GitLab

* Adding [T5/MT5/UMT5]ForTokenClassification

* Add auto mappings for T5ForTokenClassification and variants

* Adding ForTokenClassification to the list of models

* Adding attention_mask param to the T5ForTokenClassification test

* Remove outdated comment in test

* Adding EncoderOnly and Token Classification tests for MT5 and UMT5

* Fix typo in umt5 string

* Add tests for all the existing MT5 models

* Fix wrong comment in dependency_versions_table

* Reverting change to common test for _keys_to_ignore_on_load_missing

The test is correctly picking up redundant keys in _keys_to_ignore_on_load_missing.

* Removing _keys_to_ignore_on_missing from MT5 since the key is not used in the model

* Add fix-copies to MT5ModelTest

* fix assisted decoding attention_cat

* fix attention_mask for assisted decoding

* fix attention_mask len

* fix attn len

* Use a more clean way to prepare assistant models inputs

* fix param meaning

* fix param name

* fix assistant model inputs

* update token type ids

* fix assistant kwargs copy

* add encoder-decoder tests of assisted decoding

* check if assistant kwargs contains updated keys

* revert test

* fix whisper tests

* fix assistant kwargs

* revert whisper test

* delete _extend funcs

* skip 4 tests

* nits

* style

* wow it's not my day

* skip new failing tests

* style

* skip for NLLB MoE as well

* Adjust length limits and allow naked conversation list inputs

* Adjust length limits and allow naked conversation list inputs

* Maybe use a slightly more reasonable limit than 1024

* Skip tests for old models that never supported this anyway

* Cleanup input docstrings

* More docstring cleanup + skip failing TF test

* Make fixup

Keep relevant weights in fp32 when `model._keep_in_fp32_modules` is set even when `accelerate` is not installed (#26225)

* fix bug where weight would not be kept in fp32

* nit

* address review comments

* fix test

* CI with layers=2

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

* update tiny_model_summary.json

* update

* update

* update

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

* Initial addition of t5forsequenceclassification

* Adding imports and adding tests

* Formatting

* Running make fix-copies

* Adding mt5forseq

* Formatting

* run make fix-copies

* Adding to docs

* Add model_parallel

* Fix bug

* Fix

* Remove TODO

* Fixing tests for T5ForSequenceClassification

* Undo changes to dependency_versions_table.py

* Change classification head to work with T5Config directly

* Change seq length to let tests pass

* PR comments for formatting

* Formatting

* Initial addition of UMT5ForSequenceClassification

* Adding to inits and formatting

* run make fix-copies

* Add doc for UMT5ForSeqClass

* Update UMT5 config

* Fix docs

* Skip torch fx test for SequenceClassification

* Formatting

* Add skip to UMT5 tests as well

* Fix umt5 tests

* Running make fix-copies

* PR comments

* Fix for change to sentence_representation

* Rename seq_len to hidden_size since that's what it is

* Use base_model to follow format of the rest of the library

* Update docs

* Extract the decoder_input_ids changes and make one liner

* Make one-liner

* Adding T5ForQuestionAnswering

* Changed weight initialization that results in better initial loss when fine-tuning

* Update to class variables

* Running make fixup

* Running make fix-copies

* Remove model_parallel

* Adding MT5ForQuestionAnswering

* Adding docs

* Fix wrong doc

* Update src/transformers/models/mt5/modeling_mt5.py
Co-authored-by: Younes Belkada <49240599+younesbelkada@users.noreply.github.com>

* Update src/transformers/models/t5/modeling_t5.py
Co-authored-by: Younes Belkada <49240599+younesbelkada@users.noreply.github.com>

* File formatting

* Undoing change

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Co-authored-by: Younes Belkada <49240599+younesbelkada@users.noreply.github.com>

* Add TFBlipForConditionalGeneration

* update pipeline_model_mapping

* Add import

* Revert changes in GPTSanJapaneseTest

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

* Add PipelineTesterMixin

* remove class PipelineTestCaseMeta

* move validate_test_components

* Add for ViT

* Add to SPECIAL_MODULE_TO_TEST_MAP

* style and quality

* Add feature-extraction

* update

* raise instead of skip

* add tiny_model_summary.json

* more explicit

* skip tasks not in mapping

* add availability check

* Add Copyright

* A way to diable irrelevant tests

* update with main

* remove disable_irrelevant_tests

* skip tests

* better skip message

* better skip message

* Add all pipeline task tests

* revert

* Import PipelineTesterMixin

* subclass test classes with PipelineTesterMixin

* Add pipieline_model_mapping

* Fix import after adding pipieline_model_mapping

* Fix style and quality after adding pipieline_model_mapping

* Fix one more import after adding pipieline_model_mapping

* Fix style and quality after adding pipieline_model_mapping

* Fix test issues

* Fix import requirements

* Fix mapping for MobileViTModelTest

* Update

* Better skip message

* pipieline_model_mapping could not be None

* Remove some PipelineTesterMixin

* Fix typo

* revert tests_fetcher.py

* update

* rename

* revert

* Remove PipelineTestCaseMeta from ZeroShotAudioClassificationPipelineTests

* style and quality

* test fetcher for all pipeline/model tests

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

* fix torchquant issue

* add tests

* Result of black 23.1

* Update target to Python 3.7

* Switch flake8 to ruff

* Configure isort

* Configure isort

* Apply isort with line limit

* Put the right black version

* adapt black in check copies

* Fix copies

* add `keep_in_fp32_modules` support

* pass it as class attribute

* few modifs

- make tests `slow`
- fix logic

* better logic

* fix failing test

* `bfloat16` support

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

* fix

* simplify tests

* simplify tests

* fix test

* modify message

* more checks

* fix failing tests

* add more conditions

- add `is_accelerate_available`
- fixes pipleine tests that failed

* add suggestions

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

* fix failing `bnb` test

* add last safety checker
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* move generation_*.py src files into generation/*.py

* populate generation.__init__ with lazy loading

* move imports and references from generation.xxx.object to generation.object

* Use beam search functionality; Add extra outputs and test

* Add full tests for contrastive search

* Add error message on unconventional cache format

* Add gated-silu to t5 architecture to support UL2

* Fix error message

* formatting

* formatting again

* refactor

* fix classnames in _init_weights

* remove is_gated

* add test

* fix test

* Try without the test?

* Add back the test.

* Improve error message.
Co-authored-by: Daniel Hesslow <daniel@lighton.ai>

* Support for Bart and LayoutLM, and partial support for XLNet

* Support for mbart

* A lot of new models supported

* Support for other models

* LayoutLM fix

* Use strings instead of classes

* Black preview

* Fixup too!

* Fix check copies

* Use the same version as the CI

* Bump black

* move test model folders (TODO: fix imports and others)

* fix (potentially partially) imports (in model test modules)

* fix (potentially partially) imports (in tokenization test modules)

* fix (potentially partially) imports (in feature extraction test modules)

* fix import utils.test_modeling_tf_core

* fix path ../fixtures/

* fix imports about generation.test_generation_flax_utils

* fix more imports

* fix fixture path

* fix get_test_dir

* update module_to_test_file

* fix get_tests_dir from wrong transformers.utils

* update config.yml (CircleCI)

* fix style

* remove missing imports

* update new model script

* update check_repo

* update SPECIAL_MODULE_TO_TEST_MAP

* fix style

* add __init__

* update self-scheduled

* fix add_new_model scripts

* check one way to get location back

* python setup.py build install

* fix import in test auto

* update self-scheduled.yml

* update slack notification script

* Add comments about artifact names

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

* update _create_and_check_torchscript

* Enable test_torchscript

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

* 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

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

* 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

* Update feature extraction pipelilne.

* Leaving 1 small model for actual values check.

* Fixes tests

- Better support for tokenizer with no pad token
- Increasing PegasusModelTesterConfig for pipelines
- Test of feature extraction are more permissive + don't test Multimodel
models + encoder-decoder.

* Fixing model loading with incorrect shape (+ model with HEAD).

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

* Revert modeling_utils modification.

* Some corrections.

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

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

* Syntax.

* Fixing text-classification tests.

* Don't modify this file.
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* Expose get_config() on ModelTesters

* Typo

* copy pytorch-t5

* init

* boom boom

* forward pass same

* make generation work

* add more tests

* make test work

* finish normal tests

* make fix-copies

* finish quality

* correct slow example

* correct slow test

* version table

* upload models

* Update tests/test_modeling_flax_t5.py

* correct incorrectly deleted line
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
Co-authored-by: Patrick von Platen <patrick@huggingface.co>

* allow tf to use uneven num of layers

* add tokenizer

* finish docs

* finish docs

* Apply suggestions from code review

* include in index

* finish

* Update docs/source/model_doc/byt5.rst
Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>

* apply sylvais suggestions

* make style
Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>

* fix_torch_device_generate_test

* remove @

* Add missing head masking for generate() function

* Add head_mask, decoder_head_mask and cross_attn_head_mask
into prepare_inputs_for_generation for generate() function
for multiple encoder-decoder models.

* Add test_genereate_with_head_masking

* [WIP] Update the new test and handle special cases

* make style

* Omit ProphetNet test so far

* make fix-copies

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 3D attention mask to T5 model (#9643)

Added code for 3D attention mask in T5 model. Similar to BERT model.

* Add test for 3D attention mask

Added test for 3D attention mask: test_decoder_model_past_with_3d_attn_mask()
3D attention mask of the shape [Batch_size, Seq_length, Seq_length] both for
attention mask and decoder attention mask. Test is passing.

* Only run one test

* Patch segfault

* Fix summarization pipeline

* Ready for merge

* add tests

* make style and fix bart bug

* fix bart past key value edge case

* correct tf bart test

* fix gpt2 tf

* fix t5 test

k

* add model parallelism to T5EncoderModel

add model parallelism to T5EncoderModel

* remove decoder from T5EncoderModel parallelize

* uodate T5EncoderModel docs

* Extend T5ModelTest for T5EncoderModel

* fix T5Stask using range for get_device_map

* fix style
Co-authored-by: Ahmed Elnaggar <elnaggar@rostlab.informatik.tu-muenchen.de>