translation/OpenNMT-py/docs/source/FAQ.md

All the example YAML configurations are partial. To get an overview of what this YAML configuration is you can start by reading the Quickstart section.

How do I use Pretrained embeddings (e.g. GloVe)?

This is handled in the initial steps of the onmt_train execution.

Pretrained embeddings can be configured in the main YAML configuration file.

Example

Get GloVe files:

mkdir "glove_dir"
wget http://nlp.stanford.edu/data/glove.6B.zip
unzip glove.6B.zip -d "glove_dir"

Adapt the configuration:

# <your_config>.yaml

<Your data config...>

...

# this means embeddings will be used for both encoder and decoder sides
both_embeddings: glove_dir/glove.6B.100d.txt
# to set src and tgt embeddings separately:
# src_embeddings: ...
# tgt_embeddings: ...

# supported types: GloVe, word2vec
embeddings_type: "GloVe"

# word_vec_size need to match with the pretrained embeddings dimensions
word_vec_size: 100

Train:

onmt_train -config <your_config>.yaml

Notes:

the matched embeddings will be saved at <save_data>.enc_embeddings.pt and <save_data>.dec_embeddings.pt;
additional flags freeze_word_vecs_enc and freeze_word_vecs_dec are available to freeze the embeddings.

How do I use the Transformer model?

The transformer model is very sensitive to hyperparameters. To run it effectively you need to set a bunch of different options that mimic the Google setup. We have confirmed the following configuration can replicate their WMT results.

<data configuration>
...

# General opts
save_model: foo
save_checkpoint_steps: 10000
valid_steps: 10000
train_steps: 200000

# Batching
queue_size: 10000
bucket_size: 32768
world_size: 4
gpu_ranks: [0, 1, 2, 3]
batch_type: "tokens"
batch_size: 4096
valid_batch_size: 8
max_generator_batches: 2
accum_count: [4]
accum_steps: [0]

# Optimization
model_dtype: "fp32"
optim: "adam"
learning_rate: 2
warmup_steps: 8000
decay_method: "noam"
adam_beta2: 0.998
max_grad_norm: 0
label_smoothing: 0.1
param_init: 0
param_init_glorot: true
normalization: "tokens"

# Model
encoder_type: transformer
decoder_type: transformer
position_encoding: true
enc_layers: 6
dec_layers: 6
heads: 8
rnn_size: 512
word_vec_size: 512
transformer_ff: 2048
dropout_steps: [0]
dropout: [0.1]
attention_dropout: [0.1]

Here are what the most important parameters mean:

param_init_glorot & param_init 0: correct initialization of parameters;
position_encoding: add sinusoidal position encoding to each embedding;
optim adam, decay_method noam, warmup_steps 8000: use special learning rate;
batch_type tokens, normalization tokens: batch and normalize based on number of tokens and not sentences;
accum_count 4: compute gradients based on four batches;
label_smoothing 0.1: use label smoothing loss.

Do you support multi-gpu?

First you need to make sure you export CUDA_VISIBLE_DEVICES=0,1,2,3.

If you want to use GPU id 1 and 3 of your OS, you will need to export CUDA_VISIBLE_DEVICES=1,3

Both -world_size and -gpu_ranks need to be set. E.g. -world_size 4 -gpu_ranks 0 1 2 3 will use 4 GPU on this node only.

Warning - Deprecated

Multi-node distributed training is not properly implemented in OpenNMT-py 2.0 yet.

If you want to use 2 nodes with 2 GPU each, you need to set -master_ip and -master_port, and

-world_size 4 -gpu_ranks 0 1: on the first node
-world_size 4 -gpu_ranks 2 3: on the second node
-accum_count 2: This will accumulate over 2 batches before updating parameters.

If you use a regular network card (1 Gbps) then we suggest to use a higher -accum_count to minimize the inter-node communication.

Note:

In the legacy version, when training on several GPUs, you couldn't have them in 'Exclusive' compute mode (nvidia-smi -c 3).

The multi-gpu setup relied on a Producer/Consumer setup. This setup means there will be 2<n_gpu> + 1 processes spawned, with 2 processes per GPU, one for model training and one (Consumer) that hosts a Queue of batches that will be processed next. The additional process is the Producer, creating batches and sending them to the Consumers. This setup is beneficial for both wall time and memory, since it loads data shards 'in advance', and does not require to load it for each GPU process.

The new codebase allows GPUs to be in exclusive mode, because batches are moved to the device later in the process. Hence, there is no 'producer' process on each GPU.

How can I ensemble Models at inference?

You can specify several models in the onmt_translate command line: -model model1_seed1 model2_seed2 Bear in mind that your models must share the same target vocabulary.

How can I weight different corpora at training?

This is naturally embedded in the data configuration format introduced in OpenNMT-py 2.0. Each entry of the data configuration will have its own weight. When building batches, we'll sequentially take weight example from each corpus.

Note: don't worry about batch homogeneity/heterogeneity, the pooling mechanism is here for that reason. Instead of building batches one at a time, we will load pool_factor of batches worth of examples, sort them by length, build batches and then yield them in a random order.

Example

In the following example, we will sequentially sample 7 examples from corpus_1, and 3 examples from corpus_2, and so on:

# <your_config>.yaml

...

# Corpus opts:
data:
    corpus_1:
        path_src: toy-ende/src-train1.txt
        path_tgt: toy-ende/tgt-train1.txt
        weight: 7
    corpus_2:
        path_src: toy-ende/src-train1.txt
        path_tgt: toy-ende/tgt-train1.txt
        weight: 3
    valid:
        path_src: toy-ende/src-val.txt
        path_tgt: toy-ende/tgt-val.txt
...

How can I apply on-the-fly tokenization and subword regularization when training?

This is naturally embedded in the data configuration format introduced in OpenNMT-py 2.0. Each entry of the data configuration will have its own transforms. transforms basically is a list of functions that will be applied sequentially to the examples when read from file.

Example

This example applies sentencepiece tokenization with pyonmttok, with nbest=20 and alpha=0.1.

# <your_config>.yaml

...

# Tokenization options
src_subword_type: sentencepiece
src_subword_model: examples/subword.spm.model
tgt_subword_type: sentencepiece
tgt_subword_model: examples/subword.spm.model

# Number of candidates for SentencePiece sampling
subword_nbest: 20
# Smoothing parameter for SentencePiece sampling
subword_alpha: 0.1
# Specific arguments for pyonmttok
src_onmttok_kwargs: "{'mode': 'none', 'spacer_annotate': True}"
tgt_onmttok_kwargs: "{'mode': 'none', 'spacer_annotate': True}"

# Corpus opts:
data:
    corpus_1:
        path_src: toy-ende/src-train1.txt
        path_tgt: toy-ende/tgt-train1.txt
        transforms: [onmt_tokenize]
        weight: 1
    valid:
        path_src: toy-ende/src-val.txt
        path_tgt: toy-ende/tgt-val.txt
        transforms: [onmt_tokenize]
...

Other tokenization methods and transforms are readily available. See the dedicated docs for more details.

What are the readily available on-the-fly data transforms?

It's your lucky day! We already embedded several transforms that can be used easily.

Note: all the details about every flag and options for each transform can be found in the train section.

General purpose

Filter examples by length

Transform name: filtertoolong

Class: onmt.transforms.misc.FilterTooLongTransform

The following options can be added to the configuration :

src_seq_length: maximum source sequence length;
tgt_seq_length: maximum target sequence length.

Add custom prefix to examples

Transform name: prefix

Class: onmt.transforms.misc.PrefixTransform

For each dataset that the prefix transform is applied to, you can set the additional src_prefix and tgt_prefix parameters in its data configuration:

data:
    corpus_1:
        path_src: toy-ende/src-train1.txt
        path_tgt: toy-ende/tgt-train1.txt
        transforms: [prefix]
        weight: 1
        src_prefix: __some_src_prefix__
        tgt_prefix: __some_tgt_prefix__

Tokenization

Common options for the tokenization transforms are the following:

src_subword_model: path of source side (or both if shared) subword model;
tgt_subword_model: path of target side subword model;
src_subword_nbest: number of candidates for subword regularization (sentencepiece), source side;
tgt_subword_nbest: number of candidates for subword regularization (sentencepiece), target_side;
src_subword_alpha: smoothing parameter for sentencepiece regularization / dropout probability for BPE, source side;
tgt_subword_alpha: smoothing parameter for sentencepiece regularization / dropout probability for BPE, target side.

OpenNMT Tokenizer

Transform name: onmt_tokenize

Class: onmt.transforms.tokenize.ONMTTokenizerTransform

Additional options are available:

src_subword_type: type of subword model for source side (from ["none", "sentencepiece", "bpe"]);
tgt_subword_type: type of subword model for target side (from ["none", "sentencepiece", "bpe"]);
src_onmttok_kwargs: additional kwargs for pyonmttok Tokenizer class, source side;
tgt_onmttok_kwargs: additional kwargs for pyonmttok Tokenizer class, target side.

SentencePiece

Transform name: sentencepiece

Class: onmt.transforms.tokenize.SentencePieceTransform

The src_subword_model and tgt_subword_model should be valid sentencepiece models.

BPE (subword-nmt)

Transform name: bpe

Class: onmt.transforms.tokenize.BPETransform

The src_subword_model and tgt_subword_model should be valid BPE models.

BART-style noise

BART-style noise is composed of several parts, as described in BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension.

These different types of noise can be controlled with the following options:

permute_sent_ratio: proportion of sentences to permute (default boundaries are ".", "?" and "!");
rotate_ratio: proportion of inputs to permute;
insert_ratio: proportion of additional random tokens to insert;
random_ratio: proportion of tokens to replace with random;
mask_ratio: proportion of words/subwords to mask;
mask_length: length of masking window (from ["subword", "word", "span-poisson"]);
poisson_lambda: $\lambda$ value for Poisson distribution to sample span length (in the case of mask_length set to span-poisson);
replace_length: when masking N tokens, replace with 0, 1, " "or N tokens. (set to -1 for N).

SwitchOut and sampling

SwitchOut

Transform name: switchout

Class: onmt.transforms.sampling.SwitchOutTransform

Options:

switchout_temperature: sampling temperature for SwitchOut.

Drop some tokens

Transform name: tokendrop

Class: onmt.transforms.sampling.TokenDropTransform

Options:

tokendrop_temperature: sampling temperature for token deletion.

Mask some tokens

Transform name: tokenmask

Class: onmt.transforms.sampling.TokenMaskTransform

Options:

tokenmask_temperature: sampling temperature for token masking.

How can I create custom on-the-fly data transforms?

The code is easily extendable with custom transforms inheriting from the Transform base class.

You can for instance have a look at the FilterTooLongTransform class as a template:

@register_transform(name='filtertoolong')
class FilterTooLongTransform(Transform):
    """Filter out sentence that are too long."""

    @classmethod
    def add_options(cls, parser):
        """Avalilable options relate to this Transform."""
        group = parser.add_argument_group("Transform/Filter")
        group.add("--src_seq_length", "-src_seq_length", type=int, default=200,
                  help="Maximum source sequence length.")
        group.add("--tgt_seq_length", "-tgt_seq_length", type=int, default=200,
                  help="Maximum target sequence length.")

    def _parse_opts(self):
        self.src_seq_length = self.opts.src_seq_length
        self.tgt_seq_length = self.opts.tgt_seq_length

    def apply(self, example, is_train=False, stats=None, **kwargs):
        """Return None if too long else return as is."""
        if (len(example['src']) > self.src_seq_length or
                len(example['tgt']) > self.tgt_seq_length):
            if stats is not None:
                stats.update(FilterTooLongStats())
            return None
        else:
            return example

    def _repr_args(self):
        """Return str represent key arguments for class."""
        return '{}={}, {}={}'.format(
            'src_seq_length', self.src_seq_length,
            'tgt_seq_length', self.tgt_seq_length
        )

Methods:

add_options allows to add custom options that would be necessary for the transform configuration;
_parse_opts allows to parse options introduced in add_options when initialize;
apply is where the transform happens;
_repr_args is for clean logging purposes.

As you can see, there is the @register_transform wrapper before the class definition. This will allow for the class to be automatically detected (if put in the proper transforms folder) and usable in your training configurations through its name argument.

You could also collect statistics for your custom transform by creating a class inheriting ObservableStats:

class FilterTooLongStats(ObservableStats):
    """Runing statistics for FilterTooLongTransform."""
    __slots__ = ["filtered"]

    def __init__(self):
        self.filtered = 1

    def update(self, other: "FilterTooLongStats"):
        self.filtered += other.filtered

NOTE:

Add elements to keep track in the __init__ and also __slot__ to make it lightweight;
Supply update logic in update method;
(Optional) override __str__ to change default log message format;
Instantiate and passing the statistic object in the apply method of the corresponding transform class;
statistics will be gathered per corpus per worker, but only first worker will report for its shard by default.

The example argument of apply is a dict of the form:

{
    "src": <source string>,
    "tgt": <target string>,
    "align": <alignment pharaoh string> # optional
}

This is defined in onmt.inputters.corpus.ParallelCorpus.load. This class is not easily extendable for now but it can be considered for future developments. For instance, we could create some CustomParallelCorpus class that would handle other kind of inputs.

Can I get word alignments while translating?

Raw alignments from averaging Transformer attention heads

Currently, we support producing word alignment while translating for Transformer based models. Using -report_align when calling translate.py will output the inferred alignments in Pharaoh format. Those alignments are computed from an argmax on the average of the attention heads of the second to last decoder layer. The resulting alignment src-tgt (Pharaoh) will be pasted to the translation sentence, separated by |||. Note: The second to last default behaviour was empirically determined. It is not the same as the paper (they take the penultimate layer), probably because of slight differences in the architecture.

alignments use the standard "Pharaoh format", where a pair i-j indicates the i_th word of source language is aligned to j_th word of target language.
Example: {'src': 'das stimmt nicht !'; 'output': 'that is not true ! ||| 0-0 0-1 1-2 2-3 1-4 1-5 3-6'}
Using the-tgt option when calling translate.py, we output alignments between the source and the gold target rather than the inferred target, assuming we're doing evaluation.
To convert subword alignments to word alignments, or symetrize bidirectional alignments, please refer to the lilt scripts.

Supervised learning on a specific head

The quality of output alignments can be further improved by providing reference alignments while training. This will invoke multi-task learning on translation and alignment. This is an implementation based on the paper Jointly Learning to Align and Translate with Transformer Models.

The data need to be preprocessed with the reference alignments in order to learn the supervised task. The reference alignment file(s) can for instance be generated by GIZA++ or fast_align.

In order to learn the supervised task, you can set for each dataset the path of its alignment file in the YAML configuration file:

<your_config>.yaml

...

# Corpus opts:
data:
    corpus_1:
        path_src: toy-ende/src-train1.txt
        path_tgt: toy-ende/tgt-train1.txt
        # src - tgt alignments in pharaoh format
        path_align: toy-ende/src-tgt.align
        transforms: []
        weight: 1
    valid:
        path_src: toy-ende/src-val.txt
        path_tgt: toy-ende/tgt-val.txt
        transforms: []

...

Notes:

Most of the transforms are for now incompatible with the joint alignment learning pipeline, because most of them make modifications at the token level, hence alignments would be made invalid.
There should be no blank lines in the alignment files provided.

Training options to learn such alignments are:

-lambda_align: set the value > 0.0 to enable joint align training, the paper suggests 0.05;
-alignment_layer: indicate the index of the decoder layer;
-alignment_heads: number of alignment heads for the alignment task - should be set to 1 for the supervised task, and preferably kept to default (or same as num_heads) for the average task;
-full_context_alignment: do full context decoder pass (no future mask) when computing alignments. This will slow down the training (~12% in terms of tok/s) but will be beneficial to generate better alignment.

How can I update a checkpoint's vocabulary?

New vocabulary can be used to continue training from a checkpoint. Existing vocabulary embeddings will be mapped to the new vocabulary, and new vocabulary tokens will be initialized as usual.

Run onmt_build_vocab as usual with the new dataset. New vocabulary files will be created.

Training options to perform vocabulary update are:

-update_vocab: set this option
-reset_optim: set the value to "states"
-train_from: checkpoint path

How can I use source word features?

Extra information can be added to the words in the source sentences by defining word features.

Features should be defined in a separate file using blank spaces as a separator and with each row corresponding to a source sentence. An example of the input files:

data.src

however, according to the logs, she is hard-working.

feat.txt

A C C C C A A B

Prior tokenization is not necessary, features will be inferred by using the FeatInferTransform transform if tokenization has been applied.

No previous tokenization:

SRC: this is a test.
FEATS: A A A B
TOKENIZED SRC: this is a test ￭.
RESULT: A A A B <null>

Previously tokenized:

SRC: this is a test ￭.
FEATS: A A A B A
RESULT: A A A B A

Notes

FilterFeatsTransform and FeatInferTransform are required in order to ensure the functionality.
Not possible to do shared embeddings (at least with feat_merge: concat method)

Sample config file:

data:
    dummy:
        path_src: data/train/data.src
        path_tgt: data/train/data.tgt
        src_feats:
            feat_0: data/train/data.src.feat_0
            feat_1: data/train/data.src.feat_1
        transforms: [filterfeats, onmt_tokenize, inferfeats, filtertoolong]
        weight: 1
    valid:
        path_src: data/valid/data.src
        path_tgt: data/valid/data.tgt
        src_feats:
            feat_0: data/valid/data.src.feat_0
            feat_1: data/valid/data.src.feat_1
        transforms: [filterfeats, onmt_tokenize, inferfeats]

# Transform options
reversible_tokenization: "joiner"
prior_tokenization: true

# Vocab opts
src_vocab: exp/data.vocab.src
tgt_vocab: exp/data.vocab.tgt
src_feats_vocab: 
    feat_0: exp/data.vocab.feat_0
    feat_1: exp/data.vocab.feat_1
feat_merge: "sum"

During inference you can pass features by using the --src_feats argument. src_feats is expected to be a Python like dict, mapping feature names with their data file.

{'feat_0': '../data.txt.feats0', 'feat_1': '../data.txt.feats1'}

Important note! During inference, input sentence is expected to be tokenized. Therefore feature inferring should be handled prior to running the translate command. Example:

python translate.py -model model_step_10.pt -src ../data.txt.tok -output ../data.out --src_feats "{'feat_0': '../data.txt.feats0', 'feat_1': '../data.txt.feats1'}"

When using the Transformer architecture make sure the following options are appropriately set:

src_word_vec_size and tgt_word_vec_size or word_vec_size
feat_merge: how to handle features vecs
feat_vec_size and maybe feat_vec_exponent

How can I set up a translation server ?

A REST server was implemented to serve OpenNMT-py models. A discussion is opened on the OpenNMT forum: discussion link.

I. How it works?

The idea behind the translation server is to make a entry point for translation with multiple models. The server will receive natural text input, tokenize it, translate it following the decoding parameters, detokenize the result and return natural text output.

A server configuration file (./available_models/conf.json) is required. It contains the path of the model checkpoint, the path of tokenizer's data along with other inference parameters.

Configuration:

models_root: (opt) folder containing model checkpoints, [default: ./available_models]
models: list of objects such as :
- id: (opt) manually assign an id (int), [default: value from counter]
- name: (opt) assing a name (str)
- model: (required) path to checkpoint file i.e. *.pt
- timeout: (opt) interval (seconds) before unloading, reset at each translation using the model
- load: (opt) whether to load the model at start [default: False]
- on_timeout: (opt) what to do on timeout: unload removes everything; to_cpu transfer the model to RAM (from GPU memory) this is faster to reload but takes RAM.
- opt: (opt) dict of translation options (see method translate_opts in ./opts.py)
- tokenizer: (opt) set tokenizer options (if any), such as:
  - type: (str) value in {sentencepiece, pyonmttok}.
  - model: (str) path to tokenizer model
- ct2_translator_args and ct2_translate_batch_args: (opt) CTranslate2 parameters to use CTranslate2 inference engine. Parameters appearing simultaneously in opt and ct2_(...)_args must be identical.
- ct2_model: (opt) CTranslate2 model path.

Example

{
    "models_root": "./available_models",
    "models": [
        {   
            "id": 100,
            "model": "model_0.pt",
            "timeout": 600,
            "on_timeout": "to_cpu",
            "load": true,
            "opt": {
                "gpu": 0,
                "beam_size": 5
            },  
            "tokenizer": {
                "type": "sentencepiece",
                "model": "wmtenfr.model"
            }   
        },{ 
            "model": "model_0.light.pt",
            "timeout": -1, 
            "on_timeout": "unload",
            "model_root": "../other_models",
            "opt": {
                "batch_size": 1,
                "beam_size": 10
            }   
        }   
    ]   
}

II. How to start the server without Docker ?

0. Get the code

The translation server has been merged into onmt-py master branch.
Keep in line with master for last fix / improvements.

1. Install `flask`

pip install flask

2. Put some models

mkdir available_models/
cp $path_to_my_model available_models

3. Start the server

export IP="0.0.0.0"
export PORT=5000
export URL_ROOT="/translator"
export CONFIG="./available_models/conf.json"

# NOTE that these parameters are optionnal
# here, we explicitely set to default values
python server.py --ip $IP --port $PORT --url_root $URL_ROOT --config $CONFIG

III. How to start the server with Docker ?

Add the following libraries a requirement file requirements.docker.txt.

ConfigArgParse==1.2.3
Flask==1.1.2
Flask-Cors==3.0.10
pyonmttok==1.22.1
torchtext==0.4.0
waitress==1.4.4

Create a Dockerfile

FROM pytorch/pytorch:1.6.0-cuda10.1-cudnn7-runtime
WORKDIR /usr/src/app

COPY requirements.docker.txt ./requirements.txt
RUN pip install --no-cache-dir -r requirements.txt

# You can copy or use a docker volume, especially for the model and config data
COPY server.py ./
COPY tools ./tools
COPY available_models ./available_models
COPY onmt ./onmt

CMD ["python", "./server.py"]

Build the image and run container

docker build -t opennmt_server .
docker run -it --rm -p 5000:5000 opennmt_server

IV. How to use the API ?

This section contains a fex examples of the API. For details on all routes, see ./bin/server.py.

0. Set the hostname

export HOST="127.0.0.1"

1. List models

curl http://$HOST:$PORT$URL_ROOT/models

Result (example):

{
  "available": [
    "wmt14.en-de_acc_69.22_ppl_4.33_e9.pt",
    "wmt14.en-de_acc_69.22_ppl_4.33_e9.light.pt"
  ],
  "loaded": []
}

2. Translate

(this example involves subwords)

curl -i -X POST -H "Content-Type: application/json" \
    -d '[{"src": "this is a test for model 0", "id": 0}]' \
    http://$HOST:$PORT$URL_ROOT/translate

Result:

{
  "model_id": 0,
  "result": "\u2581die \u2581Formen kant en \u2581( K \u00f6r ner ) \u2581des \u2581Stahl g u\u00df form .\n",
  "status": "ok",
  "time": {
    "total": 8.510261535644531,
    "translation": 8.509992599487305,
    "writing_src": 0.0002689361572265625
  }
}

How do I use Pretrained embeddings (e.g. GloVe)?

Example

How do I use the Transformer model?

Do you support multi-gpu?

How can I ensemble Models at inference?

How can I weight different corpora at training?

Example

How can I apply on-the-fly tokenization and subword regularization when training?

Example

What are the readily available on-the-fly data transforms?

General purpose

Filter examples by length

Add custom prefix to examples

Tokenization

OpenNMT Tokenizer

SentencePiece

BPE (subword-nmt)

BART-style noise

SwitchOut and sampling

SwitchOut

Drop some tokens

Mask some tokens

How can I create custom on-the-fly data transforms?

Can I get word alignments while translating?

Raw alignments from averaging Transformer attention heads

Supervised learning on a specific head

How can I update a checkpoint's vocabulary?

How can I use source word features?

How can I set up a translation server ?

I. How it works?

Configuration:

Example

II. How to start the server without Docker ?

0. Get the code

1. Install flask

2. Put some models

3. Start the server

III. How to start the server with Docker ?

IV. How to use the API ?

0. Set the hostname

1. List models

2. Translate

1. Install `flask`