v4.0.0

This release supports Pytorch >= 0.4.1 including the recent 1.0 release. The relevant
setup.py and environment.yml files will default to 1.0.0 installation.

v4.0.0 (18/12/2018)

• Critical: NumpyDataset now returns tensors of shape HxW, N, C for 3D/4D convolutional features, 1, N, C for 2D feature files. Models should be adjusted to adapt to this new shaping.
• An order_file per split (ord: path/to/txt file with integer per line) can be given from the configurations to change the feature order of numpy tensors to flexibly revert, shuffle, tile, etc. them.
• Better dimension checking to ensure that everything is OK.
• Added LabelDataset for single label input/outputs with associated Vocabulary for integer mapping.
• Added handle_oom=(True|False) argument for [train] section to recover from GPU out-of-memory (OOM) errors during training. This is disabled by default, you need to enable it from the experiment configuration file. Note that it is still possible to get an OOM during validation perplexity computation. If you hit that, reduce the eval_batch_size parameter.
• Added de-hyphen post-processing filter to stitch back the aggressive hyphen splitting of Moses during early-stopping evaluations.
• Added optional projection layer and layer normalization to TextEncoder.
• Added enc_lnorm, sched_sampling options to NMT to enable layer normalization for encoder and use scheduled sampling at a given probability.
• ConditionalDecoder can now be initialized with max-pooled encoder states or the last state as well.
• You can now experiment with different decoders for NMT by changing the dec_variant option.
• Collect all attention weights in self.history dictionary of the decoders.
• Added n-best output to nmtpy translate with the argument -N.
• Changed the way -S works for nmtpy translate. Now you need to give the split name with -s all the time but -S is used to override the input data sources defined for that split in the configuration file.
• Removed decoder-initialized multimodal NMT MNMTDecInit. Same functionality exists within the NMT model by using the model option dec_init=feats.
• New model MultimodalNMT: that supports encoder initialization, decoder initialization, both, concatenation of embeddings with visual features, prepending and appending. This model covers almost all the models from LIUM-CVC's WMT17 multimodal systems except the multiplicative interaction variants such as trgmul.
• New model MultimodalASR: encoder-decoder initialized ASR model. See the paper
• New Model AttentiveCaptioning: Similar but not an exact reproduction of show-attend-and-tell, it uses feature files instead of raw images.
• New model AttentiveMNMTFeaturesFA: LIUM-CVC's WMT18 multimodal system i.e. filtered attention
• New (experimental) model NLI: A simple LSTM-based NLI baseline for SNLI dataset:
  • direction should be defined as direction: pre:Text, hyp:Text -> lb:Label
  • pre, hyp and lb keys point to plain text files with one sentence per line. A vocabulary should be constructed even for the labels to fit the nmtpy architecture.
  • acc should be added to eval_metrics to compute accuracy.

v2.0.0

• Ability to install through pip.
• Advanced layers are now organized into subfolders.
• New basic layers: Convolution over sequence, MaxMargin.
• New attention layers: Co-attention, multi-head attention, hierarchical attention.
• New encoders: Arbitrary sequence-of-vectors encoder, BiLSTMp speech feature encoder.
• New decoders: Multi-source decoder, switching decoder, vector decoder.
• New datasets: Kaldi dataset (.ark/.scp reader), Shelve dataset, Numpy sequence dataset.
• Added learning rate annealing: See lr_decay* options in config.py.
• Removed subword-nmt and METEOR files from repository. We now depend on
  the PIP package for subword-nmt. For METEOR, nmtpy-install-extra should
  be launched after installation.
• More multi-task and multi-input/output translate and training regimes.
• New early-stopping metrics: Character and word error rate (cer,wer) and ROUGE (rouge).
• Curriculum learning option for the BucketBatchSampler, i.e. length-ordered batches.
• New models:
  • ASR: Listen-attend-and-spell like automatic speech recognition
  • Multitask*: Experimental multi-tasking & scheduling between many inputs/outputs.

v1.4.0

• Add different environment.yml files for easy installation using conda. You can now
  create a ready-to-use conda environment by just calling conda env create -f environment-cuda<VER>.yml.
• Make NumpyDataset memory efficient by keeping float16 arrays as they are
  until batch creation time.
• Rename Multi30kRawDataset to Multi30kDataset which now supports both
  raw image files and pre-extracted visual features file stored as .npy.
• Add CNN feature extraction script under scripts/.
• Add doubly stochastic attention to ShowAttendAndTell and multimodal NMT.
• New model MNMTDecinit to initialize decoder with auxiliary features.
• New model AMNMTFeatures which is the attentive MMT but with features file
  instead of end-to-end feature extraction which was memory hungry.

v1.3.2

Updates for ShowAttendAndTell model.

v1.3.1

• Removed old Multi30kDataset.
• Sort batches by source sequence length instead of target.
• Fix ShowAttendAndTell model. It should now work.

v1.3.0

• Added Multi30kRawDataset for training end-to-end systems from raw images as input.
• Added NumpyDataset to read .npy/.npz tensor files as input features.
• You can now pass -S to nmtpy train to produce shorter experiment files with not all the hyperparameters in file name.
• New post-processing filter option de-spm for Google SentencePiece (SPM) processed files.
• sacrebleu is now a dependency as it is now accepted as an early-stopping metric.
  It only makes sense to use it with SPM processed files since they are detokenized
  once post-processed.
• Added sklearn as a dependency for some metrics.
• Added momentum and nesterov parameters to [train] section for SGD.
• ImageEncoder layer is improved in many ways. Please see the code for further details.
• Added unmerged upstream PR for ModuleDict() support.
• METEOR will now fallback to English if language can not be detected from file suffixes.
• -f now produces a separate numpy file for token frequencies when building vocabulary files with nmtpy-build-vocab.
• Added new command nmtpy test for non beam-search inference modes.
• Removed nmtpy resume command and added pretrained_file option for [train] to initialize model weights from a checkpoint.
• Added freeze_layers option for [train] to give comma-separated list of layer name prefixes to freeze.
• Improved seeding: seed is now printed in order to reproduce the results.
• Added IPython notebook for attention visualization.
• Layers
  • New shallow SimpleGRUDecoder layer.
  • TextEncoder: Ability to set maxnorm and gradscale of embeddings and work with or without sorted-length batches.
  • ConditionalDecoder: Make it work with GRU/LSTM, allow setting maxnorm/gradscale for embeddings.
  • ConditionalMMDecoder: Same as above.
• nmtpy translate
  • --avoid-double and --avoid-unk removed for now.
  • Added Google's length penalty normalization switch --lp-alpha.
  • Added ensembling which is enabled automatically if you give more than 1 model checkpoints.
• New machine learning metric wrappers in utils/ml_metrics.py:
  • Label-ranking average precision lrap
  • Coverage error
  • Mean reciprocal rank

Release v1.2.0

Release Notes

• You can now use $HOME and $USER in your configuration files.
• Fixed an overflow error that would cause NMT with more than 255 tokens to fail.
• METEOR worker process is now correctly killed after validations.
• Many runs of an experiment are now suffixed with a unique random string instead of incremental integers to avoid race conditions in cluster setups.
• Replaced utils.nn.get_network_topology() with a new Topology class that will parse the direction string of the model in a more smart way.
• If CUDA_VISIBLE_DEVICES is set, the GPUManager will always honor it.
• Dropped creation of temporary/advisory lock files under /tmp for GPU reservation.
• Time measurements during training are now structered into batch overhead, training and evaluation timings.
• Datasets
  • Added TextDataset for standalone text file reading.
  • Added OneHotDataset, a variant of TextDataset where the sequences are not prefixed/suffixed with <bos> and <eos> respectively.
  • Added experimental MultiParallelDataset that merges an arbitrary number of parallel datasets together.
• nmtpy translate
  • .nodbl and .nounk suffixes are now added to output files for --avoid-double and --avoid-unk arguments respectively.
  • A model-agnostic enough beam_search() is now separated out into its own file nmtpytorch/search.py.
  • max_len default is increased to 200.

v1.1

v1.1 (25/01/2018)

• New experimental Multi30kDataset and ImageFolderDataset classes
• torchvision dependency added for CNN support
• nmtpy-coco-metrics now computes one METEOR without norm=True
• Mainloop mechanism is completely refactored with backward-incompatible
  configuration option changes for [train] section:
  • patience_delta option is removed
  • Added eval_batch_size to define batch size for GPU beam-search during training
  • eval_freq default is now 3000 which means per 3000 minibatches
  • eval_metrics now defaults to loss. As before, you can provide a list
    of metrics like bleu,meteor,loss to compute all of them and early-stop
    based on the first
  • Added eval_zero (default: False) which tells to evaluate the model
    once on dev set right before the training starts. Useful for sanity
    checking if you fine-tune a model initialized with pre-trained weights
  • Removed save_best_n: we no longer save the best N models on dev set
    w.r.t. early-stopping metric
  • Added save_best_metrics (default: True) which will save best models
    on dev set w.r.t each metric provided in eval_metrics. This kind of
    remedies the removal of save_best_n
  • checkpoint_freq now to defaults to 5000 which means per 5000
    minibatches.
  • Added n_checkpoints (default: 5) to define the number of last
    checkpoints that will be kept if checkpoint_freq > 0 i.e. checkpointing enabled
• Added ExtendedInterpolation support to configuration files:
  • You can now define intermediate variables in .conf files to avoid
    typing same paths again and again. A variable can be referenced
    from within its section using tensorboard_dir: ${save_path}/tb notation
    Cross-section references are also possible: ${data:root} will be replaced
    by the value of the root variable defined in the [data] section.
• Added -p/--pretrained to nmtpy train to initialize the weights of
  the model using another checkpoint .ckpt.
• Improved input/output handling for nmtpy translate:
  • -s accepts a comma-separated test sets defined in the configuration
    file of the experiment to translate them at once. Example: -s val,newstest2016,newstest2017
  • The mutually exclusive counterpart of -s is -S which receives a
    single input file of source sentences.
  • For both cases, an output prefix should now be provided with -o.
    In the case of multiple test sets, the output prefix will be appended
    the name of the test set and the beam size. If you just provide a single file with -S
    the final output name will only reflect the beam size information.
• Two new arguments for nmtpy-build-vocab:
  • -f: Stores frequency counts as well inside the final json vocabulary
  • -x: Does not add special markers <eos>,<bos>,<unk>,<pad> into the vocabulary

Layers/Architectures

• Added Fusion() layer to concat,sum,mul an arbitrary number of inputs
• Added experimental ImageEncoder() layer to seamlessly plug a VGG or ResNet
  CNN using torchvision pretrained models
• Attention layer arguments improved. You can now select the bottleneck
  dimensionality for MLP attention with att_bottleneck. The dot
  attention is still not tested and probably broken.

New stuff

• Added AttentiveMNMT which implements modality-specific multimodal attention
  from the paper Multimodal Attention for Neural Machine Translation
• Added ShowAttendAndTell model

Changes in NMT

• dec_init defaults to mean_ctx, i.e. the decoder will be initialized
  with the mean context computed from the source encoder
• enc_lnorm which was just a placeholder is now removed since we do not
  provided layer-normalization for now
• Beam Search is completely moved to GPU