GitLab

* add tests with multiple eos_token_ids

* make math.prod instead of sum

* make fixup

* fix long and also use np.prod since math.prod does not exist <python 3.8

* make fixup

* add prod util

* use prod util instead of np.prod

* make fixup

* previous .long location

* use tensor ops

* remove prod

* remove prod

* update device

* make fixup

* fix none

check for mapping/dict in distributed_concat function

Co-authored-by: prajwal967 <user.email>

* doc: introduce new section for XLM-V model

* doc: mention more details for XLM-V integration

* docs: paper abstract in italics, model identifier for base model added

* doc: mention new XLM-V support

* auto: add XLM-V mapping

* doc: run make fix-copies ;)

* added inverse sqrt lr scheduler

* Updated get_scheduler in src/transformers/optimization.py

* Updated src/transformers/__init__.py

* Added inverse sqrt lr scheduler test

* Updated docs/source/en/main_classes/optimizer_schedules.mdx

* Ran style and quality scripts

* Fix get_inverse_sqrt_schedule docstring

* Comment implementation URL

* [tokenizer] sanitize saved config

* rm config["name_or_path"] test

* Remove mentions of flake8/isort

* Clean up inits

* Deall with all other inits

* Last special rule for dummy files

* Add limit_all_gathers option to fsdp_config and fix forward_prefetch bug

* Fix black issue

* Fix ruff failure

* Incorporate PR feedbacks

* Incorporate PR feedbacks

* Incorporate PR feedbacks

* Add ("opt", ("GPT2Tokenizer", "GPT2TokenizerFast" if is_tokenizers_available() else None)),

* skip failing test

* Add ("opt", ("GPT2Tokenizer", "GPT2TokenizerFast" if is_tokenizers_available() else None)),

* skip failing test

Sanity check the type of id2label and label2id arguments of from_pretrained for TokenClassification models (#21490)

* Sanity check the type of id2label and label2id arguments of from_pretrained for TokenClassification models

* Incorporate PR feedbacks

* Incorporate PR feedbacks

* fix past renamed to past_key_value

* update more `past`that were ski^êd

* fixup

* remove changes made to rag

* refactor `_reorder_cache` to use `past_key_values`

* fix git `prepare_inputs_for_generation` to pass tests when false is needed in use_cache

* Deprecate parallelize API

* Add documentation

* Fix copies

* 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

* Removing `more_itertools` dependency.

* Update examples/research_projects/vqgan-clip/requirements.txt

* make doc examples deterministic

* add IGNORE_RESULT

updated documentation

* fix

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

For IterableDataset, return DataLoader using self._train_batch_size. This is consistent with how we generate a regular DataLoader, and leads to the correct args.per_device_train_batch_size eventually ending up on each GPU.

* make SpeechT5 model by copying Wav2Vec2

* add paper to docs

* whoops added docs in wrong file

* remove SpeechT5Tokenizer + put CTC back in the name

* remove deprecated class

* remove unused docstring

* delete SpeechT5FeatureExtractor, use Wav2Vec2FeatureExtractor instead

* remove classes we don't need right now

* initial stab at speech encoder prenet

* add more speech encoder prenet stuff

* improve SpeechEncoderPrenet

* add encoder (not finished yet)

* add relative position bias to self-attention

* add encoder CTC layers

* fix formatting

* add decoder from BART, doesn't work yet

* make it work with generate loop

* wrap the encoder into a speech encoder class

* wrap the decoder in a text decoder class

* changed my mind

* changed my mind again ;-)

* load decoder weights, make it work

* add weights for text decoder postnet

* add SpeechT5ForCTC model that uses only the encoder

* clean up EncoderLayer and DecoderLayer

* implement _init_weights in SpeechT5PreTrainedModel

* cleanup config + Encoder and Decoder

* add head + cross attention masks

* improve doc comments

* fixup

* more cleanup

* more fixup

* TextDecoderPrenet works now, thanks Kendall

* add CTC loss

* add placeholders for other pre/postnets

* add type annotation

* fix freeze_feature_encoder

* set padding tokens to 0 in decoder attention mask

* encoder attention mask downsampling

* remove features_pen calculation

* disable the padding tokens thing again

* fixup

* more fixup

* code review fixes

* rename encoder/decoder wrapper classes

* allow checkpoints to be loaded into SpeechT5Model

* put encoder into wrapper for CTC model

* clean up conversion script

* add encoder for TTS model

* add speech decoder prenet

* add speech decoder post-net

* attempt to reconstruct the generation loop

* add speech generation loop

* clean up generate_speech

* small tweaks

* fix forward pass

* enable always dropout on speech decoder prenet

* sort declaration

* rename models

* fixup

* fix copies

* more fixup

* make consistency checker happy

* add Seq2SeqSpectrogramOutput class

* doc comments

* quick note about loss and labels

* add HiFi-GAN implementation (from Speech2Speech PR)

* rename file

* add vocoder to TTS model

* improve vocoder

* working on tokenizer

* more better tokenizer

* add CTC tokenizer

* fix decode and batch_code in CTC tokenizer

* fix processor

* two processors and feature extractors

* use SpeechT5WaveformFeatureExtractor instead of Wav2Vec2

* cleanup

* more cleanup

* even more fixup

* notebooks

* fix log-mel spectrograms

* support reduction factor

* fixup

* shift spectrograms to right to create decoder inputs

* return correct labels

* add labels for stop token prediction

* fix doc comments

* fixup

* remove SpeechT5ForPreTraining

* more fixup

* update copyright headers

* add usage examples

* add SpeechT5ProcessorForCTC

* fixup

* push unofficial checkpoints to hub

* initial version of tokenizer unit tests

* add slow test

* fix failing tests

* tests for CTC tokenizer

* finish CTC tokenizer tests

* processor tests

* initial test for feature extractors

* tests for spectrogram feature extractor

* fixup

* more fixup

* add decorators

* require speech for tests

* modeling tests

* more tests for ASR model

* fix imports

* add fake tests for the other models

* fixup

* remove jupyter notebooks

* add missing SpeechT5Model tests

* add missing tests for SpeechT5ForCTC

* add missing tests for SpeechT5ForTextToSpeech

* sort tests by name

* fix Hi-Fi GAN tests

* fixup

* add speech-to-speech model

* refactor duplicate speech generation code

* add processor for SpeechToSpeech model

* add usage example

* add tests for speech-to-speech model

* fixup

* enable gradient checkpointing for SpeechT5FeatureEncoder

* code review

* push_to_hub now takes repo_id

* improve doc comments for HiFi-GAN config

* add missing test

* add integration tests

* make number of layers in speech decoder prenet configurable

* rename variable

* rename variables

* add auto classes for TTS and S2S

* REMOVE CTC!!!

* S2S processor does not support save/load_pretrained

* fixup

* these models are now in an auto mapping

* fix doc links

* rename HiFiGAN to HifiGan, remove separate config file

* REMOVE auto classes

* there can be only one

* fixup

* replace assert

* reformat

* feature extractor can process input and target at same time

* update checkpoint names

* fix commit hash

* no dot scale gradient in bf16 mode

* fix since args.fp16 might be none

* fixed typo

* typo

* only do if grad scaling is true

* self.amp_dtype == torch.float16 is true

* put back prop when fsdp is not none

* * Remove unused type_vocab_size

* Remove unused initializer_factor

* Remove unused n_embd

* Remove unused scale_embedding

* Remove unused scale_attn_weights

* fix

* fix

* Remove unused head_hidden_scale

* Remove unused activation_dropout

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

Add accepting `.generate()` calls with `inputs_embeds` on BLOOM models

input_ids_seq_length doesn't exist in the GenerationConfig, it exists as local variable in the function.

Setting exponential_decay_length_penalty therefore results in an error:
`AttributeError: 'GenerationConfig' object has no attribute 'input_ids_seq_length'`

This simple change fixes this issue, and the exponential_decay_length_penalty works as expected.

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

* Allow to add more information

* fix style

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

* force `memory_efficient_backward=True`

* enhancements

- trainer support
- add new flag

* some changes

- internal changes in `Trainer`
- small refactor

* make quality

* Fixes

- add new testing util
- add new test
- change test in Trainer

* fix CI test

* educate users on how to ft 8bit models

* more checks

* fix `logger` error

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

* adapt from review

* fix

* add comment

* use return instead

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

* [FIX] path for Graphormer checkpoint

* [FIX] Test suite for graphormer

* [FIX] Update graphormer default num_classes

* gelu_python_tanh

* rename

* Version check, add test

* Pr comment

Co-authored-by: Shreshth Tuli <shreshthtuli@gmail.com>