Can't Generate from Pretrained Story Models

I ran the following command from the examples stories tutorial using the pretrained checkpoints and couldn’t get it to work. What is the correct command to generate from the pretrained story model? I saw https://github.com/pytorch/fairseq/issues/285 had a similar question, but I wasn’t sure if it was resolved and if so what the correct command was.

python generate.py data-bin/writingPrompts --path data-bin/models/fusion_checkpoint.pt --batch-size 32 --beam 1 --sampling --sampling-topk 10 --sampling-temperature 0.8 --nbest 1 --model-overrides "{'pretrained_checkpoint':'data-bin/models/pretrained_checkpoint.pt'}"

My error is pasted below.

| [wp_target] dictionary: 104960 types
| data-bin/writingPrompts test 15138 examples
| ['data-bin/writingPrompts'] test 15138 examples
| loading model(s) from data-bin/models/fusion_checkpoint.pt
| loading pretrained model
  0%|                                         | 0/474 [00:00<?, ?it/s]Traceback (most recent call last):
  File "generate.py", line 171, in <module>
    main(args)
  File "generate.py", line 104, in main
    for sample_id, src_tokens, target_tokens, hypos in translations:
  File "/juicier/scr121/scr/hughz/fairseq/fairseq/sequence_generator.py", line 95, in generate_batched_itr
    prefix_tokens=s['target'][:, :prefix_size] if prefix_size > 0 else None,
  File "/juicier/scr121/scr/hughz/fairseq/fairseq/sequence_generator.py", line 117, in generate
    return self._generate(encoder_input, beam_size, maxlen, prefix_tokens)
  File "/juicier/scr121/scr/hughz/fairseq/fairseq/sequence_generator.py", line 143, in _generate
    encoder_out = model.encoder.reorder_encoder_out(encoder_out, new_order)
  File "/juicier/scr121/scr/hughz/fairseq/fairseq/models/composite_encoder.py", line 48, in reorder_encoder_out
    encoder_out[key] = self.encoders[key].reorder_encoder_out(encoder_out[key], new_order)
  File "/juicier/scr121/scr/hughz/fairseq/fairseq/models/fconv_self_att.py", line 231, in reorder_encoder_out
    eo.index_select(0, new_order) for eo in encoder_out['encoder_out']
  File "/juicier/scr121/scr/hughz/fairseq/fairseq/models/fconv_self_att.py", line 231, in <genexpr>
    eo.index_select(0, new_order) for eo in encoder_out['encoder_out']
RuntimeError: Expected object of type torch.cuda.LongTensor but found type torch.cuda.FloatTensor for argument #3 'index'```

[frankang]

upgrading to pytorch 0.4.1 solved the above problem.

[myleott]

I think the attached commit (#393) should also fix this on 0.4.0, right? The problem is that arange on 0.4.0 returns a FloatTensor, whereas from 0.4.1 forward it returns a LongTensor.