1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
|
"""
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:
"""Foo."""
def __init__(self, gpio_pin = None, pta = None, log_return_values = False):
"""
Create a new TransitionHarness
:param gpio_pin: multipass GPIO Pin used for transition synchronization, e.g. `GPIO::p1_0`. Optional.
The GPIO output is high iff a transition is executing
:param pta: PTA object
:param log_return_values: Log return values of transition function calls?
"""
self.gpio_pin = gpio_pin
self.pta = pta
self.log_return_values = log_return_values
self.reset()
def copy(self):
new_object = __class__(gpio_pin = self.gpio_pin, pta = self.pta, log_return_values = self.log_return_values)
new_object.traces = self.traces.copy()
new_object.trace_id = self.trace_id
return new_object
def reset(self):
self.traces = []
self.trace_id = 0
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):
"""Bar."""
def __init__(self, counter_limits, **kwargs):
super().__init__(**kwargs)
self.trace_length = 0
self.one_cycle_in_us, self.one_overflow_in_us, self.counter_max_overflow = counter_limits
def copy(self):
new_harness = __class__((self.one_cycle_in_us, self.one_overflow_in_us, self.counter_max_overflow), gpio_pin = self.gpio_pin, pta = self.pta, log_return_values = self.log_return_values)
new_harness.traces = self.traces.copy()
new_harness.trace_id = self.trace_id
return new_harness
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)
self._append_nondeterministic_parameter_value(log_data_target, handler['parameter'], parameter_value)
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)
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
|
