### Description
- Makes QNN EP a shared library **by default** when building with
`--use_qnn` or `--use_qnn shared_lib`. Generates the following build
artifacts:
- **Windows**: `onnxruntime_providers_qnn.dll` and
`onnxruntime_providers_shared.dll`
- **Linux**: `libonnxruntime_providers_qnn.so` and
`libonnxruntime_providers_shared.so`
- **Android**: Not supported. Must build QNN EP as a static library.
- Allows QNN EP to still be built as a static library with `--use_qnn
static_lib`. This is primarily for the Android QNN AAR package.
- Unit tests run for both the static and shared QNN EP builds.
### Detailed changes
- Updates Java bindings to support both shared and static QNN EP builds.
- Provider bridge API:
- Adds logging sink ETW to the provider bridge. Allows EPs to register
ETW callbacks for ORT logging.
- Adds a variety of methods for onnxruntime objects that are needed by
QNN EP.
- QNN EP:
- Adds `ort_api.h` and `ort_api.cc` that encapsulates the API provided
by ORT in a manner that allows the EP to be built as either a shared or
static library.
- Adds custom function to transpose weights for Conv and Gemm (instead
of adding util to provider bridge API).
- Adds custom function to quantize data for LeakyRelu (instead of adding
util to provider bridge API).
- Adds custom ETW tracing for QNN profiling events:
- shared library: defines its own TraceLogging provider handle
- static library: uses ORT's TraceLogging provider handle and existing
telemetry provider.
- ORT-QNN Packages:
- **Python**: Pipelines build QNN EP as a shared library by default.
User can build a local python wheel with QNN EP as a static library by
passing `--use_qnn static_lib`.
- **NuGet**: Pipelines build QNN EP as a shared library by default.
`build.py` currently enforces QNN EP to be built as a shared library.
Can add support for building a QNN NuGet package with static later if
deemed necessary.
- **Android**: Pipelines build QNN EP as a **static library**.
`build.py` enforces QNN EP to be built as a static library. Packaging
multiple shared libraries into an Android AAR package is not currently
supported due to the added need to also distribute a shared libcpp.so
library.
### 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. -->
* Remove deprecated gpu arch to control nuget/python package size
(latest TRT supports sm75 Turing and newer arch)
* Add 90 to support blackwell series in next release (86;89 not
considered as adding them will rapidly increase package size)
| arch_range | Python-cuda12 | Nuget-cuda12 |
| -------------- |
------------------------------------------------------------ |
---------------------------------- |
| 60;61;70;75;80 | Linux: 279MB Win: 267MB | Linux: 247MB Win: 235MB |
| 75;80 | Linux: 174MB Win: 162MB | Linux: 168MB Win: 156MB |
| **75;80;90** | **Linux: 299MB Win: 277MB** | **Linux: 294MB Win:
271MB** |
| 75;80;86;89 | [Linux: MB Win:
390MB](https://aiinfra.visualstudio.com/Lotus/_build/results?buildId=647457&view=results)
| Linux: 416MB Win: 383MB |
| 75;80;86;89;90 | [Linux: MB Win:
505MB](https://aiinfra.visualstudio.com/Lotus/_build/results?buildId=646536&view=results)
| Linux: 541MB Win: 498MB |
### 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. -->
Callout: While adding sm90 support, the build of cuda11.8+cudnn8 will be
dropped in the coming ORT release,
as the build has issue with blackwell (mentioned in comments) and demand
on cuda 11 is minor, according to internal ort-cuda11 repo.
The new images contain the following updates:
1. Added Git, Ninja and VCPKG to all docker images
2. Updated CPU containers' GCC version from 12 to 14
3. Pinned CUDA 12 images' CUDNN version to 9.5(The latest one is 9.6)
4. Addressed container supply chain warnings by building CUDA 12 images
from scratch(avoid using Nvidia's prebuilt images)
5. Updated manylinux commit id to
75aeda9d18eafb323b00620537c8b4097d4bef48
Also, this PR updated some source code to make the CPU EP's source code
compatible with GCC 14.
### Description
Making ::p optional in the Linux python CUDA package pipeline
### Motivation and Context
Linux stage from Python-CUDA-Packaging-Pipeline has failed since merge
of #22773
### Description
1. Add XNNPack build on Linux ARM64
2. Build only one python wheel for PR request.
[AB#49763](https://aiinfra.visualstudio.com/6a833879-cd9b-44a4-a9de-adc2d818f13c/_workitems/edit/49763)
### Motivation and Context
Why I add xnnpack build on Linux ARM64 rather than Windows ARM64.
Becuase KleidiAI doesn't support Windows
```
IF(XNNPACK_TARGET_PROCESSOR STREQUAL "arm64" AND XNNPACK_ENABLE_ARM_I8MM AND NOT CMAKE_C_COMPILER_ID STREQUAL "MSVC")
IF (XNNPACK_ENABLE_KLEIDIAI)
MESSAGE(STATUS "Enabling KleidiAI for Arm64")
ENDIF()
ELSE()
SET(XNNPACK_ENABLE_KLEIDIAI OFF)
ENDIF()
```
---------
1. Add python 3.13 to our python packaging pipelines
2. Because numpy 2.0.0 doesn't support thread free python, this PR also
upgrades numpy to the latest
3. Delete some unused files.
### Description
Similar to #20786 . The last PR was able to update all pipelines and all
docker files. This is a follow-up to that PR.
### Motivation and Context
1. To extract the common part as a reusable build infra among different
ONNX Runtime projects.
2. Avoid hitting docker hub's limit: 429 Too Many Requests - Server
message: toomanyrequests: You have reached your pull rate limit. You may
increase the limit by authenticating and upgrading:
https://www.docker.com/increase-rate-limit
### Description
In PR #19073 I mistunderstood the value of "--parallel". Instead of
testing if args.parallel is None or not , I should test the returned
value of number_of_parallel_jobs function.
If build.py was invoked without --parallel, then args.parallel equals to
1. Because it is the default value. Then we should not add "/MP".
However, the current code adds it. Because if `args.paralllel` is
evaluated to `if 1` , which is True.
If build.py was invoked with --parallel with additional numbers, then
args.parallel equals to 0. Because it is unspecified. Then we should add
"/MP". However, the current code does not add it. Because `if
args.paralllel` is evaluated to `if 0` , which is False.
This also adds a new build flag: use_binskim_compliant_compile_flags, which is intended to be only used in ONNX Runtime team's build pipelines for compliance reasons.
### Motivation and Context
### Description
1. Add two build jobs for enabling Address Sanitizer in CI. One for
Windows CPU, One for Linux CPU.
2. Set default compiler flags/linker flags in build.py for normal
Windows/Linux/MacOS build. This can help control compiler flags in a
more centralized way.
3. All Windows binaries in our official packages will be built with
"/PROFILE" flag. Symbols of onnxruntime.dll can be found at [Microsoft
public symbol
server](https://learn.microsoft.com/en-us/windows-hardware/drivers/debugger/microsoft-public-symbols).
Limitations:
1. On Linux Address Sanitizer ignores RPATH settings in ELF binaries.
Therefore once Address Sanitizer is enabled, before running tests we
need to manually set LD_LIBRARY_PATH properly otherwise
libonnxruntime.so may not be able to find custom ops and shared EPs.
4. On Linux we also need to set LD_PRELOAD before running some tests(if
the main executable, like python, is not built with address sanitizer.
On Windows we do not need to.
5. On Windows before running python tests we should manually copy
address sanitizer DLL to the onnxruntime/capi directory, because python
3.8 and above has enabled "Safe DLL Search Mode" that wouldn't use the
information provided by PATH env.
6. On Linux Address Sanitizer found a lot of memory leaks from our
python binding code. Therefore right now we cannot enable Address
Sanitizer when building ONNX Runtime with python binding.
7. Address Sanitizer itself uses a lot of memory address space and
delays memory deallocations, which is easy to cause OOM issues in 32-bit
applications. We cannot run all the tests in onnxruntime_test_all in
32-bit mode with Address Sanitizer due to this reason. However, we still
can run individual tests in such a way. We just cannot run all of them
in one process.
### Motivation and Context
To catch memory issues.
### Description
Adding python3.12 support to ORT
### 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. -->
2023-11-20 09:48:28 -08:00
Renamed from tools/ci_build/github/linux/build_linux_arm64_python_package.sh (Browse further)
