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
|
"""Parallel building utilities."""
from __future__ import annotations
import os
import time
import traceback
from math import sqrt
from typing import Any, Callable, Sequence
try:
import multiprocessing
HAS_MULTIPROCESSING = True
except ImportError:
HAS_MULTIPROCESSING = False
from sphinx.errors import SphinxParallelError
from sphinx.util import logging
logger = logging.getLogger(__name__)
# our parallel functionality only works for the forking Process
parallel_available = multiprocessing and os.name == 'posix'
class SerialTasks:
"""Has the same interface as ParallelTasks, but executes tasks directly."""
def __init__(self, nproc: int = 1) -> None:
pass
def add_task(
self, task_func: Callable, arg: Any = None, result_func: Callable | None = None,
) -> None:
if arg is not None:
res = task_func(arg)
else:
res = task_func()
if result_func:
result_func(res)
def join(self) -> None:
pass
class ParallelTasks:
"""Executes *nproc* tasks in parallel after forking."""
def __init__(self, nproc: int) -> None:
self.nproc = nproc
# (optional) function performed by each task on the result of main task
self._result_funcs: dict[int, Callable] = {}
# task arguments
self._args: dict[int, list[Any] | None] = {}
# list of subprocesses (both started and waiting)
self._procs: dict[int, Any] = {}
# list of receiving pipe connections of running subprocesses
self._precvs: dict[int, Any] = {}
# list of receiving pipe connections of waiting subprocesses
self._precvsWaiting: dict[int, Any] = {}
# number of working subprocesses
self._pworking = 0
# task number of each subprocess
self._taskid = 0
def _process(self, pipe: Any, func: Callable, arg: Any) -> None:
try:
collector = logging.LogCollector()
with collector.collect():
if arg is None:
ret = func()
else:
ret = func(arg)
failed = False
except BaseException as err:
failed = True
errmsg = traceback.format_exception_only(err.__class__, err)[0].strip()
ret = (errmsg, traceback.format_exc())
logging.convert_serializable(collector.logs)
pipe.send((failed, collector.logs, ret))
def add_task(
self, task_func: Callable, arg: Any = None, result_func: Callable | None = None,
) -> None:
tid = self._taskid
self._taskid += 1
self._result_funcs[tid] = result_func or (lambda arg, result: None)
self._args[tid] = arg
precv, psend = multiprocessing.Pipe(False)
context: Any = multiprocessing.get_context('fork')
proc = context.Process(target=self._process, args=(psend, task_func, arg))
self._procs[tid] = proc
self._precvsWaiting[tid] = precv
self._join_one()
def join(self) -> None:
try:
while self._pworking:
if not self._join_one():
time.sleep(0.02)
finally:
# shutdown other child processes on failure
self.terminate()
def terminate(self) -> None:
for tid in list(self._precvs):
self._procs[tid].terminate()
self._result_funcs.pop(tid)
self._procs.pop(tid)
self._precvs.pop(tid)
self._pworking -= 1
def _join_one(self) -> bool:
joined_any = False
for tid, pipe in self._precvs.items():
if pipe.poll():
exc, logs, result = pipe.recv()
if exc:
raise SphinxParallelError(*result)
for log in logs:
logger.handle(log)
self._result_funcs.pop(tid)(self._args.pop(tid), result)
self._procs[tid].join()
self._precvs.pop(tid)
self._pworking -= 1
joined_any = True
break
while self._precvsWaiting and self._pworking < self.nproc:
newtid, newprecv = self._precvsWaiting.popitem()
self._precvs[newtid] = newprecv
self._procs[newtid].start()
self._pworking += 1
return joined_any
def make_chunks(arguments: Sequence[str], nproc: int, maxbatch: int = 10) -> list[Any]:
# determine how many documents to read in one go
nargs = len(arguments)
chunksize = nargs // nproc
if chunksize >= maxbatch:
# try to improve batch size vs. number of batches
chunksize = int(sqrt(nargs / nproc * maxbatch))
if chunksize == 0:
chunksize = 1
nchunks, rest = divmod(nargs, chunksize)
if rest:
nchunks += 1
# partition documents in "chunks" that will be written by one Process
return [arguments[i * chunksize:(i + 1) * chunksize] for i in range(nchunks)]
|
