Marian v1.12.0

[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

Marian v1.11.0

[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

Marian v1.10.0

[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
• 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.

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

Marian v1.9.0

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

Marian v1.8.0

[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

Marian v1.7.0

[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

Marian v1.6.0

[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)

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.
• Seamless training continuation with exponential smoothing

Marian v1.5.0

[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

Marian 1.4.0

[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

Marian 1.3.0

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