GitLab

* Add GPT2ForSequenceClassification based on DialogRPT

* Better documentation

* Code quality

* clean T5

* fix t5 tests

* fix index typo

* fix tf common test

* fix examples

* change positional ordering for Bart and FSTM

* add signature test

* clean docs and add tests

* add docs to encoder decoder

* clean docs

* correct two doc strings

* remove sig test for TF Elektra & Funnel

* fix tf t5 slow tests

* fix input_ids to inputs in tf

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

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

* implement lysandre results

* make style

* fix encoder decoder typo

* fix tf slow tests

* fix slow tests

* renaming

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

* GPT2 gradient checkpointing

* find_unused_parameters removed if checkpointing

* find_unused_parameters removed if checkpointing

* Update src/transformers/configuration_gpt2.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

* Added a test for generation with checkpointing

* Update src/transformers/configuration_gpt2.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* fix generate for GPT2 Double Head

* fix gpt2 double head model

* fix  bart / t5

* also add for no beam search

* fix no beam search

* fix encoder decoder

* simplify t5

* simplify t5

* fix t5 tests

* fix BART

* fix transfo-xl

* fix conflict

* integrating sylvains and sams comments

* fix tf past_decoder_key_values

* fix enc dec test

* Run new isort

* More changes

* Update CI, CONTRIBUTING and benchmarks

* fix distilbert

* fix typo

* add cross attention layers for gpt2

* make gpt2 cross attention work

* finish bert2gpt2

* add explicit comments

* remove attention mask since not yet supported

* revert attn mask in pipeline

* Update src/transformers/modeling_gpt2.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

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

* cleanup torch unittests: part 2

* remove trailing comma added by isort, and which breaks flake

* one more comma

* revert odd balls

* part 3: odd cases

* more ["key"] -> .key refactoring

* .numpy() is not needed

* more unncessary .numpy() removed

* more simplification

* improve unit tests

this is a sample of one test according to the request in https://github.com/huggingface/transformers/issues/5973
before I apply it to the rest

* batch 1

* batch 2

* batch 3

* batch 4

* batch 5

* style

* non-tf template

* last deletion of check_loss_output

* Use return_dict=True in all tests

* Formatting

* Avoid unnecessary warnings when loading pretrained model

* Fix test

* Add other keys to ignore

* keys_to_ignore_at_load -> authorized_missing_keys

* fix use cache

* add bart use cache

* fix bart

* finish bart

Co-authored-by: Sam Shleifer <sshleifer@gmail.com>

* Kill model archive maps

* Fixup

* Also kill model_archive_map for MaskedBertPreTrainedModel

* Unhook config_archive_map

* Tokenizers: align with model id changes

* make style && make quality

* Fix CI

* fix gpu slow tests in pytorch

* change model to device syntax

* Test case for #3936

* multigpu tests pass on pytorch 1.4.0

* Fixup

* multigpu tests pass on pytorch 1.5.0

* Update src/transformers/modeling_utils.py

* Update src/transformers/modeling_utils.py

* rename multigpu to require_multigpu

* mode doc

There's an inconsistency right now where:
- we load some models into CACHE_DIR
- and some models in the default cache
- and often, in both for the same models

When running the RUN_SLOW tests, this takes a lot of disk space, time, and bandwidth.

I'd rather always use the default cache

* make style

* fix conflicts

* add first copy past test to tf 2 generate

* add tf top_k_top_p_filter fn

* add generate function for TF

* add generate function for TF

* implemented generate for all models expect transfoXL

* implemented generate for all models expect transfoXL

* implemented generate for all models expect transfoXL

* make style

* change permission of test file to correct ones

* delete ipdb

* delete ipdb

* fix bug and finish simple gpt2 integration test

* clean test file

* clean test file

* make style

* make style

* make style

* make style

* change import style

* change import style

* make style

* make style

* add decorators

* add decorators

* fix tf ctrl bug dim => axis in TF

* make style

* make style

* refactored test file

* refactored test file

* take out test_torch_tf_conversion if nothing is defined

* take out test_torch_tf_conversion if nothing is defined

* remove useless files

* remove useless files

* fix conflicts

* fix conflicts

* fix conflicts

* fix conflicts

* fix conflicts

* solve conflicts

* solve conflicts

* fix conflicts

* fix conflicts

* merge conflicts

* delete ipdb

* exposed top_k_top_p_filtering fns

* delete weirdly created w! file

* add comment to test tf common modeling

* fix conflicts

* fix conflicts

* make style

* merge conflicts

* make style

* change tf.tensor.shape to shape_list(tensor)

* fix issue and add some tests

* fix issue and add some tests

* updated doc string gpt2

* add slow generate lm_model tests

* fix conflicts

* merge conflicts

* fix conflicts

* add slow generate lm_model tests

* make style

* delete unused variable

* fix conflicts

* fix conflicts

* fix conflicts

* delete unused variable

* fix conflicts

* finished hard coded tests

* improving generation

* finalized special token behaviour for no_beam_search generation

* solved modeling_utils merge conflict

* solve merge conflicts in modeling_utils.py

* add run_generation improvements from PR #2749

* adapted language generation to not use hardcoded -1 if no padding token is available

* remove the -1 removal as hard coded -1`s are not necessary anymore

* add lightweight language generation testing for randomely initialized models - just checking whether no errors are thrown

* add slow language generation tests for pretrained models using hardcoded output with pytorch seed

* delete ipdb

* check that all generated tokens are valid

* renaming

* renaming Generation -> Generate

* make style

* updated so that generate_beam_search has same token behavior than generate_no_beam_search

* consistent return format for run_generation.py

* deleted pretrain lm generate tests -> will be added in another PR

* cleaning of unused if statements and renaming

* run_generate will always return an iterable

* make style

* consistent renaming

* improve naming, make sure generate function always returns the same tensor, add docstring

* add slow tests for all lmhead models

* make style and improve example comments modeling_utils

* better naming and refactoring in modeling_utils

* improving generation

* finalized special token behaviour for no_beam_search generation

* solved modeling_utils merge conflict

* solve merge conflicts in modeling_utils.py

* add run_generation improvements from PR #2749

* adapted language generation to not use hardcoded -1 if no padding token is available

* remove the -1 removal as hard coded -1`s are not necessary anymore

* add lightweight language generation testing for randomely initialized models - just checking whether no errors are thrown

* add slow language generation tests for pretrained models using hardcoded output with pytorch seed

* delete ipdb

* check that all generated tokens are valid

* renaming

* renaming Generation -> Generate

* make style

* updated so that generate_beam_search has same token behavior than generate_no_beam_search

* consistent return format for run_generation.py

* deleted pretrain lm generate tests -> will be added in another PR

* cleaning of unused if statements and renaming

* run_generate will always return an iterable

* make style

* consistent renaming

* improve naming, make sure generate function always returns the same tensor, add docstring

* add slow tests for all lmhead models

* make style and improve example comments modeling_utils

* better naming and refactoring in modeling_utils

* changed fast random lm generation testing design to more general one

* delete in old testing design in gpt2

* correct old variable name

* temporary fix for encoder_decoder lm generation tests - has to be updated when t5 is fixed

* adapted all fast random generate tests to new design

* better warning description in modeling_utils

* better comment

* better comment and error message
Co-authored-by: Thomas Wolf <thomwolf@users.noreply.github.com>

I suspect the wrapper classes were created in order to prevent the
abstract base class (TF)CommonModelTester from being included in test
discovery and running, because that would fail.

I solved this by replacing the abstract base class with a mixin.

Code changes are just de-indenting and automatic reformattings
performed by black to use the extra line space.

This construct isn't used anymore these days.

Running python tests/test_foo.py puts the tests/ directory on
PYTHONPATH, which isn't representative of how we run tests.

Use python -m unittest tests/test_foo.py instead.

This is the result of:

    $ isort --recursive examples templates transformers utils hubconf.py setup.py