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