### Description
Current linux-ci-pipeline was broken due to missing parameters from
`py-packaging-linux-test-cpu.yml` template
### Motivation and Context
Fix Linux CI pipeline
### Description
1. Add XNNPack build on Linux ARM64
2. Build only one python wheel for PR request.
[AB#49763](https://aiinfra.visualstudio.com/6a833879-cd9b-44a4-a9de-adc2d818f13c/_workitems/edit/49763)
### Motivation and Context
Why I add xnnpack build on Linux ARM64 rather than Windows ARM64.
Becuase KleidiAI doesn't support Windows
```
IF(XNNPACK_TARGET_PROCESSOR STREQUAL "arm64" AND XNNPACK_ENABLE_ARM_I8MM AND NOT CMAKE_C_COMPILER_ID STREQUAL "MSVC")
IF (XNNPACK_ENABLE_KLEIDIAI)
MESSAGE(STATUS "Enabling KleidiAI for Arm64")
ENDIF()
ELSE()
SET(XNNPACK_ENABLE_KLEIDIAI OFF)
ENDIF()
```
---------
### Description
<!-- Describe your changes. -->
### Motivation and Context
<!-- - Why is this change required? What problem does it solve?
- If it fixes an open issue, please link to the issue here. -->
1. Add python 3.13 to our python packaging pipelines
2. Because numpy 2.0.0 doesn't support thread free python, this PR also
upgrades numpy to the latest
3. Delete some unused files.
### Description
<!-- Describe your changes. -->
Update various test projects to .net8 from EOL frameworks.
Replace the Xamarin based Android and iOS test projects with a MAUI
based project that uses .net 8.
Add new CoreML flags to C# bindings
### Motivation and Context
<!-- - Why is this change required? What problem does it solve?
- If it fixes an open issue, please link to the issue here. -->
Remove usage of EOL frameworks.
### Description
After editing the set-trigger-rules.py, we must run the file.
### Motivation and Context
Obviously the script wasn't run because some files's name are incorrect.
### Description
Similar to #20786 . The last PR was able to update all pipelines and all
docker files. This is a follow-up to that PR.
### Motivation and Context
1. To extract the common part as a reusable build infra among different
ONNX Runtime projects.
2. Avoid hitting docker hub's limit: 429 Too Many Requests - Server
message: toomanyrequests: You have reached your pull rate limit. You may
increase the limit by authenticating and upgrading:
https://www.docker.com/increase-rate-limit
### Description
Use a common set of prebuilt manylinux base images to build the
packages, to avoid building the manylinux part again and again. The base
images can be used in GenAI and other projects too.
This PR also updates the GCC version for inference python CUDA11/CUDA12
builds from 8 to 11. Later on I will update all other CUDA pipelines to
use GCC 11, to avoid the issue described in
https://github.com/onnx/onnx/issues/6047 and
https://github.com/microsoft/onnxruntime-genai/issues/257 .
### Motivation and Context
To extract the common part as a reusable build infra among different
ONNX Runtime projects.
This reverts commit f396748ed6.
### Description
<!-- Describe your changes. -->
### Motivation and Context
<!-- - Why is this change required? What problem does it solve?
- If it fixes an open issue, please link to the issue here. -->
### Description
<!-- Describe your changes. -->
Add Nuget package changes for adding new 'net6.0-maccatalyst' platform.
The output ORT Nuget package was manually tested and verified in a .NET
MAUI app setup.
### Motivation and Context
<!-- - Why is this change required? What problem does it solve?
- If it fixes an open issue, please link to the issue here. -->
---------
Co-authored-by: rachguo <rachguo@rachguos-Mini.attlocal.net>
Co-authored-by: Yi Zhang <zhanyi@microsoft.com>
Co-authored-by: rachguo <rachguo@rachguos-Mac-mini.local>
### Description
Check the onnx node tests and model tests worked
### Motivation and Context
onnx node test data and model data are mount in one dir.
And onnxruntime_test_all search the dir and load the data.
If the dir does exist or there's some change in onnxruntime_test_all,
those tests may not be executed.
For example, all onnx node test data is 32M. It's hardly for us aware of
the regression.
So I add the simple check to ensure those tests are executed.
---------
Co-authored-by: Yi Zhang <your@email.com>
### Description
In PR #19073 I mistunderstood the value of "--parallel". Instead of
testing if args.parallel is None or not , I should test the returned
value of number_of_parallel_jobs function.
If build.py was invoked without --parallel, then args.parallel equals to
1. Because it is the default value. Then we should not add "/MP".
However, the current code adds it. Because if `args.paralllel` is
evaluated to `if 1` , which is True.
If build.py was invoked with --parallel with additional numbers, then
args.parallel equals to 0. Because it is unspecified. Then we should add
"/MP". However, the current code does not add it. Because `if
args.paralllel` is evaluated to `if 0` , which is False.
This also adds a new build flag: use_binskim_compliant_compile_flags, which is intended to be only used in ONNX Runtime team's build pipelines for compliance reasons.
### Motivation and Context
### Description
1. Add two build jobs for enabling Address Sanitizer in CI. One for
Windows CPU, One for Linux CPU.
2. Set default compiler flags/linker flags in build.py for normal
Windows/Linux/MacOS build. This can help control compiler flags in a
more centralized way.
3. All Windows binaries in our official packages will be built with
"/PROFILE" flag. Symbols of onnxruntime.dll can be found at [Microsoft
public symbol
server](https://learn.microsoft.com/en-us/windows-hardware/drivers/debugger/microsoft-public-symbols).
Limitations:
1. On Linux Address Sanitizer ignores RPATH settings in ELF binaries.
Therefore once Address Sanitizer is enabled, before running tests we
need to manually set LD_LIBRARY_PATH properly otherwise
libonnxruntime.so may not be able to find custom ops and shared EPs.
4. On Linux we also need to set LD_PRELOAD before running some tests(if
the main executable, like python, is not built with address sanitizer.
On Windows we do not need to.
5. On Windows before running python tests we should manually copy
address sanitizer DLL to the onnxruntime/capi directory, because python
3.8 and above has enabled "Safe DLL Search Mode" that wouldn't use the
information provided by PATH env.
6. On Linux Address Sanitizer found a lot of memory leaks from our
python binding code. Therefore right now we cannot enable Address
Sanitizer when building ONNX Runtime with python binding.
7. Address Sanitizer itself uses a lot of memory address space and
delays memory deallocations, which is easy to cause OOM issues in 32-bit
applications. We cannot run all the tests in onnxruntime_test_all in
32-bit mode with Address Sanitizer due to this reason. However, we still
can run individual tests in such a way. We just cannot run all of them
in one process.
### Motivation and Context
To catch memory issues.
### Description
The warning is:
```
C:\a\_work\1\s\onnxruntime\contrib_ops\cpu\math\sparse_dense_matmul.cc(88,54): warning C4244: 'argument': conversion from 'const __int64' to 'Eigen::EigenBase<Derived>::Index', possible loss of data [C:\a\_work\1\b\RelWithDebInfo\onnxruntime_providers.vcxproj]
2023-12-08T20:58:48.1812949Z with
2023-12-08T20:58:48.2144272Z [
2023-12-08T20:58:48.2145285Z Derived=Eigen::Map<const Eigen::SparseMatrix<uint64_t,1,int64_t>,0,Eigen::Stride<0,0>>
2023-12-08T20:58:48.2801935Z ]
2023-12-08T20:58:48.2804047Z C:\a\_work\1\s\onnxruntime\contrib_ops\cpu\math\sparse_dense_matmul.cc(82,8): message : while compiling class template member function 'void onnxruntime::contrib::`anonymous-namespace'::SparseToDenseCsr<uint64_t>::operator ()(const onnxruntime::contrib::`anonymous-namespace'::ComputeCtx &,const onnxruntime::SparseTensor &,const onnxruntime::Tensor &,onnxruntime::Tensor &) const' [C:\a\_work\1\b\RelWithDebInfo\onnxruntime_providers.vcxproj]
2023-12-08T20:58:48.2806197Z C:\a\_work\1\s\include\onnxruntime\core/framework/data_types_internal.h(302,27): message : see the first reference to 'onnxruntime::contrib::`anonymous-namespace'::SparseToDenseCsr<uint64_t>::operator ()' in 'onnxruntime::utils::mltype_dispatcher_internal::CallableDispatchableHelper::Invoke' (compiling source file C:\a\_work\1\s\onnxruntime\contrib_ops\cpu\math\sparse_dense_matmul.cc) [C:\a\_work\1\b\RelWithDebInfo\onnxruntime_providers.vcxproj]
2023-12-08T20:58:48.2871783Z C:\a\_work\1\s\include\onnxruntime\core/framework/data_types_internal.h(438,100): message : see reference to class template instantiation 'onnxruntime::contrib::`anonymous-namespace'::SparseToDenseCsr<uint64_t>' being compiled (compiling source file C:\a\_work\1\s\onnxruntime\contrib_ops\cpu\math\sparse_dense_matmul.cc) [C:\a\_work\1\b\RelWithDebInfo\onnxruntime_providers.vcxproj]
2023-12-08T20:58:48.2893010Z C:\a\_work\1\s\include\onnxruntime\core/framework/data_types_internal.h(414,5): message : see reference to function template instantiation 'void onnxruntime::utils::MLTypeCallDispatcher<float,double,int32_t,uint32_t,int64_t,uint64_t>::InvokeWithLeadingTemplateArgs<Fn,onnxruntime::TypeList<>,onnxruntime::contrib::`anonymous-namespace'::ComputeCtx&,const T&,const onnxruntime::Tensor&,onnxruntime::Tensor&>(onnxruntime::contrib::`anonymous-namespace'::ComputeCtx &,const T &,const onnxruntime::Tensor &,onnxruntime::Tensor &) const' being compiled [C:\a\_work\1\b\RelWithDebInfo\onnxruntime_providers.vcxproj]
2023-12-08T20:58:48.2894476Z with
2023-12-08T20:58:48.2911521Z [
2023-12-08T20:58:48.2912457Z Fn=onnxruntime::contrib::`anonymous-namespace'::SparseToDenseCsr,
2023-12-08T20:58:48.3067840Z T=onnxruntime::SparseTensor
2023-12-08T20:58:48.3068863Z ] (compiling source file C:\a\_work\1\s\onnxruntime\contrib_ops\cpu\math\sparse_dense_matmul.cc)
2023-12-08T20:58:48.3195854Z C:\a\_work\1\s\onnxruntime\contrib_ops\cpu\math\sparse_dense_matmul.cc(198,11): message : see reference to function template instantiation 'void onnxruntime::utils::MLTypeCallDispatcher<float,double,int32_t,uint32_t,int64_t,uint64_t>::Invoke<onnxruntime::contrib::`anonymous-namespace'::SparseToDenseCsr,onnxruntime::contrib::`anonymous-namespace'::ComputeCtx&,const T&,const onnxruntime::Tensor&,onnxruntime::Tensor&>(onnxruntime::contrib::`anonymous-namespace'::ComputeCtx &,const T &,const onnxruntime::Tensor &,onnxruntime::Tensor &) const' being compiled [C:\a\_work\1\b\RelWithDebInfo\onnxruntime_providers.vcxproj]
2023-12-08T20:58:48.3197946Z with
2023-12-08T20:58:48.3198565Z [
2023-12-08T20:58:48.3199093Z T=onnxruntime::SparseTensor
2023-12-08T20:58:48.3905678Z ]
2023-12-08T20:58:48.3907275Z C:\a\_work\1\s\onnxruntime\contrib_ops\cpu\math\sparse_dense_matmul.cc(198,36): message : see the first reference to 'onnxruntime::utils::MLTypeCallDispatcher<float,double,int32_t,uint32_t,int64_t,uint64_t>::Invoke' in 'onnxruntime::contrib::SparseToDenseMatMul::Compute' [C:\a\_work\1\b\RelWithDebInfo\onnxruntime_providers.vcxproj]
2023-12-08T20:58:48.3910999Z ##[warning]onnxruntime\contrib_ops\cpu\math\sparse_dense_matmul.cc(88,43): Warning C4244: 'argument': conversion from 'const __int64' to 'Eigen::EigenBase<Derived>::Index', possible loss of data
2023-12-08T20:58:48.3912734Z 182>C:\a\_work\1\s\onnxruntime\contrib_ops\cpu\math\sparse_dense_matmul.cc(88,43): warning C4244: 'argument': conversion from 'const __int64' to 'Eigen::EigenBase<Derived>::Index', possible loss of data [C:\a\_work\1\b\RelWithDebInfo\onnxruntime_providers.vcxproj]
2023-12-08T20:58:48.3913414Z with
2023-12-08T20:58:48.3913660Z [
2023-12-08T20:58:48.3914001Z Derived=Eigen::Map<const Eigen::SparseMatrix<uint64_t,1,int64_t>,0,Eigen::Stride<0,0>>
2023-12-08T20:58:48.3914499Z ]
2023-12-08T20:58:48.3914743Z qlinear_concat.cc
2023-12-08T20:58:48.3917082Z ##[warning]onnxruntime\contrib_ops\cpu\math\sparse_dense_matmul.cc(92,74): Warning C4244: 'argument': conversion from 'const __int64' to 'Eigen::EigenBase<Derived>::Index', possible loss of data
2023-12-08T20:58:48.3918624Z 182>C:\a\_work\1\s\onnxruntime\contrib_ops\cpu\math\sparse_dense_matmul.cc(92,74): warning C4244: 'argument': conversion from 'const __int64' to 'Eigen::EigenBase<Derived>::Index', possible loss of data [C:\a\_work\1\b\RelWithDebInfo\onnxruntime_providers.vcxproj]
2023-12-08T20:58:48.5534583Z with
2023-12-08T20:58:48.5541266Z [
2023-12-08T20:58:48.5542401Z Derived=Eigen::Map<const Eigen::Matrix<uint64_t,-1,-1,1,-1,-1>,0,Eigen::Stride<0,0>>
2023-12-08T20:58:48.5544914Z ]
2023-12-08T20:58:48.5548670Z ##[warning]onnxruntime\contrib_ops\cpu\math\sparse_dense_matmul.cc(92,63): Warning C4244: 'argument': conversion from 'const __int64' to 'Eigen::EigenBase<Derived>::Index', possible loss of data
2023-12-08T20:58:48.5552099Z 182>C:\a\_work\1\s\onnxruntime\contrib_ops\cpu\math\sparse_dense_matmul.cc(92,63): warning C4244: 'argument': conversion from 'const __int64' to 'Eigen::EigenBase<Derived>::Index', possible loss of data [C:\a\_work\1\b\RelWithDebInfo\onnxruntime_providers.vcxproj]
2023-12-08T20:58:48.5553712Z with
2023-12-08T20:58:48.5555569Z [
2023-12-08T20:58:48.5556779Z Derived=Eigen::Map<const Eigen::Matrix<uint64_t,-1,-1,1,-1,-1>,0,Eigen::Stride<0,0>>
2023-12-08T20:58:48.5558707Z ]
2023-12-08T20:58:48.5561428Z ##[warning]onnxruntime\contrib_ops\cpu\math\sparse_dense_matmul.cc(93,90): Warning C4244: 'argument': conversion from 'const __int64' to 'Eigen::EigenBase<Derived>::Index', possible loss of data
2023-12-08T20:58:48.5565624Z 182>C:\a\_work\1\s\onnxruntime\contrib_ops\cpu\math\sparse_dense_matmul.cc(93,90): warning C4244: 'argument': conversion from 'const __int64' to 'Eigen::EigenBase<Derived>::Index', possible loss of data [C:\a\_work\1\b\RelWithDebInfo\onnxruntime_providers.vcxproj]
2023-12-08T20:58:48.5566354Z with
2023-12-08T20:58:48.5568185Z [
2023-12-08T20:58:48.5569305Z Derived=Eigen::Map<Eigen::Matrix<uint64_t,-1,-1,1,-1,-1>,0,Eigen::Stride<0,0>>
2023-12-08T20:58:48.5571339Z ]
2023-12-08T20:58:48.5574864Z ##[warning]onnxruntime\contrib_ops\cpu\math\sparse_dense_matmul.cc(93,77): Warning C4244: 'argument': conversion from 'const __int64' to 'Eigen::EigenBase<Derived>::Index', possible loss of data
2023-12-08T20:58:48.5577866Z 182>C:\a\_work\1\s\onnxruntime\contrib_ops\cpu\math\sparse_dense_matmul.cc(93,77): warning C4244: 'argument': conversion from 'const __int64' to 'Eigen::EigenBase<Derived>::Index', possible loss of data [C:\a\_work\1\b\RelWithDebInfo\onnxruntime_providers.vcxproj]
2023-12-08T20:58:48.5578562Z with
2023-12-08T20:58:48.5580399Z [
2023-12-08T20:58:48.5581503Z Derived=Eigen::Map<Eigen::Matrix<uint64_t,-1,-1,1,-1,-1>,0,Eigen::Stride<0,0>>
2023-12-08T20:58:48.5583465Z ]
2023-12-08T20:58:48.5587661Z ##[warning]onnxruntime\contrib_ops\cpu\math\sparse_dense_matmul.cc(88,54): Warning C4244: 'argument': conversion from 'const __int64' to 'Eigen::EigenBase<Derived>::Index', possible loss of data
2023-12-08T20:58:48.5590705Z 182>C:\a\_work\1\s\onnxruntime\contrib_ops\cpu\math\sparse_dense_matmul.cc(88,54): warning C4244: 'argument': conversion from 'const __int64' to 'Eigen::EigenBase<Derived>::Index', possible loss of data [C:\a\_work\1\b\RelWithDebInfo\onnxruntime_providers.vcxproj]
2023-12-08T20:58:48.5591396Z with
2023-12-08T20:58:48.5593220Z [
2023-12-08T20:58:48.5593693Z Derived=Eigen::Map<const Eigen::SparseMatrix<int64_t,1,int64_t>,0,Eigen::Stride<0,0>>
2023-12-08T20:58:48.5595955Z ]
```
And the warning in #18195
### Motivation and Context
AB#22894
---------
Co-authored-by: Dmitri Smirnov <yuslepukhin@users.noreply.github.com>
### Description
1. Add a build validation for Linux ARM64/ARM32 cross-compile to catch
issues listed in #18195 .
2. Revert eigen's commit id back to what we had before.
### Motivation and Context
To catch cross-compile issues.
Added a TODO item for fixing the compile warnings in Linux ARM32 build: AB#21639
1. Upgrade nodejs from 16.x to 18.x for Windows pipelines
2. Avoid using Azure DevOps "NodeTool" on Linux. The tool installs
nodejs from internet or local disk cache. But we already moved all Linux
tests to docker. So we do not need the installer anymore.
3. Remove some other unused code.
### Description
1. Remove 'dnf update' from docker build scripts, because it upgrades TRT
packages from CUDA 11.x to CUDA 12.x.
To reproduce it, you can run the following commands in a CentOS CUDA
11.x docker image such as nvidia/cuda:11.8.0-cudnn8-devel-ubi8.
```
export v=8.6.1.6-1.cuda11.8
dnf install -y libnvinfer8-${v} libnvparsers8-${v} libnvonnxparsers8-${v} libnvinfer-plugin8-${v} libnvinfer-vc-plugin8-${v} libnvinfer-devel-${v} libnvparsers-devel-${v} libnvonnxparsers-devel-${v} libnvinfer-plugin-devel-${v} libnvinfer-vc-plugin-devel-${v} libnvinfer-headers-devel-${v} libnvinfer-headers-plugin-devel-${v}
dnf update -y
```
The last command will generate the following outputs:
```
========================================================================================================================
Package Architecture Version Repository Size
========================================================================================================================
Upgrading:
libnvinfer-devel x86_64 8.6.1.6-1.cuda12.0 cuda 542 M
libnvinfer-headers-devel x86_64 8.6.1.6-1.cuda12.0 cuda 118 k
libnvinfer-headers-plugin-devel x86_64 8.6.1.6-1.cuda12.0 cuda 14 k
libnvinfer-plugin-devel x86_64 8.6.1.6-1.cuda12.0 cuda 13 M
libnvinfer-plugin8 x86_64 8.6.1.6-1.cuda12.0 cuda 13 M
libnvinfer-vc-plugin-devel x86_64 8.6.1.6-1.cuda12.0 cuda 107 k
libnvinfer-vc-plugin8 x86_64 8.6.1.6-1.cuda12.0 cuda 251 k
libnvinfer8 x86_64 8.6.1.6-1.cuda12.0 cuda 543 M
libnvonnxparsers-devel x86_64 8.6.1.6-1.cuda12.0 cuda 467 k
libnvonnxparsers8 x86_64 8.6.1.6-1.cuda12.0 cuda 757 k
libnvparsers-devel x86_64 8.6.1.6-1.cuda12.0 cuda 2.0 M
libnvparsers8 x86_64 8.6.1.6-1.cuda12.0 cuda 854 k
Installing dependencies:
cuda-toolkit-12-0-config-common noarch 12.0.146-1 cuda 7.7 k
cuda-toolkit-12-config-common noarch 12.2.140-1 cuda 7.9 k
libcublas-12-0 x86_64 12.0.2.224-1 cuda 361 M
libcublas-devel-12-0 x86_64 12.0.2.224-1 cuda 397 M
Transaction Summary
========================================================================================================================
```
As you can see from the output, they are CUDA 12 packages.
The problem can also be solved by lock the packages' versions by using
"dnf versionlock" command right after installing the CUDA/TRT packages.
However, going forward, to get the better reproducibility, I suggest
manually fix dnf package versions in the installation scripts like we do
for TRT now.
```bash
v="8.6.1.6-1.cuda11.8" &&\
yum-config-manager --add-repo https://developer.download.nvidia.com/compute/cuda/repos/rhel8/x86_64/cuda-rhel8.repo &&\
yum -y install libnvinfer8-${v} libnvparsers8-${v} libnvonnxparsers8-${v} libnvinfer-plugin8-${v} libnvinfer-vc-plugin8-${v}\
libnvinfer-devel-${v} libnvparsers-devel-${v} libnvonnxparsers-devel-${v} libnvinfer-plugin-devel-${v} libnvinfer-vc-plugin-devel-${v} libnvinfer-headers-devel-${v} libnvinfer-headers-plugin-devel-${v}
```
When we have a need to upgrade a package due to security alert or some
other reasons, we manually change the version string instead of relying
on "dnf update". Though this approach increases efforts, it can make our
pipeines more stable.
2. Move python test to docker
### Motivation and Context
Right now the nightly gpu package mixes using CUDA 11.x and CUDA 12.x
and the result package is totally not usable(crashes every time)
### Description
supplement of #17417
### Motivation and Context
<!-- - Why is this change required? What problem does it solve?
- If it fixes an open issue, please link to the issue here. -->
### Description
install dotnet 6.0 in the docker image.
move C# build and test into docker.
### Motivation and Context
### Note
The Unit tests and Symbolic shape infer's migration will be in another
PR.
### Description
1. Update docker files and their build instructions.
ARM64 and x86_64 can use the same docker file.
2. Upgrade Linux CUDA pipeline's base docker image from CentOS7 to UBI8
AB#18990
### Description
<!-- Describe your changes. -->
### Motivation and Context
<!-- - Why is this change required? What problem does it solve?
- If it fixes an open issue, please link to the issue here. -->
Get the latest gcc 12 by default
---------
Co-authored-by: Changming Sun <chasun@microsoft.com>
### Description
1. Fix python packaging test pipeline. There was an error in
tools/ci_build/github/linux/run_python_tests.sh that it installed a
released version of onnxruntime python package from pypi.org to run the
test. Supposedly it should pick one from the current build.
2. Refactor the pipeline to allow choosing cmake build type from the web
UI when manually trigger a build. Now this feature is for Linux only.
Because I don't want to change too much when we are about to cut a
release branch. After that I will expand it to all platforms. This
feature is useful for debugging pipeline issues, also, we may consider
having a nightly pipeline to run all tests in Debug mode which may catch
extra bugs because in debug mode we can enforce range check.
Test run:
https://aiinfra.visualstudio.com/Lotus/_build/results?buildId=342674&view=results
### Motivation and Context
Currently the pipeline has a crash error.
AB#18580
- 'js/web'
- 'js/node'
- 'onnxruntime/core/providers/js'
is updated
### Description
<!-- Describe your changes. -->
### Motivation and Context
<!-- - Why is this change required? What problem does it solve?
- If it fixes an open issue, please link to the issue here. -->
### Description
<!-- Describe your changes. -->
### Motivation and Context
<!-- - Why is this change required? What problem does it solve?
- If it fixes an open issue, please link to the issue here. -->
### Description
1. Avoid taking dependency on dl.fedoraproject.org
The website is not very stable. Our build pipelines often fail to fetch
packages from there.
2. Update manylinux to the latest version
### Description
<!-- Describe your changes. -->
Various fixes to the CSharp setup
- fix warnings
- fix invalid tests
- update test sdk nuget package
- enables testing on linux
- fixes issue with some unit tests not running in CI
- run unit tests in linux pipeline using dotnet
### Motivation and Context
<!-- - Why is this change required? What problem does it solve?
- If it fixes an open issue, please link to the issue here. -->
Unit tests weren't breaking in CIs for both Windows and Linux builds and
should have been.
Rename onnxruntime-Linux-CPU-2019 machine pool to
"onnxruntime-Ubuntu2004-AMD-CPU". The old one has an internal error and
stuck there. I cannot make any change to it. It has been like this for
more than 1 week. So I created a new pool with the same setting except
the name is different.
Also, move some android pipelines to
"onnxruntime-Linux-CPU-For-Android-CI" which uses a standard image from
https://github.com/actions/runner-images
### Description
1. Move Linux CPU pipelines to an AMD CPU pool which is cheaper
2. Enable CCache for orttraining pipeline
### Motivation and Context
Azure AMD CPU machines are generally much cheaper than Intel CPU
machines. However, they don't have local disks.
- Update Gradle version used in most places from 6.8.3 to 8.0.1. Update Android Gradle Plugin version where applicable.
Not updated in this change: React Native Android projects (under `js/react_native/`). That can be done later along with updating the React Native projects.
- Add Gradle wrapper in `java/` to make it easier to consistently use a specific Gradle version.
### Description
Add date value of today into the cache key.
### Motivation and Context
Microsoft-host agent has only 10GB for build.
To limit cache size, pipeline only use cache generated today.
### Description
update versions of a few build dependencies for onnxruntime NPM
packages.
update nodejs version to v16.x in linux CI. v12 is too out-of-dated. see
[nodejs release
schedule](https://github.com/nodejs/release#release-schedule)
### Motivation and Context
- upgrade to latest webpack allows using of latest Node.js LTS version.
previous version of webpack does not work on Node.js v18 and it is fixed
in latest version
- upgrade to latest typescript, ts-loader and other dev deps to
accelerate the build and bundling.
- upgrade also helps to resolve security warnings that may be vulnerable
in out-of-dated version
### Description
<!-- Describe your changes. -->
### Motivation and Context
<!-- - Why is this change required? What problem does it solve?
- If it fixes an open issue, please link to the issue here. -->
### Description
For compilation in container, ADO Cache task doesn't work directly.
The workaround is to mount the cache directory to the container, and let
CCache in container to read/write cache data.
In short, we just leverage ADO API to download/upload cache data.
The Post-jobs works in stack-mode, So the PostBuildCleanUp Tasks should
be defined first.
Thus, The PostBuildCleanUp would be executed lastly.
Else, Cache Task would fail to upload cache because the Agent Directory
is cleaned.
## 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.
1. Update CUDA version from 11.4 to 11.6.
2. Update Manylinux version
3. Upgrade GCC version from 10 to 11 for most x86_64 pipelines. CentOS 7 ARM64 doesn't have GCC 11 yet.
4. Refactor python packaging pipeline:
a. Split Linux GPU build job to two parts, build and test, so that the
build part doesn't need to use a GPU machine
b. Make the Linux GPU build job and Linux CPU build job more similar: share the same bash script and yaml file.
5. Temporarily disable Attention_Mask1D_Fp16_B2_FusedNoPadding because it is causing one of our packaging pipeline to fail. I have created an ADO task for this.
1. Delete the build scripts that were copied from manylinux project. Use "git checkout" instead.
2. Update manylinux version to get python 3.11. Related issue: Python 3.11 support #12343
3. Change the cuda version of linux gpu build job of nuget packaging pipeline from cuda 11.4 to cuda 11.6 to match the TRT job within the same pipeline.. (A lot other places need be updated as well, but I'd prefer to put them in another PR)
4. Make dockerfile names static. For example, replace tools/ci_build/github/linux/docker/$(DockerFile) to tools/ci_build/github/linux/docker/Dockerfile.manylinux2014_cpu . The former one relies on a runtime variable $(DockerFile), Template Parameters are expanded early in processing a pipeline run when most variables are not available. It like C++ macros vs variables.
* Update orttraining release pipelines to use torch 1.11.0
* Change requirements_torch...txt to requirements.txt
* Update cuda cmake architectures and clean up old files
