GitLab

* fix missing import jnp

* Fix missing jax and k=1
Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>

* Add missing __spec__ for transformers.models.auto

* Moves the __spec__-test to the UnitTest class

* Adds module_spec to all instances of _LazyModule

* Refactors an old test from pytest to unittest

* Rename compute_loss to hf_compute_loss to avoid conflicts with the new Keras method

* make style

* Adding deprecation warning to `compute_loss`

* Fix sneaky reference to compute_loss

* Replace logger.warning with warnings.warn

* Clarifying warning and deprecation timeline

* Check the repo consistency in model templates test

* Fix doc template

* Fix docstrings

* Fix last docstring

* fix doc examples

* remove double colons

* Convert all tutorials and guides

* Convert all remaining rst to mdx

* Track and fix bad links

* Convert docstrings of all configurations and tokenizers

* Processors and fixes

* Last modeling files and fixes to models

* Pipeline modules

* Utils files

* Data submodule

* All the other files

* Style

* Missing examples

* Style again

* Fix copies

* Say bye bye to rst docstrings forever

* Convert file_utils docstrings to Markdown

* Test on BERT

* Return block indent

* Temporarily disable doc styler

* Remove from quality checks as well

* Remove doc styler mess

* Remove check from circleCI

* Fix typo

* Convert file_utils docstrings to Markdown

* Test on BERT

* Return block indent

* Temporarily disable doc styler

* Remove from quality checks as well

* Remove doc styler mess

* Remove check from circleCI

* Fix typo

* Let's go on all other model files

* Add templates too

* Styling and quality

* Implement head_mask for Flax BERT and other models copied from BERT

* Remove `from jax._src.nn.functions import sigmoid`

Remove `from jax._src.nn.functions import sigmoid` unintentionally added by IDE

* Remove no more valid copy statement

* Apply patil-suraj's suggestions from code review

* Apply suggestions from the code review

* Update Flax template

* Fix a typo

* Also update template for CausalLM modules

* First try

* Update instructions

* avoid tf.tile in embeddings

* remove more tf.tile in embeddings

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

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

Fix examples: 'CausalLMOutputWithCrossAttentions' object has no attribute 'last_hidden_state' (#14678)
Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>

* use functional interface instead of instantiating module and immediately calling it

* fix torch.nn.functional to nn.functional. Thank you Stas!

* Add a post init method to all models

* Fix tests

* Fix last tests

* Fix templates

* Add comment

* Forgot to save

* fix inits

* fix embed dtype

* fix embed dtype

* add test to check default dtype

* quality

* add type conversion methods for flax models

* more robust casting

* cast sinusoidal positions

* update pegasus

* update albert

* update test

* make sure dtype is passed to every module

* style

* fix electra dense

* fix t5

* quality

* add more tests

* better name

* use the dtype for lm head computation

* fix albert

* style

* fix albert embed dtype

* more tests

* fix vision enc-dec

* cleanup

* fix embed dtype pegasus

* fix default param test

* doc

* update template

* fix final_logits_bias dtype

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

* fix doc

* fix doc

* add detailed docstring for dtype parameter

* remove un-necessary import
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

* [Bert2Bert] allow bert2bert + relative embeddings

* up

* Update README_ko.md

* up

* up

* 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

* finish

* up

* up

* finish

* Add cross attentions to TFGPT2Model

* Add TFEncoderDecoderModel

* Add TFBaseModelOutputWithPoolingAndCrossAttentions

* Add cross attentions to TFBertModel

* Fix past or past_key_values argument issue

* Fix generation

* Fix save and load

* Add some checks and comments

* Clean the code that deals with past keys/values

* Add kwargs to processing_inputs

* Add serving_output to TFEncoderDecoderModel

* Some cleaning + fix use_cache value issue

* Fix tests + add bert2bert/bert2gpt2 tests

* Fix more tests

* Ignore crossattention.bias when loading GPT2 weights into TFGPT2

* Fix return_dict_in_generate in tf generation

* Fix is_token_logit_eos_token bug in tf generation

* Finalize the tests after fixing some bugs

* Fix another is_token_logit_eos_token bug in tf generation

* Add/Update docs

* Add TFBertEncoderDecoderModelTest

* Clean test script

* Add TFEncoderDecoderModel to the library

* Add cross attentions to TFRobertaModel

* Add TFRobertaEncoderDecoderModelTest

* make style

* Change the way of position_ids computation

* bug fix

* Fix copies in tf_albert

* Remove some copied from and apply some fix-copies

* Remove some copied

* Add cross attentions to some other TF models

* Remove encoder_hidden_states from TFLayoutLMModel.call for now

* Make style

* Fix TFRemBertForCausalLM

* Revert the change to longformer + Remove copies

* Revert the change to albert and convbert + Remove copies

* make quality

* make style

* Add TFRembertEncoderDecoderModelTest

* make quality and fix-copies

* test TFRobertaForCausalLM

* Fixes for failed tests

* Fixes for failed tests

* fix more tests

* Fixes for failed tests

* Fix Auto mapping order

* Fix TFRemBertEncoder return value

* fix tf_rembert

* Check copies are OK

* Fix missing TFBaseModelOutputWithPastAndCrossAttentions is not defined

* Add TFEncoderDecoderModelSaveLoadTests

* fix tf weight loading

* check the change of use_cache

* Revert the change

* Add missing test_for_causal_lm for TFRobertaModelTest

* Try cleaning past

* fix _reorder_cache

* Revert some files to original versions

* Keep as many copies as possible

* Apply suggested changes - Use raise ValueError instead of assert

* Move import to top

* Fix wrong require_torch

* Replace more assert by raise ValueError

* Add test_pt_tf_model_equivalence (the test won't pass for now)

* add test for loading/saving

* finish

* finish

* Remove test_pt_tf_model_equivalence

* Update tf modeling template

* Remove pooling, added in the prev. commit, from MainLayer

* Update tf modeling test template

* Move inputs["use_cache"] = False to modeling_tf_utils.py

* Fix torch.Tensor in the comment

* fix use_cache

* Fix missing use_cache in ElectraConfig

* Add a note to from_pretrained

* Fix style

* Change test_encoder_decoder_save_load_from_encoder_decoder_from_pt

* Fix TFMLP (in TFGPT2) activation issue

* Fix None past_key_values value in serving_output

* Don't call get_encoderdecoder_model in TFEncoderDecoderModelTest.test_configuration_tie until we have a TF checkpoint on Hub

* Apply review suggestions - style for cross_attns in serving_output

* Apply review suggestions - change assert + docstrings

* break the error message to respect the char limit

* deprecate the argument past

* fix docstring style

* Update the encoder-decoder rst file

* fix Unknown interpreted text role "method"

* fix typo
Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

We use `torch.unique` here only to check whether every elements have
the same value.
Therefore, we can use `torch.unique_consecutive` here.

This function eliminates all but the first element from every consecutive
group of equivalent elements.
Like, if we apply this function to `[1, 2, 2, 1]`, it will result in
`[1, 2, 1]`.

As you could see, this is enough for checking whether every elements
have the same value.

Since `torch.unique_consecutive` do less thing, it is much more faster.
On my computer, it is 25x faster on GPU and 15x faster on CPU.

* 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>

* 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

* Add option to add flax

* Add flax template for __init__.py

* Add flax template for .rst

* Copy TF modeling template

* Add a missing line in modeling_tf_... template

* Update first half of modeling_flax_..

* Update encoder flax template

* Copy test_modeling_tf... as test_modeling_flax...

* Replace some TF to Flax in test_modeling_flax_...

* Replace tf to np

some function might not work, like _assert_tensors_equal

* Replace remaining tf to np (might not work)

* Fix cookiecutter

* Add Flax in to_replace_... template

* Update transformers-cli add-new-model

* Save generate_flax in configuration.json

This will be read by transformers-cli

* Fix to_replace_... and cli

* Fix replace cli

* Fix cookiecutter name

* Move docstring earlier to avoid not defined error

* Fix a missing Module

* Add encoder-decoder flax template from bart

* Fix flax test

* Make style

* Fix endif

* Fix replace all "utf-8 -> unp-8"

* Update comment

* Fix flax template (add missing ..._DOCSTRING)

* Use flax_bart imports in template (was t5)

* Fix unp

* Update templates/adding_a_new_model/tests

* Revert "Fix unp"

This reverts commit dc9002a41d902c4f9b07343eab1cb350c8b7fd57.

* Remove one line of copied from to suppress CI error

* Use generate_tensorflow_pytorch_and_flax

* Add a missing part

* fix typo

* fix flax config

* add examples for flax

* small rename

* correct modeling imports

* correct auto loading

* corrects some flax tests

* correct small typo

* correct as type

* finish modif

* correct more templates

* final fixes

* add file testers

* up

* make sure tests match template regex

* correct pytorch

* correct tf

* correct more tf

* correct imports

* minor error

* minor error

* correct init

* more fixes

* correct more flax tests

* correct flax test

* more fixes

* correct docs

* update

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

Set missing seq_length variable when using inputs_embeds with ALBERT & Remove code duplication (#13152)

* Set seq_length variable when using inputs_embeds

* remove code duplication

* Initial work

* All auto models

* All tf auto models

* All flax auto models

* Tokenizers

* Add feature extractors

* Fix typos

* Fix other typo

* Use the right config

* Remove old mapping names and update logic in AutoTokenizer

* Update check_table

* Fix copies and check_repo script

* Fix last test

* Add back name

* clean up

* Update template

* Update template

* Forgot a )

* Use alternative to fixup

* Fix TF model template

* Address review comments

* Address review comments

* Style

* Expose get_config() on ModelTesters

* Typo

* Try to pickle transformers

* Deal with special objs better

* Make picklable

It was used in shift_right.
After this change TF code is more similar to Pytorch implementations
Also, TF graphs are optimized (one node less)

* Add all XxxPreTrainedModel to the main init

* Add to template

* Add to template bis

* Add FlaxT5

* registering a buffer for token_type_ids, to pass the error of device-id getting hardcoded when tracing

* sytle format

* adding persistent flag to the resgitered buffers that prevent from adding them to the state_dict and addresses the Backward compatibility issue

* adding the try catch to the fix as persistent flag is only available from PT >1.6

* adding version check

* added the condition to only use the token_type_ids buffer when its autogenerated not passed by user

* adding comments and making the conidtion where token_type_ids are None to use the registered buffer

* taking out position-embeddding from the if block

* adding comments

* handling the case if buffer for position_ids was not registered

* reverted the changes on position_ids, fix the issue with size of token_type_ids buffer, moved the modification for generated token_type_ids to Bertmodel, instead of Embeddings

* reverting the token_type_ids in case of None to the previous version

* reverting changes on position_ids adding back the if block

* changes added by running make fix-copies

* changes added by running make fix-copies and added the import version as it was getting used

* changes added by running make fix-copies

* changes added by running make fix-copies

* fixing the import format

* fixing the import format

* modified to use temp tensor for trimed and expanded token_type_ids buffer

* changes made by fix-copies after temp tensor modifications

* changes made by fix-copies after temp tensor modifications

* changes made by fix-copies after temp tensor modifications

* clean up

* clean up

* clean up

* clean up

* Nit

* Nit

* Nit

* modified according to support device conversion on traced models

* modified according to support device conversion on traced models

* modified according to support device conversion on traced models

* modified according to support device conversion on traced models

* changes based on latest in master

* Adapt templates

* Add version import
Co-authored-by: Ubuntu <ubuntu@ip-172-31-32-81.us-west-2.compute.internal>
Co-authored-by: Lysandre <lysandre.debut@reseau.eseo.fr>

* Moved Mish to Torch 1.9 version

* Run black formatting

* Fixing bug that appears when using distilation (and potentially other uses).
During backward pass Pytorch complains with:
RuntimeError: one of the variables needed for gradient computation has been modified by an inplace operation
This happens because the QA model code modifies the start_positions and end_positions input tensors, using clamp_ function: as a consequence the teacher and the student both modifies the inputs, and backward pass fails.

* Fixing all models QA clamp_ bug.