Add cls pooling

Signed-off-by: Jael Gu <mengjia.gu@zilliz.com>

auto_transformers.py

@@ -97,15 +97,22 @@ class AutoTransformers(NNOperator):
             The local checkpoint path.
         tokenizer (`object`):
             The tokenizer to tokenize input text as model inputs.
+        pool (`str`):
+            The type of post-process pooling after token embeddings, defaults to "mean". Options: "mean", "cls"
     """
 
     def __init__(self,
                  model_name: str = None,
                  checkpoint_path: str = None,
                  tokenizer: object = None,
+                 pool: str = 'mean',
                  device: str = None,
                  ):
         super().__init__()
+        if pool not in ['mean', 'cls']:
+            log.warning('Invalid pool %s, using mean pooling instead.', pool)
+            pool = 'mean'
+        self.pool = pool
         if device:
             self.device = device
         else:
@@ -145,7 +152,10 @@ class AutoTransformers(NNOperator):
         except Exception as e:
             log.error(f'Invalid input for the model: {self.model_name}')
             raise e
-        outs = self.post_proc(outs, inputs)
+        if self.pool == 'mean':
+            outs = self.mean_pool(outs, inputs)
+        elif self.pool == 'cls':
+            outs = self.cls_pool(outs)
         features = outs.detach().numpy()
         if isinstance(data, str):
             features = features.squeeze(0)
@@ -184,7 +194,7 @@ class AutoTransformers(NNOperator):
             }
         return onnx_config
 
-    def post_proc(self, token_embeddings, inputs):
+    def mean_pool(self, token_embeddings, inputs):
         token_embeddings = token_embeddings
         attention_mask = inputs['attention_mask']
         input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
@@ -192,6 +202,15 @@ class AutoTransformers(NNOperator):
             token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
         return sentence_embs
 
+    def cls_pool(self, token_embeddings):
+        _shape = token_embeddings.shape
+        if len(_shape) == 3:
+            return token_embeddings[:, 0, :]
+        elif len(_shape) == 2:
+            return token_embeddings[0]
+        else:
+            raise RuntimeError(f'Invalid shape of token embeddings: {_shape}')
+
     def save_model(self, model_type: str = 'pytorch', output_file: str = 'default'):
         if output_file == 'default':
             output_file = str(Path(__file__).parent)