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add tests

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suluyan
2022-06-17 00:36:25 +08:00
parent 951077c729
commit 3aa1a70ac8
7 changed files with 116 additions and 79 deletions
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2
modelscope/models/nlp/__init__.py
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@@ -1,4 +1,4 @@
from .masked_language_model import * # noqa F403
from .sentence_similarity_model import * # noqa F403
from .sequence_classification_model import * # noqa F403
from .text_generation_model import * # noqa F403
from .masked_language_model import * # noqa F403

2
modelscope/pipelines/nlp/__init__.py
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@@ -1,4 +1,4 @@
from .fill_mask_pipeline import * # noqa F403
from .sentence_similarity_pipeline import * # noqa F403
from .sequence_classification_pipeline import * # noqa F403
from .text_generation_pipeline import * # noqa F403
from .fill_mask_pipeline import * # noqa F403

27
modelscope/pipelines/nlp/fill_mask_pipeline.py
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@@ -1,5 +1,6 @@
from typing import Dict
from typing import Dict, Optional
from modelscope.models import Model
from modelscope.models.nlp import MaskedLanguageModel
from modelscope.preprocessors import FillMaskPreprocessor
from modelscope.utils.constant import Tasks
@@ -13,15 +14,23 @@ __all__ = ['FillMaskPipeline']
@PIPELINES.register_module(Tasks.fill_mask, module_name=r'veco')
class FillMaskPipeline(Pipeline):
def __init__(self, model: MaskedLanguageModel,
preprocessor: FillMaskPreprocessor, **kwargs):
"""use `model` and `preprocessor` to create a nlp text classification pipeline for prediction
def __init__(self,
model: MaskedLanguageModel,
preprocessor: Optional[FillMaskPreprocessor] = None,
**kwargs):
"""use `model` and `preprocessor` to create a nlp fill mask pipeline for prediction
Args:
model (SequenceClassificationModel): a model instance
preprocessor (SequenceClassificationPreprocessor): a preprocessor instance
model (MaskedLanguageModel): a model instance
preprocessor (FillMaskPreprocessor): a preprocessor instance
"""
sc_model = model if isinstance(
model, MaskedLanguageModel) else Model.from_pretrained(model)
if preprocessor is None:
preprocessor = FillMaskPreprocessor(
sc_model.model_dir,
first_sequence='sentence',
second_sequence=None)
super().__init__(model=model, preprocessor=preprocessor, **kwargs)
self.preprocessor = preprocessor
self.tokenizer = preprocessor.tokenizer
@@ -55,10 +64,10 @@ class FillMaskPipeline(Pipeline):
pred_string = ''.join(pred_string).replace('##', '')
pred_string = pred_string.split('[SEP]')[0].replace(
'[CLS]', '').replace('[SEP]', '').replace('[UNK]', '')
else: ## en bert
else: # en bert
pred_string = self.tokenizer.decode(ids)
pred_string = pred_string.split('[SEP]')[0].replace(
'[CLS]', '').replace('[SEP]', '').replace('[UNK]', '')
pred_strings.append(pred_string)
return {'pred_string': pred_strings}
return {'text': pred_strings}

6
modelscope/pipelines/outputs.py
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@@ -69,6 +69,12 @@ TASK_OUTPUTS = {
# }
Tasks.text_generation: ['text'],
# fill mask result for single sample
# {
# "text": "this is the text which masks filled by model."
# }
Tasks.fill_mask: ['text'],
# ============ audio tasks ===================
# ============ multi-modal tasks ===================

18
modelscope/preprocessors/nlp.py
View File

@@ -12,8 +12,7 @@ from .builder import PREPROCESSORS
__all__ = [
'Tokenize', 'SequenceClassificationPreprocessor',
'TextGenerationPreprocessor',
'FillMaskPreprocessor'
'TextGenerationPreprocessor', 'FillMaskPreprocessor'
]
@@ -173,8 +172,7 @@ class TextGenerationPreprocessor(Preprocessor):
return {k: torch.tensor(v) for k, v in rst.items()}
@PREPROCESSORS.register_module(
Fields.nlp, module_name=r'sbert')
@PREPROCESSORS.register_module(Fields.nlp, module_name=r'sbert')
class FillMaskPreprocessor(Preprocessor):
def __init__(self, model_dir: str, *args, **kwargs):
@@ -190,7 +188,8 @@ class FillMaskPreprocessor(Preprocessor):
'first_sequence')
self.sequence_length = kwargs.pop('sequence_length', 128)
self.tokenizer = AutoTokenizer.from_pretrained(model_dir)
self.tokenizer = AutoTokenizer.from_pretrained(
model_dir, use_fast=False)
@type_assert(object, str)
def __call__(self, data: str) -> Dict[str, Any]:
@@ -205,15 +204,11 @@ class FillMaskPreprocessor(Preprocessor):
Dict[str, Any]: the preprocessed data
"""
import torch
new_data = {self.first_sequence: data}
# preprocess the data for the model input
rst = {
'input_ids': [],
'attention_mask': [],
'token_type_ids': []
}
rst = {'input_ids': [], 'attention_mask': [], 'token_type_ids': []}
max_seq_length = self.sequence_length
@@ -230,4 +225,3 @@ class FillMaskPreprocessor(Preprocessor):
rst['token_type_ids'].append(feature['token_type_ids'])
return {k: torch.tensor(v) for k, v in rst.items()}

2
requirements/nlp.txt
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@@ -1 +1 @@
https://alinlp.alibaba-inc.com/pypi/sofa-1.0.1.3-py3-none-any.whl
https://alinlp.alibaba-inc.com/pypi/sofa-1.0.3-py3-none-any.whl

138
tests/pipelines/test_fill_mask.py
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@@ -23,82 +23,110 @@ class FillMaskTest(unittest.TestCase):
ori_texts = {
'zh':
f'段誉轻挥折扇，摇了摇头，说道：“你师父是你的师父，你师父可不是我的师父。'
f'你师父差得动你，你师父可差不动我。',
'段誉轻挥折扇，摇了摇头，说道：“你师父是你的师父，你师父可不是我的师父。'
'你师父差得动你，你师父可差不动我。',
'en':
f'Everything in what you call reality is really just a r'
f'eflection of your consciousness. Your whole universe is'
f'just a mirror reflection of your story.'
'Everything in what you call reality is really just a reflection of your '
'consciousness. Your whole universe is just a mirror reflection of your story.'
}
test_inputs = {
'zh':
f'段誉轻[MASK]折扇，摇了摇[MASK]，[MASK]道：“你师父是你的[MASK][MASK]'
f'，你师父可不是[MASK]的师父。你师父差得动你，你师父可[MASK]不动我。',
'段誉轻[MASK]折扇，摇了摇[MASK]，[MASK]道：“你师父是你的[MASK][MASK]，你'
'师父可不是[MASK]的师父。你师父差得动你，你师父可[MASK]不动我。',
'en':
f'Everything in [MASK] you call reality is really [MASK] a '
f'reflection of your [MASK]. Your whole universe is just a '
f'mirror [MASK] of your story.'
'Everything in [MASK] you call reality is really [MASK] a reflection of your '
'[MASK]. Your [MASK] universe is just a mirror [MASK] of your story.'
}
#def test_run(self):
# # sbert
# for language in ["zh", "en"]:
# model_dir = snapshot_download(self.model_id_sbert[language])
# preprocessor = FillMaskPreprocessor(
# model_dir, first_sequence='sentence', second_sequence=None)
# model = MaskedLanguageModel(model_dir)
# pipeline1 = FillMaskPipeline(model, preprocessor)
# pipeline2 = pipeline(
# Tasks.fill_mask, model=model, preprocessor=preprocessor)
# ori_text = self.ori_texts[language]
# test_input = self.test_inputs[language]
# print(
# f'ori_text: {ori_text}\ninput: {test_input}\npipeline1: '
# f'{pipeline1(test_input)}\npipeline2: {pipeline2(test_input)}'
# )
@unittest.skipUnless(test_level() >= 2, 'skip test in current test level')
def test_run_by_direct_model_download(self):
# sbert
for language in ['zh', 'en']:
model_dir = snapshot_download(self.model_id_sbert[language])
preprocessor = FillMaskPreprocessor(
model_dir, first_sequence='sentence', second_sequence=None)
model = MaskedLanguageModel(model_dir)
pipeline1 = FillMaskPipeline(model, preprocessor)
pipeline2 = pipeline(
Tasks.fill_mask, model=model, preprocessor=preprocessor)
ori_text = self.ori_texts[language]
test_input = self.test_inputs[language]
print(
f'\nori_text: {ori_text}\ninput: {test_input}\npipeline1: '
f'{pipeline1(test_input)}\npipeline2: {pipeline2(test_input)}\n'
)
## veco
#model_dir = snapshot_download(self.model_id_veco)
#preprocessor = FillMaskPreprocessor(
# model_dir, first_sequence='sentence', second_sequence=None)
#model = MaskedLanguageModel(model_dir)
#pipeline1 = FillMaskPipeline(model, preprocessor)
#pipeline2 = pipeline(
# Tasks.fill_mask, model=model, preprocessor=preprocessor)
#for language in ["zh", "en"]:
# ori_text = self.ori_texts[language]
# test_input = self.test_inputs["zh"].replace("[MASK]", "<mask>")
# print(
# f'ori_text: {ori_text}\ninput: {test_input}\npipeline1: '
# f'{pipeline1(test_input)}\npipeline2: {pipeline2(test_input)}'
# veco
model_dir = snapshot_download(self.model_id_veco)
preprocessor = FillMaskPreprocessor(
model_dir, first_sequence='sentence', second_sequence=None)
model = MaskedLanguageModel(model_dir)
pipeline1 = FillMaskPipeline(model, preprocessor)
pipeline2 = pipeline(
Tasks.fill_mask, model=model, preprocessor=preprocessor)
for language in ['zh', 'en']:
ori_text = self.ori_texts[language]
test_input = self.test_inputs[language].replace('[MASK]', '<mask>')
print(
f'\nori_text: {ori_text}\ninput: {test_input}\npipeline1: '
f'{pipeline1(test_input)}\npipeline2: {pipeline2(test_input)}\n'
)
@unittest.skipUnless(test_level() >= 2, 'skip test in current test level')
def test_run_with_model_from_modelhub(self):
for language in ['zh']:
# sbert
for language in ['zh', 'en']:
print(self.model_id_sbert[language])
model = Model.from_pretrained(self.model_id_sbert[language])
print('model', model.model_dir)
preprocessor = FillMaskPreprocessor(
model.model_dir,
first_sequence='sentence',
second_sequence=None)
pipeline_ins = pipeline(
task=Tasks.fill_mask, model=model, preprocessor=preprocessor)
print(pipeline_ins(self.test_inputs[language]))
print(
f'\nori_text: {self.ori_texts[language]}\ninput: {self.test_inputs[language]}\npipeline: '
f'{pipeline_ins(self.test_inputs[language])}\n')
#def test_run_with_model_name(self):
## veco
#pipeline_ins = pipeline(
# task=Tasks.fill_mask, model=self.model_id_veco)
#for language in ["zh", "en"]:
# input_ = self.test_inputs[language].replace("[MASK]", "<mask>")
# print(pipeline_ins(input_))
# veco
model = Model.from_pretrained(self.model_id_veco)
preprocessor = FillMaskPreprocessor(
model.model_dir, first_sequence='sentence', second_sequence=None)
pipeline_ins = pipeline(
Tasks.fill_mask, model=model, preprocessor=preprocessor)
for language in ['zh', 'en']:
ori_text = self.ori_texts[language]
test_input = self.test_inputs[language].replace('[MASK]', '<mask>')
print(f'\nori_text: {ori_text}\ninput: {test_input}\npipeline: '
f'{pipeline_ins(test_input)}\n')
## structBert
#for language in ["zh"]:
# pipeline_ins = pipeline(
# task=Tasks.fill_mask, model=self.model_id_sbert[language])
# print(pipeline_ins(self_test_inputs[language]))
@unittest.skipUnless(test_level() >= 2, 'skip test in current test level')
def test_run_with_model_name(self):
# veco
pipeline_ins = pipeline(task=Tasks.fill_mask, model=self.model_id_veco)
for language in ['zh', 'en']:
ori_text = self.ori_texts[language]
test_input = self.test_inputs[language].replace('[MASK]', '<mask>')
print(f'\nori_text: {ori_text}\ninput: {test_input}\npipeline: '
f'{pipeline_ins(test_input)}\n')
# structBert
language = 'zh'
pipeline_ins = pipeline(
task=Tasks.fill_mask, model=self.model_id_sbert[language])
print(
f'\nori_text: {self.ori_texts[language]}\ninput: {self.test_inputs[language]}\npipeline: '
f'{pipeline_ins(self.test_inputs[language])}\n')
@unittest.skipUnless(test_level() >= 2, 'skip test in current test level')
def test_run_with_default_model(self):
pipeline_ins = pipeline(task=Tasks.fill_mask)
language = 'en'
ori_text = self.ori_texts[language]
test_input = self.test_inputs[language].replace('[MASK]', '<mask>')
print(f'\nori_text: {ori_text}\ninput: {test_input}\npipeline: '
f'{pipeline_ins(test_input)}\n')
if __name__ == '__main__':