 [to #42322933] NLP 1030 Refactor
Features:
1. Refactor the directory structure of nlp models. All model files are placed into either the model folder or the task_model folder
2. Refactor all the comments to google style
3. Add detail comments to important tasks and nlp models, to list the description of the model, and its preprocessor&trainer
4. Model Exporting now supports a direct all to TorchModelExporter(no need to derive from it)
5. Refactor model save_pretrained method to support direct running(independent from trainer)
6. Remove the judgement of Model in the pipeline base class, to support outer register models running in our pipelines
7. Nlp trainer now has a NLPTrainingArguments class , user can pass arguments into the dataclass, and use it as a normal cfg_modify_fn, to simplify the operation of modify cfg.
8. Merge the BACKBONES and the MODELS, so user can get a backbone with the Model.from_pretrained call
9. Model.from_pretrained now support a task argument, so user can use a backbone and load it with a specific task class.
10. Support Preprocessor.from_pretrained method
11. Add standard return classes to important nlp tasks, so some of the pipelines and the models are independent now, the return values of the models will always be tensors, and the pipelines will take care of the conversion to numpy and the following stuffs.
12. Split the file of the nlp preprocessors, to make the dir structure more clear.
Bugs Fixing:
1. Fix a bug that lr_scheduler can be called earlier than the optimizer's step
2. Fix a bug that the direct call of Pipelines (not from pipeline(xxx)) throws error
3. Fix a bug that the trainer will not call the correct TaskDataset class
4. Fix a bug that the internal loading of dataset will throws error in the trainer class
Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/10490585
2 years ago |
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- # Copyright (c) Alibaba, Inc. and its affiliates.
- import unittest
-
- import numpy as np
-
- from modelscope.hub.api import HubApi
- from modelscope.hub.snapshot_download import snapshot_download
- from modelscope.models import Model
- from modelscope.models.nlp import SbertForFaqQuestionAnswering
- from modelscope.pipelines import pipeline
- from modelscope.pipelines.nlp import FaqQuestionAnsweringPipeline
- from modelscope.preprocessors import FaqQuestionAnsweringPreprocessor
- from modelscope.utils.constant import Tasks
- from modelscope.utils.demo_utils import DemoCompatibilityCheck
- from modelscope.utils.test_utils import test_level
-
-
- class FaqQuestionAnsweringTest(unittest.TestCase, DemoCompatibilityCheck):
-
- def setUp(self) -> None:
- self.task = Tasks.faq_question_answering
- self.model_id = 'damo/nlp_structbert_faq-question-answering_chinese-base'
-
- param = {
- 'query_set': ['如何使用优惠券', '在哪里领券', '在哪里领券'],
- 'support_set': [{
- 'text': '卖品代金券怎么用',
- 'label': '6527856'
- }, {
- 'text': '怎么使用优惠券',
- 'label': '6527856'
- }, {
- 'text': '这个可以一起领吗',
- 'label': '1000012000'
- }, {
- 'text': '付款时送的优惠券哪里领',
- 'label': '1000012000'
- }, {
- 'text': '购物等级怎么长',
- 'label': '13421097'
- }, {
- 'text': '购物等级二心',
- 'label': '13421097'
- }]
- }
-
- @unittest.skipUnless(test_level() >= 2, 'skip test in current test level')
- def test_run_with_direct_file_download(self):
- cache_path = snapshot_download(self.model_id)
- preprocessor = FaqQuestionAnsweringPreprocessor.from_pretrained(
- cache_path)
- model = SbertForFaqQuestionAnswering.from_pretrained(cache_path)
- pipeline_ins = FaqQuestionAnsweringPipeline(
- model, preprocessor=preprocessor)
- result = pipeline_ins(self.param)
- print(result)
-
- @unittest.skipUnless(test_level() >= 1, 'skip test in current test level')
- def test_run_with_model_from_modelhub(self):
- model = Model.from_pretrained(self.model_id)
- preprocessor = FaqQuestionAnsweringPreprocessor(model.model_dir)
- pipeline_ins = pipeline(
- task=Tasks.faq_question_answering,
- model=model,
- preprocessor=preprocessor)
- result = pipeline_ins(self.param)
- print(result)
-
- @unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
- def test_run_with_model_name(self):
- pipeline_ins = pipeline(
- task=Tasks.faq_question_answering, model=self.model_id)
- result = pipeline_ins(self.param)
- print(result)
-
- @unittest.skipUnless(test_level() >= 2, 'skip test in current test level')
- def test_run_with_default_model(self):
- pipeline_ins = pipeline(task=Tasks.faq_question_answering)
- print(pipeline_ins(self.param, max_seq_length=20))
-
- @unittest.skipUnless(test_level() >= 2, 'skip test in current test level')
- def test_sentence_embedding(self):
- pipeline_ins = pipeline(task=Tasks.faq_question_answering)
- sentence_vec = pipeline_ins.get_sentence_embedding(
- ['今天星期六', '明天星期几明天星期几'])
- print(np.shape(sentence_vec))
-
- @unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
- def test_demo_compatibility(self):
- self.compatibility_check()
-
-
- if __name__ == '__main__':
- unittest.main()
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