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163 lines
5.6 KiB
Markdown
163 lines
5.6 KiB
Markdown
# Changelog
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All notable changes to this project will be documented in this file.
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The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/)
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and this project adheres to [Semantic Versioning](http://semver.org/spec/v2.0.0.html).
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## [Unreleased]
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### Added
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- Faster training (20-30%) by optimizing gradient popagation of biases
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- Returning hard alignments during decoding
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## [1.5.0] - 2018-06-17
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### Added
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- Average Attention Networks for Transformer model
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- 16-bit matrix multiplication on CPU
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- Memoization for constant nodes for decoding
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- Autotuning for decoding
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### Fixed
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- GPU decoding optimizations, about 2x faster decoding of transformer models
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- Multi-node MPI-based training on GPUs
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## [1.4.0] - 2018-03-13
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### Added
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- Data weighting with `--data-weighting` at sentence or word level
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- Persistent SQLite3 corpus storage with `--sqlite file.db`
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- Experimental multi-node asynchronous training
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- Restoring optimizer and training parameters such as learning rate, validation
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results, etc.
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- Experimental multi-CPU training/translation/scoring with `--cpu-threads=N`
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- Restoring corpus iteration after training is restarted
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- N-best-list scoring in marian-scorer
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### Fixed
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- Deterministic data shuffling with specific seed for SQLite3 corpus storage
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- Mini-batch fitting with binary search for faster fitting
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- Better batch packing due to sorting
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## [1.3.1] - 2018-02-04
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### Fixed
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- Missing final validation when done with training
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- Differing summaries for marian-scorer when used with multiple GPUs
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## [1.3.0] - 2018-01-24
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### Added
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- SQLite3 based corpus storage for on-disk shuffling etc. with `--sqlite`
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- Asynchronous maxi-batch preloading
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- Using transpose in SGEMM to tie embeddings in output layer
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## [1.2.1] - 2018-01-19
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### Fixed
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- Use valid-mini-batch size during validation with "translation" instead of mini-batch
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- Normalize gradients with multi-gpu synchronous SGD
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- Fix divergence between saved models and validated models in asynchronous SGD
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## [1.2.0] - 2018-01-13
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### Added
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- Option `--pretrained-model` to be used for network weights initialization
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with a pretrained model
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- Version number saved in the model file
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- CMake option `-DCOMPILE_SERVER=ON`
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- Right-to-left training, scoring, decoding with `--right-left`
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### Fixed
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- Fixed marian-server compilation with Boost 1.66
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- Fixed compilation on g++-4.8.4
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- Fixed compilation without marian-server if openssl is not available
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## [1.1.3] - 2017-12-06
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### Added
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- Added back gradient-dropping
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### Fixed
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- Fixed parameters initialization for `--tied-embeddings` during translation
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## [1.1.2] - 2017-12-05
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### Fixed
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- Fixed ensembling with language model and batched decoding
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- Fixed attention reduction kernel with large matrices (added missing
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`syncthreads()`), which should fix stability with large batches and beam-size
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during batched decoding.
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## [1.1.1] - 2017-11-30
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### Added
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- Option `--max-length-crop` to be used together with `--max-length N` to crop
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sentences to length N rather than omitting them.
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- Experimental model with convolution over input characters
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### Fixed
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- Fixed a number of bugs for vocabulary and directory handling
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## [1.1.0] - 2017-11-21
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### Added
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- Batched translation for all model types, significant translation speed-up
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- Batched translation during validation with translation
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- `--maxi-batch-sort` option for `marian-decoder`
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- Support for CUBLAS_TENSOR_OP_MATH mode for cublas in cuda 9.0
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- The "marian-vocab" tool to create vocabularies
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## [1.0.0] - 2017-11-13
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### Added
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- Multi-gpu validation, scorer and in-training translation
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- summary-mode for scorer
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- New "transformer" model based on [Attention is all you
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need](https://arxiv.org/abs/1706.03762)
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- Options specific for the transformer model
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- Linear learning rate warmup with and without initial value
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- Cyclic learning rate warmup
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- More options for learning rate decay, including: optimizer history reset,
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repeated warmup
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- Continuous inverted square root decay of learning (`--lr-decay-inv-sqrt`)
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rate based on number of updates
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- Exposed optimizer parameters (e.g. momentum etc. for Adam)
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- Version of deep RNN-based models compatible with Nematus (`--type nematus`)
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- Synchronous SGD training for multi-gpu (enable with `--sync-sgd`)
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- Dynamic construction of complex models with different encoders and decoders,
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currently only available through the C++ API
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- Option `--quiet` to suppress output to stderr
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- Option to choose different variants of optimization criterion: mean
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cross-entropy, perplexity, cross-entropy sum
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- In-process translation for validation, uses the same memory as training
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- Label Smoothing
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- CHANGELOG.md
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- CONTRIBUTING.md
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- Swish activation function default for Transformer
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(https://arxiv.org/pdf/1710.05941.pdf)
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### Changed
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- Changed shape organization to follow numpy.
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- Changed option `--moving-average` to `--exponential-smoothing` and inverted formula to `s_t = (1 - \alpha) * s_{t-1} + \alpha * x_t`, `\alpha` is now
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`1-e4` by default
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- Got rid of thrust for compile-time mathematical expressions
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- Changed boolean option `--normalize` to `--normalize [arg=1] (=0)`. New
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behaviour is backwards-compatible and can also be specified as
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`--normalize=0.6`
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- Renamed "s2s" binary to "marian-decoder"
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- Renamed "rescorer" binary to "marian-scorer"
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- Renamed "server" binary to "marian-server"
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- Renamed option name `--dynamic-batching` to `--mini-batch-fit`
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- Unified cross-entropy-based validation, supports now perplexity and other CE
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- Changed `--normalize (bool)` to `--normalize (float)arg`, allow to change
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length normalization weight as `score / pow(length, arg)`.
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### Removed
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- Temporarily removed gradient dropping (`--drop-rate X`) until refactoring.
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