# # Retrain the models used for CI. # # Should be done rarely, indicates a major breaking change. my_python=python ############### TEST regular RNN choose either -rnn_type LSTM / GRU / SRU and set input_feed 0 for SRU if false; then $my_python build_vocab.py \ -config data/data.yaml -save_data data/data \ -src_vocab data/data.vocab.src -tgt_vocab data/data.vocab.tgt \ -overwrite true $my_python train.py \ -config data/data.yaml -src_vocab data/data.vocab.src -tgt_vocab data/data.vocab.tgt \ -src_vocab_size 1000 -tgt_vocab_size 1000 \ -save_model tmp -world_size 1 -gpu_ranks 0 \ -rnn_type LSTM -input_feed 0 \ -rnn_size 256 -word_vec_size 256 \ -layers 1 -train_steps 10000 \ -optim adam -learning_rate 0.001 # -truncated_decoder 5 # -label_smoothing 0.1 mv tmp*10000.pt onmt/tests/test_model.pt rm tmp*.pt fi ############### TEST CNN if false; then $my_python build_vocab.py \ -config data/data.yaml -save_data data/data \ -src_vocab data/data.vocab.src -tgt_vocab data/data.vocab.tgt \ -overwrite true $my_python train.py \ -config data/data.yaml -src_vocab data/data.vocab.src -tgt_vocab data/data.vocab.tgt \ -src_vocab_size 1000 -tgt_vocab_size 1000 \ -save_model /tmp/tmp -world_size 1 -gpu_ranks 0 \ -encoder_type cnn -decoder_type cnn \ -rnn_size 256 -word_vec_size 256 \ -layers 2 -train_steps 10000 \ -optim adam -learning_rate 0.001 mv /tmp/tmp*10000.pt onmt/tests/test_model.pt rm /tmp/tmp*.pt fi ################# MORPH DATA if false; then $my_python build_vocab.py \ -config data/morph_data.yaml -save_data data/data \ -src_vocab data/morph_data.vocab.src -tgt_vocab data/morph_data.vocab.tgt \ -overwrite true $my_python train.py \ -config data/morph_data.yaml -src_vocab data/morph_data.vocab.src -tgt_vocab data/morph_data.vocab.tgt \ -save_model tmp -world_size 1 -gpu_ranks 0 \ -rnn_size 400 -word_vec_size 100 \ -layers 1 -train_steps 8000 \ -optim adam -learning_rate 0.001 mv tmp*8000.pt onmt/tests/test_model2.pt rm tmp*.pt fi ############### TEST TRANSFORMER if false; then $my_python build_vocab.py \ -config data/data.yaml -save_data data/data \ -src_vocab data/data.vocab.src -tgt_vocab data/data.vocab.tgt \ -overwrite true -share_vocab $my_python train.py \ -config data/data.yaml -src_vocab data/data.vocab.src -tgt_vocab data/data.vocab.tgt \ -save_model /tmp/tmp \ -batch_type tokens -batch_size 1024 -accum_count 4 \ -layers 4 -rnn_size 256 -word_vec_size 256 \ -encoder_type transformer -decoder_type transformer \ -share_embedding -share_vocab \ -train_steps 10000 -world_size 1 -gpu_ranks 0 \ -max_generator_batches 4 -dropout 0.1 \ -normalization tokens \ -max_grad_norm 0 -optim adam -decay_method noam \ -learning_rate 2 -label_smoothing 0.1 \ -position_encoding -param_init 0 \ -warmup_steps 100 -param_init_glorot -adam_beta2 0.998 mv /tmp/tmp*10000.pt onmt/tests/test_model.pt rm /tmp/tmp*.pt fi if false; then $my_python translate.py -gpu 0 -model onmt/tests/test_model.pt \ -src data/src-val.txt -output onmt/tests/output_hyp.txt -beam 5 -batch_size 16 fi ############### TEST LANGUAGE MODEL if false; then rm data/data_lm/*.python $my_python build_vocab.py \ -config data/lm_data.yaml -save_data data/data_lm -share_vocab \ -src_vocab data/data_lm/data.vocab.src -tgt_vocab data/data_lm/data.vocab.tgt \ -overwrite true $my_python train.py -config data/lm_data.yaml -save_model /tmp/tmp \ -accum_count 2 -dec_layers 2 -rnn_size 64 -word_vec_size 64 -batch_size 256 \ -encoder_type transformer_lm -decoder_type transformer_lm -share_embedding \ -train_steps 2000 -max_generator_batches 4 -dropout 0.1 -normalization tokens \ -share_vocab -transformer_ff 256 -max_grad_norm 0 -optim adam -decay_method noam \ -learning_rate 2 -label_smoothing 0.1 -model_task lm -world_size 1 -gpu_ranks 0 \ -attention_dropout 0.1 -heads 2 -position_encoding -param_init 0 -warmup_steps 100 \ -param_init_glorot -adam_beta2 0.998 -src_vocab data/data_lm/data.vocab.src # mv /tmp/tmp*2000.pt onmt/tests/test_model_lm.pt rm /tmp/tmp*.pt fi # if false; then $my_python translate.py -gpu 0 -model onmt/tests/test_model_lm.pt \ -src data/src-val.txt -output onmt/tests/output_hyp.txt -beam 5 -batch_size 16 fi