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- from typing import Tuple
- from torch import nn
- def replace_submodule(model: nn.Module, module_name: str,
- new_module: nn.Module) -> nn.Module:
- """Replace a submodule in a model with a new module."""
- parent = model.get_submodule(".".join(module_name.split(".")[:-1]))
- target_name = module_name.split(".")[-1]
- setattr(parent, target_name, new_module)
- return new_module
- def parse_fine_tuned_lora_name(name: str) -> Tuple[str, bool]:
- """Parse the name of lora weights.
- args:
- name: the name of the fine-tuned LoRA, e.g.
- base_model.model.dense1.weight
- return:
- Tuple(module_name, is_lora_a):
- module_name: the name of the module, e.g. model.dense1,
- is_lora_a whether the tensor is lora_a or lora_b.
- """
- parts = name.split(".")
- assert parts[0] == "base_model"
- assert parts[1] == "model"
- if parts[-1] == "weight":
- if parts[-2] == "lora_A" or parts[-2] == "lora_B":
- return ".".join(parts[2:-2]), parts[-2] == "lora_A"
- else:
- # Handle the case where the tensor name is
- # "base_model.model.lm_head.weight"
- return ".".join(parts[2:]), True
- if parts[-1] == "lora_embedding_A" or parts[-1] == "lora_embedding_B":
- return ".".join(parts[2:-1]), parts[-1] == "lora_embedding_A"
- raise ValueError(f"{name} is unsupported format")
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