update the opearator for newer transformers.

Signed-off-by: wxywb <xy.wang@zilliz.com>

README.md

@@ -68,14 +68,14 @@ Create the operator via the following factory method
 
 ## Interface
 
-An image-text embedding operator takes a [towhee image](link/to/towhee/image/api/doc) as input and generate the correspoing caption.
+An image captioning operator takes a [towhee image](link/to/towhee/image/api/doc) as input and generate the correspoing caption.
 
 
 **Parameters:**
 
 ​	***img:*** *towhee.types.Image (a sub-class of numpy.ndarray)*
 
-​  The image to generate embedding.	
+​  The image to generate caption.	
 
 
 

models/blip.py

@@ -136,7 +136,7 @@ class BLIP_Decoder(nn.Module):
             image_embeds = image_embeds.repeat_interleave(num_beams,dim=0)
 
         image_atts = torch.ones(image_embeds.size()[:-1],dtype=torch.long).to(image.device)
-        model_kwargs = {"encoder_hidden_states": image_embeds, "encoder_attention_mask":image_atts}
+        kwargs = {"encoder_hidden_states": image_embeds, "encoder_attention_mask":image_atts}
 
         prompt = [self.prompt] * image.size(0)
         input_ids = self.tokenizer(prompt, return_tensors="pt").input_ids.to(image.device)
@@ -154,7 +154,7 @@ class BLIP_Decoder(nn.Module):
                                                   eos_token_id=self.tokenizer.sep_token_id,
                                                   pad_token_id=self.tokenizer.pad_token_id,
                                                   repetition_penalty=1.1,
-                                                  **model_kwargs)
+                                                  **kwargs)
         else:
             #beam search
             outputs = self.text_decoder.generate(input_ids=input_ids,
@@ -164,7 +164,7 @@ class BLIP_Decoder(nn.Module):
                                                   eos_token_id=self.tokenizer.sep_token_id,
                                                   pad_token_id=self.tokenizer.pad_token_id,
                                                   repetition_penalty=repetition_penalty,
-                                                  **model_kwargs)
+                                                  **kwargs)
 
         captions = []
         for output in outputs:

models/med.py

@@ -929,7 +929,7 @@ class BertLMHeadModel(BertPreTrainedModel):
             cross_attentions=outputs.cross_attentions,
         )
 
-    def prepare_inputs_for_generation(self, input_ids, past=None, attention_mask=None, **model_kwargs):
+    def prepare_inputs_for_generation(self, input_ids, past=None, attention_mask=None, **kwargs):
         input_shape = input_ids.shape
         # if model is used as a decoder in encoder-decoder model, the decoder attention mask is created on the fly
         if attention_mask is None:
@@ -943,8 +943,8 @@ class BertLMHeadModel(BertPreTrainedModel):
             "input_ids": input_ids, 
             "attention_mask": attention_mask, 
             "past_key_values": past,
-            "encoder_hidden_states": model_kwargs.get("encoder_hidden_states", None),
-            "encoder_attention_mask": model_kwargs.get("encoder_attention_mask", None),
+            "encoder_hidden_states": kwargs.get("encoder_hidden_states", None),
+            "encoder_attention_mask": kwargs.get("encoder_attention_mask", None),
             "is_decoder": True,
         }