def _compile_subroutine_no_cache(self,
builder,
impl,
sig,
locals={},
flags=None):
"""
Invoke the compiler to compile a function to be used inside a
nopython function, but without generating code to call that
function.
Note this context's flags are not inherited.
"""
# Compile
from numba.core import compiler
with global_compiler_lock:
codegen = self.codegen()
library = codegen.create_library(impl.__name__)
if flags is None:
cstk = utils.ConfigStack()
flags = compiler.Flags()
if cstk:
tls_flags = cstk.top()
if tls_flags.is_set("nrt") and tls_flags.nrt:
flags.nrt = True
flags.no_compile = True
flags.no_cpython_wrapper = True
flags.no_cfunc_wrapper = True
cres = compiler.compile_internal(self.typing_context,
self,
library,
impl,
sig.args,
sig.return_type,
flags,
locals=locals)
# Allow inlining the function inside callers.
self.active_code_library.add_linking_library(cres.library)
return cres
def _compile_core(self):
"""
Populate and run compiler pipeline
"""
with utils.ConfigStack().enter(self.state.flags.copy()):
pms = self.define_pipelines()
for pm in pms:
pipeline_name = pm.pipeline_name
func_name = "%s.%s" % (self.state.func_id.modname,
self.state.func_id.func_qualname)
event("Pipeline: %s for %s" % (pipeline_name, func_name))
self.state.metadata['pipeline_times'] = {
pipeline_name: pm.exec_times
}
is_final_pipeline = pm == pms[-1]
res = None
try:
pm.run(self.state)
if self.state.cr is not None:
break
except _EarlyPipelineCompletion as e:
res = e.result
break
except Exception as e:
self.state.status.fail_reason = e
if is_final_pipeline:
raise e
else:
raise CompilerError("All available pipelines exhausted")
# Pipeline is done, remove self reference to release refs to user
# code
self.state.pipeline = None
# organise a return
if res is not None:
# Early pipeline completion
return res
else:
assert self.state.cr is not None
return self.state.cr
def inherit_if_not_set(self, name, default=_NotSet):
"""Inherit flag from ``ConfigStack``.
Parameters
----------
name : str
Option name.
default : optional
When given, it overrides the default value.
It is only used when the flag is not defined locally and there is
no entry in the ``ConfigStack``.
"""
self._guard_option(name)
if not self.is_set(name):
cstk = utils.ConfigStack()
if cstk:
# inherit
top = cstk.top()
setattr(self, name, getattr(top, name))
elif default is not _NotSet:
setattr(self, name, default)
def _lower_array_expr(lowerer, expr):
'''Lower an array expression built by RewriteArrayExprs.
'''
expr_name = "__numba_array_expr_%s" % (hex(hash(expr)).replace("-", "_"))
expr_filename = expr.loc.filename
expr_var_list = expr.list_vars()
# The expression may use a given variable several times, but we
# should only create one parameter for it.
expr_var_unique = sorted(set(expr_var_list), key=lambda var: var.name)
# Arguments are the names external to the new closure
expr_args = [var.name for var in expr_var_unique]
# 1. Create an AST tree from the array expression.
with _legalize_parameter_names(expr_var_unique) as expr_params:
ast_args = [ast.arg(param_name, None) for param_name in expr_params]
# Parse a stub function to ensure the AST is populated with
# reasonable defaults for the Python version.
ast_module = ast.parse('def {0}(): return'.format(expr_name),
expr_filename, 'exec')
assert hasattr(ast_module, 'body') and len(ast_module.body) == 1
ast_fn = ast_module.body[0]
ast_fn.args.args = ast_args
ast_fn.body[0].value, namespace = _arr_expr_to_ast(expr.expr)
ast.fix_missing_locations(ast_module)
# 2. Compile the AST module and extract the Python function.
code_obj = compile(ast_module, expr_filename, 'exec')
exec(code_obj, namespace)
impl = namespace[expr_name]
# 3. Now compile a ufunc using the Python function as kernel.
context = lowerer.context
builder = lowerer.builder
outer_sig = expr.ty(*(lowerer.typeof(name) for name in expr_args))
inner_sig_args = []
for argty in outer_sig.args:
if isinstance(argty, types.Optional):
argty = argty.type
if isinstance(argty, types.Array):
inner_sig_args.append(argty.dtype)
else:
inner_sig_args.append(argty)
inner_sig = outer_sig.return_type.dtype(*inner_sig_args)
flags = utils.ConfigStack().top_or_none()
flags = compiler.Flags() if flags is None else flags.copy(
) # make sure it's a clone or a fresh instance
# Follow the Numpy error model. Note this also allows e.g. vectorizing
# division (issue #1223).
flags.error_model = 'numpy'
cres = context.compile_subroutine(builder,
impl,
inner_sig,
flags=flags,
caching=False)
# Create kernel subclass calling our native function
from numba.np import npyimpl
class ExprKernel(npyimpl._Kernel):
def generate(self, *args):
arg_zip = zip(args, self.outer_sig.args, inner_sig.args)
cast_args = [
self.cast(val, inty, outty) for val, inty, outty in arg_zip
]
result = self.context.call_internal(builder, cres.fndesc,
inner_sig, cast_args)
return self.cast(result, inner_sig.return_type,
self.outer_sig.return_type)
# create a fake ufunc object which is enough to trick numpy_ufunc_kernel
ufunc = SimpleNamespace(nin=len(expr_args), nout=1, __name__=expr_name)
ufunc.nargs = ufunc.nin + ufunc.nout
args = [lowerer.loadvar(name) for name in expr_args]
return npyimpl.numpy_ufunc_kernel(context, builder, outer_sig, args, ufunc,
ExprKernel)