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| from typing import TYPE_CHECKING, Optional, Union
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| import torch
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| from peft import PeftModel
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| from transformers import AutoModelForCausalLM
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| from trl import AutoModelForCausalLMWithValueHead
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| from ..data import get_dataset, get_template_and_fix_tokenizer
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| from ..extras.misc import get_current_device
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| from ..hparams import get_infer_args, get_train_args
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| from ..model import load_model, load_tokenizer
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| if TYPE_CHECKING:
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| from peft import LoraModel
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| from transformers import PreTrainedModel
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| from ..data.data_utils import DatasetModule
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| def compare_model(model_a: "torch.nn.Module", model_b: "torch.nn.Module", diff_keys: list[str] = []) -> None:
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| state_dict_a = model_a.state_dict()
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| state_dict_b = model_b.state_dict()
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| assert set(state_dict_a.keys()) == set(state_dict_b.keys())
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| for name in state_dict_a.keys():
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| if any(key in name for key in diff_keys):
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| assert torch.allclose(state_dict_a[name], state_dict_b[name], rtol=1e-4, atol=1e-5) is False
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| else:
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| assert torch.allclose(state_dict_a[name], state_dict_b[name], rtol=1e-4, atol=1e-5) is True
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| def check_lora_model(model: "LoraModel") -> tuple[set[str], set[str]]:
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| linear_modules, extra_modules = set(), set()
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| for name, param in model.named_parameters():
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| if any(module in name for module in ["lora_A", "lora_B"]):
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| linear_modules.add(name.split(".lora_", maxsplit=1)[0].split(".")[-1])
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| assert param.requires_grad is True
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| assert param.dtype == torch.float32
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| elif "modules_to_save" in name:
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| extra_modules.add(name.split(".modules_to_save", maxsplit=1)[0].split(".")[-1])
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| assert param.requires_grad is True
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| assert param.dtype == torch.float32
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| else:
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| assert param.requires_grad is False
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| assert param.dtype == torch.float16
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| return linear_modules, extra_modules
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| def load_train_model(add_valuehead: bool = False, **kwargs) -> "PreTrainedModel":
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| model_args, _, _, finetuning_args, _ = get_train_args(kwargs)
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| tokenizer = load_tokenizer(model_args)["tokenizer"]
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| return load_model(tokenizer, model_args, finetuning_args, is_trainable=True, add_valuehead=add_valuehead)
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| def load_infer_model(add_valuehead: bool = False, **kwargs) -> "PreTrainedModel":
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| model_args, _, finetuning_args, _ = get_infer_args(kwargs)
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| tokenizer = load_tokenizer(model_args)["tokenizer"]
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| return load_model(tokenizer, model_args, finetuning_args, is_trainable=False, add_valuehead=add_valuehead)
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| def load_reference_model(
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| model_path: str,
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| lora_path: Optional[str] = None,
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| use_lora: bool = False,
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| use_pissa: bool = False,
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| is_trainable: bool = False,
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| add_valuehead: bool = False,
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| ) -> Union["PreTrainedModel", "LoraModel"]:
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| current_device = get_current_device()
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| if add_valuehead:
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| model: AutoModelForCausalLMWithValueHead = AutoModelForCausalLMWithValueHead.from_pretrained(
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| model_path, torch_dtype=torch.float16, device_map=current_device
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| )
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| if not is_trainable:
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| model.v_head = model.v_head.to(torch.float16)
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| return model
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| model = AutoModelForCausalLM.from_pretrained(model_path, torch_dtype=torch.float16, device_map=current_device)
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| if use_lora or use_pissa:
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| model = PeftModel.from_pretrained(
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| model, lora_path, subfolder="pissa_init" if use_pissa else None, is_trainable=is_trainable
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| )
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| for param in filter(lambda p: p.requires_grad, model.parameters()):
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| param.data = param.data.to(torch.float32)
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| return model
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| def load_dataset_module(**kwargs) -> "DatasetModule":
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| model_args, data_args, training_args, _, _ = get_train_args(kwargs)
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| tokenizer_module = load_tokenizer(model_args)
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| template = get_template_and_fix_tokenizer(tokenizer_module["tokenizer"], data_args)
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| dataset_module = get_dataset(template, model_args, data_args, training_args, kwargs["stage"], **tokenizer_module)
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| return dataset_module
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| def patch_valuehead_model() -> None:
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| def post_init(self: "AutoModelForCausalLMWithValueHead", state_dict: dict[str, "torch.Tensor"]) -> None:
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| state_dict = {k[7:]: state_dict[k] for k in state_dict.keys() if k.startswith("v_head.")}
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| self.v_head.load_state_dict(state_dict, strict=False)
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| del state_dict
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| AutoModelForCausalLMWithValueHead.post_init = post_init
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|
