GitLab

* finish

* delete bogus file

* correct some stuff

* finish

* finish

* added initial files

* fixes pipeline

* fixes style and quality

* fixes doc issue and positional encoding

* fixes layer norm and test

* fixes quality issue

* fixes code quality

* removed extra layer norm

* added layer norm back in encoder and decoder

* added more code copy quality checks

* update tests

* Apply suggestions from code review

* fix import

* fix test
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* Push to hub when saving checkpoints

* Add model card

* Revert partial model card

* Small fix for checkpoint

* Add tests

* Add documentation

* Fix tests

* Bump huggingface_hub

* Fix test

apparently).

* simplify local attention

* update tests

* add a comment and use torch.bitwise_xor

* finalize

* Apply suggestions from code review

* finish cleaner implementation

* more tests

* small fix

* finish

* up

* Enabling dataset iteration on pipelines.

Enabling dataset iteration on pipelines.

Unifying parameters under `set_parameters` function.

Small fix.

Last fixes after rebase

Remove print.

Fixing text2text `generate_kwargs`

No more `self.max_length`.

Fixing tf only conversational.

Consistency in start/stop index over TF/PT.

Speeding up drastically on TF (nasty bug where max_length would increase
a ton.)

Adding test for support for non fast tokenizers.

Fixign GPU usage on zero-shot.

Fix working on Tf.

Update src/transformers/pipelines/base.py
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Update src/transformers/pipelines/base.py
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Small cleanup.

Remove all asserts + simple format.

* Fixing audio-classification for large PR.

* Overly explicity null checking.

* Encapsulating GPU/CPU pytorch manipulation directly within `base.py`.

* Removed internal state for parameters of the  pipeline.

Instead of overriding implicitly internal state, we moved
to real named arguments on every `preprocess`, `_forward`,
`postprocess` function.

Instead `_sanitize_parameters` will be used to split all kwargs
of both __init__ and __call__ into the 3 kinds of named parameters.

* Move import warnings.

* Small fixes.

* Quality.

* Another small fix, using the CI to debug faster.

* Last fixes.

* Last fix.

* Small cleanup of tensor moving.

* is not None.

* Adding a bunch of docs + a iteration test.

* Fixing doc style.

* KeyDataset = None guard.

* RRemoving the Cuda test for pipelines (was testing).

* Even more simple iteration test.

* Correct import .

* Long day.

* Fixes in docs.

* [WIP] migrating object detection.

* Fixed the target_size bug.

* Fixup.

* Bad variable name.

* Fixing `ensure_on_device` respects original ModelOutput.

* Fixing #13381

* Enabling automatic LED models.

* Moving slow tokenizer to the Trie world.

* Adding more docstrings to the Trie.

* Fixing doctest (incompatible wiht our format? )

* Update src/transformers/tokenization_utils.py
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* Adding a lot more comment into the internals of this algorithm.

* Cleaner doc.

* Fixing the namings.

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

* quality.

* Fixing longest first match.

* Small improvements to cuts + more test + canine resistant test.

* Fixing fast test.
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* Implement object-detection pipeline

* Define threshold const

* Add `threshold` argument

* Refactor

* Uncomment test inputs

* `rm
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* Fix typo
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* Fix typo
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* Chore better doc
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* Rm unnecessary lines
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* Chore better naming
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* Update src/transformers/pipelines/object_detection.py
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* Update src/transformers/pipelines/object_detection.py
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* Fix typo

* Add `detr-tiny` for tests

* Add `ObjectDetectionPipeline` to `trnsfrmrs/init`

* Implement new bbox format

* Update detr post_process

* Update `load_img` method obj det pipeline

* make style

* Implement new testing format for obj det pipeln

* Add guard pytorch specific code in pipeline

* Add doc

* Make pipeline_obj_tet tests deterministic

* Revert some changes to `post_process` COCO api

* Chore

* Update src/transformers/pipelines/object_detection.py
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* Update src/transformers/pipelines/object_detection.py
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* Update src/transformers/pipelines/object_detection.py
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* Update src/transformers/pipelines/object_detection.py
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* Update src/transformers/pipelines/object_detection.py
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* Update src/transformers/pipelines/object_detection.py
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* Rm timm requirement

* make fixup

* Add timm requirement to test

* Make fixup

* Guard torch.Tensor

* Chore

* Delete unnecessary comment
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* Complete basic mechanism

* Save

* Complete everything

* Style & Quality

* Update READMEs

* Add testing

* Fix README.md format

* Apply suggestions

* Fix format

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

* fix logit_scale init

* add logit_scale_init_value as config param

* ✅ Update image-classification example's tests

* 🔥 remove cats_and_dogs test samples

* 💄 fix flake8

* refactor GPT Config to allow dyn. properties

* make attribute_map a class attribute

* remove old code

* update unit test to test config: Add test for common properties setter

* update unit test to test config: Add test for common properties passed as parameters to __init__

* update to black code format

* Allow that setters are not defined for certain config classes

* update config classes to implement attribute_map

* bugfix lxmert config - id2labels was not defined when num_labels was set

* update broken configs - add attribute_maps

* update bart config

* update black codestyle

* update documentation on common config attributes

* update GPTJ config to new attribute map

* update docs on common attributes

* gptj config: add max_position_embeddings

* gptj config: format with black

* update speech to text 2 config

* format doc file to max_len 119

* update config template

* Adding a test for multibytes unicode.

* Adding some accents.

* Making sure decoding works.

* Make tests passing by being cheesy.

* Add inputs to pretrained tests

* Make style

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

* ✨ add pytorch image classification example

* 🔥 remove utils.py

* 💄 fix flake8 style issues

* 🔥 remove unnecessary line

* ✨ limit dataset sizes

* 📌 update reqs

* 🎨 restructure - use datasets lib

* 🎨 import transforms directly

* 📝 add comments

* 💄 style

* 🔥 remove flag

* 📌 update requirement warning

* 📝 add vision README.md

* 📝 update README.md

* 📝 update README.md

* 🎨 add image-classification tag to model card

* 🚚 rename vision ➡️ image-classification

* 📝 update image-classification README.md

* correct order of overflowing_tokens for slow tokenizer (issue fix #13148)

* python 3.9 requires sentencepiece version 0.1.94 or above

* slicing of ids fixed in truncated_sequence()

* Update setup.py

* Correct order of overflowing tokens for pair of sentences

* code reformatted

* Update tokenization_utils_base.py

* reformatting file

* test to check single_input added

* missing function restored

* test to check pair_input overflowing tokens order

* test to check pair_input overflowing tokens order

* test to check pair_input overflowing tokens order

* added an error message for pair of seq and longest_first strategy

* test for pair_input modified

* variable name corrected

* fixed a typo in error message

* requested changes implemented

* required test added

* Corrected the message to match test message

* added error message for Luke Tokenizer

* lost test recovered

* docstring for truncate_sequences and prepare_for_model updated

* docstring for luke tokenizer updated

* updated ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING

* aligned text and fixed puncuatations

* improved style and quality of code

* fixed error_msg in truncate_sequences

* replaced encode_plus method with regular call method

* clean up

* rephrased the docstring

`audio-classification`.

* add test in trainer and test tokenizer saving wi
th trainer

* quality

* reverse trainer changes

* replace test in test_trainer by a test for all the tokenizers

* format

* add can_save_slow_tokenizer attribute to all tokenizers

* fix Herbert

* format

* Change comment in error

* add comments and a new assert

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

* change ValueError barthez

* change ValueError BigBird

* change ValueError Camembert

* change ValueError Mbart50

* change ValueError Pegasus

* change ValueError ReFormer

* change ValueError T5

* change ValueError RoBERTa

* XLNET fast

* Update tests/test_tokenization_common.py
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* change `assert` into `self.assertIn`

* format
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* fix_torch_device_generate_test

* remove @

* up

* correct some bugs

* correct model

* finish speech2text extension

* up

* up

* up

* up

* Update utils/custom_init_isort.py

* up

* up

* update with tokenizer

* correct old tok

* correct old tok

* fix bug

* up

* up

* add more tests

* up

* fix docs

* up

* fix some more tests

* add better config

* correct some more things
"

* fix tests

* improve docs

* Apply suggestions from code review

* Apply suggestions from code review

* final fixes

* finalize

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

* apply suggestions Lysandre and Sylvain

* apply nicos suggestions

* upload everything

* finish
Co-authored-by: Patrick von Platen <patrick@huggingface.co>
Co-authored-by: your_github_username <your_github_email>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
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* Torchscript test for Flaubert

* Update tests/test_modeling_flaubert.py

* Update tests/test_modeling_flaubert.py

* Torchscript test for ConvBERT

* Apply suggestions from code review

* Torchscript test for DistilBERT

* Update tests/test_modeling_distilbert.py

* Torchscript test

* Remove print statement

* Add the audio classification pipeline

* Remove autoconfig exception

* Mark ffmpeg test as slow

* Rearrange pipeline tests

* Add small test

* Replace asserts with ValueError

* Test GPTJ implementation

* Fixed conflicts

* Update __init__.py

* Update __init__.py

* change GPT_J to GPTJ

* fix missing imports and typos

* use einops for now
(need to change to torch ops later)

* Use torch ops instead of einsum

* remove einops deps

* Update configuration_auto.py

* Added GPT J

* Update gptj.rst

* Update __init__.py

* Update test_modeling_gptj.py

* Added GPT J

* Changed configs to match GPT2 instead of GPT Neo

* Removed non-existent sequence model

* Update configuration_auto.py

* Update configuration_auto.py

* Update configuration_auto.py

* Update modeling_gptj.py

* Update modeling_gptj.py

* Progress on updating configs to agree with GPT2

* Update modeling_gptj.py

* num_layers -> n_layer

* layer_norm_eps -> layer_norm_epsilon

* attention_layers -> num_hidden_layers

* Update modeling_gptj.py

* attention_pdrop -> attn_pdrop

* hidden_act -> activation_function

* Update configuration_gptj.py

* Update configuration_gptj.py

* Update configuration_gptj.py

* Update configuration_gptj.py

* Update configuration_gptj.py

* Update modeling_gptj.py

* Update modeling_gptj.py

* Update modeling_gptj.py

* Update modeling_gptj.py

* Update modeling_gptj.py

* Update modeling_gptj.py

* fix layernorm and lm_head size
delete attn_type

* Update docs/source/model_doc/gptj.rst
Co-authored-by: Suraj Patil <surajp815@gmail.com>

* removed claim that GPT J uses local attention

* Removed GPTJForSequenceClassification

* Update src/transformers/models/gptj/configuration_gptj.py
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* Removed unsupported boilerplate

* Update tests/test_modeling_gptj.py
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* Update src/transformers/models/gptj/modeling_gptj.py
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* Update src/transformers/models/gptj/modeling_gptj.py
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* Update src/transformers/models/gptj/modeling_gptj.py
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* Update tests/test_modeling_gptj.py
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* Update tests/test_modeling_gptj.py
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* Update tests/test_modeling_gptj.py
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* Update src/transformers/models/gptj/modeling_gptj.py
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* Update __init__.py

* Update configuration_gptj.py

* Update modeling_gptj.py

* Corrected indentation

* Remove stray backslash

* Delete .DS_Store

* Delete .DS_Store

* Delete .DS_Store

* Delete .DS_Store

* Delete .DS_Store

* Update docs to match

* Remove tf loading

* Remove config.jax

* Remove stray `else:` statement

* Remove references to `load_tf_weights_in_gptj`

* Adapt tests to match output from GPT-J 6B

* Apply suggestions from code review
Co-authored-by: Suraj Patil <surajp815@gmail.com>

* Default `activation_function` to `gelu_new`

- Specify the approximate formulation of GELU to ensure parity with the default setting of `jax.nn.gelu()`

* Fix part of the config documentation

* Revert "Update configuration_auto.py"

This reverts commit e9860e9c043b6ebf57a0e705044e9ec9ba2263bb.

* Revert "Update configuration_auto.py"

This reverts commit cfaaae4c4dc70f1fbe9abd60fc8bd0b863b8c011.

* Revert "Update configuration_auto.py"

This reverts commit 687788954fd0cfbc567fa1202d56a4ff9271944f.

* Revert "Update configuration_auto.py"

This reverts commit 194d024ea87d4fcef0dcb08e57f52c47511a9fc6.

* Hyphenate GPT-J

* Undid sorting of the models alphabetically

* Reverting previous commit

* fix style and quality issues

* Update docs/source/model_doc/gptj.rst
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* Update src/transformers/__init__.py
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* Update tests/test_modeling_gptj.py
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* Update src/transformers/models/gptj/modeling_gptj.py
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* Update src/transformers/__init__.py
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* Update src/transformers/models/gptj/modeling_gptj.py
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* Update src/transformers/models/gptj/modeling_gptj.py
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* Update src/transformers/models/gptj/configuration_gptj.py
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* Update src/transformers/models/gptj/configuration_gptj.py
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* Update src/transformers/models/gptj/configuration_gptj.py
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* Update src/transformers/models/gptj/modeling_gptj.py
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* Update src/transformers/models/gptj/modeling_gptj.py
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* Update src/transformers/models/gptj/modeling_gptj.py
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* Update src/transformers/models/gptj/modeling_gptj.py
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* Update src/transformers/models/gptj/modeling_gptj.py
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* Replaced GPTJ-specific code with generic code

* Update src/transformers/models/gptj/modeling_gptj.py
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* Made the code always use rotary positional encodings

* Update index.rst

* Fix documentation

* Combine attention classes

- Condense all attention operations into `GPTJAttention`
- Replicate GPT-2 and improve code clarity by renaming `GPTJAttention.attn_pdrop` and `GPTJAttention.resid_pdrop` to `GPTJAttention.attn_dropout` and `GPTJAttention.resid_dropout`

* Removed `config.rotary_dim` from tests

* Update test_modeling_gptj.py

* Update test_modeling_gptj.py

* Fix formatting

* Removed depreciated argument `layer_id` to `GPTJAttention`

* Update modeling_gptj.py

* Update modeling_gptj.py

* Fix code quality

* Restore model functionality

* Save `lm_head.weight` in checkpoints

* Fix crashes when loading with reduced precision

* refactor self._attn(...)` and rename layer weights"

* make sure logits are in fp32 for sampling

* improve docs

* Add `GPTJForCausalLM` to `TextGenerationPipeline` whitelist

* Added GPT-J to the README

* Fix doc/readme consistency

* Add rough parallelization support

- Remove unused imports and variables
- Clean up docstrings
- Port experimental parallelization code from GPT-2 into GPT-J

* Clean up loose ends

* Fix index.rst
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* Adding a TF variant of the DataCollatorForTokenClassification to get feedback

* Added a Numpy variant and a post_init check to fail early if a missing import is found

* Fixed call to Numpy variant

* Added a couple more of the collators

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

* Fixes, style pass, finished DataCollatorForSeqToSeq

* Added all the LanguageModeling DataCollators, except SOP and PermutationLanguageModeling

* Adding DataCollatorForPermutationLanguageModeling

* Style pass

* Add missing `__call__` for PLM

* Remove `post_init` checks for frameworks because the imports inside them were making us fail code quality checks

* Remove unused imports

* First attempt at some TF tests

* A second attempt to make any of those tests actually work

* TF tests, round three

* TF tests, round four

* TF tests, round five

* TF tests, all enabled!

* Style pass

* Merging tests into `test_data_collator.py`

* Merging tests into `test_data_collator.py`

* Fixing up test imports

* Fixing up test imports

* Trying shuffling the conditionals around

* Commenting out non-functional old tests

* Completed all tests for all three frameworks

* Style pass

* Fixed test typo

* Style pass

* Move standard `__call__` method to mixin

* Rearranged imports for `test_data_collator`

* Fix data collator typo "torch" -> "pt"

* Fixed the most embarrassingly obvious bug

* Update src/transformers/data/data_collator.py
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* Renaming mixin

* Updating docs
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