annotate mercurial/worker.py @ 20943:003cb972178d

merge: include ancestor node in merge actions
author Mads Kiilerich <madski@unity3d.com>
date Fri, 28 Feb 2014 02:38:33 +0100
parents 1e5b38a919dd
children b3e51675f98e
Ignore whitespace changes - Everywhere: Within whitespace: At end of lines:
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1 # worker.py - master-slave parallelism support
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2 #
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3 # Copyright 2013 Facebook, Inc.
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4 #
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5 # This software may be used and distributed according to the terms of the
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6 # GNU General Public License version 2 or any later version.
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7
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8 from i18n import _
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9 import errno, os, signal, sys, threading
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10 import util
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11
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12 def countcpus():
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13 '''try to count the number of CPUs on the system'''
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14
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15 # posix
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16 try:
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17 n = int(os.sysconf('SC_NPROCESSORS_ONLN'))
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18 if n > 0:
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19 return n
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20 except (AttributeError, ValueError):
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21 pass
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22
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23 # windows
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24 try:
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25 n = int(os.environ['NUMBER_OF_PROCESSORS'])
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26 if n > 0:
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27 return n
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28 except (KeyError, ValueError):
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29 pass
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30
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31 return 1
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32
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33 def _numworkers(ui):
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34 s = ui.config('worker', 'numcpus')
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35 if s:
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36 try:
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37 n = int(s)
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38 if n >= 1:
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39 return n
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40 except ValueError:
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41 raise util.Abort(_('number of cpus must be an integer'))
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42 return min(max(countcpus(), 4), 32)
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43
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44 if os.name == 'posix':
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45 _startupcost = 0.01
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46 else:
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47 _startupcost = 1e30
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48
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49 def worthwhile(ui, costperop, nops):
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50 '''try to determine whether the benefit of multiple processes can
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51 outweigh the cost of starting them'''
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52 linear = costperop * nops
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53 workers = _numworkers(ui)
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54 benefit = linear - (_startupcost * workers + linear / workers)
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55 return benefit >= 0.15
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56
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57 def worker(ui, costperarg, func, staticargs, args):
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58 '''run a function, possibly in parallel in multiple worker
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59 processes.
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60
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61 returns a progress iterator
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62
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63 costperarg - cost of a single task
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64
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65 func - function to run
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66
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67 staticargs - arguments to pass to every invocation of the function
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68
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69 args - arguments to split into chunks, to pass to individual
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70 workers
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71 '''
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72 if worthwhile(ui, costperarg, len(args)):
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73 return _platformworker(ui, func, staticargs, args)
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74 return func(*staticargs + (args,))
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75
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76 def _posixworker(ui, func, staticargs, args):
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77 rfd, wfd = os.pipe()
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78 workers = _numworkers(ui)
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79 oldhandler = signal.getsignal(signal.SIGINT)
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80 signal.signal(signal.SIGINT, signal.SIG_IGN)
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81 pids, problem = [], [0]
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82 for pargs in partition(args, workers):
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83 pid = os.fork()
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84 if pid == 0:
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85 signal.signal(signal.SIGINT, oldhandler)
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86 try:
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87 os.close(rfd)
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88 for i, item in func(*(staticargs + (pargs,))):
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89 os.write(wfd, '%d %s\n' % (i, item))
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90 os._exit(0)
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91 except KeyboardInterrupt:
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92 os._exit(255)
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93 # other exceptions are allowed to propagate, we rely
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94 # on lock.py's pid checks to avoid release callbacks
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95 pids.append(pid)
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96 pids.reverse()
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97 os.close(wfd)
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98 fp = os.fdopen(rfd, 'rb', 0)
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99 def killworkers():
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100 # if one worker bails, there's no good reason to wait for the rest
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101 for p in pids:
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102 try:
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103 os.kill(p, signal.SIGTERM)
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104 except OSError, err:
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105 if err.errno != errno.ESRCH:
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106 raise
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107 def waitforworkers():
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108 for _ in pids:
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109 st = _exitstatus(os.wait()[1])
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110 if st and not problem[0]:
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111 problem[0] = st
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112 killworkers()
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113 t = threading.Thread(target=waitforworkers)
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114 t.start()
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115 def cleanup():
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116 signal.signal(signal.SIGINT, oldhandler)
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117 t.join()
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118 status = problem[0]
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119 if status:
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120 if status < 0:
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121 os.kill(os.getpid(), -status)
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122 sys.exit(status)
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123 try:
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124 for line in fp:
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125 l = line.split(' ', 1)
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126 yield int(l[0]), l[1][:-1]
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127 except: # re-raises
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128 killworkers()
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129 cleanup()
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130 raise
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131 cleanup()
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132
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133 def _posixexitstatus(code):
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134 '''convert a posix exit status into the same form returned by
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135 os.spawnv
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136
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137 returns None if the process was stopped instead of exiting'''
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138 if os.WIFEXITED(code):
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139 return os.WEXITSTATUS(code)
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140 elif os.WIFSIGNALED(code):
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141 return -os.WTERMSIG(code)
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142
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143 if os.name != 'nt':
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144 _platformworker = _posixworker
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145 _exitstatus = _posixexitstatus
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146
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147 def partition(lst, nslices):
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148 '''partition a list into N slices of equal size'''
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149 n = len(lst)
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150 chunk, slop = n / nslices, n % nslices
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151 end = 0
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152 for i in xrange(nslices):
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153 start = end
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154 end = start + chunk
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155 if slop:
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156 end += 1
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157 slop -= 1
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158 yield lst[start:end]