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# # 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
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