### 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. -->
both arm64ec and x64 packages are needed.
x64 is needed for offline context binary generation
and arm64ec is needed for interop with python packages that don't have
prebuilt arm64 packages and only have x64.
### Description
- Update pipelines to use QNN SDK 2.25 by default
- Update ifdef condition to apply workaround for QNN LayerNorm
validation bug to QNN SDK 2.25 (as well as 2.24)
### Motivation and Context
Use the latest QNN SDK
### 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
- 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
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
- 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>
### 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
- 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
- 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
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
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
1. Add two build jobs for enabling Address Sanitizer in CI. One for
Windows CPU, One for Linux CPU.
2. Set default compiler flags/linker flags in build.py for normal
Windows/Linux/MacOS build. This can help control compiler flags in a
more centralized way.
3. All Windows binaries in our official packages will be built with
"/PROFILE" flag. Symbols of onnxruntime.dll can be found at [Microsoft
public symbol
server](https://learn.microsoft.com/en-us/windows-hardware/drivers/debugger/microsoft-public-symbols).
Limitations:
1. On Linux Address Sanitizer ignores RPATH settings in ELF binaries.
Therefore once Address Sanitizer is enabled, before running tests we
need to manually set LD_LIBRARY_PATH properly otherwise
libonnxruntime.so may not be able to find custom ops and shared EPs.
4. On Linux we also need to set LD_PRELOAD before running some tests(if
the main executable, like python, is not built with address sanitizer.
On Windows we do not need to.
5. On Windows before running python tests we should manually copy
address sanitizer DLL to the onnxruntime/capi directory, because python
3.8 and above has enabled "Safe DLL Search Mode" that wouldn't use the
information provided by PATH env.
6. On Linux Address Sanitizer found a lot of memory leaks from our
python binding code. Therefore right now we cannot enable Address
Sanitizer when building ONNX Runtime with python binding.
7. Address Sanitizer itself uses a lot of memory address space and
delays memory deallocations, which is easy to cause OOM issues in 32-bit
applications. We cannot run all the tests in onnxruntime_test_all in
32-bit mode with Address Sanitizer due to this reason. However, we still
can run individual tests in such a way. We just cannot run all of them
in one process.
### Motivation and Context
To catch memory issues.
### Description
1. Fix python packaging test pipeline. There was an error in
tools/ci_build/github/linux/run_python_tests.sh that it installed a
released version of onnxruntime python package from pypi.org to run the
test. Supposedly it should pick one from the current build.
2. Refactor the pipeline to allow choosing cmake build type from the web
UI when manually trigger a build. Now this feature is for Linux only.
Because I don't want to change too much when we are about to cut a
release branch. After that I will expand it to all platforms. This
feature is useful for debugging pipeline issues, also, we may consider
having a nightly pipeline to run all tests in Debug mode which may catch
extra bugs because in debug mode we can enforce range check.
Test run:
https://aiinfra.visualstudio.com/Lotus/_build/results?buildId=342674&view=results
### Motivation and Context
Currently the pipeline has a crash error.
AB#18580
### Description
1. Avoid taking dependency on dl.fedoraproject.org
The website is not very stable. Our build pipelines often fail to fetch
packages from there.
2. Update manylinux to the latest version
Implement CloudEP for hybrid inferencing.
The PR introduces zero new API, customers could configure session and
run options to do inferencing with Azure [triton
endpoint.](https://learn.microsoft.com/en-us/azure/machine-learning/how-to-deploy-with-triton?tabs=azure-cli%2Cendpoint)
Sample configuration in python be like:
```
sess_opt.add_session_config_entry('cloud.endpoint_type', 'triton');
sess_opt.add_session_config_entry('cloud.uri', 'https://cloud.com');
sess_opt.add_session_config_entry('cloud.model_name', 'detection2');
sess_opt.add_session_config_entry('cloud.model_version', '7'); // optional, default 1
sess_opt.add_session_config_entry('cloud.verbose', '1'); // optional, default '0', meaning no verbose
...
run_opt.add_run_config_entry('use_cloud', '1') # 0 for local inferencing, 1 for cloud endpoint.
run_opt.add_run_config_entry('cloud.auth_key', '...')
...
sess.run(None, {'input':input_}, run_opt)
```
Co-authored-by: Randy Shuai <rashuai@microsoft.com>
1. Update CUDA version from 11.4 to 11.6.
2. Update Manylinux version
3. Upgrade GCC version from 10 to 11 for most x86_64 pipelines. CentOS 7 ARM64 doesn't have GCC 11 yet.
4. Refactor python packaging pipeline:
a. Split Linux GPU build job to two parts, build and test, so that the
build part doesn't need to use a GPU machine
b. Make the Linux GPU build job and Linux CPU build job more similar: share the same bash script and yaml file.
5. Temporarily disable Attention_Mask1D_Fp16_B2_FusedNoPadding because it is causing one of our packaging pipeline to fail. I have created an ADO task for this.
1. Delete the build scripts that were copied from manylinux project. Use "git checkout" instead.
2. Update manylinux version to get python 3.11. Related issue: Python 3.11 support #12343
3. Change the cuda version of linux gpu build job of nuget packaging pipeline from cuda 11.4 to cuda 11.6 to match the TRT job within the same pipeline.. (A lot other places need be updated as well, but I'd prefer to put them in another PR)
4. Make dockerfile names static. For example, replace tools/ci_build/github/linux/docker/$(DockerFile) to tools/ci_build/github/linux/docker/Dockerfile.manylinux2014_cpu . The former one relies on a runtime variable $(DockerFile), Template Parameters are expanded early in processing a pipeline run when most variables are not available. It like C++ macros vs variables.
1. Update manylinux build scripts. This will add [PEP600](https://www.python.org/dev/peps/pep-0600/)(manylinux2 tags) support. numpy has adopted this new feature, we should do the same. The old build script files were copied from https://github.com/pypa/manylinux, but they has been deleted and replaced in the upstream repo. The manylinux repo doesn't have a manylinux2014 branch anymore. So I'm removing the obsolete code, sync the files with the latest master.
2. Update GPU CUDA version from 11.0 to 11.1(after a discussion with PMs).
3. Delete tools/ci_build/github/linux/docker/Dockerfile.manylinux2014_cuda10_2. (Merged the content to tools/ci_build/github/linux/docker/Dockerfile.manylinux2014_cuda11)
4. Modernize the cmake code of how to locate python devel files. It was suggested in https://github.com/onnx/onnx/pull/1631 .
5. Remove `onnxruntime_MSVC_STATIC_RUNTIME` and `onnxruntime_GCC_STATIC_CPP_RUNTIME` build options. Now cmake has builtin support for it. Starting from cmake 3.15, we can use `CMAKE_MSVC_RUNTIME_LIBRARY` cmake variable to choose which MSVC runtime library we want to use.
6. Update Ubuntu docker images that used in our CI build from Ubuntu 18.04 to Ubuntu 20.04.
7. Update GCC version in CUDA 11.1 pipelines from 8.x to 9.3.1
8. Split Linux GPU CI pipeline to two jobs: build the code on a CPU machine then run the tests on another GPU machines. In the past we didn't test our python packages. We only tested the pre-packed files. So we didn't catch the rpath issue in CI build.
9. Add a CentOS machine pool and test our Linux GPU build on real CentOS machines.
10. Rework ARM64 Linux GPU python packaging pipeline. Previously it uses cross-compiling therefore we must static link to C Runtime. But now have pluggable EP API and it doesn't support static link. So I changed to use qemu emulation instead. Now the build is 10x slower than before. But it is more extensible.
