def __init__(self, arg_count, py_func):
self._tm = default_type_manager
#_dispatcher.Dispatcher.__init__(self, self._tm.get_pointer(), arg_count)
# A mapping of signatures to entry points
self.overloads = {}
# A mapping of signatures to compile results
self._compileinfos = {}
# A list of nopython signatures
self._npsigs = []
self.py_func = py_func
# other parts of Numba assume the old Python 2 name for code object
self.func_code = get_code_object(py_func)
# but newer python uses a different name
self.__code__ = self.func_code
self._pysig = utils.pysignature(self.py_func)
_argnames = tuple(self._pysig.parameters)
_dispatcher.Dispatcher.__init__(self, self._tm.get_pointer(),
arg_count, _argnames)
self.doc = py_func.__doc__
self._compile_lock = utils.NonReentrantLock()
utils.finalize(self, self._make_finalizer())
def __init__(self, func):
func = get_function_object(func)
code = get_code_object(func)
pysig = utils.pysignature(func)
if not code:
raise errors.ByteCodeSupportError(
"%s does not provide its bytecode" % func)
if code.co_cellvars:
raise NotImplementedError("cell vars are not supported")
table = utils.SortedMap(ByteCodeIter(code))
labels = set(dis.findlabels(code.co_code))
labels.add(0)
try:
func_qualname = func.__qualname__
except AttributeError:
func_qualname = func.__name__
self._mark_lineno(table, code)
super(ByteCode, self).__init__(func=func,
func_qualname=func_qualname,
is_generator=inspect.isgeneratorfunction(func),
pysig=pysig,
filename=code.co_filename,
co_names=code.co_names,
co_varnames=code.co_varnames,
co_consts=code.co_consts,
co_freevars=code.co_freevars,
table=table,
labels=list(sorted(labels)))
def __init__(self, arg_count, py_func):
self._tm = default_type_manager
# _dispatcher.Dispatcher.__init__(self, self._tm.get_pointer(), arg_count)
# A mapping of signatures to entry points
self.overloads = {}
# A mapping of signatures to compile results
self._compileinfos = {}
# A list of nopython signatures
self._npsigs = []
self.py_func = py_func
# other parts of Numba assume the old Python 2 name for code object
self.func_code = get_code_object(py_func)
# but newer python uses a different name
self.__code__ = self.func_code
self._pysig = utils.pysignature(self.py_func)
_argnames = tuple(self._pysig.parameters)
_dispatcher.Dispatcher.__init__(self, self._tm.get_pointer(), arg_count, _argnames)
self.doc = py_func.__doc__
self._compile_lock = utils.NonReentrantLock()
utils.finalize(self, self._make_finalizer())
def __init__(self, py_func, locals={}, targetoptions={}):
"""
Parameters
----------
py_func: function object to be compiled
locals: dict, optional
Mapping of local variable names to Numba types. Used to override
the types deduced by the type inference engine.
targetoptions: dict, optional
Target-specific config options.
"""
self.typingctx = self.targetdescr.typing_context
self.targetctx = self.targetdescr.target_context
pysig = utils.pysignature(py_func)
arg_count = len(pysig.parameters)
_OverloadedBase.__init__(self, arg_count, py_func, pysig)
functools.update_wrapper(self, py_func)
self.targetoptions = targetoptions
self.locals = locals
self._cache = NullCache()
self.typingctx.insert_overloaded(self)
def __init__(self, py_func, locals={}, targetoptions={}):
"""
Parameters
----------
py_func: function object to be compiled
locals: dict, optional
Mapping of local variable names to Numba types. Used to override
the types deduced by the type inference engine.
targetoptions: dict, optional
Target-specific config options.
"""
self.typingctx = self.targetdescr.typing_context
self.targetctx = self.targetdescr.target_context
pysig = utils.pysignature(py_func)
arg_count = len(pysig.parameters)
_OverloadedBase.__init__(self, arg_count, py_func, pysig)
functools.update_wrapper(self, py_func)
self.targetoptions = targetoptions
self.locals = locals
self.typingctx.insert_overloaded(self)
def __init__(self, py_func, locals={}, targetoptions={}, impl_kind='direct'):
"""
Parameters
----------
py_func: function object to be compiled
locals: dict, optional
Mapping of local variable names to Numba types. Used to override
the types deduced by the type inference engine.
targetoptions: dict, optional
Target-specific config options.
"""
self.typingctx = self.targetdescr.typing_context
self.targetctx = self.targetdescr.target_context
pysig = utils.pysignature(py_func)
arg_count = len(pysig.parameters)
can_fallback = not targetoptions.get('nopython', False)
_DispatcherBase.__init__(self, arg_count, py_func, pysig, can_fallback)
functools.update_wrapper(self, py_func)
self.targetoptions = targetoptions
self.locals = locals
self._cache = NullCache()
compiler_class = self._impl_kinds[impl_kind]
self._impl_kind = impl_kind
self._compiler = compiler_class(py_func, self.targetdescr,
targetoptions, locals)
self._cache_hits = collections.Counter()
self._cache_misses = collections.Counter()
self._type = types.Dispatcher(self)
self.typingctx.insert_global(self, self._type)
def sum_expand(self, args, kws):
"""
sum can be called with or without an axis parameter.
"""
pysig = None
if kws:
def sum_stub(axis):
pass
pysig = utils.pysignature(sum_stub)
# rewrite args
args = list(args) + [kws['axis']]
kws = None
args_len = len(args)
assert args_len <= 1
if args_len == 0:
# No axis parameter so the return type of the summation is a scalar
# of the type of the array.
out = signature(_expand_integer(self.this.dtype),
*args,
recvr=self.this)
else:
# There is an axis paramter so the return type of this summation is
# an array of dimension one less than the input array.
return_type = types.Array(dtype=_expand_integer(self.this.dtype),
ndim=self.this.ndim - 1,
layout='C')
out = signature(return_type, *args, recvr=self.this)
return out.replace(pysig=pysig)
def from_function(cls, pyfunc):
"""
Create the FunctionIdentity of the given function.
"""
func = get_function_object(pyfunc)
code = get_code_object(func)
pysig = utils.pysignature(func)
if not code:
raise errors.ByteCodeSupportError(
"%s does not provide its bytecode" % func)
try:
func_qualname = func.__qualname__
except AttributeError:
func_qualname = func.__name__
self = cls()
self.func = pyfunc
self.func_qualname = func_qualname
self.func_name = func_qualname.split('.')[-1]
self.code = code
self.module = inspect.getmodule(func)
self.is_generator = inspect.isgeneratorfunction(func)
self.pysig = pysig
self.filename = code.co_filename
self.firstlineno = code.co_firstlineno
self.arg_count = len(pysig.parameters)
self.arg_names = list(pysig.parameters)
return self
def __init__(self, py_func, locals={}, targetoptions={}, impl_kind='direct'):
"""
Parameters
----------
py_func: function object to be compiled
locals: dict, optional
Mapping of local variable names to Numba types. Used to override
the types deduced by the type inference engine.
targetoptions: dict, optional
Target-specific config options.
"""
self.typingctx = self.targetdescr.typing_context
self.targetctx = self.targetdescr.target_context
pysig = utils.pysignature(py_func)
arg_count = len(pysig.parameters)
can_fallback = not targetoptions.get('nopython', False)
_DispatcherBase.__init__(self, arg_count, py_func, pysig, can_fallback)
functools.update_wrapper(self, py_func)
self.targetoptions = targetoptions
self.locals = locals
self._cache = NullCache()
compiler_class = self._impl_kinds[impl_kind]
self._impl_kind = impl_kind
self._compiler = compiler_class(py_func, self.targetdescr,
targetoptions, locals)
self._cache_hits = collections.Counter()
self._cache_misses = collections.Counter()
self._type = types.Dispatcher(self)
self.typingctx.insert_global(self, self._type)
def sum_expand(self, args, kws):
"""
sum can be called with or without an axis parameter.
"""
pysig = None
if kws:
def sum_stub(axis):
pass
pysig = utils.pysignature(sum_stub)
# rewrite args
args = list(args) + [kws['axis']]
kws = None
args_len = len(args)
assert args_len <= 1
if args_len == 0:
# No axis parameter so the return type of the summation is a scalar
# of the type of the array.
out = signature(_expand_integer(self.this.dtype), *args,
recvr=self.this)
else:
# There is an axis paramter so the return type of this summation is
# an array of dimension one less than the input array.
return_type = types.Array(dtype=_expand_integer(self.this.dtype),
ndim=self.this.ndim-1, layout='C')
out = signature(return_type, *args, recvr=self.this)
return out.replace(pysig=pysig)
def __init__(self, func):
func = get_function_object(func)
code = get_code_object(func)
pysig = utils.pysignature(func)
if not code:
raise errors.ByteCodeSupportError(
"%s does not provide its bytecode" % func)
# A map of {offset: ByteCodeInst}
table = utils.SortedMap(ByteCodeIter(code))
labels = set(dis.findlabels(code.co_code))
labels.add(0)
try:
func_qualname = func.__qualname__
except AttributeError:
func_qualname = func.__name__
self._mark_lineno(table, code)
super(ByteCode, self).__init__(func=func,
func_qualname=func_qualname,
is_generator=inspect.isgeneratorfunction(func),
pysig=pysig,
filename=code.co_filename,
co_names=code.co_names,
co_varnames=code.co_varnames,
co_consts=code.co_consts,
co_freevars=code.co_freevars,
table=table,
labels=list(sorted(labels)))
def __init__(self, func, incols, outcols, kwargs):
# Get signature of user function
sig = pysignature(func)
self.sig = sig
self.incols = incols
self.outcols = outcols
self.kwargs = kwargs
self.kernel = self.compile(func, sig.parameters.keys(), kwargs.keys())
def __init__(self, py_func, targetdescr, targetoptions, locals,
pipeline_class):
self.py_func = py_func
self.targetdescr = targetdescr
self.targetoptions = targetoptions
self.locals = locals
self.pysig = utils.pysignature(self.py_func)
self.pipeline_class = pipeline_class
def __init__(self, py_func, targetdescr, targetoptions, locals,
pipeline_class):
self.py_func = py_func
self.targetdescr = targetdescr
self.targetoptions = targetoptions
self.locals = locals
self.pysig = utils.pysignature(self.py_func)
self.pipeline_class = pipeline_class
def _getargs(fn):
"""
Returns list of positional and keyword argument names in order.
"""
sig = utils.pysignature(fn)
params = sig.parameters
args = [k for k, v in params.items()
if (v.kind & v.POSITIONAL_OR_KEYWORD) == v.POSITIONAL_OR_KEYWORD]
return args
def __init__(self, func, incols, outcols, kwargs, pessimistic_nulls,
cache_key):
# Get signature of user function
sig = pysignature(func)
self.sig = sig
self.incols = incols
self.outcols = outcols
self.kwargs = kwargs
self.pessimistic_nulls = pessimistic_nulls
self.cache_key = cache_key
self.kernel = self.compile(func, sig.parameters.keys(), kwargs.keys())
def __init__(self, py_func, targetdescr, targetoptions, locals,
pipeline_class):
self.py_func = py_func
self.targetdescr = targetdescr
self.targetoptions = targetoptions
self.locals = locals
self.pysig = utils.pysignature(self.py_func)
self.pipeline_class = pipeline_class
# Remember key=(args, return_type) combinations that will fail
# compilation to avoid compilation attempt on them. The values are
# the exceptions.
self._failed_cache = {}
def resolve_argsort(self, ary, args, kws):
assert not args
kwargs = dict(kws)
kind = kwargs.pop('kind', types.StringLiteral('quicksort'))
if kwargs:
msg = "Unsupported keywords: {!r}"
raise TypingError(msg.format([k for k in kwargs.keys()]))
if ary.ndim == 1:
def argsort_stub(kind='quicksort'):
pass
pysig = utils.pysignature(argsort_stub)
sig = signature(types.Array(types.intp, 1, 'C'), kind).replace(pysig=pysig)
return sig
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 resolve_argsort(self, ary, args, kws):
assert not args
kwargs = dict(kws)
kind = kwargs.pop('kind', types.Const('quicksort'))
if kwargs:
msg = "Unsupported keywords: {!r}"
raise TypingError(msg.format([k for k in kwargs.keys()]))
if ary.ndim == 1:
def argsort_stub(kind='quicksort'):
pass
pysig = utils.pysignature(argsort_stub)
sig = signature(types.Array(types.intp, 1, 'C'), kind).replace(pysig=pysig)
return sig
def _get_implementation(self, args, kws):
impl = self.py_func(*args, **kws)
# Check the generating function and implementation signatures are
# compatible, otherwise compiling would fail later.
pysig = utils.pysignature(self.py_func)
implsig = utils.pysignature(impl)
ok = len(pysig.parameters) == len(implsig.parameters)
if ok:
for pyparam, implparam in zip(pysig.parameters.values(),
implsig.parameters.values()):
# We allow the implementation to omit default values, but
# if it mentions them, they should have the same value...
if (pyparam.name != implparam.name
or pyparam.kind != implparam.kind
or (implparam.default is not implparam.empty
and implparam.default != pyparam.default)):
ok = False
if not ok:
raise TypeError("generated implementation %s should be compatible "
"with signature '%s', but has signature '%s'" %
(impl, pysig, implsig))
self.impls.add(impl)
return impl
def _get_implementation(self, args, kws):
impl = self.py_func(*args, **kws)
# Check the generating function and implementation signatures are
# compatible, otherwise compiling would fail later.
pysig = utils.pysignature(self.py_func)
implsig = utils.pysignature(impl)
ok = len(pysig.parameters) == len(implsig.parameters)
if ok:
for pyparam, implparam in zip(pysig.parameters.values(),
implsig.parameters.values()):
# We allow the implementation to omit default values, but
# if it mentions them, they should have the same value...
if (pyparam.name != implparam.name or
pyparam.kind != implparam.kind or
(implparam.default is not implparam.empty and
implparam.default != pyparam.default)):
ok = False
if not ok:
raise TypeError("generated implementation %s should be compatible "
"with signature '%s', but has signature '%s'"
% (impl, pysig, implsig))
self.impls.add(impl)
return impl
def _set_init(cls):
"""
Generate a wrapper for calling the constructor from pure Python.
Note the wrapper will only accept positional arguments.
"""
init = cls.class_type.instance_type.methods['__init__']
init_sig = utils.pysignature(init)
# get postitional and keyword arguments
# offset by one to exclude the `self` arg
args = _getargs(init_sig)[1:]
cls._ctor_sig = init_sig
ctor_source = _ctor_template.format(args=', '.join(args))
glbls = {"__numba_cls_": cls}
exec_(ctor_source, glbls)
ctor = glbls['ctor']
cls._ctor = njit(ctor)
def _set_init(cls):
"""
Generate a wrapper for calling the constructor from pure Python.
Note the wrapper will only accept positional arguments.
"""
init = cls.class_type.instance_type.methods['__init__']
init_sig = utils.pysignature(init)
# get postitional and keyword arguments
# offset by one to exclude the `self` arg
args = _getargs(init_sig)[1:]
cls._ctor_sig = init_sig
ctor_source = _ctor_template.format(args=', '.join(args))
glbls = {"__numba_cls_": cls}
exec_(ctor_source, glbls)
ctor = glbls['ctor']
cls._ctor = njit(ctor)
def _type_me(self, argtys, kwtys):
"""
Implement AbstractTemplate.generic() for the typing class
built by StencilFunc._install_type().
Return the call-site signature.
"""
if (self.neighborhood is not None
and len(self.neighborhood) != argtys[0].ndim):
raise ValueError("%d dimensional neighborhood specified "
"for %d dimensional input array" %
(len(self.neighborhood), argtys[0].ndim))
argtys_extra = argtys
sig_extra = ""
result = None
if 'out' in kwtys:
argtys_extra += (kwtys['out'], )
sig_extra += ", out=None"
result = kwtys['out']
if 'neighborhood' in kwtys:
argtys_extra += (kwtys['neighborhood'], )
sig_extra += ", neighborhood=None"
# look in the type cache first
if argtys_extra in self._type_cache:
(_sig, _, _, _) = self._type_cache[argtys_extra]
return _sig
(real_ret, typemap, calltypes) = self.get_return_type(argtys)
sig = signature(real_ret, *argtys_extra)
dummy_text = ("def __numba_dummy_stencil({}{}):\n pass\n".format(
",".join(self.kernel_ir.arg_names), sig_extra))
exec_(dummy_text) in globals(), locals()
dummy_func = eval("__numba_dummy_stencil")
sig.pysig = utils.pysignature(dummy_func)
self._targetctx.insert_func_defn([(self._lower_me, self, argtys_extra)
])
self._type_cache[argtys_extra] = (sig, result, typemap, calltypes)
return sig
def _type_me(self, argtys, kwtys):
"""
Implement AbstractTemplate.generic() for the typing class
built by StencilFunc._install_type().
Return the call-site signature.
"""
if (self.neighborhood is not None and
len(self.neighborhood) != argtys[0].ndim):
raise ValueError("%d dimensional neighborhood specified "
"for %d dimensional input array" %
(len(self.neighborhood), argtys[0].ndim))
argtys_extra = argtys
sig_extra = ""
result = None
if 'out' in kwtys:
argtys_extra += (kwtys['out'],)
sig_extra += ", out=None"
result = kwtys['out']
if 'neighborhood' in kwtys:
argtys_extra += (kwtys['neighborhood'],)
sig_extra += ", neighborhood=None"
# look in the type cache first
if argtys_extra in self._type_cache:
(_sig, _, _, _) = self._type_cache[argtys_extra]
return _sig
(real_ret, typemap, calltypes) = self.get_return_type(argtys)
sig = signature(real_ret, *argtys_extra)
dummy_text = ("def __numba_dummy_stencil({}{}):\n pass\n".format(
",".join(self.kernel_ir.arg_names), sig_extra))
exec_(dummy_text) in globals(), locals()
dummy_func = eval("__numba_dummy_stencil")
sig.pysig = utils.pysignature(dummy_func)
self._targetctx.insert_func_defn([(self._lower_me, self, argtys_extra)])
self._type_cache[argtys_extra] = (sig, result, typemap, calltypes)
return sig
def from_function(cls, pyfunc):
"""
Create the FunctionIdentity of the given function.
"""
func = get_function_object(pyfunc)
code = get_code_object(func)
pysig = utils.pysignature(func)
if not code:
raise errors.ByteCodeSupportError(
"%s does not provide its bytecode" % func)
try:
func_qualname = func.__qualname__
except AttributeError:
func_qualname = func.__name__
self = cls()
self.func = func
self.func_qualname = func_qualname
self.func_name = func_qualname.split('.')[-1]
self.code = code
self.module = inspect.getmodule(func)
self.modname = (utils._dynamic_modname
if self.module is None
else self.module.__name__)
self.is_generator = inspect.isgeneratorfunction(func)
self.pysig = pysig
self.filename = code.co_filename
self.firstlineno = code.co_firstlineno
self.arg_count = len(pysig.parameters)
self.arg_names = list(pysig.parameters)
# Even the same function definition can be compiled into
# several different function objects with distinct closure
# variables, so we make sure to disambiguate using an unique id.
uid = next(cls._unique_ids)
self.unique_name = '{}${}'.format(self.func_qualname, uid)
return self
def from_function(cls, pyfunc):
"""
Create the FunctionIdentity of the given function.
"""
func = get_function_object(pyfunc)
code = get_code_object(func)
pysig = utils.pysignature(func)
if not code:
raise errors.ByteCodeSupportError(
"%s does not provide its bytecode" % func)
try:
func_qualname = func.__qualname__
except AttributeError:
func_qualname = func.__name__
self = cls()
self.func = pyfunc
self.func_qualname = func_qualname
self.func_name = func_qualname.split('.')[-1]
self.code = code
self.module = inspect.getmodule(func)
self.modname = (utils._dynamic_modname
if self.module is None else self.module.__name__)
self.is_generator = inspect.isgeneratorfunction(func)
self.pysig = pysig
self.filename = code.co_filename
self.firstlineno = code.co_firstlineno
self.arg_count = len(pysig.parameters)
self.arg_names = list(pysig.parameters)
# Even the same function definition can be compiled into
# several different function objects with distinct closure
# variables, so we make sure to disambiguate using an unique id.
uid = next(cls._unique_ids)
self.unique_name = '{}${}'.format(self.func_qualname, uid)
return self
def _has_loc(fn):
"""Does function *fn* take ``loc`` argument?
"""
sig = utils.pysignature(fn)
return 'loc' in sig.parameters
def _stencil_wrapper(self, result, sigret, return_type, typemap, calltypes,
*args):
# Overall approach:
# 1) Construct a string containing a function definition for the stencil function
# that will execute the stencil kernel. This function definition includes a
# unique stencil function name, the parameters to the stencil kernel, loop
# nests across the dimenions of the input array. Those loop nests use the
# computed stencil kernel size so as not to try to compute elements where
# elements outside the bounds of the input array would be needed.
# 2) The but of the loop nest in this new function is a special sentinel
# assignment.
# 3) Get the IR of this new function.
# 4) Split the block containing the sentinel assignment and remove the sentinel
# assignment. Insert the stencil kernel IR into the stencil function IR
# after label and variable renaming of the stencil kernel IR to prevent
# conflicts with the stencil function IR.
# 5) Compile the combined stencil function IR + stencil kernel IR into existence.
# Copy the kernel so that our changes for this callsite
# won't effect other callsites.
(kernel_copy,
copy_calltypes) = self.copy_ir_with_calltypes(self.kernel_ir,
calltypes)
# The stencil kernel body becomes the body of a loop, for which args aren't needed.
ir_utils.remove_args(kernel_copy.blocks)
first_arg = kernel_copy.arg_names[0]
in_cps, out_cps = ir_utils.copy_propagate(kernel_copy.blocks, typemap)
name_var_table = ir_utils.get_name_var_table(kernel_copy.blocks)
ir_utils.apply_copy_propagate(kernel_copy.blocks, in_cps,
name_var_table, typemap, copy_calltypes)
if "out" in name_var_table:
raise ValueError(
"Cannot use the reserved word 'out' in stencil kernels.")
sentinel_name = ir_utils.get_unused_var_name("__sentinel__",
name_var_table)
if config.DEBUG_ARRAY_OPT == 1:
print("name_var_table", name_var_table, sentinel_name)
the_array = args[0]
if config.DEBUG_ARRAY_OPT == 1:
print("_stencil_wrapper", return_type, return_type.dtype,
type(return_type.dtype), args)
ir_utils.dump_blocks(kernel_copy.blocks)
# We generate a Numba function to execute this stencil and here
# create the unique name of this function.
stencil_func_name = "__numba_stencil_%s_%s" % (hex(
id(the_array)).replace("-", "_"), self.id)
# We will put a loop nest in the generated function for each
# dimension in the input array. Here we create the name for
# the index variable for each dimension. index0, index1, ...
index_vars = []
for i in range(the_array.ndim):
index_var_name = ir_utils.get_unused_var_name(
"index" + str(i), name_var_table)
index_vars += [index_var_name]
# Create extra signature for out and neighborhood.
out_name = ir_utils.get_unused_var_name("out", name_var_table)
neighborhood_name = ir_utils.get_unused_var_name(
"neighborhood", name_var_table)
sig_extra = ""
if result is not None:
sig_extra += ", {}=None".format(out_name)
if "neighborhood" in dict(self.kws):
sig_extra += ", {}=None".format(neighborhood_name)
# Get a list of the standard indexed array names.
standard_indexed = self.options.get("standard_indexing", [])
if first_arg in standard_indexed:
raise ValueError("The first argument to a stencil kernel must "
"use relative indexing, not standard indexing.")
if len(set(standard_indexed) - set(kernel_copy.arg_names)) != 0:
raise ValueError("Standard indexing requested for an array name "
"not present in the stencil kernel definition.")
# Add index variables to getitems in the IR to transition the accesses
# in the kernel from relative to regular Python indexing. Returns the
# computed size of the stencil kernel and a list of the relatively indexed
# arrays.
kernel_size, relatively_indexed = self.add_indices_to_kernel(
kernel_copy, index_vars, the_array.ndim, self.neighborhood,
standard_indexed)
if self.neighborhood is None:
self.neighborhood = kernel_size
if config.DEBUG_ARRAY_OPT == 1:
print("After add_indices_to_kernel")
ir_utils.dump_blocks(kernel_copy.blocks)
# The return in the stencil kernel becomes a setitem for that
# particular point in the iteration space.
ret_blocks = self.replace_return_with_setitem(kernel_copy.blocks,
index_vars, out_name)
if config.DEBUG_ARRAY_OPT == 1:
print("After replace_return_with_setitem", ret_blocks)
ir_utils.dump_blocks(kernel_copy.blocks)
# Start to form the new function to execute the stencil kernel.
func_text = "def {}({}{}):\n".format(stencil_func_name,
",".join(kernel_copy.arg_names),
sig_extra)
# Get loop ranges for each dimension, which could be either int
# or variable. In the latter case we'll use the extra neighborhood
# argument to the function.
ranges = []
for i in range(the_array.ndim):
if isinstance(kernel_size[i][0], int):
lo = kernel_size[i][0]
hi = kernel_size[i][1]
else:
lo = "{}[{}][0]".format(neighborhood_name, i)
hi = "{}[{}][1]".format(neighborhood_name, i)
ranges.append((lo, hi))
# If there are more than one relatively indexed arrays, add a call to
# a function that will raise an error if any of the relatively indexed
# arrays are of different size than the first input array.
if len(relatively_indexed) > 1:
func_text += " raise_if_incompatible_array_sizes(" + first_arg
for other_array in relatively_indexed:
if other_array != first_arg:
func_text += "," + other_array
func_text += ")\n"
# Get the shape of the first input array.
shape_name = ir_utils.get_unused_var_name("full_shape", name_var_table)
func_text += " {} = {}.shape\n".format(shape_name, first_arg)
# If we have to allocate the output array (the out argument was not used)
# then us numpy.full if the user specified a cval stencil decorator option
# or np.zeros if they didn't to allocate the array.
if result is None:
return_type_name = numpy_support.as_dtype(
return_type.dtype).type.__name__
if "cval" in self.options:
cval = self.options["cval"]
if return_type.dtype != typing.typeof.typeof(cval):
raise ValueError(
"cval type does not match stencil return type.")
out_init = "{} = np.full({}, {}, dtype=np.{})\n".format(
out_name, shape_name, cval, return_type_name)
else:
out_init = "{} = np.zeros({}, dtype=np.{})\n".format(
out_name, shape_name, return_type_name)
func_text += " " + out_init
offset = 1
# Add the loop nests to the new function.
for i in range(the_array.ndim):
for j in range(offset):
func_text += " "
# ranges[i][0] is the minimum index used in the i'th dimension
# but minimum's greater than 0 don't preclude any entry in the array.
# So, take the minimum of 0 and the minimum index found in the kernel
# and this will be a negative number (potentially -0). Then, we do
# unary - on that to get the positive offset in this dimension whose
# use is precluded.
# ranges[i][1] is the maximum of 0 and the observed maximum index
# in this dimension because negative maximums would not cause us to
# preclude any entry in the array from being used.
func_text += ("for {} in range(-min(0,{}),"
"{}[{}]-max(0,{})):\n").format(
index_vars[i], ranges[i][0], shape_name, i,
ranges[i][1])
offset += 1
for j in range(offset):
func_text += " "
# Put a sentinel in the code so we can locate it in the IR. We will
# remove this sentinel assignment and replace it with the IR for the
# stencil kernel body.
func_text += "{} = 0\n".format(sentinel_name)
func_text += " return {}\n".format(out_name)
if config.DEBUG_ARRAY_OPT == 1:
print("new stencil func text")
print(func_text)
# Force the new stencil function into existence.
exec_(func_text) in globals(), locals()
stencil_func = eval(stencil_func_name)
if sigret is not None:
pysig = utils.pysignature(stencil_func)
sigret.pysig = pysig
# Get the IR for the newly created stencil function.
stencil_ir = compiler.run_frontend(stencil_func)
ir_utils.remove_dels(stencil_ir.blocks)
# rename all variables in stencil_ir afresh
var_table = ir_utils.get_name_var_table(stencil_ir.blocks)
new_var_dict = {}
reserved_names = (
[sentinel_name, out_name, neighborhood_name, shape_name] +
kernel_copy.arg_names + index_vars)
for name, var in var_table.items():
if not name in reserved_names:
new_var_dict[name] = ir_utils.mk_unique_var(name)
ir_utils.replace_var_names(stencil_ir.blocks, new_var_dict)
stencil_stub_last_label = max(stencil_ir.blocks.keys()) + 1
# Shift lables in the kernel copy so they are guaranteed unique
# and don't conflict with any labels in the stencil_ir.
kernel_copy.blocks = ir_utils.add_offset_to_labels(
kernel_copy.blocks, stencil_stub_last_label)
new_label = max(kernel_copy.blocks.keys()) + 1
# Adjust ret_blocks to account for addition of the offset.
ret_blocks = [x + stencil_stub_last_label for x in ret_blocks]
if config.DEBUG_ARRAY_OPT == 1:
print("ret_blocks w/ offsets", ret_blocks, stencil_stub_last_label)
print("before replace sentinel stencil_ir")
ir_utils.dump_blocks(stencil_ir.blocks)
print("before replace sentinel kernel_copy")
ir_utils.dump_blocks(kernel_copy.blocks)
# Search all the block in the stencil outline for the sentinel.
for label, block in stencil_ir.blocks.items():
for i, inst in enumerate(block.body):
if (isinstance(inst, ir.Assign)
and inst.target.name == sentinel_name):
# We found the sentinel assignment.
loc = inst.loc
scope = block.scope
# split block across __sentinel__
# A new block is allocated for the statements prior to the
# sentinel but the new block maintains the current block
# label.
prev_block = ir.Block(scope, loc)
prev_block.body = block.body[:i]
# The current block is used for statements after sentinel.
block.body = block.body[i + 1:]
# But the current block gets a new label.
body_first_label = min(kernel_copy.blocks.keys())
# The previous block jumps to the minimum labelled block of
# the parfor body.
prev_block.append(ir.Jump(body_first_label, loc))
# Add all the parfor loop body blocks to the gufunc
# function's IR.
for (l, b) in kernel_copy.blocks.items():
stencil_ir.blocks[l] = b
stencil_ir.blocks[new_label] = block
stencil_ir.blocks[label] = prev_block
# Add a jump from all the blocks that previously contained
# a return in the stencil kernel to the block
# containing statements after the sentinel.
for ret_block in ret_blocks:
stencil_ir.blocks[ret_block].append(
ir.Jump(new_label, loc))
break
else:
continue
break
stencil_ir.blocks = ir_utils.rename_labels(stencil_ir.blocks)
ir_utils.remove_dels(stencil_ir.blocks)
assert (isinstance(the_array, types.Type))
array_types = args
new_stencil_param_types = list(array_types)
if config.DEBUG_ARRAY_OPT == 1:
print("new_stencil_param_types", new_stencil_param_types)
ir_utils.dump_blocks(stencil_ir.blocks)
# Compile the combined stencil function with the replaced loop
# body in it.
new_func = compiler.compile_ir(self._typingctx, self._targetctx,
stencil_ir, new_stencil_param_types,
None, compiler.DEFAULT_FLAGS, {})
return new_func
def sum_expand(self, args, kws):
"""
sum can be called with or without an axis parameter, and with or without
a dtype parameter
"""
pysig = None
if 'axis' in kws and 'dtype' not in kws:
def sum_stub(axis):
pass
pysig = utils.pysignature(sum_stub)
# rewrite args
args = list(args) + [kws['axis']]
elif 'dtype' in kws and 'axis' not in kws:
def sum_stub(dtype):
pass
pysig = utils.pysignature(sum_stub)
# rewrite args
args = list(args) + [kws['dtype']]
elif 'dtype' in kws and 'axis' in kws:
def sum_stub(axis, dtype):
pass
pysig = utils.pysignature(sum_stub)
# rewrite args
args = list(args) + [kws['axis'], kws['dtype']]
args_len = len(args)
assert args_len <= 2
if args_len == 0:
# No axis or dtype parameter so the return type of the summation is a scalar
# of the type of the array.
out = signature(_expand_integer(self.this.dtype), *args,
recvr=self.this)
elif args_len == 1 and 'dtype' not in kws:
# There is an axis parameter, either arg or kwarg
if self.this.ndim == 1:
# 1d reduces to a scalar
return_type = self.this.dtype
else:
# the return type of this summation is an array of dimension one
# less than the input array.
return_type = types.Array(dtype=_expand_integer(self.this.dtype),
ndim=self.this.ndim-1, layout='C')
out = signature(return_type, *args, recvr=self.this)
elif args_len == 1 and 'dtype' in kws:
# No axis parameter so the return type of the summation is a scalar
# of the dtype parameter.
from .npydecl import _parse_dtype
dtype, = args
dtype = _parse_dtype(dtype)
out = signature(dtype, *args, recvr=self.this)
elif args_len == 2:
# There is an axis and dtype parameter, either arg or kwarg
from .npydecl import _parse_dtype
dtype = _parse_dtype(args[1])
return_type = dtype
if self.this.ndim != 1:
# 1d reduces to a scalar, 2d and above reduce dim by 1
# the return type of this summation is an array of dimension one
# less than the input array.
return_type = types.Array(dtype=return_type,
ndim=self.this.ndim-1, layout='C')
out = signature(return_type, *args, recvr=self.this)
else:
pass
return out.replace(pysig=pysig)
def _stencil_wrapper(self, result, sigret, return_type, typemap, calltypes, *args):
# Overall approach:
# 1) Construct a string containing a function definition for the stencil function
# that will execute the stencil kernel. This function definition includes a
# unique stencil function name, the parameters to the stencil kernel, loop
# nests across the dimenions of the input array. Those loop nests use the
# computed stencil kernel size so as not to try to compute elements where
# elements outside the bounds of the input array would be needed.
# 2) The but of the loop nest in this new function is a special sentinel
# assignment.
# 3) Get the IR of this new function.
# 4) Split the block containing the sentinel assignment and remove the sentinel
# assignment. Insert the stencil kernel IR into the stencil function IR
# after label and variable renaming of the stencil kernel IR to prevent
# conflicts with the stencil function IR.
# 5) Compile the combined stencil function IR + stencil kernel IR into existence.
# Copy the kernel so that our changes for this callsite
# won't effect other callsites.
(kernel_copy, copy_calltypes) = self.copy_ir_with_calltypes(
self.kernel_ir, calltypes)
# The stencil kernel body becomes the body of a loop, for which args aren't needed.
ir_utils.remove_args(kernel_copy.blocks)
first_arg = kernel_copy.arg_names[0]
in_cps, out_cps = ir_utils.copy_propagate(kernel_copy.blocks, typemap)
name_var_table = ir_utils.get_name_var_table(kernel_copy.blocks)
ir_utils.apply_copy_propagate(
kernel_copy.blocks,
in_cps,
name_var_table,
typemap,
copy_calltypes)
if "out" in name_var_table:
raise ValueError("Cannot use the reserved word 'out' in stencil kernels.")
sentinel_name = ir_utils.get_unused_var_name("__sentinel__", name_var_table)
if config.DEBUG_ARRAY_OPT == 1:
print("name_var_table", name_var_table, sentinel_name)
the_array = args[0]
if config.DEBUG_ARRAY_OPT == 1:
print("_stencil_wrapper", return_type, return_type.dtype,
type(return_type.dtype), args)
ir_utils.dump_blocks(kernel_copy.blocks)
# We generate a Numba function to execute this stencil and here
# create the unique name of this function.
stencil_func_name = "__numba_stencil_%s_%s" % (
hex(id(the_array)).replace("-", "_"),
self.id)
# We will put a loop nest in the generated function for each
# dimension in the input array. Here we create the name for
# the index variable for each dimension. index0, index1, ...
index_vars = []
for i in range(the_array.ndim):
index_var_name = ir_utils.get_unused_var_name("index" + str(i),
name_var_table)
index_vars += [index_var_name]
# Create extra signature for out and neighborhood.
out_name = ir_utils.get_unused_var_name("out", name_var_table)
neighborhood_name = ir_utils.get_unused_var_name("neighborhood",
name_var_table)
sig_extra = ""
if result is not None:
sig_extra += ", {}=None".format(out_name)
if "neighborhood" in dict(self.kws):
sig_extra += ", {}=None".format(neighborhood_name)
# Get a list of the standard indexed array names.
standard_indexed = self.options.get("standard_indexing", [])
if first_arg in standard_indexed:
raise ValueError("The first argument to a stencil kernel must "
"use relative indexing, not standard indexing.")
if len(set(standard_indexed) - set(kernel_copy.arg_names)) != 0:
raise ValueError("Standard indexing requested for an array name "
"not present in the stencil kernel definition.")
# Add index variables to getitems in the IR to transition the accesses
# in the kernel from relative to regular Python indexing. Returns the
# computed size of the stencil kernel and a list of the relatively indexed
# arrays.
kernel_size, relatively_indexed = self.add_indices_to_kernel(
kernel_copy, index_vars, the_array.ndim,
self.neighborhood, standard_indexed)
if self.neighborhood is None:
self.neighborhood = kernel_size
if config.DEBUG_ARRAY_OPT == 1:
print("After add_indices_to_kernel")
ir_utils.dump_blocks(kernel_copy.blocks)
# The return in the stencil kernel becomes a setitem for that
# particular point in the iteration space.
ret_blocks = self.replace_return_with_setitem(kernel_copy.blocks,
index_vars, out_name)
if config.DEBUG_ARRAY_OPT == 1:
print("After replace_return_with_setitem", ret_blocks)
ir_utils.dump_blocks(kernel_copy.blocks)
# Start to form the new function to execute the stencil kernel.
func_text = "def {}({}{}):\n".format(stencil_func_name,
",".join(kernel_copy.arg_names), sig_extra)
# Get loop ranges for each dimension, which could be either int
# or variable. In the latter case we'll use the extra neighborhood
# argument to the function.
ranges = []
for i in range(the_array.ndim):
if isinstance(kernel_size[i][0], int):
lo = kernel_size[i][0]
hi = kernel_size[i][1]
else:
lo = "{}[{}][0]".format(neighborhood_name, i)
hi = "{}[{}][1]".format(neighborhood_name, i)
ranges.append((lo, hi))
# If there are more than one relatively indexed arrays, add a call to
# a function that will raise an error if any of the relatively indexed
# arrays are of different size than the first input array.
if len(relatively_indexed) > 1:
func_text += " raise_if_incompatible_array_sizes(" + first_arg
for other_array in relatively_indexed:
if other_array != first_arg:
func_text += "," + other_array
func_text += ")\n"
# Get the shape of the first input array.
shape_name = ir_utils.get_unused_var_name("full_shape", name_var_table)
func_text += " {} = {}.shape\n".format(shape_name, first_arg)
# If we have to allocate the output array (the out argument was not used)
# then us numpy.full if the user specified a cval stencil decorator option
# or np.zeros if they didn't to allocate the array.
if result is None:
if "cval" in self.options:
cval = self.options["cval"]
if return_type.dtype != typing.typeof.typeof(cval):
raise ValueError(
"cval type does not match stencil return type.")
out_init ="{} = np.full({}, {}, dtype=np.{})\n".format(
out_name, shape_name, cval, return_type.dtype)
else:
out_init ="{} = np.zeros({}, dtype=np.{})\n".format(
out_name, shape_name, return_type.dtype)
func_text += " " + out_init
offset = 1
# Add the loop nests to the new function.
for i in range(the_array.ndim):
for j in range(offset):
func_text += " "
# ranges[i][0] is the minimum index used in the i'th dimension
# but minimum's greater than 0 don't preclude any entry in the array.
# So, take the minimum of 0 and the minimum index found in the kernel
# and this will be a negative number (potentially -0). Then, we do
# unary - on that to get the positive offset in this dimension whose
# use is precluded.
# ranges[i][1] is the maximum of 0 and the observed maximum index
# in this dimension because negative maximums would not cause us to
# preclude any entry in the array from being used.
func_text += ("for {} in range(-min(0,{}),"
"{}[{}]-max(0,{})):\n").format(
index_vars[i],
ranges[i][0],
shape_name,
i,
ranges[i][1])
offset += 1
for j in range(offset):
func_text += " "
# Put a sentinel in the code so we can locate it in the IR. We will
# remove this sentinel assignment and replace it with the IR for the
# stencil kernel body.
func_text += "{} = 0\n".format(sentinel_name)
func_text += " return {}\n".format(out_name)
if config.DEBUG_ARRAY_OPT == 1:
print("new stencil func text")
print(func_text)
# Force the new stencil function into existence.
exec_(func_text) in globals(), locals()
stencil_func = eval(stencil_func_name)
if sigret is not None:
pysig = utils.pysignature(stencil_func)
sigret.pysig = pysig
# Get the IR for the newly created stencil function.
stencil_ir = compiler.run_frontend(stencil_func)
ir_utils.remove_dels(stencil_ir.blocks)
# rename all variables in stencil_ir afresh
var_table = ir_utils.get_name_var_table(stencil_ir.blocks)
new_var_dict = {}
reserved_names = ([sentinel_name, out_name, neighborhood_name,
shape_name] + kernel_copy.arg_names + index_vars)
for name, var in var_table.items():
if not name in reserved_names:
new_var_dict[name] = ir_utils.mk_unique_var(name)
ir_utils.replace_var_names(stencil_ir.blocks, new_var_dict)
stencil_stub_last_label = max(stencil_ir.blocks.keys()) + 1
# Shift lables in the kernel copy so they are guaranteed unique
# and don't conflict with any labels in the stencil_ir.
kernel_copy.blocks = ir_utils.add_offset_to_labels(
kernel_copy.blocks, stencil_stub_last_label)
new_label = max(kernel_copy.blocks.keys()) + 1
# Adjust ret_blocks to account for addition of the offset.
ret_blocks = [x + stencil_stub_last_label for x in ret_blocks]
if config.DEBUG_ARRAY_OPT == 1:
print("ret_blocks w/ offsets", ret_blocks, stencil_stub_last_label)
print("before replace sentinel stencil_ir")
ir_utils.dump_blocks(stencil_ir.blocks)
print("before replace sentinel kernel_copy")
ir_utils.dump_blocks(kernel_copy.blocks)
# Search all the block in the stencil outline for the sentinel.
for label, block in stencil_ir.blocks.items():
for i, inst in enumerate(block.body):
if (isinstance( inst, ir.Assign) and
inst.target.name == sentinel_name):
# We found the sentinel assignment.
loc = inst.loc
scope = block.scope
# split block across __sentinel__
# A new block is allocated for the statements prior to the
# sentinel but the new block maintains the current block
# label.
prev_block = ir.Block(scope, loc)
prev_block.body = block.body[:i]
# The current block is used for statements after sentinel.
block.body = block.body[i + 1:]
# But the current block gets a new label.
body_first_label = min(kernel_copy.blocks.keys())
# The previous block jumps to the minimum labelled block of
# the parfor body.
prev_block.append(ir.Jump(body_first_label, loc))
# Add all the parfor loop body blocks to the gufunc
# function's IR.
for (l, b) in kernel_copy.blocks.items():
stencil_ir.blocks[l] = b
stencil_ir.blocks[new_label] = block
stencil_ir.blocks[label] = prev_block
# Add a jump from all the blocks that previously contained
# a return in the stencil kernel to the block
# containing statements after the sentinel.
for ret_block in ret_blocks:
stencil_ir.blocks[ret_block].append(
ir.Jump(new_label, loc))
break
else:
continue
break
stencil_ir.blocks = ir_utils.rename_labels(stencil_ir.blocks)
ir_utils.remove_dels(stencil_ir.blocks)
assert(isinstance(the_array, types.Type))
array_types = args
new_stencil_param_types = list(array_types)
if config.DEBUG_ARRAY_OPT == 1:
print("new_stencil_param_types", new_stencil_param_types)
ir_utils.dump_blocks(stencil_ir.blocks)
# Compile the combined stencil function with the replaced loop
# body in it.
new_func = compiler.compile_ir(
self._typingctx,
self._targetctx,
stencil_ir,
new_stencil_param_types,
None,
compiler.DEFAULT_FLAGS,
{})
return new_func
def generic(self, args, kws):
sig = signature(svc_type, types.intp)
pysig = utils.pysignature(SVC_dummy)
sig.pysig = pysig
return sig
def _has_loc(fn):
"""Does function *fn* take ``loc`` argument?
"""
sig = utils.pysignature(fn)
return 'loc' in sig.parameters
def generic(self, args, kws):
sig = signature(mnb_type, types.intp)
pysig = utils.pysignature(MultinomialNB_dummy)
sig.pysig = pysig
return sig
