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
|
#!/usr/bin/env python3
from ..utils import soft_cast_int_or_float, soft_cast_float
import os
import re
import logging
logger = logging.getLogger(__name__)
class CSVfile:
def __init__(self):
self.ignore_names = os.environ.get("DFATOOL_CSV_IGNORE", "").split(",")
self.observation_names = os.environ.get("DFATOOL_CSV_OBSERVATIONS", "").split(
","
)
pass
def load(self, f):
self.column_type = dict()
observations = list()
param_names = list()
attr_names = list()
for lineno, line in enumerate(f):
line = line.removesuffix("\n")
if lineno == 0:
for i, col_name in enumerate(line.split(",")):
if col_name in self.ignore_names:
self.column_type[i] = 0
elif col_name in self.observation_names:
self.column_type[i] = 2
attr_names.append(col_name)
else:
self.column_type[i] = 1
param_names.append(col_name)
else:
param_values = list(
map(
soft_cast_int_or_float,
map(
lambda iv: iv[1],
filter(
lambda iv: self.column_type[iv[0]] == 1,
enumerate(line.split(",")),
),
),
)
)
attr_values = list(
map(
soft_cast_float,
map(
lambda iv: iv[1],
filter(
lambda iv: self.column_type[iv[0]] == 2,
enumerate(line.split(",")),
),
),
)
)
observations.append(
{
"name": "CSVFile",
"param": dict(zip(param_names, param_values)),
"attribute": dict(zip(attr_names, attr_values)),
}
)
return observations
class TraceAnnotation:
offset = None
name = None
param = dict()
def __init__(self, **kwargs):
self.__dict__.update(kwargs)
def apply_offset(self, offset):
self.offset += offset
return self
def __repr__(self):
param_desc = " ".join(map(lambda kv: f"{kv[0]}={kv[1]}", self.param.items()))
return f"{self.name}<{param_desc} @ {self.offset}>"
class RunAnnotation:
start = None
kernels = list()
end = None
# start: offset points to first run entry
# kernel: offset points to first kernel run entry
# end: offset points to first non-run entry (i.e., for all run entries: offset < end.offset)
def __init__(self, **kwargs):
self.__dict__.update(kwargs)
def apply_offset(self, offset):
self.start.apply_offset(offset)
for kernel in self.kernels:
kernel.apply_offset(offset)
self.end.apply_offset(offset)
return self
def __repr__(self):
return (
f"RunAnnotation<start={self.start}, kernels={self.kernels}, end={self.end}>"
)
class Logfile:
def kv_to_param(self, kv_str, cast):
try:
key, value = kv_str.split("=")
value = cast(value)
return key, value
except ValueError:
logger.warning(f"Invalid key-value pair: {kv_str}")
raise
def kv_to_param_f(self, kv_str):
return self.kv_to_param(kv_str, soft_cast_float)
def kv_to_param_i(self, kv_str):
return self.kv_to_param(kv_str, soft_cast_int_or_float)
def load(self, f, is_trace=False):
observations = list()
if is_trace:
trace_status = None
trace_start = None
trace_kernels = list()
trace_end = None
annotations = list()
for lineno, line in enumerate(f):
if m := re.search(r"\[::\] *([^|]*?) *[|] *([^|]*?) *[|] *(.*)", line):
name_str = m.group(1)
param_str = m.group(2)
attr_str = m.group(3)
if is_trace:
name_str, name_annot = name_str.split("@")
name_str = name_str.strip()
name_annot = name_annot.strip()
try:
param = dict(map(self.kv_to_param_i, param_str.split()))
attr = dict(map(self.kv_to_param_f, attr_str.split()))
observations.append(
{
"name": name_str,
"param": param,
"attribute": attr,
}
)
if is_trace:
observations[-1]["place"] = name_annot
except ValueError:
logger.warning(
f"Error parsing {f}: invalid key-value pair in line {lineno+1}"
)
logger.warning(f"Offending entry:\n{line}")
raise
# only relevant for is_trace == True
if m := re.fullmatch(r"\[>>\] *([^|]*?) *[|] *([^|]*?) *", line):
trace_status = 1
trace_kernels = list()
name_str = m.group(1)
param_str = m.group(2)
try:
param = dict(map(self.kv_to_param_i, param_str.split()))
except ValueError:
logger.warning(
f"Error parsing {f}: invalid key-value pair in line {lineno+1}"
)
logger.warning(f"Offending entry:\n{line}")
raise
trace_start = TraceAnnotation(
offset=len(observations), name=name_str, param=param
)
if m := re.fullmatch(r"\[--\] *([^|]*?) *[|] *([^|]*?) *", line):
trace_status = 2
name_str = m.group(1)
param_str = m.group(2)
try:
param = dict(map(self.kv_to_param_i, param_str.split()))
except ValueError:
logger.warning(
f"Error parsing {f}: invalid key-value pair in line {lineno+1}"
)
logger.warning(f"Offending entry:\n{line}")
raise
trace_kernels.append(
TraceAnnotation(
offset=len(observations), name=name_str, param=param
)
)
if m := re.fullmatch(r"\[<<\] *([^|]*?) *[|] *([^|]*?) *", line):
trace_status = None
name_str = m.group(1)
param_str = m.group(2)
try:
param = dict(map(self.kv_to_param_i, param_str.split()))
except ValueError:
logger.warning(
f"Error parsing {f}: invalid key-value pair in line {lineno+1}"
)
logger.warning(f"Offending entry:\n{line}")
raise
trace_end = TraceAnnotation(
offset=len(observations), name=name_str, param=param
)
if trace_start is not None:
annotations.append(
RunAnnotation(
start=trace_start, kernels=trace_kernels, end=trace_end
)
)
trace_status = None
trace_start = None
trace_kernels = list()
trace_end = None
if is_trace:
return observations, annotations
return observations
def dump(self, observations, f):
for observation in observations:
name = observation["name"]
param = observation["param"]
attr = observation["attribute"]
param_str = " ".join(
map(
lambda kv: f"{kv[0]}={kv[1]}",
sorted(param.items(), key=lambda kv: kv[0]),
)
)
attr_str = " ".join(
map(
lambda kv: f"{kv[0]}={kv[1]}",
sorted(attr.items(), key=lambda kv: kv[0]),
)
)
print(f"[::] {name} | {param_str} | {attr_str}", file=f)
|
