mirror of https://github.com/hpcaitech/ColossalAI
64 lines
2.7 KiB
Python
64 lines
2.7 KiB
Python
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#!/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.registry import DIST_GROUP_INITIALIZER
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from .process_group_initializer import ProcessGroupInitializer
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from ..parallel_mode import ParallelMode
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@DIST_GROUP_INITIALIZER.register_module
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class Initializer_Pipeline(ProcessGroupInitializer):
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def __init__(self, *args, **kwargs):
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super().__init__(*args, **kwargs)
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self.data_group_size = self.world_size // self.data_parallel_size
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self.pipeline_stage_size = self.data_group_size // self.pipeline_parallel_size
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def init_dist_group(self):
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dist_settings = list()
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for i in range(self.data_parallel_size):
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for j in range(self.pipeline_stage_size):
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pipe_ranks = list(
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range(i * self.data_group_size + j,
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(i + 1) * self.data_group_size,
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self.pipeline_stage_size))
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pipe_group_size = len(pipe_ranks)
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pipe_group = dist.new_group(pipe_ranks)
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if self.rank in pipe_ranks:
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local_rank = pipe_ranks.index(self.rank)
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group_world_size = pipe_group_size
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process_group = pipe_group
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ranks_in_group = pipe_ranks
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dist_settings.append(
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tuple((local_rank, group_world_size,
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process_group, ranks_in_group,
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ParallelMode.PIPELINE)))
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for k in range(pipe_group_size):
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first = pipe_ranks[k]
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second = pipe_ranks[(k + 1) % pipe_group_size]
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ranks = [first, second]
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group = dist.new_group(ranks)
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if self.rank == first:
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local_rank = 0
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group_world_size = 2
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process_group = group
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ranks_in_group = ranks
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dist_settings.append(
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tuple((local_rank, group_world_size,
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process_group, ranks_in_group,
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ParallelMode.PIPELINE_NEXT)))
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elif self.rank == second:
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local_rank = 1
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group_world_size = 2
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process_group = group
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ranks_in_group = ranks
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dist_settings.append(
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tuple((local_rank, group_world_size,
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process_group, ranks_in_group,
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ParallelMode.PIPELINE_PREV)))
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return dist_settings
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