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65 行
2.1 KiB
Python
65 行
2.1 KiB
Python
# Copyright 2025 the LlamaFactory team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import os
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import pytest
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from llamafactory.train.test_utils import compare_model, load_infer_model, load_reference_model, load_train_model
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TINY_LLAMA3 = os.getenv("TINY_LLAMA3", "llamafactory/tiny-random-Llama-3")
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TINY_LLAMA_PISSA = os.getenv("TINY_LLAMA_ADAPTER", "llamafactory/tiny-random-Llama-3-pissa")
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TRAIN_ARGS = {
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"model_name_or_path": TINY_LLAMA3,
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"stage": "sft",
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"do_train": True,
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"finetuning_type": "lora",
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"pissa_init": True,
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"pissa_iter": -1,
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"dataset": "llamafactory/tiny-supervised-dataset",
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"dataset_dir": "ONLINE",
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"template": "llama3",
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"cutoff_len": 1024,
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"output_dir": "dummy_dir",
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"overwrite_output_dir": True,
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"fp16": True,
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}
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INFER_ARGS = {
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"model_name_or_path": TINY_LLAMA_PISSA,
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"adapter_name_or_path": TINY_LLAMA_PISSA,
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"adapter_folder": "pissa_init",
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"finetuning_type": "lora",
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"template": "llama3",
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"infer_dtype": "float16",
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}
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@pytest.mark.xfail(reason="PiSSA initialization is not stable in different platform.")
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def test_pissa_train():
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model = load_train_model(**TRAIN_ARGS)
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ref_model = load_reference_model(TINY_LLAMA_PISSA, TINY_LLAMA_PISSA, use_pissa=True, is_trainable=True)
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compare_model(model, ref_model)
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@pytest.mark.xfail(reason="Known connection error.")
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def test_pissa_inference():
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model = load_infer_model(**INFER_ARGS)
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ref_model = load_reference_model(TINY_LLAMA_PISSA, TINY_LLAMA_PISSA, use_pissa=True, is_trainable=False)
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ref_model = ref_model.merge_and_unload()
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compare_model(model, ref_model)
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