GitLab

Changed `num_beams` to `num_beams // num_beam_groups` when initialising `PrefixConstrainedLogitsProcessor` in `_get_logits_processor` to fix compatibility issue when constrained decoding is used together with grouped beam search (#10475)

* smal fixes

* don't check for None

* add encode labels function to tokenizer

* start adding finetuning

* init dropout

* upload

* correct convert script

* apply changes

* fix second typo

* make first dummy training run

* adapt convert script

* push confg for comparison

* remove conf

* finish training

* adapt data collator

* add research folder

* update according to fairseq feedback

* some minor corrections

* refactor masking indices a bit

* some minor changes

* clean tokenizer

* finish clean-up

* remove previous logic

* update run script

* correct training

* finish changes

* finish model

* correct bug

* fix training a bit more

* add some tests

* finish gradient checkpointing

* finish example

* correct gradient checkpointing

* improve tokenization method

* revert changes in tokenizer

* revert general change

* adapt fine-tuning

* update

* save intermediate test

* Update README.md

* finish finetuning

* delete conversion script

* Update src/transformers/models/wav2vec2/configuration_wav2vec2.py

* Update src/transformers/models/wav2vec2/processing_wav2vec2.py
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>

* finish wav2vec2 script

* finish wav2vec2 fine-tuning

* finalize test

* correct test

* adapt tests

* finish

* remove test file
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>

* feat: Adds three definitions to glossary from @cronoik

Needed a definition for transformer which in turn needed 2 more definitions

To do with issue https://github.com/huggingface/transformers/issues/9078

* fix: Adjusts definition of neural network to make it easier to read

* Introduce save_strategy training argument

* deprecate EvaluationStrategy

* collapse EvaluationStrategy and LoggingStrategy into a single
  IntervalStrategy enum

* modify tests to use modified enum

* updated logging and saving metrics

* space removal

This PR restores the original functionality that for some reason was modified.

Fixes: https://github.com/huggingface/transformers/issues/10381

@sgugger

* refactors

* typo

* fixes

* update resources

* formatting

* remove import

* add log statement

* use fstring

* add period

* Update src/transformers/integrations.py

* correct docs

* correct tf model docs as well

* Added tb fix

* Removed local rank condition

* Updated reference to args

😂

* Make Barthez tokenizer tests a bit faster

* Quality

* Fix None in add_token_positions - issue #10210

Fix None in add_token_positions related to the issue #10210

* add_token_positions fix None values in end_positions vector

add_token_positions fix None in end_positions vector as proposed by @joeddav

* Ass support for ZeRO-2/3 and ZeRO-offload in fairscale

* Quality

* Rework from review comments

* Add doc

* Apply suggestions from code review
Co-authored-by: Stas Bekman <stas00@users.noreply.github.com>

* Address review comments
Co-authored-by: Stas Bekman <stas00@users.noreply.github.com>

* IBertConfig, IBertTokentizer added

* IBert Model names moified

* tokenizer bugfix

* embedding -> QuantEmbedding

* quant utils added

* quant_mode added to configuration

* QuantAct added, Embedding layer + QuantAct addition

* QuantAct added

* unused path removed, QKV quantized

* self attention layer all quantized, except softmax

* temporarl commit

* all liner layers quantized

* quant_utils bugfix

* bugfix: requantization missing

* IntGELU added

* IntSoftmax added

* LayerNorm implemented

* LayerNorm implemented all

* names changed: roberta->ibert

* config not inherit from ROberta

* No support for CausalLM

* static quantization added, quantize_model.py removed

* import modules uncommented

* copyrights fixed

* minor bugfix

* quant_modules, quant_utils merged as one file

* import * fixed

* unused runfile removed

* make style run

* configutration.py docstring fixed

* refactoring: comments removed, function name fixed

* unused dependency removed

* typo fixed

* comments(Copied from), assertion string added

* refactoring: super(..) -> super(), etc.

* refactoring

* refarctoring

* make style

* refactoring

* cuda -> to(x.device)

* weight initialization removed

* QuantLinear set_param removed

* QuantEmbedding set_param removed

* IntLayerNorm set_param removed

* assert string added

* assertion error message fixed

* is_decoder removed

* enc-dec arguments/functions removed

* Converter removed

* quant_modules docstring fixed

* conver_slow_tokenizer rolled back

* quant_utils docstring fixed

* unused aruments e.g. use_cache removed from config

* weight initialization condition fixed

* x_min, x_max initialized with small values to avoid div-zero exceptions

* testing code for ibert

* test emb, linear, gelu, softmax added

* test ln and act added

* style reformatted

* force_dequant added

* error tests overrided

* make style

* Style + Docs

* force dequant tests added

* Fix fast tokenizer in init

* Fix doc

* Remove space

* docstring, IBertConfig, chunk_size

* test_modeling_ibert refactoring

* quant_modules.py refactoring

* e2e integration test added

* tokenizers removed

* IBertConfig added to tokenizer_auto.py

* bugfix

* fix docs & test

* fix style num 2

* final fixes
Co-authored-by: Sehoon Kim <sehoonkim@berkeley.edu>
Co-authored-by: Lysandre <lysandre.debut@reseau.eseo.fr>
Co-authored-by: Sylvain Gugger <sylvain.gugger@gmail.com>
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

[PretrainedFeatureExtractor] + Wav2Vec2FeatureExtractor, Wav2Vec2Processor, Wav2Vec2Tokenizer (#10324)

* push to show

* small improvement

* small improvement

* Update src/transformers/feature_extraction_utils.py

* Update src/transformers/feature_extraction_utils.py

* implement base

* add common tests

* make all tests pass for wav2vec2

* make padding work & add more tests

* finalize feature extractor utils

* add call method to feature extraction

* finalize feature processor

* finish tokenizer

* finish general processor design

* finish tests

* typo

* remove bogus file

* finish docstring

* add docs

* finish docs

* small fix

* correct docs

* save intermediate

* load changes

* apply changes

* apply changes to doc

* change tests

* apply surajs recommend

* final changes

* Apply suggestions from code review

* fix typo

* fix import

* correct docstring

* Assumption of padding_idx <2 might not stand

* Use offset instead of 2

* Fix with black

* Change behavior to warning instead for backward compatibility.

* Fix with black

* Remove warning

* Make padding_idx non-required

* padding_idx fix for blenderbot

* padding_idx fix for blenderbot_small

* padding_idx fix for led

* padding_idx fix for mbart

* Remove extra whitespaces

* padding_idx fix for template

* Fix padding_idx passed to nn.Embedding mistake

* Fixed padding_idx passed to positional embedding in template

* Remove padding_idx from pytorch learned positional embeddings

* Remove accidentally added quotes

* Remove padding_idx from tf learned positional embeddings

* Remove zeroing of weights in __init__
Co-authored-by: Wang Ming Rui <mingrui.wang@C02CJTUYMD6M.local>

* move secondary methods into a separate file

* cleanup

* style

This is to fix deprecated reference to `tokenizer.max_len` with `tokenizer.model_max_length` - similar to [issue 8739](https://github.com/huggingface/transformers/issues/8739) and [PR 8604](https://github.com/huggingface/transformers/pull/8604). 
Example [here](https://colab.research.google.com/gist/poedator/f8776349e5c625ce287fc6fcd312fa1e/tokenizer-max_len-error-in-transformers_glue.ipynb). The error happens when `glue_convert_examples_to_features` is called without `max_length` parameter specified. In that case line 119 with wrong reference gets called. This simple fix should  do it.

* convbert conversion test

* fin

* fin

* fin

* clean up tf<->pt conversion

* remove from_pt
Co-authored-by: patrickvonplaten <patrick.v.platen@gmail.com>

* handle get_last_lr() before first step()

* abstract away the lr getting logic

* cleanup

* add test

* move to utils

* make logging and saving trainer built-in

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