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import os
import uuid
import weakref
import collections
import numba
from numba.core import types, errors, utils, config
# Exported symbols
from numba.core.typing.typeof import typeof_impl # noqa: F401
from numba.core.typing.asnumbatype import as_numba_type # noqa: F401
from numba.core.typing.templates import infer, infer_getattr # noqa: F401
from numba.core.imputils import ( # noqa: F401
lower_builtin, lower_getattr, lower_getattr_generic, # noqa: F401
lower_setattr, lower_setattr_generic, lower_cast) # noqa: F401
from numba.core.datamodel import models # noqa: F401
from numba.core.datamodel import register_default as register_model # noqa: F401, E501
from numba.core.pythonapi import box, unbox, reflect, NativeValue # noqa: F401
from numba._helperlib import _import_cython_function # noqa: F401
from numba.core.serialize import ReduceMixin
def type_callable(func):
"""
Decorate a function as implementing typing for the callable *func*.
*func* can be a callable object (probably a global) or a string
denoting a built-in operation (such 'getitem' or '__array_wrap__')
"""
from numba.core.typing.templates import (CallableTemplate, infer,
infer_global)
if not callable(func) and not isinstance(func, str):
raise TypeError("`func` should be a function or string")
try:
func_name = func.__name__
except AttributeError:
func_name = str(func)
def decorate(typing_func):
def generic(self):
return typing_func(self.context)
name = "%s_CallableTemplate" % (func_name,)
bases = (CallableTemplate,)
class_dict = dict(key=func, generic=generic)
template = type(name, bases, class_dict)
infer(template)
if callable(func):
infer_global(func, types.Function(template))
return typing_func
return decorate
# By default, an *overload* does not have a cpython wrapper because it is not
# callable from python.
_overload_default_jit_options = {'no_cpython_wrapper': True}
def overload(func, jit_options={}, strict=True, inline='never',
prefer_literal=False):
"""
A decorator marking the decorated function as typing and implementing
*func* in nopython mode.
The decorated function will have the same formal parameters as *func*
and be passed the Numba types of those parameters. It should return
a function implementing *func* for the given types.
Here is an example implementing len() for tuple types::
@overload(len)
def tuple_len(seq):
if isinstance(seq, types.BaseTuple):
n = len(seq)
def len_impl(seq):
return n
return len_impl
Compiler options can be passed as an dictionary using the **jit_options**
argument.
Overloading strictness (that the typing and implementing signatures match)
is enforced by the **strict** keyword argument, it is recommended that this
is set to True (default).
To handle a function that accepts imprecise types, an overload
definition can return 2-tuple of ``(signature, impl_function)``, where
the ``signature`` is a ``typing.Signature`` specifying the precise
signature to be used; and ``impl_function`` is the same implementation
function as in the simple case.
If the kwarg inline determines whether the overload is inlined in the
calling function and can be one of three values:
* 'never' (default) - the overload is never inlined.
* 'always' - the overload is always inlined.
* a function that takes two arguments, both of which are instances of a
namedtuple with fields:
* func_ir
* typemap
* calltypes
* signature
The first argument holds the information from the caller, the second
holds the information from the callee. The function should return Truthy
to determine whether to inline, this essentially permitting custom
inlining rules (typical use might be cost models).
The *prefer_literal* option allows users to control if literal types should
be tried first or last. The default (`False`) is to use non-literal types.
Implementations that can specialize based on literal values should set the
option to `True`. Note, this option maybe expanded in the near future to
allow for more control (e.g. disabling non-literal types).
"""
from numba.core.typing.templates import make_overload_template, infer_global
# set default options
opts = _overload_default_jit_options.copy()
opts.update(jit_options) # let user options override
def decorate(overload_func):
template = make_overload_template(func, overload_func, opts, strict,
inline, prefer_literal)
infer(template)
if callable(func):
infer_global(func, types.Function(template))
return overload_func
return decorate
def register_jitable(*args, **kwargs):
"""
Register a regular python function that can be executed by the python
interpreter and can be compiled into a nopython function when referenced
by other jit'ed functions. Can be used as::
@register_jitable
def foo(x, y):
return x + y
Or, with compiler options::
@register_jitable(_nrt=False) # disable runtime allocation
def foo(x, y):
return x + y
"""
def wrap(fn):
# It is just a wrapper for @overload
inline = kwargs.pop('inline', 'never')
@overload(fn, jit_options=kwargs, inline=inline, strict=False)
def ov_wrap(*args, **kwargs):
return fn
return fn
if kwargs:
return wrap
else:
return wrap(*args)
def overload_attribute(typ, attr, **kwargs):
"""
A decorator marking the decorated function as typing and implementing
attribute *attr* for the given Numba type in nopython mode.
*kwargs* are passed to the underlying `@overload` call.
Here is an example implementing .nbytes for array types::
@overload_attribute(types.Array, 'nbytes')
def array_nbytes(arr):
def get(arr):
return arr.size * arr.itemsize
return get
"""
# TODO implement setters
from numba.core.typing.templates import make_overload_attribute_template
def decorate(overload_func):
template = make_overload_attribute_template(
typ, attr, overload_func,
inline=kwargs.get('inline', 'never'),
)
infer_getattr(template)
overload(overload_func, **kwargs)(overload_func)
return overload_func
return decorate
def overload_method(typ, attr, **kwargs):
"""
A decorator marking the decorated function as typing and implementing
attribute *attr* for the given Numba type in nopython mode.
*kwargs* are passed to the underlying `@overload` call.
Here is an example implementing .take() for array types::
@overload_method(types.Array, 'take')
def array_take(arr, indices):
if isinstance(indices, types.Array):
def take_impl(arr, indices):
n = indices.shape[0]
res = np.empty(n, arr.dtype)
for i in range(n):
res[i] = arr[indices[i]]
return res
return take_impl
"""
from numba.core.typing.templates import make_overload_method_template
def decorate(overload_func):
template = make_overload_method_template(
typ, attr, overload_func,
inline=kwargs.get('inline', 'never'),
prefer_literal=kwargs.get('prefer_literal', False)
)
infer_getattr(template)
overload(overload_func, **kwargs)(overload_func)
return overload_func
return decorate
def make_attribute_wrapper(typeclass, struct_attr, python_attr):
"""
Make an automatic attribute wrapper exposing member named *struct_attr*
as a read-only attribute named *python_attr*.
The given *typeclass*'s model must be a StructModel subclass.
"""
from numba.core.typing.templates import AttributeTemplate
from numba.core.datamodel import default_manager
from numba.core.datamodel.models import StructModel
from numba.core.imputils import impl_ret_borrowed
from numba.core import cgutils
if not isinstance(typeclass, type) or not issubclass(typeclass, types.Type):
raise TypeError("typeclass should be a Type subclass, got %s"
% (typeclass,))
def get_attr_fe_type(typ):
"""
Get the Numba type of member *struct_attr* in *typ*.
"""
model = default_manager.lookup(typ)
if not isinstance(model, StructModel):
raise TypeError("make_struct_attribute_wrapper() needs a type "
"with a StructModel, but got %s" % (model,))
return model.get_member_fe_type(struct_attr)
@infer_getattr
class StructAttribute(AttributeTemplate):
key = typeclass
def generic_resolve(self, typ, attr):
if attr == python_attr:
return get_attr_fe_type(typ)
@lower_getattr(typeclass, python_attr)
def struct_getattr_impl(context, builder, typ, val):
val = cgutils.create_struct_proxy(typ)(context, builder, value=val)
attrty = get_attr_fe_type(typ)
attrval = getattr(val, struct_attr)
return impl_ret_borrowed(context, builder, attrty, attrval)
class _Intrinsic(ReduceMixin):
"""
Dummy callable for intrinsic
"""
_memo = weakref.WeakValueDictionary()
# hold refs to last N functions deserialized, retaining them in _memo
# regardless of whether there is another reference
_recent = collections.deque(maxlen=config.FUNCTION_CACHE_SIZE)
__uuid = None
def __init__(self, name, defn):
self._name = name
self._defn = defn
@property
def _uuid(self):
"""
An instance-specific UUID, to avoid multiple deserializations of
a given instance.
Note this is lazily-generated, for performance reasons.
"""
u = self.__uuid
if u is None:
u = str(uuid.uuid1())
self._set_uuid(u)
return u
def _set_uuid(self, u):
assert self.__uuid is None
self.__uuid = u
self._memo[u] = self
self._recent.append(self)
def _register(self):
from numba.core.typing.templates import (make_intrinsic_template,
infer_global)
template = make_intrinsic_template(self, self._defn, self._name)
infer(template)
infer_global(self, types.Function(template))
def __call__(self, *args, **kwargs):
"""
This is only defined to pretend to be a callable from CPython.
"""
msg = '{0} is not usable in pure-python'.format(self)
raise NotImplementedError(msg)
def __repr__(self):
return "<intrinsic {0}>".format(self._name)
def __deepcopy__(self, memo):
# NOTE: Intrinsic are immutable and we don't need to copy.
# This is triggered from deepcopy of statements.
return self
def _reduce_states(self):
"""
NOTE: part of ReduceMixin protocol
"""
return dict(uuid=self._uuid, name=self._name, defn=self._defn)
@classmethod
def _rebuild(cls, uuid, name, defn):
"""
NOTE: part of ReduceMixin protocol
"""
try:
return cls._memo[uuid]
except KeyError:
llc = cls(name=name, defn=defn)
llc._register()
llc._set_uuid(uuid)
return llc
def intrinsic(*args, **kwargs):
"""
A decorator marking the decorated function as typing and implementing
*func* in nopython mode using the llvmlite IRBuilder API. This is an escape
hatch for expert users to build custom LLVM IR that will be inlined to
the caller.
The first argument to *func* is the typing context. The rest of the
arguments corresponds to the type of arguments of the decorated function.
These arguments are also used as the formal argument of the decorated
function. If *func* has the signature ``foo(typing_context, arg0, arg1)``,
the decorated function will have the signature ``foo(arg0, arg1)``.
The return values of *func* should be a 2-tuple of expected type signature,
and a code-generation function that will passed to ``lower_builtin``.
For unsupported operation, return None.
Here is an example implementing a ``cast_int_to_byte_ptr`` that cast
any integer to a byte pointer::
@intrinsic
def cast_int_to_byte_ptr(typingctx, src):
# check for accepted types
if isinstance(src, types.Integer):
# create the expected type signature
result_type = types.CPointer(types.uint8)
sig = result_type(types.uintp)
# defines the custom code generation
def codegen(context, builder, signature, args):
# llvm IRBuilder code here
[src] = args
rtype = signature.return_type
llrtype = context.get_value_type(rtype)
return builder.inttoptr(src, llrtype)
return sig, codegen
"""
# Make inner function for the actual work
def _intrinsic(func):
name = getattr(func, '__name__', str(func))
llc = _Intrinsic(name, func, **kwargs)
llc._register()
return llc
if not kwargs:
# No option is given
return _intrinsic(*args)
else:
# options are given, create a new callable to recv the
# definition function
def wrapper(func):
return _intrinsic(func)
return wrapper
def get_cython_function_address(module_name, function_name):
"""
Get the address of a Cython function.
Args
----
module_name:
Name of the Cython module
function_name:
Name of the Cython function
Returns
-------
A Python int containing the address of the function
"""
return _import_cython_function(module_name, function_name)
def include_path():
"""Returns the C include directory path.
"""
include_dir = os.path.dirname(os.path.dirname(numba.__file__))
path = os.path.abspath(include_dir)
return path
def sentry_literal_args(pysig, literal_args, args, kwargs):
"""Ensures that the given argument types (in *args* and *kwargs*) are
literally typed for a function with the python signature *pysig* and the
list of literal argument names in *literal_args*.
Alternatively, this is the same as::
SentryLiteralArgs(literal_args).for_pysig(pysig).bind(*args, **kwargs)
"""
boundargs = pysig.bind(*args, **kwargs)
# Find literal argument positions and whether it is satisfied.
request_pos = set()
missing = False
for i, (k, v) in enumerate(boundargs.arguments.items()):
if k in literal_args:
request_pos.add(i)
if not isinstance(v, types.Literal):
missing = True
if missing:
# Yes, there are missing required literal arguments
e = errors.ForceLiteralArg(request_pos)
# A helper function to fold arguments
def folded(args, kwargs):
out = pysig.bind(*args, **kwargs).arguments.values()
return tuple(out)
raise e.bind_fold_arguments(folded)
class SentryLiteralArgs(collections.namedtuple(
'_SentryLiteralArgs', ['literal_args'])):
"""
Parameters
----------
literal_args : Sequence[str]
A sequence of names for literal arguments
Examples
--------
The following line:
>>> SentryLiteralArgs(literal_args).for_pysig(pysig).bind(*args, **kwargs)
is equivalent to:
>>> sentry_literal_args(pysig, literal_args, args, kwargs)
"""
def for_function(self, func):
"""Bind the sentry to the signature of *func*.
Parameters
----------
func : Function
A python function.
Returns
-------
obj : BoundLiteralArgs
"""
return self.for_pysig(utils.pysignature(func))
def for_pysig(self, pysig):
"""Bind the sentry to the given signature *pysig*.
Parameters
----------
pysig : inspect.Signature
Returns
-------
obj : BoundLiteralArgs
"""
return BoundLiteralArgs(
pysig=pysig,
literal_args=self.literal_args,
)
class BoundLiteralArgs(collections.namedtuple(
'BoundLiteralArgs', ['pysig', 'literal_args'])):
"""
This class is usually created by SentryLiteralArgs.
"""
def bind(self, *args, **kwargs):
"""Bind to argument types.
"""
return sentry_literal_args(
self.pysig,
self.literal_args,
args,
kwargs,
)
def is_jitted(function):
"""Returns True if a function is wrapped by one of the Numba @jit
decorators, for example: numba.jit, numba.njit
The purpose of this function is to provide a means to check if a function is
already JIT decorated.
"""
# don't want to export this so import locally
from numba.core.dispatcher import Dispatcher
return isinstance(function, Dispatcher)
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