refactor to support trt accelerate

Signed-off-by: ChengZi <chen.zhang@zilliz.com>

rerank.py

@@ -21,16 +21,17 @@ class Model:
         self.model = AutoModelForSequenceClassification.from_pretrained(model_name, config=self.config)
         self.model.to(self.device)
         self.model.eval()
-        if self.config.num_labels == 1:
-            self.activation_fct = torch.sigmoid
-        else:
-            self.activation_fct = partial(torch.softmax, dim=1)
 
-    def __call__(self, **features):
-        with torch.no_grad():
-            logits = self.model(**features, return_dict=True).logits
-            scores = self.activation_fct(logits)
-        return scores
+
+    def __call__(self, *args, **kwargs):
+        new_args = []
+        for x in args:
+            new_args.append(x.to(self.device))
+        new_kwargs = {}
+        for k, v in kwargs.items():
+            new_kwargs[k] = v.to(self.device)
+        outs = self.model(*new_args, **new_kwargs, return_dict=True)
+        return outs.logits
 
 
 class ReRank(NNOperator):
@@ -43,6 +44,10 @@ class ReRank(NNOperator):
         self.tokenizer = AutoTokenizer.from_pretrained(model_name)
         self.max_length = max_length
         self._threshold = threshold
+        if self.config.num_labels == 1:
+            self.activation_fct = torch.sigmoid
+        else:
+            self.activation_fct = partial(torch.softmax, dim=1)
 
     def __call__(self, query: str, docs: List):
         if len(docs) == 0:
@@ -60,12 +65,8 @@ class ReRank(NNOperator):
         for name in tokenized:
             tokenized[name] = tokenized[name].to(self.device)
 
-        scores = self.model(**tokenized).detach().cpu().numpy()
-        if self.config.num_labels == 1:
-            scores = [score[0] for score in scores]
-        else:
-            scores = scores[:, 1]
-            scores = [score for score in scores]
+        logits = self.model(**tokenized)
+        scores = self.post_proc(logits)
 
         re_ids = sorted(range(len(scores)), key=lambda k: scores[k], reverse=True)
         if self._threshold is None:
@@ -77,6 +78,16 @@ class ReRank(NNOperator):
         return re_docs, re_scores
 
 
+    def post_proc(self, logits):
+        scores = self.activation_fct(logits).detach().cpu().numpy()
+        if self.config.num_labels == 1:
+            scores = [score[0] for score in scores]
+        else:
+            scores = scores[:, 1]
+            scores = [score for score in scores]
+        return scores
+
+
     @property
     def _model(self):
         return self.model.model
@@ -114,3 +125,29 @@ class ReRank(NNOperator):
                 output=Path(path)
             )
         return Path(path).resolve()
+
+
+
+if __name__ == '__main__':
+    model_name_list = [
+        'cross-encoder/ms-marco-TinyBERT-L-2-v2',
+        'cross-encoder/ms-marco-MiniLM-L-2-v2',
+        'cross-encoder/ms-marco-MiniLM-L-4-v2',
+        'cross-encoder/ms-marco-MiniLM-L-6-v2',
+        'cross-encoder/ms-marco-MiniLM-L-12-v2',
+        'cross-encoder/ms-marco-TinyBERT-L-2',
+        'cross-encoder/ms-marco-TinyBERT-L-4',
+        'cross-encoder/ms-marco-TinyBERT-L-6',
+        'cross-encoder/ms-marco-electra-base',
+        'nboost/pt-tinybert-msmarco',
+        'nboost/pt-bert-base-uncased-msmarco',
+        'nboost/pt-bert-large-msmarco',
+        'Capreolus/electra-base-msmarco',
+        'amberoad/bert-multilingual-passage-reranking-msmarco',
+        ]
+    for model_name in model_name_list:
+        print('\n' + model_name)
+        op = ReRank(model_name, threshold=0)
+        res = op('abc', ['123', 'ABC', 'ABCabc'])
+        print(res)
+        op.save_model('onnx')