### Description
The machine has multiple python installations and none of them is in
PATH. Therefore we should explicitly set python version via this task to
avoid having surprises.
### Motivation and Context
Similar to #21095
### Description
Delete RoslynAnalyzers. Use CodeQL instead.
### Motivation and Context
Now we already have CodeQL which is modern and also covers C# code. The
RoslynAnalyzers one is not in our pull request pipelines. The
"RoslynAnalyzers@2" task is outdated and needs be upgraded. I will
delete it for now since we already have CodeQL.
### Description
Remove the "--enable_language_interop_ops" build flag, because the code
is incompatible with the latest numpy, and the build flag is not used
anywhere except a macOS CI pipeline. It does not seem to have a ship
plan.
### Motivation and Context
The build error was:
```
onnxruntime/core/language_interop_ops/pyop/pyop.cc:122:85: error: no member named 'elsize' in '_PyArray_Descr'
static_cast<int64_t>(PyArray_DescrFromType(type)->elsize),
~~~~~~~~~~~~~~~~~~~~~~~~~~~ ^
```
### Description
- Updates CI pipelines to use QNN SDK 2.23.0 by default.
- QNN SDK adds support for int64 Cast. This allows QNN EP to support
ONNX ArgMax/ArgMin/TopK operators that generate an int64 graph output.
Example translation of ArgMax:
- **ONNX**: input --> ArgMax --> output (int64)
- **QNN**: input --> ArgMax --> Cast (int32 to int64) --> output (int64)
### Motivation and Context
Update onnxruntime to use the latest QNN SDK.
### Description
<!-- Describe your changes. -->
Xamarin is EOL so remove support.
The MAUI targets are EOL and need updating.
https://dotnet.microsoft.com/en-us/platform/support/policy/maui
Other cleanups:
- netcoreapp3.1 is EOL
- the net6 macos target was added in the mistaken belief that was for
MAUI mac support, but that is actually via the mac-catalyst target which
we recently added support for.
- some CIs that were using the old build setup of splitting pre-net6
targets. The ORT C# bindings csproj was updated last year and the
`PreNet6` and `SelectedTargets` properties no longer exist as they were
replaced by the simpler `IncludeMobileTargets` property.
### 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 EOL components.
#21058
### Description
### 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
Upgrade pybind11 to the latest as suggested by @gnought in #21063
### Motivation and Context
Recently numpy released a new version, which caused compatibility issue
between the latest numpy version and the latest ONNX Runtime version.
### Description
<!-- Describe your changes. -->
- Add check for CoreML MLProgram supported ops
- Only check usability with ORT Mobile package if requested
- this package will be deprecated so info is a) of minimal value and b)
can be confusing.
- Output more things at INFO level
- a lot of meaningful info was only output at DEBUG level. The default
INFO level is more useful
- dump full partition info at DEBUG level
- Check subgraphs fully
- CoreML can handle a subgraph
- TBD if we want to add support for adding a subgraph to the parent
graph for Loop and If nodes
- most likely will be required for simple If nodes to be performant
- Check 5D CoreML limitation
### 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. -->
Improve helper tools
---------
Co-authored-by: Edward Chen <18449977+edgchen1@users.noreply.github.com>
Avoid using command line flags to pass in CMAKE_PREFIX_PATH. Use
environment variables instead.
Because, otherwise the value of CMAKE_PREFIX_PATH could get encoded
twice. For example, if the prefix is `C:\a\root`, then in
tools/ci_build/github/windows/helpers.ps1 we set it in Env:CMAKE_ARGS
which will be consumed by ONNX. Then when ONNX get it and decoded it,
ONNX will get `C:aroot` instead. Then because the path doesn't exist,
the CMAKE_PREFIX_PATH couldn't take effect when the script installs
ONNX. This PR fixes the issue.
The issue got discovered when I tried to upgrade cmake to a newer
version. Now our Windows CPU CI build pipeline uses cmake 3.27. In the
main branch even the CMAKE_PREFIX_PATH setting does not work, cmake
still can find protoc.exe from the directories. However, starting from
3.28 cmake changed it. With the newer cmake versions the find_library(),
find_path(), and find_file() cmake commands no longer search in
installation prefixes derived from the PATH environment variable.
### Description
<!-- Describe your changes. -->
Conditionally route to custom AllReduce kernel when buffer size and gpu
numbers meet certain requirements. Otherwise, keep using NCCL's
AllReduce.
### 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: Ye Wang <wangye@microsoft.com@h100vm-ort.kxelwkzfzxguje5bxvwxxs135a.gvxx.internal.cloudapp.net>
Co-authored-by: Your Name <you@example.com>
### 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
Move jobs in onnxruntime-Win2022-GPU-T4 machine pool to
onnxruntime-Win2022-GPU-A10
### Motivation and Context
To reduce the variants of VM images we need to maintain. Now we have 3:
1. Windows 2022 CPU
2. Windows 2022 GPU A10
3. Windows 2022 GPU T4
This change allows us removing the last one.
### Description
Fix a few issues in the Windows TRT job in "Zip-Nuget-Java-Nodejs
Packaging Pipeline":
1. It is a Windows job. It should not use bash(which is usually not
available on Windows).
2. When it sets ADO vars, it missed a semicolon
Here is the doc of how to set ADO vars via scripts:
https://learn.microsoft.com/en-us/azure/devops/pipelines/process/set-variables-scripts?view=azure-devops&tabs=bash
You could see it needs a semicolon . Without the semicolon , the vars
will have an extra quotation mark in their values.
### 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
Upgrade cutlass to 3.5 to fix build errors using CUDA 12.4 or 12.5 in
Windows
- [x] Upgrade cutlass to 3.5.0.
- [x] Fix flash attention build error with latest cutlass header files
and APIs. This fix is provided by @wangyems.
- [x] Update efficient attention to use new cutlass fmha interface.
- [x] Patch cutlass to fix `hrsqrt` not found error for sm < 53.
- [x] Disable TF32 Staged Accumulation to fix blkq4_fp16_gemm_sm80_test
build error for cuda 11.8 to 12.3.
- [x] Disable TRT 10 deprecate warnings.
The following are not included in this PR:
* TRT provider replaces the deprecated APIs.
* Fix blkq4_fp16_gemm_sm80_test build error for cuda 12.4 or 12.5. This
test is not built by default unless you add `--cmake_extra_defines
onnxruntime_ENABLE_CUDA_EP_INTERNAL_TESTS=ON` in build command.
To integrate to rel-1.18.1: Either bring in other changes (like onnx
1.16.1), or generate manifest and upload a new ONNX Runtime Build Time
Deps artifact based on rel-1.18.1.
### Motivation and Context
https://github.com/microsoft/onnxruntime/issues/19891https://github.com/microsoft/onnxruntime/issues/20924https://github.com/microsoft/onnxruntime/issues/20953
### Description
1. Publish debug symbols for Windows python packages. This PR will
publish them to ADO. Later on I will also replicate them to Microsoft
Symbol Server.
2. Build the packages in Release mode instead of RelWithDebInfo, to be
consistent with the other platforms(Linux/macOS/...)
### Motivation and Context
To help debug things. Sometimes we found an issue, but we couldn't debug
it because we didn't have symbols, and once we rebuilt the package
locally the issue was gone. This change would be helpful for such
scenarios.
Build log:
https://aiinfra.visualstudio.com/Lotus/_build?definitionId=841
# Description
This PR removes the building of the ORT "mobile" packages and much of the associated infrastructure which is no longer needed.
Not removed yet - tools/ci_build/github/android/mobile_package.required_operators.config and the helper scripts that depend on it.
# Motivation and Context
The mobile packages were deprecated in 1.18. Users should use the full packages (Android - onnxruntime-android, iOS - onnxruntime-c/onnxruntime-objc) instead or do a custom build.
### Description
Update c-api-noopenmp-packaging-pipelines.yml: remove CUDA version
parameter
To reduce confusion. This pipeline is for generating CUDA 11 packages.
Just it. Not CUDA 12.
### Motivation and Context
In the last release we accidentally published CUDA 12(instead of CUDA
11) packages to nuget.org.
We also tried to publish CUDA 12 packages to
https://aiinfra.visualstudio.com/PublicPackages/_artifacts/feed/ORT-Nightly.
Luckily it didn't go through because a package with the same version
number already existed there. Every time when someone runs this pipeline
with CUDA version set to 12, the built packages will be published to
https://aiinfra.visualstudio.com/PublicPackages/_artifacts/feed/ORT-Nightly.
And GenAI team's build pipelines are based on the nightly packages. So
sometimes GenAI team builds their packages with CUDA 12 and sometimes
with CUDA 11, which is very random.
Therefore, please limit the use of pipeline parameters. Most Azure
DevOps yml files are template files. They should use parameters. But the
top level yml files should be more careful on that.
To replaced deprecated API.
Should verify with the `Gradle cmakeCheck` step from
`Windows_Packaging_CPU_x64_default` stage from the Zip-Nuge-...
pipeline.
### Description
- Updates pipelines to use QNN SDK 2.22 by default.
- Linux QNN pipeline now uses an Ubuntu 22.04 image (required by QNN
SDK)
- Android QNN pipeline still uses the current Ubuntu 20.04 image. Will
update in a separate PR.
- Disables QDQ LayerNorm test that triggers QNN's graph finalization
error on QNN 2.22
- Increases accuracy tolerance for various HTP tests so that they pass
on Windows arm64.
### Motivation and Context
Test QNN EP with latest QNN SDK version by default.
---------
Signed-off-by: adrianlizarraga <adlizarraga@microsoft.com>
To align with Office and other MS products.
Office's support policy is:
"Office for iPad and iPhone is supported on the two most recent versions
of iOS and iPadOS. When a new version of iOS or iPadOS is released, the
Office Operating System requirement becomes the two most recent
versions: the new version of iOS or iPadOS and the previous version."
(from https://products.office.com/office-system-requirements)
The latest iOS version is 17. So they support both 17 and 16. Here I set
our min iOS version to 13 so that it will be a superset of what Office
supports.
This change would allow us using C++17's std::filesystem feature in the
core framework. The modifications were generated by running
```bash
find . -type f -exec sed -i "s/apple_deploy_target[ =]12.0/apple_deploy_target=13.0/g" {} \;
```
Cannot use 15.0 because otherwise iOS packaging would fail with:
```
/Users/runner/work/1/b/apple_framework/intermediates/iphoneos_arm64/Release/_deps/coremltools-src/mlmodel/src/MILBlob/Util/Span.hpp:288:9: error: cannot use 'throw' with exceptions disabled
MILVerifyIsTrue(index < Size(), std::range_error, "index out of bounds");
```
The Google OSS libraries we use only officially support iOS 15+.
### Description
1. Add one image into whitelist, but if the image is hit, the pipeline
status is warning.
2. adjust the image parity test tolerance
### Motivation and Context
improve pipeline stability
### Description
This PR to allow `./gradlew cmakeCheck` failed on
Windows_Packaging_(CUDA|TensorRT) Job. This way, it will still generate
all nessary jar and pom file need for later stage to consume while
`./gradlew cmakeCheck`will be also run again in the
Windows_Packaging_(CUDA|TensorRT)_Testing stage.
### Motivation and Context
Reduce the time of All java packaging stages by 30+ min.
### Description
This PR upgrades CUDA 11 build pipelines' GCC version from 8 to 11.
### Motivation and Context
GCC8 has an experimental std::filesystem implementation which is not ABI
compatible with the formal one in later GCC releases. It didn't cause
trouble for us, however, ONNX community has encountered this issue much.
For example, https://github.com/onnx/onnx/issues/6047 . So this PR
increases the minimum supported GCC version from 8 to 9, and removes the
references to GCC's "stdc++fs" library. Please note we compile our code
on RHEL8 and RHEL8's libstdc++ doesn't have the fs library, which means
the binaries in ONNX Runtime's official packages always static link to
the fs library. It is just a matter of which version of the library, an
experimental one or a more mature one. And it is an implementation
detail that is not visible from outside. Anyway, a newer GCC is better.
It will give us the chance to use many C++20 features.
#### Why we were using GCC 8?
It is because all our Linux packages were built on RHEL8 or its
equivalents. The default GCC version in RHEL8 is 8. RHEL also provides
additional GCC versions from RH devtoolset. UBI8 is the abbreviation of
Red Hat Universal Base Image 8, which is the containerized RHEL8. UBI8
is free, which means it doesn't require a subscription(while RHEL does).
The only devtoolset that UBI8 provides is GCC 12, which is too new for
being used with CUDA 11.8. And our CUDA 11.8's build env is a docker
image from Nvidia that is based on UBI8.
#### How the problem is solved
Almalinux is an alternative to RHEL. Almalinux 8 provides GCC 11. And
the CUDA 11.8 docker image from Nvidia is open source, which means we
can rebuild the image based on Almalinux 8 to get GCC 11. I've done
this, but I cannot republish the new image due to various complicated
license restrictions. Therefore I put them at an internal location in
onnxruntimebuildcache.azurecr.io.
### 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
mac-react-native-ci-pipeline.yml:
- We don't need to run component detection for PR builds so just disable it there.
npm-packaging-pipeline.yml:
- Manually added component detection task was being added twice - removed one.
- Increased timeout of stage where component detection is run since the existing timeout was close for some builds.
1. Move azcopy environment variables out of script and into an Azure DevOps variable group. Move towards consolidating the managed identity client ID definition in one place.
2. Disable azcopy overwrite. We don't want to accidentally change the files for a released package.
### Description
Runs of the React Native CI are timing out during ComponentDetection
after 8 minutes. This increases the timeout value.
### Motivation and Context
Runs of the React Native CI are timing out during ComponentDetection.
### Description
Adding java build/packaging stage to `cuda-packaging-pipeline.yml`
### Motivation and Context
This way we can enable publishing the Java Cuda 12 along with Nuget CUDA
12
### Description
Temporarily remove TVM EP's pipeline until someone helps us upgrade TVM
to a newer version which is compatible with the latest ONNX.
### Motivation and Context
The ONNX version that TVM EP uses has a known security vulnerability. We
cannot continue using it in our hosted build environment. This change is temporary
### Description
<!-- Describe your changes. -->
This PR adding protoc.exe to make the Nuget Cuda Pipleine, which also
allowing it to get build Java for various CUDA version
### 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
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.
### Description
Previous all feed are set to nightly, the offcial released feed-id is
not set
### 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. -->
