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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 pytest
from llamafactory.train.test_utils import compare_model, load_infer_model, load_reference_model, load_train_model
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",
}
OS_NAME = os.environ.get("OS_NAME", "")
@pytest.mark.xfail(OS_NAME.startswith("windows"), reason="Known connection error on Windows.")
def test_pissa_train():
model = load_train_model(**TRAIN_ARGS)
ref_model = load_reference_model(TINY_LLAMA_PISSA, TINY_LLAMA_PISSA, use_pissa=True, is_trainable=True)
compare_model(model, ref_model)
@pytest.mark.xfail(OS_NAME.startswith("windows"), reason="Known connection error on Windows.")
def test_pissa_inference():
model = load_infer_model(**INFER_ARGS)
ref_model = load_reference_model(TINY_LLAMA_PISSA, TINY_LLAMA_PISSA, use_pissa=True, is_trainable=False)
ref_model = ref_model.merge_and_unload()
compare_model(model, ref_model)
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