GitLab

* add model scaling section

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

* integrate reviewer feedback

* initialize GPU properly

* add note about BnB optimizer

* move doc from `scaling.mdx` to `performance.mdx`

* integrate reviewer feedback

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

* PoC for a ProcessorMixin class

* Documentation

* Apply suggestions from code review
Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>
Co-authored-by: Suraj Patil <surajp815@gmail.com>
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

* Roll out to other processors

* Add base feature extractor class in init

* Use args and kwargs
Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>
Co-authored-by: Suraj Patil <surajp815@gmail.com>
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

* change logger.warn to logger.warning

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

* fix test_model_outputs_equivalence

* fix tuple outputs for blenderbot

* feat(flax): allow encoder_outputs in generate

* doc(flax): encoder_outputs in generate

* fix: style

* fix: style

* Add wrapper classes

* convert inner layers to tf

* Add TF Encoder and Decoder layers

* TFSpeech2Text models

* Loadable model

* TF model with same outputs as PT model

* test skeleton

* correct tests and run the fixup

* correct attention expansion

* TFSpeech2Text pask_key_values with TF format

* use_cache = False for PT models if labels is passed

* Fix for BigBirdPegasusForConditionalGeneration

* add warning if users specify use_cache=True

* Use logger.warning instead of warnings.warn
Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>

* electra is added to onnx supported model

* add google/electra-base-generator for test onnx module
Co-authored-by: Lewis Tunstall <lewis.c.tunstall@gmail.com>

* Change the way tracing happens, enabling dynamic axes out of the box

* Update the tests and modeling xlnet

* Add the non recoding of leaf modules to avoid recording more values for the methods to record than what will be seen at tracing time (which would otherwise desynchronize the recorded values and the values that need to be given to the proxies during tracing, causing errors).

* Comments and making tracing work for gpt-j and xlnet

* Refactore things related to num_choices (and batch_size, sequence_length)

* Update fx to work on PyTorch 1.10

* Postpone autowrap_function feature usage for later

* Add copyrights

* Remove unnecessary file

* Fix issue with add_new_model_like

* Apply suggestions

* 📝 add config section

* 📝 finish first draft

* 📝 add feature extractor and processor

* 🖍 apply feedback from review

* 📝 minor edits

* last review

* fix outputs

* fix for CTC

* fix doc

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

* add cross attn to outputs

* add cross attn to outputs for TFLED

* add undo padding

* remove unused import

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

* Wav2Vec2 models must either throw or deal with add_apater
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

* Add pre-add_adapter backwards compatibility

* Add pre-add_adapter backwards compatibility

* Fix issue in tests/test_modeling_wav2vec2.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

* Single-epoch run

* Apply suggestions from code review
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

* Infinite dataset

* Trainer fix + distributed benchmark

* Benchmark fix

* unused import

* interleaved splits

* interleaved splits

* has_length util

* Move to research projects

* Leftover Sized checks

* Bump min version

* Unused import

* Revert trainer changes
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

* Unused import

* Make `has_length()` torch-independent to use in callbacks

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

* First draft

* Add conversion script

* Improve conversion script

* Improve docs and implement tests

* Define model output class

* Fix tests

* Fix more tests

* Add model to README

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

* Apply more suggestions from code review

* Apply suggestions from code review

* Rename dims to hidden_sizes

* Fix equivalence test

* Rename gamma to gamma_parameter

* Clean up conversion script

* Add ConvNextFeatureExtractor

* Add corresponding tests

* Implement feature extractor correctly

* Make implementation cleaner

* Add ConvNextStem class

* Improve design

* Update design to also include encoder

* Fix gamma parameter

* Use sample docstrings

* Finish conversion, add center cropping

* Replace nielsr by facebook, make feature extractor tests smaller

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

* [torch_int_div] Correct true division in generation

* up

* up

This reverts commit 854a0d52.

* [deepspeed docs] DeepSpeed ZeRO Inference

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

* tweak

* deal with black

* extra cleanup, better comments
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* Standardize instance segmentation models outputs

* Rename output

* Update src/transformers/modeling_outputs.py
Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>

* Add legacy argument to the config and model forward

* Update src/transformers/models/beit/modeling_beit.py
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>

* Copy fix in Segformer
Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>

* fix

* fix test

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

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

* Remove return_loss from Flax models

* fix more

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

* Add preprocess_logits_for_metrics Trainer param

* Compute accuracy in LM examples

* Improve comments

* [deepspeed] fix a bug in a test

* consistency

* Add general docstrings

* Remove legacy docstrings

* Add BEiT

* Add DEiT

* Add SegFormer

* Fix beit output class

* Fix missing return_dict

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

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

* Correct eos_token_id set in generate

* Set eos_token_id in test

* Correct eos_token_id set in generate

* Set eos_token_id in test

* change truncation_side in init of `PreTrainedTokenizerBase`
Co-authored-by: LSinev <LSinev@users.noreply.github.com>

* add test

* Revert "replace assert with exception for `padding_side` arg in `PreTrainedTokenizerBase` `__init__`"

This reverts commit 7a98b87962d2635c7e4d4f00db3948b694624843.

* fix kwargs

* Revert "fix kwargs"

This reverts commit 67b0a5270e8cf1dbf70e6b0232e94c0452b6946f.

* Update tests/test_tokenization_common.py
Co-authored-by: Nicolas Patry <patry.nicolas@protonmail.com>

* delete truncation_side variable

* reorganize test

* format

* complete doc

* Revert "Revert "replace assert with exception for `padding_side` arg in `PreTrainedTokenizerBase` `__init__`""

This reverts commit d5a10a7e2680539e5d9e98ae5d896c893d224b80.

* fix typo

* fix typos to render documentation

* Revert "Revert "Revert "replace assert with exception for `padding_side` arg in `PreTrainedTokenizerBase` `__init__`"""

This reverts commit 16cf58811943a08f43409a7c83eaa330686591d0.

* format
Co-authored-by: LSinev <LSinev@users.noreply.github.com>
Co-authored-by: Nicolas Patry <patry.nicolas@protonmail.com>

* Playing

* Properly set labels in model config for token classification example

* Port to run_ner_no_trainer

* Quality

# 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"
)
```