mirror of https://github.com/InternLM/InternLM
33 lines
990 B
Python
33 lines
990 B
Python
from typing import TYPE_CHECKING, Union
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from torch import Tensor
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from torch.nn import Module, ModuleList
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from internlm.core.context import global_context as gpc
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from internlm.moe.experts import Experts
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if TYPE_CHECKING:
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Base = Module[Tensor]
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else:
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Base = Module
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class BaseMoELayer(Base):
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"""
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Base MoE Layer.
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"""
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def __init__(
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self, gate: Module, experts: Union[Module, ModuleList], ep_group, ep_size: int, num_local_experts: int
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) -> None:
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super().__init__()
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# for elastic expert paralle, experts may have multiple groups
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expert_group_name = f"moe_ep_size_{ep_size}"
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if expert_group_name not in gpc.expert_parallel_group_names:
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gpc.expert_parallel_group_names.append(expert_group_name)
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self.gate = gate
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self.experts = Experts(experts, num_local_experts, expert_group_name)
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self.ep_group = ep_group
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self.ep_size = ep_size
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self.num_local_experts = num_local_experts
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