GitLab

* Add a post init method to all models

* Fix tests

* Fix last tests

* Fix templates

* Add comment

* Forgot to save

* Fix gradient_checkpointing backward compatibility

* Remove needless line

* make sure mask prob is big enough and length small enough

* Fix tests
Co-authored-by: patrickvonplaten <patrick.v.platen@gmail.com>

* Start the work for TFViTModel

* Convert to TF code - need to check in the follow up commits

* Clean up model code

* Expose TFViTModel

* make style

* make quality

* Add test

* make style & quality

* Fix some imports

* fix wrong usage - *kwargs => ** kwargs

* Fix Conv2D weight loading (PT->TF) issue

* Add tests for images with different sizes + fix model

* Fix some common tests for TFViTModel

* Use inputs instead of input_ids in test_compile_tf_model

* Add a comment about transpose and Conv2D in convert_tf_weight_name_to_pt_weight_name

* Avoid transpose in TFViT call

* Fix Conv2D issue in load_tf2_weights_in_pytorch_model

* Use tf.keras.layers.Conv2D instead of tf.nn.conv2d

* Using simpler heuristic to detect Conv2D layer

* Change convert_tf_weight_name_to_pt_weight_name to return TransposeType

* Check tf_weight_shape is not None before using it

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

* fix missing comma

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

* Dynamic configs

* Add config test

* Better tests

* Add tokenizer and test

* Add to from_config

* With save

* Update Transformers to huggingface_hub >= 0.1.0

* Forgot to save...

* Style

* Fix test

* Add first draft

* Make forward pass work

* Improve conversion script

* Add notebook that checks if it works

* Add BeitForSemanticSegmentation to the tests

* More improvements

* Make BeitForSemanticSegmentation consistent with Segformer

* Small bug fix

* Add BeitForSemanticSegmentation to docs

* Make sure model doesn't output hidden states when the user doesn't want to

* Make it possible to convert the large model

* Fix issue

* Fix conversion script for large model

* Add auxiliary_head option to semantic segmentation model

* Apply suggestions from @sgugger's review

* Apply suggestions from code review

* Fix failing test
Co-authored-by: Lysandre <lysandre.debut@reseau.eseo.fr>

* Generalize problem_type to all classification models

* Missing import

* Deberta BC and fix tests

* Fix template

* Missing imports

* Revert change to reformer test

* Fix style

* up

* up

* up

* up

* up

* up

* up

* add clip

* fix clip PyTorch

* fix clip PyTorch

* up

* up

* up

* up

* up

* up

* up

* Fix

* Style

* Name

* Fix tests

* Style

* Remove embed sizes checking

* Disable some tests

* Fix

* Apply suggestion

[Gradient checkpoining] Correct disabling `find_unused_parameters` in Trainer when gradient checkpointing is enabled (#13961)

* up

* correct test

* Symbolic trace dynamic axes support for BERT like models (albert, bert, distilbert, mobilebert, electra, megatron-bert)
* Sanity checks before tracing that make sure the model to trace is supported
* Adapted to PyTorch 1.9
Co-authored-by: Michael Benayoun <michael@huggingface.co>

* Make gradient_checkpointing a training argument

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

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

* Fix tests

* Style

* document Gradient Checkpointing as a performance feature

* Small rename

* PoC for not using the config

* Adapt BC to new PoC

* Forgot to save

* Rollout changes to all other models

* Fix typo
Co-authored-by: Stas Bekman <stas00@users.noreply.github.com>
Co-authored-by: Stas Bekman <stas@stason.org>

* Dynamic model

* Use defensive flag

* Style

* Doc and arg rename

* Arg rename

* Add tests

* Apply suggestions from code review
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>

* Apply suggestions from code review
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>

* Address review comments

* Apply suggestions from code review
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>

* finish

* delete bogus file

* correct some stuff

* finish

* finish

* Incorporate tests dependencies in tests_fetcher

* Harder modif

* Debug

* Loop through all files

* Last modules

* Remove debug statement

* fix AutoModel.from_pretrained(..., torch_dtype=...)

* fix to_diff_dict

* add better test

* torch is not always available when a model has self.torch_dtype

* Add option to load a pretrained model with mismatched shapes

* Fail at loading when mismatched shapes in Flax

* Fix tests

* Update src/transformers/modeling_flax_utils.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

* Address review comments
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

* fix lm_head.decoder.weight ignore_key handling

* fix the mutable class variable

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

* replicate the comment

* make deterministic
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>

* [models] respect dtype of the model when instantiating it

* cleanup

* cleanup

* rework to handle non-float dtype

* fix

* switch to fp32 tiny model

* improve

* use dtype.is_floating_point

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

* fix the doc

* recode to use explicit torch_dtype_auto_detect, torch_dtype args

* docs and tweaks

* docs and tweaks

* docs and tweaks

* merge 2 args, add docs

* fix

* fix

* better doc

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

* Fix torchscript tests

* Better test

* Remove bogus print

Co-authored-by: Michael Benayoun <michael@huggingface.co>

* Clean push to hub API

* Create working dir if it does not exist

* Different tweak

* New API + all models + test Flax

* Adds the Trainer clean up

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

* Address review comments

* (nit) output types

* No need to set clone_from when folder exists

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

* Add generated_from_trainer tag

* Update to new version

* Fixes
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
Co-authored-by: Julien Chaumond <julien@huggingface.co>
Co-authored-by: Lysandre <lysandre.debut@reseau.eseo.fr>

* consistent nn. and nn.functional: p3 templates

* restore

* Squash all commits of modeling_detr_v7 branch into one

* Improve docs

* Fix tests

* Style

* Improve docs some more and fix most tests

* Fix slow tests of ViT, DeiT and DETR

* Improve replacement of batch norm

* Restructure timm backbone forward

* Make DetrForSegmentation support any timm backbone

* Fix name of output

* Address most comments by @LysandreJik

* Give better names for variables

* Conditional imports + timm in setup.py

* Address additional comments by @sgugger

* Make style, add require_timm and require_vision to testsé

* Remove train_backbone attribute of DetrConfig, add methods to freeze/unfreeze backbone

* Add png files to fixtures

* Fix type hint

* Add timm to workflows

* Add `BatchNorm2d` to the weight initialization

* Fix retain_grad test

* Replace model checkpoints by Facebook namespace

* Fix name of checkpoint in test

* Add user-friendly message when scipy is not available

* Address most comments by @patrickvonplaten

* Remove return_intermediate_layers attribute of DetrConfig and simplify Joiner

* Better initialization

* Scipy is necessary to get sklearn metrics

* Rename TimmBackbone to DetrTimmConvEncoder and rename DetrJoiner to DetrConvModel

* Make style

* Improve docs and add 2 community notebooks
Co-authored-by: Lysandre <lysandre.debut@reseau.eseo.fr>

Cleaner and more scalable implementation of symbolic tracing with torch.fx, and provides support for new architectures:
- ALBERT
- DistilBERT
- MobileBERT
- MegatronBERT
- GPT2
- GPT Neo
Co-authored-by: Michael Benayoun <michael@huggingface.co>

* Fix regression in regression

* Add test

Symbolic tracing feature for BERT, ELECTRA and T5
Co-authored-by: Michael Benayoun <michael@huggingface.co>
Co-authored-by: Stas Bekman <stas@stason.org>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* Add test and see where CI is unhappy

* Load with strict=False

* init bigbird pegasus

* add debugging nb ; update config

* init conversion

* update conversion script

* complete conversion script

* init forward()

* complete forward()

* add tokenizer

* add some slow tests

* commit current

* fix copies

* add docs

* add conversion script for bigbird-roberta-summarization

* remove TODO

* small fixups

* correct tokenizer

* add bigbird core for now

* fix config

* fix more

* revert pegasus-tokenizer back

* make style

* everything working for pubmed; yayygit status

* complete tests finally

* remove bigbird pegasus tok

* correct tokenizer

* correct tests

* add tokenizer files

* finish make style

* fix test

* update

* make style

* fix tok utils base file

* make fix-copies

* clean a bit

* small update

* fix some suggestions

* add to readme

* fix a bit, clean tests

* fix more tests

* Update src/transformers/__init__.py

* Update src/transformers/__init__.py

* make fix-copies

* complete attn switching, auto-padding left

* make style

* fix auto-padding test

* make style

* fix batched attention tests

* put tolerance at 1e-1 for stand-alone decoder test

* fix docs

* fix tests

* correct slow tokenizer conversion

* Apply suggestions from code review
Co-authored-by: Suraj Patil <surajp815@gmail.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* complete remaining suggestions

* fix test
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
Co-authored-by: Suraj Patil <surajp815@gmail.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* lazy_init_weights

* remove ipdb

* save int

* add necessary code

* remove unnecessary utils

* Update src/transformers/models/t5/modeling_t5.py

* clean

* add tests

* correct

* finish tests

* finish tests

* fix some more tests

* fix xlnet & transfo-xl

* fix more tests

* make sure tests are independent

* fix tests more

* finist tests

* final touches

* Update src/transformers/modeling_utils.py

* Apply suggestions from code review

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

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

* clean tests

* give arg positive name

* add more mock weights to xlnet
Co-authored-by: Stas Bekman <stas00@users.noreply.github.com>

* add to  bert

* review comments

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

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

* self.config.problem_type

* fix style

* fix

* fin

* fix

* update doc

* fix

* test

* Test more problem types

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

* fix

* remove

* fix

* quality

* make fix-copies

* remove test
Co-authored-by: abhishek thakur <abhishekkrthakur@users.noreply.github.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
Co-authored-by: Lysandre <lysandre.debut@reseau.eseo.fr>

* add attentions & hidden states

* add model outputs + docs

* finish docs

* finish tests

* finish impl

* del @

* finish

* finish

* correct test

* apply sylvains suggestions

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

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

* Fix cross-attention head mask for Torch BART models

* Fix head masking for cross-attention module for the following
models: BART, Blenderbot, Blenderbot_small, M2M_100, Marian, MBart,
Pegasus

* Enable test_headmasking for M2M_100 model

* Fix cross_head_mask for FSMT, LED and T5

* This commit fixes `head_mask` for cross-attention modules
in the following models: FSMT, LED, T5

* It also contains some smaller changes in doc so that
it is be perfectly clear the shape of `cross_head_mask`
is the same as of `decoder_head_mask`

* Update template

* Fix template for BartForCausalLM

* Fix cross_head_mask for Speech2Text models

* Fix cross_head_mask in templates

* Fix args order in BartForCausalLM template

* Fix doc in BART templates

* Make more explicit naming

* `cross_head_mask` -> `cross_attn_head_mask`

* `cross_layer_head_mask` -> `cross_attn_layer_head_mask`

* Fix doc

* make style quality

* Fix speech2text docstring

* Initial support for upload to hub

* push -> upload

* Fixes + examples

* Fix torchhub test

* Torchhub test I hate you

* push_model_to_hub -> push_to_hub

* Apply mixin to other pretrained models

* Remove ABC inheritance

* Add tests

* Typo

* Run tests

* Install git-lfs

* Change approach

* Add push_to_hub to all

* Staging test suite

* Typo

* Maybe like this?

* More deps

* Cache

* Adapt name

* Quality

* MOAR tests

* Put it in testing_utils

* Docs + torchhub last hope

* Styling

* Wrong method

* Typos

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

* Address review comments

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

* Replace error by warning when loading an architecture in another

* Style

* Style again

* Add a test

* Adapt old test