def generate_scheme(self):
# declaring target and instantiating optimization engine
vx = self.implementation.add_input_variable("x", self.precision)
Log.set_dump_stdout(True)
Log.report(Log.Info,
"\033[33;1m generating implementation scheme \033[0m")
if self.debug_flag:
Log.report(Log.Info, "\033[31;1m debug has been enabled \033[0;m")
# local overloading of RaiseReturn operation
def ExpRaiseReturn(*args, **kwords):
kwords["arg_value"] = vx
kwords["function_name"] = self.function_name
if self.libm_compliant:
return RaiseReturn(*args, precision=self.precision, **kwords)
else:
return Return(kwords["return_value"], precision=self.precision)
test_nan_or_inf = Test(vx,
specifier=Test.IsInfOrNaN,
likely=False,
debug=debug_multi,
tag="nan_or_inf")
test_nan = Test(vx,
specifier=Test.IsNaN,
debug=debug_multi,
tag="is_nan_test")
test_positive = Comparison(vx,
0,
specifier=Comparison.GreaterOrEqual,
debug=debug_multi,
tag="inf_sign")
test_signaling_nan = Test(vx,
specifier=Test.IsSignalingNaN,
debug=debug_multi,
tag="is_signaling_nan")
return_snan = Statement(
ExpRaiseReturn(ML_FPE_Invalid,
return_value=FP_QNaN(self.precision)))
# return in case of infinity input
infty_return = Statement(
ConditionBlock(
test_positive,
Return(FP_PlusInfty(self.precision), precision=self.precision),
Return(FP_PlusZero(self.precision), precision=self.precision)))
# return in case of specific value input (NaN or inf)
specific_return = ConditionBlock(
test_nan,
ConditionBlock(
test_signaling_nan, return_snan,
Return(FP_QNaN(self.precision), precision=self.precision)),
infty_return)
# return in case of standard (non-special) input
# exclusion of early overflow and underflow cases
precision_emax = self.precision.get_emax()
precision_max_value = S2 * S2**precision_emax
exp_overflow_bound = sollya.ceil(log(precision_max_value))
early_overflow_test = Comparison(vx,
exp_overflow_bound,
likely=False,
specifier=Comparison.Greater)
early_overflow_return = Statement(
ClearException() if self.libm_compliant else Statement(),
ExpRaiseReturn(ML_FPE_Inexact,
ML_FPE_Overflow,
return_value=FP_PlusInfty(self.precision)))
precision_emin = self.precision.get_emin_subnormal()
precision_min_value = S2**precision_emin
exp_underflow_bound = floor(log(precision_min_value))
early_underflow_test = Comparison(vx,
exp_underflow_bound,
likely=False,
specifier=Comparison.Less)
early_underflow_return = Statement(
ClearException() if self.libm_compliant else Statement(),
ExpRaiseReturn(ML_FPE_Inexact,
ML_FPE_Underflow,
return_value=FP_PlusZero(self.precision)))
# constant computation
invlog2 = self.precision.round_sollya_object(1 / log(2), sollya.RN)
interval_vx = Interval(exp_underflow_bound, exp_overflow_bound)
interval_fk = interval_vx * invlog2
interval_k = Interval(floor(inf(interval_fk)),
sollya.ceil(sup(interval_fk)))
log2_hi_precision = self.precision.get_field_size() - (
sollya.ceil(log2(sup(abs(interval_k)))) + 2)
Log.report(Log.Info, "log2_hi_precision: %d" % log2_hi_precision)
invlog2_cst = Constant(invlog2, precision=self.precision)
log2_hi = round(log(2), log2_hi_precision, sollya.RN)
log2_lo = self.precision.round_sollya_object(
log(2) - log2_hi, sollya.RN)
# argument reduction
unround_k = vx * invlog2
unround_k.set_attributes(tag="unround_k", debug=debug_multi)
k = NearestInteger(unround_k,
precision=self.precision,
debug=debug_multi)
ik = NearestInteger(unround_k,
precision=self.precision.get_integer_format(),
debug=debug_multi,
tag="ik")
ik.set_tag("ik")
k.set_tag("k")
exact_pre_mul = (k * log2_hi)
exact_pre_mul.set_attributes(exact=True)
exact_hi_part = vx - exact_pre_mul
exact_hi_part.set_attributes(exact=True,
tag="exact_hi",
debug=debug_multi,
prevent_optimization=True)
exact_lo_part = -k * log2_lo
exact_lo_part.set_attributes(tag="exact_lo",
debug=debug_multi,
prevent_optimization=True)
r = exact_hi_part + exact_lo_part
r.set_tag("r")
r.set_attributes(debug=debug_multi)
approx_interval = Interval(-log(2) / 2, log(2) / 2)
approx_interval_half = approx_interval / 2
approx_interval_split = [
Interval(-log(2) / 2, inf(approx_interval_half)),
approx_interval_half,
Interval(sup(approx_interval_half),
log(2) / 2)
]
# TODO: should be computed automatically
exact_hi_interval = approx_interval
exact_lo_interval = -interval_k * log2_lo
opt_r = self.optimise_scheme(r, copy={})
tag_map = {}
self.opt_engine.register_nodes_by_tag(opt_r, tag_map)
cg_eval_error_copy_map = {
vx:
Variable("x", precision=self.precision, interval=interval_vx),
tag_map["k"]:
Variable("k", interval=interval_k, precision=self.precision)
}
#try:
if is_gappa_installed():
eval_error = self.gappa_engine.get_eval_error_v2(
self.opt_engine,
opt_r,
cg_eval_error_copy_map,
gappa_filename="red_arg.g")
else:
eval_error = 0.0
Log.report(Log.Warning,
"gappa is not installed in this environnement")
Log.report(Log.Info, "eval error: %s" % eval_error)
local_ulp = sup(ulp(sollya.exp(approx_interval), self.precision))
# FIXME refactor error_goal from accuracy
Log.report(Log.Info, "accuracy: %s" % self.accuracy)
if isinstance(self.accuracy, ML_Faithful):
error_goal = local_ulp
elif isinstance(self.accuracy, ML_CorrectlyRounded):
error_goal = S2**-1 * local_ulp
elif isinstance(self.accuracy, ML_DegradedAccuracyAbsolute):
error_goal = self.accuracy.goal
elif isinstance(self.accuracy, ML_DegradedAccuracyRelative):
error_goal = self.accuracy.goal
else:
Log.report(Log.Error, "unknown accuracy: %s" % self.accuracy)
# error_goal = local_ulp #S2**-(self.precision.get_field_size()+1)
error_goal_approx = S2**-1 * error_goal
Log.report(Log.Info,
"\033[33;1m building mathematical polynomial \033[0m\n")
poly_degree = max(
sup(
guessdegree(
expm1(sollya.x) / sollya.x, approx_interval,
error_goal_approx)) - 1, 2)
init_poly_degree = poly_degree
error_function = lambda p, f, ai, mod, t: dirtyinfnorm(f - p, ai)
polynomial_scheme_builder = PolynomialSchemeEvaluator.generate_estrin_scheme
#polynomial_scheme_builder = PolynomialSchemeEvaluator.generate_horner_scheme
while 1:
Log.report(Log.Info, "attempting poly degree: %d" % poly_degree)
precision_list = [1] + [self.precision] * (poly_degree)
poly_object, poly_approx_error = Polynomial.build_from_approximation_with_error(
expm1(sollya.x),
poly_degree,
precision_list,
approx_interval,
sollya.absolute,
error_function=error_function)
Log.report(Log.Info, "polynomial: %s " % poly_object)
sub_poly = poly_object.sub_poly(start_index=2)
Log.report(Log.Info, "polynomial: %s " % sub_poly)
Log.report(Log.Info, "poly approx error: %s" % poly_approx_error)
Log.report(
Log.Info,
"\033[33;1m generating polynomial evaluation scheme \033[0m")
pre_poly = polynomial_scheme_builder(
poly_object, r, unified_precision=self.precision)
pre_poly.set_attributes(tag="pre_poly", debug=debug_multi)
pre_sub_poly = polynomial_scheme_builder(
sub_poly, r, unified_precision=self.precision)
pre_sub_poly.set_attributes(tag="pre_sub_poly", debug=debug_multi)
poly = 1 + (exact_hi_part + (exact_lo_part + pre_sub_poly))
poly.set_tag("poly")
# optimizing poly before evaluation error computation
#opt_poly = self.opt_engine.optimization_process(poly, self.precision, fuse_fma = fuse_fma)
#opt_sub_poly = self.opt_engine.optimization_process(pre_sub_poly, self.precision, fuse_fma = fuse_fma)
opt_poly = self.optimise_scheme(poly)
opt_sub_poly = self.optimise_scheme(pre_sub_poly)
# evaluating error of the polynomial approximation
r_gappa_var = Variable("r",
precision=self.precision,
interval=approx_interval)
exact_hi_gappa_var = Variable("exact_hi",
precision=self.precision,
interval=exact_hi_interval)
exact_lo_gappa_var = Variable("exact_lo",
precision=self.precision,
interval=exact_lo_interval)
vx_gappa_var = Variable("x",
precision=self.precision,
interval=interval_vx)
k_gappa_var = Variable("k",
interval=interval_k,
precision=self.precision)
#print "exact_hi interval: ", exact_hi_interval
sub_poly_error_copy_map = {
#r.get_handle().get_node(): r_gappa_var,
#vx.get_handle().get_node(): vx_gappa_var,
exact_hi_part.get_handle().get_node():
exact_hi_gappa_var,
exact_lo_part.get_handle().get_node():
exact_lo_gappa_var,
#k.get_handle().get_node(): k_gappa_var,
}
poly_error_copy_map = {
exact_hi_part.get_handle().get_node(): exact_hi_gappa_var,
exact_lo_part.get_handle().get_node(): exact_lo_gappa_var,
}
if is_gappa_installed():
sub_poly_eval_error = -1.0
sub_poly_eval_error = self.gappa_engine.get_eval_error_v2(
self.opt_engine,
opt_sub_poly,
sub_poly_error_copy_map,
gappa_filename="%s_gappa_sub_poly.g" % self.function_name)
dichotomy_map = [
{
exact_hi_part.get_handle().get_node():
approx_interval_split[0],
},
{
exact_hi_part.get_handle().get_node():
approx_interval_split[1],
},
{
exact_hi_part.get_handle().get_node():
approx_interval_split[2],
},
]
poly_eval_error_dico = self.gappa_engine.get_eval_error_v3(
self.opt_engine,
opt_poly,
poly_error_copy_map,
gappa_filename="gappa_poly.g",
dichotomy=dichotomy_map)
poly_eval_error = max(
[sup(abs(err)) for err in poly_eval_error_dico])
else:
poly_eval_error = 0.0
sub_poly_eval_error = 0.0
Log.report(Log.Warning,
"gappa is not installed in this environnement")
Log.report(Log.Info, "stopping autonomous degree research")
# incrementing polynomial degree to counteract initial decrementation effect
poly_degree += 1
break
Log.report(Log.Info, "poly evaluation error: %s" % poly_eval_error)
Log.report(Log.Info,
"sub poly evaluation error: %s" % sub_poly_eval_error)
global_poly_error = None
global_rel_poly_error = None
for case_index in range(3):
poly_error = poly_approx_error + poly_eval_error_dico[
case_index]
rel_poly_error = sup(
abs(poly_error /
sollya.exp(approx_interval_split[case_index])))
if global_rel_poly_error == None or rel_poly_error > global_rel_poly_error:
global_rel_poly_error = rel_poly_error
global_poly_error = poly_error
flag = error_goal > global_rel_poly_error
if flag:
break
else:
poly_degree += 1
late_overflow_test = Comparison(ik,
self.precision.get_emax(),
specifier=Comparison.Greater,
likely=False,
debug=debug_multi,
tag="late_overflow_test")
overflow_exp_offset = (self.precision.get_emax() -
self.precision.get_field_size() / 2)
diff_k = Subtraction(
ik,
Constant(overflow_exp_offset,
precision=self.precision.get_integer_format()),
precision=self.precision.get_integer_format(),
debug=debug_multi,
tag="diff_k",
)
late_overflow_result = (ExponentInsertion(
diff_k, precision=self.precision) * poly) * ExponentInsertion(
overflow_exp_offset, precision=self.precision)
late_overflow_result.set_attributes(silent=False,
tag="late_overflow_result",
debug=debug_multi,
precision=self.precision)
late_overflow_return = ConditionBlock(
Test(late_overflow_result, specifier=Test.IsInfty, likely=False),
ExpRaiseReturn(ML_FPE_Overflow,
return_value=FP_PlusInfty(self.precision)),
Return(late_overflow_result, precision=self.precision))
late_underflow_test = Comparison(k,
self.precision.get_emin_normal(),
specifier=Comparison.LessOrEqual,
likely=False)
underflow_exp_offset = 2 * self.precision.get_field_size()
corrected_exp = Addition(
ik,
Constant(underflow_exp_offset,
precision=self.precision.get_integer_format()),
precision=self.precision.get_integer_format(),
tag="corrected_exp")
late_underflow_result = (
ExponentInsertion(corrected_exp, precision=self.precision) *
poly) * ExponentInsertion(-underflow_exp_offset,
precision=self.precision)
late_underflow_result.set_attributes(debug=debug_multi,
tag="late_underflow_result",
silent=False)
test_subnormal = Test(late_underflow_result,
specifier=Test.IsSubnormal)
late_underflow_return = Statement(
ConditionBlock(
test_subnormal,
ExpRaiseReturn(ML_FPE_Underflow,
return_value=late_underflow_result)),
Return(late_underflow_result, precision=self.precision))
twok = ExponentInsertion(ik,
tag="exp_ik",
debug=debug_multi,
precision=self.precision)
#std_result = twok * ((1 + exact_hi_part * pre_poly) + exact_lo_part * pre_poly)
std_result = twok * poly
std_result.set_attributes(tag="std_result", debug=debug_multi)
result_scheme = ConditionBlock(
late_overflow_test, late_overflow_return,
ConditionBlock(late_underflow_test, late_underflow_return,
Return(std_result, precision=self.precision)))
std_return = ConditionBlock(
early_overflow_test, early_overflow_return,
ConditionBlock(early_underflow_test, early_underflow_return,
result_scheme))
# main scheme
Log.report(Log.Info, "\033[33;1m MDL scheme \033[0m")
scheme = ConditionBlock(
test_nan_or_inf,
Statement(ClearException() if self.libm_compliant else Statement(),
specific_return), std_return)
return scheme
def generate_scheme(self):
# declaring CodeFunction and retrieving input variable
vx = Abs(self.implementation.add_input_variable("x", self.precision), tag = "vx")
Log.report(Log.Info, "generating implementation scheme")
if self.debug_flag:
Log.report(Log.Info, "debug has been enabled")
# local overloading of RaiseReturn operation
def ExpRaiseReturn(*args, **kwords):
kwords["arg_value"] = vx
kwords["function_name"] = self.function_name
return RaiseReturn(*args, **kwords)
debug_precision = {ML_Binary32: debug_ftox, ML_Binary64: debug_lftolx}[self.precision]
test_nan_or_inf = Test(vx, specifier = Test.IsInfOrNaN, likely = False, debug = True, tag = "nan_or_inf")
test_nan = Test(vx, specifier = Test.IsNaN, debug = True, tag = "is_nan_test")
test_positive = Comparison(vx, 0, specifier = Comparison.GreaterOrEqual, debug = True, tag = "inf_sign")
test_signaling_nan = Test(vx, specifier = Test.IsSignalingNaN, debug = True, tag = "is_signaling_nan")
return_snan = Statement(ExpRaiseReturn(ML_FPE_Invalid, return_value = FP_QNaN(self.precision)))
# return in case of infinity input
infty_return = Statement(ConditionBlock(test_positive, Return(FP_PlusInfty(self.precision)), Return(FP_PlusZero(self.precision))))
# return in case of specific value input (NaN or inf)
specific_return = ConditionBlock(test_nan, ConditionBlock(test_signaling_nan, return_snan, Return(FP_QNaN(self.precision))), infty_return)
# return in case of standard (non-special) input
sollya_precision = self.precision.get_sollya_object()
hi_precision = self.precision.get_field_size() - 3
# argument reduction
frac_pi_index = 3
frac_pi = round(S2**frac_pi_index / pi, sollya_precision, sollya.RN)
inv_frac_pi = round(pi / S2**frac_pi_index, hi_precision, sollya.RN)
inv_frac_pi_lo = round(pi / S2**frac_pi_index - inv_frac_pi, sollya_precision, sollya.RN)
# computing k = E(x * frac_pi)
vx_pi = Multiplication(vx, frac_pi, precision = self.precision)
k = NearestInteger(vx_pi, precision = ML_Int32, tag = "k", debug = True)
fk = Conversion(k, precision = self.precision, tag = "fk")
inv_frac_pi_cst = Constant(inv_frac_pi, tag = "inv_frac_pi", precision = self.precision)
inv_frac_pi_lo_cst = Constant(inv_frac_pi_lo, tag = "inv_frac_pi_lo", precision = self.precision)
red_vx_hi = (vx - inv_frac_pi_cst * fk)
red_vx_hi.set_attributes(tag = "red_vx_hi", debug = debug_precision, precision = self.precision)
red_vx_lo_sub = inv_frac_pi_lo_cst * fk
red_vx_lo_sub.set_attributes(tag = "red_vx_lo_sub", debug = debug_precision, unbreakable = True, precision = self.precision)
vx_d = Conversion(vx, precision = ML_Binary64, tag = "vx_d")
pre_red_vx = red_vx_hi - inv_frac_pi_lo_cst * fk
pre_red_vx_d_hi = (vx_d - inv_frac_pi_cst * fk)
pre_red_vx_d_hi.set_attributes(tag = "pre_red_vx_d_hi", precision = ML_Binary64, debug = debug_lftolx)
pre_red_vx_d = pre_red_vx_d_hi - inv_frac_pi_lo_cst * fk
pre_red_vx_d.set_attributes(tag = "pre_red_vx_d", debug = debug_lftolx, precision = ML_Binary64)
modk = Modulo(k, 2**(frac_pi_index+1), precision = ML_Int32, tag = "switch_value", debug = True)
sel_c = Equal(BitLogicAnd(modk, 2**(frac_pi_index-1)), 2**(frac_pi_index-1))
red_vx = Select(sel_c, -pre_red_vx, pre_red_vx)
red_vx.set_attributes(tag = "red_vx", debug = debug_precision, precision = self.precision)
red_vx_d = Select(sel_c, -pre_red_vx_d, pre_red_vx_d)
red_vx_d.set_attributes(tag = "red_vx_d", debug = debug_lftolx, precision = ML_Binary64)
approx_interval = Interval(-pi/(S2**(frac_pi_index+1)), pi / S2**(frac_pi_index+1))
Log.report(Log.Info, "approx interval: %s\n" % approx_interval)
error_goal_approx = S2**-self.precision.get_precision()
Log.report(Log.Info, "building mathematical polynomial")
poly_degree_vector = [None] * 2**(frac_pi_index+1)
error_function = lambda p, f, ai, mod, t: dirtyinfnorm(f - p, ai)
#polynomial_scheme_builder = PolynomialSchemeEvaluator.generate_estrin_scheme
polynomial_scheme_builder = PolynomialSchemeEvaluator.generate_horner_scheme
index_relative = []
poly_object_vector = [None] * 2**(frac_pi_index+1)
for i in range(2**(frac_pi_index+1)):
sub_func = cos(sollya.x+i*pi/S2**frac_pi_index)
degree = int(sup(guessdegree(sub_func, approx_interval, error_goal_approx))) + 1
degree_list = range(degree+1)
a_interval = approx_interval
if i == 0:
# ad-hoc, TODO: to be cleaned
degree = 6
degree_list = range(0, degree+1, 2)
elif i % 2**(frac_pi_index) == 2**(frac_pi_index-1):
# for pi/2 and 3pi/2, an approx to sin=cos(pi/2+x)
# must be generated
degree_list = range(1, degree+1, 2)
if i == 3 or i == 5 or i == 7 or i == 9:
precision_list = [sollya.binary64] + [sollya.binary32] *(degree)
else:
precision_list = [sollya.binary32] * (degree+1)
poly_degree_vector[i] = degree
constraint = sollya.absolute
delta = (2**(frac_pi_index - 3))
centered_i = (i % 2**(frac_pi_index)) - 2**(frac_pi_index-1)
if centered_i < delta and centered_i > -delta and centered_i != 0:
constraint = sollya.relative
index_relative.append(i)
Log.report(Log.Info, "generating approximation for %d/%d" % (i, 2**(frac_pi_index+1)))
poly_object_vector[i], _ = Polynomial.build_from_approximation_with_error(sub_func, degree_list, precision_list, a_interval, constraint, error_function = error_function)
# unified power map for red_sx^n
upm = {}
rel_error_list = []
poly_scheme_vector = [None] * (2**(frac_pi_index+1))
for i in range(2**(frac_pi_index+1)):
poly_object = poly_object_vector[i]
poly_precision = self.precision
if i == 3 or i == 5 or i == 7 or i == 9:
poly_precision = ML_Binary64
c0 = Constant(coeff(poly_object.get_sollya_object(), 0), precision = ML_Binary64)
c1 = Constant(coeff(poly_object.get_sollya_object(), 1), precision = self.precision)
poly_hi = (c0 + c1 * red_vx)
poly_hi.set_precision(ML_Binary64)
red_vx_d_2 = red_vx_d * red_vx_d
poly_scheme = poly_hi + red_vx_d_2 * polynomial_scheme_builder(poly_object.sub_poly(start_index = 2, offset = 2), red_vx, unified_precision = self.precision, power_map_ = upm)
poly_scheme.set_attributes(unbreakable = True)
elif i == 4:
c1 = Constant(coeff(poly_object.get_sollya_object(), 1), precision = ML_Binary64)
poly_scheme = c1 * red_vx_d + polynomial_scheme_builder(poly_object.sub_poly(start_index = 2), red_vx, unified_precision = self.precision, power_map_ = upm)
poly_scheme.set_precision(ML_Binary64)
else:
poly_scheme = polynomial_scheme_builder(poly_object, red_vx, unified_precision = poly_precision, power_map_ = upm)
#if i == 3:
# c0 = Constant(coeff(poly_object.get_sollya_object(), 0), precision = self.precision)
# c1 = Constant(coeff(poly_object.get_sollya_object(), 1), precision = self.precision)
# poly_scheme = (c0 + c1 * red_vx) + polynomial_scheme_builder(poly_object.sub_poly(start_index = 2), red_vx, unified_precision = self.precision, power_map_ = upm)
poly_scheme.set_attributes(tag = "poly_cos%dpi%d" % (i, 2**(frac_pi_index)), debug = debug_precision)
poly_scheme_vector[i] = poly_scheme
#try:
if is_gappa_installed() and i == 3:
opt_scheme = self.opt_engine.optimization_process(poly_scheme, self.precision, copy = True, fuse_fma = self.fuse_fma)
tag_map = {}
self.opt_engine.register_nodes_by_tag(opt_scheme, tag_map)
gappa_vx = Variable("red_vx", precision = self.precision, interval = approx_interval)
cg_eval_error_copy_map = {
tag_map["red_vx"]: gappa_vx,
tag_map["red_vx_d"]: gappa_vx,
}
def generate_scalar_scheme(self, vx):
Log.set_dump_stdout(True)
Log.report(Log.Info, "\033[33;1m generating implementation scheme \033[0m")
if self.debug_flag:
Log.report(Log.Info, "\033[31;1m debug has been enabled \033[0;m")
index_size = 5
comp_lo = (vx < 0)
comp_lo.set_attributes(tag = "comp_lo", precision = ML_Bool)
sign = Select(comp_lo, -1, 1, precision = self.precision)
# as sinh is an odd function, we can simplify the input to its absolute
# value once the sign has been extracted
vx = Abs(vx)
int_precision = self.precision.get_integer_format()
# argument reduction
arg_reg_value = log(2)/2**index_size
inv_log2_value = round(1/arg_reg_value, self.precision.get_sollya_object(), sollya.RN)
inv_log2_cst = Constant(inv_log2_value, precision = self.precision, tag = "inv_log2")
# for r_hi to be accurate we ensure k * log2_hi_value_cst is exact
# by limiting the number of non-zero bits in log2_hi_value_cst
# cosh(x) ~ exp(abs(x))/2 for a big enough x
# cosh(x) > 2^1023 <=> exp(x) > 2^1024 <=> x > log(2^1024)
# k = inv_log2_value * x
# -1 for guard
max_k_approx = inv_log2_value * log(sollya.SollyaObject(2)**1024)
max_k_bitsize = int(ceil(log2(max_k_approx)))
Log.report(Log.Info, "max_k_bitsize: %d" % max_k_bitsize)
log2_hi_value_precision = self.precision.get_precision() - max_k_bitsize - 1
log2_hi_value = round(arg_reg_value, log2_hi_value_precision, sollya.RN)
log2_lo_value = round(arg_reg_value - log2_hi_value, self.precision.get_sollya_object(), sollya.RN)
log2_hi_value_cst = Constant(log2_hi_value, tag = "log2_hi_value", precision = self.precision)
log2_lo_value_cst = Constant(log2_lo_value, tag = "log2_lo_value", precision = self.precision)
k = Trunc(Multiplication(inv_log2_cst, vx), precision = self.precision)
k_log2 = Multiplication(k, log2_hi_value_cst, precision = self.precision, exact = True, tag = "k_log2", unbreakable = True)
r_hi = vx - k_log2
r_hi.set_attributes(tag = "r_hi", debug = debug_multi, unbreakable = True)
r_lo = -k * log2_lo_value_cst
# reduced argument
r = r_hi + r_lo
r.set_attributes(tag = "r", debug = debug_multi)
if is_gappa_installed():
r_eval_error = self.get_eval_error(r_hi, variable_copy_map =
{
vx: Variable("vx", interval = Interval(0, 715), precision = self.precision),
k: Variable("k", interval = Interval(0, 1024), precision = self.precision)
})
Log.report(Log.Verbose, "r_eval_error: ", r_eval_error)
approx_interval = Interval(-arg_reg_value, arg_reg_value)
error_goal_approx = 2**-(self.precision.get_precision())
poly_degree = sup(guessdegree(exp(sollya.x), approx_interval, error_goal_approx)) + 3
precision_list = [1] + [self.precision] * (poly_degree)
k_integer = Conversion(k, precision = int_precision, tag = "k_integer", debug = debug_multi)
k_hi = BitLogicRightShift(k_integer, Constant(index_size, precision=int_precision), tag = "k_int_hi", precision = int_precision, debug = debug_multi)
k_lo = Modulo(k_integer, 2**index_size, tag = "k_int_lo", precision = int_precision, debug = debug_multi)
pow_exp = ExponentInsertion(Conversion(k_hi, precision = int_precision), precision = self.precision, tag = "pow_exp", debug = debug_multi)
exp_table = ML_NewTable(dimensions = [2 * 2**index_size, 4], storage_precision = self.precision, tag = self.uniquify_name("exp2_table"))
for i in range(2 * 2**index_size):
input_value = i - 2**index_size if i >= 2**index_size else i
reduced_hi_prec = int(self.precision.get_mantissa_size() - 8)
# using SollyaObject wrapper to force evaluation by sollya
# with higher precision
exp_value = sollya.SollyaObject(2)**((input_value)* 2**-index_size)
mexp_value = sollya.SollyaObject(2)**((-input_value)* 2**-index_size)
pos_value_hi = round(exp_value, reduced_hi_prec, sollya.RN)
pos_value_lo = round(exp_value - pos_value_hi, self.precision.get_sollya_object(), sollya.RN)
neg_value_hi = round(mexp_value, reduced_hi_prec, sollya.RN)
neg_value_lo = round(mexp_value - neg_value_hi, self.precision.get_sollya_object(), sollya.RN)
exp_table[i][0] = neg_value_hi
exp_table[i][1] = neg_value_lo
exp_table[i][2] = pos_value_hi
exp_table[i][3] = pos_value_lo
# log2_value = log(2) / 2^index_size
# sinh(x) = 1/2 * (exp(x) - exp(-x))
# exp(x) = exp(x - k * log2_value + k * log2_value)
#
# r = x - k * log2_value
# exp(x) = exp(r) * 2 ^ (k / 2^index_size)
#
# k / 2^index_size = h + l * 2^-index_size, with k, h, l integers
# exp(x) = exp(r) * 2^h * 2^(l *2^-index_size)
#
# sinh(x) = exp(r) * 2^(h-1) * 2^(l *2^-index_size) - exp(-r) * 2^(-h-1) * 2^(-l *2^-index_size)
# S=2^(h-1), T = 2^(-h-1)
# exp(r) = 1 + poly_pos(r)
# exp(-r) = 1 + poly_neg(r)
# 2^(l / 2^index_size) = pos_value_hi + pos_value_lo
# 2^(-l / 2^index_size) = neg_value_hi + neg_value_lo
#
error_function = lambda p, f, ai, mod, t: dirtyinfnorm(f - p, ai)
poly_object, poly_approx_error = Polynomial.build_from_approximation_with_error(exp(sollya.x), poly_degree, precision_list, approx_interval, sollya.absolute, error_function = error_function)
Log.report(Log.Verbose, "poly_approx_error: {}, {}".format(poly_approx_error, float(log2(poly_approx_error))))
polynomial_scheme_builder = PolynomialSchemeEvaluator.generate_horner_scheme
poly_pos = polynomial_scheme_builder(poly_object.sub_poly(start_index = 1), r, unified_precision = self.precision)
poly_pos.set_attributes(tag = "poly_pos", debug = debug_multi)
poly_neg = polynomial_scheme_builder(poly_object.sub_poly(start_index = 1), -r, unified_precision = self.precision)
poly_neg.set_attributes(tag = "poly_neg", debug = debug_multi)
table_index = Addition(k_lo, Constant(2**index_size, precision = int_precision), precision = int_precision, tag = "table_index", debug = debug_multi)
neg_value_load_hi = TableLoad(exp_table, table_index, 0, tag = "neg_value_load_hi", debug = debug_multi)
neg_value_load_lo = TableLoad(exp_table, table_index, 1, tag = "neg_value_load_lo", debug = debug_multi)
pos_value_load_hi = TableLoad(exp_table, table_index, 2, tag = "pos_value_load_hi", debug = debug_multi)
pos_value_load_lo = TableLoad(exp_table, table_index, 3, tag = "pos_value_load_lo", debug = debug_multi)
k_plus = Max(
Subtraction(k_hi, Constant(1, precision = int_precision), precision=int_precision, tag="k_plus", debug=debug_multi),
Constant(self.precision.get_emin_normal(), precision = int_precision))
k_neg = Max(
Subtraction(-k_hi, Constant(1, precision=int_precision), precision=int_precision, tag="k_neg", debug=debug_multi),
Constant(self.precision.get_emin_normal(), precision = int_precision))
# 2^(h-1)
pow_exp_pos = ExponentInsertion(k_plus, precision = self.precision, tag="pow_exp_pos", debug=debug_multi)
# 2^(-h-1)
pow_exp_neg = ExponentInsertion(k_neg, precision = self.precision, tag="pow_exp_neg", debug=debug_multi)
hi_terms = (pos_value_load_hi * pow_exp_pos - neg_value_load_hi * pow_exp_neg)
hi_terms.set_attributes(tag = "hi_terms", debug=debug_multi)
pos_exp = (pos_value_load_hi * poly_pos + (pos_value_load_lo + pos_value_load_lo * poly_pos)) * pow_exp_pos
pos_exp.set_attributes(tag = "pos_exp", debug = debug_multi)
neg_exp = (neg_value_load_hi * poly_neg + (neg_value_load_lo + neg_value_load_lo * poly_neg)) * pow_exp_neg
neg_exp.set_attributes(tag = "neg_exp", debug = debug_multi)
result = Addition(
Subtraction(
pos_exp,
neg_exp,
precision=self.precision,
),
hi_terms,
precision=self.precision,
tag="result",
debug=debug_multi
)
# ov_value
ov_value = round(asinh(self.precision.get_max_value()), self.precision.get_sollya_object(), sollya.RD)
ov_flag = Comparison(Abs(vx), Constant(ov_value, precision = self.precision), specifier = Comparison.Greater)
# main scheme
scheme = Statement(
Return(
Select(
ov_flag,
sign*FP_PlusInfty(self.precision),
sign*result
)))
return scheme
def generate_scheme(self):
# declaring CodeFunction and retrieving input variable
vx = Abs(self.implementation.add_input_variable("x", self.precision),
tag="vx")
Log.report(Log.Info, "generating implementation scheme")
if self.debug_flag:
Log.report(Log.Info, "debug has been enabled")
# local overloading of RaiseReturn operation
def ExpRaiseReturn(*args, **kwords):
kwords["arg_value"] = vx
kwords["function_name"] = self.function_name
return RaiseReturn(*args, **kwords)
debug_precision = {
ML_Binary32: debug_ftox,
ML_Binary64: debug_lftolx
}[self.precision]
test_nan_or_inf = Test(vx,
specifier=Test.IsInfOrNaN,
likely=False,
debug=True,
tag="nan_or_inf")
test_nan = Test(vx,
specifier=Test.IsNaN,
debug=True,
tag="is_nan_test")
test_positive = Comparison(vx,
0,
specifier=Comparison.GreaterOrEqual,
debug=True,
tag="inf_sign")
test_signaling_nan = Test(vx,
specifier=Test.IsSignalingNaN,
debug=True,
tag="is_signaling_nan")
return_snan = Statement(
ExpRaiseReturn(ML_FPE_Invalid,
return_value=FP_QNaN(self.precision)))
# return in case of infinity input
infty_return = Statement(
ConditionBlock(test_positive, Return(FP_PlusInfty(self.precision)),
Return(FP_PlusZero(self.precision))))
# return in case of specific value input (NaN or inf)
specific_return = ConditionBlock(
test_nan,
ConditionBlock(test_signaling_nan, return_snan,
Return(FP_QNaN(self.precision))), infty_return)
# return in case of standard (non-special) input
sollya_precision = self.precision.get_sollya_object()
hi_precision = self.precision.get_field_size() - 3
# argument reduction
frac_pi_index = 3
frac_pi = round(S2**frac_pi_index / pi, sollya_precision, sollya.RN)
inv_frac_pi = round(pi / S2**frac_pi_index, hi_precision, sollya.RN)
inv_frac_pi_lo = round(pi / S2**frac_pi_index - inv_frac_pi,
sollya_precision, sollya.RN)
# computing k = E(x * frac_pi)
vx_pi = Multiplication(vx, frac_pi, precision=self.precision)
k = NearestInteger(vx_pi, precision=ML_Int32, tag="k", debug=True)
fk = Conversion(k, precision=self.precision, tag="fk")
inv_frac_pi_cst = Constant(inv_frac_pi,
tag="inv_frac_pi",
precision=self.precision)
inv_frac_pi_lo_cst = Constant(inv_frac_pi_lo,
tag="inv_frac_pi_lo",
precision=self.precision)
red_vx_hi = (vx - inv_frac_pi_cst * fk)
red_vx_hi.set_attributes(tag="red_vx_hi",
debug=debug_precision,
precision=self.precision)
red_vx_lo_sub = inv_frac_pi_lo_cst * fk
red_vx_lo_sub.set_attributes(tag="red_vx_lo_sub",
debug=debug_precision,
unbreakable=True,
precision=self.precision)
vx_d = Conversion(vx, precision=ML_Binary64, tag="vx_d")
pre_red_vx = red_vx_hi - inv_frac_pi_lo_cst * fk
pre_red_vx_d_hi = (vx_d - inv_frac_pi_cst * fk)
pre_red_vx_d_hi.set_attributes(tag="pre_red_vx_d_hi",
precision=ML_Binary64,
debug=debug_lftolx)
pre_red_vx_d = pre_red_vx_d_hi - inv_frac_pi_lo_cst * fk
pre_red_vx_d.set_attributes(tag="pre_red_vx_d",
debug=debug_lftolx,
precision=ML_Binary64)
modk = Modulo(k,
2**(frac_pi_index + 1),
precision=ML_Int32,
tag="switch_value",
debug=True)
sel_c = Equal(BitLogicAnd(modk, 2**(frac_pi_index - 1)),
2**(frac_pi_index - 1))
red_vx = Select(sel_c, -pre_red_vx, pre_red_vx)
red_vx.set_attributes(tag="red_vx",
debug=debug_precision,
precision=self.precision)
red_vx_d = Select(sel_c, -pre_red_vx_d, pre_red_vx_d)
red_vx_d.set_attributes(tag="red_vx_d",
debug=debug_lftolx,
precision=ML_Binary64)
approx_interval = Interval(-pi / (S2**(frac_pi_index + 1)),
pi / S2**(frac_pi_index + 1))
Log.report(Log.Info, "approx interval: %s\n" % approx_interval)
error_goal_approx = S2**-self.precision.get_precision()
Log.report(Log.Info, "building mathematical polynomial")
poly_degree_vector = [None] * 2**(frac_pi_index + 1)
error_function = lambda p, f, ai, mod, t: dirtyinfnorm(f - p, ai)
#polynomial_scheme_builder = PolynomialSchemeEvaluator.generate_estrin_scheme
polynomial_scheme_builder = PolynomialSchemeEvaluator.generate_horner_scheme
index_relative = []
poly_object_vector = [None] * 2**(frac_pi_index + 1)
for i in range(2**(frac_pi_index + 1)):
sub_func = cos(sollya.x + i * pi / S2**frac_pi_index)
degree = int(
sup(guessdegree(sub_func, approx_interval,
error_goal_approx))) + 1
degree_list = range(degree + 1)
a_interval = approx_interval
if i == 0:
# ad-hoc, TODO: to be cleaned
degree = 6
degree_list = range(0, degree + 1, 2)
elif i % 2**(frac_pi_index) == 2**(frac_pi_index - 1):
# for pi/2 and 3pi/2, an approx to sin=cos(pi/2+x)
# must be generated
degree_list = range(1, degree + 1, 2)
if i == 3 or i == 5 or i == 7 or i == 9:
precision_list = [sollya.binary64
] + [sollya.binary32] * (degree)
else:
precision_list = [sollya.binary32] * (degree + 1)
poly_degree_vector[i] = degree
constraint = sollya.absolute
delta = (2**(frac_pi_index - 3))
centered_i = (i % 2**(frac_pi_index)) - 2**(frac_pi_index - 1)
if centered_i < delta and centered_i > -delta and centered_i != 0:
constraint = sollya.relative
index_relative.append(i)
Log.report(
Log.Info, "generating approximation for %d/%d" %
(i, 2**(frac_pi_index + 1)))
poly_object_vector[
i], _ = Polynomial.build_from_approximation_with_error(
sub_func,
degree_list,
precision_list,
a_interval,
constraint,
error_function=error_function)
# unified power map for red_sx^n
upm = {}
rel_error_list = []
poly_scheme_vector = [None] * (2**(frac_pi_index + 1))
for i in range(2**(frac_pi_index + 1)):
poly_object = poly_object_vector[i]
poly_precision = self.precision
if i == 3 or i == 5 or i == 7 or i == 9:
poly_precision = ML_Binary64
c0 = Constant(coeff(poly_object.get_sollya_object(), 0),
precision=ML_Binary64)
c1 = Constant(coeff(poly_object.get_sollya_object(), 1),
precision=self.precision)
poly_hi = (c0 + c1 * red_vx)
poly_hi.set_precision(ML_Binary64)
red_vx_d_2 = red_vx_d * red_vx_d
poly_scheme = poly_hi + red_vx_d_2 * polynomial_scheme_builder(
poly_object.sub_poly(start_index=2, offset=2),
red_vx,
unified_precision=self.precision,
power_map_=upm)
poly_scheme.set_attributes(unbreakable=True)
elif i == 4:
c1 = Constant(coeff(poly_object.get_sollya_object(), 1),
precision=ML_Binary64)
poly_scheme = c1 * red_vx_d + polynomial_scheme_builder(
poly_object.sub_poly(start_index=2),
red_vx,
unified_precision=self.precision,
power_map_=upm)
poly_scheme.set_precision(ML_Binary64)
else:
poly_scheme = polynomial_scheme_builder(
poly_object,
red_vx,
unified_precision=poly_precision,
power_map_=upm)
#if i == 3:
# c0 = Constant(coeff(poly_object.get_sollya_object(), 0), precision = self.precision)
# c1 = Constant(coeff(poly_object.get_sollya_object(), 1), precision = self.precision)
# poly_scheme = (c0 + c1 * red_vx) + polynomial_scheme_builder(poly_object.sub_poly(start_index = 2), red_vx, unified_precision = self.precision, power_map_ = upm)
poly_scheme.set_attributes(tag="poly_cos%dpi%d" %
(i, 2**(frac_pi_index)),
debug=debug_precision)
poly_scheme_vector[i] = poly_scheme
#try:
if is_gappa_installed() and i == 3:
opt_scheme = self.opt_engine.optimization_process(
poly_scheme,
self.precision,
copy=True,
fuse_fma=self.fuse_fma)
tag_map = {}
self.opt_engine.register_nodes_by_tag(opt_scheme, tag_map)
gappa_vx = Variable("red_vx",
precision=self.precision,
interval=approx_interval)
cg_eval_error_copy_map = {
tag_map["red_vx"]: gappa_vx,
tag_map["red_vx_d"]: gappa_vx,
}
print "opt_scheme"
print opt_scheme.get_str(depth=None,
display_precision=True,
memoization_map={})
eval_error = self.gappa_engine.get_eval_error_v2(
self.opt_engine,
opt_scheme,
cg_eval_error_copy_map,
gappa_filename="red_arg_%d.g" % i)
poly_range = cos(approx_interval + i * pi / S2**frac_pi_index)
rel_error_list.append(eval_error / poly_range)
#for rel_error in rel_error_list:
# print sup(abs(rel_error))
#return
# case 17
#poly17 = poly_object_vector[17]
#c0 = Constant(coeff(poly17.get_sollya_object(), 0), precision = self.precision)
#c1 = Constant(coeff(poly17.get_sollya_object(), 1), precision = self.precision)
#poly_scheme_vector[17] = FusedMultiplyAdd(c1, red_vx, c0, specifier = FusedMultiplyAdd.Standard) + polynomial_scheme_builder(poly17.sub_poly(start_index = 2), red_vx, unified_precision = self.precision, power_map_ = upm)
half = 2**frac_pi_index
sub_half = 2**(frac_pi_index - 1)
# determine if the reduced input is within the second and third quarter (not first nor fourth)
# to negate the cosine output
factor_cond = BitLogicAnd(BitLogicXor(
BitLogicRightShift(modk, frac_pi_index),
BitLogicRightShift(modk, frac_pi_index - 1)),
1,
tag="factor_cond",
debug=True)
CM1 = Constant(-1, precision=self.precision)
C1 = Constant(1, precision=self.precision)
factor = Select(factor_cond,
CM1,
C1,
tag="factor",
debug=debug_precision)
factor2 = Select(Equal(modk, Constant(sub_half)),
CM1,
C1,
tag="factor2",
debug=debug_precision)
switch_map = {}
if 0:
for i in range(2**(frac_pi_index + 1)):
switch_map[i] = Return(poly_scheme_vector[i])
else:
for i in range(2**(frac_pi_index - 1)):
switch_case = (i, half - i)
#switch_map[i] = Return(poly_scheme_vector[i])
#switch_map[half-i] = Return(-poly_scheme_vector[i])
if i != 0:
switch_case = switch_case + (half + i, 2 * half - i)
#switch_map[half+i] = Return(-poly_scheme_vector[i])
#switch_map[2*half-i] = Return(poly_scheme_vector[i])
if poly_scheme_vector[i].get_precision() != self.precision:
poly_result = Conversion(poly_scheme_vector[i],
precision=self.precision)
else:
poly_result = poly_scheme_vector[i]
switch_map[switch_case] = Return(factor * poly_result)
#switch_map[sub_half] = Return(-poly_scheme_vector[sub_half])
#switch_map[half + sub_half] = Return(poly_scheme_vector[sub_half])
switch_map[(sub_half, half + sub_half)] = Return(
factor2 * poly_scheme_vector[sub_half])
result = SwitchBlock(modk, switch_map)
#######################################################################
# LARGE ARGUMENT MANAGEMENT #
# (lar: Large Argument Reduction) #
#######################################################################
# payne and hanek argument reduction for large arguments
#red_func_name = "payne_hanek_cosfp32" # "payne_hanek_fp32_asm"
red_func_name = "payne_hanek_fp32_asm"
payne_hanek_func_op = FunctionOperator(
red_func_name,
arg_map={0: FO_Arg(0)},
require_header=["support_lib/ml_red_arg.h"])
payne_hanek_func = FunctionObject(red_func_name, [ML_Binary32],
ML_Binary64, payne_hanek_func_op)
payne_hanek_func_op.declare_prototype = payne_hanek_func
#large_arg_red = FunctionCall(payne_hanek_func, vx)
large_arg_red = payne_hanek_func(vx)
red_bound = S2**20
cond = Abs(vx) >= red_bound
cond.set_attributes(tag="cond", likely=False)
lar_neark = NearestInteger(large_arg_red, precision=ML_Int64)
lar_modk = Modulo(lar_neark,
Constant(16, precision=ML_Int64),
tag="lar_modk",
debug=True)
# Modulo is supposed to be already performed (by payne_hanek_cosfp32)
#lar_modk = NearestInteger(large_arg_red, precision = ML_Int64)
pre_lar_red_vx = large_arg_red - Conversion(lar_neark,
precision=ML_Binary64)
pre_lar_red_vx.set_attributes(precision=ML_Binary64,
debug=debug_lftolx,
tag="pre_lar_red_vx")
lar_red_vx = Conversion(pre_lar_red_vx,
precision=self.precision,
debug=debug_precision,
tag="lar_red_vx")
lar_red_vx_lo = Conversion(
pre_lar_red_vx - Conversion(lar_red_vx, precision=ML_Binary64),
precision=self.precision)
lar_red_vx_lo.set_attributes(tag="lar_red_vx_lo",
precision=self.precision)
lar_k = 3
# large arg reduction Universal Power Map
lar_upm = {}
lar_switch_map = {}
approx_interval = Interval(-0.5, 0.5)
for i in range(2**(lar_k + 1)):
frac_pi = pi / S2**lar_k
func = cos(frac_pi * i + frac_pi * sollya.x)
degree = 6
error_mode = sollya.absolute
if i % 2**(lar_k) == 2**(lar_k - 1):
# close to sin(x) cases
func = -sin(frac_pi * x) if i == 2**(lar_k -
1) else sin(frac_pi * x)
degree_list = range(0, degree + 1, 2)
precision_list = [sollya.binary32] * len(degree_list)
poly_object, _ = Polynomial.build_from_approximation_with_error(
func / x, degree_list, precision_list, approx_interval,
error_mode)
poly_object = poly_object.sub_poly(offset=-1)
else:
degree_list = range(degree + 1)
precision_list = [sollya.binary32] * len(degree_list)
poly_object, _ = Polynomial.build_from_approximation_with_error(
func, degree_list, precision_list, approx_interval,
error_mode)
if i == 3 or i == 5 or i == 7 or i == 9 or i == 11 or i == 13:
poly_precision = ML_Binary64
c0 = Constant(coeff(poly_object.get_sollya_object(), 0),
precision=ML_Binary64)
c1 = Constant(coeff(poly_object.get_sollya_object(), 1),
precision=self.precision)
poly_hi = (c0 + c1 * lar_red_vx)
poly_hi.set_precision(ML_Binary64)
pre_poly_scheme = poly_hi + polynomial_scheme_builder(
poly_object.sub_poly(start_index=2),
lar_red_vx,
unified_precision=self.precision,
power_map_=lar_upm)
pre_poly_scheme.set_attributes(precision=ML_Binary64)
poly_scheme = Conversion(pre_poly_scheme,
precision=self.precision)
elif i == 4 or i == 12:
c1 = Constant(coeff(poly_object.get_sollya_object(), 1),
precision=self.precision)
c3 = Constant(coeff(poly_object.get_sollya_object(), 3),
precision=self.precision)
c5 = Constant(coeff(poly_object.get_sollya_object(), 5),
precision=self.precision)
poly_hi = polynomial_scheme_builder(
poly_object.sub_poly(start_index=3),
lar_red_vx,
unified_precision=self.precision,
power_map_=lar_upm)
poly_hi.set_attributes(tag="poly_lar_%d_hi" % i,
precision=ML_Binary64)
poly_scheme = Conversion(FusedMultiplyAdd(
c1, lar_red_vx, poly_hi, precision=ML_Binary64) +
c1 * lar_red_vx_lo,
precision=self.precision)
else:
poly_scheme = polynomial_scheme_builder(
poly_object,
lar_red_vx,
unified_precision=self.precision,
power_map_=lar_upm)
# poly_scheme = polynomial_scheme_builder(poly_object, lar_red_vx, unified_precision = self.precision, power_map_ = lar_upm)
poly_scheme.set_attributes(tag="lar_poly_%d" % i,
debug=debug_precision)
lar_switch_map[(i, )] = Return(poly_scheme)
lar_result = SwitchBlock(lar_modk, lar_switch_map)
# main scheme
#Log.report(Log.Info, "\033[33;1m MDL scheme \033[0m")
# scheme = Statement(ConditionBlock(cond, lar_result, result))
Log.report(Log.Info, "Construction of the initial MDL scheme")
scheme = Statement(pre_red_vx_d, red_vx_lo_sub,
ConditionBlock(cond, lar_result, result))
return scheme
def generate_scheme(self):
# declaring function input variable
vx = self.implementation.add_input_variable("x", ML_Binary32)
# declaring specific interval for input variable <x>
vx.set_interval(Interval(-1, 1))
# declaring free Variable y
vy = Variable("y", precision = ML_Exact)
# declaring expression with vx variable
expr = vx * vx - vx * 2
# declaring second expression with vx variable
expr2 = vx * vx - vx
# optimizing expressions (defining every unknown precision as the
# default one + some optimization as FMA merging if enabled)
opt_expr = self.optimise_scheme(expr)
opt_expr2 = self.optimise_scheme(expr2)
# setting specific tag name for optimized expression (to be extracted
# from gappa script )
opt_expr.set_tag("goal")
opt_expr2.set_tag("new_goal")
# defining default goal to gappa execution
gappa_goal = opt_expr
# declaring EXACT expression to be used as hint in Gappa's script
annotation = self.opt_engine.exactify(vy * (1 / vy))
# the dict var_bound is used to limit the DAG part to be explored when
# generating the gappa script, each pair (key, value), indicate a node to stop at <key>
# and a node to replace it with during the generation: <node>,
# <node> must be a Variable instance with defined interval
# vx.get_handle().get_node() is used to retrieve the node instanciating the abstract node <vx>
# after the call to self.optimise_scheme
var_bound = {
vx.get_handle().get_node(): Variable("x", precision = ML_Binary32, interval = vx.get_interval())
}
# generating gappa code to determine interval for <opt_expr>
gappa_code = self.gappa_engine.get_interval_code(opt_expr, var_bound)
# add a manual hint to the gappa code
# which state thtat vy * (1 / vy) -> 1 { vy <> 0 };
self.gappa_engine.add_hint(gappa_code, annotation, Constant(1, precision = ML_Exact), Comparison(vy, Constant(0, precision = ML_Integer), specifier = Comparison.NotEqual, precision = ML_Bool))
# adding the expression <opt_expr2> as an extra goal in the gappa script
self.gappa_engine.add_goal(gappa_code, opt_expr2)
# executing gappa on the script generated from <gappa_code>
# extract the result and store them into <gappa_result>
# which is a dict indexed by the goals' tag
if is_gappa_installed():
gappa_result = execute_gappa_script_extract(gappa_code.get(self.gappa_engine))
Log.report(Log.Info, "eval error: ", gappa_result["new_goal"])
else:
Log.report(Log.Warning, "gappa was not installed: unable to check execute_gappa_script_extract")
# dummy scheme to make functionnal code generation
scheme = Statement(Return(vx))
return scheme
def generate_scheme(self):
vx = self.implementation.add_input_variable("x",
self.get_input_precision())
sollya_precision = self.get_input_precision().get_sollya_object()
log_f = sollya.log(sollya.x) # /sollya.log(self.basis)
# local overloading of RaiseReturn operation
def ExpRaiseReturn(*args, **kwords):
kwords["arg_value"] = vx
kwords["function_name"] = self.function_name
return RaiseReturn(*args, **kwords)
test_nan_or_inf = Test(vx,
specifier=Test.IsInfOrNaN,
likely=False,
debug=True,
tag="nan_or_inf")
test_nan = Test(vx,
specifier=Test.IsNaN,
debug=True,
tag="is_nan_test")
test_positive = Comparison(vx,
0,
specifier=Comparison.GreaterOrEqual,
debug=True,
tag="inf_sign")
test_signaling_nan = Test(vx,
specifier=Test.IsSignalingNaN,
debug=True,
tag="is_signaling_nan")
return_snan = Statement(
ExpRaiseReturn(ML_FPE_Invalid,
return_value=FP_QNaN(self.precision)))
log2_hi_value = round(
log_f(2),
self.precision.get_field_size() -
(self.precision.get_exponent_size() + 1), RN)
log2_lo_value = round(
log_f(2) - log2_hi_value, self.precision.sollya_object, RN)
log2_hi = Constant(log2_hi_value, precision=self.precision)
log2_lo = Constant(log2_lo_value, precision=self.precision)
int_precision = self.precision.get_integer_format()
vx_exp = ExponentExtraction(vx, tag="vx_exp", debug=debug_multi)
#---------------------
# Approximation scheme
#---------------------
# log10(x) = log10(m.2^e) = log10(m.2^(e-t+t))
# = log10(m.2^-t) + (e+t) log10(2)
# t = (m > sqrt(2)) ? 1 : 0 is used to avoid catastrophic cancellation
# when e = -1 and m ~ 2
#
#
# log10(m.2^-t) = log10(m.r/r.2^-t) = log10(m.r) + log10(2^-t/r)
# = log10(m.r) - log10(r.2^t)
# where r = rcp(m) an approximation of 1/m such that r.m ~ 1
# retrieving processor inverse approximation table
dummy_var = Variable("dummy", precision=self.precision)
dummy_div_seed = ReciprocalSeed(dummy_var, precision=self.precision)
inv_approx_table = self.processor.get_recursive_implementation(
dummy_div_seed,
language=None,
table_getter=lambda self: self.approx_table_map)
# table creation
table_index_size = inv_approx_table.index_size
table_index_range = range(1, 2**table_index_size)
log_table = ML_NewTable(dimensions=[2**table_index_size, 2],
storage_precision=self.precision)
log_table_tho = ML_NewTable(dimensions=[2**table_index_size, 2],
storage_precision=self.precision)
log_table[0][0] = 0.0
log_table[0][1] = 0.0
log_table_tho[0][0] = 0.0
log_table_tho[0][1] = 0.0
hi_size = self.precision.get_field_size() - (
self.precision.get_exponent_size() + 1)
for i in table_index_range:
#inv_value = (1.0 + (self.processor.inv_approx_table[i] / S2**9) + S2**-52) * S2**-1
#inv_value = (1.0 + (inv_approx_table[i][0] / S2**9) ) * S2**-1
inv_value = inv_approx_table[i]
value_high = round(log_f(inv_value), hi_size, sollya.RN)
value_low = round(
log_f(inv_value) - value_high, sollya_precision, sollya.RN)
log_table[i][0] = value_high
log_table[i][1] = value_low
inv_value_tho = S2 * inv_approx_table[i]
value_high_tho = round(log_f(inv_value_tho), hi_size, sollya.RN)
value_low_tho = round(
log_f(inv_value_tho) - value_high_tho, sollya_precision,
sollya.RN)
log_table_tho[i][0] = value_high_tho
log_table_tho[i][1] = value_low_tho
# determining log_table range
high_index_function = lambda table, i: table[i][0]
low_index_function = lambda table, i: table[i][1]
table_high_interval = log_table.get_subset_interval(
high_index_function, table_index_range)
table_low_interval = log_table.get_subset_interval(
low_index_function, table_index_range)
def compute_log(_vx, exp_corr_factor=None):
_vx_mant = MantissaExtraction(_vx,
tag="_vx_mant",
precision=self.precision,
debug=debug_multi)
_vx_exp = ExponentExtraction(_vx, tag="_vx_exp", debug=debug_multi)
table_index = inv_approx_table.index_function(_vx_mant)
table_index.set_attributes(tag="table_index", debug=debug_multi)
tho_cond = _vx_mant > Constant(sollya.sqrt(2),
precision=self.precision)
tho = Select(tho_cond,
Constant(1.0, precision=self.precision),
Constant(0.0, precision=self.precision),
precision=self.precision,
tag="tho",
debug=debug_multi)
rcp = ReciprocalSeed(_vx_mant, precision=self.precision, tag="rcp")
r = Multiplication(rcp,
_vx_mant,
precision=self.precision,
tag="r")
int_format = self.precision.get_integer_format()
# argument reduction
# TODO: detect if single operand inverse seed is supported by the targeted architecture
pre_arg_red_index = TypeCast(BitLogicAnd(
TypeCast(ReciprocalSeed(_vx_mant,
precision=self.precision,
tag="seed",
debug=debug_multi,
silent=True),
precision=int_format),
Constant(-2, precision=int_format),
precision=int_format),
precision=self.precision,
tag="pre_arg_red_index",
debug=debug_multi)
arg_red_index = Select(Equal(table_index, 0),
1.0,
pre_arg_red_index,
tag="arg_red_index",
debug=debug_multi)
_red_vx = arg_red_index * _vx_mant - 1.0
inv_err = S2**-6
red_interval = Interval(1 - inv_err, 1 + inv_err)
_red_vx.set_attributes(tag="_red_vx",
debug=debug_multi,
interval=red_interval)
# return in case of standard (non-special) input
_log_inv_lo = Select(tho_cond,
TableLoad(log_table_tho, table_index, 1),
TableLoad(log_table, table_index, 1),
tag="log_inv_lo",
debug=debug_multi)
_log_inv_hi = Select(tho_cond,
TableLoad(log_table_tho, table_index, 0),
TableLoad(log_table, table_index, 0),
tag="log_inv_hi",
debug=debug_multi)
Log.report(Log.Info, "building mathematical polynomial")
approx_interval = Interval(-inv_err, inv_err)
poly_degree = sup(
guessdegree(
log(1 + sollya.x) / sollya.x, approx_interval, S2**
-(self.precision.get_field_size() + 1))) + 1
global_poly_object = Polynomial.build_from_approximation(
log(1 + x) / x, poly_degree,
[self.precision] * (poly_degree + 1), approx_interval,
sollya.absolute)
poly_object = global_poly_object.sub_poly(start_index=1)
Log.report(Log.Info, "generating polynomial evaluation scheme")
_poly = PolynomialSchemeEvaluator.generate_horner_scheme(
poly_object, _red_vx, unified_precision=self.precision)
_poly.set_attributes(tag="poly", debug=debug_multi)
Log.report(Log.Info, poly_object.get_sollya_object())
corr_exp = Conversion(_vx_exp if exp_corr_factor == None else
_vx_exp + exp_corr_factor,
precision=self.precision) + tho
corr_exp.set_attributes(tag="corr_exp", debug=debug_multi)
# _poly approximates log10(1+r)/r
# _poly * red_vx approximates log10(x)
m0h, m0l = Mul211(_red_vx, _poly)
m0h, m0l = Add212(_red_vx, m0h, m0l)
m0h.set_attributes(tag="m0h", debug=debug_multi)
m0l.set_attributes(tag="m0l")
l0_h = corr_exp * log2_hi
l0_l = corr_exp * log2_lo
l0_h.set_attributes(tag="l0_h")
l0_l.set_attributes(tag="l0_l")
rh, rl = Add222(l0_h, l0_l, m0h, m0l)
rh.set_attributes(tag="rh0", debug=debug_multi)
rl.set_attributes(tag="rl0", debug=debug_multi)
rh, rl = Add222(-_log_inv_hi, -_log_inv_lo, rh, rl)
rh.set_attributes(tag="rh", debug=debug_multi)
rl.set_attributes(tag="rl", debug=debug_multi)
if sollya.log(self.basis) != 1.0:
lbh = self.precision.round_sollya_object(
1 / sollya.log(self.basis))
lbl = self.precision.round_sollya_object(
1 / sollya.log(self.basis) - lbh)
rh, rl = Mul222(rh, rl, lbh, lbl)
return rh
else:
return rh
result = compute_log(vx)
result.set_attributes(tag="result", debug=debug_multi)
if False:
# building eval error map
eval_error_map = {
red_vx:
Variable("red_vx",
precision=self.precision,
interval=red_vx.get_interval()),
log_inv_hi:
Variable("log_inv_hi",
precision=self.precision,
interval=table_high_interval),
log_inv_lo:
Variable("log_inv_lo",
precision=self.precision,
interval=table_low_interval),
corr_exp:
Variable("corr_exp_g",
precision=self.precision,
interval=self.precision.get_exponent_interval()),
}
# computing gappa error
if is_gappa_installed():
poly_eval_error = self.get_eval_error(result, eval_error_map)
Log.report(Log.Info, "poly_eval_error: ", poly_eval_error)
neg_input = Comparison(vx,
0,
likely=False,
specifier=Comparison.Less,
debug=debug_multi,
tag="neg_input")
vx_nan_or_inf = Test(vx,
specifier=Test.IsInfOrNaN,
likely=False,
debug=debug_multi,
tag="nan_or_inf")
vx_snan = Test(vx,
specifier=Test.IsSignalingNaN,
likely=False,
debug=debug_multi,
tag="snan")
vx_inf = Test(vx,
specifier=Test.IsInfty,
likely=False,
debug=debug_multi,
tag="inf")
vx_subnormal = Test(vx,
specifier=Test.IsSubnormal,
likely=False,
debug=debug_multi,
tag="vx_subnormal")
vx_zero = Test(vx,
specifier=Test.IsZero,
likely=False,
debug=debug_multi,
tag="vx_zero")
exp_mone = Equal(vx_exp,
-1,
tag="exp_minus_one",
debug=debug_multi,
likely=False)
# exp=-1 case
Log.report(Log.Info, "managing exp=-1 case")
#red_vx_2 = arg_red_index * vx_mant * 0.5
#approx_interval2 = Interval(0.5 - inv_err, 0.5 + inv_err)
#poly_degree2 = sup(guessdegree(log(x), approx_interval2, S2**-(self.precision.get_field_size()+1))) + 1
#poly_object2 = Polynomial.build_from_approximation(log(sollya.x), poly_degree, [self.precision]*(poly_degree+1), approx_interval2, sollya.absolute)
#print "poly_object2: ", poly_object2.get_sollya_object()
#poly2 = PolynomialSchemeEvaluator.generate_horner_scheme(poly_object2, red_vx_2, unified_precision = self.precision)
#poly2.set_attributes(tag = "poly2", debug = debug_multi)
#result2 = (poly2 - log_inv_hi - log_inv_lo)
m100 = -100
S2100 = Constant(S2**100, precision=self.precision)
result_subnormal = compute_log(vx * S2100, exp_corr_factor=m100)
# main scheme
Log.report(Log.Info, "MDL scheme")
pre_scheme = ConditionBlock(
neg_input,
Statement(ClearException(), Raise(ML_FPE_Invalid),
Return(FP_QNaN(self.precision))),
ConditionBlock(
vx_nan_or_inf,
ConditionBlock(
vx_inf,
Statement(
ClearException(),
Return(FP_PlusInfty(self.precision)),
),
Statement(ClearException(),
ConditionBlock(vx_snan, Raise(ML_FPE_Invalid)),
Return(FP_QNaN(self.precision)))),
ConditionBlock(
vx_subnormal,
ConditionBlock(
vx_zero,
Statement(
ClearException(),
Raise(ML_FPE_DivideByZero),
Return(FP_MinusInfty(self.precision)),
), Return(result_subnormal)), Return(result))))
scheme = pre_scheme
return scheme
def generate_scalar_scheme(self, vx):
# local overloading of RaiseReturn operation
def ExpRaiseReturn(*args, **kwords):
kwords["arg_value"] = vx
kwords["function_name"] = self.function_name
return RaiseReturn(*args, **kwords)
test_nan_or_inf = Test(vx,
specifier=Test.IsInfOrNaN,
likely=False,
debug=True,
tag="nan_or_inf")
test_nan = Test(vx,
specifier=Test.IsNaN,
debug=True,
tag="is_nan_test")
test_positive = Comparison(vx,
0,
specifier=Comparison.GreaterOrEqual,
debug=True,
tag="inf_sign")
test_signaling_nan = Test(vx,
specifier=Test.IsSignalingNaN,
debug=True,
tag="is_signaling_nan")
return_snan = Statement(
ExpRaiseReturn(ML_FPE_Invalid,
return_value=FP_QNaN(self.precision)))
int_precision = self.precision.get_integer_format()
vx_exp = ExponentExtraction(vx,
tag="vx_exp",
precision=int_precision,
debug=debug_multi)
#---------------------
# Approximation scheme
#---------------------
# log(x) = log(m.2^e) = log(m.2^(e-tho+tho))
# = log(m.2^-tho) + (e+tho) log(2)
# tho = (m > sqrt(2)) ? 1 : 0 is used to avoid catastrophic cancellation
# when e = -1 and m ~ 2
#
#
# log(m.2^-tho) = log(m.r/r.2^-tho) = log(m.r) + log(2^-tho/r)
# = log(m.r) - log(r.2^tho)
# where r = rcp(m) an approximation of 1/m such that r.m ~ 1
# retrieving processor inverse approximation table
dummy_var = Variable("dummy", precision=self.precision)
dummy_div_seed = ReciprocalSeed(dummy_var, precision=self.precision)
# table of the reciprocal approximation of the targeted processor
inv_approx_table = self.processor.get_recursive_implementation(
dummy_div_seed,
language=None,
table_getter=lambda self: self.approx_table_map)
log_f = sollya.log(sollya.x) # /sollya.log(self.basis)
log_table, log_table_tho, table_index_range = self.generate_log_table(
log_f, inv_approx_table)
# determining log_table range
high_index_function = lambda table, i: table[i][0]
low_index_function = lambda table, i: table[i][1]
table_high_interval = log_table.get_subset_interval(
high_index_function, table_index_range)
table_low_interval = log_table.get_subset_interval(
low_index_function, table_index_range)
result = self.generate_reduced_log(vx, log_f, inv_approx_table,
log_table, log_table_tho)
result.set_attributes(tag="result", debug=debug_multi)
if False:
# building eval error map
eval_error_map = {
red_vx:
Variable("red_vx",
precision=self.precision,
interval=red_vx.get_interval()),
log_inv_hi:
Variable("log_inv_hi",
precision=self.precision,
interval=table_high_interval),
log_inv_lo:
Variable("log_inv_lo",
precision=self.precision,
interval=table_low_interval),
corr_exp:
Variable("corr_exp_g",
precision=self.precision,
interval=self.precision.get_exponent_interval()),
}
# computing gappa error
if is_gappa_installed():
poly_eval_error = self.get_eval_error(result, eval_error_map)
Log.report(Log.Info, "poly_eval_error: ", poly_eval_error)
neg_input = Comparison(vx,
0,
likely=False,
specifier=Comparison.Less,
debug=debug_multi,
tag="neg_input")
vx_nan_or_inf = Test(vx,
specifier=Test.IsInfOrNaN,
likely=False,
debug=debug_multi,
tag="nan_or_inf")
vx_snan = Test(vx,
specifier=Test.IsSignalingNaN,
likely=False,
debug=debug_multi,
tag="snan")
vx_inf = Test(vx,
specifier=Test.IsInfty,
likely=False,
debug=debug_multi,
tag="inf")
vx_subnormal = Test(vx,
specifier=Test.IsSubnormal,
likely=False,
debug=debug_multi,
tag="vx_subnormal")
vx_zero = Test(vx,
specifier=Test.IsZero,
likely=False,
debug=debug_multi,
tag="vx_zero")
exp_mone = Equal(vx_exp,
-1,
tag="exp_minus_one",
debug=debug_multi,
likely=False)
# exp=-1 case
Log.report(Log.Info, "managing exp=-1 case")
#red_vx_2 = arg_red_index * vx_mant * 0.5
#approx_interval2 = Interval(0.5 - inv_err, 0.5 + inv_err)
#poly_degree2 = sup(guessdegree(log(x), approx_interval2, S2**-(self.precision.get_field_size()+1))) + 1
#poly_object2 = Polynomial.build_from_approximation(log(sollya.x), poly_degree, [self.precision]*(poly_degree+1), approx_interval2, sollya.absolute)
#print "poly_object2: ", poly_object2.get_sollya_object()
#poly2 = PolynomialSchemeEvaluator.generate_horner_scheme(poly_object2, red_vx_2, unified_precision = self.precision)
#poly2.set_attributes(tag = "poly2", debug = debug_multi)
#result2 = (poly2 - log_inv_hi - log_inv_lo)
m100 = Constant(-100, precision=int_precision)
S2100 = Constant(S2**100, precision=self.precision)
result_subnormal = self.generate_reduced_log(vx * S2100,
log_f,
inv_approx_table,
log_table,
log_table_tho,
exp_corr_factor=m100)
# main scheme
Log.report(Log.Info, "MDL scheme")
pre_scheme = ConditionBlock(
neg_input,
Statement(
ClearException(), Raise(ML_FPE_Invalid),
Return(FP_QNaN(self.precision), precision=self.precision)),
ConditionBlock(
vx_nan_or_inf,
ConditionBlock(
vx_inf,
Statement(
ClearException(),
Return(FP_PlusInfty(self.precision),
precision=self.precision),
),
Statement(
ClearException(),
ConditionBlock(vx_snan, Raise(ML_FPE_Invalid)),
Return(FP_QNaN(self.precision),
precision=self.precision))),
ConditionBlock(
vx_subnormal,
ConditionBlock(
vx_zero,
Statement(
ClearException(),
Raise(ML_FPE_DivideByZero),
Return(FP_MinusInfty(self.precision),
precision=self.precision),
), Return(result_subnormal)), Return(result))))
scheme = pre_scheme
return scheme
action="store_const",
const=True,
default=False,
help="test gappa install")
arg_template.get_parser().add_argument("--test-cgpe",
dest="test_cgpe",
action="store_const",
const=True,
default=False,
help="test cgpe install")
arg_template.get_parser().add_argument("--full-status",
dest="full_status",
action="store_const",
const=True,
default=False,
help="test full Metalibm status")
# argument extraction
args = parse_arg_index_list = arg_template.arg_extraction()
if args.test_cgpe or args.full_status:
Log.report(
Log.Info,
"CPGE available: {}".format(polynomials.is_cgpe_available()))
if args.test_gappa or args.full_status:
Log.report(Log.Info,
"Gappa available: {}".format(gappa.is_gappa_installed()))
if args.full_status:
Log.report(Log.Info, "List of registered targets:")
template.list_targets()
def generate_scheme(self):
#func_implementation = CodeFunction(self.function_name, output_format = self.precision)
vx = self.implementation.add_input_variable("x",
self.get_input_precision())
sollya_precision = self.get_input_precision().get_sollya_object()
# local overloading of RaiseReturn operation
def ExpRaiseReturn(*args, **kwords):
kwords["arg_value"] = vx
kwords["function_name"] = self.function_name
return RaiseReturn(*args, **kwords)
test_nan_or_inf = Test(vx,
specifier=Test.IsInfOrNaN,
likely=False,
debug=True,
tag="nan_or_inf")
test_nan = Test(vx,
specifier=Test.IsNaN,
debug=True,
tag="is_nan_test")
test_positive = Comparison(vx,
0,
specifier=Comparison.GreaterOrEqual,
debug=True,
tag="inf_sign")
test_signaling_nan = Test(vx,
specifier=Test.IsSignalingNaN,
debug=True,
tag="is_signaling_nan")
return_snan = Statement(
ExpRaiseReturn(ML_FPE_Invalid,
return_value=FP_QNaN(self.precision)))
log2_hi_value = round(
log10(2),
self.precision.get_field_size() -
(self.precision.get_exponent_size() + 1), RN)
log2_lo_value = round(
log10(2) - log2_hi_value, self.precision.sollya_object, RN)
log2_hi = Constant(log2_hi_value, precision=self.precision)
log2_lo = Constant(log2_lo_value, precision=self.precision)
vx_exp = ExponentExtraction(vx, tag="vx_exp", debug=debugd)
int_precision = self.precision.get_integer_format()
# retrieving processor inverse approximation table
dummy_var = Variable("dummy", precision=self.precision)
dummy_div_seed = DivisionSeed(dummy_var, precision=self.precision)
inv_approx_table = self.processor.get_recursive_implementation(
dummy_div_seed,
language=None,
table_getter=lambda self: self.approx_table_map)
# table creation
table_index_size = 7
table_index_range = range(1, 2**table_index_size)
log_table = ML_NewTable(dimensions=[2**table_index_size, 2],
storage_precision=self.precision)
log_table[0][0] = 0.0
log_table[0][1] = 0.0
for i in table_index_range:
#inv_value = (1.0 + (self.processor.inv_approx_table[i] / S2**9) + S2**-52) * S2**-1
#inv_value = (1.0 + (inv_approx_table[i][0] / S2**9) ) * S2**-1
inv_value = inv_approx_table[i][0]
value_high = round(
log10(inv_value),
self.precision.get_field_size() -
(self.precision.get_exponent_size() + 1), sollya.RN)
value_low = round(
log10(inv_value) - value_high, sollya_precision, sollya.RN)
log_table[i][0] = value_high
log_table[i][1] = value_low
# determining log_table range
high_index_function = lambda table, i: table[i][0]
low_index_function = lambda table, i: table[i][1]
table_high_interval = log_table.get_subset_interval(
high_index_function, table_index_range)
table_low_interval = log_table.get_subset_interval(
low_index_function, table_index_range)
def compute_log(_vx, exp_corr_factor=None):
_vx_mant = MantissaExtraction(_vx,
tag="_vx_mant",
debug=debug_lftolx)
_vx_exp = ExponentExtraction(_vx, tag="_vx_exp", debug=debugd)
table_index = BitLogicAnd(BitLogicRightShift(
TypeCast(_vx_mant, precision=int_precision, debug=debuglx),
self.precision.get_field_size() - 7,
debug=debuglx),
0x7f,
tag="table_index",
debug=debuglld)
# argument reduction
# TODO: detect if single operand inverse seed is supported by the targeted architecture
pre_arg_red_index = TypeCast(BitLogicAnd(
TypeCast(DivisionSeed(_vx_mant,
precision=self.precision,
tag="seed",
debug=debug_lftolx,
silent=True),
precision=ML_UInt64),
Constant(-2, precision=ML_UInt64),
precision=ML_UInt64),
precision=self.precision,
tag="pre_arg_red_index",
debug=debug_lftolx)
arg_red_index = Select(Equal(table_index, 0),
1.0,
pre_arg_red_index,
tag="arg_red_index",
debug=debug_lftolx)
#if not processor.is_supported_operation(arg_red_index):
# if self.precision != ML_Binary32:
# arg_red_index = DivisionSeed(Conversion(_vx_mant, precision = ML_Binary32), precision = ML_Binary32,
_red_vx = arg_red_index * _vx_mant - 1.0
inv_err = S2**-7
red_interval = Interval(1 - inv_err, 1 + inv_err)
_red_vx.set_attributes(tag="_red_vx",
debug=debug_lftolx,
interval=red_interval)
# return in case of standard (non-special) input
_log_inv_lo = TableLoad(log_table,
table_index,
1,
tag="log_inv_lo",
debug=debug_lftolx)
_log_inv_hi = TableLoad(log_table,
table_index,
0,
tag="log_inv_hi",
debug=debug_lftolx)
print("building mathematical polynomial")
approx_interval = Interval(-inv_err, inv_err)
poly_degree = sup(
guessdegree(
log10(1 + sollya.x) / sollya.x, approx_interval, S2**
-(self.precision.get_field_size() + 1))) + 1
global_poly_object = Polynomial.build_from_approximation(
log10(1 + x) / x, poly_degree,
[self.precision] * (poly_degree + 1), approx_interval,
sollya.absolute)
poly_object = global_poly_object #.sub_poly(start_index = 1)
print("generating polynomial evaluation scheme")
_poly = PolynomialSchemeEvaluator.generate_horner_scheme(
poly_object, _red_vx, unified_precision=self.precision)
_poly.set_attributes(tag="poly", debug=debug_lftolx)
print(global_poly_object.get_sollya_object())
corr_exp = Conversion(
_vx_exp if exp_corr_factor == None else _vx_exp +
exp_corr_factor,
precision=self.precision)
split_red_vx = Split(_red_vx,
precision=ML_DoubleDouble,
tag="split_red_vx",
debug=debug_ddtolx)
red_vx_hi = split_red_vx.hi
red_vx_lo = split_red_vx.lo
# result = _red_vx * poly - log_inv_hi - log_inv_lo + _vx_exp * log2_hi + _vx_exp * log2_lo
pre_result = -_log_inv_hi + ((_red_vx * _poly +
(corr_exp * log2_lo - _log_inv_lo)))
pre_result.set_attributes(tag="pre_result", debug=debug_lftolx)
exact_log2_hi_exp = corr_exp * log2_hi
exact_log2_hi_exp.set_attributes(tag="exact_log2_hi_hex",
debug=debug_lftolx)
cancel_part = (corr_exp * log2_hi - _log_inv_hi)
cancel_part.set_attributes(tag="cancel_part", debug=debug_lftolx)
sub_part = red_vx_hi + cancel_part
sub_part.set_attributes(tag="sub_part", debug=debug_lftolx)
#result_one_low_part = (red_vx_hi * _poly + (red_vx_lo + (red_vx_lo * _poly + (corr_exp * log2_lo - _log_inv_lo))))
result_one_low_part = ((red_vx_lo +
(red_vx_lo * _poly +
(corr_exp * log2_lo - _log_inv_lo))))
result_one_low_part.set_attributes(tag="result_one_low_part",
debug=debug_lftolx)
_result_one = (
(sub_part) + red_vx_hi * _poly) + result_one_low_part
_result = exact_log2_hi_exp + pre_result
return _result, _poly, _log_inv_lo, _log_inv_hi, _red_vx, _result_one, corr_exp
result, poly, log_inv_lo, log_inv_hi, red_vx, new_result_one, corr_exp = compute_log(
vx)
result.set_attributes(tag="result", debug=debug_lftolx)
new_result_one.set_attributes(tag="new_result_one", debug=debug_lftolx)
# building eval error map
eval_error_map = {
red_vx:
Variable("red_vx",
precision=self.precision,
interval=red_vx.get_interval()),
log_inv_hi:
Variable("log_inv_hi",
precision=self.precision,
interval=table_high_interval),
log_inv_lo:
Variable("log_inv_lo",
precision=self.precision,
interval=table_low_interval),
corr_exp:
Variable("corr_exp_g",
precision=self.precision,
interval=self.precision.get_exponent_interval()),
}
# computing gappa error
if is_gappa_installed():
poly_eval_error = self.get_eval_error(result, eval_error_map)
print("poly_eval_error: ", poly_eval_error)
neg_input = Comparison(vx,
0,
likely=False,
specifier=Comparison.Less,
debug=debugd,
tag="neg_input")
vx_nan_or_inf = Test(vx,
specifier=Test.IsInfOrNaN,
likely=False,
debug=debugd,
tag="nan_or_inf")
vx_snan = Test(vx,
specifier=Test.IsSignalingNaN,
likely=False,
debug=debugd,
tag="snan")
vx_inf = Test(vx,
specifier=Test.IsInfty,
likely=False,
debug=debugd,
tag="inf")
vx_subnormal = Test(vx,
specifier=Test.IsSubnormal,
likely=False,
debug=debugd,
tag="vx_subnormal")
vx_zero = Test(vx,
specifier=Test.IsZero,
likely=False,
debug=debugd,
tag="vx_zero")
exp_mone = Equal(vx_exp,
-1,
tag="exp_minus_one",
debug=debugd,
likely=False)
vx_one = Equal(vx, 1.0, tag="vx_one", likely=False, debug=debugd)
# exp=-1 case
print("managing exp=-1 case")
#red_vx_2 = arg_red_index * vx_mant * 0.5
#approx_interval2 = Interval(0.5 - inv_err, 0.5 + inv_err)
#poly_degree2 = sup(guessdegree(log(x), approx_interval2, S2**-(self.precision.get_field_size()+1))) + 1
#poly_object2 = Polynomial.build_from_approximation(log(sollya.x), poly_degree, [self.precision]*(poly_degree+1), approx_interval2, sollya.absolute)
#print "poly_object2: ", poly_object2.get_sollya_object()
#poly2 = PolynomialSchemeEvaluator.generate_horner_scheme(poly_object2, red_vx_2, unified_precision = self.precision)
#poly2.set_attributes(tag = "poly2", debug = debug_lftolx)
#result2 = (poly2 - log_inv_hi - log_inv_lo)
log_subtract = -log_inv_hi - log2_hi
log_subtract.set_attributes(tag="log_subtract", debug=debug_lftolx)
result2 = (log_subtract) + ((poly * red_vx) - (log_inv_lo + log2_lo))
result2.set_attributes(tag="result2", debug=debug_lftolx)
m100 = -100
S2100 = Constant(S2**100, precision=self.precision)
result_subnormal, _, _, _, _, _, _ = compute_log(vx * S2100,
exp_corr_factor=m100)
print("managing close to 1.0 cases")
one_err = S2**-7
approx_interval_one = Interval(-one_err, one_err)
red_vx_one = vx - 1.0
poly_degree_one = sup(
guessdegree(
log10(1 + sollya.x) / sollya.x, approx_interval_one, S2**
-(self.precision.get_field_size() + 1))) + 1
poly_object_one = Polynomial.build_from_approximation(
log10(1 + sollya.x) / sollya.x, poly_degree_one,
[self.precision] * (poly_degree_one + 1), approx_interval_one,
sollya.absolute).sub_poly(start_index=1)
poly_one = PolynomialSchemeEvaluator.generate_horner_scheme(
poly_object_one, red_vx_one, unified_precision=self.precision)
poly_one.set_attributes(tag="poly_one", debug=debug_lftolx)
result_one = red_vx_one + red_vx_one * poly_one
cond_one = (vx < (1 + one_err)) & (vx > (1 - one_err))
cond_one.set_attributes(tag="cond_one", debug=debugd, likely=False)
# main scheme
print("MDL scheme")
pre_scheme = ConditionBlock(
neg_input,
Statement(ClearException(), Raise(ML_FPE_Invalid),
Return(FP_QNaN(self.precision))),
ConditionBlock(
vx_nan_or_inf,
ConditionBlock(
vx_inf,
Statement(
ClearException(),
Return(FP_PlusInfty(self.precision)),
),
Statement(ClearException(),
ConditionBlock(vx_snan, Raise(ML_FPE_Invalid)),
Return(FP_QNaN(self.precision)))),
ConditionBlock(
vx_subnormal,
ConditionBlock(
vx_zero,
Statement(
ClearException(),
Raise(ML_FPE_DivideByZero),
Return(FP_MinusInfty(self.precision)),
), Return(result_subnormal)),
ConditionBlock(
vx_one,
Statement(
ClearException(),
Return(FP_PlusZero(self.precision)),
),
ConditionBlock(exp_mone, Return(result2),
Return(result))
#ConditionBlock(cond_one,
#Return(new_result_one),
#ConditionBlock(exp_mone,
#Return(result2),
#Return(result)
#)
#)
))))
scheme = pre_scheme
return scheme
language = language,
vector_size = vector_size,
arg_template = arg_template
)
self.accuracy = accuracy
self.precision = precision
if __name__ == "__main__":
# auto-test
arg_template = ML_NewArgTemplate(default_function_name = "new_exp", default_output_file = "new_exp.c" )
arg_template.get_parser().add_argument("--test-gappa", dest = "test_gappa", action = "store_const", const = True, default = False, help = "test gappa install")
arg_template.get_parser().add_argument("--test-cgpe", dest = "test_cgpe", action = "store_const", const = True, default = False, help = "test cgpe install")
arg_template.get_parser().add_argument("--full-status", dest = "full_status", action = "store_const", const = True, default = False, help = "test full Metalibm status")
# argument extraction
args = parse_arg_index_list = arg_template.arg_extraction()
if args.test_cgpe or args.full_status:
Log.report(Log.Info, "CPGE available: {}".format(polynomials.is_cgpe_available()))
if args.test_gappa or args.full_status:
Log.report(Log.Info, "Gappa available: {}".format(gappa.is_gappa_installed()))
if args.full_status:
Log.report(Log.Info, "List of registered targets:")
template.list_targets()