* Enabling fasterrcnn variant and vehicle detector
* changes for 2021_2 branch
* yolov3_pytorch commit
* fixed braces in basic_backend.cc
* ci information added
* faster rcnn variant and vehicle detector changes were made in 2021.1 and not in 2021.2
* some changes to support unit tests
* disable some tests which are failing
* fix myriad tests for vehicle detector
* Did some cleanup
*cleaned up comments
*Disabled Add_Broadcast_0x1 and Add_Broadcast_1x0
tests on MYRIAD_FP16 backend due to a bug
*cleaned up capability_2021_2.cc file
*Removed extra conditions which were added
for some validation in backend_utils
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* yolov3 pytorch workaround to ensure that the output names are matched
* gemmoptest fixed on myriad
* Fixed MYRIADX CPP Test Failures
*Expand,GatherND,Range,Round op's
are only supported in model
*where op with float input data
types are not supported and fixed
*Scatter and ScatterElements op's with
negative axis are fixed
*Reshape op with 0 dim value are not
supported and fixed
*Disabled InstanceNorm_2 test on MYRIADX
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* make changes to yolov3 pytorch
* Fixed python unit tests
*Fixed failing python tests on vpu,
GPU and CPU
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Fixes POW op failures on GPU_FP16
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Clean up capability_2021_2.cc
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Updated docx for MultiThreading option
*Added extra info on setting the num_of_threads
option using the API and it's actual usage
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* fixed slice and removed extra prints
* Disabled failing python tests
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Minor changes added in capabilty_2021_2
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* made changes to slice to avoid failures
* Disabling FP16 support for GPU_FP32
->Inferencing an FP16 model on GPU_FP32
leads to accuracy mismatches. so, we would
rather use GPU_FP16 to infer an FP16 model
on GPU Device
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Updated docx for Inferencing a FP16 Model
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* fix for mask rcnn
* Script for installing openvino from source
* Updated with openvino 2021.2 online installation
* code comment fixes
fixed accuracy mismatch for div
* Update OpenvinoEP-ExecutionProvider.md
updated for 2021.2 branch
* Update README.md
updated dockerfile documentation
* Update BUILD.md
build.md update documentation
* permissiong change of install_openvino.sh
* made changes to align with microsoft onnxruntime changes
* Updated with ov 2021.2.200
Co-authored-by: suryasidd <surya.siddharth.pemmaraju@intel.com>
Co-authored-by: sfatimar <sahar.fatima@intel/com>
Co-authored-by: MaajidKhan <n.maajidkhan@gmail.com>
Co-authored-by: mohdansx <mohdx.ansari@intel.com>
Fix clean_docker_image_cache.py detection of image pushes. They were being ignored because the expected HTTP status code was wrong. For pushes, it's 201 instead of 200.
Move CudaKernel from cuda_common.h to a new separate header, cuda_kernel.h. Update include sites to use cuda_kernel.h instead if they need CudaKernel. Inclusions of cuda_common.h are now more lightweight.
Make corresponding changes for ROCM execution provider code.
Other minor cleanup.
* build for .net5
* only reference cswinrt for .net5
* remove netstandard2.0 references
* upgrade language version
* net5
* remove extra comment closure
* add targetframework
* set target framework
* remove net*
* pep8 errors
* make test project build with .net windows SDK projection
* disable c# builds for non-x64 builds
* fix pep8 errors
* disable for store build
* fix tests
* remove cswinrt and sdk references from package
* bump cswinrt down to 1.0.1
* fix bin path
Co-authored-by: Sheil Kumar <sheilk@microsoft.com>
* ReduceL2Grad and ClipGrad.
* fix win build and amd ci pipeline
* resolve comments.
Co-authored-by: Vincent Wang <weicwang@AiFramework2080ti2.corp.microsoft.com>
Fix a typo in tools/ci_build/github/azure-pipelines/templates/get-docker-image-steps.yml.
Add logging to tools/ci_build/get_docker_image.py for easier debugging.
* save python dictionary to hdf5 representation and load an hdf5 file into a python dictionary
* unit tests for saving data to and loading data from hdf5 file
* Added Onnxruntime_GCOV_COVERAGE flag for Android.
* Set CMAKE_SYSTEM_NAME explicityly for Android.
* Added GCOV_PREFIX option to collect code coverage data.
Added a new python script to generate code coverage info.
Modified build pipeline to geneate Android code coverage info
* Added build command line option --android_coverage
* Added a comment describing the GCOV environment variables
* Fixed PEP8 issues.
* Added --android_coverage option to the build command.
* Increased Android emulator memory from 3K to 8K.
* Increased Android partition-size from 2GB to 4GB to overcome no-space-left-on-device error
* Removed source_dir from command line args.
* Use cwd absolute path to run tests.
* Added commands to output the contents of /data/local/tmp on the emulator.
* Added run_adb_shell function.
* Format changes.
* Removed keywd argument cwd.
* Removed Android in the --build_dir path.
* Removed commands added for debugging.
* Removed exxtra new-lines.
* Fix MacOs build pipeline failures by uninstalling openssl before running build script.
* Revert "Fix MacOs build pipeline failures by uninstalling openssl before running build script."
This reverts commit 90d0568fe533e9456c20d061a2d435c8fea48266.
* Change dir to the build directory where the tar file is copied.
* Changed the option from --android_coverage to --code_coverage
* Moved steps to generate Android code coverage to run_nnap_code_coverage.sh
* Require --android option if --code_coverage is specified.
* No code coverage needed for onnx_test_runner.
* Expect that the emulator is running when the script is executed.
* Fixed the title in the buildpipeline step.
* Fixed the formatting issue.
* Added a command line argument, ORT_ROOT, to run_nnapi_code_coverage.sh script
Co-authored-by: Satya Jandhyala <satyajandhyala@Satyas-Mac-mini.local>
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.
* Expand the documentation on using compiling EPs with a minimal build to call out a 'simple' option that is easier to use. Provide more background on what happens to help users choose the best option for them.
Tweak conversion script to be noisier about attempted usage of 'all' optimization level.
Co-authored-by: manashgoswami <magoswam@microsoft.com>
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>
Update ORT model conversion script
- add args for specifying optimization level and whether to use NNAPI
- add logic to create a list of required ops and ORT format model that can be used with NNAPI
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
* Make NNAPI EP build on non-Android Platform
* minor updates
* Adress CR comments
* Fix build issue using Windows, address CR comments
* Fix linux build warnings
* Fix for test failure
* Fix for test failure
* Fix model_tests failure
* Enabling Multi Device support for UEP
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Minor fix added
*Added a simple fix to determine OpenVINO
version for Arm build as well
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* cpu send/recv
* clean up send/recv
* remove unused code
* assert and nccl option for mnist
* add build option to enable build with only cpu. Without this, nccl is always enabled which will break build on machine that only contains cpu
* Add USE_MPI distinct from USE_NCCL/USE_HOROVOD
* fix
* fix
* exclude cpu send/recv for machines without mpi
Co-authored-by: Tim Harris <tiharr@microsoft.com>
* 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>
Some part of code for reduction kernels has been changed in 858040fa,
which cause failures in rocm build since ROCm EP shares some code with
CUDA EP. This PR is to quick fix this failure by not sharing two files
for now to unblock CI enabling on ROCm EP. Another PR for leveraging
858040fa for ROCm EP will be done later.
* Add kernels for AMD GPU.
This PR is mostly about GPU kernels for ROCm EP. Due to similar GPU programming language (CUDA and HIP and similar math library calls, one principle in ROCM EP design is to share CUDA kernels as much as possible for ROCm. Thus, the script amd_hipify.py has been created for converting CUDA kernels to ROCm HIP kernels automatically during compilation phase. But, for some reasons such as perf issue, syntax difference..., some converted kernels need some manual intervention. These kernels will be checked in the repo physically for now. In order to avoid manual intervention, the plan is to refactor CUDA kernels to make them portable between CUDA EP and ROCm EP as much as possible.
Please refer to "HIP Porting Guide" for details.
* like lamb, multi-tensor-apply needs to be disabled for IsAllFiniteOp and ReduceAllL2, current AMD GPU compiler has perf issue for kernel parameter which is a structure with "pass by value".
* Use hipMemsetAsync and add checks on HIP calls.
* move the generated files to build folder.
Co-authored-by: Jesse Benson <jesseb@microsoft.com>
* 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>
* Implement Hetero in UEP
* Added security checks to take valid Hetero combinations
as device type
* Integrating Hetero features
* Get the statistics Report in Debug Mode
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Passing right device type for vadm_baackend
Added simple fix to pick the right device type
when using vadm_backend with Hetero as well.
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Fixed batching logic for 2020.4 and above
* Fixed flake8 PEP8 errors
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Minor Fixes Added
*Added security checks for device_type passed
in for Hetero build during run time
*code cleanup
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Minor changes Added
*Fixed batch_size bug in vadm_backend
*code cleanup
*Documentation updated for Hetero
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
Co-authored-by: suryasidd <surya.siddharth.pemmaraju@intel.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>
Introduce sparse_initializers support.
Convert them to dense on model load and prune graph_proto_
so they don't consume space. Convert back to sparse on ORT Format model save.
Implement serializing sparse initializers to OrtFormat.
Fix Model::ToProto() to return original sparse initializers
Set a flag that graph_sync is needed when loading a simple ORT Format model.
otherwise nothing is resolved.
Add ORT Format history to README.md
ifdef MINIMAL build for DenseToSparseTensorInitializer
Allow duplicate initializers to support existing models.
Issue a warning instead of aborting.
* Revert "Remove SparseTensor support from minimal build. (#5114)"
This reverts commit 59ee8ffb17.
Signed-off-by: Dmitri Smirnov <dmitrism@microsoft.com>
* Build ACL and ArmNN with custom library path
* Define import to tensor as a separate function for maintenance and readability
* Enabled optimized depthwise convolution for ACL v20.02
* Check operation status for ACL and ArmNN Execution Providers
* Enabled fused operation for convolution-activation
Co-authored-by: Andrei-Alexandru <andrei-alexandru.avram@nxp.com>
* 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>
* use run_orttraining_test_orttrainer_frontend_separately to work around a sporadic segfault.
Co-authored-by: liqun <liqun@OrtTrainingDev4.af05slrtruoetgaxwwjv5nsq5e.px.internal.cloudapp.net>
* 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.
* Add iOS test pipeline and a sample app.
* clean up the unused code.
* clean up.
* revert the unknown change
* disable the shared library for iOS.
* add open source notice text.
* ignore the skipped test.
* extract the common ortenv setup
* 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>
