* Update onnx (#5720)
* update onnx
* update docker image for testing
(cherry picked from commit 705d093167)
* cherry pick PR 5720
* C#: Add CreateFromMemory to FixedBufferOnnxValue to allow bind user buffers and pass custom binary compatible types (#5886)
Add CreateFromMemory to FixedBufferOnnxValue so users can bind their own custom binary compatible buffers to feed/fetch data.
(cherry picked from commit c2d610066a)
* [Java] Initial Apple Silicon support (#5891)
* Rearranging checks in onnxruntime_mlas.cmake to pickup Apple Silicon.
On an M1 Macbook Pro clang reports:
$ clang -dumpmachine
arm64-apple-darwin20.1.0
So the regex check needs to look for "arm64" first, as otherwise it
matches 32-bit ARM and you get NEON compilation failures.
* Adding Java side library loading support for Apple Silicon (and other aarch64 architectures).
* Adding Qgemm fix from @tracysh
* Fixes the java packaging on Windows.
* Missed a check in the java platform detector.
(cherry picked from commit 8b83c51a35)
* Add OpenVINO EP shared lib to Py Wheel (#5920)
* Add OpenVINO EP shared lib to Py Wheel
Include the libonnxruntime_providers_openvino.so/.dll to the wheel
* Follow libs.extend pattern as other EPs
(cherry picked from commit 40926867c3)
* Make NNAPI EP reject nodes with no-shape inputs (#5927)
(cherry picked from commit 87368655e2)
* Sahar/fix documentation shared lib (#5926)
* Update OpenVINO-ExecutionProvider.Md
update openvino-executionprovider.md for shared library
* Update Build.md
updated --build_shared_lib flag for building openvino shared provider lib
* Update Dockerfile.openvino
building for shared library with the new changes for openvino shared lib
* Revert "Update Build.md"
This reverts commit c9cf5fee76be7fdc10cadf07259f1d4ed5b45b93.
* Revert "Update Dockerfile.openvino "
This reverts commit e1624e4f93a4cfb425b6f21d7fb71b299a146740.
* Update OpenVINO-ExecutionProvider.md
fix documentation to the shared library
Co-authored-by: sfatimar <sahar.fatima@intel/com>
(cherry picked from commit 8168c91978)
* Update dockerfiles (#5929)
1. Remove conda from the images. Because conda contains a file named /opt/miniconda/lib/libcrypto.so.1.0.0 which can't pass our security scan. Also, it will be easier for us to manage the third party usage registrations.
2. Remove openssh from the images. Because the official openssh package provided by Ubuntu can't pass our security scan.
3. Reduce the image size to 1/3 by using stages. Also, because it contains less packages, it will be less often needed to update.
4. Put the LICENSE-IMAGE.txt file in right place. It is missed in current images. You can see it was added to a temp folder "/code" but it got deleted afterwards.
5. Update the CPU docker image's base image to Ubuntu 18.04. The GPU one is already 18.04. It's better to keep them the same.
6. Remove the build arg ONNXRUNTIME_REPO/ONNXRUNTIME_BRANCH. Instead, the new one always uses the local source. I feel it can reduce confusion.
(cherry picked from commit 1dbabb2362)
* Add Longformer Attention Cuda Op(#5932)
Limitation: Global tokens must be at the beginning of sequence.
(cherry picked from commit 31a6be3d67)
* Bug fix for MaskRCNN and FasterRCNN (#5935)
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
(cherry picked from commit e39e82b43a)
* Fix publishing pipelines. (#5942)
Fix publishing pipelines.
(cherry picked from commit c4b55d29fe)
* Fix Python Linux GPU package name (#5943)
Fix Python Linux GPU package name. I accidentally added "noopenmp" to it.
(cherry picked from commit 5fdd9f0fd2)
* Update BUILD.md with shared provider information (#5944)
* Update build instructions to include information about shared providers
(cherry picked from commit 27513d1fd7)
* [OpenVINO]Fix memory leak in `IsDebugEnabled()` under Windows (#5948)
* w
* w
Co-authored-by: modav <modav@microsoft.com>
(cherry picked from commit e207589631)
* Add support for Python 3.8+ on Windows when CUDA is enabled (#5956)
(cherry picked from commit 015fbb3dbb)
* Support the cross compiling for Apple Silicon (#5974)
* support macos_arm64 cross compiling
* update the build docs
* update as commented.
* Update BUILD.md
(cherry picked from commit 2ec211ea7b)
* Update docker files to put 'unattended-upgrades' in a right place(#5983)
(cherry picked from commit 3323fb6082)
* Enable the xcode build for Apple Silicon (arm64 MacOS) (#5924)
* fix the build script for macos/xcode
* add the version check
* correct the osx-arch configuration
* typo
(cherry picked from commit 1852ade75d)
* Add python 3.9 support (#5874)
1. Add python 3.9 support(except Linux ARM)
2. Add Windows GPU python 3.8 to our packaging pipeline.
* Revert some pipeline changes in #5874
Co-authored-by: Ashwini Khade <askhade@microsoft.com>
Co-authored-by: Du Li <duli@OrtTrainingDev0.af05slrtruoetgaxwwjv5nsq5e.px.internal.cloudapp.net>
Co-authored-by: Dmitri Smirnov <yuslepukhin@users.noreply.github.com>
Co-authored-by: Adam Pocock <craigacp@gmail.com>
Co-authored-by: S. Manohar Karlapalem <manohar.karlapalem@intel.com>
Co-authored-by: Guoyu Wang <62914304+gwang-msft@users.noreply.github.com>
Co-authored-by: sfatimar <64512376+sfatimar@users.noreply.github.com>
Co-authored-by: Changming Sun <chasun@microsoft.com>
Co-authored-by: Tianlei Wu <tlwu@microsoft.com>
Co-authored-by: Maajid khan <n.maajidkhan@gmail.com>
Co-authored-by: Ryan Hill <38674843+RyanUnderhill@users.noreply.github.com>
Co-authored-by: Moshe David <mosdav165@gmail.com>
Co-authored-by: Ivan Stojiljkovic <17503404+ivanst0@users.noreply.github.com>
Co-authored-by: Wenbing Li <10278425+wenbingl@users.noreply.github.com>
* Add ORTTrainerOptions class for the new pytorch frontend (#4382)
Add ORTTrainerOptions class and some placeholders
* Add _ORTTrainerModelDesc to perform validation for model description (#4416)
* Add Loss Scaler classes to the new frontend (#4306)
* Add TrainStepInfo used on the new frontend API (#4256)
* Add Optimizer classes to the new frontend (#4280)
* Add LRScheduler implementation (#4357)
* Add basic ORTTrainer API (#4435)
This PR presents the public API for ORTTrainer for the short term
development.
It also validates and saves input parameters, which will be used in the
next stages, such as building ONNX model, post processing the model and
configuring the training session
* Add opset_version into ORTTrainerOptions and change type of ORTTrainer.loss_fn (#4592)
* Update ModelDescription and minor fix on ORTTrainer ctor (#4605)
* Update ModelDescription and minor fix on ORTTrainer/ORTTrainerOptions
This PR keeps the public API intact, but changes how model description is stored on the backend
Currently, users creates a dict with two lists of tuples.
One list called 'inputs' and each tuple has the following format tuple(name, shape).
The second list is called 'outputs' and each tuple can be either tuple(name, shape) or tuple(name, shape, is_loss).
With this PR, when this dict is passed in to ORTTrainer, it is fully validated as usual.
However, tuples are internally replaced by namedtuples and all output tuples will have
tuple(name, shape, is_loss) format instead of is_loss being optionally present.
Additionally to that normalization in the internal representation (which eases coding),
two internal methods were created to replace a namedtuple(name, shape) to namedtuple(name, shape, dtype)
or namedtuple(name, shape, is_loss, dtype) dependeing whether the tuple is an input or output.
This is necessary as ORTTRainer finds out data types of each input/output during model export to onnx.
Finally, a minor fix was done on ORTTrainer. It could initialize ORTTrainerOptions incorrectly when options=None
* Rename input name for test
* Add ONNX Model Export to New Frontend (#4612)
Co-authored-by: Rayan Krishnan <t-rakr@OrtDevTest2v100.af05slrtruoetgaxwwjv5nsq5e.px.internal.cloudapp.net>
Co-authored-by: Thiago Crepaldi <thiago.crepaldi@microsoft.com>
* Create training session + minor improvements (#4668)
Co-authored-by: Rayan Krishnan <t-rakr@OrtDevTest2v100.af05slrtruoetgaxwwjv5nsq5e.px.internal.cloudapp.net>
* Save ONNX model in file (#4671)
Co-authored-by: Rayan Krishnan <t-rakr@OrtDevTest2v100.af05slrtruoetgaxwwjv5nsq5e.px.internal.cloudapp.net>
* Add eval step (#4674)
Co-authored-by: Rayan Krishnan <t-rakr@OrtDevTest2v100.af05slrtruoetgaxwwjv5nsq5e.px.internal.cloudapp.net>
* Add train_step (#4677)
Co-authored-by: Rayan Krishnan <t-rakr@OrtDevTest2v100.af05slrtruoetgaxwwjv5nsq5e.px.internal.cloudapp.net>
* Add LR Scheduler (#4694)
Co-authored-by: Rayan Krishnan <t-rakr@OrtDevTest2v100.af05slrtruoetgaxwwjv5nsq5e.px.internal.cloudapp.net>
Co-authored-by: Thiago Crepaldi <thiago.crepaldi@microsoft.com>
* Add deterministic compute tests (#4716)
Co-authored-by: Rayan Krishnan <t-rakr@OrtDevTest2v100.af05slrtruoetgaxwwjv5nsq5e.px.internal.cloudapp.net>
Co-authored-by: Thiago Crepaldi <thiago.crepaldi@microsoft.com>
* Add legacy vs experimental ORTTrainer accuracy comparison (#4727)
Co-authored-by: Rayan Krishnan <t-rakr@OrtDevTest2v100.af05slrtruoetgaxwwjv5nsq5e.px.internal.cloudapp.net>
Co-authored-by: Thiago Crepaldi <thiago.crepaldi@microsoft.com>
* Add Mixed precision/LossScaler + several fixes (#4739)
Additionally to the mixed precision/loss scaler code, this PR includes:
* Fix CUDA training
* Add optimization_step into TrainStepInfo class
* Refactor LRSCheduler to use optimization_step instead of step
* Updated several default values at ORTTrainerOptions
* Add initial Gradient Accumulation supported. Untested
* Fix ONNX model post processing
* Refactor unit tests
* Add ONNX BERT example + minor fixes (#4757)
* Fix training issue when passing ONNX file into ORTTrainer
Co-authored-by: Thiago Crepaldi <thiago.crepaldi@microsoft.com>
Co-authored-by: Rayan Krishnan <t-rakr@OrtDevTest2v100.af05slrtruoetgaxwwjv5nsq5e.px.internal.cloudapp.net>
* Add Dynamic Shape support (#4758)
* Update DeepSpeed Zero Stage option to a separate option group (#4772)
* Add support to fetches (#4777)
* Add Gradient Accumulation Steps support (#4793)
* Fix Dynamic Axes feature and add unit test (#4795)
* Add frozen weights test (#4807)
* Move new pytorch front-end to 'experimental' namespace (#4814)
* Fix build
Co-authored-by: Rayan-Krishnan <rayankrishnan@live.com>
Co-authored-by: Rayan Krishnan <t-rakr@OrtDevTest2v100.af05slrtruoetgaxwwjv5nsq5e.px.internal.cloudapp.net>
1. Publish the image ACR, instead of building it every time for every PR
2. Make USE_MKLML and USE_OPENMP be able to co-exist. Currently both of them are enabled in our Linux CI build but indeed only one of them is taking effect.
3. Split nuphar and DNNL to separated pipelines.
4. Fix two warnings in onnxruntime/core/optimizer/matmul_scale_fusion.cc and onnxruntime/test/tvm/tvm_basic_test.cc.
5. Update the manylinux2010_x86_64 image to the latest.
1. Avoid building ONNX of every history ONNX versions in our CI, it is costly and easy to fail.
2. Run docker command without sudo. Previously the user is not in docker group, now Azure DevOps Service have added it in.
