def generate_fptaylor(x):
x_low = sollya.inf(x)
x_high = sollya.sup(x)
query = "\n".join([
"Variables", " real x in [{},{}];".format(x_low, x_high),
"Definitions", " r rnd64= x;",
" retval rnd64= {};".format(poly_expr), "Expressions",
" retval;"
])
rnd_rel_err = None
rnd_abs_err = None
try:
res = fptaylor.Result(query, {
**config, "--rel-error": "true",
"--abs-error": "true"
})
rnd_rel_err = float(
res.result["relative_errors"]["final_total"]["value"])
rnd_abs_err = float(
res.result["absolute_errors"]["final_total"]["value"])
except AssertionError:
pass
except KeyError:
try:
rnd_abs_err = float(
res.result["absolute_errors"]["final_total"]["value"])
except KeyError:
pass
if rnd_abs_err is None:
try:
res = fptaylor.Result(query, {
**config, "--rel-error": "false",
"--abs-error": "true"
})
rnd_abs_err = float(
res.result["absolute_errors"]["final_total"]["value"])
except AssertionError:
pass
err_int = sollya.supnorm(self.poly_object.get_sollya_object(),
sollya.exp(sollya.x), x, sollya.relative,
2**-100)
algo_rel_err = sollya.sup(err_int)
err_int = sollya.supnorm(self.poly_object.get_sollya_object(),
sollya.exp(sollya.x), x, sollya.absolute,
2**-100)
algo_abs_err = sollya.sup(err_int)
if rnd_rel_err is None or str(algo_rel_err) == "error":
rel_err = float("inf")
else:
rel_err = rnd_rel_err + algo_rel_err
abs_err = rnd_abs_err + algo_abs_err
return rel_err, abs_err
def get_value_str(self, value):
if value is Gappa_Unknown:
return "?"
elif isinstance(value, sollya.SollyaObject) and value.is_range():
return "[%s, %s]" % (sollya.inf(value), sollya.sup(value))
else:
return str(value)
def generate_approx_poly_near_zero(self, function, high_bound, error_bound,
variable):
""" Generate polynomial approximation scheme """
error_function = lambda p, f, ai, mod, t: sollya.dirtyinfnorm(
p - f, ai)
# Some issues encountered when 0 is one of the interval bound
# so we use a symetric interval around it
approx_interval = Interval(2**-100, high_bound)
local_function = function / sollya.x
degree = sollya.sup(
sollya.guessdegree(local_function, approx_interval, error_bound))
degree_list = range(0, int(degree) + 4, 2)
poly_object, approx_error = Polynomial.build_from_approximation_with_error(
function / sollya.x,
degree_list, [1] + [self.precision] * (len(degree_list) - 1),
approx_interval,
sollya.absolute,
error_function=error_function)
Log.report(
Log.Info, "approximation poly: {}\n with error {}".format(
poly_object, approx_error))
poly_scheme = Multiplication(
variable,
PolynomialSchemeEvaluator.generate_horner_scheme(
poly_object, variable, self.precision))
return poly_scheme, approx_error
def sollya_gamma_fct(x, diff_order, prec):
""" wrapper to use bigfloat implementation of exponential
rather than sollya's implementation directly.
This wrapper implements sollya's function API.
:param x: numerical input value (may be an Interval)
:param diff_order: differential order
:param prec: numerical precision expected (min)
"""
fct = None
if diff_order == 0:
fct = sollya_gamma
elif diff_order == 1:
fct = sollya_gamma_d0
elif diff_order == 2:
fct = sollya_gamma_d1
else:
raise NotImplementedError
with bigfloat.precision(prec):
if x.is_range():
lo = sollya.inf(x)
hi = sollya.sup(x)
return sollya.Interval(fct(lo), fct(hi))
else:
return fct(x)
def generate_test_case(self,
input_signals,
io_map,
index,
test_range=Interval(-1.0, 1.0)):
""" generic test case generation: generate a random input
with index @p index
Args:
index (int): integer index of the test case
Returns:
dict: mapping (input tag -> numeric value)
"""
# extracting test interval boundaries
low_input = sollya.inf(test_range)
high_input = sollya.sup(test_range)
input_values = {}
for input_tag in input_signals:
input_signal = io_map[input_tag]
# FIXME: correct value generation depending on signal precision
input_precision = input_signal.get_precision().get_base_format()
if isinstance(input_precision, ML_FP_Format):
input_value = generate_random_fp_value(input_precision,
low_input, high_input)
elif isinstance(input_precision, ML_Fixed_Format):
# TODO: does not depend on low and high range bounds
input_value = generate_random_fixed_value(input_precision)
else:
input_value = random.randrange(
2**input_precision.get_bit_size())
# registering input value
input_values[input_tag] = input_value
return input_values
def sup(obj):
""" generic getter for interval superior bound """
if isinstance(obj, SollyaObject) and obj.is_range():
return sollya.sup(obj)
elif isinstance(obj, (MetaInterval, MetaIntervalList)):
return obj.sup
else:
raise NotImplementedError
def get_integer_format(backend, optree):
""" return integer format to use for optree """
int_range = optree.get_interval()
if int_range == None:
return backend.default_integer_format
elif inf(int_range) < 0:
# signed
if sup(int_range) > 2**31 - 1 or inf(int_range) < -2**31:
return ML_Int64
else:
return ML_Int32
else:
# unsigned
if sup(int_range) >= 2**32 - 1:
return ML_UInt64
else:
return ML_UInt32
def test_interval_out_of_bound_risk(x_range, y_range):
""" Try to determine from x and y's interval if there is a risk
of underflow or overflow """
div_range = abs(x_range / y_range)
underflow_risk = sollya.inf(div_range) < S2**(
self.precision.get_emin_normal() + 2)
overflow_risk = sollya.sup(div_range) > S2**(
self.precision.get_emax() - 2)
return underflow_risk or overflow_risk
def is_simplifiable_to_cst(node):
""" node can be simplified to a constant """
node_interval = node.get_interval()
if node_interval is None or isinstance(node, Constant):
return False
elif isinstance(node_interval, SollyaObject) and node_interval.is_range():
return sollya.inf(node_interval) == sollya.sup(node_interval)
elif isinstance(node_interval, (MetaInterval, MetaIntervalList)):
return not node_interval.is_empty and (node_interval.inf == node_interval.sup)
else:
return False
def split_domain(starting_domain, slivers):
in_domains = [starting_domain]
# abs
out_domains = list()
for I in in_domains:
if sollya.inf(I) < 0 and sollya.sup(I) > 0:
out_domains.append(sollya.Interval(sollya.inf(I), 0))
out_domains.append(sollya.Interval(0, sollya.sup(I)))
else:
out_domains.append(I)
in_domains = out_domains
# k
out_domains = list()
while len(in_domains) > 0:
I = in_domains.pop()
#print("in: [{}, {}]".format(float(sollya.inf(I)), float(sollya.sup(I))))
unround_mult = I * n_invpi
mult_low = sollya.floor(sollya.inf(unround_mult))
mult_high = sollya.floor(sollya.sup(unround_mult))
if mult_low == mult_high or (mult_low == -1
and mult_high == 0):
#print(" accepted")
out_domains.append(I)
continue
if sollya.sup(I) <= 0:
divider_low = (mult_low + 1) * n_pi
divider_high = divider_low - divider_low * 2**-53
else:
divider_high = (mult_low + 1) * n_pi
divider_low = divider_high - divider_high * 2**-53
lower_part = sollya.Interval(sollya.inf(I), divider_low)
upper_part = sollya.Interval(divider_high, sollya.sup(I))
#print(" -> [{}, {}]".format(float(sollya.inf(lower_part)), float(sollya.sup(lower_part))))
#print(" -> [{}, {}]".format(float(sollya.inf(upper_part)), float(sollya.sup(upper_part))))
in_domains.append(lower_part)
in_domains.append(upper_part)
in_domains = out_domains
# subdivide each section into 2**subd sections
for _ in range(slivers):
out_domains = list()
for I in in_domains:
mid = sollya.mid(I)
out_domains.append(sollya.Interval(sollya.inf(I), mid))
out_domains.append(sollya.Interval(mid, sollya.sup(I)))
in_domains = out_domains
in_domains = set(in_domains)
in_domains = sorted(in_domains, key=lambda x: float(sollya.inf(x)))
in_domains = [
d for d in in_domains if sollya.inf(d) != sollya.sup(d)
]
return in_domains
def get_value_str(self, value):
if value is Gappa_Unknown:
return "?"
elif isinstance(value, MetaInterval):
return self.get_value_str(value.interval)
elif isinstance(value, MetaIntervalList):
# MetaIntervalList should have been catched early and
# should have generated a disjonction of cases
raise NotImplementedError
elif isinstance(value, sollya.SollyaObject) and value.is_range():
return "[%s, %s]" % (sollya.inf(value), sollya.sup(value))
else:
return str(value)
def get_add_error_budget(lhs, rhs, eps_target):
""" How accurate should be the addition of lhs + rhs to
ensure that the overall global relative error is limited to eps_target
"""
# lhs_eps = lhs.epsilon
# rhs_eps = rhs.epsilon
# real result = (lhs (1 + lhs_eps) + rhs (1 + rhs_eps)) (1 + add_eps)
# real result = (lhs + rhs + lhs . lhs_eps + rhs . rhs_eps) (1 + add_eps)
# = exact result (1 + (lhs . lhs_eps + rhs . rhs_eps) / exact_result) (1 + add_eps)
# eps_in = (lhs . lhs_eps + rhs . rhs_eps) / exact_result
# real result = exact resul * (1 + eps_in + add_eps + eps_in * add_eps)
#
# objective (eps_in + add_eps + eps_in * add_eps) <= eps_target
# |eps_in| + |add_eps| + |eps_in| * |add_eps| <= eps_target
# |add_eps| (1 + |eps_in|) <= |eps_target| - |eps_in|
# |add_eps| <= (|eps_target| - |eps_in|) / (1 + |eps_in|)
# assuming |eps_target| > |eps_in|
eps_in = (sup(abs(lhs.internal)) * lhs.epsilon + sup(abs(rhs.interval)) *
rhs.epsilon) / inf(abs(lhs.interval + rhs.interval))
assert eps_in > 0
assert eps_in >= eps_target
add_eps_bound = (eps_target - eps_in) / (1 + eps_in)
return add_eps_bound
def evaluate_argument_reduction(self, in_interval, in_prec, inv_size, inv_prec):
one = Constant(1, precision = ML_Exact, tag = "one")
dx = Variable("dx",
precision = ML_Custom_FixedPoint_Format(0, in_prec, False),
interval = in_interval)
# do the argument reduction
x = Addition(dx, one, tag = "x",
precision = ML_Exact)
x1 = Conversion(x, tag = "x1",
precision = ML_Custom_FixedPoint_Format(0, inv_size, False),
rounding_mode = ML_RoundTowardMinusInfty)
s = Multiplication(dx, Constant(S2**inv_size, precision = ML_Exact),
precision = ML_Exact,
tag = "interval_index_table")
inv_x1 = Division(one, x1, tag = "ix1",
precision = ML_Exact)
inv_x = Conversion(inv_x1, tag = "ix",
precision = ML_Custom_FixedPoint_Format(1, inv_prec, False),
rounding_mode = ML_RoundTowardPlusInfty)
y = Multiplication(x, inv_x, tag = "y",
precision = ML_Exact)
dy = Subtraction(y, one, tag = "dy",
precision = ML_Exact)
# add the necessary goals and hints
dx_gappa = Variable("dx_gappa", interval = dx.get_interval(), precision = dx.get_precision())
swap_map = {dx: dx_gappa}
# goal: dz (result of the argument reduction)
gappa_code = self.gappa_engine.get_interval_code_no_copy(dy.copy(swap_map), bound_list = [swap_map[dx]])
#self.gappa_engine.add_goal(gappa_code, s.copy(swap_map)) # range of index of table
# hints. are the ones with isAppox=True really necessary ?
self.gappa_engine.add_hint(gappa_code, x.copy(swap_map), x1.copy(swap_map), isApprox = True)
self.gappa_engine.add_hint(gappa_code, inv_x1.copy(swap_map), inv_x.copy(swap_map), isApprox = True)
self.gappa_engine.add_hint(gappa_code,
Multiplication(x1, inv_x1, precision = ML_Exact).copy(swap_map), one,
Comparison(swap_map[inv_x1], Constant(0, precision = ML_Exact),
specifier = Comparison.NotEqual, precision = ML_Bool))
# execute and parse the result
result = execute_gappa_script_extract(gappa_code.get(self.gappa_engine))
out_interval = result['goal']
length_table = 1 + floor(sup(in_interval) * S2**inv_size).getConstantAsInt()
sizeof_table = length_table * (16 + ML_Custom_FixedPoint_Format(1, inv_prec, False).get_c_bit_size()/8)
return {
'out_interval': out_interval,
'length_table': length_table,
'sizeof_table': sizeof_table,
}
def generate_json(errors, domain):
errors = [err for err in errors if err[0] in domain]
errors.sort(key=lambda err: err[2])
epsilon = errors[0][2]
delta = max(err[1] for err in errors)
d = {
"cname": self.function_name,
"delta": float(delta),
"domain": [float(sollya.inf(domain)),
float(sollya.sup(domain)),],
"epsilon": float(epsilon),
"operation": "log"
}
return d
def piecewise_approximation_degree_generator(function,
bound_low=-1.0,
bound_high=1.0,
num_intervals=16,
max_degree=2,
error_threshold=S2**-24):
""" """
interval_size = (bound_high - bound_low) / num_intervals
for i in range(num_intervals):
subint_low = bound_low + i * interval_size
subint_high = bound_low + (i + 1) * interval_size
local_function = function(sollya.x + subint_low)
local_interval = Interval(-interval_size, interval_size)
local_degree = sollya.guessdegree(local_function, local_interval,
error_threshold)
yield int(sollya.sup(local_degree))
def get_precision_rng(precision, value_range=None):
if value_range is None:
# default full-range value generation
base_format = precision.get_base_format()
if isinstance(base_format, ML_FP_MultiElementFormat):
return MPFPRandomGen(precision)
elif isinstance(base_format, ML_FP_Format):
return FPRandomGen(precision, include_snan=False)
elif isinstance(base_format, ML_Fixed_Format):
return FixedPointRandomGen(precision)
else:
Log.report(Log.Error,
"unsupported format {}/{} in get_precision_rng",
precision, base_format)
else:
low_bound = sollya.inf(value_range)
high_bound = sollya.sup(value_range)
return get_precision_rng_with_defined_range(precision, low_bound,
high_bound)
def split_domain(starting_domain, slivers):
in_domains = [starting_domain]
out_domains = list()
while len(in_domains) > 0:
I = in_domains.pop()
unround_e = sollya.log2(I)
e_low = sollya.floor(sollya.inf(unround_e))
e_high = sollya.floor(sollya.sup(unround_e))
#print("in: [{}, {}] ({}, {})".format(float(sollya.inf(I)), float(sollya.sup(I)), int(e_low), int(e_high)))
if e_low == e_high:
#print(" accepted")
out_domains.append(I)
continue
e_range = sollya.Interval(e_low, e_low+1)
I_range = 2**e_range
for _ in range(100):
mid = sollya.mid(I_range)
e = sollya.floor(sollya.log2(mid))
if e == e_low:
I_range = sollya.Interval(mid, sollya.sup(I_range))
else:
I_range = sollya.Interval(sollya.inf(I_range), mid)
divider_high = sollya.sup(I_range)
divider_low = sollya.inf(I_range)
lower_part = sollya.Interval(sollya.inf(I), divider_low)
upper_part = sollya.Interval(divider_high, sollya.sup(I))
#print(" -> [{}, {}]".format(float(sollya.inf(lower_part)), float(sollya.sup(lower_part))))
#print(" -> [{}, {}]".format(float(sollya.inf(upper_part)), float(sollya.sup(upper_part))))
in_domains.append(upper_part)
in_domains.append(lower_part)
in_domains = out_domains
# subdivide each section into 2**subd sections
for _ in range(slivers):
out_domains = list()
for I in in_domains:
mid = sollya.mid(I)
out_domains.append(sollya.Interval(sollya.inf(I), mid))
out_domains.append(sollya.Interval(mid, sollya.sup(I)))
in_domains = out_domains
in_domains = set(in_domains)
in_domains = sorted(in_domains, key=lambda x:float(sollya.inf(x)))
in_domains = [d for d in in_domains if sollya.inf(d) != sollya.sup(d)]
return in_domains
def findMaxIssue(res): """ Find the issue with the maximum error Parameters: - res: (sollya object) result from the checkModulusFilterInSpecification function Returns the maximum value (0 if not available) """ maxError = 0 for b in dict(res)["results"]: # for every band okay = dict(b)["okay"] if not okay: for i in dict(b)["issue"]: # for every issues H = dict(i)["H"] betaInf = dict(dict(i)["specification"])["betaInf"] betaSup = dict(dict(i)["specification"])["betaSup"] if sollya.inf(H) > betaSup: maxError = sollya.max(maxError, sollya.sup(H) - betaSup) else: maxError = sollya.max(maxError, betaSup - sollya.inf(H)) return maxError
def __init__(self,
precision=ML_Binary32,
abs_accuracy=S2**-24,
libm_compliant=True,
debug_flag=False,
fuse_fma=True,
fast_path_extract=True,
target=GenericProcessor(),
output_file="log1pf.c",
function_name="log1pf"):
# declaring CodeFunction and retrieving input variable
self.function_name = function_name
self.precision = precision
self.processor = target
func_implementation = CodeFunction(self.function_name,
output_format=self.precision)
vx = func_implementation.add_input_variable("x", self.precision)
sollya_precision = self.precision.sollya_object
# debug utilities
debugf = ML_Debug(display_format="%f")
debuglf = ML_Debug(display_format="%lf")
debugx = ML_Debug(display_format="%x")
debuglx = ML_Debug(display_format="%\"PRIx64\"", )
debugd = ML_Debug(display_format="%d",
pre_process=lambda v: "(int) %s" % v)
debugld = ML_Debug(display_format="%ld")
#debug_lftolx = ML_Debug(display_format = "%\"PRIx64\"", pre_process = lambda v: "double_to_64b_encoding(%s)" % v)
debug_lftolx = ML_Debug(
display_format="%\"PRIx64\" ev=%x",
pre_process=lambda v:
"double_to_64b_encoding(%s), __k1_fpu_get_exceptions()" % v)
debug_ddtolx = ML_Debug(
display_format="%\"PRIx64\" %\"PRIx64\"",
pre_process=lambda v:
"double_to_64b_encoding(%s.hi), double_to_64b_encoding(%s.lo)" %
(v, v))
debug_dd = ML_Debug(display_format="{.hi=%lf, .lo=%lf}",
pre_process=lambda v: "%s.hi, %s.lo" % (v, v))
# local overloading of RaiseReturn operation
def ExpRaiseReturn(*args, **kwords):
kwords["arg_value"] = vx
kwords["function_name"] = self.function_name
return RaiseReturn(*args, **kwords)
log2_hi_value = round(
log(2),
self.precision.get_field_size() -
(self.precision.get_exponent_size() + 1), sollya.RN)
log2_lo_value = round(
log(2) - log2_hi_value, self.precision.sollya_object, sollya.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 = ML_Int64 if self.precision is ML_Binary64 else ML_Int32
# 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
log_table = ML_Table(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 xrange(1, 2**table_index_size):
#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
value_high = round(
log(inv_value),
self.precision.get_field_size() -
(self.precision.get_exponent_size() + 1), sollya.RN)
value_low = round(
log(inv_value) - value_high, sollya_precision, sollya.RN)
log_table[i][0] = value_high
log_table[i][1] = value_low
vx_exp = ExponentExtraction(vx, tag="vx_exp", debug=debugd)
# case close to 0: ctz
ctz_exp_limit = -7
ctz_cond = vx_exp < ctz_exp_limit
ctz_interval = Interval(-S2**ctz_exp_limit, S2**ctz_exp_limit)
ctz_poly_degree = sup(
guessdegree(
log1p(sollya.x) / sollya.x, ctz_interval, S2**
-(self.precision.get_field_size() + 1))) + 1
ctz_poly_object = Polynomial.build_from_approximation(
log1p(sollya.x) / sollya.x, ctz_poly_degree,
[self.precision] * (ctz_poly_degree + 1), ctz_interval,
sollya.absolute)
print "generating polynomial evaluation scheme"
ctz_poly = PolynomialSchemeEvaluator.generate_horner_scheme(
ctz_poly_object, vx, unified_precision=self.precision)
ctz_poly.set_attributes(tag="ctz_poly", debug=debug_lftolx)
ctz_result = vx * ctz_poly
neg_input = Comparison(vx,
-1,
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")
log_function_code = CodeFunction(
"new_log", [Variable("x", precision=ML_Binary64)],
output_format=ML_Binary64)
log_call_generator = FunctionOperator(
log_function_code.get_name(),
arity=1,
output_precision=ML_Binary64,
declare_prototype=log_function_code)
newlog_function = FunctionObject(log_function_code.get_name(),
(ML_Binary64, ), ML_Binary64,
log_call_generator)
# case away from 0.0
pre_vxp1 = vx + 1.0
pre_vxp1.set_attributes(tag="pre_vxp1", debug=debug_lftolx)
pre_vxp1_exp = ExponentExtraction(pre_vxp1,
tag="pre_vxp1_exp",
debug=debugd)
cm500 = Constant(-500, precision=ML_Int32)
c0 = Constant(0, precision=ML_Int32)
cond_scaling = pre_vxp1_exp > 2**(self.precision.get_exponent_size() -
2)
scaling_factor_exp = Select(cond_scaling, cm500, c0)
scaling_factor = ExponentInsertion(scaling_factor_exp,
precision=self.precision,
tag="scaling_factor")
vxp1 = pre_vxp1 * scaling_factor
vxp1.set_attributes(tag="vxp1", debug=debug_lftolx)
vxp1_exp = ExponentExtraction(vxp1, tag="vxp1_exp", debug=debugd)
vxp1_inv = DivisionSeed(vxp1,
precision=self.precision,
tag="vxp1_inv",
debug=debug_lftolx,
silent=True)
vxp1_dirty_inv = ExponentInsertion(-vxp1_exp,
precision=self.precision,
tag="vxp1_dirty_inv",
debug=debug_lftolx)
table_index = BitLogicAnd(BitLogicRightShift(
TypeCast(vxp1, precision=int_precision, debug=debuglx),
self.precision.get_field_size() - 7,
debug=debuglx),
0x7f,
tag="table_index",
debug=debuglx)
# argument reduction
# TODO: detect if single operand inverse seed is supported by the targeted architecture
pre_arg_red_index = TypeCast(BitLogicAnd(TypeCast(vxp1_inv,
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),
vxp1_dirty_inv,
pre_arg_red_index,
tag="arg_red_index",
debug=debug_lftolx)
red_vxp1 = Select(cond_scaling, arg_red_index * vxp1 - 1.0,
(arg_red_index * vx - 1.0) + arg_red_index)
#red_vxp1 = arg_red_index * vxp1 - 1.0
red_vxp1.set_attributes(tag="red_vxp1", debug=debug_lftolx)
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)
inv_err = S2**-6 # TODO: link to target DivisionSeed precision
print "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 + sollya.x) / sollya.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_vxp1, unified_precision=self.precision)
_poly.set_attributes(tag="poly", debug=debug_lftolx)
print global_poly_object.get_sollya_object()
vxp1_inv_exp = ExponentExtraction(vxp1_inv,
tag="vxp1_inv_exp",
debug=debugd)
corr_exp = -vxp1_exp + scaling_factor_exp # vxp1_inv_exp
#poly = (red_vxp1) * (1 + _poly)
#poly.set_attributes(tag = "poly", debug = debug_lftolx, prevent_optimization = True)
pre_result = -log_inv_hi + (red_vxp1 + red_vxp1 * _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_exp",
debug=debug_lftolx,
prevent_optimization=True)
#std_result = exact_log2_hi_exp + pre_result
exact_log2_lo_exp = -corr_exp * log2_lo
exact_log2_lo_exp.set_attributes(
tag="exact_log2_lo_exp",
debug=debug_lftolx) #, prevent_optimization = True)
init = exact_log2_lo_exp - log_inv_lo
init.set_attributes(tag="init",
debug=debug_lftolx,
prevent_optimization=True)
fma0 = (red_vxp1 * _poly + init) # - log_inv_lo)
fma0.set_attributes(tag="fma0", debug=debug_lftolx)
step0 = fma0
step0.set_attributes(
tag="step0", debug=debug_lftolx) #, prevent_optimization = True)
step1 = step0 + red_vxp1
step1.set_attributes(tag="step1",
debug=debug_lftolx,
prevent_optimization=True)
step2 = -log_inv_hi + step1
step2.set_attributes(tag="step2",
debug=debug_lftolx,
prevent_optimization=True)
std_result = exact_log2_hi_exp + step2
std_result.set_attributes(tag="std_result",
debug=debug_lftolx,
prevent_optimization=True)
# 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, Return(vx),
ConditionBlock(ctz_cond, Statement(Return(ctz_result), ),
Statement(Return(std_result))))))
scheme = pre_scheme
#print scheme.get_str(depth = None, display_precision = True)
opt_eng = OptimizationEngine(self.processor)
# fusing FMA
print "MDL fusing FMA"
scheme = opt_eng.fuse_multiply_add(scheme, silence=True)
print "MDL abstract scheme"
opt_eng.instantiate_abstract_precision(scheme, None)
#print scheme.get_str(depth = None, display_precision = True)
print "MDL instantiated scheme"
opt_eng.instantiate_precision(scheme, default_precision=ML_Binary32)
print "subexpression sharing"
opt_eng.subexpression_sharing(scheme)
print "silencing operation"
opt_eng.silence_fp_operations(scheme)
# registering scheme as function implementation
func_implementation.set_scheme(scheme)
# check processor support
opt_eng.check_processor_support(scheme)
# factorizing fast path
opt_eng.factorize_fast_path(scheme)
#print scheme.get_str(depth = None, display_precision = True)
cg = CCodeGenerator(self.processor,
declare_cst=False,
disable_debug=not debug_flag,
libm_compliant=libm_compliant)
self.result = func_implementation.get_definition(cg,
C_Code,
static_cst=True)
self.result.add_header("support_lib/ml_special_values.h")
self.result.add_header("math.h")
self.result.add_header("stdio.h")
self.result.add_header("inttypes.h")
#print self.result.get(cg)
output_stream = open("%s.c" % func_implementation.get_name(), "w")
output_stream.write(self.result.get(cg))
output_stream.close()
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_sollya_precision()
# 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)
lo_bound_global = SollyaObject(0.0)
hi_bound_global = SollyaObject(0.75)
approx_interval = Interval(lo_bound_global, hi_bound_global)
approx_interval_size = hi_bound_global - lo_bound_global
# table creation
table_index_size = 7
field_index_size = 2
exp_index_size = table_index_size - field_index_size
table_size = 2**table_index_size
table_index_range = range(table_size)
local_degree = 9
coeff_table = ML_Table(dimensions = [table_size, local_degree], storage_precision = self.precision)
#local_interval_size = approx_interval_size / SollyaObject(table_size)
#for i in table_index_range:
# degree = 6
# lo_bound = lo_bound_global + i * local_interval_size
# hi_bound = lo_bound_global + (i+1) * local_interval_size
# approx_interval = Interval(lo_bound, hi_bound)
# local_poly_object, local_error = Polynomial.build_from_approximation_with_error(acos(x), degree, [self.precision] * (degree+1), approx_interval, absolute)
# local_error = int(log2(sup(abs(local_error / acos(approx_interval)))))
# print approx_interval, local_error
exp_lo = 2**exp_index_size
for i in table_index_range:
lo_bound = (1.0 + (i % 2**field_index_size) * S2**-field_index_size) * S2**(i / 2**field_index_size - exp_lo)
hi_bound = (1.0 + ((i % 2**field_index_size) + 1) * S2**-field_index_size) * S2**(i / 2**field_index_size - exp_lo)
local_approx_interval = Interval(lo_bound, hi_bound)
local_poly_object, local_error = Polynomial.build_from_approximation_with_error(acos(1 - x), local_degree, [self.precision] * (local_degree+1), local_approx_interval, sollya.absolute)
local_error = int(log2(sup(abs(local_error / acos(1 - local_approx_interval)))))
coeff_table
print local_approx_interval, local_error
for d in xrange(local_degree):
coeff_table[i][d] = sollya.coeff(local_poly_object.get_sollya_object(), d)
table_index = BitLogicRightShift(vx, vx.get_precision().get_field_size() - field_index_size) - (exp_lo << field_index_size)
print "building mathematical polynomial"
poly_degree = sup(sollya.guessdegree(acos(x), approx_interval, S2**-(self.precision.get_field_size())))
print "guessed polynomial degree: ", int(poly_degree)
#global_poly_object = Polynomial.build_from_approximation(log10(1+x)/x, poly_degree, [self.precision]*(poly_degree+1), approx_interval, absolute)
print "generating polynomial evaluation scheme"
#_poly = PolynomialSchemeEvaluator.generate_horner_scheme(poly_object, _red_vx, unified_precision = self.precision)
# 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),
#}
# computing gappa error
#poly_eval_error = self.get_eval_error(result, eval_error_map)
# main scheme
print "MDL scheme"
scheme = Statement(Return(vx))
return scheme
def generic_poly_split(offset_fct, indexing, target_eps, coeff_precision, vx):
""" generate the meta approximation for @p offset_fct over several
intervals defined by @p indexing object
For each sub-interval, a polynomial approximation with
maximal_error @p target_eps is tabulated, and evaluated using format
@p coeff_precision.
The input variable is @p vx """
# computing degree for a different polynomial approximation on each
# sub-interval
poly_degree_list = [
int(sup(guessdegree(offset_fct(offset), sub_interval, target_eps)))
for offset, sub_interval in indexing.get_offseted_sub_list()
]
poly_max_degree = max(poly_degree_list)
# tabulating polynomial coefficients on split_num sub-interval of interval
poly_table = ML_NewTable(
dimensions=[indexing.split_num, poly_max_degree + 1],
storage_precision=coeff_precision,
const=True)
offset_table = ML_NewTable(dimensions=[indexing.split_num],
storage_precision=coeff_precision,
const=True)
max_error = 0.0
for sub_index in range(indexing.split_num):
poly_degree = poly_degree_list[sub_index]
offset, approx_interval = indexing.get_offseted_sub_interval(sub_index)
offset_table[sub_index] = offset
if poly_degree == 0:
# managing constant approximation separately since it seems
# to break sollya
local_approx = coeff_precision.round_sollya_object(
offset_fct(offset)(inf(approx_interval)))
poly_table[sub_index][0] = local_approx
for monomial_index in range(1, poly_max_degree + 1):
poly_table[sub_index][monomial_index] = 0
approx_error = sollya.infnorm(
offset_fct(offset) - local_approx, approx_interval)
else:
poly_object, approx_error = Polynomial.build_from_approximation_with_error(
offset_fct(offset), poly_degree,
[coeff_precision] * (poly_degree + 1), approx_interval,
sollya.relative)
for monomial_index in range(poly_max_degree + 1):
if monomial_index <= poly_degree:
poly_table[sub_index][
monomial_index] = poly_object.coeff_map[monomial_index]
else:
poly_table[sub_index][monomial_index] = 0
max_error = max(approx_error, max_error)
Log.report(Log.Debug, "max approx error is {}", max_error)
# indexing function: derive index from input @p vx value
poly_index = indexing.get_index_node(vx)
poly_index.set_attributes(tag="poly_index", debug=debug_multi)
ext_precision = get_extended_fp_precision(coeff_precision)
# building polynomial evaluation scheme
offset = TableLoad(offset_table,
poly_index,
precision=coeff_precision,
tag="offset",
debug=debug_multi)
poly = TableLoad(poly_table,
poly_index,
poly_max_degree,
precision=coeff_precision,
tag="poly_init",
debug=debug_multi)
red_vx = Subtraction(vx,
offset,
precision=vx.precision,
tag="red_vx",
debug=debug_multi)
for monomial_index in range(poly_max_degree, -1, -1):
coeff = TableLoad(poly_table,
poly_index,
monomial_index,
precision=coeff_precision,
tag="poly_%d" % monomial_index,
debug=debug_multi)
#fma_precision = coeff_precision if monomial_index > 1 else ext_precision
fma_precision = coeff_precision
poly = FMA(red_vx, poly, coeff, precision=fma_precision)
#return Conversion(poly, precision=coeff_precision)
#return poly.hi
return poly
def generate_expr(
self,
code_object,
optree,
folded=False,
result_var=None,
initial=False,
language=None,
## force to store result in a variable, wrapping CodeExpression
# in CodeVariable
force_variable_storing=False):
""" code generation function """
language = self.language if language is None else language
# search if <optree> has already been processed
if self.has_memoization(optree):
result = self.get_memoization(optree)
if isinstance(result, CodeExpression) and force_variable_storing:
# forcing storing and translation CodeExpression to CodeVariable
# if force_variable_storing is set
result_precision = result.precision
prefix_tag = optree.get_tag(
default="var_result"
) if force_variable_storing else "tmp_result"
final_var = result_var if result_var else code_object.get_free_var_name(
result_precision, prefix=prefix_tag, declare=True)
code_object << self.generate_code_assignation(
code_object, final_var, result.get())
result = CodeVariable(final_var, result_precision)
return result
result = None
# implementation generation
if isinstance(optree, CodeVariable):
result = optree
elif isinstance(optree, Variable):
if optree.get_var_type() is Variable.Local:
final_var = code_object.get_free_var_name(
optree.get_precision(),
prefix=optree.get_tag(),
declare=True,
var_ctor=Variable)
result = CodeVariable(final_var, optree.get_precision())
else:
result = CodeVariable(optree.get_tag(), optree.get_precision())
elif isinstance(optree, Signal):
if optree.get_var_type() is Variable.Local:
final_var = code_object.declare_signal(optree,
optree.get_precision(),
prefix=optree.get_tag())
result = CodeVariable(final_var, optree.get_precision())
else:
result = CodeVariable(optree.get_tag(), optree.get_precision())
elif isinstance(optree, Constant):
precision = optree.get_precision() # .get_base_format()
if force_variable_storing or self.declare_cst or optree.get_precision(
).is_cst_decl_required():
cst_prefix = "cst" if optree.get_tag(
) is None else optree.get_tag()
cst_varname = code_object.declare_cst(optree,
prefix=cst_prefix)
result = CodeVariable(cst_varname, precision)
else:
if precision is ML_Integer:
result = CodeExpression("%d" % optree.get_value(),
precision)
else:
try:
result = CodeExpression(
precision.get_cst(optree.get_value(),
language=language), precision)
except:
result = CodeExpression(
precision.get_cst(optree.get_value(),
language=language), precision)
Log.report(
Log.Error,
"Error during get_cst call for Constant: {} ",
optree) # Exception print
elif isinstance(optree, Assert):
cond = optree.get_input(0)
error_msg = optree.get_error_msg()
severity = optree.get_severity()
cond_code = self.generate_expr(code_object,
cond,
folded=False,
language=language)
code_object << " assert {cond} report {error_msg} severity {severity};\n".format(
cond=cond_code.get(),
error_msg=error_msg,
severity=severity.descriptor)
return None
elif isinstance(optree, Wait):
time_ns = optree.get_time_ns()
code_object << "wait for {time_ns} ns;\n".format(time_ns=time_ns)
return None
elif isinstance(optree, SwitchBlock):
switch_value = optree.inputs[0]
# generating pre_statement
self.generate_expr(code_object,
optree.get_pre_statement(),
folded=folded,
language=language)
switch_value_code = self.generate_expr(code_object,
switch_value,
folded=folded,
language=language)
case_map = optree.get_case_map()
code_object << "\nswitch(%s) {\n" % switch_value_code.get()
for case in case_map:
case_value = case
case_statement = case_map[case]
if isinstance(case_value, tuple):
for sub_case in case:
code_object << "case %s:\n" % sub_case
else:
code_object << "case %s:\n" % case
code_object.open_level()
self.generate_expr(code_object,
case_statement,
folded=folded,
language=language)
code_object.close_level()
code_object << "}\n"
return None
elif isinstance(optree, ReferenceAssign):
output_var = optree.inputs[0]
result_value = optree.inputs[1]
output_var_code = self.generate_expr(code_object,
output_var,
folded=False,
language=language)
def get_assign_symbol(node):
if isinstance(node, Signal):
assign_sign = "<="
elif isinstance(node, Variable):
assign_sign = ":="
else:
Log.report(Log.Error,
"unsupported node for assign symbol:\n {}",
node)
return assign_sign
if isinstance(output_var, Signal) or isinstance(
output_var, Variable):
assign_sign = get_assign_symbol(output_var)
elif isinstance(output_var, VectorElementSelection) or isinstance(
output_var, SubSignalSelection):
select_input = output_var.get_input(0)
assign_sign = get_assign_symbol(select_input)
else:
Log.report(Log.Error,
"unsupported node for assign symbol:\n {}", node)
if isinstance(result_value, Constant):
# generate assignation
result_value_code = self.generate_expr(code_object,
result_value,
folded=folded,
language=language)
code_object << self.generate_assignation(
output_var_code.get(),
result_value_code.get(),
assign_sign=assign_sign)
else:
#result_value_code = self.generate_expr(code_object, result_value, folded = True, force_variable_storing = True, language = language)
result_value_code = self.generate_expr(code_object,
result_value,
folded=True,
language=language)
code_object << self.generate_assignation(
output_var_code.get(),
result_value_code.get(),
assign_sign=assign_sign)
if optree.get_debug() and not self.disable_debug:
self.generate_debug_msg(result_value,
result_value_code,
code_object,
debug_object=optree.get_debug())
#code_object << self.generate_assignation(output_var_code.get(), result_value_code.get())
#code_object << output_var.get_precision().generate_c_assignation(output_var_code, result_value_code)
return None
elif isinstance(optree, RangeLoop):
iterator = optree.get_input(0)
loop_body = optree.get_input(1)
loop_range = optree.get_loop_range()
specifier = optree.get_specifier()
range_pattern = "{lower} to {upper}" if specifier is RangeLoop.Increasing else "{upper} dowto {lower}"
range_code = range_pattern.format(lower=sollya.inf(loop_range),
upper=sollya.sup(loop_range))
iterator_code = self.generate_expr(code_object,
iterator,
folded=folded,
language=language)
code_object << "\n for {iterator} in {loop_range} loop\n".format(
iterator=iterator_code.get(), loop_range=range_code)
code_object.inc_level()
body_code = self.generate_expr(code_object,
loop_body,
folded=folded,
language=language)
assert body_code is None
code_object.dec_level()
code_object << "end loop;\n"
return None
elif isinstance(optree, Loop):
init_statement = optree.inputs[0]
exit_condition = optree.inputs[1]
loop_body = optree.inputs[2]
self.generate_expr(code_object,
init_statement,
folded=folded,
language=language)
code_object << "\nfor (;%s;)" % self.generate_expr(
code_object, exit_condition, folded=False,
language=language).get()
code_object.open_level()
self.generate_expr(code_object,
loop_body,
folded=folded,
language=language)
code_object.close_level()
return None
elif isinstance(optree, Process):
# generating pre_statement for process
pre_statement = optree.get_pre_statement()
self.generate_expr(code_object,
optree.get_pre_statement(),
folded=folded,
language=language)
sensibility_list = [
self.generate_expr(code_object,
op,
folded=True,
language=language).get()
for op in optree.get_sensibility_list()
]
sensibility_list = "({})".format(", ".join(
sensibility_list)) if len(sensibility_list) != 0 else ""
code_object << "process{}\n".format(sensibility_list)
self.open_memoization_level()
code_object.open_level(
extra_shared_tables=[MultiSymbolTable.SignalSymbol],
var_ctor=Variable)
for process_stat in optree.inputs:
self.generate_expr(code_object,
process_stat,
folded=folded,
initial=False,
language=language)
code_object.close_level()
self.close_memoization_level()
code_object << "end process;\n\n"
return None
elif isinstance(optree, PlaceHolder):
first_input = optree.get_input(0)
first_input_code = self.generate_expr(code_object,
first_input,
folded=folded,
language=language)
for op in optree.get_inputs()[1:]:
_ = self.generate_expr(code_object,
op,
folded=folded,
language=language)
result = first_input_code
elif isinstance(optree, ComponentInstance):
component_object = optree.get_component_object()
component_name = component_object.get_name()
code_object.declare_component(component_name, component_object)
io_map = optree.get_io_map()
component_tag = optree.get_tag()
if component_tag is None:
component_tag = "{component_name}_i{instance_id}".format(
component_name=component_name,
instance_id=optree.get_instance_id())
# component tag uniquifying
component_tag = code_object.get_free_name(component_object,
prefix=component_tag)
mapped_io = {}
for io_tag in io_map:
mapped_io[io_tag] = self.generate_expr(code_object,
io_map[io_tag],
folded=True,
language=language)
code_object << "\n{component_tag} : {component_name}\n".format(
component_name=component_name, component_tag=component_tag)
code_object << " port map (\n"
code_object << " " + ", \n ".join(
"{} => {}".format(io_tag, mapped_io[io_tag].get())
for io_tag in mapped_io)
code_object << "\n);\n"
return None
elif isinstance(optree, ConditionBlock):
condition = optree.inputs[0]
if_branch = optree.inputs[1]
else_branch = optree.inputs[2] if len(optree.inputs) > 2 else None
# generating pre_statement
self.generate_expr(code_object,
optree.get_pre_statement(),
folded=folded,
language=language)
cond_code = self.generate_expr(code_object,
condition,
folded=False,
language=language)
try:
cond_likely = condition.get_likely()
except AttributeError:
Log.report(
Log.Error,
"The following condition has no (usable) likely attribute: {}",
condition)
code_object << "if %s then\n " % cond_code.get()
code_object.inc_level()
if_branch_code = self.generate_expr(code_object,
if_branch,
folded=False,
language=language)
code_object.dec_level()
if else_branch:
code_object << " else\n "
code_object.inc_level()
else_branch_code = self.generate_expr(code_object,
else_branch,
folded=True,
language=language)
code_object.dec_level()
else:
# code_object << "\n"
pass
code_object << "end if;\n"
return None
elif isinstance(optree, Select):
# we go through all of select operands to
# flatten the select tree
def flatten_select(op, cond=None):
""" Process recursively a Select operation to build a list
of tuple (result, condition) """
if not isinstance(op, Select): return [(op, cond)]
lcond = op.inputs[0] if cond is None else LogicalAnd(
op.inputs[0], cond, precision=cond.get_precision())
return flatten_select(op.inputs[1], lcond) + flatten_select(
op.inputs[2], cond)
def legalize_select_input(select_input):
if select_input.get_precision().get_bit_size(
) != optree.get_precision().get_bit_size():
return Conversion(select_input,
precision=optree.get_precision())
else:
return select_input
prefix = optree.get_tag(default="setmp")
result_varname = result_var if result_var != None else code_object.get_free_var_name(
optree.get_precision(), prefix=prefix)
result = CodeVariable(result_varname, optree.get_precision())
select_opcond_list = flatten_select(optree)
if not select_opcond_list[-1][1] is None:
Log.report(
Log.Error,
"last condition in flatten select differs from None")
gen_list = []
for op, cond in select_opcond_list:
op = legalize_select_input(op)
op_code = self.generate_expr(code_object,
op,
folded=folded,
language=language)
if not cond is None:
cond_code = self.generate_expr(code_object,
cond,
folded=True,
force_variable_storing=True,
language=language)
gen_list.append((op_code, cond_code))
else:
gen_list.append((op_code, None))
code_object << "{result} <= \n".format(result=result.get())
code_object.inc_level()
for op_code, cond_code in gen_list:
if not cond_code is None:
code_object << "{op_code} when {cond_code} else\n".format(
op_code=op_code.get(), cond_code=cond_code.get())
else:
code_object << "{op_code};\n".format(op_code=op_code.get())
code_object.dec_level()
elif isinstance(optree, TableLoad):
table = optree.get_input(0)
index = optree.get_input(1)
index_code = self.generate_expr(code_object,
index,
folded=folded,
language=language)
prefix = optree.get_tag(default="table_value")
result_varname = result_var if result_var != None else code_object.get_free_var_name(
optree.get_precision(), prefix=prefix)
result = CodeVariable(result_varname, optree.get_precision())
code_object << "with {index} select {result} <=\n".format(
index=index_code.get(), result=result.get())
table_dimensions = table.get_precision().get_dimensions()
assert len(table_dimensions) == 1
table_size = table_dimensions[0]
default_value = 0
# linearizing table selection
for tabid, value in enumerate(table.get_data()):
code_object << "\t{} when {},\n".format(
table.get_precision().get_storage_precision().get_cst(
value),
index.get_precision().get_cst(tabid))
code_object << "\t{} when others;\n".format(table.get_precision(
).get_storage_precision().get_cst(default_value))
# result is set
elif isinstance(optree, Return):
return_result = optree.inputs[0]
return_code = self.generate_expr(code_object,
return_result,
folded=folded,
language=language)
code_object << "return %s;\n" % return_code.get()
return None #return_code
elif isinstance(optree, ExceptionOperation):
if optree.get_specifier() in [
ExceptionOperation.RaiseException,
ExceptionOperation.ClearException,
ExceptionOperation.RaiseReturn
]:
result_code = self.processor.generate_expr(
self,
code_object,
optree,
optree.inputs,
folded=False,
result_var=result_var,
language=language)
code_object << "%s;\n" % result_code.get()
if optree.get_specifier() == ExceptionOperation.RaiseReturn:
if self.libm_compliant:
# libm compliant exception management
code_object.add_header(
"support_lib/ml_libm_compatibility.h")
return_value = self.generate_expr(
code_object,
optree.get_return_value(),
folded=folded,
language=language)
arg_value = self.generate_expr(code_object,
optree.get_arg_value(),
folded=folded,
language=language)
function_name = optree.function_name
exception_list = [
op.get_value() for op in optree.inputs
]
if ML_FPE_Inexact in exception_list:
exception_list.remove(ML_FPE_Inexact)
if len(exception_list) > 1:
raise NotImplementedError
if ML_FPE_Overflow in exception_list:
code_object << "return ml_raise_libm_overflowf(%s, %s, \"%s\");\n" % (
return_value.get(), arg_value.get(),
function_name)
elif ML_FPE_Underflow in exception_list:
code_object << "return ml_raise_libm_underflowf(%s, %s, \"%s\");\n" % (
return_value.get(), arg_value.get(),
function_name)
elif ML_FPE_Invalid in exception_list:
code_object << "return %s;\n" % return_value.get()
else:
return_precision = optree.get_return_value(
).get_precision()
self.generate_expr(code_object,
Return(optree.get_return_value(),
precision=return_precision),
folded=folded,
language=language)
return None
else:
result = self.processor.generate_expr(self,
code_object,
optree,
optree.inputs,
folded=folded,
result_var=result_var,
language=language)
elif isinstance(optree, NoResultOperation):
result_code = self.processor.generate_expr(self,
code_object,
optree,
optree.inputs,
folded=False,
result_var=result_var,
language=language)
code_object << "%s;\n" % result_code.get()
return None
elif isinstance(optree, Statement):
for op in optree.inputs:
if not self.has_memoization(op):
self.generate_expr(code_object,
op,
folded=folded,
initial=True,
language=language)
return None
else:
# building ordered list of required node by depth
working_list = [op for op in optree.get_inputs()]
processing_list = [op for op in working_list]
resolved = {}
while working_list != []:
op = working_list.pop(0)
# node has already been processed: SKIP
if op in resolved:
continue
if isinstance(op, ML_Table):
# ML_Table instances are skipped (should be generated directly by TableLoad)
continue
elif isinstance(op, ML_LeafNode):
processing_list.append(op)
else:
memo = self.get_memoization(op)
if not memo is None:
# node has already been generated: STOP HERE
resolved[op] = memo
else:
# enqueue node to be processed
processing_list.append(op)
# enqueue node inputs
working_list += [op for op in op.get_inputs()]
resolved[op] = memo
# processing list in reverse order (starting with deeper node to avoid too much recursion)
for op in processing_list[::-1]:
_ = self.generate_expr(code_object,
op,
folded=folded,
initial=initial,
language=language)
# processing main node
generate_pre_process = self.generate_clear_exception if optree.get_clearprevious(
) else None
result = self.processor.generate_expr(
self,
code_object,
optree,
optree.inputs,
generate_pre_process=generate_pre_process,
folded=folded,
result_var=result_var,
language=language)
# registering result into memoization table
self.add_memoization(optree, result)
# debug management
if optree.get_debug() and not self.disable_debug:
self.generate_debug_msg(optree, result, code_object)
if (initial or force_variable_storing
or result_too_long(result)) and not isinstance(
result, CodeVariable) and not result is None:
# result could have been modified from initial optree
result_precision = result.precision
prefix_tag = optree.get_tag(
default="var_result"
) if force_variable_storing else "tmp_result"
final_var = result_var if result_var else code_object.get_free_var_name(
result_precision, prefix=prefix_tag, declare=True)
code_object << self.generate_code_assignation(
code_object, final_var, result.get())
return CodeVariable(final_var, result_precision)
return result
def random_log_sample(interval):
lo = sollya.inf(interval)
hi = sollya.sup(interval)
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
return RaiseReturn(*args, **kwords)
C_m1 = Constant(-1, precision = self.precision)
test_NaN_or_inf = Test(vx, specifier = Test.IsInfOrNaN, likely = False, debug = debug_multi, tag = "NaN_or_inf", precision = ML_Bool)
test_NaN = Test(vx, specifier = Test.IsNaN, likely = False, debug = debug_multi, tag = "is_NaN", precision = ML_Bool)
test_inf = Comparison(vx, 0, specifier = Comparison.Greater, debug = debug_multi, tag = "sign", precision = ML_Bool, likely = False);
# Infnty input
infty_return = Statement(ConditionBlock(test_inf, Return(FP_PlusInfty(self.precision)), Return(C_m1)))
# non-std input (inf/nan)
specific_return = ConditionBlock(test_NaN, Return(FP_QNaN(self.precision)), infty_return)
# Over/Underflow Tests
precision_emax = self.precision.get_emax()
precision_max_value = S2**(precision_emax + 1)
expm1_overflow_bound = ceil(log(precision_max_value + 1))
overflow_test = Comparison(vx, expm1_overflow_bound, likely = False, specifier = Comparison.Greater, precision = ML_Bool)
overflow_return = Statement(Return(FP_PlusInfty(self.precision)))
precision_emin = self.precision.get_emin_subnormal()
precision_min_value = S2** precision_emin
expm1_underflow_bound = floor(log(precision_min_value) + 1)
underflow_test = Comparison(vx, expm1_underflow_bound, likely = False, specifier = Comparison.Less, precision = ML_Bool)
underflow_return = Statement(Return(C_m1))
sollya_precision = {ML_Binary32: sollya.binary32, ML_Binary64: sollya.binary64}[self.precision]
int_precision = {ML_Binary32: ML_Int32, ML_Binary64: ML_Int64}[self.precision]
# Constants
log_2 = round(log(2), sollya_precision, sollya.RN)
invlog2 = round(1/log(2), sollya_precision, sollya.RN)
log_2_cst = Constant(log_2, precision = self.precision)
interval_vx = Interval(expm1_underflow_bound, expm1_overflow_bound)
interval_fk = interval_vx * invlog2
interval_k = Interval(floor(inf(interval_fk)), ceil(sup(interval_fk)))
log2_hi_precision = self.precision.get_field_size() - 6
log2_hi = round(log(2), log2_hi_precision, sollya.RN)
log2_lo = round(log(2) - log2_hi, sollya_precision, sollya.RN)
# Reduction
unround_k = vx * invlog2
ik = NearestInteger(unround_k, precision = int_precision, debug = debug_multi, tag = "ik")
k = Conversion(ik, precision = self.precision, tag = "k")
red_coeff1 = Multiplication(k, log2_hi, precision = self.precision)
red_coeff2 = Multiplication(Negation(k, precision = self.precision), log2_lo, precision = self.precision)
pre_sub_mul = Subtraction(vx, red_coeff1, precision = self.precision)
s = Addition(pre_sub_mul, red_coeff2, precision = self.precision)
z = Subtraction(s, pre_sub_mul, precision = self.precision)
t = Subtraction(red_coeff2, z, precision = self.precision)
r = Addition(s, t, precision = self.precision)
r.set_attributes(tag = "r", debug = debug_multi)
r_interval = Interval(-log_2/S2, log_2/S2)
local_ulp = sup(ulp(exp(r_interval), self.precision))
print("ulp: ", local_ulp)
error_goal = S2**-1*local_ulp
print("error goal: ", error_goal)
# Polynomial Approx
error_function = lambda p, f, ai, mod, t: dirtyinfnorm(f - p, ai)
Log.report(Log.Info, "\033[33;1m Building polynomial \033[0m\n")
poly_degree = sup(guessdegree(expm1(sollya.x), r_interval, error_goal) + 1)
polynomial_scheme_builder = PolynomialSchemeEvaluator.generate_horner_scheme
poly_degree_list = range(0, poly_degree)
precision_list = [self.precision] *(len(poly_degree_list) + 1)
poly_object, poly_error = Polynomial.build_from_approximation_with_error(expm1(sollya.x), poly_degree, precision_list, r_interval, sollya.absolute, error_function = error_function)
sub_poly = poly_object.sub_poly(start_index = 2)
Log.report(Log.Info, "Poly : %s" % sub_poly)
Log.report(Log.Info, "poly error : {} / {:d}".format(poly_error, int(sollya.log2(poly_error))))
pre_sub_poly = polynomial_scheme_builder(sub_poly, r, unified_precision = self.precision)
poly = r + pre_sub_poly
poly.set_attributes(tag = "poly", debug = debug_multi)
exp_k = ExponentInsertion(ik, tag = "exp_k", debug = debug_multi, precision = self.precision)
exp_mk = ExponentInsertion(-ik, tag = "exp_mk", debug = debug_multi, precision = self.precision)
diff = 1 - exp_mk
diff.set_attributes(tag = "diff", debug = debug_multi)
# Late Tests
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 = ik - overflow_exp_offset
exp_diff_k = ExponentInsertion(diff_k, precision = self.precision, tag = "exp_diff_k", debug = debug_multi)
exp_oflow_offset = ExponentInsertion(overflow_exp_offset, precision = self.precision, tag = "exp_offset", debug = debug_multi)
late_overflow_result = (exp_diff_k * (1 + poly)) * exp_oflow_offset - 1.0
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)
)
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_coeff = ik + underflow_exp_offset
exp_corrected = ExponentInsertion(corrected_coeff, precision = self.precision)
exp_uflow_offset = ExponentInsertion(-underflow_exp_offset, precision = self.precision)
late_underflow_result = ( exp_corrected * (1 + poly)) * exp_uflow_offset - 1.0
test_subnormal = Test(late_underflow_result, specifier = Test.IsSubnormal, likely = False)
late_underflow_return = Statement(
ConditionBlock(
test_subnormal,
ExpRaiseReturn(ML_FPE_Underflow, return_value = late_underflow_result)),
Return(late_underflow_result)
)
# Reconstruction
std_result = exp_k * ( poly + diff )
std_result.set_attributes(tag = "result", debug = debug_multi)
result_scheme = ConditionBlock(
late_overflow_test,
late_overflow_return,
ConditionBlock(
late_underflow_test,
late_underflow_return,
Return(std_result)
)
)
std_return = ConditionBlock(
overflow_test,
overflow_return,
ConditionBlock(
underflow_test,
underflow_return,
result_scheme)
)
scheme = ConditionBlock(
test_NaN_or_inf,
Statement(specific_return),
std_return
)
return scheme
def generate_reduction_fptaylor(x):
# get sign and abs_x, must be the same at endpoints
if sollya.sup(x) <= 0:
sign_x_expr = "-1.0"
abs_x_expr = "-x"
abs_x = -x
elif sollya.inf(x) >= 0:
sign_x_expr = "1.0"
abs_x_expr = "x"
abs_x = x
else:
assert False, "Interval must not straddle 0"
# get k, must be the same at endpoints
unround_k = abs_x * n_invpi
k_low = sollya.floor(sollya.inf(unround_k))
k_high = sollya.floor(sollya.sup(unround_k))
if k_low != k_high:
assert False, "Interval must not straddle multples of pi"
k = int(k_low)
part = k % 2
r_expr = "abs_x - whole"
r = abs_x - k * n_pi
z_expr = "r"
z = r
if part == 1:
flipped_poly_expr = "-poly"
else:
flipped_poly_expr = "poly"
x_low = sollya.inf(x)
x_high = sollya.sup(x)
query = "\n".join([
"Variables", " real x in [{},{}];".format(x_low, x_high),
"Definitions", " abs_x rnd64= {};".format(abs_x_expr),
" whole rnd64= {} * {};".format(k, n_pi),
" r rnd64= abs_x - whole;", " z rnd64= {};".format(z_expr),
" poly rnd64= {};".format(poly_expr),
" flipped_poly rnd64= {};".format(flipped_poly_expr),
" retval rnd64= flipped_poly*{};".format(sign_x_expr),
"Expressions", " retval;"
])
rnd_rel_err = None
rnd_abs_err = None
try:
res = fptaylor.Result(query, {
**config, "--rel-error": "true",
"--abs-error": "true"
})
rnd_rel_err = float(
res.result["relative_errors"]["final_total"]["value"])
rnd_abs_err = float(
res.result["absolute_errors"]["final_total"]["value"])
except AssertionError:
pass
except KeyError:
try:
rnd_abs_err = float(
res.result["absolute_errors"]["final_total"]["value"])
except KeyError:
pass
if rnd_abs_err is None:
try:
res = fptaylor.Result(query, {
**config, "--rel-error": "false",
"--abs-error": "true"
})
rnd_abs_err = float(
res.result["absolute_errors"]["final_total"]["value"])
except AssertionError:
pass
err_int = sollya.supnorm(self.poly_object.get_sollya_object(),
sollya.sin(sollya.x), z, sollya.relative,
2**-100)
algo_rel_err = sollya.sup(err_int)
err_int = sollya.supnorm(self.poly_object.get_sollya_object(),
sollya.sin(sollya.x), z, sollya.absolute,
2**-100)
algo_abs_err = sollya.sup(err_int)
if rnd_rel_err is None or str(algo_rel_err) == "error":
rel_err = float("inf")
else:
rel_err = rnd_rel_err + algo_rel_err
abs_err = rnd_abs_err + algo_abs_err
return rel_err, abs_err
def determine_error(self):
sollya.settings.display = sollya.hexadecimal
n_pi = self.precision.round_sollya_object(sollya.pi, sollya.RN)
n_invpi = self.precision.round_sollya_object(1 / sollya.pi, sollya.RN)
poly_expr = str(sollya.horner(self.poly_object.get_sollya_object()))
poly_expr = poly_expr.replace("_x_", "z")
poly_expr = poly_expr.replace("z^0x1p1", "z*z")
config = fptaylor.CHECK_CONFIG.copy()
del config["--abs-error"]
config["--opt"] = "bb-eval"
config["--rel-error-threshold"] = "0.0"
config["--intermediate-opt"] = "false"
config["--uncertainty"] = "false"
def generate_fptaylor(x):
x_low = sollya.inf(x)
x_high = sollya.sup(x)
query = "\n".join([
"Variables", " real z in [{},{}];".format(x_low, x_high),
"Definitions", " retval rnd64= {};".format(poly_expr),
"Expressions", " retval;"
])
rnd_rel_err = None
rnd_abs_err = None
try:
res = fptaylor.Result(query, {
**config, "--rel-error": "true",
"--abs-error": "true"
})
rnd_rel_err = float(
res.result["relative_errors"]["final_total"]["value"])
rnd_abs_err = float(
res.result["absolute_errors"]["final_total"]["value"])
except AssertionError:
pass
except KeyError:
try:
rnd_abs_err = float(
res.result["absolute_errors"]["final_total"]["value"])
except KeyError:
pass
if rnd_abs_err is None:
try:
res = fptaylor.Result(query, {
**config, "--rel-error": "false",
"--abs-error": "true"
})
rnd_abs_err = float(
res.result["absolute_errors"]["final_total"]["value"])
except AssertionError:
pass
err_int = sollya.supnorm(self.poly_object.get_sollya_object(),
sollya.sin(sollya.x), x, sollya.relative,
2**-100)
algo_rel_err = sollya.sup(err_int)
err_int = sollya.supnorm(self.poly_object.get_sollya_object(),
sollya.sin(sollya.x), x, sollya.absolute,
2**-100)
algo_abs_err = sollya.sup(err_int)
if rnd_rel_err is None or str(algo_rel_err) == "error":
rel_err = float("inf")
else:
rel_err = rnd_rel_err + algo_rel_err
abs_err = rnd_abs_err + algo_abs_err
return rel_err, abs_err
def generate_reduction_fptaylor(x):
# get sign and abs_x, must be the same at endpoints
if sollya.sup(x) <= 0:
sign_x_expr = "-1.0"
abs_x_expr = "-x"
abs_x = -x
elif sollya.inf(x) >= 0:
sign_x_expr = "1.0"
abs_x_expr = "x"
abs_x = x
else:
assert False, "Interval must not straddle 0"
# get k, must be the same at endpoints
unround_k = abs_x * n_invpi
k_low = sollya.floor(sollya.inf(unround_k))
k_high = sollya.floor(sollya.sup(unround_k))
if k_low != k_high:
assert False, "Interval must not straddle multples of pi"
k = int(k_low)
part = k % 2
r_expr = "abs_x - whole"
r = abs_x - k * n_pi
z_expr = "r"
z = r
if part == 1:
flipped_poly_expr = "-poly"
else:
flipped_poly_expr = "poly"
x_low = sollya.inf(x)
x_high = sollya.sup(x)
query = "\n".join([
"Variables", " real x in [{},{}];".format(x_low, x_high),
"Definitions", " abs_x rnd64= {};".format(abs_x_expr),
" whole rnd64= {} * {};".format(k, n_pi),
" r rnd64= abs_x - whole;", " z rnd64= {};".format(z_expr),
" poly rnd64= {};".format(poly_expr),
" flipped_poly rnd64= {};".format(flipped_poly_expr),
" retval rnd64= flipped_poly*{};".format(sign_x_expr),
"Expressions", " retval;"
])
rnd_rel_err = None
rnd_abs_err = None
try:
res = fptaylor.Result(query, {
**config, "--rel-error": "true",
"--abs-error": "true"
})
rnd_rel_err = float(
res.result["relative_errors"]["final_total"]["value"])
rnd_abs_err = float(
res.result["absolute_errors"]["final_total"]["value"])
except AssertionError:
pass
except KeyError:
try:
rnd_abs_err = float(
res.result["absolute_errors"]["final_total"]["value"])
except KeyError:
pass
if rnd_abs_err is None:
try:
res = fptaylor.Result(query, {
**config, "--rel-error": "false",
"--abs-error": "true"
})
rnd_abs_err = float(
res.result["absolute_errors"]["final_total"]["value"])
except AssertionError:
pass
err_int = sollya.supnorm(self.poly_object.get_sollya_object(),
sollya.sin(sollya.x), z, sollya.relative,
2**-100)
algo_rel_err = sollya.sup(err_int)
err_int = sollya.supnorm(self.poly_object.get_sollya_object(),
sollya.sin(sollya.x), z, sollya.absolute,
2**-100)
algo_abs_err = sollya.sup(err_int)
if rnd_rel_err is None or str(algo_rel_err) == "error":
rel_err = float("inf")
else:
rel_err = rnd_rel_err + algo_rel_err
abs_err = rnd_abs_err + algo_abs_err
return rel_err, abs_err
def split_domain(starting_domain, slivers):
in_domains = [starting_domain]
# abs
out_domains = list()
for I in in_domains:
if sollya.inf(I) < 0 and sollya.sup(I) > 0:
out_domains.append(sollya.Interval(sollya.inf(I), 0))
out_domains.append(sollya.Interval(0, sollya.sup(I)))
else:
out_domains.append(I)
in_domains = out_domains
# k
out_domains = list()
while len(in_domains) > 0:
I = in_domains.pop()
#print("in: [{}, {}]".format(float(sollya.inf(I)), float(sollya.sup(I))))
unround_mult = I * n_invpi
mult_low = sollya.floor(sollya.inf(unround_mult))
mult_high = sollya.floor(sollya.sup(unround_mult))
if mult_low == mult_high or (mult_low == -1
and mult_high == 0):
#print(" accepted")
out_domains.append(I)
continue
if sollya.sup(I) <= 0:
divider_low = (mult_low + 1) * n_pi
divider_high = divider_low - divider_low * 2**-53
else:
divider_high = (mult_low + 1) * n_pi
divider_low = divider_high - divider_high * 2**-53
lower_part = sollya.Interval(sollya.inf(I), divider_low)
upper_part = sollya.Interval(divider_high, sollya.sup(I))
#print(" -> [{}, {}]".format(float(sollya.inf(lower_part)), float(sollya.sup(lower_part))))
#print(" -> [{}, {}]".format(float(sollya.inf(upper_part)), float(sollya.sup(upper_part))))
in_domains.append(lower_part)
in_domains.append(upper_part)
in_domains = out_domains
# subdivide each section into 2**subd sections
for _ in range(slivers):
out_domains = list()
for I in in_domains:
mid = sollya.mid(I)
out_domains.append(sollya.Interval(sollya.inf(I), mid))
out_domains.append(sollya.Interval(mid, sollya.sup(I)))
in_domains = out_domains
in_domains = set(in_domains)
in_domains = sorted(in_domains, key=lambda x: float(sollya.inf(x)))
in_domains = [
d for d in in_domains if sollya.inf(d) != sollya.sup(d)
]
return in_domains
if self.skip_reduction:
starting_domain = sollya.Interval(-n_pi - 2**-7, n_pi + 2**-7)
else:
reduction_k = 20
starting_domain = sollya.Interval(-reduction_k * n_pi,
reduction_k * n_pi)
# analyse each piece
in_domains = split_domain(starting_domain, self.slivers)
errors = list()
for I in in_domains:
if self.skip_reduction:
rel_err, abs_err = generate_fptaylor(I)
else:
rel_err, abs_err = generate_reduction_fptaylor(I)
print("{}\t{}\t{}\t{}".format(float(sollya.inf(I)),
float(sollya.sup(I)), float(abs_err),
float(rel_err)))
errors.append((I, abs_err, rel_err))
def generate_json(errors, domain):
errors = [err for err in errors if err[0] in domain]
errors.sort(key=lambda err: err[2])
epsilon = errors[0][2]
delta = max(err[1] for err in errors)
d = {
"cname": self.function_name,
"delta": float(delta),
"domain": [
float(sollya.inf(domain)),
float(sollya.sup(domain)),
],
"epsilon": float(epsilon),
"operation": "sin"
}
return d
if self.skip_reduction:
d = generate_json(errors,
sollya.Interval(-n_pi - 2**-7, n_pi + 2**-7))
json_str = json.dumps(d, sort_keys=True, indent=4)
json_str = "spec: " + json_str.replace("\n", "\nspec: ")
print(json_str)
else:
specs = list()
for k in range(1, reduction_k):
d = generate_json(errors, sollya.Interval(-k * n_pi, k * n_pi))
specs.append(d)
for i in range(len(specs)):
d = specs[i]
if i == len(specs) - 1:
json_str = json.dumps(d, sort_keys=True, indent=4)
json_str = "spec: " + json_str.replace("\n", "\nspec: ")
print(json_str)
break
nd = specs[i + 1]
if d["epsilon"] == nd["epsilon"] and d["delta"] == nd["delta"]:
continue
json_str = json.dumps(d, sort_keys=True, indent=4)
json_str = "spec: " + json_str.replace("\n", "\nspec: ")
print(json_str)
def compute_log(_vx, exp_corr_factor=None):
_vx_mant = MantissaExtraction(_vx,
tag="_vx_mant",
precision=self.precision,
debug=debug_lftolx)
_vx_exp = ExponentExtraction(_vx, tag="_vx_exp", debug=debugd)
# The main table is indexed by the 7 most significant bits
# of the mantissa
table_index = inv_approx_table.index_function(_vx_mant)
table_index.set_attributes(tag="table_index", debug=debuglld)
# argument reduction
# Using AND -2 to exclude LSB set to 1 for Newton-Raphson convergence
# 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)
_red_vx = FMA(arg_red_index, _vx_mant, -1.0)
_red_vx.set_attributes(tag="_red_vx", debug=debug_lftolx)
inv_err = S2**-inv_approx_table.index_size
red_interval = Interval(1 - inv_err, 1 + inv_err)
# 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)
Log.report(Log.Verbose, "building mathematical polynomial")
approx_interval = Interval(-inv_err, inv_err)
poly_degree = sup(
guessdegree(
log2(1 + sollya.x) / sollya.x, approx_interval, S2**
-(self.precision.get_field_size() * 1.1))) + 1
sollya.settings.display = sollya.hexadecimal
global_poly_object, approx_error = Polynomial.build_from_approximation_with_error(
log2(1 + sollya.x) / sollya.x,
poly_degree, [self.precision] * (poly_degree + 1),
approx_interval,
sollya.absolute,
error_function=lambda p, f, ai, mod, t: sollya.dirtyinfnorm(
p - f, ai))
Log.report(
Log.Info, "poly_degree={}, approx_error={}".format(
poly_degree, approx_error))
poly_object = global_poly_object.sub_poly(start_index=1, offset=1)
#poly_object = global_poly_object.sub_poly(start_index=0,offset=0)
Attributes.set_default_silent(True)
Attributes.set_default_rounding_mode(ML_RoundToNearest)
Log.report(Log.Verbose, "generating polynomial evaluation scheme")
pre_poly = PolynomialSchemeEvaluator.generate_horner_scheme(
poly_object, _red_vx, unified_precision=self.precision)
_poly = FMA(pre_poly, _red_vx,
global_poly_object.get_cst_coeff(0, self.precision))
_poly.set_attributes(tag="poly", debug=debug_lftolx)
Log.report(
Log.Verbose, "sollya global_poly_object: {}".format(
global_poly_object.get_sollya_object()))
Log.report(
Log.Verbose, "sollya poly_object: {}".format(
poly_object.get_sollya_object()))
corr_exp = _vx_exp if exp_corr_factor == None else _vx_exp + exp_corr_factor
Attributes.unset_default_rounding_mode()
Attributes.unset_default_silent()
pre_result = -_log_inv_hi + (_red_vx * _poly + (-_log_inv_lo))
pre_result.set_attributes(tag="pre_result", debug=debug_lftolx)
exact_log2_hi_exp = Conversion(corr_exp, precision=self.precision)
exact_log2_hi_exp.set_attributes(tag="exact_log2_hi_hex",
debug=debug_lftolx)
_result = exact_log2_hi_exp + pre_result
return _result, _poly, _log_inv_lo, _log_inv_hi, _red_vx
def generate_scheme(self):
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)
# testing special value inputs
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")
# if input is a signaling NaN, raise an invalid exception and returns
# a quiet NaN
return_snan = Statement(
ExpRaiseReturn(ML_FPE_Invalid,
return_value=FP_QNaN(self.precision)))
vx_exp = ExponentExtraction(vx, tag="vx_exp", debug=debugd)
int_precision = self.precision.get_integer_format()
# log2(vx)
# r = vx_mant
# e = vx_exp
# vx reduced to r in [1, 2[
# log2(vx) = log2(r * 2^e)
# = log2(r) + e
#
## log2(r) is approximated by
# log2(r) = log2(inv_seed(r) * r / inv_seed(r)
# = log2(inv_seed(r) * r) - log2(inv_seed(r))
# inv_seed(r) in ]1/2, 1] => log2(inv_seed(r)) in ]-1, 0]
#
# inv_seed(r) * r ~ 1
# we can easily tabulate -log2(inv_seed(r))
#
# 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
log_table = ML_NewTable(dimensions=[2**table_index_size, 2],
storage_precision=self.precision,
tag=self.uniquify_name("inv_table"))
# value for index 0 is set to 0.0
log_table[0][0] = 0.0
log_table[0][1] = 0.0
for i in range(1, 2**table_index_size):
#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
#print inv_approx_table[i][0], inv_value
inv_value = inv_approx_table[i][0]
value_high_bitsize = self.precision.get_field_size() - (
self.precision.get_exponent_size() + 1)
value_high = round(log2(inv_value), value_high_bitsize, sollya.RN)
value_low = round(
log2(inv_value) - value_high, sollya_precision, sollya.RN)
log_table[i][0] = value_high
log_table[i][1] = value_low
def compute_log(_vx, exp_corr_factor=None):
_vx_mant = MantissaExtraction(_vx,
tag="_vx_mant",
precision=self.precision,
debug=debug_lftolx)
_vx_exp = ExponentExtraction(_vx, tag="_vx_exp", debug=debugd)
# The main table is indexed by the 7 most significant bits
# of the mantissa
table_index = inv_approx_table.index_function(_vx_mant)
table_index.set_attributes(tag="table_index", debug=debuglld)
# argument reduction
# Using AND -2 to exclude LSB set to 1 for Newton-Raphson convergence
# 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)
_red_vx = FMA(arg_red_index, _vx_mant, -1.0)
_red_vx.set_attributes(tag="_red_vx", debug=debug_lftolx)
inv_err = S2**-inv_approx_table.index_size
red_interval = Interval(1 - inv_err, 1 + inv_err)
# 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)
Log.report(Log.Verbose, "building mathematical polynomial")
approx_interval = Interval(-inv_err, inv_err)
poly_degree = sup(
guessdegree(
log2(1 + sollya.x) / sollya.x, approx_interval, S2**
-(self.precision.get_field_size() * 1.1))) + 1
sollya.settings.display = sollya.hexadecimal
global_poly_object, approx_error = Polynomial.build_from_approximation_with_error(
log2(1 + sollya.x) / sollya.x,
poly_degree, [self.precision] * (poly_degree + 1),
approx_interval,
sollya.absolute,
error_function=lambda p, f, ai, mod, t: sollya.dirtyinfnorm(
p - f, ai))
Log.report(
Log.Info, "poly_degree={}, approx_error={}".format(
poly_degree, approx_error))
poly_object = global_poly_object.sub_poly(start_index=1, offset=1)
#poly_object = global_poly_object.sub_poly(start_index=0,offset=0)
Attributes.set_default_silent(True)
Attributes.set_default_rounding_mode(ML_RoundToNearest)
Log.report(Log.Verbose, "generating polynomial evaluation scheme")
pre_poly = PolynomialSchemeEvaluator.generate_horner_scheme(
poly_object, _red_vx, unified_precision=self.precision)
_poly = FMA(pre_poly, _red_vx,
global_poly_object.get_cst_coeff(0, self.precision))
_poly.set_attributes(tag="poly", debug=debug_lftolx)
Log.report(
Log.Verbose, "sollya global_poly_object: {}".format(
global_poly_object.get_sollya_object()))
Log.report(
Log.Verbose, "sollya poly_object: {}".format(
poly_object.get_sollya_object()))
corr_exp = _vx_exp if exp_corr_factor == None else _vx_exp + exp_corr_factor
Attributes.unset_default_rounding_mode()
Attributes.unset_default_silent()
pre_result = -_log_inv_hi + (_red_vx * _poly + (-_log_inv_lo))
pre_result.set_attributes(tag="pre_result", debug=debug_lftolx)
exact_log2_hi_exp = Conversion(corr_exp, precision=self.precision)
exact_log2_hi_exp.set_attributes(tag="exact_log2_hi_hex",
debug=debug_lftolx)
_result = exact_log2_hi_exp + pre_result
return _result, _poly, _log_inv_lo, _log_inv_hi, _red_vx
result, poly, log_inv_lo, log_inv_hi, red_vx = compute_log(vx)
result.set_attributes(tag="result", debug=debug_lftolx)
# specific input value predicate
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="vx_snan")
vx_inf = Test(vx,
specifier=Test.IsInfty,
likely=False,
debug=debugd,
tag="vx_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)
# Specific specific for the case exp == -1
# log2(x) = log2(m) - 1
#
# as m in [1, 2[, log2(m) in [0, 1[
# if r is close to 2, a catastrophic cancellation can occur
#
# r = seed(m)
# log2(x) = log2(seed(m) * m / seed(m)) - 1
# = log2(seed(m) * m) - log2(seed(m)) - 1
#
# for m really close to 2 => seed(m) = 0.5
# => log2(x) = log2(0.5 * m)
# =
result_exp_m1 = (-log_inv_hi - 1.0) + FMA(poly, red_vx, -log_inv_lo)
result_exp_m1.set_attributes(tag="result_exp_m1", debug=debug_lftolx)
m100 = -100
S2100 = Constant(S2**100, precision=self.precision)
result_subnormal, _, _, _, _ = compute_log(vx * S2100,
exp_corr_factor=m100)
result_subnormal.set_attributes(tag="result_subnormal",
debug=debug_lftolx)
one_err = S2**-7
approx_interval_one = Interval(-one_err, one_err)
red_vx_one = vx - 1.0
poly_degree_one = sup(
guessdegree(
log(1 + x) / x, approx_interval_one, S2**
-(self.precision.get_field_size() + 1))) + 1
poly_object_one = Polynomial.build_from_approximation(
log(1 + sollya.x) / sollya.x, poly_degree_one,
[self.precision] * (poly_degree_one + 1), approx_interval_one,
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
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)),
),
Statement(ClearException(), result_subnormal,
Return(result_subnormal))),
ConditionBlock(
vx_one,
Statement(
ClearException(),
Return(FP_PlusZero(self.precision)),
),
ConditionBlock(exp_mone, Return(result_exp_m1),
Return(result))))))
scheme = Statement(result, pre_scheme)
return scheme
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 piecewise_approximation(function,
variable,
precision,
bound_low=-1.0,
bound_high=1.0,
num_intervals=16,
max_degree=2,
error_threshold=S2**-24,
odd=False,
even=False):
""" Generate a piecewise approximation
:param function: function to be approximated
:type function: SollyaObject
:param variable: input variable
:type variable: Variable
:param precision: variable's format
:type precision: ML_Format
:param bound_low: lower bound for the approximation interval
:param bound_high: upper bound for the approximation interval
:param num_intervals: number of sub-interval / sub-division of the main interval
:param max_degree: maximum degree for an approximation on any sub-interval
:param error_threshold: error bound for an approximation on any sub-interval
:return: pair (scheme, error) where scheme is a graph node for an
approximation scheme of function evaluated at variable, and error
is the maximum approximation error encountered
:rtype tuple(ML_Operation, SollyaObject): """
degree_generator = piecewise_approximation_degree_generator(
function,
bound_low,
bound_high,
num_intervals=num_intervals,
error_threshold=error_threshold,
)
degree_list = list(degree_generator)
# if max_degree is None then we determine it locally
if max_degree is None:
max_degree = max(degree_list)
# table to store coefficients of the approximation on each segment
coeff_table = ML_NewTable(
dimensions=[num_intervals, max_degree + 1],
storage_precision=precision,
tag="coeff_table",
const=True # by default all approximation coeff table are const
)
error_function = lambda p, f, ai, mod, t: sollya.dirtyinfnorm(p - f, ai)
max_approx_error = 0.0
interval_size = (bound_high - bound_low) / num_intervals
for i in range(num_intervals):
subint_low = bound_low + i * interval_size
subint_high = bound_low + (i + 1) * interval_size
local_function = function(sollya.x + subint_low)
local_interval = Interval(-interval_size, interval_size)
local_degree = degree_list[i]
if local_degree > max_degree:
Log.report(
Log.Warning,
"local_degree {} exceeds max_degree bound ({}) in piecewise_approximation",
local_degree, max_degree)
# as max_degree defines the size of the table we can use
# it as the degree for each sub-interval polynomial
# as there is nothing to gain (yet) by using a smaller polynomial
degree = max_degree # min(max_degree, local_degree)
if function(subint_low) == 0.0:
# if the lower bound is a zero to the function, we
# need to force value=0 for the constant coefficient
# and extend the approximation interval
local_poly_degree_list = list(
range(1 if even else 0, degree + 1, 2 if odd or even else 1))
poly_object, approx_error = Polynomial.build_from_approximation_with_error(
function(sollya.x) / sollya.x,
local_poly_degree_list,
[precision] * len(local_poly_degree_list),
Interval(-subint_high * 0.95, subint_high),
sollya.absolute,
error_function=error_function)
# multiply by sollya.x
poly_object = poly_object.sub_poly(offset=-1)
else:
try:
poly_object, approx_error = Polynomial.build_from_approximation_with_error(
local_function,
degree, [precision] * (degree + 1),
local_interval,
sollya.absolute,
error_function=error_function)
except SollyaError as err:
# try to see if function is constant on the interval (possible
# failure cause for fpminmax)
cst_value = precision.round_sollya_object(
function(subint_low), sollya.RN)
accuracy = error_threshold
diff_with_cst_range = sollya.supnorm(cst_value, local_function,
local_interval,
sollya.absolute, accuracy)
diff_with_cst = sup(abs(diff_with_cst_range))
if diff_with_cst < error_threshold:
Log.report(Log.Info, "constant polynomial detected")
poly_object = Polynomial([function(subint_low)] +
[0] * degree)
approx_error = diff_with_cst
else:
Log.report(
Log.error,
"degree: {} for index {}, diff_with_cst={} (vs error_threshold={}) ",
degree,
i,
diff_with_cst,
error_threshold,
error=err)
for ci in range(max_degree + 1):
if ci in poly_object.coeff_map:
coeff_table[i][ci] = poly_object.coeff_map[ci]
else:
coeff_table[i][ci] = 0.0
if approx_error > error_threshold:
Log.report(
Log.Warning,
"piecewise_approximation on index {} exceeds error threshold: {} > {}",
i, approx_error, error_threshold)
max_approx_error = max(max_approx_error, abs(approx_error))
# computing offset
diff = Subtraction(variable,
Constant(bound_low, precision=precision),
tag="diff",
debug=debug_multi,
precision=precision)
int_prec = precision.get_integer_format()
# delta = bound_high - bound_low
delta_ratio = Constant(num_intervals / (bound_high - bound_low),
precision=precision)
# computing table index
# index = nearestint(diff / delta * <num_intervals>)
index = Max(0,
Min(
NearestInteger(
Multiplication(diff, delta_ratio, precision=precision),
precision=int_prec,
), num_intervals - 1),
tag="index",
debug=debug_multi,
precision=int_prec)
poly_var = Subtraction(diff,
Multiplication(
Conversion(index, precision=precision),
Constant(interval_size, precision=precision)),
precision=precision,
tag="poly_var",
debug=debug_multi)
# generating indexed polynomial
coeffs = [(ci, TableLoad(coeff_table, index, ci))
for ci in range(max_degree + 1)][::-1]
poly_scheme = PolynomialSchemeEvaluator.generate_horner_scheme2(
coeffs, poly_var, precision, {}, precision)
return poly_scheme, max_approx_error
