def generate_scheme(self):
# declaring function input variable
vx = self.implementation.add_input_variable("x", self.precision)
vy = self.implementation.add_input_variable("y", self.precision)
Cst0 = Constant(5, precision=self.precision)
Cst1 = Constant(7, precision=self.precision)
comp = Comparison(vx,
vy,
specifier=Comparison.Greater,
precision=ML_Bool,
tag="comp")
comp_eq = Comparison(vx,
vy,
specifier=Comparison.Equal,
precision=ML_Bool,
tag="comp_eq")
scheme = Statement(
ConditionBlock(
comp, Return(vy, precision=self.precision),
ConditionBlock(
comp_eq,
Return(vx + vy * Cst0 - Cst1, precision=self.precision))),
ConditionBlock(comp_eq, Return(Cst1 * vy,
precision=self.precision)),
Return(vx * vy, precision=self.precision))
return scheme
def get_output_check_statement(output_signal, output_tag, output_value):
""" Generate output value check statement """
test_pass_cond = Comparison(
output_signal,
output_value,
specifier=Comparison.Equal,
precision=ML_Bool
)
check_statement = ConditionBlock(
LogicalNot(
test_pass_cond,
precision = ML_Bool
),
Report(
Concatenation(
" result for {}: ".format(output_tag),
Conversion(
output_signal if output_signal.get_precision() is ML_StdLogic else
TypeCast(
output_signal,
precision=ML_StdLogicVectorFormat(
output_signal.get_precision().get_bit_size()
)
),
precision = ML_String
),
precision = ML_String
)
)
)
return test_pass_cond, check_statement
def minmax_legalizer(optree):
op0 = optree.get_input(0)
op1 = optree.get_input(1)
bool_prec = get_compatible_bool_format(optree)
comp = Comparison(op0,
op1,
specifier=predicate,
precision=bool_prec,
tag="minmax_pred")
# forward_stage_attributes(optree, comp)
result = Select(comp, op0, op1, precision=optree.get_precision())
forward_attributes(optree, result)
return result
def legalize_vector_reduction_test(optree):
""" Legalize a vector test (e.g. IsMaskNotAnyZero) to a sub-graph
of basic operations """
op_input = optree.get_input(0)
vector_size = op_input.get_precision().get_vector_size()
conv_format = {
2: v2int32,
4: v4int32,
8: v8int32,
}[vector_size]
cast_format = {
2: ML_Int64,
4: ML_Int128,
8: ML_Int256,
}[vector_size]
return Comparison(TypeCast(Conversion(op_input, precision=conv_format),
precision=cast_format),
Constant(0, precision=cast_format),
specifier=Comparison.Equal,
precision=ML_Bool)
def generate_pipeline_stage(entity):
""" Process a entity to generate pipeline stages required """
retiming_map = {}
retime_map = RetimeMap()
output_assign_list = entity.implementation.get_output_assign()
for output in output_assign_list:
Log.report(
Log.Verbose,
"generating pipeline from output %s " % (output.get_str(depth=1)))
retime_op(output, retime_map)
process_statement = Statement()
# adding stage forward process
clk = entity.get_clk_input()
clock_statement = Statement()
for stage_id in sorted(retime_map.stage_forward.keys()):
stage_statement = Statement(*tuple(
assign for assign in retime_map.stage_forward[stage_id]))
clock_statement.add(stage_statement)
# To meet simulation / synthesis tools, we build
# a single if clock predicate block which contains all
# the stage register allocation
clock_block = ConditionBlock(
LogicalAnd(Event(clk, precision=ML_Bool),
Comparison(clk,
Constant(1, precision=ML_StdLogic),
specifier=Comparison.Equal,
precision=ML_Bool),
precision=ML_Bool), clock_statement)
process_statement.add(clock_block)
pipeline_process = Process(process_statement, sensibility_list=[clk])
for op in retime_map.pre_statement:
pipeline_process.add_to_pre_statement(op)
entity.implementation.add_process(pipeline_process)
stage_num = len(retime_map.stage_forward.keys())
#print "there are %d pipeline stages" % (stage_num)
return stage_num
def mantissa_extraction_modifier(optree):
""" Legalizing a MantissaExtraction node into a sub-graph
of basic operation """
init_stage = optree.attributes.get_dyn_attribute("init_stage")
op = optree.get_input(0)
tag = optree.get_tag() or "mant_extr"
op_precision = op.get_precision().get_base_format()
exp_prec = ML_StdLogicVectorFormat(op_precision.get_exponent_size())
field_prec = ML_StdLogicVectorFormat(op_precision.get_field_size())
exp_op = RawExponentExtraction(op,
precision=exp_prec,
init_stage=init_stage,
tag=tag + "_exp_extr")
field_op = SubSignalSelection(TypeCast(
op,
precision=op.get_precision().get_support_format(),
init_stage=init_stage,
tag=tag + "_field_cast"),
0,
op_precision.get_field_size() - 1,
precision=field_prec,
init_stage=init_stage,
tag=tag + "_field")
exp_is_zero = Comparison(exp_op,
Constant(op_precision.get_zero_exponent_value(),
precision=exp_prec,
init_stage=init_stage),
precision=ML_Bool,
specifier=Comparison.Equal,
init_stage=init_stage)
result = mantissa_extraction_modifier_from_fields(op, field_op,
exp_is_zero)
forward_attributes(optree, result)
return result
def legalize_mp_2elt_comparison(optree):
""" Transform comparison on ML_Compound_FP_Format object into
comparison on sub-fields """
specifier = optree.specifier
lhs = optree.get_input(0)
rhs = optree.get_input(1)
# TODO/FIXME: assume than multi-limb operand are normalized
if specifier == Comparison.Equal:
return LogicalAnd(
Comparison(lhs.hi, rhs.hi, specifier=Comparison.Equal, precision=ML_Bool),
Comparison(lhs.lo, rhs.lo, specifier=Comparison.Equal, precision=ML_Bool),
precision=ML_Bool
)
elif specifier == Comparison.NotEqual:
return LogicalOr(
Comparison(lhs.hi, rhs.hi, specifier=Comparison.NotEqual, precision=ML_Bool),
Comparison(lhs.lo, rhs.lo, specifier=Comparison.NotEqual, precision=ML_Bool),
precision=ML_Bool
)
elif specifier in [Comparison.Less, Comparison.Greater, Comparison.GreaterOrEqual, Comparison.LessOrEqual]:
strict_specifier = {
Comparison.Less: Comparison.Less,
Comparison.Greater: Comparison.Greater,
Comparison.LessOrEqual: Comparison.Less,
Comparison.GreaterOrEqual: Comparison.Greater
}[specifier]
return LogicalOr(
Comparison(lhs.hi, rhs.hi, specifier=strict_specifier, precision=ML_Bool),
LogicalAnd(
Comparison(lhs.hi, rhs.hi, specifier=Comparison.Equal, precision=ML_Bool),
Comparison(lhs.lo, rhs.lo, specifier=specifier, precision=ML_Bool),
precision=ML_Bool
),
precision=ML_Bool
)
else:
Log.report(Log.Error, "unsupported specifier {} in legalize_mp_2elt_comparison", specifier)
def generate_scheme(self):
# declaring target and instantiating optimization engine
vx = self.implementation.add_input_variable("x", self.precision)
Log.set_dump_stdout(True)
Log.report(Log.Info,
"\033[33;1m generating implementation scheme \033[0m")
if self.debug_flag:
Log.report(Log.Info, "\033[31;1m debug has been enabled \033[0;m")
# local overloading of RaiseReturn operation
def ExpRaiseReturn(*args, **kwords):
kwords["arg_value"] = vx
kwords["function_name"] = self.function_name
if self.libm_compliant:
return RaiseReturn(*args, precision=self.precision, **kwords)
else:
return Return(kwords["return_value"], precision=self.precision)
test_nan_or_inf = Test(vx,
specifier=Test.IsInfOrNaN,
likely=False,
debug=debug_multi,
tag="nan_or_inf")
test_nan = Test(vx,
specifier=Test.IsNaN,
debug=debug_multi,
tag="is_nan_test")
test_positive = Comparison(vx,
0,
specifier=Comparison.GreaterOrEqual,
debug=debug_multi,
tag="inf_sign")
test_signaling_nan = Test(vx,
specifier=Test.IsSignalingNaN,
debug=debug_multi,
tag="is_signaling_nan")
return_snan = Statement(
ExpRaiseReturn(ML_FPE_Invalid,
return_value=FP_QNaN(self.precision)))
# return in case of infinity input
infty_return = Statement(
ConditionBlock(
test_positive,
Return(FP_PlusInfty(self.precision), precision=self.precision),
Return(FP_PlusZero(self.precision), precision=self.precision)))
# return in case of specific value input (NaN or inf)
specific_return = ConditionBlock(
test_nan,
ConditionBlock(
test_signaling_nan, return_snan,
Return(FP_QNaN(self.precision), precision=self.precision)),
infty_return)
# return in case of standard (non-special) input
# exclusion of early overflow and underflow cases
precision_emax = self.precision.get_emax()
precision_max_value = S2 * S2**precision_emax
exp_overflow_bound = sollya.ceil(log(precision_max_value))
early_overflow_test = Comparison(vx,
exp_overflow_bound,
likely=False,
specifier=Comparison.Greater)
early_overflow_return = Statement(
ClearException() if self.libm_compliant else Statement(),
ExpRaiseReturn(ML_FPE_Inexact,
ML_FPE_Overflow,
return_value=FP_PlusInfty(self.precision)))
precision_emin = self.precision.get_emin_subnormal()
precision_min_value = S2**precision_emin
exp_underflow_bound = floor(log(precision_min_value))
early_underflow_test = Comparison(vx,
exp_underflow_bound,
likely=False,
specifier=Comparison.Less)
early_underflow_return = Statement(
ClearException() if self.libm_compliant else Statement(),
ExpRaiseReturn(ML_FPE_Inexact,
ML_FPE_Underflow,
return_value=FP_PlusZero(self.precision)))
# constant computation
invlog2 = self.precision.round_sollya_object(1 / log(2), sollya.RN)
interval_vx = Interval(exp_underflow_bound, exp_overflow_bound)
interval_fk = interval_vx * invlog2
interval_k = Interval(floor(inf(interval_fk)),
sollya.ceil(sup(interval_fk)))
log2_hi_precision = self.precision.get_field_size() - (
sollya.ceil(log2(sup(abs(interval_k)))) + 2)
Log.report(Log.Info, "log2_hi_precision: %d" % log2_hi_precision)
invlog2_cst = Constant(invlog2, precision=self.precision)
log2_hi = round(log(2), log2_hi_precision, sollya.RN)
log2_lo = self.precision.round_sollya_object(
log(2) - log2_hi, sollya.RN)
# argument reduction
unround_k = vx * invlog2
unround_k.set_attributes(tag="unround_k", debug=debug_multi)
k = NearestInteger(unround_k,
precision=self.precision,
debug=debug_multi)
ik = NearestInteger(unround_k,
precision=self.precision.get_integer_format(),
debug=debug_multi,
tag="ik")
ik.set_tag("ik")
k.set_tag("k")
exact_pre_mul = (k * log2_hi)
exact_pre_mul.set_attributes(exact=True)
exact_hi_part = vx - exact_pre_mul
exact_hi_part.set_attributes(exact=True,
tag="exact_hi",
debug=debug_multi,
prevent_optimization=True)
exact_lo_part = -k * log2_lo
exact_lo_part.set_attributes(tag="exact_lo",
debug=debug_multi,
prevent_optimization=True)
r = exact_hi_part + exact_lo_part
r.set_tag("r")
r.set_attributes(debug=debug_multi)
approx_interval = Interval(-log(2) / 2, log(2) / 2)
approx_interval_half = approx_interval / 2
approx_interval_split = [
Interval(-log(2) / 2, inf(approx_interval_half)),
approx_interval_half,
Interval(sup(approx_interval_half),
log(2) / 2)
]
# TODO: should be computed automatically
exact_hi_interval = approx_interval
exact_lo_interval = -interval_k * log2_lo
opt_r = self.optimise_scheme(r, copy={})
tag_map = {}
self.opt_engine.register_nodes_by_tag(opt_r, tag_map)
cg_eval_error_copy_map = {
vx:
Variable("x", precision=self.precision, interval=interval_vx),
tag_map["k"]:
Variable("k", interval=interval_k, precision=self.precision)
}
#try:
if is_gappa_installed():
eval_error = self.gappa_engine.get_eval_error_v2(
self.opt_engine,
opt_r,
cg_eval_error_copy_map,
gappa_filename="red_arg.g")
else:
eval_error = 0.0
Log.report(Log.Warning,
"gappa is not installed in this environnement")
Log.report(Log.Info, "eval error: %s" % eval_error)
local_ulp = sup(ulp(sollya.exp(approx_interval), self.precision))
# FIXME refactor error_goal from accuracy
Log.report(Log.Info, "accuracy: %s" % self.accuracy)
if isinstance(self.accuracy, ML_Faithful):
error_goal = local_ulp
elif isinstance(self.accuracy, ML_CorrectlyRounded):
error_goal = S2**-1 * local_ulp
elif isinstance(self.accuracy, ML_DegradedAccuracyAbsolute):
error_goal = self.accuracy.goal
elif isinstance(self.accuracy, ML_DegradedAccuracyRelative):
error_goal = self.accuracy.goal
else:
Log.report(Log.Error, "unknown accuracy: %s" % self.accuracy)
# error_goal = local_ulp #S2**-(self.precision.get_field_size()+1)
error_goal_approx = S2**-1 * error_goal
Log.report(Log.Info,
"\033[33;1m building mathematical polynomial \033[0m\n")
poly_degree = max(
sup(
guessdegree(
expm1(sollya.x) / sollya.x, approx_interval,
error_goal_approx)) - 1, 2)
init_poly_degree = poly_degree
error_function = lambda p, f, ai, mod, t: dirtyinfnorm(f - p, ai)
polynomial_scheme_builder = PolynomialSchemeEvaluator.generate_estrin_scheme
#polynomial_scheme_builder = PolynomialSchemeEvaluator.generate_horner_scheme
while 1:
Log.report(Log.Info, "attempting poly degree: %d" % poly_degree)
precision_list = [1] + [self.precision] * (poly_degree)
poly_object, poly_approx_error = Polynomial.build_from_approximation_with_error(
expm1(sollya.x),
poly_degree,
precision_list,
approx_interval,
sollya.absolute,
error_function=error_function)
Log.report(Log.Info, "polynomial: %s " % poly_object)
sub_poly = poly_object.sub_poly(start_index=2)
Log.report(Log.Info, "polynomial: %s " % sub_poly)
Log.report(Log.Info, "poly approx error: %s" % poly_approx_error)
Log.report(
Log.Info,
"\033[33;1m generating polynomial evaluation scheme \033[0m")
pre_poly = polynomial_scheme_builder(
poly_object, r, unified_precision=self.precision)
pre_poly.set_attributes(tag="pre_poly", debug=debug_multi)
pre_sub_poly = polynomial_scheme_builder(
sub_poly, r, unified_precision=self.precision)
pre_sub_poly.set_attributes(tag="pre_sub_poly", debug=debug_multi)
poly = 1 + (exact_hi_part + (exact_lo_part + pre_sub_poly))
poly.set_tag("poly")
# optimizing poly before evaluation error computation
#opt_poly = self.opt_engine.optimization_process(poly, self.precision, fuse_fma = fuse_fma)
#opt_sub_poly = self.opt_engine.optimization_process(pre_sub_poly, self.precision, fuse_fma = fuse_fma)
opt_poly = self.optimise_scheme(poly)
opt_sub_poly = self.optimise_scheme(pre_sub_poly)
# evaluating error of the polynomial approximation
r_gappa_var = Variable("r",
precision=self.precision,
interval=approx_interval)
exact_hi_gappa_var = Variable("exact_hi",
precision=self.precision,
interval=exact_hi_interval)
exact_lo_gappa_var = Variable("exact_lo",
precision=self.precision,
interval=exact_lo_interval)
vx_gappa_var = Variable("x",
precision=self.precision,
interval=interval_vx)
k_gappa_var = Variable("k",
interval=interval_k,
precision=self.precision)
#print "exact_hi interval: ", exact_hi_interval
sub_poly_error_copy_map = {
#r.get_handle().get_node(): r_gappa_var,
#vx.get_handle().get_node(): vx_gappa_var,
exact_hi_part.get_handle().get_node():
exact_hi_gappa_var,
exact_lo_part.get_handle().get_node():
exact_lo_gappa_var,
#k.get_handle().get_node(): k_gappa_var,
}
poly_error_copy_map = {
exact_hi_part.get_handle().get_node(): exact_hi_gappa_var,
exact_lo_part.get_handle().get_node(): exact_lo_gappa_var,
}
if is_gappa_installed():
sub_poly_eval_error = -1.0
sub_poly_eval_error = self.gappa_engine.get_eval_error_v2(
self.opt_engine,
opt_sub_poly,
sub_poly_error_copy_map,
gappa_filename="%s_gappa_sub_poly.g" % self.function_name)
dichotomy_map = [
{
exact_hi_part.get_handle().get_node():
approx_interval_split[0],
},
{
exact_hi_part.get_handle().get_node():
approx_interval_split[1],
},
{
exact_hi_part.get_handle().get_node():
approx_interval_split[2],
},
]
poly_eval_error_dico = self.gappa_engine.get_eval_error_v3(
self.opt_engine,
opt_poly,
poly_error_copy_map,
gappa_filename="gappa_poly.g",
dichotomy=dichotomy_map)
poly_eval_error = max(
[sup(abs(err)) for err in poly_eval_error_dico])
else:
poly_eval_error = 0.0
sub_poly_eval_error = 0.0
Log.report(Log.Warning,
"gappa is not installed in this environnement")
Log.report(Log.Info, "stopping autonomous degree research")
# incrementing polynomial degree to counteract initial decrementation effect
poly_degree += 1
break
Log.report(Log.Info, "poly evaluation error: %s" % poly_eval_error)
Log.report(Log.Info,
"sub poly evaluation error: %s" % sub_poly_eval_error)
global_poly_error = None
global_rel_poly_error = None
for case_index in range(3):
poly_error = poly_approx_error + poly_eval_error_dico[
case_index]
rel_poly_error = sup(
abs(poly_error /
sollya.exp(approx_interval_split[case_index])))
if global_rel_poly_error == None or rel_poly_error > global_rel_poly_error:
global_rel_poly_error = rel_poly_error
global_poly_error = poly_error
flag = error_goal > global_rel_poly_error
if flag:
break
else:
poly_degree += 1
late_overflow_test = Comparison(ik,
self.precision.get_emax(),
specifier=Comparison.Greater,
likely=False,
debug=debug_multi,
tag="late_overflow_test")
overflow_exp_offset = (self.precision.get_emax() -
self.precision.get_field_size() / 2)
diff_k = Subtraction(
ik,
Constant(overflow_exp_offset,
precision=self.precision.get_integer_format()),
precision=self.precision.get_integer_format(),
debug=debug_multi,
tag="diff_k",
)
late_overflow_result = (ExponentInsertion(
diff_k, precision=self.precision) * poly) * ExponentInsertion(
overflow_exp_offset, precision=self.precision)
late_overflow_result.set_attributes(silent=False,
tag="late_overflow_result",
debug=debug_multi,
precision=self.precision)
late_overflow_return = ConditionBlock(
Test(late_overflow_result, specifier=Test.IsInfty, likely=False),
ExpRaiseReturn(ML_FPE_Overflow,
return_value=FP_PlusInfty(self.precision)),
Return(late_overflow_result, precision=self.precision))
late_underflow_test = Comparison(k,
self.precision.get_emin_normal(),
specifier=Comparison.LessOrEqual,
likely=False)
underflow_exp_offset = 2 * self.precision.get_field_size()
corrected_exp = Addition(
ik,
Constant(underflow_exp_offset,
precision=self.precision.get_integer_format()),
precision=self.precision.get_integer_format(),
tag="corrected_exp")
late_underflow_result = (
ExponentInsertion(corrected_exp, precision=self.precision) *
poly) * ExponentInsertion(-underflow_exp_offset,
precision=self.precision)
late_underflow_result.set_attributes(debug=debug_multi,
tag="late_underflow_result",
silent=False)
test_subnormal = Test(late_underflow_result,
specifier=Test.IsSubnormal)
late_underflow_return = Statement(
ConditionBlock(
test_subnormal,
ExpRaiseReturn(ML_FPE_Underflow,
return_value=late_underflow_result)),
Return(late_underflow_result, precision=self.precision))
twok = ExponentInsertion(ik,
tag="exp_ik",
debug=debug_multi,
precision=self.precision)
#std_result = twok * ((1 + exact_hi_part * pre_poly) + exact_lo_part * pre_poly)
std_result = twok * poly
std_result.set_attributes(tag="std_result", debug=debug_multi)
result_scheme = ConditionBlock(
late_overflow_test, late_overflow_return,
ConditionBlock(late_underflow_test, late_underflow_return,
Return(std_result, precision=self.precision)))
std_return = ConditionBlock(
early_overflow_test, early_overflow_return,
ConditionBlock(early_underflow_test, early_underflow_return,
result_scheme))
# main scheme
Log.report(Log.Info, "\033[33;1m MDL scheme \033[0m")
scheme = ConditionBlock(
test_nan_or_inf,
Statement(ClearException() if self.libm_compliant else Statement(),
specific_return), std_return)
return scheme
def generate_pipeline_stage(entity,
reset=False,
recirculate=False,
one_process_per_stage=True):
""" Process a entity to generate pipeline stages required """
retiming_map = {}
retime_map = RetimeMap()
output_assign_list = entity.implementation.get_output_assign()
for output in output_assign_list:
Log.report(Log.Verbose, "generating pipeline from output {} ", output)
retime_op(output, retime_map)
for recirculate_stage in entity.recirculate_signal_map:
recirculate_ctrl = entity.recirculate_signal_map[recirculate_stage]
Log.report(Log.Verbose,
"generating pipeline from recirculation control signal {}",
recirculate_ctrl)
retime_op(recirculate_ctrl, retime_map)
process_statement = Statement()
# adding stage forward process
clk = entity.get_clk_input()
clock_statement = Statement()
# handle towards the first clock Process (in generation order)
# which must be the one whose pre_statement is filled with
# signal required to be generated outside the processes
first_process = False
for stage_id in sorted(retime_map.stage_forward.keys()):
stage_statement = Statement(*tuple(
assign for assign in retime_map.stage_forward[stage_id]))
if reset:
reset_statement = Statement()
for assign in retime_map.stage_forward[stage_id]:
target = assign.get_input(0)
reset_value = Constant(0, precision=target.get_precision())
reset_statement.push(ReferenceAssign(target, reset_value))
if recirculate:
# inserting recirculation condition
recirculate_signal = entity.get_recirculate_signal(stage_id)
stage_statement = ConditionBlock(
Comparison(
recirculate_signal,
Constant(0,
precision=recirculate_signal.get_precision()),
specifier=Comparison.Equal,
precision=ML_Bool), stage_statement)
stage_statement = ConditionBlock(
Comparison(entity.reset_signal,
Constant(1, precision=ML_StdLogic),
specifier=Comparison.Equal,
precision=ML_Bool), reset_statement,
stage_statement)
# To meet simulation / synthesis tools, we build
# a single if clock predicate block per stage
clock_block = ConditionBlock(
LogicalAnd(Event(clk, precision=ML_Bool),
Comparison(clk,
Constant(1, precision=ML_StdLogic),
specifier=Comparison.Equal,
precision=ML_Bool),
precision=ML_Bool), stage_statement)
if one_process_per_stage:
clock_process = Process(clock_block, sensibility_list=[clk])
entity.implementation.add_process(clock_process)
first_process = first_process or clock_process
else:
clock_statement.add(clock_block)
if one_process_per_stage:
pass
else:
process_statement.add(clock_statement)
pipeline_process = Process(process_statement, sensibility_list=[clk])
entity.implementation.add_process(pipeline_process)
first_process = pipeline_process
# statement that gather signals which must be pre-computed
for op in retime_map.pre_statement:
first_process.add_to_pre_statement(op)
stage_num = len(retime_map.stage_forward.keys())
#print "there are %d pipeline stages" % (stage_num)
return stage_num
def legalize_mp_3elt_comparison(optree):
""" Transform comparison on ML_Compound_FP_Format object into
comparison on sub-fields """
specifier = optree.specifier
lhs = optree.get_input(0)
rhs = optree.get_input(1)
# TODO/FIXME: assume than multi-limb operand are normalized
if specifier == Comparison.Equal:
# renormalize if not constant
lhs = lhs if is_constant(lhs) else BuildFromComponent(*Normalize_33(lhs.hi, lhs.me, lhs.lo, precision=lhs.precision.get_limb_precision(0)), precision=lhs.precision)
rhs = rhs if is_constant(rhs) else BuildFromComponent(*Normalize_33(rhs.hi, rhs.me, rhs.lo, precision=rhs.precision.get_limb_precision(0)), precision=rhs.precision)
return LogicalAnd(
Comparison(lhs.hi, rhs.hi, specifier=Comparison.Equal, precision=ML_Bool),
LogicalAnd(
Comparison(lhs.me, rhs.me, specifier=Comparison.Equal, precision=ML_Bool),
Comparison(lhs.lo, rhs.lo, specifier=Comparison.Equal, precision=ML_Bool),
precision=ML_Bool
),
precision=ML_Bool
)
elif specifier == Comparison.NotEqual:
# renormalize if not constant
lhs = lhs if is_constant(lhs) else BuildFromComponent(*Normalize_33(lhs.hi, lhs.me, lhs.lo, precision=lhs.precision.get_limb_precision(0)), precision=lhs.precision)
rhs = rhs if is_constant(rhs) else BuildFromComponent(*Normalize_33(rhs.hi, rhs.me, rhs.lo, precision=rhs.precision.get_limb_precision(0)), precision=rhs.precision)
return LogicalOr(
Comparison(lhs.hi, rhs.hi, specifier=Comparison.NotEqual, precision=ML_Bool),
LogicalOr(
Comparison(lhs.me, rhs.me, specifier=Comparison.NotEqual, precision=ML_Bool),
Comparison(lhs.lo, rhs.lo, specifier=Comparison.NotEqual, precision=ML_Bool),
precision=ML_Bool
),
precision=ML_Bool
)
elif specifier in [Comparison.LessOrEqual, Comparison.GreaterOrEqual, Comparison.Greater, Comparison.Less]:
strict_specifier = {
Comparison.Less: Comparison.Less,
Comparison.Greater: Comparison.Greater,
Comparison.LessOrEqual: Comparison.Less,
Comparison.GreaterOrEqual: Comparison.Greater
}[specifier]
# renormalize if not constant
lhs = lhs if is_constant(lhs) else BuildFromComponent(*Normalize_33(lhs.hi, lhs.me, lhs.lo, precision=lhs.precision.get_limb_precision(0)), precision=lhs.precision)
rhs = rhs if is_constant(rhs) else BuildFromComponent(*Normalize_33(rhs.hi, rhs.me, rhs.lo, precision=rhs.precision.get_limb_precision(0)), precision=rhs.precision)
return LogicalOr(
Comparison(lhs.hi, rhs.hi, specifier=strict_specifier, precision=ML_Bool),
LogicalAnd(
Comparison(lhs.hi, rhs.hi, specifier=Comparison.Equal, precision=ML_Bool),
LogicalOr(
Comparison(lhs.me, rhs.me, specifier=strict_specifier, precision=ML_Bool),
LogicalAnd(
Comparison(lhs.me, rhs.me, specifier=Comparison.Equal, precision=ML_Bool),
Comparison(lhs.lo, rhs.lo, specifier=specifier, precision=ML_Bool),
precision=ML_Bool
),
precision=ML_Bool
),
precision=ML_Bool
),
precision=ML_Bool
)
else:
Log.report(Log.Error, "unsupported specifier {} in legalize_mp_2elt_comparison", specifier)
def legalize_test(optree):
""" transform a Test optree into a sequence of basic
node """
op_input = optree.get_input(0)
predicate = optree.specifier
test_bool_format = get_compatible_bool_format(op_input)
if op_input.precision.is_vector_format():
input_scalar_precision = op_input.precision.get_scalar_format()
vector_size = op_input.precision.get_vector_size()
int_precision = {
ML_Int32: {
2: v2int32,
4: v4int32,
8: v8int32,
},
ML_Int64: {
2: v2int64,
4: v4int64,
8: v8int64,
}
}[input_scalar_precision.get_integer_format()][vector_size]
nanorinf_cst = [input_scalar_precision.get_nanorinf_exp_field()
] * vector_size
zero_cst = [0] * vector_size
one_cst = [1] * vector_size
else:
input_scalar_precision = op_input.precision
int_precision = input_scalar_precision.get_integer_format()
nanorinf_cst = input_scalar_precision.get_nanorinf_exp_field()
zero_cst = 0
one_cst = 1
if predicate is Test.IsInfOrNaN:
return Comparison(generate_exp_extraction(op_input),
Constant(nanorinf_cst, precision=int_precision),
specifier=Comparison.Equal,
precision=test_bool_format)
elif predicate is Test.IsNaN:
return LogicalAnd(Comparison(generate_exp_extraction(op_input),
Constant(nanorinf_cst,
precision=int_precision),
specifier=Comparison.Equal,
precision=test_bool_format),
Comparison(
generate_raw_mantissa_extraction(op_input),
Constant(zero_cst, precision=int_precision),
specifier=Comparison.NotEqual,
precision=test_bool_format),
precision=test_bool_format)
elif predicate is Test.IsSubnormal:
return Comparison(generate_exp_extraction(op_input),
Constant(zero_cst, precision=int_precision),
specifier=Comparison.Equal,
precision=test_bool_format)
elif predicate is Test.IsSignalingNaN:
quiet_bit_index = input_scalar_precision.get_field_size() - 1
return LogicalAnd(
Comparison(generate_exp_extraction(op_input),
Constant(nanorinf_cst, precision=int_precision),
specifier=Comparison.Equal,
precision=test_bool_format),
LogicalAnd(Comparison(generate_raw_mantissa_extraction(op_input),
Constant(zero_cst, precision=int_precision),
specifier=Comparison.NotEqual,
precision=test_bool_format),
Comparison(generate_field_extraction(
op_input, int_precision, quiet_bit_index,
quiet_bit_index),
Constant(zero_cst, precision=int_precision),
specifier=Comparison.Equal,
precision=test_bool_format),
precision=test_bool_format),
precision=test_bool_format)
elif predicate is Test.IsQuietNaN:
quiet_bit_index = input_scalar_precision.get_field_size() - 1
return LogicalAnd(
Comparison(generate_exp_extraction(op_input),
Constant(nanorinf_cst, precision=int_precision),
specifier=Comparison.Equal,
precision=test_bool_format),
LogicalAnd(Comparison(generate_raw_mantissa_extraction(op_input),
Constant(zero_cst, precision=int_precision),
specifier=Comparison.NotEqual,
precision=test_bool_format),
Comparison(generate_field_extraction(
op_input, int_precision, quiet_bit_index,
quiet_bit_index),
Constant(one_cst, precision=int_precision),
specifier=Comparison.Equal,
precision=test_bool_format),
precision=test_bool_format),
precision=test_bool_format)
elif predicate is Test.IsInfty:
return LogicalAnd(Comparison(generate_exp_extraction(op_input),
Constant(nanorinf_cst,
precision=int_precision),
specifier=Comparison.Equal,
precision=test_bool_format),
Comparison(
generate_raw_mantissa_extraction(op_input),
Constant(zero_cst, precision=int_precision),
specifier=Comparison.Equal,
precision=test_bool_format),
precision=test_bool_format)
else:
Log.report(Log.Error,
"unsupported predicate {}".format(predicate),
error=NotImplementedError)
def generate_scheme(self):
# declaring function input variable
vx = self.implementation.add_input_variable("x", ML_Binary32)
# declaring specific interval for input variable <x>
vx.set_interval(Interval(-1, 1))
# declaring free Variable y
vy = Variable("y", precision=ML_Exact)
# declaring expression with vx variable
expr = vx * vx - vx * 2
# declaring second expression with vx variable
expr2 = vx * vx - vx
# optimizing expressions (defining every unknown precision as the
# default one + some optimization as FMA merging if enabled)
opt_expr = self.optimise_scheme(expr)
opt_expr2 = self.optimise_scheme(expr2)
# setting specific tag name for optimized expression (to be extracted
# from gappa script )
opt_expr.set_tag("goal")
opt_expr2.set_tag("new_goal")
# defining default goal to gappa execution
gappa_goal = opt_expr
# declaring EXACT expression to be used as hint in Gappa's script
annotation = self.opt_engine.exactify(vy * (1 / vy))
# the dict var_bound is used to limit the DAG part to be explored when
# generating the gappa script, each pair (key, value), indicate a node to
# stop at <key>
# and a node to replace it with during the generation: <node>,
# <node> must be a Variable instance with defined interval
# vx.get_handle().get_node() is used to retrieve the node instanciating
# the abstract node <vx> after the call to self.optimise_scheme
var_bound = {
vx.get_handle().get_node():
Variable("x", precision=ML_Binary32, interval=vx.get_interval())
}
# generating gappa code to determine interval for <opt_expr>
# NOTES: var_bound must be converted from an iterator to a list to avoid
# implicit modification by get_interval_code
gappa_code = self.gappa_engine.get_interval_code(
[opt_expr], list(var_bound.keys()), var_bound)
# add a manual hint to the gappa code
# which state thtat vy * (1 / vy) -> 1 { vy <> 0 };
self.gappa_engine.add_hint(
gappa_code, annotation, Constant(1, precision=ML_Exact),
Comparison(vy,
Constant(0, precision=ML_Integer),
specifier=Comparison.NotEqual,
precision=ML_Bool))
# adding the expression <opt_expr2> as an extra goal in the gappa script
self.gappa_engine.add_goal(gappa_code, opt_expr2)
# executing gappa on the script generated from <gappa_code>
# extract the result and store them into <gappa_result>
# which is a dict indexed by the goals' tag
if is_gappa_installed():
gappa_result = execute_gappa_script_extract(
gappa_code.get(self.gappa_engine))
Log.report(Log.Info, "eval error: ", gappa_result["new_goal"])
else:
Log.report(
Log.Warning,
"gappa was not installed: unable to check execute_gappa_script_extract"
)
# dummy scheme to make functionnal code generation
scheme = Statement(Return(vx))
return scheme
def generate_pipeline_stage(entity, reset=False, recirculate=False, one_process_per_stage=True, synchronous_reset=True, negate_reset=False):
""" Process a entity to generate pipeline stages required to implement
pipeline structure described by node's stage attributes.
:param entity: input entity to pipeline
:type entity: ML_EntityBasis
:param reset: indicate if a reset must be generated for pipeline registers
:type reset: bool
:param recirculate: trigger the integration of a recirculation signal to the stage
flopping condition
:type recirculate: bool
:param one_process_per_stage:forces the generation of a separate process for each
pipeline stage (else a unique process is generated for all the stages
:type one_process_per_stage: bool
:param synchronous_reset: triggers the generation of a clocked reset
:type synchronous_reset: bool
:param negate_reset: if set indicates the reset is triggered when reset signal is 0
(else 1)
:type negate_reset: bool
"""
retiming_map = {}
retime_map = RetimeMap()
output_assign_list = entity.implementation.get_output_assign()
for output in output_assign_list:
Log.report(Log.Verbose, "generating pipeline from output {} ", output)
retime_op(output, retime_map)
for recirculate_stage in entity.recirculate_signal_map:
recirculate_ctrl = entity.recirculate_signal_map[recirculate_stage]
Log.report(Log.Verbose, "generating pipeline from recirculation control signal {}", recirculate_ctrl)
retime_op(recirculate_ctrl, retime_map)
process_statement = Statement()
# adding stage forward process
clk = entity.get_clk_input()
clock_statement = Statement()
global_reset_statement = Statement()
Log.report(Log.Info, "design has {} flip-flop(s).", retime_map.register_count)
# handle towards the first clock Process (in generation order)
# which must be the one whose pre_statement is filled with
# signal required to be generated outside the processes
first_process = False
for stage_id in sorted(retime_map.stage_forward.keys()):
stage_statement = Statement(
*tuple(assign for assign in retime_map.stage_forward[stage_id]))
if reset:
reset_statement = Statement()
for assign in retime_map.stage_forward[stage_id]:
target = assign.get_input(0)
reset_value = Constant(0, precision=target.get_precision())
reset_statement.push(ReferenceAssign(target, reset_value))
if recirculate:
# inserting recirculation condition
recirculate_signal = entity.get_recirculate_signal(stage_id)
stage_statement = ConditionBlock(
Comparison(
recirculate_signal,
Constant(0, precision=recirculate_signal.get_precision()),
specifier=Comparison.Equal,
precision=ML_Bool
),
stage_statement
)
if synchronous_reset:
# build a compound statement with reset and flops statement
stage_statement = ConditionBlock(
Comparison(
entity.reset_signal,
Constant(0 if negate_reset else 1, precision=ML_StdLogic),
specifier=Comparison.Equal, precision=ML_Bool
),
reset_statement,
stage_statement
)
else:
# for asynchronous reset, reset is in a non-clocked statement
# and will be added at the end of stage to the same process than
# register clocking
global_reset_statement.add(reset_statement)
# To meet simulation / synthesis tools, we build
# a single if clock predicate block per stage
clock_block = ConditionBlock(
LogicalAnd(
Event(clk, precision=ML_Bool),
Comparison(
clk,
Constant(1, precision=ML_StdLogic),
specifier=Comparison.Equal,
precision=ML_Bool
),
precision=ML_Bool
),
stage_statement
)
if one_process_per_stage:
if reset and not synchronous_reset:
clock_block = ConditionBlock(
Comparison(
entity.reset_signal,
Constant(0 if negate_reset else 1, precision=ML_StdLogic),
specifier=Comparison.Equal, precision=ML_Bool
),
reset_statement,
clock_block
)
clock_process = Process(clock_block, sensibility_list=[clk, entity.reset_signal])
else:
# no reset, or synchronous reset (already appended to clock_block)
clock_process = Process(clock_block, sensibility_list=[clk])
entity.implementation.add_process(clock_process)
first_process = first_process or clock_process
else:
clock_statement.add(clock_block)
if one_process_per_stage:
# reset and clock processed where generated at each stage loop
pass
else:
process_statement.add(clock_statement)
if synchronous_reset:
pipeline_process = Process(process_statement, sensibility_list=[clk])
else:
process_statement.add(global_reset_statement)
pipeline_process = Process(process_statement, sensibility_list=[clk, entity.reset_signal])
entity.implementation.add_process(pipeline_process)
first_process = pipeline_process
# statement that gather signals which must be pre-computed
for op in retime_map.pre_statement:
first_process.add_to_pre_statement(op)
stage_num = len(retime_map.stage_forward.keys())
Log.report(Log.Info, "there are {} pipeline stage(s)", stage_num)
return stage_num
def generate_auto_test(self,
test_num=10,
test_range=Interval(-1.0, 1.0),
debug=False,
time_step=10):
""" time_step: duration of a stage (in ns) """
# instanciating tested component
# map of input_tag -> input_signal and output_tag -> output_signal
io_map = {}
# map of input_tag -> input_signal, excludind commodity signals
# (e.g. clock and reset)
input_signals = {}
# map of output_tag -> output_signal
output_signals = {}
# excluding clock and reset signals from argument list
# reduced_arg_list = [input_port for input_port in self.implementation.get_arg_list() if not input_port.get_tag() in ["clk", "reset"]]
reduced_arg_list = self.implementation.get_arg_list()
for input_port in reduced_arg_list:
input_tag = input_port.get_tag()
input_signal = Signal(input_tag + "_i",
precision=input_port.get_precision(),
var_type=Signal.Local)
io_map[input_tag] = input_signal
if not input_tag in ["clk", "reset"]:
input_signals[input_tag] = input_signal
for output_port in self.implementation.get_output_port():
output_tag = output_port.get_tag()
output_signal = Signal(output_tag + "_o",
precision=output_port.get_precision(),
var_type=Signal.Local)
io_map[output_tag] = output_signal
output_signals[output_tag] = output_signal
# building list of test cases
tc_list = []
self_component = self.implementation.get_component_object()
self_instance = self_component(io_map=io_map, tag="tested_entity")
test_statement = Statement()
# initializing random test case generator
self.init_test_generator()
# Appending standard test cases if required
if self.auto_test_std:
tc_list += self.standard_test_cases
for i in range(test_num):
input_values = self.generate_test_case(input_signals, io_map, i,
test_range)
tc_list.append((input_values, None))
def compute_results(tc):
""" update test case with output values if required """
input_values, output_values = tc
if output_values is None:
return input_values, self.numeric_emulate(input_values)
else:
return tc
# filling output values
tc_list = [compute_results(tc) for tc in tc_list]
for input_values, output_values in tc_list:
input_msg = ""
# Adding input setting
for input_tag in input_values:
input_signal = io_map[input_tag]
# FIXME: correct value generation depending on signal precision
input_value = input_values[input_tag]
test_statement.add(
ReferenceAssign(
input_signal,
Constant(input_value,
precision=input_signal.get_precision())))
value_msg = input_signal.get_precision().get_cst(
input_value, language=VHDL_Code).replace('"', "'")
value_msg += " / " + hex(input_signal.get_precision(
).get_base_format().get_integer_coding(input_value))
input_msg += " {}={} ".format(input_tag, value_msg)
test_statement.add(Wait(time_step * self.stage_num))
# Adding output value comparison
for output_tag in output_signals:
output_signal = output_signals[output_tag]
output_value = Constant(
output_values[output_tag],
precision=output_signal.get_precision())
output_precision = output_signal.get_precision()
expected_dec = output_precision.get_cst(
output_values[output_tag],
language=VHDL_Code).replace('"', "'")
expected_hex = " / " + hex(
output_precision.get_base_format().get_integer_coding(
output_values[output_tag]))
value_msg = "{} / {}".format(expected_dec, expected_hex)
test_pass_cond = Comparison(output_signal,
output_value,
specifier=Comparison.Equal,
precision=ML_Bool)
test_statement.add(
ConditionBlock(
LogicalNot(test_pass_cond, precision=ML_Bool),
Report(
Concatenation(
" result for {}: ".format(output_tag),
Conversion(TypeCast(
output_signal,
precision=ML_StdLogicVectorFormat(
output_signal.get_precision(
).get_bit_size())),
precision=ML_String),
precision=ML_String))))
test_statement.add(
Assert(
test_pass_cond,
"\"unexpected value for inputs {input_msg}, output {output_tag}, expecting {value_msg}, got: \""
.format(input_msg=input_msg,
output_tag=output_tag,
value_msg=value_msg),
severity=Assert.Failure))
testbench = CodeEntity("testbench")
test_process = Process(
test_statement,
# end of test
Assert(Constant(0, precision=ML_Bool),
" \"end of test, no error encountered \"",
severity=Assert.Failure))
testbench_scheme = Statement(self_instance, test_process)
if self.pipelined:
half_time_step = time_step / 2
assert (half_time_step * 2) == time_step
# adding clock process for pipelined bench
clk_process = Process(
Statement(
ReferenceAssign(io_map["clk"],
Constant(1, precision=ML_StdLogic)),
Wait(half_time_step),
ReferenceAssign(io_map["clk"],
Constant(0, precision=ML_StdLogic)),
Wait(half_time_step),
))
testbench_scheme.push(clk_process)
testbench.add_process(testbench_scheme)
return [testbench]
def generate_scheme(self):
# declaring target and instantiating optimization engine
vx = self.implementation.add_input_variable("x", self.precision)
vx.set_attributes(precision=self.precision,
tag="vx",
debug=debug_multi)
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")
C0 = Constant(0, precision=self.precision)
C0_plus = Constant(FP_PlusZero(self.precision))
C0_minus = Constant(FP_MinusZero(self.precision))
def local_test(specifier, tag):
""" Local wrapper to generate Test operations """
return Test(vx,
specifier=specifier,
likely=False,
debug=debug_multi,
tag="is_%s" % tag,
precision=ML_Bool)
test_NaN = local_test(Test.IsNaN, "is_NaN")
test_inf = local_test(Test.IsInfty, "is_Inf")
test_NaN_or_Inf = local_test(Test.IsInfOrNaN, "is_Inf_Or_Nan")
test_negative = Comparison(vx,
C0,
specifier=Comparison.Less,
debug=debug_multi,
tag="is_Negative",
precision=ML_Bool,
likely=False)
test_NaN_or_Neg = LogicalOr(test_NaN, test_negative, precision=ML_Bool)
test_std = LogicalNot(LogicalOr(test_NaN_or_Inf,
test_negative,
precision=ML_Bool,
likely=False),
precision=ML_Bool,
likely=True)
test_zero = Comparison(vx,
C0,
specifier=Comparison.Equal,
likely=False,
debug=debug_multi,
tag="Is_Zero",
precision=ML_Bool)
return_NaN_or_neg = Statement(Return(FP_QNaN(self.precision)))
return_inf = Statement(Return(FP_PlusInfty(self.precision)))
return_PosZero = Return(C0_plus)
return_NegZero = Return(C0_minus)
NR_init = ReciprocalSquareRootSeed(vx,
precision=self.precision,
tag="sqrt_seed",
debug=debug_multi)
result = compute_sqrt(vx,
NR_init,
int(self.num_iter),
precision=self.precision)
return_non_std = ConditionBlock(
test_NaN_or_Neg, return_NaN_or_neg,
ConditionBlock(
test_inf, return_inf,
ConditionBlock(test_zero, return_PosZero, return_NegZero)))
return_std = Return(result)
scheme = ConditionBlock(test_std, return_std, return_non_std)
return scheme
def generate_datafile_testbench(self, tc_list, io_map, input_signals, output_signals, time_step, test_fname="test.input"):
""" Generate testbench with input and output data externalized in
a data file """
# textio function to read hexadecimal text
def FCT_HexaRead_gen(input_format):
legalized_input_format = input_format
FCT_HexaRead = FunctionObject("hread", [HDL_LINE, legalized_input_format], ML_Void, FunctionOperator("hread", void_function=True, arity=2))
return FCT_HexaRead
# textio function to read binary text
FCT_Read = FunctionObject("read", [HDL_LINE, ML_StdLogic], ML_Void, FunctionOperator("read", void_function=True, arity=2))
input_line = Variable("input_line", precision=HDL_LINE, var_type=Variable.Local)
# building ordered list of input and output signal names
input_signal_list = [sname for sname in input_signals.keys()]
input_statement = Statement()
for input_name in input_signal_list:
input_format = input_signals[input_name].precision
input_var = Variable(
"v_" + input_name,
precision=input_format,
var_type=Variable.Local)
if input_format is ML_StdLogic:
input_statement.add(FCT_Read(input_line, input_var))
else:
input_statement.add(FCT_HexaRead_gen(input_format)(input_line, input_var))
input_statement.add(ReferenceAssign(input_signals[input_name], input_var))
output_signal_list = [sname for sname in output_signals.keys()]
output_statement = Statement()
for output_name in output_signal_list:
output_format = output_signals[output_name].precision
output_var = Variable(
"v_" + output_name,
precision=output_format,
var_type=Variable.Local)
if output_format is ML_StdLogic:
output_statement.add(FCT_Read(input_line, output_var))
else:
output_statement.add(FCT_HexaRead_gen(output_format)(input_line, output_var))
output_signal = output_signals[output_name]
#value_msg = get_output_value_msg(output_signal, output_value)
test_pass_cond, check_statement = get_output_check_statement(output_signal, output_name, output_var)
input_msg = multi_Concatenation(*tuple(sum([[" %s=" % input_tag, signal_str_conversion(input_signals[input_tag], input_signals[input_tag].precision)] for input_tag in input_signal_list], [])))
output_statement.add(check_statement)
assert_statement = Assert(
test_pass_cond,
multi_Concatenation(
"unexpected value for inputs ",
input_msg,
" expecting :",
signal_str_conversion(output_var, output_format),
" got :",
signal_str_conversion(output_signal, output_format),
precision = ML_String
),
severity=Assert.Failure
)
output_statement.add(assert_statement)
self_component = self.implementation.get_component_object()
self_instance = self_component(io_map = io_map, tag = "tested_entity")
test_statement = Statement()
DATA_FILE_NAME = test_fname
with open(DATA_FILE_NAME, "w") as data_file:
# dumping column tags
data_file.write("# " + " ".join(input_signal_list + output_signal_list) + "\n")
def get_raw_cst_string(cst_format, cst_value):
size = int((cst_format.get_bit_size() + 3) / 4)
return ("{:x}").format(cst_format.get_base_format().get_integer_coding(cst_value)).zfill(size)
for input_values, output_values in tc_list:
# TODO; generate test data file
cst_list = []
for input_name in input_signal_list:
input_value = input_values[input_name]
input_format = input_signals[input_name].get_precision()
cst_list.append(get_raw_cst_string(input_format, input_value))
for output_name in output_signal_list:
output_value = output_values[output_name]
output_format = output_signals[output_name].get_precision()
cst_list.append(get_raw_cst_string(output_format, output_value))
# dumping line into file
data_file.write(" ".join(cst_list) + "\n")
input_stream = Variable("data_file", precision=HDL_FILE, var_type=Variable.Local)
file_status = Variable("file_status", precision=HDL_OPEN_FILE_STATUS, var_type=Variable.Local)
FCT_EndFile = FunctionObject("endfile", [HDL_FILE], ML_Bool, FunctionOperator("endfile", arity=1))
FCT_OpenFile = FunctionObject(
"FILE_OPEN", [HDL_OPEN_FILE_STATUS, HDL_FILE, ML_String], ML_Void,
FunctionOperator(
"FILE_OPEN",
arg_map={0: FO_Arg(0), 1: FO_Arg(1), 2: FO_Arg(2), 3: "READ_MODE"},
void_function=True))
FCT_ReadLine = FunctionObject(
"readline", [HDL_FILE, HDL_LINE], ML_Void,
FunctionOperator("readline", void_function=True, arity=2))
reset_statement = self.get_reset_statement(io_map, time_step)
OPEN_OK = Constant("OPEN_OK", precision=HDL_OPEN_FILE_STATUS)
testbench = CodeEntity("testbench")
test_process = Process(
reset_statement,
FCT_OpenFile(file_status, input_stream, DATA_FILE_NAME),
ConditionBlock(
Comparison(file_status, OPEN_OK, specifier=Comparison.NotEqual),
Assert(
Constant(0, precision=ML_Bool),
" \"failed to open file {}\"".format(DATA_FILE_NAME),
severity=Assert.Failure
)
),
# consume legend line
FCT_ReadLine(input_stream, input_line),
WhileLoop(
LogicalNot(FCT_EndFile(input_stream)),
Statement(
FCT_ReadLine(input_stream, input_line),
input_statement,
Wait(time_step * (self.stage_num + 2)),
output_statement,
),
),
# end of test
Assert(
Constant(0, precision = ML_Bool),
" \"end of test, no error encountered \"",
severity = Assert.Warning
),
# infinite end loop
WhileLoop(
Constant(1, precision=ML_Bool),
Statement(
Wait(time_step * (self.stage_num + 2)),
)
)
)
testbench_scheme = Statement(
self_instance,
test_process
)
if self.pipelined:
half_time_step = time_step / 2
assert (half_time_step * 2) == time_step
# adding clock process for pipelined bench
clk_process = Process(
Statement(
ReferenceAssign(
io_map["clk"],
Constant(1, precision = ML_StdLogic)
),
Wait(half_time_step),
ReferenceAssign(
io_map["clk"],
Constant(0, precision = ML_StdLogic)
),
Wait(half_time_step),
)
)
testbench_scheme.push(clk_process)
testbench.add_process(testbench_scheme)
return [testbench]
