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launcher.py 4.9 kB

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  1. # -*- coding: utf-8 -*-
  2. # MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
  3. #
  4. # Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
  5. #
  6. # Unless required by applicable law or agreed to in writing,
  7. # software distributed under the License is distributed on an
  8. # "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  9. import functools
  10. import multiprocessing as mp
  11. import queue
  12. from ..core._imperative_rt.core2 import sync
  13. from ..logger import get_logger
  14. from .group import group_barrier, init_process_group
  15. from .helper import get_device_count_by_fork
  16. from .server import Client, Server
  17. WARN_SUBPROCESS_EXIT_WITHOUT_RETURN = (
  18. "subprocess exited with code 0 but did not return a value"
  19. )
  20. def _run_wrapped(
  21. func,
  22. is_multimachine,
  23. master_ip,
  24. port,
  25. world_size,
  26. rank,
  27. dev,
  28. device_type,
  29. args,
  30. kwargs,
  31. queue: mp.Queue,
  32. ):
  33. """Init distributed process group and run wrapped function."""
  34. init_process_group(
  35. master_ip=master_ip,
  36. port=port,
  37. world_size=world_size,
  38. rank=rank,
  39. device=dev,
  40. device_type=device_type,
  41. )
  42. if is_multimachine:
  43. group_barrier()
  44. ret = func(*args, **kwargs)
  45. queue.put((dev, ret))
  46. sync()
  47. if is_multimachine:
  48. group_barrier()
  49. class launcher:
  50. """Decorator for launching multiple processes in single-machine multi-gpu training.
  51. :param func: the function you want to launch in distributed mode.
  52. :param n_gpus: how many devices each node.
  53. :param world_size: how many devices totally.
  54. :param rank_start: start number for rank.
  55. :param master_ip: ip address for master node (where the rank 0 is).
  56. :param port: server port for distributed server.
  57. """
  58. def __new__(cls, *args, **kwargs):
  59. if not args:
  60. return functools.partial(cls, **kwargs)
  61. return super().__new__(cls)
  62. def __init__(
  63. self,
  64. func,
  65. n_gpus=None,
  66. world_size=None,
  67. rank_start=0,
  68. master_ip="localhost",
  69. port=0,
  70. device_type="xpu",
  71. ):
  72. self.func = func
  73. self.n_gpus = (
  74. n_gpus if n_gpus is not None else get_device_count_by_fork(device_type)
  75. )
  76. self.world_size = world_size if world_size is not None else self.n_gpus
  77. self.rank_start = rank_start
  78. self.master_ip = master_ip
  79. self.port = port
  80. self.device_type = device_type
  81. # master node create server
  82. if self.rank_start == 0:
  83. self.server = Server(self.port)
  84. self.port = self.server.py_server_port
  85. else:
  86. assert self.port != 0, "you have to assign a port for distributed server"
  87. def __call__(self, *args, **kwargs):
  88. procs = []
  89. queue = mp.Queue(self.n_gpus)
  90. results = [None] * self.n_gpus
  91. for dev in range(self.n_gpus):
  92. p = mp.Process(
  93. target=_run_wrapped,
  94. args=(
  95. self.func,
  96. self.world_size > self.n_gpus,
  97. self.master_ip,
  98. self.port,
  99. self.world_size,
  100. dev + self.rank_start,
  101. dev,
  102. self.device_type,
  103. args,
  104. kwargs,
  105. queue,
  106. ),
  107. )
  108. p.start()
  109. procs.append(p)
  110. devs = list(range(self.n_gpus))
  111. def terminate():
  112. for dev in devs:
  113. procs[dev].terminate()
  114. devs.clear()
  115. result_count = 0
  116. while len(devs) > 0:
  117. left = []
  118. # check all processes in one second
  119. time_to_wait = 1.0 / len(devs)
  120. for dev in devs:
  121. procs[dev].join(time_to_wait)
  122. code = procs[dev].exitcode
  123. # terminate processes if one of them has failed
  124. if code != 0 and code != None:
  125. terminate()
  126. assert (
  127. code == 0 or code == None
  128. ), "subprocess {} exit with code {}".format(dev + self.rank_start, code)
  129. if code == None:
  130. left.append(dev)
  131. # DO NOT delete it, multiprocess.Queue has small buffer
  132. # fetch data early to avoid dead lock
  133. if not queue.empty():
  134. result_count += 1
  135. dev, ret = queue.get_nowait()
  136. results[dev] = ret
  137. devs = left
  138. while not queue.empty():
  139. result_count += 1
  140. dev, ret = queue.get_nowait()
  141. results[dev] = ret
  142. if result_count < self.n_gpus:
  143. get_logger().warning(WARN_SUBPROCESS_EXIT_WITHOUT_RETURN)
  144. return results

MegEngine 安装包中集成了使用 GPU 运行代码所需的 CUDA 环境,不用区分 CPU 和 GPU 版。 如果想要运行 GPU 程序,请确保机器本身配有 GPU 硬件设备并安装好驱动。 如果你想体验在云端 GPU 算力平台进行深度学习开发的感觉,欢迎访问 MegStudio 平台