mirror of
https://github.com/hiyouga/LLaMA-Factory.git
synced 2025-08-04 20:52:59 +08:00
40 lines
1.6 KiB
Python
40 lines
1.6 KiB
Python
import torch
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from typing import TYPE_CHECKING, Dict, List, Literal, Optional, Tuple
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from llmtuner.extras.constants import LAYERNORM_NAMES
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if TYPE_CHECKING:
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from trl import AutoModelForCausalLMWithValueHead
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def replace_model(model: "AutoModelForCausalLMWithValueHead", target: Literal["default", "reward"]) -> None:
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if target == "reward": # save default head temporarily
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valuehead_state_dict = model.v_head.state_dict()
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setattr(model, "default_head_weight", valuehead_state_dict["summary.weight"])
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setattr(model, "default_head_bias", valuehead_state_dict["summary.bias"])
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model.pretrained_model.set_adapter(target) # set the LoRA adapter to be active
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model.v_head.load_state_dict({
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"summary.weight": getattr(model, "{}_head_weight".format(target)),
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"summary.bias": getattr(model, "{}_head_bias".format(target))
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})
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def cast_layernorm_dtype(
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model: "AutoModelForCausalLMWithValueHead",
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layer_norm_names: List[str] = LAYERNORM_NAMES,
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layer_norm_params: Optional[Dict[str, torch.Tensor]] = None
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) -> Tuple["AutoModelForCausalLMWithValueHead", Dict[str, torch.Tensor]]:
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layer_norm_state_dict = {}
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for name, param in model.named_parameters():
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if param.ndim == 1 and any(layer_norm_name in name for layer_norm_name in layer_norm_names):
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if layer_norm_params is not None:
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param.data = layer_norm_params[name] # restore float32 weights
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else:
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layer_norm_state_dict[name] = param.data.detach().clone() # store float32 weights for stability
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param.data = param.data.to(torch.float16)
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return model, layer_norm_state_dict
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