* Nuget store packaging
* Move DNNL workaround to EP
* Fix warning as error
* Disable store tests
* Skip store tests
* msbuild target
* Cross compile protoc in Store
* Disable DML in store
* Move store builds to CPU queue
* Copy uap10 to final nuget
* Fix pip8 error
* Remove extra dml copies
* Fix argparse
* pep8
* Forward IsStoreBuild
* Apply is_store_build to duplicate generate_nuspec
* runtimes
* Refactor uap10
* Store .NET
* uap
* PR feedback
* cancel night build on pyop
* setup win cuda11 pipeline
* add debug build
* test base gpu settings
* setup pipelines to test cuda 10.2 and 11
* rename linux docker images
* rename docker image tag and add clean up job
* fix typo in cuda 11 config
* set cuda11 env
* update linux cuda 11 pipeline
* reset docker image name
* disable uninitialized warning from linux build
* change the way to silence uninitialized warning
* add flags to linux gpu pipeline
* switch docker image for linux cuda 10.2
* switch linuc cuda 10.2 image
* test cuda11 with devtool8
* try latest built images
Co-authored-by: Randy Shuai <rashuai@microsoft.com>
* initial test version
* update yml
* minor updates
* minor updates
* Test minimal build
* update with include ops for minimal build ut only
* error case to see build failure
* test no_exceptio
* Remove error cases
* address pr comments
Co-authored-by: gwang0000 <62914304+gwang0000@users.noreply.github.com>
* Remove serialization of outer scope node arg info in ORT format model. We don't currently need it in a minimal build as only SessionState calls Graph::IsConstantInitializer and it doesn't search outer scope. If we do need it in the future the information can be calculated at runtime (small binary size cost to do so).
Motivation: ORT format model was 32% bigger for a BERT model with multiple levels of subgraph and a lot of nodes due to this. Size is about 5% larger of the original ONNX model with the change. ORT format has type/shape info for all nodes, and this model has 2000 nodes so this seems reasonable.
Added example code to dump ORT format model to json.
Fixed misc bug in python test script around handling float and non-float expected output.
* match new/old api numbers
* new golden numbers for Roberta and MC
Co-authored-by: liqun <liqun@OrtTrainingDev4.af05slrtruoetgaxwwjv5nsq5e.px.internal.cloudapp.net>
* Add minimal build option to build.py
Group some of the build settings so binary size reduction options are all together
Make some cmake variable naming more consistent
Replace usage of std::hash with murmurhash3 for kernel. std::hash is implementation dependent so can't be used.
Add initial doco and ONNX to ORT model conversion script
Misc cleanups of minimal build breaks.
* Add ACL version 20.02
* fix loging typo
* check depthwise operation based on group param
* Generate ArmNN runtime inside class constructor
* Update to the latest ONNX operation set
* Update BUILD.md
Co-authored-by: Andrei-Alexandru <andrei-alexandru.avram@nxp.com>
* Changes to enable saving and loading an ORT format model via the public APIs.
Cleanup session.py to try and make slightly more understandable. More refactoring is needed here.
Couple of bug fixes
* Fix bug in handling NodeArg serialization for optional inputs which has a name and no type info.
* Address PR comments
- tweak SessionOptions config to avoid double lookup
- merge duplicated functionality in python binding around registering an EP with optional options
Fix a couple of build issues.
* Update C API to be consistent with python API
- only load model in InferenceSession ctor if required
- support loading ORT model in minimal build
* Fix nodejs test.
We get an invalid path error from LoadInterOp first now
* Another attempt at fixing nodejs test.
Error message depends on whether ENABLE_LANGUAGE_INTEROP_OPS is defined. Make the output consistent.
The interop implementation looks suspicious given it appears to be internal code that is going via the public api. TBD if that should be fixed.
* Fix couple of build issues.
* Disable test temporarily so PR can be checked in.
Will fix in separate PR that adds final pieces for minimal build as the test is required there.
* Give up on nodejs test and make the match simpler.
Fix init call in TrainingSession python to not pass through sess. it wasn't being used in Session anyway so passing it through just adds confusion.
* Fix call to Session.__init__ in TrainingSession.
Session now initializes Session._sess to None to make it clearer where the 'ownership' of that member is, and that needs to happen before TrainingSession sets it.
* cancel night build on pyop
* setup ci pipeline for build of reduced ops
* add back c# test
* remove debugging print
* add testing model
* add more arg in pipeline script
* disable pipeline trigger temporarily
* fix yaml format
* fix yaml format
* fix pipeline error
* rid c# test
* add ops for test cases
* add Conv from domain com.microsoft.nchwc
* remove --reduce_ops
* fix typo
* remove --build_java
* add test case for excluded op
* update doc with --skip_test
* formatting code, renaming files and simplify yaml
* remove debug build from yaml
* remove surplus ops from included_ops.txt
* add MinSizeRel build to yaml
* rename test cases and models
* exclude ir test from minimum build
* restrict ir test to be only applied to reduced ops build
* enable rejecting models based on onnx opset
* enable unreleased opsets in linux and mac CI
* test fixes and more updates
* enable unreleased opsets in CI builds
* enable released opsets in linux cis
* try fix windows ci yml
* yml fixes
* update yml
* yml updates post master merge
* review comments
* bug fix
* Copy samples to build folder and load models from there. Fix CI
* This PR also includes a fix to path validation for save_as_onnx API
* Add torchtext to CI for GPU training
* Remove new frontend tests from CI
Co-authored-by: Thiago Crepaldi <thiago.crepaldi@microsoft.com>
* cancel night build on pyop
* add rewriter to rewrite cpu provider
* skip BuildKernelCreateInfo<void>
* refactor variable name and comment
* include ops from csv file
* process multiple eps
* add default function to cuda provider
* rename function and add license header
* fix import
* add doc
* fix typo
* deal with empty kernel entry in cuda
* rename the rewriter file
* add comment into provider file
* add comment and rename function
* log warnings
* refactor extracting logic
* add entry for script to run solo
* add better example
* avoid onnx importing
* fix flake8 alerts
* minor fixes to better comments and doc
* add entries for all domains
* add void entry into contrib providers
* format cuda_contrib_kernels.cc
* format cpu_contrib_kernels.cc
* add all providers
* add default entry to all providers
* include op_kernel header
* cancelling change in providers beyond cpu/cuda
* rename file and switch file format to domain;opset;op1,op2...
* update doc
* restore non-regular ending grammar in cuda_contrib_kernels.cc
* add ort_root as input argument of script
* enable test in ci
* update doc
* update doc
* revert change on linux gnu ci
* switch to set to host ops
* simplify trimming logic
* add domain map to track current model
* allow ort_root to take relative path
* Removed building ngraph from source
* Disabled some tests temporarily
* Enabled softmax for all dims
* Added onnx importer to link libraries
* int64 changes
* fixed
* temp
* slice update start and end need to be initializer
* Disabled GatherND, ScatterND, ReverseSequence operators
* Added supported ops instead of unsupported ops
* Set precision only for CPU
* Removed some unecessary conditions
* Fixed segfault in slice
* Softmax restriction removed
* changes
* Setting precision for all plugins
* Changes added to include precision
and supported ops for gpu and vpu
* branch op support
* checking for disabled python test failure
* mapped input names and tensors directly rather than copying which was leading to mismatch
* last index is not supported
mkldnn does not support pow between integers
* included the code changes
* Rename inner-scoped variable to avoid MSVC warning
* applied changed to vadm as well and removed the utility function
getinputtensors() completely
* OpenVINO multi version support: CMake changes
* OpenVINO multi version support: C++ support
* removed commented code
* Remove redundant code lines
* Revert "Rename inner-scoped variable to avoid MSVC warning"
This reverts commit 2f650493162675bc6fb70730de9656ec400be332.
Merged separately in master.
* vadm changes disabled reduction op test
* putting test_gather_negative_indices in unsupported list for now
* Update MCR Dockerfile with 2020.4
Installs OpenVINO 2020.4 from deb packages via APT tool.
* Update build docs with 2020.4 info
* Update dockerfile with OV 2020.4 info
Instructions for building OpenVINO based docker image no longer require
downloading installer package as it is installed by the dockerfile
using OpenVINO 2020.4 APT package for Ubuntu 18.04
* Added constant folding bypass logic
* Added cout statements for ci
* Added NDEBUG flag for debug symbols
* Update Ops info in docs
* fixes multiple unit tests
* mathoptest.ceil disabled for gpu and myriad
* activation test temp disabled
* Fix models for CPU
* Fixed a syntax error
* local cmmit
* fixing unit tests for myriad
* Fixed Variadic Split, Topk issues
* fix_model commit
* Fix models in myriad
* Added ifdefs for OpenVINO 2020.4
* temp
* made some changes to not operator
* Added unused parameter
* relu enabled
* Fixed bug in Conv output
* Consolidated GPU failing tests into one category
* Made it compatible to InternalCI 2020.4
* Made changes for ngraph
* Disabled test for mask,fastercnn,tinyyolov3
* Removed proxy for ci
* run_dockerbuild.sh restored to same version
* run_dockerbuild.sh restored to same version
* run_dockerbuild.sh restored to same version
* Updated documentation for 2020.4
* Removed FP32 to FP16 transformation for GPU
* Disabled Coreml-FNS-Candy model test
* Added FP16 transformations
Co-authored-by: sfatimar <sahar.fatima@intel.com>
Co-authored-by: Manohar Karlapalem <manohar.karlapalem@intel.com>
Co-authored-by: sfatimar <sahar.fatima@intel/com>
Co-authored-by: sfatimar <64512376+sfatimar@users.noreply.github.com>
Co-authored-by: intel <you@example.com>
Co-authored-by: gundaarx <aravindx.gunda@intel.com>
* Sahar/csharp support openvino (#4703)
* Temp changes and include openvino to ensure nuget package is created with linux till we configure azure ci pipeline
* string id change
* native nuget indentation changes
* documentation changes
* Update Openvino_execution_provider.md
Documentation includes openvino execution provider
* Update OpenVino-ExecutionProvider.md
update details to build csharp api for openvino execution provider .
* vadm backend revert
* Update Openvino-Execution-Provider.md
updated for review comments
* Update OpenVino-Execution-Provider.md
* Update OpenVINO-ExecutionProvider.md
* nuget package custome support for openvino
change in native nuget spec python script for including linux runtime
* change to make path to boolean flag
* removed the tab
* Update OpenVINO-ExecutionProvider.md
updated for review comments
* chnages to include pep8 warnings
modification to documentation
Co-authored-by: saharfraza <sfatima.3001@gmail.com>
Co-authored-by: sfatimar <sahar.fatima@intel/com>
* Changes to include csharp support for openvino
* Fix flake error
* Fix
Co-authored-by: sfatimar <64512376+sfatimar@users.noreply.github.com>
Co-authored-by: saharfraza <sfatima.3001@gmail.com>
Co-authored-by: sfatimar <sahar.fatima@intel/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.
* Temp changes and include openvino to ensure nuget package is created with linux till we configure azure ci pipeline
* string id change
* native nuget indentation changes
* documentation changes
* Update Openvino_execution_provider.md
Documentation includes openvino execution provider
* Update OpenVino-ExecutionProvider.md
update details to build csharp api for openvino execution provider .
* vadm backend revert
* Update Openvino-Execution-Provider.md
updated for review comments
* Update OpenVino-Execution-Provider.md
* Update OpenVINO-ExecutionProvider.md
* nuget package custome support for openvino
change in native nuget spec python script for including linux runtime
* change to make path to boolean flag
* removed the tab
* Update OpenVINO-ExecutionProvider.md
updated for review comments
* chnages to include pep8 warnings
modification to documentation
Co-authored-by: saharfraza <sfatima.3001@gmail.com>
Co-authored-by: sfatimar <sahar.fatima@intel/com>
* update batch_norm test, enable dev_mode for nnapi, ignore onnx protobuf warning for nnapi ep
* fix some issues in concat and mark input without shape as not supported for now
* address review comments
* addressed comments
* bump cswinrt version
* add cswinrt
* test dotnetcore 3.0
* rename buildpacakge source
* set folder path to the package source and not the version
* refactor .netframework tests
* build .net core anycpu
Co-authored-by: Sheil Kumar <sheilk@microsoft.com>
Sometimes there is a file named "version.txt" in your CUDA installation dir, but sometimes there isn't one. I couldn't figure out it why, but the latest CUDA 11 on our CI build machines doesn't have this file. As the file is not needed for building onnxruntime, so I removed the check.
Add 'Install ONNX' step to Windows GPU pipeline
Previously it's not a problem because onnxruntime python package explicitly said it depends on ONNX, so ONNX will get installed when we test onnxruntime. However, it was removed in #4073
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.
* minor fix for test dir util
* add pause option for onnx_test_runner
* add flush std to show pause prompt text
Co-authored-by: gwang0000 <62914304+gwang0000@users.noreply.github.com>
* Add BN to ArmNN EP
* Add Concat to ArmNN EP
* ACL logging improvements
* ArmNN logging improvements
* Fallback to CPU for 9x9 convolution in ACL EP
* Fallback to CPU for 9x9 convolution in ArmNN EP
* Enable python support for ACL and ArmNN EPs when compiled with BSP toolchain
* Removed the matmul operator
* Fix conv infer shape function
* Fix provider_names list for armnn
Co-authored-by: Andrei-Alexandru <andrei-alexandru.avram@nxp.com>
* Revert "Temporarily remove dnnl from Linux CI build to unblock the whole team (#4266)"
Previously it fails because it used too much memory.
Now we only run dnnl EP with opset12 models in unit tests, to reduce peak memory usage.
* Enable onnxruntime_test_all for NNAPI EP
* switch to use ninja for ANdroid CI
* make android elumator boot faster in android ci
* simplify adb push
* more style change
* more tweaking on android ci
* build.py style update
* build e2e cppwinrt tests
* add use nuget task
* make all referenced to package version prop/target-ified
* remove dupe props/targets reference
* work around project.assets.json error by deleting it
* powershell test invocation
* switch to batch script
* print debug info
* update x86->x64
* stdio.h
* pushd/popd
* add csharp tests
* package.config -> packages.config
* typo
* x86 -> anycpu
* debug is default
* add test path
* update csproj as well
* debug
* really replace all package versions
* debug output
* really use [PackageVersion]
* sleep intead of converting async operation to task and waiting
* dont close software bitmap
* switch to powershell script
* remove binding check
* continue on failure
* continuse on error action
* continueOnError and errorActionPreference
* tabbing
Co-authored-by: Sheil Kumar <sheilk@microsoft.com>
* Add protobuf mutator library as a git submodule
* Added files and instructions to build the protobuf mutator library in CMake
* Added fuzzing flag to build system and added fuzzing dependency library. To run fuzzing test use the flags --fuzz_testing --build_shared_lib --use_full_protobuf --cmake_generator 'Visual Studio 16 2019'
* Added src files and build instructions for the main fuzzing engine
* Removed Random number generation test from inside the engine
* Added license header to files
* Removed all pep8 violations introduced by this change and other E501 violations
* Change NNAPI CI to run on new NNAPI EP
* update android ci to mac 10.15 and remove in install cmake
* update the android ci to targe android api level 29
* remove unnecessary ndk install git submodule call
* Move nnapi dnnlib to subfolder
* dnnlib compile settings
* add nnapi buildin build.py
* add onnxruntime_USE_NNAPI_BUILTIN
* compile using onnxruntime_USE_NNAPI_BUILTIN
* remove dnnlib from built in code
* Group onnxruntime_USE_NNAPI_BUILTIN sources
* add file stubs
* java 32bit compile error
* built in nnapi support 5-26
* init working version
* initializer support
* fix crash on free execution
* add dynamic input support
* bug fixes for dynamic input shape, add mul support, working on conv and batchnorm
* Add batchnormalization, add overflow check for int64 attributes
* add global average/max pool and reshape
* minor changes
* minor changes
* add skip relu and options to use different type of memory
* small bug fix for in operator relu
* bug fix for nnapi
* add transpose support, minor bug fix
* Add transpose support
* minor bug fixes, depthwise conv weight fix
* fixed the bug where the onnx model input has mismatch order than the nnapi model input
* add helper to add scalar operand
* add separated opbuilder to handle single operator
* add cast operator
* fixed reshape, moved some logs to verbose
* Add softmax and identity support, change shaper calling signature, and add support for int32 output
* changed the way to execute the NNAPI
* move NNMemory and InputOutputInfo into Model class
* add limited support for input dynamic shape
* add gemm support, fixed crash when allocating big array on stack
* add abs/exp/floor/log/sigmoid/neg/sin/sqrt/tanh support
* better dynamic input shape support;
* add more check for IsOpSupportedImpl, refactored some code
* some code style fix, switch to safeint
* Move opbuilders to a map with single instance, minor bug fixes
* add GetUniqueName for new temp tensors
* change from throw std to ort_throw
* build settings change and 3rd party notice update
* add readme for nnapi_lib, move to ort log, add comments to public functions, clean the code
* add android log sink and more logging changes, add new string for NnApiErrorDescription
* add nnapi execution options/fp16 relax
* fix a dnnlibrary build break
* addressed review comments
* address review comments, changed adding output for subgraph in NnapiExecutionProvider::GetCapability, minor issue fixes
* formatting in build.py
* more formatting fix in build.py, return fail status instead of throw in compute_func
* moved android_log_sink to platform folder, minor coding style changes
* addressed review comments
1. Increase job timeout, while we are investigating why the tests take much longer
2. Upgrade the linux docker image to manylinux2010, by request from Tianlei. (We had an offline discussion with Pranav and Tracy)
3. Remove the installation of "devtoolset-7" in the CUDA image. It was added for CUDA 10.0, it is not needed for CUDA 10.1. We have moved to CUDA 10.1.
* Add build option to disable traditional ML ops from the binary.
* Fix python tests by splitting tests for ML ops to a separate file. Exclude ML tests from onnx_test_runner and C# tests. Exclude ML op sources.
* Update Edge pkg pipelines with new MLops env variable and fix C# packaging pipeline tests to skip ML ops.
* expose ACL/ARMNN providers to python
* add -acl / -armnn to package name when use_acl / use_armnn is specified
* build python wheel for ARMNN EP
* link ACL/ARMNN EPs into onnxruntime_pybind11_state
* wrong argument order in build_python_wheel for wheel_name_suffix
* ORT on CUDA 11
1. Seperate HOROVOD and MPI
2. Seperate NCCL from HOROVOD in CMakeLists.txt
2. Remove dependency on external cub
3. cudnnSetRNNDescriptor is changed in cuDNN 8.0
* polish the code about MPI/NCCL in CMakeLists.txt and build.py
* check CUDA version
* ${MPI_INCLUDE_DIRS} should be PUBLIC
* sm30, sm50 are deprecated in CUDA 11 Toolkit
* update change based on code review feedback.
* add sm_52
* improve MPI/NCCL build path
Co-authored-by: Weixing Zhang <wezhan@microsoft.com>
