remove unnecessary guard code related with pytorch versions 1.4.2 ~
1.7.0

* [whisper] fix short-form output type

* add test

* make style

* update long-form tests

* fixes

* last fix

* finalise test

Replaced deprecated unittest method with the correct one.

* No more default chat templates

* Add the template to the GPT-SW3 tests since it's not available by default now

* Fix GPT2 test

* Fix Bloom test

* Fix Bloom test

* Remove default templates again

* support gguf fp16

* support gguf bf16 with pytorch

* add gguf f16 test

* remove bf16

* Fix float8_e4m3fn in modeling_utils

* style

* fix

* comment

fix resize when deepspeed

* let's not warn when someone is running a foward without cache + self.training

* more models

* fixup

* relaxed rope check

* lets also accept rope_type=None, defaulting to the original implementation

* type and rope_type can coexist

Remove conversation pipeline tests

* Update qwen2.md

outdated description

* Update qwen2.md

amended

* Update qwen2.md

Update

* Update qwen2.md

fix wrong version code, now good to go

fix: default value reflects the runtime environment variables rather than the ones present at import time. (#32153)

* fix: default value reflects the runtime environment variables rather than the ones present at import time.

* Fix: Change `deterministic` to None by default; use env var if None

* adds: extra_repr() to MambaRMSNorm to include the hidden size of the layer

* style fix with ruff:

fix

Fixed an if condition always evaluating to true.

Co-authored-by: Arthur Zucker <arthur.zucker@gmail.com>

* Updated ruff version and fixed the required code accorindg to the latest version.

* Updated ruff version and fixed the required code accorindg to the latest version.

* Added noqa directive to ignore 1 error shown by ruff

* add DataCollatorBatchFlattening

* Update data_collator.py

* change name

* new FA2 flow if position_ids is provided

* add comments

* minor fix

* minor fix data collator

* add test cases for models

* add test case for data collator

* remove extra code

* formating for ruff check and check_repo.py

* ruff format

ruff format tests src utils

* custom_init_isort.py

Update integration_utils.py

Added additional kwarg

* feat(cache): StaticCache uses index_copy_ to avoid useless copy

Using index_copy_ allows for explicit in-place change of the tensor.
Some backends (XLA) will otherwise copy the tensor, making the code
slower and using more memory.

Proposed implementation will end up using less memory and on XLA will
result in less compilation, but the change is also quite generic, making
no change whatsoever on CUDA or CPU backend.

* feat(cache): SlidingWindowCache uses index_copy_ to avoid useless copy

Applying the same change done in StaticCache.

* fix(cache): fallback of index_copy_ when not implemented

* fix(cache): in index_copy_ ensure tensors are on same device

* [run slow] llama

* fix(cache): add move of cache_position to same device in SlidingWindowCache

* Revert "[run slow] llama"

This reverts commit 02608dd14253ccd464e31c108e0cd94364f0e8b9.

Revert "Incorrect Whisper long-form decoding timestamps  (#32003)"

This reverts commit cd48553f.

* renamed phi3 rope_scaling type

* fixed trailing whitespaces

* fixed test

* added warning

* fixed format

* Update README.md

* tests: forward ok

* backward test done

* done testing

* removed check. scripts

* Update README.md

* added use_mambapy arg

* fixed typo in warning

* protected imports w/ mambapy package

* delete pscan.py + raise rather than assert

* Update import_utils.py

* fix whitespaces and unused import

* trailing whitespace + import block unformatted

* Update modeling_mamba.py

* transpose before pscan

* shape comment

* ran make style

* use_mambapy=False by default

Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>

* ran make fix-copies

---------

Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>

---------

Co-authored-by: Merve Noyan <mervenoyan@Merve-MacBook-Pro.local>

Add the lru_cache for speed

* gguf conversion forces add_prefix_space=False for llama3, this is not required and forces from_slow, which fails. changing to None + test

* typo

* clean test

Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>

* Change resize_token_embeddings to make it return same Class that is passed to it

* Add explanatory comment as requested in review

* Add explanatory comments for add resizing function in lxmert

* Add comment for padding_idx and moving _resize_bias in lxmert to LxmertForPreTraining

---------

Co-authored-by: Prashanth Sateesh <prasatee@Prashanths-MBP.attlocal.net>
Co-authored-by: Prashanth Sateesh <prasatee@Prashanths-MacBook-Pro.local>

add attribute to model

Signed-off-by: Daniel Lok <daniel.lok@databricks.com>

* Add YaRN and Dynamic-YaRN RoPE Scaling Methods

YaRN (Yet another RoPE extension method) combines the NTK-By-Parts
Interpolation and Attention Scaling methods, improving upon existing
RoPE interpolation methods for longer context window sizes.

Fine-tuned models maintain their original performance across benchmarks
while enabling efficient extrapolation and transfer learning for
quicker convergence, especially in compute-limited environments.

We implement YaRN and Dynamic-YaRN for the following list of models:

 - LLaMA
 - Falcon
 - GPT-NeoX
 - Olmo
 - Persimmon
 - Phi
 - StableLM
 - OpenLLaMA

New unit tests are added to assert YaRN's correct behavior on both
short and long sequence inputs.

For more details, please refer to https://arxiv.org/abs/2309.00071

.

Co-authored-by: Miguel Almeida <miguel.pessanha.almeida@tecnico.ulisboa.pt>

* Refactor YaRN implementation for LLaMA

Iterate on YaRN implementation for LLaMA and remove diff from remaining
models for increased PR modularity.

This commit includes the following changes:
- Merge 'yarn_rope_scaling' and 'rope_scaling' dictionaries
- Remove unnecessary attributes ('extrapolation_factor' and 'finetuned')
  from YaRN classes
- Inherit 'forward' method in YaRN classes from superclass
- Rename 'yarn' method to 'compute_yarn_scaling'
- Extend YaRN tests with further assertions
- Fix style inconsistencies

Co-authored-by: Miguel Monte e Freitas <miguelmontefreitas@tecnico.ulisboa.pt>

* Refactor Tensor Building Logic for YaRN

- Comply with the the tensor building logic introduced in #30743
- Add referencing to the optimized Attention Factor equation
- Remove Dynamic YaRN for a more agile deployment

Co-authored-by: mig-mfreitas <mig-mfreitas@users.noreply.github.com>

* remove unwanted file

---------

Co-authored-by: Miguel Almeida <miguel.pessanha.almeida@tecnico.ulisboa.pt>
Co-authored-by: mig-mfreitas <mig-mfreitas@users.noreply.github.com>
Co-authored-by: Joao Gante <joao@huggingface.co>

encapsulate chat template logic

* fix mask creation of gpt2 and gpt_neox caused by me

* forgot the reshape of masks when shape > 2

* add tests for gpt neox and gpt2

* nit on a comment

* [whisper] remove un-necessary transpose for fa2 attention

* propagate

* [whisper integration] use parquet dataset for testing

* propagate to others

* more propagation

* last one

* pad on right if training

* docs

* add tests

* Add llama3-llava-next-8b to llava_next conversion script

Adds support for the lmms-lab/llama3-llava-next-8b model to the
convert_llava_next_weights_to_hf.py script, along with an example
prompt generated from the llava_llama_3 conv_template in the LLaVA-NeXT
repo.

* Exclude <|begin_of_text|> from prompt example

This token gets added automatically, so it should not be included in the
prompt example.

* Add llava-next-72b and llava-next-110b

Adds the Qwen-based LLaVA-Next models to the conversion script, along
with changes to load the models on multiple GPUs for inference.

* Add llama3 and qwen prompt formats to docs

* Chat prompt and padding side left for llama3 batched

* update

* Update src/transformers/models/llava_next/convert_llava_next_weights_to_hf.py

Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* Update src/transformers/models/llava_next/convert_llava_next_weights_to_hf.py

Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* remove code

* better naming

---------

Co-authored-by: raushan <raushan@huggingface.co>
Co-authored-by: Raushan Turganbay <raushan.turganbay@alumni.nu.edu.kz>
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* Add new quant method

* update

* fix multi-device

* add test

* add offload

* style

* style

* add simple example

* initial doc

* docstring

* style again

* works ?

* better docs

* switch to non persistant

* remove print

* fix init

* code review