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1119f6e47a
Signed-off-by: Che Ruan <cr623@ic.ac.uk> Signed-off-by: mengxingkongzhouhan <117415539+mengxingkongzhouhan@users.noreply.github.com> Signed-off-by: Mercykid-bash <ruanche0218@gmail.com> Signed-off-by: Harry Mellor <19981378+hmellor@users.noreply.github.com> Co-authored-by: Che Ruan <cr623@ic.ac.uk> Co-authored-by: mengxingkongzhouhan <117415539+mengxingkongzhouhan@users.noreply.github.com> Co-authored-by: Harry Mellor <19981378+hmellor@users.noreply.github.com>
41 lines
1.3 KiB
Python
41 lines
1.3 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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from abc import ABC, abstractmethod
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import torch
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class AbstractEplbPolicy(ABC):
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@classmethod
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@abstractmethod
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def rebalance_experts(
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cls,
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weight: torch.Tensor,
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num_replicas: int,
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num_groups: int,
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num_nodes: int,
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num_ranks: int,
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) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
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"""
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Entry point for expert-parallelism load balancer.
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Parameters:
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weight: [layers, num_logical_experts], the load statistics
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for all logical experts
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num_replicas: number of physical experts, must be a multiple of
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`num_ranks`
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num_groups: number of expert groups
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num_nodes: number of server nodes
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num_ranks: number of ranks, must be a multiple of `num_nodes`
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Returns:
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physical_to_logical_map: [layers, num_replicas], the expert
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index of each replica
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logical_to_physical_map: [layers, num_logical_experts, X],
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the replica indices for each expert
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expert_count: [layers, num_logical_experts], number of
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physical replicas for each logical expert
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"""
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raise NotImplementedError
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