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When modifying a model with `get_peft_model` that was already modified in the same way, even specifying a different config may not change the trainable parameter count, e.g. when specifying target modules that are only a subset of the previous target modules. With this patch a warning will be issued with a hint to `.unload()` when calling `get_peft_model` on an already modified model.
56 lines
2.2 KiB
Python
56 lines
2.2 KiB
Python
# Copyright 2025-present the HuggingFace Inc. 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 pytest
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import torch
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from peft import LoraConfig, get_peft_model
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class TestGetPeftModel:
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RELOAD_WARNING_EXPECTED_MATCH = r"You are trying to modify a model .*"
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@pytest.fixture
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def lora_config_0(self):
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return LoraConfig(target_modules="0")
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@pytest.fixture
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def base_model(self):
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return torch.nn.Sequential(torch.nn.Linear(10, 2), torch.nn.Linear(2, 10))
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def test_get_peft_model_warns_when_reloading_model(self, lora_config_0, base_model):
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get_peft_model(base_model, lora_config_0)
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with pytest.warns(UserWarning, match=self.RELOAD_WARNING_EXPECTED_MATCH):
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get_peft_model(base_model, lora_config_0)
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def test_get_peft_model_proposed_fix_in_warning_helps(self, lora_config_0, base_model, recwarn):
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peft_model = get_peft_model(base_model, lora_config_0)
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peft_model.unload()
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get_peft_model(base_model, lora_config_0)
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warning_checker = pytest.warns(UserWarning, match=self.RELOAD_WARNING_EXPECTED_MATCH)
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for warning in recwarn:
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if warning_checker.matches(warning):
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pytest.fail("Warning raised even though model was unloaded.")
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def test_get_peft_model_repeated_invocation(self, lora_config_0, base_model):
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peft_model = get_peft_model(base_model, lora_config_0)
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# use direct-addressing of the other layer to accomodate for the nested model
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lora_config_1 = LoraConfig(target_modules="base_model.model.1")
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with pytest.warns(UserWarning, match=self.RELOAD_WARNING_EXPECTED_MATCH):
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get_peft_model(peft_model, lora_config_1)
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