[to #42322933]Add text-generation-pipeline with Palm model.

将 Palm 中文模型接入 MaaS，添加了文本生成 pipeline
        Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/8934393

    * add text_generation model and pipeline

* fix bug

* fix bug

* add TextGenerator in pipeline

* fix bug

* update checkpoint and test inputs

* remove magic number..

* fix bug

* adjust code with AutoModel

* clear comments and tidy up the code

* move model.eval() into generator

* update master interface and lint code

* replace 'palm-text-generation' with 'palm'

* add text_generation model and pipeline

* fix bug

* fix bug

* add TextGenerator in pipeline

* fix bug

* fix conflict of pipeline.txt

* remove magic number..

* fix bug

* adjust code with AutoModel

* clear comments and tidy up the code

* move model.eval() into generator

* fix conflict

* replace 'palm-text-generation' with 'palm'

* fix conflict

* add test_run_modelhub

* update sofa version

* modify sofa version

* add test_run_with_model_name

* fix bug

maas_lib/models/nlp/__init__.py

@@ -1 +1,2 @@
 from .sequence_classification_model import *  # noqa F403
+from .text_generation_model import *  # noqa F403

maas_lib/models/nlp/text_generation_model.py

@@ -0,0 +1,52 @@
+from typing import Any, Dict
+
+from maas_lib.utils.constant import Tasks
+from ..base import Model, Tensor
+from ..builder import MODELS
+
+__all__ = ['PalmForTextGenerationModel']
+
+
+@MODELS.register_module(Tasks.text_generation, module_name=r'palm')
+class PalmForTextGenerationModel(Model):
+
+    def __init__(self, model_dir: str, *args, **kwargs):
+        """initialize the text generation model from the `model_dir` path.
+
+        Args:
+            model_dir (str): the model path.
+            model_cls (Optional[Any], optional): model loader, if None, use the
+                default loader to load model weights, by default None.
+        """
+        from sofa import PalmTokenizer
+
+        super().__init__(model_dir, *args, **kwargs)
+        self.model_dir = model_dir
+
+        from sofa.models.palm import PalmForConditionalGeneration, TextGenerator
+        tokenizer = kwargs.pop('tokenizer',
+                               PalmTokenizer.from_pretrained(model_dir))
+        model = PalmForConditionalGeneration.from_pretrained(model_dir)
+        self.generator = TextGenerator(model, tokenizer)
+
+    def forward(self, input: Dict[str, Tensor]) -> Dict[str, Tensor]:
+        """return the result by the model
+
+        Args:
+            input (Dict[str, Any]): the preprocessed data
+
+        Returns:
+            Dict[str, np.ndarray]: results
+                Example:
+                    {
+                        'predictions': array([1]), # lable 0-negative 1-positive
+                        'probabilities': array([[0.11491239, 0.8850876 ]], dtype=float32),
+                        'logits': array([[-0.53860897,  1.5029076 ]], dtype=float32) # true value
+                    }
+        """
+
+        encoder_inputs = [
+            input['input_ids'], input['token_type_ids'],
+            input['attention_mask']
+        ]
+        return self.generator(encoder_inputs)

maas_lib/pipelines/nlp/__init__.py

@@ -1 +1,2 @@
 from .sequence_classification_pipeline import *  # noqa F403
+from .text_generation_pipeline import *  # noqa F403

maas_lib/pipelines/nlp/text_generation_pipeline.py

@@ -0,0 +1,59 @@
+from typing import Dict, Optional, Union
+
+from maas_lib.models import Model
+from maas_lib.models.nlp import PalmForTextGenerationModel
+from maas_lib.preprocessors import TextGenerationPreprocessor
+from maas_lib.utils.constant import Tasks
+from ..base import Pipeline, Tensor
+from ..builder import PIPELINES
+
+__all__ = ['TextGenerationPipeline']
+
+
+@PIPELINES.register_module(Tasks.text_generation, module_name=r'palm')
+class TextGenerationPipeline(Pipeline):
+
+    def __init__(self,
+                 model: Union[PalmForTextGenerationModel, str],
+                 preprocessor: Optional[TextGenerationPreprocessor] = None,
+                 **kwargs):
+        """use `model` and `preprocessor` to create a nlp text classification pipeline for prediction
+
+        Args:
+            model (SequenceClassificationModel): a model instance
+            preprocessor (SequenceClassificationPreprocessor): a preprocessor instance
+        """
+        sc_model = model if isinstance(
+            model,
+            PalmForTextGenerationModel) else Model.from_pretrained(model)
+        if preprocessor is None:
+            preprocessor = TextGenerationPreprocessor(
+                sc_model.model_dir,
+                first_sequence='sentence',
+                second_sequence=None)
+        super().__init__(model=sc_model, preprocessor=preprocessor, **kwargs)
+        self.tokenizer = preprocessor.tokenizer
+
+    def postprocess(self, inputs: Dict[str, Tensor]) -> Dict[str, str]:
+        """process the prediction results
+
+        Args:
+            inputs (Dict[str, Any]): _description_
+
+        Returns:
+            Dict[str, str]: the prediction results
+        """
+
+        vocab_size = len(self.tokenizer.vocab)
+        pred_list = inputs['predictions']
+        pred_ids = pred_list[0][0].cpu().numpy().tolist()
+        for j in range(len(pred_ids)):
+            if pred_ids[j] >= vocab_size:
+                pred_ids[j] = 100
+        pred = self.tokenizer.convert_ids_to_tokens(pred_ids)
+        pred_string = ''.join(pred).replace(
+            '##',
+            '').split('[SEP]')[0].replace('[CLS]',
+                                          '').replace('[SEP]',
+                                                      '').replace('[UNK]', '')
+        return {'pred_string': pred_string}

maas_lib/preprocessors/__init__.py

@@ -5,3 +5,4 @@ from .builder import PREPROCESSORS, build_preprocessor
 from .common import Compose
 from .image import LoadImage, load_image
 from .nlp import *  # noqa F403
+from .nlp import TextGenerationPreprocessor

maas_lib/preprocessors/nlp.py

@@ -89,3 +89,61 @@ class SequenceClassificationPreprocessor(Preprocessor):
         rst['token_type_ids'].append(feature['token_type_ids'])
 
         return rst
+
+
+@PREPROCESSORS.register_module(Fields.nlp, module_name=r'palm')
+class TextGenerationPreprocessor(Preprocessor):
+
+    def __init__(self, model_dir: str, *args, **kwargs):
+        """preprocess the data using the vocab.txt from the `model_dir` path
+
+        Args:
+            model_dir (str): model path
+        """
+        from sofa import PalmTokenizer
+
+        super().__init__(*args, **kwargs)
+
+        self.model_dir: str = model_dir
+        self.first_sequence: str = kwargs.pop('first_sequence',
+                                              'first_sequence')
+        self.second_sequence: str = kwargs.pop('second_sequence',
+                                               'second_sequence')
+        self.sequence_length: int = kwargs.pop('sequence_length', 128)
+        self.tokenizer = PalmTokenizer.from_pretrained(model_dir)
+
+    @type_assert(object, str)
+    def __call__(self, data: str) -> Dict[str, Any]:
+        """process the raw input data
+
+        Args:
+            data (str): a sentence
+                Example:
+                    'you are so handsome.'
+
+        Returns:
+            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': []}
+
+        max_seq_length = self.sequence_length
+
+        text_a = new_data.get(self.first_sequence, None)
+        text_b = new_data.get(self.second_sequence, None)
+        feature = self.tokenizer(
+            text_a,
+            text_b,
+            padding='max_length',
+            truncation=True,
+            max_length=max_seq_length)
+
+        rst['input_ids'].append(feature['input_ids'])
+        rst['attention_mask'].append(feature['attention_mask'])
+        rst['token_type_ids'].append(feature['token_type_ids'])
+
+        return {k: torch.tensor(v) for k, v in rst.items()}

requirements.txt

@@ -1,2 +1,3 @@
 -r requirements/runtime.txt
 -r requirements/pipeline.txt
+-r requirements/nlp.txt

requirements/nlp.txt

@@ -0,0 +1 @@
+https://alinlp.alibaba-inc.com/pypi/sofa-1.0.1.3-py3-none-any.whl

tests/pipelines/test_text_generation.py

@@ -0,0 +1,46 @@
+# Copyright (c) Alibaba, Inc. and its affiliates.
+import unittest
+
+from maas_hub.snapshot_download import snapshot_download
+
+from maas_lib.models import Model
+from maas_lib.models.nlp import PalmForTextGenerationModel
+from maas_lib.pipelines import TextGenerationPipeline, pipeline
+from maas_lib.preprocessors import TextGenerationPreprocessor
+from maas_lib.utils.constant import Tasks
+
+
+class TextGenerationTest(unittest.TestCase):
+    model_id = 'damo/nlp_palm_text-generation_chinese'
+    input1 = "今日天气类型='晴'&温度变化趋势='大幅上升'&最低气温='28℃'&最高气温='31℃'&体感='湿热'"
+    input2 = "今日天气类型='多云'&体感='舒适'&最低气温='26℃'&最高气温='30℃'"
+
+    def test_run(self):
+        cache_path = snapshot_download(self.model_id)
+        preprocessor = TextGenerationPreprocessor(
+            cache_path, first_sequence='sentence', second_sequence=None)
+        model = PalmForTextGenerationModel(
+            cache_path, tokenizer=preprocessor.tokenizer)
+        pipeline1 = TextGenerationPipeline(model, preprocessor)
+        pipeline2 = pipeline(
+            Tasks.text_generation, model=model, preprocessor=preprocessor)
+        print(f'input: {self.input1}\npipeline1: {pipeline1(self.input1)}')
+        print()
+        print(f'input: {self.input2}\npipeline2: {pipeline2(self.input2)}')
+
+    def test_run_with_model_from_modelhub(self):
+        model = Model.from_pretrained(self.model_id)
+        preprocessor = TextGenerationPreprocessor(
+            model.model_dir, first_sequence='sentence', second_sequence=None)
+        pipeline_ins = pipeline(
+            task=Tasks.text_generation, model=model, preprocessor=preprocessor)
+        print(pipeline_ins(self.input1))
+
+    def test_run_with_model_name(self):
+        pipeline_ins = pipeline(
+            task=Tasks.text_generation, model=self.model_id)
+        print(pipeline_ins(self.input2))
+
+
+if __name__ == '__main__':
+    unittest.main()