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41 lines
1.4 KiB
41 lines
1.4 KiB
import torch
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import torch.distributed as dist
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import torch.nn as nn
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from .experts import MoeExperts
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def save_moe_model(model: nn.Module, save_path: str):
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state_dict = model.state_dict()
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if dist.get_rank() == 0:
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torch.save(state_dict, save_path)
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dist.barrier()
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def load_moe_model(model: nn.Module, load_path: str):
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state_dict = torch.load(load_path)
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for prefix, module in model.named_modules():
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if prefix.endswith('.moe_layer.experts'):
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# this module should be an Experts instance
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assert isinstance(module, MoeExperts)
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ep_rank = dist.get_rank(module.dist_info.ep_group)
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num_local = module.num_local_experts
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for i in range(num_local):
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expert_id = ep_rank * num_local + i
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for name, _ in module.experts[i].named_parameters():
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cur_key = f'{prefix}.experts.{i}.{name}'
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param_key = f'{prefix}.experts.{expert_id}.{name}'
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load_param = state_dict[param_key]
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state_dict[cur_key] = load_param
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for name, _ in module.experts[0].named_parameters():
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pop_pre = f'{prefix}.experts.'
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pop_suf = f'.{name}'
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for i in range(num_local, module.num_total_experts):
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pop_key = f'{pop_pre}{i}{pop_suf}'
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state_dict.pop(pop_key)
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model.load_state_dict(state_dict)
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