Where communities thrive


  • Join over 1.5M+ people
  • Join over 100K+ communities
  • Free without limits
  • Create your own community
People
Repo info
Activity
  • Jun 23 10:37
    francoishernandez closed #2167
  • Jun 23 10:37
    francoishernandez commented #2167
  • Jun 22 19:14
    n8rob opened #2167
  • Jun 22 16:04
    francoishernandez commented #2158
  • Jun 22 16:04
    francoishernandez closed #2158
  • Jun 22 16:01
    francoishernandez commented #2165
  • Jun 22 16:01
    francoishernandez closed #2165
  • Jun 22 15:55
    francoishernandez commented #2166
  • Jun 13 09:35
    Sanghyuk-Choi commented #1918
  • Jun 09 14:15
    ShadowVicky opened #2166
  • May 20 06:50
    KevinZhoutianyi edited #2165
  • May 20 06:49
    KevinZhoutianyi edited #2165
  • May 20 06:48
    KevinZhoutianyi edited #2165
  • May 20 06:47
    KevinZhoutianyi opened #2165
  • May 18 20:09
    paruul commented #239
  • May 18 08:21
    Sanghyuk-Choi commented #1918
  • May 18 08:20
    Sanghyuk-Choi commented #1918
  • May 13 15:45
    PosoSAgapo closed #2162
  • May 13 15:45
    PosoSAgapo closed #2163
  • May 13 13:01
    Ch3nYe closed #2148
nashid
@nashid
At the end, failing with " File "/root/miniconda3/envs/open-nmt-env/lib/python3.7/site-packages/torch-1.8.1-py3.7-linux-x86_64.egg/torch/multiprocessing/reductions.py", line 312, in reduce_storage
metadata = storage._sharefilename()
RuntimeError: error executing torch_shm_manager at "/root/miniconda3/envs/open-nmt-env/lib/python3.7/site-packages/torch-1.8.1-py3.7-linux-x86_64.egg/torch/bin/torch_shm_manager" at /pytorch/torch/lib/libshm/core.cpp:99
"
nashid
@nashid
OpenNMT cannot find GPU resources. Any help is welcome.

I can see pytorch can find GPU resources.

>>> torch.cuda.is_available()
True
>>> torch.cuda.current_device()
0
>>> torch.cuda.device(0)
<torch.cuda.device object at 0x7f8c0a3cec50>
>>> torch.cuda.device_count()
1
>>> torch.cuda.get_device_name(0)
'NVIDIA Tesla K80'
>>> torch.cuda.get_device_name(0)
'NVIDIA Tesla K80'

However, OpenNMT is not finding the GPU resource. What is going on here?
I am using OpenNMT from https://github.com/OpenNMT/OpenNMT-py/tree/1.2.0

Traceback (most recent call last):
  File "../../OpenNMT-py/train.py", line 6, in <module>
    main()
  File "/root/work/context/huggingface-models/OpenNMT-py/onmt/bin/train.py", line 197, in main
    train(opt)
  File "/root/work/context/huggingface-models/OpenNMT-py/onmt/bin/train.py", line 91, in train
    p.join()
  File "/root/miniconda3/envs/open-nmt-env/lib/python3.7/multiprocessing/process.py", line 140, in join
    res = self._popen.wait(timeout)
  File "/root/miniconda3/envs/open-nmt-env/lib/python3.7/multiprocessing/popen_fork.py", line 48, in wait
    return self.poll(os.WNOHANG if timeout == 0.0 else 0)
  File "/root/miniconda3/envs/open-nmt-env/lib/python3.7/multiprocessing/popen_fork.py", line 28, in poll
    pid, sts = os.waitpid(self.pid, flag)
  File "/root/work/context/huggingface-models/OpenNMT-py/onmt/bin/train.py", line 181, in signal_handler
    raise Exception(msg)
Exception: 

-- Tracebacks above this line can probably
                 be ignored --

Traceback (most recent call last):
  File "/root/work/context/huggingface-models/OpenNMT-py/onmt/bin/train.py", line 135, in run
    gpu_rank = onmt.utils.distributed.multi_init(opt, device_id)
  File "/root/work/context/huggingface-models/OpenNMT-py/onmt/utils/distributed.py", line 27, in multi_init
    world_size=dist_world_size, rank=opt.gpu_ranks[device_id])
  File "/root/miniconda3/envs/open-nmt-env/lib/python3.7/site-packages/torch/distributed/distributed_c10d.py", line 510, in init_process_group
    timeout=timeout))
  File "/root/miniconda3/envs/open-nmt-env/lib/python3.7/site-packages/torch/distributed/distributed_c10d.py", line 603, in _new_process_group_helper
    timeout)
RuntimeError: ProcessGroupNCCL is only supported with GPUs, no GPUs found!
François Hernandez
@francoishernandez
So readme refers to "PyTorch 1.4" and setup.py is saying "torch>=1.6.0"
Trust the latest information, which is setup.py -- the readme was probably not updated when upgrading for 1.6 support
For your GPU issue, I don't know, never seen it. Maybe create a detailed issue on github with your setup, commands, etc.
Peng Shi
@shipengAlan
Hi everyone. I try to do some experiments by customizing Transfomer. Does OpenNMT-py support this ? And how I can do that ? Thanks a lot
ANASUA BANERJEE
@anasua123:matrix.org
[m]
Hi everyone. I try to execute the preprocess part but it throws an error "No module named 'onmt.bin.preprocess'" and if i installed the omnt then it is showing another error . can anyone plz guide me how to resolve this one
François Hernandez
@francoishernandez
@anasua123:matrix.org the whole preprocess logic has been removed since 2.0. Check the docs for quickstart and some more comprehensive examples: https://opennmt.net/OpenNMT-py/quickstart.html
bcabgil
@bcabgil
Hello! Is it possible to build a text classification model from a pre trained bert sequence to sequence translation model using openNMT-py? Is there a way to get the outputs of a specific layer of the pre-trained model instead of the translated sequence?
Suzenne
@Suzenne
Why I got a very low bleu score (bleu1 is around 2.9 ) ,using an about 180 thousand en-zh corpus to train the model with the config is consist of quick-start and config-transformer-base-1GPU.yaml ? Is there something wrong ?
πόσο σ' αγαπώ.
@PosoSAgapo
Why the model behaves differently in training and tranlsation? Even translation on the training dataset cannot give the same performance when the model is training, like the model gets 70% on training, after training, using the model to translate the train set, the accuracy is just 30%.
Guillaume Klein
@guillaumekln
Suzenne
@Suzenne
What's the meaning of these weights?
76c2f8ce25967709c248e1765cc5658.png
Karen Lastmann Assaraf
@karen.lastmannassaraf_gitlab
Hi!
Did someone already converted an opennmt self attention encoder-decoder to a hugging face transformer?
Thanks!
1190301804
@1190301804
does Opennmt support the simultaneous translation just like fairseq ?
Guillaume Klein
@guillaumekln
No, it does not.
neenaloysius
@neenaloysius
I need to run the Multi-Unit Transformer https://github.com/ElliottYan/Multi_Unit_Transformer. Tht repo suggests to use the preprocessing in OpenNMT and expects vocab.pt file. But now vocab.pt file is not generated as per the latest NMT version. can anyone please help
James
@JOHW85

https://huggingface.co/blog/constrained-beam-search

Wonder if this can be implemented in OpenNMT/Ctranslate?

Guillaume Klein
@guillaumekln
Maybe. Can you open a feature request in the CTranslate2 GitHub repository? Make sure to specify what should be implemented from this blog post and add the relevant links/papers. Thanks.
James
@JOHW85
OpenNMT/CTranslate2#739
I've posted a feature request
neo
@code2graph

For a Neural Machine Translation (NMT) task, my input data has relational information. Probably I can use Graph Neural Network (GNN) and use a Graph2Seq model. But I can't find a good generational model for GNN.

So I want to use a Transformer model. But then the massive problem is how can I embed structural information in a Transformer? Is there any open source artefact for Relational Transformer that I can use out of the box?

neo
@code2graph
I read to alleviate the problem of Out of Vocabulary (OOV), there are two techniques:
  1. BPE
  2. Copy mechanism
    It appears to me they are two orthogonal approaches. Can we combine the two, i.e., we use both the copy mechanism and BPE? Are there any work out there that combines the two? I cant find any.
neo
@code2graph
Anyone have a pointer regarding BPE and copy mechanism. Please help.
πόσο σ' αγαπώ.
@PosoSAgapo

For a Neural Machine Translation (NMT) task, my input data has relational information. Probably I can use Graph Neural Network (GNN) and use a Graph2Seq model. But I can't find a good generational model for GNN.

So I want to use a Transformer model. But then the massive problem is how can I embed structural information in a Transformer? Is there any open source artefact for Relational Transformer that I can use out of the box?

OpenNMT only implemented the vanilla Transformer model, so there is such thing as a Relational Transformer that you can directly use. Using Copy with BPE may not be a good choice, the BPE is able to generate any combination of tokens as you want, I don't see any reason that you need both in your model since the Copy mechanism in most situations does not work very well and is not able to copy the target tokens that you want.

neo
@code2graph
But copy mechanism can help to handle the unknown tokens. Isnt it?
πόσο σ' αγαπώ.
@PosoSAgapo

But copy mechanism can help to handle the unknown tokens. Isnt it?

BPE is also able to handle unknown tokens, every word after BPE tokenization will be a combination of sub-words, therefore the BPE will not have the problem of unseen tokens in almost every situation. As I said, the Copy mechanism may not give what you want, the Copy mechanism is a relatively outdated structure considering the current Seq2Seq pre-trained models like GPT2 or variants of Transformer, so I don't think you need them both in your case. It will be lots of work to incorporate them both and may not give a good performance.

neo
@code2graph
@PosoSAgapo what would happen if I just use an existing framework and do BPE and also enable the copy mechanism.
πόσο σ' αγαπώ.
@PosoSAgapo

@PosoSAgapo what would happen if I just use an existing framework and do BPE and also enable the copy mechanism.

I can't say what will happen, but I don't think it will be a good option to combine them both. Truth is SOTA models are all based on the pre-trained models or various variants of Transformers, so I think this somehow tells the fact that the Copy mechanism may not be a good solution to slove unseen tokens. I also use the Copy mechanism many times when I use the OpenNMT, but it does not work very well as far as my experience.

paruul
@paruul
How can I run translation without command line, unfortunately I couldn't find it in the documentation. Could anyone please direct me to the documentation if it already exists. Thank you!