[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

tests/models/gptj/test_modeling_tf_gptj.py

@@ -148,7 +148,7 @@ class TFGPTJModelTester:
         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)
@@ -159,7 +159,9 @@ class TFGPTJModelTester:
         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]))
@@ -181,7 +183,7 @@ class TFGPTJModelTester:
         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)
@@ -201,7 +203,9 @@ class TFGPTJModelTester:
 
         # 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]))
@@ -224,7 +228,7 @@ class TFGPTJModelTester:
         # 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)
@@ -240,7 +244,10 @@ class TFGPTJModelTester:
             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])