From 0eb823b76490bb3249bf1420143873293a132fb7 Mon Sep 17 00:00:00 2001 From: "zhangzhicheng.zzc" Date: Thu, 13 Oct 2022 10:52:40 +0800 Subject: [PATCH] [to #42322933] support t5_with_translation Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/10383770 * T5 support translate --- modelscope/metainfo.py | 4 ++ .../nlp/text2text_generation_pipeline.py | 39 ++++++++++++++++--- modelscope/preprocessors/nlp/nlp_base.py | 3 +- modelscope/utils/config.py | 10 +++++ tests/pipelines/test_text2text_generation.py | 26 +++++++------ 5 files changed, 63 insertions(+), 19 deletions(-) diff --git a/modelscope/metainfo.py b/modelscope/metainfo.py index 46c3b138..59c779e9 100644 --- a/modelscope/metainfo.py +++ b/modelscope/metainfo.py @@ -228,6 +228,9 @@ class Pipelines(object): relation_extraction = 'relation-extraction' document_segmentation = 'document-segmentation' feature_extraction = 'feature-extraction' + translation_en_to_de = 'translation_en_to_de' # keep it underscore + translation_en_to_ro = 'translation_en_to_ro' # keep it underscore + translation_en_to_fr = 'translation_en_to_fr' # keep it underscore # audio tasks sambert_hifigan_tts = 'sambert-hifigan-tts' @@ -314,6 +317,7 @@ class Preprocessors(object): bert_seq_cls_tokenizer = 'bert-seq-cls-tokenizer' text_gen_tokenizer = 'text-gen-tokenizer' text2text_gen_preprocessor = 'text2text-gen-preprocessor' + text2text_translate_preprocessor = 'text2text-translate-preprocessor' token_cls_tokenizer = 'token-cls-tokenizer' ner_tokenizer = 'ner-tokenizer' nli_tokenizer = 'nli-tokenizer' diff --git a/modelscope/pipelines/nlp/text2text_generation_pipeline.py b/modelscope/pipelines/nlp/text2text_generation_pipeline.py index 21aacf54..a739df69 100644 --- a/modelscope/pipelines/nlp/text2text_generation_pipeline.py +++ b/modelscope/pipelines/nlp/text2text_generation_pipeline.py @@ -1,21 +1,35 @@ # Copyright (c) Alibaba, Inc. and its affiliates. -from typing import Any, Dict, Optional, Union +from typing import Any, Dict, List, Optional, Union import torch +from numpy import isin from modelscope.metainfo import Pipelines from modelscope.models.base import Model from modelscope.outputs import OutputKeys -from modelscope.pipelines.base import Pipeline, Tensor +from modelscope.pipelines.base import Input, Pipeline, Tensor from modelscope.pipelines.builder import PIPELINES from modelscope.preprocessors import Text2TextGenerationPreprocessor +from modelscope.utils.config import use_task_specific_params from modelscope.utils.constant import Tasks __all__ = ['Text2TextGenerationPipeline'] +TRANSLATE_PIPELINES = [ + Pipelines.translation_en_to_de, + Pipelines.translation_en_to_ro, + Pipelines.translation_en_to_fr, +] + @PIPELINES.register_module( Tasks.text2text_generation, module_name=Pipelines.text2text_generation) +@PIPELINES.register_module( + Tasks.text2text_generation, module_name=Pipelines.translation_en_to_de) +@PIPELINES.register_module( + Tasks.text2text_generation, module_name=Pipelines.translation_en_to_ro) +@PIPELINES.register_module( + Tasks.text2text_generation, module_name=Pipelines.translation_en_to_fr) class Text2TextGenerationPipeline(Pipeline): def __init__( @@ -39,13 +53,13 @@ class Text2TextGenerationPipeline(Pipeline): Example: >>> from modelscope.pipelines import pipeline - >>> pipeline_ins = pipeline(task='text-generation', - >>> model='damo/nlp_palm2.0_text-generation_chinese-base') - >>> sentence1 = '本文总结了十个可穿戴产品的设计原则，而这些原则，同样也是笔者认为是这个行业最吸引人的地方：' - >>> '1.为人们解决重复性问题；2.从人开始，而不是从机器开始；3.要引起注意，但不要刻意；4.提升用户能力，而不是取代' + >>> pipeline_ins = pipeline(task='text2text-generation', + >>> model='damo/nlp_t5_text2text-generation_chinese-base') + >>> sentence1 = '中国的首都位于。' >>> print(pipeline_ins(sentence1)) >>> # Or use the dict input: >>> print(pipeline_ins({'sentence': sentence1})) + >>> # 北京 To view other examples plese check the tests/pipelines/test_text_generation.py. """ @@ -56,9 +70,22 @@ class Text2TextGenerationPipeline(Pipeline): model.model_dir, sequence_length=kwargs.pop('sequence_length', 128)) self.tokenizer = preprocessor.tokenizer + self.pipeline = model.pipeline.type model.eval() super().__init__(model=model, preprocessor=preprocessor, **kwargs) + def preprocess(self, inputs: Input, **preprocess_params) -> Dict[str, Any]: + """ Provide specific preprocess for text2text generation pipeline in order to handl multi tasks + """ + if not isinstance(inputs, str): + raise ValueError(f'Not supported input type: {type(inputs)}') + + if self.pipeline in TRANSLATE_PIPELINES: + use_task_specific_params(self.model, self.pipeline) + inputs = self.model.config.prefix + inputs + + return super().preprocess(inputs, **preprocess_params) + def forward(self, inputs: Dict[str, Any], **forward_params) -> Dict[str, Any]: diff --git a/modelscope/preprocessors/nlp/nlp_base.py b/modelscope/preprocessors/nlp/nlp_base.py index a9be0cb0..bec7e4e1 100644 --- a/modelscope/preprocessors/nlp/nlp_base.py +++ b/modelscope/preprocessors/nlp/nlp_base.py @@ -12,7 +12,8 @@ from modelscope.metainfo import Models, Preprocessors from modelscope.outputs import OutputKeys from modelscope.preprocessors.base import Preprocessor from modelscope.preprocessors.builder import PREPROCESSORS -from modelscope.utils.config import Config, ConfigFields +from modelscope.utils.config import (Config, ConfigFields, + use_task_specific_params) from modelscope.utils.constant import Fields, InputFields, ModeKeys, ModelFile from modelscope.utils.hub import get_model_type, parse_label_mapping from modelscope.utils.logger import get_logger diff --git a/modelscope/utils/config.py b/modelscope/utils/config.py index 0b966bef..c4fa3c1b 100644 --- a/modelscope/utils/config.py +++ b/modelscope/utils/config.py @@ -633,6 +633,16 @@ def check_config(cfg: Union[str, ConfigDict]): check_attr(ConfigFields.evaluation) +def use_task_specific_params(model, task): + """Update config with summarization specific params.""" + task_specific_params = model.config.task_specific_params + + if task_specific_params is not None: + pars = task_specific_params.get(task, {}) + logger.info(f'using task specific params for {task}: {pars}') + model.config.update(pars) + + class JSONIteratorEncoder(json.JSONEncoder): """Implement this method in order that supporting arbitrary iterators, it returns a serializable object for ``obj``, or calls the base implementation diff --git a/tests/pipelines/test_text2text_generation.py b/tests/pipelines/test_text2text_generation.py index 2506547e..d90263c4 100644 --- a/tests/pipelines/test_text2text_generation.py +++ b/tests/pipelines/test_text2text_generation.py @@ -15,42 +15,44 @@ from modelscope.utils.test_utils import test_level class Text2TextGenerationTest(unittest.TestCase, DemoCompatibilityCheck): def setUp(self) -> None: - self.model_id = 'damo/t5-cn-base-test' - self.input = '中国的首都位于。' + self.model_id_generate = 'damo/t5-cn-base-test' + self.input_generate = '中国的首都位于。' + self.model_id_translate = 'damo/t5-translate-base-test' + self.input_translate = 'My name is Wolfgang and I live in Berlin' - @unittest.skipUnless(test_level() >= 1, 'skip test in current test level') + @unittest.skipUnless(test_level() >= 0, 'skip test in current test level') def test_run_T5(self): - cache_path = snapshot_download(self.model_id) - model = T5ForConditionalGeneration(cache_path) + cache_path = snapshot_download(self.model_id_generate) + model = T5ForConditionalGeneration.from_pretrained(cache_path) preprocessor = Text2TextGenerationPreprocessor(cache_path) pipeline1 = Text2TextGenerationPipeline(model, preprocessor) pipeline2 = pipeline( Tasks.text2text_generation, model=model, preprocessor=preprocessor) print( - f'pipeline1: {pipeline1(self.input)}\npipeline2: {pipeline2(self.input)}' + f'pipeline1: {pipeline1(self.input_generate)}\npipeline2: {pipeline2(self.input_generate)}' ) - @unittest.skipUnless(test_level() >= 2, 'skip test in current test level') + @unittest.skipUnless(test_level() >= 0, 'skip test in current test level') def test_run_pipeline_with_model_instance(self): - model = Model.from_pretrained(self.model_id) + model = Model.from_pretrained(self.model_id_translate) preprocessor = Text2TextGenerationPreprocessor(model.model_dir) pipeline_ins = pipeline( task=Tasks.text2text_generation, model=model, preprocessor=preprocessor) - print(pipeline_ins(self.input)) + print(pipeline_ins(self.input_translate)) @unittest.skipUnless(test_level() >= 0, 'skip test in current test level') def test_run_pipeline_with_model_id(self): pipeline_ins = pipeline( - task=Tasks.text2text_generation, model=self.model_id) - print(pipeline_ins(self.input)) + task=Tasks.text2text_generation, model=self.model_id_translate) + print(pipeline_ins(self.input_translate)) @unittest.skip( 'only for test cases, there is no default official model yet') def test_run_pipeline_without_model_id(self): pipeline_ins = pipeline(task=Tasks.text2text_generation) - print(pipeline_ins(self.input)) + print(pipeline_ins(self.input_generate)) @unittest.skip('demo compatibility test is only enabled on a needed-basis') def test_demo_compatibility(self):