Mariannmt

Added
- Average Attention Networks for Transformer model
- 16-bit matrix multiplication on CPU
- Memoization for constant nodes for decoding
- Autotuning for decoding

Fixed
- GPU decoding optimizations, about 2x faster decoding of transformer models
- Multi-node MPI-based training on GPUs

Added
- Data weighting with `--data-weighting` at sentence or word level
- Persistent SQLite3 corpus storage with `--sqlite file.db`
- Experimental multi-node asynchronous training
- Restoring optimizer and training parameters such as learning rate, validation
results, etc.
- Experimental multi-CPU training/translation/scoring with `--cpu-threads=N`
- Restoring corpus iteration after training is restarted
- N-best-list scoring in marian-scorer

Fixed
- Deterministic data shuffling with specific seed for SQLite3 corpus storage
- Mini-batch fitting with binary search for faster fitting
- Better batch packing due to sorting

Fixed
- Missing final validation when done with training
- Differing summaries for marian-scorer when used with multiple GPUs

Added
- SQLite3 based corpus storage for on-disk shuffling etc. with `--sqlite`
- Asynchronous maxi-batch preloading
- Using transpose in SGEMM to tie embeddings in output layer

Fixed
- Use valid-mini-batch size during validation with "translation" instead of
mini-batch
- Normalize gradients with multi-gpu synchronous SGD
- Fix divergence between saved models and validated models in asynchronous SGD

Added
- Option `--pretrained-model` to be used for network weights initialization
with a pretrained model
- Version number saved in the model file
- CMake option `-DCOMPILE_SERVER=ON`
- Right-to-left training, scoring, decoding with `--right-left`

Fixed
- Fixed marian-server compilation with Boost 1.66
- Fixed compilation on g++-4.8.4
- Fixed compilation without marian-server if openssl is not available