The current image cache cleanup is not removing many images. Upon examining the cache container registry logs, it appears there are some infrequent pulls of old images which may be made by something other than CI builds (perhaps some automated scan of the registry).
This change adds a minimum access count for images in the cache so that infrequently but periodically accessed images can be removed. The idea is that images used by CI builds that are worth caching will have a higher volume of accesses.
Update training Python packaging build to use get_docker_image.py.
Remove BUILD_EXTR_PAR docker build argument.
Update get_docker_image.py to check again for the image in the cache after building and before pushing to reduce the chance of a redundant push.
Description: Add ORT minimal with NNAPI EP to Android CI
Motivation and Context
The added build/test to Android CI will only run UT, additional onnx_test_runner with customer .ort models will be added later
Fix Python 3.5 compatibility issue in tools/ci_build/get_docker_image.py.
Fix line endings in tools/ci_build/github/azure-pipelines/clean-build-docker-image-cache-pipeline.yml.
* Run only required steps relevant to fuzz testing.
* Exit status non-zero for any uncaught exception other than ort_exception in the driver code
Co-authored-by: Satya Jandhyala <sajandhy@microsoft.com>
Follow up to #5811 to automate cleanup of the build docker image cache.
Added a script and build definition to clean up docker images that haven't been accessed recently.
* Remove nGraph Execution Provider
Pursuant to nGraph deprecation notice: https://github.com/microsoft/onnxruntime/blob/master/docs/execution_providers/nGraph-ExecutionProvider.md#deprecation-notice
**Deprecation Notice**
| | |
| --- | --- |
| Deprecation Begins | June 1, 2020 |
| Removal Date | December 1, 2020 |
Starting with the OpenVINO™ toolkit 2020.2 release, all of the features
previously available through nGraph have been merged into the OpenVINO™
toolkit. As a result, all the features previously available through
ONNX RT Execution Provider for nGraph have been merged with ONNX RT
Execution Provider for OpenVINO™ toolkit.
Therefore, ONNX RT Execution Provider for **nGraph** will be deprecated
starting June 1, 2020 and will be completely removed on December 1,
2020. Users are recommended to migrate to the ONNX RT Execution Provider
for OpenVINO™ toolkit as the unified solution for all AI inferencing on
Intel® hardware.
* Remove nGraph Licence info from ThirdPartyNotices.txt
* Use simple Test.Run() for tests without EP exclusions
To be consistent with rest of test code.
* Remove nGraph EP functions from Java code
* Added fuzz testing using ORT model.
* The onnxruntime_security_fuzz driver code should accept either ONNX or ORT (based on the file extension) input file if /f flag is provided.
* Added ValidateOrtFormatModelDoesNotRunOptimizersInFullBuild test.
* Added win-ci-fuzz-testing.yml to run build pipeline.
* Prevent out-of-range access in the graph.cpp.
This PR adds infrastructure to automatically cache docker images used in CI builds in a container registry.
Currently, build images are pulled from a container registry for some builds and built every time for others. The container registry requires maintenance to keep the images up to date and building images every time wastes build agent resources.
With this change, a given build image can be looked up in a cache container registry and if present, pulled, and otherwise, built and pushed. The uniqueness of a build image is determined by a hash digest of the dockerfile, docker build context directory, and certain "docker build" options. This digest is part of the image tag in the cache container repository.
The cache container registry will need to be cleaned up periodically. This is not automated yet.
Transitions from the ORT-only DML NuGet (hosted on the onnxruntime_public feed) to the new unified DirectML NuGet (Microsoft.AI.DirectML) on nuget.org. In addition, the Microsoft.AI.MachineLearning (WinML) and Microsoft.ML.OnnxRuntime.DirectML packages now take a dependency on the Microsoft.AI.DirectML package. This means we can remove the extra copy of DML binaries in these packages since they will be installed by the DML package.
* Add validation of operator registrations to the reduction script
- the script has all the logic to process the registrations, and there's a CI that uses it
Fix some operator registrations
* Fix CUDA PRelu registration
* Refactor to split out kernel registration file parsing and use in the exclude ops script and an op registration validation script.
Run op validation in minimal build CI
* Fix PEP8 error and some comments
* Add copy sparse model in minimal CI
* Add squeeze 13 support
* fix small typo
* Add ut for squeeze in NNAPI
* Fix some issue in the UT and code
* Modify based on the master change
* Fix build break
* Create an Azure Pipeline to merge cpp and python e2e pipelines into one. Still keep cpp 2e2 pipeline until this new pipeline is stable.
Co-authored-by: liqun <liqun@OrtTrainingDev4.af05slrtruoetgaxwwjv5nsq5e.px.internal.cloudapp.net>
* Add YAML file for pipeline
* Modify typo
* Add working directory
* Modify and test
* Modfiy and test
* Modify and test
* Modify and test
* Modify
* Modify
* Modify
* Modify
* Make sure to copy all the result files
* Add clearn up
* Modify
* Modify agent pool name
* Upload only specific artifacts
* Modify
* Integrated CI Pipeline for running TRT perf as well as added the “large amount of models” into perf model target
* Fix bug
* Fix bug
* Add reading the information regarding previously known failing models
and then skip testing them during benchmark/validation
* Modify the script file for CI
* Replace print with logger.info
* Fix bug
* Fix bug
* Refine the code
* Modify the script so that it can capture script segmentation fault while
running ORT
* Fix bug
* fix bug
* fix bug
* Add debug info
* fix bug
* Refine perf code
* Refine the code
* fix bug
* Code refactoring
* change many-models path
* remove metadata after validation/benchmark are done
* Update README.md
* Fix bug so that metadata doesn't hold stale value
* Remove hardcode and update README
* Add arguments to the script to make it run correctly
* Update linux-gpu-tensorrt-ci-perf-pipeline.yml for Azure Pipelines
* Update linux-gpu-tensorrt-ci-perf-pipeline.yml for Azure Pipelines
* Fix bug so that metadata doesn't hold stale value
* Fix small bug of finding test dataset directory for FP16 test data, as
well as modification of some output information
* use -i random for perf test of TRT changes
Co-authored-by: Olivia Jain <oljain@microsoft.com>
* create new nuget packaging pipeline without openmp
* rename package
* update image name
* rename package name
* rename managed package
* reset project attribute
* merge master
* set package name
* set NoOpenMP as cpu build
* shorten line length
Co-authored-by: Randy Shuai <rashuai@microsoft.com>
The ROCm EP is designed and implemented based on AMD GPU software stack named ROCm. Here is the link for the details about ROCm: https://rocmdocs.amd.com/en/latest/
ROCm EP was created based on the following things:
1. AMD GPU programming language: HIP
2. AMD GPU HIP language runtime: amdhip64
3. BLAS: rocBLAS, hipBLAS
4. DNN: miOpen
5. Collective Communication library: RCCL
6. cub: hipCub
7. …
Current status:
BERT-L and GPT2 training can be ran on AMD GPU with data parallel.
Next:
1. Make more GPU code be sharable between ROCm EP and CUDA EP since HIP language and HIP runtime API are very close to CUDA.
2. Continue improving the implementation.
3. Continue GPU kernel optimization.
4. Support model parallelism on ROCm EP.
……
The rocm kernels have been removed from this commit and will be in a separate PR. Since the original PR was too big(~180 files), it was suggested to split the PR into two parts, one is rocm-kernels, the other is non rocm kernels.
Co-authored-by: Weixing Zhang <wezhan@microsoft.com>
Co-authored-by: sabreshao <sabre.shao@amd.com>
Co-authored-by: anghostcici <11013544+anghostcici@users.noreply.github.com>
Co-authored-by: Suffian Khan <sukha@microsoft.com>
Co-authored-by: Edward Chen <18449977+edgchen1@users.noreply.github.com>
replace number matching with relaxed comparison in frontend tests
Co-authored-by: liqun <liqun@OrtTrainingDev4.af05slrtruoetgaxwwjv5nsq5e.px.internal.cloudapp.net>
* Cmake changes for 2021.1
* added new ov version 2020.1 for faster rcnn
* Added missing defs
* equal op modified
* changes to incoroporate faster rcnn
* backend util.cc
* hddl_plugin_config.hpp is depreceated . instead use hddl_config.hpp
* changing myriad precision bool to i32
* gather is not enabled for gpu
* conv2D and pooltest auto_pad attribute should not be null
* negative indices are not valid for scatter op in myriad
* non max suppression op only supported in faster rcnn mode
* maxpool indices output is not supported
* Cleaned redundant code in backends
* Added ifdefs for HDDL config
* cast output dimensions check
topk operator k input it seems only resolved for myriad as it is
throwing issues for ask rcnn . need to verify
* we are limiting the subgraph size to 3 here
* taking care of review comments
* Fixed minor bugs
* Modified Slice op checks
* Added NonZero, Upsample
* Removed TopK if it's in the middle of a subgraph
* incorporated upsample conditions too
* Dockerfile changes for 2021.1 release
* dockerfile aptkey update
* Minor fixes
* ceil condition added again
* Fixed few gpu models
* Disabled LSTM and yolov3 in ModelTests
* python softmax cross entropy tests and negative log likelihood
* Update Build.md
Updated for openvino 2021.1
* Update OpenVINO-ExecutionProvider.md
update openvino execution provider for 2021.1
* Update READMe.md
updated new openvino version
* Update Dockerfile.openvino
added environment variable for DEBIAN Frontend
* Fixed myriad models
* Fixed gather condition
* Fixed mask rcnn model on myriad
* Modified Gather condition
* set default target of MCR dockerfile to MYRIAD_FP16
* Fixed tinyolov3 on CPU
* Update OpenVINO-ExecutionProvider.md
update openvino execution provider documentation
* Update Dockerfile.openvino
Removed environment variable
* Update OpenVINO-ExecutionProvider.md
update image manipulation networks supported
* Update onnx_backend_test_series_filters.jsonc
removed test_upsample_nearest from cpu test cases
* New InternalCI changes for 2021.1
* Full protobuf removed for OpenVINO
* Protobuf added
* Updated with apt installation for openvino
* Revert the testing changes
* Reverted testing changes
* File permessions are changed to original
* Deleted openvino installation and cmake change
* Optimized Dockerfile
Removed unnecessary cmake installation, numpy
* Added missing ifdefs
* delete array fix
* backend_utils.cc output_shape
* Revert "set default target of MCR dockerfile to MYRIAD_FP16"
This reverts commit 928d3e2b71e2f589cf51dacd3a133951cf9ca18d.
Co-authored-by: suryasidd <surya.siddharth.pemmaraju@intel.com>
Co-authored-by: sfatimar <sahar.fatima@intel/com>
Co-authored-by: suryasidd <48925384+suryasidd@users.noreply.github.com>
Co-authored-by: S. Manohar Karlapalem <manohar.karlapalem@intel.com>
Co-authored-by: Aravind <aravindx.gunda@intel.com>
Co-authored-by: Aravind Gunda <38353114+gundaarx@users.noreply.github.com>
* add tensor board, remove torch.distributed.lanuch because ort nccl depends on MPI. Use MPI to launch parallel training.
Co-authored-by: liqun <liqun@OrtTrainingDev4.af05slrtruoetgaxwwjv5nsq5e.px.internal.cloudapp.net>
* add the ios ci build.
* no dependency on mac ci pipeline.
* fix the command line.
* keep sync
* automatically retrieve sdpath
* fix the case errors and warnings
* fix the vlog switch issue.
* add parallel flag for build.
* update the display name of the pipeline.
- Update docker image release build to use build commit.
- Use valid default in component governance detection step.
- Use smaller docker build context.
* 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>
* 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.
* 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>
* 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>
* 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.
* 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>
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.
* 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>
* 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
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.
Modify gradle build so artifactID has _gpu for GPU builds.
Pass USE_CUDA flag on CUDA build
Adjust publishing pipelines to extract POM from a correct path.
Co-Authored-By: @Craigacp
1. Enlarge the read buffer size further, so that our code can run even faster. TODO: need apply the similar changes to python some other language bindings.
2. Add coreml_VGG16_ImageNet to the test exclusion set of x86_32. It is not a new model but previously we didn't run the test against x86_32.
* try mac pipeline
* fix path separator
* copy prebuilds folder
* split esrp yaml for win/mac
* disable mac signing temporarily
* add linux
* fix indent
* add nodetool in linux
* add nodetool in win-ci-2019
* replace linux build by custom docker scripts
* use manylinux as node 12.16 not working on centos6
* try ubuntu
* loosen timeout for test case - multiple runs calls
1. Fix the nuget cpu pipeline and put code coverage pipeline back.
2. Reduce onnx_test_runner's default logging level from WARNING to ERROR. Because there are too many log messages now.
3. Enlarge the protobuf read buffer size for onnx_test_runner. It was missed from PR #4020.
- Add support for ENABLE_LANGUAGE_INTEROP_OPS in training build which is enabled for nightly builds
- Fix passing of environment variables to `sudo docker run` in build definitions
- Fix setup.py package naming logic
* Add flake8 to Win CI build so it's re-enabled. It was in the static analysis build that is currently disabled so checks are not running.
Fix build.py to be compliant again.
Add prefix to flake8 output so it's (hopefully) easier to identify the errors in build output.
* Add to all builds in Windows CPU CI so they all fail quickly if there's an issue.
Add transformer glue test example to show how to use ORTTrainer to fine-tune a transformer model
Co-authored-by: liqun <liqun@OrtTrainingDev4.af05slrtruoetgaxwwjv5nsq5e.px.internal.cloudapp.net>
In this PR, we
1. create some APIs for creating NVTX objects
2. apply those APIs in pipeline-related operators and sequential executor.
As a result, we can explicitly see how a pipeline schedule is run by GPUs in
Nvidia's visual profiler. Note that these APIs are Linux only due to Nvidia's
limited support.
* Remove 'model_.' prefix for onnx model initializers in training
* fix test case remove redundant device test
* rename
* Fix state_dict/load_state_dict with frozen_weight
* nit
* Add monkey patch for pt opset 10
* remove pt patch in CI
* nit: newline
Change training perf test build to use "docker" instead of "sudo docker". The training perf test build runs in an environment that supports calling "docker" and not "sudo docker".
* gpt2 training perf
* gpt2 training perf
* debug
* debug
* debug
* fix bug
* minor
* on comments
* dynamic sql
* fix build
* minor
* linked hash
* on comments
* minor
* mem
* minor
Co-authored-by: Ethan Tao <ettao@microsoft.com>
Update install_deps.sh to use relative path from script directory to symbolic_opset10.py. This allows install_deps.sh to be called from different working directories.
* [java] - adding a cuda enabled test.
* Adding --build_java to the windows gpu ci pipeline.
* Removing a stray line from the unit tests that always enabled CUDA for Java.
* Enable running PEP8 checks via flake8 as part of the build if flake8 is installed.
Update scripts in \tools and \onnxruntime\python. Excluding \onnxruntime\python\tools which needs a lot more work to be PEP8 compliant. Also excluding orttraining\tools for the same reason.
Install flake8 as part of the static_analysis build task in the Win-CPU CI so the checks are run in one CI build.
Update coding standards doc.
* Added aarch64 build pipeline
* Fix build error
* Remove auditwheel repair which doesn't work with cross compiling
* Statically link C++
* Added auditwheel repair back and fix stdlib.h
* Remove extra space
* Add signed nuget package to publish ort-nightly nuget feed
* Push managed nuget as well
* Indentation fix
* Indentation fix
* Update gpu.yml to also publish directml nuget
* Fix typo in naming of task
* Fix C# log APIs. Fixes github issue #3409.
* Fix build error due to accidental duplication of GraphOptimizationLevel
* Fix runoptions
* Fix broken test. Add --blame switch to dotnet test cmd line to print the failed test in case of crash.
* initial change to transformer.py
* prepare e2e transformer tests
* refactor transformer tests
* put test python files in a flat folder
* fix typo pip install transform(s)
* python 3.6
* python version to 3.6 in install_ubuntu.sh
* remove argparser
* to use opset ver 12
* workaround loss_scale naming patch in case of loss_fn_
* assign self.loss_fn_ so it can be checked
* skip a few un-needed post-process steps
* fix loss_scale_input_name, clean up post process steps
* skip non-frontend tests
* move cpu/cuda related files to coresponding cpu/cuda folder (#3668)
Co-authored-by: Weixing Zhang <wezhan@microsoft.com>
* type cast for ratio is not necessary for dropout (#3682)
Co-authored-by: Weixing Zhang <wezhan@microsoft.com>
* thrustallocator is not needed since cub is used directly for gather now. (#3683)
Co-authored-by: Weixing Zhang <wezhan@microsoft.com>
* GatherND-12 Implementation (#3645)
* Renamed, UT passing
* Move GatherND CUDA Kerenl into onnxruntime
* Merge GatherNDOpTest
* Refactor Test code
* Merge CPU Kernel Impl
* Handle Negative Indice, Fix UT
* Improve CUDA kernel to handle negative index
* Minor Fixes
* Preserve GatherND-1 Cuda kernel
* Fix Mac build
* fix UT
* Fix Build
* fix GatherNDOpTest.double > CUDA error cudaErrorInvalidDeviceFunction:invalid device function
Co-authored-by: Sherlock Huang <bahuang@OrtTrainingDev3.af05slrtruoetgaxwwjv5nsq5e.px.internal.cloudapp.net>
Co-authored-by: Peng Wang (pengwa) <pengwa@microsoft.com>
* update with reviewers' comments
* testBertTrainingGradientAccumulation was not using rtol and may fail occasionally with small (e-06) difference
* fix merge mistakes
Co-authored-by: liqun <liqun@OrtTrainingDev4.af05slrtruoetgaxwwjv5nsq5e.px.internal.cloudapp.net>
Co-authored-by: Weixing Zhang <weixingzhang@users.noreply.github.com>
Co-authored-by: Weixing Zhang <wezhan@microsoft.com>
Co-authored-by: Sherlock <baihan.huang@gmail.com>
Co-authored-by: Sherlock Huang <bahuang@OrtTrainingDev3.af05slrtruoetgaxwwjv5nsq5e.px.internal.cloudapp.net>
Co-authored-by: Peng Wang (pengwa) <pengwa@microsoft.com>
The flags "--enable_wcos --use_winml" don't work with the latest VC++ and CMake. I don't know which caused the failure. But it doesn't work. Remove it to make the pipelines work first.
Will add them back before 1.3 release.
* add windowsai.yml for new Microsoft.AI.MachineLearning nuget
* temporarily add windowsai.yml to gpu.yml
* pass in build arch
* remove install onnx task
* no dml for arm or arm64
* refactor nuget pipeline defs
* update package creation
* pass in build and sources path
* missing hyphens
* copy license file
* fix parameter variable
* disable arm builds for now
* remove commented script block
* download pipeline atifcat name update
* set working dir
* Add bundling nuget script
* path combine
* null path
* combine needs parentheses
* binplace microsoft.* dlls in new nuget package
* update artifact name
* move merged nuget to artifacts directory
* move to merged subfolder in artifacts staging dir
* forward slash to back
* enable arm
* vcvarsall needs x64 vars setup
* Run Tests
* fix tests
* move global variables
* update yml to not have global variable in template
* removed parameters
* fixes
* Add build arch as an env variable
* ne not neq
* %Var% for batch script
* dont pass argument for x64
* disable arm tests
* skip csharp/cxx tests for microsoft nuget package
* remove test-win as it tests only c# cxx and capi
* test build for store apps
* dont build for store
* tools/nuget/generate_nuspec_for_native_nuget.py
* remove args.
* add new props and targets for microsoft.ai
* make windowsai props/targets static
* add dependency
* dont ship dot net props
* Remove c# fom windowsai nuget
* copy license file
* native packages must have win10 as the platform, not win
* cuda header in wrong if branch
* no dml for arm builds
* only build dml for x64/ x86
* User/sheilk/props update (#3616)
* prelim store work
* props
* Fix desktop nuget props/targets
* clean up targets and make store apps work
Co-authored-by: Sheil Kumar <sheilk@microsoft.com>
* update windowsai.yml with latest
* remove extra dloadhelpers
* Add abi headers to abi dir, and reference native includes
* update windowsai.yml
* minor update
* remove parameters
* add doesrp param
* hard code esrp to true
* add directml for x86/x64
* revert gpu yml changes
* add store builds
* add store builds
* add checks again in old way
* dup job names for store and desktop builds
* move all of the runtime binaries to win10 folder
* only set safeseh on x86
* disable the store builds for now... missing msvcprt.lib
* copy paste deletion...
* switch back to win- (#3646)
Co-authored-by: Sheil Kumar <sheilk@microsoft.com>
* use stahlworks
* & not supported in ado
* add cuda to cpu nuget(???) and EnableDelayedExpansion to enable x86 dml package
* revert nocontribops
* add underscore...
* extra win/win10 change
* merged nuget... still not being bundled...
* files in merged directory
* missing parens causing dml to be included in cpu package
* more diagnostic info
* switch dir to get-childitem
* wait for compression to complete
* add winml_adapter to mkml and gpu packages
* enable_wcos
* add mklml binaries
* props and targets missing from mklml
Co-authored-by: Sheil Kumar <sheilk@microsoft.com>
* Added FP16 transformations
* Revert "Added CMAKE_BUILD_TYPE to make building dynamic"
This reverts commit d3e17af1af655cfdc4d2fec33f52055caa525e85.
* Added FP16 transformations for FP16 builds
* Backend logic cleanup
Cleans the backend(intel_graph.*) code in the following ways:-
1. Minimize global usage: Since all the IR graphs need to be
re-generated on every Infer, it is bad practice to rely on globals
for their saving and usage as there would be multiple readers and
writers to the same global variable leading to incorrect usages or
contentions. This change replaces globals with locals where possible.
This change also fixes an existing bug with due to
incorrect global usage.
2. Remove all unused functions.
3. Remove all unused headers and prepocessor directives.
* removed commented out code
* Disabled default optimization for Intel EP
Signed-off-by: suryasidd <surya.siddharth.pemmaraju@intel.com>
* Fix missed plugins.xml for python bindings
* Fixed the build after latest master changes
Signed-off-by: suryasidd <surya.siddharth.pemmaraju@intel.com>
* Disabled unsupported ops for accelerators
Signed-off-by: suryasidd <surya.siddharth.pemmaraju@intel.com>
* Added some more disabled ops
Signed-off-by: suryasidd <surya.siddharth.pemmaraju@intel.com>
* Added environment variable to enable debugging
Signed-off-by: suryasidd <surya.siddharth.pemmaraju@intel.com>
* Added more debug statements
Signed-off-by: suryasidd <surya.siddharth.pemmaraju@intel.com>
* Fixed unsupported ops list for GPU and VPU
Signed-off-by: suryasidd <surya.siddharth.pemmaraju@intel.com>
* Fixed unsqueeze unit tests
Signed-off-by: suryasidd <surya.siddharth.pemmaraju@intel.com>
* Added error message to the status
Signed-off-by: suryasidd <surya.siddharth.pemmaraju@intel.com>
* Overwrite Model proto with shape info from data
Overwrites the shape info of Model proto with the shape from
actual input data. Needed for inferring models with Dynamic
shapes.
* Removed print statement and disabled where op
Signed-off-by: suryasidd <surya.siddharth.pemmaraju@intel.com>
* Disabled Reshape with Empty initializer
* Added more debug statements for 1P
* Don't allow 1D inputs with symbol for dimension
* Disabled some 3rd phase ops
* Disabled split and added zero dimension check for OutputDefs
* Cleanup zero dimensionality check
* Added different data type check for inputs and initializers
* Added conditions for Mod, Cast and Pad
* Removed unused variable
* Disabled scan and added conditions for squeeze
* Added changes for fixing all C++ unit tests
* Implements Backend Manager class for caching
Backend Manager provides a layer of indirection between EP interface
and OV backend that provides caching services for models with
symbolic dims in input shapes.
* clean up commented blocks
* clang-formatting
* Read I/O type info from ModleProto
Read the tensor element type information from ModelProto object,
as FusedNode is no longer available.
* code cleanup
* clang-formatting
* Added print statement for jenkins
* Disabled some python tests
* Changed the path of convert fp32 to fp16 hpp
* Added conditions for BatchNorm in GetCapability
* Fixed failed tests
* Revert "Added conditions for BatchNorm in GetCapability"
This reverts commit c3c28c3b00d27892c42546b35dacdd807a48ee90.
* Added Intel to onnxruntime backends
* pick up vars set by OV package setupvars.sh
* Added conditions for Identity
* remove a few cout prints
* Added conditions for GPU_FP32 unit tests
* Revert "pick up vars set by OV package setupvars.sh"
This reverts commit 8199e029c03eae21a1a7ef6bfdc93d00e5d0198b.
* Commented out fatal message for protobuf
* Might need to be removed
* Add interface class for current backend
* moved common logic to base class
* simplified cpu backend
* Removed unused headers
* use vectors to save i/o tensors for windows compatibility
* move utils fxns to backend_utils namespace
* rename ov_backend to ibackend
* Factory pattern for backend creation
* rename CPU backend to Basic backend
* renamed to vad-M and added to factory list
* Added conditions for VPU
* Added print statements
* Changed the logic for checking for symbolic shapes
* Modified logic for zero dimension check
* Removed VPU single dimension condition
* Removed comments
* Modified logic in DimensionCheck method
* Remove legacy OpenVINO EP
Remove all the legacy code for OpenVINO EP. UEP code will take its
place going forward.
This change does NOT remove OVEP files in the following areas asa
they will be reused by UEP:-
1. Documentation: All .md files
2. Docker releated files
3. Python bindings
4. Java bindings
5. C# bindings
6. ORT Server
7. CI pipeline setup files
* Rename Intel EP to OpenVINO EP
* Added unique names to the subgraphs
* Removed subgraphs with only constant inputs
* Modified subgraph partitioning algorithm to remove const input subgraphs
* Apply suggestion to onnxruntime/core/providers/openvino/openvino_execution_provider.cc
* Tracking output names to fix the output order bug
* Changed output names to a unordered map
* Modified logic to check for symbolic input shapes
* Fixed a bug in Reshape check
* Added empty model path to Model constructor
* Made necessary changes to cmake to build from the binary package
* Changed INTEL_CVSDK_DIR to INTEL_OPENVINO_DIR
* Enable dyn device selection with C++ API
* Added Round operator to unsupported list
* Modified subgraph partition logic for MYRIAD
* Removed supported ops from the list
* Enable dyn dev selection in Py API's
* Add documentation for dynamic device selection
* Use MYRIAD || HDDL instead of VPU
* Removed temporary cast of Int64 to FP32
* Disabled unit Tests for CPU_FP32 and GPU_FP32
* Removed default "CPU" from unit tests to allow overriding
* Removed ops Concat, Squeeze, Unsqueeze from unsupported list
* Get the device id from info
* Removed overwriting device_id and precision
* Enabled ConvTranspose and EyeLike
* Reordered unsupported ops in alphabetical order
* Fixed syntax error
* Fixed syntax error
* Code clean-up: Handle exceptions, logs and formatting
Code formatted according to ORT coding guidelines.
* remove debug print from pybind code
* updated docs with ops and models
* formatting prints
* Added default values for c and j for openvino
* Overriding the values set for c and j to be 1
* BACKEND_OPENVINO should be empty if openvino is not in build
* Overriding c value with default for perftest
* fix VAD-M device string bug
* Add IE error details to exceptions
* Use IE specific device names in EP
* Add VAD-F (FPGA) device support
* Removed unecessary libraries from whl package
* Code changes for Windows compatibility
* Add VAD-F option to python API
* [revert before merge] cmake changes for RC
* Enable Windows build in CMake
* Unset macro OPTIONAL for windows builds
inference_engine.hpp's include chain defines a macro 'OPTIONAL'
which conflicts with onnx project's headers when using MSVC. So
would need to explictly unset it for MSVC.
* Use a single copy of plugin/IE::Core
Defined as a static member in Backend manager
* Remove restriction of single subgraphs for myriad
* Passed subgraph name to Backend to enhance log statements
* Disabled zero dimension conditions
* Disabled concat to remove zero dims
* Enabled building ngraph as part of ORT
* Removed serializing and added versioning
* Fix CPU_FP32 unit tests
* Removed unecessary condition
* add ngraph.so.0.0 to .whl
* Check for zero dimensions only for inputs and outputs
* Restrict loading only 10 subgraphs on myriad
* Build ngraph.dll within UEP. Doesn't link yet
* Rename Linux included libngraph.so to libovep_ngraph.so
Renames locally built libngraph.so containing ONNX importer to
libovep_ngraph.so in order to avoid linkage conflicts with
libngraph.so supplied by OpenVINO binary installer.
Applies only for Linux builds.
* use output_name cmake properties for lib name
* fix .so name format in lib_name.patch
* CMake code cleanup
* Rename WIN32 included ngraph.dll to ovep_ngraph.dll
To avoid conflict with ngraph.dll distributed by openvino.
* Added myriad config for networks without 4 dimensions
* Loading the 10 max clusters for inference on myriad
* Refactor code and add Batching support
Encapsulate subgraph settings into context structs.
Add batching support for completely supported models.
* Disabled some broken tests
* use input_indexes to avoid batch-checking initializers
* Avoid static initialization order error on WOS
* Added candy to broken tests
* InternalCI changes for 2020.2
* Updated DLDT instructions
* Unsaved changed in install_openvino.sh
* Changes after manual check
* Remove custom ngraph onnx_import build for WOS
ONNX Importer on WOS does not have protobuf issue.
* Remove FP32ToFP16 ngraph pass
This conversion is performed implicitly within IE.
* Surround debug logic by #ifndef NDEBUG
* remove invalid TODO comments
* removed references to ngrpah-ep
* clang-formatting
* remove commented code
* comment edits
* updating copyright year to that of first OpenVINO-EP release
* remove redundant log msg
* Modified operator and topology support
* Update build instructions
* doc formatting
* Fixed clip unit tests
* Revert "Remove FP32ToFP16 ngraph pass"
This reverts commit ec962ca5f315a5658ad980e740196f19de2639c1.
* Applying FP16 transformation only for GPU FP16
* Fixed GPU FP32 python tests
* automatically use full protobuf
* disable onnxrt server for now
* Disabled upsample
* update dockerfile instructions
* Removed MO paths and added ngraph path
* Remove OVEP from ORT Server docs
Will put it back in after validation
* Updated path to Ngraph lib
* Disabled Resize and some other python tests
* Removed unnecesary header files
* Use commit SHA to fetch ngraph repo
* Avoid un-needed file changes due to version update
* Fixed clip tests
* Fixed Pow, max and min onnx tests
* build.md doc typo
* Update cmake patch command for ngraph src
* remove dead cmake code for onnxruntime_USE_OPENVINO_BINARY
* use spaces instead of tab
* remove commented code
* Add info about protobuf version
* edit debug env var and enable for WIN32
* specify only version tag of 2020.2 for dockerbuilds
* remove unnecessary file changes
* Pass empty string as default argument to C# tests
* Use ${OPENVINO_VERSION} to name openvino install directory in CI builds
* Enabled unnecessarily disabled tests
* Fixed ngraph protobuf patch
* Fixed error in protobuf patch
* Revert "Use ${OPENVINO_VERSION} to name openvino install directory in CI builds"
This reverts commit 89e72adb8bf3b9712f5c81c5e13fe68c6c0df002.
* Remove unsetting OPTIONAL macro
This is no longer used in recent ONNX update onnx/onnx@da13be2,
so this unset workaround is no longer necessary.
* Use a null string default argument for C# API
* Set OpenVINO version yml files and pass to CI Docker builds
Git Tag info for DLDT as well as install directory are set
using this value.
This reverts commit 9fa9c20348ed72ae360a95c98e9b074d2f9fafc5.
* Documentation: recommendation and instructions for disabling ORT graph optimizations
* more doc updates
* Reduced the number of models according to CI time constraints
Co-authored-by: ynimmaga <yamini.nimmagadda@intel.com>
Co-authored-by: suryasidd <surya.siddharth.pemmaraju@intel.com>
Co-authored-by: Mikhail Treskin <mikhail.treskin@intel.com>
Co-authored-by: mbencer <mateusz.bencer@intel.com>
Co-authored-by: Aravind <aravindx.gunda@intel.com>
Co-authored-by: suryasidd <48925384+suryasidd@users.noreply.github.com>
