def get_default_args(cls, **kw):
""" Return a structure containing the arguments for MetaAtan,
builtin from a default argument mapping overloaded with @p kw
"""
arg_dict = cls.default_args_atan.copy()
arg_dict.update(kw)
return DefaultArgTemplate(**arg_dict)
def get_default_args(**kw):
""" generate default argument structure for OpUnitBench """
default_values = {
"precision": ML_Int32,
}
default_values.update(kw)
return DefaultArgTemplate(**default_values)
def get_default_args(**kw):
default_arg = {
"function_name": "new_lzcnt",
"output_file": "ut_lzcnt.c",
"precision": ML_Int32
}
default_arg.update(**kw)
return DefaultArgTemplate(**default_arg)
def get_default_args(**kw):
default_args_sin = {
"output_file": "ml2_wide_sin.c",
"function_name": "ml2_wide_sin",
"precision": ML_Binary64,
"poly_degree": 1,
"skip_reduction": False,
}
default_args_sin.update(kw)
return DefaultArgTemplate(**default_args_sin)
def get_default_args(**kw):
""" Return a structure containing the arguments for ML_HyperbolicSine,
builtin from a default argument mapping overloaded with @p kw """
default_args_sinh = {
"output_file": "my_sinh.c",
"function_name": "my_sinh",
"precision": ML_Binary32,
"accuracy": ML_Faithful,
"target": GenericProcessor.get_target_instance()
}
default_args_sinh.update(kw)
return DefaultArgTemplate(**default_args_sinh)
def get_default_args(**kw):
""" Return a structure containing the arguments for current class,
builtin from a default argument mapping overloaded with @p kw """
default_args = {
"output_file": "ut_block_lzcnt.c",
"function_name": "ut_lzcnt",
"precision": ML_Int32,
"auto_test_range": Interval(0, 2**31),
"auto_test_execute": 1000,
}
default_args.update(kw)
return DefaultArgTemplate(**default_args)
def get_default_args(cls, **kw):
""" Return a structure containing the arguments for MetaAtan,
builtin from a default argument mapping overloaded with @p kw
"""
arg_dict = cls.default_args_atan.copy()
arg_dict.update({
"output_file": "my_atan2.c",
"function_name": "my_atan2",
"input_intervals": [Interval(-5, 5)] * 2,
})
arg_dict.update(kw)
return DefaultArgTemplate(**arg_dict)
def get_default_args(**kw):
""" Return a structure containing the arguments for ML_Exponential,
builtin from a default argument mapping overloaded with @p kw """
default_args_exp = {
"output_file": "ml_exp.c",
"function_name": "ml_exp",
"precision": ML_Binary32,
"accuracy": ML_Faithful,
"target": GenericProcessor()
}
default_args_exp.update(kw)
return DefaultArgTemplate(**default_args_exp)
def get_default_args(**kw):
""" Return a structure containing the arguments for ML_Log1p,
builtin from a default argument mapping overloaded with @p kw """
default_args_log1p = {
"output_file": "my_log1p.c",
"function_name": "my_log1pf",
"precision": ML_Binary32,
"accuracy": ML_Faithful,
"target": GenericProcessor.get_target_instance(),
"passes": [("start:instantiate_abstract_prec"), ("start:instantiate_prec"), ("start:basic_legalization"), ("start:expand_multi_precision")],
}
default_args_log1p.update(kw)
return DefaultArgTemplate(**default_args_log1p)
def get_default_args(**kw):
""" Return a structure containing the arguments for MetaFMOD,
builtin from a default argument mapping overloaded with @p kw """
default_args_exp = {
"output_file": "ml_fmod.c",
"function_name": "ml_fmod",
"input_intervals": (Interval(-100, 100), Interval(-100, 100)),
"precision": ML_Binary32,
"accuracy": ML_Faithful,
"target": GenericProcessor.get_target_instance(),
}
default_args_exp.update(kw)
return DefaultArgTemplate(**default_args_exp)
def get_default_args(**kw):
""" Return a structure containing the arguments for ML_GenericLog,
builtin from a default argument mapping overloaded with @p kw """
default_args_log = {
"output_file": "ml_genlog.c",
"function_name": "ml_genlog",
"precision": ML_Binary32,
"accuracy": ML_Faithful,
"target": GenericProcessor(),
"basis": exp(1),
}
default_args_log.update(kw)
return DefaultArgTemplate(**default_args_log)
def get_default_args(**kw):
""" Return a structure containing the arguments for MetalibmSqrt,
builtin from a default argument mapping overloaded with @p kw """
default_args_sqrt = {
"output_file": "my_sqrtf.c",
"function_name": "my_sqrtf",
"num_iter": 3,
"precision": ML_Binary32,
"accuracy": ML_Faithful,
"target": GenericProcessor()
}
default_args_sqrt.update(kw)
return DefaultArgTemplate(**default_args_sqrt)
def get_default_args(**kw):
""" Return a structure containing the arguments for ML_ImplementPoly """
default_args_log = {
"output_file": "POLY.c",
"function_name": "POLY",
"precision": ML_Binary64,
"target": GenericProcessor(),
"function": None,
"interval": None,
"epsilon": None
}
default_args_log.update(kw)
return DefaultArgTemplate(**default_args_log)
def get_default_args(**kw):
""" Return a structure containing the arguments for ML_SinCos,
builtin from a default argument mapping overloaded with @p kw """
default_args_sincos = {
"output_file": "my_sincos.c",
"function_name": "my_sincos",
"precision": ML_Binary32,
"accuracy": ML_Faithful,
"target": GenericProcessor(),
"sin_output": False
}
default_args_sincos.update(kw)
return DefaultArgTemplate(**default_args_sincos)
def get_default_args(**args):
""" Generate a default argument structure set specifically for
the Hyperbolic Cosine """
default_cosh_args = {
"precision": ML_Binary32,
"accuracy": ML_Faithful,
"target": GenericProcessor.get_target_instance(),
"output_file": "my_cosh.c",
"function_name": "my_cosh",
"language": C_Code,
"vector_size": 1
}
default_cosh_args.update(args)
return DefaultArgTemplate(**default_cosh_args)
def get_default_args(**kw):
""" Return a structure containing the arguments for ML_Log,
builtin from a default argument mapping overloaded with @p kw """
default_args_log = {
"output_file": "LOG.c",
"function_name": "LOG",
"precision": ML_Binary32,
"accuracy": ML_Faithful,
"target": GenericProcessor(),
"cgpe_index": 0,
"tbl_index_size": 7,
"no_subnormal": False,
"no_fma": False,
"no_rcp": False,
"log_radix": "e",
"force_division": False,
}
default_args_log.update(kw)
return DefaultArgTemplate(**default_args_log)
def get_default_args(**kw):
""" Return a structure containing the arguments for ML_Exponential,
builtin from a default argument mapping overloaded with @p kw """
default_args = {
"output_file":
"ut_num_simplification.c",
"function_name":
"ut_num_simplification",
"passes": [
"beforecodegen:dump", "beforecodegen:numerical_simplification",
"beforecodegen:dump"
],
"precision":
ML_Binary32,
"accuracy":
ML_Faithful,
"target":
GenericProcessor.get_target_instance()
}
default_args.update(kw)
return DefaultArgTemplate(**default_args)
)
@staticmethod
def get_default_args(**kw):
""" Return a structure containing the arguments for ML_Cosine,
builtin from a default argument mapping overloaded with @p kw """
default_args_cos = {
"output_file": "my_cosf.c",
"function_name": "my_cosf",
"precision": ML_Binary32,
"accuracy": ML_Faithful,
"target": GenericProcessor()
}
default_args_cos.update(kw)
return DefaultArgTemplate(**default_args_cos)
def generate_emulate(self, result, mpfr_x, mpfr_rnd):
""" generate the emulation code for ML_Log2 functions
mpfr_x is a mpfr_t variable which should have the right precision
mpfr_rnd is the rounding mode
"""
emulate_func_name = "mpfr_cos"
emulate_func_op = FunctionOperator(emulate_func_name, arg_map = {0: FO_Result(0), 1: FO_Arg(0), 2: FO_Arg(1)}, require_header = ["mpfr.h"])
emulate_func = FunctionObject(emulate_func_name, [ML_Mpfr_t, ML_Int32], ML_Mpfr_t, emulate_func_op)
mpfr_call = Statement(ReferenceAssign(result, emulate_func(mpfr_x, mpfr_rnd)))
return mpfr_call
def get_default_args(**kw):
arg_template = DefaultArgTemplate(precision=ML_Binary32,
output_file="ut_out.c",
function_name="ut_test")
return arg_template