GitLab

* [`Whisper`] raise better erros
fixes #27893

* update torch as well

* nits

* nits

* actual fix

* style

* ze fix

* fix fix fix style

* fix sdpa with non-contiguous inputs for gpt_bigcode

* fix other archs

* add currently comment

* format

* Improve the error printed when loading an unrecognized architecture

* Improve the error printed when loading an unrecognized architecture

* Raise a ValueError instead because KeyError prints weirdly

* make fixup

Update the link for vision encoder decoder doc used by
FlaxVisionEncoderDecoderModel link.

* fix loss computation

* compute on GPU if possible

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

* fixed typos (issue 27919)

* Update docs/source/en/tasks/knowledge_distillation_for_image_classification.md
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

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

* Support PeftModel signature inspect

* Use get_base_model() to get the base model

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Co-authored-by: shujunhua1 <shujunhua1@jd.com>

streamline

Update formats

up

* fix no sequence length models error

* block size check

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Co-authored-by: Adam Louly <adamlouly@microsoft.com@orttrainingdev9.d32nl1ml4oruzj4qz3bqlggovf.px.internal.cloudapp.net>

Update modeling_timesformer.py

Fixing typo to correct the stochastic depth decay rule

fix bug: cost matrix is infeasible

* fix a typo and add an illustrative test

* appease black

* reduce code duplication and add Annotion type back with a pending deprecation warning

* remove unused code

* change warning type

* black formatting fix

* change enum deprecation approach to support 3.8 and earlier

* add stacklevel

* fix black issue

* fix ruff issues

* fix ruff issues

* move tests to own mixin

* include yolos

* fix black formatting issue

* fix black formatting issue

* use logger instead of warnings and include target version for deprecation

* add deepspeed scheduled test for amd

* fix image

* add dockerfile

* add comment

* enable tests

* trigger

* remove trigger for this branch

* trigger

* change runner env to trigger the docker build image test

* use new docker image

* remove test suffix from docker image tag

* replace test docker image with original image

* push new image

* Trigger

* add back amd tests

* fix typo

* add amd tests back

* fix

* comment until docker image build scheduled test fix

* remove deprecated deepspeed build option

* upgrade torch

* update docker & make tests pass

* Update docker/transformers-pytorch-deepspeed-amd-gpu/Dockerfile

* fix

* tmp disable test

* precompile deepspeed to avoid timeout during tests

* fix comment

* trigger deepspeed tests with new image

* comment tests

* trigger

* add sklearn dependency to fix slow tests

* enable back other tests

* final update

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Co-authored-by: Felix Marty <felix@hf.co>
Co-authored-by: Félix Marty <9808326+fxmarty@users.noreply.github.com>
Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>

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

"text input must of type" -> "text input must be of type"

fix parameter count in readme

* Update import message

* Update message

* Fix test for multi-GPU

* WIth CPU handle

* Initial commit for AutoBackbone & Backbone

* Added timm and clarified out_indices

* Swapped the example to out_indices

* fix toctree

* Update autoclass_tutorial.md

* Update backbones.md

* Update autoclass_tutorial.md

* Add dummy torch input instead

* Add dummy torch input

* Update autoclass_tutorial.md

* Update backbones.md

* minor fix

* Update docs/source/en/main_classes/backbones.md
Co-authored-by: Maria Khalusova <kafooster@gmail.com>

* Update docs/source/en/autoclass_tutorial.md
Co-authored-by: Maria Khalusova <kafooster@gmail.com>

* Added illustrations and explained backbone & neck

* Update docs/source/en/main_classes/backbones.md
Co-authored-by: Maria Khalusova <kafooster@gmail.com>

* Update backbones.md

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Co-authored-by: Maria Khalusova <kafooster@gmail.com>

* use logger.warning_once to avoid massive outputs when training/finetuning longformer

* update more

* docstring corrections

* style make

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Co-authored-by: vijaye12 <vijaye12@in.ibm.com>

* up

* up

* test

* logits ok

* up

* up

* few fixes

* conversion script

* up

* nits

* nits

* update

* nuke

* more updates

* nites

* fix many issues

* nit

* scatter

* nit

* nuke megablocks

* nits

* fix conversion script

* nit

* remove

* nits

* nit

* update

* oupsssss

* change

* nits device

* nits

* fixup

* update

* merge

* add copied from

* fix the copy mentions

* update tests

* more fixes

* nits

* conversion script

* add parts of the readme

* Update tests/models/mixtral/test_modeling_mixtral.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>

* new test + conversion script

* Apply suggestions from code review
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>

* Apply suggestions from code review

* fix

* fix copies

* fix copies

* ooops

* fix config

* Apply suggestions from code review

* fix nits

* nit

* add copies

* add batched tests

* docs

* fix flash attention

* let's add more verbose

* add correct outputs

* support router ouptus

* ignore copies where needed

* fix

* cat list if list is given for now

* nits

* Update docs/source/en/model_doc/mixtral.md

* finish router refactoring

* fix forward

* fix expected values

* nits

* fixup

* fix

* fix bug

* fix

* fix dtype mismatch

* fix

* grrr grrr I support item assignment

* fix CI

* docs

* fixup

* remove some copied form

* fix weird diff

* skip doctest fast on the config and modeling

* mark that is supports flash attention in the doc

* update

* Update src/transformers/models/mixtral/modeling_mixtral.py
Co-authored-by: Lysandre Debut <hi@lysand.re>

* Update docs/source/en/model_doc/mixtral.md
Co-authored-by: Lysandre Debut <hi@lysand.re>

* revert router logits config issue

* update doc accordingly

* Update src/transformers/models/mixtral/convert_mixtral_weights_to_hf.py

* nits

* use torch testing asssert close

* fixup

* doc nits

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Co-authored-by: younesbelkada <younesbelkada@gmail.com>
Co-authored-by: Younes Belkada <49240599+younesbelkada@users.noreply.github.com>
Co-authored-by: Lysandre Debut <hi@lysand.re>

* Skip nn.Module.reset_parameters

* Actually skip

* Check quality

* Maybe change all inits

* Fix init issues: only modify public functions

* Add a small test for now

* Style

* test updates

* style

* nice tes

* style

* make it even faster

* one more second

* remove fx icompatible

* Update tests/test_modeling_common.py
Co-authored-by: Lysandre Debut <hi@lysand.re>

* Update tests/test_modeling_common.py
Co-authored-by: Lysandre Debut <hi@lysand.re>

* skip

* fix quality

* protect the import

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Co-authored-by: Lysandre Debut <hi@lysand.re>

fix sdpa dispatch

* More improvements

* Improve variable names

* Update READMEs, improve docs

change dtype of some integration tests

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

Fix test

* [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

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

* fix tune integration for ray 2.7+
Signed-off-by: Justin Yu <justinvyu@anyscale.com>

* add version check for ray tune backend availability
Signed-off-by: Justin Yu <justinvyu@anyscale.com>

* missing import
Signed-off-by: Justin Yu <justinvyu@anyscale.com>

* pin min version instead
Signed-off-by: Justin Yu <justinvyu@anyscale.com>

* address comments
Signed-off-by: Justin Yu <justinvyu@anyscale.com>

* some fixes
Signed-off-by: Justin Yu <justinvyu@anyscale.com>

* fix unnecessary final checkpoint
Signed-off-by: Justin Yu <justinvyu@anyscale.com>

* fix lint
Signed-off-by: Justin Yu <justinvyu@anyscale.com>

* dep table fix
Signed-off-by: Justin Yu <justinvyu@anyscale.com>

* fix lint
Signed-off-by: Justin Yu <justinvyu@anyscale.com>

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Signed-off-by: Justin Yu <justinvyu@anyscale.com>

* [CLAP] Replace hard-coded batch size to enable dynamic ONNX export

* Add back docstring

* add sdpa

* wip

* cleaning

* add ref

* yet more cleaning

* and more :)

* wip llama

* working llama

* add output_attentions=True support

* bigcode sdpa support

* fixes

* gpt-bigcode support, require torch>=2.1.1

* add falcon support

* fix conflicts falcon

* style

* fix attention_mask definition

* remove output_attentions from attnmaskconverter

* support whisper without removing any Copied from statement

* fix mbart default to eager renaming

* fix typo in falcon

* fix is_causal in SDPA

* check is_flash_attn_2_available in the models init as well in case the model is not initialized through from_pretrained

* add warnings when falling back on the manual implementation

* precise doc

* wip replace _flash_attn_enabled by config.attn_implementation

* fix typo

* add tests

* style

* add a copy.deepcopy on the config in from_pretrained, as we do not want to modify it inplace

* obey ...