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47 lines
2.1 KiB
47 lines
2.1 KiB
import torch.nn as nn
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import torch.distributed as dist
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from typing import List, Tuple, Union
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from colossalai.context import ParallelMode
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from colossalai.core import global_context as gpc
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class PipelineSharedModuleWrapper:
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def __init__(self, pipeline_ranks: Union[List[int], Tuple[int]]) -> None:
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assert len(pipeline_ranks) > 1, f'Expect len(pipeline_ranks) > 1, got {len(pipeline_ranks)}'
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self.pipeline_ranks = pipeline_ranks
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self.group = None
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self.ranks_in_group = None
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self._init_group()
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def _init_group(self):
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world_size = gpc.get_world_size(ParallelMode.GLOBAL)
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dp_size = gpc.get_world_size(ParallelMode.DATA)
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pp_size = gpc.get_world_size(ParallelMode.PIPELINE)
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rank = gpc.get_global_rank()
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num_dp_groups = world_size // dp_size
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num_pp_stages = num_dp_groups // pp_size
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for i in range(dp_size):
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for j in range(num_pp_stages):
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pipeline_ranks = list(range(i * num_dp_groups + j, (i + 1) * num_dp_groups, num_pp_stages))
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sub_ranks = [pipeline_ranks[idx] for idx in self.pipeline_ranks]
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group = dist.new_group(sub_ranks)
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if rank in sub_ranks:
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self.group = group
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self.ranks_in_group = sub_ranks
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def register_module(self, module: nn.Module):
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assert self.ranks_in_group is not None,\
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f'Rank {gpc.get_local_rank(ParallelMode.PIPELINE)} is not in pipeline_ranks {self.pipeline_ranks}'
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src = self.ranks_in_group[self.pipeline_ranks[0]]
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for p in module.parameters():
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setattr(p, 'pipeline_shared_module_pg', self.group)
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dist.broadcast(p, src, group=self.group)
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def register_parameter(self, param: nn.Parameter):
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assert self.ranks_in_group is not None,\
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f'Rank {gpc.get_local_rank(ParallelMode.PIPELINE)} is not in pipeline_ranks {self.pipeline_ranks}'
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src = self.ranks_in_group[self.pipeline_ranks[0]]
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setattr(param, 'pipeline_shared_module_pg', self.group)
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dist.broadcast(param, src, group=self.group)
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