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120 lines
4.4 KiB
120 lines
4.4 KiB
import torch.distributed as dist
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from colossalai.registry import DIST_GROUP_INITIALIZER
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from colossalai.global_variables import moe_env
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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_Moemodel(ProcessGroupInitializer):
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"""Model parallel initialization for MoE system.
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:param moe_moel: Size of moe model parallel
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:param moe_data: Size of moe data parallel
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:param args: Args used in base class
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:param kwargs: Kwargs used in base class
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:type moe_model: int
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:type moe_data: int
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"""
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def __init__(self, moe_model, moe_data, *args, **kwargs):
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super().__init__(*args, **kwargs)
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self.moe_model = moe_model
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self.moe_data = moe_data
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def init_dist_group(self):
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"""Initialize model parallel groups in moe parallel environment,
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and assign local_ranks and groups to each gpu.
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:return: MoE model parallelism's information
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:rtype: Tuple(local_rank, group_world_size, process_group, ranks_in_group, mode)
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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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group_world_size = None
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mode = ParallelMode.MOE_MODEL
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for i in range(self.moe_data):
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ranks = [i * self.moe_model + j for j in range(self.moe_model)]
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group = dist.new_group(ranks)
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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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ranks_in_group = ranks
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return local_rank, group_world_size, process_group, ranks_in_group, mode
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@DIST_GROUP_INITIALIZER.register_module
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class Initializer_Moedata(ProcessGroupInitializer):
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"""Data parallel initialization for MoE system.
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:param moe_moel: Size of moe model parallel
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:param moe_data: Size of moe data parallel
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:param args: Args used in base class
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:param kwargs: Kwargs used in base class
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:type moe_model: int
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:type moe_data: int
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"""
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def __init__(self, moe_model, moe_data, *args, **kwargs):
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super().__init__(*args, **kwargs)
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self.moe_model = moe_model
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self.moe_data = moe_data
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def init_dist_group(self):
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"""Initialize data parallel groups in moe parallel environment,
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and assign local_ranks and groups to each gpu.
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:return: MoE data parallelism's information
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:rtype: Tuple(local_rank, group_world_size, process_group, ranks_in_group, mode)
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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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group_world_size = None
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mode = ParallelMode.MOE_DATA
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for i in range(self.moe_model):
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ranks = [i + j * self.moe_model for j in range(self.moe_data)]
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group = dist.new_group(ranks)
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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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ranks_in_group = ranks
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return local_rank, group_world_size, process_group, ranks_in_group, mode
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@DIST_GROUP_INITIALIZER.register_module
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class Initializer_Moe(ProcessGroupInitializer):
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"""Serves as the single entry point to MoE parallel initialization.
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:param args: Args used to initialize ProcessGroupInitializer
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:param kwargs: Kwargs used to initialize ProcessGroupInitializer
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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.moe_model = moe_env.model_parallel_size
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self.moe_data = moe_env.data_parallel_size
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self.model_initializer = Initializer_Moemodel(
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self.moe_model, self.moe_data, *args, **kwargs)
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self.data_initializer = Initializer_Moedata(
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self.moe_model, self.moe_data, *args, **kwargs)
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def init_dist_group(self):
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"""Initializes MoE parallel communication groups.
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:return: MoE parallelism's information
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:rtype: list of Tuples (local_rank, group_world_size, process_group, ranks_in_group, mode)
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"""
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parallel_setting = [self.model_initializer.init_dist_group(),
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self.data_initializer.init_dist_group()]
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return parallel_setting
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