Advance ONNX commit to pickup the latest ArgMax, ArgMin,
ReduceMax/ReduceMin, MaxPool
Declare new versions for CPU/CUDA.
Implement infrastructure support for int8/uint8.
Adust GatherOp test for a new error.
Adjust Scan9.BadShape test.
Add exclusions for index out of bounds checks.
Rework result verification for SVDTransformer.
* added support for ios crosscompilation under linux
* reverted cmake generator change
* if --ios is added protoc can be compiled for host system
* accidently reverted change to compile protoc for host system for ios if protoc exe is not set
* wdata is now used
* accidentally pasted CMAKE_OSX_ARCHITECTURES into CmakeLists.txt, also made bad merge on build.py previously
* removed print
* fixed typeo, deleted commented statements for earlier debugging
* reverted accidental delete
* added asmmacro.h for aarch64 asm
now MlasSgemmKernel**** gets underscore added if needed
no need anymote to differentiate between iOS arm64 and normal amr64 build
onnxruntime.cmake: added check if iOSCross is set to properly set RPATH
* removed 2 spaces
* fix: logcial error fixed, now protoc gets compiled if not supplied with --path_to_protoc_exe
* removed unecessarily added spaces
* removed some more spaces
1. Copy tensorflow's thread pool class to ORT, so that we can get a better implementation of thread pool based parallelfor
2. Copy Eigen's thread pool class to ORT
3. Support thread affinity
4. Remove RNN kernel’s private thread pool
5. Modify pool kernels to use the thread pool when openmp is disabled.
* Enable sequence of tensor
* add tests
* small updates
* There should only be 2 elements returned
* CR feedback, and another 6->2 check update in the test.
* missing semicolon...
* Add explicit to constructor taking pointer paramter
Co-authored-by: Sheil Kumar <sheilk@microsoft.com>
* Fix WCOS/Win32 linking bugs
* Remove unused NODEFAULTLIB flags
* Avoid plain target_link_libraries signature
* Avoid plain target_link_libraries signature
* Fix library list escaping
* Use library list instead of string
* Remove duplicate link to windowsapp.lib
* Remove Win32 build workarounds
* Specify CMake policies before initializing language
* Expose Win32 header definitions during build
* Force set API family
* Enable Win32 APIs in featurizer
* Use MT dynamic CRT
* Expose Win32 specific functions
* Disable app container globally
* Disable default wide functions in featurizers
* Add featurizers to test include path
* Workaround https://gitlab.kitware.com/cmake/cmake/issues/19428
* Revert pipeline debugging hacks
* Skip /FI in CUDA sources
* Default to Win32 builds
* Enable WCOS when using WinML
* Use generator expression to apply CMAKE_MSVC_RUNTIME_LIBRARY to C++ only
* Add support for sessions to share a global threadpool.
* Fix build issues
* Add tests, fix build issues.
* Added some documentation
* Fix centos issue when threadpools become nullptr due to 1 core.
* Fix mac and x86 build issues
* Address some PR comments
* Disabled test for android, added few more tests and addressed more PR comments.
* const_cast
Moved path_lib.h/cc from onnxruntime/core/framework to onnxruntime/core/platform and from the onnxruntime_framework to the onnxruntime_common libraries.
This change fixes#3129. When running onnxruntime as dll on Windows, CUDA does some internal cleanups when process exits. After this, any call to CUDA would cause crash. Delayload makes thread_local destructor to happen after CUDA cleanup, thus the crash.
* add dml gpu pipelines
* add x86 to the gpu dml dev build pipeline
* Enable DML x86 builds
* Fix uint64_t -> size_t warning
* fix warnings
* enable dml on x86 ci builds
* operatorHelper 773 error uint32_t vs uint64_t
* operatorHelper 773 error uint32_t vs uint64_t
* make x86 pipeline use the gpu pool
* more warnings
* fix x86 directml path
* make dml nuget package
* disable tf_pnasnet_large
* disable zfnet512
* make validation use wildcards
* disable x86 dml gpu tests
* add args.
* update gpu.yml
* change nupkg wildcard
* add debug statements
* package x86 dml nupkg
* dont drop managed nuget again from dml pipeline build
* Add DML EULA
* directml license should be renamed to not clobber the existing license
* casing on dml package....
* {} to ()
* fix license name
* disable dml from x86 ci
* typo and cr feedback
* remove featurizers
* ship the dml pdb as well
* Switch to CUDA10.2
* Update win-gpu-tensorrt-ci-pipeline.yml
* Update win-gpu-tensorrt-ci-pipeline.yml
* remove dynamic_shape
* update onnx-tensorrt submodule
* check if input shape is specified for TensorRT subgraph input and enable some TensorRT unit tests
* fix format issue
* add shape inference instruction for TensorRT
* update according to the reviews
* Update win-gpu-tensorrt-ci-pipeline.yml
* port the mimalloc allocator
* hook mimalloc opt into common.h and reduction ops
* repurpose USE_MIMALLOC to only denote subbing in of default allocator with mimalloc and some refactoring
* fix unintended cherry pick diffs
* polish alloctor_mimalloc
* explicitly disable mimalloc where it already had been disabled
* update mimalloc to pull in stl allocator
* switch mimalloc stl allocator to use mimalloc library version
* turn mimalloc on by default (only the stl changes are enabled, the python interacting ones are off already and shall remain so)
* move FastAllocVector into cpu specific code
* separate out defines into arena and stl changes
* the rest of the define renames
* bfc arena allocator
* some typos and rename the bfc arena allocator to fit existing class naming conventions
* adjustments in response to comments
* different template instantiations are friends
* merge training kernels to master
* merge training kernels to master
* revert two files
* merge training kernels to master
* merge training kernels to master
* merge training kernels to master
* merge training kernels to master
* merge training kernels to master
* merge training kernels to master
* merge training kernels to master
* merge training kernels to master
* merge training kernels to master
* merge training kernels to master
* merge training kernels to master
* merge training kernels to master
* merge training kernels to master
* merge training kernels to master
* merge training kernels to master
* merge training kernels to master
* merge training kernels to master
* merge training kernels to master
