Facing errors in basic test run

[purealpha001]

Unable to run either examples/mm_all.py or the basic test script provided.

Steps:

cd ~
git clone https://github.com/jerinphilip/fairseq-ilmt.git
cd fairseq-ilmt
pip3 install --editable .
cd ~
git clone https://github.com/jerinphilip/ilmulti.git
cd ilmulti
pwd
python3 -m pip install -r requirements.txt 
python3 setup.py install 

No errors reported.

Downloaded models via"

cd ~
bash ilmulti/scripts/download-and-setup-models.sh

Test Code:

from ilmulti.translator import from_pretrained

translator = from_pretrained(tag='mm-all-iter0')
sample = translator("The quick brown fox jumps over the lazy dog", tgt_lang='hi')

Error log:

| [src] dictionary: 40897 types
| [tgt] dictionary: 40897 types
./ilmulti/translator/translator.py:23: UserWarning: utils.load_ensemble_for_inference is deprecated. Please use checkpoint_utils.load_model_ensemble instead.
  self.models, model_args = fairseq.utils.load_ensemble_for_inference(model_paths, self.task, model_arg_overrides=eval(args.model_overrides))
---------------------------------------------------------------------------
RuntimeError                              Traceback (most recent call last)
<ipython-input-19-d75d9cb5891a> in <module>()
      2 
      3 translator = from_pretrained(tag='mm-all-iter0')
----> 4 sample = translator("The quick brown fox jumps over the lazy dog", tgt_lang='hi')

5 frames
/content/ilmulti/ilmulti/translator/mt_engine.py in __call__(self, source, tgt_lang, src_lang, detokenize)
     22             sources.append(content)
     23 
---> 24         export = self.translator(sources)
     25         export = self._handle_empty_lines_noise(export)
     26         if detokenize:

/content/ilmulti/ilmulti/translator/translator.py in __call__(self, lines, attention)
     65                 },
     66             }
---> 67             translations = self.task.inference_step(self.generator, self.models, sample)
     68             for i, (id, hypos) in enumerate(zip(batch.ids.tolist(), translations)):
     69                 src_tokens_i = utils.strip_pad(src_tokens[i], tgt_dict.pad())

/usr/local/lib/python3.6/dist-packages/fairseq/tasks/fairseq_task.py in inference_step(self, generator, models, sample, prefix_tokens)
    242     def inference_step(self, generator, models, sample, prefix_tokens=None):
    243         with torch.no_grad():
--> 244             return generator.generate(models, sample, prefix_tokens=prefix_tokens)
    245 
    246     def update_step(self, num_updates):

/usr/local/lib/python3.6/dist-packages/torch/autograd/grad_mode.py in decorate_context(*args, **kwargs)
     13         def decorate_context(*args, **kwargs):
     14             with self:
---> 15                 return func(*args, **kwargs)
     16         return decorate_context
     17 

/usr/local/lib/python3.6/dist-packages/fairseq/sequence_generator.py in generate(self, models, sample, prefix_tokens, bos_token, **kwargs)
    374                 step,
    375                 lprobs.view(bsz, -1, self.vocab_size),
--> 376                 scores.view(bsz, beam_size, -1)[:, :, :step],
    377             )
    378 

/usr/local/lib/python3.6/dist-packages/fairseq/search.py in step(self, step, lprobs, scores)
     79             out=(self.scores_buf, self.indices_buf),
     80         )
---> 81         torch.div(self.indices_buf, vocab_size, out=self.beams_buf)
     82         self.indices_buf.fmod_(vocab_size)
     83         return self.scores_buf, self.indices_buf, self.beams_buf

RuntimeError: Integer division of tensors using div or / is no longer supported, and in a future release div will perform true division as in Python 3. Use true_divide or floor_divide (// in Python) instead.

[jerinphilip]

Use torch 1.0.0 for now, that should do okay. Will fix it later.

[rahulraj80]

Alternatively:
Change

torch.div(self.indices_buf, vocab_size, out=self.beams_buf)

to

torch.floor_divide(self.indices_buf, vocab_size, out=self.beams_buf)

on the offending line. It works.

[jerinphilip]

^ Not advisable. The torch version might break more places, create inconsistencies and will have to fix everywhere. If you want to translate a large batch of samples, I'd recommend the fairseq-ilmt which is minimal mods on fairseq. I know this works with fairseq v0.7.2 (where I branched to make some mods which was compatible with pytorch 1.0.0 and maybe 1.1.0).

The example here is just to for some specific use-cases and demonstrate this model works. If you have higher volumes you should switch to fairseq level batching optimizations which are in fairseq-ilmt.

[rahulraj80]

OK - makes sense. Tested it on a colab so was more adventurous. :-)