add unmerged fixes (#1)

- update pr (afa2fbbd041133a280b818ea4b23a9e61634384c)

config.json

@@ -80,6 +80,6 @@
   },
   "vocab_size": 51289,
   "torch_dtype": "float16",
-  "transformers_version": "4.41.0.dev0",
+  "transformers_version": "4.49.0",
   "is_encoder_decoder": true
 }

configuration_florence2.py

@@ -77,7 +77,7 @@ class Florence2VisionConfig(PretrainedConfig):
     >>> configuration = model.config
     ```"""
 
-    model_type = "florence2_vision"
+    model_type = "davit"
     keys_to_ignore_at_inference = ["past_key_values"]
 
     def __init__(
@@ -327,7 +327,7 @@ class Florence2Config(PretrainedConfig):
         self.vocab_size = vocab_size
         self.projection_dim = projection_dim
         if vision_config is not None:
-            vision_config = PretrainedConfig(**vision_config)
+            vision_config = Florence2VisionConfig(**vision_config)
         self.vision_config = vision_config
         self.vocab_size = self.vocab_size
 

modeling_florence2.py

@@ -26,7 +26,7 @@ import torch.utils.checkpoint as checkpoint
 from torch.nn import CrossEntropyLoss 
 from collections import OrderedDict
 from einops import rearrange
-from timm.models.layers import DropPath, trunc_normal_
+from timm.layers import DropPath, trunc_normal_
 
 from transformers.modeling_utils import PreTrainedModel
 from transformers.generation.utils import GenerationMixin
@@ -2080,8 +2080,8 @@ class Florence2LanguageForConditionalGeneration(Florence2LanguagePreTrainedModel
     def get_decoder(self):
         return self.model.get_decoder()
 
-    def resize_token_embeddings(self, new_num_tokens: int, pad_to_multiple_of: Optional[int] = None) -> nn.Embedding:
-        new_embeddings = super().resize_token_embeddings(new_num_tokens, pad_to_multiple_of)
+    def resize_token_embeddings(self, new_num_tokens: int, pad_to_multiple_of: Optional[int] = None, **kwargs) -> nn.Embedding:
+        new_embeddings = super().resize_token_embeddings(new_num_tokens, pad_to_multiple_of, **kwargs)
         self._resize_final_logits_bias(new_embeddings.weight.shape[0])
         return new_embeddings
 
@@ -2589,8 +2589,8 @@ class Florence2ForConditionalGeneration(Florence2PreTrainedModel):
     def get_input_embeddings(self):
         return self.language_model.get_input_embeddings()
 
-    def resize_token_embeddings(self, new_num_tokens: Optional[int] = None, pad_to_multiple_of=None) -> nn.Embedding:
-        model_embeds = self.language_model.resize_token_embeddings(new_num_tokens, pad_to_multiple_of)
+    def resize_token_embeddings(self, new_num_tokens: Optional[int] = None, pad_to_multiple_of=None, **kwargs) -> nn.Embedding:
+        model_embeds = self.language_model.resize_token_embeddings(new_num_tokens, pad_to_multiple_of, **kwargs)
         # update vocab size
         self.config.text_config.vocab_size = model_embeds.num_embeddings
         self.config.vocab_size = model_embeds.num_embeddings
@@ -2644,7 +2644,7 @@ class Florence2ForConditionalGeneration(Florence2PreTrainedModel):
         return x 
 
     def _merge_input_ids_with_image_features(
-        self, image_features, inputs_embeds 
+        self, image_features, inputs_embeds, task_prefix_attention_mask=None
     ):
         batch_size, image_token_length = image_features.size()[:-1]
         device = image_features.device
@@ -2656,10 +2656,11 @@ class Florence2ForConditionalGeneration(Florence2PreTrainedModel):
             return image_features, image_attention_mask
 
         task_prefix_embeds = inputs_embeds
-        task_prefix_attention_mask = torch.ones(batch_size, task_prefix_embeds.size(1), device=device)
+        if task_prefix_attention_mask is None:
+            task_prefix_attention_mask = torch.ones(batch_size, task_prefix_embeds.size(1), device=device)
 
-        if len(task_prefix_attention_mask.shape) == 3:
-            task_prefix_attention_mask = task_prefix_attention_mask[:, 0]
+            if len(task_prefix_attention_mask.shape) == 3:
+                task_prefix_attention_mask = task_prefix_attention_mask[:, 0]
 
         # concat [image embeds, task prefix embeds]
         inputs_embeds = torch.cat([image_features, task_prefix_embeds], dim=1)
@@ -2735,7 +2736,7 @@ class Florence2ForConditionalGeneration(Florence2PreTrainedModel):
             if pixel_values is not None:
                 # (batch_size, num_image_tokens, hidden_size)
                 image_features = self._encode_image(pixel_values)
-                inputs_embeds, attention_mask = self._merge_input_ids_with_image_features(image_features, inputs_embeds)
+                inputs_embeds, attention_mask = self._merge_input_ids_with_image_features(image_features, inputs_embeds, task_prefix_attention_mask=attention_mask)
 
         if inputs_embeds is not None:
             attention_mask = attention_mask.to(inputs_embeds.dtype)
@@ -2782,6 +2783,7 @@ class Florence2ForConditionalGeneration(Florence2PreTrainedModel):
         input_ids, 
         inputs_embeds=None,
         pixel_values=None,
+        attention_mask=None,
         **kwargs
         ):
 
@@ -2792,11 +2794,12 @@ class Florence2ForConditionalGeneration(Florence2PreTrainedModel):
             # 2. Merge text and images
             if pixel_values is not None:
                 image_features = self._encode_image(pixel_values)
-                inputs_embeds, attention_mask = self._merge_input_ids_with_image_features(image_features, inputs_embeds)
+                inputs_embeds, attention_mask = self._merge_input_ids_with_image_features(image_features, inputs_embeds, task_prefix_attention_mask=attention_mask)
         
         return self.language_model.generate(
             input_ids=None,
             inputs_embeds=inputs_embeds,
+            attention_mask=attention_mask,
             **kwargs
         )