[to #42322933] Fix bug for TextGenerationPreprocessor

1. 修复 TextGenerationPreprocessor 中 padding 设置为 True 导致输入序列长度不同时训练报错的 bug
2. 将 vqa pipeline 的输入从 image_path 调整为 Image.Image 类型
        Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/9630285

modelscope/models/nlp/gpt3/gpt3_for_text_generation.py

@@ -43,8 +43,14 @@ class GPT3ForTextGeneration(TorchModel):
 
     def generate(self, input: Dict[str, Tensor]) -> Dict[str, str]:
         assert 'input_ids' in input, "generate function must accept 'input_ids' key"
+        input_ids = input['input_ids']
+        if 'attention_mask' in input:
+            attention_mask = input['attention_mask']
+            input_ids = input_ids[0][attention_mask[0].nonzero()] \
+                .squeeze().unsqueeze(0)
+
         gen_params = dict()
-        gen_params['inputs'] = input['input_ids']
+        gen_params['inputs'] = input_ids
         gen_params['do_sample'] = input.pop('do_sample', True)
         gen_params['max_length'] = input.pop('max_length', 128)
         gen_params['top_k'] = input.pop('top_k', 10)

modelscope/preprocessors/multi_modal.py

@@ -118,10 +118,12 @@ class MPlugVisualQuestionAnsweringPreprocessor(Preprocessor):
             transforms.Normalize(mean=mean, std=std),
         ])
 
-    def __call__(self, data: Dict[str, Any]) -> Dict[str, Any]:
-        image, question = data['image'], data['question']
-        image = Image.open(image).convert('RGB') if isinstance(image,
-                                                               str) else image
+    def __call__(self, data: Union[tuple, Dict[str, Any]]) -> Dict[str, Any]:
+        image: Image.Image = data[0] if isinstance(data,
+                                                   tuple) else data['image']
+        question: str = data[1] if isinstance(data,
+                                              tuple) else data['question']
+        image = image.convert('RGB')
         image = self.patch_resize_transform(image)
         image = torch.stack([image], dim=0)
         question = self.tokenizer([question.lower()],

modelscope/preprocessors/nlp.py

@@ -286,7 +286,7 @@ class TextGenerationPreprocessor(NLPTokenizerPreprocessorBase):
         self.tokenizer = self.build_tokenizer(
             model_dir) if tokenizer is None else tokenizer
         kwargs['truncation'] = kwargs.get('truncation', True)
-        kwargs['padding'] = kwargs.get('padding', True)
+        kwargs['padding'] = kwargs.get('padding', 'max_length')
         kwargs['return_token_type_ids'] = kwargs.get('return_token_type_ids',
                                                      False)
         kwargs['max_length'] = kwargs.pop('sequence_length', 128)

tests/pipelines/test_visual_question_answering.py

@@ -2,6 +2,8 @@
 
 import unittest
 
+from PIL import Image
+
 from modelscope.hub.snapshot_download import snapshot_download
 from modelscope.models import Model
 from modelscope.models.multi_modal import MPlugForVisualQuestionAnswering
@@ -13,11 +15,13 @@ from modelscope.utils.test_utils import test_level
 
 
 class VisualQuestionAnsweringTest(unittest.TestCase):
-    model_id = 'damo/mplug_visual-question-answering_coco_large_en'
-    input_vqa = {
-        'image': 'data/test/images/image_mplug_vqa.jpg',
-        'question': 'What is the woman doing?',
-    }
+
+    def setUp(self):
+        self.model_id = 'damo/mplug_visual-question-answering_coco_large_en'
+        self.input_vqa = {
+            'image': Image.open('data/test/images/image_mplug_vqa.jpg'),
+            'question': 'What is the woman doing?',
+        }
 
     @unittest.skipUnless(test_level() >= 2, 'skip test in current test level')
     def test_run(self):