Here's the motivating issue:
https://github.com/microsoft/azure-pipelines-tasks/issues/10331
Noticed some problems in other repos so also updating usages in ORT.
We may be fine now without it, but this change adds some safeguard against future additions of 'set -x' for debugging.
### Description
Change CUDA pipelines to download CUDA SDK in every build 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
1. Set gtest output while ctest is set to empty.
2. onnx_src in _deps shouldn't be removed because
onnx_test_pytorch_converted and onnx_test_pytorch_converted need to read
data from onnx/backend/test/data/..
### Motivation and Context
Test result report is important to find the flaky tests.
### To do
Tests are not inconsistent.
If ctest_path is empty, onnx_test_pytorch_converted and
onnx_test_pytorch_converted will not be executed, if it's not,
onnxruntime_mlas_test will not be executed.
270c09a37f/tools/ci_build/build.py (L1743-L1753)
### Description
After this PR there are following pool need to be updated.
old|new|note
---|---|---
onnxruntime-Win2019-GPU-dml-A10|tbd|
onnxruntime-Win2019-GPU-T4|onnxruntime-Win2022-GPU-T4|
onnxruntime-Win2019-GPU-training-T4|onnxruntime-Win2022-GPU-T4|ame as
the above because we do not have many T4 GPUs
onnxruntime-tensorrt8-winbuild-T4|tbd|
aiinfra-dml-winbuild|tbd|
### 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. -->
Old pool | New pool | Notes
-- | -- | --
onnxruntime-Win-CPU-2019 | onnxruntime-Win-CPU-2022 |
onnxruntime-Win2019-CPU-training | onnxruntime-Win2022-CPU-training-AMD
|
onnxruntime-Win2019-CPU-training-AMD |
onnxruntime-Win2022-CPU-training-AMD | Same as the above
onnxruntime-Win2019-GPU-dml-A10 | Need be created | You need to create a
new image for it first
onnxruntime-Win2019-GPU-T4 | onnxruntime-Win2022-GPU-T4 |
onnxruntime-Win2019-GPU-training-T4 | onnxruntime-Win2022-GPU-T4 | Same
as the above because we do not have many T4 GPUs
onnxruntime-tensorrt8-winbuild-T4| TBD|TBD
Win-CPU-2021|onnxruntime-Win-CPU-2022| will do it in next PR
Win-CPU-2019|onnxruntime-Win2022-Intel-CPU'| Intel CPU needed for
win-ci-pipeline.yml -> `stage: x64_release_dnnl`
<br class="Apple-interchange-newline">
### Motivation and Context
With vs2022 we can take the advantage of 64bit compiler. It also with
better c++20 support
Set default value for parameters in nuget-zip pipeline, and only apply
the configurations when they are not "NONE".
---------
Co-authored-by: Randy Shuai <rashuai@microsoft.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
Updates the default QNN SDK version to 2.10 for the QNN NuGet pipeline.
### Motivation and Context
Ensures that the daily QNN NuGet pipeline builds ORT using the latest
QNN SDK by default.
update ROCm/MIGraphX CI to ROC5.5.
TODO:
two PR to fix failure on
orttraining/orttraining/test/python/orttraining_test_ortmodule_api.py
-
test_gradient_correctness_minmax/test_gradient_correctness_argmax_unfold/test_gradient_correctness_argmax_diagonal
(https://github.com/microsoft/onnxruntime/pull/15903)
- test_ortmodule_attribute_name_collision_warning
(https://github.com/microsoft/onnxruntime/pull/15884)
### Description
The CI is extremely slow on downloading source code (~1MB/sec) so the
web CI went timeout. This is blocking the PR/checks.
Increase the timeout temporarily.
### Description
this is for ort 1.15 release to work with onnx 1.14
It shall be merged after onnx 1.14 release and before ort 1.15 release.
### Motivation and Context
---------
Signed-off-by: Liqun Fu <liqfu@microsoft.com>
### Description
Fix the bug in #15693
### 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 for CI pipelines to version 2.10.0.
- Disables convolution op tests that run on the QNN CPU backend due to a
potential bug with QNN SDK 2.10.0.
### Motivation and Context
Allows us to test the latest QNN SDK in default CI pipeline runs.
### Description
<!-- Describe your changes. -->
Various fixes to the CSharp setup
- fix warnings
- fix invalid tests
- update test sdk nuget package
- enables testing on linux
- fixes issue with some unit tests not running in CI
- run unit tests in linux pipeline using dotnet
### 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. -->
Unit tests weren't breaking in CIs for both Windows and Linux builds and
should have been.
### Description
add target ort.webgpu.min.js
WebGPU is experimental feature, so I don't want to put webgpu into the
ort.min.js file. This change adds 2 ways for users to access ort-web
with webgpu:
- using script tag: by URL
`https://cdn.jsdelivr.net/npm/onnxruntime-web@1.15.0/dist/ort.webgpu.min.js`
( this URL is not ready yet )
- using `import()`: use `import { Tensor, InferenceSession } from
'onnxruntime-web/webgpu';` - 'onnxruntime-web/webgpu' instead of
'onnxruntime-web'
### Description
latest emsdk generated multi-thread version sometimes crash with unknown
reason ( error: memory access out of bounds ).
we don't want to break existing ort-web users, so revert emsdk back to
3.1.19 (same to what ort v1.14.0 uses)
### Description
They were missed in #15707 , because they are not in common places for Dockerfiles.
Though this commit updated tools/ci_build/github/pai/rocm-ci-pipeline-env.Dockerfile, it won't automatically take effect. The image needs to be manually generated and pushed to a place, and before doing that our CMakeLists.txt also needs to be tweaked a little bit.
### Description
This is the first part to create a webassembly artifacts for ort-web
webgpu EP (wasm build).
there will be following steps to consume the artifacts in web build
### Description
Download protoc from Github Release instead of Nuget to avoid having
dependency on nuget.exe on Linux
### Motivation and Context
To avoid having dependency on nuget.exe on Linux. Many users' build
environment do not have nuget or dotnet.
### Description
This PR creates Nuget and Android for Training.
### Motivation and Context
These packages are intended to be released in ORT 1.15 to enable
On-Device Training Scenarios.
## Packaging Story for Learning On The Edge Release
### Nuget Packages:
1. New Native package -> **Microsoft.ML.OnnxRuntime.Training** (Native
package will contain binaries for: win-x86, win-x64, win-arm, win-arm64,
linux-x64, linux-arm64, android)
2. C# bindings will be added to existing package ->
**Microsoft.ML.OnnxRuntime.Managed**
### Android Package published to Maven:
1. New package for training (full build) ->
**onnxruntime-training-android-full-aar**
### Python Package published to PyPi:
1. Python bindings and offline tooling will be added to the existing ort
training package -> **onnxruntime-training**
### Description
All our Windows build pipelines already uses cmake 3.26 except one
pipeline: QNN ARM64.
This PR does the same for Linux build pipelines.
### Motivation and Context
This change is related to #15704 .
### Description
- Update to QNN SDK 2.9.0 for QNN pipelines
- Temporarily disable warnings as errors for QNN Windows x64 pipeline
- Note that this pipeline did not previously run to completion. It also
currently does not run for pull requests.
### Motivation and Context
Need to update and test the latest available version of the QNN SDK.
Rename onnxruntime-Linux-CPU-2019 machine pool to
"onnxruntime-Ubuntu2004-AMD-CPU". The old one has an internal error and
stuck there. I cannot make any change to it. It has been like this for
more than 1 week. So I created a new pool with the same setting except
the name is different.
Also, move some android pipelines to
"onnxruntime-Linux-CPU-For-Android-CI" which uses a standard image from
https://github.com/actions/runner-images
### Description
* Update TensorRT 8.6 lib dependencies in dockerfile of TRT EP Perf
pipeline
* Avoid using `--allow_running_as_root` and build ORT with non-root user
### Motivation and Context
To fix the build issue on EP perf pipeline
Fixed
[AB#14615]
### Description
Add parameters to make some stages could use other run's intermediate
output.
### Motivation and Context
nuget workflow has 38 stages of 4 layers.
We had to run the whole workflow from begining to test one stage.
It could make life easier to run only one stage for testing.
like
### N.B.
In this PR, Nuget_Test_Linux_CPU, Nuget_Test_LinuxGPU and
Jar_Packaging_GPU are enabled as the first step.
So I can start to move tests from Linux host to container
### Description
In 2021 we restricted onnx node test CI execution in range of opset
14-15 for ORT-TRT, which was the latest opset that TRT EP could support
Update this range to opset 14-17 to improve the ORT-TRT unit test
coverage, as [Nvidia announced that TRT 8.6 supported
opset17](https://github.com/onnx/onnx-tensorrt/blob/main/docs/operators.md)
### Description
* Reverting default TensorRT version to 8.5 as temporary fix
* Apart from that, this PR temporarily leaves this CI as a place to
validate user behavior that uses TRT 8.5 with latest ORT
### Context
* This CI pool equips 2xTesla M60 GPUs, which are no longer supported by
TensorRT 8.6.
* Currently, other CIs are using single-T4 VM but there's no VM with
2xT4 or other suitable dualGPU in the range.
* Once we decide which VM instance for this CI to migrate to, TRT8.6 can
be enabled on this CI
* According to
[Nvidia](https://docs.nvidia.com/deeplearning/tensorrt/release-notes/index.html):
* TensorRT 8.5.3 was the last release supporting NVIDIA Kepler (SM 3.x)
and NVIDIA Maxwell (SM 5.x) devices. *These devices are no longer
supported in TensorRT 8.6*. NVIDIA Pascal (SM 6.x) devices are
deprecated in TensorRT 8.6.
### Description
This PR resolves a part of non-critical comments from code review
comments in #14579.
- use `USE_JSEP` instead of `USE_JS` in build definition to make it less
ambiguous
- remove unused util functions from util.ts
- fix transpose.h
- other misc fixes
