[cache] make all classes cache compatible finally (#38635)

* dump

* push other models

* fix simple greedy generation

* xmod

* add fmst and clean up some mentions of old cache format

* gpt-bigcode now follows standards

* delete tuple cache reference in generation

* fix some models

* fix some models

* fix mambas and support cache in tapas

* fix some more tests

* fix copies

* delete `_reorder_cache`

* another fix copies

* fix typos and delete unnecessary test

* fix rag generate, needs special cache reordering

* fix tapas and superglue

* reformer create special cache

* recurrent gemma `reorder_cache` was a no-op, delete

* fix-copies

* fix blio and musicgen pipeline tests

* fix reformer

* fix reformer, again...

* delete `_supports_cache_class`

* delete `supports_quantized_cache`

* fix failing tests

* fix copies

* some minor clean up

* style

* style

* fix copies

* fix tests

* fix copies

* create causal mask now needs positions?

* fixc copies

* style

* Update tests/test_modeling_common.py

Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com>

* clean-up of non-generative model after merging main

* check `is_decoder` for cache

* delete transpose for scores

* remove tuple cache from docs everywhere

* fix tests

* fix copies

* fix copies once more

* properly deprecate `encoder_attention_mask` in Bert-like models

* import `deprecate_kwarg` where needed

* fix copies again

* fix copies

* delete `nex_decoder_cache`

* fix copies asks to update for PLM

* fix copies

* rebasing had a few new models, fix them and merge asap!

* fix copies once more

* fix slow tests

* fix tests and updare PLM checkpoint

* add read token and revert accidentally removed line

* oh com -on, style

* just skip it, read token has no access to PLM yet

---------

Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com>

tests/models/speecht5/test_modeling_speecht5.py

@@ -354,9 +354,9 @@ class SpeechT5ForSpeechToTextTester:
         next_attention_mask = torch.cat([attention_mask, next_attn_mask], dim=-1)
 
         output_from_no_past = model(next_input_ids, attention_mask=next_attention_mask)["last_hidden_state"]
-        output_from_past = model(next_tokens, attention_mask=next_attention_mask, past_key_values=past_key_values)[
-            "last_hidden_state"
-        ]
+        output_from_past = model(
+            next_tokens, attention_mask=next_attention_mask, past_key_values=past_key_values, use_cache=True
+        )["last_hidden_state"]
 
         # select random slice
         random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()