GitLab

* Fix test

* Remove commented test

* Fix name

* Apply style

* Fix check copies

* Remove prints

* Restore boolean

* Fix reshape

* Add head masking to TF LED

* Add head_mask to Longformer + one doc piece to LED

* Fix integration tests

* add wav2vec2CTC and deprecate for maskedlm

* remove from docs

* claiming this issue

* Integration test for BertGeneration(Encoder and Decoder)

* fix code quality

* Refacto BERT

* Restore all the concerned models

* Remove print

* Update template

* Apply Sylvain's and Morgan's comments

* Fix cast

* Put the cast inside call

* Remove cond in ebds

* Fix funnel

* Restore previous dot product (attention_scores) computation

* Add ConvBERT and BART

* Make all the S2S models ONNX compliant

* Fix test

* Fix check copies

* Disable temporarily too slow tests

* Fix style

* Fix template

* Cleaning up `ConversationalPipeline` to support more than DialoGPT.

Currently ConversationalPipeline was heavily biased towards DialoGPT
,which is the default model for this pipeline.

This PR proposes changes to put back the modifications specific to
DialoGPT into tokenizer-specific behavior wherever possible, by
creating `_build_conversation_input_ids` function that takes
conversation as input, and returns a list of ints corresponding
to the tokens. It feels natural to put here because all models
have probably different strategies to build input_ids from the
full conversation and it's the tokenizer's job to transform strings
into tokens (and vice-versa)

If `_build_conversation_input_ids` is missing, previous behavior is
used so we don't break anything so far (except for blenderbot where it's a fix).

This PR also contains a fix for too long inputs. There used
to be dead code for trying to limit the size of incoming input.
The introduced fixed is that we limit
within `_build_conversation_input_ids` to `tokenizer.model_max_length`.
It corresponds to the intent of the removed dead code and is actually
better because it corresponds to `model_max_length` which is different
from `max_length` (which is a default parameter for `generate`).

- Removed `history` logic from the Conversation as it's not relevant
anymore because tokenization logic has been moved to tokenizer.
And tokenizer cannot save any cache, and conversation cannot know
what is relevant or not.
Also it's not usable from `blenderbot` because the input_ids are
not append only (EOS tokens is always at the end).

- Added `iter_texts` method on `Conversation` because all
the code was literred with some form of this iteration of
past/generated_responses.

* Removing torch mention in types.

* Adding type checking to `_build_conversation_input_ids`.

* Fixing import in strings.

* Correct cast to device

* Comment back the slow test

`encoder_no_repeat_ngram_size` from their config.

Adding new `encoder_no_repeat_ngram_size` to `generate`.

Blenderbot results seemed off compared to original ParlAI script:
`https://parl.ai/projects/recipes/`

. Notably the model seems
to repeat a lot what was said during the conversation.

The actual problem was that `no_repeat_ngram_size` actually applies
to the `encoder_input_ids` but HF's `no_repeat_ngram_size` applies
to the previously generated ids (within the decoder). The history
conversation of blenderbot is within the `encoder` part so that
explains why HF's implementation had the repetitions.

This fix was focused on blenderbot *not* small and added tests
for those because they are quite different in configuration.

This change includes:

- Adding a new EncoderNoRepeatLogitProcessor.
- Adding 1 new arg to `generate` (`encoder_no_repeat_ngram_size`)
- Adding 1 new config parameter `encoder_no_repeat_ngram_size`.
- Adding 2 tests, one for the pipeline (high level, inputs exhibited
repeat behavior, one low level for EncoderNoRepeatLogitProcessor)
- Factored NoRepeatLogitProcessor so that logic could be reused.

Further work:

- Blenderbot conversational pipeline still does not behave correctly
 as they way input is prepared within the pipeline is still incorrect
(follow up PR)
- Blenderbot allows the bot to have personas, which is done by
prepending "your personna: XXXX" to the input, this could be explored
too in a follow up PR.

@patrickvonplaten
@LysandreJik

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

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

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

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

* Doc quality.

* Fixing test.

* Last fixes.

* Fixing to account for batch_size.

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

* Update src/transformers/generation_utils.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>

* initiliaze bart4causalLM

* create BartDecoderWrapper, setters/getters

* delete spaces

* forward and additional methods

* update cache function, loss function, remove ngram* params in data class.

* add bartcausallm, bartdecoder testing

* correct bart for causal lm

* remove at

* add mbart as well

* up

* fix typo

* up

* correct

* add pegasusforcausallm

* add blenderbotforcausallm

* add blenderbotsmallforcausallm

* add marianforcausallm

* add test for MarianForCausalLM

* add Pegasus test

* add BlenderbotSmall test

* add blenderbot test

* fix a fail

* fix an import fail

* a fix

* fix

* Update modeling_pegasus.py

* fix models

* fix inputs_embeds setting getter

* adapt tests

* correct repo utils check

* finish test improvement

* fix tf models as well

* make style

* make fix-copies

* fix copies

* run all tests

* last changes

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

* TF DistilBERT integration test

* Update test_modeling_tf_distilbert.py

* TF Albert integration test

* TF Alber integration test added

* Fix Longformer and LED

* Add a test for graph execution with inputs_embeds

* Apply style

* Add {decoder_,}head_mask to LED

* Fix create_custom_forward signatue in encoder

* Add head_mask to longformer

* Add head_mask to longformer to fix dependencies
of LED on Longformer.

* Not working yet

* Add mising one input in longofrmer_modeling.py

* make fix-copies

* add raw scaffold

* implement feat extract layers

* make style

* remove +

* correctly convert weights

* make feat extractor work

* make feature extraction proj work

* run forward pass

* finish forward pass

* Succesful decoding example

* remove unused files

* more changes

* add wav2vec tokenizer

* add new structure

* fix run forward

* add other layer norm architecture

* finish 2nd structure

* add model tests

* finish tests for tok and model

* clean-up

* make style

* finish docstring for model and config

* make style

* correct docstring

* correct tests

* change checkpoints to fairseq

* fix examples

* finish wav2vec2

* make style

* apply sylvains suggestions

* apply lysandres suggestions

* change print to log.info

* re-add assert statement

* add input_values as required input name

* finish wav2vec2 tokenizer

* Update tests/test_tokenization_wav2vec2.py
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>

* apply sylvains suggestions
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>

* ALBERT Tokenizer integration test

* Batching

* Style

* change tokenizer requirement

* split line

* Correct typo from list to str

* improve style

* make other function pretty as well

* add comment

* correct typo

* add new test

* pass tests for tok without padding token

* Apply suggestions from code review

* Add {decoder_,}head_mask to fsmt_modeling.py

* Enable test_headmasking and some changes to docs

* Remove test_head_masking flag from fsmt test file

Remove test_head_masking flag from test_modeling_fsmt.py
since test_head_masking is set to be True by default (thus it is redundant to store).

* Merge master and remove test_head_masking = True

* Rebase necessary due to an update of jaxlib

* Remove test_head_masking=True in tests/test_modeling_fsmt.py
as it is redundant.

* Adding a new `return_full_text` parameter to TextGenerationPipeline.

For text-generation, it's sometimes used as prompting text.
In that context, prefixing `generated_text` with the actual input
forces the caller to take an extra step to remove it.

The proposed change adds a new parameter (for backward compatibility).
`return_full_text` that enables the caller to prevent adding the prefix.

* Doc quality.

* Remove redundant test_head_masking = True flags

* Remove all redundant test_head_masking flags in PyTorch test_modeling_* files

* Make test_head_masking = True as a default choice in test_modeling_tf_commong.py

* Remove all redundant test_head_masking flags in TensorFlow
test_modeling_tf_* files

* Put back test_head_masking=False fot TFT5 models

* tests: add integration tests for new Bort model

* bort: add conversion script from Gluonnlp to Transformers 🚀



* bort: minor cleanup (BORT -> Bort)

* add docs

* make fix-copies

* clean doc a bit

* correct docs

* Update docs/source/model_doc/bort.rst
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* Update docs/source/model_doc/bort.rst
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* correct dialogpt doc

* correct link

* Update docs/source/model_doc/bort.rst

* Update docs/source/model_doc/dialogpt.rst
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

* make style
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* fix --lr_scheduler_type choices

* rewrite to fix for all enum-based cl args

* cleanup

* adjust test

* style

* Proposal that should work

* Remove needless code

* Fix test
Co-authored-by: Sylvain Gugger <sylvain.gugger@gmail.com>

* Allow --arg Value for booleans in HfArgumentParser

* Update last test

* Better error message

* Whenresuming training from checkpoint, Trainer loads model

* Finish cleaning tests

* Address review comment

* Use global_step from state

pipeline.

- If table is empty then the line that contain `answer[0]` will fail.
- This PR add a check to prevent `answer[0]`.
- Also adds an early check for presence of `table` and `query` to
prevent late failure and give better error message.
- Adds a few tests to make sure these errors are correctly raised.