mirror of https://github.com/hpcaitech/ColossalAI
56 lines
2.0 KiB
Python
56 lines
2.0 KiB
Python
#!/usr/bin/env python
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# -*- encoding: utf-8 -*-
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from torch import distributed as dist
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from colossalai.legacy.registry import DIST_GROUP_INITIALIZER
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from ..parallel_mode import ParallelMode
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from .process_group_initializer import ProcessGroupInitializer
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@DIST_GROUP_INITIALIZER.register_module
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class Initializer_Data(ProcessGroupInitializer):
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"""A ProcessGroupInitializer for data parallelism.
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Args:
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rank (int): The rank of current process.
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world_size (int): Size of whole communication world.
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config (Config): Running configuration.
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data_parallel_size (int): Size of data parallel.
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pipeline_parallel_size (int): Size of pipeline parallel.
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tensor_parallel_size (int): Size of tensor parallel.
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"""
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def __init__(self, *args, **kwargs):
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super().__init__(*args, **kwargs)
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self.num_data_parallel_group = self.world_size // self.data_parallel_size
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def init_dist_group(self):
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"""Initialize data parallel groups, and assign local_ranks and groups to each gpu.
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Returns:
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Tuple (local_rank, group_world_size, process_group, ranks_in_group, mode):
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A Data parallelism's information tuple.
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"""
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local_rank = None
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ranks_in_group = None
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process_group = None
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cpu_group = None
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group_world_size = None
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mode = ParallelMode.DATA
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for i in range(self.num_data_parallel_group):
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ranks = [i + j * self.num_data_parallel_group for j in range(self.data_parallel_size)]
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group = dist.new_group(ranks)
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group_cpu = dist.new_group(ranks, backend="gloo") if dist.get_backend() != "gloo" else group
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if self.rank in ranks:
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local_rank = ranks.index(self.rank)
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group_world_size = len(ranks)
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process_group = group
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cpu_group = group_cpu
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ranks_in_group = ranks
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return local_rank, group_world_size, process_group, cpu_group, ranks_in_group, mode
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