mirror of
https://github.com/modelscope/modelscope.git
synced 2025-12-16 08:17:45 +01:00
a58be34384
Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/12770413 * add prompt and lora * add adapter * add prefix * add tests * adapter smoke test passed * prompt test passed * support model id in petl * migrate chatglm6b * add train script for chatglm6b * move gen_kwargs to finetune.py * add chatglm2 * add model definination
119 lines
3.9 KiB
Python
119 lines
3.9 KiB
Python
# Copyright (c) Alibaba, Inc. and its affiliates.
|
|
import os
|
|
import shutil
|
|
import tempfile
|
|
import unittest
|
|
|
|
import numpy as np
|
|
import torch
|
|
|
|
from modelscope.hub.snapshot_download import snapshot_download
|
|
from modelscope.models.base import Model
|
|
from modelscope.msdatasets import MsDataset
|
|
from modelscope.pipelines import pipeline
|
|
from modelscope.swift import Swift
|
|
from modelscope.swift.lora import (Linear, LoRA, LoRAConfig,
|
|
mark_only_lora_as_trainable)
|
|
from modelscope.trainers import build_trainer
|
|
from modelscope.utils.constant import ModelFile, Tasks
|
|
from modelscope.utils.test_utils import test_level
|
|
|
|
|
|
class TestLora(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()
|
|
|
|
@unittest.skipUnless(test_level() >= 0, 'skip in this level')
|
|
def test_lora_base(self):
|
|
|
|
class TestModel(torch.nn.Module):
|
|
|
|
def __init__(self):
|
|
super().__init__()
|
|
self.lora = Linear(16, 16, r=4)
|
|
|
|
model = TestModel()
|
|
mark_only_lora_as_trainable(model)
|
|
model.train()
|
|
loss = model.lora(torch.ones(16, 16))
|
|
loss = loss.sum()
|
|
loss.backward()
|
|
|
|
model = TestModel()
|
|
mark_only_lora_as_trainable(model)
|
|
model.eval()
|
|
loss = model.lora(torch.ones(16, 16))
|
|
loss = loss.sum()
|
|
try:
|
|
loss.backward()
|
|
except Exception:
|
|
pass
|
|
else:
|
|
raise Exception('No tensor needs grad, should throw en error here')
|
|
|
|
@unittest.skipUnless(test_level() >= 0, 'skip in this level')
|
|
def test_lora_smoke_test(self):
|
|
dataset = MsDataset.load(
|
|
'clue', subset_name='afqmc',
|
|
split='train').to_hf_dataset().select(range(2))
|
|
|
|
model_dir = snapshot_download(
|
|
'damo/nlp_structbert_sentence-similarity_chinese-tiny')
|
|
model = Model.from_pretrained(model_dir, adv_grad_factor=None)
|
|
|
|
cfg_file = os.path.join(model_dir, 'configuration.json')
|
|
lora_config = LoRAConfig(replace_modules=['query', 'key', 'value'])
|
|
model = Swift.prepare_model(model, lora_config)
|
|
|
|
kwargs = dict(
|
|
model=model,
|
|
cfg_file=cfg_file,
|
|
train_dataset=dataset,
|
|
eval_dataset=dataset,
|
|
work_dir=self.tmp_dir)
|
|
|
|
trainer = build_trainer(default_args=kwargs)
|
|
trainer.train()
|
|
output_dir = os.path.join(self.tmp_dir, ModelFile.TRAIN_OUTPUT_DIR)
|
|
|
|
def pipeline_sentence_similarity(model_dir):
|
|
model = Model.from_pretrained(model_dir)
|
|
lora_config.pretrained_weights = output_dir
|
|
Swift.prepare_model(model, lora_config)
|
|
model.load_state_dict(
|
|
torch.load(os.path.join(output_dir, 'pytorch_model.bin')))
|
|
model.eval()
|
|
pipeline_ins = pipeline(
|
|
task=Tasks.sentence_similarity, model=model)
|
|
return pipeline_ins(input=('test', 'this is a test'))
|
|
|
|
output1 = pipeline_sentence_similarity(
|
|
'damo/nlp_structbert_sentence-similarity_chinese-tiny')
|
|
|
|
LoRA.unpatch_lora(model, lora_config)
|
|
model.save_pretrained(
|
|
output_dir, save_checkpoint_names='pytorch_model.bin')
|
|
|
|
def pipeline_sentence_similarity_origin():
|
|
model = Model.from_pretrained(output_dir)
|
|
model.eval()
|
|
pipeline_ins = pipeline(
|
|
task=Tasks.sentence_similarity, model=model)
|
|
return pipeline_ins(input=('test', 'this is a test'))
|
|
|
|
output2 = pipeline_sentence_similarity_origin()
|
|
print(output1, output2)
|
|
self.assertTrue(all(np.isclose(output1['scores'], output2['scores'])))
|
|
|
|
|
|
if __name__ == '__main__':
|
|
unittest.main()
|