1. Merge Nuget CPU pipeline, Java CPU pipeline, C-API pipeline into a single one.
2. Enable compile warnings for cuda files(*.cu) on Windows.
3. Enable static code analyze for the Windows builds in these jobs. For example, this is our first time scanning the JNI code.
4. Fix some warnings in the training code.
5. Enable code sign for Java. Previously we forgot it.
6. Update TPN.txt to remove Jemalloc.
* Add type reduction support to Min, Max and Pow
Update the C++ type reduction infrastructure to allow specifying an opset for the supported types list, as those can change across opset versions.
Minor updates to the type usage tracking script
* Add 'all opsets' macros and constant
* 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
* Permit dense seq optimization on BERT-L pytorch export by enabling ReduceSumTraining, Equal, and NonZero on AMD
* enable Equal tests
* enable fast_matrix_reduction test case
* Update to match new test setup.
* Add Gemm(7) manually for now.
Will fix properly on Monday. It's used by mnist.ort as that is created by optimizing mnist.onnx to level 1 causing 2 nodes to be replaced by a Gemm and the op to be missing from the required list as that is created using the original onnx model.
* optimize cuda conv by fused activation
* remove needless print out
* exclude test from cpu
* handle status error from cudnn 8.x
* add reference to base class
* add hipify
* 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
* Add macos coreml CI and coreml_flags
* Move save debuggubg model to use environment var
* Move pipeline off from macos CI template
* Fix an issue building using unix make, add parallel to build script
* Fixed build break for shared_lib and cmpile warning
* Fix a compile warning
* test
* Revert the accidental push from another branch
This reverts commit 472029ba25d50f9508474c9eeceb3454cead7877.
* 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
* assert sequence tensor and remove skips
* update testdata json
* use ONNX 1.8 in cgmanifest.json
* use previous commit to workaround
* update ONNX commit ID in docker
* skip test_maxpool_2d_dilations test for now
* update function name
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.
* 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>
