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from llvmlite.llvmpy.core import Type, Builder, Constant
import llvmlite.llvmpy.core as lc
from llvmlite import ir
from numba.core import types, config, cgutils
class _ArgManager(object):
A utility class to handle argument unboxing and cleanup
def __init__(self, context, builder, api, env_manager, endblk, nargs):
self.context = context
self.builder = builder
self.api = api
self.env_manager = env_manager
self.arg_count = 0 # how many function arguments have been processed
self.cleanups = []
self.nextblk = endblk
def add_arg(self, obj, ty):
Unbox argument and emit code that handles any error during unboxing.
Args are cleaned up in reverse order of the parameter list, and
cleanup begins as soon as unboxing of any argument fails. E.g. failure
on arg2 will result in control flow going through:
arg2.err -> arg1.err -> arg0.err -> arg.end (returns)
# Unbox argument
native = self.api.to_native_value(ty, obj)
# If an error occurred, go to the cleanup block for
# the previous argument
with cgutils.if_unlikely(self.builder, native.is_error):
# Define the cleanup function for the argument
def cleanup_arg():
# Native value reflection
self.api.reflect_native_value(ty, native.value, self.env_manager)
# Native value cleanup
if native.cleanup is not None:
# NRT cleanup
# (happens after the native value cleanup as the latter
# may need the native value)
if self.context.enable_nrt:
self.context.nrt.decref(self.builder, ty, native.value)
# Write the on-error cleanup block for this argument
cleanupblk = self.builder.append_basic_block(
"arg%d.err" % self.arg_count)
with self.builder.goto_block(cleanupblk):
# Go to next cleanup block
self.nextblk = cleanupblk
self.arg_count += 1
return native.value
def emit_cleanup(self):
Emit the cleanup code after returning from the wrapped function.
for dtor in self.cleanups:
class _GilManager(object):
A utility class to handle releasing the GIL and then re-acquiring it
def __init__(self, builder, api, argman):
self.builder = builder
self.api = api
self.argman = argman
self.thread_state = api.save_thread()
def emit_cleanup(self):
class PyCallWrapper(object):
def __init__(self, context, module, func, fndesc, env, call_helper,
self.context = context
self.module = module
self.func = func
self.fndesc = fndesc
self.env = env
self.release_gil = release_gil
def build(self):
wrapname = self.fndesc.llvm_cpython_wrapper_name
# This is the signature of PyCFunctionWithKeywords
# (see CPython's methodobject.h)
pyobj = self.context.get_argument_type(types.pyobject)
wrapty = Type.function(pyobj, [pyobj, pyobj, pyobj])
wrapper = ir.Function(self.module, wrapty, name=wrapname)
builder = Builder(wrapper.append_basic_block('entry'))
# - `closure` will receive the `self` pointer stored in the
# PyCFunction object (see _dynfunc.c)
# - `args` and `kws` will receive the tuple and dict objects
# of positional and keyword arguments, respectively.
closure, args, kws = wrapper.args = 'py_closure' = 'py_args' = 'py_kws'
api = self.context.get_python_api(builder)
self.build_wrapper(api, builder, closure, args, kws)
return wrapper, api
def build_wrapper(self, api, builder, closure, args, kws):
nargs = len(self.fndesc.argtypes)
objs = [api.alloca_obj() for _ in range(nargs)]
parseok = api.unpack_tuple(args, self.fndesc.qualname,
nargs, nargs, *objs)
pred = builder.icmp(lc.ICMP_EQ, parseok, Constant.null(parseok.type))
with cgutils.if_unlikely(builder, pred):
# Block that returns after erroneous argument unboxing/cleanup
endblk = builder.append_basic_block("arg.end")
with builder.goto_block(endblk):
# Get the Environment object
env_manager = self.get_env(api, builder)
cleanup_manager = _ArgManager(self.context, builder, api,
env_manager, endblk, nargs)
# Compute the arguments to the compiled Numba function.
innerargs = []
for obj, ty in zip(objs, self.fndesc.argtypes):
if isinstance(ty, types.Omitted):
# It's an omitted value => ignore dummy Python object
val = cleanup_manager.add_arg(builder.load(obj), ty)
if self.release_gil:
cleanup_manager = _GilManager(builder, api, cleanup_manager)
# We elect to not inline the top level user function into the call
# wrapper, this incurs an overhead of a function call, however, it
# increases optimisation stability in that the optimised user function
# is what will actually be run and it is this function that all the
# inspection tools "see". Further, this makes optimisation "stable" in
# that calling the user function from e.g. C or from this wrapper will
# result in the same code executing, were inlining permitted this may
# not be the case as the inline could trigger additional optimisation
# as the function goes into the wrapper, this resulting in the executing
# instruction stream being different from that of the instruction stream
# present in the user function.
status, retval = self.context.call_conv.call_function(
builder, self.func, self.fndesc.restype, self.fndesc.argtypes,
innerargs, attrs=('noinline',))
# Do clean up
self.debug_print(builder, "# callwrapper: emit_cleanup")
self.debug_print(builder, "# callwrapper: emit_cleanup end")
# Determine return status
with builder.if_then(status.is_ok, likely=True):
# Ok => return boxed Python value
with builder.if_then(status.is_none):
retty = self._simplified_return_type()
obj = api.from_native_return(retty, retval, env_manager)
# Error out
self.context.call_conv.raise_error(builder, api, status)
def get_env(self, api, builder):
"""Get the Environment object which is declared as a global
in the module of the wrapped function.
envname = self.context.get_env_name(self.fndesc)
gvptr = self.context.declare_env_global(builder.module, envname)
envptr = builder.load(gvptr)
env_body = self.context.get_env_body(builder, envptr)
api.emit_environment_sentry(envptr, return_pyobject=True,
env_manager = api.get_env_manager(self.env, env_body, envptr)
return env_manager
def _simplified_return_type(self):
The NPM callconv has already converted simplified optional types.
We can simply use the value type from it.
restype = self.fndesc.restype
# Optional type
if isinstance(restype, types.Optional):
return restype.type
return restype
def debug_print(self, builder, msg):
if config.DEBUG_JIT:
self.context.debug_print(builder, "DEBUGJIT: {0}".format(msg))