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Changelog

All notable changes to this project will be documented in this file.

The format is based on Keep a Changelog and this project adheres to Semantic Versioning.

[Unreleased]

  • Fixed compilation with clang 16.0.6
  • Added Threads::Threads to EXT_LIBS
  • Updates to pymarian: building for multiple python versions; disabling tcmalloc; hosting gated COMETs on HuggingFace

Added

  • Added --normalize-gradient-by-ratio to mildly adapt gradient magnitude if effective batch size diverges from running average effective batch size.
  • Added --no-optimizer-reload to skip optimizer state loading during continued training or fallback.
  • Added pymarian-eval, CLI for scoring metrics
  • Added --input-reorder pos1 pos2 option to re-ordering inputs internally when reading in batches. This is mostly a model property.
  • Added pymarian: python bindings based on pybind11
  • Added implementation of COMET-KIWI
  • Added implementation of xCOMET-XL/XXL regressor parts (MQM interpolation missing for now)
  • Added implementation of COMET-22 (reference-based) model and conversion
  • Added sparsemax operator (slow version)
  • Added sampling variants nucleus and epsilon, e.g. --output-sampling nucleus 0.9 and --output-sampling epsilon 0.02, respectively.
  • Added ALIBI related options to new layer framework.
  • Added --no-spm-encode option, allowing the model to use vocabulary IDs directly to train/decode.
  • Added MSE and MAE costs to COMET-QE training.
  • Added augmentation of shuffled examples to COMET-QE training via --comet-augment-bad.
  • Minor changes and fixes related to metric training.
  • Added --quiet-validation option that disables printing Hyp/Ref samples during validation
  • Added --custom-fallbacks option that allows to specify a list of option sets that get traversed for subsequent fallbacks upon divergence
  • Added --overwrite-checkpoint option that (when set to false) can be used to dump checkpoints with iteration numbers.
  • Implementations of COMET-20 (reference-based) and BLEURT-20 for inference with conversion scripts.
  • ./marian evaluate sub command for evaluation with COMET-QE-20, COMET-20 and BLEURT-20
  • A bunch of scripts for metrics use and early MBR experiments
  • LSH vocab filtering for GPU. Speed is not competitive with non-LSH. Checking in for completeness and possible future use of LSH on GPU for non-filtering stuff
  • Added --throw-on-divergence and --fp16-fallback-to-fp32 options to detect (fp16 and fp32) and recover (only fp16) diverged runs. If not recoverable, exception gets rethrown and goes unhandled to force fatal error and shutdown.
  • Re-implementation of COMET-QE for inference and training; conversion scripts from Unbabel-Comet to Marian.
  • Validator that generates embeddings and can be used during COMET training with an external script.
  • New experimental layer framework for Transformer-like models.

Fixed

  • Do not mmap files for conversion via Quicksand API
  • Fixed ALiBI states and caching in new layer framework
  • Throw exception when forcing with FS vocabs
  • Fixed force-decoding with LSH
  • Fixed force-decoding for beam-size > 1
  • Fixed lost node in mt-detect metrics
  • Fixed BLEURT logmask computation
  • Fixed wrong paramter name for norm in new layer framework
  • Fixed unit test for LayerNorm
  • Only collect batch statistics during mini-batch-fit up to actual max-length.
  • Implemented fully correct version of GELU instead of using bad approximatin via Swish.
  • Handle copying from fp32 or fp16 embeddings in embedder mode correctly.
  • Correct defaults for factored embeddings such that shared library use works (move out of config.h/cpp).

Changed

  • Refactoring of model loading, mmapping happens now opportunistically, --mmap-models for decoding forces mmap and croaks if not possible.
  • Removed --num-devices N option that wasn't really used by anyone (I assume).

[1.12.0] - 2023-02-20

Added

  • Fused inplace-dropout in FFN layer in Transformer
  • --force-decode option for marian-decoder
  • --output-sampling now works with ensembles (requires proper normalization via e.g --weights 0.5 0.5)
  • --valid-reset-all option

Fixed

  • Make concat factors not break old vector implementation
  • Use allocator in hashing
  • Read/restore checkpoints from main process only when training with MPI
  • Multi-loss casts type to first loss-type before accumulation (aborted before due to missing cast)
  • Throw ShapeSizeException if total expanded shape size exceeds numeric capacity of the maximum int value (2^31-1)
  • During mini-batch-fitting, catch ShapeSizeException and use another sizing hint. Aborts outside mini-batch-fitting.
  • Fix incorrect/missing gradient accumulation with delay > 1 or large effective batch size of biases of affine operations.
  • Fixed case augmentation with multi-threaded reading.
  • Scripts using PyYAML now use safe_load; see https://msg.pyyaml.org/load
  • Fixed check for fortran_ordering in cnpy
  • Fixed fp16 training/inference with factors-combine concat method
  • Fixed clang 13.0.1 compatibility
  • Fixed potential vulnerabilities from lxml<4.9.1 or mistune<2.0.31
  • Fixed the --best-deep RNN alias not setting the s2s model type

Changed

  • Parameter synchronization in local sharding model now executes hash checksum before syncing
  • Make guided-alignment faster via sparse memory layout, add alignment points for EOS, remove losses other than ce
  • Negative --workspace -N value allocates workspace as total available GPU memory minus N megabytes.
  • Set default parameters for cost-scaling to 8.f 10000 1.f 8.f, i.e. when scaling scale by 8 and do not try to automatically scale up or down. This seems most stable.
  • Make guided-alignment faster via sparse memory layout, add alignment points for EOS, remove losses other than ce.
  • Changed minimal C++ standard to C++-17
  • Faster LSH top-k search on CPU
  • Updated intgemm to the latest upstream version
  • Parameters in npz files are no longer implicitly assumed to be row-ordered. Non row-ordered parameters will result in an abort
  • Updated Catch2 header from 2.10.1 to 2.13.9

[1.11.0] - 2022-02-08

Added

  • Parallelized data reading with e.g. --data-threads 8
  • Top-k sampling during decoding with e.g. --output-sampling topk 10
  • Improved mixed precision training with --fp16
  • Set FFN width in decoder independently from encoder with e.g. --transformer-dim-ffn 4096 --transformer-decoder-dim-ffn 2048
  • Adds option --add-lsh to marian-conv which allows the LSH to be memory-mapped.
  • Early stopping based on first, all, or any validation metrics via --early-stopping-on
  • Compute 8.6 support if using CUDA>=11.1
  • Support for RMSNorm as drop-in replace for LayerNorm from Biao Zhang; Rico Sennrich (2019). Root Mean Square Layer Normalization. Enabled in Transformer model via --transformer-postprocess dar instead of dan.
  • Extend suppression of unwanted output symbols, specifically "\n" from default vocabulary if generated by SentencePiece with byte-fallback. Deactivates with --allow-special
  • Allow for fine-grained CPU intrinsics overrides when BUILD_ARCH != native e.g. -DBUILD_ARCH=x86-64 -DCOMPILE_AVX512=off
  • Adds custom bias epilogue kernel.
  • Adds support for fusing relu and bias addition into gemms when using cuda 11.
  • Better suppression of unwanted output symbols, specifically "\n" from SentencePiece with byte-fallback. Can be deactivated with --allow-special
  • Display decoder time statistics with marian-decoder --stat-freq 10 ...
  • Support for MS-internal binary shortlist
  • Local/global sharding with MPI training via --sharding local
  • fp16 support for factors.
  • Correct training with fp16 via --fp16.
  • Dynamic cost-scaling with --cost-scaling.
  • Dynamic gradient-scaling with --dynamic-gradient-scaling.
  • Add unit tests for binary files.
  • Fix compilation with OMP
  • Added --model-mmap option to enable mmap loading for CPU-based translation
  • Compute aligned memory sizes using exact sizing
  • Support for loading lexical shortlist from a binary blob
  • Integrate a shortlist converter (which can convert a text lexical shortlist to a binary shortlist) into marian-conv with --shortlist option

Fixed

  • Fix AVX2 and AVX512 detection on MacOS
  • Add GCC11 support into FBGEMM
  • Added pragma to ignore unused-private-field error on elementType_ on macOS
  • Do not set guided alignments for case augmented data if vocab is not factored
  • Various fixes to enable LSH in Quicksand
  • Added support to MPIWrappest::bcast (and similar) for count of type size_t
  • Adding new validation metrics when training is restarted and --reset-valid-stalled is used
  • Missing depth-scaling in transformer FFN
  • Fixed an issue when loading intgemm16 models from unaligned memory.
  • Fix building marian with gcc 9.3+ and FBGEMM
  • Find MKL installed under Ubuntu 20.04 via apt-get
  • Support for CUDA 11.
  • General improvements and fixes for MPI handling, was essentially non-functional before (syncing, random seeds, deadlocks during saving, validation etc.)
  • Allow to compile -DUSE_MPI=on with -DUSE_STATIC_LIBS=on although MPI gets still linked dynamically since it has so many dependencies.
  • Fix building server with Boost 1.75
  • Missing implementation for cos/tan expression operator
  • Fixed loading binary models on architectures where size_t != uint64_t.
  • Missing float template specialisation for elem::Plus
  • Broken links to MNIST data sets
  • Enforce validation for the task alias in training mode.

Changed

  • MacOS marian uses Apple Accelerate framework by default, as opposed to openblas/mkl.
  • Optimize LSH for speed by treating is as a shortlist generator. No option changes in decoder
  • Set REQUIRED_BIAS_ALIGNMENT = 16 in tensors/gpu/prod.cpp to avoid memory-misalignment on certain Ampere GPUs.
  • For BUILD_ARCH != native enable all intrinsics types by default, can be disabled like this: -DCOMPILE_AVX512=off
  • Moved FBGEMM pointer to commit c258054 for gcc 9.3+ fix
  • Change compile options a la -DCOMPILE_CUDA_SM35 to -DCOMPILE_KEPLER, -DCOMPILE_MAXWELL, -DCOMPILE_PASCAL, -DCOMPILE_VOLTA, -DCOMPILE_TURING and -DCOMPILE_AMPERE
  • Disable -DCOMPILE_KEPLER, -DCOMPILE_MAXWELL by default.
  • Dropped support for legacy graph groups.
  • Developer documentation framework based on Sphinx+Doxygen+Breathe+Exhale
  • Expresion graph documentation (#788)
  • Graph operators documentation (#801)
  • Remove unused variable from expression graph
  • Factor groups and concatenation: doc/factors.md

[1.10.0] - 2021-02-06

Added

  • Added intgemm8(ssse3|avx|avx512)?, intgemm16(sse2|avx|avx512)? types to marian-conv with uses intgemm backend. Types intgemm8 and intgemm16 are hardware-agnostic, the other ones hardware-specific.
  • Shortlist is now always multiple-of-eight.
  • Added intgemm 8/16bit integer binary architecture agnostic format.
  • Add --train-embedder-rank for fine-tuning any encoder(-decoder) model for multi-lingual similarity via softmax-margin loss
  • Add --logical-epoch that allows to redefine the displayed epoch counter as a multiple of n data epochs, updates or labels. Also allows to define width of fractional part with second argument.
  • Add --metrics chrf for computing ChrF according to https://www.aclweb.org/anthology/W15-3049/ and SacreBLEU reference implementation
  • Add --after option which is meant to replace --after-batches and --after-epochs and can take label based criteria
  • Add --transformer-postprocess-top option to enable correctly normalized prenorm behavior
  • Add --task transformer-base-prenorm and --task transformer-big-prenorm
  • Turing and Ampere GPU optimisation support, if the CUDA version supports it.
  • Printing word-level scores in marian-scorer
  • Optimize LayerNormalization on CPU by 6x through vectorization (ffast-math) and fixing performance regression introduced with strides in 77a420
  • Decoding multi-source models in marian-server with --tsv
  • GitHub workflows on Ubuntu, Windows, and MacOS
  • LSH indexing to replace short list
  • ONNX support for transformer models (very experimental)
  • Add topk operator like PyTorch's topk
  • Use cblas_sgemm_batch instead of a for loop of cblas_sgemm on CPU as the batched_gemm implementation
  • Supporting relative paths in shortlist and sqlite options
  • Training and scoring from STDIN
  • Support for reading from TSV files from STDIN and other sources during training and translation with options --tsv and --tsv-fields n.
  • Internal optional parameter in n-best list generation that skips empty hypotheses.
  • Quantized training (fixed point or log-based quantization) with --quantize-bits N command
  • Support for using Apple Accelerate as the BLAS library

Fixed

  • Segfault of spm_train when compiled with -DUSE_STATIC_LIBS=ON seems to have gone away with update to newer SentencePiece version.
  • Fix bug causing certain reductions into scalars to be 0 on the GPU backend. Removed unnecessary warp shuffle instructions.
  • Do not apply dropout in embeddings layers during inference with dropout-src/trg
  • Print "server is listening on port" message after it is accepting connections
  • Fix compilation without BLAS installed
  • Providing a single value to vector-like options using the equals sign, e.g. --models=model.npz
  • Fix quiet-translation in marian-server
  • CMake-based compilation on Windows
  • Fix minor issues with compilation on MacOS
  • Fix warnings in Windows MSVC builds using CMake
  • Fix building server with Boost 1.72
  • Make mini-batch scaling depend on mini-batch-words and not on mini-batch-words-ref
  • In concatenation make sure that we do not multiply 0 with nan (which results in nan)
  • Change Approx.epsilon(0.01) to Approx.margin(0.001) in unit tests. Tolerance is now absolute and not relative. We assumed incorrectly that epsilon is absolute tolerance.
  • Fixed bug in finding .git/logs/HEAD when Marian is a submodule in another project.
  • Properly record cmake variables in the cmake build directory instead of the source tree.
  • Added default "none" for option shuffle in BatchGenerator, so that it works in executables where shuffle is not an option.
  • Added a few missing header files in shortlist.h and beam_search.h.
  • Improved handling for receiving SIGTERM during training. By default, SIGTERM triggers 'save (now) and exit'. Prior to this fix, batch pre-fetching did not check for this sigal, potentially delaying exit considerably. It now pays attention to that. Also, the default behaviour of save-and-exit can now be disabled on the command line with --sigterm exit-immediately.
  • Fix the runtime failures for FASTOPT on 32-bit builds (wasm just happens to be 32-bit) because it uses hashing with an inconsistent mix of uint64_t and size_t.
  • fix beam_search ABORT_IF(beamHypIdx >= beam.size(), "Out of bounds beamHypIdx??"); when enable openmp and OMP_NUM_THREADS > 1

Changed

  • Remove --clip-gemm which is obsolete and was never used anyway
  • Removed --optimize switch, instead we now determine compute type based on binary model.
  • Updated SentencePiece repository to version 8336bbd0c1cfba02a879afe625bf1ddaf7cd93c5 from https://github.com/google/sentencepiece.
  • Enabled compilation of SentencePiece by default since no dependency on protobuf anymore.
  • Changed default value of --sentencepiece-max-lines from 10000000 to 2000000 since apparently the new version doesn't sample automatically anymore (Not quite clear how that affects quality of the vocabulary).
  • Change mini-batch-fit search stopping criterion to stop at ideal binary search threshold.
  • --metric bleu now always detokenizes SacreBLEU-style if a vocabulary knows how to, use bleu-segmented to compute BLEU on word ids. bleu-detok is now a synonym for bleu.
  • Move label-smoothing computation into Cross-entropy node
  • Move Simple-WebSocket-Server to submodule
  • Python scripts start with #!/usr/bin/env python3 instead of python
  • Changed compile flags -Ofast to -O3 and remove --ffinite-math
  • Moved old graph groups to depracated folder
  • Make cublas and cusparse handle inits lazy to save memory when unused
  • Replaced exception-based implementation for type determination in FastOpt::makeScalar

[1.9.0] - 2020-03-10

Added

  • An option to print cached variables from CMake
  • Add support for compiling on Mac (and clang)
  • An option for resetting stalled validation metrics
  • Add CMAKE options to disable compilation for specific GPU SM types
  • An option to print word-level translation scores
  • An option to turn off automatic detokenization from SentencePiece
  • Separate quantization types for 8-bit FBGEMM for AVX2 and AVX512
  • Sequence-level unliklihood training
  • Allow file name templated valid-translation-output files
  • Support for lexical shortlists in marian-server
  • Support for 8-bit matrix multiplication with FBGEMM
  • CMakeLists.txt now looks for SSE 4.2
  • Purging of finished hypotheses during beam-search. A lot faster for large batches.
  • Faster option look-up, up to 20-30% faster translation
  • Added --cite and --authors flag
  • Added optional support for ccache
  • Switch to change abort to exception, only to be used in library mode
  • Support for 16-bit packed models with FBGEMM
  • Multiple separated parameter types in ExpressionGraph, currently inference-only
  • Safe handling of sigterm signal
  • Automatic vectorization of elementwise operations on CPU for tensors dims that are divisible by 4 (AVX) and 8 (AVX2)
  • Replacing std::shared_ptr with custom IntrusivePtr for small objects like Tensors, Hypotheses and Expressions.
  • Fp16 inference working for translation
  • Gradient-checkpointing

Fixed

  • Replace value for INVALID_PATH_SCORE with std::numer_limits::lowest() to avoid overflow with long sequences
  • Break up potential circular references for GraphGroup*
  • Fix empty source batch entries with batch purging
  • Clear RNN chache in transformer model, add correct hash functions to nodes
  • Gather-operation for all index sizes
  • Fix word weighting with max length cropping
  • Fixed compilation on CPUs without support for AVX
  • FastOpt now reads "n" and "y" values as strings, not as boolean values
  • Fixed multiple reduction kernels on GPU
  • Fixed guided-alignment training with cross-entropy
  • Replace IntrusivePtr with std::uniq_ptr in FastOpt, fixes random segfaults due to thread-non-safty of reference counting.
  • Make sure that items are 256-byte aligned during saving
  • Make explicit matmul functions respect setting of cublasMathMode
  • Fix memory mapping for mixed paramter models
  • Removed naked pointer and potential memory-leak from file_stream.{cpp,h}
  • Compilation for GCC >= 7 due to exception thrown in destructor
  • Sort parameters by lexicographical order during allocation to ensure consistent memory-layout during allocation, loading, saving.
  • Output empty line when input is empty line. Previous behavior might result in hallucinated outputs.
  • Compilation with CUDA 10.1

Changed

  • Combine two for-loops in nth_element.cpp on CPU
  • Revert LayerNorm eps to old position, i.e. sigma' = sqrt(sigma^2 + eps)
  • Downgrade NCCL to 2.3.7 as 2.4.2 is buggy (hangs with larger models)
  • Return error signal on SIGTERM
  • Dropped support for CUDA 8.0, CUDA 9.0 is now minimal requirement
  • Removed autotuner for now, will be switched back on later
  • Boost depdendency is now optional and only required for marian_server
  • Dropped support for g++-4.9
  • Simplified file stream and temporary file handling
  • Unified node intializers, same function API.
  • Remove overstuff/understuff code

[1.8.0] - 2019-09-04

Added

  • Alias options and new --task option
  • Automatic detection of CPU intrisics when building with -arch=native
  • First version of BERT-training and BERT-classifier, currently not compatible with TF models
  • New reduction operators
  • Use Cmake's ExternalProject to build NCCL and potentially other external libs
  • Code for Factored Vocabulary, currently not usable yet without outside tools

Fixed

  • Issue with relative paths in automatically generated decoder config files
  • Bug with overlapping CXX flags and building spm_train executable
  • Compilation with gcc 8
  • Overwriting and unsetting vector options
  • Windows build with recent changes
  • Bug with read-ahead buffer
  • Handling of "dump-config: false" in YAML config
  • Errors due to warnings
  • Issue concerning failed saving with single GPU training and --sync-sgd option.
  • NaN problem when training with Tensor Cores on Volta GPUs
  • Fix pipe-handling
  • Fix compilation with GCC 9.1
  • Fix CMake build types

Changed

  • Error message when using left-to-right and right-to-left models together in ensembles
  • Regression tests included as a submodule
  • Update NCCL to 2.4.2
  • Add zlib source to Marian's source tree, builds now as object lib
  • -DUSE_STATIC_LIBS=on now also looks for static versions of CUDA libraries
  • Include NCCL build from github.com/marian-nmt/nccl and compile within source tree
  • Set nearly all warnings as errors for Marian's own targets. Disable warnings for 3rd party
  • Refactored beam search

[1.7.0] - 2018-11-27

Added

  • Word alignment generation in scorer
  • Attention output generation in decoder and scorer with --alignment soft
  • Support for SentencePiece vocabularies and run-time segmentation/desegmentation
  • Support for SentencePiece vocabulary training during model training
  • Group training files by filename when creating vocabularies for joint vocabularies
  • Updated examples
  • Synchronous multi-node training (early version)

Fixed

  • Delayed output in line-by-line translation

Changed

  • Generated word alignments include alignments for target EOS tokens
  • Boost::program_options has been replaced by another CLI library
  • Replace boost::file_system with Pathie
  • Expansion of unambiguous command-line arguments is no longer supported

[1.6.0] - 2018-08-08

Added

  • Faster training (20-30%) by optimizing gradient popagation of biases
  • Returning Moses-style hard alignments during decoding single models, ensembles and n-best lists
  • Hard alignment extraction strategy taking source words that have the attention value greater than the threshold
  • Refactored sync sgd for easier communication and integration with NCCL
  • Smaller memory-overhead for sync-sgd
  • NCCL integration (version 2.2.13)
  • New binary format for saving/load of models, can be used with *.bin extension (can be memory mapped)
  • Memory-mapping of graphs for inferece with ExpressionGraph::mmap(const void* ptr) function. (assumes *.bin model is mapped or in buffer)
  • Added SRU (--dec-cell sru) and ReLU (--dec-cell relu) cells to inventory of RNN cells
  • RNN auto-regression layers in transformer (--transformer-decoder-autreg rnn), work with gru, lstm, tanh, relu, sru cells
  • Recurrently stacked layers in transformer (--transformer-tied-layers 1 1 1 2 2 2 means 6 layers with 1-3 and 4-6 tied parameters, two groups of parameters)
  • Seamless training continuation with exponential smoothing

Fixed

  • A couple of bugs in "selection" (transpose, shift, cols, rows) operators during back-prob for a very specific case: one of the operators is the first operator after a branch, in that case gradient propgation might be interrupted. This did not affect any of the existing models as such a case was not present, but might have caused future models to not train properly
  • Bug in mini-batch-fit, tied embeddings would result in identical embeddings in fake source and target batch. Caused under-estimation of memory usage and re-allocation

[1.5.0] - 2018-06-17

Added

  • Average Attention Networks for Transformer model
  • 16-bit matrix multiplication on CPU
  • Memoization for constant nodes for decoding
  • Autotuning for decoding

Fixed

  • GPU decoding optimizations, about 2x faster decoding of transformer models
  • Multi-node MPI-based training on GPUs

[1.4.0] - 2018-03-13

Added

  • Data weighting with --data-weighting at sentence or word level
  • Persistent SQLite3 corpus storage with --sqlite file.db
  • Experimental multi-node asynchronous training
  • Restoring optimizer and training parameters such as learning rate, validation results, etc.
  • Experimental multi-CPU training/translation/scoring with --cpu-threads=N
  • Restoring corpus iteration after training is restarted
  • N-best-list scoring in marian-scorer

Fixed

  • Deterministic data shuffling with specific seed for SQLite3 corpus storage
  • Mini-batch fitting with binary search for faster fitting
  • Better batch packing due to sorting

[1.3.1] - 2018-02-04

Fixed

  • Missing final validation when done with training
  • Differing summaries for marian-scorer when used with multiple GPUs

[1.3.0] - 2018-01-24

Added

  • SQLite3 based corpus storage for on-disk shuffling etc. with --sqlite
  • Asynchronous maxi-batch preloading
  • Using transpose in SGEMM to tie embeddings in output layer

[1.2.1] - 2018-01-19

Fixed

  • Use valid-mini-batch size during validation with "translation" instead of mini-batch
  • Normalize gradients with multi-gpu synchronous SGD
  • Fix divergence between saved models and validated models in asynchronous SGD

[1.2.0] - 2018-01-13

Added

  • Option --pretrained-model to be used for network weights initialization with a pretrained model
  • Version number saved in the model file
  • CMake option -DCOMPILE_SERVER=ON
  • Right-to-left training, scoring, decoding with --right-left

Fixed

  • Fixed marian-server compilation with Boost 1.66
  • Fixed compilation on g++-4.8.4
  • Fixed compilation without marian-server if openssl is not available

[1.1.3] - 2017-12-06

Added

  • Added back gradient-dropping

Fixed

  • Fixed parameters initialization for --tied-embeddings during translation

[1.1.2] - 2017-12-05

Fixed

  • Fixed ensembling with language model and batched decoding
  • Fixed attention reduction kernel with large matrices (added missing syncthreads()), which should fix stability with large batches and beam-size during batched decoding

[1.1.1] - 2017-11-30

Added

  • Option --max-length-crop to be used together with --max-length N to crop sentences to length N rather than omitting them.
  • Experimental model with convolution over input characters

Fixed

  • Fixed a number of bugs for vocabulary and directory handling

[1.1.0] - 2017-11-21

Added

  • Batched translation for all model types, significant translation speed-up
  • Batched translation during validation with translation
  • --maxi-batch-sort option for marian-decoder
  • Support for CUBLAS_TENSOR_OP_MATH mode for cublas in cuda 9.0
  • The "marian-vocab" tool to create vocabularies

[1.0.0] - 2017-11-13

Added

  • Multi-gpu validation, scorer and in-training translation
  • summary-mode for scorer
  • New "transformer" model based on Attention is all you need
  • Options specific for the transformer model
  • Linear learning rate warmup with and without initial value
  • Cyclic learning rate warmup
  • More options for learning rate decay, including: optimizer history reset, repeated warmup
  • Continuous inverted square root decay of learning (--lr-decay-inv-sqrt) rate based on number of updates
  • Exposed optimizer parameters (e.g. momentum etc. for Adam)
  • Version of deep RNN-based models compatible with Nematus (--type nematus)
  • Synchronous SGD training for multi-gpu (enable with --sync-sgd)
  • Dynamic construction of complex models with different encoders and decoders, currently only available through the C++ API
  • Option --quiet to suppress output to stderr
  • Option to choose different variants of optimization criterion: mean cross-entropy, perplexity, cross-entropy sum
  • In-process translation for validation, uses the same memory as training
  • Label Smoothing
  • CHANGELOG.md
  • CONTRIBUTING.md
  • Swish activation function default for Transformer (https://arxiv.org/pdf/1710.05941.pdf)

Changed

  • Changed shape organization to follow numpy.
  • Changed option --moving-average to --exponential-smoothing and inverted formula to s_t = (1 - \alpha) * s_{t-1} + \alpha * x_t, \alpha is now 1-e4 by default
  • Got rid of thrust for compile-time mathematical expressions
  • Changed boolean option --normalize to --normalize [arg=1] (=0). New behaviour is backwards-compatible and can also be specified as --normalize=0.6
  • Renamed "s2s" binary to "marian-decoder"
  • Renamed "rescorer" binary to "marian-scorer"
  • Renamed "server" binary to "marian-server"
  • Renamed option name --dynamic-batching to --mini-batch-fit
  • Unified cross-entropy-based validation, supports now perplexity and other CE
  • Changed --normalize (bool) to --normalize (float)arg, allow to change length normalization weight as score / pow(length, arg)

Removed

  • Temporarily removed gradient dropping (--drop-rate X) until refactoring.