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"""
Harnesses for various types of benchmark logs.
tbd
"""
import subprocess
import re
# TODO prepare benchmark log JSON with parameters etc.
# Should be independent of PTA class, as benchmarks may also be
# generated otherwise and it should also work with AnalyticModel (which does
# not have states)
class TransitionHarness:
def __init__(self, gpio_pin = None, pta = None, log_return_values = False):
self.gpio_pin = gpio_pin
self.pta = pta
self.log_return_values = log_return_values
self.reset()
def reset(self):
self.traces = []
self.trace_id = 1
self.synced = False
def global_code(self):
ret = ''
if self.gpio_pin != None:
ret += '#define PTALOG_GPIO {}\n'.format(self.gpio_pin)
if self.log_return_values:
ret += '#define PTALOG_WITH_RETURNVALUES\n'
ret += 'uint16_t transition_return_value;\n'
ret += '#include "object/ptalog.h"\n'
if self.gpio_pin != None:
ret += 'PTALog ptalog({});\n'.format(self.gpio_pin)
else:
ret += 'PTALog ptalog;\n'
return ret
def start_benchmark(self, benchmark_id = 0):
return 'ptalog.startBenchmark({:d});\n'.format(benchmark_id)
def start_trace(self):
self.traces.append({
'id' : self.trace_id,
'trace' : list(),
})
self.trace_id += 1
def append_state(self, state_name, param):
self.traces[-1]['trace'].append({
'name': state_name,
'isa': 'state',
'parameter': param,
})
def append_transition(self, transition_name, param, args = []):
self.traces[-1]['trace'].append({
'name': transition_name,
'isa': 'transition',
'parameter': param,
'args' : args,
})
def start_run(self):
return 'ptalog.reset();\n'
def pass_transition(self, transition_id, transition_code, transition: object = None):
ret = 'ptalog.passTransition({:d});\n'.format(transition_id)
ret += 'ptalog.startTransition();\n'
if self.log_return_values and transition and len(transition.return_value_handlers):
ret += 'transition_return_value = {}\n'.format(transition_code)
ret += 'ptalog.logReturn(transition_return_value);\n'
else:
ret += '{}\n'.format(transition_code)
ret += 'ptalog.stopTransition();\n'
return ret
def stop_run(self, num_traces = 0):
return 'ptalog.dump({:d});\n'.format(num_traces)
def stop_benchmark(self):
return ''
def parser_cb(self, line):
pass
def parse_log(self, lines):
sync = False
for line in lines:
print(line)
res = re.fullmatch(r'\[PTA\] (.*=.*)', line)
if re.fullmatch(r'\[PTA\] benchmark start, id=(.*)', line):
print('> got sync')
sync = True
elif not sync:
continue
elif re.fullmatch(r'\[PTA\] trace, count=(.*)', line):
print('> got transition')
pass
elif res:
print(dict(map(lambda x: x.split('='), res.group(1).split())))
pass
class OnboardTimerHarness(TransitionHarness):
def __init__(self, counter_limits, **kwargs):
super().__init__(**kwargs)
self.trace_id = 0
self.trace_length = 0
self.one_cycle_in_us, self.one_overflow_in_us, self.counter_max_overflow = counter_limits
def global_code(self):
ret = '#include "driver/counter.h"\n'
ret += '#define PTALOG_TIMING\n'
ret += super().global_code()
return ret
def start_benchmark(self, benchmark_id = 0):
ret = 'counter.start();\n'
ret += 'counter.stop();\n'
ret += 'ptalog.passNop(counter);\n'
ret += super().start_benchmark(benchmark_id)
return ret
def pass_transition(self, transition_id, transition_code, transition: object = None):
ret = 'ptalog.passTransition({:d});\n'.format(transition_id)
ret += 'ptalog.startTransition();\n'
ret += 'counter.start();\n'
if self.log_return_values and transition and len(transition.return_value_handlers):
ret += 'transition_return_value = {}\n'.format(transition_code)
else:
ret += '{}\n'.format(transition_code)
ret += 'counter.stop();\n'
if self.log_return_values and transition and len(transition.return_value_handlers):
ret += 'ptalog.logReturn(transition_return_value);\n'
ret += 'ptalog.stopTransition(counter);\n'
return ret
def _append_nondeterministic_parameter_value(self, log_data_target, parameter_name, parameter_value):
if log_data_target['parameter'][parameter_name] is None:
log_data_target['parameter'][parameter_name] = list()
log_data_target['parameter'][parameter_name].append(parameter_value)
def parser_cb(self, line):
#print('[HARNESS] got line {}'.format(line))
if re.match(r'\[PTA\] benchmark start, id=(\S+)', line):
self.synced = True
print('[HARNESS] synced')
if self.synced:
res = re.match(r'\[PTA\] trace=(\S+) count=(\S+)', line)
if res:
self.trace_id = int(res.group(1))
self.trace_length = int(res.group(2))
self.current_transition_in_trace = 0
#print('[HARNESS] trace {:d} contains {:d} transitions. Expecting {:d} transitions.'.format(self.trace_id, self.trace_length, len(self.traces[self.trace_id]['trace']) // 2))
if self.log_return_values:
res = re.match(r'\[PTA\] transition=(\S+) cycles=(\S+)/(\S+) return=(\S+)', line)
else:
res = re.match(r'\[PTA\] transition=(\S+) cycles=(\S+)/(\S+)', line)
if res:
transition_id = int(res.group(1))
# TODO Handle Overflows (requires knowledge of arch-specific max cycle value)
cycles = int(res.group(2))
overflow = int(res.group(3))
if overflow >= self.counter_max_overflow:
raise RuntimeError('Counter overflow ({:d}/{:d}) in benchmark id={:d} trace={:d}: transition #{:d} (ID {:d})'.format(cycles, overflow, 0, self.trace_id, self.current_transition_in_trace, transition_id))
duration_us = cycles * self.one_cycle_in_us + overflow * self.one_overflow_in_us
# self.traces contains transitions and states, UART output only contains trnasitions -> use index * 2
try:
log_data_target = self.traces[self.trace_id]['trace'][self.current_transition_in_trace * 2]
except IndexError:
transition_name = None
if self.pta:
transition_name = self.pta.transitions[transition_id].name
print('[HARNESS] benchmark id={:d} trace={:d}: transition #{:d} (ID {:d}, name {}) is out of bounds'.format(0, self.trace_id, self.current_transition_in_trace, transition_id, transition_name))
print(' Offending line: {}'.format(line))
return
if log_data_target['isa'] != 'transition':
raise RuntimeError('Log mismatch: Expected transition, got {:s}'.format(log_data_target['isa']))
if self.pta:
transition = self.pta.transitions[transition_id]
if transition.name != log_data_target['name']:
raise RuntimeError('Log mismatch: Expected transition {:s}, got transition {:s}'.format(log_data_target['name'], transition.name))
if self.log_return_values and len(transition.return_value_handlers):
for handler in transition.return_value_handlers:
if 'parameter' in handler:
parameter_value = return_value = int(res.group(4))
if 'return_values' not in log_data_target:
log_data_target['return_values'] = list()
log_data_target['return_values'].append(return_value)
if 'formula' in handler:
parameter_value = handler['formula'].eval(return_value)
print('append current')
self._append_nondeterministic_parameter_value(log_data_target, handler['parameter'], parameter_value)
print('append following')
for following_log_data_target in self.traces[self.trace_id]['trace'][(self.current_transition_in_trace * 2 + 1) :]:
self._append_nondeterministic_parameter_value(following_log_data_target, handler['parameter'], parameter_value)
print('append preceding')
if 'apply_from' in handler and any(map(lambda x: x['name'] == handler['apply_from'], self.traces[self.trace_id]['trace'][: (self.current_transition_in_trace * 2 + 1)])):
for preceding_log_data_target in reversed(self.traces[self.trace_id]['trace'][: (self.current_transition_in_trace * 2)]):
self._append_nondeterministic_parameter_value(preceding_log_data_target, handler['parameter'], parameter_value)
if preceding_log_data_target['name'] == handler['apply_from']:
break
if 'offline_aggregates' not in log_data_target:
log_data_target['offline_aggregates'] = {
'duration' : list()
}
log_data_target['offline_aggregates']['duration'].append(duration_us)
self.current_transition_in_trace += 1
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