How to use Bert or Bart or Roberta or GPT for translation

[allhelllooz]

I am running various architectures mentioned here in --arch option to benchmark and I am using workpiece-tokenizer externally before pre-process step.

I was able to run following transformer based architectures with following command and able to inference as well. transformer, transformer_iwslt_de_en, transformer_wmt_en_de, transformer_vaswani_wmt_en_de_big, transformer_vaswani_wmt_en_fr_big, transformer_wmt_en_de_big, transformer_wmt_en_de_big_t2t

Command I use -> CUDA_VISIBLE_DEVICES=0,1,2,3 fairseq-train /home/translation_task/mr2en_token_data --arch transformer_wmt_en_de --share-decoder-input-output-embed --optimizer adam --adam-betas '(0.9,0.98)' --clip-norm 0.0 --lr 5e-4 --lr-scheduler inverse_sqrt --warmup-updates 10000 --dropout 0.3 --weight-decay 0.0001 --criterion label_smoothed_cross_entropy --label-smoothing 0.1 --max-tokens 4096 --update-freq 2 --max-source-positions 512 --max-target-positions 512 --skip-invalid-size-inputs-valid-test

Now I am trying to run following and not able to. Can someone suggest about the same.

• bert_base, roberta_base, xlm_base (after removing --share-decoder-input-output-embed)

  
  Traceback (most recent call last):
  File "/home/dh/anaconda3/bin/fairseq-train", line 11, in <module>
  load_entry_point('fairseq', 'console_scripts', 'fairseq-train')()
  File "/home/dh/swapnil/fairseq/fairseq_cli/train.py", line 354, in cli_main
  nprocs=args.distributed_world_size,
  File "/home/dh/anaconda3/lib/python3.7/site-packages/torch/multiprocessing/spawn.py", line 171, in spawn
  while not spawn_context.join():
  File "/home/dh/anaconda3/lib/python3.7/site-packages/torch/multiprocessing/spawn.py", line 118, in join
  raise Exception(msg)
  Exception: 

-- Process 1 terminated with the following error: Traceback (most recent call last): File "/home/dh/anaconda3/lib/python3.7/site-packages/torch/multiprocessing/spawn.py", line 19, in _wrap fn(i, *args) File "/home/dh/swapnil/fairseq/fairseq_cli/train.py", line 321, in distributed_main main(args, init_distributed=True) File "/home/dh/swapnil/fairseq/fairseq_cli/train.py", line 51, in main model = task.build_model(args) File "/home/dh/swapnil/fairseq/fairseq/tasks/fairseq_task.py", line 185, in build_model return models.build_model(args, self) File "/home/dh/swapnil/fairseq/fairseq/models/init.py", line 48, in build_model return ARCH_MODEL_REGISTRY[args.arch].build_model(args, task) File "/home/dh/swapnil/fairseq/fairseq/models/masked_lm.py", line 116, in build_model args.max_positions = args.tokens_per_sample AttributeError: 'Namespace' object has no attribute 'tokens_per_sample'

 - bart_large, transformer_lm_gpt2_small

Traceback (most recent call last):
File "/home/dh/anaconda3/bin/fairseq-train", line 11, in load_entry_point('fairseq', 'console_scripts', 'fairseq-train')() File "/home/dh/swapnil/fairseq/fairseq_cli/train.py", line 354, in cli_main nprocs=args.distributed_world_size, File "/home/dh/anaconda3/lib/python3.7/site-packages/torch/multiprocessing/spawn.py", line 171, in spawn while not spawn_context.join(): File "/home/dh/anaconda3/lib/python3.7/site-packages/torch/multiprocessing/spawn.py", line 118, in join raise Exception(msg) Exception:

-- Process 0 terminated with the following error: Traceback (most recent call last): File "/home/dh/anaconda3/lib/python3.7/site-packages/torch/multiprocessing/spawn.py", line 19, in _wrap fn(i, args) File "/home/dh/swapnil/fairseq/fairseq_cli/train.py", line 321, in distributed_main main(args, init_distributed=True) File "/home/dh/swapnil/fairseq/fairseq_cli/train.py", line 89, in main train(args, trainer, task, epoch_itr) File "/home/dh/swapnil/fairseq/fairseq_cli/train.py", line 152, in train log_output = trainer.train_step(samples) File "/home/dh/swapnil/fairseq/fairseq/trainer.py", line 327, in train_step sample, self.model, self.criterion, self.optimizer, ignore_grad File "/home/dh/swapnil/fairseq/fairseq/tasks/fairseq_task.py", line 251, in train_step loss, sample_size, logging_output = criterion(model, sample) File "/home/dh/anaconda3/lib/python3.7/site-packages/torch/nn/modules/module.py", line 541, in call result = self.forward(input, kwargs) File "/home/dh/swapnil/fairseq/fairseq/criterions/label_smoothed_cross_entropy.py", line 57, in forward net_output = model(sample['net_input']) File "/home/dh/anaconda3/lib/python3.7/site-packages/torch/nn/modules/module.py", line 541, in call result = self.forward(*input, kwargs) File "/home/dh/anaconda3/lib/python3.7/site-packages/torch/nn/parallel/distributed.py", line 442, in forward output = self.module(*inputs[0], *kwargs[0]) File "/home/dh/anaconda3/lib/python3.7/site-packages/torch/nn/modules/module.py", line 541, in call result = self.forward(input, kwargs) File "/home/dh/swapnil/fairseq/fairseq/models/fairseq_model.py", line 385, in forward return self.decoder(src_tokens, *kwargs) File "/home/dh/anaconda3/lib/python3.7/site-packages/torch/nn/modules/module.py", line 541, in call result = self.forward(input, **kwargs) TypeError: forward() got multiple values for argument 'prev_output_tokens'

Traceback (most recent call last): File "", line 1, in File "/home/dh/anaconda3/lib/python3.7/multiprocessing/spawn.py", line 105, in spawn_main exitcode = _main(fd) File "/home/dh/anaconda3/lib/python3.7/multiprocessing/spawn.py", line 115, in _main self = reduction.pickle.load(from_parent) _pickle.UnpicklingError: pickle data was truncated

#### Environment?
 - fairseq Version : '0.9.0'
 - PyTorch Version : '1.3.1'
 - OS (e.g., Linux): Ubuntu 16.04
 - How you installed fairseq (`pip`, source): pip
 - Python version: 3.7 anaconda
 - CUDA/cuDNN version: 10.0
 - GPU models and configuration: v100 x 4 (32 Gb)

[huihuifan]

you have preprocessed data with source and target to run machine translation architectures, however roberta requires different data preprocessing. You can check examples/ folder for tutorials on how to preprocess data for roberta

[allhelllooz]

I want to run the translations only. And I wondering how to use roberta or bart for the same.

So once the preprocessing is done ... would there be changes in above command for roberta ? Are you refering to this -> https://github.com/pytorch/fairseq/blob/master/examples/roberta/README.pretraining.md

Also, what would be preprocessing steps for Bart ? Couldn't find anything here -> https://github.com/pytorch/fairseq/tree/master/examples/bart

[ButteredGroove]

I want to run the translations only. And I wondering how to use roberta or bart for the same.

I'm interested in this too. Are there tutorials for using RoBERTa (as embeddings?) or BART pre-trained models along with train, test, and eval files to train and evaluate a model to do seq2seq translations?

[mukhal]

I am running into the same issues trying to use pre-trained XLM-R for translation. I think the main problem is that Roberta and XLM-R are encoder-only architectures. I think the solution is to use XLM-R or Roberta as an encoder for feature-extraction with a newly initialized decoder.

[allhelllooz]

BART is supposed to be built for translation according to its paper. And for Roberta it looks like a slim chance to use it for translation as it is not seq-2-seq. @huihuifan I was able to run Roberta as its own not for translation. What would you suggest for BART.

[ButteredGroove]

What about using RoBERTa to generate embeddings for words to train seq2seq models?

[tuhinjubcse]

What was your gpu configuration for BART ? https://github.com/pytorch/fairseq/blob/master/examples/bart/README.cnn.md did you guy follow this configuration ?

[ButteredGroove]

What was your gpu configuration for BART ? https://github.com/pytorch/fairseq/blob/master/examples/bart/README.cnn.md did you guy follow this configuration ?

I tried that demo, but step 2) tries to process train.source, val.source, train.target, and val.target. No files with those names exist in the CNN-Dailymail files in step 1). However, in the CNN-Dailymail subdirectory finished_files there are train.bin and val.bin. Looks like the demo is missing a step?

[yuchenlin]

same problem here.. the example of using BART is so unclear. it would be much better if we can see some toy examples of using BART with simple input/output format for seq2seq tasks

[loganlebanoff]

To do the BART preprocessing, you have to look here https://github.com/pytorch/fairseq/issues/1391, specifically zhaoguangxiang's comment

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[karin0018]

same problem here.. the example of using BART is so unclear. it would be much better if we can see some toy examples of using BART with simple input/output format for seq2seq tasks

couldn't agree more...