* Add robust dependency check for Python package
* Add version_info.py to .gitignore
* Fix Linux build
* Fix Windows CPU build
* Fix Windows 32-bit build
* Minor tweak
* Generate version_info.py earlier in onnxruntime_python.cmake
* Print a user-friendly message if cuDNN is not found in
* Relax version requirements for CUDA 11 - only the major version has to match
* Fix PATH environment variable to include CUDA 11 in 'Python packaging pipeline' (Windows/GPU)
* Fix the build with cuDNN 7
* Remove support from custom ops from the base minimal build as they contribute too much binary growth to an Android build.
Add ability to explicitly enable custom op support in a minimal build.
Change one minimal build CI to test adding custom op support (unit tests are run in that build to validate)
* model building
* fix build
* winml adapter model building api
* model building
* make build
* make build again
* add model building with audio op
* inplace and inorder fft
* add ifft
* works!
* cleanup
* add comments
* switch to iterative rather than recursive and use parallelization
* batched parallelization
* fft->dft
* cleanup
* window functions
* add melweightmatrix op
* updates to make spectrogram test work
* push latest
* add onesided
* cleanup
* Clean up building apis and fix mel
* cleanup
* cleanup
* naive stft
* fix test output
* middle c complete
* 3 tones
* cleanup
* signal def new line
* Add save functionality
* Perf improvements, 10x improvement
* cleanup
* use bitreverse lookup table for performance
* implement constant initializers for tensors
* small changes
* add matmul tests
* merge issues
* support add attribute
* add tests for double data type windowfunctions and minor cleanup
* stft onesided/and not tests
* cleanup
* cleanup
* clean up
* cleanup
* remove threading attribute
* forward declare orttypeinfo
* warnings
* fwd declare
* fix warnings
* 1 more warning
* remove saving to e drive...
* cleanup and fix stft test
* add opset picker
* small additions
* add onnxruntime tests
* add signed/unsigned
* fix warning
* fix warning
* finish onnxruntime tests
* make windows namespace build succeed
* add experimental flag
* add experimental api into nuget package
* add experimental api build flag and add to windows ai nuget package
* turn experimental for tests
* add minimum opset version to new experimental domain
* api cleanup
* disable ms experimental ops test when --ms_experimental is not enabled
* add macro behind flag
* remove unused x
* pr feedback
Co-authored-by: Sheil Kumar <sheilk@microsoft.com>
* minimal_build with training ops
* Removing redundant comment from an earlier attempt at a fix
* Fixing a bad merge conflict resolution
* Responding to PR feedback
* tweaking the makefiles based on feedback
* combining two enable_training blocks in CMakeLists.txt
* Add ability to generate configuration that includes required types for individual operators, to allow build size reduction based on that.
- Add python bindings for ORT format models
- Add script to update bindings and help info
- Add parsing of ORT format models
- Add ability to enable type reduction to config generation
- Update build.py to only allow operator/type reduction via config
- simpler to require config to be generated first
- can't mix a type aware (ORT format model only) and non-type aware config as that may result in insufficient types being enabled
- Add script to create reduced build config
- Update CIs
* merged alloc_plan
* pass compilation
* Start running, incorrect allocation memory info
* add in comments
* fix a bug of recording pattern too early.
* debugging lifetime
* fix lifetime
* passed mnist
* in process of visualization
* Add code to generate chrome trace for allocations.
* in process of collecting fragmentation
* before rebuild
* passed mnist
* passed bert tiny
* fix the inplace reuse
* fix the exception of weight in pinned memory
* add guards to ensure the tensor is in AllocPlan
* add customized profiling
* debugging
* debugging
* fix the reuse of differnt location type
* add rank
* add the rank
* add fragmentation
* add time_step_trace
* Add summary for each execution step (total bytes, used/free bytes).
* add top k
* change type of top k parameter
* remove prints
* change heap to set{
* add the name pattern
* add the useage for pattern
* add partition
* change to static class
* add custom group
* remove const
* update memory_info
* in process of adding it as runtime config
* change the memory profiling to be an argument
* add some comments
* add checks to recored meomry_info in traaining session
* set the "local rank setting" to correct argument.
* addressing comments
* format adjustment
* formatting
* remove alloc_interval
* update memory_info.cc to skip session when there is no tensor for a particular memory type
* fix memory_info multiple iteration seg-fault
* consolidate mainz changes
* fixed some minor errors
* guard by ORT_MINIMAL_BUILD
* add ORT_MEMORY_PROFILE flag
* added compiler flag to turn on/off memory profiling related code
* clean up the code regarding comments
* add comments
* revoke the onnx version
* clean up the code to match master
* clean up the code to match master
* clean up the code to match master
Co-authored-by: Jesse Benson <benson.jesse@gmail.com>
Co-authored-by: Wei Zuo <wezuo@OrtTrainingDev3.af05slrtruoetgaxwwjv5nsq5e.px.internal.cloudapp.net>
Co-authored-by: wezuo <wezuo@az-eus-v100-32gb-5-worker-mgtbby.eastus.cloudapp.azure.com>
Co-authored-by: wezuo <wezuo@az-eus-v100-32gb-5-worker-yclzsf.eastus.cloudapp.azure.com>
* Use readelf for minimal build binary size checks.
The on-disk size grows in 4KB chunks which makes it hard to see how much growth an individual checkin causes.
Only downside is that the sum of the sections is larger than the on-disk size (assumably things get packed smaller on disk and some of the section alignment constraints can be ignored)
* Remove unused function
1. Update the ProtoSrc path. The old one is not used anymore.
2. Regenerate OnnxMl.cs
3. Delete some unused code in tools/ci_build/build.py
4. Avoid set intra_op_param.thread_pool_size in ModelTests in OpenMP build.
5. Fix a typo in the C API pipeline.
* 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>
