Deprecation Notices¶

This section contains information about deprecation of behaviours, features and APIs that have become undesirable/obsolete. Any information about the schedule for their deprecation and reasoning behind the changes, along with examples, is provided. However, first is a small section on how to suppress deprecation warnings that may be raised from Numba so as to prevent warnings propagating into code that is consuming Numba.

Suppressing Deprecation warnings¶

All Numba deprecations are issued via NumbaDeprecationWarning or NumbaPendingDeprecationWarning s, to suppress the reporting of these the following code snippet can be used:

from numba.core.errors import NumbaDeprecationWarning, NumbaPendingDeprecationWarning
import warnings

warnings.simplefilter('ignore', category=NumbaDeprecationWarning)
warnings.simplefilter('ignore', category=NumbaPendingDeprecationWarning)

The action used above is 'ignore', other actions are available, see The Warnings Filter documentation for more information.

Note

It is strongly recommended that applications and libraries which choose to suppress these warnings should pin their Numba dependency to a suitable version because their users will no longer be aware of the coming incompatibility.

Deprecation of reflection for List and Set types¶

Reflection (reflection) is the jargon used in Numba to describe the process of ensuring that changes made by compiled code to arguments that are mutable Python container data types are visible in the Python interpreter when the compiled function returns. Numba has for some time supported reflection of list and set data types and it is support for this reflection that is scheduled for deprecation with view to replace with a better implementation.

Reason for deprecation¶

First recall that for Numba to be able to compile a function in nopython mode all the variables must have a concrete type ascertained through type inference. In simple cases, it is clear how to reflect changes to containers inside nopython mode back to the original Python containers. However, reflecting changes to complex data structures with nested container types (for example, lists of lists of integers) quickly becomes impossible to do efficiently and consistently. After a number of years of experience with this problem, it is clear that providing this behaviour is both fraught with difficulty and often leads to code which does not have good performance (all reflected data has to go through special APIs to convert the data to native formats at call time and then back to CPython formats at return time). As a result of this, the sheer number of reported problems in the issue tracker, and how well a new approach that was taken with typed.Dict (typed dictionaries) has gone, the core developers have decided to deprecate the noted reflection behaviour.

Example(s) of the impact¶

At present only a warning of the upcoming change is issued. In future code such as:

from numba import njit

@njit
def foo(x):
    x.append(10)

a = [1, 2, 3]
foo(a)

will require adjustment to use a typed.List instance, this typed container is synonymous to the Typed Dict. An example of translating the above is:

from numba import njit
from numba.typed import List

@njit
def foo(x):
    x.append(10)

a = [1, 2, 3]
typed_a = List()
[typed_a.append(x) for x in a]
foo(typed_a)

For more information about typed.List see Typed List. Further usability enhancements for this feature were made in the 0.47.0 release cycle.

Schedule¶

This feature will be removed with respect to this schedule:

Pending-deprecation warnings will be issued in version 0.44.0
Prominent notice will be given for a minimum of two releases prior to full removal.

Recommendations¶

Projects that need/rely on the deprecated behaviour should pin their dependency on Numba to a version prior to removal of this behaviour, or consider following replacement instructions that will be issued outlining how to adjust to the change.

Expected Replacement¶

As noted above typed.List will be used to permit similar functionality to reflection in the case of list s, a typed.Set will provide the equivalent for set (not implemented yet!). The advantages to this approach are:

That the containers are typed means type inference has to work less hard.
Nested containers (containers of containers of …) are more easily supported.
Performance penalties currently incurred translating data to/from native formats are largely avoided.
Numba’s typed.Dict will be able to use these containers as values.

Deprecation of object mode fall-back behaviour when using `@jit`¶

The numba.jit decorator has for a long time followed the behaviour of first attempting to compile the decorated function in nopython mode and should this compilation fail it will fall-back and try again to compile but this time in object mode. It it this fall-back behaviour which is being deprecated, the result of which will be that numba.jit will by default compile in nopython mode and object mode compilation will become opt-in only.

Reason for deprecation¶

The fall-back has repeatedly caused confusion for users as seemingly innocuous changes in user code can lead to drastic performance changes as code which may have once compiled in nopython mode mode may silently switch to compiling in object mode e.g:

from numba import jit

@jit
def foo():
    l = []
    for x in range(10):
        l.append(x)
    return l

foo()

assert foo.nopython_signatures # this was compiled in nopython mode

@jit
def bar():
    l = []
    for x in range(10):
        l.append(x)
    return reversed(l) # innocuous change, but no reversed support in nopython mode

bar()

assert not bar.nopython_signatures # this was not compiled in nopython mode

Another reason to remove the fall-back is that it is confusing for the compiler engineers developing Numba as it causes internal state problems that are really hard to debug and it makes manipulating the compiler pipelines incredibly challenging.

Further, it has long been considered best practice that the nopython mode keyword argument in the numba.jit decorator is set to True and that any user effort spent should go into making code work in this mode as there’s very little gain if it does not. The result is that, as Numba has evolved, the amount of use object mode gets in practice and its general utility has decreased. It can be noted that there are some minor improvements available through the notion of loop-lifting, the cases of this being used in practice are, however, rare and often a legacy from use of less-recent Numba whereby such behaviour was better accommodated/the use of @jit with fall-back was recommended.

Example(s) of the impact¶

At present a warning of the upcoming change is issued if @jit decorated code uses the fall-back compilation path. In future code such as:

@jit
def bar():
    l = []
    for x in range(10):
        l.append(x)
    return reversed(l)

bar()

will simply not compile, a TypingError would be raised.

Schedule¶

This feature will be removed with respect to this schedule:

Deprecation warnings will be issued in version 0.44.0
Prominent notice will be given for a minimum of two releases prior to full removal.

Recommendations¶

Projects that need/rely on the deprecated behaviour should pin their dependency on Numba to a version prior to removal of this behaviour. Alternatively, to accommodate the scheduled deprecations, users with code compiled at present with @jit can supply the nopython=True keyword argument, if the code continues to compile then the code is already ready for this change. If the code does not compile, continue using the @jit decorator without nopython=True and profile the performance of the function. Then remove the decorator and again check the performance of the function. If there is no benefit to having the @jit decorator present consider removing it! If there is benefit to having the @jit decorator present, then to be future proof supply the keyword argument forceobj=True to ensure the function is always compiled in object mode.

Deprecation of the target kwarg¶

There have been a number of users attempting to use the target keyword argument that’s meant for internal use only. We are deprecating this argument, as alternative solutions are available to achieve the same behaviour.

Recommendations¶

Update the jit decorator as follows:

Change @numba.jit(..., target='cuda') to numba.cuda.jit(...).

Schedule¶

This feature will be moved with respect to this schedule:

Deprecation warnings will be issued in 0.51.0.
The target kwarg will be removed in version 0.54.0.

Deprecation of the role of compute capability for CUDA inspection methods¶

The following methods of the Dispatcher class:

accept a kwarg called compute_capability. This kwarg is deprecated - it is ignored and accepted for backwards compatibility only. The use of the kwarg was already problematic, as in most cases the returned values pertain to the device in the current context, instead of the requested compute capability.

When compute_capability is not provided, these methods return a dict of variants, which was keyed by a (compute_capability, argtypes) tuple. The dict is now only keyed by argument types, and items in the dict are for the device in the current context. For backwards compatibility, the returned dict is temporarily a subclass that will also allow indexing by (compute_capability, argtypes) as well as by argtypes only.

For specialized Dispatchers (those whose kernels were eagerly compiled by providing a signature), the methods return only one variant, instead of a dict of variants. For consistency with the CPU target and for support for multiple signatures to be added to the CUDA target, these methods will always return a dict in future.

The ptx property also returns one variant directly for specialized Dispatchers, and a dict for un-specialized Dispatchers. It too will always return a dict in future.

Recommendations¶

Update calls to these methods such that:

They are always called when the device for which their output is required is in the current CUDA context.
The compute_capability kwarg is not passed to them.
Any use of their results indexes into them using only a tuple of argument types.
For specialized Dispatchers, check whether the result is a dict and index into it accordingly if so.

Schedule¶

In 0.53.0:

The compute_capability kwarg is deprecated.
Returned values from the inspection methods will support indexing by (compute_capability, argtypes) and argtypes.
Specialized dispatchers and will return a single variant from these methods and the ptx property rather than a dict, but will produce a warning.

In 0.54.0:

The compute_capability kwarg will be removed.
ptx and the inspection methods will always return a dict.
Support for indexing into the results of these methods using (cc, argtypes) will be removed.

Deprecation Notices¶

Suppressing Deprecation warnings¶

Deprecation of reflection for List and Set types¶

Reason for deprecation¶

Example(s) of the impact¶

Schedule¶

Recommendations¶

Expected Replacement¶

Deprecation of object mode fall-back behaviour when using @jit¶

Reason for deprecation¶

Example(s) of the impact¶

Schedule¶

Recommendations¶

Deprecation of the target kwarg¶

Recommendations¶

Schedule¶

Deprecation of the role of compute capability for CUDA inspection methods¶

Recommendations¶

Schedule¶

Deprecation of object mode fall-back behaviour when using `@jit`¶