def autojit_method_compiler(env, extclass, method, signature):
"""
Called to compile a new specialized method. The result should be
added to the perfect hash-based vtable.
"""
# compiled_method = numba.jit(argtypes=argtypes)(method.py_func)
func_env = pipeline.compile2(env, method.py_func,
restype=signature.return_type,
argtypes=signature.args)
# Create Method for the specialization
new_method = signatures.Method(
method.py_func,
method.name,
func_env.func_signature,
is_class=method.is_class,
is_static=method.is_static)
new_method.update_from_env(func_env)
# Update vtable type
vtable_wrapper = extclass.__numba_vtab
vtable_type = extclass.exttype.vtab_type
vtable_type.specialized_methods[new_method.name,
signature.args] = new_method
# Replace vtable (which will update the vtable all (live) objects use)
new_vtable = virtual.build_hashing_vtab(vtable_type)
vtable_wrapper.replace_vtable(new_vtable)
return func_env.numba_wrapper_func
def compile_function(env, func, argtypes, restype=None, **kwds):
"""
Compile a python function given the argument types. Compile only
if not compiled already, and only if it is registered to the function
cache.
Returns a triplet of (signature, llvm_func, python_callable)
`python_callable` is the wrapper function (NumbaFunction).
"""
function_cache = env.specializations
# For NumbaFunction, we get the original python function.
func = getattr(func, "py_func", func)
# get the compile flags
flags = None # stub
# Search in cache
result = function_cache.get_function(func, argtypes, flags)
if result is not None:
sig, lfunc, pycall = result
return sig, lfunc, pycall
# Compile the function
from numba import pipeline
compile_only = getattr(func, "_numba_compile_only", False)
kwds["compile_only"] = kwds.get("compile_only", compile_only)
assert kwds.get("llvm_module") is None, kwds.get("llvm_module")
func_env = pipeline.compile2(env, func, restype, argtypes, **kwds)
function_cache.register_specialization(func_env)
return (func_env.func_signature, func_env.lfunc, func_env.numba_wrapper_func)
def _compile_methods(class_dict, env, ext_type, lmethods, method_pointers,
flags):
parent_method_pointers = getattr(
ext_type.py_class, '__numba_method_pointers', None)
for i, (method_name, func_signature) in enumerate(ext_type.methods):
if method_name not in class_dict:
# Inherited method
assert parent_method_pointers is not None
name, p = parent_method_pointers[i]
assert name == method_name
method_pointers.append((method_name, p))
continue
method = class_dict[method_name]
# Don't use compile_after_type_inference, re-infer, since we may
# have inferred some return types
# TODO: delayed types and circular calls/variable assignments
logger.debug(method.py_func)
func_env = pipeline.compile2(
env, method.py_func, func_signature.return_type,
func_signature.args, name=method.py_func.__name__,
**flags)
lmethods.append(func_env.lfunc)
method_pointers.append((method_name, func_env.translator.lfunc_pointer))
class_dict[method_name] = method.result(func_env.numba_wrapper_func)
def autojit_method_compiler(env, extclass, method, signature):
"""
Called to compile a new specialized method. The result should be
added to the perfect hash-based vtable.
"""
# compiled_method = numba.jit(argtypes=argtypes)(method.py_func)
func_env = pipeline.compile2(env,
method.py_func,
restype=signature.return_type,
argtypes=signature.args)
# Create Method for the specialization
new_method = signatures.Method(method.py_func,
method.name,
func_env.func_signature,
is_class=method.is_class,
is_static=method.is_static)
new_method.update_from_env(func_env)
# Update vtable type
vtable_wrapper = extclass.__numba_vtab
vtable_type = extclass.exttype.vtab_type
vtable_type.specialized_methods[new_method.name,
signature.args] = new_method
# Replace vtable (which will update the vtable all (live) objects use)
new_vtable = virtual.build_hashing_vtab(vtable_type)
vtable_wrapper.replace_vtable(new_vtable)
return func_env.numba_wrapper_func
def type_infer_method(self, method):
func_env = pipeline.compile2(self.env, method.py_func,
method.signature.return_type,
method.signature.args,
# don't use qualified name
name=method.name,
pipeline_name='type_infer',
**self.flags)
self.func_envs[method] = func_env
# Verify signature after type inference with registered
# (user-declared) signature
method.signature = methods.ExtMethodType(
method.signature.return_type,
method.signature.args,
method.name,
is_class_method=method.is_class,
is_static_method=method.is_static)
self.ext_type.add_method(method)
def compile_function(env, func, argtypes, restype=None, func_ast=None, **kwds):
"""
Compile a python function given the argument types. Compile only
if not compiled already, and only if it is registered to the function
cache.
Returns a triplet of (signature, llvm_func, python_callable)
`python_callable` is the wrapper function (NumbaFunction).
"""
function_cache = env.specializations
# For NumbaFunction, we get the original python function.
func = getattr(func, 'py_func', func)
# get the compile flags
flags = None # stub
# Search in cache
result = function_cache.get_function(func, argtypes, flags)
if result is not None:
sig, lfunc, pycall = result
return sig, lfunc, pycall
# Compile the function
from numba import pipeline
compile_only = getattr(func, '_numba_compile_only', False)
kwds['compile_only'] = kwds.get('compile_only', compile_only)
assert kwds.get('llvm_module') is None, kwds.get('llvm_module')
func_env = pipeline.compile2(env,
func,
restype,
argtypes,
func_ast=func_ast,
**kwds)
function_cache.register_specialization(func_env)
return (func_env.func_signature, func_env.lfunc,
func_env.numba_wrapper_func)
def type_infer_method(self, method):
func_env = pipeline.compile2(
self.env,
method.py_func,
method.signature.return_type,
method.signature.args,
# don't use qualified name
name=method.name,
pipeline_name='type_infer',
**self.flags)
self.func_envs[method] = func_env
# Verify signature after type inference with registered
# (user-declared) signature
method.signature = methods.ExtMethodType(method.signature.return_type,
method.signature.args,
method.name,
is_class=method.is_class,
is_static=method.is_static)
self.ext_type.add_method(method)
