### Description
Previously we wanted to add DirectML EP to existing onnxruntime Windows
CUDA packages. After careful consideration, we will postpone the change.
This PR reverts some pipeline changes previously made by @mszhanyi and
@jchen351 .
### Description
OVEP development changes for ORT 1.21 Release
### Motivation and Context
- Has Critical Bug Fixes
- Improved Performance optimizations for both memory & inference latency
(https://github.com/intel/onnxruntime/pull/513)
- Enabled Model Compilation using NPUW
(https://github.com/intel/onnxruntime/pull/508)
- Fixed support for EPContext embed mode 0 for lower memory utilization
- Updated NuGet package name as `Intel.ML.OnnxRuntime.OpenVino`
- Fixed QDQ Stripping logic on NPU
### Description
The default thread count methodology by onnxruntime did not account for
new upcoming Intel microarchitectures leading to a suboptimal thread
count. Optimizing the thread count for new Intel microarchitectures
reveal gains on the majority of models across datatypes and shows gains
up to ~1.5x speedup.
### Motivation and Context
Applications should run on Intel with the most performant thread
configuration for the majority of models. With new microarchitectures,
adjusting the thread count methodology is required to take advantage of
their differences.
<!-- - Why is this change required? What problem does it solve?
- If it fixes an open issue, please link to the issue here. -->
### Description
Upgrade version of Dawn.
Removed dawn.patch, because all patches are included in upstream.
Updated code that affected by API changes (`const char*` ->
`WGPUStringView`)
### 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
This pull request introduces several enhancements and new
functionalities to the `tools/python/util/android/android.py` file,
focusing on improving the management of Android emulators. The most
important changes include adding a timeout parameter to the
`start_emulator` function, adding checks to prevent multiple emulators
from running simultaneously, and introducing new utility functions to
manage emulator processes more effectively.
Enhancements to `start_emulator` function:
* Added a `timeout_minutes` parameter to the `start_emulator` function
to make the startup timeout configurable.
[[1]](diffhunk://#diff-c54db556a9c445989f830c09ab90ce2704e648deaccce9c9e0ee4875ddaa864dL108-R117)
[[2]](diffhunk://#diff-c54db556a9c445989f830c09ab90ce2704e648deaccce9c9e0ee4875ddaa864dL158-R170)
* Added a check to prevent starting a new emulator if one with the same
AVD name is already running.
* Included additional emulator arguments `-verbose` for better control
and debugging.
* Added a final verification step to ensure the emulator has started
successfully.
New utility functions for managing emulator processes:
* Introduced `check_emulator_running_using_avd_name `,
`check_emulator_running_using_process`, and
`check_emulator_running_using_pid` to check if an emulator is running
based on AVD name, process instance, or PID, respectively.
* Added `stop_emulator_by_proc` and `stop_emulator_by_pid` functions to
stop the emulator process using a `subprocess.Popen` instance or PID,
with a configurable timeout.
* Updated the `stop_emulator` function to use the new utility functions
for stopping the emulator process.
These changes enhance the robustness and flexibility of the emulator
management utilities, making it easier to handle different scenarios in
CI environments and development workflows.
### 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: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
Co-authored-by: Scott McKay <skottmckay@gmail.com>
### Description
- Erf
- Round
- Max
- ReduceMax
- ReduceMean
- ReduceSum
- Unsqueeze
- Squeeze
- Softmax
### 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: Scott McKay <skottmckay@gmail.com>
Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
### Description
Fixes regression in post merge pipeline caused by #22612
### Motivation and Context
So far, there isn't the artifactFeeds in Public Project
We need to be able to control/override the exact version of qnn sdk used
for the android build as qnn-runtime (maven package) releases are slower
to QNN SDK releases.
### Description
Add a new stage to build cuda and dml in Windows GPU CI pipeline (PR
checks) to prevent regressions introduced by new cuda tests.
Update all tests in cuda/testcases name prefix to CudaEp for skipping
them easily
### Motivation and Context
1. CudaNhwcEP is added by default when using cuda ep
2. if onnxruntime_ENABLE_CUDA_EP_INTERNAL_TES is enable, the tests in
tests/provider/cuda/testcases is added too.
### To do
add enable_pybind in the new stage.
Now, --enable_pybind will trigger some python test, like
onnxruntime_test_python.py.
It uses the API of get_avaible_providers() .
More discussions are needed to decide how to make it works
### Description
* Install PyTorch for transformers tests. The installation is before
python tests so that it can use torch if needed.
* Update protobuf and numpy versions used in transformers test.
### Motivation and Context
Currently, transformers tests are enabled in the following CI pipelines:
* Linux CPU CI Pipeline (torch for cpu-only)
* Linux GPU CI Pipeline (torch for cuda 12)
* Windows GPU CUDA CI Pipeline (torch for cpu-only right now, note that
we might change it to torch for cuda 12 in the future).
For ROCm CI Pipeline, transformer tests are enabled but skipped since
onnx package is not installed in CI.
Previously, torch was not installed before python tests, so some tests
depending on torch were skipped like
[test_bind_onnx_types_not_supported_by_numpy](f6e1d44829/onnxruntime/test/python/onnxruntime_test_python_iobinding.py (L199))
or [test
user_compute_stream](https://github.com/microsoft/onnxruntime/blob/main/onnxruntime/test/python/onnxruntime_test_python.py#L465-L476).
In this PR, we changed build.py to install torch before running python
tests.
### Description
* Reduce GQA test combinations to save about 35 minutes test time in CI
pipelines.
* Show latency of transformers tests
* Use seed in DMMHA test to avoid random failure.
* For test_flash_attn_rocm.py, test skipping condition from "has cuda
ep" to "not has rocm ep", so that it does not run in cpu build.
* For test_flash_attn_cuda.py, move flash attention and memory efficient
attention tests to different classes, so that we can skip a test suite
instead of checking in each test.
### Motivation and Context
It takes too long to run GQA tests in CI pipelines since there are too
many combinations.
###### Linux GPU CI Pipeline
Before: 5097 passed, 68 skipped, 8 warnings in 1954.64s (0:32:34)
After: 150 passed, 176 skipped, 8 warnings in 530.38s (0:08:50)
Time Saved: **1424** seconds (0:23:44)
###### Windows GPU CUDA CI Pipeline
Before: 1781 passed, 72 skipped, 6 warnings in 605.48s (0:10:05)
After: 116 passed, 118 skipped, 6 warnings in 275.48s (0:04:35)
Time Saved: **330** seconds (0:05:30)
###### Linux CPU CI Pipeline
Before: 5093 passed, 72 skipped, 4 warnings in 467.04s (0:07:47)
- 212.96s transformers/test_gqa_cpu.py::TestGQA::test_gqa_past
- 154.12s transformers/test_gqa_cpu.py::TestGQA::test_gqa_no_past
- 26.45s
transformers/test_gqa_cpu.py::TestGQA::test_gqa_interactive_one_batch
After: 116 passed, 210 skipped, 4 warnings in 93.41s (0:01:33)
- 0.97s transformers/test_gqa_cpu.py::TestGQA::test_gqa_past
- 19.23s transformers/test_gqa_cpu.py::TestGQA::test_gqa_no_past
- 2.41s
transformers/test_gqa_cpu.py::TestGQA::test_gqa_interactive_one_batch
Time Saved: **374** seconds (0:06:14).
### Description
Match new SDPA pattern for huggingface BERT model that exported from
latest transformers package.
Some changes of transformers tests in CI pipeline:
(1) Enable tests for bert, distilbert and roberta models in CI.
(2) Remove out-of-date tests for huggingface models that were marked as
slow and not enabled in CI pipeline.
(3) Upgrade transformers package version to the latest.
### Motivation and Context
Recent huggingface transformers use torch SDPA in bert modeling. The
graph pattern change causes attention fusion not working anymore. Update
the fusion script to match the new pattern.
### Description
<!-- Describe your changes. -->
when updating from cp38 to cp310, there has some issues for bigmodel
pipeine. there are two jobs failed: stable_diffusion and whisper.
1. for stable_diffusion, we are now using
"nvcr.io/nvidia/pytorch:22.11-py3" from nvidia repo. it is for cuda11
and python3.8. and they are not providing python3.10 version for cuda
11. the latest version of this docker image is for cuda12 and
python3.10. To solve this problem, i use a docker image of ubuntu22.04,
and then install all need python package for this job.
2. for whisper. the original docker image is ubuntu20.04 which doesn't
have python3.10, and has to update to ubuntu22.04.
### 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. -->
…ime/java (#22771)"
This reverts commit 632a36a233.
### Description
<!-- Describe your changes. -->
### Motivation and Context
Run E2E tests using Browserstack failed due to this PR.
### Description
<!-- Describe your changes. -->
Extend timeout for always failed job.
### 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
Appends variant name to the Browserstack artifacts that are published so
that we don't run into the error:
"##[error]Artifact browserstack_test_artifacts already exists for build
609095."
[Working pipeline
run](https://aiinfra.visualstudio.com/Lotus/_build/results?buildId=609503&view=results)
### Motivation and Context
- onnxruntime-ios-packaging-pipeline has been failing
### Description
- Updates pipelines to use QNN SDK 2.28.2.241116.
- Re-enable LayerNormalization unit tests that failed with accuracy
errors with the previous QNN SDK (2.28.0).
- Update QNN EP to no longer provide a dummy bias for LayerNorm if the
QNN SDK version is >= 2.28.0.
### Motivation and Context
Use the latest QNN SDK. This version improves inference latency for
certain customer models.
### Description
Now, ENABLE_CUDA_NHWC_OPS is enabled by default.
It adds a new chance to create cuda provider while both cuda/dml are
enabled
### 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
OVEP development changes for ORT 1.21 Release
### Motivation and Context
Has critical bug fixes
Support for concurrency execution of models is enabled
Support for OV 2024.5
Memory optimizations for NPU platform
---------
Co-authored-by: jatinwadhwa921 <jatin.wadhwa@intel.com>
Co-authored-by: Ankit Maheshkar <ankit.maheshkar@intel.com>
Co-authored-by: sfatimar <sahar.fatima@intel.com>
Co-authored-by: saurabhkale17 <saurabh1.kale@intel.com>
Co-authored-by: TejalKhade28 <tejal.khade@intel.com>
Co-authored-by: Javier E. Martinez <javier.e.martinez@intel.com>
### Description
This change is to update the Gradle version within java project to 8.7,
it also upgrades the JAVA to 17. Gradle version from react-native was
also updated to 7.5 to make it compatible with changes from the Java
directory. However, the target java version remains the same. Java
version from these will be upgraded in a separated PR.
This is spited from #22206
### Motivation and Context
This is the first step to upgrade the react native version.
### Description
<!-- Describe your changes. -->
It seems after CI updated to py310, numpy got updated to 2.0 and sympy
1.2 failed to cast float numpy array.
Pointing sympy to 1.13 when py>=3.9 and re-enable unit test
### 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. -->
Error: Linux CPU
CI
### 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
Current linux-ci-pipeline was broken due to missing parameters from
`py-packaging-linux-test-cpu.yml` template
### Motivation and Context
Fix Linux CI pipeline
### Description
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
Fixes command for building Linux python packages by preventing an empty
`-p` command-line option from being passed to a subsequent build script:
1f3b675453/tools/ci_build/github/linux/run_python_dockerbuild.sh (L37)
### Motivation and Context
A recent [PR
](https://github.com/microsoft/onnxruntime/pull/22773)introduced a new
optional command-line option (`-p`) to pass custom python exe paths. We
need to check if the option is empty before forwarding the option to a
separate build script.
### Description
This PR Fix warning - `LegacyKeyValueFormat: "ENV key=value" should be
used instead of legacy "ENV key value" format` from all Dockerfile
### 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. Add XNNPack build on Linux ARM64
2. Build only one python wheel for PR request.
[AB#49763](https://aiinfra.visualstudio.com/6a833879-cd9b-44a4-a9de-adc2d818f13c/_workitems/edit/49763)
### Motivation and Context
Why I add xnnpack build on Linux ARM64 rather than Windows ARM64.
Becuase KleidiAI doesn't support Windows
```
IF(XNNPACK_TARGET_PROCESSOR STREQUAL "arm64" AND XNNPACK_ENABLE_ARM_I8MM AND NOT CMAKE_C_COMPILER_ID STREQUAL "MSVC")
IF (XNNPACK_ENABLE_KLEIDIAI)
MESSAGE(STATUS "Enabling KleidiAI for Arm64")
ENDIF()
ELSE()
SET(XNNPACK_ENABLE_KLEIDIAI OFF)
ENDIF()
```
---------
### Description
This PR will set default python to 3.10 except
tools/ci_build/github/azure-pipelines/bigmodels-ci-pipeline.yml. This is
needed because we are no longer using python 3.8
This PR excludes changes for Big Models CI, because it will require
additional changes. Which will be track in
USER STORY 52729
### 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 CI with TRT 10.6
* Update oss parser to [10.6-GA-ORT-DDS
](https://github.com/onnx/onnx-tensorrt/tree/10.6-GA-ORT-DDS) and update
dependency version
* Update Py-cuda11 CI to use trt10.6
### 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. -->
(There will be 3rd PR to further reduce trt_version hardcoding)
### Description
- Changes running the E2E iOS tests from running in App Center to
running in BrowserStack
- Steps for running locally can be found in the OneNote
### Motivation and Context
- Follow-up of #22117
- App Center (the previous platform for running E2E mobile tests) is
getting deprecated in 2025
### Misc info
Additional build steps were required to get the necessary testing
artifacts for BrowserStack. App Center consumed an entire folder, while
BrowserStack requests the following:
1. a ZIP file of all the tests
2. an IPA file of the test app
#### Flow
Here is a rough outline of what is happening in the pipeline:
1. The build_and_assemble_apple_pods.py script builds the relevant
frameworks (currently, this means packages for iOS and Mac)
4. The test_apple_packages.py script installs the necessary cocoapods
for later steps
5. XCode task to build for testing builds the iOS target for the test
app
6. Now that the test app and the tests have been built, we can zip them,
creating the tests .zip file
7. To create the IPA file, we need to create a .plist XML file which is
generated by the generate_plist.py script.
- Attempts to use the Xcode@5 task to automatically generate the plist
file failed.
- Also, building for testing generates some plist files -- these cannot
be used to export an IPA file.
8. We run the Xcode task to build an .xcarchive file, which is required
for creating an IPA file.
9. We use xcodebuild in a script step to build an IPA file with the
xcarchive and plist files from the last two steps.
10. Finally, we can run the tests using the BrowserStack script.
---------
Co-authored-by: Scott McKay <skottmckay@gmail.com>
Co-authored-by: Edward Chen <18449977+edgchen1@users.noreply.github.com>
### Description
* Build cuda nhwc ops by default.
* Deprecate `--enable_cuda_nhwc_ops` in build.py and add
`--disable_cuda_nhwc_ops` option
Note that it requires cuDNN 9.x. If you build with cuDNN 8, NHWC ops
will be disabled automatically.
### Motivation and Context
In general, NHWC is faster than NCHW for convolution in Nvidia GPUs with
Tensor Cores, and this could improve performance for vision models.
This is the first step to prefer NHWC for CUDA in 1.21 release. Next
step is to do some tests on popular vision models. If it help in most
models and devices, set `prefer_nhwc=1` as default cuda provider option.
### 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
Refactor the cmake code that is related to delay loading. Provide a
cmake option to control if delay loading should be enabled or not.
Disabling the option when python is enabled, due to a known issue.
### Motivation and Context
ONNX Runtime's python package depends on DirectML.dll, but supposedly
the DLL should be delay loaded.
This PR only refactor the code. It doesn't change the behavior.
### Description
<!-- Describe your changes. -->
Set SDL's git submodule to false.
### 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. -->
* Previous job's SDL logs:It has 'git submodule sync' command, which
means 'git submodule sync synchronizes all submodules while git
submodule sync'
* After set sdl git submodules to false, the logs don't have 'git
submodule sync' command.
- cast
- argmax
- gelu
- cast
- LayerNorm
- GroupNorm
- InstanceNorm
### 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: Edward Chen <18449977+edgchen1@users.noreply.github.com>
Co-authored-by: Scott McKay <skottmckay@gmail.com>
Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
### Description
Now, we need to build cuda and dml in one package.
But CUDA EP and DML EP can't run in one process.
It will throw the exception of `the GPU device instance has been
suspended`
So the issue is CUDA EP and DML EP coexist in compile time but can't
exist in run time.
This PR is to split cuda ep test and dml ep test in all unit tests.
The solution is to use 2 environment variable, NO_CUDA_TEST and
NO_DML_TEST, in CI.
For example, if NO_CUDA_TEST is set, the DefaultCudaExecutionProvider
will be nullptr, and the test will not run with CUDA EP.
In debugging, the CUDAExecutionProvider will not be called.
I think, as long as cuda functions, like cudaSetDevice, are not called,
DML EP tests can pass.
Disabled java test of testDIrectML because it doesn't work now even
without CUDA EP.
### Description
[DML EP] Update DML to 1.15.4
### 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 want the customer to use the latest DirectML.
### Description
<!-- Describe your changes. -->
* Leverage template `common-variables.yml` and reduce usage of hardcoded
trt_version
8391b24447/tools/ci_build/github/azure-pipelines/templates/common-variables.yml (L2-L7)
* Among all CI yamls, this PR reduces usage of hardcoding trt_version
from 40 to 6, by importing trt_version from `common-variables.yml`
* Apply TRT 10.5 and re-enable control flow op test
### 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. -->
- Reduce usage of hardcoding trt_version among all CI ymls
### Next refactor PR
will work on reducing usage of hardcoding trt_version among
`.dockerfile`, `.bat` and remaining 2 yml files
(download_win_gpu_library.yml & set-winenv.yml, which are step-template
yaml that can't import variables)
### Description
1. Add pipauth to more ADO pipeline. (We will use a private ADO feed to
fetch python packages in these pipeline, to improve security)
2. Enforce codeSignValidation(CSV).
### Motivation and Context
Fulfill some internal compliance requirements.
### Description
<!-- Describe your changes. -->
hipblasLt library is released with rocm6.x, and current onnxruntime's
code need some modifications to match new hipblasLt API.
### 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 python from 3.9 to 3.10 in ROCm and MigraphX docker files and CI
pipelines. Upgrade ROCm version to 6.2.3 in most places except ROCm CI,
see comment below.
Some improvements/upgrades on ROCm/Migraphx docker or pipeline:
* rocm 6.0/6.1.3 => 6.2.3
* python 3.9 => 3.10
* Ubuntu 20.04 => 22.04
* Also upgrade ml_dtypes, numpy and scipy packages.
* Fix message "ROCm version from ..." with correct file path in
CMakeList.txt
* Exclude some NHWC tests since ROCm EP lacks support for NHWC
convolution.
#### ROCm CI Pipeline:
ROCm 6.1.3 is kept in the pipeline for now.
- Failed after upgrading to ROCm 6.2.3: `HIPBLAS_STATUS_INVALID_VALUE ;
GPU=0 ; hostname=76123b390aed ;
file=/onnxruntime_src/onnxruntime/core/providers/rocm/rocm_execution_provider.cc
; line=170 ; expr=hipblasSetStream(hipblas_handle_, stream);` . It need
further investigation.
- cupy issues:
(1) It currently supports numpy < 1.27, might not work with numpy 2.x.
So we locked numpy==1.26.4 for now.
(2) cupy support of ROCm 6.2 is still in progress:
https://github.com/cupy/cupy/issues/8606.
Note that miniconda issues: its libstdc++.so.6 and libgcc_s.so.1 might
have conflict with the system ones. So we created links to use the
system ones.
#### MigraphX CI pipeline
MigraphX CI does not use cupy, and we are able to use ROCm 6.2.3 and
numpy 2.x in the pipeline.
#### Other attempts
Other things that I've tried which might help in the future:
Attempt to use a single docker file for both ROCm and Migraphx:
https://github.com/microsoft/onnxruntime/pull/22478
Upgrade to ubuntu 24.04 and python 3.12, and use venv like
[this](27903e7ff1/tools/ci_build/github/linux/docker/rocm-ci-pipeline-env.Dockerfile).
### Motivation and Context
In 1.20 release, ROCm nuget packaging pipeline will use 6.2:
https://github.com/microsoft/onnxruntime/pull/22461.
This upgrades rocm to 6.2.3 in CI pipelines to be consistent.
### Description
<!-- Describe your changes. -->
**Changes applied to maven related signing:**
* Windows sha256 file encoded by utf8(no BOM)
* powershell script task used latest version, previous 5.1 version only
supports utf8 with BOM.
* Windows sha256 file content in format 'sha256value
*filename.extension'.
* Linux sha256 file content in format 'sha256value *filename.extension'.
**More information about powershell encoding:**
Windows powershell encoding reference: [about_Character_Encoding -
PowerShell | Microsoft
Learn](https://learn.microsoft.com/en-us/powershell/module/microsoft.powershell.core/about/about_character_encoding?view=powershell-7.4)
- for version 5.1, it only has 'UTF8 Uses UTF-8 (with BOM).'
- for version v7.1 and higher, it has:
utf8: Encodes in UTF-8 format (no BOM).
utf8BOM: Encodes in UTF-8 format with Byte Order Mark (BOM)
utf8NoBOM: Encodes in UTF-8 format without Byte Order Mark (BOM)
### 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. -->
Should make the binary size report more stable as changes < 4K can occur
when a padding boundary is crossed.
### Motivation and Context
<!-- - Why is this change required? What problem does it solve?
- If it fixes an open issue, please link to the issue here. -->
### Description
<!-- Describe your changes. -->
Add DoEsrp Check for Signature Verification
### 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 ORT Training pipeline to github actions and enable CodeQL scan for the code(including inference code).
We will move all pull request pipelines to Github Actions.
### 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
Update list of CI pipelines to trigger for external PRs.
### Motivation and Context
The pipelines triggered for external PRs are not consistent with
internal PRs.
### Description
(1) Upgrade opencv
(2) Add some comments about onnxruntime-gpu installation
### Motivation and Context
opencv-python was locked to an older version, which has security
vulnerabilities: see https://github.com/microsoft/onnxruntime/pull/22445
for more info
### Description
1. Remove the onnxruntime::OrtMutex class and replace it with
~absl::Mutex~ std::mutex.
2. After this change, most source files will not include <Windows.h>
indirectly.
### Motivation and Context
To reduce the number of deps we have, and address some Github issues
that are related to build ONNX Runtime from source.
In PR #3000 , I added a custom implementation of std::mutex . It was
mainly because at that time std::mutex's default constructor was not
trivial on Windows. If you had such a mutex as a global var, it could
not be initialized at compile time. Then VC++ team fixed this issue.
Therefore we don't need this custom implementation anymore.
This PR also removes nsync. I ran several models tests on Linux. I
didn't see any perf difference.
This PR also reverts PR #21005 , which is no longer needed since conda
has updated its msvc runtime DLL.
This PR unblocks #22173 and resolves#22092 . We have a lot of open
issues with nsync. This PR can resolve all of them.
### Description
Updates the ROCm EP opsets to match the current CUDA EP opsets. Also
enable the test CApiTest.basic_cuda_graph_with_annotation.
Note that some changes are whitespace-only. These changes were made to
improve the comparison of corresponding ROCm and CUDA EP source files
when using a side by side diff tool.
### Motivation and Context
The ROCm EP derives from the CUDA EP. Many source files are shared
between the EPs and "hipified" during the ROCm EP build, however quite a
few files within the ROCm EP are under source control after their
initial hipification. Over time these ROCm EP files get stale relative
to their CUDA EP counterparts. It becomes necessary to re-hipify these
otherwise static files in order to pick up important changes such as
opset differences.
- Allow specification of iOS simulator runtime version to use.
- Pick simulator runtime version (iphonesimulator 16.4) that is supported by the Xcode version (14.3.1) that we use.
- Disable CoreML EP's DepthToSpace op support for CoreML version less than 7, with DCR mode, and FP16 input. It doesn't produce the correct output in this case.
- Some cleanup of iOS test infrastructure.
### Description
Our nightly CPU python package's name is "ort-nightly" instead of
"onnxruntime". It was because of some historical reasons. Tensorflow was
like that.
Now we would prefer to make them the same.
Do this change for all nightly python packages, including CPU,
GPU(CUDA), and maybe others.
### 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
Change the hipify step to remove the -roc option to hipify-perl. This
will prefer hipblas over rocblas. rocblas can still be called directly
such as in TunableOp.
### Motivation and Context
hip interfaces are preferred over roc for porting from cuda to hip.
Calling roc interfaces is meant for ROCm-specific enhancements or
extensions.
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.
- Work around Xcode 16 iOS test build issue: `error: Multiple commands produce '.../PlugIns'`.
- Fix link error in iOS static framework test.
- Update build.py to check for the right kind of build before running iOS tests on the simulator.
- Update Xcode 16 build images to 'macos-15' because that's the only image that will have Xcode 16 soon. See https://github.com/actions/runner-images/issues/10703.
### Description
Add a new pipeline to publish ROCM package to ADO
### 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?definitionId=1615
### Description
* Add digital signature to dll files in jar files.
* Jar file names: onnxruntime-{version}.jar,
onnxruntime_gpu-{version}.jar
### Motivation and Context
#19204
### Description
Aallows alpha, beta and rc version releases to Maven for Android
artifacts.
### Motivation and Context
Helpful to release rc versions or test artifacts to Maven for testing.
For example, a new QNN android package is being released and it will be
nice to test the RC version for dependencies before release
## Future Work
Allow RC version for all Maven artifacts.
### Description
Pre built QNN Android package
### Future Work
1. Setting up CI with Browserstack- onnxruntime_tests and Android test
2. ESRP Release to Maven
### Description
Resolve#21976 .
ABSL generally does not have forward/backward compatibility. Our code is
only compatible with one fixed LTS version. So it's important to fix the
version number there when using find_package to detect an installed
version.
### Description
It runs after "Python-CUDA-Packaging-Pipeline" that runs on a CPU
machine that skipped all tests.
This testing pipeline is for doing the tests.
Fix the QNN nuget package issue
### Description
Inside the package, folder name \runtimes\win-arm64\ was changed to \runtimes\win-ARM64\, which breaks lib copy settings in Microsoft.ML.OnnxRuntime.QNN.props.
### Motivation and Context
Fix issue: https://github.com/microsoft/onnxruntime/issues/21692
### Description
Update the commit from 59600894a2c1c18290944b83e989bfe618975230 to
1887322ed36d522409a6b805d4e7942cf76a8e40
### Motivation and Context
The new one has python 3.13.
AB#50959
### Description
This change introduces the WebGPU EP into ONNX Runtime.
To make the PR as simple as possible, this PR excluded the following:
- C API changes for WebGPU EP
- actual implementation of WebGPU EP. Currently in this PR, WebGPU is a
stub implementation that does not register any kernel.
- Python IO Binding update
- Node.js IO Binding update
This PR now contains only 43 file changes (while the working branch
contains 130+) and hopefully this makes it easier to review.
There is going to be separated PRs for each mentioned above.
Current working branch: #21904
### Description
With TensorRT 10.4 update, the name of TensorRT windows package changed
### 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
- removed installing AppCenter + pipeline step that runs AppCenter
Espresso tests
- added script for running AppCenter tests
### Motivation and Context
App Center is getting deprecated in the next year + we have upcoming
Android work that depends on working E2E testing.
---------
Co-authored-by: Edward Chen <18449977+edgchen1@users.noreply.github.com>
- Add Java API for appending QNN EP
- Update Java unit test setup
- Fix issues with setting system properties for tests
- Unify Windows/non-Windows setup to simplify
