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# Copyright 2023-present the HuggingFace Inc. 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 pytest
import torch
from torch import nn
from transformers import AutoModelForCausalLM
from peft import LoraConfig, get_peft_model
class ModelWithModuleDict(nn.Module):
def __init__(self):
super().__init__()
self.other_layer = nn.Linear(10, 10)
self.module = nn.ModuleDict({"foo": nn.Linear(10, 10)})
def forward(self):
return self.module["foo"](torch.rand(1, 10))
class ModelWithModuleList(nn.Module):
def __init__(self):
super().__init__()
self.other_layer = nn.Linear(10, 10)
self.module = nn.ModuleList([nn.Linear(10, 10)])
def forward(self):
return self.module[0](torch.rand(1, 10))
class ModelWithParameterDict(nn.Module):
def __init__(self):
super().__init__()
self.other_layer = nn.Linear(10, 10)
self.module = nn.ParameterDict({"foo": nn.Parameter(torch.rand(10, 10))})
def forward(self):
return self.module["foo"]
class ModelWithParameterList(nn.Module):
def __init__(self):
super().__init__()
self.other_layer = nn.Linear(10, 10)
self.module = nn.ParameterList([nn.Parameter(torch.rand(10, 10))])
def forward(self):
return self.module[0]
@pytest.mark.parametrize(
"cls", [ModelWithModuleDict, ModelWithModuleList, ModelWithParameterDict, ModelWithParameterList]
)
def test_modules_to_save_targets_module_dict_raises(cls):
model = cls()
peft_config = LoraConfig(
target_modules=["other_layer"],
modules_to_save=["module"],
)
model() # sanity check that the model would normally work
msg = "modules_to_save cannot be applied to modules of type"
with pytest.raises(TypeError, match=msg):
get_peft_model(model=model, peft_config=peft_config)
def test_get_peft_model_revision_warning(tmp_path):
base_model_id = "peft-internal-testing/tiny-random-BertModel"
base_revision = "v2.0.0"
base_model = AutoModelForCausalLM.from_pretrained(base_model_id, revision=base_revision).eval()
lora_config = LoraConfig(revision=base_revision)
overwrite_revision = "main"
overwrite_warning = f"peft config has already set base model revision to {base_revision}, overwriting with revision {overwrite_revision}"
with pytest.warns(UserWarning, match=overwrite_warning):
_ = get_peft_model(base_model, lora_config, revision=overwrite_revision)
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