):
if path is not None:
path = [path]
file, path, description = imp.find_module(part, path)
if file is not None:
file.close()
return path, description
"""Constructors for 3rd party libraries
Note: These can never be renamed due to client compatibility issues"""
def _getobject(modname, attribute):
mod = __import__(modname, fromlist=[attribute])
return mod.__dict__[attribute]
""" Use copy_reg to extend global pickle definitions """
if sys.version_info < (3, 4):
method_descriptor = type(str.upper)
def _reduce_method_descriptor(obj):
return (getattr, (obj.__objclass__, obj.__name__))
try:
import copy_reg as copyreg
except ImportError:
import copyreg
copyreg.pickle(method_descriptor, _reduce_method_descriptor)