CHANGELOG.md

Changelog

All notable changes to the project are documented in this file.

Version numbers are of the form 1.0.0. Any version bump in the last digit is backwards-compatible, in that a model trained with the previous version can still be used for translation with the new version. Any bump in the second digit indicates a backwards-incompatible change, e.g. due to changing the architecture or simply modifying model parameter names. Note that Sockeye has checks in place to not translate with an old model that was trained with an incompatible version.

Each version section may have have subsections for: Added, Changed, Removed, Deprecated, and Fixed.

[1.15.5]

Added

• Optionally apply query, key and value projections to the source and target hidden vectors in the CNN model before applying the attention mechanism. CLI parameter: --cnn-project-qkv.

[1.15.4]

Added

• A warning will be printed if the checkpoint decoder slows down training.

[1.15.3]

Added

• Exposing the xavier random number generator through --weight-init-xavier-rand-type.

[1.15.2]

Added

• Exposing MXNet's Nesterov Accelerated Gradient, Adadelta and Adadelta optimizers.

[1.15.1]

Added

• A tool that initializes embedding weights with pretrained word representations, sockeye.init_embedding.

[1.15.0]

Added

• Added support for Swish-1 (SiLU) activation to transformer models (Ramachandran et al. 2017: Searching for Activation Functions, Elfwing et al. 2017: Sigmoid-Weighted Linear Units for Neural Network Function Approximation in Reinforcement Learning). Use --transformer-activation-type swish1.
• Added support for GELU activation to transformer models (Hendrycks and Gimpel 2016: Bridging Nonlinearities and Stochastic Regularizers with Gaussian Error Linear Units. Use --transformer-activation-type gelu.

[1.14.3]

Changed

• Fast decoding for transformer models. Caches keys and values of self-attention before softmax. Changed decoding flag --bucket-width to apply only to source length.

[1.14.2]

Added

• Gradient norm clipping (--gradient-clipping-type) and monitoring.

Changed

• Changed --clip-gradient to --gradient-clipping-threshold for consistency.

[1.14.1]

Changed

• Sorting sentences during decoding before splitting them into batches.
• Default chunk size: The default chunk size when batching is enabled is now batch_size * 500 during decoding to avoid users accidentally forgetting to increase the chunk size.

[1.14.0]

Changed

• Downscaled fixed positional embeddings for CNN models.
• Renamed --monitor-bleu flag to --decode-and-evaluate to illustrate that it computes other metrics in addition to BLEU.

Added

• --decode-and-evaluate-use-cpu flag to use CPU for decoding validation data.
• --decode-and-evaluate-device-id flag to use a separate GPU device for validation decoding. If not specified, the existing and still default behavior is to use the last acquired GPU for training.

[1.13.2]

Added

• A tool that extracts specified parameters from params.x into a .npz file for downstream applications or analysis.

[1.13.1]

Added

• Added chrF metric (Popovic 2015: chrF: character n-gram F-score for automatic MT evaluation) to Sockeye. sockeye.evaluate now accepts bleu and chrf as values for --metrics

[1.13.0]

Fixed

• Transformer models do not ignore --num-embed anymore as they did silently before. As a result there is an error thrown if --num-embed != --transformer-model-size.
• Fixed the attention in upper layers (--rnn-attention-in-upper-layers), which was previously not passed correctly to the decoder.

Removed

• Removed RNN parameter (un-)packing and support for FusedRNNCells (removed --use-fused-rnns flag). These were not used, not correctly initialized, and performed worse than regular RNN cells. Moreover, they made the code much more complex. RNN models trained with previous versions are no longer compatible.
• Removed the lexical biasing functionality (Arthur ETAL'16) (removed arguments --lexical-bias and --learn-lexical-bias).

[1.12.2]

Changed

• Updated to MXNet 0.12.1, which includes an important bug fix for CPU decoding.

[1.12.1]

Changed

• Removed dependency on sacrebleu pip package. Now imports directly from contrib/.

[1.12.0]

Changed

• Transformers now always use the linear output transformation after combining attention heads, even if input & output depth do not differ.

[1.11.2]

Fixed

• Fixed a bug where vocabulary slice padding was defaulting to CPU context. This was affecting decoding on GPUs with very small vocabularies.

[1.11.1]

Fixed

• Fixed an issue with the use of ignore in CrossEntropyMetric::cross_entropy_smoothed. This was affecting runs with Eve optimizer and label smoothing. Thanks @kobenaxie for reporting.

[1.11.0]

Added

• Lexicon-based target vocabulary restriction for faster decoding. New CLI for top-k lexicon creation, sockeye.lexicon. New translate CLI argument --restrict-lexicon.

Changed

• Bleu computation based on Sacrebleu.

[1.10.5]

Fixed

• Fixed yet another bug with the data iterator.

[1.10.4]

Fixed

• Fixed a bug with the revised data iterator not correctly appending EOS symbols for variable-length batches. This reverts part of the commit added in 1.10.1 but is now correct again.

[1.10.3]

Changed

• Fixed a bug with max_observed_{source,target}_len being computed on the complete data set, not only on the sentences actually added to the buckets based on --max_seq_len.

[1.10.2]

Added

• --max-num-epochs flag to train for a maximum number of passes through the training data.

[1.10.1]

Changed

• Reduced memory footprint when creating data iterators: integer sequences are streamed from disk when being assigned to buckets.

[1.10.0]

Changed

• Updated MXNet dependency to 0.12 (w/ MKL support by default).
• Changed --smoothed-cross-entropy-alpha to --label-smoothing. Label smoothing should now require significantly less memory due to its addition to MXNet's SoftmaxOutput operator.
• --weight-normalization now applies not only to convolutional weight matrices, but to output layers of all decoders. It is also independent of weight tying.
• Transformers now use --embed-dropout. Before they were using --transformer-dropout-prepost for this.
• Transformers now scale their embedding vectors before adding fixed positional embeddings. This turns out to be crucial for effective learning.
• .param files now use 5 digit identifiers to reduce risk of overflowing with many checkpoints.

Added

• Added CUDA 9.0 requirements file.
• --loss-normalization-type. Added a new flag to control loss normalization. New default is to normalize by the number of valid, non-PAD tokens instead of the batch size.
• --weight-init-xavier-factor-type. Added new flag to control Xavier factor type when --weight-init=xavier.
• --embed-weight-init. Added new flag for initialization of embeddings matrices.

Removed

• --smoothed-cross-entropy-alpha argument. See above.
• --normalize-loss argument. See above.

[1.9.0]

Added

• Batch decoding. New options for the translate CLI: --batch-size and --chunk-size. Translator.translate() now accepts and returns lists of inputs and outputs.

[1.8.4]

Added

• Exposing the MXNet KVStore through the --kvstore argument, potentially enabling distributed training.

[1.8.3]

Added

• Optional smart rollback of parameters and optimizer states after updating the learning rate if not improved for x checkpoints. New flags: --learning-rate-decay-param-reset, --learning-rate-decay-optimizer-states-reset

[1.8.2]

Fixed

• The RNN variational dropout mask is now independent of the input (previously any zero initial state led to the first state being canceled).
• Correctly pass self.dropout_inputs float to mx.sym.Dropout in VariationalDropoutCell.

[1.8.1]

Changed

• Instead of truncating sentences exceeding the maximum input length they are now translated in chunks.

[1.8.0]

Added

• Convolutional decoder.
• Weight normalization (for CNN only so far).
• Learned positional embeddings for the transformer.

Changed

• --attention-* CLI params renamed to --rnn-attention-*.
• --transformer-no-positional-encodings generalized to --transformer-positional-embedding-type.