8.2.1. NBEP 2: Extension points¶
Author: | Antoine Pitrou |
---|---|
Date: | July 2015 |
Status: | Draft |
Implementing new types or functions in Numba requires hooking into various mechanisms along the compilation chain (and potentially outside of it). This document aims, first, at examining the current ways of doing so and, second, at making proposals to make extending easier.
If some of the proposals are implemented, we should first strive to use and exercise them internally, before exposing the APIs to the public.
Note
This document doesn’t cover CUDA or any other non-CPU backend.
8.2.1.1. High-level API¶
There is currently no high-level API, making some use cases more complicated than they should be.
8.2.1.1.1. Proposed changes¶
8.2.1.1.1.1. Dedicated module¶
We propose the addition of a numba.extending
module exposing the main
APIs useful for extending Numba.
8.2.1.1.1.2. Implementing a function¶
We propose the addition of a @overload
decorator allowing the
implementation of a given function for use in nopython mode.
The overloading function has the same formal signature as the implemented
function, and receives the actual argument types. It should return a
Python function implementing the overloaded function for the given types.
The following example implements numpy.where()
with
this approach.
import numpy as np
from numba import types
from numba.extending import overload
@overload(np.where)
def where(cond, x, y):
"""
Implement np.where().
"""
# Choose implementation based on argument types.
if isinstance(cond, types.Array):
# Array where() => return an array of the same shape
if all(ty.layout == 'C' for ty in (cond, x, y)):
def where_impl(cond, x, y):
"""
Fast implementation for C-contiguous arrays
"""
shape = cond.shape
if x.shape != shape or y.shape != shape:
raise ValueError("all inputs should have the same shape")
res = np.empty_like(x)
cf = cond.flat
xf = x.flat
yf = y.flat
rf = res.flat
for i in range(cond.size):
rf[i] = xf[i] if cf[i] else yf[i]
return res
else:
def where_impl(cond, x, y):
"""
Generic implementation for other arrays
"""
shape = cond.shape
if x.shape != shape or y.shape != shape:
raise ValueError("all inputs should have the same shape")
res = np.empty_like(x)
for idx, c in np.ndenumerate(cond):
res[idx] = x[idx] if c else y[idx]
return res
else:
def where_impl(cond, x, y):
"""
Scalar where() => return a 0-dim array
"""
scal = x if cond else y
return np.full_like(scal, scal)
return where_impl
It is also possible to implement functions already known to Numba, to
support additional types. The following example implements the
built-in function len()
for tuples with this approach:
@overload(len)
def tuple_len(x):
if isinstance(x, types.BaseTuple):
# The tuple length is known at compile-time, so simply reify it
# as a constant.
n = len(x)
def len_impl(x):
return n
return len_impl
8.2.1.1.1.3. Implementing an attribute¶
We propose the addition of a @overload_attribute
decorator allowing
the implementation of an attribute getter for use in nopython mode.
The following example implements the .nbytes
attribute on Numpy arrays:
@overload_attribute(types.Array, 'nbytes')
def array_nbytes(arr):
def get(arr):
return arr.size * arr.itemsize
return get
Note
The overload_attribute() signature allows for expansion to also define
setters and deleters, by letting the decorated function return a
getter, setter, deleter
tuple instead of a single getter
.
8.2.1.1.1.4. Implementing a method¶
We propose the addition of a @overload_method
decorator allowing the
implementation of an instance method for use in nopython mode.
The following example implements the .take()
method on Numpy arrays:
@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
8.2.1.1.1.5. Exposing a structure member¶
We propose the addition of a make_attribute_wrapper()
function exposing
an internal field as a visible read-only attribute, for those types backed
by a StructModel
data model.
For example, assuming PdIndexType
is the Numba type of pandas indices,
here is how to expose the underlying Numpy array as a ._data
attribute:
@register_model(PdIndexType)
class PdIndexModel(models.StructModel):
def __init__(self, dmm, fe_type):
members = [
('values', fe_type.as_array),
]
models.StructModel.__init__(self, dmm, fe_type, members)
make_attribute_wrapper(PdIndexType, 'values', '_data')
8.2.1.2. Typing¶
8.2.1.2.1. Numba types¶
Numba’s standard types are declared in numba.types
. To declare
a new type, one subclasses the base Type
class or one of its
existing abstract subclasses, and implements the required functionality.
8.2.1.2.1.1. Proposed changes¶
No change required.
8.2.1.2.2. Type inference on values¶
Values of a new type need to be type-inferred if they can appear as
function arguments or constants. The core machinery is in
numba.typing.typeof
.
In the common case where some Python class or classes map exclusively to the new type, one can extend a generic function to dispatch on said classes, e.g.:
from numba.typing.typeof import typeof_impl
@typeof_impl(MyClass)
def _typeof_myclass(val, c):
if "some condition":
return MyType(...)
The typeof_impl
specialization must return a Numba type instance,
or None if the value failed typing.
(when one controls the class being type-inferred, an alternative
to typeof_impl
is to define a _numba_type_
property on the class)
In the rarer case where the new type can denote various Python classes
that are impossible to enumerate, one must insert a manual check in the
fallback implementation of the typeof_impl
generic function.
8.2.1.2.2.1. Proposed changes¶
Allow people to define a generic hook without monkeypatching the fallback implementation.
8.2.1.2.3. Fast path for type inference on function arguments¶
Optionally, one may want to allow a new type to participate in the
fast type resolution (written in C code) to minimize function call
overhead when a JIT-compiled function is called with the new type.
One must then insert the required checks and implementation in
the _typeof.c
file, presumably inside the compute_fingerprint()
function.
8.2.1.2.3.1. Proposed changes¶
None. Adding generic hooks to C code embedded in a C Python extension is too delicate a change.
8.2.1.2.4. Type inference on operations¶
Values resulting from various operations (function calls, operators, etc.) are typed using a set of helpers called “templates”. One can define a new template by subclass one of the existing base classes and implement the desired inference mechanism. The template is explicitly registered with the type inference machinery using a decorator.
The ConcreteTemplate
base class allows one to define inference as
a set of supported signatures for a given operation. The following example
types the modulo operator:
@builtin
class BinOpMod(ConcreteTemplate):
key = "%"
cases = [signature(op, op, op)
for op in sorted(types.signed_domain)]
cases += [signature(op, op, op)
for op in sorted(types.unsigned_domain)]
cases += [signature(op, op, op) for op in sorted(types.real_domain)]
(note that type instances are used in the signatures, severely limiting the amount of genericity that can be expressed)
The AbstractTemplate
base class allows to define inference
programmatically, giving it full flexibility. Here is a simplistic
example of how tuple indexing (i.e. the __getitem__
operator) can
be expressed:
@builtin
class GetItemUniTuple(AbstractTemplate):
key = "getitem"
def generic(self, args, kws):
tup, idx = args
if isinstance(tup, types.UniTuple) and isinstance(idx, types.Integer):
return signature(tup.dtype, tup, idx)
The AttributeTemplate
base class allows to type the attributes
and methods of a given type. Here is an example, typing the .real
and .imag
attributes of complex numbers:
@builtin_attr
class ComplexAttribute(AttributeTemplate):
key = types.Complex
def resolve_real(self, ty):
return ty.underlying_float
def resolve_imag(self, ty):
return ty.underlying_float
Note
AttributeTemplate
only works for getting attributes. Setting
an attribute’s value is hardcoded in numba.typeinfer
.
The CallableTemplate
base class offers an easier way to parse
flexible function signatures, by letting one define a callable that has
the same definition as the function being typed. For example, here is how
one could hypothetically type Python’s sorted
function if Numba supported
lists:
@builtin
class Sorted(CallableTemplate):
key = sorted
def generic(self):
def typer(iterable, key=None, reverse=None):
if reverse is not None and not isinstance(reverse, types.Boolean):
return
if key is not None and not isinstance(key, types.Callable):
return
if not isinstance(iterable, types.Iterable):
return
return types.List(iterable.iterator_type.yield_type)
return typer
(note you can return just the function’s return type instead of the full signature)
8.2.1.2.4.1. Proposed changes¶
Naming of the various decorators is quite vague and confusing. We propose
renaming @builtin
to @infer
, @builtin_attr
to @infer_getattr
and builtin_global
to infer_global
.
The two-step declaration for global values is a bit verbose, we propose
simplifying it by allowing the use of infer_global
as a decorator:
@infer_global(len)
class Len(AbstractTemplate):
key = len
def generic(self, args, kws):
assert not kws
(val,) = args
if isinstance(val, (types.Buffer, types.BaseTuple)):
return signature(types.intp, val)
The class-based API can feel clumsy, we can add a functional API for some of the template kinds:
@type_callable(sorted)
def type_sorted(context):
def typer(iterable, key=None, reverse=None):
# [same function as above]
return typer
8.2.1.3. Code generation¶
8.2.1.3.1. Concrete representation of values of a Numba type¶
Any concrete Numba type must be able to be represented in LLVM form
(for variable storage, argument passing, etc.). One defines that
representation by implementing a datamodel class and registering it
with a decorator. Datamodel classes for standard types are defined
in numba.datamodel.models
.
8.2.1.3.1.1. Proposed changes¶
No change required.
8.2.1.3.2. Conversion between types¶
Implicit conversion between Numba types is currently implemented as a
monolithic sequence of choices and type checks in the
BaseContext.cast()
method. To add a new implicit conversion, one
appends a type-specific check in that method.
Boolean evaluation is a special case of implicit conversion (the
destination type being types.Boolean
).
Note
Explicit conversion is seen as a regular operation, e.g. a constructor call.
8.2.1.3.2.1. Proposed changes¶
Add a generic function for implicit conversion, with multiple dispatch based on the source and destination types. Here is an example showing how to write a float-to-integer conversion:
@lower_cast(types.Float, types.Integer)
def float_to_integer(context, builder, fromty, toty, val):
lty = context.get_value_type(toty)
if toty.signed:
return builder.fptosi(val, lty)
else:
return builder.fptoui(val, lty)
8.2.1.3.3. Implementation of an operation¶
Other operations are implemented and registered using a set of generic
functions and decorators. For example, here is how lookup for a the .ndim
attribute on Numpy arrays is implemented:
@builtin_attr
@impl_attribute(types.Kind(types.Array), "ndim", types.intp)
def array_ndim(context, builder, typ, value):
return context.get_constant(types.intp, typ.ndim)
And here is how calling len()
on a tuple value is implemented:
@builtin
@implement(types.len_type, types.Kind(types.BaseTuple))
def tuple_len(context, builder, sig, args):
tupty, = sig.args
retty = sig.return_type
return context.get_constant(retty, len(tupty.types))
8.2.1.3.3.1. Proposed changes¶
Review and streamine the API. Drop the requirement to write
types.Kind(...)
explicitly. Remove the separate @implement
decorator and rename @builtin
to @lower_builtin
, @builtin_attr
to @lower_getattr
, etc.
Add decorators to implement setattr()
operations, named
@lower_setattr
and @lower_setattr_generic
.
8.2.1.3.4. Conversion from / to Python objects¶
Some types need to be converted from or to Python objects, if they can
be passed as function arguments or returned from a function. The
corresponding boxing and unboxing operations are implemented using
a generic function. The implementations for standard Numba types
are in numba.targets.boxing
. For example, here is the boxing
implementation for a boolean value:
@box(types.Boolean)
def box_bool(c, typ, val):
longval = c.builder.zext(val, c.pyapi.long)
return c.pyapi.bool_from_long(longval)
8.2.1.3.4.1. Proposed changes¶
Change the implementation signature from (c, typ, val)
to
(typ, val, c)
, to match the one chosen for the typeof_impl
generic function.