added fix

florence2_models/configuration_florence2.py

@@ -249,6 +249,7 @@ class Florence2LanguageConfig(PretrainedConfig):
         self.use_cache = use_cache
         self.num_hidden_layers = encoder_layers
         self.scale_embedding = scale_embedding  # scale factor will be sqrt(d_model) if True
+        self.forced_bos_token_id = bos_token_id
 
         super().__init__(
             num_labels=num_labels,
@@ -262,12 +263,12 @@ class Florence2LanguageConfig(PretrainedConfig):
         )
 
         # ensure backward compatibility for BART CNN models
-        if self.forced_bos_token_id is None and kwargs.get("force_bos_token_to_be_generated", False):
-            self.forced_bos_token_id = self.bos_token_id
-            warnings.warn(
-                f"Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. "
-                "The config can simply be saved and uploaded again to be fixed."
-            )
+        # if self.forced_bos_token_id is None and kwargs.get("force_bos_token_to_be_generated", False):
+        #     self.forced_bos_token_id = self.bos_token_id
+        #     warnings.warn(
+        #         f"Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. "
+        #         "The config can simply be saved and uploaded again to be fixed."
+        #     )
 
 class Florence2Config(PretrainedConfig):
     r"""

florence2_models/modeling_florence2.py

@@ -61,6 +61,8 @@ from transformers.modeling_outputs import (
     Seq2SeqLMOutput,
     Seq2SeqModelOutput,
 )
+import transformers
+from packaging import version
 
 
 if is_flash_attn_2_available():
@@ -1934,7 +1936,8 @@ class Florence2Decoder(Florence2LanguagePreTrainedModel):
 
 
 class Florence2LanguageModel(Florence2LanguagePreTrainedModel):
-    _tied_weights_keys = ["encoder.embed_tokens.weight", "decoder.embed_tokens.weight"]
+    if not version.parse(transformers.__version__) >= version.parse('5.0.0'):
+        _tied_weights_keys = ["encoder.embed_tokens.weight", "decoder.embed_tokens.weight"]
 
     def __init__(self, config: Florence2LanguageConfig):
         super().__init__(config)
@@ -2057,7 +2060,8 @@ class Florence2LanguageModel(Florence2LanguagePreTrainedModel):
 
 class Florence2LanguageForConditionalGeneration(Florence2LanguagePreTrainedModel, GenerationMixin):
     base_model_prefix = "model"
-    _tied_weights_keys = ["encoder.embed_tokens.weight", "decoder.embed_tokens.weight", "lm_head.weight"]
+    if not version.parse(transformers.__version__) >= version.parse('5.0.0'):
+        _tied_weights_keys = ["encoder.embed_tokens.weight", "decoder.embed_tokens.weight", "lm_head.weight"]
     _keys_to_ignore_on_load_missing = ["final_logits_bias"]
 
     def __init__(self, config: Florence2LanguageConfig):
@@ -2067,7 +2071,8 @@ class Florence2LanguageForConditionalGeneration(Florence2LanguagePreTrainedModel
         self.lm_head = nn.Linear(config.d_model, self.model.shared.num_embeddings, bias=False)
 
         # Initialize weights and apply final processing
-        self.post_init()
+        if not version.parse(transformers.__version__) >= version.parse('5.0.0'):
+            self.post_init()
 
     def _tie_weights(self):
         if self.config.tie_word_embeddings:
@@ -2075,6 +2080,11 @@ class Florence2LanguageForConditionalGeneration(Florence2LanguagePreTrainedModel
             self._tie_or_clone_weights(self.model.decoder.embed_tokens, self.model.shared)
             self._tie_or_clone_weights(self.lm_head, self.model.shared)
 
+    def tie_weights(self):
+        self.model.encoder.embed_tokens.weight = self.model.shared.weight
+        self.model.decoder.embed_tokens.weight = self.model.shared.weight
+        self.lm_head.weight = self.model.shared.weight
+
     def get_encoder(self):
         return self.model.get_encoder()
 
@@ -2536,7 +2546,8 @@ class Florence2VisionModelWithProjection(Florence2PreTrainedModel):
     FLORENCE2_START_DOCSTRING,
 )
 class Florence2ForConditionalGeneration(Florence2PreTrainedModel, GenerationMixin):
-    _tied_weights_keys = ["language_model.encoder.embed_tokens.weight", "language_model.decoder.embed_tokens.weight", "language_model.lm_head.weight"]
+    if not version.parse(transformers.__version__) >= version.parse('5.0.0'):
+        _tied_weights_keys = ["language_model.encoder.embed_tokens.weight", "language_model.decoder.embed_tokens.weight", "language_model.lm_head.weight"]
     def __init__(self, config: Florence2Config):
         super().__init__(config)
         assert config.vision_config.model_type == 'davit', 'only DaViT is supported for now'