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[Unreleased]

New features

Fixes and improvements

v3.24.0 (2024-01-08)

New features

  • Support of new option offset to ignore token score of special tokens

v3.23.0 (2023-12-05)

New features

  • Support Phi model

Fixes and improvements

  • Fix the conversion for whisper without the "alignment_heads" in the "generation_config.json"
  • Fix forward batch

v3.22.0 (2023-11-22)

New features

  • Support "sliding window" and "chunking input" for Mistral

Fixes and improvements

  • Take into account the "generation_config.json" and fix "lang_ids" getter for Whisper converter
  • Accept callback even on "generate_tokens" method
  • Fix iomp5 linking with latest Intel OpenAPI on Ubuntu
  • Fixed "decoder_start_token_id" for T5

v3.21.0 (2023-11-09)

New features

  • Minimal Support for Mistral (Loader and Rotary extension for long sequence). No sliding yet
  • Support Distil-Whisper
  • Support Whisper-large-v3

v3.20.0 (2023-09-18)

New features

  • Update the Transformers converter to support more model architectures:
    • MixFormerSequential (used by microsoft/phi-1_5)
  • Accept batch inputs in method generate_tokens
  • Add method async_generate_tokens to return an asynchronous generator compatible with asyncio

Fixes and improvements

  • Remove the epsilon value in the softmax CPU kernel for consistency with other implementations
  • Optimize implementation of the Dynamic Time Wrapping (DTW) function (used for Whisper alignment)
  • Avoid an unnecessary copy of the input arguments in method Whisper::align

v3.19.0 (2023-08-31)

Changes

  • Binary wheels for Python 3.7 are no longer built

New features

  • Build wheels for Python 3.12
  • Update the Transformers converter to support more model architectures:
    • Falcon-RW
    • DistilBERT
    • Llama with linear RoPE scaling (e.g. Vicuna v1.5)
    • Llama with a non default RoPE base period (e.g. CodeLlama)
  • Accept the token type IDs as inputs for encoder models
  • Add property GenerationStepResult.hypothesis_id to identify the different hypotheses when running random sampling with num_hypotheses > 1

Fixes and improvements

  • Improve performance of 8-bit models on CPU:
    • Vectorize the GEMM output dequantization
    • Fuse the GEMM output dequantization with bias and activation
  • Allow inputs shorter than 30 seconds in Whisper methods
  • Fix incorrect batch_id values passed to the callback function
  • Fix a shape error in models using both MQA and relative positions
  • Fix compilation error related to AVX512 when using GCC 7
  • Call .detach() on PyTorch tensors before getting the Numpy array in converters

v3.18.0 (2023-08-03)

Changes

Converted models now uses the same floating point precision as the original models. For example, a model saved in float16 will be converted to a float16 model. Before this change, the weights were casted to float32 by default.

Similarly, selecting int8 keeps non quantized weights in their original precision unless a more specific quantization type is selected:

  • int8_float32
  • int8_float16
  • int8_bfloat16

New features

  • Add property compute_type to model instances
  • Extend the Python class StorageView with additional methods and properties:
    • to(dtype)
    • device_index
    • device
    • dtype
    • shape

Fixes and improvements

  • Update the function get_supported_compute_types to correctly return bfloat16 when supported
  • Update the HF Llama converter to accept extra tokens in the vocabulary
  • Fix a shape error when enabling return_alternatives with a model using relative positions
  • Fix a conversion error when using torch<1.13
  • Fix a type error when running Whisper models with the bfloat16 type
  • Update pybind11 to 2.11.1

v3.17.1 (2023-07-20)

Fixes and improvements

  • Fix an error when running models with the new int8_bfloat16 computation type
  • Fix a vocabulary error when converting Llama 2 models with the Transformers converter
  • Update the Transformers converter to correctly convert Llama models using GQA
  • Stop the decoding when the generator returned by the method generate_tokens is closed

v3.17.0 (2023-07-18)

New features

  • Add new computation types: bfloat16 and int8_bfloat16 (require a GPU with Compute Capability 8.0 or above)
  • Support multi-query attention for encoder-decoder models
  • Allow converters to register weights as PyTorch tensors instead of Numpy arrays

Fixes and improvements

  • Pass the flag trust_remote_code when loading the tokenizer in the Transformers converter
  • Improve performance of T5 models by reusing the same relative position bias in every layers
  • Whisper: disable the first timestamp decoding rule when a prefix is used
  • Install the CMake configuration in the correct library directory (e.g. some platforms use lib64 instead of lib)

v3.16.1 (2023-07-03)

Fixes and improvements

  • Fix repeated outputs in version 3.16.0 when using include_prompt_in_result=False and a batch input with variable lengths: a typo in the code led to min_length being incorrectly applied
  • Update the Transformers converter to accept extra tokens for Falcon models
  • Release the Python GIL when loading the model
  • Initialize the rotary embeddings on the GPU instead of the CPU
  • Avoid a copy for the input features passed to the Whisper methods
  • Vectorize copy in the Tile CUDA operator

v3.16.0 (2023-06-15)

New features

  • Update the Transformers converter to support more architectures:
    • Falcon-40B
    • XLM-RoBERTa
  • Add the generation option sampling_topp to enable top-p (nucleus) sampling
  • Save vocabulary files in the JSON format to better support tokens containing newlines or carriage returns

Fixes and improvements

  • Fix the application of min_length and max_length when using include_prompt_in_result=False and a batch input with variable lengths: the length constraint should only apply to the sequence after the prompt
  • Update oneDNN to 3.1.1

v3.15.1 (2023-06-09)

Fixes and improvements

  • Fix an error when using the new static_prompt argument in the methods generate_tokens and generate_batch
  • Improve the performance of models using ALiBi

v3.15.0 (2023-06-06)

New features

  • Initial support of encoder-only Transformer model via a new class ctranslate2.Encoder
  • Update the Transformers converter to support the Falcon models
  • Add a generation argument static_prompt to optimize the execution for models using system prompts: the model state for this prompt is cached and reused in future calls
  • Support early stopping in greedy search when the callback function returns True
  • Make the layer norm epsilon value configurable in the model configuration file config.json
  • Add Tanh as a possible activation function

Fixes and improvements

  • Fix a performance issue when running models using ALiBi on the GPU
  • Fix application of the rotary embeddings when the multi-query attention is used
  • Fix conversion of Marian models using tied-embeddings-all: false
  • Remove use_fast argument when loading Hugging Face tokenizers to use the default tokenizer for the model

v3.14.0 (2023-05-26)

New features

  • Update the Transformers converter with new architectures:
    • CodeGen
    • GPTBigCode
    • LLaMa
    • MPT
  • Update the OpenNMT-py converter to support some recent options:
    • layer_norm="rms"
    • max_relative_positions=-1 (rotary embeddings)
    • max_relative_positions=-2 (ALiBi)
    • pos_ffn_activation_fn="silu"
  • Update the OpenNMT-tf converter to support models using different configurations for the encoder and decoder (e.g. post-norm in the encoder and pre-norm in the decoder)
  • Implement the multi-query attention (used by GPTBigCode)

Fixes and improvements

  • Support paths containing Unicode characters on Windows
  • Fix the generate_tokens method to properly raise the underlying exception instead of hanging indefinitely
  • Fix compilation error when using -DBUILD_SHARED_LIBS=OFF
  • Fix runtime errors when linking against libctranslate2.a without using the "whole archive" flags

v3.13.0 (2023-04-25)

New features

  • Support conversion of GPT-NeoX models with the Transformers converter
  • Extend the end_token argument to also accept a list of tokens
  • Add option return_end_token to include the end token in the results of the methods generate_batch and translate_batch (by default the end token is removed)
  • Expose the callback argument for the methods generate_batch and translate_batch to get early results from the decoding loop
  • Fallback to a custom threading implementation when OpenMP is not used (which is currently the case for the macOS ARM64 Python wheels)
  • Define the CMake package CTranslate2::ctranslate2 to facilitate the library integration in other CMake projects

Fixes and improvements

  • Fix the vocabulary loading when some tokens end with the carriage return
  • Implement a fused kernel to apply the rotary embeddings
  • Update the Ruy library to commit 363f2522

v3.12.0 (2023-04-17)

New features

  • Add methods Generator.generate_tokens and Translator.generate_tokens returning a generator that yields tokens as soon as they are generated by the model (not compatible with beam search)
  • Improve performance of rotary embeddings on CPU with an alternative implementation that is enabled when setting rotary_interleave=False in the model specification (may require to permute QK weights)
  • Support a variable number of input frames in method Whisper.align to improve batch support
  • Expose flag low_cpu_mem_usage in the Transformers converter to reduce the memory usage when loading large models (requires the package accelerate)

Fixes and improvements

  • Fix crash in Whisper.align when num_frames // 2 <= median_filter_width
  • Raise an error if arguments end_token or suppress_sequences contain tokens that are not in the vocabulary
  • Optimize the quantization of FP16 weights during the model conversion
  • In the Transformers converter, also load the model weights in FP16 when the selected quantization is int8_float16
  • Update the Whisper timestamp decoding rules to prevent the generation of segments with zero duration

v3.11.0 (2023-04-06)

Changes

  • The Python wheels for macOS ARM are now built with the Ruy backend to support INT8 computation. This will change the performance and results when loading an INT8 model and/or using the auto compute type. To keep the previous behavior, set compute_type="float32".

New features

  • Support conversion of the GPT-J architecture
  • Support conversion of models using rotary position embeddings
  • Apply the new OpenNMT-py option decoder_start_token
  • Add option revision in the Transformers converter to download a specific revision of the model from the Hugging Face Hub

v3.10.3 (2023-03-28)

Fixes and improvements

  • Fix a synchronization issue when the model input is a CUDA storage

v3.10.2 (2023-03-27)

Fixes and improvements

  • Select the correct device when copying a StorageView instance

v3.10.1 (2023-03-25)

Fixes and improvements

  • Add missing device setter in Whisper.encode

v3.10.0 (2023-03-24)

New features

  • Add Generator option include_prompt_in_result (True by default)
  • Add method Whisper.encode to only run the Whisper encoder
  • Add model properties Whisper.device and Whisper.device_index

Fixes and improvements

  • Update the methods Whisper.detect_language, Whisper.generate, and Whisper.align to accept the encoder output
  • Fix a crash when running Generator.forward on GPU and the generator object is destroyed before the forward output
  • Fix parsing of Marian YAML vocabulary files containing "complex key mappings" and escaped sequences such as "\x84"

v3.9.1 (2023-03-17)

Fixes and improvements

  • Fix missing alignments in the Whisper.align result due to a bug in the DTW implementation
  • Fix error when converting a Whisper model from a path

v3.9.0 (2023-03-15)

New features

  • Support BLOOM language models
  • Add method Whisper.align to return the text/audio alignment and implement word-level timestamps

Fixes and improvements

  • Do not force intra_threads to 1 when loading a model on the GPU as some ops may still run on the CPU
  • Disable multithreading when copying a batch of small arrays

v3.8.0 (2023-03-06)

New features

  • Experimental support of AVX512 in manually vectorized functions: this code path is not enabled by default but can be enabled by setting the environment variable CT2_FORCE_CPU_ISA=AVX512
  • Add Transformers converter option copy_files to copy any files from the Hugging Face model to the converted model directory
  • Expose some Whisper parameters:
    • max_initial_timestamp_index
    • suppress_blank
    • suppress_tokens

Fixes and improvements

  • Reduce conversion time for large models by skipping some weights comparisons
  • Reduce maximum memory usage when converting Transformers models with --quantization float16
  • Set FP32 compute type for FP16 convolutions to match the PyTorch behavior and accuracy
  • Update oneDNN to 3.0.1

v3.7.0 (2023-02-23)

Changes

  • Rename the "float" compute type to "float32" for clarity. "float" is still accepted for backward compatibility.

New features

  • Add the environment variable CT2_CUDA_TRUE_FP16_GEMM. This flag is enabled by default so that FP16 GEMMs are running in full FP16. When disabled, the compute type of FP16 GEMMs is set to FP32, which is what PyTorch and TensorFlow do by default.

Fixes and improvements

  • Improve the numerical precision of Whisper models running in FP16 by setting the FP32 compute type for GEMMs (same behavior as PyTorch)
  • Improve support for running the Whisper models with INT16 quantization
  • Ensure the Whisper decoding does not continue past max_length, which could previously happen when the prompt was longer than max_length/2
  • Include the EOS score in the score returned by Whisper during greedy search

v3.6.0 (2023-02-16)

New features

  • Build the Windows Python wheels with cuDNN to enable GPU execution of Whisper models
  • Add the model attribute Whisper.is_multilingual

Fixes and improvements

  • Reduce the beam search memory usage by not duplicating the decoder states that are the same in each beam (e.g. the projected memory keys and values)
  • Optimize the dot product attention during beam search by moving the query beam dimension to the time dimension
  • Fix support of English-only Whisper models
  • Include the prefix tokens (if they exist) in the output of Whisper.generate
  • Log a warning when the model weights are implicitly converted to another type

v3.5.1 (2023-02-13)

Fixes and improvements

  • Whisper: fix an incorrect timestamp rule that prevented timestamps to be generated in pairs
  • Whisper: ignore the EOS token when applying the length penalty to match the original implementation

v3.5.0 (2023-02-10)

New features

  • Add a patience factor for beam search to continue decoding until beam_size * patience hypotheses are finished, as described in Kasai et al. 2022
  • Implement all GELU variants and select them accordingly when converting models:
    • Tanh approximation (already implemented)
    • Sigmoid approximation
    • Reference implementation based on the CDF

Fixes and improvements

  • Fix incorrect outputs of T5 models due to a bug in the CUDA kernel of the RMS normalization
  • Raise an error if the Whisper input shape is incorrect
  • Optimize the transposition operator used in the multi-head attention when running on GPU
  • Remove the upper limit in python_requires to facilitate the package installation with tools like Poetry and PDM

v3.4.0 (2023-02-03)

Fixes and improvements

  • Fix incorrect vocabulary in M2M100 models after conversion with transformers>=4.24
  • Fix incorrect model outputs when executing with very large batch sizes on GPU
  • Fix memory error in biased decoding: the vector of divergence was read and updated past its length
  • Allow setting prefix_bias_beta > 0 with beam_size == 1
  • Prevent timestamps from decreasing during Whisper generation
  • Make some error messages more helpful when implementing a custom converter

v3.3.0 (2023-01-02)

New features

  • Support T5 models, including the variants T5v1.1 and mT5
  • Support loading the model files from memory:
    • Python: see the files argument in the constructor of classes loading models
    • C++: see the models::ModelMemoryReader class

Fixes and improvements

  • Improve the quantization accuracy of OPT models by applying the SmoothQuant technique during conversion (pre-computed activation scales should be passed to the converter option --activation_scales)
  • Fix conversion of BART-like models from HuggingFace that are using a different number of encoder and decoder layers
  • Fix compilation when no BLAS CPU backend is selected
  • Remove no longer relevant CMake warning when the project is compiled without oneDNN
  • Update oneDNN to 3.0
  • Update oneMKL to 2023.0

v3.2.0 (2022-12-12)

New features

  • Add decoding option suppress_sequences to prevent specific sequences of tokens from being generated
  • Add decoding option end_token to stop the decoding on a different token than the model EOS token
  • Allow returning multiple random hypotheses from greedy search + random sampling when setting num_hypotheses > 1

Fixes and improvements

  • Improve support for batch generation with the Whisper model:
    • Improve performance of batch generation with a context (we only require the prompts to have the same length, which is easily done by adapting the number of previous text tokens)
    • Support batch mode for option return_no_speech_prob
    • Support cases where some prompts in the batch have the token <|notimestamps|> but not others
  • Enable the Conv1D layer in more Python wheels:
    • macOS x64 (using oneDNN)
    • macOS ARM64 (using a custom implementation)
    • Linux AArch64 (using a custom implementation)
  • Update the OpenNMT-py converter to support the latest checkpoint structure
  • Generalize the TransformerSpec constructor to accept arbitrary encoder and decoder specifications
  • Remove the global compilation flag -ffast-math which introduces unwanted side effects and enable it only for the layer norm CPU kernel where it is actually useful
  • Fix CMake error on Windows when setting -DOPENMP_RUNTIME=COMP

v3.1.0 (2022-11-29)

Changes

  • The input prompt is no longer included in the result of Whisper.generate as it is usually not useful in a transcription loop
  • The default beam size in Whisper.generate is updated from 1 to 5 to match the default value in openai/whisper
  • Generation options min_length and no_repeat_ngram_size now penalize the logits instead of the log probs which may change some scores
  • Raise a deprecation warning when reading the TranslationResult object as a list of dictionaries

New features

  • Allow configuring the C++ logs from Python with the function ctranslate2.set_log_level
  • Implement the timestamp decoding rules when the Whisper prompt does not include the token <|notimestamps|>
  • Add option return_no_speech_prob to the method Whisper.generate for the result to include the probability of the no speech token

Fixes and improvements

  • Improve performance of the Whisper model when generating with a context
  • Fix timestamp tokens in the Whisper vocabulary to use the correct format (<|X.XX|>)
  • Fix AVX and NEON log functions to return -inf on log(0) instead of NaN
  • When info logs are enabled, log the system configuration only when the first model is loaded and not immediately when the library is loaded
  • Define a LogitsProcessor abstract class to apply arbitrary updates to the logits during decoding
  • Update oneDNN to 2.7.2

v3.0.2 (2022-11-14)

Fixes and improvements

  • Whisper: fix generate arguments that were not correctly passed to the model

v3.0.1 (2022-11-10)

Fixes and improvements

  • Whisper: do not implicitly add <|startoftranscript|> in generate since it is not always the first token

v3.0.0 (2022-11-07)

This major version integrates the Whisper speech recognition model published by OpenAI. It also introduces some breaking changes to remove deprecated usages and simplify some modules.

Breaking changes

General

  • Remove option normalize_scores: the scores are now always divided by pow(length, length_penalty) with length_penalty defaulting to 1
  • Remove option allow_early_exit: the beam search now exits early only when no penalties are used

Python

  • Rename some classes:
    • OpenNMTTFConverterV2 -> OpenNMTTFConverter
    • TranslationStats -> ExecutionStats
  • Remove compatibility for reading ScoringResult as a list of scores: the scores can be accessed with the attribute log_probs
  • Remove compatibility for reading ExecutionStats as a tuple
  • Remove support for deprecated Python version 3.6

CLI

  • Rename the client executable translate to a more specific name ct2-translator

C++

  • Rename or remove some classes and methods:
    • TranslationStats -> ExecutionStats
    • GeneratorPool -> Generator
    • TranslatorPool -> Translator
    • TranslatorPool::consume_* -> Translator::translate_*
    • TranslatorPool::consume_stream -> removed
    • TranslatorPool::score_stream -> removed
  • Remove support for building with CUDA 10

New features

  • Integrate the Whisper speech recognition model published by OpenAI
  • Support conversion of models trained with OpenNMT-py V3
  • Add method Generator.forward_batch to get the full model output for a batch of sequences
  • Add Python class StorageView to expose C++ methods taking or returning N-dimensional arrays: the class implements the array interface for interoperability with Numpy and PyTorch
  • Add a new configuration file config.json in the model directory that contains non structual model parameters (e.g. related to the input, the vocabulary, etc.)
  • Implement the Conv1D layer and operator on CPU and GPU (using oneDNN and cuDNN respectively)
  • [C++] Allow registration of external models with models::ModelFactory

Fixes and improvements

  • Fix conversion of models that use biases only for some QKV projections but not for all
  • Fuse masking of the output log probs by aggregating disabled tokens from all related options: disable_unk, min_length, no_repeat_ngram_size, etc.
  • Reduce the layer norm epsilon value on GPU to 1e-5 to match the default value in PyTorch
  • Move some Transformer model attributes under the encoder/decoder scopes to simplify loading
  • Redesign the ReplicaPool base class to simplify adding new classes with multiple model workers
  • Compile the library with C++17
  • Update oneDNN to 2.7.1
  • Update oneMKL to 2022.2
  • Update pybind11 to 2.10.1
  • Update cibuildwheel to 2.11.2

v2.24.0 (2022-10-03)

Changes

  • The Linux binaries now use the GNU OpenMP runtime instead of Intel OpenMP to workaround an initialization error on systems without /dev/shm

Fixes and improvements

  • Fix a memory error when running random sampling on GPU
  • Optimize the model loading on multiple GPUs by copying the finalized model weights instead of reading the model from disk multiple times
  • In the methods Translator.translate_iterable and Translator.score_iterable, raise an error if the input iterables don't have the same length
  • Fix some compilation warnings

v2.23.0 (2022-09-16)

New features

  • Build wheels for Python 3.11

Fixes and improvements

  • In beam search, get more candidates from the model output and replace finished hypotheses by these additional candidates
  • Fix possibly incorrect attention vectors returned from the beam search
  • Fix coverage penalty that was actually not applied
  • Fix crash when the beam size is larger than the vocabulary size
  • Add missing compilation flag -fvisibility=hidden when building the Python module
  • Update oneDNN to 2.6.2
  • Update OpenBLAS to 0.3.21

v2.22.0 (2022-09-02)

Changes

  • score_batch methods now return a list of ScoringResult instances instead of plain lists of probabilities. In most cases you should not need to update your code: the result object implements the methods __len__, __iter__, and __getitem__ so that it can still be used as a list.

New features

  • Add methods to efficiently process long iterables:
    • Translator.translate_iterable
    • Translator.score_iterable
    • Generator.generate_iterable
    • Generator.score_iterable
  • Add decoding option min_alternative_expansion_prob to filter out unlikely alternatives in return_alternatives mode
  • Return ScoringResult instances from score_batch to include additional outputs. The current attributes are:
    • tokens: the list of tokens that were actually scored (including special tokens)
    • log_probs: the log probability of each scored token
  • Support running score_batch asynchronously by setting the asynchronous flag

Fixes and improvements

  • Fix possibly incorrect results when using disable_unk or use_vmap with one of the following options:
    • min_decoding_length
    • no_repeat_ngram_size
    • prefix_bias_beta
    • repetition_penalty
  • Also pad the output layer during scoring to enable Tensor Cores
  • Improve the correctness of the model output probabilities when the output layer is padded
  • Skip translation when the NLLB input is empty (i.e. when the input only contains EOS and the language token)

v2.21.1 (2022-07-29)

Fixes and improvements

  • Fix conversion of NLLB models when tokenizer_class is missing from the configuration

v2.21.0 (2022-07-27)

New features

  • Support NLLB multilingual models via the Transformers converter
  • Support Pegasus summarization models via the Transformers converter

Fixes and improvements

  • Do not stop decoding when the EOS token is coming from the user input: this is required by some text generation models like microsoft/DialoGPT where EOS is used as a separator
  • Fix conversion error for language models trained with OpenNMT-py
  • Fix conversion of models that are not using bias terms in the multi-head attention
  • Fix data type error when enabling the translation options return_alternatives and return_attention with a float16 model
  • Improve CPU performance of language models quantized to int8
  • Implement a new vectorized GELU operator on CPU
  • Raise a more explicit error when trying to convert a unsupported Fairseq model
  • Update pybind11 to 2.10.0

v2.20.0 (2022-07-06)

New features

  • Generation option no_repeat_ngram_size to prevent the repetitions of N-grams with a minimum size

Fixes and improvements

  • Fix conversion of OpenNMT-tf models that use static position embeddings
  • Fix a segmentation fault in return_alternatives mode when the target prefix is longer than max_decoding_length
  • Fix inconsistent state of asynchronous results in Python when a runtime exception is raised
  • Remove <pad> token when converting MarianMT models from Transformers: this token is only used to start the decoder from a zero embedding, but it is not included in the original Marian model
  • Optimize CPU kernels with vectorized reduction of accumulated values
  • Do not modify the configuration passed to OpenNMTTFConverterV2.from_config
  • Improve Python classes documentation by listing members at the top

v2.19.1 (2022-06-23)

Fixes and improvements

  • Fix missing final bias in some MarianMT models converted from Transformers
  • Fix missing final layer normalization in OPT models converted from Transformers
  • Fix error when converting OpenNMT-tf V1 checkpoints with the new OpenNMT-tf converter
  • Reduce model conversion memory usage when the loaded weights are in FP16 and the model is converted with quantization
  • Add missing C++ type ctranslate2::float16_t in the public headers that is required to use some functions
  • Fix some Python typing annotations

v2.19.0 (2022-06-08)

New features

  • Support conversion of decoder-only Transformer models trained with OpenNMT-tf

Fixes and improvements

  • Fix conversion error for Transformers' model facebook/bart-large-cnn
  • Fix crash when scoring empty sequences
  • Apply max_input_length after all special tokens have been added to the input
  • Clear the GPU memory cache when no new batches are immediately available for execution
  • Improve functions signature in the generated Python API documentation
  • Update oneDNN to 2.6
  • Update spdlog to 1.10.0
  • Update OpenBLAS to 0.3.20

v2.18.0 (2022-05-23)

New features

  • Support Meta's OPT models via the Transformers converter
  • Extend the Fairseq converter to support transformer_lm models

Fixes and improvements

  • Fix conversion error for Marian's pre-norm Transformer models
  • Fix conversion error for Transformers' MarianMT models that are missing some configuration fields
  • Improve conversion speed of Marian models (optimize the generation of the sinusoidal position encodings)

v2.17.0 (2022-05-09)

New features

  • Add a converter for Hugging Face's Transformers. The following models are currently supported:
    • BART
    • M2M100
    • MarianMT
    • MBART
    • OpenAI GPT2
  • Revisit the OpenNMT-tf converter to better support custom models and configurations:
    • Extend the conversion script to accept the training configuration
    • Add a new converter class ctranslate2.converters.OpenNMTTFConverterV2
  • Move all documentation and guides to the website to improve navigation and clarity

Fixes and improvements

  • In text generation, include the start token in the output if it is not the BOS token

v2.16.0 (2022-04-28)

New features

  • Initial support of language models:
    • Add a high-level class ctranslate2.Generator to generate text with language models
    • Add a converter for OpenAI GPT-2 models
    • Update the OpenNMT-py converter to support transformer_lm decoders
  • Build ARM64 wheels for macOS
  • Allow loading custom Fairseq extensions and architectures during conversion with the option --user_dir
  • Enable conversion of the Fairseq architectures multilingual_transformer and multilingual_transformer_iwslt_de_en
  • Implement random sampling in beam search using the Gumbel-max trick
  • Generate and publish the Python API reference to https://opennmt.net/CTranslate2

Fixes and improvements

  • Fix model loading on a GPU with index > 0
  • Fix memory error when running random sampling on GPU with certain batch sizes
  • Fix incorrect tokens order in some converted Marian vocabularies
  • Properly count the number of layers before building the encoder/decoder instead of relying on runtime exceptions

v2.15.1 (2022-04-04)

Fixes and improvements

  • Fix missing deactivation of OpenMP threading in GPU execution (regression introduced in version 2.15.0)

v2.15.0 (2022-04-04)

New features

  • Expose translator option max_queued_batches to configure the maximum number of queued batches (when the queue is full, future requests will block until a free slot is available)
  • Allow converters to customize the vocabulary special tokens <unk>, <s>, and </s>

Fixes and improvements

  • Fix compatibility of models converted on Windows with other platforms by saving the vocabulary files with the newline character "\n" instead of "\r\n"
  • Clarify conversion error when no TensorFlow checkpoints are found in the configured model directory
  • Enable fused QKV transposition by switching the heads and time dimensions before the QKV split
  • Cache the prepared source lengths mask in the Transformer decoder state and reuse it in the next decoding steps
  • Pad the output layer to enable Tensor Cores only once instead of updating the layer on each batch
  • Vectorize copy in Concat and Split ops on GPU
  • Factorize all OpenMP parallel for loops to call the parallel_for function
  • Compile CUDA kernels for deprecated Compute Capabilities that are not yet dropped by CUDA:
    • CUDA 11: 3.5 and 5.0
    • CUDA 10: 3.0

v2.14.0 (2022-03-16)

New features

  • Include BART and MBART in the list of supported Fairseq architectures
  • Add Fairseq converter option --no_default_special_tokens to require all special tokens to be set by the user during inference, including the decoder start tokens (for example, this is required by MBART-25 to properly set the language tokens)

Fixes and improvements

  • Fix conversion of Post-Norm Transformers trained with OpenNMT-tf
  • Fix scoring with Fairseq models that used an incorrect decoder start token (Fairseq uses </s> as the decoder start token, not <s>)
  • Fix scoring result to include the end of sentence token
  • Ignore OpenNMT-py options --alignment_layer and --alignment_heads for models that are not trained with alignments
  • Enable batch encoding in return_alternatives translation mode (the decoding still runs sequentially)
  • Make enumerations ctranslate2.specs.Activation and ctranslate2.specs.EmbeddingsMerge public since they could be used to configure the Transformer specification
  • Update oneDNN to 2.5.3
  • Update cpu_features to 0.7.0
  • Update cxxopts to 3.0.0
  • Update spdlog to 1.9.2

v2.13.1 (2022-03-02)

Fixes and improvements

  • Fix conversion error for old OpenNMT-py models that do not have the option self_attn_type

v2.13.0 (2022-02-28)

New features

  • Add converter for Marian and support the collection of OPUS-MT pretrained models
  • Support models applying a layer normalization after the embedding layer (cf. option --layernorm-embedding in Fairseq)
  • Support models using the Swish (a.k.a SiLU) activation function
  • Support models using custom decoder start tokens, which can be passed in the target prefix

Fixes and improvements

  • Remove unexcepted call to a CUDA function in CPU execution when unloading models
  • Add option groups in the translation client help output
  • Use new thrust::cuda::par_nosync execution policy when calling Thrust functions
  • Update Thrust to 1.16.0
  • Update pybind11 to 2.9.1

v2.12.0 (2022-02-01)

New features

  • Support models using additional source features (a.k.a. factors)

Fixes and improvements

  • Fix compilation with CUDA < 11.2
  • Fix incorrect revision number reported in the error message for unsupported model revisions
  • Improve quantization correctness by rounding the value instead of truncating (this change will only apply to newly converted models)
  • Improve default value of intra_threads when the system has less than 4 logical cores
  • Update oneDNN to 2.5.2

v2.11.0 (2022-01-11)

Changes

  • With CUDA >= 11.2, the environment variable CT2_CUDA_ALLOCATOR now defaults to cuda_malloc_async which should improve performance on GPU.

New features

  • Build Python wheels for AArch64 Linux

Fixes and improvements

  • Improve performance of Gather CUDA kernel by using vectorized copy
  • Update Intel oneAPI to 2022.1
  • Update oneDNN to 2.5.1
  • Log some additional information with CT2_VERBOSE >= 1:
    • Location and compute type of loaded models
    • Version of the dynamically loaded cuBLAS library
    • Selected CUDA memory allocator

v2.10.1 (2021-12-15)

Fixes and improvements

  • Fix stuck execution when loading a model on a second GPU
  • Fix numerical error in INT8 quantization on macOS

v2.10.0 (2021-12-13)

Changes

  • inter_threads now also applies to GPU translation, where each translation thread is using a different CUDA stream to allow some parts of the GPU execution to overlap

New features

  • Add option disable_unk to disable the generation of unknown tokens
  • Add function set_random_seed to fix the seed in random sampling
  • [C++] Add constructors in Translator and TranslatorPool classes with ModelReader parameter

Fixes and improvements

  • Fix incorrect output from the Multinomial op when running on GPU with a small batch size
  • Fix Thrust and CUB headers that were included from the CUDA installation instead of the submodule
  • Fix static library compilation with the default build options (cmake -DBUILD_SHARED_LIBS=OFF)
  • Compile the Docker image and the Linux Python wheels with SSE 4.1 (vectorized kernels are still compiled for AVX and AVX2 with automatic dispatch, but other source files are now compiled with SSE 4.1)
  • Enable /fp:fast for MSVC to mirror -ffast-math that is enabled for GCC and Clang
  • Statically link against oneDNN to reduce the size of published binaries:
    • Linux Python wheels: 43MB -> 17MB
    • Windows Python wheels: 41MB -> 11MB
    • Docker image: 733MB -> 600MB

v2.9.0 (2021-12-01)

New features

  • Add GPU support to the Windows Python wheels
  • Support OpenNMT-py and Fairseq options --alignment_layer and --alignment_heads which specify how the multi-head attention is reduced and returned by the Transformer decoder
  • Support dynamic loading of CUDA libraries on Windows

Fixes and improvements

  • Fix division by zero when normalizing the score of an empty target
  • Fix error that was not raised when the input length is greater than the number of position encodings
  • Improve performance of random sampling on GPU for large values of sampling_topk or when sampling over the full vocabulary
  • Include transformer_align and transformer_wmt_en_de_big_align in the list of supported Fairseq architectures
  • Add a CUDA kernel to prepare the length mask to avoid moving back to the CPU

v2.8.1 (2021-11-17)

Fixes and improvements

  • Fix dtype error when reading float16 scores in greedy search
  • Fix usage of MSVC linker option /nodefaultlib that was not correctly passed to the linker

v2.8.0 (2021-11-15)

Changes

  • The Linux Python wheels now use Intel OpenMP instead of GNU OpenMP for consistency with other published binaries

New features

  • Build Python wheels for Windows

Fixes and improvements

  • Fix segmentation fault when calling Translator.unload_model while an asynchronous translation is running
  • Fix implementation of repetition penalty that should be applied to all previously generated tokens and not just the tokens of the last step
  • Fix missing application of repetition penalty in greedy search
  • Fix incorrect token index when using a target prefix and a vocabulary mapping file
  • Set the OpenMP flag when compiling on Windows with -DOPENMP_RUNTIME=INTEL or -DOPENMP_RUNTIME=COMP

v2.7.0 (2021-11-03)

Changes

  • Inputs are now truncated after 1024 tokens by default (see translation option max_input_length)

New features

  • Add translation option max_input_length to limit the model input length
  • Add translation option repetition_penalty to apply an exponential penalty on repeated sequences
  • Add scoring option with_tokens_score to also output token-level scores when scoring a file

Fixes and improvements

  • Adapt the length penalty formula when using normalize_scores to match other implementations: the scores are divided by pow(length, length_penalty)
  • Implement LayerNorm with a single CUDA kernel instead of 2
  • Simplify the beam search implementation

v2.6.0 (2021-10-15)

New features

  • Build wheels for Python 3.10
  • Accept passing the vocabulary as a opennmt.data.Vocab object or a list of tokens in the OpenNMT-tf converter

Fixes and improvements

  • Fix segmentation fault in greedy search when normalize_scores is enabled but not return_scores
  • Fix segmentation fault when min_decoding_length and max_decoding_length are both set to 0
  • Fix segmentation fault when option sampling_topk is larger than the vocabulary size
  • Fix incorrect score normalization in greedy search when max_decoding_length is reached
  • Fix incorrect score normalization in the return_alternatives translation mode
  • Improve error checking when reading the binary model file
  • Apply LogSoftMax in-place during decoding and scoring

v2.5.1 (2021-10-04)

Fixes and improvements

  • Fix logic error in the in-place implementation of the Gather op that could lead to incorrect beam search outputs

v2.5.0 (2021-10-01)

New features

  • Add an 8-bit GEMM backend on AArch64 using Ruy

Fixes and improvements

  • Skip unnecessary transpositions of the projected decoder queries in the multi-head attention
  • Use 32-bit indexing in all CUDA kernels to slightly improve performance
  • Let the compiler auto-vectorize the LayerNorm CPU kernel
  • Update Intel oneAPI to 2021.4

v2.4.0 (2021-09-10)

New features

  • [Python] Support asynchronous translation: translate_batch can return future-like objects with argument asynchronous=True
  • [Python] translate_batch now returns a list of TranslationResult objects instead of a list of dictionaries (this object can also be indexed as a list of dictionaries for backward compatibility)
  • Add options --source_lang and --target_lang to the Fairseq converter for models that do not include these information

Fixes and improvements

  • Fix Fairseq model conversion when the model options are stored in model["cfg"]["model"]
  • Compile the CPU INT8 quantization kernel with FMA instructions
  • Enable packing of the last linear weight when not using dynamic vocabulary reduction
  • Replace the generic Tile implementation by dedicated CPU and CUDA kernels
  • [Python] Implement __repr__ method for TranslationStats objects
  • [Python] Update pybind11 to 2.7.1

v2.3.2 (2021-08-05)

Fixes and improvements

  • Fix GPU execution that gets stuck when applying the GELU activation

v2.3.1 (2021-07-28)

Fixes and improvements

  • Fix compilation with CUDA < 10.2

v2.3.0 (2021-07-26)

New features

  • Add compute type int8_float16 for mixed INT8 and FP16 computation on GPU (requires Compute Capability >= 7.0)
  • Add methods Translator.score_batch and Translator.score_file to score existing translations

Fixes and improvements

  • Relax the GPU driver requirement for running the Docker image to >= 450.80.02 (same as the published Python package)

v2.2.0 (2021-07-06)

New features

  • Add Python utility functions to query the system capabilities:
    • ctranslate2.get_cuda_device_count
    • ctranslate2.get_supported_compute_types
  • Add option fixed_dictionary in the Fairseq converter to support multilingual models
  • Extend environment variable CT2_VERBOSE to configure more log levels (see README)

Fixes and improvements

  • Fuse activation with bias addition on GPU for a small performance increase
  • Make the GELU activation compatible with FP16 execution
  • Improve the log format using the spdlog library
  • Improve the accuracy of the profiling results on GPU
  • Update Intel oneAPI to 2021.3

v2.1.0 (2021-06-14)

New features

  • Support conversion of Transformer models trained with Fairseq (see script ct2-fairseq-converter)
  • Support conversion of models using GELU activations
  • Add translation option normalize_scores to return scores normalized by the hypotheses length: enabling this option can improve the beam search output for some models
  • Add translation option allow_early_exit to toggle the beam search early exit optimization: disabling this option has a small negative impact on performance, but it can improve the beam search output when using penalties or normalized scores
  • [C++] Add class BufferedTranslationWrapper to buffer and batch independent inputs to the same model

Fixes and improvements

  • Read value of environment variable OMP_NUM_THREADS when intra_threads is not set
  • Improve file translation performance by enabling local sorting by default
  • [Python] Improve error message when converting unsupported models and list all options that are unuspported
  • [Python] Return statistics of Translator.translate_file as an object with named properties
  • [C++] Fix compilation of method TranlatorPool::consume_raw_text_file that takes streams as inputs

v2.0.0 (2021-06-03)

This major version introduces some breaking changes to simplify model conversion, improve the consistency of user options, and update the Python package to CUDA 11.x. It also comes with internal improvements to facilitate future changes.

Breaking changes

General

  • Disable return_scores by default as most applications do not use translation scores
  • Replace all Docker images by a single one: <version>-ubuntu20.04-cuda11.2
  • Replace CMake option LIB_ONLY by BUILD_CLI
  • Require CMake version >= 3.15 for GPU compilation

Python

  • For GPU execution, the Linux Python wheels published on PyPI now require CUDA 11.x to be installed on the system. The CUDA dependencies (e.g. cuBLAS) are no longer included in the package and are loaded dynamically.
  • Remove support for converting the TensorFlow SavedModel format (checkpoints should be converted instead)
  • Remove the model_spec option for converters that can automatically detect it from the checkpoints
  • Force translation options to be set with keyword arguments only (see the API reference)
  • Rename tokenization callables arguments in translate_file for clarity:
    • tokenize_fn to source_tokenize_fn
    • detokenize_fn to target_detokenize_fn

CLI

  • Rename length contraints options for consistency with other APIs:
    • max_sent_length to max_decoding_length
    • min_sent_length to min_decoding_length

C++

  • Move the max_batch_size and batch_type options from the TranslationOptions structure to the translation methods of TranslatorPool
  • Simplify the TranslationResult structure with public attributes instead of methods
  • Asynchronous translation API now returns one future per example instead of a single future for the batch

New features

  • Add translation option prefix_bias_beta to bias the decoding towards the target prefix (see Arivazhagan et al. 2020)
  • Automatically detect the model specification when converting OpenNMT-py models
  • Support conversion and execution of Post-Norm Transformers
  • Add an experimental asynchronous memory allocator for CUDA 11.2 and above (can be enabled with the environment variable CT2_CUDA_ALLOCATOR=cuda_malloc_async)
  • Expose the Python package version in ctranslate2.__version__

Fixes and improvements

  • Fix silent activation of replace_unknowns when enabling return_attention
  • Improve support for the NVIDIA Ampere architecture in prebuilt binaries
  • Reduce the size of the Python wheels published on PyPI
  • Define a custom CUDA kernel for the GEMM output dequantization instead of a Thrust-based implementation
  • Update Thrust to 1.12.0

v1.20.1 (2021-04-29)

Fixes and improvements

  • Do not return scores for empty outputs when return_scores is disabled
  • Do not include google/cpu_features library in CTranslate2 installation

v1.20.0 (2021-04-20)

Changes

  • Drop Python 3.5 support
  • Docker image tags suffixed with -gpu are no longer updated to prefer tags with an explicit CUDA version

Fixes and improvements

  • Fix int8 quantization for rows that only contains zeros
  • Fix type error when running the CUDA code path of the Multinomial operator
  • Add EOS score to the greedy search final score for consistency with the beam search output
  • Use third party library google/cpu_features to resolve CPU features at runtime
  • Small optimizations when manipulating tensor shapes and indices
  • Internal refactoring of Transformer layers

v1.19.0 (2021-03-31)

Changes

  • Rename CMake option WITH_TESTS to BUILD_TESTS

New features

  • Add "auto" compute type to automatically select the fastest compute type on the current system

Fixes and improvements

  • [Python] Clear memory allocator cache when calling unload_model
  • [Python] Make methods unload_model and load_model thread safe
  • Fix conversion of TensorFlow SavedModel with shared embeddings
  • Update Intel oneAPI to 2021.2
  • Compile core library with C++14 standard

v1.18.3 (2021-03-02)

Fixes and improvements

  • Use Intel OpenMP instead of GNU OpenMP in the Docker images as a workaround for issue #409.

v1.18.2 (2021-02-23)

Fixes and improvements

  • Fix crash when enabling coverage penalty in GPU translation
  • Fix incorrect value of AVX2 flag in CT2_VERBOSE output

v1.18.1 (2021-02-01)

Fixes and improvements

  • Fix conversion of models setting the attributes with_source_bos or with_source_eos

v1.18.0 (2021-01-28)

Changes

  • Some options default value in the translate client have been changed to match the Python API:
    • batch_size = 32 (instead of 30)
    • beam_size = 2 (instead of 5)
    • intra_threads = 4 (instead of 0)

New features

  • Support multi-GPU translation: device_index argument can now be set to a list of GPU IDs (see example)

Fixes and improvements

  • Improve performance when using multiple GPU translators concurrently in the same process
  • [Python] Do nothing when calling unload_model(to_cpu=True) on CPU translators
  • [Python] Set a default value for max_batch_size argument in method Translator.translate_file
  • Disable CT2_TRANSLATORS_CORE_OFFSET in OpenMP builds as setting thread affinity does not work when OpenMP is enabled

v1.17.1 (2021-01-15)

Fixes and improvements

  • Fix Python wheel loading error on macOS

v1.17.0 (2021-01-11)

Changes

  • Linux Python wheels are now compiled under manylinux2014 and require pip version >= 19.3

New features

  • Publish Python wheels for macOS (CPU only)
  • Support compilation for ARM 64-bit architecture and add NEON vectorization
  • Add new optional GEMM backends: Apple Accelerate and OpenBLAS
  • Add replace_unknowns translation option to replace unknown target tokens by source tokens with the highest attention
  • Add flags in the model specification to declare that BOS and/or EOS tokens should be added to the source sequences

Fixes and improvements

  • Fix segmentation fault when the model is converted with a wrong vocabulary and predicts an out-of-vocabulary index
  • Fix result of vectorized array reduction when the array length is not a multiple of the SIMD registers width
  • Fix exit code when running cli/translate -h
  • Improve performance of vectorized vector math by inlining calls to intrinsics functions
  • Improve accuracy of LogSoftMax CUDA implementation
  • Improve error message when --model option is not set in cli/translate
  • Update oneMKL to 2020.1 in published binaries
  • Update oneDNN to 2.0 in published binaries
  • Update default search paths to support compilation with oneMKL and oneDNN installed from the oneAPI toolkit

v1.16.2 (2020-11-27)

Fixes and improvements

  • Fix cuBLAS version included in the Python wheels published to PyPI. The included library was targetting CUDA 10.2 instead of CUDA 10.1.
  • Re-add Python 3.5 wheels on PyPI to give users more time to transition

v1.16.1 (2020-11-23)

Fixes and improvements

  • Fuse dequantization and bias addition on GPU for improved INT8 performance
  • Improve performance of masked softmax on GPU
  • Fix error when building the CentOS 7 GPU Docker image
  • The previous version listed "Pad size of INT8 matrices to a multiple of 16 when the GPU has INT8 Tensor Cores". However, the padding was not applied due to a bug and fixing it degraded the performance, so this behavior is not implemented for now.

v1.16.0 (2020-11-18)

Changes

  • Drop support for Python 2.7 and 3.5

New features

  • Add Docker images using CUDA 11.0

Fixes and improvements

  • Enable parallel CPU translations from translate_batch in Python when setting inter_threads > 1 and max_batch_size > 0
  • Improve GPU performance on Turing architecture when using a Docker image or the Python package
  • Pad size of INT8 matrices to a multiple of 16 when the GPU has INT8 Tensor Cores
  • Add information about detected GPU devices in CT2_VERBOSE output
  • Update oneDNN to 1.7
  • [Python] Improve type checking for some arguments

v1.15.0 (2020-11-06)

New features

  • [Experimental] The Python package published on PyPI now includes GPU support. The binary is compiled with CUDA 10.1, but all CUDA dependencies are integrated in the package and do not need to be installed on the system. The only requirement should be a working GPU with driver version >= 418.39.

Fixes and improvements

  • Remove the TensorRT dependency to simplify installation and reduce memory usage:
    • Reduce GPU Docker images size by 600MB
    • Reduce memory usage on the GPU and the system by up 1GB
    • Reduce initialization time during the first GPU translation
  • Improve TopK performance on GPU for K < 5
  • Improve INT8 performance on GPU
  • Accept linear layers without bias when converting models
  • Update Intel MKL to 2020.4
  • [Python] Improve compatibility with Python 3.9

v1.14.0 (2020-10-13)

New features

  • Accept target prefix in file translation APIs

Fixes and improvements

  • Fix CUDA illegal memory access when changing the beam size in the same process
  • Fix decoding with target prefix that sometimes did not go beyond the prefix
  • Fix Intel MKl search paths on macOS
  • Update Intel MKL to 2020.3
  • Clarify error message when selecting a CUDA device in CPU-only builds

v1.13.2 (2020-08-31)

Fixes and improvements

  • Fix model conversion to float16 when using the Python converters: weights were duplicated and not correctly converted
  • Fix incorrect code logic that could lead to incorrect translation results

v1.13.1 (2020-08-06)

Fixes and improvements

  • Fix performance regression when decoding with a large beam size on GPU

v1.13.0 (2020-07-30)

New features

  • Environment variable CT2_TRANSLATORS_CORE_OFFSET to pin parallel translators to a range of CPU cores (only for intra_threads = 1)
  • [Python] Add some properties to the Translator object:
    • device
    • device_index
    • num_translators
    • num_queued_batches
    • model_is_loaded

Fixes and improvements

  • Improve batch performance of target prefix
  • Improve performance when the input batch contains sentences with very different lengths
  • Improve beam search performance by expanding the batch size only after the first decoding step
  • Optimize Transpose op on GPU for the permutation used in multi-head attention
  • Remove padding in returned attention vectors
  • Update Intel MKL to 2020.2

v1.12.1 (2020-07-20)

Fixes and improvements

  • Fix implicit int16 to float16 model conversion on compatible GPUs

v1.12.0 (2020-07-16)

Changes

  • Docker images based on Ubuntu 16.04 are no longer updated

New features

  • Support float16 data type for model conversion (with --quantization float16) and computation (with --compute_type float16). FP16 execution can improve performance by up to 50% on NVIDIA GPUs with Compute Capability >= 7.0.
  • Add Docker images with newer CUDA versions, which can improve performance in some cases:
    • latest-ubuntu18-cuda10.0 (same as latest-ubuntu18-gpu)
    • latest-ubuntu18-cuda10.1
    • latest-ubuntu18-cuda10.2
    • latest-centos7-cuda10.0 (same as latest-centos7-gpu)
    • latest-centos7-cuda10.1
    • latest-centos7-cuda10.2
  • Allow setting a computation type per device (e.g. Translator(..., compute_type={"cuda": "float16", "cpu": "int8"}) with the Python API)
  • [C++] Add ModelReader interface to customize model loading

Fixes and improvements

  • Optimize Transpose op on CPU for the permutation used in multi-head attention
  • Optimize GELU op CPU with Intel MKL
  • Fix compilation when targeting an architecture and disabling ISA dispatch (e.g.: -DCMAKE_CXX_FLAGS="-march=skylake" -DENABLE_CPU_DISPATCH=OFF)
  • Inline some frequently called methods

v1.11.0 (2020-06-29)

New features

  • Add tokenization and detokenization hooks for file translation APIs
  • Add alternatives to Intel MKL:
    • Integrate oneDNN for GEMM functions
    • Implement vectorized operators that automatically select the instruction set architecture (ISA) (can be manually controlled with the CT2_FORCE_CPU_ISA environment variable)
  • When alternatives are available, avoid using Intel MKL on non Intel processors (can be manually controlled with the CT2_USE_MKL environment variable)
  • Enable a verbose mode with the environment variable CT2_VERBOSE=1 to help debugging the run configuration (e.g. the detected CPU, whether Intel MKL is being used, etc.)

Fixes and improvements

  • Improve numerical precision of SoftMax and LogSoftMax layers on CPU
  • Parallelize INT16 quantization/dequantization and ReLU on CPU
  • Add back the translation client in CentOS 7 Docker images

v1.10.2 (2020-06-23)

Fixes and improvements

  • [Python] Fix error when calling unload_model(to_cpu=True) for models with shared weights
  • [Python] Do not ignore errors when importing the compiled translator extension

v1.10.1 (2020-05-25)

Fixes and improvements

  • Force intra_threads to 1 when running a model on GPU to prevent high CPU load
  • Improve handling of decoding length constraints when using a target prefix
  • Do not raise an error when setting use_vmap but no vocabulary map exists

v1.10.0 (2020-04-17)

New features

  • Coverage penalty as in Wu et al. 2016 with the option coverage_penalty
  • Batch size can be expressed in number of tokens with the option batch_type
  • Translation scores can be disabled with the option return_scores (if disabled, the final SoftMax is skipped during greedy decoding)
  • Support compilation without TensorRT by setting -DWITH_TENSORRT=OFF during CMake configuration (in this case, beam search is no longer supported)
  • Experimental integration of Intel MKL's packed GEMM which can be enabled by setting the environment variable CT2_USE_EXPERIMENTAL_PACKED_GEMM=1

Fixes and improvements

  • Remove direct dependency to cuDNN (still an indirect dependency via TensorRT)
  • Static AVX optimization for the ReLU operator
  • Remove unnecessary memory initialization when creating temporary buffers
  • Dissociate SoftMax and LogSoftMax in profiling report

v1.9.1 (2020-04-08)

Fixes and improvements

  • Fix parallel translations when calling Translator.translate_batch from multiple Python threads
  • Fix crash on invalid num_hypotheses value

v1.9.0 (2020-03-24)

New features

  • Return 2 additional statistics from file translation APIs:
    • the number of translated examples
    • the total translation time in milliseconds

Fixes and improvements

  • Fix exceptions that were not catched by the Python wrapper
  • Fix an invalid insertion in the variables collection while iterating over it
  • Optimize filling operation of float storages
  • Internal refactoring of decoding functions to make them reusable for other tasks (e.g. generative language models)

v1.8.0 (2020-03-10)

New features

  • [Python] Add methods Translator.unload_model and Translator.load_model to manually manage memory
  • [Docker] Move all images to Python 3 only
  • Expose options that enable an internal sorting by length to increase the translation efficiency:
    • for file translation: read_batch_size contiguous examples will be loaded, sorted by length, and batched with size max_batch_size
    • for batch translation: if the batch is larger than max_batch_size, examples will be sorted by length and batched with size max_batch_size

Fixes and improvements

  • Fix another error when releasing a translator that is placed on a GPU that is not GPU 0
  • Fix possible memory corruption when creating GPU translators in parallel
  • Fix memory that is briefly allocated on GPU 0 when destroying a translator that is placed on another GPU
  • Reduce latency of model loading, especially on GPU

v1.7.1 (2020-03-03)

Fixes and improvements

  • Revert "Parallelize some low level transformations on CPU" which caused incorrect computation
  • Avoid unnecessary TensorFlow runtime initialization when converting checkpoints
  • Fix compilation without MKL

v1.7.0 (2020-02-28)

New features

  • Translation option return_alternatives to return multiple choices at the first unconstrained decoding position: combined with a target prefix, this could be used to provide alternative words and translations at a specific location in the target
  • Support Transformers with different number of encoder/decoder layers
  • Allow compilation without OpenMP with -DOPENMP_RUNTIME=NONE

Fixes and improvements

  • Fix SavedModel conversion when TensorFlow Addons 0.8 is installed
  • Fix error when releasing a translator/model that is placed on a GPU that is not GPU 0
  • Fix memory that was allocated on GPU 0 even when the translator/model was placed on another GPU
  • Query GPU int8 support on the first model load, and then cache the result for future loads
  • Avoid creating an empty model directory on conversion errors
  • Parallelize some low level transformations on CPU
  • Reduce memory usage when translating large files by limiting the work queue size

v1.6.3 (2020-02-24)

Fixes and improvements

  • Fix incorrectness in relative representation computation

v1.6.2 (2020-02-21)

Fixes and improvements

  • Fix conversion of models with shared embeddings

v1.6.1 (2020-02-11)

Fixes and improvements

  • [Docker] Remove translation client in CentOS 7 images as it can cause compatibility issues with downstream images

v1.6.0 (2020-02-14)

New features

  • Support Transformers with relative position representations (as in Shaw et al. 2018)
  • Accept target prefix in batch request
  • Support return_attention with prefixed translation

v1.5.1 (2020-02-06)

Fixes and improvements

  • Fix INT8 translation on CPU with vocabulary map

v1.5.0 (2020-02-06)

New features

  • [C++] Add max_batch_size translation options for single translators

Fixes and improvements

  • Improve INT8 performance on CPU
  • Enable INT8 support on default Intel MKL build
  • Simplify project dependencies:
    • Replace boost::program_options with cxxopts for client options
    • Include header-only dependencies as Git submodules (cxxopts, cub, and thrust)
    • Remove MKL-DNN
  • Harmonize Python/C++ default values:
    • [Python] Change default beam size from 4 to 2
    • [C++] Load models on the CPU by default

v1.4.0 (2020-01-20)

New features

  • Publish a package on PyPI (without GPU support)
  • Add method to convert OpenNMT-tf models directly from a dictionary of variables
  • Return statistics from Python method Translator.translate_file
  • Add set_model methods to support changing models without creating a new Translator
  • Add a contains_model function to check whether a directory could contain a CTranslate2 model

v1.3.0 (2020-01-14)

New features

  • Support random sampling (see the sampling_topk and sampling_temperature translation options)
  • CT2_CUDA_CACHING_ALLOCATOR_CONFIG environment variable to configure the CUDA caching allocator

Fixes and improvements

  • Fix incorrect translations on Windows due to incompatibility between the compiler OpenMP and Intel OpenMP
  • Release cuDNN/cuBLAS/TensorRT handles on thread exit when destroying a TranslatorPool
  • Remove use of --{start,end}-group compiler options when compiling on Mac OS
  • Update Intel MKL to 2020.0 in Docker images
  • Load vocabulary assets for SavedModel exported with OpenNMT-tf 2.5 and above

v1.2.3 (2019-12-11)

Fixes and improvements

  • Improve translator robustness on empty batch and inputs
  • Speed optimization for LayerNorm
  • Check vocabulary size when converting OpenNMT-tf models
  • Add more samples in the execution profiling output which now supports nested functions

v1.2.2 (2019-11-25)

Fixes and improvements

  • Fix PositionEncoder internal state that was shared with other instances on the same thread
  • Replace Boost.Python by pybind11
  • Include a Python source distribution in the Docker images

v1.2.1 (2019-11-06)

Fixes and improvements

  • Avoid copying decoder states when possible to improve decoding performance (10% to 20% faster)
  • Fix execution profiling on GPU (device was not synchronized before measuring the time)
  • Include Mul operation in profiling report
  • Add a Python 3 wheel in Ubuntu Docker images

v1.2.0 (2019-10-28)

New features

  • Accept Transformer models with custom number of layers and heads
  • --log-profiling client option to profile ops execution

Fixes and improvements

  • Fix conversion error for models having 2 different weights with the same values
  • Fix invalid MKL function override after a refactoring
  • Add more information and context to several error messages

v1.1.0 (2019-10-18)

New features

  • New Docker images: latest-ubuntu16-gpu, latest-ubuntu18, latest-ubuntu18-gpu
  • Support OpenNMT-tf Transformer models with shared embeddings
  • Update to TensorRT 6
  • Make OpenMP runtime configurable

Fixes and improvements

  • Reduce the size of models with shared weights on disk and in memory
  • Shared words vocabulary is no longer duplicated on disk and in memory
  • Improve performance of translation with a vocabulary map on GPU
  • Statically link against Intel MKL
  • Remove some implementation details from public headers

v1.0.1 (2019-10-08)

Fixes and improvements

  • Fix loading of newer OpenNMT-py models
  • Promote FP16 to FP32 in model converter scripts
  • Improve INT8 performance on CPU and GPU
  • Improve performance on GPU by fusing the layer normalization operation x * gamma + beta
  • Enable INT8 and INT16 computation on all platforms with Intel MKL 2019.5 and above

v1.0.0 (2019-09-23)

First stable release.