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sordonia
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Oct 24, 2024
| @dataclass | ||
| class PEERConfig(ModifierConfig): | ||
| n_heads: int = 8 | ||
| moe__num_experts: int = 100 |
| def experts_names(self): | ||
| return self.model.experts_names | ||
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| def get_expert_instance(self, name): |
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this is only available for multiexpert, i am not sure to add this dependency here
| module = MultiExpertModule(**vars(expert.training_config)).to("cuda") | ||
| module.add_expert_instance(expert, is_default=True) | ||
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| elif args.merge_or_route in ["peer"]: |
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can you explain what you are trying to do?
| for expert_name in module.experts_names: | ||
| expert = module.get_expert_instance(expert_name) | ||
| expert: Expert = module.get_expert_instance(expert_name) | ||
| expert.expert_info.training_config = args |
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remove this, not a good idea to store complex object in training_config, it will be transformed to a Dict in the next PR :)
| self.selector = selector or TaskNameSelector() | ||
| self._default_expert_name = None | ||
| self.expert_infos = {} | ||
| self.experts = nn.ModuleDict({}) |
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not all containers have "experts", see my last PR on making LoRA faster
| self.experts = nn.ModuleDict({}) | ||
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| @property | ||
| def num_experts(self): |
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TODO: