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.


  1. Get GloVe files:

mkdir "glove_dir"
unzip -d "glove_dir"
  1. 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
  1. Train:

onmt_train -config <your_config>.yaml


  • the matched embeddings will be saved at <save_data> and <save_data>;

  • additional flags fix_word_vecs_enc and fix_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.


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.


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:
        path_src: toy-ende/src-train1.txt
        path_tgt: toy-ende/tgt-train1.txt
        weight: 7
        path_src: toy-ende/src-train1.txt
        path_tgt: toy-ende/tgt-train1.txt
        weight: 3
        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.


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
onmttok_kwargs: "{'mode': 'none', 'spacer_annotate': True}"

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

        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__


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.misc.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;

  • src_onmttok_kwargs: additional kwargs for pyonmttok Tokenizer class, target side.


Transform name: sentencepiece

Class: onmt.transforms.misc.SentencePieceTransform

The src_subword_model and tgt_subword_model should be valid sentencepiece models.

BPE (subword-nmt)

Transform name: bpe

Class: onmt.transforms.misc.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


Transform name: switchout

Class: onmt.transforms.misc.SwitchOutTransform


  • switchout_temperature: sampling temperature for SwitchOut.

Drop some tokens

Transform name: tokendrop

Class: onmt.transforms.misc.TokenDropTransform


  • tokendrop_temperature: sampling temperature for token deletion.

Mask some tokens

Transform name: tokenmask

Class: onmt.transforms.misc.TokenMaskTransform


  • 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:

class FilterTooLongTransform(Transform):
    """Filter out sentence that are too long."""

    def __init__(self, opts):
        self.src_seq_length = opts.src_seq_length
        self.tgt_seq_length = opts.tgt_seq_length

    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 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:
            return None
            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


  • add_options allows to add custom options that would be necessary for the transform configuration;

  • 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.

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 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 ith word of source language is aligned to jth 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, 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:



# Corpus opts:
        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
        path_src: toy-ende/src-val.txt
        path_tgt: toy-ende/tgt-val.txt
        transforms: []



  • 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.