mirror of
https://github.com/hiyouga/LLaMA-Factory.git
synced 2025-08-04 20:52:59 +08:00
120 lines
4.9 KiB
Python
120 lines
4.9 KiB
Python
# Copyright 2025 the LlamaFactory team.
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
|
|
from typing import TYPE_CHECKING, Optional, Union
|
|
|
|
import torch
|
|
from peft import PeftModel
|
|
from transformers import AutoModelForCausalLM
|
|
from trl import AutoModelForCausalLMWithValueHead
|
|
|
|
from ..data import get_dataset, get_template_and_fix_tokenizer
|
|
from ..extras.misc import get_current_device
|
|
from ..hparams import get_infer_args, get_train_args
|
|
from ..model import load_model, load_tokenizer
|
|
|
|
|
|
if TYPE_CHECKING:
|
|
from peft import LoraModel
|
|
from transformers import PreTrainedModel
|
|
|
|
from ..data.data_utils import DatasetModule
|
|
|
|
|
|
def compare_model(model_a: "torch.nn.Module", model_b: "torch.nn.Module", diff_keys: list[str] = []) -> None:
|
|
state_dict_a = model_a.state_dict()
|
|
state_dict_b = model_b.state_dict()
|
|
assert set(state_dict_a.keys()) == set(state_dict_b.keys())
|
|
for name in state_dict_a.keys():
|
|
if any(key in name for key in diff_keys):
|
|
assert torch.allclose(state_dict_a[name], state_dict_b[name], rtol=1e-4, atol=1e-5) is False
|
|
else:
|
|
assert torch.allclose(state_dict_a[name], state_dict_b[name], rtol=1e-4, atol=1e-5) is True
|
|
|
|
|
|
def check_lora_model(model: "LoraModel") -> tuple[set[str], set[str]]:
|
|
linear_modules, extra_modules = set(), set()
|
|
for name, param in model.named_parameters():
|
|
if any(module in name for module in ["lora_A", "lora_B"]):
|
|
linear_modules.add(name.split(".lora_", maxsplit=1)[0].split(".")[-1])
|
|
assert param.requires_grad is True
|
|
assert param.dtype == torch.float32
|
|
elif "modules_to_save" in name:
|
|
extra_modules.add(name.split(".modules_to_save", maxsplit=1)[0].split(".")[-1])
|
|
assert param.requires_grad is True
|
|
assert param.dtype == torch.float32
|
|
else:
|
|
assert param.requires_grad is False
|
|
assert param.dtype == torch.float16
|
|
|
|
return linear_modules, extra_modules
|
|
|
|
|
|
def load_train_model(add_valuehead: bool = False, **kwargs) -> "PreTrainedModel":
|
|
model_args, _, _, finetuning_args, _ = get_train_args(kwargs)
|
|
tokenizer = load_tokenizer(model_args)["tokenizer"]
|
|
return load_model(tokenizer, model_args, finetuning_args, is_trainable=True, add_valuehead=add_valuehead)
|
|
|
|
|
|
def load_infer_model(add_valuehead: bool = False, **kwargs) -> "PreTrainedModel":
|
|
model_args, _, finetuning_args, _ = get_infer_args(kwargs)
|
|
tokenizer = load_tokenizer(model_args)["tokenizer"]
|
|
return load_model(tokenizer, model_args, finetuning_args, is_trainable=False, add_valuehead=add_valuehead)
|
|
|
|
|
|
def load_reference_model(
|
|
model_path: str,
|
|
lora_path: Optional[str] = None,
|
|
use_lora: bool = False,
|
|
use_pissa: bool = False,
|
|
is_trainable: bool = False,
|
|
add_valuehead: bool = False,
|
|
) -> Union["PreTrainedModel", "LoraModel"]:
|
|
current_device = get_current_device()
|
|
if add_valuehead:
|
|
model: AutoModelForCausalLMWithValueHead = AutoModelForCausalLMWithValueHead.from_pretrained(
|
|
model_path, torch_dtype=torch.float16, device_map=current_device
|
|
)
|
|
if not is_trainable:
|
|
model.v_head = model.v_head.to(torch.float16)
|
|
|
|
return model
|
|
|
|
model = AutoModelForCausalLM.from_pretrained(model_path, torch_dtype=torch.float16, device_map=current_device)
|
|
if use_lora or use_pissa:
|
|
model = PeftModel.from_pretrained(
|
|
model, lora_path, subfolder="pissa_init" if use_pissa else None, is_trainable=is_trainable
|
|
)
|
|
for param in filter(lambda p: p.requires_grad, model.parameters()):
|
|
param.data = param.data.to(torch.float32)
|
|
|
|
return model
|
|
|
|
|
|
def load_dataset_module(**kwargs) -> "DatasetModule":
|
|
model_args, data_args, training_args, _, _ = get_train_args(kwargs)
|
|
tokenizer_module = load_tokenizer(model_args)
|
|
template = get_template_and_fix_tokenizer(tokenizer_module["tokenizer"], data_args)
|
|
dataset_module = get_dataset(template, model_args, data_args, training_args, kwargs["stage"], **tokenizer_module)
|
|
return dataset_module
|
|
|
|
|
|
def patch_valuehead_model() -> None:
|
|
def post_init(self: "AutoModelForCausalLMWithValueHead", state_dict: dict[str, "torch.Tensor"]) -> None:
|
|
state_dict = {k[7:]: state_dict[k] for k in state_dict.keys() if k.startswith("v_head.")}
|
|
self.v_head.load_state_dict(state_dict, strict=False)
|
|
del state_dict
|
|
|
|
AutoModelForCausalLMWithValueHead.post_init = post_init
|