GitLab

* fix fa2 autocasting when using quantization

* Update src/transformers/models/distilbert/modeling_distilbert.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>

* Update src/transformers/models/distilbert/modeling_distilbert.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>

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

* [DETA] fix freeze/unfreeze function

* Update src/transformers/models/deta/modeling_deta.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>

* Update src/transformers/models/deta/modeling_deta.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>

* add freeze/unfreeze test case in DETA

* fix type

* fix typo 2

* fix : enable aux and enc loss in training pipeline

* Add unsynced variables from original DETA for training

* modification for passing CI test

* make style

* make fix

* manual make fix

* change deta_modeling_test of configuration 'two_stage' default to TRUE and minor change of dist checking

* remove print

* divide configuration in DetaModel and DetaForObjectDetection

* image smaller size than 224 will give topk error

* pred_boxes and logits should be equivalent to two_stage_num_proposals

* add missing part in DetaConfig

* Update src/transformers/models/deta/modeling_deta.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* add docstring in configure and prettify TO DO part

* change distribute related code to accelerate

* Update src/transformers/models/deta/configuration_deta.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* Update tests/models/deta/test_modeling_deta.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* protect importing accelerate

* change variable name to specific value

* wrong import

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

* Fix pos_mask application and update tests accordingly

* Fix style

* Adding comments

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Co-authored-by: Fernando Rodriguez <fernando.rodriguez@nielseniq.com>

When running the case on multi-cards server with devcie_map-auto, It will not always be allocated to device 0,
Because other processes may be using these cards. It will select the devices that can accommodate this model.
Signed-off-by: yuanwu <yuan.wu@intel.com>

Switch to the conda-forge channel for transformer installation,
as the huggingface channel does not offer the latest version.

Fixes #28248

* fix M4T FE error when no attention mask

* modify logic

* add test

* go back to initial test situation + add other tests

* fix get_num_masks output as [int] to int

* fix loss size from torch.Size([1]) to torch.Size([])

* Sort es/_toctree.yml like en/_toctree.yml

* Run make style

* Add -Rendimiento y escalabilidad- section to es/_toctree.yml

* Run make style

* Add s to section

* Add translate of performance.md

* Add performance.md to es/_toctree.yml

* Run make styele

* Fix docs links

* Run make style

* Translate contributing.md into Chinese

* Update review comments

* remove token_type_ids from model_input_names (like #24788)

* removed test that assumed token_type_ids should be present and updated a model reference so that it points to an available model)

* start - docs, SpeechT5 copy and rename

* add relevant code from FastSpeech2 draft, have tests pass

* make it an actual conformer, demo ex.

* matching inference with original repo, includes debug code

* refactor nn.Sequentials, start more desc. var names

* more renaming

* more renaming

* vocoder scratchwork

* matching vocoder outputs

* hifigan vocoder conversion script

* convert model script, rename some config vars

* replace postnet with speecht5's implementation

* passing common tests, file cleanup

* expand testing, add output hidden states and attention

* tokenizer + passing tokenizer tests

* variety of updates and tests

* g2p_en pckg setup

* import structure edits

* docstrings and cleanup

* repo consistency

* deps

* small cleanup

* forward signature param order

* address comments except for masks and labels

* address comments on attention_mask and labels

* address second round of comments

* remove old unneeded line

* address comments part 1

* address comments pt 2

* rename auto mapping

* fixes for failing tests

* address comments part 3 (bart-like, train loss)

* make style

* pass config where possible

* add forward method + tests to WithHifiGan model

* make style

* address arg passing and generate_speech comments

* address Arthur comments

* address Arthur comments pt2

* lint  changes

* Sanchit comment

* add g2p-en to doctest deps

* move up self.encoder

* onnx compatible tensor method

* fix is symbolic

* fix paper url

* move models to espnet org

* make style

* make fix-copies

* update docstring

* Arthur comments

* update docstring w/ new updates

* add model architecture images

* header size

* md wording update

* make style

remove broken space

Bumps [tj-actions/changed-files](https://github.com/tj-actions/changed-files) from 22.2 to 41.
- [Release notes](https://github.com/tj-actions/changed-files/releases)
- [Changelog](https://github.com/tj-actions/changed-files/blob/main/HISTORY.md)
- [Commits](https://github.com/tj-actions/changed-files/compare/v22.2...v41

)

---
updated-dependencies:
- dependency-name: tj-actions/changed-files
  dependency-type: direct:production
...
Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>

[Whisper] Fix errors with MPS backend introduced by new code on word-level timestamps computation (#28288)

* Update modeling_whisper.py to support MPS backend

Fixed some issue with MPS backend.

First, the torch.std_mean is not implemented and is not scheduled for implementation, while the single torch.std and torch.mean are.
Second, MPS backend does not support float64, so it can not cast from float32 to float64. Inverting the double() when the matrix is in the cpu fixes the issue while should not change the logic.

* Found another instruction in modeling_whisper.py not implemented byor MPS

After a load test, where I transcribed a 2 hours audio file, I got into a branch that did not fix in the previous commit.
Similar fix, where the torch.std_mean is changed into torch.std and torch.mean

* Update modeling_whisper.py removed trailing white spaces

Removed trailing white spaces

* Update modeling_whisper.py to use is_torch_mps_available()

Using is_torch_mps_available() instead of capturing the NotImplemented exception

* Update modeling_whisper.py sorting the import block

Sorting the utils import block

* Update src/transformers/models/whisper/modeling_whisper.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* Update src/transformers/models/whisper/modeling_whisper.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* Update src/transformers/models/whisper/modeling_whisper.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

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

Co-authored-by: liujizhong1 <liujizhong1@xiaomi.com>

* Update trainer.py

* format

update docs around mixing hf scheduler with deepspeed optimizer

Update modeling_utils.py

* v1

* add docstring

* add tests

* add awq 0.1.8

* oops

* fix test

* fix llava index errors

* forward contrib credits from original implementation and fix

* better fix

* final fixes and fix all tests

* fix

* fix nit

* fix tests

* add regression tests

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

Co-authored-by: yudong.lin <yudong.lin@funplus.com>

* fix: minor enhancement and fix in bounding box visualization example

The example that was trying to visualize the bounding box was not considering an edge case,
where the bounding box can be un-normalized. So using the same set of code, we can not get
results with a different dataset with un-normalized bounding box. This commit fixes that.

* run make clean

* add an additional note on the scenarios where the box viz code works

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Co-authored-by: Anindyadeep <anindya@pop-os.localdomain>

* fix frames

* use smaller chunk length

* correct beam search + tentative stride

* fix whisper word timestamp in batch

* add test batch generation with return token timestamps

* Apply suggestions from code review
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
Co-authored-by: Sanchit Gandhi <93869735+sanchit-gandhi@users.noreply.github.com>

* clean a test

* make style + correct typo

* write clearer comments

* explain test in comment

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Co-authored-by: sanchit-gandhi <sanchit@huggingface.co>
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
Co-authored-by: Sanchit Gandhi <93869735+sanchit-gandhi@users.noreply.github.com>

fix
Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>

* add check_support_list.py

* fix

* fix

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

Bug: `training_args.py` fix missing import with accelerate with version `accelerate==0.20.1` (#28171)

* fix-accelerate-version

* updated with exported ACCELERATE_MIN_VERSION,

* update string in ACCELERATE_MIN_VERSION

* First draft

* More improvements

* More improvements

* Make all tests pass

* Remove script

* Update image processor

* Address comments

* Use new gradient checkpointing method

* Convert checkpoints, add integration test

* Do not keep aspect ratio for now

* Set keep_aspect_ratio=False for beit, add integration test

* Remove print statement

Fix: [SeamlessM4T - S2TT] Bug in batch loading of audio in torch.Tensor format in the SeamlessM4TFeatureExtractor class (#27914)

* fixes: code fixes on is_batched condition to also check for batched audio data in torch.Tensor format instead of only just checking for batched audio data in np.ndarray format

* Update src/transformers/models/seamless_m4t/feature_extraction_seamless_m4t.py
Co-authored-by: Yoach Lacombe <52246514+ylacombe@users.noreply.github.com>

* refactor: code refactoring to remove torch framework dependency

* docs: updated docstring to add torch tensor compatibility

* test: add test cases to incorporate torch tensor inputs

* test: ran make fix-copies for code conformity

* test: refactor test to separate the test_call into test_call_numpy and test_call_torch

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Co-authored-by: Yoach Lacombe <52246514+ylacombe@users.noreply.github.com>

* normalize reverse indexing for causal lm sequence classifiers

* normalize reverse indexing for causal lm sequence classifiers

* normalize reverse indexing for causal lm sequence classifiers

* use modulo instead

* unify modulo-based sequence lengths

fix
Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>

* add 4-bit serialization docs

* up

* up

Update test values

Indices must match stage name ordering

* Fix vision text dual encoder

* Small cleanup for wav2vec2 (not fixed yet)

* Small fix for vision_encoder_decoder

* Fix SAM builds

* Update TFBertTokenizer test with modern exporting + tokenizer

* Fix DeBERTa

* Fix DeBERTav2

* Try RAG fix but it's impossible to test locally

* Actually fix RAG now that I got FAISS working somehow

* Fix Wav2Vec2, add sermon

* Fix Hubert

* some nits

* update test

* add support d\sd[a

* remove some dummy inputs

* all good

* style

* nits

* fixes

* fix more copies

* nits

* styling

* fix

* Update src/transformers/models/mistral/modeling_mistral.py
Co-authored-by: Younes Belkada <49240599+younesbelkada@users.noreply.github.com>

* add a slow test just to be sure

* fixup

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Co-authored-by: Younes Belkada <49240599+younesbelkada@users.noreply.github.com>

* [Whisper] Use torch for stft if available

* update docstring

* mock patch decorator

* fit on one line

* updated bitsandbytes.py

* rm test_raise_* from test_4bit.py

* add test_4bit_serialization.py

* modeling_utils bulk edits

* bnb_ver 0.41.3 in integrations/bitsandbytes.py
Co-authored-by: Younes Belkada <49240599+younesbelkada@users.noreply.github.com>

* @slow reinstated
Co-authored-by: Younes Belkada <49240599+younesbelkada@users.noreply.github.com>

* bnb ver 0.41.3 in  src/transformers/modeling_utils.py
Co-authored-by: Younes Belkada <49240599+younesbelkada@users.noreply.github.com>

* rm bnb version todo in  integrations/bitsandbytes.py
Co-authored-by: Younes Belkada <49240599+younesbelkada@users.noreply.github.com>

* moved 4b serialization tests to test_4bit

* tests upd for opt

* to torch_device
Co-authored-by: Younes Belkada <49240599+younesbelkada@users.noreply.github.com>

* ruff fixes to tests

* rm redundant bnb version check in mod_utils
Co-authored-by: Younes Belkada <49240599+younesbelkada@users.noreply.github.com>

* restore _hf_peft_config_loaded  modeling_utils.py::2188
Co-authored-by: Younes Belkada <49240599+younesbelkada@users.noreply.github.com>

* restore _hf_peft_config_loaded  test in modeling_utils.py::2199
Co-authored-by: Younes Belkada <49240599+younesbelkada@users.noreply.github.com>

* fixed NOT getattr(self, "is_8bit_serializable")
Co-authored-by: Younes Belkada <49240599+younesbelkada@users.noreply.github.com>

* setting model.is_4bit_serializable

* rm separate fp16_statistics arg from set_module...

* rm else branch in integrations::bnb::set_module

* bnb 4bit dtype check

* upd comment on 4bit weights

* upd tests for FP4 safe

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Co-authored-by: Younes Belkada <49240599+younesbelkada@users.noreply.github.com>