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Notes on versioning

[Unreleased]

2.2.0 (2021-09-14)

New features

  • Support source features (thanks @anderleich !)

Fixes and improvements

  • Adaptations to relax torch version
  • Customizable transform statistics (#2059)
  • Adapt release code for ctranslate2 2.0

2.1.2 (2021-04-30)

Fixes and improvements

  • Fix update_vocab for LM (#2056)

2.1.1 (2021-04-30)

Fixes and improvements

  • Fix potential deadlock (b1a4615)
  • Add more CT2 conversion checks (e4ab06c)

2.1.0 (2021-04-16)

New features

  • Allow vocab update when training from a checkpoint (cec3cc8, 2f70dfc)

Fixes and improvements

  • Various transforms related bug fixes
  • Fix beam warning and buffers reuse
  • Handle invalid lines in vocab file gracefully

2.0.1 (2021-01-27)

Fixes and improvements

  • Support embedding layer for larger vocabularies with GGNN (e8065b7)
  • Reorganize some inference options (9fb5f30)

2.0.0 (2021-01-20)

First official release for OpenNMT-py major upgdate to 2.0!

New features

  • Language Model (GPT-2 style) training and inference
  • Nucleus (top-p) sampling decoding

Fixes and improvements

  • Fix some BART default values

2.0.0rc2 (2020-11-10)

Fixes and improvements

  • Parallelize onmt_build_vocab (422d824)
  • Some fixes to the on-the-fly transforms
  • Some CTranslate2 related updates
  • Some fixes to the docs

2.0.0rc1 (2020-09-25)

This is the first release candidate for OpenNMT-py major upgdate to 2.0.0!

The major idea behind this release is the -- almost -- complete makeover of the data loading pipeline . A new 'dynamic' paradigm is introduced, allowing to apply on the fly transforms to the data.

This has a few advantages, amongst which:

  • remove or drastically reduce the preprocessing required to train a model;
  • increase and simplify the possibilities of data augmentation and manipulation through on-the fly transforms.

These transforms can be specific tokenization methods, filters, noising, or any custom transform users may want to implement. Custom transform implementation is quite straightforward thanks to the existing base class and example implementations.

You can check out how to use this new data loading pipeline in the updated docs and examples.

All the readily available transforms are described here.

Performance

Given sufficient CPU resources according to GPU computing power, most of the transforms should not slow the training down. (Note: for now, one producer process per GPU is spawned -- meaning you would ideally need 2N CPU threads for N GPUs).

Breaking changes

A few features are dropped, at least for now:

  • audio, image and video inputs;
  • source word features.

Some very old checkpoints with previous fields and vocab structure are also incompatible with this new version.

For any user that still need some of these features, the previous codebase will be retained as legacy in a separate branch. It will no longer receive extensive development from the core team but PRs may still be accepted.

1.2.0 (2020-08-17)

Fixes and improvements

  • Support pytorch 1.6 (e813f4d, eaaae6a)
  • Support official torch 1.6 AMP for mixed precision training (2ac1ed0)
  • Flag to override batch_size_multiple in FP16 mode, useful in some memory constrained setups (23e5018)
  • Pass a dict and allow custom options in preprocess/postprocess functions of REST server (41f0c02, 8ec54d2)
  • Allow different tokenization for source and target in REST server (bb2d045, 4659170)
  • Various bug fixes

New features

  • Gated Graph Sequence Neural Networks encoder (11e8d0), thanks @SteveKommrusch
  • Decoding with a target prefix (95aeefb, 0e143ff, 91ab592), thanks @Zenglinxiao

1.1.1 (2020-03-20)

Fixes and improvements

  • Fix backcompatibility when no 'corpus_id' field (c313c28)

1.1.0 (2020-03-19)

New features

  • Support CTranslate2 models in REST server (91d5d57)
  • Extend support for custom preprocessing/postprocessing function in REST server by using return dictionaries (d14613d, 9619ac3, 92a7ba5)
  • Experimental: BART-like source noising (5940dcf)

Fixes and improvements

  • Add options to CTranslate2 release (e442f3f)
  • Fix dataset shard order (458fc48)
  • Rotate only the server logs, not training (189583a)
  • Fix alignment error with empty prediction (91287eb)

1.0.2 (2020-03-05)

Fixes and improvements

  • Enable CTranslate2 conversion of Transformers with relative position (db11135)
  • Adapt -replace_unk to use with learned alignments if they exist (7625b53)

1.0.1 (2020-02-17)

Fixes and improvements

  • Ctranslate2 conversion handled in release script (1b50e0c)
  • Use attention_dropout properly in MHA (f5c9cd4)
  • Update apex FP16_Optimizer path (d3e2268)
  • Some REST server optimizations
  • Fix and add some docs

1.0.0 (2019-10-01)

New features

  • Implementation of "Jointly Learning to Align & Translate with Transformer" (@Zenglinxiao)

Fixes and improvements

  • Add nbest support to REST server (@Zenglinxiao)
  • Merge greedy and beam search codepaths (@Zenglinxiao)
  • Fix "block ngram repeats" (@KaijuML, @pltrdy)
  • Small fixes, some more docs

1.0.0.rc2 (2019-10-01)

  • Fix Apex / FP16 training (Apex new API is buggy)
  • Multithread preprocessing way faster (Thanks @francoishernandez)
  • Pip Installation v1.0.0.rc1 (thanks @pltrdy)

0.9.2 (2019-09-04)

  • Switch to Pytorch 1.2
  • Pre/post processing on the translation server
  • option to remove the FFN layer in AAN + AAN optimization (faster)
  • Coverage loss (per Abisee paper 2017) implementation
  • Video Captioning task: Thanks Dylan Flaute!
  • Token batch at inference
  • Small fixes and add-ons

0.9.1 (2019-06-13)

  • New mechanism for MultiGPU training "1 batch producer / multi batch consumers" resulting in big memory saving when handling huge datasets
  • New APEX AMP (mixed precision) API
  • Option to overwrite shards when preprocessing
  • Small fixes and add-ons

0.9.0 (2019-05-16)

  • Faster vocab building when processing shards (no reloading)
  • New dataweighting feature
  • New dropout scheduler.
  • Small fixes and add-ons

0.8.2 (2019-02-16)

  • Update documentation and Library example
  • Revamp args
  • Bug fixes, save moving average in FP32
  • Allow FP32 inference for FP16 models

0.8.1 (2019-02-12)

  • Update documentation
  • Random sampling scores fixes
  • Bug fixes

0.8.0 (2019-02-09)

  • Many fixes and code cleaning thanks @flauted, @guillaumekln
  • Datasets code refactor (thanks @flauted) you need to r-preeprocess datasets

New features

  • FP16 Support: Experimental, using Apex, Checkpoints may break in future version.
  • Continuous exponential moving average (thanks @francoishernandez, and Marian)
  • Relative positions encoding (thanks @francoishernanndez, and Google T2T)
  • Deprecate the old beam search, fast batched beam search supports all options

0.7.2 (2019-01-31)

  • Many fixes and code cleaning thanks @bpopeters, @flauted, @guillaumekln

New features

  • Multilevel fields for better handling of text featuer embeddinggs.

0.7.1 (2019-01-24)

  • Many fixes and code refactoring thanks @bpopeters, @flauted, @guillaumekln

New features

  • Random sampling thanks @daphnei
  • Enable sharding for huge files at translation

0.7.0 (2019-01-02)

  • Many fixes and code refactoring thanks @benopeters
  • Migrated to Pytorch 1.0

0.6.0 (2018-11-28)

  • Many fixes and code improvements
  • New: Ability to load a yml config file. See examples in config folder.

0.5.0 (2018-10-24)

  • Fixed advance n_best beam in translate_batch_fast
  • Fixed remove valid set vocab from total vocab
  • New: Ability to reset optimizer when using train_from
  • New: create_vocabulary tool + fix when loading existing vocab.

0.4.1 (2018-10-11)

  • Fixed preprocessing files names, cleaning intermediary files.

0.4.0 (2018-10-08)

  • Fixed Speech2Text training (thanks Yuntian)

  • Removed -max_shard_size, replaced by -shard_size = number of examples in a shard. Default value = 1M which works fine in most Text dataset cases. (will avoid Ram OOM in most cases)

0.3.0 (2018-09-27)

  • Now requires Pytorch 0.4.1

  • Multi-node Multi-GPU with Torch Distributed

    New options are: -master_ip: ip address of the master node -master_port: port number of th emaster node -world_size = total number of processes to be run (total GPUs accross all nodes) -gpu_ranks = list of indices of processes accross all nodes

  • gpuid is deprecated See examples in https://github.com/OpenNMT/OpenNMT-py/blob/master/docs/source/FAQ.md

  • Fixes to img2text now working

  • New sharding based on number of examples

  • Fixes to avoid 0.4.1 deprecated functions.

0.2.1 (2018-08-31)

Fixes and improvements

  • First compatibility steps with Pytorch 0.4.1 (non breaking)
  • Fix TranslationServer (when various request try to load the same model at the same time)
  • Fix StopIteration error (python 3.7)

New features

  • Ensemble at inference (thanks @Waino)

0.2 (2018-08-28)

improvements

  • Compatibility fixes with Pytorch 0.4 / Torchtext 0.3
  • Multi-GPU based on Torch Distributed
  • Average Attention Network (AAN) for the Transformer (thanks @francoishernandez )
  • New fast beam search (see -fast in translate.py) (thanks @guillaumekln)
  • Sparse attention / sparsemax (thanks to @bpopeters)
  • Refactoring of many parts of the code base:
  • change from -epoch to -train_steps -valid_steps (see opts.py)
  • reorg of the logic train => train_multi / train_single => trainer
  • Many fixes / improvements in the translationserver (thanks @pltrdy @francoishernandez)
  • fix BPTT

0.1 (2018-06-08)

First and Last Release using Pytorch 0.3.x