class ML_EntityBasis(object):
name = "entity_basis"
## constructor
# @param base_name string function name (without precision considerations)
# @param function_name
# @param output_file string name of source code output file
# @param debug_file string name of debug script output file
# @param io_precisions input/output ML_Format list
# @param abs_accuracy absolute accuracy
# @param libm_compliant boolean flag indicating whether or not the function
# should be compliant with standard libm specification
# (wrt exception, error ...)
# @param fast_path_extract boolean flag indicating whether or not fast
# path extraction optimization must be applied
# @param debug_flag boolean flag, indicating whether or not debug code
# must be generated
def __init__(
self,
# Naming
base_name=ArgDefault("unknown_entity", 2),
entity_name=ArgDefault(None, 2),
output_file=ArgDefault(None, 2),
# Specification
io_precisions=ArgDefault([ML_Binary32], 2),
abs_accuracy=ArgDefault(None, 2),
libm_compliant=ArgDefault(True, 2),
# Optimization parameters
backend=ArgDefault(VHDLBackend(), 2),
fast_path_extract=ArgDefault(True, 2),
# Debug verbosity
debug_flag=ArgDefault(False, 2),
language=ArgDefault(VHDL_Code, 2),
arg_template=DefaultEntityArgTemplate):
# selecting argument values among defaults
base_name = ArgDefault.select_value([base_name])
Log.report(
Log.Info, "pre entity_name: %s %s " %
(entity_name, arg_template.entity_name))
entity_name = ArgDefault.select_value(
[arg_template.entity_name, entity_name])
Log.report(Log.Info, "entity_name: %s " % entity_name)
Log.report(
Log.Info,
"output_file: %s %s " % (arg_template.output_file, output_file))
Log.report(Log.Info, "debug_file: %s " % arg_template.debug_file)
output_file = ArgDefault.select_value(
[arg_template.output_file, output_file])
debug_file = arg_template.debug_file
# Specification
io_precisions = ArgDefault.select_value([io_precisions])
abs_accuracy = ArgDefault.select_value([abs_accuracy])
# Optimization parameters
backend = ArgDefault.select_value([arg_template.backend, backend])
fast_path_extract = ArgDefault.select_value(
[arg_template.fast_path_extract, fast_path_extract])
# Debug verbosity
debug_flag = ArgDefault.select_value([arg_template.debug, debug_flag])
language = ArgDefault.select_value([arg_template.language, language])
auto_test = arg_template.auto_test
auto_test_std = arg_template.auto_test_std
self.precision = arg_template.precision
self.pipelined = arg_template.pipelined
# io_precisions must be a list
# -> with a single element
# XOR -> with as many elements as function arity (input + output arities)
self.io_precisions = io_precisions
## enable the generation of numeric/functionnal auto-test
self.auto_test_enable = (auto_test != False or auto_test_std != False)
self.auto_test_number = auto_test
self.auto_test_range = arg_template.auto_test_range
self.auto_test_std = auto_test_std
# enable/disable automatic exit once functional test is finished
self.exit_after_test = arg_template.exit_after_test
# enable post-generation RTL elaboration
self.build_enable = arg_template.build_enable
# enable post-elaboration simulation
self.execute_trigger = arg_template.execute_trigger
self.language = language
# Naming logic, using provided information if available, otherwise deriving from base_name
# base_name is e.g. exp
# entity_name is e.g. expf or expd or whatever
self.entity_name = entity_name if entity_name else generic_naming(
base_name, self.io_precisions)
self.output_file = output_file if output_file else self.entity_name + ".vhd"
self.debug_file = debug_file if debug_file else "{}_dbg.do".format(
self.entity_name)
# debug version
self.debug_flag = debug_flag
# debug display
self.display_after_gen = arg_template.display_after_gen
self.display_after_opt = arg_template.display_after_opt
# TODO: FIX which i/o precision to select
# TODO: incompatible with fixed-point formats
# self.sollya_precision = self.get_output_precision().get_sollya_object()
self.abs_accuracy = abs_accuracy if abs_accuracy else S2**(
-self.get_output_precision().get_precision())
# target selection
self.backend = backend
# register control
self.reset_pipeline = arg_template.reset_pipeline
self.recirculate_pipeline = arg_template.recirculate_pipeline
# optimization parameters
self.fast_path_extract = fast_path_extract
self.implementation = CodeEntity(self.entity_name)
self.vhdl_code_generator = VHDLCodeGenerator(
self.backend,
declare_cst=False,
disable_debug=not self.debug_flag,
language=self.language)
uniquifier = self.entity_name
self.main_code_object = NestedCode(self.vhdl_code_generator,
static_cst=False,
uniquifier="{0}_".format(
self.entity_name),
code_ctor=VHDLCodeObject)
if self.debug_flag:
self.debug_code_object = CodeObject(self.language)
self.debug_code_object << debug_utils_lib
self.vhdl_code_generator.set_debug_code_object(
self.debug_code_object)
# pass scheduler instanciation
self.pass_scheduler = PassScheduler()
# recursive pass dependency
pass_dep = PassDependency()
for pass_uplet in arg_template.passes:
pass_slot_tag, pass_tag = pass_uplet.split(":")
pass_slot = PassScheduler.get_tag_class(pass_slot_tag)
pass_class = Pass.get_pass_by_tag(pass_tag)
pass_object = pass_class(self.backend)
self.pass_scheduler.register_pass(pass_object,
pass_dep=pass_dep,
pass_slot=pass_slot)
# linearly linking pass in the order they appear
pass_dep = AfterPassById(pass_object.get_pass_id())
# TODO/FIXME: can be overloaded
if self.reset_pipeline:
self.reset_signal = self.implementation.add_input_signal(
"reset", ML_StdLogic)
self.recirculate_signal_map = {}
def get_pass_scheduler(self):
return self.pass_scheduler
## Class method to generate a structure containing default arguments
# which must be overloaded by @p kw
@staticmethod
def get_default_args(**kw):
return DefaultEntityArgTemplate(**kw)
def get_implementation(self):
""" return meta-entity CodeEntity object """
return self.implementation
## name generation
# @param base_name string, name to be extended for unifiquation
def uniquify_name(self, base_name):
""" return a unique identifier, combining base_name + function_name """
return "%s_%s" % (self.function_name, base_name)
## emulation code generation
def generate_emulate(self):
raise NotImplementedError
# try to extract 'clk' input or create it if
# it does not exist
def get_clk_input(self):
clk_in = self.implementation.get_input_by_tag("clk")
if not clk_in is None:
return clk_in
else:
return self.implementation.add_input_signal('clk', ML_StdLogic)
def get_output_precision(self):
return self.io_precisions[0]
def get_input_precision(self):
return self.io_precisions[-1]
def get_sollya_precision(self):
""" return the main precision use for sollya calls """
return self.sollya_precision
def generate_interfaces(self):
""" Generate entity interfaces """
raise NotImplementedError
def generate_scheme(self):
""" generate MDL scheme for function implementation """
Log.report(
Log.Error,
"generate_scheme must be overloaded by ML_EntityBasis child")
##
# @return main_scheme, [list of sub-CodeFunction object]
def generate_entity_list(self):
return self.generate_scheme()
## submit operation node to a standard optimization procedure
# @param pre_scheme ML_Operation object to be optimized
# @param copy dict(optree -> optree) copy map to be used while duplicating pre_scheme (if None disable copy)
# @param enable_subexpr_sharing boolean flag, enables sub-expression sharing optimization
# @param verbose boolean flag, enable verbose mode
# @return optimizated scheme
def optimise_scheme(self,
pre_scheme,
copy=None,
enable_subexpr_sharing=True,
verbose=True):
""" default scheme optimization """
# copying when required
scheme = pre_scheme if copy is None else pre_scheme.copy(copy)
return scheme
##
# @return main code object associted with function implementation
def get_main_code_object(self):
return self.main_code_object
def get_recirculate_signal(self, stage_id):
""" generate / retrieve the signal used to recirculate
register at pipeline stage <stage_id> """
return self.recirculate_signal_map[stage_id]
def is_main_entity(self, entity):
""" Overloadable predicate to determine which sub entities should be
considered principal """
return True
## generate VHDL code for entity implenetation
# Code is generated within the main code object
# and dumped to a file named after implementation's name
# @param code_function_list list of CodeFunction to be generated (as sub-function )
# @return void
def generate_code(self, code_entity_list, language=VHDL_Code):
""" Final VHDL generation, once the evaluation scheme has been optimized"""
# registering scheme as function implementation
#self.implementation.set_scheme(scheme)
# main code object
code_object = self.get_main_code_object()
# list of ComponentObject which are entities on which self depends
common_entity_list = []
generated_entity = []
self.result = code_object
code_str = ""
while len(code_entity_list) > 0:
code_entity = code_entity_list.pop(0)
if code_entity in generated_entity:
continue
entity_code_object = NestedCode(self.vhdl_code_generator,
static_cst=False,
uniquifier="{0}_".format(
self.entity_name),
code_ctor=VHDLCodeObject)
if self.is_main_entity(code_entity):
self.vhdl_code_generator.disable_debug = not self.debug_flag
else:
self.vhdl_code_generator.disable_debug = True
result = code_entity.add_definition(self.vhdl_code_generator,
language,
entity_code_object,
static_cst=False)
result.add_library("ieee")
result.add_header("ieee.std_logic_1164.all")
result.add_header("ieee.std_logic_arith.all")
result.add_header("ieee.std_logic_misc.all")
code_str += result.get(self.vhdl_code_generator, headers=True)
generated_entity.append(code_entity)
# adding the entities encountered during code generation
# for future generation
# TODO: document
extra_entity_list = [
comp_object.get_code_entity()
for comp_object in entity_code_object.get_entity_list()
]
Log.report(
Log.Info, "appending {} extra entit(y/ies)\n".format(
len(extra_entity_list)))
code_entity_list += extra_entity_list
Log.report(Log.Verbose, "Generating VHDL code in " + self.output_file)
output_stream = open(self.output_file, "w")
output_stream.write(code_str)
output_stream.close()
if self.debug_flag:
Log.report(Log.Verbose,
"Generating Debug code in {}".format(self.debug_file))
debug_code_str = self.debug_code_object.get(None)
debug_stream = open(self.debug_file, "w")
debug_stream.write(debug_code_str)
debug_stream.close()
def gen_implementation(self,
display_after_gen=False,
display_after_opt=False,
enable_subexpr_sharing=True):
## apply @p pass_object optimization pass
# to the scheme of each entity in code_entity_list
def entity_execute_pass(scheduler, pass_object, code_entity_list):
for code_entity in code_entity_list:
entity_scheme = code_entity.get_scheme()
processed_scheme = pass_object.execute(entity_scheme)
# todo check pass effect
# code_entity.set_scheme(processed_scheme)
return code_entity_list
# generate scheme
code_entity_list = self.generate_entity_list()
# defaulting pipeline stage to None
self.implementation.set_current_stage(None)
Log.report(Log.Info, "Applying passes just before pipelining")
code_entity_list = self.pass_scheduler.get_full_execute_from_slot(
code_entity_list, PassScheduler.BeforePipelining,
entity_execute_pass)
#print "before pipelining dump: "
#for code_entity in code_entity_list:
# scheme = code_entity.get_scheme()
# print scheme.get_str(
# depth = None,
# display_precision = True,
# memoization_map = {},
# custom_callback = lambda op: " [S={}] ".format(op.attributes.init_stage)
# )
if self.pipelined:
self.stage_num = generate_pipeline_stage(
self,
reset=self.reset_pipeline,
recirculate=self.recirculate_pipeline)
else:
self.stage_num = 1
Log.report(Log.Info,
"there is/are {} pipeline stage(s)".format(self.stage_num))
Log.report(Log.Info, "Applying passes just after pipelining")
code_entity_list = self.pass_scheduler.get_full_execute_from_slot(
code_entity_list, PassScheduler.AfterPipelining,
entity_execute_pass)
# stage duration (in ns)
time_step = 10
if self.auto_test_enable:
code_entity_list += self.generate_auto_test(
test_num=self.auto_test_number if self.auto_test_number else 0,
test_range=self.auto_test_range,
time_step=time_step)
for code_entity in code_entity_list:
scheme = code_entity.get_scheme()
if display_after_gen or self.display_after_gen:
print("function %s, after gen " % code_entity.get_name())
print(
scheme.get_str(depth=None,
display_precision=True,
memoization_map={}))
# optimize scheme
opt_scheme = self.optimise_scheme(
scheme, enable_subexpr_sharing=enable_subexpr_sharing)
if display_after_opt or self.display_after_opt:
print("function %s, after opt " % code_entity.get_name())
print(
scheme.get_str(depth=None,
display_precision=True,
memoization_map={}))
print("Applying passes just before codegen")
code_entity_list = self.pass_scheduler.get_full_execute_from_slot(
code_entity_list, PassScheduler.JustBeforeCodeGen,
entity_execute_pass)
# generate VHDL code to implement scheme
self.generate_code(code_entity_list, language=self.language)
if self.execute_trigger:
# rtl elaboration
print("Elaborating {}".format(self.output_file))
elab_cmd = "vlib work && vcom -2008 {}".format(self.output_file)
elab_result = subprocess.call(elab_cmd, shell=True)
print("Elaboration result: ", elab_result)
# debug cmd
debug_cmd = "do {debug_file};".format(
debug_file=self.debug_file) if self.debug_flag else ""
debug_cmd += " exit;" if self.exit_after_test else ""
# simulation
test_delay = time_step * (self.stage_num + 2) * (
self.auto_test_number +
(len(self.standard_test_cases) if self.auto_test_std else 0) +
100)
sim_cmd = "vsim -c work.testbench -do \"run {test_delay} ns; {debug_cmd}\"".format(
entity=self.entity_name,
debug_cmd=debug_cmd,
test_delay=test_delay)
Log.report(Log.Info, "simulation command:\n{}".format(sim_cmd))
sim_result = subprocess.call(sim_cmd, shell=True)
if sim_result:
Log.report(Log.Error,
"simulation failed [{}]".format(sim_result))
else:
Log.report(Log.Info, "simulation success")
elif self.build_enable:
print("Elaborating {}".format(self.output_file))
elab_cmd = "vlib work && vcom -2008 {}".format(self.output_file)
Log.report(Log.Info, "elaboration command:\n{}".format(elab_cmd))
elab_result = subprocess.call(elab_cmd, shell=True)
if elab_result:
Log.report(Log.Error,
"failed to elaborate [{}]".format(elab_result))
else:
Log.report(Log.Info, "elaboration success")
# Currently mostly empty, to be populated someday
def gen_emulation_code(self, precode, code, postcode):
"""generate C code that emulates the function, typically using MPFR.
precode is declaration code (before the test loop)
postcode is clean-up code (after the test loop)
Takes the input and output names from input_list and output_list.
Must postfix output names with "ref_", "ref_ru_", "ref_rd_"
This class method performs commonly used initializations.
It initializes the MPFR versions of the inputs and outputs,
with the same names prefixed with "mp" and possibly postfixed with "rd" and "ru".
It should be overloaded by actual metafunctions, and called by the overloading function.
"""
## provide numeric evaluation of the main function on @p input_value
def numeric_emulate(self, input_value):
raise NotImplementedError
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 init_test_generator(self):
""" Generic initialization of test case generator """
return
def implement_test_case(self, io_map, input_values, output_signals,
output_values, time_step):
""" Implement the test case check and assertion whose I/Os values
are described in input_values and output_values dict """
test_statement = Statement()
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(get_input_assign(input_signal, input_value))
input_msg += get_input_msg(input_tag, input_signal, input_value)
test_statement.add(Wait(time_step * (self.stage_num + 2)))
# Adding output value comparison
for output_tag in output_signals:
output_signal = output_signals[output_tag]
output_value = output_values[output_tag]
output_cst_value = Constant(
output_value, precision=output_signal.get_precision())
value_msg = get_output_value_msg(output_signal, output_value)
test_pass_cond, check_statement = get_output_check_statement(
output_signal, output_tag, output_cst_value)
test_statement.add(check_statement)
assert_statement = 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)
test_statement.add(assert_statement)
return test_statement
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:
test_statement.add(
self.implement_test_case(io_map, input_values, output_signals,
output_values, time_step))
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]
@staticmethod
def get_name():
return ML_EntityBasis.function_name
# list of input to be used for standard test validation
standard_test_cases = []
def __init__(
self,
# Naming
base_name=ArgDefault("unknown_entity", 2),
entity_name=ArgDefault(None, 2),
output_file=ArgDefault(None, 2),
# Specification
io_precisions=ArgDefault([ML_Binary32], 2),
abs_accuracy=ArgDefault(None, 2),
libm_compliant=ArgDefault(True, 2),
# Optimization parameters
backend=ArgDefault(VHDLBackend(), 2),
fast_path_extract=ArgDefault(True, 2),
# Debug verbosity
debug_flag=ArgDefault(False, 2),
language=ArgDefault(VHDL_Code, 2),
arg_template=DefaultEntityArgTemplate):
# selecting argument values among defaults
base_name = ArgDefault.select_value([base_name])
Log.report(
Log.Info, "pre entity_name: %s %s " %
(entity_name, arg_template.entity_name))
entity_name = ArgDefault.select_value(
[arg_template.entity_name, entity_name])
Log.report(Log.Info, "entity_name: %s " % entity_name)
Log.report(
Log.Info,
"output_file: %s %s " % (arg_template.output_file, output_file))
Log.report(Log.Info, "debug_file: %s " % arg_template.debug_file)
output_file = ArgDefault.select_value(
[arg_template.output_file, output_file])
debug_file = arg_template.debug_file
# Specification
io_precisions = ArgDefault.select_value([io_precisions])
abs_accuracy = ArgDefault.select_value([abs_accuracy])
# Optimization parameters
backend = ArgDefault.select_value([arg_template.backend, backend])
fast_path_extract = ArgDefault.select_value(
[arg_template.fast_path_extract, fast_path_extract])
# Debug verbosity
debug_flag = ArgDefault.select_value([arg_template.debug, debug_flag])
language = ArgDefault.select_value([arg_template.language, language])
auto_test = arg_template.auto_test
auto_test_std = arg_template.auto_test_std
self.precision = arg_template.precision
self.pipelined = arg_template.pipelined
# io_precisions must be a list
# -> with a single element
# XOR -> with as many elements as function arity (input + output arities)
self.io_precisions = io_precisions
## enable the generation of numeric/functionnal auto-test
self.auto_test_enable = (auto_test != False or auto_test_std != False)
self.auto_test_number = auto_test
self.auto_test_range = arg_template.auto_test_range
self.auto_test_std = auto_test_std
# enable/disable automatic exit once functional test is finished
self.exit_after_test = arg_template.exit_after_test
# enable post-generation RTL elaboration
self.build_enable = arg_template.build_enable
# enable post-elaboration simulation
self.execute_trigger = arg_template.execute_trigger
self.language = language
# Naming logic, using provided information if available, otherwise deriving from base_name
# base_name is e.g. exp
# entity_name is e.g. expf or expd or whatever
self.entity_name = entity_name if entity_name else generic_naming(
base_name, self.io_precisions)
self.output_file = output_file if output_file else self.entity_name + ".vhd"
self.debug_file = debug_file if debug_file else "{}_dbg.do".format(
self.entity_name)
# debug version
self.debug_flag = debug_flag
# debug display
self.display_after_gen = arg_template.display_after_gen
self.display_after_opt = arg_template.display_after_opt
# TODO: FIX which i/o precision to select
# TODO: incompatible with fixed-point formats
# self.sollya_precision = self.get_output_precision().get_sollya_object()
self.abs_accuracy = abs_accuracy if abs_accuracy else S2**(
-self.get_output_precision().get_precision())
# target selection
self.backend = backend
# register control
self.reset_pipeline = arg_template.reset_pipeline
self.recirculate_pipeline = arg_template.recirculate_pipeline
# optimization parameters
self.fast_path_extract = fast_path_extract
self.implementation = CodeEntity(self.entity_name)
self.vhdl_code_generator = VHDLCodeGenerator(
self.backend,
declare_cst=False,
disable_debug=not self.debug_flag,
language=self.language)
uniquifier = self.entity_name
self.main_code_object = NestedCode(self.vhdl_code_generator,
static_cst=False,
uniquifier="{0}_".format(
self.entity_name),
code_ctor=VHDLCodeObject)
if self.debug_flag:
self.debug_code_object = CodeObject(self.language)
self.debug_code_object << debug_utils_lib
self.vhdl_code_generator.set_debug_code_object(
self.debug_code_object)
# pass scheduler instanciation
self.pass_scheduler = PassScheduler()
# recursive pass dependency
pass_dep = PassDependency()
for pass_uplet in arg_template.passes:
pass_slot_tag, pass_tag = pass_uplet.split(":")
pass_slot = PassScheduler.get_tag_class(pass_slot_tag)
pass_class = Pass.get_pass_by_tag(pass_tag)
pass_object = pass_class(self.backend)
self.pass_scheduler.register_pass(pass_object,
pass_dep=pass_dep,
pass_slot=pass_slot)
# linearly linking pass in the order they appear
pass_dep = AfterPassById(pass_object.get_pass_id())
# TODO/FIXME: can be overloaded
if self.reset_pipeline:
self.reset_signal = self.implementation.add_input_signal(
"reset", ML_StdLogic)
self.recirculate_signal_map = {}
class ML_EntityBasis(object):
name = "entity_basis"
## constructor
# @param base_name string function name (without precision considerations)
# @param function_name
# @param output_file string name of source code output file
# @param debug_file string name of debug script output file
# @param io_precisions input/output ML_Format list
# @param libm_compliant boolean flag indicating whether or not the function
# should be compliant with standard libm specification
# (wrt exception, error ...)
# @param fast_path_extract boolean flag indicating whether or not fast
# path extraction optimization must be applied
# @param debug_flag boolean flag, indicating whether or not debug code
# must be generated
def __init__(self,
# Naming
base_name = ArgDefault("unknown_entity", 2),
entity_name= ArgDefault(None, 2),
output_file = ArgDefault(None, 2),
# Specification
io_precisions = ArgDefault([ML_Binary32], 2),
libm_compliant = ArgDefault(True, 2),
# Optimization parameters
backend = ArgDefault(VHDLBackend(), 2),
fast_path_extract = ArgDefault(True, 2),
# Debug verbosity
debug_flag = ArgDefault(False, 2),
language = ArgDefault(VHDL_Code, 2),
arg_template = DefaultEntityArgTemplate
):
# selecting argument values among defaults
base_name = ArgDefault.select_value([base_name])
Log.report(Log.Info, "pre entity_name: %s %s " % (entity_name, arg_template.entity_name))
entity_name = ArgDefault.select_value([arg_template.entity_name, entity_name])
Log.report(Log.Info, "entity_name: %s " % entity_name)
Log.report(Log.Info, "output_file: %s %s " % (arg_template.output_file, output_file))
Log.report(Log.Info, "debug_file: %s "% arg_template.debug_file)
output_file = ArgDefault.select_value([arg_template.output_file, output_file])
debug_file = arg_template.debug_file
# Specification
io_precisions = ArgDefault.select_value([io_precisions])
# Optimization parameters
backend = ArgDefault.select_value([arg_template.backend, backend])
fast_path_extract = ArgDefault.select_value([arg_template.fast_path_extract, fast_path_extract])
# Debug verbosity
debug_flag = ArgDefault.select_value([arg_template.debug, debug_flag])
language = ArgDefault.select_value([arg_template.language, language])
auto_test = arg_template.auto_test
auto_test_std = arg_template.auto_test_std
self.precision = arg_template.precision
self.io_formats = arg_template.io_formats
self.pipelined = arg_template.pipelined
# io_precisions must be a list
# -> with a single element
# XOR -> with as many elements as function arity (input + output arities)
self.io_precisions = io_precisions
## enable the generation of numeric/functionnal auto-test
self.auto_test_enable = (auto_test != False or auto_test_std != False)
self.auto_test_number = auto_test
self.auto_test_range = arg_template.auto_test_range
self.auto_test_std = auto_test_std
# embedded test in behavior or externalize inputs/expected in data file
self.externalized_test_data = arg_template.externalized_test_data
# enable/disable automatic exit once functional test is finished
self.exit_after_test = arg_template.exit_after_test
# enable post-generation RTL elaboration
self.build_enable = arg_template.build_enable
# enable post-elaboration simulation
self.execute_trigger = arg_template.execute_trigger
self.simulator = arg_template.simulator
self.language = language
# Naming logic, using provided information if available, otherwise deriving from base_name
# base_name is e.g. exp
# entity_name is e.g. expf or expd or whatever
self.entity_name = entity_name if entity_name else generic_naming(base_name, self.io_precisions)
self.output_file = output_file if output_file else self.entity_name + ".vhd"
self.debug_file = debug_file if debug_file else "{}_dbg.do".format(self.entity_name)
# debug version
self.debug_flag = debug_flag
# debug display
self.display_after_gen = arg_template.display_after_gen
self.display_after_opt = arg_template.display_after_opt
self.decorate_code=arg_template.decorate_code
# target selection
self.backend = backend
# register control
self.reset_pipeline, self.synchronous_reset = arg_template.reset_pipeline
self.negate_reset = arg_template.negate_reset
self.reset_name = arg_template.reset_name
self.recirculate_pipeline = arg_template.recirculate_pipeline
# optimization parameters
self.fast_path_extract = fast_path_extract
self.implementation = CodeEntity(self.entity_name)
self.vhdl_code_generator = VHDLCodeGenerator(self.backend, declare_cst = False, disable_debug = not self.debug_flag, language = self.language, decorate_code=self.decorate_code)
uniquifier = self.entity_name
self.main_code_object = NestedCode(
self.vhdl_code_generator, static_cst=False,
uniquifier="{0}_".format(self.entity_name),
code_ctor=VHDLCodeObject,
shared_symbol_list=[MultiSymbolTable.EntitySymbol, MultiSymbolTable.ProtectedSymbol]
)
if self.debug_flag:
self.debug_code_object = CodeObject(self.language)
self.debug_code_object << debug_utils_lib
self.vhdl_code_generator.set_debug_code_object(self.debug_code_object)
# pass scheduler instanciation
self.pass_scheduler = PassScheduler()
# recursive pass dependency
pass_dep = PassDependency()
Log.report(Log.Verbose, "extra_passes: {}".format(arg_template.extra_passes))
for pass_uplet in arg_template.passes + arg_template.extra_passes:
pass_slot_tag, pass_tag = pass_uplet.split(":")
pass_slot = PassScheduler.get_tag_class(pass_slot_tag)
pass_class = Pass.get_pass_by_tag(pass_tag)
pass_object = pass_class(self.backend)
self.pass_scheduler.register_pass(pass_object, pass_dep = pass_dep, pass_slot = pass_slot)
# linearly linking pass in the order they appear
pass_dep = AfterPassById(pass_object.get_pass_id())
# TODO/FIXME: can be overloaded
if self.reset_pipeline:
self.reset_signal = self.implementation.add_input_signal(self.reset_name, ML_StdLogic)
self.recirculate_signal_map = dict((index, self.implementation.add_input_signal(v, ML_StdLogic)) for index, v in arg_template.recirculate_signal_map.items())
def get_pass_scheduler(self):
return self.pass_scheduler
## Class method to generate a structure containing default arguments
# which must be overloaded by @p kw
@staticmethod
def get_default_args(**kw):
return DefaultEntityArgTemplate(**kw)
def get_implementation(self):
""" return meta-entity CodeEntity object """
return self.implementation
## name generation
# @param base_name string, name to be extended for unifiquation
def uniquify_name(self, base_name):
""" return a unique identifier, combining base_name + function_name """
return "%s_%s" % (self.function_name, base_name)
## emulation code generation
def generate_emulate(self):
raise NotImplementedError
# try to extract 'clk' input or create it if
# it does not exist
def get_clk_input(self):
clk_in = self.implementation.get_input_by_tag("clk")
if not clk_in is None:
return clk_in
else:
return self.implementation.add_input_signal('clk', ML_StdLogic)
def get_output_precision(self):
return self.io_precisions[0]
def get_input_precision(self):
return self.io_precisions[-1]
def get_io_format(self, tag):
return self.io_formats[tag]
def get_sollya_precision(self):
""" return the main precision use for sollya calls """
return self.sollya_precision
def generate_interfaces(self):
""" Generate entity interfaces """
raise NotImplementedError
def generate_scheme(self):
""" generate MDL scheme for function implementation """
Log.report(Log.Error, "generate_scheme must be overloaded by ML_EntityBasis child")
##
# @return main_scheme, [list of sub-CodeFunction object]
def generate_entity_list(self):
return self.generate_scheme()
## submit operation node to a standard optimization procedure
# @param pre_scheme ML_Operation object to be optimized
# @param copy dict(optree -> optree) copy map to be used while duplicating pre_scheme (if None disable copy)
# @param enable_subexpr_sharing boolean flag, enables sub-expression sharing optimization
# @param verbose boolean flag, enable verbose mode
# @return optimizated scheme
def optimise_scheme(self, pre_scheme, copy = None, enable_subexpr_sharing = True, verbose = True):
""" default scheme optimization """
# copying when required
scheme = pre_scheme if copy is None else pre_scheme.copy(copy)
return scheme
##
# @return main code object associted with function implementation
def get_main_code_object(self):
return self.main_code_object
def get_recirculate_signal(self, stage_id):
""" generate / retrieve the signal used to recirculate
register at pipeline stage <stage_id> """
try:
return self.recirculate_signal_map[stage_id]
except KeyError as e:
Log.report(Log.Error, "stage {} has no associated recirculation signal in recirculate_signal_map", stage_id, error=e)
def is_main_entity(self, entity):
""" Overloadable predicate to determine which sub entities should be
considered principal """
return True
## generate VHDL code for entity implenetation
# Code is generated within the main code object
# and dumped to a file named after implementation's name
# @param code_function_list list of CodeFunction to be generated (as sub-function )
# @return void
def generate_code(self, code_entity_list, language = VHDL_Code):
""" Final VHDL generation, once the evaluation scheme has been optimized"""
# registering scheme as function implementation
#self.implementation.set_scheme(scheme)
# main code object
code_object = self.get_main_code_object()
# list of ComponentObject which are entities on which self depends
common_entity_list = []
generated_entity = []
self.result = code_object
code_str = ""
while len(code_entity_list) > 0:
code_entity = code_entity_list.pop(0)
if code_entity in generated_entity:
continue
entity_code_object = NestedCode(
self.vhdl_code_generator, static_cst=False,
uniquifier="{0}_".format(self.entity_name),
code_ctor=VHDLCodeObject,
shared_symbol_list=[MultiSymbolTable.EntitySymbol, MultiSymbolTable.ProtectedSymbol],
)
if self.is_main_entity(code_entity):
self.vhdl_code_generator.disable_debug = not self.debug_flag
else:
self.vhdl_code_generator.disable_debug = True
result = code_entity.add_definition(self.vhdl_code_generator, language, entity_code_object, static_cst = False)
result.add_library("ieee")
result.add_header("ieee.std_logic_1164.all")
result.add_header("ieee.std_logic_arith.all")
result.add_header("ieee.std_logic_misc.all")
result.add_header("STD.textio.all")
result.add_header("ieee.std_logic_textio.all")
code_str += result.get(self.vhdl_code_generator, headers = True)
generated_entity.append(code_entity)
# adding the entities encountered during code generation
# for future generation
# TODO: document
extra_entity_list = [
comp_object.get_code_entity() for comp_object in
entity_code_object.get_entity_list()
]
Log.report(Log.Info, "appending {} extra entit(y/ies)\n".format(len(extra_entity_list)))
code_entity_list += extra_entity_list
Log.report(Log.Verbose, "Generating VHDL code in " + self.output_file)
output_stream = open(self.output_file, "w")
output_stream.write(code_str)
output_stream.close()
if self.debug_flag:
Log.report(Log.Verbose, "Generating Debug code in {}".format(self.debug_file))
debug_code_str = self.debug_code_object.get(None)
debug_stream = open(self.debug_file, "w")
debug_stream.write(debug_code_str)
debug_stream.close()
def gen_implementation(self,
display_after_gen = False,
display_after_opt = False,
enable_subexpr_sharing = True
):
## apply @p pass_object optimization pass
# to the scheme of each entity in code_entity_list
def entity_execute_pass(scheduler, pass_object, code_entity_list):
for code_entity in code_entity_list:
entity_scheme = code_entity.get_scheme()
processed_scheme = pass_object.execute(entity_scheme)
# todo check pass effect
# code_entity.set_scheme(processed_scheme)
return code_entity_list
# generate scheme
code_entity_list = self.generate_entity_list()
Log.report(Log.Info, "Applying passes at start of generation")
code_entity_list = self.pass_scheduler.get_full_execute_from_slot(
code_entity_list,
PassScheduler.Start,
entity_execute_pass
)
# defaulting pipeline stage to None
self.implementation.set_current_stage(None)
Log.report(Log.Info, "Applying passes just before pipelining")
code_entity_list = self.pass_scheduler.get_full_execute_from_slot(
code_entity_list,
PassScheduler.BeforePipelining,
entity_execute_pass
)
if self.pipelined:
self.stage_num = generate_pipeline_stage(
self, reset=self.reset_pipeline,
recirculate=self.recirculate_pipeline,
synchronous_reset=self.synchronous_reset,
negate_reset=self.negate_reset)
else:
self.stage_num = 1
Log.report(Log.Info, "there is/are {} pipeline stage(s)".format(self.stage_num))
Log.report(Log.Info, "Applying passes just after pipelining")
code_entity_list = self.pass_scheduler.get_full_execute_from_slot(
code_entity_list,
PassScheduler.AfterPipelining,
entity_execute_pass
)
# stage duration (in ns)
time_step = 10
if self.auto_test_enable:
code_entity_list += self.generate_auto_test(
test_num = self.auto_test_number if self.auto_test_number else 0,
test_range = self.auto_test_range,
time_step = time_step
)
for code_entity in code_entity_list:
scheme = code_entity.get_scheme()
if display_after_gen or self.display_after_gen:
print("function %s, after gen " % code_entity.get_name())
print(scheme.get_str(depth = None, display_precision = True, memoization_map = {}))
# optimize scheme
opt_scheme = self.optimise_scheme(scheme, enable_subexpr_sharing = enable_subexpr_sharing)
if display_after_opt or self.display_after_opt:
print("function %s, after opt " % code_entity.get_name())
print(scheme.get_str(depth = None, display_precision = True, memoization_map = {}))
Log.report(Log.Info, "Applying passes just before codegen")
code_entity_list = self.pass_scheduler.get_full_execute_from_slot(
code_entity_list,
PassScheduler.JustBeforeCodeGen,
entity_execute_pass
)
# generate VHDL code to implement scheme
self.generate_code(code_entity_list, language = self.language)
if self.simulator == "vsim":
simulation = ModelsimSimulation([self.output_file], self.debug_file)
elif self.simulator == "ghdl":
simulation = GHDLSimulation([self.output_file])
else:
Log.report(Log.Error, "unknown RTL elab and simulation tool: {}", self.simulator)
if self.execute_trigger:
test_delay = time_step * (self.stage_num + 2) * (self.auto_test_number + (len(self.standard_test_cases) if self.auto_test_std else 0) + 100)
sim_result = simulation.elab_and_run(test_delay, debug=self.debug_flag, exit_after_test=self.exit_after_test)
# rtl elaboration
if sim_result:
Log.report(Log.Error, "simulation failed [{}]".format(sim_result))
else:
Log.report(Log.Info, "simulation success")
elif self.build_enable:
elab_result = simulation.elab(self.entity_name)
if elab_result:
Log.report(Log.Error, "failed to elaborate [{}]".format(elab_result))
else:
Log.report(Log.Info, "elaboration success")
# Currently mostly empty, to be populated someday
def gen_emulation_code(self, precode, code, postcode):
"""generate C code that emulates the function, typically using MPFR.
precode is declaration code (before the test loop)
postcode is clean-up code (after the test loop)
Takes the input and output names from input_list and output_list.
Must postfix output names with "ref_", "ref_ru_", "ref_rd_"
This class method performs commonly used initializations.
It initializes the MPFR versions of the inputs and outputs,
with the same names prefixed with "mp" and possibly postfixed with "rd" and "ru".
It should be overloaded by actual metafunctions, and called by the overloading function.
"""
## provide numeric evaluation of the main function on @p input_value
def numeric_emulate(self, input_value):
raise NotImplementedError
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 init_test_generator(self):
""" Generic initialization of test case generator """
return
def implement_test_case(self, io_map, input_values, output_signals,
output_values, time_step, index=None):
""" Implement the test case check and assertion whose I/Os values
are described in input_values and output_values dict
:param index: index of the corresponding test case (to be displayed as info)
"""
test_statement = Statement()
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(get_input_assign(input_signal, input_value))
input_msg += get_input_msg(input_tag, input_signal, input_value)
test_statement.add(Wait(time_step * (self.stage_num + 2)))
# Adding output value comparison
for output_tag in output_signals:
output_signal = output_signals[output_tag]
output_value = output_values[output_tag]
output_cst_value = Constant(output_value, precision=output_signal.get_precision())
value_msg = get_output_value_msg(output_signal, output_value)
test_pass_cond, check_statement = get_output_check_statement(output_signal, output_tag, output_cst_value)
test_statement.add(check_statement)
assert_statement = Assert(
test_pass_cond,
"\"unexpected value for {test_id}, inputs {input_msg}, output {output_tag}, expecting {value_msg}, got: \"".format(input_msg = input_msg, output_tag = output_tag, value_msg = value_msg, test_id=("" if index is None else "test #{}".format(index))),
severity = Assert.Failure
)
test_statement.add(assert_statement)
return test_statement
def get_input_signal_map(self, io_map):
# map of input_tag -> input_signal, excludind commodity signals
# (e.g. clock and reset)
input_signals = {}
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
# excluding clk, reset and recirculate signals
if not input_tag in ["clk", self.reset_name] + [sig.get_tag() for sig in self.recirculate_signal_map.values()]:
input_signals[input_tag] = input_signal
return input_signals
def get_output_signal_map(self, io_map):
""" generate map of output signals """
# map of output_tag -> output_signal
output_signals = {}
# excluding clock and reset signals from argument list
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
return output_signals
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]
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 = self.get_input_signal_map(io_map)
# map of output_tag -> output_signal
output_signals = self.get_output_signal_map(io_map)
# building list of test cases
tc_list = []
# 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]
if self.externalized_test_data:
return self.generate_datafile_testbench(tc_list, io_map, input_signals, output_signals, time_step, test_fname=self.externalized_test_data)
else:
return self.generate_embedded_testbench(tc_list, io_map, input_signals, output_signals, time_step)
def get_reset_statement(self, io_map, time_step):
reset_statement = Statement()
if self.reset_pipeline:
# TODO: fix pipeline register reset
reset_value = 0 if self.negate_reset else 1
unreset_value = 1 - reset_value
reset_signal = io_map[self.reset_name]
reset_statement.add(ReferenceAssign(reset_signal, Constant(reset_value, precision=ML_StdLogic)))
# to account for synchronous reset
reset_statement.add(Wait(time_step * 3))
reset_statement.add(ReferenceAssign(reset_signal, Constant(unreset_value, precision=ML_StdLogic)))
reset_statement.add(Wait(time_step * 3))
for recirculate_signal in self.recirculate_signal_map.values():
reset_statement.add(ReferenceAssign(io_map[recirculate_signal.get_tag()], Constant(0, precision=ML_StdLogic)))
return reset_statement
def generate_embedded_testbench(self, tc_list, io_map, input_signals, output_signals, time_step, test_fname="test.input"):
""" Generate testbench with embedded input and output data """
self_component = self.implementation.get_component_object()
self_instance = self_component(io_map = io_map, tag = "tested_entity")
test_statement = Statement()
for index, (input_values, output_values) in enumerate(tc_list):
test_statement.add(
self.implement_test_case(io_map, input_values, output_signals, output_values, time_step, index=index)
)
reset_statement = self.get_reset_statement(io_map, time_step)
testbench = CodeEntity("testbench")
test_process = Process(
reset_statement,
test_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]
@staticmethod
def get_name():
return ML_EntityBasis.function_name
# list of input to be used for standard test validation
standard_test_cases = []