.. decorator:: numba.jit(signature=None, nopython=False, nogil=False, cache=False, forceobj=False, parallel=False, error_model='python', fastmath=False, locals={}, boundscheck=False)
Compile the decorated function on-the-fly to produce efficient machine
code. All parameters are optional.
If present, the *signature* is either a single signature or a list of
signatures representing the expected :ref:`numba-types` of function
arguments and return values. Each signature can be given in several
forms:
* A tuple of :ref:`numba-types` arguments (for example
``(numba.int32, numba.double)``) representing the types of the
function's arguments; Numba will then infer an appropriate return
type from the arguments.
* A call signature using :ref:`numba-types`, specifying both return
type and argument types. This can be given in intuitive form
(for example ``numba.void(numba.int32, numba.double)``).
* A string representation of one of the above, for example
``"void(int32, double)"``. All type names used in the string are assumed
to be defined in the ``numba.types`` module.
*nopython* and *nogil* are boolean flags. *locals* is a mapping of
local variable names to :ref:`numba-types`.
This decorator has several modes of operation:
* If one or more signatures are given in *signature*, a specialization is
compiled for each of them. Calling the decorated function will then try
to choose the best matching signature, and raise a :class:`TypeError` if
no appropriate conversion is available for the function arguments. If
converting succeeds, the compiled machine code is executed with the
converted arguments and the return value is converted back according to
the signature.
* If no *signature* is given, the decorated function implements
lazy compilation. Each call to the decorated function will try to
re-use an existing specialization if it exists (for example, a call
with two integer arguments may re-use a specialization for argument
types ``(numba.int64, numba.int64)``). If no suitable specialization
exists, a new specialization is compiled on-the-fly, stored for later
use, and executed with the converted arguments.
If true, *nopython* forces the function to be compiled in :term:`nopython
mode`. If not possible, compilation will raise an error.
If true, *forceobj* forces the function to be compiled in :term:`object
mode`. Since object mode is slower than nopython mode, this is mostly
useful for testing purposes.
If true, *nogil* tries to release the :py:term:`global interpreter lock`
inside the compiled function. The GIL will only be released if Numba can
compile the function in :term:`nopython mode`, otherwise a compilation
warning will be printed.
.. _jit-decorator-cache:
If true, *cache* enables a file-based cache to shorten compilation times
when the function was already compiled in a previous invocation.
The cache is maintained in the ``__pycache__`` subdirectory of
the directory containing the source file; if the current user is not
allowed to write to it, though, it falls back to a platform-specific
user-wide cache directory (such as ``$HOME/.cache/numba`` on Unix
platforms).
.. _jit-decorator-parallel:
If true, *parallel* enables the automatic parallelization of a number of
common NumPy constructs as well as the fusion of adjacent parallel
operations to maximize cache locality.
The *error_model* option controls the divide-by-zero behavior.
Setting it to 'python' causes divide-by-zero to raise exception like CPython.
Setting it to 'numpy' causes divide-by-zero to set the result to *+/-inf* or
*nan*.
Not all functions can be cached, since some functionality cannot be
always persisted to disk. When a function cannot be cached, a
warning is emitted.
.. _jit-decorator-fastmath:
If true, *fastmath* enables the use of otherwise unsafe floating point
transforms as described in the
`LLVM documentation <https://llvm.org/docs/LangRef.html#fast-math-flags>`_.
Further, if :ref:`Intel SVML <intel-svml>` is installed faster but less
accurate versions of some math intrinsics are used (answers to within
``4 ULP``).
.. _jit-decorator-boundscheck:
If true, *boundscheck* enables bounds checking for array indices. Out of
bounds accesses will raise IndexError. The default is to not do bounds
checking. If bounds checking is disabled, out of bounds accesses can
produce garbage results or segfaults. However, enabling bounds checking
will slow down typical functions, so it is recommended to only use this
flag for debugging. You can also set the `NUMBA_BOUNDSCHECK` environment
variable to 0 or 1 to globally override this flag.
The *locals* dictionary may be used to force the :ref:`numba-types`
of particular local variables, for example if you want to force the
use of single precision floats at some point. In general, we recommend
you let Numba's compiler infer the types of local variables by itself.
Here is an example with two signatures::
@jit(["int32(int32)", "float32(float32)"], nopython=True)
def f(x): ...
Not putting any parentheses after the decorator is equivalent to calling
the decorator without any arguments, i.e.::
@jit
def f(x): ...
is equivalent to::
@jit()
def f(x): ...
The decorator returns a :class:`Dispatcher` object.
.. note::
If no *signature* is given, compilation errors will be raised when
the actual compilation occurs, i.e. when the function is first called
with some given argument types.
.. note::
Compilation can be influenced by some dedicated :ref:`numba-envvars`.
.. decorator:: numba.generated_jit(nopython=False, nogil=False, cache=False, forceobj=False, locals={})
Like the :func:`~numba.jit` decorator, but calls the decorated function at
compile-time, passing the *types* of the function's arguments.
The decorated function must return a callable which will be compiled as
the function's implementation for those types, allowing flexible kinds of
specialization.
The :func:`~numba.generated_jit` decorator returns a :class:`Dispatcher` object.
The class of objects created by calling :func:`~numba.jit` or :func:`~numba.generated_jit`. You shouldn't try to create such an object in any other way. Calling a Dispatcher object calls the compiled specialization for the arguments with which it is called, letting it act as an accelerated replacement for the Python function which was compiled.
In addition, Dispatcher objects have the following methods and attributes:
.. attribute:: py_func The pure Python function which was compiled.
.. method:: inspect_types(file=None, pretty=False) Print out a listing of the function source code annotated line-by-line with the corresponding Numba IR, and the inferred types of the various variables. If *file* is specified, printing is done to that file object, otherwise to sys.stdout. If *pretty* is set to True then colored ANSI will be produced in a terminal and HTML in a notebook. .. seealso:: :ref:`architecture`
.. method:: inspect_llvm(signature=None) Return a dictionary keying compiled function signatures to the human readable LLVM IR generated for the function. If the signature keyword is specified a string corresponding to that individual signature is returned.
.. method:: inspect_asm(signature=None) Return a dictionary keying compiled function signatures to the human-readable native assembly code for the function. If the signature keyword is specified a string corresponding to that individual signature is returned.
.. method:: inspect_cfg(signature=None, show_wrapped)
Return a dictionary keying compiled function signatures to the
control-flow graph objects for the function. If the signature keyword is
specified a string corresponding to that individual signature is returned.
The control-flow graph objects can be stringified (``str`` or ``repr``)
to get the textual representation of the graph in DOT format. Or, use
its ``.display(filename=None, view=False)`` method to plot the graph.
The *filename* option can be set to a specific path for the rendered
output to write to. If *view* option is True, the plot is opened by
the system default application for the image format (PDF). In IPython
notebook, the returned object can be plot inlined.
Usage::
@jit
def foo():
...
# opens the CFG in system default application
foo.inspect_cfg(foo.signatures[0]).display(view=True)
.. method:: inspect_disasm_cfg(signature=None)
Return a dictionary keying compiled function signatures to the
control-flow graph of the disassembly of the underlying compiled ``ELF``
object. If the signature keyword is specified a control-flow graph
corresponding to that individual signature is returned. This function is
execution environment aware and will produce SVG output in Jupyter
notebooks and ASCII in terminals.
Example::
@njit
def foo(x):
if x < 3:
return x + 1
return x + 2
foo(10)
print(foo.inspect_disasm_cfg(signature=foo.signatures[0]))
Gives::
[0x08000040]> # method.__main__.foo_241_long_long (int64_t arg1, int64_t arg3);
─────────────────────────────────────────────────────────────────────┐
│ 0x8000040 │
│ ; arg3 ; [02] -r-x section size 279 named .text │
│ ;-- section..text: │
│ ;-- .text: │
│ ;-- __main__::foo$241(long long): │
│ ;-- rip: │
│ 25: method.__main__.foo_241_long_long (int64_t arg1, int64_t arg3); │
│ ; arg int64_t arg1 @ rdi │
│ ; arg int64_t arg3 @ rdx │
│ ; 2 │
│ cmp rdx, 2 │
│ jg 0x800004f │
└─────────────────────────────────────────────────────────────────────┘
f t
│ │
│ └──────────────────────────────┐
└──┐ │
│ │
┌─────────────────────────┐ ┌─────────────────────────┐
│ 0x8000046 │ │ 0x800004f │
│ ; arg3 │ │ ; arg3 │
│ inc rdx │ │ add rdx, 2 │
│ ; arg3 │ │ ; arg3 │
│ mov qword [rdi], rdx │ │ mov qword [rdi], rdx │
│ xor eax, eax │ │ xor eax, eax │
│ ret │ │ ret │
└─────────────────────────┘ └─────────────────────────┘
.. method:: recompile() Recompile all existing signatures. This can be useful for example if a global or closure variable was frozen by your function and its value in Python has changed. Since compiling isn't cheap, this is mainly for testing and interactive use.
.. method:: parallel_diagnostics(signature=None, level=1) Print parallel diagnostic information for the given signature. If no signature is present it is printed for all known signatures. ``level`` is used to adjust the verbosity, ``level=1`` (default) is minimum verbosity, levels 2, 3, and 4 provide increasing levels of verbosity.
.. method:: get_metadata(signature=None) Obtain the compilation metadata for a given signature. This is useful for developers of Numba and Numba extensions.
.. decorator:: numba.vectorize(*, signatures=[], identity=None, nopython=True, target='cpu', forceobj=False, cache=False, locals={})
Compile the decorated function and wrap it either as a `NumPy
ufunc`_ or a Numba :class:`~numba.DUFunc`. The optional
*nopython*, *forceobj* and *locals* arguments have the same meaning
as in :func:`numba.jit`.
*signatures* is an optional list of signatures expressed in the
same form as in the :func:`numba.jit` *signature* argument. If
*signatures* is non-empty, then the decorator will compile the user
Python function into a NumPy ufunc. If no *signatures* are given,
then the decorator will wrap the user Python function in a
:class:`~numba.DUFunc` instance, which will compile the user
function at call time whenever NumPy can not find a matching loop
for the input arguments. *signatures* is required if *target* is
``"parallel"``.
*identity* is the identity (or unit) value of the function being
implemented. Possible values are 0, 1, None, and the string
``"reorderable"``. The default is None. Both None and
``"reorderable"`` mean the function has no identity value;
``"reorderable"`` additionally specifies that reductions along multiple
axes can be reordered.
If there are several *signatures*, they must be ordered from the more
specific to the least specific. Otherwise, NumPy's type-based
dispatching may not work as expected. For example, the following is
wrong::
@vectorize(["float64(float64)", "float32(float32)"])
def f(x): ...
as running it over a single-precision array will choose the ``float64``
version of the compiled function, leading to much less efficient
execution. The correct invocation is::
@vectorize(["float32(float32)", "float64(float64)"])
def f(x): ...
*target* is a string for backend target; Available values are "cpu",
"parallel", and "cuda". To use a multithreaded version, change the
target to "parallel" (which requires signatures to be specified)::
@vectorize(["float64(float64)", "float32(float32)"], target='parallel')
def f(x): ...
For the CUDA target, use "cuda"::
@vectorize(["float64(float64)", "float32(float32)"], target='cuda')
def f(x): ...
The compiled function can be cached to reduce future compilation time.
It is enabled by setting *cache* to True. Only the "cpu" and "parallel"
targets support caching.
.. decorator:: numba.guvectorize(signatures, layout, *, identity=None, nopython=True, target='cpu', forceobj=False, cache=False, locals={})
Generalized version of :func:`numba.vectorize`. While
:func:`numba.vectorize` will produce a simple ufunc whose core
functionality (the function you are decorating) operates on scalar
operands and returns a scalar value, :func:`numba.guvectorize`
allows you to create a `NumPy ufunc`_ whose core function takes array
arguments of various dimensions.
The additional argument *layout* is a string specifying, in symbolic
form, the dimensionality and size relationship of the argument types
and return types. For example, a matrix multiplication will have
a layout string of ``"(m,n),(n,p)->(m,p)"``. Its definition might
be (function body omitted)::
@guvectorize(["void(float64[:,:], float64[:,:], float64[:,:])"],
"(m,n),(n,p)->(m,p)")
def f(a, b, result):
"""Fill-in *result* matrix such as result := a * b"""
...
If one of the arguments should be a scalar, the corresponding layout
specification is ``()`` and the argument will really be given to
you as a zero-dimension array (you have to dereference it to get the
scalar value). For example, a :ref:`one-dimension moving average <example-movemean>`
with a parameterable window width may have a layout string of ``"(n),()->(n)"``.
Note that any output will be given to you preallocated as an additional
function argument: your code has to fill it with the appropriate values
for the function you are implementing.
If your function doesn't take an output array, you should omit the "arrow"
in the layout string (e.g. ``"(n),(n)"``). When doing this, it is important
to be aware that changes to the input arrays cannot always be relied on to be
visible outside the execution of the ufunc, as NumPy may pass in temporary
arrays as inputs (for example, if a cast is required).
.. seealso::
Specification of the `layout string <https://numpy.org/doc/stable/reference/c-api/generalized-ufuncs.html#details-of-signature>`_
as supported by NumPy. Note that NumPy uses the term "signature",
which we unfortunately use for something else.
The compiled function can be cached to reduce future compilation time.
It is enabled by setting *cache* to True. Only the "cpu" and "parallel"
targets support caching.
The class of objects created by calling :func:`numba.vectorize` with no signatures.
DUFunc instances should behave similarly to NumPy :class:`~numpy.ufunc` objects with one important difference: call-time loop generation. When calling a ufunc, NumPy looks at the existing loops registered for that ufunc, and will raise a :class:`~python.TypeError` if it cannot find a loop that it cannot safely cast the inputs to suit. When calling a DUFunc, Numba delegates the call to NumPy. If the NumPy ufunc call fails, then Numba attempts to build a new loop for the given input types, and calls the ufunc again. If this second call attempt fails or a compilation error occurs, then DUFunc passes along the exception to the caller.
.. seealso:: The ":ref:`dynamic-universal-functions`" section in the user's guide demonstrates the call-time behavior of :class:`~numba.DUFunc`, and discusses the impact of call order on how Numba generates the underlying :class:`~numpy.ufunc`.
.. attribute:: ufunc The actual NumPy :class:`~numpy.ufunc` object being built by the :class:`~numba.DUFunc` instance. Note that the :class:`~numba.DUFunc` object maintains several important data structures required for proper ufunc functionality (specifically the dynamically compiled loops). Users should not pass the :class:`~numpy.ufunc` value around without ensuring the underlying :class:`~numba.DUFunc` will not be garbage collected.
.. attribute:: nin The number of DUFunc (ufunc) inputs. See `ufunc.nin`_.
.. attribute:: nout The number of DUFunc outputs. See `ufunc.nout`_.
.. attribute:: nargs The total number of possible DUFunc arguments (should be :attr:`~numba.DUFunc.nin` + :attr:`~numba.DUFunc.nout`). See `ufunc.nargs`_.
.. attribute:: ntypes The number of input types supported by the DUFunc. See `ufunc.ntypes`_.
.. attribute:: types A list of the supported types given as strings. See `ufunc.types`_.
.. attribute:: identity The identity value when using the ufunc as a reduction. See `ufunc.identity`_.
.. method:: reduce(A, *, axis, dtype, out, keepdims) Reduces *A*\'s dimension by one by applying the DUFunc along one axis. See `ufunc.reduce`_.
.. method:: accumulate(A, *, axis, dtype, out) Accumulate the result of applying the operator to all elements. See `ufunc.accumulate`_.
.. method:: reduceat(A, indices, *, axis, dtype, out) Performs a (local) reduce with specified slices over a single axis. See `ufunc.reduceat`_.
.. method:: outer(A, B) Apply the ufunc to all pairs (*a*, *b*) with *a* in *A*, and *b* in *B*. See `ufunc.outer`_.
.. method:: at(A, indices, *, B) Performs unbuffered in place operation on operand *A* for elements specified by *indices*. If you are using NumPy 1.7 or earlier, this method will not be present. See `ufunc.at`_.
Note
Vectorized functions can, in rare circumstances, show :ref:`unexpected warnings or errors <ufunc-fpu-errors>`.
.. decorator:: numba.cfunc(signature, nopython=False, cache=False, locals={})
Compile the decorated function on-the-fly to produce efficient machine
code. The compiled code is wrapped in a thin C callback that makes it
callable using the natural C ABI.
The *signature* is a single signature representing the signature of the
C callback. It must have the same form as in :func:`~numba.jit`.
The decorator does not check that the types in the signature have
a well-defined representation in C.
*nopython* and *cache* are boolean flags. *locals* is a mapping of
local variable names to :ref:`numba-types`. They all have the same
meaning as in :func:`~numba.jit`.
The decorator returns a :class:`CFunc` object.
.. note::
C callbacks currently do not support :term:`object mode`.
The class of objects created by :func:`~numba.cfunc`. :class:`CFunc` objects expose the following attributes and methods:
.. attribute:: address The address of the compiled C callback, as an integer.
.. attribute:: cffi A `cffi`_ function pointer instance, to be passed as an argument to `cffi`_-wrapped functions. The pointer's type is ``void *``, so only minimal type checking will happen when passing it to `cffi`_.
.. attribute:: ctypes A :mod:`ctypes` callback instance, as if it were created using :func:`ctypes.CFUNCTYPE`.
.. attribute:: native_name The name of the compiled C callback.
.. method:: inspect_llvm() Return the human-readable LLVM IR generated for the C callback. :attr:`native_name` is the name under which this callback is defined in the IR.