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

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* 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>
This commit is contained in:
Raushan Turganbay
2025-07-16 17:00:17 +05:00
committed by GitHub
parent 6cb43defd0
commit c8524aeb07
268 changed files with 5707 additions and 6831 deletions
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docs/source/en/cache_explanation.md
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@@ -141,7 +141,7 @@ The legacy format is essentially the same data structure but organized different
- The tensors have the same shape `[batch_size, num_heads, seq_len, head_dim]`.
- The format is less flexible and doesn't support features like quantization or offloading.
If your project depends on this legacy format, you can convert between [`DynamicCache`] and a tuple of tuples as shown below with the [`~DynamicCache.from_legacy_cache`] and [`DynamicCache.to_legacy_cache`] functions. This is helpful if you have custom logic for manipulating a cache in a specific format.
If your project depends on this legacy format, we recommend to convert to [`DynamicCache`] with [`~DynamicCache.from_legacy_cache`]. Note that legacy cache format is deprecated and not used anymore in `Transformers`. You can convert back to tuple format with [`DynamicCache.to_legacy_cache`] functions, which is helpful if you have custom logic for manipulating a cache in a specific format.
```py
import torch