test nie udany

modeling_florence2.py

@@ -2201,22 +2201,16 @@ class Florence2LanguageForConditionalGeneration(Florence2LanguagePreTrainedModel
     ):
         # cut decoder_input_ids if past_key_values is used
         if past_key_values is not None:
-            # Dodatkowe zabezpieczenie na wypadek, gdyby `past_key_values` nie było krotką
-            if not isinstance(past_key_values, tuple):
-                past_key_values = tuple(past_key_values)
+            past_length = past_key_values[0][0].shape[2]
 
-            # Sprawdzamy, czy wewnętrzne elementy nie są None, zanim uzyskamy do nich dostęp
-            if past_key_values[0] is not None and past_key_values[0][0] is not None:
-                past_length = past_key_values[0][0].shape[2]
-
-                # Some generation methods already pass only the last input ID
-                if decoder_input_ids.shape[1] > past_length:
-                    remove_prefix_length = past_length
-                else:
-                    # Default to old behavior: keep only final ID
-                    remove_prefix_length = decoder_input_ids.shape[1] - 1
+            # Some generation methods already pass only the last input ID
+            if decoder_input_ids.shape[1] > past_length:
+                remove_prefix_length = past_length
+            else:
+                # Default to old behavior: keep only final ID
+                remove_prefix_length = decoder_input_ids.shape[1] - 1
 
-                decoder_input_ids = decoder_input_ids[:, remove_prefix_length:]
+            decoder_input_ids = decoder_input_ids[:, remove_prefix_length:]
 
         return {
             "input_ids": None,  # encoder_outputs is defined. input_ids not needed
@@ -3067,21 +3061,32 @@ class Florence2ForConditionalGeneration(Florence2PreTrainedModel):
         encoder_outputs=None,
         **kwargs,
     ):
-        # Ta funkcja powinna delegować wywołanie do `language_model`,
-        # a nie zwracać własny słownik.
-        # Poprawione wywołanie przekazuje wszystkie argumenty.
-        return self.language_model.prepare_inputs_for_generation(
-            decoder_input_ids,
-            past_key_values=past_key_values,
-            attention_mask=attention_mask,
-            decoder_attention_mask=decoder_attention_mask,
-            head_mask=head_mask,
-            decoder_head_mask=decoder_head_mask,
-            cross_attn_head_mask=cross_attn_head_mask,
-            use_cache=use_cache,
-            encoder_outputs=encoder_outputs,
-            **kwargs,
-        )
+        # cut decoder_input_ids if past_key_values is used
+        if past_key_values is not None:
+            past_length = past_key_values[0][0].shape[2]
+
+            # Some generation methods already pass only the last input ID
+            if decoder_input_ids.shape[1] > past_length:
+                remove_prefix_length = past_length
+            else:
+                # Default to old behavior: keep only final ID
+                remove_prefix_length = decoder_input_ids.shape[1] - 1
+
+            decoder_input_ids = decoder_input_ids[:, remove_prefix_length:]
+        
+        return {
+            "input_ids": None,  # encoder_outputs is defined. input_ids not needed
+            "encoder_outputs": encoder_outputs,
+            "past_key_values": past_key_values,
+            "decoder_input_ids": decoder_input_ids,
+            "attention_mask": attention_mask,
+            "pixel_values": pixel_values,
+            "decoder_attention_mask": decoder_attention_mask,
+            "head_mask": head_mask,
+            "decoder_head_mask": decoder_head_mask,
+            "cross_attn_head_mask": cross_attn_head_mask,
+            "use_cache": use_cache,  # change this to avoid caching (presumably for debugging)
+        }
     
     def prepare_decoder_input_ids_from_labels(self, labels: torch.Tensor):
         return self.language_model.shift_tokens_right(labels)