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# Copyright 2024 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.
import os
import torch
from peft import LoraModel, PeftModel
from transformers import AutoModelForCausalLM
from llamafactory.extras.misc import get_current_device
from llamafactory.hparams import get_infer_args, get_train_args
from llamafactory.model import load_model, load_tokenizer
TINY_LLAMA = os.environ.get("TINY_LLAMA", "llamafactory/tiny-random-Llama-3")
TINY_LLAMA_PISSA = os.environ.get("TINY_LLAMA_ADAPTER", "llamafactory/tiny-random-Llama-3-pissa")
TRAIN_ARGS = {
"model_name_or_path": TINY_LLAMA,
"stage": "sft",
"do_train": True,
"finetuning_type": "lora",
"pissa_init": True,
"pissa_iter": -1,
"dataset": "llamafactory/tiny-supervised-dataset",
"dataset_dir": "ONLINE",
"template": "llama3",
"cutoff_len": 1024,
"overwrite_cache": True,
"output_dir": "dummy_dir",
"overwrite_output_dir": True,
"fp16": True,
}
INFER_ARGS = {
"model_name_or_path": TINY_LLAMA_PISSA,
"adapter_name_or_path": TINY_LLAMA_PISSA,
"adapter_folder": "pissa_init",
"finetuning_type": "lora",
"template": "llama3",
"infer_dtype": "float16",
}
def compare_model(model_a: "torch.nn.Module", model_b: "torch.nn.Module"):
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():
assert torch.allclose(state_dict_a[name], state_dict_b[name], rtol=1e-4, atol=1e-5)
def test_pissa_init():
model_args, _, _, finetuning_args, _ = get_train_args(TRAIN_ARGS)
tokenizer_module = load_tokenizer(model_args)
model = load_model(tokenizer_module["tokenizer"], model_args, finetuning_args, is_trainable=True)
base_model = AutoModelForCausalLM.from_pretrained(
TINY_LLAMA_PISSA, torch_dtype=torch.float16, device_map=get_current_device()
)
ref_model = PeftModel.from_pretrained(base_model, TINY_LLAMA_PISSA, subfolder="pissa_init", is_trainable=True)
for param in filter(lambda p: p.requires_grad, ref_model.parameters()):
param.data = param.data.to(torch.float32)
compare_model(model, ref_model)
def test_pissa_inference():
model_args, _, finetuning_args, _ = get_infer_args(INFER_ARGS)
tokenizer_module = load_tokenizer(model_args)
model = load_model(tokenizer_module["tokenizer"], model_args, finetuning_args, is_trainable=False)
base_model = AutoModelForCausalLM.from_pretrained(
TINY_LLAMA_PISSA, torch_dtype=torch.float16, device_map=get_current_device()
)
ref_model: "LoraModel" = PeftModel.from_pretrained(base_model, TINY_LLAMA_PISSA, subfolder="pissa_init")
ref_model = ref_model.merge_and_unload()
compare_model(model, ref_model)
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