Skip to content

Releases: microsoft/onnxruntime

ONNX Runtime v1.18.0

21 May 00:28
@yihonglyu yihonglyu
v1.18.0
4573740
This commit was created on GitHub.com and signed with GitHub’s verified signature.
GPG key ID: B5690EEEBB952194
Learn about vigilant mode.
Compare
Choose a tag to compare
ONNX Runtime v1.18.0 Latest
Latest

Announcements

  • Windows ARM32 support has been dropped at the source code level.
  • Python version >=3.8 is now required for build.bat/build.sh (previously >=3.7). Note: If you have Python version <3.8, you can bypass the tools and use CMake directly.
  • The onnxruntime-mobile Android package and onnxruntime-mobile-c/onnxruntime-mobile-objc iOS cocoapods are being deprecated. Please use the onnxruntime-android Android package, and onnxruntime-c/onnxruntime-objc cocoapods, which support ONNX and ORT format models and all operators and data types. Note: If you require a smaller binary size, a custom build is required. See details on creating a custom Android or iOS package on Custom build | onnxruntime.

Build System & Packages

  • CoreML execution provider now depends on coremltools.
  • Flatbuffers has been upgraded from 1.12.0 → 23.5.26.
  • ONNX has been upgraded from 1.15 → 1.16.
  • EMSDK has been upgraded from 3.1.51 → 3.1.57.
  • Intel neural_speed library has been upgraded from v0.1.1 → v0.3 with several important bug fixes.
  • There is a new onnxruntime_CUDA_MINIMAL CMake option for building ONNX Runtime CUDA execution provider without any operations apart from memcpy ops.
  • Added support for Catalyst for macOS build support.
  • Added initial support for RISC-V and three new build options for it: --rv64, --riscv_toolchain_root, and --riscv_qemu_path.
  • Now you can build TensorRT EP with protobuf-lite instead of the full version of protobuf.
  • Some security-related compile/link flags have been moved from the default setting → new build option: --use_binskim_compliant_compile_flags. Note: All our release binaries are built with this flag, but when building ONNX Runtime from source, this flag is default OFF.
  • Windows ARM64 build now depends on PyTorch CPUINFO library.
  • Windows OneCore build now uses “Reverse forwarding” apisets instead of “Direct forwarding”, so onnxruntime.dll in our Nuget packages will depend on kernel32.dll. Note: Windows systems without kernel32.dll need to have reverse forwarders (see API set loader operation - Win32 apps | Microsoft Learn for more information).

Core

  • Added ONNX 1.16 support.
  • Added additional optimizations related to Dynamo-exported models.
  • Improved testing infrastructure for EPs developed as shared libraries.
  • Exposed Reserve() in OrtAllocator to allow custom allocators to work when session.use_device_allocator_for_initializers is specified.
  • Improved lock contention due to memory allocations.
  • Improved session creation time (graph and graph transformer optimizations).
  • Added new SessionOptions config entry to disable specific transformers and rules.
  • [C# API] Exposed SessionOptions.DisablePerSessionThreads to allow sharing of threadpool between sessions.
  • [Java API] Added CUDA 12 Java support.

Performance

  • Improved 4bit quant support:
    • Added HQQ quantization support to improve accuracy.
    • Implemented general GEMM kernel and improved GEMV kernel performance on GPU.
    • Improved GEMM kernel quality and performance on x64.
    • Implemented general GEMM kernel and improved GEMV performance on ARM64.
  • Improved MultiheadAttention performance on CPU.

Execution Providers

  • TensorRT

    • Added support for TensorRT 10.
    • Finalized support for DDS ops.
    • Added Python support for user provided CUDA stream.
    • Fixed various bugs.

  • CUDA

    • Added support of multiple CUDA graphs.
    • Added a provider option to disable TF32.
    • Added Python support for user provided CUDA stream.
    • Extended MoE to support of Tensor Parallelism and int4 quantization.
    • Fixed bugs in BatchNorm and TopK kernel.

  • QNN

    • Added support for up to QNN SDK 2.22.
    • Upgraded support from A16W8 → mixed 8/16-bit precision configurability per layer.
    • Added fp16 execution support via enable_htp_fp16 option.
    • Added multiple partition support for QNN context binary.
    • Expanded operator support and fixed various bugs.
    • Added support for per-channel quantized weights for Conv.
    • Integration with Qualcomm’s AIHub.

  • OpenVINO

    • Added support for up to OpenVINO 2024.1.
    • Added support for importing pre-compiled blob as EPContext blob.
    • Separated device and precision as inputs by removing support for device_id in provider options and adding precision as separate CLI option.
    • Deprecated CPU_FP32 and GPU_FP32 terminology and introduced CPU and GPU terminology.
    • AUTO:GPU,CPU will only create GPU blob, not CPU blob.

  • DirectML

    • Additional ONNX operator support: Resize-18 and Resize-19, Col2Im-18, InNaN-20, IsInf-20, and ReduceMax-20.
    • Additional contrib op support: SimplifiedLayerNormalization, SkipSimplifiedLayerNormalization, QLinearAveragePool, MatMulIntegerToFloat, GroupQueryAttention, DynamicQuantizeMatMul, and QAttention.

Mobile

  • Improved performance of ARM64 4-bit quantization.
  • Added support for building with QNN on Android.
  • Added MacCatalyst support.
  • Added visionOS support.
  • Added initial support for creating ML Program format CoreML models.
  • Added support for 1D Conv and ConvTranspose to XNNPACK EP.

Web

  • Added WebNN EP preview.
  • Improved WebGPU performance (MHA, ROE).
  • Added more WebGPU and WebNN examples.
  • Increased generative model support.
  • Optimized Buffer management to reduce memory footprint.

Training

  • Large Model Training
    • Added optimizations for Dynamo-exported models.
    • Added Mixtral integration using ORT backend.
  • On-Device Training
    • Added support for models >2GB to enable SLM training on edge devices.

GenAI

  • Added additional model support: Phi-3, Gemma, LLama-3.
  • Added DML EP support.
  • Improved tokenizer quality.
  • Improved sampling method and ORT model performance.

Extensions

  • Created Java packaging pipeline and published to Maven repository.
  • Added support for conversion of Huggingface FastTokenizer into ONNX custom operator.
  • Unified the SentencePiece tokenizer with other Byte Pair Encoding (BPE) based tokenizers.
  • Fixed Whisper large model pre-processing bug.
  • Enabled eager execution for custom operator and refactored the header file structure.

Known Issues

  • We are still in the process of uploading ORT Training packages to ADO and PyPI.

Contributors

Yi Zhang, Yulong Wang, Adrian Lizarraga, Changming Sun, Scott McKay, Tianlei Wu, Peng Wang, Hector Li, Edward Chen, Dmitri Smirnov, Patrice Vignola, Guenther Schmuelling, Ye Wang, Chi Lo, Wanming Lin, Xu Xing, Baiju Meswani, Peixuan Zuo, Vincent Wang, Markus Tavenrath, Lei Cao, Kunal Vaishnavi, Rachel Guo, Satya Kumar Jandhyala, Sheil Kumar, Yifan Li, Jiajia Qin, Maximilian Müller, Xavier Dupré, Yi-Hong Lyu, Yufeng Li, Alejandro Cid Delgado, Adam Louly, Prathik Rao, wejoncy, Zesong Wang, Adam Pocock, George Wu, Jian Chen, Justin Chu, Xiaoyu, guyang3532, Jingyan Wang, raoanag, Satya Jandhyala, Hariharan Seshadri, Jiajie Hu, Sumit Agarwal, Peter Mcaughan, Zhijiang Xu, Abhishek Jindal, Jake Mathern, Jeff Bloomfield, Jeff Daily, Linnea May, Phoebe Chen, Preetha Veeramalai, Shubham Bhokare, Wei-Sheng Chin, Yang Gu, Yueqing Zhang, Guangyun Han, inisis, ironman, Ivan Berg, Liqun Fu, Yu Luo, Rui Ren, Sahar Fatima, snadampal, wangshuai09, Zhenze Wang, Andrew Fantino, Andrew Grigorev, Ashwini Khade, Atanas Dimitrov, AtomicVar, Belem Zhang, Bowen Bao, Chen Fu, Dhruv Matani, Fangrui Song, Francesco, Frank Dong, Hans Chen, He Li, Heflin Stephen Raj, Jambay Kinley, Masayoshi Tsutsui, Matttttt, Nanashi, Phoebe Chen, Pranav Sharma, Segev Finer, Sophie Schoenmeyer, TP Boudreau, Ted Themistokleous, Thomas Boby, Xiang Zhang, Yongxin Wang, Zhang Lei, aamajumder, danyue, Duansheng Liu, enximi, fxmarty, kailums, maggie1059, mindest, mo-ja, moyo1997
Big thank you to everyone who contributed to this release!

Assets 24

ONNX Runtime v1.17.3

18 Apr 15:46
@sophies927 sophies927
v1.17.3
56b660f
This commit was created on GitHub.com and signed with GitHub’s verified signature.
GPG key ID: B5690EEEBB952194
Learn about vigilant mode.
Compare
Choose a tag to compare
ONNX Runtime v1.17.3

What's new?

General:

  • Update copying API header files to make Linux logic consistent with Windows (#19736) - @mszhanyi
  • Pin ONNX version to fix DML and Python packaging pipeline exceptions (#20073) - @mszhanyi

Build System & Packages:

  • Fix minimal build with training APIs enabled bug affecting Apple framework (#19858) - @edgchen1

Core:

CUDA EP:

TensorRT EP:

Web:

Windows:

  • Fix Windows memory mapping bug affecting some larger models (#19623) - @yufenglee

Kernel Optimizations:

  • Fix GQA and Rotary Embedding bugs affecting some models (#19801, #19874) - @aciddelgado
  • Update replacement of MultiHeadAttention (MHA) and GroupQueryAttention (GQA) (#19882) - @kunal-vaishnavi
  • Add support for packed QKV input and Rotary Embedding with sm<80 using Memory Efficient Attention kernel (#20012) - @aciddelgado

Models:

This patch release also includes additional fixes by @spampana95 and @enximi. Big thank you to all our contributors!

Contributors

gyagp, Craigacp, and 22 other contributors
Assets 24

ONNX Runtime v1.17.1

27 Feb 18:34
@YUNQIUGUO YUNQIUGUO
v1.17.1
8f5c79c
This commit was created on GitHub.com and signed with GitHub’s verified signature.
GPG key ID: B5690EEEBB952194
Learn about vigilant mode.
Compare
Choose a tag to compare
ONNX Runtime v1.17.1

This patch release includes the following updates:

General

  • Update thread affinity on server so it is only set with auto affinity (#19318) - @ivberg

Build System and Packages

  • Fix bug that was breaking arm64 build by disabling __cpuid check on arm64 builds since intrinsic is not available (#19574) - @smk2007

Core

  • Add capturestate / rundown ETW support logging for session and provider options (#19397) - @ivberg
  • Restrict L2 cache core check on Intel devices (#19483) - @smk2007

Performance

  • Optimize KahnsTopologicalSort and PriorityNodeCompare to fix performance degradation in session creation time that was affecting many models (#19475) - @smk2007

EPs

  • Enable DirectML on Windows and CUDA on Linux for Node.js binding (#19274) - @jchen351

QNN

OpenVINO

DirectML

Web

Training

  • Reduce onnxruntime-training package size so it can be published on PyPI (#19486) - @baijumeswani
  • Update default std flag used during torch extensions compilation (#19516) - @baijumeswani
  • Add ATen fallback support for bicubic interpolation algorithm (#19380) - @prathikr

Quantization

Whisper Model

Contributors

PatriceVignola, smk2007, and 12 other contributors
Assets 23

ONNX Runtime v1.17.0

03 Feb 00:25
@YUNQIUGUO YUNQIUGUO
v1.17.0
5f0b62c
This commit was created on GitHub.com and signed with GitHub’s verified signature.
GPG key ID: B5690EEEBB952194
Learn about vigilant mode.
Compare
Choose a tag to compare
ONNX Runtime v1.17.0

Announcements

In the next release, we will totally drop support for Windows ARM32.

General

Build System and Packages

  • Dropped CentOS 7 support. All Linux binaries now require glibc version >=2.28, but users can still build the source code for a lower glibc version.
  • Added CUDA 12 packages for Python and Nuget.
  • Added Python 3.12 packages for ONNX Runtime Inference. ONNX Runtime Training Python 3.12 packages cannot be provided at this time since training packages depend on PyTorch, which does not support Python 3.12 yet.
  • Linux binaries (except those in AMD GPU packages) are built in a more secure way that is compliant with BinSkim's default policy (e.g., the binaries no longer have an executable stack).
  • Added support for Windows ARM64X for users who build ONNX Runtime from source. No prebuilt package provided yet.
  • Removed Windows ARM32 binaries from official packages. Users who still need these binaries can build them from source.
  • Added AMD GPU package with ROCm and MiGraphX (Python + Linux only).
  • Split ONNX Runtime GPU Nuget package into two packages.
  • When building the source code for Linux ARM64 or Android, the C/C++ compiler must support BFloat16. Support for Android NDK 24.x has been removed. Please use NDK 25.x or 26.x instead.
  • Link time code generation (LTCG or LTO) is now disabled by default when building from source. To re-enable it, users can add "--enable_lto" to the build command. All prebuilt binaries are still built with LTO.

Core

  • Optimized graph inlining.
  • Allow custom op to invoke internal thread-pool for parallelism.
  • Added support for supplying a custom logger at the session level.
  • Added new logging and tracing of session and execution provider options.
  • Added new dynamic ETW provider that can trace/diagnose ONNX internals while maintaining great performance.

Performance

  • Added 4bit quant support on NVIDIA GPU and ARM64.

EPs

TensorRT EP

  • Added support for direct load of precompiled TensorRT engines and customizable engine prefix.
  • Added Python support for TensorRT plugins via ORT custom ops.
  • Fixed concurrent Session::Run bugs.
  • Updated calls to deprecated TensorRT APIs (e.g., enqueue_v2 → enqueue_v3).
  • Fixed various memory leak bugs.

QNN EP

  • Added support for QNN SDK 2.18.
  • Added context binary caching and model initialization optimizations.
  • Added mixed precision (8/16 bit) quantization support.
  • Add device-level session options (soc_model, htp_arch, device_id), extreme_power_saver for htp_performance_mode, and vtcm_mb settings.
  • Fixed multi-threaded inference bug.
  • Fixed various other bugs and added performance improvements.
  • QNN profiling of the NPU can be enabled dynamically with ETW or write out to CSV.

OpenVINO EP

  • Added support for OpenVINO 2023.2.
  • Added AppendExecutionProvider_OpenVINO_V2 API for supporting new OpenVINO EP options.

DirectML EP

  • Updated to DirectML 1.13.1.
  • Updated operators LpPool-18 and AveragePool-19 with dilations.
  • Improved Python I/O binding support.
  • Added RotaryEmbedding.
  • Added support for fusing subgraphs into DirectML execution plans.
  • Added new Python API to choose a specific GPU on multi-GPU devices with the DirectML EP.

Mobile

  • Added initial support for 4bit quantization on ARM64.
  • Extended CoreML/NNAPI operator coverage.
  • Added support for YOLOv8 pose detection pre/post processing.
  • Added support for macOS in CocoaPods package.

Web

  • Added support for external data format.
  • Added support for I/O bindings.
  • Added support for training.
  • Added WebGPU optimizations.
  • Transitioned WebGPU out of experimental.
  • Added FP16 support for WebGPU.

Training

Large Model Training

  • Enabled support for QLoRA (with support for BFloat16).
  • Added symbolic shape support for Triton codegen (see PR).
  • Made improvements to recompute optimizer with easy ON/OFF to allow layer-wise recompute (see PR).
  • Enabled memory-efficient gradient management. For Mistral, we see ~10GB drop in memory consumption when this feature is ON (see PR).
  • Enabled embedding sparsity optimizations.
  • Added support for Aten efficient attention and Triton Flash Attention (see PR).
  • Packages now available for CUDA 11.8 and 12.1.

On Device Training

  • On-Device training will now support training on the web. This release focuses on federated learning and developer exploration scenarios. More features coming soon in future releases.

Extensions

  • Modified gen_processing_model tokenizer model to output int64, unifying output datatype of all tokenizers.
  • Implemented support for post-processing of YOLO v8 within the Python extensions package.
  • Introduced 'fairseq' flag to enhance compatibility with certain Hugging Face tokenizers.
  • Incorporated 'added_token' attribute into the BPE tokenizer to improve CodeGen tokenizer functionality.
  • Enhanced the SentencePiece tokenizer by integrating token indices into the output.
  • Added support for the custom operator implemented with CUDA kernels, including two example operators.
  • Added more tests on the Hugging Face tokenizer and fixed identified bugs.

Known Issues

  • The onnxruntime-training package is not yet available in PyPI but can be accessed in ADO as follows: 
    python -m pip install cerberus flatbuffers h5py numpy>=1.16.6 onnx packaging protobuf sympy setuptools>=41.4.0
pip install -i https://aiinfra.pkgs.visualstudio.com/PublicPackages/_packaging/ORT/pypi/simple/ onnxruntime-training
pip install torch-ort
python -m torch_ort.configure
    Installation instructions can also be accessed here.
  • For models with int4 kernel only:
    • Crash may occur when int4 is applied on Intel CPUs with hybrid core if the E-cores are disabled in BIOS. Fix is in progress to be patched.
    • The "neural-speed" library used by int4 kernels has a bug that could lead to out-of-bounds memory read/write.
    • Performance regression on the int4 kernel on x64 makes the op following MatMulNBits much slower. Fix is in progress to be patched.
  • Current bug in BeamSearch implementation of T5, GPT, and Whisper may break models under heavy inference load using BeamSearch on CUDA. See #19345. Fix is in progress to be patched.
  • Full support of ONNX 1.15 opsets is still in progress. A list of new ONNX 1.15 opset support that has been included in this release can be found above in the 'General' section.
  • Some Cast nodes will not be removed (see #17953): Cast node from higher precision to lower precision (like fp32 to fp16) will be kept. If model result is different from ORT 1.16 and 1.17, check whether some Cast nodes was removed in 1.16 but kept in 1.17.
  • When running ONNX Runtime's python 3.12 package on Windows 11, you may see a warning like: “Unsupported Windows version (11). ONNX Runtime supports Windows 10 and above, only.” You may safely ignore it.

Contributions

Contributors to ONNX Runtime include members across teams at Microsoft, along with our community members:
Changming Sun, Yulong Wang, Tianlei Wu, Yi Zhang, Jian Chen, Jiajia Qin, Adrian Lizarraga, Scott McKay, Wanming Lin, pengwa, Hector Li, Chi Lo, Dmitri Smirnov, Edward Chen, Xu Xing, satyajandhyala, Rachel Guo, PeixuanZuo, RandySheriffH, Xavier Dupré, Patrice Vignola, Baiju Meswani, Guenther Schmuelling, Jeff Bloomfield, Vincent Wang, cloudhan, zesongw, Arthur Islamov, Wei-Sheng Chin, Yifan Li, raoanag, Caroline Zhu, Sheil Kumar, Ashwini Khade, liqun Fu, xhcao, aciddelgado, kunal-vaishnavi, Aditya Goel, Hariharan Seshadri, Ye Wang, Adam Pocock, Chen Fu, Jambay Kinley, Kaz Nishimura, Maximilian Müller, Yang Gu, guyang3532, mindest, Abhishek Jindal, Justin Chu, Numfor Tiapo, Prathik Rao, Yufeng Li, cao lei, snadampal, sophies927, BoarQing, Bowen Bao, George Wu, Jiajie Hu, MistEO, Nat Kershaw (M...

Read more
Assets 23

ONNX Runtime v1.16.3

20 Nov 20:51
@snnn snnn
v1.16.3
2ac381c
This commit was created on GitHub.com and signed with GitHub’s verified signature. The key has expired.
GPG key ID: 4AEE18F83AFDEB23
Expired
Learn about vigilant mode.
Compare
Choose a tag to compare
ONNX Runtime v1.16.3

What's Changed

  1. Stable Diffusion XL demo update by @tianleiwu in #18496
  2. Fixed a memory leak issue(#18466) in TensorRT EP by @chilo-ms in #18467
  3. Fix a use-after-free bug in SaveInputOutputNamesToNodeMapping function by @snnn in #18456 . The issue was found by AddressSanitizer.

Contributors

snnn, tianleiwu, and chilo-ms
Assets 22

ONNX Runtime v1.16.2

09 Nov 12:08
@snnn snnn
v1.16.2
0c5b95f
This commit was created on GitHub.com and signed with GitHub’s verified signature. The key has expired.
GPG key ID: 4AEE18F83AFDEB23
Expired
Learn about vigilant mode.
Compare
Choose a tag to compare
ONNX Runtime v1.16.2

The patch release includes updates on:

  • Performance optimizations for Llama2 on CUDA EP and DirectML EP
  • Performance optimizations for Stable Diffusion XL model for CUDA EP
    • Demos for text to image generation
  • Mobile bug fixes for crash on some older 64-bit ARM devices and AOT inlining issue on iOS with C# bindings
  • TensorRT EP bug fixes for user provided compute stream and stream synchronization
Assets 22

ONNX Runtime v1.16.1

11 Oct 18:28
@snnn snnn
v1.16.1
2a1fd25
This commit was created on GitHub.com and signed with GitHub’s verified signature. The key has expired.
GPG key ID: 4AEE18F83AFDEB23
Expired
Learn about vigilant mode.
Compare
Choose a tag to compare
ONNX Runtime v1.16.1

Patch release for 1.16

  • Fix type of weights and activations in the ONNX quantizer
  • Fix quantization bug in historic quantizer #17619
  • Enable session option access in nodejs API
  • Update nodejs to v18
  • Align ONNX Runtime extensions inclusion in source and build
  • Limit per thread context to 1 in the TensorRT EP to avoid error caused by input shape changes
Assets 22

ONNX Runtime v1.16.0

20 Sep 21:27
@er3x3 er3x3
v1.16.0
e7a0495
This commit was created on GitHub.com and signed with GitHub’s verified signature. The key has expired.
GPG key ID: 4AEE18F83AFDEB23
Expired
Learn about vigilant mode.
Compare
Choose a tag to compare
ONNX Runtime v1.16.0

General

  • Support for serialization of models >=2GB

APIs

  • New session option to disable default CPU EP fallback session.disable_cpu_ep_fallback
  • Java
    • Support for fp16 and bf16 tensors as inputs and outputs, along with utilities to convert between these and fp32 data. On JDK 20 and newer the fp16 conversion methods use the JDK's Float.float16ToFloat and Float.floatToFloat16 methods which can be hardware accelerated and vectorized on some platforms.
    • Support for external initializers so that large models that can be instantiated without filesystem access
  • C#
    • Expose OrtValue API as the new preferred API to run inference in C#. This reduces garbage and exposes direct native memory access via Slice like interfaces.
    • Make Float16 and BFloat16 full featured fp16 interfaces that support conversion and expose floating properties (e.g. IsNaN, IsInfinity, etc)
  • C++
    • Make Float16_t and BFloat16_t full featured fp16 interfaces that support conversion and expose floating properties (e.g. IsNaN, IsInfinity, etc)

Performance

  • Improve LLM quantization accuracy with smoothquant
  • Support 4-bit quantization on CPU
  • Optimize BeamScore to improve BeamSearch performance
  • Add FlashAttention v2 support for Attention, MultiHeadAttention and PackedMultiHeadAttention ops

Execution Providers

  • CUDA EP
    • Initial fp8 support (QDQ, Cast, MatMul)
    • Relax CUDA Graph constraints to allow more models to utilize
    • Allow CUDA allocator to be registered with ONNX Runtime externally
    • Fixed a build issue with CUDA 12.2 (#16713)
  • TensorRT EP
    • CUDA Graph support
    • Support user provided cuda compute stream
    • Misc bug fixes and improvements
  • OpenVINO EP
    • Support OpenVINO 2023.1
  • QNN EP
    • Enable context binary cache to reduce initialization time
    • Support QNN 2.12
    • Support for resize with asymmetric transformation mode on HTP backend
    • Ops support: Equal, Less, LessOrEqual, Greater, GreaterOrEqual, LayerNorm, Asin, Sign, DepthToSpace, SpaceToDepth
    • Support 1D Conv/ConvTranspose
    • Misc bug fixes and improvements

Mobile

  • Initial support for Azure EP
  • Dynamic shape support for CoreML
  • Improve React Native performance with JSI
  • Mobile support for CLIPImageProcessor pre-processing and CLIP scenario
  • Swift Package Manager support for ONNX Runtime inference and ONNX Runtime extensions via onnxruntime-swift-package-manager

Web

  • webgpu ops coverage improvements (SAM, T5, Whisper)
  • webnn ops coverage improvements (SAM, Stable Diffusion)
  • Stability/usability improvements for webgpu

Large model training

  • ORTModule + OpenAI Triton Integration now available. See details here
  • Label Sparsity compute optimization support complete and enabled by default starting release 1.16
  • New experimental embedding sparsity related optimizations available (disabled by default).
    • Improves training performance of Roberta in Transformers by 20-30%
  • Other compute optimizations like Gather/Slice/Reshape upstream support enabled.
  • Optimizations for LLaMAv2 (~10% acceleration) and OpenAI Whisper
  • Improvements to logging and metrics (initialization overhead, memory usage, statistics convergence tool, etc) system improvements.
  • PythonOp enhancement: bool and tuple[bool] constants, materialize grads, empty inputs, save in context, customized shape inference, use full qualified name for export.
  • SCELossInternal/SCELossGradInternal CUDA kernels can handle elements more than std::numeric_limits<int32_t>::max.
  • Improvements to LayerNorm fusion
  • Model cache for exported onnx model is introduced to avoid repeatedly exporting a model that is not changed across.

On-Device Training

  • iOS support available starting this release
  • Minimal build now available for On-Device Training. Basic binary size ~1.5 MB
  • ORT-Extensions custom op support enabled through onnxblock for on-device training scenarios

ORT Extensions

This ORT release is accompanied by updates to onnxruntime-extensions. Features include:

  • New Python API gen_processing_models to export ONNX data processing model from Huggingface Tokenizers such as LLaMA , CLIP, XLM-Roberta, Falcon, BERT, etc.
  • New TrieTokenizer operator for RWKV-like LLM models, and other tokenizer operator enhancements.
  • New operators for Azure EP compatibility: AzureAudioToText, AzureTextToText, AzureTritonInvoker for Python and NuGet packages.
  • Processing operators have been migrated to the new Lite Custom Op API

Known Issues

  • ORT CPU Python package requires execution provider to be explicitly provided. See #17631. Fix is in progress to be patched.

Contributions

Contributors to ONNX Runtime include members across teams at Microsoft, along with our community members:
fs-eire, edgchen1, snnn, pengwa, mszhanyi, PeixuanZuo, tianleiwu, adrianlizarraga, baijumeswani, cloudhan, satyajandhyala, yuslepukhin, RandyShuai, RandySheriffH, skottmckay, Honry, dependabot[bot], HectorSVC, jchen351, chilo-ms, YUNQIUGUO, justinchuby, PatriceVignola, guschmue, yf711, Craigacp, smk2007, RyanUnderhill, jslhcl, wschin, kunal-vaishnavi, mindest, xadupre, fdwr, hariharans29, AdamLouly, wejoncy, chenfucn, pranavsharma, yufenglee, zhijxu-MS, jeffdaily, natke, jeffbloo, liqunfu, wangyems, er3x3, nums11, yihonglyu, sumitsays, zhanghuanrong, askhade, wenbingl, jingyanwangms, ashari4, gramalingam, georgen117, sfatimar, BowenBao, hanbitmyths, stevenlix, jywu-msft

Assets 22
8 Join discussion

ONNX Runtime v1.15.1

16 Jun 21:12
@snnn snnn
v1.15.1
baeece4
This commit was created on GitHub.com and signed with GitHub’s verified signature. The key has expired.
GPG key ID: 4AEE18F83AFDEB23
Expired
Learn about vigilant mode.
Compare
Choose a tag to compare
ONNX Runtime v1.15.1

This release fixed the following issues:

  1. A coding problem in test/shared_lib/test_inference.cc that it should use ASSERT_NEAR to test float values instead of ASSERT_EQ. Without this change, some DNNL/OpenVino tests would fail on some AMD CPUs.
  2. A misaligned error in cublasGemmBatchedHelper function. The error only occurs when CUDA version = 11.8 and the GPU's CUDA Compute capability >=80. (In other words: with TensorFloat-32 support) (#15981)
  3. A build issue that build with onnxruntime_ENABLE_MEMORY_PROFILE was broken in 1.15.0 release. (#16124)
  4. Native onnxruntime library not loading in Azure App Service. It is because in 1.15.0 we introduced a Windows API call to SetThreadDescription. Though the API is available in all Windows 10 versions, some sandbox environments block using the API. (#15375)
  5. An alignment problem for xnnpack EP on Intel/AMD CPUs on PC platforms.
  6. Some training header files were missing in the 1.15.0 training nuget package.
  7. Some fields in OrtCUDAProviderOptionsV2 struct are not initialized
  8. The *.dylib files in ONNX Runtime nuget package are not signed. (#16168)

Known issue

  1. Segfaults when loading model with local functions, works fine if model is inlined by ONNX (#16170)
  2. Cross building for iOS requires manually downloading protoc (#16238)
Assets 22

ONNX Runtime v1.15.0

25 May 01:44
@snnn snnn
v1.15.0
638146b
This commit was created on GitHub.com and signed with GitHub’s verified signature. The key has expired.
GPG key ID: 4AEE18F83AFDEB23
Expired
Learn about vigilant mode.
Compare
Choose a tag to compare
ONNX Runtime v1.15.0

Announcements

Starting from the next release(ONNX Runtime 1.16.0), at operating system level we will drop the support for

  • iOS 11 and below. iOS 12 will be the minimum supported version.
  • CentOS 7, Ubuntu 18.04, and any Linux distro without glibc version >=2.28.

At compiler level we will drop the support for

  • GCC version <= 9
  • Visual Studio 2019

Also, we will remove the onnxruntime_DISABLE_ABSEIL build option since we will upgrade protobuf and the new protobuf version will need abseil.

General

  • Added support for ONNX Optional type in C# API
  • Added collectives to support multi-GPU inferencing
  • Updated macOS build machines to macOS-12, which comes with Xcode 14.2 and we should stop using Xcode 12.4
  • Added Python 3.11 support (deprecate 3.7, support 3.8-3.11) in packages for Onnxruntime CPU, Onnxruntime-GPU, Onnxruntime-directml, and onnxruntime-training.
  • Updated to CUDA 11.8. ONNX Runtime source code is still compatible with CUDA 11.4 and 12.x.
  • Dropped the support for Windows 8.1 and below
  • Eager mode code and onnxruntime_ENABLE_EAGER_MODE cmake option are deleted.
  • Upgraded Mimalloc version from 2.0.3 to 2.1.1
  • Upgraded protobuf version from 3.18.3 to 21.12
  • New dependency: cutlass, which is only used in CUDA/TensorRT packages.
  • Upgraded DNNL from 2.7.1 to 3.0

Build System

  • On POSIX systems by default we disallow using "root" user to build the code. If needed, you can append "--allow_running_as_root" to your build command to bypass the check.
  • Add the support for building the source natively on Windows ARM64 with Visual Studio 2022.
  • Added a Gradle wrapper and updated Gradle version from 6.8.3 to 8.0.1. (Gradle is the tool for building ORT Java package)
  • When doing cross-compiling, the build scripts will try to download a prebuit protoc from Github instead of building the binary from source. Because now protobuf has many dependencies. It is not easy to setup a build environment for protobuf.

Performance

Execution Providers

Two new execution providers: JS EP and QNN EP.

TensorRT EP

  • Official support for TensorRT 8.6
  • Explicit shape profile overrides
  • Support for TensorRT plugins via ORT custom op
  • Improve support for TensorRT options (heuristics, sparsity, optimization level, auxiliary stream, tactic source selection etc.)
  • Support for TensorRT timing cache
  • Improvements to our test coverage, specifically for opset16-17 models and package pipeline unit test coverage.
  • Other misc bugfixes and improvements.

OpenVINO EP

  • Support for OpenVINO 2023.0
  • Dynamic shapes support for iGPU
  • Changes to OpenVINO backend to improve first inference latency
  • Deprecation of HDDL-VADM and Myriad VPU support
  • Misc bug fixes.

QNN EP

DirectML EP:

AzureEP

  • Added support for OpenAI whisper model
  • Available in a Nuget pkg in addition to Python

Mobile

New packages

  • Swift Package Manager for onnxruntime
  • Nuget package for onnxruntime-extensions (supports Android/iOS for MAUI/Xamarin)
  • React Native package for onnxruntime can optionally include onnxruntime-extensions

Pre/Post processing

  • Added support for built-in pre and post processing for NLP scenarios: classification, question-answering, text-prediction

  • Added support for built-in pre and post processing for Speech Recognition (Whisper)

  • Added support for built-in post processing for Object Detection (YOLO). Non-max suppression, draw bounding boxes

  • Additional CoreML and NNAPI kernels to support customer scenarios

    • NNAPI: BatchNormalization, LRN
    • CoreML: Div, Flatten, LeakyRelu, LRN, Mul, Pad, Pow, Sub

Web

  • [preview] WebGPU support
  • Support building the source code with "MinGW make" on Windows.

ORT Training

On-device training:

  • Official package for On-Device Training now available. On-device training extends ORT Inference solutions to enable training on edge devices.
  • APIs and Language bindings supported for C, C++, Python, C#, Java.
  • Packages available for Desktop and Android.
  • For custom builds refer build instructions.

Others

  • Added graph optimizations which leverage the sparsity in the label data to improve performance. With these optimizations we see performance gains ranging from 4% to 15% for popular HF models over baseline ORT.
  • Vision transformer models like ViT, BEIT and SwinV2 see upto 44% speedup with ORT Training+ DeepSpeed over PyTorch eager mode on AzureML.
  • Added optimizations for SOTA models like Dolly and Whisper. ORT Training + DS now gives ~17% speedup for Whisper and ~4% speedup for Dolly over PyTorch eager mode. Dolly optimizations on main branch show a ~40% over eager mode.

Known Issues

  • The onnxruntime-training 1.15.0 packages published to pypi.org were actually built in Debug mode instead of Release mode. You can get the right one from https://download.onnxruntime.ai/ . We will fix the issue in the next patch release.
  • XNNPack EP does not work on x86 CPUs without AVX-512 instructions, because we used wrong alignment when allocating buffers for XNNPack to use.
  • The CUDA EP source code has a build error when CUDA version <11.6. See #16000.
  • The onnxruntime-training builds are missing the training header files.

Contributions

Contributors to ONNX Runtime include members across teams at Microsoft, along with our community members:
snnn, fs-eire, edgchen1, wejoncy, mszhanyi, PeixuanZuo, pengwa, jchen351, cloudhan, tianleiwu, PatriceVignola, wangyems, adrianlizarraga, chenfucn, HectorSVC, baijumeswani, justinchuby, skottmckay, yuslepukhin, RandyShuai, RandySheriffH, natke, YUNQIUGUO, smk2007, jslhcl, chilo-ms, yufenglee, RyanUnderhill, hariharans29, zhanghuanrong, askhade, wschin, jywu-msft, mindest, zhijxu-MS, dependabot[bot], xadupre, liqunfu, nums11, gramalingam, Craigacp, fdwr, shalvamist, jstoecker, yihonglyu, sumitsays, stevenlix, iK1D, pranavsharma, georgen117, sfatimar, MaajidKhan, satyajandhyala, faxu, jcwchen, hanbitmyths, jeffbloo, souptc, ytaous kunal-vaishnavi

Assets 22
17 Join discussion