[CI ] Remove `past` in favor of `pat_key_values` (#21443)

* fix past renamed to past_key_value

* update more `past`that were ski^êd

* fixup

* remove changes made to rag

* refactor `_reorder_cache` to use `past_key_values`

* fix git `prepare_inputs_for_generation` to pass tests when false is needed in use_cache

src/transformers/generation/utils.py

@@ -724,7 +724,7 @@ class GenerationMixin:
 
         return model_kwargs
 
-    def _reorder_cache(self, past, beam_idx):
+    def _reorder_cache(self, past_key_values, beam_idx):
         raise NotImplementedError(
             f"Make sure that a `_reorder_cache` function is correctly implemented in {self.__class__.__module__} to"
             f" enable beam search for {self.__class__}"

src/transformers/models/bart/modeling_bart.py

@@ -1444,9 +1444,9 @@ class BartForConditionalGeneration(BartPretrainedModel):
         return shift_tokens_right(labels, self.config.pad_token_id, self.config.decoder_start_token_id)
 
     @staticmethod
-    def _reorder_cache(past, beam_idx):
+    def _reorder_cache(past_key_values, beam_idx):
         reordered_past = ()
-        for layer_past in past:
+        for layer_past in past_key_values:
             # cached cross_attention states don't have to be reordered -> they are always the same
             reordered_past += (
                 tuple(past_state.index_select(0, beam_idx) for past_state in layer_past[:2]) + layer_past[2:],
@@ -1921,8 +1921,8 @@ class BartForCausalLM(BartPretrainedModel):
         }
 
     @staticmethod
-    def _reorder_cache(past, beam_idx):
+    def _reorder_cache(past_key_values, beam_idx):
         reordered_past = ()
-        for layer_past in past:
+        for layer_past in past_key_values:
             reordered_past += (tuple(past_state.index_select(0, beam_idx) for past_state in layer_past),)
         return reordered_past

src/transformers/models/bert/modeling_bert.py

@@ -1288,9 +1288,9 @@ class BertLMHeadModel(BertPreTrainedModel):
             "use_cache": use_cache,
         }
 
-    def _reorder_cache(self, past, beam_idx):
+    def _reorder_cache(self, past_key_values, beam_idx):
         reordered_past = ()
-        for layer_past in past:
+        for layer_past in past_key_values:
             reordered_past += (tuple(past_state.index_select(0, beam_idx) for past_state in layer_past),)
         return reordered_past
 

src/transformers/models/bigbird_pegasus/modeling_bigbird_pegasus.py

@@ -2629,9 +2629,9 @@ class BigBirdPegasusForConditionalGeneration(BigBirdPegasusPreTrainedModel):
         return shift_tokens_right(labels, self.config.pad_token_id, self.config.decoder_start_token_id)
 
     @staticmethod
-    def _reorder_cache(past, beam_idx):
+    def _reorder_cache(past_key_values, beam_idx):
         reordered_past = ()
-        for layer_past in past:
+        for layer_past in past_key_values:
             # cached cross_attention states don't have to be reordered -> they are always the same
             reordered_past += (
                 tuple(past_state.index_select(0, beam_idx) for past_state in layer_past[:2]) + layer_past[2:],
@@ -3098,8 +3098,8 @@ class BigBirdPegasusForCausalLM(BigBirdPegasusPreTrainedModel):
         }
 
     @staticmethod
-    def _reorder_cache(past, beam_idx):
+    def _reorder_cache(past_key_values, beam_idx):
         reordered_past = ()
-        for layer_past in past:
+        for layer_past in past_key_values:
             reordered_past += (tuple(past_state.index_select(0, beam_idx) for past_state in layer_past),)
         return reordered_past

src/transformers/models/biogpt/modeling_biogpt.py

@@ -714,8 +714,8 @@ class BioGptForCausalLM(BioGptPreTrainedModel):
         }
 
     @staticmethod
-    def _reorder_cache(past, beam_idx):
+    def _reorder_cache(past_key_values, beam_idx):
         reordered_past = ()
-        for layer_past in past:
+        for layer_past in past_key_values:
             reordered_past += (tuple(past_state.index_select(0, beam_idx) for past_state in layer_past),)
         return reordered_past

src/transformers/models/blenderbot/modeling_blenderbot.py

@@ -1401,9 +1401,9 @@ class BlenderbotForConditionalGeneration(BlenderbotPreTrainedModel):
         }
 
     @staticmethod
-    def _reorder_cache(past, beam_idx):
+    def _reorder_cache(past_key_values, beam_idx):
         reordered_past = ()
-        for layer_past in past:
+        for layer_past in past_key_values:
             # cached cross_attention states don't have to be reordered -> they are always the same
             reordered_past += (
                 tuple(past_state.index_select(0, beam_idx) for past_state in layer_past[:2]) + layer_past[2:],
@@ -1624,8 +1624,8 @@ class BlenderbotForCausalLM(BlenderbotPreTrainedModel):
         }
 
     @staticmethod
-    def _reorder_cache(past, beam_idx):
+    def _reorder_cache(past_key_values, beam_idx):
         reordered_past = ()
-        for layer_past in past:
+        for layer_past in past_key_values:
             reordered_past += (tuple(past_state.index_select(0, beam_idx) for past_state in layer_past),)
         return reordered_past

src/transformers/models/blenderbot_small/modeling_blenderbot_small.py

@@ -1367,9 +1367,9 @@ class BlenderbotSmallForConditionalGeneration(BlenderbotSmallPreTrainedModel):
         }
 
     @staticmethod
-    def _reorder_cache(past, beam_idx):
+    def _reorder_cache(past_key_values, beam_idx):
         reordered_past = ()
-        for layer_past in past:
+        for layer_past in past_key_values:
             # cached cross_attention states don't have to be reordered -> they are always the same
             reordered_past += (
                 tuple(past_state.index_select(0, beam_idx) for past_state in layer_past[:2]) + layer_past[2:],
@@ -1590,8 +1590,8 @@ class BlenderbotSmallForCausalLM(BlenderbotSmallPreTrainedModel):
         }
 
     @staticmethod
-    def _reorder_cache(past, beam_idx):
+    def _reorder_cache(past_key_values, beam_idx):
         reordered_past = ()
-        for layer_past in past:
+        for layer_past in past_key_values:
             reordered_past += (tuple(past_state.index_select(0, beam_idx) for past_state in layer_past),)
         return reordered_past

src/transformers/models/blip/modeling_blip_text.py

@@ -925,8 +925,8 @@ class BlipTextLMHeadModel(BlipTextPreTrainedModel):
             "is_decoder": True,
         }
 
-    def _reorder_cache(self, past, beam_idx):
+    def _reorder_cache(self, past_key_values, beam_idx):
         reordered_past = ()
-        for layer_past in past:
+        for layer_past in past_key_values:
             reordered_past += (tuple(past_state.index_select(0, beam_idx) for past_state in layer_past),)
         return reordered_past

src/transformers/models/camembert/modeling_camembert.py

@@ -1563,9 +1563,9 @@ class CamembertForCausalLM(CamembertPreTrainedModel):
 
         return {"input_ids": input_ids, "attention_mask": attention_mask, "past_key_values": past_key_values}
 
-    def _reorder_cache(self, past, beam_idx):
+    def _reorder_cache(self, past_key_values, beam_idx):
         reordered_past = ()
-        for layer_past in past:
+        for layer_past in past_key_values:
             reordered_past += (tuple(past_state.index_select(0, beam_idx) for past_state in layer_past),)
         return reordered_past
 

src/transformers/models/codegen/modeling_codegen.py

@@ -727,7 +727,9 @@ class CodeGenForCausalLM(CodeGenPreTrainedModel):
         )
 
     @staticmethod
-    def _reorder_cache(past: Tuple[Tuple[torch.Tensor]], beam_idx: torch.Tensor) -> Tuple[Tuple[torch.Tensor]]:
+    def _reorder_cache(
+        past_key_values: Tuple[Tuple[torch.Tensor]], beam_idx: torch.Tensor
+    ) -> Tuple[Tuple[torch.Tensor]]:
         """
         This function is used to re-order the `past_key_values` cache if [`~PretrainedModel.beam_search`] or
         [`~PretrainedModel.beam_sample`] is called. This is required to match `past_key_values` with the correct
@@ -735,5 +737,5 @@ class CodeGenForCausalLM(CodeGenPreTrainedModel):
         """
         return tuple(
             tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past)
-            for layer_past in past
+            for layer_past in past_key_values
         )

src/transformers/models/ctrl/modeling_ctrl.py

@@ -624,7 +624,9 @@ class CTRLLMHeadModel(CTRLPreTrainedModel):
         )
 
     @staticmethod
-    def _reorder_cache(past: Tuple[Tuple[torch.Tensor]], beam_idx: torch.Tensor) -> Tuple[Tuple[torch.Tensor]]:
+    def _reorder_cache(
+        past_key_values: Tuple[Tuple[torch.Tensor]], beam_idx: torch.Tensor
+    ) -> Tuple[Tuple[torch.Tensor]]:
         """
         This function is used to re-order the `past_key_values` cache if [`~PreTrainedModel.beam_search`] or
         [`~PreTrainedModel.beam_sample`] is called. This is required to match `past_key_values` with the correct
@@ -632,7 +634,7 @@ class CTRLLMHeadModel(CTRLPreTrainedModel):
         """
         return tuple(
             tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past)
-            for layer_past in past
+            for layer_past in past_key_values
         )
 
 

src/transformers/models/data2vec/modeling_data2vec_text.py

@@ -1024,9 +1024,9 @@ class Data2VecTextForCausalLM(Data2VecTextPreTrainedModel):
 
         return {"input_ids": input_ids, "attention_mask": attention_mask, "past_key_values": past_key_values}
 
-    def _reorder_cache(self, past, beam_idx):
+    def _reorder_cache(self, past_key_values, beam_idx):
         reordered_past = ()
-        for layer_past in past:
+        for layer_past in past_key_values:
             reordered_past += (tuple(past_state.index_select(0, beam_idx) for past_state in layer_past),)
         return reordered_past
 

src/transformers/models/electra/modeling_electra.py

@@ -1671,8 +1671,8 @@ class ElectraForCausalLM(ElectraPreTrainedModel):
         return {"input_ids": input_ids, "attention_mask": attention_mask, "past_key_values": past_key_values}
 
     # Copied from transformers.models.roberta.modeling_roberta.RobertaForCausalLM._reorder_cache
-    def _reorder_cache(self, past, beam_idx):
+    def _reorder_cache(self, past_key_values, beam_idx):
         reordered_past = ()
-        for layer_past in past:
+        for layer_past in past_key_values:
             reordered_past += (tuple(past_state.index_select(0, beam_idx) for past_state in layer_past),)
         return reordered_past

src/transformers/models/encoder_decoder/modeling_tf_encoder_decoder.py

@@ -690,9 +690,9 @@ class TFEncoderDecoderModel(TFPreTrainedModel, TFCausalLanguageModelingLoss):
         )
 
     def prepare_inputs_for_generation(
-        self, input_ids, past=None, attention_mask=None, use_cache=None, encoder_outputs=None, **kwargs
+        self, input_ids, past_key_values=None, attention_mask=None, use_cache=None, encoder_outputs=None, **kwargs
     ):
-        decoder_inputs = self.decoder.prepare_inputs_for_generation(input_ids, past=past)
+        decoder_inputs = self.decoder.prepare_inputs_for_generation(input_ids, past_key_values=past_key_values)
         decoder_attention_mask = decoder_inputs["attention_mask"] if "attention_mask" in decoder_inputs else None
         past_key_values = decoder_inputs.get("past_key_values")
         if past_key_values is None:

src/transformers/models/ernie/modeling_ernie.py

@@ -1230,9 +1230,9 @@ class ErnieForCausalLM(ErniePreTrainedModel):
         }
 
     # Copied from transformers.models.bert.modeling_bert.BertLMHeadModel._reorder_cache
-    def _reorder_cache(self, past, beam_idx):
+    def _reorder_cache(self, past_key_values, beam_idx):
         reordered_past = ()
-        for layer_past in past:
+        for layer_past in past_key_values:
             reordered_past += (tuple(past_state.index_select(0, beam_idx) for past_state in layer_past),)
         return reordered_past
 

src/transformers/models/fsmt/modeling_fsmt.py

@@ -1291,9 +1291,9 @@ class FSMTForConditionalGeneration(PretrainedFSMTModel):
         return shift_tokens_right(labels, self.config.pad_token_id)
 
     @staticmethod
-    def _reorder_cache(past, beam_idx):
+    def _reorder_cache(past_key_values, beam_idx):
         reordered_past = []
-        for layer_past in past:
+        for layer_past in past_key_values:
             # get the correct batch idx from decoder layer's batch dim for cross and self-attn
             layer_past_new = {
                 attn_key: _reorder_buffer(attn_cache, beam_idx) for attn_key, attn_cache in layer_past.items()

src/transformers/models/git/modeling_git.py

@@ -1512,9 +1512,11 @@ class GitForCausalLM(GitPreTrainedModel):
             attentions=outputs.attentions,
         )
 
-    def prepare_inputs_for_generation(self, input_ids, past=None, attention_mask=None, use_cache=True, **kwargs):
-        # cut decoder_input_ids if past is used
-        if past is not None:
+    def prepare_inputs_for_generation(
+        self, input_ids, past_key_values=None, attention_mask=None, use_cache=None, **kwargs
+    ):
+        # cut decoder_input_ids if past_key_values is used
+        if past_key_values is not None:
             input_ids = input_ids[:, -1:]
 
         # if model is used as a decoder in encoder-decoder model, the decoder attention mask is created on the fly
@@ -1526,12 +1528,12 @@ class GitForCausalLM(GitPreTrainedModel):
             "input_ids": input_ids,
             "attention_mask": attention_mask,
             "pixel_values": kwargs.get("pixel_values", None),
-            "past_key_values": past,
+            "past_key_values": past_key_values,
             "use_cache": use_cache,
         }
 
-    def _reorder_cache(self, past, beam_idx):
+    def _reorder_cache(self, past_key_values, beam_idx):
         reordered_past = ()
-        for layer_past in past:
+        for layer_past in past_key_values:
             reordered_past += (tuple(past_state.index_select(0, beam_idx) for past_state in layer_past),)
         return reordered_past

src/transformers/models/gpt2/modeling_gpt2.py

@@ -1117,7 +1117,9 @@ class GPT2LMHeadModel(GPT2PreTrainedModel):
         )
 
     @staticmethod
-    def _reorder_cache(past: Tuple[Tuple[torch.Tensor]], beam_idx: torch.Tensor) -> Tuple[Tuple[torch.Tensor]]:
+    def _reorder_cache(
+        past_key_values: Tuple[Tuple[torch.Tensor]], beam_idx: torch.Tensor
+    ) -> Tuple[Tuple[torch.Tensor]]:
         """
         This function is used to re-order the `past_key_values` cache if [`~PreTrainedModel.beam_search`] or
         [`~PreTrainedModel.beam_sample`] is called. This is required to match `past_key_values` with the correct
@@ -1125,7 +1127,7 @@ class GPT2LMHeadModel(GPT2PreTrainedModel):
         """
         return tuple(
             tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past)
-            for layer_past in past
+            for layer_past in past_key_values
         )
 
 
@@ -1336,7 +1338,9 @@ class GPT2DoubleHeadsModel(GPT2PreTrainedModel):
         )
 
     @staticmethod
-    def _reorder_cache(past: Tuple[Tuple[torch.Tensor]], beam_idx: torch.Tensor) -> Tuple[Tuple[torch.Tensor]]:
+    def _reorder_cache(
+        past_key_values: Tuple[Tuple[torch.Tensor]], beam_idx: torch.Tensor
+    ) -> Tuple[Tuple[torch.Tensor]]:
         """
         This function is used to re-order the `past_key_values` cache if [`~PreTrainedModel.beam_search`] or
         [`~PreTrainedModel.beam_sample`] is called. This is required to match `past_key_values` with the correct
@@ -1344,7 +1348,7 @@ class GPT2DoubleHeadsModel(GPT2PreTrainedModel):
         """
         return tuple(
             tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past)
-            for layer_past in past
+            for layer_past in past_key_values
         )
 
 

src/transformers/models/gpt_neo/modeling_gpt_neo.py

@@ -782,7 +782,9 @@ class GPTNeoForCausalLM(GPTNeoPreTrainedModel):
         )
 
     @staticmethod
-    def _reorder_cache(past: Tuple[Tuple[torch.Tensor]], beam_idx: torch.Tensor) -> Tuple[Tuple[torch.Tensor]]:
+    def _reorder_cache(
+        past_key_values: Tuple[Tuple[torch.Tensor]], beam_idx: torch.Tensor
+    ) -> Tuple[Tuple[torch.Tensor]]:
         """
         This function is used to re-order the `past_key_values` cache if [`~PretrainedModel.beam_search`] or
         [`~PretrainedModel.beam_sample`] is called. This is required to match `past_key_values` with the correct
@@ -790,7 +792,7 @@ class GPTNeoForCausalLM(GPTNeoPreTrainedModel):
         """
         return tuple(
             tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past)
-            for layer_past in past
+            for layer_past in past_key_values
         )
 
 

src/transformers/models/gpt_neox/modeling_gpt_neox.py

@@ -700,9 +700,9 @@ class GPTNeoXForCausalLM(GPTNeoXPreTrainedModel):
             "past_key_values": past_key_values,
         }
 
-    def _reorder_cache(self, past, beam_idx):
+    def _reorder_cache(self, past_key_values, beam_idx):
         reordered_past = ()
-        for layer_past in past:
+        for layer_past in past_key_values:
             reordered_past += (
                 tuple(past_state.index_select(0, beam_idx) for past_state in layer_past[:2]) + layer_past[2:],
             )