[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/models/rembert/modeling_rembert.py

@@ -1148,9 +1148,9 @@ class RemBertForCausalLM(RemBertPreTrainedModel):
 
         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[:2]) + layer_past[2:],
             )

src/transformers/models/roberta/modeling_roberta.py

@@ -1022,9 +1022,9 @@ class RobertaForCausalLM(RobertaPreTrainedModel):
 
         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/roberta_prelayernorm/modeling_roberta_prelayernorm.py

@@ -1029,9 +1029,9 @@ class RobertaPreLayerNormForCausalLM(RobertaPreLayerNormPreTrainedModel):
 
         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/roc_bert/modeling_roc_bert.py

@@ -1575,9 +1575,9 @@ class RoCBertForCausalLM(RoCBertPreTrainedModel):
         }
 
     # 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/roformer/modeling_roformer.py

@@ -1187,9 +1187,9 @@ class RoFormerForCausalLM(RoFormerPreTrainedModel):
 
         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[:2]) + layer_past[2:],
             )

src/transformers/models/speech_encoder_decoder/modeling_speech_encoder_decoder.py

@@ -583,9 +583,9 @@ class SpeechEncoderDecoderModel(PreTrainedModel):
         return shift_tokens_right(labels, self.config.pad_token_id, self.config.decoder_start_token_id)
 
     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
         input_dict = {
             "attention_mask": attention_mask,
@@ -603,6 +603,6 @@ class SpeechEncoderDecoderModel(PreTrainedModel):
             " respective methods of the wrapped decoder object (model.decoder.resize_token_embeddings(...))"
         )
 
-    def _reorder_cache(self, past, beam_idx):
+    def _reorder_cache(self, past_key_values, beam_idx):
         # apply decoder cache reordering here
-        return self.decoder._reorder_cache(past, beam_idx)
+        return self.decoder._reorder_cache(past_key_values, beam_idx)

src/transformers/models/speech_to_text/modeling_speech_to_text.py

@@ -1418,8 +1418,8 @@ class Speech2TextForConditionalGeneration(Speech2TextPreTrainedModel):
         }
 
     @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/speech_to_text_2/modeling_speech_to_text_2.py

@@ -967,8 +967,8 @@ class Speech2Text2ForCausalLM(Speech2Text2PreTrainedModel):
         }
 
     @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/speecht5/modeling_speecht5.py

@@ -2368,7 +2368,7 @@ class SpeechT5ForSpeechToText(SpeechT5PreTrainedModel):
     def prepare_inputs_for_generation(
         self,
         decoder_input_ids,
-        past=None,
+        past_key_values=None,
         attention_mask=None,
         head_mask=None,
         decoder_head_mask=None,
@@ -2378,12 +2378,12 @@ class SpeechT5ForSpeechToText(SpeechT5PreTrainedModel):
         **kwargs,
     ):
         # cut decoder_input_ids if past is used
-        if past is not None:
+        if past_key_values is not None:
             decoder_input_ids = decoder_input_ids[:, -1:]
 
         return {
             "encoder_outputs": encoder_outputs,
-            "past_key_values": past,
+            "past_key_values": past_key_values,
             "decoder_input_ids": decoder_input_ids,
             "attention_mask": attention_mask,
             "head_mask": head_mask,
@@ -2393,9 +2393,9 @@ class SpeechT5ForSpeechToText(SpeechT5PreTrainedModel):
         }
 
     @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/switch_transformers/modeling_switch_transformers.py

@@ -1774,15 +1774,15 @@ class SwitchTransformersForConditionalGeneration(SwitchTransformersPreTrainedMod
     def prepare_decoder_input_ids_from_labels(self, labels: torch.Tensor):
         return self._shift_right(labels)
 
-    def _reorder_cache(self, past, beam_idx):
+    def _reorder_cache(self, past_key_values, beam_idx):
         # if decoder past is not included in output
         # speedy decoding is disabled and no need to reorder
-        if past is None:
+        if past_key_values is None:
             logger.warning("You might want to consider setting `use_cache=True` to speed up decoding")
-            return past
+            return past_key_values
 
         reordered_decoder_past = ()
-        for layer_past_states in past:
+        for layer_past_states in past_key_values:
             # get the correct batch idx from layer past batch dim
             # batch dim of `past` is at 2nd position
             reordered_layer_past_states = ()

src/transformers/models/t5/modeling_t5.py

@@ -1773,15 +1773,15 @@ class T5ForConditionalGeneration(T5PreTrainedModel):
     def prepare_decoder_input_ids_from_labels(self, labels: torch.Tensor):
         return self._shift_right(labels)
 
-    def _reorder_cache(self, past, beam_idx):
+    def _reorder_cache(self, past_key_values, beam_idx):
         # if decoder past is not included in output
         # speedy decoding is disabled and no need to reorder
-        if past is None:
+        if past_key_values is None:
             logger.warning("You might want to consider setting `use_cache=True` to speed up decoding")
-            return past
+            return past_key_values
 
         reordered_decoder_past = ()
-        for layer_past_states in past:
+        for layer_past_states in past_key_values:
             # get the correct batch idx from layer past batch dim
             # batch dim of `past` is at 2nd position
             reordered_layer_past_states = ()

src/transformers/models/trocr/modeling_trocr.py

@@ -1007,8 +1007,8 @@ class TrOCRForCausalLM(TrOCRPreTrainedModel):
         }
 
     @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/vision_encoder_decoder/modeling_vision_encoder_decoder.py

@@ -669,6 +669,6 @@ class VisionEncoderDecoderModel(PreTrainedModel):
             " respective methods of the wrapped decoder object (model.decoder.resize_token_embeddings(...))"
         )
 
-    def _reorder_cache(self, past, beam_idx):
+    def _reorder_cache(self, past_key_values, beam_idx):
         # apply decoder cache reordering here
-        return self.decoder._reorder_cache(past, beam_idx)
+        return self.decoder._reorder_cache(past_key_values, beam_idx)

src/transformers/models/whisper/modeling_whisper.py

@@ -1396,8 +1396,8 @@ class WhisperForConditionalGeneration(WhisperPreTrainedModel):
 
     #
     @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/xglm/modeling_xglm.py

@@ -942,8 +942,8 @@ class XGLMForCausalLM(XGLMPreTrainedModel):
         }
 
     @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/xlm_prophetnet/modeling_xlm_prophetnet.py

@@ -2117,9 +2117,9 @@ class XLMProphetNetForConditionalGeneration(XLMProphetNetPreTrainedModel):
 
     @staticmethod
     # Copied from transformers.models.bart.modeling_bart.BartForConditionalGeneration._reorder_cache
-    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:],
@@ -2364,9 +2364,9 @@ class XLMProphetNetForCausalLM(XLMProphetNetPreTrainedModel):
 
     @staticmethod
     # Copied from transformers.models.bart.modeling_bart.BartForCausalLM._reorder_cache
-    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/xlm_roberta/modeling_xlm_roberta.py

@@ -1026,9 +1026,9 @@ class XLMRobertaForCausalLM(XLMRobertaPreTrainedModel):
 
         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/xlm_roberta_xl/modeling_xlm_roberta_xl.py

@@ -988,9 +988,9 @@ class XLMRobertaXLForCausalLM(XLMRobertaXLPreTrainedModel):
 
         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
 

templates/adding_a_new_model/cookiecutter-template-{{cookiecutter.modelname}}/modeling_{{cookiecutter.lowercase_modelname}}.py

@@ -1180,9 +1180,9 @@ class {{cookiecutter.camelcase_modelname}}ForCausalLM({{cookiecutter.camelcase_m
 
         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[:2]) + layer_past[2:],)
         return reordered_past
 
@@ -2905,9 +2905,9 @@ class {{cookiecutter.camelcase_modelname}}ForConditionalGeneration({{cookiecutte
         }
 
     @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
 
@@ -3344,9 +3344,9 @@ class {{cookiecutter.camelcase_modelname}}ForCausalLM({{cookiecutter.camelcase_m
         }
 
     @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
 {% endif -%}

tests/models/gpt2/test_modeling_tf_gpt2.py

@@ -180,7 +180,7 @@ class TFGPT2ModelTester:
         self.parent.assertTrue(len(outputs) == len(outputs_use_cache_conf))
         self.parent.assertTrue(len(outputs) == len(outputs_no_past) + 1)
 
-        output, past = outputs.to_tuple()
+        output, past_key_values = outputs.to_tuple()
 
         # create hypothetical next token and extent to next_input_ids
         next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size)
@@ -191,7 +191,9 @@ class TFGPT2ModelTester:
         next_token_type_ids = tf.concat([token_type_ids, next_token_types], axis=-1)
 
         output_from_no_past = model(next_input_ids, token_type_ids=next_token_type_ids)["last_hidden_state"]
-        output_from_past = model(next_tokens, token_type_ids=next_token_types, past=past)["last_hidden_state"]
+        output_from_past = model(next_tokens, token_type_ids=next_token_types, past_key_values=past_key_values)[
+            "last_hidden_state"
+        ]
 
         # select random slice
         random_slice_idx = int(ids_tensor((1,), shape_list(output_from_past)[-1]))
@@ -213,7 +215,7 @@ class TFGPT2ModelTester:
         attn_mask = tf.concat([attn_mask_begin, attn_mask_end], axis=1)
 
         # first forward pass
-        output, past = model(input_ids, attention_mask=attn_mask).to_tuple()
+        output, past_key_values = model(input_ids, attention_mask=attn_mask).to_tuple()
 
         # create hypothetical next token and extent to next_input_ids
         next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size)
@@ -233,7 +235,9 @@ class TFGPT2ModelTester:
 
         # get two different outputs
         output_from_no_past = model(next_input_ids, attention_mask=attn_mask)["last_hidden_state"]
-        output_from_past = model(next_tokens, past=past, attention_mask=attn_mask)["last_hidden_state"]
+        output_from_past = model(next_tokens, past_key_values=past_key_values, attention_mask=attn_mask)[
+            "last_hidden_state"
+        ]
 
         # select random slice
         random_slice_idx = int(ids_tensor((1,), shape_list(output_from_past)[-1]))
@@ -256,7 +260,7 @@ class TFGPT2ModelTester:
         # first forward pass
         outputs = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, use_cache=True)
 
-        output, past = outputs.to_tuple()
+        output, past_key_values = outputs.to_tuple()
 
         # create hypothetical next token and extent to next_input_ids
         next_tokens = ids_tensor((self.batch_size, 3), config.vocab_size)
@@ -272,7 +276,10 @@ class TFGPT2ModelTester:
             next_input_ids, token_type_ids=next_token_type_ids, attention_mask=next_attention_mask
         )["last_hidden_state"]
         output_from_past = model(
-            next_tokens, token_type_ids=next_token_types, attention_mask=next_attention_mask, past=past
+            next_tokens,
+            token_type_ids=next_token_types,
+            attention_mask=next_attention_mask,
+            past_key_values=past_key_values,
         )["last_hidden_state"]
         self.parent.assertTrue(output_from_past.shape[1] == next_tokens.shape[1])