numba 0.13.2 documentation
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numba package

Subpackages

Submodules

numba.assume module

A place to store all assumptions made in various part of the code base.

This allow us to do a usage analysis to discover all code that is assuming something.

Each assumption is defined as a global variable. Its value is the description of the assumption. Code that makes the assumption should assert the_assumption

numba.bytecode module

From NumbaPro

class numba.bytecode.ByteCode(func)

Bases: numba.bytecode.ByteCodeBase

class numba.bytecode.ByteCodeBase(func, func_name, argspec, filename, co_names, co_varnames, co_consts, co_freevars, table, labels)

Bases: object

argspec
co_consts
co_freevars
co_names
co_varnames
dump()
filename
func
func_name
labels
table
class numba.bytecode.ByteCodeInst(offset, opcode, arg)

Bases: object

  • offset:

    byte offset of opcode

  • opcode:

    opcode integer value

  • arg:

    instruction arg

  • lineno:

    -1 means unknown

arg
classmethod get(offset, opname, arg)
get_jump_target()
is_jump
is_terminator
lineno
next
offset
opcode
opname
class numba.bytecode.ByteCodeIter(code)

Bases: object

next()
read_arg(size)
class numba.bytecode.ByteCodeOperation(inst, args)

Bases: object

exception numba.bytecode.ByteCodeSupportError

Bases: exceptions.Exception

class numba.bytecode.CustomByteCode(func, func_name, argspec, filename, co_names, co_varnames, co_consts, co_freevars, table, labels)

Bases: numba.bytecode.ByteCodeBase

numba.bytecode.get_code_object(obj)

Shamelessly borrowed from llpython

numba.bytecode.get_function_object(obj)

Objects that wraps function should provide a “__numba__” magic attribute that contains a name of an attribute that contains the actual python function object.

class numba.bytecode.opcode_info

Bases: tuple

opcode_info(argsize,)

argsize

Alias for field number 0

numba.callwrapper module

class numba.callwrapper.PyCallWrapper(context, module, func, fndesc, exceptions)

Bases: object

build()
build_wrapper(api, builder, args, kws)
make_const_string(string)
make_exception_switch(api, builder, code)

Handle user defined exceptions. Build a switch to check which exception class was raised.

make_keywords(kws)

numba.cffi_support module

Support for CFFI. Allows checking whether objects are CFFI functions and obtaining the pointer and numba signature.

class numba.cffi_support.ExternCFunction(symbol, cstring)

Bases: numba.types.Function

numba.cffi_support.get_pointer(cffi_func)

Get a pointer to the underlying function for a CFFI function as an integer.

numba.cffi_support.is_cffi_func(obj)

Check whether the obj is a CFFI function

numba.cffi_support.make_function_type(cffi_func)
numba.cffi_support.map_type(cffi_type)

Map CFFI type to numba type

numba.cgutils module

class numba.cgutils.IfBranchObj(builder, bbenter, bbend)

Bases: object

class numba.cgutils.MetadataKeyStore(module, name)

Bases: object

get()

Get string value

set(value)

Add a string value

class numba.cgutils.Structure(context, builder, value=None, ref=None)

Bases: object

class numba.cgutils.VerboseProxy(obj)

Bases: object

Use to wrap llvm.core.Builder to track where segfault happens

numba.cgutils.alloca_once(builder, ty, name='')
numba.cgutils.append_basic_block(builder, name='')
numba.cgutils.as_bool_byte(builder, value)
numba.cgutils.for_range(*args, **kwds)
numba.cgutils.gep(builder, ptr, *inds)
numba.cgutils.get_function(builder)
numba.cgutils.get_item_pointer(builder, aryty, ary, inds, wraparound=False)
numba.cgutils.get_item_pointer2(builder, data, shape, strides, layout, inds, wraparound=False)
numba.cgutils.get_module(builder)
numba.cgutils.get_null_value(ltype)
numba.cgutils.get_range_from_slice(builder, slicestruct)
numba.cgutils.get_record_data(builder, record)
numba.cgutils.get_record_member(builder, record, offset, typ)
numba.cgutils.get_strides_from_slice(builder, ndim, strides, slice, ax)
numba.cgutils.goto_block(*args, **kwds)
numba.cgutils.goto_entry_block(*args, **kwds)
numba.cgutils.guard_null(context, builder, value)
numba.cgutils.guard_zero(context, builder, value)
numba.cgutils.if_likely(*args, **kwds)
numba.cgutils.if_unlikely(*args, **kwds)
numba.cgutils.ifelse(*args, **kwds)
numba.cgutils.ifnot(*args, **kwds)
numba.cgutils.ifthen(*args, **kwds)
numba.cgutils.inbound_gep(builder, ptr, *inds)
numba.cgutils.init_record_by_ptr(builder, ltyp, ptr)
numba.cgutils.is_false(builder, val)
numba.cgutils.is_neg_int(builder, val)
numba.cgutils.is_not_null(builder, val)
numba.cgutils.is_null(builder, val)
numba.cgutils.is_pointer(ltyp)
numba.cgutils.is_scalar_zero(builder, value)
numba.cgutils.is_struct(ltyp)
numba.cgutils.is_struct_ptr(ltyp)
numba.cgutils.is_true(builder, val)
numba.cgutils.loop_nest(*args, **kwds)
numba.cgutils.normalize_slice(builder, slice, length)

Clip stop

numba.cgutils.pack_array(builder, values)
numba.cgutils.printf(builder, format_string, *values)
numba.cgutils.set_branch_weight(builder, brinst, trueweight, falseweight)
numba.cgutils.set_record_data(builder, record, buf)
numba.cgutils.terminate(builder, bbend)
numba.cgutils.unpack_tuple(builder, tup, count)

numba.compiler module

class numba.compiler.CompileResult

Bases: tuple

CompileResult(typing_context, target_context, entry_point, entry_point_addr, typing_error, type_annotation, llvm_module, llvm_func, signature, objectmode, lifted, fndesc)

entry_point

Alias for field number 2

entry_point_addr

Alias for field number 3

fndesc

Alias for field number 11

lifted

Alias for field number 10

llvm_func

Alias for field number 7

llvm_module

Alias for field number 6

objectmode

Alias for field number 9

signature

Alias for field number 8

target_context

Alias for field number 1

type_annotation

Alias for field number 5

typing_context

Alias for field number 0

typing_error

Alias for field number 4

class numba.compiler.Flags

Bases: numba.utils.ConfigOptions

OPTIONS = frozenset(['no_wraparound', 'enable_looplift', 'no_compile', 'boundcheck', 'force_pyobject', 'enable_pyobject'])
numba.compiler.compile_bytecode(typingctx, targetctx, bc, args, return_type, flags, locals, lifted=())
numba.compiler.compile_extra(typingctx, targetctx, func, args, return_type, flags, locals)
  • return_type

    Use None to indicate

numba.compiler.compile_isolated(func, args, return_type=None, flags=Flags(), locals={})

Compile the function is an isolated environment. Good for testing.

numba.compiler.compile_result(**kws)
numba.compiler.ir_optimize_for_py_stage(interp)

This passes breaks semantic for the type inferer but they reduces refct calls for object mode.

numba.compiler.legalize_given_types(args, return_type)
numba.compiler.legalize_return_type(return_type, interp, targetctx)

Only accept array return type iff it is passed into the function.

numba.compiler.native_lowering_stage(targetctx, interp, typemap, restype, calltypes, nocompile)
numba.compiler.py_lowering_stage(targetctx, interp, nocompile)
numba.compiler.translate_stage(bytecode)
numba.compiler.type_inference_stage(typingctx, interp, args, return_type, locals={})

numba.config module

numba.controlflow module

class numba.controlflow.CFBlock(offset)

Bases: object

class numba.controlflow.ControlFlowAnalysis(bytecode)

Bases: object

  • bytecode

  • blocks

  • blockseq

  • doms: dict of set

    Dominators

  • backbone: set of block offsets

    The set of block that is common to all possible code path.

dead_block_elimin()
iterblocks()

Return all blocks in sequence of occurrence

iterliveblocks()

Return all live blocks in sequence of occurrence

jump(target)
op_BREAK_LOOP(inst)
op_FOR_ITER(inst)
op_JUMP_ABSOLUTE(inst)
op_JUMP_FORWARD(inst)
op_JUMP_IF_FALSE(inst)
op_JUMP_IF_FALSE_OR_POP(inst)
op_JUMP_IF_TRUE(inst)
op_JUMP_IF_TRUE_OR_POP(inst)
op_POP_BLOCK(inst)
op_POP_JUMP_IF_FALSE(inst)
op_POP_JUMP_IF_TRUE(inst)
op_RAISE_VARARGS(inst)
op_RETURN_VALUE(inst)
op_SETUP_LOOP(inst)
run()
numba.controlflow.find_dominators(blocks)

numba.ctypes_support module

This file fixes portability issues for ctypes

numba.ctypes_utils module

This file fixes portability issues for ctypes

numba.ctypes_utils.convert_ctypes(ctypeobj)
numba.ctypes_utils.is_ctypes_funcptr(obj)
numba.ctypes_utils.make_function_type(cfnptr)

numba.dataflow module

class numba.dataflow.BlockInfo(offset)

Bases: object

append(inst, **kws)
dump()
make_temp()
pop()
push(val)
terminator
tos
class numba.dataflow.DataFlowAnalysis(cfa)

Bases: object

Perform stack2reg

This is necessary to resolve blocks that propagates stack value. This would allow the use of and and or and python2.6 jumps.

dispatch(info, inst)
dump()
dup_topx(info, count)
op_BINARY_ADD(info, inst)
op_BINARY_AND(info, inst)
op_BINARY_DIVIDE(info, inst)
op_BINARY_FLOOR_DIVIDE(info, inst)
op_BINARY_LSHIFT(info, inst)
op_BINARY_MODULO(info, inst)
op_BINARY_MULTIPLY(info, inst)
op_BINARY_OR(info, inst)
op_BINARY_POWER(info, inst)
op_BINARY_RSHIFT(info, inst)
op_BINARY_SUBSCR(info, inst)
op_BINARY_SUBTRACT(info, inst)
op_BINARY_TRUE_DIVIDE(info, inst)
op_BINARY_XOR(info, inst)
op_BREAK_LOOP(info, inst)
op_BUILD_LIST(info, inst)
op_BUILD_SLICE(info, inst)

slice(TOS1, TOS) or slice(TOS2, TOS1, TOS)

op_BUILD_TUPLE(info, inst)
op_CALL_FUNCTION(info, inst)
op_COMPARE_OP(info, inst)
op_DUP_TOP(info, inst)
op_DUP_TOPX(info, inst)
op_DUP_TOP_TWO(info, inst)
op_FOR_ITER(info, inst)
op_GET_ITER(info, inst)
op_INPLACE_ADD(info, inst)
op_INPLACE_AND(info, inst)
op_INPLACE_DIVIDE(info, inst)
op_INPLACE_FLOOR_DIVIDE(info, inst)
op_INPLACE_LSHIFT(info, inst)
op_INPLACE_MODULO(info, inst)
op_INPLACE_MULTIPLY(info, inst)
op_INPLACE_OR(info, inst)
op_INPLACE_POWER(info, inst)
op_INPLACE_RSHIFT(info, inst)
op_INPLACE_SUBTRACT(info, inst)
op_INPLACE_TRUE_DIVIDE(info, inst)
op_INPLACE_XOR(info, inst)
op_JUMP_ABSOLUTE(info, inst)
op_JUMP_FORWARD(info, inst)
op_JUMP_IF_FALSE(info, inst)
op_JUMP_IF_FALSE_OR_POP(info, inst)
op_JUMP_IF_TRUE(info, inst)
op_JUMP_IF_TRUE_OR_POP(info, inst)
op_LOAD_ATTR(info, inst)
op_LOAD_CONST(info, inst)
op_LOAD_DEREF(info, inst)
op_LOAD_FAST(info, inst)
op_LOAD_GLOBAL(info, inst)
op_POP_BLOCK(info, inst)
op_POP_JUMP_IF_FALSE(info, inst)
op_POP_JUMP_IF_TRUE(info, inst)
op_POP_TOP(info, inst)
op_PRINT_ITEM(info, inst)
op_PRINT_NEWLINE(info, inst)
op_RAISE_VARARGS(info, inst)
op_RETURN_VALUE(info, inst)
op_ROT_FOUR(info, inst)
op_ROT_THREE(info, inst)
op_ROT_TWO(info, inst)
op_SETUP_LOOP(info, inst)
op_SLICE_0(info, inst)

TOS = TOS[:]

op_SLICE_1(info, inst)

TOS = TOS1[TOS:]

op_SLICE_2(info, inst)

TOS = TOS1[:TOS]

op_SLICE_3(info, inst)

TOS = TOS2[TOS1:TOS]

op_STORE_ATTR(info, inst)
op_STORE_FAST(info, inst)
op_STORE_SLICE_0(info, inst)

TOS[:] = TOS1

op_STORE_SLICE_1(info, inst)

TOS1[TOS:] = TOS2

op_STORE_SLICE_2(info, inst)

TOS1[:TOS] = TOS2

op_STORE_SLICE_3(info, inst)

TOS2[TOS1:TOS] = TOS3

op_STORE_SUBSCR(info, inst)
op_UNARY_INVERT(info, inst)
op_UNARY_NEGATIVE(info, inst)
op_UNARY_NOT(info, inst)
op_UNARY_POSITIVE(info, inst)
op_UNPACK_SEQUENCE(info, inst)
run()
run_on_block(blk)
class numba.dataflow.LoopBlock

Bases: object

iterator

numba.decorators module

Contains function decorators and target_registry

numba.decorators.autojit(*args, **kws)

Deprecated.

Use jit instead. Calls to jit internally.

numba.decorators.jit(signature_or_function=None, argtypes=None, restype=None, locals={}, target='cpu', **targetoptions)
jit([signature_or_function, [locals={}, [target=’cpu’,
[**targetoptions]]]])

The function can be used as the following versions:

  1. jit(signature, [target=’cpu’, [**targetoptions]]) -> jit(function)

    Equivalent to:

    d = dispatcher(function, targetoptions) d.compile(signature)

    Create a dispatcher object for a python function and default target-options. Then, compile the funciton with the given signature.

    Example:

    @jit(“void(int32, float32)”) def foo(x, y):

    return x + y

  2. jit(function) -> dispatcher

    Same as old autojit. Create a dispatcher function object that specialize at call site.

    Example:

    @jit def foo(x, y):

    return x + y

  3. jit([target=’cpu’, [**targetoptions]]) -> configured_jit(function)

    Same as old autojit and 2). But configure with target and default target-options.

    Example:

    @jit(target=’cpu’, nopython=True) def foo(x, y):

    return x + y

The CPU (default target) defines the following:

  • nopython: [bool]

    Set to True to disable the use of PyObjects and Python API calls. The default behavior is to allow the use of PyObjects and Python API. Default value is False.

  • forceobj: [bool]

    Set to True to force the use of PyObjects for every value. Default value is False.

numba.decorators.njit(*args, **kws)

Equavilent to jit(nopython=True)

numba.dispatcher module

class numba.dispatcher.LiftedLoop(bytecode, typingctx, targetctx, locals, flags)

Bases: numba.dispatcher.Overloaded

compile(sig)
class numba.dispatcher.Overloaded(py_func, locals={}, targetoptions={})

Bases: _dispatcher.Dispatcher

Abstract class. Subclass should define targetdescr class attribute.

add_overload(cres)
compile(sig, locals={}, **targetoptions)
disable_compile(val=True)

Disable the compilation of new signatures at call time.

get_overload(sig)
inspect_types()
is_compiling
jit(sig, **kws)

Alias of compile(sig, **kws)

signatures

Returns a list of compiled function signatures.

classmethod typeof_pyval(val)

This is called from numba._dispatcher as a fallback if the native code cannot decide the type.

numba.dummyarray module

class numba.dummyarray.Array(dims, itemsize)

Bases: object

A dummy numpy array-like object. Consider it an array without the actual data, but offset from the base data pointer.

dims: tuple of Dim
describing each dimension of the array
ndim: int
number of dimension
shape: tuple of int
size of each dimension
strides: tuple of int
stride of each dimension
itemsize: int
itemsize
extent: (start, end)
start and end offset containing the memory region
classmethod from_desc(offset, shape, strides, itemsize)
is_c_contig
is_f_contig
iter_contiguous_extent()

Generates extents

ravel(order='C')
reshape(*newshape, **kws)
class numba.dummyarray.Dim(start, stop, size, stride)

Bases: object

A single dimension of the array

start:
start offset
stop:
stop offset
size:
number of items
stride:
item stride
copy(start=None, stop=None, size=None, stride=None)
get_offset(idx)
is_contiguous(itemsize)
normalize(base)
size
start
stop
stride
class numba.dummyarray.Extent

Bases: tuple

Extent(begin, end)

begin

Alias for field number 0

end

Alias for field number 1

numba.dummyarray.compute_index(indices, dims)
numba.dummyarray.is_element_indexing(item, ndim)
numba.dummyarray.iter_strides_c_contig(arr, shape=None)

yields the c-contigous strides

numba.dummyarray.iter_strides_f_contig(arr, shape=None)

yields the f-contigous strides

numba.errcode module

class numba.errcode.Enum(init=0)

Bases: object

get()

numba.findlib module

numba.findlib.find_file(pat, libdir=None)
numba.findlib.find_lib(libname, libdir=None, platform=None)
numba.findlib.get_lib_dir()

Anaconda specific

numba.interpreter module

class numba.interpreter.Interpreter(bytecode)

Bases: object

A bytecode interpreter that builds up the IR.

block_constains_opname(offset, opname)
code_consts
code_freevars
code_locals
code_names
current_scope
dump(file=None)
get(name)
get_closure_value(index)

Get a value from the cell contained in this function’s closure.

get_global_value(name)

Get a global value from the func_global (first) or as a builtins (second). If both failed, return a ir.UNDEFINED.

init_first_block()
insert_block(offset, scope=None, loc=None)
interpret()
op_BINARY_ADD(inst, lhs, rhs, res)
op_BINARY_AND(inst, lhs, rhs, res)
op_BINARY_DIVIDE(inst, lhs, rhs, res)
op_BINARY_FLOOR_DIVIDE(inst, lhs, rhs, res)
op_BINARY_LSHIFT(inst, lhs, rhs, res)
op_BINARY_MODULO(inst, lhs, rhs, res)
op_BINARY_MULTIPLY(inst, lhs, rhs, res)
op_BINARY_OR(inst, lhs, rhs, res)
op_BINARY_POWER(inst, lhs, rhs, res)
op_BINARY_RSHIFT(inst, lhs, rhs, res)
op_BINARY_SUBSCR(inst, target, index, res)
op_BINARY_SUBTRACT(inst, lhs, rhs, res)
op_BINARY_TRUE_DIVIDE(inst, lhs, rhs, res)
op_BINARY_XOR(inst, lhs, rhs, res)
op_BREAK_LOOP(inst)
op_BUILD_LIST(inst, items, res)
op_BUILD_SLICE(inst, start, stop, step, res, slicevar)
op_BUILD_TUPLE(inst, items, res)
op_CALL_FUNCTION(inst, func, args, kws, res)
op_COMPARE_OP(inst, lhs, rhs, res)
op_FOR_ITER(inst, iterator, indval, pred)

Assign new block other this instruction.

op_GET_ITER(inst, value, res)
op_INPLACE_ADD(inst, lhs, rhs, res)
op_INPLACE_AND(inst, lhs, rhs, res)
op_INPLACE_DIVIDE(inst, lhs, rhs, res)
op_INPLACE_FLOOR_DIVIDE(inst, lhs, rhs, res)
op_INPLACE_LSHIFT(inst, lhs, rhs, res)
op_INPLACE_MODULO(inst, lhs, rhs, res)
op_INPLACE_MULTIPLY(inst, lhs, rhs, res)
op_INPLACE_OR(inst, lhs, rhs, res)
op_INPLACE_POWER(inst, lhs, rhs, res)
op_INPLACE_RSHIFT(inst, lhs, rhs, res)
op_INPLACE_SUBTRACT(inst, lhs, rhs, res)
op_INPLACE_TRUE_DIVIDE(inst, lhs, rhs, res)
op_INPLACE_XOR(inst, lhs, rhs, res)
op_JUMP_ABSOLUTE(inst)
op_JUMP_FORWARD(inst)
op_JUMP_IF_FALSE(inst, pred)
op_JUMP_IF_FALSE_OR_POP(inst, pred)
op_JUMP_IF_TRUE(inst, pred)
op_JUMP_IF_TRUE_OR_POP(inst, pred)
op_LOAD_ATTR(inst, item, res)
op_LOAD_CONST(inst, res)
op_LOAD_DEREF(inst, res)
op_LOAD_GLOBAL(inst, res)
op_POP_BLOCK(inst, delitem=None)
op_POP_JUMP_IF_FALSE(inst, pred)
op_POP_JUMP_IF_TRUE(inst, pred)
op_PRINT_ITEM(inst, item, printvar, res)
op_PRINT_NEWLINE(inst, printvar, res)
op_RAISE_VARARGS(inst, exc)
op_RETURN_VALUE(inst, retval)
op_SETUP_LOOP(inst)
op_SLICE_0(inst, base, res, slicevar, indexvar, nonevar)
op_SLICE_1(inst, base, start, nonevar, res, slicevar, indexvar)
op_SLICE_2(inst, base, nonevar, stop, res, slicevar, indexvar)
op_SLICE_3(inst, base, start, stop, res, slicevar, indexvar)
op_STORE_ATTR(inst, target, value)
op_STORE_FAST(inst, value)
op_STORE_SLICE_0(inst, base, value, slicevar, indexvar, nonevar)
op_STORE_SLICE_1(inst, base, start, nonevar, value, slicevar, indexvar)
op_STORE_SLICE_2(inst, base, nonevar, stop, value, slicevar, indexvar)
op_STORE_SLICE_3(inst, base, start, stop, value, slicevar, indexvar)
op_STORE_SUBSCR(inst, target, index, value)
op_UNARY_INVERT(inst, value, res)
op_UNARY_NEGATIVE(inst, value, res)
op_UNARY_NOT(inst, value, res)
op_UNARY_POSITIVE(inst, value, res)
op_UNPACK_SEQUENCE(inst, sequence, stores, iterobj)
store(value, name)
verify()

numba.io_support module

numba.ir module

class numba.ir.Assign(value, target, loc)

Bases: numba.ir.Stmt

class numba.ir.Block(scope, loc)

Bases: object

A code block

append(inst)
dump(file=<open file '<stdout>', mode 'w' at 0x10028d150>)
insert_before_terminator(stmt)
is_terminated
remove(inst)
terminator
verify()
class numba.ir.Branch(cond, truebr, falsebr, loc)

Bases: numba.ir.Stmt

is_terminator = True
class numba.ir.Const(value, loc)

Bases: object

class numba.ir.Del(value, loc)

Bases: numba.ir.Stmt

class numba.ir.Expr(op, loc, **kws)

Bases: object

classmethod binop(fn, lhs, rhs, loc)
classmethod build_list(items, loc)
classmethod build_tuple(items, loc)
classmethod call(func, args, kws, loc)
classmethod getattr(value, attr, loc)
classmethod getitem(target, index, loc)
classmethod getiter(value, loc)
classmethod iternext(value, loc)
classmethod iternextsafe(value, loc)
classmethod itervalid(value, loc)
list_vars()
classmethod unary(fn, value, loc)
class numba.ir.Global(name, value, loc)

Bases: object

class numba.ir.Intrinsic(name, type, args)

Bases: object

For inserting intrinsic node into the IR

class numba.ir.Jump(target, loc)

Bases: numba.ir.Stmt

is_terminator = True
class numba.ir.Loc(filename, line, col=None)

Bases: object

Source location

strformat()
class numba.ir.Loop(entry, exit, condition=None)

Bases: object

body
condition
entry
exit
valid()
verify()
exception numba.ir.NotDefinedError

Bases: exceptions.NameError

class numba.ir.Raise(exception, loc)

Bases: numba.ir.Stmt

is_terminator = True
exception numba.ir.RedefinedError

Bases: exceptions.NameError

class numba.ir.Return(value, loc)

Bases: numba.ir.Stmt

is_terminator = True
class numba.ir.Scope(parent, loc)

Bases: object

  • parent: Scope

    Parent scope

  • localvars: VarMap

    Scope-local variable map

  • loc: Loc

    Start of scope location

define(name, loc)

Define a variable

get(name)

Refer to a variable

get_or_define(name, loc)
has_parent
make_temp(loc)
redefine(name, loc)

Redefine if the name is already defined

class numba.ir.SetAttr(target, attr, value, loc)

Bases: numba.ir.Stmt

class numba.ir.SetItem(target, index, value, loc)

Bases: numba.ir.Stmt

class numba.ir.Stmt

Bases: object

is_terminator = False
class numba.ir.Var(scope, name, loc)

Bases: object

  • scope: Scope

  • name: str

  • loc: Loc

    Definition location

is_temp
class numba.ir.VarMap

Bases: object

define(name, var)
get(name)
exception numba.ir.VerificationError

Bases: exceptions.Exception

numba.irpasses module

Contains optimization passes for the IR.

class numba.irpasses.RemoveRedundantAssign(interp)

Bases: object

Turn assignment pairs into one assignment

mark_asssignment(tempassign, offset, inst)
run()
run_block(blk)

numba.looplifting module

numba.looplifting.bind(loops, typingctx, targetctx, locals, flags)

Install loop dispatchers into the module

numba.looplifting.bind_loop(loopbc, typingctx, targetctx, locals, flags)
numba.looplifting.discover_args_and_returns(bytecode, insts, outer_rds, outer_wrs)

Basic analysis for args and returns This completely ignores the ordering or the read-writes.

numba.looplifting.find_previous_inst(insts, offset)
numba.looplifting.find_varnames_uses(bytecode, insts)
numba.looplifting.insert_instruction(insts, item)
numba.looplifting.insert_loop_call(bytecode, loop, args, lbclist, outer, outerlabels, outernames)
numba.looplifting.lift_loop(bytecode)

Lift the top-level loops.

  • outer: ByteCode of a copy of the loop-less function.
  • loops: a list of ByteCode of the loops.
numba.looplifting.make_loop_bytecode(bytecode, loop, args)
numba.looplifting.separate_loops(bytecode, outer, loops)

Separate top-level loops from the function

Stores loopless instructions from the original function into outer. Stores list of loop instructions into loops. Both outer and loops are list-like (append(item) defined).

numba.looplifting.stitch_instructions(outer, loop)

numba.lowering module

class numba.lowering.BaseLower(context, fndesc)

Bases: object

Lower IR to LLVM

init()
init_argument(arg)
lower()
lower_block(block)
post_lower()

Called after all blocks are lowered

typeof(varname)
class numba.lowering.ExternalFunctionDescriptor(name, restype, argtypes)

Bases: numba.lowering.FunctionDescriptor

A FunctionDescriptor subclass for opaque external functions (e.g. raw C functions).

class numba.lowering.FunctionDescriptor(native, modname, qualname, unique_name, doc, blocks, typemap, restype, calltypes, args, kws, mangler=None, argtypes=None, globals=None)

Bases: object

args
argtypes
blocks
calltypes
doc
globals
kws
lookup_module()

Return the module in which this function is supposed to exist. This may be a dummy module if the function was dynamically generated.

mangled_name
modname
native
qualname
restype
typemap
unique_name
class numba.lowering.Lower(context, fndesc)

Bases: numba.lowering.BaseLower

alloca(name, type)
getvar(name)
loadvar(name)
lower_assign(ty, inst)
lower_expr(resty, expr)
lower_inst(inst)
storevar(value, name)
exception numba.lowering.LoweringError(msg, loc)

Bases: exceptions.Exception

class numba.lowering.PyIterState(context, builder, value=None, ref=None)

Bases: numba.cgutils.Structure

class numba.lowering.PyLower(context, fndesc)

Bases: numba.lowering.BaseLower

alloca(name, ltype=None)

Allocate a stack slot and initialize it to NULL. The default is to allocate a pyobject pointer. Use ltype to override.

builtin_lookup(mod, name)
mod:
The __builtins__ dictionary or module
name: str
The object to lookup
check_error(obj)
check_occurred()
cleanup()
decref(value)

This is allow to be called on non pyobject pointer, in which case no code is inserted.

If the value is a PyIterState, it unpack the structure and decref the iterator.

get_builtin_obj(name)
getvar(name, ltype=None)
incref(value)
init()
init_argument(arg)
is_null(obj)
loadvar(name)
lower_assign(inst)

The returned object must have a new reference

lower_const(const)
lower_expr(expr)
lower_global(name, value)

  1. Check global scope dictionary.

  2. Check __builtins__.

    2a) is it a dictionary (for non __main__ module) 2b) is it a module (for __main__ module)

lower_inst(inst)
pack_iter(obj)
post_lower()
return_error_occurred()
return_exception_raised()
set_iter_valid(state, item)
storevar(value, name)

Stores a llvm value and allocate stack slot if necessary. The llvm value can be of arbitrary type.

unpack_iter(state)
class numba.lowering.PythonFunctionDescriptor(native, modname, qualname, unique_name, doc, blocks, typemap, restype, calltypes, args, kws, mangler=None, argtypes=None, globals=None)

Bases: numba.lowering.FunctionDescriptor

classmethod from_object_mode_function(interp)

Build a FunctionDescriptor for a Python function to be compiled and executed in object mode.

classmethod from_specialized_function(interp, typemap, restype, calltypes, mangler)

Build a FunctionDescriptor for a specialized Python function.

numba.lowering.default_mangler(name, argtypes)

numba.macro module

Macro handling passes

Macros are expanded on block-by-block

class numba.macro.Macro(name, func, callable=False, argnames=None)

Bases: object

A macro object is expanded to a function call

argnames
callable
func
name
numba.macro.expand_macros(blocks)
numba.macro.expand_macros_in_block(constants, block)
numba.macro.module_getattr_folding(constants, block)

numba.numpy_support module

numba.numpy_support.from_dtype(dtype)
numba.numpy_support.from_struct_dtype(dtype)
numba.numpy_support.is_array(val)
numba.numpy_support.is_arrayscalar(val)
numba.numpy_support.map_arrayscalar_type(val)
numba.numpy_support.map_layout(val)
numba.numpy_support.numba_types_to_numpy_letter_types(numba_type_seq)
numba.numpy_support.numpy_letter_types_to_numba_types(numpy_letter_types_seq)
numba.numpy_support.ufunc_find_matching_loop(ufunc, op_dtypes)

numba.pythonapi module

exception numba.pythonapi.NativeError

Bases: exceptions.RuntimeError

class numba.pythonapi.PythonAPI(context, builder)

Bases: object

alloca_obj()
bool_from_long(ival)
borrow_none()
bytes_from_string_and_size(string, size)
call(callee, args, kws)
call_function_objargs(callee, objargs)
complex_adaptor(cobj, cmplx)
complex_from_doubles(realval, imagval)
complex_imag_as_double(cobj)
complex_real_as_double(cobj)
decref(obj)
dict_getitem_string(dic, name)

Returns a borrowed reference

dict_new()
dict_pack(keyvalues)

keyvalues: iterable of (str, llvm.Value of PyObject*)

dict_setitem_string(dictobj, name, valobj)
err_clear()
err_occurred()
err_set_object(exctype, excval)
err_set_string(exctype, msg)
extract_record_data(obj, pbuf)
float_as_double(fobj)
float_from_double(fval)
from_native_array(typ, ary)
from_native_return(val, typ)
from_native_value(val, typ)
from_unituple(typ, val)
get_module_dict()
get_module_dict_symbol()
get_null_object()
import_module_noblock(modname)
incref(obj)
iter_next(iterobj)
list_new(szval)
list_pack(items)
list_setitem(seq, idx, val)

Warning: Steals reference to val

long_as_longlong(numobj)
long_as_ulonglong(numobj)
long_from_long(ival)
long_from_longlong(numobj)
long_from_ssize_t(ival)
long_from_ulonglong(numobj)
make_none()
name_error_type
native_error_type
numba_array_adaptor(ary, ptr)
number_add(lhs, rhs)
number_and(lhs, rhs)
number_as_ssize_t(numobj)
number_divide(lhs, rhs)
number_float(val)
number_floordivide(lhs, rhs)
number_invert(obj)
number_long(numobj)
number_lshift(lhs, rhs)
number_multiply(lhs, rhs)
number_negative(obj)
number_or(lhs, rhs)
number_positive(obj)
number_power(lhs, rhs)
number_remainder(lhs, rhs)
number_rshift(lhs, rhs)
number_subtract(lhs, rhs)
number_truedivide(lhs, rhs)
number_xor(lhs, rhs)
object_getattr_string(obj, attr)
object_getitem(obj, key)
object_getiter(obj)
object_istrue(obj)
object_not(obj)
object_richcompare(lhs, rhs, opstr)

Refer to Python source Include/object.h for macros definition of the opid.

object_setitem(obj, key, val)
object_str(obj)
parse_tuple(args, fmt, *objs)
parse_tuple_and_keywords(args, kws, fmt, keywords, *objs)
print_object(obj)
print_string(text)
raise_exception(exctype, excval)
raise_missing_global_error(name)
raise_native_error(msg)
recreate_record(pdata, size, dtypeaddr)
release_record_buffer(pbuf)
return_none()
sequence_getslice(obj, start, stop)
string_as_string(strobj)
string_from_constant_string(string)
string_from_string_and_size(string, size)
sys_write_stdout(fmt, *args)
to_native_arg(obj, typ)
to_native_array(typ, ary)
to_native_value(obj, typ)
tuple_getitem(tup, idx)

Borrow reference

tuple_new(count)
tuple_pack(items)
tuple_setitem(tuple_val, index, item)
numba.pythonapi.fix_python_api()

Execute once to install special symbols into the LLVM symbol table

numba.sigutils module

numba.sigutils.is_signature(sig)
numba.sigutils.normalize_signature(sig)
numba.sigutils.parse_signature(signature_str)

numba.special module

numba.special.typeof(val)

Get the type of a variable or value.

Used outside of Numba code, infers the type for the object.

numba.testing module

numba.testing.discover_tests(startdir)
numba.testing.multitest()

Run tests in multiple processes.

numba.testing.run_tests(suite, descriptions=True, verbosity=2, buffer=True, failfast=False)
numba.testing.test()

numba.type_annotations module

class numba.type_annotations.SourceLines(func)

Bases: _abcoll.Mapping

avail
class numba.type_annotations.TypeAnnotation(interp, typemap, calltypes, lifted)

Bases: object

annotate()

numba.typeinfer module

Type inference base on CPA. The algorithm guarantees monotonic growth of type-sets for each variable.

Steps:
  1. seed initial types
  2. build constrains
  3. propagate constrains
  4. unify types

Constrain propagation is precise and does not regret (no backtracing). Constrains push types forward following the dataflow.

class numba.typeinfer.BuildTupleConstrain(target, items, loc)

Bases: object

class numba.typeinfer.CallConstrain(target, func, args, kws, loc)

Bases: object

Constrain for calling functions. Perform case analysis foreach combinations of argument types.

resolve(context, typevars, fnty)
class numba.typeinfer.ConstrainNetwork

Bases: object

TODO: It is possible to optimize constrain propagation to consider only
dirty type variables.
append(constrain)
propagate(context, typevars)
class numba.typeinfer.GetAttrConstrain(target, attr, value, loc, inst)

Bases: object

class numba.typeinfer.IntrinsicCallConstrain(target, func, args, kws, loc)

Bases: numba.typeinfer.CallConstrain

class numba.typeinfer.Propagate(dst, src, loc)

Bases: object

A simple constrain for direct propagation of types for assignments.

class numba.typeinfer.SetAttrConstrain(target, attr, value, loc)

Bases: object

class numba.typeinfer.SetItemConstrain(target, index, value, loc)

Bases: object

class numba.typeinfer.TypeInferer(context, blocks)

Bases: object

Operates on block that shares the same ir.Scope.

build_constrain()
constrain_statement(inst)
dump()
get_function_types(typemap)
get_return_type(typemap)
get_state_token()

The algorithm is monotonic. It can only grow the typesets. The sum of all lengths of type sets is a cheap and accurate description of our progress.

propagate()
seed_return(typ)

Seeding of return value is optional.

seed_type(name, typ)

All arguments should be seeded.

typeof_assign(inst)
typeof_call(inst, target, call)
typeof_const(inst, target, const)
typeof_expr(inst, target, expr)
typeof_global(inst, target, gvar)
typeof_intrinsic_call(inst, target, func, *args)
typeof_setattr(inst)
typeof_setitem(inst)
unify()
class numba.typeinfer.TypeVar(context, var)

Bases: object

add_types(*types)
get()
getone()
lock(typ)
union(other)
class numba.typeinfer.TypeVarMap

Bases: dict

set_context(context)
exception numba.typeinfer.TypingError(msg, loc=None)

Bases: exceptions.Exception

numba.types module

These type objects do not have a fixed machine representation. It is up to the targets to choose their representation.

numba.unittest_support module

This file fixes portability issues for unittest

numba.utils module

class numba.utils.BenchmarkResult(func, records, loop)

Bases: object

class numba.utils.ConfigOptions

Bases: object

OPTIONS = ()
copy()
set(name)
unset(name)
class numba.utils.SortedMap(seq)

Bases: _abcoll.Mapping

Immutable

class numba.utils.SortedSet(seq)

Bases: _abcoll.Set

class numba.utils.UniqueDict

Bases: dict

numba.utils.benchmark(func, maxsec=1)
numba.utils.bit_length(intval)
numba.utils.dict_iteritems(d)
numba.utils.dict_itervalues(d)
numba.utils.dict_keys(d)
numba.utils.dict_values(d)
numba.utils.format_time(tm)
numba.utils.func_globals(f)
numba.utils.iter_next(it)
numba.utils.longint(v)
numba.utils.runonce(fn)

Module contents

Expose top-level symbols that are safe for import *

numba.jit(signature_or_function=None, argtypes=None, restype=None, locals={}, target='cpu', **targetoptions)
jit([signature_or_function, [locals={}, [target=’cpu’,
[**targetoptions]]]])

The function can be used as the following versions:

  1. jit(signature, [target=’cpu’, [**targetoptions]]) -> jit(function)

    Equivalent to:

    d = dispatcher(function, targetoptions) d.compile(signature)

    Create a dispatcher object for a python function and default target-options. Then, compile the funciton with the given signature.

    Example:

    @jit(“void(int32, float32)”) def foo(x, y):

    return x + y

  2. jit(function) -> dispatcher

    Same as old autojit. Create a dispatcher function object that specialize at call site.

    Example:

    @jit def foo(x, y):

    return x + y

  3. jit([target=’cpu’, [**targetoptions]]) -> configured_jit(function)

    Same as old autojit and 2). But configure with target and default target-options.

    Example:

    @jit(target=’cpu’, nopython=True) def foo(x, y):

    return x + y

The CPU (default target) defines the following:

  • nopython: [bool]

    Set to True to disable the use of PyObjects and Python API calls. The default behavior is to allow the use of PyObjects and Python API. Default value is False.

  • forceobj: [bool]

    Set to True to force the use of PyObjects for every value. Default value is False.

numba.autojit(*args, **kws)

Deprecated.

Use jit instead. Calls to jit internally.

numba.njit(*args, **kws)

Equavilent to jit(nopython=True)

numba.vectorize(ftylist[, target='cpu'[, **kws]])

A decorator to create numpy ufunc object from Numba compiled code.

ftylist: iterable
An iterable of type signatures, which are either function type object or a string describing the function type.
target: str
A string for code generation target. Default to “cpu”.

A NumPy universal function

@vectorize([‘float32(float32, float32)’,
‘float64(float64, float64)’])
def sum(a, b):
return a + b
numba.guvectorize(ftylist, signature[, target='cpu'[, **kws]])

A decorator to create numpy generialized-ufunc object from Numba compiled code.

ftylist: iterable
An iterable of type signatures, which are either function type object or a string describing the function type.
signature: str
A NumPy generialized-ufunc signature. e.g. “(m, n), (n, p)->(m, p)”
target: str
A string for code generation target. Defaults to “cpu”.

A NumPy generialized universal-function

@guvectorize([‘void(int32[:,:], int32[:,:], int32[:,:])’,
‘void(float32[:,:], float32[:,:], float32[:,:])’], ‘(x, y),(x, y)->(x, y)’)
def add_2d_array(a, b):
for i in range(c.shape[0]):
for j in range(c.shape[1]):
c[i, j] = a[i, j] + b[i, j]
numba.export(prototype)
numba.exportmany(prototypes)
numba.typeof(val)

Get the type of a variable or value.

Used outside of Numba code, infers the type for the object.