* 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>
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
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.
* 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>
* 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>
* 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>
* 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.
* 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
