GitLab

* [FT] First commit for graphormer architecture.

The model has no tokenizer, as it uses a collator and preprocessing function for its input management.
Architecture to be tested against original one.
The arch might need to be changed to fit the checkpoint, but a revert to the original arch will make the code less nice to read.
TODO: doc

* [FIX] removed test model

* [FIX] import error

* [FIX] black and flake

* [DOC] added paper refs

* [FIX] [DOC]

* [FIX] black

* [DOC] Updated READMEs

* [FIX] Order of imports + rm Tokenizer calls

* [FIX] Moved assert in class to prevent doc build failure

* [FIX] make fix-copies

* [Doc] update from code review
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* [FIX] Removed Graphormer from Sequence classification model list

* [DOC] Added HF copyright to Cython file

* [DOC] Fixed comments

* [FIX] typos in class doc + removed config classes.

Todo: update doc from paper definitions

* [FIX] Removed dependency to fairseq, and replaced all asserts with Exception management

* [FIX] Homogeneized initialization of weights to pretrained constructor

* [FIX] [CP] Updated multi_hop parameter to get same results as in original implementation

* [DOC] Relevant parameter description in the configuration file

* [DOC] Updated doc and comments in main graphormer file

* [FIX] make style and quality checks

* [DOC] Fix doc format

* [FIX] [WIP] Updated part of the tests, though still a wip

* [FIX] [WIP]

* [FIX] repo consistency

* [FIX] Changed input names for more understandability

* [FIX] [BUG] updated num_classes params for propagation in the model

* simplified collator

* [FIX] Updated tests to follow new naming pattern

* [TESTS] Updated test suite along with model

* |FIX] rm tokenizer import

* [DOC] add link to graphormerdoc

* Changed section in doc from text model to graph model

* Apply suggestions from code review

Spacing, inits
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* [DOC] Explain algos_graphormer functions

* Cython soft import protection

* Rm call to Callable in configuration graphormer

* [FIX] replaced asserts with Exceptions

* Add org to graphormer checkpoints

* Prefixed classes with Graphormer

* Management of init functions

* format

* fixes

* fix length file

* update indent

* relaunching ci

* Errors for missing cython imports

* fix style

* fix style doc
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* Add XLA torchrun support

* Clarify that currently DDP doesn't work with torch.distributed XLA backend yet

* Enable DDP with torchrun and XLA (now available in PT-XLA 1.13)

* Add check for AWS Neuron availability and AWS Neuron specific compiler flag

* Change the new test's name to TestTrainerDistributedNeuronCore

* Remove "assert" and replace raised exception

* Remove compiler flag as it is optional. If needed, will be another PR.

* Use TORCHELASTIC_RUN_ID to determine whether torchrun is used

* 🚧 wip video classification pipeline

* 🚧 wip - add is_decord_available check

* 🐛 add missing import

* ✅ add tests

* 🔧 add decord to setup extras

* 🚧 add is_decord_available

* ✨ add video-classification pipeline

* 📝 add video classification pipe to docs

* 🐛 add missing VideoClassificationPipeline import

* 📌 add decord install in test runner

* ✅ fix url inputs to video-classification pipeline

* ✨ updates from review

* 📝 add video cls pipeline to docs

* 📝 add docstring

* 🔥 remove unused import

* 🔥 remove some code

* 📝 docfix

* Fixed test_saved_model_extended

* Fix TFGPT2 tests

* make fixup

* Make sure keras-nlp utils are available for type hinting too

* Update src/transformers/testing_utils.py
Co-authored-by: Yih-Dar <2521628+ydshieh@users.noreply.github.com>

* make fixup
Co-authored-by: Yih-Dar <2521628+ydshieh@users.noreply.github.com>

* Add DiNAT

* Adds DiNAT + tests

* Minor fixes

* Added HF model

* Add natten to dependencies.

* Cleanup

* Minor fixup

* Reformat

* Optional NATTEN import.

* Reformat & add doc to _toctree

* Reformat (finally)

* Dummy objects for DiNAT

* Add NAT + minor changes

Adds NAT as its own independent model + docs, tests
Adds NATTEN to ext deps to ensure ci picks it up.

* Remove natten from `all` and `dev-torch` deps, add manual pip install to ci tests

* Minor fixes.

* Fix READMEs.

* Requested changes to docs + minor fixes.

* Requested changes.

* Add NAT/DiNAT tests to layoutlm_job

* Correction to Dinat doc.

* Requested changes.

* Init Update

* ClearML Callbacks integration

* update corrections

* args reporting updated

* {'tensorboard': False, 'pytorch': False}

* ClearML Tests added

* add clearml

* output_uri=True in Task.init

* reformatted integrations.py

* reformatted and fixed

* IF-ELSE statement issue on "has_clearml" resolved

* Add clearml in main callback docs

* Add additional clearml documentation

* Update src/transformers/integrations.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* Accept suggestion
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* Accept suggestion
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* Small change in comments

* Make style clearml

* Accept suggestion
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
Co-authored-by: Victor Sonck <victor.sonck@gmail.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* First draft

* Remove scatter dependency

* Add require_torch

* update vectorized sum test, add clone call

* remove artifacts

* fix style

* fix style v2

* remove "scatter" mentions from the code base

* fix isort error
Co-authored-by: Niels Rogge <nielsrogge@Nielss-MacBook-Pro.local>
Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>

* Run some TF Whisper tests in subprocesses to avoid GPU OOM
Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>

This PR (https://github.com/huggingface/transformers/pull/19367) introduced a few breaking changes:

- Removed an argument `mask_threshold`.
- Broke the default behavior (instance vs panoptic in the function call)
  https://github.com/huggingface/transformers/pull/19367/files#diff-60f846b86fb6a21d4caf60f5b3d593a04accb8f248de3029cccae2ff898c5bc3R119-R120
- Broke the actual masks: https://github.com/huggingface/transformers/pull/1961

This PR is the start of a handful that will aim at bringing back the old
behavior(s).

- tests should not have to specify `task` by default, unless we want to
  modify the behavior and have a lower form of segmentation running)
- `test_small_model_pt` should be working.

This specific PR starts with adding more information to the masks hash
because missing the actual mask was actual easy to miss (the hashes do
change, but it was easy to miss that one code path wasn't properly
updated).

So we go from a simple `hash` to
```
{"hash": #smaller hash, "shape": (h, w), "white_pixels": n}
```

The `shape` should help make sure the interpolation of the mask works
correctly, the `white_pixels` hopefully helps detect big regressions in
their amount when the hash gets modified.

* Add a decorator for flaky tests

* Quality

* Don't break the rest

* Address review comments

* Fix test name

* Fix typo and print to stderr

* Rework pipeline tests

* Try to fix Flax tests

* Try to put it before

* Use a new decorator instead

* Remove ignore marker since it doesn't work

* Filter pipeline tests

* Woopsie

* Use the fitlered list

* Clean up and fake modif

* Remove init

* Revert fake modif

* add sudachipy and jumanpp tokenizers for bert_japanese

* use ImportError instead of ModuleNotFoundError in SudachiTokenizer and JumanppTokenizer

* put test cases of test_tokenization_bert_japanese in one line

* add require_sudachi and require_jumanpp decorator for testing

* add sudachi and pyknp(jumanpp) to dependencies

* remove sudachi_dict_small and sudachi_dict_full from dependencies

* empty commit for ci

* Poc to use safetensors

* Typo

* Final version

* Add tests

* Save with the right name!

* Update tests/test_modeling_common.py
Co-authored-by: Julien Chaumond <julien@huggingface.co>

* Support for sharded checkpoints

* Test from Hub part 1

* Test from hub part 2

* Fix regular checkpoint sharding

* Bump for fixes
Co-authored-by: Julien Chaumond <julien@huggingface.co>

* First draft

* Make basic test work

* Fix most tokenizer tests

* More improvements

* Make more tests pass

* Fix more tests

* Fix some code quality

* Improve truncation

* Implement feature extractor

* Improve feature extractor and add tests

* Improve feature extractor tests

* Fix pair_input test partly

* Add fast tokenizer

* Improve implementation

* Fix rebase

* Fix rebase

* Fix most of the tokenizer tests.

* propose solution for fast

* add: integration test for fasttokenizer, warning for decode, fix template in slow tokenizer

* add: modify markuplmconverter

* add: some modify on converter and tokenizerfast

* Fix style, copies

* Make fixup

* Update tokenization_markuplm.py

* Update test_tokenization_markuplm.py

* Update markuplm related

* Improve processor, add integration test

* Add processor test file

* Improve processor

* Improve processor tests

* Fix more processor tests

* Fix processor tests

* Update docstrings

* Add Copied from statements

* Add more Copied from statements

* Add code examples

* Improve code examples

* Add model to doc tests

* Adding dependency check

* Add dummy file

* Add requires_backends

* Add model to toctree

* Fix more things, disable dependency check for now

* Apply more suggestions

* Add soft dependency

* Add annotators to tests

* Fix style

* Remove from_slow=True

* Remove print statements

* Add sanity check

* Fix processor test

* Fix processor tests, add more docs

* Add doc tests for mdx file

* Add more tips

* Apply suggestions
Co-authored-by: Niels Rogge <nielsrogge@Nielss-MacBook-Pro.local>
Co-authored-by: lockon-n <45759388+lockon-n@users.noreply.github.com>
Co-authored-by: SaulLu <lucilesaul.com@gmail.com>
Co-authored-by: lockon-n <dd098309@126.com>

* Fix docstrings with last version of hf-doc-builder styler

* Remove empty Parameter block

* Use commit hash to look in cache instead of calling head

* Add tests

* Add attr for local configs too

* Stupid typos

* Fix tests

* Update src/transformers/utils/hub.py
Co-authored-by: Julien Chaumond <julien@huggingface.co>

* Address Julien's comments
Co-authored-by: Julien Chaumond <julien@huggingface.co>

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

* Fix all tests

* enable fx2trt

* Update perf_train_gpu_one.mdx

* Update perf_train_gpu_one.mdx

* add lib check

* update

* format

* update

* fix import check

* fix isort

* improve doc

* refactor ctx manager

* fix isort

* black format

* isort fix

* fix format

* update args

* update black

* cleanups

* Update perf_train_gpu_one.mdx

* code refactor

* code refactor to init

* remove redundancy

* isort

* replace self.args with args
Co-authored-by: Stas Bekman <stas@stason.org>

* enhance ipex import

* refine codes

* refine style

* add link

* style
Co-authored-by: Stas Bekman <stas@stason.org>

* Fix all is_torch_tpu_available 

* Add a TF in-graph tokenizer for BERT

* Add from_pretrained

* Add proper truncation, option handling to match other tokenizers

* Add proper imports and guards

* Add test, fix all the bugs exposed by said test

* Fix truncation of paired texts in graph mode, more test updates

* Small fixes, add a (very careful) test for savedmodel

* Add tensorflow-text dependency, make fixup

* Update documentation

* Update documentation

* make fixup

* Slight changes to tests

* Add some docstring examples

* Update tests

* Update tests and add proper lowercasing/normalization

* make fixup

* Add docstring for padding!

* Mark slow tests

* make fixup

* Fall back to BertTokenizerFast if BertTokenizer is unavailable

* Fall back to BertTokenizerFast if BertTokenizer is unavailable

* make fixup

* Properly handle tensorflow-text dummies

* Improve vision models

* Add a lot of improvements

* Remove to_2tuple from swin tests

* Fix TF Swin

* Fix more tests

* Fix copies

* Improve more models

* Fix ViTMAE test

* Add channel check for TF models

* Add proper channel check for TF models

* Apply suggestion from code review

* Apply suggestions from code review

* Add channel check for Flax models, apply suggestion

* Fix bug

* Add tests for greyscale images

* Add test for interpolation of pos encodigns
Co-authored-by: Niels Rogge <nielsrogge@Nielss-MacBook-Pro.local>

* Prepare CI for v0.8.0

* pin hfh (revert before merge)

* Revert "pin hfh (revert before merge)"

This reverts commit a0103140e1c77b810ffcb735192968bc03be3e1f.

* Test rc3

* Test latest rc

* Unpin to the RC
Co-authored-by: Sylvain Gugger <Sylvain.gugger@gmail.com>

* deprecate is_torch_bf16_available

* address suggestions

Extend Transformers Trainer Class to Enable CPU AMP and Integrate Intel Extension for PyTorch (#17138)

* init PR

* fix import ipex

* minor fix on bf16

* refine optimizer

* refine args notes

* refine code

* refine ipex optimize args

* refine half_precision_backend

* black format

* isort format

* isort format files

* flake8 format

* doc builder format

* refine codes

* remove jit and optim bits

* black preview format

* Update src/transformers/trainer.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* refine code

* refine notes

* Update src/transformers/trainer.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* Update src/transformers/trainer.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* code refine

* add ipex ut

* add performance cpu doc

* link to the cpu doc from main perf doc

* install ipex into CI's docker

* Update perf_train_cpu.mdx

* Update docs/source/en/perf_train_cpu.mdx
Co-authored-by: Stas Bekman <stas00@users.noreply.github.com>

* Update perf_train_cpu.mdx

* Update perf_train_cpu.mdx
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
Co-authored-by: Stas Bekman <stas@stason.org>
Co-authored-by: Stas Bekman <stas00@users.noreply.github.com>

[Json configs] Make json prettier for all saved tokenizer files & ensure same json format for all processors (tok + feat_extract) (#17457)

* [Json dump] Make json prettier

* correct more tokenizeirs

* more patterns

* add aggressive test

* the aggressive test was actually useful :-)

* more tests

* Apply suggestions from code review

* Support compilation via Torchdynamo, AOT Autograd, NVFuser

* Address comments

* Lint

* Stas comments - missing quality test

* Lintere

* Quality test

* Doc lint

* Reset CUDA peak mem

* Add CustomTrainer

* require a single gpu
Co-authored-by: Stas Bekman <stas@stason.org>

Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

- Adds auto_batch_size finder 
- Moves training loop to an inner training loop

* Replace dict/BatchEncoding instance checks by Mapping

* Typo

* Add initial BNB integration

* fixup! Add initial BNB integration

* Add bnb test decorator

* Update Adamw8bit option name

* Use the full bnb package name

* Overide bnb for all embedding layers

* Fix package name

* Formatting

* Remove unnecessary import

* Update src/transformers/trainer.py
Co-authored-by: Stas Bekman <stas00@users.noreply.github.com>

* Rename AdamwBNB optimizer option

* Add training test checking that bnb memory utilization is lower

* fix merge

* fix merge; fix + extend new test

* cleanup

* expand bnb

* move all require_* candidates to testing_utils.py
Co-authored-by: Stas Bekman <stas00@users.noreply.github.com>
Co-authored-by: Stas Bekman <stas@stason.org>

* add low_cpu_mem_usage tests

* wip: revamping

* wip

* install /usr/bin/time

* wip

* cleanup

* cleanup

* cleanup

* cleanup

* cleanup

* fix assert

* put the wrapper back

* cleanup; switch to bert-base-cased

* Trigger CI

* Trigger CI

* Split file_utils in several submodules

* Fixes

* Add back more objects

* More fixes

* Who exactly decided to import that from there?

* Second suggestion to code with code review

* Revert wront move

* Fix imports

* Adapt all imports

* Adapt all imports everywhere

* Revert this import, will fix in a separate commit

* encoder works

* addded files

* norm in stage

* convertion script

* tests

* fix copies

* make fix-copies

* fixed __init__

* make fix-copies

* fix

* shapiro test needed

* make fix-copie

* minor changes

* make style + quality

* minor refactor conversion script

* rebase + tests

* removed unused variables

* updated doc

* toctree

* CI

* doc

* Apply suggestions from code review
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* resolved conversations

* make fixup

* config passed to modules

* config passed to modules

* Apply suggestions from code review
Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>

* conversations

* conversations

* copyrights

* normal test

* tests
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>

* Per-folder tests reorganization
Co-authored-by: sgugger <sylvain.gugger@gmail.com>
Co-authored-by: Stas Bekman <stas@stason.org>

# Add support for W&B hyperparameter sweep
This PR:
* allows using wandb for running hyperparameter search.
* The runs are visualized on W&B sweeps dashboard
* This supports runnning sweeps on parallel devices, all reporting to the same central dashboard.

### Usage
**To run new a hyperparameter search:**
```
trainer.hyperparameter_search(
    backend="wandb", 
    project="transformers_sweep", # name of the project
    n_trials=5,
    metric="eval/loss", # metric to be optimized, default 'eval/loss'. A warning is raised if the passed metric is not found
)
```
This outputs a sweep id. Eg. `my_project/sweep_id`

**To run sweeps on parallel devices:**
Just pass sweep id which you want to run parallel
```
trainer.hyperparameter_search(
    backend="wandb", 
    sweep_id = "my_project/sweep_id"
)
```

* Make OpenAIGPTTokenizer work with SpaCy 3.x

SpaCy 3.x introduced an API change to creating the tokenizer that
breaks OpenAIGPTTokenizer. The old API for creating the tokenizer in
SpaCy 2.x no longer works under SpaCy 3.x, but the new API for creating
the tokenizer in SpaCy 3.x DOES work under SpaCy 2.x. Switching to the
new API should allow OpenAIGPTTokenizer to work under both SpaCy 2.x and
SpaCy 3.x versions.

* Add is_spacy_available and is_ftfy_available methods to file utils

* Add spacy and ftfy unittest decorator to testing utils

* Add tests for OpenAIGPTTokenizer that require spacy and ftfy

* Modify CircleCI config to run tests that require spacy and ftfy

* Remove unneeded unittest decorators are reuse test code

* Run make fixup