2.0 Transition Guide¶

This document details some changes introduced in OpenNMT-tf 2.0 and actions required by the user.

New requirements¶

Python 3¶

Python 3.5 (or above) is now required by OpenNMT-tf. See python3statement.org for more context about this decision.

TensorFlow 2.0¶

OpenNMT-tf has been completely redesigned for TensorFlow 2.0 which is now the minimal required TensorFlow version.

The correct TensorFlow version is declared as a dependency of OpenNMT-tf and will be automatically installed as part of the pip installation:

pip install --upgrade pip
pip install OpenNMT-tf

Changed checkpoint layout¶

TensorFlow 2.0 introduced a new way to save checkpoints: variables are no longer matched by their name but by where they are stored relative to the root model object. Consequently, OpenNMT-tf V1 checkpoints are no longer compatible without conversion.

To smooth this transition, V1 checkpoints of the following models are automatically upgraded on load:

NMTBigV1
NMTMediumV1
NMTSmallV1
Transformer
TransformerBig

Improved main script command line¶

The command line parser has been improved to better manage task specifc options that are now located after the run type:

onmt-main <general options> train <train options>

Also, some options have changed:

the run type train_and_eval has been replaced by train --with_eval
the main script now includes the average_checkpoints and update_vocab tasks
distributed training options are currently missing in 2.0
--session_config has been removed as no longer applicable in TensorFlow 2.0

See onmt-main -h for more details.

Changed vocabulary configuration¶

In OpenNMT-tf V1, models were responsible to declare the name of the vocabulary to look for, e.g.:

# V1 model inputter.
source_inputter=onmt.inputters.WordEmbedder(
    vocabulary_file_key="source_words_vocabulary",
    embedding_size=512)

meant that the user should configure the vocabulary like this:

# V1 vocabulary configuration.
data:
  source_words_vocabulary: src_vocab.txt

This is no longer the case in V2 where vocabulary configuration now follows a general pattern, the same that is currently used for “embedding” and “tokenization” configurations.

Single vocabulary (e.g. language model):

data:
  vocabulary: vocab.txt

Source and target vocabularies (e.g. sequence to sequence, tagging, etc.):

data:
  source_vocabulary: src_vocab.txt
  target_vocabulary: tgt_vocab.txt

Multi-source and target vocabularies:

data:
  source_1_vocabulary: src_1_vocab.txt
  source_2_vocabulary: src_2_vocab.txt

Nested inputs:

data:
  source_1_1_vocabulary: src_1_1_vocab.txt
  source_1_2_vocabulary: src_1_2_vocab.txt
  source_2_vocabulary: src_2_vocab.txt

Changed predefined models¶

Predefined models do not require a model definition file and can be directly set to the --model_type command line argument. Some of them have been changed for clarity:

V1	V2	Comment
`NMTBig`	`NMTBigV1`
`NMTMedium`	`NMTMediumV1`
`NMTSmall`	`NMTSmallV1`
`SeqTagger`	`LstmCnnCrfTagger`
`TransformerAAN`		Not considered useful compared to the standard Transformer
`TransformerBigFP16`		Use `TransformerBig` with `--mixed_precision` flag on the command line
`TransformerFP16`		Use `Transformer` with `--mixed_precision` flag on the command line

Changed parameters¶

Some parameters in the YAML configuration have been renamed or removed:

V1	V2	Comment
`*/bucket_width`	`*/length_bucket_width`
`*/num_threads`		Automatic value
`*/prefetch_buffer_size`		Automatic value
`eval/eval_delay`	`eval/steps`	Use steps instead of seconds to set the evaluation frequency
`eval/exporters`		Not implemented
`params/clip_gradients`		Set `clipnorm` or `clipvalue` in `params/optimizer_params/`
`params/freeze_variables`	`params/freeze_layers`	Use layer names instead of variable regexps
`params/gradients_accum`		Use `train/effective_batch_size` instead
`params/horovod`		Not implemented
`params/loss_scale`		Dynamic loss scaling by default
`params/maximum_iterations`	`params/maximum_decoding_length`
`params/maximum_learning_rate`		Not implemented
`params/param_init`		Not implemented
`params/weight_decay`	`params/optimizer_params/weight_decay`
`train/save_checkpoints_secs`		Not implemented
`train/train_steps`	`train/max_step`

Parameters taking reference to Python classes should also be revised when upgrading to V2, as the class likely changed in the process. This concerns:

params/optimizer and params/optimizer_params
params/decay_type and params/decay_params

New mixed precision workflow¶

OpenNMT-tf 2.0 is using the newly introduced graph rewriter to automatically convert parts of the graph from float32 to float16.

Variables are casted on the fly and checkpoints no longer need to be converted for inference or to continue training in float32. This means mixed precision is no longer a property of the model but should be enabled on the command line instead, e.g.:

onmt-main --model_type Transformer --config data.yml --auto_config --mixed_precision train