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# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# 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 tempfile
import unittest
from transformers import AutoModelForCausalLM, OPTForCausalLM
from transformers.testing_utils import require_peft, require_torch, require_torch_gpu, slow, torch_device
from transformers.utils import is_torch_available
if is_torch_available():
import torch
@require_peft
@require_torch
class PeftTesterMixin:
peft_test_model_ids = ("peft-internal-testing/tiny-OPTForCausalLM-lora",)
transformers_test_model_ids = ("hf-internal-testing/tiny-random-OPTForCausalLM",)
transformers_test_model_classes = (AutoModelForCausalLM, OPTForCausalLM)
# TODO: run it with CI after PEFT release.
@slow
class PeftIntegrationTester(unittest.TestCase, PeftTesterMixin):
"""
A testing suite that makes sure that the PeftModel class is correctly integrated into the transformers library.
"""
def _check_lora_correctly_converted(self, model):
"""
Utility method to check if the model has correctly adapters injected on it.
"""
from peft.tuners.tuners_utils import BaseTunerLayer
is_peft_loaded = False
for _, m in model.named_modules():
if isinstance(m, BaseTunerLayer):
is_peft_loaded = True
break
return is_peft_loaded
def test_peft_from_pretrained(self):
"""
Simple test that tests the basic usage of PEFT model through `from_pretrained`.
This checks if we pass a remote folder that contains an adapter config and adapter weights, it
should correctly load a model that has adapters injected on it.
"""
for model_id in self.peft_test_model_ids:
for transformers_class in self.transformers_test_model_classes:
peft_model = transformers_class.from_pretrained(model_id).to(torch_device)
self.assertTrue(self._check_lora_correctly_converted(peft_model))
self.assertTrue(peft_model._hf_peft_config_loaded)
# dummy generation
_ = peft_model.generate(input_ids=torch.LongTensor([[0, 1, 2, 3, 4, 5, 6, 7]]).to(torch_device))
def test_peft_state_dict(self):
"""
Simple test that checks if the returned state dict of `get_adapter_state_dict()` method contains
the expected keys.
"""
for model_id in self.peft_test_model_ids:
for transformers_class in self.transformers_test_model_classes:
peft_model = transformers_class.from_pretrained(model_id).to(torch_device)
state_dict = peft_model.get_adapter_state_dict()
for key in state_dict.keys():
self.assertTrue("lora" in key)
def test_peft_save_pretrained(self):
"""
Test that checks various combinations of `save_pretrained` with a model that has adapters loaded
on it. This checks if the saved model contains the expected files (adapter weights and adapter config).
"""
for model_id in self.peft_test_model_ids:
for transformers_class in self.transformers_test_model_classes:
peft_model = transformers_class.from_pretrained(model_id).to(torch_device)
with tempfile.TemporaryDirectory() as tmpdirname:
peft_model.save_pretrained(tmpdirname)
self.assertTrue("adapter_model.bin" in os.listdir(tmpdirname))
self.assertTrue("adapter_config.json" in os.listdir(tmpdirname))
self.assertTrue("config.json" not in os.listdir(tmpdirname))
self.assertTrue("pytorch_model.bin" not in os.listdir(tmpdirname))
peft_model = transformers_class.from_pretrained(tmpdirname).to(torch_device)
self.assertTrue(self._check_lora_correctly_converted(peft_model))
peft_model.save_pretrained(tmpdirname, safe_serialization=True)
self.assertTrue("adapter_model.safetensors" in os.listdir(tmpdirname))
self.assertTrue("adapter_config.json" in os.listdir(tmpdirname))
peft_model = transformers_class.from_pretrained(tmpdirname).to(torch_device)
self.assertTrue(self._check_lora_correctly_converted(peft_model))
def test_peft_enable_disable_adapters(self):
"""
A test that checks if `enable_adapters` and `disable_adapters` methods work as expected.
"""
from peft import LoraConfig
dummy_input = torch.LongTensor([[0, 1, 2, 3, 4, 5, 6, 7]]).to(torch_device)
for model_id in self.transformers_test_model_ids:
for transformers_class in self.transformers_test_model_classes:
peft_model = transformers_class.from_pretrained(model_id).to(torch_device)
peft_config = LoraConfig(init_lora_weights=False)
peft_model.add_adapter(peft_config)
peft_logits = peft_model(dummy_input).logits
peft_model.disable_adapters()
peft_logits_disabled = peft_model(dummy_input).logits
peft_model.enable_adapters()
peft_logits_enabled = peft_model(dummy_input).logits
self.assertTrue(torch.allclose(peft_logits, peft_logits_enabled, atol=1e-12, rtol=1e-12))
self.assertFalse(torch.allclose(peft_logits_enabled, peft_logits_disabled, atol=1e-12, rtol=1e-12))
def test_peft_add_adapter(self):
"""
Simple test that tests if `add_adapter` works as expected
"""
from peft import LoraConfig
for model_id in self.transformers_test_model_ids:
for transformers_class in self.transformers_test_model_classes:
model = transformers_class.from_pretrained(model_id).to(torch_device)
peft_config = LoraConfig(init_lora_weights=False)
model.add_adapter(peft_config)
self.assertTrue(self._check_lora_correctly_converted(model))
# dummy generation
_ = model.generate(input_ids=torch.LongTensor([[0, 1, 2, 3, 4, 5, 6, 7]]).to(torch_device))
def test_peft_add_adapter_from_pretrained(self):
"""
Simple test that tests if `add_adapter` works as expected
"""
from peft import LoraConfig
for model_id in self.transformers_test_model_ids:
for transformers_class in self.transformers_test_model_classes:
model = transformers_class.from_pretrained(model_id).to(torch_device)
peft_config = LoraConfig(init_lora_weights=False)
model.add_adapter(peft_config)
self.assertTrue(self._check_lora_correctly_converted(model))
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(tmpdirname)
model_from_pretrained = transformers_class.from_pretrained(tmpdirname).to(torch_device)
self.assertTrue(self._check_lora_correctly_converted(model_from_pretrained))
def test_peft_add_multi_adapter(self):
"""
Simple test that tests the basic usage of PEFT model through `from_pretrained`. This test tests if
add_adapter works as expected in multi-adapter setting.
"""
from peft import LoraConfig
from peft.tuners.tuners_utils import BaseTunerLayer
dummy_input = torch.LongTensor([[0, 1, 2, 3, 4, 5, 6, 7]]).to(torch_device)
for model_id in self.transformers_test_model_ids:
for transformers_class in self.transformers_test_model_classes:
is_peft_loaded = False
model = transformers_class.from_pretrained(model_id).to(torch_device)
logits_original_model = model(dummy_input).logits
peft_config = LoraConfig(init_lora_weights=False)
model.add_adapter(peft_config)
logits_adapter_1 = model(dummy_input)
model.add_adapter(peft_config, adapter_name="adapter-2")
logits_adapter_2 = model(dummy_input)
for _, m in model.named_modules():
if isinstance(m, BaseTunerLayer):
is_peft_loaded = True
break
self.assertTrue(is_peft_loaded)
# dummy generation
_ = model.generate(input_ids=dummy_input)
model.set_adapter("default")
self.assertTrue(model.active_adapter() == "default")
model.set_adapter("adapter-2")
self.assertTrue(model.active_adapter() == "adapter-2")
# Logits comparison
self.assertFalse(
torch.allclose(logits_adapter_1.logits, logits_adapter_2.logits, atol=1e-6, rtol=1e-6)
)
self.assertFalse(torch.allclose(logits_original_model, logits_adapter_2.logits, atol=1e-6, rtol=1e-6))
@require_torch_gpu
def test_peft_from_pretrained_kwargs(self):
"""
Simple test that tests the basic usage of PEFT model through `from_pretrained` + additional kwargs
and see if the integraiton behaves as expected.
"""
for model_id in self.peft_test_model_ids:
for transformers_class in self.transformers_test_model_classes:
peft_model = transformers_class.from_pretrained(model_id, load_in_8bit=True, device_map="auto")
module = peft_model.model.decoder.layers[0].self_attn.v_proj
self.assertTrue(module.__class__.__name__ == "Linear8bitLt")
self.assertTrue(peft_model.hf_device_map is not None)
# dummy generation
_ = peft_model.generate(input_ids=torch.LongTensor([[0, 1, 2, 3, 4, 5, 6, 7]]).to(torch_device))
def test_peft_pipeline(self):
"""
Simple test that tests the basic usage of PEFT model + pipeline
"""
from transformers import pipeline
for model_id in self.peft_test_model_ids:
pipe = pipeline("text-generation", model_id)
_ = pipe("Hello")
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