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import copy
import os
import shutil
import tempfile
import unittest
import peft
import torch
from modelscope import Preprocessor
from modelscope.models.nlp.structbert import SbertConfig, SbertForSequenceClassification
from peft import PeftModel, inject_adapter_in_model
from peft.config import PeftConfigMixin
from peft.tuners.lora import Linear
from peft.utils import WEIGHTS_NAME
from torch import nn
from swift import AdaLoraConfig, LoraConfig, LoRAConfig, Swift, get_peft_model
class TestPeft(unittest.TestCase):
def setUp(self):
print(('Testing %s.%s' % (type(self).__name__, self._testMethodName)))
self.tmp_dir = tempfile.TemporaryDirectory().name
if not os.path.exists(self.tmp_dir):
os.makedirs(self.tmp_dir)
def tearDown(self):
shutil.rmtree(self.tmp_dir)
super().tearDown()
def test_peft_lora_injection(self):
model = SbertForSequenceClassification(SbertConfig())
model2 = copy.deepcopy(model)
lora_config = LoraConfig(target_modules=['query', 'key', 'value'])
model = Swift.prepare_model(model, lora_config)
model.save_pretrained(self.tmp_dir, safe_serialization=False)
with open(os.path.join(self.tmp_dir, 'configuration.json'), 'w') as f:
f.write('{}')
self.assertTrue(os.path.exists(os.path.join(self.tmp_dir, WEIGHTS_NAME)))
model2 = Swift.from_pretrained(model2, self.tmp_dir)
state_dict = model.state_dict()
state_dict2 = model2.state_dict()
for key in state_dict:
self.assertTrue(key in state_dict2)
self.assertTrue(all(torch.isclose(state_dict[key], state_dict2[key]).flatten().detach().cpu()))
@unittest.skip
def test_lora_merge(self):
def reset_lora_parameters(self, adapter_name, init_lora_weights):
if init_lora_weights is False:
return
if adapter_name == 'default':
ratio = 1.0
elif adapter_name == 'second':
ratio = 2.0
else:
ratio = 3.0
if adapter_name in self.lora_A.keys():
nn.init.ones_(self.lora_A[adapter_name].weight)
self.lora_A[adapter_name].weight.data = self.lora_A[adapter_name].weight.data * ratio
nn.init.ones_(self.lora_B[adapter_name].weight)
Linear.reset_lora_parameters = reset_lora_parameters
model = SbertForSequenceClassification(SbertConfig())
lora_config = LoRAConfig(target_modules=['query', 'key', 'value'])
model = Swift.prepare_model(model, lora_config)
lora_config2 = LoRAConfig(target_modules=['query', 'key', 'value'])
model = Swift.prepare_model(model, {'second': lora_config2})
model.add_weighted_adapter(['default', 'second'],
weights=[0.7, 0.3],
adapter_name='test',
combination_type='cat')
self.assertTrue(model.base_model.bert.encoder.layer[0].attention.self.key.active_adapter == ['test'])
model2 = SbertForSequenceClassification(SbertConfig())
lora_config = LoraConfig(target_modules=['query', 'key', 'value'])
model2 = get_peft_model(model2, lora_config)
lora_config2 = LoraConfig(target_modules=['query', 'key', 'value'])
inject_adapter_in_model(lora_config2, model2, adapter_name='second')
model2.add_weighted_adapter(['default', 'second'],
weights=[0.7, 0.3],
adapter_name='test',
combination_type='cat')
state_dict = model.state_dict()
state_dict2 = model2.state_dict()
state_dict2 = {key[len('base_model.model.'):]: value for key, value in state_dict2.items() if 'lora' in key}
for key in state_dict:
self.assertTrue(key in state_dict2)
self.assertTrue(all(torch.isclose(state_dict[key], state_dict2[key]).flatten().detach().cpu()))
preprocessor = Preprocessor.from_pretrained('damo/nlp_structbert_sentence-similarity_chinese-base')
inputs = preprocessor('how are you')
print(model(**inputs))
model.save_pretrained(self.tmp_dir)
model3 = SbertForSequenceClassification(SbertConfig())
model3 = Swift.from_pretrained(model3, self.tmp_dir)
state_dict3 = model3.state_dict()
for key in state_dict:
self.assertTrue(key in state_dict3)
self.assertTrue(all(torch.isclose(state_dict[key], state_dict3[key]).flatten().detach().cpu()))
def test_lora_reload_by_peft(self):
lora_config = LoRAConfig(target_modules=['query', 'key', 'value'])
model = SbertForSequenceClassification(SbertConfig())
model2 = copy.deepcopy(model)
model = Swift.prepare_model(model, lora_config)
model.save_pretrained(self.tmp_dir, peft_format=True)
model2 = PeftModel.from_pretrained(model2, self.tmp_dir)
state_dict = model.state_dict()
state_dict2 = model2.state_dict()
state_dict2 = {key[len('base_model.model.'):]: value for key, value in state_dict2.items() if 'lora' in key}
for key in state_dict:
self.assertTrue(key in state_dict2)
self.assertTrue(all(torch.isclose(state_dict[key], state_dict2[key]).flatten().detach().cpu()))
def test_peft_adalora_injection(self):
model = SbertForSequenceClassification(SbertConfig())
model2 = copy.deepcopy(model)
adalora_config = AdaLoraConfig(target_modules=['query', 'key', 'value'], total_step=1)
model = Swift.prepare_model(model, adalora_config)
model.save_pretrained(self.tmp_dir, safe_serialization=False)
with open(os.path.join(self.tmp_dir, 'configuration.json'), 'w') as f:
f.write('{}')
self.assertTrue(os.path.exists(os.path.join(self.tmp_dir, WEIGHTS_NAME)))
model2 = Swift.from_pretrained(model2, self.tmp_dir)
state_dict = model.state_dict()
state_dict2 = model2.state_dict()
for key in state_dict:
self.assertTrue(key in state_dict2)
self.assertTrue(all(torch.isclose(state_dict[key], state_dict2[key]).flatten().detach().cpu()))
@unittest.skip
def test_peft_lora_dtype(self):
model = SbertForSequenceClassification(SbertConfig())
model2 = copy.deepcopy(model)
model3 = copy.deepcopy(model)
lora_config = LoraConfig(target_modules=['query', 'key', 'value'], lora_dtype='float16')
model = Swift.prepare_model(model, lora_config)
model.save_pretrained(self.tmp_dir, safe_serialization=False)
self.assertTrue(os.path.exists(os.path.join(self.tmp_dir, 'additional_config.json')))
model2 = Swift.from_pretrained(model2, self.tmp_dir)
self.assertTrue(model2.base_model.model.bert.encoder.layer[0].attention.self.key.lora_A.default.weight.dtype ==
torch.float16)
self.assertTrue(model2.peft_config['default'].lora_dtype == 'float16')
state_dict = model.state_dict()
state_dict2 = model2.state_dict()
for key in state_dict:
self.assertTrue(key in state_dict2)
self.assertTrue(all(torch.isclose(state_dict[key], state_dict2[key]).flatten().detach().cpu()))
PeftConfigMixin.from_pretrained = PeftConfigMixin.from_pretrained_origin
model3 = Swift.from_pretrained(model3, self.tmp_dir)
self.assertTrue(model3.base_model.model.bert.encoder.layer[0].attention.self.key.lora_A.default.weight.dtype ==
torch.float32)
self.assertTrue(isinstance(model3.peft_config['default'], peft.LoraConfig))
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