GitLab

Changelog:
- `f472d141`: Support tied embeddings in LSTM encoder/decoder
- `89e19d42`: Don't print alignment by default (use `--print-alignment` to re-enable it)
- `d2e2a1d4`: Add Transformer-based language model
- `c2794070`: Add new Transformer configuration for IWSLT
- `2fbfda0d`: Misc changes for pytorch-translate
- Miscellaneous bug fixes

This implements transformer based language model. It already obtains better perplexity on wikitext103 without any tuning. I will also train it on gbw where I also expect to get better ppl

Example training command:

python train.py /private/home/abaevski/data/wiki103 —save-dir /tmp —fp16 —max-epoch 80 —save-interval 1 —arch transformer_lm —task language_modeling —optimizer nag —lr 0.008 —lr-scheduler reduce_lr_on_plateau —lr-shrink 0.6 —dropout 0.2 —criterion adaptive_loss —adaptive-softmax-cutoff 10000,50000,200000 —max-tokens 512 —tokens-per-sample 512 —seed 1 —sample-break-mode none —log-format json —log-interval 50 —save-interval-updates 2500 —keep-interval-updates 25
small transformer got to 31.3 ppl on wiki text 103 (compared to 35 with fconv) while @myleott got a big transformer lm to 27 something ppl on wiki text 103

add model override argument from load_ensemble_for_inference at generation time, updating readme for stories

default samples_per_token in eval_lm