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launcher.py 5.1 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 os
  12. import queue
  13. from .. import _exit
  14. from ..core._imperative_rt.core2 import full_sync
  15. from ..logger import get_logger
  16. from .group import group_barrier, init_process_group
  17. from .helper import _check_device_initialized, get_device_count_by_fork
  18. from .server import Client, Server
  19. WARN_SUBPROCESS_EXIT_WITHOUT_RETURN = (
  20. "subprocess exited with code 0 but did not return a value"
  21. )
  22. def _run_wrapped(
  23. func,
  24. is_multimachine,
  25. master_ip,
  26. port,
  27. world_size,
  28. rank,
  29. dev,
  30. device_type,
  31. args,
  32. kwargs,
  33. queue: mp.Queue,
  34. ):
  35. """Init distributed process group and run wrapped function."""
  36. _check_device_initialized(device_type)
  37. init_process_group(
  38. master_ip=master_ip,
  39. port=port,
  40. world_size=world_size,
  41. rank=rank,
  42. device=dev,
  43. device_type=device_type,
  44. )
  45. # set NCCL_LAUNCH_MODE to avoid deadlock
  46. os.environ["NCCL_LAUNCH_MODE"] = "PARALLEL"
  47. if is_multimachine:
  48. group_barrier()
  49. ret = func(*args, **kwargs)
  50. queue.put((dev, ret))
  51. full_sync()
  52. if is_multimachine:
  53. group_barrier()
  54. _exit(0)
  55. class launcher:
  56. """Decorator for launching multiple processes in single-machine multi-gpu training.
  57. :param func: the function you want to launch in distributed mode.
  58. :param n_gpus: how many devices each node.
  59. :param world_size: how many devices totally.
  60. :param rank_start: start number for rank.
  61. :param master_ip: ip address for master node (where the rank 0 is).
  62. :param port: server port for distributed server.
  63. """
  64. def __new__(cls, *args, **kwargs):
  65. if not args:
  66. return functools.partial(cls, **kwargs)
  67. return super().__new__(cls)
  68. def __init__(
  69. self,
  70. func,
  71. n_gpus=None,
  72. world_size=None,
  73. rank_start=0,
  74. master_ip="localhost",
  75. port=0,
  76. device_type="xpu",
  77. ):
  78. self.func = func
  79. self.n_gpus = (
  80. n_gpus if n_gpus is not None else get_device_count_by_fork(device_type)
  81. )
  82. self.world_size = world_size if world_size is not None else self.n_gpus
  83. self.rank_start = rank_start
  84. self.master_ip = master_ip
  85. self.port = port
  86. self.device_type = device_type
  87. # master node create server
  88. if self.rank_start == 0:
  89. self.server = Server(self.port)
  90. self.port = self.server.py_server_port
  91. else:
  92. assert self.port != 0, "you have to assign a port for distributed server"
  93. def __call__(self, *args, **kwargs):
  94. procs = []
  95. queue = mp.Queue(self.n_gpus)
  96. results = [None] * self.n_gpus
  97. for dev in range(self.n_gpus):
  98. p = mp.Process(
  99. target=_run_wrapped,
  100. args=(
  101. self.func,
  102. self.world_size > self.n_gpus,
  103. self.master_ip,
  104. self.port,
  105. self.world_size,
  106. dev + self.rank_start,
  107. dev,
  108. self.device_type,
  109. args,
  110. kwargs,
  111. queue,
  112. ),
  113. )
  114. p.start()
  115. procs.append(p)
  116. devs = list(range(self.n_gpus))
  117. def terminate():
  118. for dev in devs:
  119. procs[dev].terminate()
  120. devs.clear()
  121. result_count = 0
  122. while len(devs) > 0:
  123. left = []
  124. # check all processes in one second
  125. time_to_wait = 1.0 / len(devs)
  126. for dev in devs:
  127. procs[dev].join(time_to_wait)
  128. code = procs[dev].exitcode
  129. # terminate processes if one of them has failed
  130. if code != 0 and code != None:
  131. terminate()
  132. assert (
  133. code == 0 or code == None
  134. ), "subprocess {} exit with code {}".format(dev + self.rank_start, code)
  135. if code == None:
  136. left.append(dev)
  137. # DO NOT delete it, multiprocess.Queue has small buffer
  138. # fetch data early to avoid dead lock
  139. if not queue.empty():
  140. result_count += 1
  141. dev, ret = queue.get_nowait()
  142. results[dev] = ret
  143. devs = left
  144. while not queue.empty():
  145. result_count += 1
  146. dev, ret = queue.get_nowait()
  147. results[dev] = ret
  148. if result_count < self.n_gpus:
  149. get_logger().warning(WARN_SUBPROCESS_EXIT_WITHOUT_RETURN)
  150. 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