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A natural language modeling framework based on PyTorch

Home Page: https://pytext.readthedocs.io/en/master/

License: Other

Python 99.94% Makefile 0.02% Shell 0.02% Batchfile 0.02%

pytext's Introduction

⚠️ Please migrate to torchtext ⚠️

PyText is deprecated and will no longer be actively maintained. Please check out torchtext and contribute there!

Overview

PyText is a deep-learning based NLP modeling framework built on PyTorch. PyText addresses the often-conflicting requirements of enabling rapid experimentation and of serving models at scale. It achieves this by providing simple and extensible interfaces and abstractions for model components, and by using PyTorch’s capabilities of exporting models for inference via the optimized Caffe2 execution engine. We are using PyText in Facebook to iterate quickly on new modeling ideas and then seamlessly ship them at scale.

Core PyText features:

Production ready models for various NLP/NLU tasks:
- Text classifiers
  - Yoon Kim (2014): Convolutional Neural Networks for Sentence Classification
  - Lin et al. (2017): A Structured Self-attentive Sentence Embedding
- Sequence taggers
  - Lample et al. (2016): Neural Architectures for Named Entity Recognition
- Joint intent-slot model
  - Zhang et al. (2016): A Joint Model of Intent Determination and Slot Filling for Spoken Language Understanding
- Contextual intent-slot models
Distributed-training support built on the new C10d backend in PyTorch 1.0
Mixed precision training support through APEX (trains faster with less GPU memory on NVIDIA Tensor Cores)
Extensible components that allows easy creation of new models and tasks
Reference implementation and a pretrained model for the paper: Gupta et al. (2018): Semantic Parsing for Task Oriented Dialog using Hierarchical Representations
Ensemble training support
Reference implementation for the paper: Babu et al. 2021: Non-autoregressive Semantic Parsing for Compositional Task Oriented Dialog

Installing PyText

PyText requires Python 3.6.1 or above.

To get started on a Cloud VM, check out our guide.

Get the source code:

  $ git clone https://github.com/facebookresearch/pytext
  $ cd pytext

Create a virtualenv and install PyText:

  $ python3 -m venv pytext_venv
  $ source pytext_venv/bin/activate
  (pytext_venv) $ pip install pytext-nlp

Detailed instructions and more installation options can be found in our Documentation. If you encounter issues with missing dependencies during installation, please refer to OS Dependencies.

Train your first text classifier

For this first example, we'll train a CNN-based text-classifier that classifies text utterances, using the examples in tests/data/train_data_tiny.tsv. The data and configs files can be obtained either by cloning the repository or by downloading the files manually from GitHub.

  (pytext_venv) $ pytext train < demo/configs/docnn.json

By default, the model is created in /tmp/model.pt

Now you can export your model as a caffe2 net:

  (pytext_venv) $ pytext export < demo/configs/docnn.json

You can use the exported caffe2 model to predict the class of raw utterances like this:

  (pytext_venv) $ pytext --config-file demo/configs/docnn.json predict <<< '{"text": "create an alarm for 1:30 pm"}'

More examples and tutorials can be found in Full Documentation.

Join the community

Facebook group: https://www.facebook.com/groups/pytext/

License

PyText is BSD-licensed, as found in the LICENSE file.

pytext's People

Contributors

Stargazers

Watchers

Forkers

github30 seayoung1112 snisarg borguz chenyangyu1988 titousensei shinichr huguanglong zwjyyc likeucode m3rlin45 beesitech pkq1688 szhl 2951283197zhang hgpatswu kldcr echo-ray allensmile zhouyonglong techeye220 hbcbh1999 shafiahmed rtriangle hantek wombatpm chenyangh srikalyan barseghyanartur brettkoonce aliosamahassan stjordanis mwu1993 yang-zhang xtagon albertbj codeaudit jaykimbravekjh lite-java im-gozmit awfssv lql0716 coldkey2003 vishalbelsare nhittt yangchunm shubhampachori12110095 prozhuchen moree0 dst1213 xhuvom fendaq bussiere intfrr xuesj walden2013 wwjiang007 shyamalschandra hunglethanh9 deeep-learning raymondseger cxz mldeveloper01 jamshaidsohail5 chengyanliang bin2000 delaiahz repoarchiver arunkumarramanan ainewsbot jithinraj wh-forker wutenghu angelo337 iaep zhoudaqing tarsbase pb-pravin speedcell4 andrewliujian javelir davidmr001 shaunstanislauslau daiwk pramabhi tsoliangwu0130 duzhanyuan rogervaas hhy5277 hongshunyang basicv8vc mahshad92 abduakhatov-ai mbrukman sohuren awesome-archive hins connietong slidersun husseinshtia

pytext's Issues

Question regarding the output of the Hierarchical Intent and Slot Filling tutorial

Hi,

I'm following the tutorial here: https://pytext-pytext.readthedocs-hosted.com/en/latest/hierarchical_intent_slot_tutorial.html

I was able to run the REPL and replicate the result from the last section of the tutorial, which takes this as input:

{"text": "order coffee from starbucks"}

and returns something like this as output:

[{'prediction': [7, 0, 5, 0, 1, 0, 3, 0, 1, 1],
'score': [
        0.44425372408062447,
        0.8018286800064633,
        0.6880680051949267,
        0.9891564979506277,
        0.9999506231665385,
        0.9992705616574005,
        0.34512090135492923,
        0.9999979545618913,
        0.9999998668826438,
        0.9999998686418744]}]

My question is, how is this data structure encoded, and how do I use it to parse "order coffee from starbucks" into the hierarchical tree structure (intents and slots)?

self-attention model is missing the "hops" feature

in the original paper, there are two equations for attention, (5) and (6) (on page 3).

near as we can tell, in the code - https://github.com/facebookresearch/pytext/blob/master/pytext/models/representations/pooling.py#L22 - the ws2 tensor is of the wrong dimension; i.e. it's only of dim, in language of the paper, d_a instead of a matrix of r x d_a.

is this intentional?

thanks!

TypeError: init() got an unexpected keyword argument 'dtype'

I followed tutorial of ATIS on https://pytext-pytext.readthedocs-hosted.com/en/latest/atis_tutorial.html. On my way to step 3: pytext train < sample_config.json this error showed up:

Traceback (most recent call last):
  File "/home/hiepph/miniconda3/bin/pytext", line 11, in <module>
    load_entry_point('pytext-nlp', 'console_scripts', 'pytext')()
  File "/home/hiepph/miniconda3/lib/python3.6/site-packages/click/core.py", line 722, in __call__
    return self.main(*args, **kwargs)
  File "/home/hiepph/miniconda3/lib/python3.6/site-packages/click/core.py", line 697, in main
    rv = self.invoke(ctx)
  File "/home/hiepph/miniconda3/lib/python3.6/site-packages/click/core.py", line 1066, in invoke
    return _process_result(sub_ctx.command.invoke(sub_ctx))
  File "/home/hiepph/miniconda3/lib/python3.6/site-packages/click/core.py", line 895, in invoke
    return ctx.invoke(self.callback, **ctx.params)
  File "/home/hiepph/miniconda3/lib/python3.6/site-packages/click/core.py", line 535, in invoke
    return callback(*args, **kwargs)
  File "/home/hiepph/miniconda3/lib/python3.6/site-packages/click/decorators.py", line 17, in new_func
    return f(get_current_context(), *args, **kwargs)
  File "/home/hiepph/src/pytext/pytext/main.py", line 232, in train
    train_model(config)
  File "/home/hiepph/src/pytext/pytext/workflow.py", line 55, in train_model
    task = prepare_task(config, dist_init_url, device_id, rank, world_size)
  File "/home/hiepph/src/pytext/pytext/workflow.py", line 78, in prepare_task
    return create_task(config.task)
  File "/home/hiepph/src/pytext/pytext/task/task.py", line 38, in create_task
    return create_component(ComponentType.TASK, task_config, metadata, model_state)
  File "/home/hiepph/src/pytext/pytext/config/component.py", line 142, in create_component
    return cls.from_config(config, *args, **kwargs)
  File "/home/hiepph/src/pytext/pytext/task/task.py", line 81, in from_config
    featurizer=featurizer,
  File "/home/hiepph/src/pytext/pytext/config/component.py", line 147, in create_data_handler
    ComponentType.DATA_HANDLER, data_handler_config, *args, **kwargs
  File "/home/hiepph/src/pytext/pytext/config/component.py", line 142, in create_component
    return cls.from_config(config, *args, **kwargs)
  File "/home/hiepph/src/pytext/pytext/data/joint_data_handler.py", line 76, in from_config
    DatasetFieldName.DOC_WEIGHT_FIELD: FloatField(),
  File "/home/hiepph/src/pytext/pytext/fields/field.py", line 288, in __init__
    unk_token=None,
  File "/home/hiepph/src/pytext/pytext/fields/field.py", line 54, in __init__
    super().__init__(*args, **kwargs)
TypeError: __init__() got an unexpected keyword argument 'dtype'

This doesn't happen with Train your first model tutorial.

F1 is a harmonic mean, not geometric

Hi,
I think there might be a tiny mistake in documentation:

https://github.com/facebookresearch/pytext/blob/master/pytext/docs/source/train_your_first_model.rst

F1 score is described as geometric mean but I think it is in fact harmonic.

export to onnx

Steps to reproduce

train for example the tiny CNN sentence classifier
try to export the model to onnx : "$ pytext export onnx < demo/configs/docnn.onnx"
see it of course does nt work, the export format being hard coded to Caffe (IIUC)

Observed Results

export to default non onnx format

Expected Results

an standard onnx file at least for simple cnn based NN

Relevant Code

For instance, add a 3rd param to the pytext export in order to choose the model file format:

 pytext export onnx < demo/configs/docnn.onnx

i cant find prf1 metrics in my console when model training is over

actually by default it will output prf1 metrics to console channel, but when dataset is bigger, with more console result , console will replace previous content with current result. can we output these metrics into another file .and testout.txt doesnt contain these metrics result

pytext export < config.json

no such file: config.json

are you sure the model output is config.json?

add attention visualisation via imgkit hacking

related to #143 ; unfortunately tensorboard doesn't let you write arbitrary html to the text field ( see tensorflow/tensorboard#830 ) but using imgkit it could be hacked in by way of an image.

this would be a really neat way to visualise the changing attention as the model trains. unfortunately, imgkit requires the dep wkhtmltopdf, but hey.

i'm happy to add this once it's clear to me how to get the right tensor out at eval time (i.e. once i properly understand #143).

Project roadmap?

Hi,

The goals are ambitious, the codebase is in flux, and future directions are outlined in your paper https://arxiv.org/pdf/1812.08729.pdf, but it's unclear what the roadmap is for this project.

What release cadence can we expect, and what features are prioritized?

I'm also curious if this has a substantial backing by Facebook / the PyTorch team.

Thanks

rnng model CUDA error: initialization error

Problems:

I had set the "use_cuda_if_available" to be true in the rnng.json file and run pytext train < demo/configs/rnng.json. Then got "RuntimeError: CUDA error: initialization error" and "RuntimeError: Input and hidden tensors are not at the same device, found input tensor at cuda:0 and hidden tensor at cpu"

ENV

Python 3.6.5 :: Anaconda, Inc.
2.NVIDIA-SMI 390.30

Thanks so much!

how does one get attention values out of the predictions?

thanks for this library! i've managed to try an simple model and i'm excited to do more.

i'm using is config:

(pytext) 12:04 PM noon ∈ pytext (master*) ♫ cat sst2.json
{
  "config": {
    "task": {
      "DocClassificationTask": {
        "model": {
          "representation": {
            "BiLSTMDocAttention": {}
          }
        },
        "data_handler": {
          "columns_to_read": [
            "doc_label",
            "text"
          ],
          "train_path": "data/train.tsv",
          "eval_path": "data/val.tsv",
          "test_path": "data/test.tsv"
        },
        "trainer": {
          "epochs": 150
        }
      }
    }
  }
}

and following the tutorial i can train and get predictions. awesome!

but how do i find the attention?

it doesn't appear to be anywhere in the results of pytext.create_predictor ... ?

thanks for any help!

The format of the training set for Sequence taggers task

Steps to reproduce

I want to ask what is the format of the training set if I want to do the word tagging task? Use the default JointModelDataHandler，Can give an example?

Expected Results

Take a snapshot of the specific annotation method or content of the training set?

can i specify channel in metric reporter in config file

seems that we can't do this now

can't find tensorboard event files in current working dir

both with and without setting use_tensorboard=true, i can not find tensorboard event files in my current working directory.
my OS Is ubuntu 18

Guide and documentation link is not exist.

The following links is broken.

https://github.com/facebookresearch/pytext/blob/master/CLOUDS.md

https://pytext.readthedocs.io/installation.html

Dimension mismatch between representation layer and decoder layer

Steps to reproduce

pytext train < test.json
test.json:

"model": {
       "representation": {
         "BiLSTMSlotAttention": {
           "dropout": 0.1,
           "lstm": {
             "dropout": 0.1,
             "lstm_dim": 256,
             "num_layers": 2,
             "bidirectional": true
           },
           "slot_attention": {
             "attn_dimension": 64,
             "attention_type": "dot"
           }
         }
       },
       "decoder": {
         "hidden_dims": [256]
       },
       "output_layer": {
         "CRFOutputLayer": {}
       }
     },

Observed Results

Rank 0 worker: Running epoch for Stage.TRAIN
Traceback (most recent call last):
  File "/home/lvzhuoran/anaconda3/bin/pytext", line 11, in <module>
    load_entry_point('pytext-nlp-yckj', 'console_scripts', 'pytext')()
  File "/home/lvzhuoran/anaconda3/lib/python3.6/site-packages/click/core.py", line 722, in __call__
    return self.main(*args, **kwargs)
  File "/home/lvzhuoran/anaconda3/lib/python3.6/site-packages/click/core.py", line 697, in main
    rv = self.invoke(ctx)
  File "/home/lvzhuoran/anaconda3/lib/python3.6/site-packages/click/core.py", line 1066, in invoke
    return _process_result(sub_ctx.command.invoke(sub_ctx))
  File "/home/lvzhuoran/anaconda3/lib/python3.6/site-packages/click/core.py", line 895, in invoke
    return ctx.invoke(self.callback, **ctx.params)
  File "/home/lvzhuoran/anaconda3/lib/python3.6/site-packages/click/core.py", line 535, in invoke
    return callback(*args, **kwargs)
  File "/home/lvzhuoran/anaconda3/lib/python3.6/site-packages/click/decorators.py", line 17, in new_func
    return f(get_current_context(), *args, **kwargs)
  File "/ssd/asr/lvzhuoran/project/nlp/pytext/pytext/main.py", line 232, in train
    train_model(config)
  File "/ssd/asr/lvzhuoran/project/nlp/pytext/pytext/workflow.py", line 57, in train_model
    trained_model, best_metric = task.train(config, rank, world_size)
  File "/ssd/asr/lvzhuoran/project/nlp/pytext/pytext/task/task.py", line 157, in train
    rank=rank,
  File "/ssd/asr/lvzhuoran/project/nlp/pytext/pytext/trainers/trainer.py", line 146, in train
    rank=rank,
  File "/ssd/asr/lvzhuoran/project/nlp/pytext/pytext/trainers/trainer.py", line 216, in _run_epoch
    logits = model(*inputs)
  File "/home/lvzhuoran/anaconda3/lib/python3.6/site-packages/torch/nn/modules/module.py", line 489, in __call__
    result = self.forward(*input, **kwargs)
  File "/ssd/asr/lvzhuoran/project/nlp/pytext/pytext/models/model.py", line 213, in forward
    *input_representation, *decoder_inputs
  File "/home/lvzhuoran/anaconda3/lib/python3.6/site-packages/torch/nn/modules/module.py", line 489, in __call__
    result = self.forward(*input, **kwargs)
  File "/ssd/asr/lvzhuoran/project/nlp/pytext/pytext/models/decoders/mlp_decoder.py", line 54, in forward
    return self.mlp(torch.cat(input, 1))
  File "/home/lvzhuoran/anaconda3/lib/python3.6/site-packages/torch/nn/modules/module.py", line 489, in __call__
    result = self.forward(*input, **kwargs)
  File "/home/lvzhuoran/anaconda3/lib/python3.6/site-packages/torch/nn/modules/container.py", line 92, in forward
    input = module(input)
  File "/home/lvzhuoran/anaconda3/lib/python3.6/site-packages/torch/nn/modules/module.py", line 489, in __call__
    result = self.forward(*input, **kwargs)
  File "/home/lvzhuoran/anaconda3/lib/python3.6/site-packages/torch/nn/modules/linear.py", line 67, in forward
    return F.linear(input, self.weight, self.bias)
  File "/home/lvzhuoran/anaconda3/lib/python3.6/site-packages/torch/nn/functional.py", line 1354, in linear
    output = input.matmul(weight.t())
RuntimeError: size mismatch, m1: [3840 x 1024], m2: [512 x 256] at /pytorch/aten/src/THC/generic/THCTensorMathBlas.cu:266

Expected Results

They should be the same size, [3840 x 1024] and [1024 x 256]

Relevant Code

https://github.com/facebookresearch/pytext/blob/fa0fea934bad76e9906c013e377ce80c77cbd5e4/pytext/models/representations/bilstm_slot_attn.py#L51

I could open a pr to fix this.

No module named 'libfb'

Excuse me, when I run the command there is a ModuleNotFoundError:

import libfb.py.mkl # noqa
ModuleNotFoundError: No module named 'libfb'

what is libfb, and how to install it?

Install Flow installs a CPU only version of PyTorch

The dependency_link in setup.py links to a wheel that doesn't support cuda

export_caffe2_path for each task in DisjointMultitask mode

I followed multitask demo,
$ pytext train < multitask_sst_lm.json

My issue is how do I export as caffe2 (.c2) for each tasks. I wanna train my own multitask and expect multiple binary-classifiers in return. The question is what should I do to export_caffe2_path if I needed outputs for each tasks??

better tensorboard output: plot train/eval curves on different summaries so they appear on the same graph

presently, the train/eval curves are plotted on different graphs (i.e. the same summary writer)

it's nicer to plot the train/eval curves under the same name, but different summary writers, so they can be shown on the same graph.

Generation of Config Docs doesn't work right with indentation

things like the incorrectly formatted and indented "attributes" section here:
https://pytext-pytext.readthedocs-hosted.com/en/latest/configs/pytext.data.bptt_lm_data_handler.BPTTLanguageModelDataHandler.Config.html
are caused by the GoogleDocstring in Napoleon not properly handling indented docstrings that we use in our config classes.

I will work on unindenting them programmatically, as it looks ugly to do it in the code

Dropout ignored in BiLSTMSlotAttention

Steps to reproduce

In bilstm_slot_attn.py#93-96 the computed dropout representation is not re-used.

Observed Results

No results observed, but might have performance impact.

Expected Results

Dropout value is being used.

Relevant Code

        rep = self.dropout(embedded_tokens)

        # LSTM representation
        rep, _ = self.lstm(embedded_tokens, seq_lengths, states)
        #Should be
        rep, _ = self.lstm(rep, seq_lengths, states)

A related question:
Is the used SlotAttention described somewhere? From looking at the code, it seems like a regular attention mechanism, but maybe I'm missing something. The readme states that the sequence tagger is based on Lample et al. (2016), but the word tagger using this slot attention would be a different architecture then.

docnn onnx export/validation failure on ConstantFill op

Steps to reproduce

install fresh (2019 Jan 27) pytext :
python3.6 -m venv pytext_venv
source pytext_venv/bin/activate
pip install pytext-nlp
train docnn :
pytext train < demo/configs/docnn.json
pytext export < demo/configs/docnn.json
see error: Context: Bad node spec: input: "75" output: "76" op_type: "ConstantFill" attribute { name: "input_as_shape" i: 1 type: INT }

Observed Results

...
Loading data...
load_path is not a config of embedding, skipping
save_path is not a config of embedding, skipping
freeze is not a config of embedding, skipping
shared_module_key is not a config of embedding, skipping
dict_feat is not a config of embedding, skipping
char_feat is not a config of embedding, skipping
pretrained_model_embedding is not a config of embedding, skipping
dense_feat is not a config of embedding, skipping
Model has 2 param groups (1 from embedding module) for optimizer
Saving caffe2 model to: /tmp/model.caffe2.predictor
Saving onnx model to: /tmp/model.onnx
/home/wtambellini/repos/pytext/pytext_venv/lib64/python3.6/site-packages/torch/nn/modules/module.py:487: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument.
result = self._slow_forward(*input, **kwargs)
Traceback (most recent call last):
File "/home/wtambellini/repos/pytext/pytext_venv/bin/pytext", line 11, in
sys.exit(main())
File "/home/wtambellini/repos/pytext/pytext_venv/lib64/python3.6/site-packages/click/core.py", line 764, in call
return self.main(*args, **kwargs)
File "/home/wtambellini/repos/pytext/pytext_venv/lib64/python3.6/site-packages/click/core.py", line 717, in main
rv = self.invoke(ctx)
File "/home/wtambellini/repos/pytext/pytext_venv/lib64/python3.6/site-packages/click/core.py", line 1137, in invoke
return _process_result(sub_ctx.command.invoke(sub_ctx))
File "/home/wtambellini/repos/pytext/pytext_venv/lib64/python3.6/site-packages/click/core.py", line 956, in invoke
return ctx.invoke(self.callback, **ctx.params)
File "/home/wtambellini/repos/pytext/pytext_venv/lib64/python3.6/site-packages/click/core.py", line 555, in invoke
return callback(*args, **kwargs)
File "/home/wtambellini/repos/pytext/pytext_venv/lib64/python3.6/site-packages/click/decorators.py", line 17, in new_func
return f(get_current_context(), *args, **kwargs)
File "/home/wtambellini/repos/pytext/pytext_venv/lib64/python3.6/site-packages/pytext/main.py", line 303, in export
export_saved_model_to_caffe2(model, output_path, output_onnx_path)
File "/home/wtambellini/repos/pytext/pytext_venv/lib64/python3.6/site-packages/pytext/workflow.py", line 130, in export_saved_model_to_caffe2
task.export(task.model, export_caffe2_path, export_onnx_path=output_onnx_path)
File "/home/wtambellini/repos/pytext/pytext_venv/lib64/python3.6/site-packages/pytext/task/task.py", line 189, in export
self.exporter.export_to_caffe2(model, export_path, export_onnx_path)
File "/home/wtambellini/repos/pytext/pytext_venv/lib64/python3.6/site-packages/pytext/exporters/exporter.py", line 192, in export_to_caffe2
export_onnx_path,
File "/home/wtambellini/repos/pytext/pytext_venv/lib64/python3.6/site-packages/pytext/utils/onnx_utils.py", line 50, in pytorch_to_caffe2
onnx.checker.check_model(onnx_model)
File "/home/wtambellini/repos/pytext/pytext_venv/lib64/python3.6/site-packages/onnx/checker.py", line 86, in check_model
C.check_model(model.SerializeToString())
onnx.onnx_cpp2py_export.checker.ValidationError: No Op or Function registered for ConstantFill with domain_version of 9
==> Context: Bad node spec: input: "75" output: "76" op_type: "ConstantFill" attribute { name: "input_as_shape" i: 1 type: INT }

Expected Results

successfull onnx export

Relevant Code

pytext train < demo/configs/docnn.json
pytext export < demo/configs/docnn.json

Note

The op ConstantFill op does not appear in the current doc :
https://github.com/onnx/onnx/blob/master/docs/Operators.md
It was shown as experimental op on some reports. Has it been retired/removed ?

cannot import name '_prctl_pr_set_pdeathsig'

Steps to reproduce

on my osx

pip install pytext-nlp
pip install caffe2

Observed Results

when I run pytext it comes out:

Traceback (most recent call last):
  File "/home/work/anaconda3/bin/pytext", line 7, in <module>
    from pytext.main import main
  File "/home/work/anaconda3/lib/python3.6/site-packages/pytext/main.py", line 28, in <module>
    from torch.multiprocessing.spawn import spawn
  File "/home/work/anaconda3/lib/python3.6/site-packages/torch/multiprocessing/spawn.py", line 8, in <module>
    from . import _prctl_pr_set_pdeathsig
ImportError: cannot import name '_prctl_pr_set_pdeathsig'

CircleCI status should reflect continuous runs, not latest PR

Steps to reproduce

Submit a PR that has failing tests
Observe that it makes the CircleCI icon on the readme red

Example usage for pretrained_embeddings_path?

Is there any documentation or examples for using pretrained embeddings?

Specifically, I've found that the config can take a pretrained_embeddings_path, which I assume is a path to a file containing embeddings- how is that file structured?

https://pytext-pytext.readthedocs-hosted.com/en/latest/configs/pytext.models.embeddings.word_embedding.WordEmbedding.Config.html

Execute your First model tutorial doesn't match Train mdoel

Steps to reproduce

in the docs:
The example used in "train your first model" is docnn for alarm intent, however the example used for "execute your first model" is using ATIS, the gap in the example categories is confusing. We should make it match the docnn results for "train your first model" because:

Users who are following the tutorials in order will expect it
Users coming to this tutorial from ATIS will have a better understanding and be less likely to get confused by the mismatch, as they have already gained some experience

implement tensorboard support via the configs

there's a use_tensorboard field, but it doesn't do anything, and there's a summary_writer that's used in the code, if it's configured, but it isn't. just need to expose these via the configs.

Stopping and resuming model training

Couldn't find in the documentation whether it is possible to stop training at some point, save the resulting model and later resume training this model, rather than starting a new model from scratch.

Seq2Label Example

Is there documentation/example on how to use pytext for seq2label tasks?

Can pytext support Chinese? If support, what should i do to train Intent-Slot model on Chinese Dataset.

"Multitask training with disjoint datasets" tutorial does now include disjoint datasets

If I understood it correctly, the tutorial shows multitask learning, but not disjoint datasets.

How to predict batch inputs?

Firstly, thanks for the library!
I have a problem that I can’t predict batch input datas, I use the pytext.create_predictor to get a predictor but looks like it can only predict one input data, for example {"raw_text": "sentence"}. I want to process batch datas like [{"raw_text": "sentence 1"}, {"raw_text": "sentence 2"}…], it is inefficient to use a for loop to call predictor, so is it possible to provide an api to predict batch?
Thx!

multi GPU problem

Problems

I had run this demo pytext train < demo/configs/distributed_docnn.json, but I got problem TypeError: can't pickle _thread.lock objects. I have inspected the code, it seems to the pickle.Pickle cause this problem.
in the main/train_model_distributed function, used to optimizing OOM, but it introduce this problem.

ENV

python: miniconda(https://conda.io/miniconda.html) 64-bit (bash installer)
others: following the Document Install From Source

CONFIG

{
  "config": {
    "task": {
      "DocClassificationTask": {
        "data_handler": {
          "columns_to_read": [
            "text",
            "doc_label"
          ],
          "train_path": "pytext/tests/data/train_data_tiny.tsv",
          "eval_path": "pytext/tests/data/test_data_tiny.tsv",
          "test_path": "pytext/tests/data/test_data_tiny.tsv"
                }
      }
    },
    "use_cuda_if_available": true,
    "distributed_world_size": 2
  }
}

Thanks a lot

find_config_class only looks for pytest Tasks

Steps to reproduce

build custom <Task>
pytext gen-dfeault-config [CustomTask]

Observed Results

Exception: Unknown task class: [CustomTask]

What happened? This could be a description, log output, etc.

I believe it's because utils.documentation_helper.find_config_class() will only look at pytext modules (

pytext/pytext/utils/documentation_helper.py

Line 145 in d998f75

if not mod_name.startswith("pytext"):

)

Expected Results

What did you expect to happen?

I was hoping to be able to generate a default config for my new Task just as I could if it were a "predefined" Task.

Trying to understand the differences between Pytext, Allennlp and Torchtext?

Thanks for the opensource. I am just trying to understand the differences between Pytext and Allennlp? Since both say they can help manage NLP experiments. What do you think on Pytext's advantages over Allennlp? Tried to find the answer in your doc but cannot. THANKS!

Running out of memory when training a text classifier

PyText crashed running a text classification task with a large dataset.

Steps to reproduce

Build custom dataset with 450k examples where the texts have 350 words on average
pytext train < config.json
where config.json is:

{
    "task": {
        "DocClassificationTask": {
            "data_handler": {
                "train_path": "datasets/train_pytext.tsv",
                "eval_path": "datasets/test_pytext.tsv",
                "test_path": "datasets/test_pytext.tsv"
            }
        },
        "distributed_world_size": 24
    }
}

Observed Results

System crash due to lack of memory. The process was running in a system with 64 CPUs and 256 GB of memory.

Expected Results

I have trained models before using pytext and smaller datasets with successful results. It was surprising to see it crash.

difference between word_feat and seq_word_feat

Forgive me that this isn't quite an issue. If there is a more appropriate forum for these kinds of questions, please let me know, and I'll close this.

But I'm wondering what the difference is between word_feat and seq_word_feat here:

pytext/pytext/config/field_config.py

Line 60 in faccba1

class FeatureConfig(ModuleConfig): # type: ignore

Any insight would be greatly appreciated.

Can't do basic POS tagging because PyText's WordTaggingTask expects int:int:string labels

I am trying to do a simple POS tagging task, for which I am using CONLL data files (without blank or commented lines), and a JSON configuration file calling WordTaggingTask per the coding documentation. (See end of issue for complete configuration file). It does not detect the labels of the dataset properly.

Steps to reproduce

Download CONLL data files (such as here).
Remove all commented lines and blank lines from the file
Create a configuration file (using the content provided below)
Launch training with pytext train config_file.json.

Observed Results

When launching the training, everything runs without error, but only 2 labels are detected (I actually only found "NoLabel" when following the execution) when around 20 are expected.

Building vocab for feature word_feat from train data only.
word_feat field's vocabulary size is 39587
Building vocab for label word_label
word_label field's vocabulary size is 2

Of course, the model cannot train on 2 labels, and I get the following at each iteration.

Stage.TRAIN
loss: 0.008701
	Per label scores                        	Precision 	Recall    	F1        	Support   

	PAD_LABEL                               	0.00      	0.00      	0.00      	0         

	Overall micro scores                    	0.00      	0.00      	0.00      	0         
	Overall macro scores                    	0.00      	0.00      	0.00      

Rank 0 worker: Running epoch for Stage.EVAL

Where could it come from?

I followed the code execution to this point:

DataHandler.preprocess(...) calls JointDataHandler.preprocess_row(...), which calls DataUtils.align_slot_labels(...) cf below

        if DatasetFieldName.WORD_LABEL_FIELD in self.labels:
            # TODO move it into word label field
            res[DatasetFieldName.WORD_LABEL_FIELD] = data_utils.align_slot_labels(
                features.token_ranges,
                row_data.get(DFColumn.WORD_LABEL),
                self.labels[DatasetFieldName.WORD_LABEL_FIELD].use_bio_labels,
            )
            res[DatasetFieldName.RAW_WORD_LABEL] = row_data.get(DFColumn.WORD_LABEL)

When looking, row_data.get(DFColumn.WORD_LABEL) contains the raw labels.

data_utils.align_slot_labels(...) then calls data_utils.parse_slot_string(...) (cf below or l.53 of data_utils.py) which systematically tries to split the labels and assumes that they are of the form int:int:string. (Which is not the case for my labels, simple strings that they are).

    slots = slots_field.split(",")
    slot_list = []
    for slot in slots:
        slot_toks = slot.split(":", 2)
        if len(slot_toks) == 3:
            curr_slot = Slot(slot_toks[2], int(slot_toks[0]), int(slot_toks[1]))
            slot_list.append(curr_slot)
    return slot_list

The snippet above returns an empty slot list, which then is converted to "NoLabel".

Expected Results

I expected pytext to properly detect simple labels and not assume they are of the shape int:int:string. If this is the expected behaviour, could you explain why?

Relevant Code

Config_file.json

{
    "task": {
        "WordTaggingTask": {
            "model": {
                "representation": {
                    "BiLSTMSlotAttention": {
                        "dropout": 0,
                        "lstm": {
                            "dropout": 0,
                            "lstm_dim": 100,
                            "num_layers": 1,
                            "bidirectional": true
                        }
                    }
                },
                "output_layer": {
                    "WordTaggingOutputLayer": {}
                }
            },
            "optimizer": {
                "type": "adam",
                "lr": "0.001"
            },
            "trainer": {
                "epochs": 2
            },
            "data_handler": {
                "columns_to_read": [
                    "other_info",
                    "text",
                    "other_info",
                    "word_label",
                    "other_info0",
                    "other_info1",
                    "other_info2",
                    "other_info3",
                    "other_info4",
                    "other_info5"
                ],
                "train_path": "../datasets/fr_gsd-ud-train.conllu",
                "eval_path": "../datasets/fr_gsd-ud-dev.conllu",
                "test_path": "../datasets/fr_gsd-ud-test.conllu",
                "train_batch_size": 128,
                "eval_batch_size": 128,
                "test_batch_size": 128
            },
            "labels": {
                "use_bio_labels": false
            }
        }
    }
}

TypeError: gen_dataset_from_path() got an unexpected keyword argument 'rank'

Environment:

Python 3.7 with Miniconda on Ubuntu 16.04, pytext-nlp==0.1.3 installed from pip.

Description

Keyword arguments rank and world_size aren't allowed to gen_dataset_from_path in pytext/data/data_handler.py, invoked through pytext/pytext/main.py:train_model

Calling the function with these as positional arguments resolves the issue.

  File "scripts/baseline", line 64, in train_hred
    trained_model, best_metric = train_model(cfg, summary_writer=summary_writer)
  File "/home/adama/miniconda3/envs/baseline/lib/python3.7/site-packages/pytext_nlp-0.1.3-py3.7.egg/pytext/workflow.py", line 71, in train_model
    trained_model, best_metric = task.train(config, rank, world_size)
  File "/home/adama/miniconda3/envs/baseline/lib/python3.7/site-packages/pytext_nlp-0.1.3-py3.7.egg/pytext/task/task.py", line 150, in train
    self.data_handler.get_train_iter(rank, world_size),
  File "/home/adama/miniconda3/envs/baseline/lib/python3.7/site-packages/pytext_nlp-0.1.3-py3.7.egg/pytext/data/data_handler.py", line 533, in get_train_iter
    self.train_path, self.train_batch_size, rank, world_size
  File "/home/adama/miniconda3/envs/baseline/lib/python3.7/site-packages/pytext_nlp-0.1.3-py3.7.egg/pytext/data/data_handler.py", line 523, in get_train_iter_from_path
    self.gen_dataset_from_path(train_path, rank=rank, world_size=world_size),
TypeError: gen_dataset_from_path() got an unexpected keyword argument 'rank'

Not able to run the pytext

I just follow the installation steps.

python3.6 -m venv pytext_venv
source pytext_venv/bin/activate
pip3.6 install pytext-nlp

I am getting following error

(pytext_venv) root@ubuntu:~/test/pytext# pytext
Traceback (most recent call last):
  File "/root/test/pytext_venv/bin/pytext", line 7, in <module>
    from pytext.main import main
  File "/root/test/pytext_venv/lib/python3.6/site-packages/pytext/__init__.py", line 11, in <module>
    from .builtin_task import register_builtin_tasks
  File "/root/test/pytext_venv/lib/python3.6/site-packages/pytext/builtin_task.py", line 5, in <module>
    from pytext.task.disjoint_multitask import DisjointMultitask
  File "/root/test/pytext_venv/lib/python3.6/site-packages/pytext/task/__init__.py", line 4, in <module>
    from .serialize import load, save
  File "/root/test/pytext_venv/lib/python3.6/site-packages/pytext/task/serialize.py", line 12, in <module>
    from .task import create_task
  File "/root/test/pytext_venv/lib/python3.6/site-packages/pytext/task/task.py", line 25, in <module>
    from pytext.metric_reporters import MetricReporter
  File "/root/test/pytext_venv/lib/python3.6/site-packages/pytext/metric_reporters/__init__.py", line 6, in <module>
    from .compositional_metric_reporter import CompositionalMetricReporter
  File "/root/test/pytext_venv/lib/python3.6/site-packages/pytext/metric_reporters/compositional_metric_reporter.py", line 17, in <module>
    from pytext.metrics.intent_slot_metrics import (
  File "/root/test/pytext_venv/lib/python3.6/site-packages/pytext/metrics/intent_slot_metrics.py", line 6, in <module>
    from typing import Any, Counter, Dict, List, NamedTuple, Optional, Sequence, Set, Tuple
ImportError: cannot import name 'Counter'

I would like to see the help page of pytext.

How do I resolve it?

CircleCI Tests don't validate Black Formatting

We are following the style of black (https://pypi.org/project/black/18.3a0/) for formatting on PyText, we should have the tests on CircleCI validate that

graph is only added to tensorboard when exporter is present in the config

it's probably best to always export the graph for viewing in tensorboard, if use_tensorboard is set.

the only problem i had when thinking about how to do this is that the add_graph function needs the dummy input; and that's only set when the exporter is defined.

so does it make sense to always have an exporter if use_tensorboard is true? i.e. some kind of simple "TensorboardExporter"?

hypothesis.errors.InvalidArgument: Invalid argument: 'min_satisfying_examples' is not a valid setting

Hello! I work on https://gitpod.io, and I'd love to try and make life easier for potential new PyText contributors by configuring Gitpod to support PyText development.

This would provide newcomers (and PyText maintainers) with free pre-configured workspaces in the cloud, allowing them to get started coding instantly, without having to first set up a complete development environment themselves.

If you like this idea, I've already started experimenting with a prototype .gitpod.yml configuration for PyText that looks like this:

tasks:
  - before: source activation_venv
    init: >
        pip install torch &&
        ./install_deps &&
        pip install -U pytest pytest-cov

This is supposed to automatically pre-configure Gitpod workspaces for PyText development.

However, I'm facing this issue in the resulting environment:

Steps to reproduce

Open this PyText workspace snapshot (it has a fresh PyText clone, and the setup steps were run)
Try running pytest

Observed Results

(pytext_venv) gitpod /workspace/pytext $ pytest
================================================================ test session starts ================================================================
platform linux -- Python 3.6.6, pytest-4.1.1, py-1.7.0, pluggy-0.8.1
hypothesis profile 'default' -> database=DirectoryBasedExampleDatabase('/workspace/pytext/.hypothesis/examples')
rootdir: /workspace/pytext, inifile: pytest.ini
plugins: cov-2.6.1, hypothesis-4.0.1
collected 91 items / 2 errors

====================================================================== ERRORS =======================================================================
________________________________________ ERROR collecting pytext/exporters/test/text_model_exporter_test.py _________________________________________
pytext/exporters/test/text_model_exporter_test.py:8: in <module>
    import caffe2.python.hypothesis_test_util as hu
pytext_venv/lib/python3.6/site-packages/caffe2/python/hypothesis_test_util.py:76: in <module>
    verbosity=hypothesis.Verbosity.verbose))
pytext_venv/lib/python3.6/site-packages/hypothesis/_settings.py:183: in __init__
    "Invalid argument: %r is not a valid setting" % (name,)
E   hypothesis.errors.InvalidArgument: Invalid argument: 'min_satisfying_examples' is not a valid setting
------------------------------------------------------------------ Captured stdout ------------------------------------------------------------------
net_drawer will not run correctly. Please install the correct dependencies.
__________________________________________________ ERROR collecting pytext/models/test/crf_test.py __________________________________________________
pytext/models/test/crf_test.py:4: in <module>
    import caffe2.python.hypothesis_test_util as hu
pytext_venv/lib/python3.6/site-packages/caffe2/python/hypothesis_test_util.py:76: in <module>
    verbosity=hypothesis.Verbosity.verbose))
pytext_venv/lib/python3.6/site-packages/hypothesis/_settings.py:183: in __init__
    "Invalid argument: %r is not a valid setting" % (name,)
E   hypothesis.errors.InvalidArgument: Invalid argument: 'min_satisfying_examples' is not a valid setting
================================================================= warnings summary ==================================================================
pytext/loss/loss.py:95
  /workspace/pytext/pytext/loss/loss.py:95: DeprecationWarning: invalid escape sequence \l
    """

pytext/config/test/component_test.py:51
  /workspace/pytext/pytext/config/test/component_test.py:51: PytestWarning: cannot collect test class 'TestConfig' because it has a __init__ constructor
    class TestConfig(ConfigBase):

-- Docs: https://docs.pytest.org/en/latest/warnings.html
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! Interrupted: 2 errors during collection !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
======================================================== 2 warnings, 2 error in 4.69 seconds ========================================================

Expected Results

The tests should pass, but I don't understand the above errors or how to fix them. Maybe I'm doing something wrong?

pytext export (Context: Bad node spec) anybody can help

Steps to reproduce

Observed Results

What happened? This could be a description, log output, etc.

Expected Results

What did you expect to happen?

Relevant Code

// TODO(you): code here to reproduce the problem

<repository>

Steps to reproduce

Observed Results

What happened? This could be a description, log output, etc.

Expected Results

What did you expect to happen?

Relevant Code

// TODO(you): code here to reproduce the problem

Pytext segfaults when running the example

Steps to reproduce

git clone [email protected]:facebookresearch/pytext.git
conda create -n pytext -y
conda activate pytext
conda intall pip
pip install pytext-nlp
cd pytext
pytext train < demo/configs/docnn.json
pytext test < demo/configs/docnn.json
pytext export --output-path exported_model.c2 < demo/configs/docnn.json

Observed Results

(pytext) ➜  pytext git:(master) ✗ pytext export --output-path exported_model.c2 < demo/configs/docnn.json
No config file specified, reading from stdin
Exporting /tmp/model.pt to caffe2 file: exported_model.c2 and onnx file: /tmp/model.onnx
Loading model from /tmp/model.pt...
Task parameters:

{'data_handler': {'columns_to_read': ['doc_label', 'text', 'dict_feat'],
                  'eval_batch_size': 128,
                  'eval_path': 'tests/data/test_data_tiny.tsv',
                  'max_seq_len': -1,
                  'shuffle': True,
                  'sort_within_batch': True,
                  'test_batch_size': 128,
                  'test_path': 'tests/data/test_data_tiny.tsv',
                  'train_batch_size': 128,
                  'train_path': 'tests/data/train_data_tiny.tsv'},
 'exporter': None,
 'features': {'char_feat': None,
              'dense_feat': None,
              'dict_feat': None,
              'freeze': False,
              'load_path': None,
              'pretrained_model_embedding': None,
              'save_path': None,
              'shared_module_key': None,
              'word_feat': {'embed_dim': 100,
                            'embedding_init_range': None,
                            'embedding_init_strategy': 'random',
                            'export_input_names': ['tokens_vals'],
                            'freeze': False,
                            'lowercase_tokens': True,
                            'min_freq': 1,
                            'mlp_layer_dims': [],
                            'pretrained_embeddings_path': '',
                            'vocab_file': '',
                            'vocab_from_all_data': False,
                            'vocab_from_pretrained_embeddings': False,
                            'vocab_from_train_data': True,
                            'vocab_size': 0}},
 'featurizer': {'SimpleFeaturizer': {'convert_to_bytes': False,
                                     'lowercase_tokens': True,
                                     'sentence_markers': None,
                                     'split_regex': '\\s+'}},
 'labels': {'export_output_names': ['doc_scores'],
            'label_weights': {},
            'target_prob': False},
 'metric_reporter': {'output_path': '/tmp/test_out.txt'},
 'model': {'decoder': {'freeze': False,
                       'hidden_dims': [],
                       'load_path': None,
                       'save_path': None,
                       'shared_module_key': None},
           'output_layer': {'freeze': False,
                            'load_path': None,
                            'loss': {'CrossEntropyLoss': {}},
                            'save_path': None,
                            'shared_module_key': None},
           'representation': {'BiLSTMDocAttention': {'dropout': 0.4,
                                                     'freeze': False,
                                                     'load_path': None,
                                                     'lstm': {'bidirectional': True,
                                                              'dropout': 0.4,
                                                              'freeze': False,
                                                              'load_path': None,
                                                              'lstm_dim': 32,
                                                              'num_layers': 1,
                                                              'save_path': None,
                                                              'shared_module_key': None},
                                                     'mlp_decoder': None,
                                                     'pooling': {'SelfAttention': {'attn_dimension': 64,
                                                                                   'dropout': 0.4}},
                                                     'save_path': None,
                                                     'shared_module_key': None}}},
 'optimizer': {'lr': 0.001,
               'momentum': 0.0,
               'type': 'adam',
               'weight_decay': 1e-05},
 'scheduler': {'T_max': 1000,
               'cooldown': 0,
               'cut_frac': 0.1,
               'eta_min': 0.0,
               'gamma': 0.1,
               'lm_gradual_unfreezing': True,
               'lm_lr_multiplier': 1.0,
               'lm_use_per_layer_lr': False,
               'non_pretrained_param_groups': 2,
               'patience': 5,
               'ratio': 32,
               'step_size': 30,
               'threshold': 0.0001,
               'threshold_is_absolute': False,
               'type': 'none'},
 'trainer': {'early_stop_after': 0,
             'epochs': 10,
             'max_clip_norm': None,
             'random_seed': 0,
             'report_train_metrics': True}}
creating field TextFeatureField
creating field DocLabelField

Loading data...
load_path is not a config of embedding, skipping
save_path is not a config of embedding, skipping
freeze is not a config of embedding, skipping
shared_module_key is not a config of embedding, skipping
dict_feat is not a config of embedding, skipping
char_feat is not a config of embedding, skipping
pretrained_model_embedding is not a config of embedding, skipping
dense_feat is not a config of embedding, skipping
Model has 2 param groups (1 from embedding module) for optimizer
Saving caffe2 model to: exported_model.c2
Saving onnx model to: /tmp/model.onnx
/data/erippeth/anaconda3/envs/pytext/lib/python3.7/site-packages/torch/nn/modules/module.py:487: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument.
  result = self._slow_forward(*input, **kwargs)
*** Error in `/data/erippeth/anaconda3/envs/pytext/bin/python': free(): invalid pointer: 0x00007ffd338d6368 ***
======= Backtrace: =========
/lib64/libc.so.6(+0x81489)[0x7f1473a2c489]
/data/erippeth/anaconda3/envs/pytext/lib/python3.7/site-packages/onnx/onnx_cpp2py_export.cpython-37m-x86_64-linux-gnu.so(+0xe3155)[0x7f145c7fd155]
/data/erippeth/anaconda3/envs/pytext/lib/python3.7/site-packages/onnx/onnx_cpp2py_export.cpython-37m-x86_64-linux-gnu.so(+0x78004)[0x7f145c792004]
/data/erippeth/anaconda3/envs/pytext/lib/python3.7/site-packages/onnx/onnx_cpp2py_export.cpython-37m-x86_64-linux-gnu.so(+0xe8c67)[0x7f145c802c67]
/data/erippeth/anaconda3/envs/pytext/lib/python3.7/site-packages/onnx/onnx_cpp2py_export.cpython-37m-x86_64-linux-gnu.so(+0xe9fb4)[0x7f145c803fb4]
/data/erippeth/anaconda3/envs/pytext/lib/python3.7/site-packages/onnx/onnx_cpp2py_export.cpython-37m-x86_64-linux-gnu.so(+0xe4365)[0x7f145c7fe365]
/data/erippeth/anaconda3/envs/pytext/lib/python3.7/site-packages/onnx/onnx_cpp2py_export.cpython-37m-x86_64-linux-gnu.so(+0xeb315)[0x7f145c805315]
/data/erippeth/anaconda3/envs/pytext/lib/python3.7/site-packages/onnx/onnx_cpp2py_export.cpython-37m-x86_64-linux-gnu.so(+0x48e3e)[0x7f145c762e3e]
/data/erippeth/anaconda3/envs/pytext/lib/python3.7/site-packages/onnx/onnx_cpp2py_export.cpython-37m-x86_64-linux-gnu.so(+0x49b33)[0x7f145c763b33]
/data/erippeth/anaconda3/envs/pytext/lib/python3.7/site-packages/onnx/onnx_cpp2py_export.cpython-37m-x86_64-linux-gnu.so(+0x4b7d3)[0x7f145c7657d3]
/data/erippeth/anaconda3/envs/pytext/lib/python3.7/site-packages/onnx/onnx_cpp2py_export.cpython-37m-x86_64-linux-gnu.so(+0x4c7b6)[0x7f145c7667b6]
/data/erippeth/anaconda3/envs/pytext/lib/python3.7/site-packages/onnx/onnx_cpp2py_export.cpython-37m-x86_64-linux-gnu.so(+0x206f3)[0x7f145c73a6f3]
/data/erippeth/anaconda3/envs/pytext/lib/python3.7/site-packages/onnx/onnx_cpp2py_export.cpython-37m-x86_64-linux-gnu.so(+0x3a86e)[0x7f145c75486e]
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7f14741a8000-7f14741a9000 r--p 0001f000 00:2c 433809370747               /autofs/nas1/data/erippeth/anaconda3/envs/pytext/lib/python3.7/lib-dynload/_ctypes.cpython-37m-x86_64-linux-gnu.so
7f14741a9000-7f14741ad000 rw-p 00020000 00:2c 433809370747               /autofs/nas1/data/erippeth/anaconda3/envs/pytext/lib/python3.7/lib-dynload/_ctypes.cpython-37m-x86_64-linux-gnu.so
7f14741ad000-7f14741b4000 r--s 00000000 09:05 393577                     /usr/lib64/gconv/gconv-modules.cache
7f14741b4000-7f14741b5000 rw-p 00000000 00:00 0
7f14741b5000-7f14741b6000 r--p 00021000 09:05 393278                     /usr/lib64/ld-2.17.so
7f14741b6000-7f14741b7000 rw-p 00022000 09:05 393278                     /usr/lib64/ld-2.17.so
7f14741b7000-7f14741b8000 rw-p 00000000 00:00 0
7ffd338b1000-7ffd338db000 rw-p 00000000 00:00 0                          [stack]
7ffd339ca000-7ffd339cc000 r-xp 00000000 00:00 0                          [vdso]
ffffffffff600000-ffffffffff601000 r-xp 00000000 00:00 0                  [vsyscall]
[1]    20013 abort (core dumped)  pytext export --output-path exported_model.c2 < demo/configs/docnn.json

Expected Results

Training a model per the documents

Error calling pytext from command line

I'm getting a syntax error at the very first step after installation of pytext. whether by calling pytext from command line or importing it in python shell I get the following error :

$ pytext

WARNING:root:This caffe2 python run does not have GPU support. Will run in CPU only mode.
Traceback (most recent call last):
  File "/home/ml/.virtualenvs/caffe2-pytext/bin/pytext", line 7, in <module>
    from pytext.main import main
  File "/home/ml/.virtualenvs/caffe2-pytext/lib/python3.5/site-packages/pytext/__init__.py", line 11, in <module>
    from .builtin_task import register_builtin_tasks
  File "/home/ml/.virtualenvs/caffe2-pytext/lib/python3.5/site-packages/pytext/builtin_task.py", line 4, in <module>
    from pytext.config.component import register_tasks
  File "/home/ml/.virtualenvs/caffe2-pytext/lib/python3.5/site-packages/pytext/config/__init__.py", line 4, in <module>
    from .pytext_config import ConfigBase, PyTextConfig, TestConfig  # noqa
  File "/home/ml/.virtualenvs/caffe2-pytext/lib/python3.5/site-packages/pytext/config/pytext_config.py", line 50
    raise TypeError(f"Failed to specify {unspecified_fields} for {type(self)}")
                                                                             ^
SyntaxError: invalid syntax

any hints as to whats the issue?

Find a way to make CircleCI artifacts get posted to PR's

Right now, when CircleCI runs, we produce a couple of very useful html artifacts, in the form of a code coverage page, and the entire Documentation. It would be great if links to these artifacts were posted directly on the PR after the build/test was complete, allowing people to see the generated docs/code test coverage directly

can't store tensors in the "context"

in say trainer.py, just after the model is run, put something like this:

logits = model(*inputs)
...
context["loss"] = logits

this gives an error:

  File "/home/noon/tools/miniconda3/envs/pytext/lib/python3.7/site-packages/torch/tensor.py", line 18, in __deepcopy__
    raise RuntimeError("Only Tensors created explicitly by the user "
RuntimeError: Only Tensors created explicitly by the user (graph leaves) support the deepcopy protocol at the moment

coming from the use of deep_copy in the ./pytext/data/data_handler.py here:

            # Due to a limitation in PyTorch's distributed training backend that
            # enforces that all the parallel workers to have the same number of
            # batches we keep yielding the last batch until the requested total
            # number of batches is fullfilled
            if i == num_batches - 1:
                context = deepcopy(context)
                context.update({BatchContext.IGNORE_LOSS: True})
                for _j in range(num_batches, self.total_num_batches):
                    yield (input, target, context)

i'm trying to use the context to pass around some tensors that i want to use as part of other calculations/visualisations; but it doesn't seem supported.

am i missing some better way to do this?

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