### Description
<!-- Describe your changes. -->
Current failure is due to a version mismatch.
Use llvm-cov from the Android NDK instead of the system gcov so that the
version is correct.
Also comment out publishing to the Azure dashboard to simplify the
setup. The CI prints out the stats for review by developers.
### 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. -->
Fix CI pipeline
### Description
Right now our "Zip-Nuget-Java-Nodejs Packaging Pipeline" is too big.
This OnDevice training part is independent of the others, so it can be
split out. Then our NPM Packaging pipeline will not depends on this
training stuff.
### Motivation and Context
Similar to #21235
Also, this PR fixed a problem that: "NuGet_Test_Linux_Training_CPU" job
downloads artifacts from "onnxruntime-linux-x64" for getting customop
shared libs, but the job forget to declare it depends on the
"Linux_C_API_Packaging_CPU_x64" which produces the artifact. Such
problems can be hard to find when a pipeline goes big.
### Description
* Swap cuda version 11.8/12.2 in GPU CIs
* Set CUDA12 as default version in yamls of publishing nuget/python/java
GPU packages
* Suppress warnings as errors of flash_api.cc during ort win-build
### Description
- Update pipelines to use QNN SDK 2.24 by default
- Update QNN_Nuget_Windows pipeline to build csharp solution without
mobile projects (fixes errors).
- Implement workaround for QNN 2.24 validation bug for LayerNorm ops
without an explicit bias input.
- Enable Relu unit test, which now passes due to the fact Relu is no
longer fused into QuantizeLinear for QNN EP.
- Fix bug where a negative quantization axis is not properly normalized
for per-channel int4 conv.
### Motivation and Context
Update QNN SDk.
### Description
Before this change, copy_strip_binary.sh manually copies each file from
onnx runtime's build folder to an artifact folder. It can be hard when
dealing with symbolic link for shared libraries.
This PR will change the packaging pipelines to run "make install" first,
before packaging shared libs .
### Motivation and Context
Recently because of feature request #21281 , we changed
libonnxruntime.so's SONAME. Now every package that contains this shared
library must also contains libonnxruntime.so.1. Therefore we need to
change the packaging scripts to include this file. Instead of manually
construct the symlink layout, using `make install` is much easier and
will make things more consistent because it is a standard way of making
packages.
**Breaking change:**
After this change, our **inference** tarballs that are published to our
Github release pages will be not contain ORT **training** headers.
### Description
<!-- Describe your changes. -->
Add ML Program ConvTranspose
- some limitations to simplify the implementation for now
- some limitations due to flaky CoreML output
Added support for non-contiguous MLMultiArray output as we see that with
some unit tests when the CPU-only flag is not set (e.g. innermost dim
has min size of 16 but test output only has 8 values).
- support only one non-contiguous dim to keep it simple
- manually tested as we don't have a setup that can test objective-c
code
- test code is in model.mm and can be enabled via ifdef if we need to
validate any future 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. -->
Address operator gaps in high priority model.
---------
Co-authored-by: Edward Chen <18449977+edgchen1@users.noreply.github.com>
### Description
<!-- Describe your changes. -->
Add GridSample ML Program support
One combination of inputs has diffs between the pytorch generated unit
tests data and CoreML. Disabling until needed as investigation may take
a while.
### 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. -->
High priorities models
1. Update google benchmark from 1.8.3 to 1.8.5
2. Update google test from commit in main branch to tag 1.15.0
3. Update pybind11 from 2.12.0 to 2.13.1
4. Update pytorch cpuinfo to include the support for Arm Neoverse V2,
Cortex X4, A720 and A520.
5. Update re2 from 2024-05-01 to 2024-07-02
6. Update cmake to 3.30.1
7. Update Linux docker images
8. Fix a warning in test/perftest/ort_test_session.cc:826:37: error:
implicit conversion loses integer precision: 'streamoff' (aka 'long
long') to 'const std::streamsize' (aka 'const long')
[-Werror,-Wshorten-64-to-32]
### Description
<!-- Describe your changes. -->
Add support for Slice
### 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. -->
High priority models.
### Description
Replace inline pip install with pip install from requirements*.txt
### Motivation and Context
so that CG can recognize
### Dependency
- [x] https://github.com/microsoft/onnxruntime/pull/21085
### Description
<!-- Describe your changes. -->
Add CoreML ML Program Resize
- refactor existing logic to try and simplify and share between
NeuralNetwork and MLProgram checks
- add handling for some new attributes
- antialias and axes - should have been done when setting the CoreML EP
max opset to 21
### 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. -->
Support priority models
### Description
<!-- Describe your changes. -->
* promote trt version to 10.2.0.19
* EP_Perf CI: clean config of legacy TRT<8.6, promote test env to
trt10.2-cu118/cu125
* skip two tests as Float8/BF16 are supported by TRT>10.0 but TRT CIs
are not hardware-compatible on these:
```
1: [ FAILED ] 2 tests, listed below:
1: [ FAILED ] IsInfTest.test_isinf_bfloat16
1: [ FAILED ] IsInfTest.test_Float8E4M3FN
```
### 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. -->
There is a bug for kernel running on rocm6.0, so change ci docker image
to rocm6.1
For the torch installed in the docker image, change to rocm repo when it
is not 6.0 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. -->
We need to prevent VitisAI EP build breaks, add a stage in Windows CPU
CI Pipeline to build Vitis AI EP on Windows.
There are no external dependencies for builds. Tests have to be disabled
though as the EP has external SW/HW dependencies.
This will at least allow us to prevent build breaks which has happened
on multiple occasions recently.
tested
https://dev.azure.com/onnxruntime/onnxruntime/_build/results?buildId=1432346&view=results
and it seems to run fine.
### Description
Combining android build and test step into one job
### Motivation and Context
Reduce runtime by removing additional machine allocation, and artifact
uploading and downloading.
---------
Co-authored-by: Edward Chen <18449977+edgchen1@users.noreply.github.com>
### Description
the exception was caused by
3dd6fcc089
Why I add skip_macos_test
because there's new an exception in
https://dev.azure.com/onnxruntime/onnxruntime/_build/results?buildId=1425579&view=logs&j=c90c5af3-67d5-5936-5a62-71c93ebfca65&t=01038f35-8e78-5801-1aa1-d9647bb65858
```
2024-07-05T14:41:09.3864740Z mkdir -p /Users/runner/Library/Developer/Xcode/DerivedData/apple_package_test-akksnidsbpojopfdqrclgsoqqerv/Build/Products/Debug/macos_package_testUITests.xctest/Contents/Frameworks
2024-07-05T14:41:09.3933430Z mkdir: /Users/runner/Library/Developer/Xcode/DerivedData/apple_package_test-akksnidsbpojopfdqrclgsoqqerv/Build/Products/Debug/macos_package_testUITests.xctest: Operation not permitted
2024-07-05T14:41:09.3996760Z /var/folders/0f/b0mzpg5d31z074x3z5lzkdxc0000gn/T/tmp97ycvwq5/apple_package_test/Pods/Target Support Files/Pods-macos_package_testUITests/Pods-macos_package_testUITests-frameworks.sh: line 7: realpath: command not found
2024-07-05T14:41:09.4003170Z :18: error: Unexpected failure
2024-07-05T14:41:11.1323470Z error: Sandbox: mkdir(72212) deny(1) file-write-create /Users/runner/Library/Developer/Xcode/DerivedData/apple_package_test-akksnidsbpojopfdqrclgsoqqerv/Build/Products/Debug/macos_package_testUITests.xctest (in target 'macos_package_testUITests' from project 'apple_package_test')
2024-07-05T14:41:11.1325620Z
2024-07-05T14:41:11.8731110Z
2024-07-05T14:41:11.8733040Z Test session results, code coverage, and logs:
2024-07-05T14:41:11.8734820Z /Users/runner/Library/Developer/Xcode/DerivedData/apple_package_test-akksnidsbpojopfdqrclgsoqqerv/Logs/Test/Test-macos_package_test-2024.07.05_14-40-38-+0000.xcresult
2024-07-05T14:41:11.8735530Z
2024-07-05T14:41:11.8906210Z Testing failed:
2024-07-05T14:41:11.8911060Z Sandbox: mkdir(72212) deny(1) file-write-create /Users/runner/Library/Developer/Xcode/DerivedData/apple_package_test-akksnidsbpojopfdqrclgsoqqerv/Build/Products/Debug/macos_package_testUITests.xctest
2024-07-05T14:41:11.8912570Z Unexpected failure
2024-07-05T14:41:11.8913690Z Testing cancelled because the build failed.
2024-07-05T14:41:11.8914380Z
2024-07-05T14:41:11.8914970Z ** TEST FAILED **
2024-07-05T14:41:11.8915480Z
2024-07-05T14:41:11.8915780Z
2024-07-05T14:41:11.8916750Z The following build commands failed:
2024-07-05T14:41:11.8919280Z PhaseScriptExecution [CP]\ Embed\ Pods\ Frameworks /Users/runner/Library/Developer/Xcode/DerivedData/apple_package_test-akksnidsbpojopfdqrclgsoqqerv/Build/Intermediates.noindex/apple_package_test.build/Debug/macos_package_testUITests.build/Script-059136A7770CA5376C30F2FD.sh (in target 'macos_package_testUITests' from project 'apple_package_test')
2024-07-05T14:41:11.8922180Z (1 failure)
```
And I find macos test is skipped in
9ef28f092f/tools/ci_build/github/azure-pipelines/templates/c-api-cpu.yml (L119-L127)
as well.
Maybe it is an known issue.
### Description
1. remove QNN stages from the big packaging pipeline
2. Add publish nightly package in the current [QNN Nuget
pipeline](https://dev.azure.com/aiinfra/Lotus/_builddefinitionId=1234])
### Motivation and Context
Reduce the complexity of the big Nuget packaging pipelines.
---------
Co-authored-by: Yi Zhang <your@email.com>
### Description
Our macOS pipeline are failing because of a build error in absl.
However, the bug fix we need is not available in the latest ABSL
release.
Here is the issue: https://github.com/abseil/abseil-cpp/pull/1536
And here is the fix:
779a3565ac
GTests uses ABSL. But this ABSL target also depends on GTest. So, it is
a circular dependency. We should be able to avoid that by avoid building
tests for ABSL. However, the version we are using has a problem with
that: it has cmake target that still depends on GTest even when testing
is disabled.
It's strange that we suddenly hit this problem and it only happens on macOS.
### Description
Make current ROCm packaging stages to a single workflow.
Reduce the possibility of all nightly packages can't be generated by one
failed stage
### Motivation and Context
Our plan is to reduce the complexity of the current zip-nuget pipeline
to improve the stability and performance of nightly packages generation.
ROCm packaging stages has no dependencies with other packaging jobs and
it's the most time-consuming route.
After this change, the most used CPU/CUDA/Mobile packaging workflow
duration can be reduced roughly from 3h20m to 2h30m.
### Description
It's the prerequisite step of reducing complexity of current zip-nuget
pipeline.
Some packaging tasks could be cut from the most complex nuget pipline
and easily be published
### 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
Update upstream packaging pipeline name to make it more meaningful.
### Motivation and Context
The upstream pipeline used to only building Nuget packages, but now it
also builds Zip and Java. So change the name will make it more
meaningful.
### 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
As suggested by SciPy's doc, we will
`Build against NumPy 2.0.0, then it will work for all NumPy versions
with the same major version number (NumPy does maintain backwards ABI
compatibility), and as far back as NumPy 1.19 series at the time of
writing`
I think it works because in
[numpyconfig.h#L64](https://github.com/numpy/numpy/blob/main/numpy/_core/include/numpy/numpyconfig.h#L64)
there is a macro NPY_FEATURE_VERSION. By default it is set to
NPY_1_19_API_VERSION. And the NPY_FEATURE_VERSION macro controls ABI.
This PR only upgrade the build time dependency; When a user installs
ONNX Runtime, they still can use numpy 1.x.
### Motivation and Context
Recently numpy published a new version, 2.0.0, which is incompatible with the latest ONNX Runtime release.
### 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. -->
### 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
<!-- 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
<!-- 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. -->
Add a new GitHub Actions workflow, `.github/workflows/mac.yml`. It contains these jobs:
- ARM64 MacOS CI build.
- Objective-C static analysis build. This was moved over from another Azure DevOps pipeline to make it more visible.
### Description
This PR make numbers of optimizations to onnxruntime-web's module export
and deployment.
See each section below for more details.
#### Preview
>
[onnxruntime-web@1.19.0-esmtest.20240513-a16cd2bd21](https://www.npmjs.com/package/onnxruntime-web/v/1.19.0-esmtest.20240513-a16cd2bd21)
> ~~onnxruntime-web@1.19.0-esmtest.20240430-c7edbcc63d~~
> ~~onnxruntime-web@1.18.0-esmtest.20240428-624c681c83~~
> ~~onnxruntime-web@1.18.0-esmtest.20240411-1abb64e894~~
<details>
<summary><h4>Breaking changes</h4></summary>
There is no code change required, but there are a few differences
regarding **code import**, **flags**, **bundler config** and
**deployment steps**.
#### Importing:
Import table is changed. See following for details.
<details>
<summary><h5>Current import table:</h5></summary>
| Target Name | Path for "import" or "require" | WebGL | JSEP | wasm |
Proxy | Training |
|------|-----|-----|-----|-----|-----|-----|
| `ort` (default) | `onnxruntime-web` | ✔️ | ❌ | ✔️ | ✔️ | ❌ |
| `ort.all` | `onnxruntime-web/experimental` | ✔️ | ✔️ | ✔️ | ✔️ | ❌ |
| `ort.node` | `onnxruntime-web` | ❌ | ❌ | ✔️ | ❌ | ❌ |
| `ort.training` | `onnxruntime-web/training` | ❌ | ❌ | ✔️ |
✔️<sup>\[1]</sup> | ✔️ |
| `ort.wasm` | `onnxruntime-web/wasm` | ❌ | ❌ | ✔️ | ✔️ | ❌ |
| `ort.wasm-core` | `onnxruntime-web/wasm-core` | ❌ | ❌ | ✔️ | ❌ | ❌ |
| `ort.webgl` | `onnxruntime-web/webgl` | ✔️ | ❌ | ❌ | ✔️<sup>\[2]</sup>
| ❌ |
| `ort.webgpu` | `onnxruntime-web/webgpu` | ❌ | ✔️ | ✔️ | ✔️ | ❌ |
* [1] didn't test. may not actually work.
* [2] not working. this is a mistake in build config.
</details>
<details>
<summary><h5>Proposed update:</h5></summary>
| Target Name | Path for "import" or "require" | WebGL | JSEP | wasm |
Proxy | Training |
|------|-----|-----|-----|-----|-----|-----|
| `ort` (default) | `onnxruntime-web` | ✔️ | ❌ | ✔️ | ✔️ | ❌ |
| `ort.all` |
~~`onnxruntime-web/experimental`~~<br/>`onnxruntime-web/all` | ✔️ | ✔️ |
✔️ | ✔️ | ❌ |
| `ort.node` | `onnxruntime-web` | ❌ | ❌ | ✔️ | ❌ | ❌ |
| `ort.training` | `onnxruntime-web/training` | ❌ | ❌ | ✔️ | ✔️ | ✔️ |
| `ort.wasm` | `onnxruntime-web/wasm` | ❌ | ❌ | ✔️ | ✔️ | ❌ |
| ~~`ort.wasm-core`~~ | ~~`onnxruntime-web/wasm-core`~~ | ~~❌~~ | ~~❌~~
| ~~✔️~~ | ~~❌~~ | ~~❌~~ |
| `ort.webgl` | `onnxruntime-web/webgl` | ✔️ | ❌ | ❌ | ~~✔️~~ ❌ | ❌ |
| `ort.webgpu` | `onnxruntime-web/webgpu` | ❌ | ✔️ | ✔️ | ✔️ | ❌ |
</details>
#### Flags:
The following flags are deprecated:
- `env.wasm.simd` (boolean): will be ignored. SIMD is always enabled in
build.
The following flags changed their type:
- `env.wasm.wasmPaths`: When using this flag as a string ( for the URL
prefix ), nothing is changed. When using this flag as an object ( for
per-file path override ), the type changed:
```diff
- export interface Old_WasmFilePaths{
- 'ort-wasm.wasm'?: string;
- 'ort-wasm-threaded.wasm'?: string;
- 'ort-wasm-simd.wasm'?: string;
- 'ort-training-wasm-simd.wasm'?: string;
- 'ort-wasm-simd-threaded.wasm'?: string;
- };
+ export interface New_WasmFilePaths {
+ /**
+ * Specify the override path for the main .wasm file.
+ *
+ * This path should be an absolute path.
+ *
+ * If not modified, the filename of the .wasm file is:
+ * - `ort-wasm-simd-threaded.wasm` for default build
+ * - `ort-wasm-simd-threaded.jsep.wasm` for JSEP build (with WebGPU and
WebNN)
+ * - `ort-training-wasm-simd-threaded.wasm` for training build
+ */
+ wasm?: URL|string;
+ /**
+ * Specify the override path for the main .mjs file.
+ *
+ * This path should be an absolute path.
+ *
+ * If not modified, the filename of the .mjs file is:
+ * - `ort-wasm-simd-threaded.mjs` for default build
+ * - `ort-wasm-simd-threaded.jsep.mjs` for JSEP build (with WebGPU and
WebNN)
+ * - `ort-training-wasm-simd-threaded.mjs` for training build
+ */
+ mjs?: URL|string;
+ }
```
#### Bundler compatibility:
Config changes are need for bundlers. See usage example in
/js/web/test/e2e/ for Webpack, parcel and rollup.
#### Deployment:
- if consuming from a CDN, there is no breaking change.
- if consuming from a local server, need to copy all `ort-*.wasm` and
`ort-*.mjs` files (totally 6 files) in the dist folder. (previously only
need to copy `ort-*.wasm` files.)
</details>
<details>
<summary><h4>Problems</h4></summary>
There are a few problems with the current module export and deployment:
- Script URL cannot be correctly inferred when imported as ESM.
- Workers are forcefully encoded using Blob URL, which makes
onnxruntime-web not working in CSP environment and Node.js, when using
proxy or multi-threading feature.
- Generated JS code (by Emscripten) is encoded using
`function.toString()`, which is unstable and error-prone.
- When running with a different Emscripten build, always need the build
step. Making it difficult to swap artifacts in deveopment/debug.
</details>
<details>
<summary><h4>Goals</h4></summary>
- Full ESM support
- Support variances of ways to import. Including:
- import from HTML's `<script>` tag (IIFE format, exporting to global
variable `ort`)
```html
<script
src="https://example.com/cdn-path-to-onnxruntime-web/dist/ort.min.js"></script>
```
- import from source code inside `<script type="module">` tag (ESM)
```html
<script type="module">
import * as ort from
"https://example.com/cdn-path-to-onnxruntime-web/dist/ort.min.mjs";
// using 'ort'
</script>
```
- import in a CommonJS project (CJS format, resolve from package.json
"exports" field)
```js
// myProject/main.js
const ort = require('onnxruntime-web');
```
- import in an ESM project (ESM format, resolve from package.json
"exports" field)
```js
// myProject/main.js (or main.mjs)
import * as ort from 'onnxruntime-web';
```
- Support popular bundlers when importing onnxruntime-web into a CJS/ESM
project.
- webpack (esm requires extra post-process step)
- rollup
- parcel (esm requires extra post-process step)
- More bundlers **TBD**
- Multi-threading support for Node.js
NOTE: keeping single JavaScript file (the all-in-one bundle) is no
longer a goal. This is because technically there is a conflict with the
other requirements.
</details>
<details>
<summary><h4>Important Design Decisions</h4></summary>
- Drop support of single JavaScript output.
- The current onnxruntime-web distribution uses a single JavaScript file
to include all code. While there are a few benefits, it also creates
problems as mentioned above. Since ESM is being used more and more
widely, and browsers are making more restricted security checks and
requirement, the old Blob based solution is going to be replaced.
- To achieve the requirement, specifically, the CSP environment support,
we have to offer a non Blob based solution. Therefore, we have to
distribute multiple files and drop the single file solution.
- Do not run parser/postprocess on Emscripten generated JavaScript.
- Emscripten is evolving quickly so we should only depends on what's in
its documentation instead of a certain implementation details. (for
example, currently we patch on its code to deal with a special variable
`_scriptDir`)
- Keep the generated files as-is also helps to:
- reduce the size of ort.min.js
- make it easier to replace build artifacts when in development/debug
- Drop support for non-SIMD and non-MultiThread. This helps to reduce
the number of artifacts in distribution.
- (fixed-sized) SIMD is supported in any mainstream JS environment.
- Multi-thread as WebAssembly feature is supported in any mainstream JS
environment. In some environment the feature is guarded with cross
origin policy, but it can still work if not trying to create any worker.
- Use ESM output for Emscripten generated JavaScript.
- There are 2 ways to dynamically import classic (umd) modules and
neither of them are recommended:
- dynamically creating a <script> tag. This changes the HTML structure
and have quite a lot of compatibility issue
- use `fetch()` and `eval()`. However `eval` is strongly suggested to be
avoid because there is a great perf hit.
- importing ESM is super easy - just use the `import()` call.
Considering ESM is widely supported in modern browsers and Node.js this
is the better option.
- Add Blob based solution as a fallback for cross-origin workers.
- There are still wide use case of importing onnxruntime-web from CDN.
In this usage, make it able create worker by using `fetch()`+`Blob` to
create a same-origin Blob URL.
</details>
<details>
<summary><h4>Distribution File Manifest</h4></summary>
The distribution folder contains the following files:
- WebAssembly artifacts. These files are the result of compiling the
ONNX Runtime C++ code to WebAssembly by Emscripten.
| File Name | Build Flags |
|------|-----|
| ort-wasm-simd-threaded.mjs <br/> ort-wasm-simd-threaded.wasm |
`--enable_wasm_simd` <br/> `--enable_wasm_threads` |
| ort-training-wasm-simd-threaded.mjs <br/>
ort-training-wasm-simd-threaded.wasm | `--enable_training_apis` <br/>
`--enable_wasm_simd` <br/> `--enable_wasm_threads` |
| ort-wasm-simd-threaded.jsep.mjs <br/> ort-wasm-simd-threaded.jsep.wasm
| `--enable_wasm_simd` <br/> `--enable_wasm_threads` <br/> `--use_jsep`
<br/> `--use_webnn` |
- onnxruntime-web JavaScript artifacts. These files are generated by
ESBuild as the entry point for onnxruntime-web.
There are multiple build targets for different use cases:
| Target Name | Path for "import" or "require" | Description |
|------|-----|-----|
| `ort` | `onnxruntime-web` | The default target. |
| `ort.all` | `onnxruntime-web/all` | The target including webgl. |
| `ort.node` | `onnxruntime-web` | The default target for Node.js. |
| `ort.training` | `onnxruntime-web/training` | The target including
training APIs |
| `ort.wasm` | `onnxruntime-web/wasm` | The target including only
WebAssembly (CPU) EP |
| `ort.webgl` | `onnxruntime-web/webgl` | The target including only
WebGL EP |
For each target, there are multiple files generated:
| File Name | Description |
|------|-----|
| [target].js | The entry point for the target. IIFE and CommonJS
format. |
| [target].mjs | The entry point for the target. ESM format. |
| [target].min.js <br/> [target].min.js.map | The entry point for the
target. Minimized with sourcemap. IIFE and CommonJS format. |
| [target].min.mjs <br/> [target].min.mjs.map | The entry point for the
target. Minimized with sourcemap. ESM format. |
| [target].proxy.mjs | (if appliable) The proxy ESM module for the
target. |
| [target].proxy.min.mjs <br/> [target].proxy.min.mjs.map | (if
appliable) The proxy ESM module for the target. Minimized with
sourcemap. |
</details>
<details>
<summary><h4>Dynamic Import Explained</h4></summary>
- Local Served | No Proxy:
```
[Bundle or ort.min.js]
|
+ import()--> [ort-wasm-simd-threaded.mjs]
|
+ WebAssembly.instantiateStreaming()--> [ort-wasm-simd-threaded.wasm]
|
+ new Worker()--> [ort-wasm-simd-threaded.mjs (worker)]
|
+ WebAssembly.instantiateStreaming()--> [ort-wasm-simd-threaded.wasm]
```
- Local Served | Proxy:
```
[Bundle or ort.min.js]
|
+ import()--> [ort.proxy.min.mjs]
|
+ new Worker()--> [ort.proxy.min.mjs (worker)]
|
+ import()--> [ort-wasm-simd-threaded.mjs]
|
+ WebAssembly.instantiateStreaming()--> [ort-wasm-simd-threaded.wasm]
|
+ new Worker()--> [ort-wasm-simd-threaded.mjs (worker)]
|
+ WebAssembly.instantiateStreaming()--> [ort-wasm-simd-threaded.wasm]
```
- Cross Origin | No Proxy:
```
[Bundle or ort.min.js]
|
+ fetch('ort-wasm-simd-threaded.mjs')
|
+ URL.createObjectURL(res.blob())
|
+ import()--> [blob:... (ort-wasm-simd-threaded)]
|
+ WebAssembly.instantiateStreaming()--> [ort-wasm-simd-threaded.wasm]
|
+ new Worker()--> [blob:... (ort-wasm-simd-threaded) (worker)]
|
+ WebAssembly.instantiateStreaming()--> [ort-wasm-simd-threaded.wasm]
```
- Cross Origin | Proxy
```
[Bundle or ort.min.js]
|
+ fetch('ort.proxy.min.mjs')
|
+ URL.createObjectURL(res.blob())
|
+ import()--> [blob:... (ort.proxy)]
|
+ new Worker()--> [blob:... (ort.proxy) (worker)]
|
+ fetch('ort-wasm-simd-threaded.mjs')
|
+ URL.createObjectURL(res.blob())
|
+ import()--> [blob:... (ort-wasm-simd-threaded)]
|
+ WebAssembly.instantiateStreaming()--> [ort-wasm-simd-threaded.wasm]
|
+ new Worker()--> [blob:... (ort-wasm-simd-threaded) (worker)]
|
+ WebAssembly.instantiateStreaming()--> [ort-wasm-simd-threaded.wasm]
```
</details>
### Description
<!-- Describe your changes. -->
* Partially revert [previous
change](https://github.com/microsoft/onnxruntime/pull/19804), and
* Redo concurrency_test_result parser outside of post.py
* Add support of syncing memtest result to db
### 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. -->
To fix the error when CI is running on two model groups.
- When running on two model groups, the [previous
change](https://github.com/microsoft/onnxruntime/pull/19804) wrongly
navigates two levels up in the directory after running one model group,
while one level is needed. After that, the script can't find another
model group.
- Running on one model group can't repro the issue
### 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. -->
- Move iOS package build to separate job so it can run in parallel with Android AAR build and be decoupled from the test stage. The test stage fails sometimes (not infrequently) and may need to be re-run.
- Update stop iOS simulator step so it doesn't fail if the start step doesn't run.
### 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
- This PR combine all CUDA 12 stage into the Zip-nuget-... pipeline.
- It also enables the cuda12 support
### 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
orttrainingtestdatascus has only save mnist whose size is only 64M in
Azure File
To meet security requirements and reduce maintenance cost, move the test
data to lotusscus and saved in Azure blob.
This pull request primarily involves changes to the build scripts in the
`tools/ci_build/github/azure-pipelines` directory. The changes add build
date and time information to the build process. This is achieved by
introducing two new parameters, `BuildDate` and `BuildTime`, and
incorporating them into the `msbuildArguments` in multiple locations.
Addition of new parameters:
*
[`tools/ci_build/github/azure-pipelines/templates/c-api-cpu.yml`](diffhunk://#diff-00815920cc190d10fdebceac0c3a4b8a59e408684ae38177dfe7f96cae276c59R309-R310):
Added `BuildDate` and `BuildTime` parameters using the pipeline's start
time.
Incorporation of new parameters in `msbuildArguments`:
*
[`tools/ci_build/github/azure-pipelines/c-api-noopenmp-packaging-pipelines.yml`](diffhunk://#diff-efb530efd945fdd9d3e1b92e53d25cc8db7df2e28071c364b07a7193092de01bL947-R948):
Added `CurrentDate` and `CurrentTime` parameters to `msbuildArguments`
in multiple locations.
[[1]](diffhunk://#diff-efb530efd945fdd9d3e1b92e53d25cc8db7df2e28071c364b07a7193092de01bL947-R948)
[[2]](diffhunk://#diff-efb530efd945fdd9d3e1b92e53d25cc8db7df2e28071c364b07a7193092de01bL1092-R1093)
[[3]](diffhunk://#diff-efb530efd945fdd9d3e1b92e53d25cc8db7df2e28071c364b07a7193092de01bL1114-R1115)
[[4]](diffhunk://#diff-efb530efd945fdd9d3e1b92e53d25cc8db7df2e28071c364b07a7193092de01bL1137-R1138)
*
[`tools/ci_build/github/azure-pipelines/templates/c-api-cpu.yml`](diffhunk://#diff-00815920cc190d10fdebceac0c3a4b8a59e408684ae38177dfe7f96cae276c59L446-R448):
Incorporated the `CurrentDate` and `CurrentTime` parameters into
`msbuildArguments`.### 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. -->
- Update method for uploading to Azure storage to use managed identity.
- Allow helper script tasks to be split across different calls.
- Rewrite helper script in Python.
Motivation:
Recently the Azure storage account configuration was changed and now the old way of uploading to it no longer works.
### Description
<!-- Describe your changes. -->
Currently figuring out if the protobuf dependency is building protoc it
is a little obtuse and inconsistent
* in some places we directly set protobuf_BUILD_PROTOC_BINARIES to OFF
to indicate the protobuf dependency is not building protoc
* e.g. macOS/iOS/visionOS builds
* for a user provided protoc path we don't set
protobuf_BUILD_PROTOC_BINARIES, and inside protobuf_function.cmake that
determines if `protobuf::protoc` is added as a dependency or not
*
0dda8b0c44/cmake/external/protobuf_function.cmake (L40-L45)
To be more consistent/explicit, set protobuf_BUILD_PROTOC_BINARIES to
OFF when ONNX_CUSTOM_PROTOC_EXECUTABLE set and valid.
Remove outdated script that built and external protoc binary which was
used in later builds. The build setup will fetch a pre-built protoc so
there's no need for this additional build.
### 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. -->
Make it easier to figure out if protoc is coming from the protobuf
dependency.
### Description
There was a bug with gqa on cpu where on token case, with batch_size >
1, and with past_present_share_buffer off, the output would occasionally
contain nans. this pr fixes that. it also updates documentation and
fixes posid gen for rotary in cuda in prompt case.
### Motivation and Context
this pr solves the GQA CPU bug as well as updates the documentation and
makes seqlens_k irrelevant for prompt case, which is useful to prevent
user error.
### Description
- Updates QNN pipelines to use QNN SDK 2.21
- Downloads QNN SDK from Azure storage to avoid having to rebuild images
when a new version is released.
### Motivation and Context
Test with the latest QNN SDK.
### Description
<!-- Describe your changes. -->
This branch is based on rel-1.18.0 and supports TensorRT 10-GA.
### 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. -->
https://github.com/microsoft/onnxruntime/pull/20418
Add back Catalyst changes only for now.
### 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>
### Description
<!-- Describe your changes. -->
Update order of steps
### 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. -->
Fix CI
### Description
<!-- Describe your changes. -->
Error:
**Artifact name input: e2e_test_logs_1364625_$(Date:yyyyMMddHHmmss)
##[error]Artifact name is not valid:
e2e_test_logs_1364625_$(Date:yyyyMMddHHmmss). It cannot contain '\', /',
"', ':', '<', '>', '|', '*', and '?'**
Date not correctly showing up in the artifact name. Use predefined
pipeline variable BuildNumber instead which also serves similarly as a
timestamp.
### 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. -->
RN CI failure
---------
Co-authored-by: rachguo <rachguo@rachguos-Mac-mini.local>
Co-authored-by: rachguo <rachguo@rachguos-Mini.attlocal.net>
### Description
<!-- Describe your changes. -->
As title.
### 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. -->
Update to more generic url
### 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. Update the image name to avoid docker image wouldn't be overwrite.
there was an mistake that variables.CUDA_VERSION_MAJOR is always empty
14fcf0a52d/tools/ci_build/github/azure-pipelines/stages/nuget-linux-cuda-packaging-stage.yml (L120)
3. set one artifact name as variable to make the job rerunnable
### 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. -->
This PR supports a build of onnxruntime.xcframework for xros/xrsimulator
for visionos via the build command of
`python3 tools/ci_build/github/apple/build_apple_framework.py --config
Release/Debug
tools/ci_build/github/apple/default_vision_os_framework_build_settings.json`.
For officially include visionos in ios cocoapods package and testing in
CI, would require separate work for upgrading the Xcode version &
upgrade macOS CI agent to macos-13-arm64 or higher.
### 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. -->
visionos support:
https://github.com/microsoft/onnxruntime/discussions/19313
---------
Co-authored-by: rachguo <rachguo@rachguos-Mini.attlocal.net>
Co-authored-by: rachguo <rachguo@rachguos-Mac-mini.local>
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
- Updates Windows QNN Nuget and Python packaging pipelines to download
QNN SDK from blob storage.
- Makes the QNN SDK version configurable when launching the python
packaging pipeline.
### Motivation and Context
Removes the need to rebuild images to update QNN SDK. Only applies to
Windows pipelines. Linux pipelines still get the SDK from disk.
### 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
These changes include
Support to OpenVINO 2024.1
Import PreCompiled Blobs with EPContext Blob
Separate Device/Precision as input
Deprecate CPU_FP32 , GPU_FP32 terminology , introduce CPU, GPU
AUTO GPU, CPU will only create GPU Blob and not CPU Blob.
### Motivation and Context
- OpenVINO 2024.1 will be out soon
- Import Precompiled Blob can greatly reduce FEIL/FIL Time.
- Separating Device/Precision will make the input cleaner
-
---------
Co-authored-by: Suryaprakash Shanmugam <suryaprakash.shanmugam@intel.com>
Co-authored-by: Preetha Veeramalai <preetha.veeramalai@intel.com>
### Description
Currently we try to include all prebuilt binaries into the NPM packages.
This was working until we added libonnxruntime_providers_cuda.so
(>400MB) into the NPM package. The NPM registry refuses to accept new
package publishment because the file is too large.
To make the new NPM package working, we have to remove the large file
from the package, and add a new script on package installation. This
script will try to dynamically install onnxruntime CUDA dynamic library
for Linux/x64.
Add `--user` option to pip install command.
Error:
```
ERROR: Could not install packages due to an OSError: [Errno 13] Permission denied: '/usr/local/bin/f2py'
Consider using the `--user` option or check the permissions.
```
See #19877.
### Description
update with ONNX 1.16.0 branch according to
https://github.com/microsoft/onnxruntime/blob/main/docs/How_To_Update_ONNX_Dev_Notes.md
ONNX 1.16.0 release notes:
https://github.com/onnx/onnx/releases/tag/v1.16.0
#### Updated ops for CPU EP:
- DequantizeLinear(21)
- Added int16 and uint16 support + various optimizer tests
- Missing int4 and uint4 support
- Missing block dequantization support
- QuantizeLinear(21)
- Added int16 and uint16 support + various optimizer tests
- Missing int4 and uint4 support
- Missing block quantization support
- Cast(21)
- Missing int4 and uint4 support
- CastLike(21)
- Missing int4 and uint4 support
- ConstantOfShape(21)
- Missing int4 and uint4 support
- Identity(21)
- Missing int4 and uint4 support
- If(21)
- Missing int4 and uint4 support
- Loop(21)
- Missing int4 and uint4 support
- Reshape(21)
- Missing int4 and uint4 support
- Scan(21)
- Missing int4 and uint4 support
- Shape(21)
- Missing int4 and uint4 support
- Size(21)
- Missing int4 and uint4 support
- Flatten(21)
- Missing float8e4m3fnuz, float8e5m2, float8e5m2fnuz, int4, and uint4
support
- Pad(21)
- Missing float8e4m3fnuz, float8e5m2, float8e5m2fnuz, int4, and uint4
support
- Squeeze(21)
- Missing float8e4m3fnuz, float8e5m2, float8e5m2fnuz, int4, and uint4
support
- Transpose(21)
- Missing float8e4m3fnuz, float8e5m2, float8e5m2fnuz, int4, and uint4
support
- Unsqueeze(21)
- Missing float8e4m3fnuz, float8e5m2, float8e5m2fnuz, int4, and uint4
support
#### Unimplemented opset 21 features/ops
- int4 and uint4 data type
- QLinearMatMul(21)
- GroupNormalization(21)
- ai.onnx.ml.TreeEnsemble(5)
### 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. -->
### Disabled tests
#### ORT Training
orttraining/orttraining/test/python/orttraining_test_ort_apis_py_bindings.py
- test_ort_custom_ops: Potential shape inference bug for custom ops
#### Python quantization unit tests
test/onnx/python/quantization (shape inference bug)
- test_op_conv_transpose.py: test_quantize_conv_transpose_u8u8_fp16
- test_op_conv_transpose.py: test_quantize_conv_transpose_s8s8_fp16
- test_op_gemm.py: test_quantize_qop_gemm_s8s8
- test_op_gemm.py: test_quantize_qop_gemm_e4m3fn_same
- test_op_gemm.py: test_quantize_qop_gemm_e4m3fn_p3
- test_op_matmul.py: test_quantize_matmul_u8u8_f16
- test_op_matmul.py: test_quantize_matmul_s8s8_f16
- test_op_matmul.py: test_quantize_matmul_s8s8_f16_entropy
- test_op_matmul.py: test_quantize_matmul_s8s8_f16_percentile
- test_op_matmul.py: test_quantize_matmul_s8s8_f16_distribution
- test_op_relu.py: test_quantize_qop_relu_s8s8
#### ONNX tests
- test_maxpool_2d_ceil_output_size_reduce_by_one: ONNX 1.16.0 fixed a
maxpool output size bug and added this test. Enable this test when [ORT
PR](https://github.com/microsoft/onnxruntime/pull/18377) is merged.
Refer to original [ONNX PR](https://github.com/onnx/onnx/pull/5741).
- test_ai_onnx_ml_tree_ensemble_set_membership_cpu: new unimplemented op
ai.onnx.ml.TreeEnsemble
- test_ai_onnx_ml_tree_ensemble_single_tree_cpu: same
- test_ai_onnx_ml_tree_ensemble_set_membership_cuda: same
- test_ai_onnx_ml_tree_ensemble_single_tree_cuda: same
- test_cast_INT4_to_FLOAT_cpu: ORT Cast(21) impl doesn't support int4
yet
- test_cast_INT4_to_INT8_cpu: same
- test_cast_UINT4_to_FLOAT_cpu: same
- test_cast_UINT4_to_UINT8_cpu: same
- test_cast_INT4_to_FLOAT_cuda
- test_cast_INT4_to_INT8_cuda
- test_cast_UINT4_to_FLOAT_cuda
- test_cast_UINT4_to_UINT8_cuda
- test_constantofshape_float_ones_cuda: ConstantOfShape(21) not
implemented for cuda
- test_constantofshape_int_shape_zero_cuda: same
- test_constantofshape_int_zeros_cuda: same
- test_flatten_axis0_cuda: Flatten(21) not implemented for cuda
- test_flatten_axis1_cuda: same
- test_flatten_axis2_cuda: same
- test_flatten_axis3_cuda: same
- test_flatten_default_axis_cuda: same
- test_flatten_negative_axis1_cuda: same
- test_flatten_negative_axis2_cuda: same
- test_flatten_negative_axis3_cuda: same
- test_flatten_negative_axis4_cuda: same
- test_qlinearmatmul_2D_int8_float16_cpu: QLinearMatMul(21) for onnx not
implemented in ORT yet
- test_qlinearmatmul_2D_int8_float32_cpu: same
- test_qlinearmatmul_2D_uint8_float16_cpu: same
- test_qlinearmatmul_2D_uint8_float32_cpu: same
- test_qlinearmatmul_3D_int8_float16_cpu: same
- test_qlinearmatmul_3D_int8_float32_cpu: same
- test_qlinearmatmul_3D_uint8_float16_cpu: same
- test_qlinearmatmul_3D_uint8_float32_cpu: same
- test_qlinearmatmul_2D_int8_float16_cuda: same
- test_qlinearmatmul_2D_int8_float32_cuda: same
- test_qlinearmatmul_2D_uint8_float16_cuda: same
- test_qlinearmatmul_2D_uint8_float32_cuda: same
- test_qlinearmatmul_3D_int8_float16_cuda: same
- test_qlinearmatmul_3D_int8_float32_cuda: same
- test_qlinearmatmul_3D_uint8_float16_cuda: same
- test_qlinearmatmul_3D_uint8_float32_cuda: same
- test_size_cuda: Size(21) not implemented for cuda
- test_size_example_cuda: same
- test_dequantizelinear_blocked: Missing implementation for block
dequant for DequantizeLinear(21)
- test_quantizelinear_blocked_asymmetric: Missing implementation for
block quant for QuantizeLinear(21)
- test_quantizelinear_blocked_symmetric: Missing implementation for
block quant for QuantizeLinear(21)
---------
Signed-off-by: liqunfu <liqun.fu@microsoft.com>
Signed-off-by: Ganesan Ramalingam <grama@microsoft.com>
Co-authored-by: Ganesan Ramalingam <grama@microsoft.com>
Co-authored-by: George Wu <jywu@microsoft.com>
Co-authored-by: adrianlizarraga <adlizarraga@microsoft.com>
### Description
make the compilation work on Azure CPU Agent by reduce the parallel
count
### Motivation and Context
The OOM issue mentioned in #20244 was caused the by low
memory/parallel_count.
### Description
It always has been out of memory in training CUDA 12.2 packaging
pipeline
https://dev.azure.com/aiinfra/Lotus/_build?definitionId=1308&_a=summary
since the PR #19910
I tried other CPU agents for example, D64as_v5(256G memory) and
D32as_v4(128G memory and 256 G SSD temp storage), which are still out of
memory like the below image
But it works on T4, though T4 only has 4 vCPUs, 28G memory and 180G temp
storage, and it takes much more time.
### Motivation and Context
Restore CUDA 12.2 training packaging pipeline first.
More time is needed to investigate the root cause
### Other Clues.
These 2 compilation steps take nearly 6 minutes with Cuda 12.2 on T4
And it runs out of memory on CPU machine. @ajindal1
cuda12.2 on T4
```
2024-03-14T05:39:08.7726865Z [ 90%] Building CUDA object CMakeFiles/onnxruntime_providers_cuda.dir/onnxruntime_src/onnxruntime/contrib_ops/cuda/bert/flash_attention/flash_fwd_split_hdim32_fp16_sm80.cu.o
2024-03-14T05:45:01.3223393Z [ 90%] Building CUDA object CMakeFiles/onnxruntime_providers_cuda.dir/onnxruntime_src/onnxruntime/contrib_ops/cuda/bert/flash_attention/flash_fwd_split_hdim64_bf16_sm80.cu.o
2024-03-14T05:46:07.9218003Z [ 90%] Building CUDA object CMakeFiles/onnxruntime_providers_cuda.dir/onnxruntime_src/onnxruntime/contrib_ops/cuda/bert/flash_attention/flash_fwd_split_hdim96_fp16_sm80.cu.o
2024-03-14T05:52:59.2387051Z [ 90%] Building CUDA object CMakeFiles/onnxruntime_providers_cuda.dir/onnxruntime_src/onnxruntime/contrib_ops/cuda/bert/group_query_attention_impl.cu.o
```
But they could be finished in about one minute with Cuda 11.8 on CPU
```
cuda11.8 on CPU
2024-04-09T11:34:35.0849836Z [ 90%] Building CUDA object CMakeFiles/onnxruntime_providers_cuda.dir/onnxruntime_src/onnxruntime/contrib_ops/cuda/bert/flash_attention/flash_fwd_split_hdim32_fp16_sm80.cu.o
2024-04-09T11:35:53.6648154Z [ 90%] Building CUDA object CMakeFiles/onnxruntime_providers_cuda.dir/onnxruntime_src/onnxruntime/contrib_ops/cuda/bert/flash_attention/flash_fwd_split_hdim64_bf16_sm80.cu.o
cuda11.8 on GPU
024-03-13T12:16:33.4102477Z [ 90%] Building CUDA object CMakeFiles/onnxruntime_providers_cuda.dir/onnxruntime_src/onnxruntime/contrib_ops/cuda/bert/flash_attention/flash_fwd_split_hdim32_fp16_sm80.cu.o
2024-03-13T12:19:58.8268272Z [ 90%] Building CUDA object CMakeFiles/onnxruntime_providers_cuda.dir/onnxruntime_src/onnxruntime/contrib_ops/cuda/bert/flash_attention/flash_fwd_split_hdim64_bf16_sm80.cu.o
```
### Description
Update QNN python packages to use QNN SDK version 2.19.2.
### Motivation and Context
Our CI builds already use QNN SDK version 2.19.2. We should make sure
the ort-nightly-qnn python packages are also built with the same QNN SDK
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
Except [Python-CUDA-Packaging
pipeline](https://dev.azure.com/aiinfra/Lotus/_build?definitionId=1299&_a=summary),
all windows cuda packaging jobs have been running well now.
After comparison, enable_lto isn't added in the pipeline, which might be
one root cause of the random hang.
### 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
Enable NPUs supporting DXCORE_ADAPTER_ATTRIBUTE_D3D12_GENERIC_ML and
D3D_FEATURE_LEVEL_1_0_GENERIC with DML EP. This also begins ingesting DX
headers through the DirectX-Headers repo.
Note that this includes an update to cgamanifest.json for onnx-tensorrt
which is triggered during re-generation due to a prior changes to
deps.txt.
### 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
Bump spotless and the Gradle wrapper to 6.25.0 and 8.6 respectively to
allow compiling ORT on Java 21. The build still targets Java 8.
I'm not sure if there will be CI changes necessary to use this PR,
specifically for the Gradle version as I don't know if that is cached
somewhere earlier in the CI build process.
The new Gradle version adds a warning that using `--source` and
`--target` to select the Java language version is obsolete which is
annoying, we can fix it if we decide to only allow building on newer
versions of Java, while still supporting running on Java 8.
### Motivation and Context
Java 21 is the latest LTS release of Java and ORT should be able to
build on it.
### 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. -->
---------
Co-authored-by: Yi Zhang <your@email.com>
### Description
reactor win-ci.yml to solve the random hang issue in more GPU workflows,
move nugget-zip packages and python cuda12 packages building to CPU
machine.
---------
Co-authored-by: Yi Zhang <your@email.com>
### Description
Address build issues and source code discrepancies.
Fix cuda_test_provider gtest argument stack corruption.
### Motivation and Context
`OpTester` class that is widely used for kernel testing is not
suitable for testing internal classes for EPs that are built as shared
objects.
Currently, CUDA EP tests run only on Linux.
We want to enable testing and developments on Windows,
and create a usable pattern for testing of other EPs internals.
Alternatives considered:
Abstracting EP unit tests into separate test executable such as
`onnxruntime_test_all`.
This alternative was rejected as it would create a lot more changes in
the established patterns,
and potentially interfere with CUDA functionality with more complex
source code maintanence.
### Description
In #20073, I use pin onnx version to unblock the whole PR CI.
In fact, we could use the onnx that installed by building source code,
that the onnx version is controlled by deps.txt.
For some history reason, DML stage installed onnx from pypi. Now, the
onnx can be installed as other stages.
add an option to skip installing onnx in win-ci-prebuild-step
### Description
Make Windows GPU Packaging stage in Python Packaging pipeline run on CPU
machine as well
### 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. -->
### Test Link
https://dev.azure.com/aiinfra/Lotus/_build/results?buildId=430961&view=results
### Description
Update Web CI to use data dir under Agent.TempDirectory
This change fixes the random failure caused by unstable access to karma
temp directory (which is under AppData\Local\Temp) on CI pipeline
### Description
Add NPU to list of device supported.
Added changes for Support to OV 2024.0
Nuget packages removes packaging of OpenVINO DLL
Bug Fixes with Python API
Reverted Dockerfiles not being maintained.
### Motivation and Context
NPU Device has been introduced by Intel in latest client systems
OpenVINO 2024.0 release is out.
---------
Co-authored-by: Suryaprakash Shanmugam <suryaprakash.shanmugam@intel.com>
Co-authored-by: Preetha Veeramalai <preetha.veeramalai@intel.com>
Co-authored-by: Ubuntu <ubuntu@ubuntu-118727.iind.intel.com>
Co-authored-by: hmamidix <hemax.sowjanya.mamidi@intel.com>
Co-authored-by: vthaniel <vishnudas.thaniel.s@intel.com>
Co-authored-by: saurabhkale17 <saurabh1.kale@intel.com>
### Description
1. Move building on CPU machine.
2. Optimize the pipeline
3. Since there isn't official ONNX package for python 12, the python 12
test stage uses the packages built with ONNX source in build stage.
### Motivation and Context
1. Resolve the random hang in compilation
4. Save a lot of GPU resources.
---------
### Description
<!-- Describe your changes. -->
### Motivation and Context
downloading deps is not needed in test stage
remove it to reduce random downloading errors
### Description
<!-- Describe your changes. -->
the crash caused by the neural_speed turns out to be a very corn case.
Turn it on by default.
### 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. -->
MAUI on macOS uses mac-catalyst which requires a different native
binary.
---------
Co-authored-by: rachguo <rachguo@rachguos-Mini.attlocal.net>
Co-authored-by: Scott McKay <skottmckay@gmail.com>
### Description
The docker image name was fixed, but the docker argument was different
in different job.
It would trigger rebuilding the docker image almost every time!!!
### Description
Fix a few warnings in typedoc (for generating JS API):
```
[warning] The signature TrainingSession.loadParametersBuffer has an @param with name "buffer", which was not used.
[warning] NonTensorType, defined in ./lib/onnx-value.ts, is referenced by OnnxValue but not included in the documentation.
[warning] TensorFactory, defined in ./lib/tensor-factory.ts, is referenced by Tensor but not included in the documentation.
[warning] ExternalDataFileType, defined in ./lib/onnx-model.ts, is referenced by InferenceSession.SessionOptions.externalData but not included in the documentation.
[warning] TensorToDataUrlOptions, defined in ./lib/tensor-conversion.ts, is referenced by Tensor.toDataURL.toDataURL.options but not included in the documentation.
[warning] TensorToImageDataOptions, defined in ./lib/tensor-conversion.ts, is referenced by Tensor.toImageData.toImageData.options but not included in the documentation.
[warning] Failed to resolve link to "GpuBufferType" in comment for Env.WebGpuFlags.adapter.
[warning] Failed to resolve link to "GpuBufferType" in comment for Env.WebGpuFlags.device.
```
Changes highlighted:
- Merge `CoreMlExecutionProviderOption` and
`CoreMLExecutionProviderOption`. They expose 2 set of different options
for React-native and ORT nodejs binding. This should be fixed in future.
- Fix a few inconsistency of names between JSDoc and parameters
- Fix broken type links
- Exclude trace functions
### Description
<!-- Describe your changes. -->
* Add concurrency test to EP Perf CI panel (impl. by onnx_test_runner)
* Model: FasterRCNN-10 model within CI image
* `-c` param configurable via CI panel when kicking off CI tasks
* Auto-replicate test input/outputs according to `-c` param
* By default, the model test will be executed in 100 iterations (~2min
added to T4 CI task load overall)
### 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. -->
To monitor potential concurrency issues of ORT-TRT
### Description
Modifications to support 2GB+ checkpoint & Upgrading Flatbuffers
### Motivation and Context
This PR includes changes that will make ort handle 2GB+ checkpoints.
To do that we need to upgrade flatbuffers to 23.5.9 -
https://github.com/google/flatbuffers/pull/7945
- Modified the commitHash and the hash for the new version
- Removed the patch for rust generator's unused variable warning as it
is no longer producing this - [Check it out
here](d121e09d89/src/idl_gen_rust.cpp)
- Updated the VerifyField calls with alignment values that were
introduced in the new version.
---------
Co-authored-by: Sumit Agarwal <sumitagarwal@microsoft.com>
### Description
1. Use stage to organize the pipeline and split building and testing
2. Move compilation on CPU machine
3. test stage can leverage existing artifacts
4. check wheel size, it gives warning if the size above 300M
5. docker image name wasn't change even the argument changed, which
caused the docker image was always rebuilt. So update the docker image
name according to the argument can save the docker build time.
Pipeline duration reduced by 60% (2 hours -> 50 minutes)
Compilation time reduced by 75% (1.5hours -> 20 minutes)
GPU time reduced by 87% ( 8 hours to 1 hours)
for debugging, the GPU time could be reduced by above 95%, because we
can choose run only one test stage and skip building.
### Motivation and Context
Make the pipeline efficient.
Optimized
https://dev.azure.com/aiinfra/Lotus/_build/results?buildId=424177&view=results
Curent
https://dev.azure.com/aiinfra/Lotus/_build/results?buildId=422393&view=results
---------
### Description
Change nuget pipeline's "Final_Jar_Testing_Windows_GPU" job to download
TRT binaries in every build. Now all the other build jobs are already
doing this. This is the only one left.
Similar to #19909
### Motivation and Context
As a follow up of #19118
### Description
Change nuget pipeline's "Final_Jar_Testing_Windows_GPU" job to download
TRT binaries in every build. Now all the other build jobs are already
doing this. This is the only one left.
### Motivation and Context
As a follow up of #19118
### Description
the `npm test` flags are difficult to memorize, because they are
different to the `ort.env` flags. This change makes those flags align
with ort JS API. eg. `--wasm-enable-proxy` became `--wasm.proxy`.
Old flags are marked as deprecated except `-x` (as a shortcut of
`--wasm.numThreads`)
### 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
<!-- Describe your changes. -->
### Motivation and Context
--extra-index-url is not allowed by injected Secure Supply Chain Step in
packaging pipelines.
```
> Starting Multifeed Python Security Analysis:
##[warning]tools/ci_build/github/azure-pipelines/bigmodels-ci-pipeline.yml - Found "extra-index-url". (https://aka.ms/cfs/pypi)
```
And those 2 packages can be installed from PyPI as well now.
Co-authored-by: Yi Zhang <your@email.com>
Fix some linker errors that come up when integrating the onnxruntime-training-c pod into another Xcode project. The problematic configuration is a minimal build with training APIs enabled.
- training_op_defs.o had some unresolved references to ONNX functions. It should not be included at all in a minimal build.
- tree_ensemble_helper.o also had unresolved references to ONNX ParseData. The containing function is unused in a minimal build.
Added a test to cover this configuration.
### Description
As a follow up of #19788, remove more remaining Windows ARM32 build
jobs.
### Motivation and Context
Our nuget packaging pipeline is failing because it could not find an
artifact for Win ARM32.
```
##[error]Artifact onnxruntime-training-win-arm was not found for build 421397.
```
Deprecation of Win ARM32 was announced by Windows team in January 2023.
We should follow it.
### Description
* Update name of existing dockerfiles and add support to test latest
TensorRT EA binary located in the image
* Add cuda 12.3/cuDNN 9/TensorRT 8.6 dockerfile
* Add detail to CI prompts and configs
Instruction to test latest TRT via BIN:
1. Select `BIN` in TensorRT Version
2. In Variables, update related tarCudaVersion, **clear**
tarCudnnVersion (not required in latest TRT tar binary) , and path to
binary.
### Description
* Add tag to distinguish if TRT `builtin` or `oss` parser is being used
* `oss` tag will be inserted with onnx-tensorrt commit id, to indicate
which version oss parser is
### Validate
DB entry before/after this PR
(during test, `builtin` or `oss_{commit_id}` tag was inserted in the
database entries):
### Motivation and Context
To distinguish perf results using builtin/oss parser in the database,
this parser tag is needed.
In future, results using different parsers will be listed in different
Perf Dashboard pages.
### Description
<!-- Describe your changes. -->
Address warnings so all the ORT projects build with /W4 on Windows.
Mainly
- unused parameters
- variables shadowing other ones
### 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. -->
#19588 started on this.
### Description
Change webgpu CI pipeline to use a preinstalled chrome. Hopefully it can
increase the stability. Now the chrome got from puppeteer often failed
to start.
### Description
It is a "Bash" task that requires running bash on Windows. Most Windows
operating systems do not have Bash installed. Given this task is only
debugging purposes, we can remove it for now.
### Motivation and Context
I am making this change because I am regenerating the VM image in a
different manner, and the new image does not contain bash. Once this PR
is in, I can switch the images.
### Description
<!-- Describe your changes. -->
* Publish the artifacts as late as possible
* once published the artifacts are immutable, and any retry will fail if
they exist
* if any step fails after publishing the stage cannot be retried
* use powershell to cleanup
* DeleteFiles is taking >30 mins and causing the stage to timeout
* powershell took < 1s
### 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. -->
Make pipeline more robust
### Description
<!-- Describe your changes. -->
Use UseMultiToolTask and limit the number of cl.exe instances running.
MultiToolTask info:
https://devblogs.microsoft.com/cppblog/improved-parallelism-in-msbuild/
Info on why limiting CL_MPCount can help:
https://github.com/Microsoft/checkedc-clang/wiki/Parallel-builds-of-clang-on-Windows
The current CIs have 4 cores (both physical and logical). Hardcoded the
GPU build in win-ci.yml to use CL_MPCount of 2 as that seems to work
fine. Can adjust if needed to base it on the actual number of cores or
to use build.py to build.
Caveat: I've run about 16 builds and haven't seen a slow build yet, but
as the root cause of the slow builds isn't really known this isn't
guaranteed to be a fix.
### 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. -->
Try and prevent super slow GPU builds by reducing number of tasks
potentially running in parallel.
### Description
<!-- Describe your changes. -->
The RN CI has intermittent failure error with "app seems to idle".
enable the most verbose logging level (and can add steps to dump
device.log from the detox folder/artifacts if necessary) to at least get
more information.
### 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>
### Description
<!-- Describe your changes. -->
Xcode UI tests seem to be flaky:
https://github.com/orgs/community/discussions/68807
Add a couple of retries if we get a "Timed out while loading
Accessibility." error which is transient.
### 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
Add Whisper Conversion and E2E into Big Models pipeline
### 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: Your Name <your@email.com>
Co-authored-by: kunal-vaishnavi <115581922+kunal-vaishnavi@users.noreply.github.com>
### Description
1. check GPU status in docker
2. use stages to make test stage can leverage existing building
artifacts
### Motivation and Context
To investigate the root cause of the random exception
`CUDA failure 100: no CUDA-capable device is detected`
### Description
<!-- Describe your changes. -->
build.py sets a few parallelization parameters when building. Using
msbuild directly lacks those.
7a5860e490/tools/ci_build/build.py (L1665-L1669)
Changed to use build.py. If there's a concern with that we _could_ set
the parameters in the yaml, but that will be uglier due to duplicating
logic in multiple places.
### 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
Updates the default QNN SDK version to 2.19.2.240210.
### Motivation and Context
Build and test the latest version of QNN SDK in our pipelines.
### Description
Some test thresholds that previously worked in T4 GPU does not work
anymore. The reason is current pipeline uses A10, and TF32 is enabled by
default.
Disable TF32 in Linux GPU CI Pipeline in testing to avoid such random
test failure.
### Motivation and Context
Linux Test has random failure at tests:
ProviderOptionsTest > testCUDAOptions() FAILED
org.opentest4j.AssertionFailedError: array contents differ at index
[446], expected: <0.0419757> but was: <0.041948937>
at
app//org.junit.jupiter.api.AssertionFailureBuilder.build(AssertionFailureBuilder.java:151)
at
app//org.junit.jupiter.api.AssertionFailureBuilder.buildAndThrow(AssertionFailureBuilder.java:132)
at
app//org.junit.jupiter.api.AssertArrayEquals.failArraysNotEqual(AssertArrayEquals.java:440)
at
app//org.junit.jupiter.api.AssertArrayEquals.assertArrayEquals(AssertArrayEquals.java:290)
at
app//org.junit.jupiter.api.AssertArrayEquals.assertArrayEquals(AssertArrayEquals.java:123)
at
app//org.junit.jupiter.api.AssertArrayEquals.assertArrayEquals(AssertArrayEquals.java:119)
at
app//org.junit.jupiter.api.Assertions.assertArrayEquals(Assertions.java:1360)
at
app//ai.onnxruntime.providers.ProviderOptionsTest.runProvider(ProviderOptionsTest.java:99)
at
app//ai.onnxruntime.providers.ProviderOptionsTest.testCUDAOptions(ProviderOptionsTest.java:43)
org.opentest4j.AssertionFailedError: array contents differ at index [6],
expected: <0.0225981> but was: <0.022587791>
at
app//org.junit.jupiter.api.AssertionFailureBuilder.build(AssertionFailureBuilder.java:151)
at
app//org.junit.jupiter.api.AssertionFailureBuilder.buildAndThrow(AssertionFailureBuilder.java:132)
at
app//org.junit.jupiter.api.AssertArrayEquals.failArraysNotEqual(AssertArrayEquals.java:440)
at
app//org.junit.jupiter.api.AssertArrayEquals.assertArrayEquals(AssertArrayEquals.java:290)
at
app//org.junit.jupiter.api.AssertArrayEquals.assertArrayEquals(AssertArrayEquals.java:123)
at
app//org.junit.jupiter.api.AssertArrayEquals.assertArrayEquals(AssertArrayEquals.java:119)
at
app//org.junit.jupiter.api.Assertions.assertArrayEquals(Assertions.java:1360)
at app//ai.onnxruntime.InferenceTest.runProvider(InferenceTest.java:676)
at app//ai.onnxruntime.InferenceTest.testCUDA(InferenceTest.java:615)
### Description
<!-- Describe your changes. -->
ROCm CI pipeline issue.
```
Downloading and preparing dataset wikitext/wikitext-2-raw-v1 (download: 4.50 MiB, generated: 12.91 MiB, post-processed: Unknown size, total: 17.41 MiB) to /home/onnxruntimedev/.cache/huggingface/datasets/wikitext/wikitext-2-raw-v1/1.0.0/aa5e094000ec7afeb74c3be92c88313cd6f132d564c7effd961c10fd47c76f20...
main()
File "/stage/huggingface-transformers/examples/pytorch/language-modeling/run_mlm.py", line 242, in main
datasets = load_dataset(data_args.dataset_name, data_args.dataset_config_name, cache_dir=model_args.cache_dir)
File "/opt/miniconda/envs/rocm-ci/lib/python3.9/site-packages/datasets/load.py", line 856, in load_dataset
builder_instance.download_and_prepare(
File "/opt/miniconda/envs/rocm-ci/lib/python3.9/site-packages/datasets/builder.py", line 583, in download_and_prepare
self._download_and_prepare(
File "/opt/miniconda/envs/rocm-ci/lib/python3.9/site-packages/datasets/builder.py", line 639, in _download_and_prepare
split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
File "/home/onnxruntimedev/.cache/huggingface/modules/datasets_modules/datasets/wikitext/aa5e094000ec7afeb74c3be92c88313cd6f132d564c7effd961c10fd47c76f20/wikitext.py", line 138, in _split_generators
data_file = dl_manager.download_and_extract(self.config.data_url)
File "/opt/miniconda/envs/rocm-ci/lib/python3.9/site-packages/datasets/utils/download_manager.py", line 289, in download_and_extract
return self.extract(self.download(url_or_urls))
File "/opt/miniconda/envs/rocm-ci/lib/python3.9/site-packages/datasets/utils/download_manager.py", line 197, in download
downloaded_path_or_paths = map_nested(
File "/opt/miniconda/envs/rocm-ci/lib/python3.9/site-packages/datasets/utils/py_utils.py", line 195, in map_nested
return function(data_struct)
File "/opt/miniconda/envs/rocm-ci/lib/python3.9/site-packages/datasets/utils/download_manager.py", line 220, in _download
return cached_path(url_or_filename, download_config=download_config)
File "/opt/miniconda/envs/rocm-ci/lib/python3.9/site-packages/datasets/utils/file_utils.py", line 281, in cached_path
output_path = get_from_cache(
File "/opt/miniconda/envs/rocm-ci/lib/python3.9/site-packages/datasets/utils/file_utils.py", line 634, in get_from_cache
raise ConnectionError("Couldn't reach {}".format(url))
ConnectionError: Couldn't reach https://s3.amazonaws.com/research.metamind.io/wikitext/wikitext-2-raw-v1.zip
```
### 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. -->
Update the `datasets` pipeline to latest version `2.17.0`.
### Description
<!-- Describe your changes. -->
This PR upgrades ORTModule's default opset from 15 to 17. Opset 17 is
the final opset supported by torchscript exporter
(https://github.com/pytorch/pytorch/pull/107829)
### 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. -->
Engineering excellence contribution for ORT Training DRI.
---------
Co-authored-by: Prathik Rao <prathikrao@microsoft.com@orttrainingdev8.d32nl1ml4oruzj4qz3bqlggovf.px.internal.cloudapp.net>
### Description
Adds a job to the python packaging pipeline that builds x64 python
wheels for QNN EP.
### Motivation and Context
Necessary to create a cached QNN model on Windows x64, which is done by
creating a properly configured onnxruntime session with QNN EP.
### Description
This pull request includes a small change to the
`Dockerfile.manylinux2_28_cuda` file in the
`tools/ci_build/github/linux/docker` directory. The change corrects the
`PREPEND_PATH` argument from `/usr/local/cuda/binet` to
`/usr/local/cuda/bin`, ensuring the correct path to CUDA binaries is
set.
This pull request includes modifications to the `c-api-cpu.yml` Azure
Pipelines configuration file. The changes mainly revolve around the
Node.js packaging stage and the handling of Node.js artifacts. The most
significant changes include renaming the Node.js packaging stage, adding
a new dependency to the stage, changing artifact names, adding a new
script to list Node.js artifacts, and updating the source folder for
copying NuGet binaries.
Changes in Node.js packaging:
*
[`tools/ci_build/github/azure-pipelines/templates/c-api-cpu.yml`](diffhunk://#diff-00815920cc190d10fdebceac0c3a4b8a59e408684ae38177dfe7f96cae276c59L503-R508):
Renamed the Node.js packaging stage from `Nodejs_Packaging_CPU` to
`Nodejs_Packaging` and added `Windows_CI_GPU_DML_Dev` as a new
dependency to the stage.
Changes in handling of Node.js artifacts:
*
[`tools/ci_build/github/azure-pipelines/templates/c-api-cpu.yml`](diffhunk://#diff-00815920cc190d10fdebceac0c3a4b8a59e408684ae38177dfe7f96cae276c59L568-R569):
Changed the artifact name from `drop-onnxruntime-nodejs-win-x64` to
`drop-onnxruntime-nodejs-win-x64-dml` in the task to download pipeline
artifacts for Windows x64.
*
[`tools/ci_build/github/azure-pipelines/templates/c-api-cpu.yml`](diffhunk://#diff-00815920cc190d10fdebceac0c3a4b8a59e408684ae38177dfe7f96cae276c59R595-R598):
Added a new script to list Node.js artifacts from the directory
`$(Build.BinariesDirectory)/nodejs-artifacts/win32/x64/`.
*
[`tools/ci_build/github/azure-pipelines/templates/c-api-cpu.yml`](diffhunk://#diff-00815920cc190d10fdebceac0c3a4b8a59e408684ae38177dfe7f96cae276c59L635-R640):
Updated the source folder from
`$(Build.BinariesDirectory)\RelWithDebInfo\RelWithDebInfo\nuget-artifacts\onnxruntime-win-x64\lib`
to `$(Build.BinariesDirectory)\nodejs-artifacts\win32\x64` in the task
to copy NuGet binaries to the directory
`$(Build.SourcesDirectory)\js\node\bin\napi-v3\win32\x64`.
---------
Co-authored-by: Yulong Wang <7679871+fs-eire@users.noreply.github.com>
### Description
1. make parity_check use local model to avoid using hf token
2. del the model didn't work because it tried to del the object define
out of the function scope.
So it caused out of memory in A10.
3. In fact, 16G GPU memory (one T4) is enough. But the conversion
process always be killed in T4 and it works on A10/24G.
Standard_NC4as_T4_v3 has 28G CPU memory
Standard_NV36ads_A10_v5 has 440G memory.
It looks that the model conversion needs very huge memory.
### Motivation and Context
Last time, I came across some issues in convert_to_onnx.py so I use the
onnx model in https://github.com/microsoft/Llama-2-Onnx for testing.
Now, these issues could be fixed. So I use onnx model generated by this
repo and the CI can cover the model conversion.
Fix pytest version to 7.4.4, higher version will cause error
`from onnxruntime.capi import onnxruntime_validation
ModuleNotFoundError: No module named 'onnxruntime.capi'`
### Description
<!-- Describe your changes. -->
Setup usage of coremltools via dependencies instead of copying files.
Pull in some changes from
https://github.com/microsoft/onnxruntime/pull/19347 in preparation for
supporting ML Program and enabling building the ML Model on all
platforms to make development and testing of CoreML EP code easier.
- Update to coremltools 7.1
- Add patch for changes required for cross platform build of ML Program
related code
- Generate coreml proto files on all platforms
- mainly to test these changes work everywhere, as the proto files will
be used on all platforms when #19347 is checked in
- rename onnxruntime_coreml_proto target to coreml_proto as it contains
purely coreml protobuf code with no ORT related chagnes
### 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 setup.
### Description
1. save the model to pipeline cache
2. lower the similarly bar to 97
3. publish the generated image that we can check it once the test fails
### Motivation and Context
Reduce model downloads
### Description
<!-- Describe your changes. -->
Updates to only include ios archs framework in artifacts included in
Nuget Package.
### 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. -->
Related issue:
https://github.com/microsoft/onnxruntime/issues/19295#issuecomment-1914143256
---------
Co-authored-by: rachguo <rachguo@rachguos-Mini.attlocal.net>
Co-authored-by: Edward Chen <18449977+edgchen1@users.noreply.github.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 visual parity test based on openai clip model
2. Add trigger rules
### Motivation and Context
1. check generated image is expected
2. reduce unnecessary triggers
### Description
Fix two issues:
(1) We can only use single quote inside `bash -c "..."`. Current
pipeline job stopped at `python3 demo_txt2img.py astronaut` and skip the
following commands. In this change, we remove the remaining commands to
get same effect (otherwise, the pipeline runtime might be 2 hours
instead of 15 minutes).
(2) Fix a typo of Stable.
### Description
Update abseil to a release tag and register neural_speed to CG.
### Motivation and Context
Now we are using a non-relesed version of abseil. Using a tag is better.
### Description
1. Update Linux GPU machine from T4 to A10, sm=8.6
2. update the tolerance
### Motivation and Context
1. Free more T4 and test with higher compute capability.
2. ORT enables TF32 in GEMM for A10/100. TF32 will cause precsion loss
and fail this test
```
2024-01-19T13:27:18.8302842Z [ RUN ] ModelTests/ModelTest.Run/cuda__models_zoo_opset12_SSD_ssd12
2024-01-19T13:27:25.8438153Z /onnxruntime_src/onnxruntime/test/providers/cpu/model_tests.cc:347: Failure
2024-01-19T13:27:25.8438641Z Expected equality of these values:
2024-01-19T13:27:25.8438841Z COMPARE_RESULT::SUCCESS
2024-01-19T13:27:25.8439276Z Which is: 4-byte object <00-00 00-00>
2024-01-19T13:27:25.8439464Z ret.first
2024-01-19T13:27:25.8445514Z Which is: 4-byte object <01-00 00-00>
2024-01-19T13:27:25.8445962Z expected 0.145984 (3e157cc1), got 0.975133 (3f79a24b), diff: 0.829149, tol=0.0114598 idx=375. 20 of 388 differ
2024-01-19T13:27:25.8446198Z
2024-01-19T13:27:25.8555736Z [ FAILED ] ModelTests/ModelTest.Run/cuda__models_zoo_opset12_SSD_ssd12, where GetParam() = "cuda_../models/zoo/opset12/SSD/ssd-12.onnx" (7025 ms)
2024-01-19T13:27:25.8556077Z [ RUN ] ModelTests/ModelTest.Run/cuda__models_zoo_opset12_YOLOv312_yolov312
2024-01-19T13:27:29.3174318Z /onnxruntime_src/onnxruntime/test/providers/cpu/model_tests.cc:347: Failure
2024-01-19T13:27:29.3175144Z Expected equality of these values:
2024-01-19T13:27:29.3175389Z COMPARE_RESULT::SUCCESS
2024-01-19T13:27:29.3175812Z Which is: 4-byte object <00-00 00-00>
2024-01-19T13:27:29.3176080Z ret.first
2024-01-19T13:27:29.3176322Z Which is: 4-byte object <01-00 00-00>
2024-01-19T13:27:29.3178431Z expected 4.34958 (408b2fb8), got 4.51324 (40906c80), diff: 0.16367, tol=0.0534958 idx=9929. 22 of 42588 differ
```
3. some other test like SSD throw other exception, so skip them
'''
2024-01-22T09:07:40.8446910Z [ RUN ]
ModelTests/ModelTest.Run/cuda__models_zoo_opset12_SSD_ssd12
2024-01-22T09:07:51.5587571Z
/onnxruntime_src/onnxruntime/test/providers/cpu/model_tests.cc:358:
Failure
2024-01-22T09:07:51.5588512Z Expected equality of these values:
2024-01-22T09:07:51.5588870Z COMPARE_RESULT::SUCCESS
2024-01-22T09:07:51.5589467Z Which is: 4-byte object <00-00 00-00>
2024-01-22T09:07:51.5589953Z ret.first
2024-01-22T09:07:51.5590462Z Which is: 4-byte object <01-00 00-00>
2024-01-22T09:07:51.5590841Z expected 1, got 63
'''
### Description
Adds a job to create a nightly python package for ORT/QNN on Windows
ARM64.
Must build onnxruntime-qnn with python 3.11 and numpy 1.25.
**Note: pipeline run may take up to 3 hrs**
### Motivation and Context
Make it possible to get a nightly python package with the latest updates
to QNN EP.
Issue #19161
### 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
<!-- 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
Linux_GPU_x64 job in the pipeline has been canceled due to timeout since
0112.
### Description
This way, we will not need to update the windows images constantly and
allow more flexibility to choose the cuda version in the future.
### Description
Disable ccache for all the jobs in in Windows CPU CI pipeline.
Before disabling it, the build has a warning that:
"MSIL .netmodule or module compiled with /GL found; restarting link with
/LTCG; add /LTCG to the link command line to improve linker performance"
After disabling it, the warning is gone and the build doesn't use /GL or
/LTCG.
Cache itself should not cause this difference.
### Motivation and Context
### 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. -->
