[to #42322933] text-error-correction support batch inference

Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/11398895

modelscope/models/nlp/bart/text_error_correction.py

@@ -78,18 +78,16 @@ class BartForTextErrorCorrection(TorchModel):
         """
         import fairseq.utils
 
-        if len(input['net_input']['src_tokens'].size()) == 1:
-            input['net_input']['src_tokens'] = input['net_input'][
-                'src_tokens'].view(1, -1)
+        batch_size = input['src_tokens'].size(0)
 
+        input = {'net_input': input}
         if torch.cuda.is_available():
             input = fairseq.utils.move_to_cuda(input, device=self._device)
 
-        sample = input
-
         translations = self.task.inference_step(self.generator, self.models,
-                                                sample)
-
-        # get 1-best List[Tensor]
-        preds = translations[0][0]['tokens']
-        return TextErrorCorrectionOutput(predictions=preds)
+                                                input)
+        batch_preds = []
+        for i in range(batch_size):
+            # get 1-best List[Tensor]
+            batch_preds.append(translations[i][0]['tokens'])
+        return TextErrorCorrectionOutput(predictions=batch_preds)

modelscope/pipelines/nlp/text_error_correction_pipeline.py

@@ -78,9 +78,14 @@ class TextErrorCorrectionPipeline(Pipeline):
 
         """
 
-        pred_str = self.vocab.string(
-            inputs['predictions'],
-            '@@',
-            extra_symbols_to_ignore={self.vocab.pad()})
+        sc_sent = []
+        for sent in inputs['predictions']:
+            pred_str = self.vocab.string(
+                sent, '@@', extra_symbols_to_ignore={self.vocab.pad()})
+            sc_sent.append(''.join(pred_str.split()))
 
-        return {OutputKeys.OUTPUT: ''.join(pred_str.split())}
+        # for  consistent with old version
+        if len(sc_sent) == 1:
+            sc_sent = sc_sent[0]
+
+        return {OutputKeys.OUTPUT: sc_sent}

modelscope/preprocessors/nlp/text_error_correction.py

@@ -3,6 +3,8 @@
 import os.path as osp
 from typing import Any, Dict
 
+import torch
+
 from modelscope.metainfo import Preprocessors
 from modelscope.preprocessors.base import Preprocessor
 from modelscope.preprocessors.builder import PREPROCESSORS
@@ -24,6 +26,8 @@ class TextErrorCorrectionPreprocessor(Preprocessor):
         """
         super().__init__(*args, **kwargs)
         self.vocab = Dictionary.load(osp.join(model_dir, 'dict.src.txt'))
+        self.max_length = 100 + 1  # 1 is eos token
+        self.padding_value = 2
 
     def __call__(self, data: str) -> Dict[str, Any]:
         """process the raw input data
@@ -44,7 +48,12 @@ class TextErrorCorrectionPreprocessor(Preprocessor):
         text = ' '.join([x for x in data])
         inputs = self.vocab.encode_line(
             text, append_eos=True, add_if_not_exist=False)
-        lengths = inputs.size()
-        sample = dict()
-        sample['net_input'] = {'src_tokens': inputs, 'src_lengths': lengths}
-        return sample
+        lengths = inputs.size()[0]
+
+        padding = torch.tensor([self.padding_value] *  # noqa: W504
+                               (self.max_length - lengths))
+        inputs = torch.unsqueeze(torch.cat([padding, inputs]), dim=0)
+        lengths = torch.tensor([lengths])
+        out = {'src_tokens': inputs, 'src_lengths': lengths}
+
+        return out

tests/pipelines/test_text_error_correction.py

@@ -19,6 +19,8 @@ class TextErrorCorrectionTest(unittest.TestCase, DemoCompatibilityCheck):
         self.model_id = 'damo/nlp_bart_text-error-correction_chinese'
 
     input = '随着中国经济突飞猛近，建造工业与日俱增'
+    input_2 = '这洋的话，下一年的福气来到自己身上。'
+    input_3 = '在拥挤时间，为了让人们尊守交通规律，派至少两个警察或者交通管理者。'
 
     @unittest.skipUnless(test_level() >= 2, 'skip test in current test level')
     def test_run_with_direct_download(self):
@@ -34,6 +36,15 @@ class TextErrorCorrectionTest(unittest.TestCase, DemoCompatibilityCheck):
             f'pipeline1: {pipeline1(self.input)}\npipeline2: {pipeline2(self.input)}'
         )
 
+    @unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
+    def test_run_with_model_name_batch(self):
+        run_kwargs = {'batch_size': 2}
+        pipeline_ins = pipeline(
+            task=Tasks.text_error_correction, model=self.model_id)
+        print(
+            'batch: ',
+            pipeline_ins([self.input, self.input_2, self.input_3], run_kwargs))
+
     @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)