### Description
CMake's
[target_link_libraries](https://cmake.org/cmake/help/latest/command/target_link_libraries.html#id2)
function accepts plain library name(like `re2`) or target name(like
`re2::re2`) or some other kinds of names. "plain library names" are
old-fashioned, for compatibility only. We should use target names.
### Motivation and Context
To make vcpkg work with winml build. See #23158
### Description
Resolve#21281 and #10589 .
1. Change libonnxruntime.so's SONAME: remove the minor and patch
version.
By default when creating an ELF shared object, linker will set the
file's internal DT_SONAME field to the specified name which is the file
name plus SOVERSION . For example, the file name for our library is
libonnxruntime.so. And by default SOVERSION is the lib's VERSION number,
which is something like 1.19.0. So the DT_SONAME field in
libonnxruntime.so is something like libonnxruntime.so.1.18.0. You can
use readelf tool to examine it.
```
readelf -d libonnxruntime.so | grep SONAME
0x000000000000000e (SONAME) Library soname: [libonnxruntime.so.1.18.0]
```
When an executable is linked with a shared object which has a DT_SONAME
field, then when the executable is run the dynamic linker will attempt
to load the shared object specified by the DT_SONAME field rather than
using the file name(which is libonnxruntime.so) given to the linker.
After this change, the SONAME will be shorten to "libonnxruntime.so.1"
instead.
2. Set default version strings for Windows DLLs, to resolve#10589
### Description
Fixes cmake function definition in winml.cmake to copy link flags.
### Motivation and Context
XFGCheck errors in WindowsAI because this function does not transfer
linker flags
### Description
1. `onnxruntime_fetchcontent_makeavailable` works around unconditional
install commands so that can be used instead of `FetchContent_Populate`
2. This dependency is Windows specific, mark it as such.
### Motivation and Context
1. This simplifies `cmake/external/wil.cmake` not to do anything
specific wether WIL was fetched or found
2. Given it's specific to Windows, it might not be available on other OS
in specific air-gapped environment such as
[conan-center-index](https://github.com/conan-io/conan-center-index).
This allows downstream builds not to require specific patches for
something not required by the build in the first place.
### Description
Use target name for flatbuffers.
Add version range for flatbuffers. It is similar to #13870
### Motivation and Context
To fix a build error:
```
CMake Error at onnxruntime_graph.cmake:88 (add_dependencies):
The dependency target "flatbuffers" of target "onnxruntime_graph" does not
exist.
Call Stack (most recent call first):
CMakeLists.txt:1490 (include)
```
It happens when flatbuffers library is already installed. For example,
on Ubuntu people may get it from apt-get. But, the one provided by
Ubuntu 20.04 is not compatible with our code. The one in Ubuntu 22.04
works fine.
## Description
1. Convert some git submodules to cmake external projects
2. Update nsync from
[1.23.0](https://github.com/google/nsync/releases/tag/1.23.0) to
[1.25.0](https://github.com/google/nsync/releases/tag/1.25.0)
3. Update re2 from 2021-06-01 to 2022-06-01
4. Update wil from an old commit to 1.0.220914.1 tag
5. Update gtest to a newer commit so that it can optionally leverage
absl/re2 for parsing command line flags.
The following git submodules are deleted:
1. FP16
2. safeint
3. XNNPACK
4. cxxopts
5. dlpack
7. flatbuffers
8. googlebenchmark
9. json
10. mimalloc
11. mp11
12. pthreadpool
More will come.
## Motivation and Context
There are 3 ways of integrating 3rd party C/C++ libraries into ONNX
Runtime:
1. Install them to a system location, then use cmake's find_package
module to locate them.
2. Use git submodules
6. Use cmake's external projects(externalproject_add).
At first when this project was just started, we considered both option 2
and option 3. We preferred option 2 because:
1. It's easier to handle authentication. At first this project was not
open source, and it had some other non-public dependencies. If we use
git submodule, ADO will handle authentication smoothly. Otherwise we
need to manually pass tokens around and be very careful on not exposing
them in build logs.
2. At that time, cmake fetched dependencies after "cmake" finished
generating vcprojects/makefiles. So it was very difficult to make cflags
consistent. Since cmake 3.11, it has a new command: FetchContent, which
fetches dependencies when it generates vcprojects/makefiles just before
add_subdirectories, so the parent project's variables/settings can be
easily passed to the child projects.
And when the project went on, we had some new concerns:
1. As we started to have more and more EPs and build configs, the number
of submodules grew quickly. For more developers, most ORT submodules are
not relevant to them. They shouldn't need to download all of them.
2. It is impossible to let two different build configs use two different
versions of the same dependency. For example, right now we have protobuf
3.18.3 in the submodules. Then every EP must use the same version.
Whenever we have a need to upgrade protobuf, we need to coordinate
across the whole team and many external developers. I can't manage it
anymore.
3. Some projects want to manage the dependencies in a different way,
either because of their preference or because of compliance
requirements. For example, some Microsoft teams want to use vcpkg, but
we don't want to force every user of onnxruntime using vcpkg.
7. Someone wants to dynamically link to protobuf, but our build script
only does static link.
8. Hard to handle security vulnerabilities. For example, whenever
protobuf has a security patch, we have a lot of things to do. But if we
allowed people to build ORT with a different version of protobuf without
changing ORT"s source code, the customer who build ORT from source will
be able to act on such things in a quicker way. They will not need to
wait ORT having a patch release.
9. Every time we do a release, github will also publish a source file
zip file and a source file tarball for us. But they are not usable,
because they miss submodules.
### New features
After this change, users will be able to:
1. Build the dependencies in the way they want, then install them to
somewhere(for example, /usr or a temp folder).
2. Or download the dependencies by using cmake commands from these
dependencies official website
3. Similar to the above, but use your private mirrors to migrate supply
chain risks.
4. Use different versions of the dependencies, as long as our source
code is compatible with them. For example, you may use you can't use
protobuf 3.20.x as they need code changes in ONNX Runtime.
6. Only download the things the current build needs.
10. Avoid building external dependencies again and again in every build.
### Breaking change
The onnxruntime_PREFER_SYSTEM_LIB build option is removed you could think from now
it is default ON. If you don't like the new behavior, you can set FETCHCONTENT_TRY_FIND_PACKAGE_MODE to NEVER.
Besides, for who relied on the onnxruntime_PREFER_SYSTEM_LIB build
option, please be aware that this PR will change find_package calls from
Module mode to Config mode. For example, in the past if you have
installed protobuf from apt-get from ubuntu 20.04's official repo,
find_package can find it and use it. But after this PR, it won't. This
is because that protobuf version provided by Ubuntu 20.04 is too old to
support the "config mode". It can be resolved by getting a newer version
of protobuf from somewhere.
Patch Protobuf and ONNX's cmake files and enforce BinSkim check.
This PR has overlap with #13523 . I would prefer to get this one merged
first so that we can finished the BinSkim work, and I try to make this
PR as small as possible.
* Remove APIs unavailable in Store in #8349, #8178, #8065
* Add UWP stubs of C runtime functions
* Remove UWP incompatible tests from UWP build
* Remove incompatible tests from Store
* Use UWP stubs in store only
* Skip partition check outside of Windows
* Remove unused WRL include
* Workaround Windows header not including what it uses
* Fix precompiled header name clash
* Workaround SDK bugs
* DXCore workaround in Win7
* Fix warning
* Fix more warnings
* Bump WinML to target Windows 8
* Fix more warnings
* Remove unnecessary workarounds
* Remove Desktop only APIs from DML adapter
* Remove APIs unavailable in Store in #8349, #8178, #8065
* Add UWP stubs of C runtime functions
* Remove UWP incompatible tests from UWP build
* Remove incompatible tests from Store
* Use UWP stubs in store only
* Skip partition check outside of Windows
* Remove unused WRL include
* Workaround Windows header not including what it uses
* Fix precompiled header name clash
* Workaround SDK bugs
* DXCore workaround in Win7
* Fix warning
* Fix more warnings
* Bump WinML to target Windows 8
* Fix more warnings
* Remove unnecessary workarounds
1. Update manylinux build scripts. This will add [PEP600](https://www.python.org/dev/peps/pep-0600/)(manylinux2 tags) support. numpy has adopted this new feature, we should do the same. The old build script files were copied from https://github.com/pypa/manylinux, but they has been deleted and replaced in the upstream repo. The manylinux repo doesn't have a manylinux2014 branch anymore. So I'm removing the obsolete code, sync the files with the latest master.
2. Update GPU CUDA version from 11.0 to 11.1(after a discussion with PMs).
3. Delete tools/ci_build/github/linux/docker/Dockerfile.manylinux2014_cuda10_2. (Merged the content to tools/ci_build/github/linux/docker/Dockerfile.manylinux2014_cuda11)
4. Modernize the cmake code of how to locate python devel files. It was suggested in https://github.com/onnx/onnx/pull/1631 .
5. Remove `onnxruntime_MSVC_STATIC_RUNTIME` and `onnxruntime_GCC_STATIC_CPP_RUNTIME` build options. Now cmake has builtin support for it. Starting from cmake 3.15, we can use `CMAKE_MSVC_RUNTIME_LIBRARY` cmake variable to choose which MSVC runtime library we want to use.
6. Update Ubuntu docker images that used in our CI build from Ubuntu 18.04 to Ubuntu 20.04.
7. Update GCC version in CUDA 11.1 pipelines from 8.x to 9.3.1
8. Split Linux GPU CI pipeline to two jobs: build the code on a CPU machine then run the tests on another GPU machines. In the past we didn't test our python packages. We only tested the pre-packed files. So we didn't catch the rpath issue in CI build.
9. Add a CentOS machine pool and test our Linux GPU build on real CentOS machines.
10. Rework ARM64 Linux GPU python packaging pipeline. Previously it uses cross-compiling therefore we must static link to C Runtime. But now have pluggable EP API and it doesn't support static link. So I changed to use qemu emulation instead. Now the build is 10x slower than before. But it is more extensible.
* 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>
* Introduce VariadicAlias, remove hardcoded alias limits
* Include optional-lite in winml build
Co-authored-by: Sherlock Huang <bahuang@OrtTrainingDev3.af05slrtruoetgaxwwjv5nsq5e.px.internal.cloudapp.net>
* Add suspend handler with new telemetry event
* Fix build warning
* Use cppwinrt from nuget
* Restore nuget packages
* add dependencies
* Add nuget_helpers
* Cleaned up
* Clean up
* Comment
* Add dependencies for the rest
* Remove unused line
* Update activation string
* PR comment to remove ALL
* switch to work PC
* back with iterable of buffers
* add raw api tests
* tensorization
* last test
* all tests pass!
* small cleanup
* whitespace
* newline
* whitespace
* refactor common code into DisjointBufferHelpers
* remove unused file
* warning
* skip gpu tests when hardware not available
* Add error condition when createreference is invoked
* add null check to cretereference
* uncomment out check
Co-authored-by: Sheil Kumar <sheilk@microsoft.com>
* make tensorizer events measures
* throttle the events and add a new one SoftwareBitmapToGPUTensorTelemetryEvent
* factor out timing code into a class
* typo
* typo
* move eventimer class into its own header file
* add throttling to detensorization and remove variable timing
* make detensorization events measures as well
* add ConvertGPUTensorToSoftwareBitmapTelemetryEvent event
* de-duplicate event names
* fix comment
* PR feedback
* support Normalized_0_1 and Normalized_1_1
* add tests for Normalized_1_1
* fix build error
* fix imagetests failure
* support denterization and add more tests
* fix build
* remove added models
* disable gpu tests for CPU pipeline
* refactor based on comments and moved two added models
* merge normalizer and Denomalizer into NominalRangeConverter
* add comments
* little change
* Add experimental winrt api idl with dummy type to satisfy the build
* remove experimental from the api_lib target
* make experimental api available on windows builds also
* remove /y /d
* revert some pathing changes
* remove experimental api call from tests
* revert cppwinrt cmake changes
* switch to stdapi
Co-authored-by: Sheil Kumar <sheilk@microsoft.com>
* add build inbox flag
* remove raw tests and wstring for utf filenames
* enable raw tests
* use ToWideString
* create new utf8 helper
* update string helper to utf8
Co-authored-by: Sheil Kumar <sheilk@microsoft.com>
* add windowsai.yml for new Microsoft.AI.MachineLearning nuget
* temporarily add windowsai.yml to gpu.yml
* pass in build arch
* remove install onnx task
* no dml for arm or arm64
* refactor nuget pipeline defs
* update package creation
* pass in build and sources path
* missing hyphens
* copy license file
* fix parameter variable
* disable arm builds for now
* remove commented script block
* download pipeline atifcat name update
* set working dir
* Add bundling nuget script
* path combine
* null path
* combine needs parentheses
* binplace microsoft.* dlls in new nuget package
* update artifact name
* move merged nuget to artifacts directory
* move to merged subfolder in artifacts staging dir
* forward slash to back
* enable arm
* vcvarsall needs x64 vars setup
* Run Tests
* fix tests
* move global variables
* update yml to not have global variable in template
* removed parameters
* fixes
* Add build arch as an env variable
* ne not neq
* %Var% for batch script
* dont pass argument for x64
* disable arm tests
* skip csharp/cxx tests for microsoft nuget package
* remove test-win as it tests only c# cxx and capi
* test build for store apps
* dont build for store
* tools/nuget/generate_nuspec_for_native_nuget.py
* remove args.
* add new props and targets for microsoft.ai
* make windowsai props/targets static
* add dependency
* dont ship dot net props
* Remove c# fom windowsai nuget
* copy license file
* native packages must have win10 as the platform, not win
* cuda header in wrong if branch
* no dml for arm builds
* only build dml for x64/ x86
* User/sheilk/props update (#3616)
* prelim store work
* props
* Fix desktop nuget props/targets
* clean up targets and make store apps work
Co-authored-by: Sheil Kumar <sheilk@microsoft.com>
* update windowsai.yml with latest
* remove extra dloadhelpers
* Add abi headers to abi dir, and reference native includes
* update windowsai.yml
* minor update
* remove parameters
* add doesrp param
* hard code esrp to true
* add directml for x86/x64
* revert gpu yml changes
* add store builds
* add store builds
* add checks again in old way
* dup job names for store and desktop builds
* move all of the runtime binaries to win10 folder
* only set safeseh on x86
* disable the store builds for now... missing msvcprt.lib
* copy paste deletion...
* switch back to win- (#3646)
Co-authored-by: Sheil Kumar <sheilk@microsoft.com>
* use stahlworks
* & not supported in ado
* add cuda to cpu nuget(???) and EnableDelayedExpansion to enable x86 dml package
* revert nocontribops
* add underscore...
* extra win/win10 change
* merged nuget... still not being bundled...
* files in merged directory
* missing parens causing dml to be included in cpu package
* more diagnostic info
* switch dir to get-childitem
* wait for compression to complete
* add winml_adapter to mkml and gpu packages
* enable_wcos
* add mklml binaries
* props and targets missing from mklml
Co-authored-by: Sheil Kumar <sheilk@microsoft.com>
