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
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
|
#!/usr/bin/env python3
"""analyze-kconfig - Generate a model for KConfig selections
analyze-kconfig builds a model determining system attributes
(e.g. ROM or RAM usage) based on KConfig configuration variables.
Only boolean variables are supported at the moment.
"""
import argparse
import hashlib
import json
import kconfiglib
import logging
import os
import time
import numpy as np
import dfatool.cli
import dfatool.utils
from dfatool.loader.kconfig import KConfigAttributes
from dfatool.model import AnalyticModel
from dfatool.validation import CrossValidator
def main():
parser = argparse.ArgumentParser(
formatter_class=argparse.RawDescriptionHelpFormatter, description=__doc__
)
dfatool.cli.add_standard_arguments(parser)
parser.add_argument(
"--boolean-parameters",
action="store_true",
help="Use boolean (not categorial) parameters when building the NFP model",
)
parser.add_argument(
"--show-failing-symbols",
action="store_true",
help="Show Kconfig symbols related to build failures. Must be used with an experiment result directory.",
)
parser.add_argument(
"--show-nop-symbols",
action="store_true",
help="Show Kconfig symbols which are only present in a single configuration. Must be used with an experiment result directory.",
)
parser.add_argument(
"--force-tree",
action="store_true",
help="Build decision tree without checking for analytic functions first. Use this for large kconfig files.",
)
parser.add_argument(
"--max-std",
type=str,
metavar="VALUE_OR_MAP",
help="Specify desired maximum standard deviation for decision tree generation, either as float (global) or <key>/<attribute>=<value>[,<key>/<attribute>=<value>,...]",
)
parser.add_argument(
"--export-observations",
type=str,
metavar="FILE.json.xz",
help="Export observations (intermediate and generic benchmark data representation) to FILE",
)
parser.add_argument(
"--export-observations-only",
action="store_true",
help="Exit after exporting observations",
)
parser.add_argument(
"--export-webconf",
type=str,
help="Export kconfig-webconf NFP model to file",
metavar="FILE",
)
parser.add_argument(
"--config",
type=str,
help="Show model results for symbols in .config file",
metavar="FILE",
)
parser.add_argument(
"--sample-size",
type=int,
help="Restrict model generation to N random samples",
metavar="N",
)
parser.add_argument(
"--show-model",
choices=["static", "paramdetection", "param", "all", "tex", "html"],
action="append",
default=list(),
help="static: show static model values as well as parameter detection heuristic.\n"
"paramdetection: show stddev of static/lut/fitted model\n"
"param: show parameterized model functions and regression variable values\n"
"all: all of the above\n"
"tex: print tex/pgfplots-compatible model data on stdout\n"
"html: print model and quality data as HTML table on stdout",
)
parser.add_argument(
"--show-quality",
choices=["table", "summary", "all", "tex", "html"],
action="append",
default=list(),
help="table: show static/fitted/lut SMAPE and MAE for each name and attribute.\n"
"summary: show static/fitted/lut SMAPE and MAE for each attribute, averaged over all states/transitions.\n"
"all: all of the above.\n"
"tex: print tex/pgfplots-compatible model quality data on stdout.",
)
parser.add_argument("kconfig_path", type=str, help="Path to Kconfig file")
parser.add_argument(
"model",
type=str,
help="Path to experiment results directory or observations.json.xz file",
)
args = parser.parse_args()
if args.log_level:
numeric_level = getattr(logging, args.log_level.upper(), None)
if not isinstance(numeric_level, int):
print(f"Invalid log level: {args.log_level}", file=sys.stderr)
sys.exit(1)
logging.basicConfig(level=numeric_level)
if args.export_dref:
dref = dict()
if os.path.isdir(args.model):
attributes = KConfigAttributes(args.kconfig_path, args.model)
if args.export_dref:
dref.update(attributes.to_dref())
if args.show_failing_symbols:
show_failing_symbols(attributes)
if args.show_nop_symbols:
show_nop_symbols(attributes)
observations = list()
for param, attr in attributes.data:
for key, value in attr.items():
observations.append(
{
"name": key,
"param": param,
"attribute": value,
}
)
if args.sample_size:
shuffled_data_indices = np.random.permutation(np.arange(len(observations)))
sample_indices = shuffled_data_indices[: args.sample_size]
new_observations = list()
for sample_index in sample_indices:
new_observations.append(observations[sample_index])
observations = new_observations
if args.export_observations:
import lzma
print(
f"Exporting {len(observations)} observations to {args.export_observations}"
)
with lzma.open(args.export_observations, "wt") as f:
json.dump(observations, f)
if args.export_observations_only:
return
else:
# show-failing-symbols, show-nop-symbols, DFATOOL_KCONF_WITH_CHOICE_NODES, DFATOOL_KCONF_IGNORE_NUMERIC, and DFATOOL_KCONF_IGNORE_STRING have no effect
# in this branch.
import lzma
with lzma.open(args.model, "rt") as f:
observations = json.load(f)
if args.boolean_parameters:
dfatool.utils.observations_enum_to_bool(observations, kconfig=True)
by_name, parameter_names = dfatool.utils.observations_to_by_name(observations)
# Release memory
del observations
if args.max_std:
max_std = dict()
if "=" in args.max_std:
for kkv in args.max_std.split(","):
kk, v = kkv.split("=")
key, attr = kk.split("/")
if key not in max_std:
max_std[key] = dict()
max_std[key][attr] = float(v)
else:
for key in by_name.keys():
max_std[key] = dict()
for attr in by_name[key]["attributes"]:
max_std[key][attr] = float(args.max_std)
else:
max_std = None
constructor_start = time.time()
model = AnalyticModel(
by_name,
parameter_names,
force_tree=args.force_tree,
max_std=max_std,
)
constructor_duration = time.time() - constructor_start
if args.info:
dfatool.cli.print_info_by_name(model, by_name)
if args.export_pgf_unparam:
dfatool.cli.export_pgf_unparam(model, args.export_pgf_unparam)
if args.cross_validate:
xv_method, xv_count = args.cross_validate.split(":")
xv_count = int(xv_count)
xv = CrossValidator(
AnalyticModel,
by_name,
parameter_names,
force_tree=args.force_tree,
max_std=max_std,
)
xv.parameter_aware = args.parameter_aware_cross_validation
else:
xv_method = None
static_model = model.get_static()
try:
lut_model = model.get_param_lut()
except RuntimeError as e:
if args.force_tree:
# this is to be expected
logging.debug(f"Skipping LUT model: {e}")
else:
logging.warning(f"Skipping LUT model: {e}")
lut_model = None
fit_start_time = time.time()
param_model, param_info = model.get_fitted()
fit_duration = time.time() - fit_start_time
if xv_method == "montecarlo":
static_quality, _ = xv.montecarlo(lambda m: m.get_static(), xv_count)
if lut_model:
lut_quality, _ = xv.montecarlo(
lambda m: m.get_param_lut(fallback=True), xv_count
)
else:
lut_quality = None
xv.export_filename = args.export_xv
analytic_quality, xv_analytic_models = xv.montecarlo(
lambda m: m.get_fitted()[0], xv_count
)
elif xv_method == "kfold":
static_quality, _ = xv.kfold(lambda m: m.get_static(), xv_count)
if lut_model:
lut_quality, _ = xv.kfold(
lambda m: m.get_param_lut(fallback=True), xv_count
)
else:
lut_quality = None
xv.export_filename = args.export_xv
analytic_quality, xv_analytic_models = xv.kfold(
lambda m: m.get_fitted()[0], xv_count
)
else:
static_quality = model.assess(static_model)
if args.export_raw_predictions:
analytic_quality, raw_results = model.assess(param_model, return_raw=True)
with open(args.export_raw_predictions, "w") as f:
json.dump(raw_results, f, cls=dfatool.utils.NpEncoder)
else:
analytic_quality = model.assess(param_model)
xv_analytic_models = [model]
if lut_model:
lut_quality = model.assess(lut_model)
else:
lut_quality = None
if "static" in args.show_model or "all" in args.show_model:
print("--- static model ---")
for name in model.names:
for attribute in model.attributes(name):
dfatool.cli.print_static(model, static_model, name, attribute)
if "param" in args.show_model or "all" in args.show_model:
print("--- param model ---")
for name in model.names:
for attribute in model.attributes(name):
info = param_info(name, attribute)
if type(info) is dfatool.cli.AnalyticFunction:
dfatool.cli.print_analyticinfo(f"{name:20s} {attribute:15s}", info)
elif type(info) is dfatool.cli.SplitFunction:
dfatool.cli.print_splitinfo(
model.parameters, info, f"{name:20s} {attribute:15s}"
)
if "table" in args.show_quality or "all" in args.show_quality:
dfatool.cli.model_quality_table(
["static", "parameterized", "LUT"],
[static_quality, analytic_quality, lut_quality],
[None, param_info, None],
)
print("Model Error on Training Data:")
for name in sorted(model.names):
for attribute, error in sorted(
analytic_quality[name].items(), key=lambda kv: kv[0]
):
mae = error["mae"]
smape = error["smape"]
print(f"{name:15s} {attribute:20s} ± {mae:10.2} / {smape:5.1f}%")
if args.show_model_size:
dfatool.cli.print_model_size(model)
if args.export_webconf:
attributes = KConfigAttributes(args.kconfig_path, None)
try:
with open(f"{attributes.kconfig_root}/nfpkeys.json", "r") as f:
nfpkeys = json.load(f)
except FileNotFoundError:
logging.error(
f"{attributes.kconfig_root}/nfpkeys.json is missing, webconf model will be incomplete"
)
nfpkeys = None
kconfig_hasher = hashlib.sha256()
with open(args.kconfig_path, "rb") as f:
kconfig_data = f.read()
while len(kconfig_data) > 0:
kconfig_hasher.update(kconfig_data)
kconfig_data = f.read()
kconfig_hash = str(kconfig_hasher.hexdigest())
complete_json_model = model.to_json(
with_param_name=True, param_names=parameter_names
)
json_model = dict()
for name, attribute_data in complete_json_model["name"].items():
for attribute, data in attribute_data.items():
json_model[attribute] = data.copy()
if nfpkeys:
json_model[attribute].update(nfpkeys[name][attribute])
out_model = {
"model": json_model,
"modelType": "dfatool-kconfig",
"project": "tbd",
"kconfigHash": kconfig_hash,
"symbols": attributes.symbol_names,
"choices": attributes.choice_names,
}
with open(args.export_webconf, "w") as f:
json.dump(out_model, f, sort_keys=True, cls=dfatool.utils.NpEncoder)
if args.export_dot:
dfatool.cli.export_dot(model, args.export_dot)
if args.export_dref:
dref.update(
model.to_dref(
static_quality,
lut_quality,
analytic_quality,
xv_models=xv_analytic_models,
)
)
dref["constructor duration"] = (constructor_duration, r"\second")
dref["regression duration"] = (fit_duration, r"\second")
dfatool.cli.export_dataref(args.export_dref, dref)
if args.config:
kconf = kconfiglib.Kconfig(args.kconfig_path)
kconf.load_config(args.config)
print(f"Model result for .config: {model.value_for_config(kconf)}")
for symbol in model.symbols:
kconf2 = kconfiglib.Kconfig(args.kconfig_path)
kconf2.load_config(args.config)
kconf_sym = kconf2.syms[symbol]
if kconf_sym.tri_value == 0 and 2 in kconf_sym.assignable:
kconf_sym.set_value(2)
elif kconf_sym.tri_value == 2 and 0 in kconf_sym.assignable:
kconf_sym.set_value(0)
else:
continue
# specific to multipass:
# Do not suggest changes which affect the application
skip = False
num_changes = 0
changed_symbols = list()
for i, csymbol in enumerate(model.symbols):
if kconf.syms[csymbol].tri_value != kconf2.syms[csymbol].tri_value:
num_changes += 1
changed_symbols.append(csymbol)
if (
csymbol.startswith("app_")
and kconf.syms[csymbol].tri_value
!= kconf2.syms[csymbol].tri_value
):
skip = True
break
if skip:
continue
try:
model_diff = model.value_for_config(kconf2) - model.value_for_config(
kconf
)
if kconf_sym.choice:
print(
f"Setting {kconf_sym.choice.name} to {kconf_sym.name} changes {num_changes:2d} symbols, model change: {model_diff:+5.0f}"
)
else:
print(
f"Setting {symbol} to {kconf_sym.str_value} changes {num_changes:2d} symbols, model change: {model_diff:+5.0f}"
)
except TypeError:
if kconf_sym.choice:
print(
f"Setting {kconf_sym.choice.name} to {kconf_sym.name} changes {num_changes:2d} symbols, model is undefined"
)
else:
print(
f"Setting {symbol} to {kconf_sym.str_value} changes {num_changes:2d} symbols, model is undefined"
)
for changed_symbol in changed_symbols:
print(
f" {changed_symbol:30s} -> {kconf2.syms[changed_symbol].str_value}"
)
def show_failing_symbols(data):
for symbol in data.param_names:
unique_values = list(set(map(lambda p: p[symbol], data.failures)))
for value in unique_values:
fail_count = len(list(filter(lambda p: p[symbol] == value, data.failures)))
success_count = len(
list(filter(lambda p: p[0][symbol] == value, data.data))
)
if success_count == 0 and fail_count > 0:
print(
f"Setting {symbol} to '{value}' reliably causes the build to fail (count = {fail_count})"
)
def show_nop_symbols(data):
for symbol in data.symbol_names:
true_count = len(
list(filter(lambda config: config[symbol] == True, data.failures))
) + len(list(filter(lambda config: config[0][symbol] == True, data.data)))
false_count = len(
list(filter(lambda config: config[symbol] == False, data.failures))
) + len(list(filter(lambda config: config[0][symbol] == False, data.data)))
if false_count == 0:
print(f"Symbol {symbol} is never n")
if true_count == 0:
print(f"Symbol {symbol} is never y")
pass
if __name__ == "__main__":
main()
|