How do I use my v2 models in v3 ? — OpenNMT-py documentation (original) (raw)

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.

Also you can have a look at this: Tutorial

As a reminder, OpenNMT-py v2.x used to rely on Torchtext 0.5

This torchtext version used “Fields”, “RawFields”, “MultiFields” which were deprecated in torchtext versions > 0.5. In order to convert old models we have to mimic those old Class and as a result you need to install a newer version of torchtext.

If you use pytorch 1.12.1 then install torchtext 0.13

If you use pytorch 1.13.0 then install torchtext 0.14

After the conversion you can eliminate completely torchtext.

Conversion is perfomed using the following script:

python tools/convertv2_v3.py -v2model myoldmodel.pt -v3model newmodel.pt

The new checkpoint will no longer have a “fields” key, replaced by “vocab” Some model options are modified as follow:

A new key add_qkvbias is set to true for old models.

New models will be trained by default with false

Special note for GPT2 type Language Model trained with v2

The special tokens of LM in v2 where not in line with NMT type models. Only 3 special tokens (<unk>, <blank>, </s>) were in the the vocab. For v3, we aligned LM and NMT models. You need to update the vocab by training from v2 converted to v3 checkpoint, with the update_vocab flag. It will make the vocab consistent with v3 structure.

How do I train the Transformer model?

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

Please have a look at the Example of WMT17 EN-DE.

...

General opts

save_model: mybasemodel save_checkpoint_steps: 10000 valid_steps: 10000 train_steps: 200000

Batching

bucket_size: 262144 world_size: 4 gpu_ranks: [0, 1, 2, 3] num_workers: 2 batch_type: "tokens" batch_size: 4096 valid_batch_size: 2048 accum_count: [4] accum_steps: [0]

Optimization

model_dtype: "fp16" 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 hidden_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:

Performance tips

Position encoding: Absolute vs Relative vs Rotary Embeddings vs Alibi

The basic feature is absolute position encoding stemming from the original Transformer Paper. However, even with this, we can use SinusoidalInterleaved (default OpenNMT-py) or SinusoidalConcat (default Fairseq imported models)

If you prefer to use relative position encoding, we support 3 modes:

In both cases, it is necessary to set position_encoding: false

In a nutshell, at the time if this writing (v3.1) absolute position encoding is managed in the Embeddings module, whereas the relative position encoding is managed directly in the multi-head self-attention module.

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 has not been properly re-implemented since OpenNMT-py 2.0.

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

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 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

  1. Get GloVe files:

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

  1. Adapt the configuration:

.yaml

...

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 .yaml

Notes:

How can I ensemble Models at inference?

You can specify several models in the onmt_translate command line: -model model1_seed1 model2_seed2Bear 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 bucketing mechanism is here for that reason. Instead of building batches one at a time, we will load bucket_size 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:

.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 ...

What special tokens does OpenNMT-py use?

In the v2, special tokens were different for SEQ2SEQ and LM: LM was BOS, PAD, EOS with IDs (0, 1, 2) and the first vocab token started at id=3 SEQ2SEQ was UNK, PAD, BOS, EOS with IDs (0, 1, 2, 3) and first vocab token started at id=4

In v3 we changed this behavior to align things: group.add( “–default_specials”, “-default_specilas”, nargs=”+”, type=str, default=[ DefaultTokens.UNK, DefaultTokens.PAD, DefaultTokens.BOS, DefaultTokens.EOS, ])

When we train a SEQ2SEQ model we use: SRC: srctok1 srctok2 srctok3 …. srctokn TGT: BOS tgttok1 tgttok2 ….. tgttokm EOS But when training a LM SRC: BOS srctok1 srctok2 srctok3 …. srctokn TGT: srctok1 srctok2 srctok3 …. srctokn EOS

Having said that, sometimes we need to finetune models (eg: NLLB-200, Llama, …) with existing vocab and special tokens are not the same.

ex with NLLB-200 BOS id=0 PAD id=1 EOS id=2 UNK id=3 And the decoder start token is EOS () which means in fact that the BOS is never used. At training, TGT needs to start with EOS instead of BOS in the default OpenNMT-py config.

Example of Llama UNK id=0 BOS id=1 EOS id=2 There was no PAD but to avoid conflicts we forced PAD id=3 (which was token ‘<0x00>’ in the original llama tokenizer)

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.

.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 :

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

At inference if you want to use the target prefix feature to prefix your target segment with a unique prefix (as opposed to a target prefix coming from a line-by-line file) you need to set your yaml file as follow (example given with a target language as in the NLLB-200 case):

tgt_prefix: "spa_Latn" tgt_file_prefix: true

Add custom suffix to examples

Transform name: suffix

Class: onmt.transforms.misc.SuffixTransform

For each dataset that the suffix transform is applied to, you can set the additional src_suffix and tgt_suffix parameters in its data configuration:

data: corpus_1: path_src: toy-ende/src-train1.txt path_tgt: toy-ende/tgt-train1.txt transforms: [suffix] weight: 1 src_suffix: some_src_suffix tgt_suffix: some_tgt_suffix

Convert examples to uppercase

Transform name: uppercase

Class: onmt.transforms.uppercase.UpperCaseTransform

Converts source and target (if present) examples to uppercase so the model can learn better to translate sentences in all caps. This transform normalizes the examples so the uppercased strings are stripped from any diacritics and accents. Usually this is desirable for most languages, although there are few exceptions.

The following option can be added to the main configuration (same ratio for all dataset with this transform):

Normalize punctuation

Transform name: normalize

Class: onmt.transforms.normalize.NormalizeTransform

Normalizes source and target (if present) examples using the same rules as Moses punctuation normalizer.

The following options can be added to the configuration of each dataset:

Clean dataset

Transform name: clean

Class: onmt.transforms.clean.CleanTransform

Cleans source and target (if present) examples using a set of rules.

The following options can be added to the configuration of each dataset:

Context / Doc aware transform

Transform name: docify

Class: onmt.transforms.docify.DocifyTransform

Concatenates several segments into one, separated with a delimiter.

Pre-requisite:

Dataset must be “Docs” separated by an empty line which will make clear a story ends at this empty line.

The following options can be added to the main configuration (same options for all dataset with this transform):

When working with several workers, this require some precaution in order to make sure “doc” are read linearly.

max_context + 1 needs to be a multiple of stride = Number of gpu x num_workers

Example: max_context=1 and 1 GPU, then num_workers must be 2 or 4.

Augment source segments with fuzzy matches for Neural Fuzzy Repair

Transform name: fuzzymatch

Class: onmt.transforms.fuzzymatch.FuzzyMatchTransform

Augments source segments with fuzzy matches for Neural Fuzzy Repair, as described in Neural Fuzzy Repair: Integrating Fuzzy Matches into Neural Machine Translation. Currently, the transform augments source segments with only a single fuzzy match. The Translation Memory (TM) format should be a flat text file, with each line containing the source and the target segment separated by a delimiter. As fuzzy matching during training is computational intensive, we offer some advice to achieve good performance and minimize overhead:

The following options can be added to the main configuration (valid for all datasets using this transform):

Augment source and target segments with inline tags

Transform name: inlinetags

Class: onmt.transforms.inlinetags.InlineTagsTransform

Augments source and target segments with inline tags (placeholders). The transform adds 2 kind of tags, paired tags (an opening and a closing tag) and isolated (standalone) tags, and requires a tab-delimited dictionary text file with source and target terms and phrases. A dictionary with 20-30k entries is recommended. User-defined tags must include the number placeholder #, e.g. “｟user_start_tag_#｠”.

The following options can be added to the main configuration (valid for all datasets using this transform):

Make the model learn to use terminology

Transform name: terminology

Class: onmt.transforms.terminology.TerminologyTransform

Augments source segments with terms so the model can learn to use user-provided terms at inference. It requires a dictionary with source and target terms, delimited with a tab. The transform uses Spacy’s lemmatization facilities in order to a) solve the word inflection problem when searching for terms in any form, and b) make the model inflect correctly most target terms at inference. The lemmatization is applied at the dictionary entries and also at the source and target examples, and the term searches during training are performed on the lemmatized examples. The format of a processed segment augmented with terms is as follows:This is an ｟src_term_start｠ augmented ｟tgt_term_start｠ target_lemma_for_augmented ｟tgt_term_end｠ example.The following options can be added to the main configuration (valid for all datasets using this transform):

Tokenization

Common options for the tokenization transforms are the following:

OpenNMT Tokenizer

Transform name: onmt_tokenize

Class: onmt.transforms.tokenize.ONMTTokenizerTransform

Additional options are available:

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:

SwitchOut and sampling

SwitchOut

Transform name: switchout

Class: onmt.transforms.sampling.SwitchOutTransform

Options:

Drop some tokens

Transform name: tokendrop

Class: onmt.transforms.sampling.TokenDropTransform

Options:

Mask some tokens

Transform name: tokenmask

Class: onmt.transforms.sampling.TokenMaskTransform

Options:

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:

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:

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

{ "src": , "tgt": , "align": # 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.

How to use LoRa and 8bit loading to finetune a big model ?

Cf paper: LoRa

LoRa is a mechanism that helps to finetune bigger model on a single GPU card by limiting the anmount of VRAM needed. The principle is to make only a few layers trainable (hence reducing the amount of required memory especially for the Adam optimizer)

You need to train_from a model (for instance NLLB-200 3.3B) and use the following options:

Bitsandbytes enables quantization of Linear layers. For more information: https://github.com/TimDettmers/bitsandbytes Also you can read the blog post here: https://huggingface.co/blog/hf-bitsandbytes-integration

You need to add the following option:

You can for instane quantize the layers of the PositionWise Feed-Forward from the Encoder/Decoder and the key/query/values/final from the Multi-head attention. Choices for quantization are [”bnb_8bit”, “bnb_FP4”, “bnb_NF4”]

How to use gradient checkpointing when dealing with a big model ?

Be carefull, the module that you use checkpointing needs to have gradients.

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.

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:

.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:

Training options to learn such alignments are:

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:

How can I use source word features?

Additional word-level information can be incorporated into the model by defining word features in the source sentence.

Word features must be appended to the actual textual data by using the special character ￨ as a feature separator. For instance:

however￨C ￭,￨N according￨L to￨L the￨L logs￨L ￭,￨N she￨L is￨L hard-working￨L ￭.￨N

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

SRC: however,￨C according￨L to￨L the￨L logs,￨L she￨L is￨L hard-working.￨L TOKENIZED SRC: however ￭, according to the logs ￭, she is hard-working ￭. RESULT: however￨C ￭,￨C according￨L to￨L the￨L logs￨L ￭,￨L she￨L is￨L hard￨L ￭-￭￨L working￨L ￭.￨L

Options

For the Transformer architecture make sure the following options are appropriately set:

Notes

Sample config file:

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

Transform options

reversible_tokenization: "joiner"

Vocab opts

src_vocab: exp/data.vocab.src tgt_vocab: exp/data.vocab.tgt

Features options

n_src_feats: 2 src_feats_defaults: "0￨1" feat_merge: "sum"

To allow source features in the server add the following parameters in the server’s config file:

"features": { "n_src_feats": 2, "src_feats_defaults": "0￨1", "reversible_tokenization": "joiner" }

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:

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

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−−portIP --port IP−−portPORT --url_root URLROOT−−configURL_ROOT --config URLR​OOT−−configCONFIG

III. How to start the server with Docker ?

  1. 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

  1. 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"]

  1. 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

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 } }